The Future of Philippine Agriculture under a Changing Climate: Policies, Investments and Scenarios 9789814818360

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The Future of Philippine Agriculture under a Changing Climate

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The ISEAS – Yusof Ishak Institute (formerly Institute of Southeast Asian Studies) is an autonomous organization established in 1968. It is a regional centre dedicated to the study of socio-political, security, and economic trends and developments in Southeast Asia and its wider geostrategic and economic environment. The Institute’s research programmes are grouped under Regional Economic Studies (RES), Regional Strategic and Political Studies (RSPS), and Regional Social and Cultural Studies (RSCS). The Institute is also home to the ASEAN Studies Centre (ASC), the Nalanda-Sriwijaya Centre (NSC) and the Singapore APEC Study Centre. ISEAS Publishing, an established academic press, has issued more than 2,000 books and journals. It is the largest scholarly publisher of research about Southeast Asia from within the region. ISEAS Publishing works with many other academic and trade publishers and distributors to disseminate important research and analyses from and about Southeast Asia to the rest of the world.

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The Future of Philippine Agriculture under a Changing Climate Policies, Investments and Scenarios edited by Mark W. Rosegrant and Mercedita A. Sombilla

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First published in Singapore in 2019 by ISEAS Publishing 30 Heng Mui Keng Terrace Singapore 119614 E-mail: [email protected] Website: All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the ISEAS – Yusof Ishak Institute. © 2019 ISEAS – Yusof Ishak Institute, Singapore The responsibility for facts and opinions in this publication rests exclusively with the authors and their interpretations do not necessarily reflect the views or the policy of the publisher or its supporters. ISEAS Library Cataloguing-in-Publication Data The Future of Philippine Agriculture under a Changing Climate: Policies, Investments and Scenarios / edited by Mark W. Rosegrant and Mercedita A. Sombilla. 1. Agriculture—Environmental aspects—Philippines. 2. Climatic changes—Philippines. 3. Alternative agriculture—Philippines. I. Rosegrant, Mark W., editor. II. Agcaoili-Sombilla, Mercedita C. (Mercedita Castro), editor. S589.76 P5F99 2019 ISBN 978-981-4818-35-3 (soft cover) ISBN 978-981-4818-36-0 (ebook, PDF) Cover design by NEDA – Development Information Staff Typeset by Superskill Graphics Pte Ltd Printed in Singapore by Mainland Press Pte Ltd

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CONTENTS List of Tables vii List of Figures xiv List of Appendices xx Preface xxii Acknowledgements xxxii List of Contributors xxxiii PART I: Setting up the Scenarios: Current Status and Potential Impacts of Climate Change to Philippine Agriculture   1. Current Structure and Future Challenges of the Agricultural Sector Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla   2. The Context of Land Cover Changes in Agriculture and Forestry David M. Wilson and Rodel D. Lasco

3

71

  3. Trends in Agricultural Water Resources Arlene B. Inocencio

133

  4. Existing Evidence of Climate Change and Variability Felino P. Lansigan

174

PART II: Climate Change Adaptation Strategies and Sustainability of Philippine Agriculture   5. The Sustainability of Agricultural Growth Asa Jose U. Sajise, Dieldre S. Harder and Paul Joseph B. Ramirez

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vi

Contents

  6. The Gendered Impacts of Climate Change Maria Emilinda T. Mendoza

260

  7. Adaptation and Mitigation Strategies Marites M. Tiongco

278

  8. Risk Management and Coping Strategies Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc

324

PART III: Investments and Supporting Policies to Alleviate Climate Change Impacts to Philippine Agriculture   9. A Biophysical Approach to Modelling Alternative Agricultural Futures under Climate Change Timothy S. Thomas, Vijay Nazareth and Renato A. Folledo, Jr.

377

10. A Partial Equilibrium Approach to Modelling Alternative Agricultural Futures under Climate Change Nicostrato D. Perez and Mark W. Rosegrant

450

11. A General Equilibrium Approach to Modelling Alternative Agricultural Futures under Climate Change Angga Pradesha and Sherman Robinson

492

PART IV: Conclusion 12. Summary and Policy Recommendations Mercedita A. Sombilla and Mark W. Rosegrant

537

Index

567

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LIST OF TABLES 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 1.11 1.12 1.13 1.14 1.15 1.16

2.1 2.2 2.3 2.4

Characteristics of Agricultural Workers, 2001–15 11 Labour Trends, 1990–2014 12 Value of Major Philippine Exports, 2001–14 15 Volume of Major Philippine Exports, 2001–14 16 Value of Major Philippine Agricultural Imports, 2001–14 17 Volume of Major Philippine Imports, 2001–14 18 Agricultural Trade with Major Partners, 2008–15 19 Growth Rates of Gross Agricultural Value-Added by Commodity, 2000–15 20 Rate of Growth of Gross Value-Added by Crop, 2000–15 21 Total Factor Productivity and Contributions to Revenue Growth, 1975–2004 27 Shares of Household Expenditures on Food, 2000–15 29 Share of Urban, Rural, and Agricultural Population 32 Poverty Incidence by Region, 1991, 2006, 2009, 2012, and 2015 34 Actual Versus Mandated Agriculture and Fisheries Modernization Programme Budget, 2000–05 41 Department of Agriculture Budget, 1998–2015 42 Allocation of the Department of Agriculture Budget for Commodity Programmes and Other Supporting Activities, 2010–15 43 Philippine Land-Cover by Type, 1990, 2000, 2005, and 2010 Remote-Sensing Land-Cover Data for the Philippines, 2001 and 2009 Forest Type and Extent, 1990, 2000, 2005, and 2010 Indirect and Underlying Drivers of Deforestation and Forest Degradation

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80 81 81 88

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List of Tables

viii 2.5 2.6 2.7 2.8 2.9 2.10

3.1 3.2 3.3 3.4

4.1

4.2 4.3 4.4

5.1 5.2 5.3 5.4 5.5 5.6 5.7

Agricultural Land Coverage, Selected Regions, 2011 Harvested Area of Primary Crops, 2012 Summary of Land-Use Transition Phases and Indicators Summary of Forest Transition Indicators, 1961–2011 Summary of Philippine Forestry Policies, 1975–2011 Major Cross-Cutting Land-Use and Climate Change Policies in the Philippines, 1991–2011 Philippine Climate Change Adaptation Activities Related to Water Resources as of 31 December 2013 “Firmed-up” Irrigation Service Area by Region, as of 2015 Distribution of Irrigation Investments by Purpose, Type, and Funding Source, 1965–2015 Average Time before First Major Rehabilitation and Number of Rehabilitated National Irrigation Systems with Recorded Information, 1965–2008

90 91 98 102 111 116

138 144 149

154

Trends in the Number of Hot Days and Warm Nights, and in the Frequency and Intensity of Extreme Daily Rainfall, 1951–2008 177 Matrix of Estimated Losses and Damages to Rice Crops Due to Extreme Weather 187 Crop Losses Due to Natural Disasters, Selected Crops 190 Good Agricultural Practices at the Farm-Level and Addressing Climate-Related Hazards for Selected Locations 193 Land-Degradation Hotspots by Region, 2003 Shares of Land Degradation by Region and Type Based on SOTER/ASSOD Studies, 1993 Severity of Soil Erosion by Region, 1993 Gross and Average Soil Erosion Rates by Region and Land Use, 1993 Estimated Physical Changes in the Regional Surface Water Stock, 1988–93 Estimated Physical Changes in Regional Groundwater Stock, 1988–93 Potential Impacts of Agriculture on Water Quality

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216 217 218 219 224 225 228

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List of Tables ix

5.8 5.9 5.10 5.11

6.1 6.2 6.3 6.4 6.5

7.1 7.2 7.3

8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8

Number of Documented Accessions of Philippine Germplasm by Crop, 2007 Improved Rice Varieties Released in the Philippines, by Time Period Trends in Adoption of Modern Rice Varieties in Central Luzon Provinces, 1996–2004 Production and Area Harvested of High-Yielding Varieties and Certified Seeds, 2001–11

230 232 234 235

Focus Group Rankings of Sectors Vulnerable to Climate Change–Related Hazards, 2014 262 Labour and Employment by Gender, 2008 and 2012 264 Gendered Division of Labour in Agricultural Production, 2012 266 Percentage of Households Employing Men and Women for Selected Farm Activities, 2012 267 Focus Group Rankings of the Importance of Different Forms of Social Capital, 2014 2739 Examples of Projected Climate Change Impacts on Philippine Agriculture, Forestry, and Fisheries Government Policies and Strategies in Response to Climate Change, 1991–2014 Official Development Assistance Initiatives with Climate Change Adaptation, Mitigation, and Disaster Risk Reduction Components Total Value of Damage to Agricultural Commodities Due to Typhoons, Floods, and Droughts, 2000–13 Value of Damage to Agricultural Facilities and Irrigation Due to Typhoons, Floods, and Droughts, 2000–13 Total Value of Economic Loss and Damages Due to Typhoon Haiyan (Yolanda) Category 3 Provinces Exposed to Multiple Hazards Economic Profile of Sample Households Incidence and Severity of Shocks Experienced Impact of Shocks Experienced Farm-Related Damages Experienced

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279 285

306

330 331 333 336 346 348 349 350

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List of Tables

x 8.9 8.10 8.11 8.12 8.13 8.14 8.15 8.16 9.1 9.2 9.3 9.4 9.5 9.6

9.7 9.8 9.9

9.10 9.11 9.12 9.13 9.14

Effect of Shocks on the Family’s Well-Being 351 Perception of Recovery from Shocks 353 Average Degree of Recovery Versus Type of Recovery 354 Adoption of Financial Coping Mechanisms 354 Most Important Coping Strategies 355 Adoption of Risk-Management Measures, by Economic Profile 356 Adoption of Precautionary Measures 356 Damages of Concern to Farm Households 357 Yearly Rainfall by Region and Percentile, 1950–2000 384 Rainfall in the Wettest Three Months by Region and Percentile, 1950–2000 386 Rainfall in the Driest Three Months by Region And Percentile, 1950–2000 388 Mean Daily Maximum Temperature in the Warmest Month by Region, 1950–2000 390 Projected Change in Yearly Rainfall from Four General Circulation Models, 2000–50 392 Projected Change in Mean Daily Maximum Temperature in the Warmest Month from Four AR5 General Circulation Models, 2000–50 394 Comparison of Rainfall and Temperature Changes from Four AR5 General Circulation Models, 2000–50 394 Comparison of Rainfall and Temperature Changes from Four AR4 General Circulation Models, 2000–50 395 Harvested Area, Production, and Yield of Leading Agricultural Commodities, 2010–12, and Percent Change, 1999–2001 397 Harvested Hectares of Irrigated and Rainfed Rice by Region, Circa 2005 405 Projected Climate Impacts on Irrigated Rice Yields under a Baseline Scenario of Low Fertilizer Use, 2000–50 408 Projected Climate Impacts on Irrigated Rice Yields under a Baseline Scenario of High Fertilizer Use, 2000–50 410 Projected Climate Impacts on Rain-Fed Rice Yields under a Baseline Scenario of Low Fertilizer Use, 2000–50 412 Projected Climate Impacts on Rain-Fed Rice Yields under a Baseline Scenario of High Fertilizer Use, 2000–50 413

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List of Tables xi

9.15 9.16 9.17 9.18 9.19 9.20 9.21 9.22 9.23 9.24 9.25 9.26 9.27 9.28

10.1 10.2 10.3 10.4 10.5 10.6

Harvested Hectares of Rain-Fed Maize by Region, Circa 2005 415 Projected Climate Impacts on Rain-Fed Maize Yields under a Baseline Scenario of Low Fertilizer Use, 2000–50 418 Projected Climate Impacts on Rain-Fed Maize Yields under a Baseline Scenario of High Fertilizer Use, 2000–50 421 Harvested Hectares of Irrigated and Rain-Fed Sugarcane by Region, Circa 2005 429 Projected Climate Impacts on Irrigated Sugarcane Yields, 2000–50 431 Projected Climate Impacts on Rain-Fed Sugarcane Yields, 2000–50 433 Harvested Hectares of Coconuts by Region, Circa 2005 435 Climate Impacts on Rain-Fed Coconut Yields from Four AR5 General Circulation Models, 2000–50 435 Harvested Hectares of Bananas by Region, Circa 2005 440 Projected Climate Impacts on Rain-Fed Banana Yields from Four AR5 General Circulation Models, 2000–50 440 Projected Improvements in Rain-Fed Maize Yields from Various Technologies, 2050 442 Projected Improvements in Irrigated Rice Yields from Various Technologies, 2050 443 Projected Improvements in Rain-Fed Rice Yields from Various Technologies, 2050 444 Summary of Crop Model Results for Yield Changes Due to Climate Change, 2000–50 446 Average Projected Impact of Climate Change on Agriculture Globally and in the Philippines, 2030 and 2050 454 Projected Impact of Climate Change on Food Security, 2011–30 and 2011–50 457 Changes in Welfare Due to Climate Change, 2011–50 458 Economic Cost of Malnutrition Due to Climate Change, 2011–50 460 Total Value of Damage to Philippine Agriculture Due to Typhoons, Floods, and Droughts, 2000–10 461 Projected Value of Damage to Philippine Agriculture Due to Extreme Events, with and without Climate Change, 2011–50 461

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List of Tables

xii 10.7 10.8 10.9 10.10 10.11 10.12 10.13 10.14 10.15 10.16

11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9

The Economic Cost of Climate Change to Philippine Agriculture, 2010–50 463 Changes in Social Welfare Due to Climate Change, with and without Adaptation, 2011–50 464 Description of Adaptation Technologies and Irrigation Development 468 The Potential Impact of Adaptation Strategies on Rice and Corn Productivity, 2050 470 Status of Irrigation Development in the Philippines as of 31 December 2013 474 Assumptions on Adoption, Development, and Application of Selected Climate Change Adaptation Technologies 476 The Projected Impact of Adaptation Technologies and Irrigation Investment on Rice and Corn Indicators, 2050 480 The Projected Impact of Adaptation Technologies and Irrigation Investment on Food Security, 2050 484 Changes in Welfare, by Adaptation Technology and Irrigation Investment, 2050 486 Effectiveness of Adaptation Strategies in Compensating for the Impact of Climate Change on Production, Consumption, and Food Security, 2050 487 List of Climate and Policy Scenarios 495 Baseline Projections of GDP by Major Sector, 2011 and 2050 499 Baseline Projections of Agricultural GDP by Commodity, 2011–50 499 Baseline Agricultural Production Shares by Region, 2011 500 Baseline Projections of Sectoral Shares of Labour Force, 2011 and 2050 501 Projected Impact of Climate Change on Real Value-Added by Sector, 2050 503 Projected Impact of Climate Change on Agricultural Production by Region, 2050 506 Projected Impact of Climate Change on Real Returns to Factor Inputs, 2050 508 Projected Impact of Climate Change on Real Household Incomes, 2050 509

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List of Tables xiii

11.10 11.11 11.12 11.13 11.14 11.15 11.16

12.1 12.2

Projected Impact of Climate Change on Total Absorption, 2011–50 510 Projected Impact of Climate Change on Household Welfare, 2011–50 511 Projected Impact of Policy Responses to Climate Change on Macroeconomic Variables, 2050 513 Projected Impact of Policy Responses to Climate Change on Agricultural Production, 2050 514 Projected Impact of Policy Responses to Climate Change on Government Revenues, 2050 515 Projected Impact of Policy Responses to Climate Change on Total Absorption, 2011–50 516 Projected Impact of Policy Responses to Climate Change on Household Welfare, 2011–50 518 Key Impediments to the Success of the Agricultural Plans and Programmes 539 Climate Change Adaptation Measures in the Agricultural Sector 547

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LIST OF FIGURES 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 1.11 1.12 1.13 1.14 1.15 1.16

2.1 2.2

Composition of Gross Domestic Product, 1960–2015 7 Composition of Employment, 2000–15 9 Labour Productivity, 1987–2015 10 Value and Share of Agricultural Trade, 1980–2015 14 Trends in Real Gross Agricultural Value-Added, 2000–15 20 Rate of Growth Rate of Gross Value-Added for Livestock and Poultry, 2001–15 21 Trends in Livestock and Poultry Production, 2000–15 23 Trends in Fishery Production by Subsector, 2000–15 24 Land Productivity, Selected Crops, 2000–15 26 Log of Per Capita Income Versus Food as a Share of Total Household Expenditures, 2015 30 Consumer Price Index Ratios for Food, 1994–2015 31 Inflation Rate and Inflation Rate of the Poorest 30 per cent of Households, 2007–15 31 Poverty Reduction, Selected Asian Countries, 1990–2014 33 Incidence of Multidimensional Versus Income-Based Poverty, 1988–2012 35 Trends of Rice Prices in Domestic and World Markets, 2000–15 48 Access to Social Services and Assets: The Poorest 25 per cent Versus the Richest 25 per cent 53 Global, Regional, and National Level Land-Use Change, 1990–2010 73 Agricultural Land, Rural Population, and Population Density in the Philippines, 1990–2012 91

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List of Figures xv

2.3 2.4 2.5 2.6 2.7 2.8

3.1 3.2 3.3 3.4 3.5

3.6

3.7

3.8

4.1 4.2 4.3 4.4

Agricultural Land Use as a Share of Agricultural Land, 1990–2011 93 Harvested Agricultural Land by Major Product, 1990–2013 94 Harvested Area of Major Cash Crops, 1990–2012 95 Harvested Area of Tree and Shrub-Based Products, 1990–2012 96 Land-Use Transition Indicators, 1961–2011 100 Greenhouse Gas Emissions from the Forestry/Agriculture Sector in the Philippines, 1961–2011 104 Trends in Public Investments in Irrigation in Real Terms, 1965–2015 141 Trends in Irrigation Investments by Type of System, 1965–2015 142 Trends in the Service Area of National Irrigation Systems by Region and Type, 1967–2015 146 Trends in Irrigation Investments by Use, 1965–2015 147 Angat Water Consumption between the Metropolitan Waterworks and Sewerage System and the National Irrigation Administration, 1968–2015 152 Actual Versus Recommended Operation and Maintenance Expenditures, and Collectible and Collected Irrigation Service Fees, 1983–2015 156 Trends in the Costs of Operations and Maintenance, Service Area, and “Firmed-Up” Service Area in National Irrigation Systems by Region, 1985–2015 157 Trends in the Ratio of Actual Wet and Dry Season Irrigated Area to Total Service Area in National Irrigation Systems by Region, 1967–2015 158 Mean Temperature Anomalies, 1951–2010 Relative to 1961–90 176 Monthly Mean Minimum Temperatures in Muñoz, Nueva Ecija, 1974–90 and 1991–2008 179 Monthly Mean Minimum Temperatures in Legazpi City, 1973–90 and 1991–2008 180 Monthly Average Number of Meteorological Rainy Days in Legazpi City, 1973–90 and 1991–2008 182

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xvi

List of Figures

4.5 4.6 4.7

Monthly Frequency of Tropical Cyclones, 1948–2010 183 Rice Production, Area Harvested, and Yield, 1987–2014 186 Rice Yield Decline as Function of Temperature Increases during Dry and Wet Seasons, Nueva Ecija 188 Projected Reduction in Crop Yields with Higher Temperatures, Selected Locations 189 Areas of the Siargao Islands Vulnerable to Rising Sea Levels of Less Than 0.5 metres 191 Combined Probabilities of Meeting Cumulative Rainfall in Malaybalay, Bukidnon, 2020 and 2050 195 Framework for a Knowledge-Based Crop-Forecasting System 197 Weekly Probability of Cumulative Rainfall Deficit for Rain-Fed Rice Production, Iloilo Province 199 Location Map of Existing Network of Weather Gauging Stations 200

4.8 4.9 4.10 4.11 4.12 4.13

5.1 5.2 5.3

7.1 7.2 7.3 7.4

8.1 8.2 8.3 8.4 8.5

The “Driving Force–State–Response” Framework 213 Total Renewable Water Resources per Capita, 1958–62 to 2008–11 223 Regional Distribution of Greenhouse Gas Emissions from Rice Cultivation, 1994–2010 237 Operating Structure of the National Framework Strategy for Climate Change, 2010–22 Strategic Actions on Food Security under the National Climate Change Action Plan, 2011–28 Provinces with the Highest Incidence of Poverty, Number of Poor Households, and Risk of Climate Hazards Adaptation Cost and Climate Change–Related Damage to Agriculture as Percentage of GDP The Incidence of Natural Disasters Frequency of Natural Disasters, Philippines, 2000–15 The Evolution of Philippine Disaster Risk Management over Time A Conceptual Framework for Managing the Risk of Natural Disasters Farm-Level Risk Management

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287 289 302 308 325 328 334 338 340

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List of Figures xvii

9.1 Regional Divisions Underlying the Analysis 379 9.2 Land Cover, 2009 380 9.3 Elevation 381 9.4 Protected Areas 382 9.5 Mean Yearly Rainfall, 1950–2000 383 9.6 Rainfall in the Wettest Three Months, 1950–2000 385 9.7 Rainfall in the Driest Three Months, 1950–2000 387 9.8 Mean Daily Maximum Temperature in the Warmest Month, 1950–2000 389 9.9 Projected Changes in Mean Yearly Precipitation from Four AR5 General Circulation Models, 2000–50 391 9.10 Projected Change in Mean Daily Maximum Temperature in the Warmest Month from Four AR5 General Circulation Models, 2000–50 393 9.11 Share of Cropland, 2010 396 9.12 Harvested Area Trends, 1990–2012 399 9.13 Yield Trends, 1990–2012 400 9.14 Intensity and Productivity of Irrigated Rice, Circa 2005 403 9.15 Intensity and Productivity of Rain-Fed Rice, Circa 2005 404 9.16 Median Percentage Change in Irrigated Rice Yields under Climate Change, and the Projected Effect of Adaptation Measures under a Baseline Scenario of Low Fertilizer Use, 2000–50 406 9.17 Median Percentage Change in Irrigated Rice Yields under Climate Change, and the Projected Effect of Adaptation Measures under a Baseline Scenario of High Fertilizer Use, 2010–50 409 9.18 Median Percentage Change in Rain-Fed Rice Yields under Climate Change, and the Projected Effect of Adaptation Measures under a Baseline Scenario of Low Fertilizer Use, 2000–50 411 9.19 Median Percentage Change in Rain-Fed Rice Yields under Climate Change, and the Projected Effect of Adaptation Measures under a Baseline Scenario of High Fertilizer Use, 2010–50 414 9.20 Intensity and Productivity of Rain-Fed Maize, Circa 2005 416 9.21 Median Percentage Change in Rain-Fed Maize Yields under Climate Change, and the Projected Effect of Adaptation Measures under a Scenario of Low Fertilizer Use, 2000–50 417

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List of Figures

xviii 9.22

9.23 9.24 9.25 9.26 9.27

9.28

9.29 9.30 9.31 9.32

10.1

11.1 11.2 11.3 11.4 11.5

Median Percentage Change in Rain-Fed Maize Yields under Climate Change, and the Projected Effect of Adaptation Measures under a Scenario of High Fertilizer Use, 2000–50 Rainfall and Temperature Profiles by Month and Model for a Location in Cagayan Valley, 2000 and 2050 Rainfall and Temperature Profiles by Month and Model for a Location in Cotabato, 2000 and 2050 Intensity and Productivity of Irrigated Sugarcane, Circa 2005 Intensity and Productivity of Rain-Fed Sugarcane, Circa 2005 Median Percentage Change in Irrigated Sugarcane Yields under Climate Change, and the Projected Effect of Adaptation Measures, 2010–50 Median Percentage Change in Rain-Fed Sugarcane Yields under Climate Change, and the Projected Effect of Adaptation Measures, 2010–50 Intensity and Productivity of Rain-Fed Coconuts, Circa 2005 Median Percentage Change in Rain-Fed Coconut Yields from Four AR5 General Circulation Models, 2000–50 Intensity and Productivity of Rain-Fed Bananas, Circa 2005 Projected Yield Change in Rain-Fed Bananas from Four AR5 General Circulation Models, 2000–50 Historical and Average Projected Agricultural Crop Yields, with and without Climate Change, 1970–2050 Baseline Changes in Shares of Major Food Commodity Exports, 2011 and 2050 Baseline Changes in Shares of Major Food Commodity Imports, 2011 and 2050 Change in the Demand for Agricultural Labour and in GDP under Climate Change, 2011–50 Projected Cost of Climate Change and Its Share of Total GDP, 2011–50 Net Benefit of Adaptation Strategies in Mitigating Climate Change, 2011–50

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420 422 425 427 428

430

432 434 436 438 439

453

501 502 505 519 520

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List of Figures xix

11.6 11.7

Net Benefits and Costs of Irrigation Expansion, 2014–50 and 2025–50 The Interlinked Modelling System Used to Assess Climate Change Impacts in the DCGE Model

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521 525

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LIST OF APPENDICES 1.1 1.2 1.3

3A.1 3A.2

5A.1 5A.2

7A.1 7A.2 7A.3

8.1 8.2 8.3

Changes in the Incidence Poverty and Growth of Agricultural Versus Nonagricultural Income, 1991–2006 Initial Conditions Affecting Sectoral Growth Elasticity of Poverty Reduction, 1991–2006 Total Department of Agriculture Budget and Shares of Major Outputs, 2000–15 Selected Foreign-Assisted Irrigation Projects by Donor Measures of Economic Performance of Selected ForeignAssisted Irrigation Projects by Donor

61 61 62 164 168

Data and Variables Used in the Construction of Malmquist Productivity Indexes 250 Irrigated Rice Area and Estimated Water Consumption, 2000–14 251 Palay Production Losses Due to Climate-Related Natural Disasters, 2007–15 315 Typhoons Causing the Most Economic Damage, 1990–2015 317 Major Department of Agriculture Programmes, Activities, and Projects on Productivity and Climate Change Resilience, 2013–16 318 Number of Farm Households Resorting to Financial Coping Strategies Incidence of Selling Goods to Cope from Shock Households Reducing Consumption Due to Shocks

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362 363 364

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List of Appendices xxi

8.4 8.5 8.6 8.7 8.8

Households Reducing Consumption Due to Shocks, by Type of Consumption Incidence of Asking for Assistance Long-Term Risk-Management/Precautionary Measures Factors Influencing Recovery, Based on Full Model Factors Influencing Recovery, Based on Reduced Model

365 366 367 368 369

11A.1 11A.2 11A.3 11A.4

Commodities Included in the Model 527 Productivity Shock Introduced in the Model 528 World Price Shock Introduced in the Model 529 Projected Productivity Gain from New Irrigation Infrastructure 529 11A.5 Import Tariff Rates 530 11A.6 Cost Structure of Building Dams Following the National Irrigation Authority’s Masterplan 531

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PREFACE The Philippines is highly vulnerable to climatic stresses, and droughts and floods have substantially affected agricultural production over time. In particular, El Niño Southern Oscillation (ENSO)–related droughts have affected the country’s water resources and temperatures, with flow-on effects to the agricultural sector, as well as to health and the environment. In addition to more frequent and severe flooding and drought, the Philippines has also experienced many catastrophic natural disasters, particularly typhoons. Yolanda (internationally known as Haiyan) devastated the country in 2013, causing 6,300 deaths; the displacement of over 5 million citizens; an estimated 89.60 billion Philippine peso (PhP) in property damage to houses, hospitals, schools, roads, bridges, and so on; and PhP42.76 billion in losses to the productive sectors, including agriculture. Consequently, the Philippines ranks high in the Global Climate Risk Index, taking first place in 2013. As recently as December 2017, Typhoons Urduja and Vinta (internationally known as Tembin and Kai-Tak, respectively) once again caused the loss of hundreds of lives, along with the destruction of property, infrastructure, and livelihoods. The current state of climate science research does not attribute extreme events like floods, droughts, or typhoons to climate change, but there is growing consensus that climate change increases the frequency and severity of such events. In combination with key drivers of food production growth — such as income and population growth, investment in research and technology, and changes in dietary patterns — climate change will have a major impact on Philippine farmers’ ability to produce sufficient food and generate enough income to support healthy and productive lives. Poor and vulnerable communities are the most severely affected based on their reliance on subsistence farming and their limited capacity to undertake measures to adapt to climate change, which is of critical importance to

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Preface xxiii

sustaining agricultural production growth in the pursuit of food security and poverty reduction. Failure to adapt to climate change would make the Philippines even more susceptible both to extreme events and to the long-term impacts of climate change. The country must prepare for these impacts and enhance its capacity to deal with them economically, institutionally, scientifically, and technically. This book has been produced as a response to this need. The volume focuses on enhancing the adaptive capacity of the Philippine agriculture sector — the most vulnerable and severely affected by climate change, first, because of its high dependence on natural resources, and second, because it has the highest incidence of poverty and lagging growth compared with the rest of the economy. The volume is designed to provide a much-needed base of knowledge and menu of policy options in support of decisionand policymaking on agriculture, climate change, and food security. The volume uses newly generated data, modelling outputs, and innovative analyses to provide a scientific basis for a variety of adaptation measures under different sets of climate change scenarios to guide decisionmakers in strategic planning and policy formulation.

OVERVIEW OF CLIMATE TRENDS AND PROJECTIONS The Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) of the Department of Science and Technology — the national agency mandated to undertake scientific and technological services in meteorology, hydrology, climatology, astronomy, and other geophysical sciences — developed climate projections as inputs for mainstreaming the analysis of vulnerability and the need for adaptation across the country’s development plans, programmes, and activities. The summary analysis below used the PAGASA climate projections in combination with available observed baseline climate data for the 1951–2010 period. The Philippines has four climate types. Type I occurs in westernmost parts of the country and is characterized by two pronounced seasons: dry from November to April and wet from May to October. Type II, in the eastern coast, has no dry season but pronounced rainfall from November to April. Type III, in the central-west, has no pronounced seasons but is characterized by a slightly drier period from November to April. Finally,

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xxiv

Preface

Type IV occurs in the country’s central-east and is characterized by an equal distribution of rainfall throughout the year. Analyses of the observed baseline climate data indicated the following changes in key climate trends during 1951–2010: 1. The yearly mean temperature rose by 0.65°C. 2. The maximum and minimum temperatures rose by 0.35°C and 0.94°C, respectively, leading to a significant increase in the number of hot days and a decrease in the number of cool nights. 3. Increased intensity of extreme daily rainfall is already being experienced in most parts of the country. 4. An average of twenty tropical cyclones formed or crossed the Philippine Area of Responsibility per year, and the number of tropical cyclones with maximum sustained winds of more than 150 kilometres per hour increased slightly, especially during the El Niño years. It should be noted that the analysis indicated significant variation in the above observed trends across the country’s regions (see Chapter 4, this volume, for more information). PAGASA projected the Philippine climate at two future points in time, 2020 and 2050, based on the observed climate trends outlined above, and the mid-range A1B emission scenario of the Fourth Intergovernmental Panel on Climate Change (see Chapter 9, this volume). The results can be summarized as follows: • All areas of the Philippines are projected to become warmer, more so in the relatively warmer summer months. • In all areas of the country, yearly mean temperatures (that is, the average of maximum and minimum temperatures) are projected to rise by 0.9°C to 1.1°C in 2020 and by 1.8°C to 2.2°C in 2050. • All seasonal mean temperatures are projected to rise in both 2020 and 2050, and be consistent in all the provinces across growing seasons. • Substantial regional differences are projected in rainfall changes in 2020 and 2050 in most parts of the country, with reduced rainfall in most provinces during the summer season, which is projected to make the usually dry season drier and increase the likelihood of drought. Rainfall is expected to be higher in most areas of Luzon

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and Visayas during the southwest monsoon and the September– November months, making these periods wetter and increasing the chances of floods. • Northeast monsoon rainfall is projected to increase, particularly in areas characterized by the country’s Type II climate. This is projected to increase the likelihood that these provinces will experience flooding. • The southwest monsoon season is projected to have larger increases in rainfall, particularly in the provinces of Luzon (0.9–63 per cent) and Visayas (2–22 per cent). In contrast, rainfall during the southwest monsoon season is generally projected to decrease in the provinces of Mindanao. • Projections for extreme events in 2020 and 2050 show that hot temperatures (indicated by the number of days with maximum temperature exceeding 35°C) will continue to become more frequent, that the number of dry days (those with less than 2.5 millimetres (mm) of rain) will escalate in all parts of the country, and that occurrences of heavy daily rainfall (exceeding 300 mm) will continue to increase in Luzon and Visayas. These climate projections were subsequently extended to 2100 and, under the same mid-range scenario, indicated a rise in temperature approaching as much as 3.1°C based on the assumption that past emissions deposited in the atmosphere will continue to induce an upward trend, ultimately with negative impacts for almost all economic sectors, but particularly for agriculture.

THE IMPACT ON PHILIPPINE AGRICULTURE Previous studies have shown that the direct impacts of climate change on agriculture could be both positive and negative. The negative impacts, however, greatly outweigh the positive ones. On the positive side, for example, typhoons increase the agricultural water supply, and soil erosion resulting from floods has the potential to improve soil fertility as nutrients flow from upland to lowland areas. Other factors being equal, these impacts are considered to produce positive effects because they facilitate increased agricultural production in the areas affected, thereby helping to improve food security.

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In contrast, natural hazards — such as severe typhoons, floods, and droughts — can reduce farm productivity, damage farm structures and facilities (including irrigation systems), limit farm planting options, and destroy infrastructure facilities that affect the flow and mobility of farm inputs and outputs. Studies have shown that crops suffer yield reductions whenever temperatures exceeded threshold values. Similarly, temperature increases coupled with rainfall changes that lead to drought conditions increase the incidence or outbreaks of pests and diseases, both in plants and animals. During the severe El Niño in 1997–98, heavy locust infestations occurred in twenty-three provinces. The country experienced a mild El Niño in 2010, followed by a severe one in 2014–15, resulting in infestations of armyworms in twenty-four provinces. Occurrences of coconut scaling insects in the provinces of Batangas, Laguna and Quezon and nearby areas have been attributed to these heat wave conditions. At the other extreme, declining temperatures in the Cordillera Autonomous Region have caused frost-related damage to vegetables and other high-value crops grown in the region. In the fisheries subsector, migration of fish to cooler and deeper waters would force fishers to travel further from the coasts to increase their catch. Seaweed production, which is already being practised as an adaptation to climate change in many poor and depressed coastal communities, has likewise been adversely affected. The negative impacts of climate change lead to increases in the overall cost of agricultural production, declines in agricultural productivity, contraction of the food supply, and increased food prices. Decreased yields and inadequate job opportunities in the sector can prompt migration and shifts in population, leading to increased pressure on already depressed urban areas. Insufficient food supply can further lead to increased incidence of malnutrition; higher poverty levels; and, potentially, heightened social unrest and civil conflict in certain areas. All these negative impacts pose threats to food security, particularly in areas directly affected. In value terms, the total damages to Philippine agriculture due to typhoons, droughts, and floods between 2000 and 2016 is estimated at PhP295.31 billion or US$6.46 billion (see Tables 8.2 and 8.3 in Chapter 8, this volume). Yearly losses averaged PhP17.37 billion, equivalent to about 1.8 per cent of the sector’s yearly average gross value-added. This includes production losses and damages to farm equipment, facilities, structures, irrigation, and road facilities. Losses were highest in 2014, at PhP40.40 billion, and lowest in 2002, at PhP1.21 billion. According to the

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Department of Agriculture, the two most recent typhoons (Urduja and Vinta) caused total agricultural losses of approximately PhP1.24 billion. The most affected commodities were rice, coconuts, corn, high-value cash crops, bananas, and fisheries (see Table 8.1 in Chapter 8 of this volume).

THE STRUCTURE OF THE BOOK In Part I, the book begins by laying out the context of Philippine agriculture, along with relevant historical evidence and information on the challenges facing the sector. Chapter 1, by Majah-Leah V. Ravago, Arsenio M. Balisacan, and Mercedita A. Sombilla, provides an overview of the patterns, composition, policies, and institutional framework that have influenced the performance of the agricultural sector in recent years, along with the changing dynamics of agricultural supply and demand in the context of a growing economy, urbanization, regional market integration, and climate change. Chapter 1 also provides evidence of the effects of a changing climate on critical agricultural resource inputs, as well as on productivity performance. Chapter 2, by David M. Wilson and Rodel D. Lasco, offers a review of the evidence of impacts of land-use change in the Philippines in the past twenty years, including the role of agricultural expansion on deforestation within both regional and global contexts. The chapter details the effects of a changing climate on landuse and ecosystem services, which are key to agricultural productivity, as well as the contributory role of land-use change to global greenhouse gas emissions. Chapter 3, by Arlene B. Inocencio, focuses on water — a critical resource not only for agriculture, but also for life itself. The chapter examines agriculture’s readiness to respond to potential climate impacts through an analysis of existing evidence on water supply and demand, and especially the changing dynamics of water demand across agricultural, industrial, and domestic uses. Specifically, the chapter outlines what the Philippine government has implemented in terms of public investments in water for agriculture in the past four decades, and illustrates the need for potential climate change impacts to purposefully be factored into agricultural water-sector investments, planning, management, and development. Chapter 4, by Felino P. Lansigan, presents scientific evidence of climate change and variability, its effects and impacts on agricultural production, responses to climate risks in agriculture, and challenges and imperatives. The implications of climate variability and

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climate change on the agricultural sector are also discussed, along with best agricultural practices and technology- and institutionally based adaptation strategies. Chapter 5, by Asa Jose U. Sajise, Dieldre S. Harder, and Paul Joseph B. Ramirez, provides an examination of the links between agricultural growth and the environment. The chapter describes the state of the Philippine natural resource base and environment in the context of agricultural productivity by reviewing current evidence on the extent of environmental externalities in four domains: land degradation, water availability, agrobiodiversity, and climate change. The impact of climate change is illustrated by assessing the sustainability of productivity growth for rice through a case study on greenhouse gas emissions. Part II of the book focuses on the challenges climate change imposes on the Philippine agricultural sector and strategies that need to be implemented to respond to these challenges. Chapter 6, by Maria Emilinda T. Mendoza, focuses on the gender-differentiated impacts of climate change, emphasizing the importance of mainstreaming gender issues into climate change–related policies and programmes. The chapter demonstrates the importance of determining women’s capabilities and vulnerabilities in being able to contribute to viable adaptation and mitigation measures, along with the challenge of supporting women’s ability to participate in decisionmaking processes related to such responses. Chapter 7, by Marites M. Tiongco, focuses on the country’s adaptation and mitigation strategies in terms of priorities and limitations in key vulnerable areas (food, land, water, and energy); trends in government budget allocations; and the roles of the institutions involved, together with their capacity and effectiveness in addressing climate change risks in the agriculture sector. Chapter 8, by Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc, provides a conceptual framework for understanding risk management and resilience at farm-household and national levels. The authors then apply the framework at the household level to explore how farm households cope with natural hazards such as typhoons, droughts, and floods. At the national level, the discussion focuses on how public policy can be designed to maximize economic welfare both before and after a disaster occurs. The discussion considers the pros and cons of alternative public policies in reducing household vulnerability before, during, and after a disaster. Part III of the book presents the results of economic modeling work designed to explore the country’s potential agricultural “futures” under

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climate change, thereby evaluating agricultural strategies to address climate change in the Philippines. The framework underlying the three chapters in this section of the book is a linked modelling approach to assess the effects on agriculture of alternative agricultural policies, technologies, and investments in combination with macroeconomic policies and climate adaptation strategies under a range of simulated climate and socioeconomic conditions. Chapter 9, by Timothy S. Thomas, Vijay Nazareth, and Renato A. Folledo, Jr. describes and presents the results of the biophysical approach to modelling alternative agricultural futures under climate change. Chapter 10, by Nicostrato D. Perez and Mark W. Rosegrant, uses a partial equilibrium agricultural model with forty-six crop and livestock commodities — the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) of the International Food Policy Research Institute (IFPRI) — to analyse and simulate the agricultural impacts and costs of climate change; the effectiveness of existing and emerging production technologies; and the contribution of demographic, development, and investment policies to food security and climate change adaptation efforts. Chapter 11, by Angga Pradesha and Sherman Robinson, uses a general equilibrium approach to assess the impacts of climate change on the Philippine agricultural sector and economy, focusing on the country’s agricultural production, income distribution, and the macroeconomic environment. In Part IV, Chapter 12, by Mercedita A. Sombilla and Mark W. Rosegrant, synthesizes the book’s recommendations to address the necessary policy and institutional challenges and enable the agricultural sector to perform its role as a key pillar for the country’s pursuit of inclusive growth, poverty reduction, and sustainable development. Strategies and investments options for agriculture, including the pros and cons of its implementation and operationalization, are identified and summarized for policymakers, development planners, and agricultural scientists.

THE BROADER RELEVANCE OF THIS WORK ACROSS ASIA The analysis and tools presented in this book have direct relevance to other countries in the Asia and Pacific regions, which share many characteristics related to agriculture and climate change with the Philippines. Climate projections in other parts of Asia are like those

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estimated for the Philippines. The temperature projections for the twentyfirst century under the mid-range scenario indicate strong warming. In Southeast Asia, warming is similar to the global mean of 2.5°C by the end of the twenty-first century. Warming at levels above the global mean is projected for South Asia (3.3°C) and East Asia (3.3°C), and much higher levels than the global mean are projected in the continental interior of Asia (3.7°C in central Asia, 3.8°C in Tibet, and 4.3°C in northern Asia). Precipitation in summer is likely to increase in northern Asia, East Asia, South Asia, and most of Southeast Asia but is projected to decrease in central Asia. Summer heat waves/hot spells are projected to be more intense, of longer duration, and more frequent in East Asia. Fewer very cold days are projected to occur in East Asia and South Asia, and an increase in the frequency of intense precipitation events is expected in parts of South Asia and in East Asia. Extreme rainfall and winds associated with tropical cyclones are also projected to increase in East Asia, Southeast Asia, and South Asia. The impacts of climate change on temperature and precipitation in much of the rest of Asia will generally have the same negative effect on food production and food security as in the Philippines. Higher temperatures threaten agricultural productivity by stressing crops and reducing yields. Projected impacts on rice and wheat yields suggest that any increases in production associated with carbon dioxide fertilization will be more than offset by reductions in yields resulting from temperature or moisture changes. Coastal erosion and land loss, inundation and sea flooding, upstream movement of the saline/freshwater front, and seawater intrusion into freshwater lenses are projected to occur in the large deltaic regions of Bangladesh, Myanmar, Vietnam, and Thailand, and in the low-lying areas of Indonesia, the Philippines, and Malaysia with rising sea levels and increased sea-surface temperatures potentially leading to the displacement of several million people from the region’s coastal zone. The costs of response measures to reduce the impact of a 30–50 centimetres sea-level rise in the region could amount to millions of dollars per year. Although many Asian countries have grown more rapidly than the Philippines, agriculture remains an important economic sector in most Asian and Pacific countries. The rising need for food and industrial crops over time has triggered to the need for increased agricultural production, which has exerted substantial strain on the environment.

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Water resources, which are already under heavy stress from increasing population and economic growth, continue to fall under considerable pressure. Furthermore, economic growth in Asia and the Pacific is also dependent on other natural resources, especially forest products. Like the Philippines, other countries in the region are confronted with the impacts of climate change, including droughts, floods, typhoons, rising sea levels, and heat waves. As in the Philippines, climate change poses significant threats to the sustainability of the region’s economic growth and poverty reduction. Given the similarity of these challenges, the analysis and analytical tools presented in this book to support policymakers, scientists, and others are relevant not only for the Philippines, but also for other Asian and Pacific countries. It is intended that this work facilitates muchneeded analysis of the effects of climate change and the identification of appropriate adaptation interventions to mitigate the impact on agriculture and food production.

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ACKNOWLEDGEMENTS The International Food Policy Research Institute and the National Economic and Development Authority would like to express their appreciation to those who made the implementation of this research project and the publication of the book possible, including Rowena Valmonte-Santos, Lorena Danessi, Mary Jane Banks, Ioannis Vasileiou, Kathleen Anne Coballes and the Environment Division of NEDA Agriculture, Natural Resources and Environment Staff. This work was implemented as part of and funded by the CGIAR Research Programs on Climate Change, Agriculture and Food Security (CCAFS) and Policies, Institutions, and Markets (PIM). CCAFS and PIM are carried out with support from CGIAR Fund Donors and through bilateral funding agreements. For details please visit and . This book has gone through the standard peer review procedure of the ISEAS – Yusof Ishak Institute. The opinions expressed here belong to the authors, and do not necessarily reflect those of CCAFS, PIM, IFPRI, CGIAR, and NEDA.

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LIST OF CONTRIBUTORS Arsenio M. Balisacan is the Chairman and Chief Executive Officer of the Philippine Competition Commission, and former Socioeconomic Planning Secretary and Director-General of the National Economic and Development Authority, Manila. Renato A. Folledo, Jr. is Consultant at the International Council for Research in Agroforestry. Dieldre S. Harder is Consultant at the Resources, Environment, and Economics Center for Studies Inc., Quezon City. Arlene B. Inocencio is Full Professor and Department Chair at the School of Economics, De La Salle University, Manila. Karl Robert L. Jandoc is Assistant Professor at the School of Economics, University of the Philippines, Diliman. Felino P. Lansigan is Professor and Dean of the College of Arts and Sciences, University of the Philippines Los Baños. Rodel D. Lasco is Country Coordinator of the World Agroforestry Centre. Maria Emilinda T. Mendoza is Associate Professor at the Department of Social Development Services, College of Human Ecology, University of the Philippines Los Baños. Vijay Nazareth is Research Analyst at the Environment and Production Technology Division, International Food Policy Research Institute.

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List of Contributors

Nicostrato D. Perez is Senior Scientist at the Environment and Production Technology Division, International Food Policy Research Institute. Angga Pradesha is Senior Research Analyst at the Development Strategy and Governance Division, International Food Policy Research Institute. Paul Joseph B. Ramirez is Assistant Professor at the Department of Economics, College of Economics and Management, University of the Philippines Los Baños. Majah-Leah V. Ravago is Research Faculty at the Department of Economics, Ateneo de Manila University, and previously Assistant Professor at the School of Economics, University of the Philippines, Diliman. Sherman Robinson is Research Fellow (emeritus) at the International Food Policy Research Institute and Professor of Economics (emeritus) at the University of Sussex. Mark W. Rosegrant is Research Fellow Emeritus in the Director General’s Office and former Director of the Environment and Production Technology Division, International Food Policy Research Institute. James A. Roumasset is Professor (emeritus) at the Department of Economics, University of Hawaii, USA. Asa Jose U. Sajise is Associate Professor at the Department of Economics, College of Economics and Management, University of the Philippines Los Baños. Mercedita A. Sombilla is Assistant Secretary of the Regional Development Office, National Economic and Development Authority, Manila. Timothy S. Thomas is Research Fellow at the Environment and Production Technology Division, International Food Policy Research Institute. Marites M. Tiongco is Full Professor and Dean at the School of Economics, De La Salle University, Manila. David M. Wilson is Consultant at the World Agroforestry Centre.

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PART I Setting up the Scenarios: Current Status and Potential Impacts of Climate Change to Philippine Agriculture

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1 CURRENT STRUCTURE AND FUTURE CHALLENGES OF THE AGRICULTURAL SECTOR Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla

After a period of lacklustre performance in the 2000s, the Philippine economy improved considerably during 2010–14. The Aquino Administration (2010–16) has anchored a platform of sustainable and inclusive growth that incorporates fighting corruption, pursuing peace and order, and instituting governmental reform. Average yearly growth during 2010–14 was 6.2 per cent — the country’s highest five-year average in forty years (peaking at 7.2 per cent in 2013). This pace of growth has put the country among the fastest growing developing economies in the world, resulting in unprecedented upgrades in credit and investment ratings. Progress, however, is slower in the social sector. Poverty is high and, so far, has responded sluggishly to economic growth. Underemployment also remains high at close to 20 per cent. Clearly, much work remains to be done. Sustaining economic growth over the medium to long term requires structural transformation — especially involving a shift from low-productivity areas and sectors to high ones. Raising agricultural

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla

productivity is a key contributor. Although agriculture’s share of the economy has continued to decline with economic development, enormous opportunities exist for income growth and poverty reduction in response to rapidly changing Asian food markets. Nevertheless, policy and governance constraints have limited Filipino farmers’ ability to seize these opportunities. Basic reforms are required to facilitate and strengthen agriculture’s contribution to the Philippine economy. This chapter provides an overview of the patterns, composition, policies, and institutional framework that have influenced the performance of the agricultural sector in recent years. The focus is the changing dynamics of agricultural supply and demand — as a whole and for key commodities — in the context of a growing economy, urbanization, and regional market integration. The chapter concludes with a discussion of the policy and institutional challenges inherent in enabling agriculture to form a key pillar in the country’s pursuit of inclusive growth, poverty reduction, and sustainable development.

AGRICULTURE IN THE CONTEXT OF STRUCTURAL TRANSFORMATION The decline of agriculture in response to economic development has been widely documented in the literature following the works of Clark (1940), Kuznets (1966), and Chenery and Syrquin (1975), using both cross-sectional and time-series data. The pattern is quite “uniform and pervasive” (Timmer 1988, p. 276), be it in socialist or capitalist countries in Asia, Latin America, or Africa. The “flying geese” metaphor exemplifies this pattern, describing Japan as the lead goose in structural transformation, followed by the new industrializing economies of Hong Kong, Singapore, South Korea, and Taiwan; then Malaysia, Thailand, and Indonesia; with the Philippines and Vietnam trailing behind (Ravago, Roumasset, and Balisacan 2010). The development process also requires that general economic growth be accompanied or preceded by rapid agricultural growth (Timmer 1988). Moreover, structural transformation involves a seeming paradox, whereby the declining importance of agriculture must be preceded or accompanied by rapid productivity growth in the sector. Anderson (1986) characterizes the underlying economic forces behind the structural transformation for a small, open economy. At the initial stage of development, the economy is largely agricultural, labour is employed

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5

mainly on the farm, and food exports defray the cost of manufactured imports. At this stage, nonfarm capital is low, per capita land endowment determines average incomes, and capital accumulation and innovation barely surpass diminishing labour productivity and the pressure of population growth. However, population growth also induces innovation and specialization within the agricultural sector (Boserup 1965, 1981). This, together with capital accumulation, eventually leads to the emergence of industrialization, whereby labour is released from the low-productivity areas of agriculture to high-productivity areas of manufacturing, and the surplus from agricultural development, combined with reinvestment of the profits from manufacturing, creates capital accumulation. This facilitates labour-intensive manufacturing industries in becoming internationally competitive. The process gradually shifts the country’s composition of export trade from primary agriculture to manufactured products. The lower the ratio of land per worker, the faster the emergence of the manufacturing sector, the faster the decline in the ratio of agricultural exports to imports, and the more rapid the technological progress from farm to nonfarm activities. Thereafter, the faster the accumulation of industrial capital, the faster the decline in agriculture’s comparative advantage, and the faster the decline in agriculture’s employment share. Specialization and capital accumulation together increase the returns to human capital formation, lowering population growth, which then enhances the virtuous circle of industrial revolution (Lucas 1993, 2001). The final stage of structural transformation is often referred to as “deindustrialization”, whereby the services sector grows relative to industry. As household incomes rise, a higher proportion of that income is spent on services, many of which are domestically produced because they are largely nontradable.1 In addition, the services sector is relatively labourintensive, which explains the eventual movement of the workforce out of manufacturing and agriculture. Furthermore, this reinforces the decline of agriculture’s shares of output and employment as development proceeds. Anderson (1986) draws two important conclusions from this simple, open, and growing model of the economy. First, while agricultural products are the most important exports at the beginning of the growth process, the reverse may eventually occur as agriculture becomes an import-competing sector. It is also likely that the country will become a net food importer and that (given land endowments) the higher the population growth, the sooner this will occur. Second agricultural employment and output will

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla

grow relatively slower as both the industrial and services sectors expand, making agriculture relatively less important to the economy. Relatedly, as incomes increase, food expenditures decline as a share of household expenditures. Hence, as development proceeds, food prices increasingly become a less important determinant of household welfare. Recent development experience also ties sustained poverty reduction to structural transformation (Dollar and Kraay 2002; Besley and Cord 2006; Timmer 2007). The movement of labour from low-productivity to highproductivity areas or sectors of the economy is a key factor in reducing poverty, especially in Asia. Such movement is also associated with sustained overall economic growth — so the empirically observed link between overall economic growth and poverty reduction is not surprising (Dollar and Kraay 2002). Similarly, the response of national poverty reduction to growth (growth elasticity) is found to be even higher, empirically, in cases where agricultural growth is robust (Timmer 2005; Timmer and Akkus 2008). Despite important role of agriculture in facilitating structural transformation and poverty reduction, a mix of market failures and political economy issues stifles the sector’s potential and undermines development efforts. The politics, institutions, and laws that shape agricultural protection (or the lack of it) is itself also a consequence of structural transformation (Anderson 1986; Anderson, Hayami, and Honma 1986; Balisacan and Roumasset 1989; Timmer 2007).

Structural Transformation in Output The Philippine experience is unique and did not follow the development experience of many countries. The country largely skipped the primary engine of growth: manufacturing for export (Balisacan and Hill 2007; de Dios and Williamson 2015). The share of industrial gross domestic product (GDP) fell from an average of 38 per cent in the 1980s to 35 per cent in the 1990s, and 33 per cent in 2000s (Figure 1.1). Similarly, agriculture’s share of total output fell from 16 per cent in the 1980s to 15 per cent in the 1990s, to 13 per cent in the 2000s. On the other hand, the corresponding share of the services sector expanded from 45 per cent in the 1980s, to 50 per cent in the 1990s, and to 54 per cent in the 2000s. The declining trend in the agricultural sector and the expansion in the services sector continued in recent years with average shares of 10.5 and 56.7 per cent, respectively, in 2011–15; however, industry’s average share of GDP remained at about

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Note: “Agriculture” includes hunting, forestry, and fishing. Source: Constructed by authors from PSA (Philippine Statistics Authority), “National Income Accounts”, various years [f] (accessed 16 May 2017).

FIGURE 1.1 Composition of Gross Domestic Product, 1960–2015

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla

33 per cent during this period. The levelling out of the manufacturing sector during that time could reflect the government’s efforts to rebalance the sources of economic growth, in particular drawing more growth from industry, especially manufacturing, investment, and exports, while reducing dependence on household consumption and services. Moreover, in the past three decades the economy shifted from agriculture towards an expanded services sector, skipping industrial development and deviating from the previously described experience of recently developed economies, particularly in East Asia. While growth in the Philippines has been restrained, South Korea’s has accelerated. Lucas (1993) refers to the continuing transformation of South Korea as a miracle, similar to what transpired in Hong Kong, Singapore, and Taiwan.

Structural Transformation in Employment Structural transformation in employment follows the development process in output, albeit with an expected time lag (Figure 1.2). Agriculture had been the largest provider of employment nationwide in the 1980s, at 51 per cent; however, its average share of total employment fell to 40 per cent in the late 1990s and to 32 per cent during 2010–15. As the services sector expanded in the 1980s it attracted labour away from agriculture, eventually exceeding agricultural employment in 1996. The sector’s share of employment continued to rise to an average of 48.2 per cent in 2000s and 52.6 per cent during 2010–14. While industry, which includes manufacturing, is responsible for a substantial share of output, it has the lowest share of employment, and shares remained constant at 15 per cent for the three decades leading to 2014. As previously discussed, growth in agriculture is required to stimulate growth in industry, which in turn generates high-quality employment, even for unskilled workers associated with manufacturing. The country’s experience had been perverse, however, with employment moving out of the low-productivity agricultural sector into an equally low-productivity services sector. Labour productivity in agriculture is the lowest among the sectors and has stagnated (Figure 1.3). Labour productivity in the services sector has also stagnated, but improved in recent years. In contrast, labour productivity in industry has grown, especially in recent years. The share of paid agricultural employment grew from 2000 onward to average 27 per cent during 2009–11, whereas the average share of self-

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Note: Data for 2014 exclude Leyte due to the onset of typhoon Haiyan (Yolanda). Source: Constructed by authors from PSA (Philippine Statistics Authority), “Labor Force Survey”, October round, various years [d] (accessed 16 May 2017).

FIGURE 1.2 Composition of Employment, 2000–15

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10

Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla FIGURE 1.3 Labour Productivity, 1987–2015

Thousand pesos per worker (2000 prices)

600,000 Agriculture labour productivity

500,000

Industry labour productivity Manufacturing labour productivity

400,000

Services labour productivity

300,000

200,000

100,000

0

Note: Labour productivity is calculated as the gross value-added divided by the number of workers. Source: Constructed by authors from PSA (Philippine Statistics Authority), “Labor Force Survey”, October round, various years [d] (accessed 16 May 2017).

employed (that is, “unpaid”) agricultural workers declined to about 25 per cent in recent years, indicating improvement in the quality of agricultural employment (Table 1.1). The ratio of male to female agricultural workers remained stable at 3 to 1. About two-thirds of those employed in the sector were under 45 years old. The share of workers under 25 years old remained relatively stable in the ten years to 2015; however, it should be noted that this age bracket constituted about 50 per cent of the agricultural workforce in the 1980s and 1990s (Ravago and Cruz 2004). The recent decline implies the sector is considerably less attractive to the younger generation. Creating opportunities for productive employment is critical to the country’s sustained economic growth and poverty reduction goals. At the turn of the millennium, the Filipino workforce comprised about 31 million people (Table 1.2), representing about 70 per cent of the economically active population (those 15–64 years old). Of the 31 million Filipinos able to work, only 28 million had work in the 2000s; those remaining were either unemployed or underemployed. As of 2012–15, the pool of

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TABLE 1.1 Characteristics of Agricultural Workers, 2001–15 Characteristic Location (%) Urban Rural Category (%) Wage/salary earners Self-employed Unpaid family workers Gender (%) Male Female Age (%) 15–19 years 20–24 years 25–34 years 35–44 years 45–54 years 55–64 years 65 years and over Total number of workers included (thousands)

2001

2004

2007

2010

2012

2014

2015

16.42 83.58

15.74 84.26

15.36 84.64

15.21 84.79

14.73 85.27

14.92 85.08

14.65 85.35

22.72 47.21 30.07

24.99 48.79 26.31

23.23 50.63 26.14

27.00 47.55 25.45

31.93 45.92 22.15

30.91 45.99 23.19

29.81 48.26 21.94

74.27 25.73

75.18 24.82

74.58 25.42

74.48 25.52

74.91 25.09

74.16 25.84

74.63 25.37

10.99 10.66 10.98 10.31 10.29 9.95 8.97 8.95 10.12 9.09 9.03 9.18 9.01 9.21 16.09 20.76 21.39 19.42 21.56 20.94 21.79 20.22 20.34 20.59 21.15 20.09 21.02 21.79 17.15 15.83 16.84 18.18 17.26 17.46 18.56 12.17 10.33 10.69 11.62 11.56 11.53 12.34 7.23 6.28 6.47 6.74 6.51 6.38 7.43 10,426 10,159 12,497 12,515 12,373 12,502 11,761

Notes: Category of worker excludes observations for categories not reported. Data for 2012 are from the July survey round; 2014 data exclude Leyte due to typhoon Haiyan (Yolanda). Source: Calculated by authors from PSA (Philippine Statistics Authority). “Labor Force Survey.” October round. Various years [d]. (accessed 16 May 2017).

workers had expanded to 41 million, comprising 38 million employed and 2.9 million unemployed. The Philippine Statistics Authority (PSA) defines “unemployed” as people in the labour force who had no job or business and did not work during the reference period but were reportedly looking for work; “underemployed” comprises employed people with the expressed desire for additional hours of work in their current job, in an additional job, or in a new job with longer hours. In terms of growth, the labour force expanded from 1990 until 2000, but growth decelerated thereafter. Employment growth fluctuated, and the unemployment rate fell from a high of 10.7 per cent in 2000 to 7.1 per cent in 2013. Underemployment remained high during this period, at about 20 per cent.

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24,665 3.0 22,345 2.6 90.6 9.4 22.7

1990 28,589 3.4 25,965 3.7 90.8 9.2 20.8

1995 31,493 3.4 28,117 3.1 89.3 10.7 20.3

2000 35,537 0.9 32,187 2.1 90.8 7.8 20.4

2005 38,930 2.8 36,096 3.0 92.7 7.3 19.1

2010 40,426 1.0 37,600 1.1 93 7.0 20.0

2012

41,022 1.5 38,118 1.4 92.9 7.1 19.3

2013

41,229 0.7 38,503 1.4 93.5 6.5 18.3

2014

Notes: Data are three-year averages centred on the year indicated. The unemployment rate for 2005 refers to 2005 only due to a change in PSA’s definitions that year. Source: Calculated by authors from PSA (Philippine Statistics Authority), “Labor Force Survey”, October round, various years [d] (accessed 16 May 2017).

Labour force (thousands) Growth rate (%) Number of employed (thousands) Growth rate (%) Employment rate (%) Unemployment rate (%) Underemployment rate (%)

Employment Category

TABLE 1.2 Labour Trends, 1990–2014

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Structural Transformation in Trade The structural transformation in Philippine trade followed the same development patterns exhibited in output and employment. In the 1980s, 1990s, and 2000s, agricultural shares of total trade averaged 15, 9, and 7 per cent, respectively, mainly driven by sharp contractions in agriculture’s share of total exports during 1980–2000 (Figure 1.4, panel a). Agriculture’s share of imports followed a series of peaks and troughs within a somewhat stable band. During 2010–15, agriculture’s yearly share of total trade averaged 9 per cent, driven by the agricultural share of total imports, which averaged 10 per cent. Nevertheless, in absolute terms, trade in agricultural products more than doubled from a yearly average of US$2.6 billion during 1980–90 to US$7.4 billion during 2000–10. During 2011–15, trade in agricultural products averaged US$14.6 billion per year (Figure 1.4, panel b). The composition and shares of major agricultural imports and exports have also changed over the years (Tables 1.3 and 1.4). The country’s major exports, including coconut oil, bananas, sugar, pineapples, and tuna, accounted for 50 per cent of the agricultural export value in 2013–15. Coconut oil remains the most valuable export product; bananas declined in importance in 2010 but rebounded thereafter. In terms of the composition of the country’s major imports, wheat and meslin recorded the highest import value during 2013–15, followed by milk, cream, and related products (Tables 1.5 and 1.6). Rice, the country’s staple, ranked fourth, with its imports registering the largest share of total agricultural imports during 2013–15. The trend in rice imports was closely linked with the government’s food sufficiency policy, which is discussed later in this chapter. The country’s major agricultural trading partners include members of the Association of Southeast Asian Nations (ASEAN) — particularly Brunei, Indonesia, Malaysia, Singapore, and Thailand — as well as Australia, the European Union, Japan, and the United States (Table 1.7). In 2008–13, a substantial share of Philippine agricultural imports (19 per cent) and exports (24 per cent) came from and went to the United States; 13 per cent of agricultural exports went to Japan. Trade with the ASEAN member countries has also been significant. In 2013–15, agricultural exports to ASEAN neighbors averaged 13 per cent, whereas imports averaged 22 per cent. As in the past ten years, the Philippines generated surpluses from trade in agricultural products with the European Union and Japan.

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14

Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla FIGURE 1.4 Value and Share of Agricultural Trade, 1980–2015

Notes: “Total exports” and “total imports” include both goods and services, whereas “goods” only includes merchandise. FOC = free on board; CIF = cost, insurance, and freight. Source: Constructed by authors from PSA (Philippine Statistics Authority), “Import/export data”, various years [c] (accessed 16 May 2017).

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TABLE 1.3 Value of Major Philippine Exports, 2001–14 2001 Commodity Ranking in 2013 Coconut oil Bananas Centrifugal sugar Pineapple and products Tuna Desiccated coconut Seaweed and carrageenan Tobacco, manufactured Fertilizer, manufactured Milk, cream, and related products Value of total agricultural exports

2005

2010

2014

(US$ million) 1,411 (21.01) 1,299 (15.29)  n.a.

1,605 (16.02) 1,364 1(9.87)  n.a.

1,155 1(7.94) 1,131 1(6.67) 1,177 1(3.93) 1,176 1(3.88)  n.a.

1,201 1(5.47) 1,134 1(3.23) 1,122 1(3.40) 1,178 1(1.89)  n.a.

1,145 1(2.30)  n.a.

1,180 1(1.88)  n.a.

1,958 1,(100)

8,660 4,(100)

1,095 (25.44) 1,378 1(9.17) 1,143 1(2.91) 1,282 1(6.88) 1,336 1(8.43) 1,195 1(4.56) 1,155 1(3.61) 1,157 1(3.71) 1,130 1(3.06) 1,136 1(3.21) 4,207 4,(100)

1,131 (19.04) 1,917 (15.04)  n.a. 1,478 1(8.16) 1,511 1(8.38) 1,204 1(3.37) 1,226 1(3.76) 1,260 1(4.30)  n.a. 11,65 1(1.12) 6,025 1,(100)

Notes: Values are three-year moving averages centred on the year indicated; figures in parentheses indicate shares of the total value of agricultural exports; n.a. indicates that data were not available. Source: Calculated by authors from PSA (Philippine Statistics Authority), “Import/export data”, various years [c] (accessed 16 May 2017).

SOURCES OF AGRICULTURAL GROWTH Output Growth Agricultural value-added growth rates reflect structural transformation within the agricultural sector (Table 1.8). The major growth drivers are the livestock and poultry, fisheries, and crop subsectors — registering average yearly growth of 4.1, 4.4, and 4.3 per cent, respectively, during 2000–05. Growth proved to be short-lived, however, with decelerations

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla TABLE 1.4 Volume of Major Philippine Exports, 2001–14 2001

Commodity Ranking in 2013 Coconut oil Bananas Centrifugal sugar Pineapple and products Tuna Desiccated coconut Seaweed and carrageenan Tobacco (manufactured) Fertilizer (manufactured) Milk and cream and products

2005

2010

2014

(thousand metric tons) 1,133 1,628 n.a. 458 70 87 45 n.a. 317 n.a.

1,059 2,042 n.a. 583 61 123 35 n.a. 373 n.a.

1,003 1,767 253 490 99 111 35 23 361 33

931 2,768 … 732 94 57 34 27 … 18

Notes: Values are three-year moving averages centred on the year indicated; ellipses indicate that the commodity was not ranked in the top ten in 2013; n.a. indicates that data were not available. Source: Calculated by authors from PSA (Philippine Statistics Authority), “Import/export data”, various years [c] (accessed 16 May 2017).

in all subsectors during 2006–10 due to weather disturbances, notably Typhoon Ketsana in 2009. Overall, the performance of the sector was volatile and erratic in more recent years; the growth of livestock and fisheries subsectors further decelerated at 2.2 and –0.5 per cent per year on average, respectively, during 2011–15. The forestry subsector reversed its growth trend — contributing positive, albeit small, growth to the sector — but its relative importance has since declined to represent less than a 1 per cent share of gross agricultural value-added. In terms of sectoral shares, crops continued to dominate, representing about 50 per cent of agricultural value-added between 2000 and 2015 (Table 1.8). The fisheries subsector remained relatively stable over this timeframe, increasing its average share of agricultural value-added from 15 per cent in the early 2000s to 19 per cent in 2011–15. In real terms, trends in the crop subsector mirror those of the agricultural sector as a whole, declining in 2008 and reaching a plateau towards the end of the period (Figure 1.5). Fisheries and livestock and poultry followed an increasing trend from 2000 until 2015, whereas, forestry was far more volatile. The major subsectors are discussed in more detail below.

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TABLE 1.5 Value of Major Philippine Agricultural Imports, 2001–14 2001) Commodity Ranking in 2013 Wheat and meslin Milk, cream, and related products Soybean oil/cake meal Rice Fertilizer, manufactured Meat, bovine Urea Tobacco, manufactured Coffee Total value of agricultural imports

2005)

2010)

2014)

(US$ million) 409.89) (14.01) 344.04) (11.82) 197.26) (6.73) 147.21) (5.00) n.a.

430.48) (10.77) 403.83) (10.21) 352.52) (8.93) 442.68) (11.00) n.a.

85.19) (2.93) 69.80) (2.41) 77.86) (2.69) n.a.

118.84) (3.01) 102.67) (2.57) 178.73) (2.57) n.a.

2,919.23) 3,976.85) (100) (100)

773) (11.06) 556) (7.76) 456) (6.48) 1,025) (14.86) 253) (3.53) 191) (2.68) 196) (2.79) 156) (2.27) 109) (1.53) 7,062) (100)

1,073) (11.32) 722) (7.78) 873) (9.25) 414) (4.17) 341) (3.66) 299) (3.14) 227) (2.44) 165) (1.74) 253) (2.66) 9,509) (100)

Notes: Values are three-year moving averages centred on the year indicated; figures in parentheses indicate shares of the value of total agricultural exports; n.a. indicates that data were not available. Source: Calculated by authors from PSA (Philippine Statistics Authority), “Import/export data”, various years [c] (accessed 16 May 2017).

Crops While crop production represents the largest share of agricultural output, its rate of growth in the recent decade was among the slowest. Growth was negative from 2006–10 (Table 1.8), in part because the expansion of arable land slowed down dramatically. Adding to the deceleration of crop production was a series of natural disasters and droughts. Of the crops constituting the overall growth trend, palay (unmilled rice grain) and corn, did fairly well (Table 1.9). Negative growth of the crops subsector during 2006–10 was driven by a slump in sugarcane and other crops (together

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla TABLE 1.6 Volume of Major Philippine Imports, 2001–14 2001

Commodity Ranking in 2013 Wheat and meslin Milk, cream, and products Soybean oil/cake meal Rice Fertilizer, manufactured Meat, bovine Urea Tobacco, manufactured Coffee

2005

2010

2014

(thousand metric tons) 2,875 252 1,064 881 n.a. 81 630 23 n.a.

2,293 275 1,317 1,513 n.a. 99 546 65 n.a.

2,589 273 1,432 1,614 804 87 579 46 58

3,446 308 1,820 990 1,083 93 694 47 n.a.

Note: Values are three-year moving averages centred on the year indicated; n.a. indicates that data were not available. Source: Calculated by authors from PSA (Philippine Statistics Authority), “Import/export data”, various years [c] (accessed 16 May 2017).

constituting less than 40 per cent of all crops). In Table 1.9, the growth rate of bananas (7.45 per cent) overtook that of palay (1.18 per cent) in 2006–10, largely due to improved farm practices. Nevertheless, growth in banana production hit an all-time low in 2011–15 (–0.62) due to external factors, such as pest infestation. This, combined with deep troughs recorded for coconuts, drove the deceleration of growth during 2006–15.

Livestock and Poultry The contribution of livestock and poultry to gross agricultural value-added rose to 23 per cent at the turn of the millennium from an average of 18 per cent in the 1970s. This growth was erratic, however, decelerating during the first half of the decade and recovering only after 2006 (Figure 1.6). The downtrend in the growth of livestock and poultry was attributed to declining production of hogs, carabaos (water buffalo), and cattle during the first three years of the 2000s. Growth in the poultry subsector was relatively faster, reaching about 4.8 per cent per year on average during 2011–15. While the livestock subsector declined sharply in 2007, recording growth of –2.61 per cent, its production rebounded in succeeding years due

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3135.75 6,079.80 31.09 261.73 452.86 96.83 777.53 1,069.04 486.28 1,970.84 606.63 486.93 781.36 2,194.87

3,889.30 7,684.74 42.00 278.80 527.49 176.34 971.68 1,402.16 579.68 2,795.98 859.63 518.37 908.82 2,513.09

2009

1,045.69 2,427.60

1,017.05 587.96

608.75 2,863.46

961.92 1,142.38

432.02 117.41

35.65 260.98

4,101.09 7,399.79

2010

1,285.39 2,687.26

1,066.03 607.94

920.71 2,186.69

1,454.27 1,655.15

653.05 138.67

52.32 564.21

5,431.76 7,839.93

2012

1,325.18 2,728.34

911.75 631.93

857.68 2,206.89

1,203.45 1,765.35

691.85 116.17

48.03 719.66

5,037.94 8,168.33

(US$ million)

2011

1,965.39 3,039.38

1,048.05 738.34

964.60 1,956.80

1,466.98 1,754.83

915.99 100.87

49.02 340.91

6,400.03 7,931.14

2013

2015

2,233.78 3,206.31

999.36 1,066.83

788.19 2,754.74

1,505.42 1,992.34

944.19 108.72

72.00 502.30

1,562.15 3,525.50

994.98 1,110.02

517.50 3,344.78

1,329.51 2,337.95

655.33 125.60

72.40 521.91

6,542.95 5,131.85 9,631.24 10,965.76

2014

Notes: ASEAN = Association of the Southeast Asian Nations. Source: Compiled by authors from PSA (Philippine Statistics Authority), “Import/export data”, various years [c] (accessed 16 May 2017).

Philippines Exports Imports Australia Exports Imports Japan Exports Imports United States Exports Imports ASEAN Exports Imports European Union Exports Imports Rest of the World Exports Imports

Region/Country

2008

TABLE 1.7 Agricultural Trade with Major Partners, 2008–15

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla

20

TABLE 1.8 Growth Rates of Gross Agricultural Value-Added by Commodity, 2000–15 Commodity Livestock and poultry Fisheries Crops Forestry Total

2000–05

2006–10

2011–15

14.1 1.(22) 14.4 1.(15) 14.3 1.(58) 13.0 (0.46)

13.2 1.(22) 17.4 1.(18) –5.7 1.(54) –3.5 (0.55)

12.2 1.(24) –0.5 1.(19) 11.4 1.(49) 19.5 (0.60)

14.3 ,(100)

–0.6 ,(100)

11.4 ,(100)

Note: Value-added is calculated in 2000 prices. Figures in parentheses indicate average shares of gross value-added for the period. Source: Calculated by authors from PSA (Philippine Statistics Authority), “Gross Value Added in Agriculture, Fishery, and Forestry”, various years [i] (accessed 16 May 2017).

FIGURE 1.5 Trends in Real Gross Agricultural Value-Added, 2000–15

200 180 160 140 120 100 80 60 40 20 0 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Source: Constructed by authors from PSA (Philippine Statistics Authority), “Gross Value Added in Agriculture, Fishery, and Forestry”, various years [i] (accessed 16 May 2017).

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TABLE 1.9 Rate of Growth of Gross Value-Added by Crop, 2000–15 Crop

2000–05

2006–10

2011–15

Palay

13.61 (36.48) 14.29 (10.28) 13.53 1(9.18) 12.63 1(5.87) 14.88 1(7.43) 12.22 (30.77) 13.28 1,(100)

11.18 (36.83) 12.89 (11.93) 10.99 1(8.55) –0.87 1(5.03) 17.45 1(9.47) 10.48 (28.20) 11.63 1,(100)

12.39 (38.13) 12.53 (12.41) –1.49 1(7.91) –1.17 1(4.91) –0.62 1(9.65) 11.37 (26.98) 11.35 1,(100)

Corn Coconuts Sugarcane Bananas Other crops Total

Note: Value-added is calculated in 2000 prices. Figures in parentheses indicate average shares of gross value-added for the period. Source: Calculated by authors from PSA (Philippine Statistics Authority), “Gross Value Added in Agriculture, Fishery, and Forestry”, various years [i] (accessed 16 May 2017).

FIGURE 1.6 Rate of Growth Rate of Gross Value-Added for Livestock and Poultry, 2001–15

Note: Value-added is calculated in 2000 prices. Source: Constructed by authors from PSA (Philippine Statistics Authority), “Gross Value Added in Agriculture, Fishery, and Forestry”, various years [i] (accessed 16 May 2017).

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla

to increased domestic demand, advances in production technology, and incentives for large commercial producers (such as duty-free importation of stock). Hog and carabao production contributed substantially to the strong performance of the livestock sector during 2000–15 (Figure 1.7, panel a). Hog production dominated the subsector in terms of volume, representing more than three-quarters of livestock production. Carabao production also grew by about 50 per cent over 1990 levels largely because the government intensified livestock distribution in the late 1990s. Goats maintained their popularity as “the poor man’s cow”, recording relatively stable production. The production of cattle, on the other hand, declined due to the widespread incidence of diseases (such as foot and mouth disease). The economic difficulties of the 1980s prompted a fall in popularity of higher priced pork and beef in favour of chicken and carabeef, and these preferences persisted into the 2000s. Chicken and chicken eggs dominated the poultry subsector, whereas ducks and duck eggs exhibited a downward trend (Figure 1.7, panel b). Chicken production accelerated faster relative to other poultry options despite the 2004 avian flu scare in Southeast Asia (partly due to the country’s efforts to contain the virus).

Fisheries Fishing is one of the most important income- and employment-generating activities in the Philippines, especially in coastal areas. On a positive note, the downtrend in the sector’s performance during the 1990s due to rapid depletion of marine and aquatic resources has been turned around, but issues — such as destructive fishing, overfishing, commercial fishing vessels encroaching municipal fishing grounds, massive degradation of mangroves, and pollution of major rivers and lakes — continue to constrain the sector (David 2003). The average rate of growth slowed down during 2011–15 posting an average of –0.5 per cent. Among the three subsectors constituting fisheries, aquaculture represented the largest average share during 2000–15 (47 per cent), whereas municipal and commercial fishing represented 28 and 25 per cent shares, respectively. Aquaculture production accelerated sharply after 2000 in real terms (Figure 1.8, panel a), starting with slow growth from the second half of the 1990s (due to scarce milkfish fries, disease problems in prawn culture, and red-tide episodes in mariculture). Municipal fishing grew in relative importance, exhibiting an upward trend

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Source: Constructed by authors from PSA (Philippine Statistics Authority), “Production”, various years [j] (accessed 16 May 2017).

FIGURE 1.7 Trends in Livestock and Poultry Production, 2000–15

Current Structure and Future Challenges of the Agricultural Sector

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a. Quantity

50

100

150

200

250

300

350

b. Value

Notes: PSA defines the three fisheries subsectors as (1) aquaculture: operations involving all forms of raising and culturing fish and other species in fresh, brackish, or marine water areas; (2) commercial fishing: catching fish using fishing boats with a capacity of more than three gross tons for trade, business, or profit beyond subsistence or sports fishing; and (3) municipal fishing: catching fish within municipal waters using fishing vessels of three gross tons or less, or not requiring the use of fishing vessels Source: Constructed by authors from PSA (Philippine Statistics Authority), “Production”, various years [j] (accessed 16 May 2017).

50

100

150

200

250

FIGURE 1.8 Trends in Fishery Production by Subsector, 2000–15

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Current Structure and Future Challenges of the Agricultural Sector

25

in both output and real value (Figure 1.8). Commercial fishing also grew steadily due to higher catches of tuna for export (David 2003). In terms of value, the fisheries output trend was positive due to higher prices.

Productivity Growth Productivity growth is key to the profitability and viability of any economic activity. In the long run, especially given land constraints and population pressures, the most-important driver of growth is improved production efficiency. Yields, defined as production output per unit of land, are a commonly used indicator of agricultural productivity (albeit partial because they only account for land as an input). On this basis, palay and corn productivity rose during the 2000s, whereas the productivity of the other crops stagnated (Figure 1.9). The productivity in sugarcane and pineapples changed little. Bananas recorded the most significant shift overall. Abaca, coffee, and mangoes all decreased in land productivity, whereas palay, corn, coconuts, and tobacco recorded significant increases during 2000–15. A more comprehensive indicator of productivity growth, total factor productivity (TFP) takes into account the growth in all inputs used in production. Teruel and Dumagan (2014) estimated Philippine agricultural TFP growth using the “superlative index number” procedure, under which revenue growth is examined in the context of prices, quantities, and TFP. Considering estimates over the 1975–2004 period, agricultural TFP growth was highest during 1975–79, at 6.22 per cent (Table 1.10). Growth was mainly driven by the earlier Green Revolution but was not sustained thereafter and even plummeted into negative values during 1985–89. At the turn of the millennium, TFP growth began to rise, averaging 3.6 per cent during 2000–04.

FOOD AND CONSUMPTION PATTERNS Food as a share of household consumption expenditures tends to decline with increased per capita incomes. This pattern has been found to be robust in both cross-sectional and time-series household data, and at local, regional, and global levels. In examining the driving forces behind this stylized pattern of development, Anderson (1986) identified the fundamental role of household preferences — that is, the universally increasing preference for nonfood relative to food purchases as per capita

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Note: Data are presented as three-year averages centred on the year indicated Source: Constructed by authors from the PSA (Philippine Statistics Authority), “Production”, various years [j] ; and “Land Use”, various years [k] (accessed 16 May 2017).

FIGURE 1.9 Land Productivity, Selected Crops, 2000–15

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1.27 19.15 3.75 1.51 1.34 3.24 0.17 0.77 1.10 0.34 4.54 0.78 –0.01 –0.29 0.36 –0.04 –0.32 0.64

0.36 0.10 0.51 0.27 0.15 0.27 –0.05 0.06 1.83 0.32 0.01 0.17 0.01 0.07 –1.82 0.90

1980–84

6.22 9.37

1975–79

0.01 0.68 –0.21 –0.01 2.19 0.54

2.14 1.09 0.79 –0.26 0.04 0.19 0.94 0.09 3.14 0.23

–0.70 10.87

1985–89

0.00 0.12 –0.12 0.02 0.34 0.62

1.71 0.44 0.26 0.02 0.07 0.26 0.59 0.08 2.65 0.20

1.70 8.96

1990–94

0.04 0.22 0.28 0.04 0.10 0.13

1.42 0.28 0.01 1.25 0.05 0.38 0.97 0.08 1.27 0.13

2.20 8.86

1995–99

0.01 0.07 0.15 0.04 0.03 –0.29

0.83 0.63 –0.01 –0.14 0.06 0.16 –0.03 –0.01 1.80 0.18

3.58 7.04

2000–04

Note: TFP = total factor productivity. Source: Teruel, Romeo, and Jesus Dumagan, “Total Factor Productivity Growth in Philippine Agriculture”, in Productivity Growth in Philippine Agriculture, edited by R. Briones, M. Sombilla, and A. Balisacan (Los Baños: Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA); Muñoz, Nueva Ecija: Philippine Rice Research Institute (PhilRice); and Quezon City: Department of Agriculture-Bureau of Agricultural Research, 2014).

TFP growth (%) Revenue growth (%) Growth in output prices (%) Rice Corn Sugar Coconuts Tobacco Root crops Fruit Vegetables Meat Eggs Growth in input quantities (%) Seed Fertilizer Animal labour Machinery Land Labour

Parameter

TABLE 1.10 Total Factor Productivity and Contributions to Revenue Growth, 1975–2004

Current Structure and Future Challenges of the Agricultural Sector

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla

incomes rise. Nevertheless, substantial variation in household responses to income changes typically exists. Demand for staples (which in the Philippines and many countries of Asia and Africa means rice) tends to shift less in response to income changes than does the demand for commodities like meat and fruit, but at some point in the development process the share of food expenses does tend to fall (see Huang, Yang, and Rozelle 2010 for the case of China). Interestingly, food consumption patterns in the Philippines appear not to conform to stylized patterns, at least based on recent data. At best, the evidence from cross-sectional and time-series data is mixed. Despite increases in per capita GDP since 2000 (averaging about 3 per cent per year), food as a share of household spending decreased only marginally between 2000 and 2015, ranging from 54 to 50 per cent (Table 1.11). This observation also extends to commodities comprising food expenditures. Spending on rice tended to rise rather than fall, and spending on meat, dairy, and fruit products unexpectedly declined or remained flat (Balisacan 1994). Cross-sectional data from the nationally representative 2015 Family Income and Expenditure Survey (PSA 2015), however, reflect the more expected trends (Figure 1.10). What could explain this conundrum? At least two, arguably fundamental, factors are involved. The first is that economic growth during the period was accompanied by rapidly rising income inequality, making growth highly exclusive. But because the consumption patterns of the very rich differ so much from those of the poor and near-poor — the overwhelming majority of the population — the average food consumption pattern of the entire population does not correlate with average income levels, as reflected by per capita GDP. The very sluggish reduction in absolute poverty in recent years could reflect initial high, and rising, income inequality. This, of course, is consistent with the earlier observation that food shares had decreased only marginally. Conceptually, this “Engel value” is a reasonable approximation of household welfare, with rising food shares indicating deterioration (Deaton 1986). The second factor in play has to do with the evolution of food prices relative to other consumer goods. In recent years, consumer food prices have tended to rise faster than nonfood prices (Figure 1.11). Movement in the Consumer Price Index (CPI) for the poorest 30 per cent of the population was even sharper (Figure 1.12) and the contrast for rice even sharper again. Since rice is such an important component in the diets of poor and near-poor people in the Philippines,

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9.5 22.1 1.5 9.9 13.3 6.6 13.8 2.5 2.8

9.7 24.2 1.7 9.9 13.1 6.1 13.9 2.4 2.6

8.5 23.7 1.3 9.2 12.6 6.4 13.2 2.7 2.7

51.1 89.5 10.5

2006

8.1 25.7 1.3 8.9 11.8 6.2 12.8 2.6 2.8

52.23 88.9 11.1

2009

8.8 24.6 2.0 9.1 12.0 6.1 13.3 3.3 2.7

52.8 86.5 13.5

2012

8.9 23.6 1.9 8.8 11.6 6.2 12.5 3.7 2.7

50.6 84.2 15.8

2015

Notes: For 2000, cereals include rice; for 2012 and 2015, roots and tubers are the sum of spending on vegetables cultivated for roots and potatoes and tubers. In 2012, a new product classification was used that might account for “kinks” in the trends. Sources: Calculated by authors from PSA (Philippine Statistics Authority), “Family Income and Expenditure Survey. Incidence of multidimensional versus income-based poverty”, various years [b] (accessed 16 May 2017).

52.4 90.7 9.3

54.0 91.9 8.1

Food as a share of total household expenditures (%) Share of food consumed at home (%) Share of food consumed outside home (%) Share of food expenditures by food category (%) Cereals, excluding rice Rice Roots and tubers Fruit and vegetables Meat products Dairy products Fish products Coffee, cocoa, and tea Nonalcoholic beverages

2003

2000

Category

TABLE 1.11 Shares of Household Expenditures on Food, 2000–15

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla FIGURE 1.10 Log of Per Capita Income Versus Food as a Share of Total Household Expenditures, 2015

Source: PSA (Philippine Statistics Authority), “Family Income and Expenditure Survey”, 2015 (accessed 16 May 2017).

its share of household expenditures actually rises with rising prices. This effect, combined with the dismally low income increases among poor and near-poor households, explains for the sluggish decline in food shares.

AGRICULTURE AND POVERTY Poverty Trends The rural sector constitutes half the national population (Table 1.12) and continues to account for about two-thirds of all poor people, the overwhelming majority of whom are employed in agriculture. Hence,

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FIGURE 1.11 Consumer Price Index Ratios for Food, 1994–2015

Note: CPI = Consumer Price Index. Source: Constructed by authors from PSA (Philippine Statistics Authority), “Family Income and Expenditure Survey”, 2015 (accessed 16 May 2017).

FIGURE 1.12 Inflation Rate and Inflation Rate of the Poorest 30 per cent of Households, 2007–15

Source: Dennis Mapa, Kristelle Castillo, and Krizia Francisco, Rice Price, Job Misery, Hunger Incidence: Need to Track Few More Statistical Indicators for the Poor (Quezon City: School of Statistics, University of the Philippines, Diliman, 2015); and PSA (Philippine Statistics Authority), “Family Income and Expenditure Survey”, 2015 (accessed 16 May 2017).

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla TABLE 1.12 Share of Urban, Rural, and Agricultural Population Total Population

Agricultural Population

Year

Urban

Rural

Urban

Rural

2003 2006 2009 2012 2015

49.1 49.3 49.4 43.9 42.9

50.9 50.7 50.6 56.1 57.1

13.2 13.6 13.9 13.6 14.2

86.8 86.3 86.2 86.4 85.8

Source: Calculated by authors from PSA (Philippine Statistics Authority), “Family Income and Expenditure Survey. Incidence of multidimensional versus income-based poverty”, various years [b] (accessed 16 May 2017).

despite rapid urbanization in recent years, poverty in the Philippines — as in many other developing countries — is a largely rural phenomenon. In the early 1990s, absolute poverty in the Philippines was much less prevalent than in China, Indonesia, or Vietnam. But the country made virtually no progress in reducing poverty in subsequent years, particularly in the first decade of the new millennium (Figure 1.13). Farmers in the major emerging ASEAN member countries of Indonesia, Thailand, and Vietnam benefited enormously from the modernization of both local and global supply chains and trade opportunities arising from the rapid expansion of Asian agri-food markets. Together with sustained growth of employment opportunities in nonfarm sectors of the economy, particularly industry, this development facilitated rapid poverty reduction, particularly in rural areas. Based on the World Bank’s poverty line of US$1.90 a day, the proportion of the population in the “absolute poor” category declined rapidly in China, Indonesia, and Vietnam between 1990 and 2014 (Figure 1.13). The same rapid decline occurred in Malaysia and Thailand in the 1970s and 1980s. The poverty trend in the Philippines is another story: the incidence of poverty both regionally and nationally has changed little since the turn of the new millennium (Table 1.13). During 2000–12, Philippine poverty levels were unresponsive to rapid income growth and other opportunities occurring in East and Southeast Asia. The country’s economic growth was considerable in the 2000s (4.7 per cent per year on average), but not as high as in neighbouring countries. Nonetheless, the incidence of poverty seems to be going down by 2015 (Table 1.13).

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FIGURE 1.13 Poverty Reduction, Select Asian Countries, 1990–2014

Notes: Estimates refer to the share of population living on less than US$1.90 a day (based on 2011 purchasing power parity exchange rates). Data for Indonesia are approximations based on urban and rural estimates. Sources: Constructed by authors from World Bank, PovcalNet, various years (accessed 20 May 2015).

It is puzzling as to why growth failed to translate into lower absolute poverty levels, but recent research has investigated the issue (Balisacan 2007, 2015; Fuwa, Balisacan, and Bresciani 2015). Beyond income levels, the poverty reduction trend improved in other areas of human deprivation. Balisacan (2015) estimated trends in the incidence of multidimensional poverty and income-based poverty (Figure 1.14). All three data sources used — the Annual Poverty Indicators Survey, Family Income and Expenditure Survey, and National Demographic and Health Survey (PSA various years [a, b, and e]) — indicate continuing reductions in the incidence of multidimensional poverty at yearly rates of 1.78, 2.04, and 2.17 per cent, respectively. All three sources also confirmed deceleration in poverty reduction in the 2000s. The pattern of poverty is quite different seen through the lens of official income-based poverty data. Trends in those data show that GDP growth from 1997 had no significant impact on poverty. The difference is apparent for estimates of multidimensional

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30.5 42.7 54.3 7.1 36.6 42.8 21.1 22.7 44.4 54.5 39.6 43.6 50.0 40.3 46.6 39.6 53.3 34.4

2009

47.1 26.0 49.2 4.7 25.9 26.8 13.1 10.3 40.6 44.2 29.1 35.9 41.5 45.0 39.0 30.6 37.9 26.6

47.4 25.1 54.4 3.6 22.0 25.5 13.7 11.9 34.5 44.2 30.8 31.0 42.6 45.8 40.1 31.4 38.3 26.3

Share of Population (%)

2006 55.8 22.8 40.3 3.9 18.5 22.1 12.9 10.9 31.0 41.1 29.1 30.2 45.2 40.1 39.5 30.7 44.7 25.2

2012

53.7 19.7 39.1 3.9 13.1 15.8 11.2 9.1 24.4 36.0 22.4 27.6 38.7 33.9 36.6 22.0 37.3 21.6

2015

Note: Caraga comprises four provinces: Agusan del Norte, Agusan del Sur, Surigao del Norte, and Surigao del Sur; CALABARZON comprises five provinces: Cavite, Laguna, Batangas, Rizal, and Quezon; MIMAROPA comprises five provinces: Mindoro Oriental, Mindoro Occidental, Marinduque, Romblon, and Palawan; and SOCCSKSARGEN comprises four provinces and one city: South Cotabato, Cotabato, Sultan Kudarat, Sarangi, and General Santos City. Based on a major revision, PSA’s estimation methodology was different in 1991 than in the other years; nevertheless, the national estimate of the incidence of poverty for that year is still used as the basis of the country’s poverty reduction target under the Millennium Development Goals. Source: Compiled by authors from PSA (Philippine Statistics Authority), “Poverty Incidence by Region”, various years [g] (accessed 16 May 2017).

Autonomous Region in Muslim Mindanao (ARMM) Cordillera Administrative Region (CAR) Caraga National Capital Region Region 1 (Ilocos Region) Region 2 (Cagayan Valley) Region 3 (Central Luzon) Region 4a (CALABARZON) Region 4b (MIMAROPA) Region 5 (Bicol Region) Region 6 (Western Visayas) Region 7 (Central Visayas) Region 8 (Eastern Visayas) Region 9 (Zamboanga Peninsula) Region 10 (Northern Mindanao) Region 11 (Davao Region) Region 12 (SOCCSKSARGEN) National total

Region

1991

TABLE 1.13 Poverty Incidence by Region, 1991, 2006, 2009, 2012, and 2015

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FIGURE 1.14 Incidence of Multidimensional Versus Income-Based Poverty, 1988–2012

Notes: APIS = Annual Poverty Indicators Survey; FIES = Family Income and Expenditure Survey; NDHS = National Demographic and Health Survey. FIES data from 2000 onward are not strictly comparable with data prior to 2000 due to changes in the survey questionnaire over time. The APIS, FIES, and NDHS trends are based on multidimensional measures of poverty (including, for example, health, education, and standard living), whereas the income-based poverty trend is based on the official 2009 per capita food poverty threshold. Source: Arsenio Balisacan, “The Growth-Poverty Nexus: Multidimensional Poverty in the Philippines”, in Sustainable Economic Development: Resources, Environment and Institutions, edited by A. Balisacan, U. Chakravorty, and M. Ravago (Oxford and San Diego: Elsevier Academic Press, 2015).

poverty using the Annual Poverty Indicators Survey and Family Income and Expenditure Survey, which both show continued improvements in poverty reduction during the 2000s.

Agricultural Growth as an Engine of Local Poverty Reduction Some of the literature points to key policy and institutional issues as an explanation for why the country has failed to seize growth and poverty reduction opportunities. This section reviews empirical evidence of the connection between agricultural growth and rural welfare outcomes, especially those associated with generating employment, reducing poverty, and supporting other aspects of human development. This discussion

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also draws from the literature to identify the role of urbanization, infrastructure development, asset/income inequality, and local geophysical characteristics in shaping the comparative advantage of agricultural growth in driving local poverty reduction. Around the world, particularly in East Asia, agriculture’s relative importance to national income levels, employment, and poverty reduction has rapidly declined. In the fast-emerging economies of Asia, invariably, this structural transformation has been accompanied by substantial poverty reduction. China’s experience in the 1980s and 1990s (and even today) illustrates the poverty-reducing effects of structural transformation. The agricultural sector declined sharply in relative importance, and national poverty levels also fell rapidly, especially in agricultural and rural areas. As a result, about 600 million people were lifted out of poverty in the past three decades. China was the single largest contributor to the global poverty reduction achieved in 1980–2010. Behind this success was the dynamic interplay of rapid agricultural production growth fuelled by productivity improvements, especially in the food sector, and even more rapid nonagricultural income growth, mainly induced by massive off-farm investments in industry and labour-intensive exports. This tremendously transformed household income sources even among farm households. In the early 1980s, about 80  per cent of the incomes of Chinese farm households were derived from agriculture, whereas by the late 2000s, this share had dropped to only about 40 per cent. The same development pattern, albeit at a slower rate, was apparent in Indonesia, Thailand, and Vietnam. Poverty reduction has varied remarkably across Philippine provinces and regions. Part of the variation has to do with the pace of local income growth, broadly suggesting that income growth is a necessary prerequisite for poverty reduction (as is evident in national and global contexts). But the source of growth is important for local poverty reduction. For the country’s seventy-three provinces, poverty reduction tended to follow expected trends whenever nonagricultural income grew faster than agricultural income (Appendix Table 1.1). This was true not only in urban areas, but also in rural areas. This does not, however, suggest that agricultural growth is inconsequential to local poverty reduction at this stage of the country’s development. On the contrary, under certain conditions, agriculture does matter and will continue to matter. A number of provinces achieved poverty reduction under a regime where agricultural income grew faster than nonagricultural income. The response of poverty to sectoral growth,

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37

whether agricultural or nonagricultural, depends on a number of factors that could vary by location. An in-depth examination of the factors influencing the response of poverty reduction to income growth reveals that the factors operating for the agricultural sector are quite different from those operating for the nonagricultural sector (Appendix Table 1.2). When it comes to agricultural growth, elasticity (that is, sensitivity to change) tends to be higher in areas where the potential for agricultural productivity is high, based on geophysical endowments, and urbanization is relatively low. Put another way, agricultural development has high potential to drive poverty reduction in areas with high potential for agricultural productivity growth (for example, through irrigation development in relatively flat landscapes), as well as in relatively more rural or remote (that is, less commercialized) areas. In Ilocos provinces, for example, agriculture is still likely to be a key driver of poverty reduction given its comparatively low asset inequality and distance from industrializing or urbanizing centers. This would be even more pronounced with improved access to the national road network, thereby linking the provinces to major markets for farm produce, including exports. For the nonagricultural sector, the response tends to be influenced by initial levels of income/asset inequality, human capital, and infrastructure development. High land inequality, such as in the Negros provinces, weakens the capacity of nonfarm income growth to serve as a key driver of poverty reduction. High levels of human capital favour nonfarm development, which also favours faster poverty reduction. Rapidly developing areas tend to have good infrastructure, which reduces transaction costs and facilitates the agglomeration (that is, urbanization) of economies. The type of infrastructure development influences poverty’s response to income growth. In another recent study of the impact of infrastructure on agricultural versus nonagricultural income growth, Fuwa, Balisacan, and Bresciani (2015) found that investing in local roads is likely to facilitate rural nonfarm growth, whereas investing in national roads is likely to reinforce agricultural growth by providing greater access to agricultural markets. Thus, investing in national road networks does not appear likely to lead to rural industrialization, but rather to further urbanization, whereas investing in local road networks could facilitate rural nonfarm sector development (and may well mitigate urban congestion).

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KEY POLICY AND GOVERNANCE ISSUES This section examines key constraints to growth and poverty reduction preventing the Philippines from achieving inclusive development and sharing in the prosperity its neighbours are already experiencing. In particular, the discussion addresses how policy reform and public investment can alter the course of agricultural and rural development to fuel poverty reduction while the economy maintains its high-growth trajectory.

Macroeconomic Constraints A fundamental development lesson in the past half-century is the overwhelming influence of macroeconomic factors, such as monetary, fiscal, and exchange rate policies, on overall economic incentives for agriculture and rural areas. In many developing countries, these policies have tended to be biased in favour of industry (and services) and against agriculture, thereby prematurely drawing resources away from agriculture to the nonagricultural sector of the economy. Specifically, unsustainably high fiscal deficits and high inflation rates accompanying attempts to spur growth, combined with exchange rate controls and protectionist policies for import-substituting industries, have prompted overvaluation of the local currency, disproportionately hurting the highly tradable agricultural sector, particularly in terms of export commodities. Moreover, the indirect effects of these policies on agricultural incentives have overwhelmingly tended to offset any favourable effects of direct policies and programmes targeting agriculture — such as input subsidies and output price support, among others (Krueger, Schiff, and Valdes 1991; Anderson et al. 2008). The policy-induced suppression of agricultural incentives has meant lower income growth in agriculture, less dynamic economic transformation, and less poverty reduction despite economic opportunities arising from rapid growth in global trade, information and communications technologies, and global food and agricultural value chains (Reardon and Timmer 2007; World Bank 2008; Reardon, Timmer, and Minten 2012). While the same pattern of incentives generally prevailed in the Philippines (David, Intal, and Balisacan 2009), the macroeconomic environment was benign for agriculture (and for the economy as a whole) during 2005–14. Unlike previous episodes of growth in most of the postwar period, when every boom was soon followed by a bust, growth

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39

since the recovery from the global financial crisis has been supported by sound macroeconomic fundamentals: declining debt burden, declining public-sector deficits, low inflation rates (within government targets), consistently strong current-account positions, and improving public-sector revenues. Outstanding public-sector debt as share of GDP declined from about 101 per cent in 2000–02 to about 72 per cent in 2011–13. Interest payments as a share of GDP declined from about 30 per cent in 2006 to 17 per cent in 2013. The national government borrowing programme increasingly shifted away from foreign to domestic sources (with the share of foreign borrowing fell from 44 per cent in 2009 to 11 per cent in 2013), thereby reducing the country’s exposure to external shocks. Although government revenues, expressed as a share of GDP, have yet to rebound from the low levels recorded in 2006–08, they have gradually improved in recent years, rising from about 13 per cent in 2010 to about 15 per cent in 2013 and 2014. Together with more effective spending management, these developments have precipitated lower fiscal deficits — down from about 3.5 per cent of GDP in 2009 and 2010 to 1 per cent in 2013 and 2014. Meanwhile, inflation has remained low and within the target range of 2–5 per cent since 2009. The current account has likewise been consistently positive since the mid-2000s on the back of robust overseas worker remittances, business process outsourcing, tourism receipts, and merchandise exports. This favourable external position has allowed the country to somehow withstand external shocks, such as recessions in major trading partners and the global financial crisis, and hence prevent sharp swings in the exchange rate and domestic interest rates (which characterized other decades after World War II). The challenge, moving forward, is to sustain the momentum of fiscal reform. Expanding the tax base and developing new revenue sources to further raise levels at least to those of the country’s neighbors will be crucial in fiscally fortifying the economy and sustaining rapid growth. Massive infrastructure development in transport, power, information and communications, irrigation and drainage, and disaster risk reduction will be crucial to building a highly competitive and resilient economy, especially in view of the onset of economic cooperation among ASEAN countries and increasing integration of the Philippine economy in global markets. Additionally, strong investment in the social sector in recent years — particularly in health, education, and social protection — has to be sustained in order to foster human development and shared prosperity. Improving

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla

access to finance for small and medium-sized enterprises, especially in rural areas, are also necessary to ensure more inclusive growth.

Modernization of the Sector The Agriculture and Fisheries Modernization Act (Republic Act 8435) of 1997 is the overarching legislation providing for the policies and measures intended to modernize and enhance the profitability of the sector, thereby preparing it for the challenges of globalization. To this end, the Act prescribes the formulation and implementation of a medium-to-long term, comprehensive Agricultural and Fisheries Modernization Plan (AFMP). The Plan is envisioned to encompass programmes and strategies covering infrastructure and market support, credit, research and development, biodiversity and environment, agrarian reform, extension services, among others. AFMP was initially to be implemented from 1998 to 2003, with a first year budget of PhP20 billion, and a yearly budget thereafter of PhP17 billion (representing a total budget of PhP105 billion over and above the Department of Agriculture’s regular budget). In actuality, only 71 per cent of AFMP’s allocated budget was released during these first six years, representing a total of PhP80.9 billion over and above the Department of Agriculture’s budget (Table 1.14). This increased the Department of Agriculture’s budget from PhP15.7 billion in 1998 to PhP22.9 billion in 2015 (Table 1.15). Overall, budget levels fluctuated significantly, dropping to PhP9.1 billion in 2005 and peaking at PhP33.6 billion in 2014 (Table 1.15). As indicated, disbursement levels have been lower than budgeted allocations and the release of funding has also been delayed (Habito and Briones 2005; Dy 2005; SEPO 2009). AFMP’s first year’s budget was released three years after the law was enacted (Table 1.15) which hindered the prompt and efficient delivery of programmed activities. Another problem is the fact that not all the Department of Agriculture’s programmed activities were allocated a fair share of the budget increases (Appendix Table 1.3). Budget cuts primarily affected the productivityenhancing components, both of AFMP specifically, and the Department of Agriculture’s programming more generally. A clear example is the actual R&D allocation, which for the first six years averaged only 4.3 per cent of AFMP’s yearly budget. By way of comparison, this is 5.7 per cent lower than the 10 per cent share mandated by the Agriculture and

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TABLE 1.14 Actual Versus Mandated Agriculture and Fisheries Modernization Programme Budget, 2000–05

Year 2000 2001 2002 2003 2004 2005 Total/average

Mandated Budget Allocation (billion PhP)

Actual AFMP Budget (billion PhP)

Actual Budget as a Share of Mandated Budget (%)

17.9 14.1 13.5 12.8 11.8 10.6 80.9

14.9 19.5 11.4 19.2 16.5 16.4 58.0

83.2 67.4 84.9 71.4 55.1 60.4 71.7

Notes: AFMP = Agriculture and Fisheries Modernization Program. Financial data are presented in real Philippine pesos, deflated by the wholesale price index (WPI) with a base year of 1998. Source: Constructed by authors from Albert Aquino, Anita Tidon, Princess Ani, and Meliza Festejo. “The Agriculture and Fisheries Modernization Act of 1997: A Collective Approach to Competitiveness”, 2013 (accessed 25 May 2016).

Fisheries Modernization Act (Dy 2005; Aragon et al. 2011). Moreover, the share of the total agricultural budget allocated to R&D fell to an average of less than 3 per cent per year during 2006–14 (Appendix Table 1.3). The share returned to 5.5 per cent of the total agricultural budget in 2015. The budget allocation for extension was similarly low, affecting the quality and frequency of extension activities, although it was increased to 7.5 per cent in 2015, compared with an average of 4.8 per cent per year during 2006–14.

AFMA’s National Banner Programmes Rice has continued to receive the largest share of Department of Agriculture’s budget (Table 1.16). It has also received sizeable shares of the budget allocated for public support services, including irrigation and postharvest facilities. A major reason for the huge support to rice relates to the country’s goal of achieving rice self-sufficiency, and the fact that most of poor, smallholder farmers engage in rice cultivation. The government’s focus on rice dwarfed its support for other commodities, ultimately hindering production diversification. Rice has continued to account for about one-third of the total agricultural area planted in the past five and a

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla TABLE 1.15 Department of Agriculture Budget, 1998–2015

Regular Budget for Agriculture Year 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Regular Budget for Agriculture Plus Additional GATT Allocation

Regular Budget for Agriculture Plus Additional AFMP Allocation

Total

(thousand PhP) 2,838,727 3,172,950 3,735,267 3,874,047 4,436,651 3,356,965 2,961,065 2,672,742 2,512,281 2,783,440 3,084,964 4,830,741

12,892,205 11,017,299

14,919,666 19,525,573 11,442,170 19,160,890 16,519,188 16,417,178 16,616,186 17,941,093 10,809,607 19,627,383 24,107,452 17,258,176 27,296,198 32,742,946 33,639,080 22,896,742

15,730,932 14,190,249 18,654,934 13,399,620 15,878,820 12,517,855 19,480,253 19,089,921 19,128,467 10,724,534 13,894,571 24,458,124 24,107,452 17,258,176 27,296,198 32,742,946 33,639,080 22,896,742

Notes: GATT = General Agreement on Tariffs and Trade; AFMP = Agriculture and Fisheries Modernization Program. Financial data are presented in real Philippine pesos, deflated by the wholesale price index (WPI) with a base year of 1998. From 1995 to 1999 the regular Department of Agriculture budget was enhanced to ensure that GATT commitments were met. With the implementation of AFMP, the GATT budget was cut. As of 2010, the General Appropriations Act consolidated the Department of Agriculture budget, so from that year AFMP allocations are not specified. Sources: Constructed by authors from DBM (Department of Budget and Management), Philippines General Appropriations Act, Various years, retrieved from .

half decades, while the combined share of other traditional crops, such as corn, coconuts, and sugarcane, has been close to 50 per cent. High-value crops, such as fruit and vegetables, have only accounted for a meagre 3 to 4 per cent of total area harvested. This pattern has persisted over time — despite the relatively high returns of high-value crops, including those with great export potential — and clearly runs counter to what would be

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6,702 3,231 1,741 472 1,012 382 1,337 246 n.d. n.d. 246 3,762 782 377 20,291

5,801 1,134 1,958 220 420 309 813 408 n.d. n.d. 306 2,883 1,320 391 15,963

2011

2013

10,966 2,243 2,772 426 600 461 1,077 416 224 n.d. 409 2,565 651 1,074 23,882

11,845 3,115 3,291 673 599 454 1,614 409 442 44 391 3,293 1,274 887 28,728

(million PhP)

2012 9,073 5,157 2,965 772 719 580 1,797 377 215 n.d. 100 3,476 4,196 705 30,131

2014

n.d. 2,794 3,131 1,027 983 722 2,042 284 224 n.d. 97 4,033 3,323 3,390 22,049

2015

Notes: NIA = National Irrigation Authority; n.d. = no data. Financial data are presented in real Philippine pesos, deflated by the wholesale price index (WPI) with a base year of 1998. Other support programmes include budgets for general management and supervision; support to operations and operations of the office of the secretary and attached agencies; and automatic appropriations, such as retirement and life insurance premiums, the Japanese increased food production programme, the agricultural competitiveness enhancement fund, and the special account in the general fund. Data for total appropriations exclude the quick response fund and credit facility to agrarian reform beneficiaries in 2010 and 2011; the credit facility to agrarian reform beneficiaries in 2012 and 2014; and the NIA irrigation projects and credit facility to agrarian reform beneficiaries in 2015. Sources: Constructed by authors from DBM (Department of Budget and Management), Philippines General Appropriations Act, various years, retrieved from .

NIA irrigation projects Farm-to-market roads National rice program National corn program National high-value crop program National livestock program National fisheries program Organic agriculture Quick response fund Credit facility to agrarian reform beneficiaries Market development Other support programs Locally funded projects Foreign-assisted projects Total appropriations

Programme

2010

TABLE 1.16 Allocation of the Department of Agriculture Budget for Commodity Programmes and Other Supporting Activities, 2010–15

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expected under competitive markets, where land use should shift from low- to high-return crops (World Bank 2007). In short, the banner programmes do not appear to have contributed to the goal of agricultural development, nor have they improved the competitive position of the commodities that have received huge government support. The yield levels of these commodities — a partial indicator of productivity performance — have not been outstanding as discussed earlier in this chapter. Importantly, the commodity-based focus of government programmes, as opposed to a whole farm system, failed to give farmers the opportunity to earn additional income. Because of meagre government support, high-value commodities with huge export and value-adding potentials — such as coffee, cacao, some fruits, and vegetables — were not fully developed to compete on the global market. The government’s budget allocation also failed to provide strong support for greater farm diversification to crops that are more resilient to natural shocks, such as increasingly severe weather aberrations resulting from climate change. Another observation relates to subsidies on farm inputs, such as seed and other planting material, fingerlings, fertilizer, animals, and postharvest facilities, which are essentially private goods and services. Balisacan, Sebastian, and associates (2006) find that these subsidies distort farmers’ technology choices, encourage the misallocation of resources, crowd out the private sector, and even disproportionately benefit farmers who are already better off. Although these subsidies have been reduced in recent years, they remain a problem.

Irrigation Development Programme Efficient irrigation systems increase agricultural productivity and income by providing farmers with at least one additional crop per year. The importance of these structures is indicated through their share of agriculture’s budget. Irrigation development was the recipient of an average share of 30 per cent per year during 2000–15, second only to production support.2 Despite the considerable resources invested in the construction of new irrigation systems and the rehabilitation of existing ones, between 2000 and 2014 the “firmed-up” service area3 only increased by 346,609 ha or 23,107 ha per year. More worrisome are problems related to the quality of operations and maintenance (O&M) especially within national irrigation systems,

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which have affected their service performance as discussed in more detail in Chapter 3, in this volume.

The Research Development and Extension Programme The country’s underinvestment in R&D and its weak extension capacity were examined by Francisco and Bordey (2014), who provide various indicators, such as the number of agricultural researchers and extension personnel per million population; the ratio of gross expenditure on R&D and extension to gross national product and agricultural value-added; and per capita research investment, all of which were shown to be below the comparable levels of other Asian countries. Underinvestment in agricultural R&D and extension in more recent years persisted, as noted earlier, with its low share of the Department of Agriculture’s budget. The issue goes beyond budget levels, however. The clear disconnect between public research and public needs has become more disturbing. The government research and extension programmes in response to farmers’ problems are ad hoc.4 No regular activities assess farmers’ productivity problems for the purpose of setting research and extension (Ponce and Dy 2014). More often than not, scientists undertake R&D activities based on their specializations and expertise rather than in response to farmers’ or the sector’s needs. As previously noted, the dominant focus of public R&D on rice discriminates against other commodities in terms of access to improved technologies to enhance quality and yields. But even rice research which is deemed to be the most organized stream of public R&D in the Philippines and which boosts its high-yielding seed varieties that is resistant to a variety of adverse environments and weather conditions has been partially successful in achieving the yield levels that are to be expected of modern varieties due to their poor adoption. Majority of farmers still use their own saved seed (from recent harvests) rather than certified seed. Sombilla and Quilloy (2014) identified a number of reasons for this, including both logistical, which includes the weak extension service, and technical difficulties.

Government Credit and Crop Insurance Programmes The credit policy reforms that took place under the AFMP phased out all the government’s direct credit programmes in the agricultural sector and

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established a market-based credit policy, among others. These reforms led to more active participation by private financial institutions in rural credit markets; the emergence of innovative micro-lending techniques; and, hence, greater access by smallholder farmers to formal financial institutions. Nevertheless, such progress is still insufficient considering that a very large number of smallholder borrowers still depend on informal lenders for production financing. The participation of formal financial services and commercial banks continues to be low due to ongoing fear of high and systemic risks, and the huge transaction costs involved compared with the low and unstable profitability of the agricultural sector (Llanto 2006). Investment credit that covers long-gestation crops, such as rubber and oil palm, is still greatly lacking. To deal with these lingering issues, policymakers need to strengthen the credibility of the regulatory system that governs the financial market, establish an efficient credit information bureau, and improve the efficiency of risk-reducing instruments like agricultural insurance that could improve the credit worthiness of smallholder farmers. The country’s agricultural insurance programme, implemented by the Philippine Crop Insurance Corporation, has to date not lived up to its main objective of managing agricultural risks. Despite the subsidies extended by the programme and the growth in the product lines it offers, penetration among farmers is still low, and its need to yield higher returns to the insurer continues to threaten its sustainability. Reyes et al. (2015) provide a comprehensive assessment of the programme, identifying contributing factors to the low penetration — estimated to be less than 10 per cent during 1981–2014 for rice and corn, and much lower for high-value crops. A key factor is the level of insurance coverage, which is lower than farmers’ actual production costs, whereas the premium rate is deemed to be unreasonably high. Implementation is also problematic in terms of the need for more careful assessment of damages, streamlining procedures for processing claims, and ensuring the proper selection of targeted beneficiaries. The challenge of sustaining higher returns to insurers relates to the risky nature of agricultural production to natural disasters, such as typhoons, that affect large number of farmers at the same time. When such disasters occur, premiums are insufficient to cover the cost of indemnity. Weather index-based insurance designed to overcome some of these problems especially those related to climate change and increasing weather variability, should be explored by the government through privatesector pilot projects.

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Overall Performance of the Agricultural Programmes The implementation issues and controversies that riddled the programmes affected the momentum and ultimately the effectiveness of the Department of Agriculture’s programmes. The continued bias against agriculture as evidenced by the country’s public investment in the sector has also contributed to the failure of these programmes to make the needed dent in improving the welfare of the Filipino farmers (Ravago and Balisacan 2016).

Food Sufficiency Policy The country’s food policy, as indicated in various Philippine Development Plans, has multiple objectives: achieving food security, increasing smallholder incomes, protecting poor consumers from high prices, and raising productivity to enhance farming’s contribution to economic growth and development. In practice, the policy largely focuses on rice and involves buying palay from producers at above-market prices and selling rice to consumers at below-market prices, especially in urban areas. The other goal of the policy is to achieve national self-sufficiency in its primary staple food, which is implemented by the National Food Authority (NFA), under the Department of Agriculture.5 NFA is empowered to monopolize the importation of rice and to implement quantitative restrictions on rice imports when the private sector is permitted to import. NFA also regulates domestic rice trade and is provided a subsidy by the national government for its operations. The national self-sufficiency goal puts pressure on NFA to restrict the volume of imports, driving domestic rice prices above comparable border prices. NFA uses this higher level of domestic prices as the basis for its “sell low” prices for consumers. The policy regime — NFA’s near monopoly on rice trade, high import tariffs, and quantitative restrictions on rice — has resulted in inadequate supply and has artificially kept domestic prices 50 to 100 per cent higher than comparable global (border) prices (World Bank 2015a). As a result, Filipinos pay more for their staples than their counterparts in Southeast Asian countries; moreover, they have not benefited from falling world rice prices in recent years (Figure 1.15). Even most rice farmers have not benefited from NFA’s support price, partly due to poor targeting and partly because NFA’s procurement represents only a small component of total rice production (typically less than 5 per cent).

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla FIGURE 1.15 Trends of Rice Prices in Domestic and World Markets, 2000–15

Notes: Palay is unmilled rice; 25 per cent broken rice is a standard grade of milled rice. The spike in 2008 is due to global food crisis. World prices were converted from U.S. dollars per ton using monthly exchange rates from the Banko Sentral ng Pilipinas. Sources: Constructed by authors from FAO (Food and Agriculture Organization of the United Nations), FAOSTAT database, various years (accessed 17 May 2015); domestic retail and farmgate palay prices are from PSA (Philippine Statistics Authority), “Prices”, various years [l] (accessed 16 May 2017).

The high rice prices have effectively reduced the purchasing power of the incomes of Filipinos, particularly poor people whose rice expenditure accounts for about 20 per cent of their total household expenditures. This means that, in order to meet their staple needs, poor people have to cut down on other expenditures, such as education and health care — or, even worse, their intake of rice, which could cause malnutrition. In recent years, the incidence of malnutrition in the Philippines has been among the highest of countries with comparable development levels (World Bank 2015b). Despite its comparatively remarkable economic growth in recent years, the Philippines is also one of the very few Asian countries that failed to achieve the Millennium Development Goals’ 2015 poverty target. The ramifications of high food prices on poverty are especially notable in 2013 and 2014, largely because NFA chose to restrict imports tightly,

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despite dwindling rice inventory. As a result, domestic rice prices rose sharply to a high of 15 per cent in the second half of 2013. From the first half of 2013 to the first half of 2014, the inflation rate for items comprising the food basket of poor people (which is used to estimate the incidence of poverty) increased by 9.4 per cent (see Figure 1.12 presented earlier). In contrast, the overall inflation rate for the period was only 4.3 per cent. Based on the results of the 2012 Family Income and Expenditure Survey, the average nominal per capita income of the poorest 30 per cent of the population increased by 7.2 per cent, whereas levels for the richest 20 per cent increased by 4.5 per cent. In the absence of highly inflated food prices (or were the inflation rate for food items only as high as the overall inflation rate), the real incomes of poor people could have risen, and the increase could have been more proportionally than the increase for the richest 20 per cent of the population. Simply put, growth could have been pro-poor and inclusive, but, instead, the poorest 30 per cent of the population experienced declines in their real incomes, not increases. So despite quite remarkable GDP growth of 7.2 per cent in 2013 and 6.1 per cent in 2014 (even by the standards of the emerging economies of the world), the incidence of poverty in the Philippines actually rose from 24.6 per cent in the first half of 2013 to 25.8 per cent in the first half of 2014 based on PSA’s official calculations. Hence, neither objective of the policy — self-sufficiency nor poverty reduction — was achieved. “Selling low” to poor people also had little effect on their welfare because NFA rice only accounts for about 11 per cent of their rice purchases (and leakage of the subsidy to the nonpoor is high). Similarly, the policy of buying high from farmers would also have had little impact because NFA’s total purchases, at an average of only 7 per cent of total production, are too small (and leakage of the subsidy to large farmers and perhaps traders is also high). In any case, the policy has proven to be a costly way both of providing income transfers to poor people and of securing the availability of rice nationally. For every peso reaching poor people, 2 pesos were spent (Roumasset 2000). Additionally, for every US$1.00 saved through the choice not to import rice, US$2.60 in domestic resources was spent to produce rice locally. Finally, uncertainty in the private food market arising from NFA’s operations (for example, the unexpected arrival of rice imports during harvest months) has discouraged private investment in storage and distribution facilities.

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At the same time, NFA accumulated debt of over PhP170 billion by 2010, which the national government partially covers each year. These outlays usually represent the single largest government expense for agriculture (David, Intal, and Balisacan 2009; Balisacan, Sombilla, and Dikitanan 2010). Historically, the rice sector’s share of the total budget of the Department of Agriculture and related (government-owned or controlled) agencies has been about 65 per cent, which is high considering its 20 per cent share of gross agricultural value-added. Moving forward, it is high time for a thorough reform of rice policy. The quantitative restriction regime needs to be replaced with tariffs, perhaps initially at the out-quota rate6 of 35 per cent, decreasing over time to align with tariffs operating for other agricultural commodities. NFA would need to be reoriented to manage buffer stocks for emergency purposes, and the private sector would require assistance in developing logistics, particularly in terms of transport.

High Transaction Costs The high cost of doing business — starting a business, dealing with construction permits, employing workers, registering property, getting credit, protecting investors, paying taxes, enforcing contracts, resolving insolvency — has stifled investments, especially in sectors that have potentials for decent, productive, and remunerative jobs. This stems from two basic sources: (1) the country’s relatively weak institutions, and (2) its poor quality infrastructure, especially transport infrastructure. Comparison on ease of doing business between the Philippines and its East Asian region is instructive. Based on World Bank (2014c), the country has a relatively poor business environment, as evidenced by its rank of 95 (the 50th percentile among respondent countries), in contrast with that of 26 (the 14th percentile) for Thailand, 18 (the 10th percentile) for Malaysia, 90 (the 46th percentile) for China, 78 (the 41st percentile) for Vietnam, and 114 (the 60th percentile) for Indonesia.7 Based on recent issues of the World Economic Forum’s Global Competitiveness Report (2010, 2011), the Philippines ranked in the bottom half of over 130 respondent countries, in terms of both the quality of institutions and quality of infrastructure. In contrast, the major East Asian countries, particularly China, Malaysia, and Thailand, ranked much higher. As noted above, all these countries have done well in reducing poverty. Of the various factors, the most problematic ones for the Philippines pertain to corruption in public institutions,

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inefficiency of government bureaucracy, and inadequacy of infrastructure. Domestic or foreign investors see the Philippines through this lens. For farmers, these inefficiencies would translate into high postharvest losses; large differentials between retail (consumer) prices and farmgate (producer) prices — that is, transaction costs; and low access to incomeenhancing opportunities towards diversification of farm-household incomes. For example, due partly to poor infrastructure, farmers cannot efficiently connect to supply and value chains, including export markets. Thus, they miss huge opportunities for income growth from the rapidly expanding markets for high-value crops in the rapidly growing and urbanizing centers of Asia. In terms of basic infrastructure, the Philippines has performed poorly in the provision of roads, railways, seaports, airports, power, and communications (Balisacan and Hill 2007; World Bank 2014a). While public investment in infrastructure (as a share of GDP) increased from about 1.5 per cent in 2011 to about 3 per cent in 2014 (and was targeted to rise to 4 per cent in 2015), the infrastructure deficits are huge, and current spending levels are still short of those of some of the country’s neighbours. This poor infrastructure connectivity has created high transaction costs and lack of spatial integration, whereby the regions and provinces are bifurcated into rapidly growing regions and poorly lagging regions (Balisacan and Hill 2007). The consequence is deepening pockets of poverty where some provinces have much higher absolute poverty than others (Fuwa, Balisacan, and Bresciani 2015). In contrast, in situations where provinces are efficiently connected and where investment in human development, particularly health and education, is location-neutral, even households in lagging provinces would benefit from growth in leading provinces. So, while concentration of production activities in certain regions, provinces, or centres is inevitable, and perhaps even desirable, possibly due to high-scale economies, efficient connectivity through infrastructure and human development would allow equitable distribution of welfare opportunities across households, regardless of economic density and geographic distance from growth centres (Balisacan, Hill, and Piza 2009; World Bank 2009a). Given the fiscal space that it currently enjoys, the country has the opportunity to address infrastructure bottlenecks and severe underinvestment in basic social services. Investment in transport infrastructure, in particular, should at least be brought up to the levels of the country’s peers in Asia (about 6 to 8 per cent of GDP). To free up more resources for the social sector and

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agriculture, and to improve efficiency in the construction, operation, and maintenance of public utilities, the regulatory and policy environment for public–private partnerships should be further improved to make infrastructure projects attractive to the private sector.

Unequal Access to Basic Social Services The quest for equitable household welfare and opportunities in a setting where production activities are spatially concentrated highlights another key aspect of Philippine development pattern: high inequity in access to social services and assets, especially in education, health, and land. For one, a large gap exists in access to certain basic social services, such as clean water, between the bottom 25 per cent and top 25 per cent of the population (Figure 1.16). To be sure, inequity in access to social services is ubiquitous in the developing world, even in Southeast Asian neighbours, particularly Indonesia, Thailand, and Vietnam; however, this inequity is far more remarkable in the Philippines than in other East Asian countries. The high inequity in access to social services, especially health and education, is likewise highly evident across regions or provinces, or between urban and rural areas. But even within rural areas, huge disparity in access to social services is the norm. Indeed, it is this inequality within geographic areas that accounts for about three-quarters of the overall inequality in the distribution of welfare across households; inequality among these areas accounts for the remaining one-quarter of the overall inequality (Balisacan 2007). As would be expected given inefficient connectivity, the state of poverty and inequality varies substantially across provinces. Poverty and health deprivation indicators in the Ilocos provinces (Ilocos Norte, Ilocos Sur, La Union and Pangasinan) are comparatively low, even though average per capita incomes in these provinces are not as high as those in the Southern Luzon and Central Luzon provinces. The Ilocos provinces have relatively low levels of income (and land) inequality. A partial explanation for this is the absence of plantations or haciendas that dominate rural settings in the Visayas. The government’s direct response to these inequities has been varied and included asset reforms and cash transfers intended for poor people, the most recent of which is the Pantawid Pamilya programme (the country’s version of the Conditional Cash Transfer programme favoured in Latin

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Source: Constructed by authors based on estimates from PSA (Philippine Statistics Authority), Family Income and Expenditure Survey, 2012 .

FIGURE 1.16 Access to Social Services and Assets: The Poorest 25 per cent Versus the Richest 25 per cent

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America and many other of the world’s developing countries). In the past two decades, however, all but a few of the major poverty-reduction programmes have either been poorly designed or badly implemented. As such, the programmes have been grossly ineffective in achieving their goals and have become extremely expensive. The high leakage of benefits to unintended groups could actually have contributed to increased inequality. According to Manasan (2009) and World Bank (2014b), included among the programmes with high leakage are Pantawid Kuryente (with a leakage rate of about 72 per cent); the Department of Education’s foodfor-school programme (59–62 per cent); Tulong para kay Lolo at Lola, which was implemented during the 2008 global financial crisis (61 per cent); Philhealth’s indigent programme (50 per cent); and NFA’s rice price subsidy (41 per cent). The Pantawid Pamilya programme, a key pillar of the Aquino Administration’s social protection strategy, is intended to break the intergenerational cycle of poverty by ensuring that young children, particularly 0–14 year olds in poor households, would grow up healthy and stay in school. Under the programme, household beneficiaries with up to three eligible children receive a total cash grant of PhP15,000 per year if they meet certain health and education conditions. Initially launched as a pilot programme in 2008, the programme expanded rapidly from only about 1 million households in 2010 to about 4.4 million in 2014. Beginning in 2015, the programme was further expanded to cover 15–18 year olds in poor households, as well as homeless street families and indigenous people. The programme’s 2015 budget was about PhP65 billion, representing about 2.5 per cent of the government’s total budget, or 20 per cent of the budget for social services. Recent assessment on the Pantawid Pamilya programme’s initial impact shows that, overall, it has succeeded in keeping children healthy and in school (World Bank 2014b). Moreover, the findings of the study indicate that household beneficiaries tend to invest in their children’s education and that, contrary to frequent assertions in public discussions, the programme has not encouraged dependency nor led to higher spending on undesirable (“vice”) goods. These results are encouraging given the very poor performance of most poverty reduction programmes in recent decades, in terms of high leakage rates, high transfer costs per peso, and unsustainable programme benefits.

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The Continuing Challenge of Property Rights Reform Another key constraint to rural development is the country’s ineffective and costly asset reform programme. In order to address high income inequality in rural areas the government has pursued asset reform programmes for the past four decades. Of these programmes, the most far-reaching was the Comprehensive Agrarian Reform Program (CARP), including its subsequent version, CARP Extension with Reforms (CARPer). The government spent an estimated PhP236 billion on CARP (in 2007 prices), which is equivalent to 20 per cent of the government’s total spending on agriculture during 1988–2007. The extension of land reform for another five years under CARPer was expected to incur another PhP150 billion. To appreciate the magnitude of the financial investment involved, a major elevated roadway in the Philippines would cost about PhP1 billion. The findings of several impact assessment studies have, at best, been mixed, in part either because the results are nonrepresentative or because comprehensive data are lacking. One result, for example, indicates a positive impact on provincial growth and hence indirectly on poverty, but a very small direct impact on poverty, especially in the past decade (Balisacan and Fuwa 2004). Using the most comprehensive dataset involving national agricultural and population censuses, nationally representative surveys of family incomes and expenditures, labour force surveys, and administrative records from implementing agencies, a team of researchers confirmed earlier results showing that the direct effect of the agrarian reform programme on poverty was disappointingly small, at least until the early 2000s (World Bank 2009b). In particular, the observed changes in household incomes of farmer beneficiaries in agrarian reform communities (ARCs) were higher than, although not much different from, the changes observed in comparable non-ARC farm households, all else being equal. The change in the poverty incidence observed in ARCs was also not much different from the change in non-ARCs. To be sure, because of their relatively favourable initial conditions (location, infrastructure development, and proximity to market centres), farms within ARCs tended to have higher productivity (by 15 per cent) than those in non-ARCs, but coverage was limited to only about half the programme’s beneficiaries. The redistribution of private lands was found to have a positive impact in reducing poverty when it was associated with complete titling and transfer, and the effect was stronger when the norm for the transfer was

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compulsory acquisition (of large, private holdings). Nevertheless, these positive effects were overwhelmed by factors relating to the programme’s design and implementation, which tended to inhibit efficiency, innovation, and poverty reduction. Overall, despite the huge spending for CARP in the past two decades, the programme has little to show in terms of improving rural household welfare. Even these modest results may exaggerate the welfare effects because the counterfactual is not known. For example, if CARP has had the effect of “freezing” the land market (as discussed below), it will also have slowed agricultural productivity and put both beneficiaries and nonbeneficiaries on lower growth paths. What has gone wrong? Although the intentions behind CARP were good, its design was poorly conceived largely because of a grossly inadequate understanding of rural development dynamics and the political economy of asset reform under a regime of weak governance. For one, CARP’s provisions were highly restrictive, especially on the transferability of land titles. RA 6657 and RA 9700 (the laws creating and extending CARP, respectively) illegalized the sale or lease of land titles for ten years from the effective date of the transfer (to agrarian reform beneficiaries) and imposed an ownership ceiling of 5 hectares.8 The transfer restriction has prevented the awarded land from being used as collateral, rendering the certificates of land ownership unbankable. This has curtailed farmers’ access to credit because the restrictions effectively made the legal rural-financial market disappear. The 5-hectare ceiling on ownership, on the other hand, has prevented farmers from adjusting their scale of operations to achieve efficiency, thereby driving private capital away from agriculture.9 Furthermore, the most common mode of ownership transfer has been collective, not individual, titles. What matters most to formal financial intermediaries are individual, unencumbered titles — not collective titles. Disturbingly, as of October 2007, about 71 per cent or about 2 million hectares of the total land distributed under the agrarian reform programme were actually under collective ownership arrangements, about one-third of which was from government-owned lands. It is probable that the 2 million hectares have remained unproductive all these years because those lands do not carry much weight in credit access — that is, they lack or have low collateral value. But even if those lands do have collateral value, farmer beneficiaries are likely to be severely constrained from choosing production arrangements, crops, or technologies that suit their particular conditions or circumstances. For example, a farmer with sufficient farming experience

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and skills may be better off operating individually rather than as part of a collective production arrangement. CARPer ended in 2014. Lessons learned from the past forty years of land reform must not be lost. CARP’s very long implementation has been extremely costly to farm efficiency and rural growth and is even detrimental to poverty reduction and equity goals. The way forward is to restore a favourable legal environment for land markets in rural areas by removing the restrictions against ownership transfer and lease of land and relaxing the land ownership ceiling to allow flexibility in the scale of farm operations. The effort should also involve urgently subdividing the collective certificate of land ownership awards into individual titles so that beneficiaries can use the lands awarded to them as collateral. Finally, any reform of land management needs to be accompanied by a strong push for the provision of public goods and support services, particularly access to well-functioning irrigation systems; profitable farming technologies; and (high-value) supply chains, including global supply chains.10

Climate Change, Natural Disasters, and Agriculture Philippine climate projections show increasing means and concentrations of rainfall (Chapters 2 and 4, in this volume), implying that wet seasons will become wetter and dry seasons drier. The country’s geographical location makes it vulnerable to naturally occurring events, which are projected to increase in frequency and hence increase the country’s disaster risk profile (Chapter 8, this volume). When the local response capacity is limited, naturally occurring events escalate into disasters that cause great damage and human suffering, often eroding or negating social, economic, and other development gains. This is one of the important lessons of the past six years, after a single natural disaster overturned gains in certain areas and sectors of the economy. This was demonstrated through the Visayan earthquake in October 2013, followed a month later by typhoon Haiyan (Yolanda) in November 2013. Damages from the typhoon alone are estimated to be PhP571 billion (NEDA 2013). In 2009, direct losses to private and public assets resulting from typhoons Ondoy and Pepeng were estimated to be PhP206 billion or about 1.8 per cent of GDP (Public Commission 2009). The agricultural sector, in particular, is highly vulnerable to weather-related shocks (Chapter 10, this volume). Direct impacts like destruction of crops, farm buildings, machinery, equipment, means of

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transport, stored commodities, cropland, irrigation works, and dams are immediately observable (Chapter 8, this volume). It is imperative that the country strengthen its institutional disaster preparedness. Broadly, the benefits of investing in technologies, using geohazard maps, establishing early warning systems, building dikes, and increasing awareness far exceed the associated costs. Studies on the Philippines show US$3 to US$30 worth of benefits per US$1 of investment, depending on type of disaster or hazard (Kelman and Shreve 2013). One implication of these climatic changes for farmers is that their prior experience of the frequency, duration, strength, and timing of rainfall is less reliable than before, which increases their risk (Chapter 8, this volume) and may necessitate the State’s role, for example, in making insurance available. Innovations in national weather-index insurance avoid the problem of all the households in a particular village experiencing the same disaster and thereby making claims on their insurance at the same time. Investments in research that offers farmers additional risk-reducing strategies also reduce their vulnerability to weather-related shocks; this includes research on drought-tolerant and flood-resistant crop varieties. Perez and Rosegrant (Chapter 10, this volume) show that crop yields are higher using climatesmart technologies. Other studies suggest that bundling insurance with tolerant varieties is more advantageous to farmers than doing either on its own (Lybert and Carter 2015). Disasters classified according to the probability of their occurrence may elicit varied responses at household and national levels. For example, responses to low-probability/low-frequency natural disasters like earthquakes and volcanic eruptions may be different from the response to high-probability/high-frequency natural disasters like typhoons. Thus, variations in risks could imply the need for different policy responses. While it is broadly recognized that the benefits of investment in preparedness exceed the costs, the body of knowledge on the economics of disaster preparedness and response is scarce, especially in highly diverse geographic areas of developing countries such as the Philippines. This is partly because of sparse data and partly because of the high diversity of conditions, institutions, and geography even within a country. Accordingly, the understanding of what does and doesn’t work in terms of local disaster preparedness and response is poor — despite the huge outpouring of good intentions in recent years, including public advocacies for making communities resilient to natural disasters, especially in rural areas. Clearly,

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governments, multilateral institutions, and philanthropic organizations have to walk their talk by investing more in research and data to improve the current understanding of the types of policies, programmes, and projects that will be economically appropriate under developing-country conditions and circumstances. What may have worked well in developedcountry settings may not provide economically efficient and sustainable solutions to the problems rural communities face in developing countries. Good-quality data and analyses are indispensable to effective, evidencebased policymaking.

CONCLUDING REMARKS The aim of food policy should be to achieve inclusive access to food while generating long-term sources of productivity and income growth. This would require reorienting food security policy towards facilitating rather than inhibiting trade, competition, and crop diversification. In particular, quantitative restrictions combined with high rice tariffs is inconsistent with the paramount development objectives of reducing poverty and generating long-term sources of productivity and income growth in rural areas. Furthermore, the current “buy high, sell low” policy does not advance inclusive access to food, even among the poorest groups of the population. Not only is the policy poorly targeted, but even the majority of smallholder farmers and landless workers do not benefit from the high prices because they are net buyers of rice. Moreover, NFA’s low consumer price is only low in reference to the domestic market, but high in relation to comparable world prices. The way forward to achieving food security is not to artificially induce high food prices by restricting trade, particularly importation, when food supply falls short of demand at competitive world prices, but by shifting the focus of policy to efficiency-enhancing measures. These include research and development (of locally appropriate technologies), road network development, irrigation and flood control development, the facilitation of public–private partnerships, and the complete conversion of collective land titles to individual ones in order to facilitate credit flows to agriculture. Conditional cash transfers to enhance the formation of human capital in poor farm households may in turn enhance productivity and directly reduce poverty. The shift will necessarily involve changing the metric of success in agricultural development purely from increases in national food

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production to increases in farm-household incomes from both agricultural and nonagricultural activities. What matters more to food security is access to food at the household level and at reasonably competitive prices. As the experience of the most food-secure countries in the world shows, access to food for all — especially among poor people — has much to with the households’ purchasing power, which rises when household incomes rise, but falls when food prices rise. The 2016 onset of the ASEAN Economic Community should provide extra pressure for the Philippines to implement long-overdue policy and governance reforms needed to foster a more competitive and shock-resilient economy, particularly in the agricultural sector. Indeed, the benefits of joining the ASEAN Economic Community — and other regional groupings — have less to do with access to larger regional markets and perhaps more to do with domestic efficiency-enhancing reforms that would otherwise be politically difficult to effect due to entrenched vested interests. The prospect of climate change makes the implementation of these reforms even more imperative. Coupled with appropriate investments, institutional reform can create a resilient Philippine economy and contribute to minimizing the impact of natural disasters when they do, inevitably, occur. Despite rather shaky global headwinds and domestic challenges, the economy is on a high growth trajectory, making it one of the world’s best- performing emerging economies. The key challenge will be sustaining this growth and ensuring it is more equitable and inclusive. Reforming food policy is paramount to this objective.

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APPENDIX 1: SUPPLEMENTARY TABLES APPENDIX TABLE 1.1 Changes in the Incidence of Poverty and Growth of Agricultural Versus Nonagricultural Income, 1991–2006

Direction of Change in the Incidence of Poverty Increase Decrease

Number of Provinces in Which Agricultural Income Growth Was Greater Than Nonagricultural Income Growth

Number of Provinces in Which Agricultural Income Growth Was Less Than Nonagricultural Income Growth

3 4

58 58

Note: Data include a total of seventy-three provinces. Source: Nobuhiko Fuwa, Arsenio Balisacan, and Fabrizio Bresciani, “In Search of a Strategy for Making Growth More Pro-Poor in the Philippines”, Asian Economic Papers 14, no. 1 (2015): 202–26.

APPENDIX TABLE 1.2 Initial Conditions Affecting Sectoral Growth Elasticity of Poverty Reduction, 1991–2006 Variable

Coefficient

Ln (nonag Y per capita) –1.670*** Ln (agri Y per hectare) –0.230*** Time trend (year) –0.010*** Ln (nonag income) interacted with initial conditions of 1991 Share of Filipinos working overseas –0.501*** Malnutrition –6.309*** Road density –0.372*** Income inequality –1.877** Ln (ag income) interacted with initial conditions of 1991 Irrigation potential –0.674** Rice yield –0.289** Constant 27.745*** Dependent variable Number of observations R-squared F-test (all coefficients zero)

Standard Error 0.358 0.083 0.003

0.116 2.122 0.134 0.846 0.312 0.075 6.324

Ln (Provincial povertyit) 402 0.550 39.116

Notes: Results are based on provincial panel data for 1991–2006 using a fixed-effects model; other provincial fixed effects, such as local political characteristics, urban–rural disparity, and schooling of household head, were not statistically significant and hence are not shown; ** and *** indicate confidence at the 5 and 1 per cent levels, respectively. Source: Nobuhiko Fuwa, Arsenio Balisacan, and Fabrizio Bresciani, “In Search of a Strategy for Making Growth More Pro-Poor in the Philippines”, Asian Economic Papers 14, no. 1 (2015): 202–26.

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13.40

Total Department of Agriculture budget (billion PhP) 18.65 15.88 12.70

79.71 82.61 12.91 7.6 92.61 90.21 7.4 9.8 19.56

79.11 9.3 88.41 11.61

0.0 3.7 0.4 6.7 2.3

19.09

81.61 9.1 90.71 9.3

0.0 3.7 0.7 1.9 3.3

18.87

81.11 8.6 89.71 10.31

0.0 2.5 0.8 1.7 3.5

3.6 0.0 0.0 2.9 0.5 1.8 1.7

3.7 0.0 0.0 2.2 0.8 1.6 2.3

5.1 0.9

0.0 2.2 2.9 2.8 3.6

5.1 0.0

0.0

0.0 3.8 1.6 2.6 5.7

8.2 0.0

0.0

0.0 2.5 0.4 2.1 2.5

4.0 0.0

0.0

0.0 2.0 0.4 3.6 3.6

4.2 0.0

0.0

0.0 3.1 0.0 1.9 0.3

4.6 0.0

5.3

0.0 5.5 0.0 3.1 0.5

7.5 0.0

8.0

10.72 13.90 24.46

24.11

17.26 27.30 32.74 33.64 22.90

82.81 83.81 84.51 76.71 86.61 83.81 83.81 83.21 88.51 7.5 5.5 3.5 3.5 4.8 3.2 3.1 4.4 6.5 90.31 89.31 88.01 80.21 91.31 87.01 86.91 87.61 95.11 9.7 10.71 12.01 19.81 8.7 13.01 13.11 12.41 4.9

0.0 3.0 1.2 1.7 3.4

3.9 0.0

0.0

Notes: AFMA = Agriculture and Fisheries Modernization Act; ESETS = Extension support, education, and training services; FMR = farm-to-market roads; GAA = General Appropriations Act; GASS = general administration and support services; MFO = major final outputs; STO = support to operation. Data for the total budget are presented in real Philippine pesos, deflated by the wholesale price index (WPI) with a base year of 1998. Source: Constructed by authors from DBM (Department of Budget and Management), Philippines General Appropriations Act, various years, retrieved from .

79.71 9.3 89.11 10.91

0.0 3.2 1.2 9.8 1.8

4.4 0.0

0.0

0.1 4.1 0.7 5.7 0.2

3.6 3.2

0.0

0.0 4.6 0.6 1.8 0.2

4.2 2.7

0.0

4.3 2.4

0.0

6.6 2.1

0.0

0.0

0.0

0.0

23.21 0.8 1.0 30.81 8.0

87.41 7.2 94.61 5.4

Total operations GASS and STO Total operations, GAS, and STO Subsidy to attached corporations

AFMA Component/MFO Production support 19.31 Market development 0.8 Credit facilitation 2.3 Irrigation development 24.91 Postharvest facilities and other 23.01 infrastructure (including FMR) Agricultural equipment and facilities 0.0 support services ESETS 6.6 ESETS salary supplement for extension 0.0 workers under local government agencies ESETS human resource development 0.0 Research and development 6.4 Information services 1.1 Regulatory services 2.9 Policy formulation, planning and advocacy 0.0 services

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 GAA Reenacted GAA Reenacted GAA GAA GAA GAA GAA GAA GAA GAA GAA Share (%) 17.81 22.41 24.81 23.41 23.91 18.91 19.51 23.11 10.31 8.0 8.9 9.4 21.81 30.11 0.4 0.3 0.4 0.4 0.4 0.5 1.0 1.6 1.6 2.9 2.0 1.8 0.6 4.0 0.6 0.7 0.9 1.0 0.9 0.6 0.4 0.4 0.2 0.2 0.2 2.7 0.1 4.0 36.31 28.31 26.11 30.51 29.61 39.11 29.91 28.71 28.41 35.51 41.61 38.91 28.71 3.2 7.1 8.0 7.0 12.31 14.21 10.51 22.31 17.81 19.61 18.21 19.61 17.31 16.91 22.71

2000 2001 2002 GAA Reenacted GAA

APPENDIX TABLE 1.3 Total Department of Agriculture Budget and Shares of Major Outputs, 2000–15

Current Structure and Future Challenges of the Agricultural Sector

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Notes The authors gratefully acknowledge the excellent research assistance of Jan Carlo Punongbayan, Shirra de Guia, and J. Kat Magadia. The authors are also grateful for comments and suggestions by participants of the IFPRI-NEDA project workshop held in Tagaytay City. Any errors of commission or omission are the sole responsibility of the authors and should not be attributed to any of the above or to their respective affiliations. This paper was completed in early 2016. Any views, statements or analyses expressed in this paper are those of the authors and should not be attributed to those of the Philippine Competition Commission, National Economic and Development Authority, or Ateneo de Manila University, except where they specify the contrary.   1. Although many services, such as the outsourcing business processes, have also become tradable due to advances in technology.   2. Note that the drastic reduction in the irrigation budget in 2015 was due to the transfer of the National Irrigation Administration from the Department of Agriculture to the Office of the Presidential Assistant for Food Security and Agricultural Modernization (OPAFSAM).   3. Firmed-up service area is equivalent to the service area, less any land either converted from agricultural to nonagricultural uses or considered permanently “nonrestorable” (that is, having either insufficient water or irrigation facilities that can no longer be completed for technical reasons).   4. Responsibility for public research and extension rests with the Department of Agriculture through its national research agencies, regional integrated research centres, the Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development within the Department of Science and Technology, and the state colleges and universities.   5. Note that oversight of NFA was transferred to the OPAFSAM in 2014.  6. Out-quota rates are tariffs imposed on imports that exceed quantitative restrictions and, hence, are typically prohibitively higher than in-quota rates.   7. For example, the cost of starting a business (as a share of per capita income) is substantially higher in the Philippines (17 per cent) than in Malaysia (7 per cent), Thailand (7 per cent), China (1 per cent), and Vietnam (5 per cent). On average, it would take 34 days to start a business in the Philippines, whereas the comparable timespans for Malaysia, Thailand, and Vietnam are 6, 28, and 34 days, respectively (World Bank 2014c).   8. An additional requirement for transferability is that the beneficiary must have paid off the Land Bank, which would likely take 30 years or more.   9. While economies of scale at five hectares could arguably be captured for rice cultivation, the same could not be said for crops like sugar and coconuts, especially if economies of scale in markets and production are considered. 10. Cognizant of the distinction between farm workers and farmers, a two-pronged approach would perhaps make sense, involving a much freer approach towards

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Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla beneficiaries wanting to sell their land and the development of market-based support services for those wanting to remain in farming. The best way to develop supply chains is through the private sector, possibly led by larger commercial farmers.

References Anderson, Kym. “Economic Growth, Structural Change and the Political Economy of Protection”. In The Political Economy of Agricultural Protection, edited by K. Anderson and Y. Hayami. London: Allen & Unwin, 1986. ———, Yujiro Hayami, and Masayoshi Honma. “The Growth of Agricultural Protection”. In The Political Economy of Agricultural Protection, edited by K. Anderson and Y. Hayami. London: Allen & Unwin, 1986. ———, Marianne Kurzweil, Will Martin, Damiano Sandri, and Ernesto Valenzuela. “Methodology for Measuring Distortions to Agricultural Incentives”. Agricultural Distortions. Washington, D.C: World Bank, 2008. Aragon, Corazon, Artemio Salaza, Antonio Quilloy, and Gideon Carnaje. “Increasing Agricultural Productivity in the Philippines”. Laguna Philippines: University of the Philippines Los Baños (UPLB) – College of Economics and Management (CEM), 2011. Aquino, Albert, Anita Tidon, Princess Ani, and Meliza Festejo. “The Agriculture and Fisheries Modernization Act of 1997: A Collective Approach to Competitiveness”. 2013 (accessed 25 May 2016). Balisacan, Arsenio. “Demand for Food in the Philippines: Responses to Price and Income Changes”. Philippine Review of Economics and Business 31 (1994): 137–63. ———. “Why Does Poverty Persist in the Philippines? Facts, Fancies, and Policies”. In Whither the Philippines in the 21st Century? edited by R. Severino and L. Salazar. Singapore: Institute of Southeast Asian Studies, 2007. ———. “The Growth-Poverty Nexus: Multidimensional Poverty in the Philippines”. In Sustainable Economic Development: Resources, Environment and Institutions, edited by A. Balisacan, U. Chakravorty, and M. Ravago. Oxford and San Diego: Elsevier Academic Press, 2015. ——— and Nobuhiko Fuwa. “Going Beyond Cross-Country Averages: Growth, Inequality and Poverty Reduction in the Philippines”. World Development 32, no. 11 (2004): 1891–907. ——— and Hal Hill, eds. The Dynamics of Regional Development: The Philippines in East Asia. Cheltenham, U.K.: Edward Elgar, 2007. ——— and James Roumasset. “Public Choice of Economic Policy: The Growth of Agricultural Protection”. Weltwirstschaftlitches Archiv 123, no. 2 (1989): 232–48. ———, Hal Hill, and Sharon Piza. “Spatial Disparities and Development Policy

18-J04349 01 Future of Philippine Agriculture.indd 64

19/11/18 12:03 PM

Current Structure and Future Challenges of the Agricultural Sector

65

in the Philippines”. In Reshaping Economic Geography in East Asia, edited by Y. Huang and A. Bocchi. Washington, D.C.: World Bank, 2009. ———, Leocadio Sebastian, and associates. Securing Rice, Reducing Poverty: Challenges and Policy Directions. Los Baños: The Southeast Asian Regional Center for Graduate Study and Research in Agriculture, Philippine Rice Research Institute, and Bureau of Agricultural Research of the Philippine Department of Agriculture, 2006. ———, Mercedita Sombilla, and Rowell Dikitanan. “Rice Crisis in the Philippines: Why Did It Occur and What Are Its Policy Implications?”. In The Rice Crisis: Markets, Policies and Food Security, edited by D. Dawe. Rome: Food and Agricultural Organization of the United Nations; and London: Earthscan, 2010. Besley, Timothy, and Louise Cord, eds. Operationalizing Pro-Poor Growth: Synthesis and Country Experiences. London: Palgrave Macmillan, 2006. Boserup, Ester. The Conditions of Agricultural Growth. London: Allen & Unwin, 1965.
 ———. Population and Technological Change: A Study of Long-Term Trends. Chicago: University of Chicago Press, 1981. Clark, Colin. The Conditions of Economic Progress. London: Macmillan, 1940. Chenery, Hollis, and Moises Syrquin. Patterns of Development, 1950–1970. New York: Oxford University Press, 1975. David, Cristina. “Agriculture”. In The Philippine Economy: Development, Policies, and Challenges, edited by A. Balisacan and H. Hill. New York: Oxford University Press, 2003. ———, Ponciano Intal, and Arsenio Balisacan. “The Philippines”. In Distortions to Agricultural Incentives in Asia, edited by K. Anderson and W. Martin. Washington, D.C.: World Bank, 2009. de Dios, Emmanuel and Jeffrey Williamson. “Deviant Behavior: A Century of Philippine Industrialization”. In Sustainable Economic Development: Risk, Resources and Governance, edited by A. Balisacan, U. Chakravorty, and M. Ravago. Oxford and San Diego: Elsevier Academic Press, 2015. Deaton, Angus. “Demand Analysis”. In Handbook of Econometrics. Amsterdam and New York: New-Holland, 1986. DBM (Department of Budget and Management). Philippines General Appropriations Act. Various years. Retrieved from . Dollar, David, and Kraay Aart. “Growth Is Good for the Poor”. Journal of Economic Growth 7, no. 3 (2002): 195–225. Dy, Rolando. “Private-Sector Investment and Rural Growth”. Report submitted to the World Bank Office Manila, 2005 (accessed 25 May 2016).

18-J04349 01 Future of Philippine Agriculture.indd 65

19/11/18 12:03 PM

66

Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla

FAO (Food and Agriculture Organization of the United Nations). FAOSTAT database. Various years (accessed 17 May 2015). Francisco, Sergio and Flordeliza Bordey. “Research, Development and Extension”. In Productivity Growth in Philippine Agriculture, edited by R. Briones, M. Sombilla, and A. Balisacan. Los Baños: Southeast Asian Regional Center for Graduate Study and Research in Agriculture, Philippine Rice Research Institute, and Bureau of Agricultural Research of the Department of Agriculture, 2014. Fuwa, Nobuhiko, Arsenio Balisacan, and Fabrizio Bresciani. “In Search of a Strategy for Making Growth More Pro-Poor in the Philippines”. Asian Economic Papers 14, no. 1 (2015): 202–26. Habito, Cielito, and Roehlano Briones. “Philippine Agriculture over the Years: Performance, Policies and Pitfalls”. Paper presented at the Conference “Policies to Strengthen Productivity in the Philippines”, Makati City, the Philippines, June 2005. Huang, Jikun, J. Yang, and Scott Rozelle.“China’s Agriculture: Drivers of Change and Implications for China and the Rest of World”. Agricultural Economics 41 (2010): 47–55. Kelman, Ilan and Cheney Shreve, ed. “Disaster Mitigation Saves”. Version 12. 2013 (accessed 5 May 2015). Krueger, Anne, Maurice Schiff, and Alberto Valdes, eds. The Political Economy of Agricultural Pricing Policy. Vol 2: Asia. Baltimore: Johns Hopkins University Press, 1991. Kuznets, Simon. Modern Economic Growth: Rate, Structure and Spread. New Haven: Yale University Press, 1966. Llanto, Gilberto. “Rural Finance”. In Securing Rice, Reducing Poverty: Challenges and Policy Direction, edited by A. Balisacan, L. Sebastian, and associates. Los Baños: The Southeast Asian Regional Center for Graduate Study and Research in Agriculture, Philippine Rice Research Institute, and Bureau of Agricultural Research of the Philippine Department of Agriculture, 2006. Lybert, Travis and Michael Carter. “Bundling Drought Tolerance and Index Insurance to Reduce Rural Household Vulnerability to Drought”. In Sustainable Economic Development: Resources, Environment, and Institutions, edited by A. Balisacan, U. Chakravorty, and M. Ravago. Oxford and San Diego: Elsevier Academic Press, 2015. Lucas, Robert. “Making A Miracle”. Econometrica 61, no. 2 (1993): 251–72. ———. Lectures on Economic Growth. Cambridge: Harvard University Press, 2001. Manasan, Rosario. “Reforming Social Protection Policy: Responding to the Global Financial Crisis and Beyond”. Makati: Philippine Institute for Development Studies, 2009.

18-J04349 01 Future of Philippine Agriculture.indd 66

19/11/18 12:03 PM

Current Structure and Future Challenges of the Agricultural Sector

67

Mapa, Dennis, Kristelle Castillo, and Krizia Francisco. Rice Price, Job Misery, Hunger Incidence: Need to Track Few More Statistical Indicators for the Poor. Quezon City: School of Statistics, University of the Philippines, Diliman, 2015. NEDA (National Economic and Development Authority). “Reconstruction Assistance on Yolanda (RAY)”. 2013. (accessed 17 May 2015). Ponce, Eliseo and Rolando Dy. “Convergence Mechanisms and Quality Improvements in the Provision of Support Services to Agrarian Reform Beneficiaries”. Report submitted by Technical Working Group 2 for the study commissioned by an Inter-Agency Committee led by the National Economic Development Authority on Institutional Arrangements for Land Management and Rural Development, 2014. PSA (Philippine Statistics Authority). “Annual Poverty Indicators Survey”. Incidence of multidimensional versus income-based poverty. Various years [a] (accessed 16 May 2017). ———. “Family Income and Expenditure Survey”. Incidence of multidimensional versus income-based poverty. Various years [b] (accessed 16 May 2017). ———. “Import/export data”. Various years [c]. (accessed 16 May 2017). ———. “Labor Force Survey”. October round. Various years [d]. (accessed 16 May 2017). ———. “National Demographic and Health Survey”. Incidence of multidimensional versus income-based poverty. Various years [e] (accessed 16 May 2017). ———. “National Income Accounts”. Various years [f] (accessed 16 May 2017). ———. “Poverty Incidence by Region”. Various years [g] (accessed 16 May 2017). ———. “Value and Share of Agricultural Trade”. Various years [h] (accessed 16 May 2017). ———. “Gross Value Added in Agriculture, Fishery, and Forestry”. Various years [i] (accessed 16 May 2017). ———. “Production”. Various years [j] (accessed 16 May 2017). ———. “Land Use”. Various years [k] (accessed 16 May 2017). ———. “Prices.” Various years [l] (accessed 16 May 2017).

18-J04349 01 Future of Philippine Agriculture.indd 67

19/11/18 12:03 PM

68

Majah-Leah V. Ravago, Arsenio M. Balisacan and Mercedita A. Sombilla

———. “Family Income and Expenditure Survey”. 2015 (accessed 16 May 2017). ———. Family Income and Expenditure Survey, 2012 . Public Commission.“Typhoons Ondoy and Pepeng: Post-Disaster Needs Assessment”. Main report by the Special National Public Reconstruction Commission and Global Facility for Disaster Reduction and Recovery, 2009 (accessed 20 May 2015). Ravago, Majah-Leah and Amy Cruz. Southeast Asian Agriculture and Development Primer Series. Los Baños: Southeast Asian Regional Center for Graduate Study and Research in Agriculture, 2004. ———, James Roumasset, and Arsenio Balisacan. “Economic Policy for Sustainable Development vs. Greedy Growth and Preservationism”. In Sustainability Science for Watershed Landscapes, edited by J. Roumasset, K. Burnett, and A. Balisacan, eds. Los Baños: Southeast Asian Regional Center for Graduate Study and Research in Agriculture; and Singapore: Institute of Southeast Asian Studies, 2010. ——— and Arsenio Balisacan. “Agricultural Policy and Institutional Reforms in the Philippines: Experiences, Impacts, and Lessons”. Southeast Asian Agriculture and Development Primer (SAADP) Second Series, SEARCA, 2016. Reardon, Thomas and C. Peter Timmer. “The Supermarket Revolution with Asian Characteristics”. In Reasserting the Rural Development Agenda: Lessons Learned and Emerging Challenges in Asia, edited by A. Balisacan and N. Fuwa. Los Baños: Southeast Asian Regional Center for Graduate Study and Research in Agriculture; and Singapore: Institute of Southeast Asian Studies, 2007. ———, C. Peter Timmer, and Bart Minten. “Supermarket Revolution in Asia and Emerging Development Strategies to Include Small Farmers”. Proceedings of the National Academy of Sciences of the United States 109, no. 31 (2012): 12332–37. Reyes, Celia, Christian Mina, Reneli Gloria, and Sarah Mercado. Review of Design and Implementation of the Agricultural Insurance Programs of the Philippine Crop Insurance Corporation. Quezon City: Philippine Institute of Development Studies, 2015. Roumasset, James. “Market Friendly Food Security: Alternatives for Restructuring NFA”. Mimeographed. Department of Economics, University of Hawaii, Honolulu, 2000. SEPO. Financing Agriculture Modernization: Risks & Opportunities. 2009. (accessed 20 May 2015). Sombilla, Mercedita and Karen Quilloy. “Strengthening the Philippine Rice Seed

18-J04349 01 Future of Philippine Agriculture.indd 68

19/11/18 12:03 PM

Current Structure and Future Challenges of the Agricultural Sector

69

System”. Paper submitted to the Regional Strategic Analysis and Knowledge Support Systems in Asia (ReSAKSS-Asia) Program, International Food Policy Research Institute. Washington, D.C., 2014. Teruel, Romeo and Jesus Dumagan. “Total Factor Productivity Growth in Philippine Agriculture”. In Productivity Growth in Philippine Agriculture, edited by R. Briones, M. Sombilla, and A. Balisacan. Los Baños: Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA); Muñoz, Nueva Ecija: Philippine Rice Research Institute (PhilRice); and Quezon City: Department of Agriculture-Bureau of Agricultural Research, 2014. Timmer, C. Peter. “The Agricultural Transformation”. In Handbook of Development Economics, Vol. 1, edited by H. Chenery and T. Srinivasan. Amsterdam: North Holland, 1988. ———. Agriculture Pro-poor Growth: An Asian Perspectives. Working Paper 63. Washington, D.C.: Center for Global Development, 2005. ———. A World without Agriculture: The Structural Transformation in Historical Perspective. Washington, D.C.: AEI Press, 2007. ——— and Selvin Akkus. The Structural Transformation as a Pathway out of Poverty: Analytics, Empirics and Politics. Washington, D.C.: Center for Global Development, 2008. World Bank. PovcalNet. Various years (accessed 20 May 2015). ———. Philippines: Agriculture Public Expenditure Review. Technical Working Paper. Washington, D.C.: Rural Development, Natural Resources, and Environment Sector Unit of the Sustainable Development Department for the East Asia and Pacific Region, 2007. ———. World Development Report: Agriculture for Development. Washington, D.C., 2008. ———. World Development Report: Reshaping Economic Geography. Washington, D.C., 2009a. ———. Land Reform, Rural Development, and Poverty in the Philippines: Revisiting the Agenda. Technical Working Paper. Washington, D.C., 2009b. ———. Investing in the Future: Sharing Growth and Job Opportunities for All. Philippine Economic Update. Manila, 2014a. ———. Keeping Children Healthy and in School: Evaluating the Pantawid Pamilya Using Regression Discontinuity Design. Second Wave Impact Evaluation Results. Manila, 2014b. ———. “Ease of Doing Business Rankings”. 2014c (accessed 20 May 2015). ———. Philippine Economic Update: Making Growth Work for the Poor. Manila, 2015a. ———. “Nutrition Country Profiles: Philippines”. 2015b. (accessed 20 May 2015). World Economic Forum. The Global Competitiveness Report 2010–2011, 2010

(accessed 20 May 2015). ———. The Global Competitiveness Report 2011–2012, 2011 (accessed 20 May 2015).

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2 THE CONTEXT OF LAND COVER CHANGES IN AGRICULTURE AND FORESTRY David M. Wilson and Rodel D. Lasco

The nexus between an increasing global population, the demand for food and the land on which it is cultivated, and emerging climate variability poses one of today’s greatest societal challenges (Rudel et al. 2009). Understanding the dynamics and drivers of global land-use change is as important now as ever. As agricultural and pasture lands expand into remaining forests, previously fertile land is lost to desertification and intensified soil degradation. The latest report from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC-AR5) suggests that land-use change may also be responsible for as much as 24 per cent of global greenhouse gas (GHG) emissions (IPCC 2014b). This poses challenges for decision-makers and farmers alike and requires a coordinated effort that balances the need to meet rising food demand with the protection of our fragile and depleted natural resource base. Underpinning this is the need for accurate information on global, regional, national, and subnational dynamics and drivers of land-use change. Increasingly, this needs to be viewed and considered through the lens of climate change.

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A tension between two major land-use types — forests and agricultural land — also exists. Prevailing opinion is that expansion of agricultural land comes at the expense of forested areas; indeed, evidence exists suggesting this was, is, and most likely will continue to be one of the major drivers of forest loss and land-use change globally (Rudel et al. 2005; Chomitz 2006; Lambin and Meyfroidt 2011; Hosonuma et al. 2012). This is not necessarily the case in all countries, however, and local contexts are often more complex and ambiguous, as is discussed in this chapter focusing on the Philippines. Agricultural lands and agroecosystems provide essential benefits to human society and, in addition to being crucial to subsistence and economic activity, are deeply embedded culturally. For developing nations, poorer, often smallholder farmers rely on agriculture to meet their daily needs, and as populations grow so too does demand for land. At the same time, forests also offer benefits to those dependent on their goods and services (who may also be smallholder farmers). Direct benefits such as food, fodder, and timber are obvious. Perhaps less tangible, but no less important, are trees and forests and the role they play in supporting agricultural activities such as micro-climatic regulation, soil nutrient cycling, and stabilization, as well as improving water retention. These aspects are given added importance at a time of climate uncertainty. Finding an optimal balance for nations still reliant on agriculture and forest resources for both daily needs and economic development remains a central research and policy challenge. During 1990–2010, the global net loss of forest cover was twice the rate of growth of agricultural land for the same period (Figure 2.1). Forest ecosystems in Asia have undergone significant transformations, with a net loss of forest of some 600,000 hectares (ha) per year in the 1990s, and a net gain of more than 2.2 million ha of forests per year between 2000 and 2010 (FAO 2010b). Regionally, agricultural land increased by over 25 per cent in Asia, but, in contrast to the global trend, total forest cover also increased by over 6 per cent. The majority of this growth in forest cover occurred in China, but significant losses have and are occurring in Cambodia, Indonesia, and Myanmar. In China, the increase in forest cover has been due, in part, to large-scale afforestation efforts, but also to the practice of “exporting deforestation” to other countries in the region to meet China’s increased demand for timber and other wood products. In the other three countries, the decrease has mainly been driven by logging and monoculture plantation expansion (particularly oil palm) with corresponding losses of

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73

FIGURE 2.1 Global, Regional, and National Level Land-Use Change, 1990–2010

Source: FAO (Food and Agriculture Organization of the United Nations). Global Forest Resources Assessment 2010. Rome, 2010b.

rich biodiversity. Thus, the overall increase in forest cover in Asia may disguise the continued decline in forest resources despite increases in the forest area designated for conservation of biological diversity and protected areas in Asia (FAO 2010b). Subregionally, agricultural land expanded by 17 per cent in Southeast Asia during 1990–2010, accompanied by a 14 per cent decrease in forest cover. This is not a universal trend, however. Some countries (the Philippines and Vietnam) gained substantial forest cover, offsetting some of the losses experienced elsewhere in the region. Perhaps most interestingly, the countries where both agricultural and forest lands increased are China, the Philippines, and Vietnam. Can lessons or

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cautionary notes be drawn from these nations and the policies that have led to the growth in both land uses, and what does this mean, if anything, in light of global and local climate change? This chapter presents a review of recent evidence relating to the impacts of land-use change in the Philippines, including the role of agricultural expansion in deforestation within a regional and global context. The extent of agricultural and forest lands, as well as the drivers of change for each are discussed, along with new evidence suggesting that the Philippines may be undergoing a land-use transition. Also detailed are the effects of a changing climate on land use and ecosystem services key to agricultural productivity, as well as the contributory role of landuse change to global greenhouse gas (GHG) emissions. Existing and proposed national policies and programmes related to land use and their impacts on forests and agricultural land are briefly summarized in the light of future climate change. Finally, the analysis highlights approaches and policies that may be able to balance the need to meet rising food demand while maintaining the important ecosystems and associated services they provide.

THE CURRENT DISCOURSE ON LAND-USE CHANGE As global population rises, the demand for food likewise increases, while the availability of productive agricultural lands remains static or declines. Thus, many developing countries are grappling with the challenge of how to preserve natural resources — including forest ecosystems and the services they provide — while enhancing food production (Lambin and Meyfroidt 2011). Projected global demand in land for crops, forest products, urban areas, and biofuels ranges from 250 million ha to over 1 billion ha by 2030 and more by mid-century, pointing to rapid and extensive land-use change (Lambin and Meyfroidt 2011). Between 1980 and 2000, more than 55 per cent of new agricultural land across the tropics came at the expense of intact forests, and another 28 per cent came from disturbed forests (Gibbs et al. 2010). The same study suggests that, even with agricultural intensification, farm land is projected to expand by a net 10 billion ha by 2050 in order to meet the rising demand for food crops, biofuels, and animal products. In Southeast Asia, the majority of new agricultural land during 1980–2000 (60 per cent) came from intact forests, whereas the figure was smaller (approximately 20 per

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cent) in the Philippines, with the majority of agricultural land converted from already disturbed forest and shrubland (Gibbs et al. 2010). In some cases, developing and transition economies — such as Chile, China, Costa Rica, El Salvador, India, and Vietnam — seemingly made more efficient use of land, increasing both forest cover and agricultural production. This was apparently achieved through a combination of agricultural intensification, appropriate land-use zoning, increased forest protection, the creation of off-farm jobs, foreign capital investments in land for food, and remittances (Lambin and Meyfroidt 2011).

The Borlaug Hypothesis Revisited: Agricultural Intensification and “Land Sparing” The hypothesis that agricultural intensification reduces pressure on forests by avoiding agricultural expansion is not a new one. Commonly known as “the Borlaug hypothesis”, it has been reignited recently, in part because of international policies such as Reducing Emissions from Deforestation and Forest Degradation (REDD+), which seeks to reduce pressure on existing forests, in part, through the intensification of agricultural activities. Some authors have questioned the validity of the Borlaug hypothesis when analysed from both a micro- and a macroeconomic perspective. Their overall assertion is that the hypothesis does not account for the increasingly globalized nature of agriculture and its response to price fluctuation and demands. The hypothesis is based on the assumption that demand for agricultural produce is fixed, so meeting the demand will reduce the need for further production and expansion, thus reducing forest conversion. This, however, does not account for a “rebound effect”, whereby increased productivity in a competitive market reduces costs and actually increases demand for cheaper products, driving further forest conversion. Additionally, the use of subsidies has been shown to override any land-sparing outcomes from agricultural intensification, although this could be moderated under certain circumstances — for example, for subsistence farmers and where expansion is limited due to physical or technological barriers (Pirard and Belna 2012). Indeed, some empirical studies have demonstrated the lack of evidence to verify the Borlaug hypothesis. Rudel et al. (2009), considers the historical evidence for the hypothesis via a global comparative study of 10 major crop types in 161 countries (including the Philippines). Country-level data

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provide no evidence that the growth in total cropland area associated with agricultural intensification either declined or was halted during 1970–2005, although the rate of expansion was lower than the rate of population growth (Rudel et al. 2009). Ewers et al. (2009) considered 23 major crop types from 124 countries, concluding that, in some cases, the land-sparing hypothesis may have positive impacts on forest cover. They suggest that — when the effects of human population increases on forest area were controlled for — declines in the area of natural forest were smaller in countries where the yield of staple crops increased most. However, these positive impacts are limited by the aforementioned rebound effect and through diversification away from staple crops, both of which can increase pressure on naturally vegetated areas, like forests. Global case studies also reveal mixed results, providing inconclusive evidence of the value of the hypothesis. As examples, in the Philippines the reduction of pressure on forests was achieved by intensifying labour on agricultural land, whereas the reverse was true in Indonesia, where greater capital investment precipitated a reduction in forest cover (Angelsen and Kaimowitz 2001). If agricultural intensification is insufficient on its own to simultaneously meet demand for food and stabilize or enhance the natural resource base in the Philippines, what is the alternative?

Blurring the Boundaries between Forest and Agriculture: Multifunctionality and “Land Sharing” The potential conflict between the growing need for agricultural land and natural resource conservation has led to the development of a proposed optimization of both in a single landscape. Known as “land sharing”, this paradigm — which has grown in prominence in recent years — proposes the co-occurrence of productive agroecosystems that simultaneously deliver food security, ecosystem services, and biodiversity conservation benefits (Grau, Kuemmerle, and Macchi 2013). Conditions potentially favouring the adoption of such land-sharing approaches include highly diversified types of agriculture (as opposed to monoculture), which are more resilient to the impacts of climatic stresses and provide a buffer to economic shocks. In the Philippines, monoculture is extremely vulnerable to the impacts of climate change, as was painfully revealed following the super typhoon Haiyan (Yolanda) in November 2013, which destroyed 600,000 hectares of crops, most of which was under monoculture production to rice, maize,

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or coconuts. Hence there could be a case for land-sharing approaches as a means of balancing conservation and production in the context of climaterelated shocks. There are suggestions that this form of multifunctional agriculture would come at the cost of productivity, and that smaller-scale farms are inherently inefficient. This is generally not supported in the literature, however, which suggests that smaller scale agriculture with diversified crops can be more productive per unit of area than larger scale farms — recounting the “productivity–size inverse relationship” proposed by Amartya Sen1 (Perfecto and Vandermeer 2010). Multifunctional landscapes that incorporate or protect trees and employ low-input, ecosystem-sensitive farming practices have thus been presented as a possible approach to relieving the tension between forest protection and agricultural productivity (Michon et al. 2007; Perfecto and Vandermeer 2010; Sayer et al. 2013). In these multifunctional landscapes in developing countries, including the Philippines, smallholders manage agroforestry, long or improved fallow, woodlots, and home gardens to both secure tree cover and meet livelihood needs (Dewi et al. 2013). While the structure and function of these landscapes may be different from both agroecosystems and primary forests, the overall benefits of each may offer a viable alternative to agricultural intensification and increased forest protection, or at least offer an additional tool to policymakers and practitioners who seek to strike a delicate balance (Tscharntke et al. 2012).

LAND-COVER CHANGE IN THE PHILIPPINES Discussions about land-cover change in the Philippines are often dominated by changes in forest cover. The prevailing narrative is that forest cover has been steadily declining since early European colonization in the sixteenth century. These declines accelerated with commercial logging activities in the early twentieth century, peaking in the late 1980s before forests began to recover towards the turn of the millennium — perhaps in part due to the introduction of purposive policies, such as a logging moratorium and the establishment of a network of protected areas. Less attention has been given to the drivers of recent changes in forest cover and productive function, changes in agricultural land use, or the relationship between these factors; quantitative analysis is largely lacking in the literature. These issues are discussed further below.

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Land Classification Perhaps one of the reasons for a lack of consistent quantitative analysis of land cover and land-cover change at the national scale has been the lack of clarity in land classifications. The current Philippine system is relatively broad with two major units, publicly owned forestlands and alienable and disposable lands (A&D). Subcategories within these classifications have been the source of intense debate.

Forestlands Forestlands in the Philippines cover almost 16 million ha, or roughly 52 per cent of the total area of the archipelago (DENR-FMB 2012). Such lands are broadly classified as those having more than an 18 per cent slope, but they also include additional lands defined by the government under the “Revised Forestry Code of 1975”. Estimates of around 7 million ha of forest cover in the Philippines suggest that more than half of all forestlands do not, in fact, have any forest cover and are inhabited by as many as 20 million people. All forestlands are considered publicly owned by the State and are administered by the Forestry Management Bureau of the Department for Environment and Natural Resources (DENR-FMB).

Alienable and Disposable Lands Lands that are outside forest areas or other designated zones are considered A&D, which means they are largely nonpublic; are available for private ownership; and can be developed for commercial, agricultural, residential, or urban uses within national and local planning frameworks. It is estimated that around 700,000 ha of these lands have tree cover (DENR-FMB 2012).

Conflicting Forest Classification It has been over ten years since the last official government statistics on forest cover in the Philippines were released. In 2003, DENR-FMB adopted the internationally recognized definitions of forests and forest types used by the Food and Agriculture Organization of the United Nations (FAO). In 2002 the reported figure was 7.2 million ha, but this increased substantially to 7.6 million ha with the 2003 reclassifications, which meant

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that previously excluded areas such as plantations, privately owned forests, and reforestation areas were included. The definition of forest cover also changed, reducing the minimum land cover for forests from 1 to 0.5 ha. This had the overall effect of increasing the reported Philippine forest cover and has contributed to the challenge of accurately tracking Philippine forest cover from year to year. In fact, some nongovernmental organizations (NGOs) have conducted separate assessments using remotely sensed data and suggest that the government figures may be overestimating the total forest cover (ESSC 2010).

Changes in Forest Cover When the Spanish arrived in the Philippines in 1521, 90 per cent of the country (approximately 27 million ha of a total of 30 million ha of land area) was covered with lush tropical rainforest (Lasco, Visco, and Pulhin 2001). By 1900, 70 per cent or 21 million ha of forest cover remained (Garrity, Kummer, and Guiang 1993). Between 1934 and 1988 it is estimated that 9.8 million ha of forests were lost and that almost all the 2.1 million ha of forest located within 1.5 kilometre of roads in 1934 had been totally removed by 1988 (Liu, Iverson, and Brown 1993). By 1996, only 6.1 million ha (20 per cent) of forest remained (DENR-FMB 1997). Thus, in the past century alone, the Philippines lost 14.9 million ha of tropical forests, and from being one of the world’s biggest exporters of tropical hardwoods in the 1960s, the Philippines has become a net importer of wood products. According to DENR-FMB figures, from the late 1990s, forest cover in the Philippines began to recover and enter a phase of net increase. According to the FAO’s 2010 Global Forest Resource Assessment for 1990–2000 (FAO 2010b), the Philippines ranked tenth in the world for net yearly gain in forest cover in absolute terms, and fifth in terms of percentage of cover. During that timeframe, an apparent increase in forest cover of more than 1 million ha to 7.67 million ha was recorded (Table 2.1). This increase can be attributed in part to deliberate policies, such as a moratorium on logging old growth and mossy forests at more than 1,000 metres above sea level, as well as reforestation efforts by government agencies, NGOs, the private sector, and civil society organizations (Mendoza 2009). Other wooded land that does not meet the FAO definition of a forest also increased during this timeframe. This land is largely considered to be shrublands

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TABLE 2.1 Philippine Land-Cover by Type, 1990, 2000, 2005, and 2010 Area (thousand hectares) Category Forest Other wooded land Other land Inland water bodies Total

1990

2000

2005

2010

6,570 3,216 20,031 183 30,000

7,117 6,672 16,028 183 30,000

7,391 8,400 14,026 183 30,000

7,665 10,128 12,024 183 30,000

Note: “Other land” includes agricultural land and grassland. Source: FAO (Food and Agriculture Organization of the United Nations). Global Forest Resource Assessment Report 2010: Country Report. Rome, 2010.

and regenerating grasslands, not agricultural lands, and the increasing trend is attributed to ecological succession in open and grassland areas (FAO 2010a). Uncertainty remains, however, about these figures based on contradictory data from the DENR-FMB suggesting that total forest cover as of 2010 was 6,839,718 ha (DENR-FMB 2012). This is especially confounding because DENR-FMB led the development of the FAO country report (the source of data presented here). Additional evidence from remote-sensing data suggests that forest cover in the Philippines may be even higher than official figures suggest, and that that there was an 11 per cent increase in forest cover during 2001–09 to 15.3 million ha, suggesting that over half the total area of the Philippines is forested (Table 2.2). Further doubt is raised about these figures, however, because coconuts may be included under forests, which is not the case in official statistics. At the very least, the range of figures reported from different sources highlights the need from both a research and a policy perspective for a definitive estimation of Philippine land cover in order to effectively manage the nation’s natural resources. In the absence of reliable data, for the purposes of this chapter the FAO figures have been adopted.

Forest Typologies According to the FAO classification system, the Philippines has three main types of forest (Table 2.3). DENR-FMB, however, also categorizes forests by ecological type, which are discussed in detail below.

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TABLE 2.2 Remote-Sensing Land-Cover Data for the Philippines, 2001 and 2009 Share of Total Area (%)

Million ha Type of Land Cover/Forest Closed shrublands Open shrublands Woody savannas Savannas Grasslands Permanent wetlands Croplands Urban and built-up areas Cropland/natural vegetation mosaic Barren or sparsely vegetated Forest Evergreen needleleaf forest Evergreen broadleaf forest Deciduous needleleaf forest Deciduous broadleaf forest Mixed forests Total

2001

2009

2001

2009

0.040 0.012 0.365 0.064 0.159 1.313 1.580 0.306 13.387 0.035 11.991 0.021 11.129 0.004 0.272 0.565 29.253

0.023 0.005 0.528 0.010 0.049 1.305 1.643 0.301 10.085 0.003 15.297 0.007 15.111 0.007 0.069 0.104 29.251

0.14 0.04 1.25 0.22 0.54 4.49 5.40 1.04 45.76 0.12 40.99 0.07 38.05 0.01 0.93 1.93 100

0.08 0.02 1.81 0.04 0.17 4.46 5.62 1.03 34.48 0.01 52.29 0.02 51.66 0.02 0.23 0.35 100

Percentage Change (%) –0.06 –0.03 0.56 –0.18 –0.37 –0.03 0.22 –0.02 –11.29 –0.11 11.30 –0.05 13.61 0.01 –0.70 –1.58

Source: NASA (National Aeronautics and Space Administration). “Moderate Resolution Imaging Spectroradiometer (MODIS) data”, 2010 (accessed April 2015).

TABLE 2.3 Forest Type and Extent, 1990, 2000, 2005, and 2010 Forest Cover (million ha) Forest Type

1990

2000

2005

2010

Other naturally regenerated forest Planted forest Primary foresta Total

5.407 0.302 0.861 6.571

5.929 0.327 0.861 7.119

6.190 0.340 0.861 7.393

6.452 0.352 0.861 7.667

Note: Data are based on official classifications by the Food and Agriculture Organization of the United Nations. a. Primary forest is categorized as “Dipterocarp forest, old-growth”. Source: FAO (Food and Agriculture Organization of the United Nations). Global Forest Resource Assessment Report 2010: Country Report. Rome, 2010.

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Old-Growth Forests Since 1992, logging on all old-growth forests, mossy forests, and forests at more than 1,000 metres above sea level and with more than a 50 per cent slope has been banned in the Philippines primarily for the purpose of conserving biodiversity. These forests are now part of a National Integrated Protected Area System. An estimated 2.7 million ha of forest is under some form of protection; these are mainly mossy forests (1.1 million ha) and old-growth forests (0.86 million ha).

Secondary Forests An estimated 3.8 million ha of land are classified as secondary forests, defined as having trees covering 5–10 per cent of the ground by area (FAO 2010a). These lands have been further degraded after clearance, through ongoing selective cutting, agricultural expansion (the logging–shifting cultivation tandem2), livestock grazing, or wildfires (Geist and Lambin 2001).

Shrublands As of 2003, an estimated 3.6 million ha of land in the Philippines were designated as shrublands, which comprise remnants of tropical forests that were progressively degraded by excessive tree cutting. The cover consists of relic trees, shrubs, and grasses, and these areas are expected to continue to regenerate to a mature tropical forest if left undisturbed.

Grasslands Except in very small, high-altitude areas, there are no natural grasslands in the Philippines; hence, the estimated 2 million ha of grasslands are anthropogenic and managed ecosystems, often periodically burned for pasture for grazing livestock. Previously forested, they are the product of severe land degradation associated with deforestation and intensive management. Because of the high population density in upland areas, most grassland areas in the Philippines are used for farming or grazing.

Industrial Tree Plantations As of 2012 there were 140 lease agreements for tree plantations covering around 1 million ha of forest and A&D lands (DENR-FMB 2012). In

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addition to producing sustainably managed timber, these plantations are also used to extract nontimber forest products (mainly rubber), as well as nontimber species used for energy (bamboo) and to support the ailing timber industry in the Philippines.

Agroforestry Agroforestry involves the deliberate incorporation of woody perennials into agricultural lands, grown in conjunction with agricultural crops or livestock. It is estimated that upland farms account for 5.7 million ha of land in the Philippines, including both forest tree–based farms, coconut plantations (which are typically intercropped), and fruit orchards.

Forest Fallows and Shifting Cultivation Shifting cultivation and swidden agriculture or kaingin3 are effectively outlawed in the Philippines because they are somewhat misleadingly considered to be a major driver of degradation of primary forests into secondary forests. This practice is generally associated with indigenous communities that have been living close to the forests for generations. Little information is available on the area covered by forest fallows in the Philippines (Schmidt-Vogt et al. 2009). This is partly due to difficulty in distinguishing forest fallows from logged-over forests in satellite images, as well as the constantly changing use of the land. Traditional fallow systems in the Philippines are important for food security based on the diversity of the crops grown and the buffer they provide against economic and climatic shocks. These systems are often central to the culture and belief systems of indigenous groups, with various rites and traditions focused on site selection, planting, and harvesting (for an in-depth review, see Vergara 1982, Olofson 1983, Rice and Dulnuan 1981, and Palao et al. 2011). The long forest fallow period (5–20 years) is important in the sustainability of traditional swidden systems through the accumulation and conservation of nutrients in the vegetation, which increases the organic matter of top soil (Nair 1988).

Drivers of Change: Deforestation, Degradation, and Agriculture Globally, the drivers of forest cover change are commonly grouped into direct drivers normally associated with human activities, such as

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agricultural expansion, timber extraction, and infrastructure projects, or indirect drivers that constitute a more complex tangle of socioeconomic, political, technological, and cultural factors that can trigger direct drivers. Agricultural expansion is often cited as one of the principal direct drivers of land-use change in general, and deforestation and forest degradation in particular (Gibbs et al. 2010). Nevertheless, this has been shown to vary significantly across countries and through time (Rudel et al. 2005; Rudel, Schneider, and Uriarte 2010). Indeed, agricultural expansion can affect forest cover in different ways depending on whether it is commercial (larger scale, clear cutting) or subsistence (smaller scale, selective cutting).

DIRECT DRIVERS OF DEFORESTATION AND DEGRADATION In Asia, a recent review of the drivers of deforestation and forest degradation between 2000 and 2010 indicates that agricultural expansion (both commercial and subsistence in roughly equal proportion) and timber extraction are the principal drivers (Hosonuma et al. 2012). In the case of the Philippines, in the past the principal drivers of deforestation were a demand for timber and agricultural expansion associated with a rapidly increasing population (Kummer 1992; Liu, Iverson, and Brown 1993; Sajise 1998; Guiang, Borlagdan, and Pulhin 2001; Lasco, Visco, and Pulhin 2001; Pulhin 2002; Rebugio et al. 2005; and Chokkalingam et al. 2006). While illegal logging remains a challenge, it is nothing like the scale of the twentieth century. Current major pressures on extant forest extent and productivity include commercial agricultural expansion (oil palm, rubber, and other monocrops), urbanization, major infrastructure development (roads, mines, and dams), and extraction of fuelwood and charcoal (Geist and Lambin 2001; Grainger, Franscisco, and Tiraswat 2003; Chokkalingam et al. 2006; GIZ 2013).

Logging and Demand for Timber Historically, the most important driving force of forest loss in the Philippines was logging activities by companies (Kummer 1992) that were granted a timber licence agreement (TLA). At the height of logging activities in the 1970s, 471 TLA holders in the Philippines controlled an

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aggregate area of more than 10 million ha, a staggering one-third of the country’s total land area. This shows how a few companies gained control of much of the country’s forest resources. Since the mid-1980s, however, the number of TLAs has steadily declined. By 1992, 71 TLAs covered an area of 2.3 million ha, and by 2012 only three licenses remained covering 177,000 ha of forest. Once access roads are built and communities are established inside the forest area, shifting cultivation and other (often illegal) commercial activities, including further cutting, commence; this process led to the formation of highly degraded secondary forests and, ultimately, to denuded grassland areas (Liu, Iverson, and Brown 1993).

Swidden or Shifting Cultivation Very limited quantitative information exists on the extent and management of indigenous forest fallows in the Philippines. Practitioners of swiddenbased systems are often blamed for deforestation and degradation and, indeed, the practice is considered illegal in the country’s forestlands. While this may be true for some shorter fallow systems, traditional longer fallow systems have in fact been shown to offer greater ecosystem benefits than more permanent tree plantation or crops to which these systems are transitioning in Southeast Asia (Cramb et al. 2009; Ziegler et al. 2009, 2012). A meta-analysis of land-cover transformations of the past ten to fifteen years in tropical forest–agriculture frontiers worldwide shows that the transition from swidden to other land uses often contributes to permanent deforestation, loss of biodiversity, increased weed pressure, declines in soil fertility, and accelerated soil erosion (van Vliet et al. 2012).

Commercial Agricultural Expansion Expansion of agrifuels — principally oil palm and other monocrops, including rubber and eucalyptus — has come at the expense of natural forests, especially in frontier areas of Mindanao and Palawan (Dressler 2011; GIZ 2013). Oil palm plantations are extensive, require large areas of land to reach economic viability, and are usually accompanied by roads that provide access for further forest exploitation. It is thought that this recent expansion may be the result of an overspill in demand from the main producers of oil palm in Indonesia and Malaysia. A global increase

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in demand for rubber has driven the expansion of plantations in the Philippines, where the tree grows well. It is estimated that between 2005 and 2011 rubber plantations almost doubled from 100,000 ha to over 180,000 ha (PSA-BAS 2012), although it is not clear whether this directly replaced natural forest.

Mining and Infrastructure Projects Large infrastructure projects, such as hydropower dams and mining operations, are estimated to be responsible for up to 20 per cent of deforestation in Asia alone (Geist and Lambin 2001). The Philippines is rich in mineral resources, and the mining industry has seen significant financial growth since the late 1990s, increasing revenues from PhP33 billion in 1997 to PhP146 billion by 2012. The number of metallic mines operating also doubled in that time from seventeen to thirty-five, covering an estimated 1 million ha or 3 per cent of total land area (DENR-MGB 2013). There exist few official statistics as to the impact of these operations on land use, in particular in relation to forests; however, many of the active and granted permits are in remote mountainous locations where much of the extant primary forest remains. Although regulations exist, ineffective monitoring and policing mean that mining activities, in particular, encroach into forest areas, sometimes within lands protected ecological or cultural reasons. Some attempts have been made to show the overlap between mining concessions and existing forests, key biodiversity areas, and indigenous people’s ancestral domains to suggest that operations may encroach into these areas (Phelps et al. 2010); no reliable figures exist, however. Additionally, small-scale mining, which is enforced and monitored by local government units, is thought to be responsible for major deforestation in the Philippines because of weak local governance and collusion (GIZ 2013).

Fuelwood and Charcoal Globally, one of the biggest drivers of forest degradation is considered to be the extraction of timber for fuelwood and charcoal production (Kissinger, Herold, and de Sy 2012), an assumption also made for Southeast Asia (ADB 1995). However, very little data exists either for fuelwood extraction or charcoal manufacturing, or of its impacts on forest cover or productivity

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in the Philippines. In fact, site-specific data from a long-term study in the province of Cebu suggests that the demand for charcoal and fuelwood may even have the opposite effect, instead driving the establishment of sustainably managed woodlots on an island with already low forest cover (Bensel 2008).

Urbanization The rapid economic development of the Philippines coupled with the increase in population has precipitated the expansion of areas for residential, industrial, and commercial buildings. This has driven further displacement of smallholder farmers, mainly in the lowlands, forcing them to occupy more marginal lands in the uplands and leading to the further fragmentation and degradation of forest frontiers (Estoque and Murayama 2011, 2013).

INDIRECT DRIVERS OF DEFORESTATION Underlying drivers of forest destruction and secondary forest formation are more complex than simply blaming loggers and shifting cultivators. Deforestation (and secondary forest formation and use) is often associated with systemic issues like corruption, poverty, high population density, and migration to marginal upland areas where conventional agricultural practices are unsuitable (Chokkalingam et al. 2006). The main indirect drivers of forest destruction in the Philippines are discussed in more detail in Table 2.4.

Policies, Governance, and Institutions The underlying causes or drivers of forest (over)exploitation, especially in the earlier phases of forest conversion, can be associated with governance and structural inequities. Elite control of wealth and natural resources is an issue in both the lowlands and the uplands, including large-scale exploitation of forest resources for private gain; inequitable access to land and assets for the majority; and a lack of urban job creation leading to poverty, migration, and overexploitation of forests and fragile uplands. Corruption among government officials has also been cited as an issue, particularly associated with illegal logging, and one that is thought to

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TABLE 2.4 Indirect and Underlying Drivers of Deforestation and Forest Degradation Indirect Driver

Description

Policies, governance, and institutions

• Issues around implementing rules and regulations of forest policies that often lead to confounding and overlapping tenure instruments and ultimately to insecurity • Logging bans in some forest types intensified extraction activities in other types (mainly secondary) • Issues with lack of capacity to police command and control policies • The financial, political, and technical weakness of institutional structures • Persisting problems with corruption and collusion • The conflicting mandates of overlapping government departments

Sociocultural

• Continuing migration of the population from lowlands to uplands • Irresponsible attitudes towards forest areas • Lack of education, knowledge, and awareness of the intrinsic value of forests

Market forces

• Declining timber production, accompanied by increasing charcoal manufacturing, resulting in the degradation of forests rather than large-scale deforestation • Continuing high market demand for timber, forcing people to source logs illegally in order to maintain livelihoods • Undeveloped sustainable, certified timber management policies and incentives

Source: GIZ (Deutsche Gesellschaft für Internationale Zusammenarbeit). Analysis of Key Drivers of Deforestation and Forest Degradation in the Philippines, 2013. (accessed April 2015).

persist, albeit on smaller scales than in the past (Chokkalingam et al. 2006). While government policies, including logging moratoriums, were intended to reduce deforestation, evidence suggests that these may simply have displaced forest loss to secondary forests and other activities associated with the loss of employment and scarcity of timber (Durst 2008, cited in GIZ 2013). Ineffective deployment of implementing rules and regulations and weak enforcement meant these policies ultimately failed to achieve the results for which they were designed.

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Increasing Population and Upland Migration Rapid deforestation in the Philippines could also be associated with increasing population over the twentieth century. The population grew enormously, from 37 million in 1970 to 98 million in 2012 (World Bank 2015), prompting people living along the lowlands to move upward in search of more space (Sajise 1998; Guiang, Borlagdan, and Pulhin 2001). Migration from the lowlands to the uplands has also been cited as a driving force for deforestation. Evidence suggests that net upland migration rates increased from 3.4 per cent during 1970–75 to 14.5 per cent during 1980–85 (Cruz, Zosa-Ferranil, and Goce 1988). The increased population also led to increased demand for food, which coincided with labour limits being reached in the lowlands; the failure of industry to absorb this surplus forced farmers to migrate upward into forest zones and uplands to establish subsistence farming (Cramb 1998, 2005). Increased population in itself should not be considered the main driver of deforestation but rather the macroeconomic policies that force people to migrate due to lack of sufficient employment opportunities and associated well-being in the lowlands (Kummer 1992).

Agricultural Land-Cover Change The dynamics of agricultural land-use change are complex and strongly linked with broader societal drivers, such as population density, economic growth, dietary preferences, the underlying incidence of poverty, and production and distribution (in)efficiencies driven by central government policy and international trade agreements. These inherent complexities make it difficult to pinpoint the precise reasons why land is converted for cultivation or livestock use or why it is abandoned. Globally, agricultural land use as a proportion of overall area is approaching 40 per cent; the figure in the Philippines is similar, at 40.6 per cent, and a little lower than the average for the East Asia and Pacific region (Table 2.5). The population of the Philippines currently stands at 98 million people with a yearly growth rate of 1.90 per cent (PSA-NSO 2010) and a population density of 303 people per square kilometre (km2). The scale and pace of population change (only 28 people per km2 in 1910) for an archipelagic nation with only 30 million ha of land represents a significant challenge to meeting food demand while avoiding the erosion of the nation’s natural resource base.

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TABLE 2.5 Agricultural Land Coverage, Selected Regions, 2011 Region The Philippines East Asia and the Pacific Latin America and the Caribbean Least-developed countries South Asia Africa south of the Sahara World

Coverage (%) 40.6 48.2 36.9 39.1 54.7 43.8 37.6

Source: World Bank. “World DataBank”. 2015 . (accessed 23 January 2015).

A time-series study conducted by Kastner and Nonhebel (2010) examined how calculations of the land required for food in the Philippines changed over the course of the twentieth century. The study concluded that for the first fifty years roughly 2,500 m2 of land was required to feed one person. This decreased to 2,000 m2 by 1960 but was halved to 1,000 m2 by 1985. Even though the land required to feed one person more than halved over the century, the increase in population over the same period indicates an increase from 2 million ha of total land in 1910 to 8 million ha by the century’s end. The reduction or stabilization of land required for food between 1960 and 1985 is largely attributed to the intensification of agricultural production through the introduction of fertilizers and higher yielding cultivars associated with the Green Revolution (Kastner and Nonhebel 2010). Indeed, the expansion of land for planting cereal crops was limited to the first half of the twentieth century (until about 1960), after which it remained largely stable due to the introduction of irrigation systems and increased output per unit of area given higher yielding cultivars and increased cropping intensities. Nonetheless, it is estimated that, as of 2012, of the more than 12 million ha of agricultural land in the Philippines, over half (7.3 million ha) was devoted to cereal production (rice and maize) and a further 3.5 million ha was devoted to coconut production (Table 2.6). Since 1990, agricultural land as a percentage of total land area has steadily risen (Figure 2.2). At the same time, the number of people

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TABLE 2.6 Harvested Area of Primary Crops, 2012 Crop Total cereals Rice (paddy) Maize Vegetables Coconuts Bananas Total agricultural land

Million hectares 7.3 4.7 2.6 0.6 3.5 0.5 12

Source: PSA-BAS (Philippine Department of Agriculture, Bureau of Agricultural Statistics). “Data on crop area”. 2012 (accessed 4 January 2014).

FIGURE 2.2 Agricultural Land, Rural Population, and Population Density in the Philippines, 1990–2012

Source: World Bank. “World DataBank”. 2015 (accessed 23 January 2015).

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occupying rural lands, after gradually decreasing from the 1960s, remains stable at about 50 per cent. Population density has also risen rapidly, although the most densely populated areas remain the large urban centres, such as the capital, Manila. A closer examination of disaggregated crop typology data broadly shows how the agricultural land is used in the Philippines (Figure 2.3). Arable lands form the majority of land, although this has been decreasing at approximately the same rate as perennial crops (mainly bananas and coconuts), have been increasing since the early twenty-first century: they both represent approximately 40 per cent of total agricultural lands. The remainder of agricultural lands is permanent pastures and meadows for livestock. Cereals (mainly rice and maize) represent the majority of harvested hectares, and this remained relatively stable between 1990 and 2010 (Figure 2.4). Land devoted to maize decreased during the 1990s and stabilized at the same time that harvested hectare of rice were increasing. Harvested hectare of coconuts exceeded maize for most of the period, representing about a quarter of all agricultural lands. Nevertheless, maize remains an important crop both economically and especially in meeting subsistence needs. As will be seen in later chapters, maize is also prone to negative impacts under some climate change scenarios. Data for major cash crops (Figure 2.5) show that the area under sugarcane cultivation, although variable, generally increased between 1990 and 2012, that harvested hectare of cassava remained relatively constant over the same period. Sugarcane, although not as extensive as cereals, remains an economically important crop, generating as much in gross production value as maize on less than a quarter of the land. With the exception of coconuts, perennial tree and shrub-based crops do not occupy as much land as annual crops; those most economically important occupy only around 0.5 million ha of land (Figure 2.6). A notable increase in mango and guava production occurred between 1990 and 2012 and could be associated with the increasing importance of these commodities for export. The harvested area of rubber and oil palm has also risen rapidly since the beginning of the twenty-first century. Production of most other crops has not changed, although the area planted to coffee has declined slightly, possibly representing a missed opportunity given the potentially lucrative, if not volatile, global market for gourmet and certified coffee.

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Source: FAO (Food and Agriculture Organization of the United Nations). FAOSTAT database. 2014 (accessed April 2014).

FIGURE 2.3 Agricultural Land Use as a Share of Agricultural Land, 1990–2011

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Source: FAO (Food and Agriculture Organization of the United Nations). FAOSTAT database. 2014 (accessed April 2014).

FIGURE 2.4 Harvested Agricultural Land by Major Product, 1990–2013

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Source: PSA-BAS (Philippine Department of Agriculture, Bureau of Agricultural Statistics). “Data on crop area”. 2012 (accessed 4 January 2014).

FIGURE 2.5 Harvested Area of Major Cash Crops, 1990–2012

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Source: PSA-BAS (Philippine Department of Agriculture, Bureau of Agricultural Statistics). “Data on crop area”. 2012 (accessed 4 January 2014).

FIGURE 2.6 Harvested Area of Tree and Shrub-Based Products, 1990–2012

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Overall, it appears that agricultural land use in the Philippines increased from 25 per cent of total land cover in 1960, to 37 per cent in 1990, and 40 per cent in 2011. This is against a backdrop of rising population density, from around 220 people per km2 in 1990 to around 330 people per km2 by 2011. Cereal crops are the most extensive type of agricultural land cover and, despite some fluctuations in the analysed period, have remained relatively constant over time, both in absolute and percentage terms. Still, with a projected increase in population to around 115 million by mid-century, pressure on precious arable land is likely to increase. Despite the continued growth in land devoted to agriculture and increased production efficiency, the contribution of the sector to overall gross domestic product (GDP) declined through the 1990s, from a high of 22 per cent in 1990 to the current 11 per cent figure (World Bank 2015). This could be a product of the diversification of the economy and rise of the increasingly important service industry.

Land-Use Transition In a similar way to near neighbours, China and Vietnam, evidence indicates that the Philippines is beginning to strike the balance between meeting daily subsistence requirements of a growing population, prospering as a nation, and preserving its natural resource base. Data suggest that the Philippines is undergoing a land-use transition by beginning to increase its low forest resource base while maintaining or increasing its agricultural lands. As a result, there is renewed interest in the forest transition hypothesis originally proposed by Mather (1992), which states that national forest cover declines as demand for agricultural land rises, in turn driven by population growth (Rudel et al. 2005; Rudel, Schneider, and Uriarte 2010). The basic premise of the forest transition theory (also known as the environmental Kuznet’s curve) is that countries or regions with high forest cover will be subject to high levels of deforestation, often associated with agricultural expansion and the development infrastructure relating to timber extraction, until a turning point is reached whereby forests begin to recover either through purposive interventions or through natural regeneration due to abandonment (Angelsen 2007). The rate of forest loss then reaches a low before stabilizing, then forest area gradually increases (Table 2.7). Based on the characteristics of the major phases of land-use transition theory and the relevant phases of changing forest cover in the Philippines

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Early phase: large-scale logging, 1900–30 Late phase: postwar–1975 1975–86

1986–mid-1990s

Late 1990s– early 2010s

Triggers Increased population density Increased agricultural rents Greater accessibility to forests due to the construction of roads, often associated with commercial logging or mining

Positive feedback loops Demand for timber products and reduced transport costs drive further deforestation Profits from exploiting previously forested areas reinvested in further deforestation Increased access for migrant agriculturalists at lowland rents

Stabilization Increasing forest land rents stimulated by a rise in demand for increasingly scarce forest products Increasing off farm labor wages and supply of agricultural products Farm abandonment due to degraded soils Forests and agricultural mosaic landscape Dynamic socioecological interactions determining the utilization and or conservation of each.

Forest transition Forests begin to return either through deliberate policies or through natural regeneration Economic drivers and policy choices Appreciation for the ecological function of forests in providing benefits and increased protection/rehabilitation

Source: Compiled by authors from sources indicated.

Phase/Timing in the Philippines

Transition Phase

TABLE 2.7 Summary of Land-Use Transition Phases and Indicators

Lambin and Meyfroidt 2011 Chokkalingam et al. 2006 Hosonuma et al. 2012

Lasco, Visco, and Pulhin 2001 Kummer 1992 Grainger and Malayang 2006

Kummer and Turner 1994 Grainger and Malayang 2006

Liu, Iverson, and Brown 1993 Garrity, Kummer, and Guiang 1993 Lasco, Visco, and Pulhin 2001 Pulhin 2002

Source

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according to the available literature, it appears that the theory could be broadly applied to the past 100 years of land-use change in the Philippines (Table 2.7). The long-term changes in forest cover in the Philippines appear to mirror this generalized theory. Could it be that the Philippines has moved beyond the turning point? According to recent attempts to classify developing countries by their stage of forest transition, the Philippines is considered a “post-transition” country in which rates of deforestation have been reduced, and total forest cover at a national level has increased (Hosonuma et al. 2012). This does not, however, account for regional variations within the country, nor the type of forests that remain under threat. Quantitative evidence is also lacking as to rates of land degradation and the impact of degradation on forest productivity or ecological integrity in the Philippines. Lambin and Meyfroidt (2011) reviewed the conditions they consider to underlie a number of countries’ land-use transition pathways, including the proportion of the population living in rural areas, forest cover and agricultural lands as a share of total land area, and agricultural versus timber production (as indicated by volume of total round wood). They evaluated these indicators for a number of developing countries; this chapter adds to that analysis using FAO data on the trade balance for biomass-based products in efforts to reveal information on “outsourcing” of deforestation. Overall, both agricultural land and forest cover increased during the 1990s, as did the rural population, albeit at a slightly slower rate than overall population growth (Figure 2.7). Unlike the trend in many developing countries, there does not appear to be a large-scale shift in population from rural to urban areas. Unsurprisingly, agricultural production has become increasingly important compared with the production of timber. In terms of the overall trade balance (imports versus exports), the Philippines was a net exporter of wood products during the 1960s and 1970s, in line with evidence of the rapacious extraction of timber previously discussed. From the late 1980s, however, the Philippines became a net importer of wood products, only beginning to reach equilibrium in the early 2010s. A similar pattern can be observed for agricultural products. Since the mid-1990s, there has been a net importation of total agricultural products, perhaps indicating that the Philippines is less food-secure. The reliance on import products, particularly agricultural and wood products, may explain why forest cover has been maintained and even increased more recently. While

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Source: Constructed by authors based on FAO (Food and Agriculture Organization of the United Nations). FAOSTAT database. 2014 (accessed April 2014).

FIGURE 2.7 Land-Use Transition Indicators, 1961–2011

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this may have positive impacts for forest cover in the Philippines, it may be displacing forest product extraction to other countries, with much of the imported timber coming from Indonesia and Malaysia. Considering all indicators mentioned above, with the exception of land deals in Africa (Table 2.8), the Philippines is comparable with countries thought to be undergoing a land-use transition in the region. A 2.5 per cent yield increase was recorded between 1961 and 2011, and agricultural GDP remains relatively high. Remittances also make a significant contribution to the GDP in the Philippines. Overall, it appears that the Philippines expanded forest protection and increased the productivity of steadily increasing agricultural lands through a combination of alternative economic activities and, hence, is undergoing a land-use transition. If the Philippines can continue to strike a balance between natural resource use and agricultural production, meeting demand for food and avoiding environmental degradation may be possible. This is especially encouraging given the potential future impacts of a changing climate.

LAND-COVER CHANGE AND CLIMATE VARIABILITY Subsequent chapters of this volume discuss the current and future potential impacts of climate change on agriculture in the Philippines in more detail. What follows is a brief discussion of the possible impacts on land cover relating to forestry and agriculture in the Philippines, as well as the contribution of each sector to global GHG emissions. The impacts of future climate change on land use are uncertain and vary spatially. Similarly, the influence of land use on climate change is complex and varies locally, regionally, and globally. The impacts on agricultural productivity and land cover are yet to be fully understood or explored, although early attempts suggest that land-use change will remain the dominant force and “set the stage on which climate alterations can act” (Dale 1997, p. 766). Modelling work based on IPCC climate change scenarios suggests that the potential net change globally may be relatively minor (Nelson et al. 2010). Nevertheless, at a country level and indeed regionally, the changes can be more dramatic. In most developed countries, total agricultural area will decline or remain static, but agricultural land could increase by as much as 43 million ha (25 per cent) in least developed countries. The same estimates suggest that the Philippines will be one of only twenty-four countries with net increases in agricultural land of more

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TABLE 2.8 Summary of Forest Transition Indicators, 1961–2011 Indicator Agricultural gross domestic product (%) Increase in crop yields from primary products (%) Protected terrestrial areas as a share of total land (%) Land deals in Africa (thousand ha) Remittances as a share of gross domestic product (%)

The Philippines China Vietnam 11.88 12.50 10.88 10.88 19.81

11 3.03 16.6 7,308 1.1

22.00 12.05 16.21 10.00 17.91

Source: Based on Lambin, Eric and Patrick Meyfroidt, “Global Land Use Change, Economic Globalization, and the Looming Land Scarcity”, in Proceedings of the National Academy of Sciences of the United States of America 108, no. 9 (2011): 3465–72. The AgGDP and crop yield data are from FAO (Food and Agriculture Organization of the United Nations). FAOSTAT database. 2014 (accessed April 2014). The land area data are from DENR-PAWB (Department for Environment and National Resources, Protected Area and Wildlife Resources), Statistics on Philippine Protected Areas and Wildlife Resources. 2004 (accessed January 2015). The land deal data are from Friis, Cecilie, and Anette Reenberg, Land Grab in Africa: Emerging Land System Drivers in a Teleconnected World, GLP Report No. 1. Copenhagen: Global Land Project International Project Office, 2010. The remittances data are from the World Bank, “World DataBank”, 2015 (accessed 23 January 2015). World Bank (2015).

than 1 million ha by 2050, with Malaysia being the only other country from Southeast Asia in that group (Nelson et al. 2010). Other scholars found that changes in productive area will also be reflected in negative impacts on social welfare and GDP for Southeast Asia (Calzadilla et al. 2014). A recent global review of predictions under climate change scenarios derived from a combination of general circulation models and observational data, points to a link between levels of deforestation, vegetative cover, and local to regional impacts on temperature and rainfall (Lawrence and Vandecar 2015). The main effects in Southeast Asia appear to be an increase in mean temperature, a reduction in mean rainfall, and a redistribution of rainfall spatially and temporally — the timing and amounts of which are crucial for agricultural productivity. In summary, evidence indicates that forest cover affects climate, which in turn affects agriculture, suggesting that, for planning and future-proofing, these two types of land use should not be considered independently of each another. Furthermore, the impacts of climate extremes on land suitability and production are already being felt in the Philippines, where it is estimated

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that a total of 4 million ha of rice and 1.5 million ha of maize were damaged by floods, drought, or tropical cyclones between 1995 and 2004, at a total cost of more than PhP23 billion (Reyes et al. 2009). Between 2000 and 2010 this figure increased to a total of 7.4 million ha of agricultural land at a cost of PhP106 billion (Israel and Briones 2013). The devastation wrought by Super Typhoon Haiyan (Yolanda) is estimated to have caused PhP21 billion in damages to agriculture and fisheries alone (almost the same as for the entire ten-year period 1995–2004) according to final estimates from the government agency responsible for managing disaster risk (NDRRMC 2014). Some estimates suggest that these losses will continue to increase with a corresponding reduction in GDP of around 2 per cent per year by the end of the century (ADB 2009).

LAND-COVER CHANGE AND GREENHOUSE GAS DYNAMICS Land-cover change is considered to be one of the major drivers of global climate change, representing roughly 24 per cent of global emissions yearly (IPCC 2014b). Forest loss not only releases carbon dioxide (CO2) and other harmful GHGs into the atmosphere as they are burned or decay, but it also halts the sequestration of the lost trees, reducing the carbon pool. Replacement with intensive agricultural practices — which require high (often fossil fuel–based) inputs; increased demand for water; and, in the case of livestock, direct emissions from enteric fermentation — further compound this issue. On the other hand, Philippine agriculture is a net emitter of GHG, especially from rice production (Figure 2.8). As previously mentioned, rice production areas have continued to increase in the Philippines and appear to be coupled with increased GHG emissions. Under certain climate scenarios, however, some predictions suggest that an increase in atmospheric CO2 may actually yield positive results for the agricultural sector. In considering the economic impact of a 1.5°C and 3°C warming scenario for Asian countries, Mendelsohn (2014) found that, if CO2 fertilization is taken into account, the net results in terms of U.S. dollars per year are actually positive under both temperature scenarios, although smaller under higher temperature scenarios. This is also the case for China, Indonesia, and Vietnam, but not for other East and Southeast Asian countries.

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Note: Forests are a net carbon sink, which offsets some emissions from the agricultural sector. Source: FAO (Food and Agriculture Organization of the United Nations). FAOSTAT database. 2014 (accessed April 2014).

FIGURE 2.8 Greenhouse Gas Emissions from the Forestry/Agriculture Sector in the Philippines, 1961–2011

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THE IMPACTS OF LAND COVER AND CLIMATE CHANGE ON ECOSYSTEM SERVICES The links between human well-being and the goods and services provided by a range of ecosystems is well established (Corvalan, Hales, and McMichael 2005). How ecosystem goods and services are affected by land-cover change and what this means for societies and communities that rely on them, either to meet day-to-day subsistence needs or for their intrinsic or cultural values, is less well understood (Nagendra, Reyers, and Lavorrel 2013). Additionally, increasing evidence suggests that climate change is already affecting the range, structure, function, and overall state of ecosystems globally, which in turn is affecting productivity (Heubes et al. 2011; Grimm et al. 2013). The observed or predicted impacts of land-cover change are discussed below, along with the potential future impact of climate change on major ecosystem service groups that support agricultural productivity within the Philippine context.

Soil Quality and Stability Like many developing countries, the Philippines relies on agricultural production to meet domestic demand and generate export revenues. Underpinning the productivity of the land on which these crops are grown is the fertility and condition of the soil. At the same time, estimates suggest that the Philippines has extremely high levels of soil degradation (Francisco and de Los Angeles 1998; DA–DENR–DST–DAR 2010). Indeed, prime agricultural land has all but been exhausted in the Philippines, which has driven farmers to seek new lands in marginal areas or ecologically sensitive uplands with as much as 74 per cent of sloping lands in active use (DA–DENR–DST–DAR 2010). Farmers migrating into these sloping uplands often bring unsuitable techniques and management practices to their new environment, which in some cases exacerbates soil erosion rates (Cramb et al. 2000). This represents a dangerous convergence: increased demand for agricultural products and reduced soil health to support productivity. The FAO estimates the average soil erosion rate for the Philippines at 81 tons per ha per year compared with an average of 30–40 tons per ha per year for the rest of Asia. Determining soil erosion rates is important, not only for identifying areas especially at risk, but also for estimating

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financial impacts (Briones 2010). An FAO report from the late 1990s suggests that the financial cost of soil erosion amounted to PhP6.39 billion yearly (approximately US$150 million) using a standard 10 per cent discount rate and based on statistics from 1988, the most recent complete available data at that time. More recent estimates of the economic costs are not available, but it can reasonably be assumed that this figure is unlikely to have reduced and may well have increased because soil erosion remains a significant issue. Indeed, these figures represent the direct costs of soil loss on agriculture but do not account for the offsite and indirect impacts on public utilities, such as hydroelectric dam facilities, drinking water, domestic water reservoirs, and impacts on fisheries and other marine resources as a result of sedimentation. According to the Revised National Action Plan to Combat Desertification, Land Degradation, and Drought (DA–DENR–DST–DAR 2010), as much as 5.2 million ha or roughly 20 per cent of land in the Philippines is severely eroded, and a further 8.5 million ha (almost 30 per cent) is moderately eroded. It is estimated that this reduces the soil productivity and water retention capacity by between 30 and 50 per cent, threatening the productivity of land and livelihoods of farmers. Displacement of soil by wind and water is thought to be a product of inappropriate land management practices, such as deforestation and overgrazing, which expose soil to the elements and make it more vulnerable to erosion (Asio et al. 2009). Increased risk of climate extremes, particularly the local to regional intensification of the water cycle, may further exacerbate soil fertility losses of already fragile and exposed soils. Maintaining soil cover by employing conservation techniques into agriculture, as well as incorporating trees, may be one appropriate solution. Soil erosion has been the principal driver of land degradation, but under climate change additional challenges such as saltwater intrusion and resulting soil salinization, as well as alternate water logging and compaction through flood and drought events, may prove to be just as detrimental. Currently, poorly drained soils account for approximately 0.3 per cent of land in the Philippines and saline or sodic soils represent 1.33 per cent or 400,000 ha mainly situated in coastal areas (DA–DENR–DST–DAR 2010). Under climate change scenarios that predict rising sea levels and an intensification of the hydrological cycle, these figures could be set to rise and in turn further reduce access to land that supports agricultural production (IPCC 2014a).

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Water Quantity, Quality, and Timing Water is unsubstitutable, and its availability varies spatially and temporally. As with many ecosystem services, access to water and its associated benefits (nutrient transfer, irrigation, drinking water, and recreation) is further threatened when considered through the lens of climate change. Intensification of the hydrological cycle in combination with other factors, such as urbanization, lack of infrastructure, and poor land-use practices, has already begun to manifest severe flooding in the Philippines. At the same time, many areas of the world periodically experience drought conditions that exceed normal seasonal variation and result in reduced agricultural productivity. For example, regional evidence in the most recent IPCC report suggests that increased heat and water stress may already be affecting rice production in the Philippines (IPCC 2014a). Regionally, studies have identified a compounding effect when quantifying the combined impact of climate change and land-cover change on hydrologic processes and especially on water quantity (that is, total streamflows) and evapotranspiration, which are both important for agricultural productivity (Qiu, Yin, and Geng 2012; Ty et al. 2012; Cuo et al. 2013). The influence of land-use change on these processes is reasonably well understood within the Philippines at a site-specific level (Hernandez, Henderson, and Oliver 2012; Sanchez et al. 2012). Earlier studies into the impacts of climate variability caused by El Niño Southern Oscillation events in the Philippines perhaps provide an insight into the possible hydrologic impacts of future climate change. During the El Niño event of 1991/92 in the Philippines, approximately 0.5 million ha of land under agricultural production (cereals, vegetables, and fruit trees) was affected by severe drought (Jose, Francisco, and Cruz 1999). Very few studies, however, have analysed the impacts of future climate change when combined with land-use change, especially within the Philippines. One such study, Combalicer and Im (2012), used statistically downscaled time-series climate data coupled with long-term streamflow records for a small forested watershed. Results demonstrated that under an A2 climate scenario (that is, high population growth and low technology use) even well-vegetated areas were shown to have reduced streamflows (2–12 per cent) and increased evapotranspiration (32–41 per cent). This suggests that the influence of future climate change is more strongly felt than the influence of land cover (Combalicer and Im 2012).

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Carbon Dynamics under Different Land Uses Terrestrial ecosystems play a significant role in the global carbon cycle, with highly vegetated ecosystems such as forests, especially those in the tropics, representing substantial carbon sinks. Conversion of forests to agricultural land or other uses therefore contributes significantly to yearly global emissions (HLPE 2012). Tropical forests in Southeast Asia store a high density of carbon depending on the type of forest and species. This sequestration is reduced by approximately 50 per cent with deforestation (Lasco 2002; Ziegler et al. 2012; Yuen et al. 2013). Minimum total ecosystem carbon values — that is, the combination of above and below ground and soil organic carbon — vary in Southeast Asia according to land use: forests are estimated to store 119 tons per ha; agroforestry, 77 tons per ha; and permanent croplands, 56 tons per ha — the lowest amount at less than half that of forests (Ziegler et al. 2012). This is also true in the Philippines, where natural forests (113 tons per ha) have been shown to store more carbon than the grasslands (5 tons per ha) and shrublands (35 tons per ha) that succeed them after deforestation (Lasco and Pulhin 2000, 2003). In the Philippines, a number of efforts have been made to quantify the carbon stocks of different types of forests, in part to understand the Philippine role in contributing to or mitigating global carbon emissions and also to improve the understanding of potential financial benefits associated with carbon market incentivized reforestation (Lasco and Pulhin 2000, 2009; Lasco, Evangelista, and Pulhin 2010; Lasco et al. 2011). These variations in carbon storage are not only important from a climate change mitigation perspective, but also have implications for agricultural productivity. Estimates suggest that soil organic carbon can fall by 50 per cent within just 5 to 10 years of conversion from forest to agricultural land in the tropics. This has implications in terms of increasing net GHG emissions and reducing soil and ultimately crop productivity (Lal 2004). Land converted from forest to agriculture may only be able to support crop growth for a limited period without being allowed to recover via a fallow period, and thus other land may be sought.

Biodiversity and Site-Species Suitability in a Changing Climate As climate variability increases, the response of different ecosystems will vary, and this will determine the future suitability of land and where and

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under what conditions species can survive under new temperature and water (both too much and too little) conditions. Forests may no longer flourish where they currently do, and current agricultural practices, cultivars, and irrigation systems may no longer be appropriate. Climate change will therefore influence all aspects of land use and suitability. Considered a hotspot for biodiversity due to its archipelagic geography and associated array of ecosystems, the Philippines has a high number of endemic species and concurrently a large number of species considered under threat. The most recent IPCC-AR5 report (IPCC 2014b), which assesses the impacts of climate on the ecological range of plant and animal species, concludes that in all but the lowest impact scenarios, trees will not be able to adapt at the same pace as the climate is changing. This has potential bearing on future land cover under medium- and high-emission climate scenarios to the century’s end (IPCC 2014a). Lasco et al. (2007) used the Holdridge Lifezones model to study how climate change may affect general forest types in the Philippines and found that under potential climate change scenarios, forests could change dramatically, especially with increasing precipitation levels. The most vulnerable are dry forests, which could be completely eliminated with a 25 per cent increase in precipitation. More recent studies used species distribution models to simulate the effects of climate change on individual tree species in the Philippines and showed that the future ranges of habitat of 14 forest threatened tree species are likely to be affected (Garcia et al. 2013).

AGRICULTURAL AND FORESTRY POLICIES AND PROGRAMMES Agricultural and forestry policies, regulation, and governance have a highly influential role in determining the type and level of land-use change and activities at the national level. The major policies and related programmes that have directly, indirectly, deliberately, or incidentally influenced land use and land-use change in the Philippines are discussed below.

Forest Land Policies and Programmes The Philippines has a long history in instituting forest-focused policies, from the exploitative phase of Spanish and subsequently U.S. colonization, through to the more ambiguous post–World War II phases, and finally

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towards more sustainable management at the end of the twentieth century (Grainger and Malayang 2006). Since the 1990s forest policies in the Philippines have been designed to balance the economic and social needs of a developing country with the dwindling natural resource base (Tumaneng-Diete, Ferguson, and MacLaren 2005). Generating income, revenue, and employment opportunities while at the same time preserving or ensuring forest ecosystem function and services, rich biodiversity, and social and cultural equity and vitality has therefore been the focus and aim of social forestry policies (Harrison and Emtage 2004; Table 2.9). Most recently, the forestry sector in the Philippines has begun to consider, and to some extent plan, for the impacts of climate change. The Revised Management Plan for Forestry Development (MPFD n.d.), still in development as of April 2015, highlights the need for the sector both to contribute to climate change mitigation efforts and plan for the future impacts of climate change via adaptation measures. Concrete strategies and associated programmes have yet to emerge, however, which is possibly one of the reasons why a recent review of forestry governance and policy considered this to be an area of weakness and recommended improvements around monitoring and reporting, as well as increased efforts to access opportunities under global initiatives such as REDD+, which is discussed in more detail below (Eleazar et al. 2013). The National Greening Program (NGP) is ambitious. It aims to plant 1.5 billion trees over 1.5 million ha between 2011 and 2016, more than doubling both the total area reforested since 1990 and the 2011–2016 Philippine Development Plan target of 600,000 ha. This would increase the overall cover by 12 per cent over 2003 levels (NEDA 2011). NGP also has an aspirational set of social and environmental goals, including poverty reduction, food security, biodiversity conservation, and climate change adaptation and mitigation (DENR-FMB 2012). The Programme is a multi-agency effort led by DENR but with oversight from the Department of Agriculture and the Department of Agrarian Reform. In fact all local, provincial, regional, and national government agencies have been instructed to fully support the programme under the Interdepartmental Convergence Initiative (ICI) for a National Greening Program. While ICI should be commended in endeavoring to drive a more cohesive delivery of NGP, some evidence suggests that much work remains to be done, particularly in terms of an operating plan in practice (Israel and Lintag 2013).

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Instituted community-based forest management as the national strategy to ensure the sustainable development of the country’s forests and promote social justice

Executive Order 263, 1995: Community Based Forest Management

DENR-FMB 2012

continued on next page

Encourages communities to organize, via people’s organizations, granting access to and limited rights over the management of forestlands via 25-year contracts, renewable for a further 25 years (an estimated 1,888 agreements were in place as of 2012 covering 1,615,403 ha of forest lands and involving some 192,090 households)

Has the effect of restricting agricultural activities on approximately 11 per cent of the area of the Philippines as of 2004 which ostensibly means that any forests under this system should be protected; lack of resources continues to pose a significant barrier to enforcement, however

Provided a stronger legal basis for the establishment and management of protected areas

Republic Act 7586, 1992: National Integrated Protected Areas Systems Act

DENR-FMB 2012

FAO 2010b

Enhanced the economic role of secondary forests as a source of wood, while removing pressure on old growth forests (reported figures since 1990 show that this type of forest cover has remained constant at approximately 0.86 million ha)

Instituted a moratorium on logging in old-growth (primary) forests, shifting timber extraction to secondary (residual) forests, effective 1992

DENR Administrative Order 24, 1991

Guiang, Borlagdan, Pulhin 2001

Mandated the adoption of a multiple-use approach to forest lands, accelerated land classification, delineated forest boundaries, rationalized woodprocessing plants, enhanced forest protection and development through industrial tree plantations, census, and recognition of some forest occupants; Continued support for the implementation of selective logging

Provided the country’s fundamental forestry laws and policies and strengthened state control over forestlands and natural resources

Presidential Decree 705, 1975: Revised Forestry Code of the Philippines

Source

Impacts on Land-Cover Change/Climate Change

Description of Main Features

Policy/Programme

TABLE 2.9 Summary of Philippine Forestry Policies, 1975–2011

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Recognized the rights of indigenous peoples over their ancestral lands within forest lands via certificates of ancestral domain titles

Was designed to increase forest cover by incentivizing reforestation via contracts awarded to households and communities

Extended the moratorium under DAO 24 to include all natural forests as well as stricter regulation, monitoring, and penalties for transgressions by logging companies

Republic Act 8371: Indigenous People’s Rights Act, 1997

The National Forestation Program, 1986–2000

Executive Order 23, 2011

Source: Compiled by authors from sources indicated.

Description of Main Features

Policy/Programme

TABLE 2.9 — cont’d Source Phelps et al. 2010

Chokkalingam et al. 2006 Pasicolan, Udo de Haes, and Sajise 1997

GIZ 2013

Impacts on Land-Cover Change/Climate Change Covered some 2.7 million ha under 96 certificates between 1997 and 2008; only around 20 per cent (0.6 million ha) of these were formally registered as ancestral domains; competing interests exist, especially in the form of concessions for mining operations Based on evidence suggesting the success of the spontaneous establishment of tree-farm holdings, attempts have been made to improve the understanding of what motivates smallholders to replant their farms/holdings with native tree species; results suggest that economic drivers have less influence than access to and rights over land and remaining forests May have led to unintentional negative impacts, forcing those whose livelihoods were affected to pursue illegal means of gathering timber in lieu of viable alternatives; may have stemmed from the policy not being accompanied by options for sustainable timber harvesting

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To date a total of 350,000 ha have been planted (based on final figures reported in 2011 and 2012), but neither the mortality rate nor a monitoring report is available. Hence, it is difficult to determine exactly how much area remains planted, although, given the success of past nationwide efforts, a figure of 50 per cent does not seem unreasonable. Whether any lost trees will be replanted, and how this would be monitored is also unclear (Israel and Briones 2013). Nor are there data relating to the climate change mitigation or adaptation benefits highlighted as target outcomes from NGP, which represents a missed opportunity to quantify and generate further investment in the programme through global initiatives such as REDD+. NGP also aimed to use high-quality indigenous tree species, but nurseries have been unable to meet demand, so exotics have increasingly been used — a fact criticized by some.

Agricultural Policies Agrarian and natural resource policies in the Philippines have undergone an evolution since the 1990s when irrational conversion of land use to urban development and industrialization had negative effects on river systems and aquifers, caused rapid deterioration of irrigation systems established in the preceding decade, and also led to the net importation of practically all food products. At that point Philippine agriculture entered a period of self-review, including the development of a number of key polices discussed below.

Republic Act 8435, 1997: Agriculture and Fisheries Modernization Act (AFMA) The Philippine government sought to promote food security, particularly in terms of national self-sufficiency in staples, especially rice. In terms of land use, the Act provides for the protection or nonconversion of all irrigated or irrigable agricultural lands under the Network of Protected Areas for Agricultural and Agro-industrial Development (NPAAAD). The establishment of Strategic Agricultural and Fisheries Development Zones (SAFDZ) acknowledges the scarcity of land as a major constraint to modernizing the agricultural sector. Zones were identified based on NPAAAD to target increased efficiency in assigning agricultural areas for food production. This policy needs to be updated, however, because not

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all areas are covered under SAFDZ, which is symptomatic of enforcement and implementation issues associated with technically robust policies in the Philippines.

Republic Act 6657, 1988: Comprehensive Agrarian Reform Program (CARP) and Republic Act 9700, 2009 or Comprehensive Agrarian Reform Program Extension with Reform (CARPER) Enacted to promote a more equitable distribution and ownership of land, CARP covers all public and private (nonforest) agricultural lands regardless of tenure arrangements or crops produced. It encourages the formation and maintenance of economically viable, family-sized farms and provides incentives for landowners to invest the proceeds of the agrarian reform programme in promoting industrialization, employment, and privatization of public-sector enterprises. Institutionally, DAR and DENR are in charge of land distribution. DAR is responsible for redistributing all private lands and some governmentowned lands, whereas the bulk of public lands were to be redistributed by DENR. The total scope of redistribution was over 9 million ha of land (5.4 million ha under DAR and 3.6 million ha under DENR). In 2009, the Act was further strengthened via RA 9700 to accelerate the acquisition and retribution of land with an original target completion date of June 2014. According to the most recent publicly available data, 4.7 million ha of land had been redistributed to over 2.5 million landless farmers by DAR (DA–BAR 2012); DENR had achieved over 95 per cent of its target (2.5 million ha). As of December 31, 2013, the majority of the remaining balance (89 per cent or about 684,777 hectares) was private agricultural lands that are difficult to distribute through compulsory acquisition; this compelled DAR to extend the issuance of “notices of coverage” until June 2016. The impacts of twenty-five years of agrarian reform are still unfolding, with success hailed by some and contested by others (Otsuka 1991; Borras, Jr. 2001; Borras 2006, 2005; Vista, Nel, and Binns 2012). Clearly, significant redistribution of lands has occurred since CARP began, with millions of beneficiaries (Borras, Carranza, and Franco 2007). However, Borras (2005) provide examples of landlords entering into further long-term contracts with CARP beneficiaries that contradict the spirit of reform and may even increase the risk of farmers to climate-related shocks (Borras and Franco

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2005). For example, an El Niño–induced drought can drastically reduce farm yields, making it difficult for farmers to meet household and farming needs for capital, in turn making them vulnerable once again to control by the former landlords (Adam 2013). Without complete control over their land, farmers are more vulnerable to climate variability and risks (including pests and disease) because they are unable to make important decisions regarding farm management, which is especially the case in areas under monocrop systems.

Cross-Cutting Land-Use and Climate Change Policies and Programmes In addition to specific forest and agriculture policies, a number of policies influence both land-cover and climate change impacts in the Philippines (Table 2.10); the most notable of these are discussed in more detail below.

House Bill 108: National Land-Use and Management Act (Proposed) In addition to its absolute limits, political, administrative, legislative, and physical constraints necessitate the appropriate allocation of land. Land also forms the basis for much economic activity, particularly in biomassbased economies like the Philippines. For more than twenty years, a proposed National Land Use and Management Act has been progressing slowly through the legislative process. The proposed act is viewed by many as essential for sustainably managing the 7 million ha of unforested public lands that are subject to competing claims and conflict over access to resources and governance. Added pressures on land, most pressingly through continued rapid population growth, compounded by the impacts of climate change, make the need for a policy rationalizing the governance of land in the Philippines all the more pressing. It is proposed that the act would provide a framework to disentangle some of the long-standing policy conflicts created through competing priorities for land use, offer more tenure security, and provide resolution for boundary disputes.

Reducing Emissions from Deforestation and Forest Degradation (REDD+) Internationally, REDD+ has the potential to stem the flow of GHG emissions arising from deforestation and forest degradation. It is essentially an

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Source NEDA 2008

DENR-MGB 2013

Impacts on Land-Use Cover/Climate Change This Act decentralized decisionmaking and policy setting to the local city or municipal level (including functions relating to local spatial, economic, and social planning) within the overall Provincial Development and Physical Framework Plan (PDPFP). Subsequently, this included the development of Forest Land Use Plans (FLUPs) for those municipalities whose borders included forestlands (~85 per cent). The proprietary rights obtained by mining operators under RA 7942 were criticized for being incompatible with the effective implementation of a community-based natural resource management regime, since the commercial interests of mining corporations and other private entities appear to be prioritized over the interests of local resident communities; partly in recognition of these criticisms, in 2012 EO 79 was issued halting new mining contracts until new legislation was in place modifying the existing revenue-sharing mechanisms and targeting more environmentally sensitive operations.

Description of Main Features

The primary instrument for spatial planning at this level is the Comprehensive Land Use Plan (CLUP), which determines landuse allocation based on locally identified priorities.

This policy was designed to promote rational exploration, development, utilization, and conservation of all mineral resources while safeguarding the environment and protecting the rights of affected communities; Section 19 specifies that operations are not allowed in old growth or virgin forests, forest reserves, mangrove forests, and mossy forests, among others.

Policy/Programme

The Local Government Code of 1991, Republic Act 7160 – Comprehensive Land Use Planning

Republic Act 7942: Philippine Mining Act, 1995, and Executive Order 79, 2012

TABLE 2.10 Major Cross-Cutting Land-Use and Climate Change Policies in the Philippines, 1991–2011

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Source: Compiled by authors from sources indicated.

This Act is designed to strengthen resilience to (natural or humaninduced) disasters and reduce the vulnerability of communities to their impacts.

Republic Act 10121: Disaster Risk Reduction and Management Act (2011)

NDRRMC 2011

NEDA 2008

From this, the more detailed National Climate Change Action Plan (NCCAP) was developed; both of these high-level, national documents explicitly call for the development of a national land-use act and the incorporation of climate change and disaster risk management considerations into local planning frameworks via the CLUP.

The Climate Change Act provided for the development of the National Framework Strategy on Climate Change (NFSCC), which outlines the 12 year plan of the country’s response to climate change mitigation and adaptation.

Republic Act 9729: Climate Change Act, 2009

The implementation mechanism is via the National Disaster Risk Reduction and Management Plan (NDRRMP), 2011-28, which is managed and delivered by multiple agencies; the Act explicitly requires the inclusion of disaster risk reduction and climate change adaptation measures into the local spatial planning process via the CLUP.

DENR-EMB 2011

More recently, the process has been strengthened to include Disaster Risk Reduction (DRR) and Climate Change Adaptation (CCA) considerations, which involves the determination of risks from disaster and climate impacts and appropriate recommendations as to mitigating measures or alternative siting of projects.

As a planning and development tool, this process determines the suitability of a proposed project based on the social and ecological conditions of the site; hence, it is highly influential on the location and size of spatial development in the Philippines.

Presidential Decree 1586, 1978: Environmental Impact Statement System Law

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international land-use policy lever to increase conservation of forest carbon stocks, sustainably manage forests, and enhance carbon stocks (through reforestation). The Philippines has developed the Philippine National REDD-Plus Strategy (PNRPS), which was completed in 2010. Under PNRPS the Philippines adopted a nested approach, encouraging activity at a subnational level towards a national scheme by 2015 and supporting demonstration projects covering over 300,000 ha of land (Lasco et al. 2013). The potential scope and benefits of REDD+ components in the Philippines have been analysed for smallholder communities (Lasco et al. 2011) and at a subnational level (Phelps et al. 2010). These findings show that reducing the rate of forest degradation by 5 to 15 per cent per year, while increasing the rate of reforestation to 1.5 per cent per year could reduce carbon emissions by up to about 60 million tons of carbon (tC) by 2030 (Lasco et al. 2012), equivalent to between US$97 and $417 million of carbon credits per year at an assumed US$5 per tC (although with current trading values of less than US$1, this may be an over estimation). An incentive may exist to pursue REDD+ as a funding mechanism to support the sustainable management of forest resources. Nevertheless, significant challenges remain, including resolution of long-standing tenure disputes, identifying and distributing carbon rights, governing benefits, and quantifying forest degradation (GIZ 2013).

POLICY RECOMMENDATIONS The summary of some of the key policies influencing land-use change in the Philippines demonstrates the complexity of the institutional landscape. Strengthening the implementation of existing policies, and designing new “fit-for-purpose” land-use planning policies will be key in reducing vulnerability to the multiple risks of current and future climate change. Based on the findings presented in this chapter, three major policy recommendations are presented below.

1. Review and Strengthen Existing Forestry and Agriculture Policies and Programmes A recent review of land-related policies and governance in the Philippines found a lack of routine monitoring of policy impacts (Eleazar et al. 2013). The impacts of two forestry policies in particular need to be assessed in

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light of existing land-cover change and future climate change impacts: EO 23 (the logging moratorium) and EO 26 (NGP). Harmonizing policies, and different departments’ interpretations of them, is needed and, to some extent, has already begun with the development of ICI as part of NGP. Still, the results of these nascent efforts remain to be seen, and any impact on land cover and benefits in terms of forest cover and agricultural productivity is yet to be determined. Any review should consider and appropriately planned for the future opportunities and impacts of a changing climate; the management, sustainability, and possible certification of fast-growing species and woodlots to meet timber demand and reduce pressure on natural forests; and increased support for Payment for Ecosystem Services (PES)4 schemes. Further analysis and research into the impacts of CARP are also required. With the break-up of larger land ownership into smaller-sized farms managed by the farmers themselves, questions abound as to their productivity and potential impacts for food security in light of a rising population, existing problems with unsustainable land use, erosion of ecosystem services, and the future impacts of climate change. Smallholder farmers possess rich, local knowledge in farming but are usually faced with limited capital to sustain their production; hence, assistance to the farmers should go beyond securing tenure instruments to developing and climate-proofing their farms.

2. Where Suitable, Consider Incentivizing Multifunctional Agriculture Support for further research into multifunctionality is needed to complement agricultural intensification pathways and reach necessary future productivity without negatively affecting already eroded ecosystem services. Evidence suggests that incorporating trees into agricultural landscapes may offer both climate change mitigation and adaptation benefits by sequestering carbon, regulating micro-climates, and enhancing the livelihood opportunities and resilience of smallholder farmers in the face of climate shocks (Jose 2009; Mbow et al. 2013; Carsan et al. 2014; Lasco et al. 2014). Interventions that have included agroforestry elements have been shown to deliver households with not only economic benefits, but also with increased resilience to climate- and market-related shocks (Santos and van Noordwijk 2011; van Noordwijk et al. 2011;

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Garrity 2004). Incentive schemes could include PES programmes to encourage the inclusion of trees in agricultural landscapes, an approach also recommended by a World Bank review of land governance in the Philippines (Eleazar et al. 2013) and one currently being tested in the field by the World Agroforestry Centre. Although such activities have begun in some parts of the country, efforts should be translated at regional and national scales for more effective delivery of ecosystem services, perhaps via a specific policy like the one recently launched by the Government of India (GOI 2014). Any policy would need to address technological aspects (for example, germplasm); improve extension activities; and develop markets for highervalue commodities (coffee, cacao, and rubber) in composite systems as opposed to monocropping, which is emerging in some regions of the Philippines (for example, oil palm in Mindanao and Palawan) (Buttoud et al. 2013).

3. Ensure that Proposed National Land Use and Management Act Is Fit for Purpose The process of mainstreaming climate change into existing spatial planning policies has already begun but appears to be disjointed. This underscores the need for a national framework in order to optimize and rationalize land-use governance in light of the uncertain future impacts of climate change. The land-use planning policies and procedures that are currently in place (some of which are described in this chapter) remain fragmented and characterized by inefficient allocation, poor enforcement of zoning arrangements, and under-resourced local government units responsible for local planning policy (Eleazar et al. 2013). This bill represents an opportunity to address the issue of optimal use of finite lands. Optimization of land use must be based on suitability, which increasingly must include risks associated with current climate variability and future change. Future-proofing land-use allocation in this way will facilitate effective planning for the anticipated changes in ecological range of different commodities and staples. For example, later chapters of this volume provide evidence of the impacts of future climate change on maize, in particular, which demonstrates that some sites in the Philippines will lose out while others gain, and adjustments to planting and management regimes will be required. Without a universal land-use

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and zoning policy in place, the opportunity to avoid the worst negative impacts and maximize any benefits will be missed. Any efforts would hinge on a widely accepted land-use classification regime based on robust and accurate data. Current systems to determine land cover, monitor changes, and identify suitable locations for certain crops are hindered by inconsistent reporting and classification. Evidencebased decisionmaking is therefore limited. Reform and rationalization of land-cover data management has long been called for and needs to be heard.

CONCLUSION Over the past century land use in the Philippines has changed dramatically, transforming the nation from a largely forested archipelago to one characterized by heavily degraded uplands and underproductive agricultural lands. More recently, according to official statistics, forest cover has begun to recover, and very recent efforts at large-scale reforestation look set to maintain this. At the same time, agricultural land has increased in extent, and these two indicators point to a country undergoing a land-use transition consistent with other countries in the region and driven by forest and agricultural policies. Overall, the Philippines is more efficient in terms of unit production per unit area than ever before, but the pressures of an expanding population mean this advantage has been lost. The challenge of how to meet demand for food with finite, degraded, and underproductive land, while at the same time nurturing a recovery of the fragile natural forest resource base will only be increased under the already perceptible influence of climate change. The analysis in this chapter has highlighted the potential negative impacts of climate change on key ecosystem services relating to water, soil, carbon, and biodiversity — all of which are important for the agriculture and forestry sector. The adoption of land management practices and policies that conserve and protect ecosystem functions as part of an improved agricultural regime, but still allow for productive rather than protected land use activities, may address some of the risks posed and be more acceptable both for farmers and policymakers. Some of the more promising potential approaches have been recommended and should be considered as possible tools in the policy toolbox when planning for a future under increasing climate uncertainty.

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Notes 1. Sen’s theory posits an inverse relationship between the size of a farm and productivity; in other words, the smaller the farm, the more productive it is per hectare of land. The theory has been tested in dozens of countries and has been largely found to hold true. 2. The logging-shifting cultivation tandem suggests a complementary relationship between logging activities, which make formerly inaccessible forest lands available for agricultural activities. 3. Shifting cultivation is the practice of temporarily clearing and cultivating land until it becomes infertile, then leaving it to revert to its natural vegetation. Practised in its long fallow form, swidden agriculture, or kaingin as it is called in the Philippines, involves slashing and burning underbrush and trees and plowing the ashes under for fertilizer. Land is cultivated for a short period before being allowed to regenerate to secondary forest, sometimes in excess of twenty years. 4. Also known as payments for “environmental” services (or benefits), these are incentives offered to farmers or landowners in exchange for managing their land to provide some sort of ecological service, which can involve contracts between consumers of ecosystem services and the suppliers of these services. The party supplying the environmental services normally holds the property rights over an environmental good or land use that secures it and that provides a flow of benefits to the demanding party in return for compensation (payment).

References Adam, Jeroen. “Land Reform, Dispossession and New Elites: A Case Study on Coconut Plantations in Davao Oriental, Philippines”. Asia Pacific Viewpoint 542 (1995): 232–45. ADB (Asian Development Bank). “The Bank’s Policy on Forestry”. Institutional Document, 1995 (accessed April 2015). Angelsen, Arild. Forest Cover Change in Space and Time: Combining the von Thünen and Forest Transition. World Bank Policy Research Working Paper 4117. Washington, D.C., 2007. ——— and David Kaimowitz. Agricultural Technologies and Tropical Deforestation. Oxon and New York: CAB International, 2001. Asio, V., R. Jahn, F. Perez, I. Navarrete, and S. Abit, Jr. “A Review of Soil Degradation in the Philippines”. Annals of Tropical Research 31, no. 2 (2009): 69–94. Bensel, Terrence. “Fuelwood, Deforestation, and Land Degradation: 10 Years of Evidence from Cebu Province, The Philippines”. Land Degradation and Development 605 (2008): 587–605.

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Borras, Jr., Saturnino. “State–Society Relations in Land Reform Implementation in the Philippines”. Development and Change 32 (2001): 545–75. ———. “Can Redistributive Reform Be Achieved Via Market-Based Voluntary Land Transfer Schemes? Evidence and Lessons from the Philippines”. Journal of Development Studies 41, no. 1 (2005): 90–134. ———. “Redistributive Land Reform in ‘Public’ (Forest) Lands: Lessons from the Philippines and their Implications for Land Reform Theory and Practice”. Progress in Development Studies 6, no. 2 (2006): 123–45. ——— and Jennifer Franco. “Struggles for Land and Livelihood”. Critical Asian Studies 37, no. 3 (2005): 331–61. ———, D. Carranza, and J. Franco. “Anti-Poverty or Anti-Poor? The World Bank’s Market-Led Agrarian Reform Experiment in the Philippines”. Third World Quarterly 28, no. 8 (2007): 1557–76. Briones, Roehlano. “Addressing Land Degradation: Benefits, Costs, and Policy Directions”. Philippines Development Journal 68 (XXXVII/1) (2010): 41–79. Buttoud, G., in collaboration with O. Ajayi, G. Detlefsen, F. Place, and E. Torquebiau. Advancing Agroforestry on the Policy Agenda: A Guide for Decision-Makers. Agroforestry Working Paper 1. Rome: Food and Agriculture Organization of the United Nations, 2013. Calzadilla, A., T. Zhu, K. Rehdanz, R. Tol, and C. Ringler. “Climate Change and Agriculture: Impacts and Adaptation Options in South Africa”. Water Resources and Economics 5 (2014): 24–48. Carsan, S., A. Stroebel, I. Dawson, R. Kindt, C. Mbow, J. Mowo, and R. Jamnadass. “Can Agroforestry Option Values Improve the Functioning of Drivers of Agricultural Intensification in Africa?”. Current Opinion in Environmental Sustainability 6 (2014): 35–40. Chokkalingam, U., A. Carandang, J. Pulhin, R. Lasco, R. Peras, and T. Toma, eds. One Century of Forest Rehabilitation in the Philippines: Approaches, Outcomes, Lessons. Bogor, Indonesia: Center for International Forestry Research, 2006. Chomitz, Kenneth. At Loggerheads? Agricultural Expansion, Poverty Reduction, and Environment in the Tropical Forests. Washington, D.C.: World Bank, 2006. Combalicer, Edwin and Sangjun Im. “Change Anomalies of Hydrologic Responses to Climate Variability and Land-use Changes in the Mt. Makiling Forest Reserve”. Journal of Environmental Science and Management (Special Issue 1) (2012): 1–13. Corvalan, C., S. Hales, and A. McMichael (core writing team). Ecosystems and Human Well-Being: Health Synthesis. A Report of the Millennium Ecosystem Assessment. Geneva: World Health Organization, 2012. Cramb, Robert. “Environment and Development in the Philippine Uplands: The Problem of Agricultural Land Degradation.” Asian Studies Review 22, no. 3 (1998): 289–308. ———. “Farmers’ Strategies for Managing Acid Upland Soils in Southeast Asia:

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124

David M. Wilson and Rodel D. Lasco

An Evolutionary Perspective”. Agriculture, Ecosystems and Environment 106, no. 1 (2005): 69–87. ———, J. Garcia, R. Gerrits, and G. Saguiguit “Conservation Farming Projects in the Philippine Uplands: Rhetoric and Reality”. World Development 28, no. 5 (2000): 911–27. ———, C. Colfer, W. Dressler, P. Laungaramsri, Q. Le, E. Mulyoutami, N. Peluso, and R. Wadley. “Swidden Transformations and Rural Livelihoods in Southeast Asia”. Human Ecology 37, no. 3 (2009): 323–46. Cruz, M., I. Zosa-Ferranil, and C. Goce. “Population Pressure and Migration: Implications for Upland Development in the Philippines”. Journal of Philippine Development 16, no. 26 (1988): 15–46. Cuo, L., Y. Zhang, Y. Gao, Z. Hao, and L. Cairang. “The Impacts of Climate Change and Land Cover/use Transition on the Hydrology in the Upper Yellow River Basin, China”. Journal of Hydrology 502 (October 2013): 37–52. DA-BAR (Department of Agriculture, Bureau of Agricultural Research). Country STAT Philippines, 2012 (accessed January 2015). DA–DENR–DST–DAR (Department of Agriculture, Bureau of Soils and Water Management; Department of Environment and Natural Resources; Department of Science and Technology; and Department of Agrarian Reform). The Updated Philippine National Action Plan to Combat Desertification, Land Degradation and Drought, 2010 (accessed April 2015). Dale, Virginia. “The Relationship between Land-Use Change and Climate Change.” Ecological Applications 7, no. 3 (1997): 753–69. DENR-EMB (Department of Environment and Natural Resources, Environmental Management Bureau). 2011. Environmental Impact Assessment (EIA) Technical Guidelines Incorporating Disaster Risk Reduction (DRR) and Climate Change Adaptation (CCA) Concerns Under the Philippine Environmental Impact Statement (EIS) System. EIA DRR/CCA Technical Guidelines, 2011 (accessed April 2015). DENR-FMB (Department for Environment and National Resources, Forest Management Bureau). Philippine Forest Statistics, 2012 (accessed April 2015). ———. Philippine Forestry Statistics, 1997 (accessed April 2015). DENR-MGB (Department of Environment and Natural Resources, Mines and Geosciences Bureau). Mining Industry Statistics. 2013 (accessed April 2015).

18-J04349 02 Future of Philippine Agriculture.indd 124

19/11/18 12:03 PM

The Context of Land Cover Changes in Agriculture and Forestry

125

DENR-PAWB (Department for Environment and National Resources, Protected Area and Wildlife Resources). Statistics on Philippine Protected Areas and Wildlife Resources. 2004 (accessed January 2015). Dewi, S., M. van Noordwijk, A. Ekadinata, and J.-L. Pfund. “Protected Areas within Multifunctional Landscapes: Squeezing Out Intermediate Land Use Intensities in the Tropics?”. Land Use Policy 30, no. 1 (2013): 38–56. Eleazar, F., B. Garcia, E. Guiang, A. Herrera, L. Isorena, R. Ravanera, and E. Serote. Improving Land Sector Governance in the Philippines: Implementation of Land Governance Assessment Framework (LGAF). 2013 (accessed April 2015). ESSC (Environmental Science for Social Change). Figuring the Forest Figures. 2010 (accessed 23 January 2015). Estoque, Ronald and Yuji Murayama. “Spatio-Temporal Urban Land Use/Cover Change Analysis in a Hill Station: The Case of Baguio City, Philippines”. Procedia: Social and Behavioral Sciences 21 (2011): 326–35. ——— and Yuji Murayama. “Landscape Pattern and Ecosystem Service Value Changes: Implications for Environmental Sustainability Planning for the Rapidly Urbanizing Summer Capital of the Philippines”. Landscape and Urban Planning 116 (2013): 60–72. Ewers, R., J. Scharlemann, A. Balmford, and R. Green. “Do Increases in Agricultural Yield Spare Land for Nature?”. Global Change Biology 15, no. 7 (2009): 1716–26. FAO (Food and Agriculture Organization of the United Nations). Global Forest Resource Assessment Report 2010: Country Report. Rome, 2010a. ———. Global Forest Resources Assessment 2010. Rome, 2010b. ———. FAOSTAT database. 2014 (accessed April 2014). Francisco, Hermina and Marian de Los Angeles. Soil Resource Depreciation and Deforestation: Philippine Case Study in Resource Accounting. Rome: Food and Agriculture Organization of the United Nations, 1998. Friis, Cecilie, and Anette Reenberg. Land Grab in Africa: Emerging Land System Drivers in a Teleconnected World. GLP Report No. 1. Copenhagen: Global Land Project International Project Office, 2010. Garcia, K., R. Lasco, A. Ines, B. Lyon, and F. Pulhin. “Predicting Geographic Distribution and Habitat Suitability Due to Climate Change of Selected Threatened Forest Tree Species in the Philippines”. Applied Geography 44 (2013): 12–22. Garrity, D. “Agroforestry and the Achievement of the Millennium Development Goals”. Agroforestry Systems 61 (2004) : 5–17. ———, D. Kummer, and E. Guiang. The Upland Ecosystem in the Philippines:

18-J04349 02 Future of Philippine Agriculture.indd 125

19/11/18 12:03 PM

126

David M. Wilson and Rodel D. Lasco

Alternatives for Sustainable Farming and Forestry. Washington, D.C.: National Academy Press, 1993. Geist, Helmut, and Eric Lambin. What Drives Tropical Deforestation? A Meta-Analysis of Proximate and Underlying Causes of Deforestation Based on Subnational Case Study Evidence. LUCC Report, Series 4. 2001 (accessed April 2014). Gibbs, H., S. Ruesch, F. Achard, M. Clayton, P. Holmgren, N. Ramankutty, and J. Foley. “Tropical Forests Were the Primary Sources of New Agricultural Land in the 1980s and 1990s”. Proceedings of the National Academy of Sciences of the United States of America 107, no. 38 (2010): 16732–37. GIZ (Deutsche Gesellschaft für Internationale Zusammenarbeit). Analysis of Key Drivers of Deforestation and Forest Degradation in the Philippines. 2013 (accessed April 2015). GOI (Government of India). National Agroforestry Policy. New Delhi: Ministry of Agriculture, Department of Agriculture and Cooperation, 2014. Grainger, Alan, and Ben Malayang. “A Model of Policy Changes to Secure Sustainable Forest Management and Control of Deforestation in the Philippines”. Forest Policy and Economics 8, no. 1 (2006): 67–80. ———, H. Francisco, and P. Tiraswat. “The Impact of Changes in Agricultural Technology on Long-Term Trends in Deforestation”, Land Use Policy 20, no. 3 (2003): 209–23. Grau, R., T. Kuemmerle, and L. Macchi. “Beyond ‘Land Sparing versus Land Sharing’: Environmental Heterogeneity, Globalization and the Balance between Agricultural Production and Nature Conservation”. Current Opinion in Environmental Sustainability 5, no. 5 (2013): 477–83. Grimm, N., F. Chapin, III, B. Bierwagen, P. Gonzalez, P. Groffman, Y. Luo, F. Melton, K. Nadelhoffer, A. Pairis, P. Raymond, J. Schimel, and G. Williamson. “The Impacts of Climate Change on Ecosystem Structure and Function”. Frontiers in Ecology and the Environment 11, no. 9 (2013): 474–82. Guiang, E., S. Borlagdan, and J. Pulhin. Community-Based Forest Management in the Philippines: A Preliminary Assessment. Institute of Philippine Culture, Ateneo de Manila University, Quezon City, in collaboration with the Department of Social Forestry and Forest Governance College of Forestry and Natural Resources University of the Philippines at Los Baños, 2001. Harrison, S., N. Emtage, and E.E. Nasayo. “Past and Present Forestry Support Programs in the Philippines, and Lessons for the Future”. Small-scale Forest Economics, Management and Policy 3, no. 3 (2004): 303–17. Hernandez, E.C., A. Henderson, and D.P. Oliver. “Effects of Changing Land Use in the Pagsanjan–Lumban Catchment on Suspended Sediment Loads to Laguna de Bay, Philippines”. Agricultural Water Management 106 (2012): 8–16.

18-J04349 02 Future of Philippine Agriculture.indd 126

19/11/18 12:03 PM

The Context of Land Cover Changes in Agriculture and Forestry

127

Heubes, J., I. Kühn, K. König, R. Wittig, G. Zizka, and K. Hahn. “Modelling Biome Shifts and Tree Cover Change for 2050 in West Africa”. Journal of Biogeography 38, no. 12 (2011): 2248–58. HLPE (High Level Panel of Experts on Food Security and Nutrition). Food Security and Climate Change. A report by the High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security. Rome, 2012. Hosonuma, N., M. Herold, V. De Sy, R. De Fries, M. Brockhaus, L. Verchot, A. Angelsen, and E. Romijn. “An Assessment of Deforestation and Forest Degradation Drivers in Developing Countries”. Environmental Research Letters 7, no. 4 (2012): 1–12. IPCC (Intergovernmental Panel on Climate Change). “Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change”. In Climate Change 2014: Impacts, Adaptation, and Vulnerability, edited by V. Barros, C. Field, D. Dokken, M. Mastrandrea, K. Mach, T. Bilir, M. Chatterjee, K. Ebi, Y. Estrada, R. Genova, B. Girma, E. Kissel, A. Levy, S. MacCracken, P. Mastrandrea, and L. White. Cambridge, U.K., and New York: Cambridge University Press, 2014. ———. “Summary for Policymakers”. In Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlomer, C. von Stechow, T. Zwickel and J. Minx. Cambridge, U.K., and New York: Cambridge University Press, 2014. Israel, Danilo and Roehlano Briones. “Impacts of Natural Disasters on Agriculture, Food Security, and Natural Resources and Environment in the Philippines”. In ERIA Discussion Paper Series 2013–15, 2013 (accessed April 2015). ——— and Jeffrey Lintag. “Assessment of the Efficiency and Effectiveness of the Reforestation Program of the Department of Environment and Natural Resources”. In Philippines Institute of Development Studies Discussion Paper Series 2013–22, 2013 (accessed April 2015). Jose, A.M., R.V. Francisco, and N.A. Cruz. “A Study on the Impact of Climate Variability/Change on Water Resources in the Philippines”. Journal of Philippine Development 47, no. 1 (1999): 115–34. Jose, S. 2009. “Agroforestry for Ecosystem services and Environmental Benefits: An Overview”. Agroforestry Systems 76, no. 1 (2009): 1–10. Kastner, Thomas and Sanderine Nonhebel. “Changes in Land Requirements for Food in the Philippines: A Historical Analysis”. Land Use Policy 27, no. 3 (2010): 853–63.

18-J04349 02 Future of Philippine Agriculture.indd 127

19/11/18 12:03 PM

128

David M. Wilson and Rodel D. Lasco

Kissinger, G., M. Herold, and V. de Sy. Drivers of Deforestation and Forest Degradation: A Synthesis for REDD+ Policymakers. Vancouver: Lexeme Consulting, 2012. Kummer, D. Deforestation in the Postwar Philippines. Manila: Ateneo de Manila University Press, 1992. Kummer, David M. and B.L. Turner, II. “The Human Causes of Deforestation in Southeast Asia”. American Institute of Biological Sciences 44, no. 5 (1994): 323–28. Lal, R. “Soil Carbon Sequestration Impacts on Global Climate Change and Food Security”. Science 304, no. 5677 (2004): 1623–27. Lambin, Eric and Patrick Meyfroidt. “Global Land Use Change, Economic Globalization, and the Looming Land Scarcity”. In Proceedings of the National Academy of Sciences of the United States of America 108, no. 9 (2011): 3465–72. Lasco, R. “Forest Carbon Budgets in Southeast Asia Following Harvesting and Land Cover Change”. Science in China 45 (2002): 55–64. ———. “Philippine Forest Ecosystems and Climate Change: Carbon Stocks, Rate of Sequestration and the Kyoto Protocol”. Annals of Tropical Research 25, no. 2 (2003): 37–51. ———. Carbon Budgets of Forest Ecosystems in the Philippines. “Carbon Budgets of Forest Ecosystems in the Philippines”. Journal of Environmental Science and Management 12, no. 1 (2009): 1–13. ———, and Florencia B. Pulhin. “Forest Land Use Change in the Philippines and Climate Change Mitigation”. Mitigation and Adaptation Strategies for Global Change 2, no. 5 (2000): 81–97. ———, R. Visco, and J. Pulhin. “Secondary Forests in The Philippines: Formation and Transformation in the 20th Century”. Journal of Tropical Forest Science 13, no. 4 (2001): 652–70. ———, R. Evangelista, and F. Pulhin. “Potential of Community-Based Forest Management to Mitigate Climate Change in the Philippines”. Small-Scale Forestry 9 (2010): 429–43. ———, F. Pulhin, R. Cruz, J. Pulhin, S. Roy, and P. Sanchez. “Forest Responses to Changing Rainfall in the Philippines”. In Climate Change and Vulnerability, edited by N. Leary, C. Conde, J. Kulkarni, A. Nyong, and J. Pulhin. London: Earthscan, 2007. ———, F. Pulhin, L. Bugayong, and M. Mendoza. “An Assessment of Potential Benefits to Smallholders of REDD+ Components in the Philippines”. Annals of Tropical Research 33, no. 1 (2011): 31–48. ———, R. Veridiano, M. Habito, and F. Pulhin. “Reducing Emissions from Deforestation and Forest Degradation plus (REDD+) in the Philippines: Will It Make a Difference in Financing Forest Development?”. Mitigation and Adaptation Strategies for Global Change 18 (2012): 1109–1124. ———, N. Mallari, F. Pulhin, A. Florece, E. Rico, R. Baliton, and J. Urquiola. “Lessons

18-J04349 02 Future of Philippine Agriculture.indd 128

19/11/18 12:03 PM

The Context of Land Cover Changes in Agriculture and Forestry

129

from Early REDD+ Experiences in the Philippines”. International Journal of Forestry Research 2013 (2013): 1–12. ———, R. Delfino, D. Catacutan, E. Simelton, and D. Wilson. “Climate Risk Adaptation by Smallholder Farmers: The Roles of Trees and Agroforestry. Current Opinion in Environmental Sustainability 6 (2014): 83–88. Lawrence, Deborah and Karen Vandecar. “Effects of Tropical Deforestation on Climate and Agriculture”. Nature Climate Change 5 (2015): 27–36. Liu, D., L. Iverson, and S. Brown. “Rates and Patterns of Deforestation in the Philippines: Application of Geographic Information System Analysis”. Forest Ecology and Management 57, no. 1 (1993): 1–16. Mather, A. “The Forest Transition”. Area 24, no. 4 (1992): 367–79. Mbow, C., P. Smith, D. Skole, L. Duguma, and M. Bustamante. “Achieving Mitigation and Adaptation to Climate Change through Sustainable Agroforestry Practices in Africa”. Current Opinion in Environmental Sustainability 6 (2013): 8–14. Mendelsohn, R. “The Impact of Climate Change on Agriculture in Asia”. Journal of Integrative Agriculture 13, no. 4 (2014): 660–65. Mendoza, M. “A Review of Opportunities and Constraints to Forest Financing in the Philippines”. Ecosystems and Development Journal 2, no. 1 (2009): 45–55. Michon, G., H. Foresta, P. Levang, and F. Verdeaux. “Domestic Forests: A New Paradigm for Integrating Local Communities’ Forestry into Tropical Forest Science. Ecology and Society 12, no. 2 (2007): 1 (online). MPFD (Management Plan for Forestry Development). “Revised Master Plan for Forestry Development”. n.d. (accessed 23 January 2015). Nagendra, H., B. Reyers, and S. Lavorel. “Impacts of Land Change on Biodiversity: Making the Link to Ecosystem Services”. Current Opinion in Environmental Sustainability 5 (2013): 503–8. Nair, P. “Use of Perennial Legumes in Asian Farming Systems”. In Green Manure in Rice Farming. Los Banos: International Rice Research Institute, 1988. NASA (National Aeronautics and Space Administration). “Moderate Resolution Imaging Spectroradiometer (MODIS) data”. 2010 (accessed April 2015). NDRRMC (National Disaster Risk Reduction and Management Council). “National Disaster Risk Reduction and Management Plan”. 2011 (accessed April 2015). ———. “NDRRMC Update”. 2014 (accessed April 2014). NEDA (National Economic and Development Authority). “Mainstreaming Disaster Risk Reduction in Subnational Development and Land Use/Physical Planning

18-J04349 02 Future of Philippine Agriculture.indd 129

19/11/18 12:03 PM

130

David M. Wilson and Rodel D. Lasco

in the Philippines: Guidelines”. 2008 (accessed April 2015). ———. “Philippine Development Plan 2011–2016”. In Ch. 10 in Conservation, Protection and Rehabilitation of the Environment and Natural Resources, 2011 (accessed April 2015). Nelson, G., M. Rosegrant, A. Palazzo, I. Gray, C. Ingersoll, R. Robertson, S. Tokgoz, T. Zhu, T. Sulser, C. Ringler, S. Msangi, and L. You. Food Security, Farming and Climate Change to 2050: Scenarios, Results, Policy Options. Washington, D.C.: International Food Policy Research Institute, 2010. Olofson 1983 [Author to supply full bibliographic information]. Otsuka, K. “Determinants and consequences of land reform implementation in the Philippines”. Journal of Development Economics 35, no. 2 (1991): 339–55. Palao, L., W. Dressler, R. Cruz, and J. Pulhin. “Changes in Indigenous Land Use System: From Swidden to Sedentary Agriculture”. Asia Life Sciences 20, no. 2 (2011): 395–416. Pasicolan, P., H. Udo de Haes, and P. Sajise. “Farm Forestry: An Alternative to Government-Driven Reforestation in the Philippines”. Forest Ecology and Management 99, no. 1 (1997): 261–74. Perfecto, Ivette and John Vandermeer. “The Agroecological Matrix as Alternative to the Land-Sparing/Agriculture Intensification Model”. In Proceedings of the National Academy of Sciences of the United States of America 107, no. 13 (2010): 5786–91. Phelps, J., M. Guerrero, D. Dalabajan, B. Young, and E. Webb. “What Makes a ‘REDD’ Country?”. Global Environmental Change 20, no. 2 (2010): 322–32. Pirard, R., and K. Belna. “Agriculture and Deforestation: Is REDD+ Rooted In Evidence?”. Forest Policy and Economics 21 (2012): 62–70. PSA-BAS (Philippine Department of Agriculture, Bureau of Agricultural Statistics). “Data on crop area”. 2012 (accessed 4 January 2014). PSA-NSO (Philippines Statistics Authority, National Statistics Office). “Population and Housing”. 2010 (accessed January 2015). Pulhin, J. “Trends in Forest Policy of the Philippines”. In Policy Trend Report, 2002

(accessed April 2015). Qiu, G., J. Yin, and S. Geng. “Impact of Climate and Land-Use Changes on Water Security for Agriculture in Northern China”. Journal of Integrative Agriculture 11, no. 1 (2012): 144–50. Rebugio, L., R. Cruz, R. Lasco, M. Calderon, W. Carandang, E. Tolentino, Jr., R. Visco, L. dela Cruz, J. Dizon, F. Pulhin, R. Dalmacio, M. Calderon, L. Camacho, and N. Bantayan. 2005. Forest Restoration in the Philippines: A Review of Research

18-J04349 02 Future of Philippine Agriculture.indd 130

19/11/18 12:03 PM

The Context of Land Cover Changes in Agriculture and Forestry

131

and Development Initiatives. Seoul: ASEAN–Korea Environmental Cooperation Unit, 2005. Reyes, C., S. Domingo, C. Mina, and K. Gonzales. “Policy Options for Rice and Corn Farmers in the Face of Seasonal Climate Variability”. Journal of Philippine Development 66, no. 1 (2009): 133–57. Rice, D. and F. Dulnuan. “Indigenous Forest Dwellers in Forest Development Planning”. In Proceedings of the Workshop on Agroforestry in the Philippines. Los Baños: Center for Forestry Education and Development, College of Forestry, University of the Philippines, 1981. Rudel, T., L. Schneider, and M. Uriarte. “Forest Transitions: An Introduction”. Land Use Policy 27, no. 2 (2010): 95–97. ———, O. Coomes, E. Moran, F. Achard, A. Angelsen, J. Xu, and E. Lambin. “Forest Transitions: Towards a Global Understanding of Land Use Change”. Global Environmental Change 15, no. 1 (2005): 23–31. ———, L. Schneider, M. Uriarte, B. Turner, R. DeFries, D. Lawrence, and J. Geoghegan. “Agricultural Intensification and Changes in Cultivated Areas, 1970–2005”. In Proceedings of the National Academy of Sciences of the United States of America 106, no. 49 (2009): 20675–80. Sajise, P. “Forest Policy in the Philippines: A Winding Trail towards Participatory Sustainable Development”. The Institute for Global Environmental Strategies, 1998. Sanchez, P., D. Oliver, H. Castillo, and R. Kookana. “Nutrient and Sediment Concentrations in the Pagsanjan–Lumban Catchment of Laguna de Bay, Philippines”. Agricultural Water Management 106 (2012): 17–26. Santos, Fernando and Meine van Noordwijk. “Is Native Timber Tree Intercropping an Economically Feasible Alternative for Smallholder Farmers in the Philippines?”. Australian Journal of Agricultural and Resource Economics 55, no. 2 (2011): 257–72. Sayer, J., T. Sunderland, J. Ghazoul, J.-L. Pfund, D. Sheil, E. Meijaard, M. Venter, A. Boedhihartono, M. Day, C. Garcia, C. van Oosten, and L. Buck. “Ten Principles for a Landscape Approach to Reconciling Agriculture, Conservation, and Other Competing Land Uses”. In Proceedings of the National Academy of Sciences of the United States of America 110, no. 21 (2013): 8349–56. Schmidt-Vogt, D., S. Leisz, O. Mertz, A. Heinmann, T. Thiha, P. Messerli, M. Epprecht, P. Van Cu, V. Kim Chi, M. Hardiono, and T. Dao. “An Assessment of Trends in the Extent of Swidden in Southeast Asia”. Human Ecology 37, no. 3 (2009): 269–80. Tscharntke, T., Y. Clough, T. Wanger, L. Jackson, I. Motzke, I. Perfecto, J. Vandermeer, and A. Whitbread. “Global Food Security, Biodiversity Conservation and the Future of Agricultural Intensification”. Biological Conservation 151, no. 1 (2012): 53–59. Tumaneng-Diete, T., I. Ferguson, and D. MacLaren. “Log Export Restrictions

18-J04349 02 Future of Philippine Agriculture.indd 131

19/11/18 12:03 PM

132

David M. Wilson and Rodel D. Lasco

and Trade Policies in the Philippines: Bane or Blessing to Sustainable Forest management?”. Forest Policy and Economics 7, no. 2 (2005): 187–98. Ty, T., K. Sunada, Y. Ichikawa, and S. Oishi. “Scenario-Based Impact Assessment of Land Use/Cover and Climate Changes on Water Resources and Demand: A Case Study in the Srepok River Basin, Vietnam — Cambodia”. Water Resources Management 26, no. 5 (2012): 1387–407. van Noordwijk, M., M. Hoang, H. Neufeldt, I. Öborn, and T. Yatich, eds. How Trees and People Can Co-Adapt to Climate Change: Reducing Vulnerability in Multifunctional Landscapes. Nairobi: World Agroforestry Centre, 2011. van Vliet, N., O. Mertz, A. Heinimann, T. Langanke, U. Pascual, B. Schmook, C. Adams, D. Schmidt-Vogt, P. Messerli, S. Leisz, J.-C. Castella, L. Jørgensen, T. Birch-Thomsen, C. Hett, T. Bech-Bruun, A. Ickowitz, K. Chi Vu, K. Yasuyuki, J. Fox, C. Padoch, W. Dressler, and A. Ziegler. “Trends, Drivers and Impacts of Changes in Swidden Cultivation in Tropical Forest-Agriculture Frontiers: A Global Assessment”. Global Environmental Change 22, no. 2 (2012): 418–29. Vergara 1982 [Author to supply full bibliographic information]. Vista, B., E. Nel, and T. Binns. “Land, Landlords and Sustainable Livelihoods: The Impact of Agrarian Reform on a Coconut Hacienda in the Philippines”. Land Use Policy 29, no. 1 (2012): 154–64. Weiss, John. The Economics of Climate Change in Southeast Asia: A Regional Review. Asian Development Bank. Manila, 2009. World Bank. “World DataBank”. 2015 (accessed 23 January 2015). Yuen, J., A. Ziegler, E. Webb, and C. Ryan. “Uncertainty in Below-Ground Carbon Biomass for Major Land Covers in Southeast Asia”. Forest Ecology and Management 310 (2013): 915–26. Ziegler, A., T. Bruun, M. Guardiola-Claramonte, T. Giambelluca, D. Lawrence, and T. Nguyen. “Environmental Consequences of the Demise in Swidden Cultivation in Montane Mainland Southeast Asia: Hydrology and Geomorphology”. Human Ecology 37, no. 3 (2009): 361–73. ———, J. Phelps, J. Yuen, E. Webb, D. Lawrence, J. Fox, and T. Bruun. “Carbon Outcomes of Major Land-Cover Transitions in SE Asia: Great Uncertainties and REDD Plus Policy Implications”. Global Change Biology 18, no. 10 (2012): 3087–99.

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3 TRENDS IN AGRICULTURAL WATER RESOURCES Arlene B. Inocencio

One of the pathways through which climate change will be felt is its impact on water resources (WEPA 2012). This includes increased risk in the form of excess water, water shortages, poor water quality, and disruptions to freshwater ecosystems due to higher water temperatures, increased intensity and duration of precipitation, longer periods of low flows, and higher operating costs (Kundzewicz et al. 2007). Dependence on and overexploitation of groundwater can result from the unreliability of surface water supplies, while at the same time changes in precipitation patterns increase the incidence of flooding. Increased water risks and growing uncertainty about future conditions are already exacerbating existing water security challenges in the Philippines and have implications for water-related planning, management, and investment decisions. Adapting to new circumstances will require knowledge-based investment strategies and adaptive water governance, taking into account climate variability and minimizing potentially costly mismatches between water systems and the future climate. This chapter outlines trends in water resource investments and agricultural water uses, focusing on irrigation projects, for the purpose

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of examining the preparedness of the agricultural sector to respond to the adverse impacts of climate change. While the literature on these impacts is mixed, lack of action could be more costly than a proactive, anticipatory approach. The increased recognition of the need to consider and mainstream the implications of climate change in government initiatives should be matched by increased preparation rather than a cursory or “business as usual” response. The next section outlines potential climate change adaptation strategies relating to water and irrigation. Thereafter, the climate change strategy of the National Irrigation Administration (NIA) is presented, followed by a discussion of trends in public investments in irrigation at national and subnational levels, which reflect planning, management, and decision making about agricultural water allocation and use. The chapter concludes with policy implications. (Details of the data and methodology utilized are provided in Box 3.1.)

CLIMATE CHANGE ADAPTATION IN THE IRRIGATION SECTOR Irrigation and water are key sectors in need of climate change adaptation. In water-stressed areas, approaches should include applying irrigation, improving irrigation efficiency, adopting water-efficient technologies to harvest water and conserve soil moisture, developing and adopting improved varieties and species with resistance to heat and drought, and reviewing or developing other policies beyond the water sector. Furthermore, in areas with increased precipitation and flooding, investments should be made in drainage infrastructure and water management to prevent waterlogging, erosion, and nutrient leaching. Blobel et al. (2006) note that technologies for irrigated agriculture under climate change need to focus both on the supply side, such as through changing tilling practices and harvesting rainwater, and from the demand side, such as through increased irrigation efficiency and change irrigation water pricing policy. Other studies (for example, Cai, Rosegrant, and Ringler 2003 and Hsiao, Steduto, and Fereres 2007) present other water-saving technologies at the irrigation system level, including reduced usage during transport from source to farm, typically along canals (conveyance efficiency), from farm gate to field (distribution efficiency), and during application to crops (application efficiency). Conveyance efficiency, for example, can be increased by lining canals; distribution

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BOX 3.1 Data and Methodology The analyses presented in this chapter are based on data from NIA and the Department of Budget and Management. Secondary national and subnational data for all systems from 1966 to 2015 were used to establish levels of investment, by various types, purposes, and combinations, and trends in rehabilitation/restoration projects versus operations and management (O&M) expenditures. Project reports — which include appraisals, completion and performance assessments, and other water project documents — are reviewed and analysed as a means of assessing economic performance. Secondary data include the yearly inventories of national, community, and private irrigation systems from the NIA’s Management Information Division for the period 2010–15; system performance data for national and community irrigation systems from the National Irrigation System Performance (NISPER) and Communal Irrigation System Performance (CISPER) databases for the 1983–2015 period; and system performance data for private irrigation systems for the 2005–15 period from NIA’s Systems Management Division. The NISPER and CISPER databases include information on service areas, programmed and actual irrigated areas in the wet and dry seasons, targeted and actual irrigation service fee collections and collectibles, O&M, average yields per system, amortization payments, and other sources of revenue and total system expenditures. Public investments in irrigation are based on total yearly obligations given in NIA’s year-end reports for the 1974–2015 period. These data are the best available representation of actual capital outlays because they include appropriations directly and indirectly allocated to NIA. Note that all time-series investment data, O&M expenses, and corporate incomes are provided in Philippine pesos (PhP) at 2000 prices using the gross domestic product implicit price index as deflator. Source: Author.

efficiency can be enhanced by lining and maintaining on-farm canals; and application efficiency can be increased by using more sophisticated irrigation technologies, such as drip irrigation or sprinklers, or through the application of deficit irrigation (Cai, Rosegrant, and Ringler 2003). Innovative adaptation to climate change through changes in agricultural water management can be implemented at the farm, community, national, and global level (IFPRI/ADB 2009). At the farm level, adaptation strategies

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include water harvesting, on-farm irrigation, soil and water conservation, drip and sprinkler systems, and groundwater use; at the community level, options include small reservoirs, watershed management, and water trading; nationally, investments in large-scale systems and reservoirs could enhance water management for agriculture; and at the global level, funding is needed to ensure investments and proper implementation of these adaptation technologies. Obviously, investments in infrastructure are needed to ensure the sufficient supply of water for agricultural uses. Rosegrant, Ringler, and Zhu (2009) highlighted the difficulty (including the increasing costs and political sensitivity) of establishing new investments, particularly to meet rising water demand. They argue that prospective new water sources need to be carefully selected and developed with economic efficiency, for example, by impounding surface water, sustainably exploiting groundwater resources, and expanding the development of nontraditional water sources. Despite these potential areas for adapting to climate change, the implementation of policies in the areas of irrigation and water management in the Philippines has been limited to date.

THE NATIONAL IRRIGATION ADMINISTRATION’S CLIMATE CHANGE STRATEGY The NIA is a government corporation mandated to sustainably develop and manage agricultural water resources and to develop, operate, and maintain Philippine irrigation systems (Box 3.2). The yearly water withdrawal of the Philippine agricultural sector is about 82 per cent of the national total of 81.56 billion cubic metres per year (FAO 2014). Given that agriculture is the largest water user, it is imperative to assess the actions being undertaken by the relevant authorities to manage this scarce and threatened resource. The NIA’s masterplan for 2014–28 incorporates a climate change strategy that includes: (1) adopting climate-resilient agri-fishery technology and infrastructure; (2) lining canals as an integrated aspect of restoring/ rehabilitating existing irrigation systems (involving 8,662 km of main canals and 20,864 km of lateral canals in national and communal irrigation systems); and (3) climate-proofing/retro-fitting irrigation infrastructure. These projects are intended to strengthen the climate resilience of vulnerable irrigation infrastructure under the Participatory Irrigation Development Program (PIDP). The approach takes into account climate

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BOX 3.2 Philippine Irrigation Systems There are three types of irrigation systems in the Philippines: national irrigation systems (NIS), communal irrigation systems (CIS), and private irrigation systems (PIS). NIS represent the largest component, comprising over 230 systems, across 16 regions, that are mostly large gravity systems ranging from 1,000 to over 100,000 hectares (ha) of irrigated area. The NIA fully owns almost all of these systems, co-managing them with irrigators’ associations. CIS, of which there are over 9,000, are typically much smaller. The NIA undertakes the design, execution, and financing of newly constructed or restored CIS, but — unlike NIS — irrigators’ associations own CIS, are involved in their planning and construction, and are fully responsible for the O&M of these systems. The CIS irrigators’ associations have two payment options. Under the first option, they pay 10 per cent of the project cost and amortize the balance at no interest (over 50 years for gravity systems, and 7 or 15 years for pump systems). In the second option, the CIS pay 30 per cent of the total project cost and the system is turned over to the irrigators’ associations upon completion of the project. However, even though the Local Government Code of 1992 devolved responsibility for CIS to local government, limited technical and financial capability at the local level has meant that, in practice, many irrigators’ associations still turn to the NIA for funding. Source: David (1995).

risks in redesigning, retrofitting, or modifying the operation of irrigation infrastructure. In 2013, the NIA spent PhP225 million in activities related to climate change adaptation, which represents 0.72 per cent of the Administration’s total budget. The allocation of funding across activities included a river bank protection dike at the Magat River Integrated Irrigation System (MRIIS); watershed planting in the Upper Pampanga River Integrated Irrigation System (UPRIIS); and some technology interventions (Table 3.1). The allocation for technologies provides for: (1) improving control structures, water delivery, canal lining, and provision and repair of gates; (2) improving irrigation water quality in UPRIIS, Angat-Maasim River Irrigation System (AMRIS), and the Pampanga Delta; (3) upgrading image-based

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TABLE 3.1 Philippine Climate Change Adaptation Activities Related to Water Resources as of 31 December 2013 Project/Intervention/Adaptation Activity

(million PhP)

Construction of water impounding facilities (a small reservoir irrigation project)

218.515

Nationwide technology interventions to existing irrigation systems

258.403

Nationwide climate change vulnerability mapping in agricultural and irrigated areas and related watersheds

254.972

Construction of a river bank protection dike in the Integrated Irrigation Systems of the Magat River, Isabela

273.720

Establishment of a watershed management programme at the Pantabangan dam complex, Nueva Ecija

219.352

Total

224.962

Source: DBM (Department of Budget and Management), General Appropriations (Manila City, 2013).

parcellary maps in 10 NIS; (4) establishing agrometeorological stations; and (5) building capacity and disseminating technology. Aside from receiving funding from the national government, the NIA was the recipient of a grant of PhP19 million from the Korean International Cooperation Agency for the construction of water-impounding facilities (a small reservoir irrigation project) in Isabela. The investment coordination committee, headed by the Secretary of Finance and co-chaired by the National Economic and Development Authority (NEDA), approved the project in November 2013 at an estimated to cost PhP1.03 billion. In 2014 the project was allocated a total budget of PhP175 million, or about 0.83 per cent of the total allocation for irrigation of PhP21 billion. The NIA’s master irrigation plan for 2014–28 spells out the direction of the country’s irrigation development, which targets strategic agricultural production areas to attain staple food sufficiency and maximize poverty alleviation. To attain this goal, three interventions have been identified: (1) raising farm productivity and competitiveness in production; (2) enhancing economic incentives and enabling mechanisms; and (3) managing the consumption of staple foods. The first intervention includes accelerating the expansion of irrigation services by frontloading investments; prioritizing the construction, rehabilitation, and restoration

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of “quick-gestating” projects; investing in small-scale irrigation systems; improving cropping intensity; and improving the efficiency of irrigation systems through modernization and integrated water resource management. The master plan prioritizes small-scale irrigation projects in smaller rural communities with minor sources of water. Major irrigation projects are intended to incorporate integrated land and water resource planning or a river basin approach. Part of the short- to medium-term strategy is the integration of climate change adaptation and mitigation measures in the government’s irrigation development programme. Beginning in 2013, the NIA included the river bank protection dike, watershed plantation, and technology intervention in its yearly budget. Specific adaptation activities have been identified, including lining canals and climate-proofing/retrofitting irrigation infrastructure. A total of 21,600 km of canals remain unlined (of which about 2,800 km are main canals and 8,200 km are lateral canals of the NIS). About 650–800 km of NIS and CIS canals were scheduled to be lined per year during 2013–16. Feasibility studies on the redesign of irrigation infrastructure of two pilot NIS have been completed, and the redesign, retrofitting, or modification guidelines have been developed. The NIA was lining canals and establishing protection dikes prior to the mainstreaming of the climate change agenda in relevant government programmes, indicating that, to a significant extent, the Administration has relabeled existing programmes as climate change initiatives rather than developing new initiatives to counteract climate change.

TRENDS IN PUBLIC INVESTMENT IN WATER FOR AGRICULTURE Historically, irrigation has been the largest public agricultural expense in the Philippines. In the 1970s and 1980s, public expenditures on irrigation represented about 45 per cent of all public agricultural spending and 12 per cent of all spending on infrastructure development (David and Inocencio 2012). Since the late 1980s, the relative importance of irrigation in public agricultural spending has declined by more than half, while its share of total spending on infrastructure has fallen to about 6 per cent. In recent years, irrigation’s share of public agricultural spending has increased to nearly 30 per cent, and to about 10 per cent of total spending on infrastructure. The relative importance of irrigation as a policy instrument is even higher

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within the rice sector because publicly supported irrigation is primarily for surface gravity systems suited for rice cultivation, and the rice sector accounts for at least two-thirds of public agricultural expenditures. In 2015, total public expenditures for irrigation reached PhP22 billion, 90 per cent of which was allocated to capital investments and the remainder to corporate expenditures (such as staffing and other operating and maintenance costs). From 1976 to 2015, capital investments averaged 85 per cent of total public expenditures for irrigation. Over the past five decades, public capital investments in irrigation have fluctuated significantly, rising in the 1970s, declining drastically in 1983, and recovering to some extent in the early 1990s (Figure 3.1). The sharp increase in world rice prices in the 1970s, together with the introduction of modern rice varieties suited to irrigated conditions, raised the marginal rates of returns to irrigation investments. Public spending on irrigation declined as world commodity prices declined, yields of modern rice varieties leveled off, and the cost of irrigation expansion increased. Investments have risen again since 2008, likely in response to increased world rice prices, and this trend has continued with the present administration’s food self-sufficiency programme. More systematic analyses indicate that public investment levels respond to short-term changes in world rice prices because these changes affect the marginal rate of return to irrigation investment and the adoption of rice self-sufficiency rather than a consideration of the long-term costs and benefits (Hayami and Kikuchi 1978; Azarcon, Barker, and associates 1992; Kikuchi, Maruyama, and Hayami 2003). With the passage of Republic Act 9729 (the Climate Change Act of 2009), climate change was mainstreamed in policy formulation. In response, the Department of Agriculture instituted the process of integrating climate change into its programmes to protect and optimize agricultural and fishery production. This consideration could potentially increase investments in specific types of projects, although current changes in allocations for agricultural water appear nominal.

TRENDS IN TYPES OF AGRICULTURAL WATER PROJECTS AND SYSTEMS Public investment by type of project and system indicates priorities over time. In the past five decades, approximately 70 per cent of public spending on irrigation has been allocated to the construction, rehabilitation,

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FIGURE 3.1 Trends in Public Investments in Irrigation in Real Terms, 1965–2015

Sources: NIA (National Irrigation Administration), Various years (a). Year-End Report to the President (Quezon City).

restoration, repair, and support services of NIS; only 21 per cent of expenditures have flowed to CIS, and 9 per cent to small water-impounding projects, tubewells, and other individual systems (Figure 3.2). NIS’s share of total public support for irrigation is higher when the costs of O&M and other support services funded through corporate revenues are included. Even if the budgets for shallow tubewells, small water-impounding projects, and small-farm reservoirs allocated by the Bureau of Soils and Water Management and other agencies were included, public expenditures for this type of irrigation would be less than 5 per cent of the total. Budgetary resources for the expansion and rehabilitation of CIS have increased, but no data are available with which to evaluate the effects of these expenditures on the performance of the systems. The fact that many locally funded CIS projects have been implemented with Congressional “pork barrel” and local government funding may partly explain the slow growth in irrigated areas. Anecdotal evidence indicates that many CIS

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Notes: NIS = national irrigation systems; CIS = communal irrigation systems; “Other” includes groundwater pumps, dams, canals, small water impounding projects, reforestation and fire protection, and feasibility studies and detailed engineering; “Other” increased starting 2013 due to the provisions for non-component of San Roque Multi-purpose project paid to NPC-PSALM. Sources: NIA (National Irrigation Administration), Various years (a). Year-End Report to the President (Quezon City).

FIGURE 3.2 Trends in Irrigation Investments by Type of System, 1965–2015

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have disbanded and are now operated as individual or private systems (Panella 2004; Euroestudios Ingenieros de consulta 2006). Until the early 1980s, about 95 per cent of public expenditures on irrigation were allocated to NIS. CIS’s share began to rise by the mid-1980s as donor agencies focused on poverty reduction, and the government embarked on the Comprehensive Agrarian Reform Program in 1988. As a result, CIS’s share of total irrigation investments rose from an average of less than 5 per cent in the 1970s, to more than 40 per cent in early 1990s. Foreign-assisted communal projects were typically part of the integrated area development projects (for example, Palawan Integrated Development Projects and the Southern Philippines Irrigation Sector Project) and agrarian reform-related projects. Local funding for communal projects had been mostly sourced from the Agrarian Reform Funds. During the late-1990s, NIS’s share of irrigation investments once again increased — despite the passage of the Agriculture and Fisheries Modernization Act in 1997, which directed increased public support for small-scale irrigation systems and groundwater resources development — but in more recent years the amount and share of investment in CIS has again expanded substantially. The NIA recalculated service area in terms of “firmed-up” service area, which is equivalent to the service area less any land either converted from agricultural to nonagricultural uses or considered permanently “nonrestorable” (that is, having either insufficient water or irrigation facilities that can no longer be completed for technical reasons). Investment in irrigation during 2011–12 almost doubled just as the NIA’s five-year rationalization programme — which was intended to generate surplus income to cover operating expenses by implementing a phased reduction in spending for NIS — was nearing completion. The new role of the NIA has been reduced to management and O&M of headworks and main canals of large NIS. The majority of irrigation systems are either publicly funded or assisted and comprise 89 per cent of firmed-up service area (Table 3.2). In terms of irrigated service area, NIS constitute 44 per cent of the total, CIS comprise 36 per cent, and PIS comprise 11 per cent. Luzon is home to over 60 per cent of the total firmed-up service area. The largest NIS areas are located in Regions 2 and 3 (46 per cent of total firmed-up service area), followed by Region 12 (8 per cent), and Regions 1 and 6 (about 6 per cent each).

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5 10 16 17 3 5 8 64 7 3 4 13 3 3 4 4 7 4 23 1,731,128

Total 2 6 20 26 3 3 3 63 6 2 3 11 3 2 3 5 8 4 26 754,666

8 9 9 11 3 6 12 58 6 4 6 16 3 4 4 4 6 4 26 615,797

13 11 24 5 3 8 13 78 8 2 3 14 0 1 3 1 2 2 9 187,767

Area as a Share of the National Total (%)

Private Irrigation Systems 2 29 12 11 1 7 9 73 9 1 2 11 0 2 2 2 6 4 16 172,899

Other GovernmentAssisted Service Areas

Notes: ARMM = Autonomous Region of Muslim Mindanao; CAR = Cordillera Administrative Region. Other government-assisted service areas can be classified as either communal or private systems. Firmed-up service area is the service area less any land either converted from agricultural to nonagricultural uses or considered nonrestorable. Source: NIA (National Irrigation Administration), Inventory of National, Communal, Private and OGA Irrigation System as of December 2015 (Quezon City, 2016).

CAR Region 1 (Ilocos Region) Region 2 (Cagayan Valley) Region 3 (Central Luzon) Region 4a (CALABARZON) Region 4b (MIMAROPA) Region 5 (Bicol Region) Total for Luzon Region 6 (Western Visayas) Region 7 (Central Visayas) Region 8 (Eastern Visayas) Total for Visayas ARMM Region 9 (Zamboanga Peninsula) Region 10 (Northern Mindanao) Region 11 (Davao Region) Region 12 (SOCCSKSARGEN) Region 13 (Caraga) Total for Mindanao Total area of the Philippines (ha)

Region

National Irrigation Communal Systems Irrigation Systems

TABLE 3.2 “Firmed-up” Irrigation Service Area by Region, as of 2015

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Most CIS are also located in Luzon, with Regions 3 and 5 having the largest service areas, followed by Regions 1, 2, and Cordillera Autonomous Region (CAR). Also in terms of firmed-up service area, most PIS are found in Luzon, particularly in Region 2. In terms of service area per region, CIS dominate in five regions (CAR, 5, 6, 7, 9) comprising over 50 per cent of total. Considering that CIS are allocated far fewer resources from the national budget than NIS, they are less likely to be able to respond to the adverse impacts of climate change compared with regions having more than 50 per cent of their area under NIS (Regions 2, 3,11, 12, and the Autonomous Region of Muslim Mindanao [ARMM]). The above trends in types of project in terms of public investments and service areas are indicative of the corresponding climate change adaptation projects and costs that may have to be developed, taking into consideration the scale and type of management by region. NIS can be categorized into three types of schemes: (1) run-of-the-river diversion, (2) storage or reservoir, and (3) pump irrigation. A diversion scheme draws water under controlled conditions directly from the flow of rivers or streams, whereas a storage or reservoir scheme involves the construction of storage dams to impound and release water as needed, drawn from a diversion dam downstream. According to the NIA, the reservoir projects are usually multipurpose and may serve power generation, flood control, fishery, and recreational functions. Pump projects lift water from underground or from rivers and streams and may be used in some storage or diversion systems to lift water in order to irrigate areas at higher elevation or pump groundwater to supplement available surface water supplies. As of 2015, reservoirs accounted for 32 per cent of irrigation projects, diversion schemes for 64 per cent, and pump systems for the remaining 4 per cent. The reservoir schemes are largely found in Regions 2 and 3, whereas most of the pump systems are located in Regions 1, 2, 3 and 5 (that is, in Luzon). Over time, much of the expansion has been in diversion and small reservoir schemes. Diversion schemes are more vulnerable during dry seasons and also to higher water stress resulting from decreased rainfall and extended dry spells (Figure 3.3).

AGRICULTURAL WATER PROJECTS On average, public investment in agricultural water during 1965–2015 was largely spent on new construction or the rehabilitation of irrigation

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Notes: Region 1 includes Cordillera Autonomous Region; Region 12 includes the Autonomous Region of Muslim Mindanao. Sources: NIA–SMD (National Irrigation Administration, Systems Management Division), Various years. National Irrigation System Performance (NISPER) database (Quezon City).

FIGURE 3.3 Trends in the Service Area of National Irrigation Systems by Region and Type, 1967–2015

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systems (Figure 3.4). A relatively small proportion of these expenditures were allocated to other areas, such as to strengthen irrigators’ associations; train NIA staff; run agricultural extension activities; and fund technical studies, particularly in foreign-assisted projects. The distribution of agricultural water projects according to purpose is indicative of the nature of these investments and their changing patterns over time. Investment projects are classified as: (1) new construction only; (2) more than 50 per cent new construction, with some rehabilitation and restoration; (3) more than 50 per cent rehabilitation and restoration, with some new construction; (4) rehabilitation and restoration only; and (5) “Other”, for projects that cannot be classified as either new or rehabilitation works (for example, the World Bank–funded Watershed and Erosion Management Project in the early 1980s). From 1965 to 2015, an average of 19 per cent of irrigation investments were allocated to totally new construction, including medium and large pump systems that draw water from major rivers, such as the Abra River FIGURE 3.4 Trends in Irrigation Investments by Use, 1965–2015

Note: “Other” increased starting 2013 due to the provisions for non-component of San Roque Multipurpose project paid to NPC-PSALM. Sources: NIA (National Irrigation Administration), Various years (a). Year-End Report to the President (Quezon City).

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in Abra, Libmanan Cabusao in Bicol, and Lower Agusan in Mindanao (David and Inocencio 2014). About two-thirds of expenditures funded irrigation projects that combined the construction of new irrigated area with the rehabilitation of existing gravity-based NIS or CIS. Most of these irrigation projects integrated, expanded, and modernized several smaller irrigation systems by constructing large reservoirs upstream (such as in the two largest systems, UPRIIS and MRIIS) or strengthened headworks further upstream (such as in the Ilocos Norte Integrated Project or the Upper Chico Irrigation Project). More recent projects established a regulating pond for the Agno River water from the hydropower plants in Ambuklao and Binga Dams to increase water supply for an integrated and rehabilitated national system in Pangasinan. Only about 12 per cent of irrigation investments were for rehabilitation and restoration purposes. The share of budgets for totally or predominantly new construction projects declined from the 1990s. Conversely, the share of rehabilitation only projects rose as high as 30 per cent during 2009–10. These patterns are consistent with the expectation that, as the more suitable sites for irrigation are developed over time, the benefit–cost ratios for rehabilitation projects become more favourable compared with the construction of new irrigated area (Kikuchi, Maruyama, and Hayami 2003; Inocencio et al. 2007). Overall, a significant share of expenditures on NIS during 1965–2015 was directed toward projects based on new or predominantly new construction — 57 and 24 per cent, respectively, for a total of 81 per cent — compared with entirely or predominantly rehabilitated areas — 10 per cent each, for a total of 18 per cent (Table 3.3). Similarly, more of the expenditures on CIS projects focused on new or predominantly new construction (66 per cent). Consistent with the overall trends, projects involving foreign-assisted funding focused increasingly on rehabilitation projects over time, both for NIS and CIS. About two-thirds of all expenditures involving foreignassistance were allocated to projects predominantly focusing on new irrigated area, whereas projects for new irrigated areas only contributed about 21 per cent. Although the World Bank, Asian Development Bank, and (in more recent years) Japan have funded mainly rehabilitation projects, the share of projects with more than 50 per cent rehabilitation, combined with new construction, was relatively small, at about 10 per cent, but increased over time. Locally funded projects were split up between new or predominantly new construction projects and projects predominantly or entirely involving

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Foreign-assisted projects New construction only More than 50 per cent new construction, combined with rehabilitation and restoration More than 50 per cent rehabilitation and restoration, combined with new construction Rehabilitation and restoration only Other Subtotal

Communal irrigation systems New construction only More than 50 per cent new construction, combined with rehabilitation and restoration More than 50 per cent rehabilitation and restoration, combined with new construction Rehabilitation and restoration only Subtotal

National irrigation systems New construction only More than 50 per cent new construction, combined with rehabilitation and restoration only More than 50 per cent rehabilitation and restoration, combined with new construction Rehabilitation and restoration only Subtotal

Irrigation System

151 149 110 110 110 100

119 113 112 100

110 100

117 100 121 165

110

128

136 100

110 100 141 159

110

118

116 150

150 114

124 157

1965–2015 1965–69

1980–89

1990–99

111 112 100

112

111 185

110 100

110

121 179

113 100

112

117 178

111 114 100

112

124 169

110 100

117

111 192

111 100

116

128 164

117 113 100

120

133 128

113 100

165

115 117

132 100

112

135 121

Share of Total Investment (%)

1970–79

113 110 100

137

119 150

116 100

111

126 148

130 100

118

114 139

2010–15

continued on next page

115 110 100

132

129 135

144 100

129

118 119

130 100

125

122 123

2000–09

TABLE 3.3 Distribution of Irrigation Investments by Purpose, Type, and Funding Source, 1965–2015 Trends in Agricultural Water Resources

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110 133 110 100

115 112 118 100

140 110 100

113 125 100 149 118

110

123

119 145

149 111

119 121

1965–2015 1965–69

1980–89

1990–99

112 116 100

112

116 174

111 127 100

115

141 116

111 113 100

116

124 165

113 110 100

146

125 126

122 112 100

129

120 117

128 123 100

140

115 114

Share of Total Investment (%)

1970–79

133 144 100

125

117 121

157 117 100

120

118 119

2000–09

114 119 100

125

113 130

110 127 100

124

114 124

2010–15

Sources: Author’s estimate and basic data from NIA (National Irrigation Administration), Various years (a). Year-End Report to the President (Quezon City).

Combined total of all projects New construction only More than 50 per cent new construction, combined with rehabilitation and restoration More than 50 per cent rehabilitation and restoration, combined with new construction Rehabilitation and restoration only Other Total

Locally funded projects New construction only More than 50 per cent rehabilitation and restoration, combined with new construction Less than 50 per cent new construction, combined with rehabilitation and restoration Rehabilitation and restoration only Other Subtotal

Irrigation System

TABLE 3.3 — cont’d

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rehabilitation (40 per cent and 36 per cent of total during 1965–2015, respectively). The share of the latter rose from about 50 per cent in the 1980s to about 70 per cent or more in the 1990s and 2000s. Much of this funding is appropriated in lump sums for repairs, restoration, rehabilitation, and new construction of NIS, CIS, or pump systems, either directly to the NIA or indirectly through other government agencies (such as the Department of Agrarian Reform, Department of Public Works and Highways, or local government agencies).

COMPETITION FOR WATER The freshwater storage capacity and high rate of precipitation usually assures that the country has an adequate supply of water for its agricultural, industrial, and domestic uses. Nevertheless, seasonal variations, uneven geographic distribution, and a changing climate can result in severe water shortages, especially during the dry season. As water demand increases, competition for finite resources intensifies, eroding the ability to withstand droughts and floods and leaving urban and rural communities more vulnerable to climate extremes. A case in point is the Angat multipurpose reservoir, which supplies water to farmers in Bulacan, urban residents in metropolitan Manila, and hydropower. Angat provides irrigation to about 27,000 ha of rice and vegetable farms in Bulacan. There are also competing domestic users in the 2.1 million customers of Metropolitan Waterworks and Sewerage System (MWSS), as of 2014 served by two private concessionaires: Manila Water Company (MWC) and Maynilad Water Services (MWS). The reservoir is also used for hydropower generation by the National Power Corporation (NPC) at a yearly average of 500 gigawatts, constituting about 5 per cent of Luzon’s power demand. In addition to these functions, the operation of the reservoir and the afterbay re-regulation dam at Bustos (for irrigation) and at Ipo (for domestic water supply), the dam is also intended to provide flood control. As of 1998, the MWSS has had water rights of 31 cubic metres per second (cms). The NIA’s water rights for AMRIS to irrigate over 28,000 ha is 36 cms (a reduction from the original 40 cms granted in 1927). Water for hydropower generation is considered nonconsumptive because the MWSS’s and the NIA’s releases transit the hydropower turbines; as a result, the NPC’s allocation is the sum of those of the MWSS and the NIA.

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In 1988, the MWSS was granted an additional allocation of 15 cms for any unused portion of the NIA’s irrigation water, provided that the NIA had sufficiently delivered and satisfied the irrigation water needs. The NIA’s water allocation at the Angat Reservoir has declined overtime (Figure 3.5). The increasing allocation for domestic water use for metropolitan Manila could be considered a cause for concern for agriculture in the region. The reservoir’s domestic water allocation may no longer be sufficient. Reduction in irrigation water allocation, however, would only be possible if certain irrigated agricultural lands were retired or converted to uses requiring less water, such as industrial uses or use as recreational parks. During dry years or drought conditions, when water is needed to satisfy domestic water requirements, it would be economically beneficial for AMRIS to sell its irrigation water entitlement to the MWSS (Tabios and David 2004). Whether the MWSS would compensate farmers and the NIA at AMRIS for foregone household incomes and irrigation service fee collection and the NPC for the foregone hydropower revenues is a different story. FIGURE 3.5 Angat Water Consumption between the Metropolitan Waterworks and Sewerage System and the National Irrigation Administration, 1968–2015

Source: Constructed by author using data from the NWRB (National Water Resources Board), Actual Water Consumption at Angat Main Units (Quezon City, 2014).

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The trading of irrigation water for domestic water supply has not been as simple as expected. During the El Niño period in 1997, which caused severe water shortages in the Philippines, the actual water released to the MWSS was 32 cms on average — based on an allocation at that time of only 22 cms. This increased release was made at the expense of Bulacan farmers who experienced substantial crop losses. In this case, the NIA agreed to give up a portion of its water allocation to fulfill the needs of residents in metropolitan Manila. To address the issue, however, the NIA suspended the operation of AMRIS for the entire 1997/98 dry cropping season in favour of domestic water supply. The MWSS was to draw more water on condition that it compensated the NIA and the adversely affected farmers during the period in question. No compensation was made to farmers, however, and discussions between the NIA and MWSS have focused on the possibility of compensating the NIA for foregone irrigation service fees, rather than on compensating the affected farmers.

TRENDS IN OPERATION AND MAINTENANCE The establishment of the NIA’s corporate structure is intended to provide the necessary incentives and resources to ensure the efficient development and management of irrigation systems in the Philippines. Nevertheless, the quality of operations and routine maintenance by the NIS has continued to be inadequate, contributing to disappointing system performance and need for early rehabilitation (David 1990, 2003; World Bank 1992; Shepley, Buenaventura, and Roca 2000; David and Inocencio 2012). For example, the five medium-sized NIS first built with Asian Development Bank (ADB) funding in Mindanao in the 1970s up until the early 1980s (Banga, Marbel, Saug, Simulao, and Pulangui) underwent major rehabilitation in the 1990s — only ten to fifteen years after their construction (also funded by the ADB’s the Irrigation Systems Improvement Project I). Major rehabilitation of UPRIIS — the largest NIS, supposedly with a fifty-year lifespan — was undertaken only twenty-five years after its completion as part of the Casecnan Irrigation Component Project, funded by a Japanese loan. These observations are consistent with estimates of the rehabilitation cycle based on information for 141 systems (Table 3.4). Whereas NIS built prior to 1965 required rehabilitation after about thirty years, the cycle for NIS built between 1965 and 1980 was only eighteen years, and for more

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TABLE 3.4 Average Time before First Major Rehabilitation and Number of Rehabilitated National Irrigation Systems with Recorded Information, 1965–2008 Time Period

Average Time Prior to Rehabilitation (years)

No. of National Irrigation System Projects with Recorded Rehabilitation

Before 1965 1965–80 1981–95 1996–2008 All systems

32 18 19 10 20

151 141 149 110 141

Source: Estimated by author from NIA (National Irrigation Administration), Various years (a). Year-End Report to the President (Quezon City).

recent projects, the average is only nine years, which is far shorter than the average across all systems of twenty years. Based on a sample of 40 NIS, Shepley, Buenaventura, and Roca (2000) found the average interval from the start of operation to the first major rehabilitation to be nineteen years, with a standard deviation of fourteen years. This can be compared with an international norm of twenty-five to thirty years. Based on a 2002 assessment of the state of NIS facilities (JICA 2002), approximately 80 per cent of the then 196 systems and 50 per cent of control structures for both lateral and main canals were in need of rehabilitation or improvement. In addition, more than 60 per cent of main and lateral canals needed desilting, reshaping, and heightening of embankments, and about three-quarters of the 13,967 km of irrigation service roads required repairs. The magnitude of the problem suggests chronic underfunding of routine preventive maintenance, which stems mostly from the low level and collection rate of irrigation service fees. In turn, the low collection rate is largely due to the inadequate water service farmers receive, especially at the tail-end of irrigation canals (Cablayan et al. 2014). Persistent underfunding of routine maintenance (and deferred maintenance) raises the cost of maintenance over time because facilities deteriorate faster. Thus, a vicious cycle is created beginning with inefficient water service, underinvestment in O&M, unabated deterioration of facilities, and persistently low rates of collection of irrigation fees. According to Shepley, Buenaventura, and Roca (2000), the recommended per ha cost of O&M to cover the average direct costs of

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water scheduling and gate operations, canal-cleaning labor, gate repairs/ greasing and locks, use of hand-held radios, and equipment rental is at least double the current fees charged in river diversion and reservoir systems (Figure 3.6). Moreover, actual spending on O&M at the field level is significantly less than the collectible service fees in the wet and dry seasons because the collection rate has averaged only about 50 per cent in the 1980s and 1990s, rising to slightly higher than 60 per cent in the decade preceding 2012. Furthermore, Shepley, Buenaventura, and Roca (2000) found that about 40 per cent of the workers’ time is devoted to service fee collection, rather than to O&M. Real O&M expenditures and O&M per ha have declined in several regions and will result in the further deterioration and poor performance of irrigation systems (Figure 3.7). The poor performance of many of these systems may be reflected in low regional cropping intensities and (wet and dry season) irrigation intensities (Figure 3.8). Levine (1982), however, argued that the NIA has little incentive to maintain the irrigation systems sustainably by raising service fees and collection rates, and that this lack of incentive has not changed over time. It has been easier to obtain local funding for repairs, restoration, and rehabilitation through lump sum appropriations in the annual General Appropriation Act, and foreign lending agencies are willing to fund rehabilitation projects as evidenced by the increase in the number supported by the World Bank, ADB, and Japanese development and funding agencies.

THE ECONOMIC PERFORMANCE OF FOREIGN-ASSISTED PROJECTS Measures of economic performance of sixty-one agricultural water projects indicate the issues and problems that relevant agencies have to take into account when making decisions about future water investments. Performance indicators reveal a poor overall trend in terms of time and cost overruns, actual versus target irrigated area, and estimated economic rates of returns (Appendix Tables 3A.1 and 3A.2). With the exception of the Upper Pampanga River Project (UPRP), Magat River Multipurpose Project in the early 1970s, and Irrigation Operations Support Project (IOSP) and Bukidnon Integrated Area Development in the mid-1990s, all the projects took significantly more time to complete than expected. Close to half of

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FIGURE 3.6 Actual Versus Recommended Operation and Maintenance Expenditures, and Collectible and Collected Irrigation Service Fees, 1983–2015

Notes: O&M = operations and maintenance; ISF = irrigation service fees. Sources: Constructed by author from NIA–SMD (National Irrigation Administration, Systems Management Division). Various years. National Irrigation System Performance (NISPER) database (Quezon City). Recommended O&M levels are from Steven Shepley, Evelyn Buenaventura, and David Roca, Review of Cost Recovery Mechanism for National Irrigation Systems, TA 3235-PHI (Manila: National Irrigation Administration and Asian Development Bank, 2000).

the projects exceeded the 75 per cent rate of time overrun. Some of the reasons given include natural disasters and adverse weather conditions, delays in the release of funds resulting from budgetary constraints or bureaucratic problems, changes in the design, the breakdown of equipment, sociopolitical and office-management issues, law and order/peacekeeping problems, and so on. On cost overruns, only about a quarter of the sixty-one projects were completed on budget; more than one-third incurred cost overruns of up to 50 per cent; and, upon completion, close to 20 per cent of the projects had

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Notes: MRIIS = Magat River Integrated Irrigation System; UPRIIS = Upper Pampanga River Integrated Irrigation System; Region 1 includes CAR; Region 2 excludes MRIIS; Region 3 excludes UPRIIS and Angat-Maasim; and Region 12 includes ARMM. Philippine pesos are in 2000 prices. Firmed-up service area is the service area less any land either converted from agricultural to nonagricultural uses or considered nonrestorable. Sources: NIA–SMD (National Irrigation Administration, Systems Management Division), Various years. National Irrigation System Performance (NISPER) database (Quezon City).

FIGURE 3.7 Trends in the Costs of Operations and Maintenance, Service Area, and “Firmed-up” Service Area in National Irrigation Systems by Region, 1985–2015 Trends in Agricultural Water Resources

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Notes: MRIIS = Magat River Integrated Irrigation System; UPRIIS = Upper Pampanga River Integrated Irrigation System; Region 1 includes the Cordillera Autonomous Region; Region 2 excludes MRIIS; Region 3 excludes UPRIIS and Angat-Maasim; and Region 12 includes the Autonomous Region of Muslim Mindanao. Sources: NIA–SMD (National Irrigation Administration, Systems Management Division), Various years. National Irrigation System Performance (NISPER) database (Quezon City).

FIGURE 3.8 Trends in the Ratio of Actual Wet and Dry Season Irrigated Area to Total Service Area in National Irrigation Systems by Region, 1967–2015

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at least tripled their original budget. The Ilocos Norte Irrigation Project I and the irrigation component of the Samar Island Rural Development Project incurred 220 and 293 per cent cost overruns, respectively. UPRP’s high cost overrun was caused by the sharp devaluation of the peso in 1970 and the concomitant rise in inflation rates. The other reasons for cost overruns include changes in system design and other supporting infrastructure, higher costs of relocating affected communities, delays in implementation, and cost escalation. In addition to incurring significant time and cost overruns, at least 10 per cent of the projects fell short of the targeted new irrigation area by at least 50 per cent (four of forty-five projects for which data were available). Only a little over 25 per cent of the projects met their targets, while more than half fell short of their targeted new area by at least 10 to 49 per cent. The performance of projects involving rehabilitation was better: 15 per cent of the twenty-six projects for which data were available met their targets, and more than 40 per cent exceeded their targets. Palawan Integrated Area Development I and the Southern Philippines Irrigation Sector Project performed worst, falling short of their targeted renovated area by at least 50 per cent. A more complete measure of performance is the economic internal rate of return (EIRR). As to be expected, at the appraisal stage (that is, ex ante) the EIRRs for all projects were above 12 per cent — the cut-off level for approval by donor agencies. Over 12 per cent of the 43 projects for which data were available recorded EIRRs of at least 12–13 per cent upon completion. The Angat-Magat Integrated Agricultural Development Project, UPRP, IOSP 2, and the Water Resources Development Project exceeded ex ante EIRR by at least 25 per cent, but more than half of the projects fell short of their appraisal EIRR by at least 10 per cent. Estimates of EIRR several years after their completion (that is, ex post) are available for only twelve projects, and in only five of the twelve were the rates of returns higher than 12 per cent. EIRR estimates were higher at the appraisal stage than upon completion of projects for several reasons. For systems built in the 1970s, very high world prices were used, which raised the estimated benefits from irrigation investments; however, as world prices dropped long-term, the rates of return also declined. Other possible causes of lower EIRR rates upon the completion of projects include the aforementioned cost overruns and inability to meet new and rehabilitated area targets.

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ISSUES WITH PROJECT PLANNING AND SYSTEM DESIGN Moya (2014) carried out case studies on the causes of the poor performance of NIS. Observations corroborated earlier findings that many of these concerns can be traced to flawed economic and technical assumptions during the planning and design phase, and problems during the construction phase. Gaps have been found between design assumptions and operational realities causing systems to underperform. For instance, estimations of field water requirements and water losses throughout the system were grossly underestimated. The 1–2 mm per day seepage and percolation rate assumed in conventional design procedures was eight to forty times lower than those measured in the field. These faulty assumptions resulted in the overestimation of the area able to be irrigated within a system. Complicating such estimations were reported degradations of watersheds affecting both the quantity and quality of available water. Watershed changes need to be taken into account to improve the planning and design of agricultural water projects, which in turn must include water reliability analyses. Aside from rainfall intensity, river water discharges that can be diverted for irrigation depend on a number of interacting factors, such as land cover and use within the watershed. The case studies indicate other design concerns that have not been addressed appropriately through O&M practices. For example, intakes in rivers with steep side slopes and sediment entrainment usually become clogged and require frequent desilting, which has not been factored into O&M programmes. This situation is made worse when natural disasters or higher rainfall intensities occur. Turnouts and other water distribution facilities have been poorly designed, misaligned, and constructed inappropriately to function within a range of canal water elevations. In addition, building dams at the foot of hillside slopes presents a high risk of structures being filled in or washed out during floods. In a number of cases, the river course has shifted away from the point of abstraction and diversion of irrigation systems. Changes in river courses due to sedimentation in turn result in numerous O&M issues in run-of-the-river diversion and surface pump systems. In extreme cases, headworks have been washed out or completely covered by sediment due to strong river flows. Resources need to be allocated each year or planting season to enable water to be redirected or diverted into the irrigation system service

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area. Another factor is the reality that the conditions of catchments are insufficiently monitored to enable changes in erosion, transport, and sedimentation to be predicted. Such changes can decrease water yields. Readily available technologies and tools to systematically address the above concerns — such as modelling, geographic information systems, and remote image sensing — are not being sufficiently utilized to facilitate more accurate calculations of available water for irrigation. While relatively recent systems may have access to such tools, they are not available to smaller and older systems. It seems logical to compare the costs of building river training structures with the costs of frequently rehabilitating washed-out or filled-in dams. Design engineers tend to use tried-and-tested design approaches but ignore potentially valuable lessons from past projects. As a result, problems persist in irrigation system performance. This situation is further exacerbated by lack of input by operations units in the design of irrigation systems. Many design parameters stipulated in manuals produced by design engineers do not reflect field realities, complicating the role of operations staff, who take over once construction is completed. Tabios and David (2014) examined discrepancies between the estimated and actual irrigated areas of NIS. The actual irrigated area of AMRIS is only 75 per cent of the estimated design area during the dry season and only 55 per cent of the estimated area during the wet season. These discrepancies occur for three reasons: (1) about 3,500 ha of the total area have elevations of at least 19 metres and hence cannot be irrigated with water from the Bustos Dam, which has a maximum crest elevation of 18.5 metres; (2) in the past few years, built-up or urbanized areas total about 4,500 ha, so about 8,000 ha of the original AMRIS design area cannot be irrigated; and (3) for the wet season, an additional 5,500 ha of the area with elevation below 7 metres would become flooded, reducing the actual wet irrigated area to a little over half the estimated design area. These case studies indicate that even the more recent agricultural water projects are flawed by numerous planning, design, and construction issues that climate change will further complicate (see also Box 3.3).

POLICY IMPLICATIONS The evidence presented in this chapter indicates that the Philippine water and irrigation sectors are only beginning to recognize and understand the

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BOX 3.3 Differences in Estimated and Actual Irrigated Area in the Pampanga Delta Irrigation System The Pampanga Delta irrigation system was completed in 2002 with an estimated area of 11,540 ha. Its water source is the Pampanga River, which is diverted through the Cong Dadong dam diversion structure. Given the dam’s parameters — elevation: 8.6 metres; fixed length: 850 metres; movable length: 150 metres; height: 1.3 metres; scour sluice gate width: 36.5 metres; intake water level: 8.5 metres, and maximum intake discharge: 20.18 cms — the water supply from the river would be adequate to irrigate the entire design area, even assuming an 80 per cent dependable flow of 108 cms. Following AMRIS, using the high estimate of 0.00167 cms per ha water requirement for irrigating paddy, the average daily water requirement would only be about 19.3 cms for the Pampanga Delta design area. The actual wet irrigated area is only about 1,000 ha or 8 per cent of the estimated design service area, whereas, during the actual dry season, actual irrigated area is about 30 per cent of the design area. Tabios and David (2014) estimated the built-up or urbanized area to be about 1,043 ha, fish ponds to total 1,645 ha, areas above 8.5 metres elevation — and thus above Cong Dadong Dam — to be about 3,000 ha, and areas below 3 metres elevation — and hence flooded during the wet season — to be about 950 ha. Despite these adjustments, about 4,940 ha of the total design area would be irrigable, yet the maximum actual irrigated area in the dry season is only about 3,500 ha, and the actual irrigated area in the wet season is about 1,000 ha (even less more recently). The remaining discrepancies could result from limitations in the data used. More accurate field data is needed to validate and improve estimates of land uses and flooded or high elevation areas. Source: Heavily drawn from Tabios and David (2014).

potential impacts of climate change, which may partly explain the slow response and nominal efforts to date. This lack of appropriate response or action could be more costly than an anticipatory or proactive stance. The increased recognition of the need to consider and mainstream climate change issues within agricultural water programmes and projects should be matched with better preparation. The analysis of trends and patterns

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indicates that the relevant public sector agencies need more inputs, direction, and guidance in order to formulate a sound development agenda for agricultural water resources that explicitly takes into account the potential adverse impacts of climate change. Most of the available literature provides a list of mitigation and adaptation measures as a basis for solid plans and concrete actions; however, these lists are either too general or specific, and hence may not directly serve the needs of the Philippine agriculture water sector. This chapter points to the urgent need for a more proactive and coordinated approach to managing, developing, and maintaining the country’s water resources — not only within the agricultural sector, but also across competing sectors. In addition, funding is needed to increase the number and improve the design of projects, learning from past lessons, and to improve O&M so that the pervasive deterioration of water systems can be arrested. Incentives will have to be formulated to ensure appropriate O&M and payment of service fees. Building resilience to current climate variability or adapting to future climate change will require interventions in several areas: institutions and policymaking, operations and management, infrastructure, technology and innovation, capacity building and awareness, and monitoring and information systems. These areas are closely interlinked. For instance, to increase water security, stronger institutions are needed, and more investments in infrastructure and new technologies for increasing water use efficiency. Also, better water management and operations, coupled with stakeholders’ capacity building and enhanced monitoring and information systems, are also essential elements for adapting to climate change. Agricultural water resources investment policy and direction need to be reoriented to take into account potential climate change impacts. Part of this would be to invest in consistent and reliable data and more studies and analysis to promote more science-based planning and actions. This chapter points to some specific agricultural water investment areas to pursue from identification of potential irrigable areas, and to the need to improve design and planning and anticipate implementation problems. Conventional interventions are important but are no longer sufficient. The agricultural water sector needs to be better informed on climate issues to be in a position to pursue more climate-responsive options and actions.

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With IFAD

With IFAD

ADB

Donor

Agricultural Inputs Programme Agrarian Reform Communities Project Agusan del Sur Irrigation Project Phase 1 Phase 2 Allah River Irrigation Project Angat Magat Integrated Agricultural Development Project Bicol River Basin Irrigation Development Bukidnon Integrated Area Development Project Bukidnon Irrigation Project Cordillera Highland Agricultural Resource Management Project Cotabato Irrigation Project Davao del Norte Irrigation Project Phase 1 Phase 2 Phase 3 Highland Agricultural Development Project Irrigation component Irrigation Systems Improvement Project 1, Northern Leyte

Name of Project

2007 1983 1993 1989 1978 2002 1989 2003

1979 1990 1992 1994 1997

2000 1975 1979 1978 1974 1997 1979 1996

1974 1977 1983 1987 1991

100

119 879 607 674 105 1,040

5

179 150 225 50 40

153 689 1,629 254 78 307 818 1,947

191 5,773

100 56 22 33 79 –29 25 25

143 40

18

121 190 –6 23

3

60 200 12 –5 –6 –68 181 81

–11 –20

Year of Time Cost Commence- Year of Overrun Actual Cost Overrun ment Completion (%) (million PhP) (%)

Appendix Table 3A.1 Selected Foreign-Assisted Irrigation Projects by Donor

APPENDIX 3A Supplementary Tables

3,111

10,830 10,964 3,699 1,457

8,457

7,300 5,368 16,539 3,810

6,791

20,524

521

67,078

–8

–6 –27 –18 –11

–26

–16 –33 –12 –58

–20

–7

–10

11

Rehabilitated (%)

New (%)

New (ha)

Rehabilitated (ha)

Actual/Targeted Irrigated Area

Actual Irrigated Area

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Name of Project

Irrigation Systems Improvement Project 2 Irrigation Sector Project Kabulnan Irrigation and Area Development Project Irrigation component (National Irrigation Administration) Laguna de Bay Development Project Laguna de Bay Irrigation Project 2 Palawan Integrated Area Development Project Phase 1 Palawan Integrated Area Development Project Phase 2 Irrigation component Pulangui River Irrigation Project Sorsogon Integrated Area Development Project with IFAD Southern Philippines Irrigation Sector Project Calayagon communal irrigation system Can-asuhan Small Reservoir Irrigation System (SRIS) Gibong right bank extension Malaig national irrigation system JICA, OECF/JBIC/Japan Bohol Irrigation Project OECF Phase 1 JBIC Phase 2 JBIC Casecnan Multipurpose Irrigation Power Project Phase 1 JICA Phase 2 JICA Ilocos Norte Irrigation Project Stage 1 JBIC Lower Agusan Development Programme, Flood Control Phase 1 Phase 2

Donor 2005 1991 2001

1991 1998 1982 1997 2010

1997 2009 2009 1995 2006 2000 2007

1997 1984 1992

1982 1991 1975 1989 2000

18-J04349 03 Future of Philippine Agriculture.indd 165

1984 1999 1999 1983 2002 1988 1997

200 57 140 67

25

160 25

75 33 100

63 138 50 40

60 40 80

1,437 2,300 1,155 4,402

16,180

1,571 3,463

1,720 1,865 2,207 1,950 64 40 1,625 3,455 751 220 952 3,694

220 76 101 66

–1

130 –4

27 45 –11

42 –21 141 94

47 50 31

Year of Time Cost Commence- Year of Overrun Actual Cost Overrun ment Completion (%) (million PhP) (%)

8,545 4,493

16,879

4,973 4,530

5,485 190 675 665 0

2,740 9,100

3,144 15,381 8,984

–16 –43

–37

0 0

0 –24 –29 0 –100

–26 –21

289 –43 –22

18

0

–25 –52

–12

–60

–3 14

continued on next page

65,141

750

1,160 2,900 2,049 5,794

1,781

12,249 11,880

Rehabilitated (%)

New (%)

New (ha)

Rehabilitated (ha)

Actual/Targeted Irrigated Area

Actual Irrigated Area

Trends in Agricultural Water Resources

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With OECF

With IFAD

Malitubog-Maridagao Irrigation Project I Pampanga Delta Irrigation Project Rehabilitation of Apron of Angat Afterbay Regulatory Dam Tarlac Groundwater Irrigation System Reactivation Project

JICA JBIC JICA JBIC World Bank

Chico River Irrigation Project Stage 1 Communal Irrigation Development Project Phase 1 Phase 2 Earthquake Reconstruction Project Irrigation Operations Support Project Phase 1 Phase 2 Jalaur Irrigation Project, Stage I Magat River Multipupose Project Phase 1A Phase 1B Phase 2 Phase 3 Mindanao Rural Development National Irrigation Systems Improvement Project Phase 1 Phase 2

Name of Project

Donor

Appendix Table 3A.1 — cont’d

1986 1992 2000 1997 1992 2000 1983 1986 1977 1983 1982 1986 1986 1987

1983 1991 1990 1988 1995 1977 1974 1974 1975 1976 1978 1999 1977 1978

2005

1999 1976

2005 2003

1990 1991

125 80

33 0 50 50 0 100 20 167

80 80 40

150

100

150 71

889 997

1,714 3,443 258 4,632 128 684 3,108 711 1,548

1,766 1,363 4,759

827

802

1,290 4,023

12 –4

19 94 2 12 99 12 3 56 –10

70 –14 –36

32

29

23 118

Year of Time Cost Commence- Year of Overrun Actual Cost Overrun ment Completion (%) (million PhP) (%)

15,909 12,590

210 2,900 45,592

22,800 34,127

17,910

4,627

7,681 8,589

28,500 63,704

5,791

72,852 20,444 51,810

29,160

1,497

3,331

–27 –54

0 7 –8

0 11

–2

–7

0

3 19

16

–13 –7 –1

173

7

0

Rehabilitated (%)

New (%)

New (ha)

Rehabilitated (ha)

Actual/Targeted Irrigated Area

Actual Irrigated Area

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2000 1981

1992 1975

1984 1977 1981 2005 1988

1974 1969 1973 1997 1980 2011

1988

1979

2003

1983

1975

33 100

100

150 0 100 60 33

80

60

550 83 99,556

2,488

355 841 424 2,663 797

1,308

190

–1 81 –20

86

54 209 57 10 41

293

75

2,613 3,427 410,605

5,232

4,154 35,152 7,100 3,249 32,072

1,009

2,882

679,071

7,796

22,235 47,317 18,200 103,880

10,727

–20 –12 –21

–13

–63 13 –17 –27 0

–50

–32

–29

1

–2 3 9 –7

0

Rehabilitated (%)

New (%)

New (ha)

Rehabilitated (ha)

Actual/Targeted Irrigated Area

Actual Irrigated Area

Notes: ADB = Asian Development Bank; IFAD = International Fund for Agricultural Development; JBIC = Japan Bank for International Cooperation; JICA = Japan International Cooperation Agency; OECF = Overseas Economic Cooperation Fund; SRIS = small reservoir irrigation system; UPRIIS = Upper Pampanga River Integrated Irrigation System; USAID = United States Agency for International Development. Source: Compiled by author based on donor reports on the completion, implementation, performance assessment, and appraisal of projects.

Banaoang Pump Irrigation System Agno River Integrated Irrigation System Visayas Communal Irrigation and Participatory Project Libmanan/Cabusao Integrated Area Development Project

Other China China Eximbank IFAD USAID Total

Participatory Irrigation Development Project Philippine Rural Development Project (Mindoro Integrated Rural Development Programme) Samar Island Rural Development Project, irrigation component Tarlac Irrigation Systems Improvement Project Upper Pampanga River Project UPRIIS Aurora-Peñaranda Irrigation Project Water Resources Development Project Watershed Management and Erosion Control Project

Name of Project

Donor

Year of Time Cost Commence- Year of Overrun Actual Cost Overrun ment Completion (%) (million PhP) (%)

Trends in Agricultural Water Resources

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Agrarian Reform Communities Project Agusan del Sur Irrigation Project Andanan Simulao Allah River Irrigation Project Angat Magat Integrated Agricultural Development Project Bicol River Basin Irrigation Development Bukidnon Integrated Area Development Project Farm-to-market road component Communal irrigation system component Community development support component Social services component Cordillera Highland Agricultural Resource Management Project Cotabato Irrigation Project Davao del Norte Irrigation Project Phase 1 Phase 2 Phase 3 Highland Agricultural Development Project Irrigation Systems Improvement Project 1, Northern Leyte Irrigation Systems Improvement Project 2 Irrigation Sector Project Kabulnan Irrigation and Area Development Project Irrigation component (National Irrigation Administration)

ADB

With IFAD

With IFAD

Name of Project

Donor

29 12 14 18

16

21 10 14

(negative) (negative) 20

20 12 13 12 11 38 12

Completion (%)

18 19 14 18 27 12 31

18 14

24 18 18 19 14 24 22 22 12 50

Appraisal (%)

18

17 12

17

Evaluation (%)

Economic Internal Rate of Return

APPENDIX TABLE 3A.2 Measures of Economic Performance of Selected Foreign-Assisted Irrigation Projects by Donor

1.10

1.09 1.04 0.13

1.15 0.50 0.29

1.09

0.82 0.67 0.69 0.63 0.79 1.56 0.09

Actual/Estimate

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Name of Project

Laguna de Bay Development Project Laguna de Bay Irrigation Project 2 Palawan Integrated Area Dev’t Proj Phase 1 Palawan Integrated Area Dev’t Proj Phase 2 Pulangui River Irrigation Project Sorsogon Integrated Area Development Project Southern Philippines Irrigation Sector Project Calayagon communal irrigation system Can-asuhan small reservoir irrigation system Gibong Right Bank Extension Malaig national irrigation system Japanese International Cooperation Agency (JICA)/OECF/JBIC/Japan Bohol Irrigation Project OECF Phase 1 JBIC Phase 2 JBIC Casecnan Multipurpose Irrigation Power Project Phase 1 Phase 2 JICA Ilocos Norte Irrigation Project Stage 1 JBIC Lower Agusan Development Programme -Irrigation Flood Control 1 JICA Malitubog-Maridagao Irrigation Project 1 JBIC Pampanga Delta Irrigation Project JICA Rehabilitation of Apron of Angat Afterbay Regulatory Dam JBIC Tarlac Groundwater Irrigation System Reactivation Project

Donor

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17 16

16 14

30 13 12 10 21 16 20

10 19

25

17

0.69

0.98

0.92 0.94

0.59 0.45

0.62 0.30

0.14 0.36 0.91

Actual/Estimate

continued on next page

11

12 15

12 16

Evaluation (%)

12 16 16

Completion (%)

15 19 17

17 19 15 19

14 17 18 18 19 18

Appraisal (%)

Economic Internal Rate of Return

Trends in Agricultural Water Resources

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Banaoang Pump Irrigation System Agno River Integrated Irrigation System Allocation for Construction Dam and its Facilities Visayas Communal Irrigation and Participatory Project Libmanan/Cabusao Integrated Area Development Project

Chico River Irrigation Project, Stage I Communal Irrigation Development Project Phase 1 Phase 2 Earthquake Reconstruction Project Irrigation Operations Support Project Phase 1 Phase 2 Jalaur Irrigation Project, Stage 1 Magat River Multipupose Project Mindanao Rural Development Participatory Irrigation Development Project Philippine Rural Development Project (Mindoro Integrated Rural Development Programme) Samar Island Rural Development Project, irrigation component Tarlac Irrigation Systems Improvement Project Upper Pampanga River Project UPRIIS Aurora-Peñaranda Irrigation Project Water Resources Development Project Watershed Management and Erosion Control Project

Name of Project

20 28 19

19 15

(negative) 15 20 12 32 14

14 15 14 17 27 18 24 18

28 21 20 12 17

34 17 20 13 22 25 14

17 15

19 19

12

19

13

20

Evaluation (%)

Economic Internal Rate of Return Completion (%)

Appraisal (%)

0.30 0.77

1.03 1.48 0.68 1.22 0.22

0.82 1.26 1.01 0.93 0.77

0.89 0.79

Actual/Estimate

Notes: ADB = Asian Development Bank; IFAD = International Fund for Agricultural Development; JBIC = Japan Bank for International Cooperation; JICA = Japan International Cooperation Agency; OECF = Overseas Economic Cooperation Fund; SRIS = small reservoir irrigation system; UPRIIS = Upper Pampanga River Integrated Irrigation System; USAID = United States Agency for International Development. Source: Compiled by author from donor reports on the completion, implementation, performance assessment, and appraisal of projects.

IFAD USAID Total

Other China China Eximbank

with ADB/IFAD

IBRD/OECF

World Bank with IFAD

Donor

Appendix Table 3A.2 — cont’d

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References Azarcon, Y., R. Barker, and Associate. Trends and Determinants of Irrigation Investments in the Philippines: APAPII. Collaborative Research Report Issue 321 of APAP II, 1992. Blobel, D., N. Meyer-Ohlendorf, C. Schlosser-Allera, and P. Steel. United Nations Framework Convention on Climate Change Handbook. Bonn: Climate Change Secretariat, UNFCCC, 2006. Cablayan, O., A. Inocencio, C. Francisco, and V. Saw, with C. Ureta. “Review of National Irrigation Service Fee”. Policy paper submitted to the Participatory Irrigation Development Program Office, National Irrigation Administration, Quezon City, 2014. Cai, X., M. Rosegrant, and C. Ringler. “Physical and Economic Efficiency of Water Use in the River Basin: Implications for Efficient Water Management”. Water Resources Research 39, no. 1 (2003): 1013 . David, W. “Irrigation Development in the Philippines: Brief History, Present Status, Problems, and Policy Recommendations”. Philippine Journal of Crop Science 15, no. 1 (1990): 17–26. ———. Averting the Water Crisis in Agriculture: Policy and Program Framework for Irrigation Development in the Philippines. Quezon City: University of the Philippines Press and Asia Pacific Policy Center, 2003. David, C. Philippine Irrigation Development: Overview, Determinants, and Policy Issues. PIDS Discussion Paper Series 95–26. Makati City: Philippine Institute for Development Studies, 1995. ——— and A. Inocencio. “Irrigation Policy and Performance Indicators in the Philippines.” Final Report under the Monitoring and Evaluation of Agricultural Policy Indicators Project. Makati City: Philippine Institute for Development Studies, 2012. ——— and A. Inocencio. A Rapid Appraisal of the Irrigation Program of the Philippine Government. Makati City: Philippine Institute for Development Studies, 2014. DBM (Department of Budget and Management). “General Appropriations”. Manila City, 2013. Euroestudios Ingenieros de consulta. “Participatory Irrigation Development Project”. Final Report. National Irrigation Administration, Quezon City, 2006. FAO (Food and Agriculture Organization of the United Nations). AQUASTAT database, 2014 (accessed 6 January 2014). Hayami, Y., and M. Kikuchi. “Investment Inducements to Public Infrastructure: Irrigation in the Philippines”. Review of Economics and Statistics 60, no. 1 (1978): 70–77. Hsiao, T., P. Steduto, and E. Fereres. “A Systematic and Quantitative Approach to Improve Water Use Efficiency in Agriculture”. Irrigation Science 25 (2007): 209–31. IFPRI/ADB (International Food Policy Research Institute and Asian Development

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19/11/18 12:03 PM

172

Arlene B. Inocencio

Bank). Addressing Climate Change in the Asia and Pacific Region Building: Climate Resilience in the Agriculture Sector. Mandaluyong City: Asian Development Bank, 2009. Inocencio, A., M. Kikuchi, M. Tonosaki, A. Maruyama, D. Merrey, H. Sally, and I. de Jong. Costs and Performance of Irrigation Projects: A Comparison of Sub-Saharan Africa and Other Developing Regions. IWMI Research Report 109. Colombo, Sri Lanka: International Water Management Institute, 2007. JICA (Japanese International Cooperation Agency). National Water Information Network (NWIN): National Irrigation Administration database on national irrigation systems through the National Water Resources Board website, 2002 [link no longer available] (accessed March 2009). Kikuchi, M., A. Maruyama and Y. Hayami. “Phases of Irrigation Development in Asian Tropics: A Case Study of the Philippines and Sri Lanka”. Journal of Development Studies 39, no. 5 (2003): 109–38. Kundzewicz, Z., L. Mata, N. Arnell, P. Döll, P. Kabat, B. Jiménez, K. Miller, T. Oki, Z. Sen, and I. Shiklomanov. “Freshwater Resources and their Management. Climate Change 2007: Impacts, Adaptation and Vulnerability”. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M. Parry, O. Canziani, J. Palutikof, P. van der Linden, and C. Hanson, eds. Cambridge: Cambridge University Press, 2007. Levine, G. Relative Water Supply: An Explanatory Variable for Irrigation Systems: The Determinants of Developing Country Irrigation Project Problems. Technical Report 6. Washington, DC: U.S. Agency for International Development, 1982. Moya, T. Analysis of Technical Assumptions and Processes of Evaluating Feasibility of Irrigation Projects. PIDS Policy Notes 2014-11. Makati City: Philippine Institute for Development Studies, 2014. NWRB (National Water Resources Board). Actual Water Consumption at Angat Main Units. Quezon City, 2014. NIA (National Irrigation Administration). Various years (a). Year-End Report to the President. Quezon City. ———. Various years (b). Annual Report. Quezon City. ———. Inventory of National, Communal, Private and OGA Irrigation System as of December 2015. Quezon City, 2016. NIA–SMD (National Irrigation Administration, Systems Management Division). Various years. National Irrigation System Performance (NISPER) database. Quezon City. Panella, T. “Irrigation Development and Management Reform in the Philippines: Stakeholder Interests and Implementation”. In The Politics of Irrigation Reform: Contested Policy Formulation and Implementation in Asia, Africa and Latin America, edited by J. Bolding and P. Molinga, Ch. 4. Burlington, Vermont: Ashgate, 2004.

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Trends in Agricultural Water Resources

173

Rosegrant, M., C. Ringler, and T. Zhu. “Water for Agriculture: Maintaining Food Security under Growing Scarcity”. Annual Review of Environment and Resources 34 (2009): 205–22. Shepley, S., E. Buenaventura, and D. Roca. “Review of Cost Recovery Mechanism for National Irrigation Systems”. TA 3235-PHI. Manila: National Irrigation Administration and Asian Development Bank, 2000. Tabios, G. and C. David. “Competing Uses of Water: Cases of Angat Reservoir, Laguna and Groundwater Systems of Batangas City and Cebu City”. In Winning the Water War: Watersheds, Water Policies and Water Institutions, edited by A. Rola, H. Francisco, and J. Liguton, Ch. 5. Makati City: Philippine Institute for Development Studies and Philippine Council for Agriculture, Forestry and Natural Resources Research and Development, 2004. ——— and C. David. Appraisal of Methodology in Estimating Irrigable Areas and Processes of Evaluating Feasibility of NIA Irrigation Projects. PIDS Policy Notes 2014-13. Makati City: Philippine Institute for Development Studies, 2014. WEPA (Water Environment Partnership in Asia). Outlook on Water Environmental Management in Asia 2012. Kanagawa, Japan: Institute for Global Environmental Strategies, 2012. World Bank. Philippines: Irrigated Agriculture Sector Review. Report No. 948-PH, Vols. I and II. Washington, D.C., 1992.

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4 EXISTING EVIDENCE OF CLIMATE CHANGE AND VARIABILITY Felino P. Lansigan

Climate change and variability have profound and wide-ranging effects on the agricultural sector, the environment, and society. Global, regional, and local evidences of climate change and variability have grown in recent years, and historical records from the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), combined with past and recent studies, provide evidence that climate change is no longer an anticipated scenario but a reality. Climate change is defined as shifts in mean climate variables or variance in the distribution of climate variables based on historical records over a significant period of time (IPCC 2007, 2014, p. 6). Changes may be evident in temperature and precipitation levels, as well as in other indicators, such as the number of wet and dry days, the number of warm days and cold nights, extremes of climate, and so on. This chapter presents historical evidences of changes in climate variables at local and national levels. The implications of climate variability and climate change on the agricultural sector are also discussed, along with some beneficial agricultural practices, climate adaptation strategies, and

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location-specific measures for coping with changing climate at different levels.

LOCAL EVIDENCES OF CLIMATE CHANGE AND VARIABILITY Observed Trends in Temperature and Precipitation One of the earliest studies analyzing observed trends climate variables in the Asia and Pacific region, as well as in specific countries, is the work of Manton et al. (2001, pp. 269–84), which made use also of historical data from selected weather gauging stations in the Philippines. Their analyses confirmed that the observed trends are statistically significant. Recently, these results were further updated by Cinco, de Guzman, Hilario, and Wilson (2014, pp. 12–26) using the historical climate data for thirty-four synoptic stations in the archipelago. Analysis of time-series data on historical yearly mean temperatures (based on available PAGASA data) shows an increasing trend relative to normal values (that is, a thirty-year average for the period 1971–2000). The plot shows that the mean temperature increased by 0.65°C between 1951 and 2010 (Figure 4.1). Averaged across the country, yearly mean temperatures increased at a rate of 0.0164°C per year during 1981–2010, and by 0.0108°C per year during 1951–2010. The same long-term trends in daily precipitation and atmospheric temperature in some locations the Philippines have also been observed (Cinco, de Guzman, Hilario, and Wilson 2014, pp. 12–26). Moreover, the recent study also reported that some locations — such as Laoag, Iloilo, Tacloban, and Cotabato — show statistically significant increases in both the frequency and intensity of extreme daily rainfall events. The distribution of daily maximum and minimum temperatures above the 99th percentile indicates that both days and nights are becoming warmer (Villafuerte, Matsumoto, Akasaka, Takahashi, Kubota, and Cinco 2014, pp. 1–13). However, analyses of available climate data in other weather gauging stations in the Philippines show varying trends (Table 4.1). For the period 1951–2008, the number of warm nights and hot days were either increasing or decreasing, and in some locations increasing or decreasing significantly. The differences in trends between the two studies could be due to the shorter length of historical records in some

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FIGURE 4.1 Mean Temperature Anomalies, 1951–2010 Relative to 1961–90

Notes: Anomalies are identified as an increase over normal (average 1961–90) values of 0.65°C or more. The running means indicate the mean value for the preceding five years. Source: Thelma Cinco, Rosalina de Guzman, Flaviana Hilario, and David Wilson, “Long-Term Trends and Extremes in Observed Precipitation and Near Surface Air Temperature in the Philippines for the Period 1951–2010”, Atmospheric Research 145/146 (2014): 12–26.

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TABLE 4.1 Trends in the Number of Hot Days and Warm Nights, and in the Frequency and Intensity of Extreme Daily Rainfall, 1951–2008 Locations/Stations Alabat Ambulong Baguio City Cabanatuan Calapan Casiguran Catarman Catbalogan Coron Cuyo Daet Dagupan Davao Dipolog General Santos Hinatuan Iloilo Infanta Laoag Legazpi Malaybalay Puerto Prinsesa Romblon Roxas Science Garden, Q.C. Surigao Tacloban Tuguegarao Vigan Virac Zamboanga

No. of Hot Days

No. of Warm Nights

Extreme Frequency

Extreme Intensity

▼ – ▲ ▲ ▲ + ▲ ▲ ▲ ▲ + + – ▼ ▲ ▲ ▲ ▲ ▲ ▼ ▲ + – ▼ + ▲ ▲ – ▲ ▼ ▲

▼ + ▲ ▲ ▼ ▼ + + + + ▲ + ▲ – ▲ ▲ + ▲ + ▲ – ▲ ▲ – ▲ + + ▼ + ▼ ▲

+ + + + ▲ + – – ▼ ▼

– + ▲ + + + + – ▼ –

+ + – + + ▲ + ▲ – + + +

+ + – + + ▲ + + – + + +

– – ▲ – + –

– – ▲ + + –

Notes: + = increasing; – = decreasing; ▲ = significantly increasing; and ▼ = significantly decreasing. Source: PAGASA–DOST (Philippine Atmospheric, Geophysical and Astronomical Services Administration and Department of Science and Technology), Climate Change in the Philippines (Quezon City, 2011).

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locations, or because weather stations are often situated in coastal areas or near the airports. On the other hand, in most of the locations in the Philippines, the frequency and intensity of extreme daily rainfall appear to be increasing or have increased based on available historical weather data from 1951 until 2008 (Table  4.1). A closer look at the seasonal variability of climate parameters in selected locations with reasonably adequate historical records indicates that climate has changed significantly. For example, the monthly mean minimum temperature in Muñoz, Nueva Ecija, had significantly increased between the time periods 1974–90 and 1991–2008 (Figure 4.2). Similarly, historical weather data in Legazpi City, Albay, indicate that the monthly mean minimum temperatures during 1991–2008 were significantly higher than those during 1973–90 (Figure 4.3), and the same trends were observed in other locations with adequate weather records (PAGASA– DOST 2011; Lansigan and Faderogao 2012; Comiso, Blanche, Sarigumba, Espaldon, and Lansigan 2014). An appreciable increase in minimum — but not maximum — daily temperatures was observed, which is critical to crop growth and development. While the increase in maximum temperatures was not significant, minimum temperatures increased by about 1.19°C over the time period, leading to decreased rice crop yields at experimental farms (IRRI 2008). The change in climate variability is also reflected in the shift in the probability distribution of the climate variables (IPCC 2012), including an increase in extreme weather events — that is, the occurrence of floods and droughts (Lansigan 2009). Yearly maximum daily rainfall levels were fairly uniformly distributed during 1959–78, but greater variation occurred during 1979–2006, including a peak frequency of 162 mm and an observed extreme event of about 348 mm (Lansigan 2009). Historical records of daily maximum rainfall events have become very common. The recurrence interval, or return period of extreme events, has decreased, implying that more rainfall extremes occur more frequently.

Sequences of Wet and Dry Days One important weather factor in agricultural production is the sequence of wet and dry periods in a given location, which can be approximated by the sequence of wet and dry days based on different threshold levels

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Source: Fatima Hadap, and Felino Lansigan, “Analysis of Trends in Extreme Rainfall Events in Selected Locations in the Philippines”, paper presented at the 6th National Convention on Statistics, Mandaluyong City, the Philippines, 1–2 October 2010.

FIGURE 4.2 Monthly Mean Minimum Temperatures in Muñoz, Nueva Ecija, 1974–90 and 1991–2008

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Source: Constructed by author based on Marife Gracilla and Felino Lansigan, “Analysis of Changing Climate in Selected Locations in the Philippines”, paper presented at the 6th National Convention on Statistics, Mandaluyong City, the Philippines, 1–2 October 2010.

FIGURE 4.3 Monthly Mean Minimum Temperatures in Legazpi City, 1973–90 and 1991–2008

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of evapotranspiration, such as for the case in Legazpi City (Figure 4.4). Data indicate that the difference in the number of rainy days is statistically significantly from January to May. Such a significant change in the sequence of wet and dry days has implications for the cropping calendar, particularly in rain-fed crop-production areas (Gracilla and Lansigan 2010). The planting window may have shifted, and the crop growing period may have changed.

Changes in Other Climate-Related Indicators Another phenomenon associated with climate change is rising sea levels stemming from increased surface sea temperatures (known in the literature as “sea level rises” and “sea surface temperatures”). Such changes are expected to have significant impacts on agricultural production, particularly in the low-lying coastal areas. Sea level rises lead to salt water intrusion, which increases the salinity of agricultural lands and contaminates the water table. Based on data from the University of the Philippines Marine Science Institute (UP-MSI 2012) for the 1985– 2006 period, increased sea surface temperatures ranged from 0.20°C per decade in southern Mindanao to 0.33°C per decade in the north of the archipelago. Observed historical time-series data on sea surface temperatures also indicated that the seas surrounding the Philippines can be clustered according to the range of increases (David, Penaflor, Goh, and Lansigan 2013).

THE PREVALENCE AND INTENSITY OF EXTREME EVENTS Statistics show that extreme weather events, such as typhoons, heat waves, and frost episodes, have caused significant damage to agricultural crops and livestock in the Philippines. Historical records indicate that these events have become more intense over time based on the analysis of extreme precipitation indices (Cinco, de Guzman, Hilario, and Wilson 2014, pp. 12–26; Villafuerte, Matsumoto, Akasaka, Takahashi, Kubota, and Cinco 2014, pp. 1–13). Moreover, areas previously not affected by such events have become affected in recent years.

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Note: Data for January, March, April, and May are significant at the 5 per cent level; data for the remaining months are not significant at the 5 per cent level. Source: Marife Gracilla and Felino Lansigan, “Analysis of Changing Climate in Selected Locations in the Philippines”, paper presented at the 6th National Convention on Statistics, Mandaluyong City, the Philippines, 1–2 October 2010.

FIGURE 4.4 Monthly Average Number of Meteorological Rainy Days in Legazpi City, 1973–90 and 1991–2008

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Intense Rainfall-Related Events The shift in climate variables in terms of mean values and distribution also resulted in changes in extreme weather events. Thus, climate change is also expected to contribute to the intensity of extreme events, such as typhoons characterized by stronger winds and heavier rainfall (IPCC 2007; Yumul, Dimalanta, Servando, and Cruz 2013, pp. 715–27). Historically, the Philippines averages twenty typhoons per year. PAGASA data for the 1948–2010 period (PAGASA-DOST, 2011) indicate a maximum of thirty typhoons in 1993 and only eleven in 1998. Most of the tropical cyclones occurred during the wet season, ranging from about three to four typhoons in July to at least two in November (Figure 4.5).

FIGURE 4.5 Monthly Frequency of Tropical Cyclones, 1948–2010

Note: Data are based on tropical cyclones entering the Philippine “Area of Responsibility” and crossing the Philippines. Source: Constructed by author from PAGASA–DOST (Philippine Atmospheric, Geophysical and Astronomical Services Administration and Department of Science and Technology), Climate Change in the Philippines (Quezon City, 2011).

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Extreme Temperature-Related Events Historically, temperature-related events have occurred in selected cropgrowing areas and have significantly affected crop production. El Niño Southern Oscillation (ENSO) episodes have occurred throughout the country with differing spatial intensities. The worst dry episode was in 1997/98, when most of the country had less than a 10 per cent probability of receiving any rainfall in the one year period from April 1997 until March 1998. The second-worst dry episode occurred in 1982/83. ENSO episodes greatly influence year-to-year variations in the extreme precipitation index; statistically drier conditions are associated with El Niño events, whereas wetter conditions are associated with La Niña events (Villafuerte, Matsumoto, Akasaka, Takahashi, Kubota, and Cinco 2014, pp. 1–13). Historical data show that El Niño and La Niña episodes have become more frequent affecting significantly agricultural production in the country. In recent years, heat waves have been blamed for the prevalence of certain pests and diseases, such as coconut scale insects in the provinces of Batangas, Laguna, Quezon, and nearby areas (PCA 2014). Climate change has triggered imbalances in the growth and population of certain species of insects and their predators, which eventually presented favorable conditions for the widespread infestation of coconut scale insects. In contrast, in the mountainous areas of the Cordillera region — particularly in the vegetable production areas in Atok and Buguias, Benguet Province — temperatures have dropped to below 9°C, increasing the occurrence of frosts. While isolated instances of frosts do not cause significant damage to vegetable crops, occurrences of two consecutive days can cause significant damage, and even a total loss, especially in the case of green leafy vegetables (MDGF 2011).

Increased Seasonal Climate Variability Trends in historical weather data lend support to characteristics projected in yearly and seasonal climate modelling in the Philippines, which indicate a 0.64°C temperature increase over a sixty-year period (PAGASA-DOST 2011). The projected change or increase in mean temperatures ranges from 1.0°C to 3.1°C under the medium-range scenario, and from 0.7°C to 3.4°C under the high-emission scenario. Moreover, downscaled seasonal mean rainfall

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projections under a medium-range emission scenario for the period centred on 2020 and 2050 indicate that yearly rainfall patterns and distributions are projected to shift. Data show that dry months become drier, and wet months become wetter — a change that has profound implications for the planting calendar and other agricultural production activities.

THE IMPACT OF CLIMATE CHANGE AND VARIABILITY ON AGRICULTURE Changing climate and extreme climate variability have had significant impacts on Philippine agriculture, particularly on the production of rice, corn, vegetables, and other high-value perennial crops, such as bananas, coconuts, and sugarcane. Statistics on crop losses and damages due to climate extremes such as ENSO events and typhoons are significant, and these climatic conditions have greatly affected the country’s economies, ecosystems, and citizens (Yumul, Dimalanta, Servando, and Cruz 2013, pp. 715–27).

The Impacts of Climate Variability on Crops Rising atmospheric temperatures, erratic and changing rainfall patterns, and extreme weather events all affect crop production. The extent of the damage depends both on the timing and duration of the event in question and on the crop’s stage of growth at the time of the event. Crop yields and area harvested dropped significantly during ENSO episodes such as those observed during the worst El Niño events of 1997/98 and 1982/83, which as previously discussed resulted in significantly reduced rice production in those years (Figure 4.6). In addition, the Department of Agriculture (DA 2010) estimated losses and damages to crops due to the occurrence of extreme events during crop growth and development (Table 4.2). Crop damages range from insignificant or negligible to a complete loss depending on the timing and duration of events. The Department of Agriculture uses these estimates to assess actual losses from events such as typhoons.

The Impact of Climate Change on Crop Growth and Development As stated previously, climate change is generally associated with rising temperatures, erratic rainfall patterns, and more intense extreme weather

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Source: Constructed by author from DA-BAS (Department of Agriculture, Bureau of Agricultural Statistics), Statistics on Rice Production, Area Harvested and Average Rice Yield in the Philippines from 1980–2012 (Quezon City, 2014).

FIGURE 4.6 Rice Production, Area Harvested, and Yield, 1987–2014

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TABLE 4.2 Matrix of Estimated Losses and Damages to Rice Crops Due to Extreme Weather 70–100 Growth stage Booting Flowering Maturity

≤12

>12

12

15–30 25–40 15–25

25–35 35–50 25–30

Source: DA (Department of Agriculture), Estimates of Losses and Damages to Rice Crop Due to Occurrence of Extreme Events during Crop Growth and Development (Quezon City, 2010).

events, such as typhoons, floods, droughts, frosts, and sea level rises (IPCC 2007; ARF 2011). Temperature drives physiological processes, such as photosynthesis and respiration, which in turn define crop growth and development. In general, temperatures beyond a certain threshold level decrease crop yields. It is known, for example, that at more than 35°C crops continue to produce biomass but not yield. For rice, the phenomenon of reduced yield due to increased temperatures is attributed to spikelet sterility (Liu et al. 2006, pp. 87–100; Wassmann 2010). Crop growth and yield can be simulated using a process-based crop model (Matthews and Stephens 2002; Tsuji, Uehara, and Balas 2004; Bouman 2006), weather data, and other input parameters to evaluate the effects of changes in climate (Penning de Vries, Jansen, ten Berge, and Bakema 1989; Lansigan and Salvacion 2007; Lansigan and Salvacion 2008). As an example, for every 1°C temperature increase during the dry and wet seasons, rice productivity decreases by 14.12 and 7.91 per cent, respectively (Figure 4.7). Similarly, yields of corn, sugarcane, peanuts, and tomatoes generally fall by 8–14 per cent per 1°C increase in ambient temperature (Figure 4.8). Although increased carbon dioxide (CO2) concentration in the atmosphere will be favorable to the crop, CO2 enrichment or fertilization cannot compensate for increased respiration due to higher temperatures. Estimates of rates of crop losses due to natural disasters, including extreme weather events, pests and diseases, and other weather-related phenomena are used to assess risks in agri-insurance, as well as in

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Source: Felino Lansigan, “Frequency Analysis of Extreme Hydrologic Events and Water Stress in a Changing Climate”, San Miguel Corporation Professorial Chair Lecture Paper (Los Baños: University of the Philippines Los Baños, 2008).

FIGURE 4.7 Rice Yield Decline as Function of Temperature Increases during Dry and Wet Seasons, Nueva Ecija

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FIGURE 4.8 Projected Reduction in Crop Yields with Higher Temperatures, Selected Locations

Source: Felino Lansigan, “Frequency Analysis of Extreme Hydrologic Events and Water Stress in a Changing Climate”, San Miguel Corporation Professorial Chair Lecture Paper (Los Baños: University of the Philippines Los Baños, 2008).

determining strategies for disaster funds and subsidies for smallholders (Table 4.3). Recent data indicate that damages and losses to crops and livestock, livelihoods, property, and people have reached more than PhP1.0 billion per occurrence of strong typhoons. In November 2013, Super typhoon Yolanda (Haiyan) caused the loss of more than 6,000 lives

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TABLE 4.3 Crop Losses Due to Natural Disasters, Selected Crops Reduction Coefficients (%) Commodity

Location

Typhoon

Flood

Drought

Pests and Diseases

Rice Corn Sugarcane Bananas Cotton Tomatoes Tobacco

Nueva Ecija Isabela n.a. n.a. Ilocos Norte Ilocos Norte Ilocos Norte

10 80–100 n.a. n.a. n.a. n.a. n.a.

30–80 85 n.a. 85 85 85 85

15 90 n.a. 90 90 90 90

10 25–90c n.a. 15–80c 20–99c 15–60c 10–70c

Note: n.a. indicates that data were not available. Source: SEARCA (Southeast Asian Regional Center for Graduate Study and Research in Agriculture), Assessment and Management of Risks Due to Natural Calamities in Support of Quedancor’s Lending Operations (Los Baños, 2006).

in the eastern Visayas region of the Philippines, and resulted in damages of at least PhP6.0 billion to agriculture. As previously stated, sea level rises are caused by increased sea surface temperatures associated with global warming. This leads to saltwater intrusion resulting in salinity problems in low-lying coastal areas used for agricultural production. Salinity stress is even expected to become more serious in coastal and deltaic areas vulnerable to sea level rises. Thus, tolerance to salinity is an important consideration in selecting crops or varieties for planting in coastal areas. Coastal areas in the Philippines cover about 34,000 square kilometres encompassing 804 municipalities and cities and 23,492 barangays, including areas like the Siargao Islands in Surigao del Norte province (Figure 4.9). Exposed areas are planted to crops like rice and corn, as well as fruit trees. Sea level rises are not only expected to reduce crop yields but also to reduce the area of crop production due to the inundation of coastal areas.

The Impact of Climate Change and Variability on Watersheds Climate change is also expected to increase the frequency of more intense hydrologic events (IPCC 2007, 2012; Lansigan 2009). Available historical climate data for the period 1951–2010 show that the yearly mean

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FIGURE 4.9 Areas of the Siargao Islands Vulnerable to Rising Sea Levels of Less Than 0.5 metres

Source: PhilCCAP (Philippine Climate Change Adaptation Project), Siargao Island Protected Landscapes and Seascapes (SIPLAS) Management Plan (Quezon City: Department of Environment and Natural Resources, 2013a).

temperature in the Philippines increased by 0.65°C relative to normal values, which are expected to cause changes in rainfall patterns. This will in turn make the water supply less dependable, thereby disrupting crop production activities, such as planting dates, irrigation, fertilization, and other activities (PAGASA–DOST 2011; Cinco, de Guzman, Hilario, and Wilson 2014, pp. 12–26). Analyses generally indicate a significant shift in the sequences of wet and dry days at certain locations (PAGASA–DOST 2011; Cinco, de Guzman, Hilario, and Wilson 2014, pp. 12–26).

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RESPONDING TO CHANGING CLIMATE A suite of agricultural production responses known as “good agricultural practices” can be applied by farmers to cope with climate risks. These practices are part of what is now known as “climate-smart agriculture” (Box 4.1). This discussion only focuses on some of the climate adaptation BOX 4.1 Climate-Smart Agriculture Climate-smart agriculture comprises efficient, knowledge-based climate risk management, institutional support mechanisms, effective communication strategies, and the uptake of relevant technologies. Recognizing the potential value and application of advances such as crop physiology, agronomy, crop science, climatology, agrometeorology, information and communications technologies, the Philippine Department of Science and Technology (DOST) and the University of the Philippines, Los Baños launched the collaborative, nationwide project “Smarter Approaches to Reinvigorate Agriculture as an Industry in the Philippines” or SARAI. The project involves establishing timely and reliable estimates of crop production forecasts, knowledge- or science-based agro-advisories, and site-specific recommendations on crop production systems using crop simulation models. It is envisioned that information will be shared in real time via radio, the Internet, SMS messaging, and other means and will include advisories on crop forecasts; area planted; crop protection; and water, nutrient, and integrated crop management practices. The effective implementation of initiatives such as Project SARAI will require infrastructure and institutional support to promote access to and sharing of data and information (such as historical weather records, soils and crop management data, crop-specific genetic coefficients needed for crop modeling, and so on). As part of its corporate social responsibility, for example, a local telecommunications company is providing free use of its INFOBOARD service to the project. It is also imperative to engage local government agencies given that local stakeholders will benefit from these activities. Capacity building to promote smarter agriculture and develop greater resilience to climate risks among stakeholders also requires active collaboration and partnerships among academic institutions, development agencies, NGOs, local government agencies, and civil organizations. Source: Author.

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measures that are applied at the farm-level and are directly climate-related. In choosing climate adaptation measures, the following criteria should be considered: (1) effectiveness in minimizing or eliminating the adverse impacts of climate hazards; (2) efficiency; (3) ability to enhance climate resilience; and (4) suitability — and social acceptability — in the context of local conditions (DA-FAO 2010).

Best Agricultural Practice Strategies Some good practices in agricultural production that have enhanced the climate resilience of farmers and other local stakeholders include agricultural crop diversification, intercropping, organic farming, and agroforestry (Table 4.4; ARF 2011; DA-FAO 2010). TABLE 4.4 Good Agricultural Practices at the Farm-Level and Addressing Climate-Related Hazards for Selected Locations Climate Hazards

Good Practices for Climate Adaptation

Warming or increased temperatures

Planting heat- and drought-tolerant crops and varieties Growing aerobic rice varieties Diversifying crop choices

Erratic rainfall

Adjusting planting dates Adaptive cropping calendar Improving water management Diversifying crop choices Using early maturing varieties

More intense extreme events (droughts, flood, typhoons)

Planting stress-tolerant varieties Using wind breaks along crop production areas Using early maturing varieties Using agri-insurance

Rising sea levels

Planting salinity-tolerant crops or varieties Planting flood- or submergence-tolerant varieties

Sources: Felino Lansigan, “Determining the Seasonal Climate-based Optimal Planting Dates for Rainfed Rice in Selected Locations in the Philippines”, National Convention on Statistics, Mandaluyong City, the Philippines, 4–5 October 2011; Helen Grace Centeno, and Reiner Wassmann, “Reviewing Impacts of Climate Change and Climate Variability on Rice production in the Philippines”, in Adaptation to Climate Variability in Rice Production: Asia Rice Foundation Annual Rice Forum 2010 (Los Baños: Asia Rice Foundation, 2011); IPCC (Intergovernmental Panel on Climate Change), Contributions of the IPCC Working Group II to the Fourth Assessment Report, (Cambridge, U.K., and New York: Cambridge University Press, 2007).

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Technology-Based Strategies Location-specific, technology-based measures for adapting to the adverse effects of climate change and variability are outlined below.

Crop Management Options These measures focus on managing crop growth and development under shifting climate conditions and include adapting the cropping calendar to ensure the best planting dates based on the seasonal or projected climate outlook. The optimal planting window can be determined using empirical and knowledge-based procedures that consider water availability from rainfall in light of crop requirements (Figure 4.10). Probabilities of meeting water requirements during a crop’s vegetative, flowering, and maturing/ripening stages can be estimated based on the cumulative rainfall received during specified periods. Alternatively, the choice of crops and varieties tolerant to the various climate-related stresses — such as flooding, drought, heat, and salinity — can minimize the adverse impacts of climate change. Examples are varieties of rice that can tolerate saline soils (Wassmann 2010) and even a new variety of rice, Sub-1, that can tolerate flooding (IRRI 2011).

Water Management Options This strategy involves ensuring the availability of water for plant growth and development and irrigation when it is needed, improving the wateruse efficiency of irrigation, and modifying crop management to reduce water use. In many rain-fed areas, water-impounding structures store rainwater during the wet season for use in the dry season. Rainwater harvesting is widely practiced in the rice and vegetable growing areas in the Cordillera region (DA-FAO 2010). In some rain-fed areas, the efficiency of rice and vegetable production is improved by intensifying production through intercropping or multiple cropping to maximize use of available soil moisture. Small-scale irrigation at the farm level is also being improved using shallow tube wells (David et al. 2005, pp. 3–6). Moreover, in recent years improved technologies, such as the alternate wetting and drying technology in rice production, have been used to mitigate the negative

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Source: Felino Lansigan, “Climate Risk for Weather Index-Based Insurance for Crop Production in the Philippines”, paper presented at the 12th National Convention on Statistics, Mandaluyong City, the Philippines, 1–2 October 2013.

FIGURE 4.10 Combined Probabilities of Meeting Cumulative Rainfall in Malaybalay, Bukidnon, 2020 and 2050

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effects of climate change because under certain conditions they can reduce greenhouse gas emissions from rice fields during fertilization (Liu et al. 2006; Wassmann 2010).

Nutrient Management Options Nutrient management options include recently developed fertilization strategies, particularly for use in irrigated areas. Science-based decisionsupport systems are now available to guide the application of fertilizers. For example, site-specific nutrient management is now being introduced to improve fertilizer use efficiency (IRRI 2013; PhilCCAP 2013a) often combined with other crop-management practices, such as irrigation and weed management. This involves being able to reliably determine soil fertility as one component of a smarter, more precise agricultural strategy.

Climate Forecasting Options In recent years, advances in science and technology have improved predictions of seasonal climate conditions with reasonable accuracy (Sivakumar and Hansen 2007). This in turn has facilitated the use of crop models (Matthews and Stephens 2002) to generate crop forecasts and crop production advisories based on the seasonal climate outlook for specific locations (Lansigan, delos Santos, and Hansen 2007). Such systems of crop forecasting have recently been piloted in selected areas of the Philippines (Project SARAI 2014). The framework for knowledge-based crop forecasting systems using crop simulation models and seasonal climate outlooks for the Philippines is depicted in Figure 4.11 (Lansigan, delos Santos, and Hansen 2007; Project SARAI 2014).

Institutionalized Strategies Institutional level strategies for adapting to climate change and variability involve mechanisms for transferring or sharing climate-related risks, thereby reducing vulnerability. Such measures include the following innovations and interventions.

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Source: Felino Lansigan, William delos Santos, and James Hansen, “Delivering Climate Forecast Products to Farmers: Ex post Assessment of Impacts of Advanced Climate Information on Corn Production Systems in Isabela, Philippines”, in Climate Prediction and Agriculture: Advances and Challenges, edited by Mannava Sivakumar and James Hansen (Berlin and Heidelberg: Springer-Verlag, 2007); Project SARAI, “UPLB-DOST Project SARAI (Smarter Approaches to Reinvigorate Agriculture as an Industry in the Philippines)”, Annual Project Technical Report (Los Baños, 2014).

FIGURE 4.11 Framework for a Knowledge-Based Crop-Forecasting System

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Agri-Insurance Coverage Regular crop insurance usually covers the cost of establishing and producing crops, and livestock insurance only partially covers the cost of purchasing livestock. In recent years, innovative crop insurance products have been introduced dealing with different aspects of risk. These include weather index-based insurance (WIBI) products, whereby coverage depends on predefined threshold values of weather variables, such as temperature and cumulative rainfall (Figure 4.12). Risk is expected to be low during the wet season given the high probability of rainfall meeting cumulative requirements for the different stages of crop growth. WIBI products have been piloted in selected locations in the Philippines, but some implementation issues have arisen. One such problem is the relatively low number of reliable weather gauging stations in the Philippines (Figure 4.13). Although a wide network of automatic weather stations exists, and additional units are planned to be installed, coverage for WIBI products remains limited. Currently, the Philippine Crop Insurance Corporation (PCIC) only recognizes PAGASA’s stations. Another constraint is the high premium associated with WIBI products, which deters smallholder farmers from subscribing. Government taxes imposed on the WIBI products are partially responsible for the high cost of premiums; they are equally a potential means of reducing premiums, were products to become tax exempt.

Disaster Funds and Subsidies The provision of immediate support or relief for victims of natural disasters, such as extreme weather events, is a common government strategy for managing climate risk. Another mechanism is making subsidized loans readily available with attractive terms, such as low interest rates and extended payment periods.

Microfinance for Farmers in Vulnerable Areas As part of the farm rehabilitation programme, some nongovernmental organizations (NGOs), rural banks, and credit facilities provide microfinance to affected farmers, and other local stakeholders, often linked with livelihood programmes and related packages to enhance the adaptive capacity of affected stakeholders.

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Note: These probabilities are used to estimate risk within each of the planting periods. Source: Felino Lansigan, “Climate Risk for Weather Index-Based Insurance for Crop Production in the Philippines”, paper presented at the 12th National Convention on Statistics, Mandaluyong City, the Philippines, 1–2 October 2013.

Figure 4.12 Weekly Probability of Cumulative Rainfall Deficit for Rain-Fed Rice Production, Iloilo Province

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FIGURE 4.13 Location Map of Existing Network of Weather Gauging Stations

Source: PAGASA-DOST (Philippine Atmospheric, Geophysical and Astronomical Services Administration and Department of Science and Technology), Network of Weather Gauging Stations of PAGASA (Quezon City, 2013).

Improved Agricultural Extension Services Government agencies, such as PAGASA and the Department of Agriculture’s Agricultural Training Institute (ATI), implement extension programmes to build farmers’ capacity to cope with climate-related risks.

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ATI, for example, has implemented climate field schools in selected pilot areas where participants are introduced to climate change concepts, issues, and adaptation measures. This also includes the proper interpretation and use of information on seasonal climate outlooks and crop advisories in farm-level operations. Some international and local NGOs working with farmers at the grassroots level are also conducting capacity-building activities through seminars, training-workshops, farm trials, and so on. These activities promote measures both for mitigating and adapting to climate change.

POLICY IMPLICATIONS Trends in climate variables and indices, and their impacts on agriculture, present a number of policy implications that need to be addressed to enhance farmers’ resilience to climate risks. These issues are discussed below.

Meeting Investment Requirements to Establish an Adequate Network of Weather Gauging Stations The Philippines currently only has a limited number of weather gauging stations, most of which need rehabilitating and updating. Such stations provide real-time weather data relevant to many local- or farm-level applications. A smarter crop-forecasting system that makes use of advances in science and technology requires reliable, almost real-time weather data in order to determine the best planting dates, select which crops to plant, and estimate crop yields. Moreover, adequate networking of such stations is also required for real-time early warning systems — such as flood forecasting and drought monitoring — to function. This capability is extremely useful to local government agencies in managing disaster and climate risks. In addition, weather gauging stations can provide data to facilitate the development and implementation of WIBI products, which also rely on real-time weather data from farms within a 20-kilometre radius. Thus, expanding the network of weather gauging stations would widen and increase local government coverage of climate risk management and disaster risk reduction. Investments by local government agencies to establish a network of automatic weather stations are imperative, particularly in vulnerable and risk-prone areas.

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Given the extent of damages and losses in agriculture and infrastructure associated with natural disasters, the return on such investments is expected to be high.

Promoting and Outscaling Best Practices and Climate Change Adaptation Measures through the Department of Agriculture’s Networks in Collaboration with Local Government Although agricultural extension needs to be enhanced in the Philippines, government agencies, such as the Department of Agriculture and local government agencies, must work together to inform, educate, and communicate with the country’s farmers to build their capacity to cope with the impacts of climate change. This involves disseminating technologies and information to increase resilience to climate risks. The Department of Agriculture’s ATI has been conducting a number of training courses promoting best agricultural practices and technologies to manage the adverse impacts of climate change. Moreover, some state universities are also involved in capacity-building activities, in collaboration with the Department of Agriculture, as part of their extension programmes. Capacity-building programmes involve training trainers, municipal agricultural technicians from the local governments, and — occasionally — farmer leaders. While collaborative training activities are occurring to some extent, a more formal, mainstreamed institutional framework for collaboration needs to be initiated between the Department of Agriculture, local government agencies, and state universities. One method of collaboration and partnership that has shown potential is through farmers’ field schools, also known as climate field schools, which are being piloted in selected locations. The schools are established by municipal government agricultural offices, and capacity building among farmers is undertaken with the technical assistance of national and local governments and development agencies working in the area. Training involves a variety of modules, from basic crop growth and development, to basic appreciation and understanding of climate change, to climate change adaptation strategies and measures based on best practices, including strategies for risk management. Additional training modules could be considered, such as accessing and using seasonal climate data and interpreting weather forecasts. Historically, the success of

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field schools depends on the active support of local government and the extent of networking with development agencies and stakeholders that provide technical assistance in developing modules and training trainers. Nevertheless, the contribution of field schools does not negate the need for an effective and efficient agricultural extension programme or unit within the Department of Agriculture catering directly to farmers’ needs.

Providing a Climate Risk Transfer Mechanism for Weather Index-Based Insurance, and Addressing Attendant Implementation Issues Agriculture is a risky enterprise involving numerous biotic and abiotic stress factors — including weather and climate — that determine its success or failure. Nevertheless, there are proven ways and best practices to cope with or adapt to the vagaries of weather and climate in agricultural production. Managing climate risks may also involve providing mechanisms to transfer risk. Strategies include exploring the possibility for local governments to either secure or provide insurance coverage in areas that are vulnerable to climate risks. In the event of natural disasters, this option may prove less expensive than rehabilitation — although the high cost of insurance premiums for agri-insurance needs to be addressed. Moreover, better and more objective indices for basic risks should be explored and developed, for example, through PCIC and private sector entities, such as insurance and microcredit providers, rural banks, and so on. Providing more objective agri-insurance products to farmers, such as WIBI, would give greater opportunities for broader subscription and coverage. WIBI, however, is constrained by an inadequate weather gauging network and a lack of reliable historical weather data in some locations. One issue in implementing WIBI is how to reduce the relatively high premiums associated with the product. Hence, strategies must be formulated to reduce the premiums and increase subscription rates, especially in the most vulnerable areas or regions (Lansigan 2015). A number of mechanisms to promote WIBI and reduce its premiums have been proposed, including group insurance coverage, government tax exemptions on insurance products, subsidies on insurance premiums, and the development of insurance indices. Nevertheless, subscription to any form of agri-insurance must be promoted, particularly in agricultural production areas most vulnerable to climate risks.

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Issuing or Accessing More Reliable, Real-Time Climate Information, Such as Seasonal Climate and Crop Forecasts Farmers mainly rely on national agencies, such as PAGASA and the Department of Agriculture, for information on climate and crop forecasts broadcast through radio and television. In more recent years, other methods of disseminating real-time information have been introduced, such as short message service (SMS) and text messaging, and community service announcements delivered through cable television or other means. The Department of Agriculture and International Rice Research Institute are collaborating with a local telecommunications company (Telcos) to pilot an SMS-based farmer advisory program under the Philippine Climate Change Adaptation Project. However, one big issue on access and use of weather and climate information at the farm level is who will generate the local-level seasonal climate forecasts, and how these downscaled forecasts will be interpreted and used for farm-level decision making. Ideally, the local government agricultural office should issue climate advisories and recommendations, but local agricultural officers are not currently equipped to downscale and interpret weather forecasts. One strategy would be to include a module on interpreting seasonal weather forecasts in farmer/climate field schools to enable farmers and other stakeholders to make use of weather and climate information. This approach has been implemented in Iloilo province through the Dumangas climate field school. PAGASA has trained a staff member at the municipal government agricultural office on how to use and interpret seasonal climate forecasts and how to disseminate this information over local radio on a near daily basis. Staff members at the field school provide daily weather forecasts and advisories on farm activities, such as the best time to plant, how to schedule farm activities, and the best crops to plant. Thus, effective and efficient strategies must be formulated on how to make this kind of information more accessible to farm-level stakeholders so that they, in turn, can make more informed farm decisions.

Redefining the Crop Suitability Map Considering Updated Data and Information on Agroecology, Vulnerability to Climate Change, and Variability The crop production practices and strategies in land management units need to be reassessed based on updated soil, weather, and climate information.

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This also includes vulnerability assessment and risk analysis, and requires updating agroecological data related to crop production at least at the province level. However, such databases only exist fora few pilot provinces; updating field data on soils, water resources, and climate for the rest of the crop growing provinces will require significant national government investments. Nevertheless, the top crop-producing provinces could be prioritized so that resources can be channelled appropriately whenever they are made available. Areas prone or vulnerable to climate hazards, such as temperature increases, erratic rainfall patterns, and increased severity of extreme events, should be identified, and crop production in these areas should be limited to periods with favourable seasonal weather forecasts. This makes it imperative that land suitability maps for different priority crops be updated with recent data and information on biophysical properties of agricultural production areas.

References ARF (Asia Rice Foundation). Adaptation to Climate Variability in Rice Production: Asia Rice Foundation Annual Rice Forum 2010. Los Baños: Asia Rice Foundation, 2011. Bouman, B. Input data to ecophysiological crop model. Los Baños: International Rice Research Institute, 2006. Centeno, H. and R. Wassmann. “Reviewing Impacts of Climate Change and Climate Variability on Rice production in the Philippines”. In Adaptation to Climate Variability in Rice Production: Asia Rice Foundation Annual Rice Forum 2010. Los Baños: Asia Rice Foundation, 2011. Cinco, T., R. de Guzman, F. Hilario, and D. Wilson. “Long-Term Trends and Extremes in Observed Precipitation and Near Surface Air Temperature in the Philippines for the Period 1951–2010”. Atmospheric Research 145/146 (2014): 12–26. Comiso, J., C. Blanche, T. Sarigumba, M. Espaldon, and F. Lansigan, eds. Changing Philippine Climate: Impacts on Agriculture and Natural Resources. Quezon City: UP Press, 2014. David, L., R. Maneja, B. Goh, F. Lansigan, P. Sereywath, I. Radhawane, B.M. Manjaji Matsumoto, et al. “Sea Level Rise Vulnerability of Southeast Asia Coasts”, LOICZ In Print (December 2005): 3–6. David, L.T., E.L. Penaflor, B. Goh, and F.P. Lansigan. Seascape Clusters According to the Range of SST Increase. Quezon City: UP Marine Science Institute, 2013. DA (Department of Agriculture). Estimates of Losses and Damages to Rice Crop Due to Occurrence of Extreme Events during Crop Growth and Development. Quezon City, 2010.

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Felino P. Lansigan

DA-BAS (Department of Agriculture, Bureau of Agricultural Statistics). Statistics on Rice Production, Area Harvested and Average Rice Yield in the Philippines from 1980–2012. Quezon City, 2014. DA-FAO (Department of Agriculture and Food and Agriculture Organization of the United Nations). Enhanced Climate Change Adaptation Capacity of Communities in Contiguous Fragile Ecosystems in the Cordilleras. SPICCAC Report to MDGF. Quezon City, 2010. Gracilla, Marife and Felino Lansigan. “Analysis of Changing Climate in Selected Locations in the Philippines”. Paper presented at the 6th National Convention on Statistics, Mandaluyong City, the Philippines, 1–2 October 2010. Hadap, Fatima and Felino Lansigan. “Analysis of Trends in Extreme Rainfall Events in Selected Locations in the Philippines”. Paper presented at the 6th National Convention on Statistics, Mandaluyong City, the Philippines, 1–2 October 2010. IPCC (Intergovernmental Panel on Climate Change). Contributions of the IPCC Working Group II to the Fourth Assessment Report. Cambridge, U.K., and New York: Cambridge University Press, 2007. ———. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, edited by C. Field, V. Barros, T. Stocker, D. Qin, D. Dokken, K. Ebi, M. Mastrandrea et al. Cambridge, U.K., and New York: Cambridge University Press, 2012. ———. Climate Change 2014: Impacts, Adaptation, and Vulnerability. IPCC Working Group II Contributions to AR5, 2014 (accessed April 2015). IRRI (International Rice Research Institute). Los Baños: International Rice Research Institute Climate Unit, 2008. ———. Sub-1 Variety Has Been Developed and Demonstrated to Be Flood-Tolerant. Los Baños: International Rice Research Institute, 2011. ———. Nutrient Manager. Los Baños: International Rice Research Institute, 2013. Lansigan, Felino. “Frequency Analysis of Extreme Hydrologic Events and Water Stress in a Changing Climate”. San Miguel Corporation Professorial Chair Lecture Paper. Los Baños: University of the Philippines Los Baños, 2008. ———. “Frequency Analysis of Extreme Hydrologic Events and Assessment of Water Stress in a Changing Climate in the Philippines”. In From Headwaters to the Ocean: Hydrological Changes and Watershed Management, edited by M. Taniguchi, W. Burnett, Y. Fukushima, M. Haigh, and Y. Umezawa. Leiden: CRC Press, Taylor and Francis Group, 2009. ———. “Determining the Seasonal Climate-based Optimal Planting Dates for Rainfed Rice in Selected Locations in the Philippines”. National Convention on Statistics, Mandaluyong City, the Philippines, 4–5 October 2011.

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———. “Climate Risk for Weather Index-Based Insurance for Crop Production in the Philippines”. Paper presented at the 12th National Convention on Statistics, Mandaluyong City, the Philippines, 1–2 October 2013. ———. Implementation Issues in Weather Index-Based Insurance for Agricultural Production: A Philippine Case Study. Hayama, Japan: Institute for Global Environmental Strategies; Los Baños: Southeast Asian Regional Center for Graduate Study and Research in Agriculture, 2015. ——— and Arnold Salvacion. “Assessing the Effects of Changing Climate on Rice and Corn Yields in Selected Locations in the Philippines”. Paper presented in DA-BAR National Research Symposium, Quezon City, 3–7 October 2007. ——— and Arnold Salvacion. “Simulating the Effects of Changing Climate on Major Crops in the Philippines”. International Society for Southeast Asian Agricultural Sciences (ISSAAS) Conference, Los Baños, 26 October 2008. ——— and Francis John Faderogao. Assessing the Effects of Climate Change and Climate Variability on Rice Productivity in Selected Locations in the Philippines. Proceedings of the Philippine Statistical Association Annual Conference, Davao City, 16–17 August 2012. ———, W. delos Santos, and J. Hansen. “Delivering Climate Forecast Products to Farmers: Ex post Assessment of Impacts of Advanced Climate Information on Corn Production Systems in Isabela, Philippines”. In Climate Prediction and Agriculture: Advances and Challenges, edited by M. Sivakumar and J. Hansen. Berlin and Heidelberg: Springer-Verlag, 2007. Liu, J., D. Liao, R. Oane, L. Estenor, X. Yang, Z. Li, and J. Bennett. “Genetic Variation in the Sensitivity of Anther Dehiscence to Drought Stress in Rice”. Field Crops Research 97 (2006): 87–100. Manton, M., P. Della-Marta, M. Haylock, K. Hennessy, N. Nicholls, L. Chambers, D. Collins, et al. “Trends in Extreme Daily Rainfall and Temperature in Southeast Asia and the South Pacific: 1961–1998”. International Journal of Climatology 21 (2001): 269–84. Matthews, R. and W. Stephens. Crop-Soil Simulation Models: Applications in Developing Countries. Wallingford, U.K.: CAB International, 2002. MDGF (Millennium Development Goal Fund). Occurrences of Frost and Damage to Crops in Benguet Province. Quezon City and Baguio City, 2011. PAGASA (Philippine Atmospheric, Geophysical and Astronomical Services Administration). Climate Change in the Philippines. SPICCAC Project Report. Quezon City, 2010. PAGASA-DOST (Philippine Atmospheric, Geophysical and Astronomical Services Administration and Department of Science and Technology). Network of Weather Gauging Stations of PAGASA. Quezon City, 2013. PAGASA–DOST (Philippine Atmospheric, Geophysical and Astronomical Services

18-J04349 04 Future of Philippine Agriculture.indd 207

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Administration and Department of Science and Technology). Climate Change in the Philippines. Quezon City, 2011. PCA (Philippine Coconut Authority). Annual Report: Research and Development. Quezon City, 2014. Penning de Vries, F., D. Jansen, H. ten Berge, and A. Bakema. Simulation of Ecophysiological Processes of Growth in Several Annual Crops. Los Baños: International Rice Research Institute, 1989. PhilCCAP (Philippine Climate Change Adaptation Project). Siargao Island Protected Landscapes and Seascapes (SIPLAS) Management Plan. Quezon City: Department of Environment and Natural Resources, 2013a. PhilCCAP (Philippine Climate Change Adaptation Project). Nutrient Manager. Quezon City: Department of Agriculture, 2013b. Project SARAI. UPLB-DOST Project SARAI (Smarter Approaches to Reinvigorate Agriculture as an Industry in the Philippines), Annual Project Technical Report. Los Baños, 2014. SEARCA (Southeast Asian Regional Center for Graduate Study and Research in Agriculture). Assessment and Management of Risks Due to Natural Calamities in Support of Quedancor’s Lending Operations. Los Baños, 2006. Sivakumar, M. and J. Hansen, eds. Climate Prediction and Agriculture: Advances and Challenges. Berlin and Heidelberg: Springer-Verlag, 2007. Tsuji G., G. Uehara, and S. Balas, eds. DSSAT Crop Simulation Models. Honolulu: University of Hawaii, 2004. UP-MSI (University of the Philippines, Marine Science Institute). Sea Surface Temperature Data. Quezon City, 2012. Villafuerte, M., J. Matsumoto, I. Akasaka, H. Takahashi, H. Kubota, and T. Cinco. “Long-Term Trends and Variability of Rainfall Extremes in the Philippines”. Atmospheric Research 137 (2014): 1–13. Wassmann, R., ed. Advanced Technologies of Rice Production from Coping with Climate Change: ‘No regret’ Options for Adaptation and Mitigation and their Potential Update. IRRI Limited Proceedings No. 16. from the workshop “Advanced Technologies of Rice Production for Coping with Climate Change: ‘No Regret’ Options for Adaptation and Mitigation and their Potential Uptake”, 23–25 June, Los Baños. Los Baños: International Rice Research Institute, 2010. Yumul, Jr., G., C. Dimalanta, N. Servando, and N. Cruz. “Abnormal Weather Events in 2009: Increased Precipitation and Disastrous Impacts in the Philippines”. Climate Change 118 (2013): 715–27 .

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PART II Climate Change Adaptation Strategies and Sustainability of Philippine Agriculture

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5 THE SUSTAINABILITY OF AGRICULTURAL GROWTH Asa Jose U. Sajise, Dieldre S. Harder, and Paul Joseph B. Ramirez

Globally, agricultural development policy and related initiatives have shifted and evolved over time, in part prompted by emerging issues and the prevailing milieu. Tracing the general eras of these shifts, Hazell (1999) notes that during the early 1950s and 1960s, agricultural researchers were preoccupied with the quest to increase agricultural growth and economic development by improving and enriching “the five eyes” of agricultural development: (1) innovation, by strengthening agricultural research and extension; (2) infrastructure; (3) input-use efficiency; (4) institutions, by liberalizing markets and trade; and (5) incentives. Experiences during these years prompted policymakers and development experts to expand the agricultural development paradigm in the 1980s and 1990s to include alleviating poverty and ensuring food security. Hazell (1999) calls these aspects the “equity modifiers” of earlier growth-centred agricultural development policy. By the 1990s policymakers began to recognize that agriculture should contribute to economic growth, development, and poverty reduction without harming the environment — thus, the concept of environmentally sustainable agricultural growth was born.

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Agricultural development in the Philippines has closely followed these trends. In fact, the country’s Agriculture and Fisheries Modernization Act of 1997 (Government of the Philippines 1997, p. 2) declared that the State should “promote development that is compatible with the preservation of the ecosystem in areas where agriculture and fisheries activities are carried out [and] ... exert care and judicious use of the country’s natural resources in order to attain long-term sustainability.” Despite these intentions, nationallevel environmental accounting has generally slowed since the early 2000s, when several donor-funded projects were completed (Bennagen 2007). Both the funding and human resource capacity to collect relevant data on the environmental impacts of national and sectoral economic activity has dwindled since the turn of the millennium. Attempts to explore the impacts of micro-level agricultural activity in 2000 were confined to rice cultivation in the uplands (NSCB–UNDP 2000). Furthermore, in addition to scope limitations, studies have failed to account for the effects of environmental degradation on agriculture, especially in terms of growth and sustainability. The concept of sustainable development is often rooted in the Bruntland Commission’s definition, which focuses on meeting present needs without compromising the needs of future generations (WCED 1987). Ongley (1996, p. 11) states that sustainable agricultural development is “the management and conservation of the natural resource base and the orientation of technological and institutional change in such a manner as to ensure the attainment and continued satisfaction of human needs for the present and future generations”. Applied to the context of agricultural growth, sustainability implies achieving increased productivity while preserving the integrity of the natural environment. It is tied to the notion that human agricultural activity, if unchecked, has a deleterious impact on the environment, thereby compromising the rights and benefits of future generations. The “Driving Force–State–Response” framework of the Organization for Economic Co-operation and Development (OECD) provides one answer to the question of how agriculture actually affects the environment (Figure 5.1). Under this framework, driving forces include human and economic activities that cause changes in farm and management practices, as well as changes in the environment itself, notably meteorological events. Together, such drivers constitute agriculture’s impact on the ecosystem and the health and welfare of farmers through its effect on the main biological

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Source: OECD. “Environmental Indicators for Agriculture: Issues and Design” (The York Workshop. Paris, 1999b).

FIGURE 5.1 The “Driving Force–State–Response” Framework

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inputs to agriculture (soil, water, and air) and the synergistic composition and function of ecosystems. In turn, the State elicits responses from the farmers, policymakers, consumers, and others resulting in behavioral changes, policy reforms, shifts in demand, and so on. Another way to look at the link between agriculture and the environment, which is also implicit in the OECD framework, is the notion of “jointness” between agricultural production and its environmental impacts. In that sense, alongside the production of commodities, agriculture also causes known and unknown ecological and environmental processes that can be both positive and negative (Sajise and Sajise 2011). Agriculture’s beneficial side effects include public goods and services that are not traded in the market (and, hence, are underprovided), such as climate regulation, the prevention of soil erosion, and the production of genetic resources. Agriculture’s unintended negative impacts are associated with agroecosystem modifications, such as pollution in the form of soil and land degradation and water pollution, which do not pass through the market and, hence, are overprovided. These excluded or nonmarketed goods and services can also be treated as input services — in particular, the use of the environment as a “waste sink”1 (Peskin and de Los Angeles undated). Among the more prominent negative consequences of agricultural activity and identified as threatening the future viability of agricultural systems are land degradation, limited water availability, loss of biodiversity and genetic diversity, and climate change (DFID 2004). While other chapters in this volume focus on the impact of the environment, in particular climate change and variability on agriculture in the Philippines, this chapter looks at the effect of agricultural productivity growth (or the lack of it) on the environment. The discussion begins with a review of existing national and regional data representing the state of the natural resource base and environment in the context of agricultural productivity. The review focuses on trends in negative externalities — that is, unintended negative consequences — in the areas of soil and land degradation, water availability and quality, agrobiodiversity, and climate change. The chapter also includes a case study on the rice sector a means of assessing the sustainability of agricultural productivity growth in relation to the specific unintended consequence of greenhouse gas (GHG) emissions. In conclusion, the chapter makes the case for the need to collect and monitor indicators of the linkages between agriculture and

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the environment and to incorporate such indicators into the Philippine Agricultural Statistical System.

SOIL AND LAND DEGRADATION Land degradation is considered one of the main impediments to agricultural productivity and economic growth (Eswaran, Lal, and Reich 2001). Global estimates on the magnitude of the economic impacts of land degradation range from 0.05 to as much as 10 per cent of the value of agricultural production per year. South Asia, for example, loses an estimated US$10 billion or 7 per cent of its Gross Domestic Product (GDP) due to land degradation (UNEP–FAO–UNDP 1994; Pimentel et al. 1995; Wiebe 2003). In general, land degradation has two main components: (1) changes in physical, chemical, and biological soil endowments, which mainly affect lowland agriculture and include nutrient loss, salinization, acidification, and compaction (Briones 2010b, citing Cummings 1999); and (2) soil erosion, which is a major concern for upland agriculture. Nearly half the arable land in the Philippines is categorized as moderately to severely degraded (representing 8.5 and 5.2 million ha, respectively), resulting in a 30–50 per cent decline in soil productivity and water retention (DA–DAR–DENR–DOST 2010). This makes degraded lands highly vulnerable to drought, which could ultimately threaten food security. The Philippine Bureau of Soils and Water Management has released a regional assessment on the extent of the country’s land degradation2 (Table 5.1). This initiative complies with the national commitment to the United Nations Convention to Combat Desertification under the 2010–2020 National Action Plan. It also fits the OECD’s (1999a) “agri-environmental” indicator for soil quality, particularly regarding the extent of soil degradation and land management practices. The OECD methodology, however, emphasizes risk measurements rather than soilquality conditions, which are more difficult and costly to measure. Another agri-environmental indicator of soil quality is the level of vulnerability to various degradation processes (OECD 1999a). To date, studies undertaken in the early 1990s by the Soil and Terrain Database (SOTER) and Soil Degradation in South and Southeast Asia (ASSOD) provide the only available data on shares of land degradation by type and region (Table 5.2). The data indicate that topsoil erosion is the most prevalent type of land degradation (Asio et al. 2009; DA–DAR–DENR–DOST 2010).

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Asa Jose U. Sajise, Dieldre S. Harder, and Paul Joseph B. Ramirez TABLE 5.1 Land Degradation Hotspots by Region, 2003

Region ARMM CAR Caraga Region 1 (Ilocos Region) Region 2 (Cagayan Valley) Region 3 (Central Luzon) Region 4a (CALABARZON) Region 4b (MIMAROPA) Region 5 (Bicol Region) Region 6 (Western Visayas) Region 7 (Central Visayas) Region 8 (Eastern Visayas) Region 9 (Zamboanga Peninsula) Region 10 (Northern Mindanao) Region 11 (Davao Region) Region 12 (SOCCSKSARGEN) Total

Degraded Land Area (ha)

Total Land Area (ha)

1,104,721 1,597,029 1,311,792 1,363,364 1,538,458 1,221,223 1,238,154 1,350,052 1,160,570 1,383,826 1,446,038 1,362,123 1,167,433 1,409,467 1,417,666 1,295,130 5,367,047

11,160,829 11,829,368 11,884,697 11,284,019 12,687,517 12,147,036 11,622,861 12,745,601 11,763,249 12,022,311 11,489,077 12,143,169 11,599,734 11,405,599 12,714,059 11,437,274 29,936,400

Share of Land That Is Degraded (%) 19 33 17 28 20 10 15 13 19 19 30 17 10 29 15 21

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region; ha = hectares. Caraga comprises four provinces: Agusan del Norte, Agusan del Sur, Surigao del Norte, and Surigao del Sur; Calabarzon comprises five provinces: Cavite, Laguna, Batangas, Rizal, and Quezon; Mimaropa comprises five provinces: Mindoro Oriental, Mindoro Occidental, Marinduque, Romblon, and Palawan; and Soccsksargen comprises four provinces and one city: South Cotabato, Cotabato, Sultan Kudarat, Sarangani, and General Santos City. Source: DA–DAR–DENR–DOST (Department of Agriculture, Bureau of Soils and Water Management; Department of Agrarian Reform; Department of Environment and Natural Resources; and Department of Science and Technology). The Updated Philippine National Action Plan to Combat Desertification, Land Degradation and Drought (DLDD): FY 2010–2020. 2010 (accessed May 2015).

Briones (2010b) noted the slow but inevitable rise in the volume of soil loss due to erosion from an estimated 340 million tons per year in the late-1980s to around 350 million tons per year by 2000.3 The severity of soil erosion by region and the rate of soil erosion by different vegetative covers are presented in Tables 5.3 and 5.4, respectively. Because of the lack of a comprehensive nationwide assessment of soil erosion in the Philippines (Asio et al. 2009), estimates relied heavily on assumptions that were often less than technically sound. Economic research on soil

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1.1 1.8 1.2 2.7 1.8 4.8 1.8 2.0 1.5 2.1 1.6 2.8 3.2 1.5

23.7 10.0 35.1 24.4 46.9 60.3 20.1 51.5 20.5 46.1 19.3 12.1 10.7 17.3

73.0 87.0 46.0 63.2 34.4 35.0 73.4 35.8 70.3 50.0 76.7 83.3 73.5 76.6

n.a. n.a. 8.7 6.6 n.a. 0.3 n.a. 10.2 1.2 0.8 3.2 0.3 13.6 n.a.

n.a. 1.6 1.9 n.a. 1.3 n.a. 0.4 n.a. n.a. n.a. n.a. 0.1 n.a. n.a.

2.6 n.a. 0.2 n.a. 3.9 0.5 6.5 0.1 2.6 1.8 n.a. n.a. 0.5 5.2

n.a. n.a. 1.4 1.0 1.8 n.a. n.a. n.a. n.a. n.a. n.a. 0.8 n.a. n.a.

0.5 0.6 4.0 1.9 5.5 2.7 n.a. 1.7 2.3 1.2 0.4 0.4 0.9 0.8

0.2 0.8 2.7 2.9 6.2 1.2 0.0 0.3 3.1 0.1 0.4 3.0 0.8 0.1

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region; SOTER = Soil and Terrain Database; ASSOD = Soil Degradation in South and Southeast Asia; ha = hectares; n.a. indicates data were not available. Source: DA–DAR–DENR–DOST (Department of Agriculture, Bureau of Soils and Water Management; Department of Agrarian Reform; Department of Environment and Natural Resources; and Department of Science and Technology). The Updated Philippine National Action Plan to Combat Desertification, Land Degradation and Drought (DLDD): FY 2010–2020. 2010 (accessed May 2015).

ARMM CAR Region 1 (Ilocos Region) Region 2 (Cagayan Valley) Region 3 (Central Luzon) Region 4 (CALABARZON) Region 5 (Bicol Region) Region 6 (Western Visayas) Region 7 (Central Visayas) Region 8 (Eastern Visayas) Region 9 (Zamboanga Peninsula) Region 10 (Northern Mindanao) Region 11 (Davao Region) Region 12 (SOCCSKSARGEN)

Region

Total No Soil (million Degradation ha) (%)

Urbanization, Loss of Soil Built up Nutrients Top Areas, Other Soil and Organic River Water Soil Matter Erosion Flooding Logging and Industry Degradation Erosion (%) (%) (%) (%) (%) (%) (%)

TABLE 5.2 Shares of Land Degradation by Region and Type Based on SOTER/ASSOD Studies, 1993

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1,130,367 1,514,881 1,726,022 1,923,250 1,341,043 1,445,705 1,551,663 1,181,247 1,507,871 1,354,500 1,330,216 1,529,049 1,595,698

1,515,825 1,242,343 1,374,153 1,400,855 1,896,162 1,635,856 1,541,365 1,376,717 1,825,550 1,582,033 1,964,821 1,090,510 1,549,128

Slight Erosion (E1) 1,737,134 1,262,226 1,158,953 1,323,659 1,134,444 1,511,197 1,497,893 1,558,010 1,388,481 1,705,116 1,920,531 1,574,877 1,706,767

Moderate Erosion (E2) 413,729 264,569 416,644 143,297 317,337 153,579 391,721 328,733 405,619 212,343 603,451 966,174 464,960

Severe Erosion (E3) 32,313 n.a. 7,986 32,021 67,030 16,912 39,698 55,233 28,764 14,518 13,751 8,680 12,767

Unclassified Erosion (EU)

(%) 1,829,368 12.9 1,284,019 19.1 2,683,758 19.0 1,823,082 12.9 4,756,016 33.6 1,763,249 12.5 2,022,340 26.4 1,499,940 38.0 2,156,285 100.0 1,868,510 27.8 2,832,770 31.1 3,169,290 22.8 2,329,320 12.9

(ha)

Total Area

Notes: CAR = Cordillera Administrative Region; ha = hectares; n.a. indicates data were not available. Source: DA–DAR–DENR–DOST (Department of Agriculture, Bureau of Soils and Water Management; Department of Agrarian Reform; Department of Environment and Natural Resources; and Department of Science and Technology). The Updated Philippine National Action Plan to Combat Desertification, Land Degradation and Drought (DLDD): FY 2010–2020. 2010 (accessed May 2015).

CAR Region 1 (Ilocos Region) Region 2 (Cagayan Valley) Region 3 (Central Luzon) Region 4 (CALABARZON) Region 5 (Bicol Region) Region 6 (Western Visayas) Region 7 (Central Visayas) Region 8 (Eastern Visayas) Region 9 (Zamboanga Peninsula) Region 10 (Northern Mindanao) Region 11 (Davao Region) Region 12 (SOCCSKSARGEN)

Region

No Apparent Erosion (E0)

Erosion Class

TABLE 5.3 Severity of Soil Erosion by Region, 1993

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115.99 114.53 118.17 116.73 168.60 150.99 139.01 136.45 150.07 142.13 146.69 157.92 129.72 457.02

Agriculture 1,131.73 1,104.02 1,199.54 1,198.84 1,190.39 1,173.61 1,136.18 1,118.21 1,195.24 1,110.76 1,125.10 1,127.18 1,151.00 1,561.80

Grassland 12.54 10.53 13.85 11.24 15.50 10.69 10.74 10.27 11.70 11.03 13.50 13.65 11.72 26.98

Woodland 1,140.26 1,109.08 1,121.57 1,106.82 1,264.49 1,125.29 1,175.94 1,154.92 1,147.00 1,153.92 1,175.29 1,188.76 1,182.45 2,045.79

Total (tons per year)

182.82 128.49 156.51 197.82 165.50 184.74 105.80 114.04 176.35 192.58 166.31 165.39 194.79 180.62

Average (tons per ha per year)

Notes: CAR = Cordillera Administrative Region; ha = hectares. Source: Herminia Francisco, “Upland Soil Resources of the Philippines: Resource Assessment and Accounting for Soil Depreciation. ENRAP Phase II Technical Report No. 6”, in The Philippine Environmental and Resources Accounting Project, Phase II: Reports. Department of Environmental and Natural Resources, United States Agency for International Development, International Resources Group, Ltd., and Edgevale Associates, 1994.

CAR Region 1 (Ilocos Region) Region 2 (Cagayan Valley) Region 3 (Central Luzon) Region 4 (CALABARZON) Region 5 (Bicol Region) Region 6 (Western Visayas) Region 7 (Central Visayas) Region 8 (Eastern Visayas) Region 9 (Zamboanga Peninsula) Region 10 (Northern Mindanao) Region 11 (Davao Region) Region 12 (SOCCSKSARGEN) Total

Region

Gross Erosion Rate (million metric tons per year)

TABLE 5.4 Gross and Average Soil Erosion Rates by Region and Land Use, 1993

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erosion in the Philippines, on the other hand, has progressed little since the 1990s. Low (2013) noted that, globally, there were few peer-reviewed publications on the economics of desertification in recent decades. It was only in the 1980s that formal economic modelling of land degradation began, although the volume of economic research undertaken in the field has not grown much since the early 1990s. While literature is replete with documented studies linking soil erosion and productivity losses, growing evidence suggests that economic losses due to soil degradation are not significant. Francisco and de Los Angeles (1998) argued that the value of soil nutrients per hectare lost through erosion is not substantial and may in fact be barely noticeable when masked with organic and inorganic fertilizers. A World Bank study also estimated onsite fertility losses in upland agriculture at about a quarter of 1 per cent of GDP, or around US$100 million (Coxhead and Shively 1995, citing World Bank 1989). Briones (2010b) calculated the impact of soil erosion at 0.6 per cent of gross agricultural value-added in 2007.4 Greater variability was noted in the value of soil losses per hectare depending on the valuation methodology used, as well as plot and site characteristics (Briones 2010b). Consequently, as farmers adopt better land management practices, reduced land degradation amounts to US$10–17 million, or roughly 0.3 per cent of agricultural value-added (Coxhead and Shively 1995). Briones (2010b) also noted that soil resources are a “stock of natural assets” in agricultural production that depreciates with increasing use of inputs, such as fertilizer and labour, per unit of land area; hence, onsite losses in soil fertility on lowland agriculture can be accounted for. In fact, literature shows that loss of soil cover can be abated through proper soil-conservation and farm-management practices (Comia, Paningbatan, and Hakanson 1994; Paningbatan et al. 1995; Dano and Midmore 2004). The same, however, does not hold true in upland areas. The unintended impacts of upland soil erosion to downstream agriculture and coastal and marine ecosystems are not borne by farmers and therefore represent a loss to society (Briones 2010b). Offsite impacts of soil erosion to agriculture are often thought to be of a much higher magnitude because of upland–lowland linkages. Saastomoinen (1994) valued the cost of sedimentation to irrigated agriculture on a national scale at around PhP2.8 billion (US$65 million). Area-level estimates of losses in irrigated agriculture due to sedimentation range from PhP2.5 to 14.8 million per year, or US$344 million per year (Baluyut 1985; Indab

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1999). Coxhead and Shively (1995), however, admitted that it is difficult to determine a full valuation of the impacts of soil erosion because some offsite damages evolve slowly over time. Although eroded soil can cause sedimentation of lakes, reservoirs, irrigation canals, and paddy fields, these damages can be offset by downstream benefits such as soil deposition in lowland farms, which can enhance soil fertility. While upland–lowland linkages cannot be negated, Rola et al. (2013) noted that impacts may not be as significant as the literature implies. First, causal linkages are not firmly established due to the challenge of disaggregating the source of soil erosion (Cruz, Francisco, and Conway 1988; Saastomoinen 1994). Second, land-use changes resulting from mining and large-scale upland development (that is, road construction) may have a stronger impact on lowland agricultural productivity than any land-use changes stemming from agriculture. Meanwhile, given soil depletion rates by region (Francisco 1994), Briones (2010b) suggested that it would take about twenty to thirty-three years for the topsoil layer to be depleted, and more than a century for the soil layer to be completely eroded. Although impacts of soil depletion may only be significantly noticeable in areas with the steepest slopes, future impacts on the sustainability of agricultural productivity will be significant. Hence, a coordinated, ongoing appraisal of soil resources is needed (Atienza, Hapal, and Moga 2008), including programmes designed to protect and enhance this country’s fragile natural resource base.

WATER AVAILABILITY Competition for water use has been increasing at more than double the rate of population growth in the past century. Increased demand for water not only stems from population growth, but also from increased demand for agricultural, industrial, and other uses. At the same time, groundwater pollution is occurring in some areas due to human activities. As a result, the quality and availability of water has risen to the top of the list of environmental concerns in recent years (OECD undated). This is especially true in the Philippines. Indicators of the impact of agriculture on the environment should therefore include measures of water demand, water supply, and water quality. Agricultural water supply is derived from both surface water and groundwater, which are renewable resources whose supply is determined

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by hydrologic processes. Groundwater, for instance, is replenished or recharged naturally by rain and melting snow, and to some extent by surface water. Not all precipitation recharges groundwater reserves, however. Some precipitation evaporates, some runs off the land surface to streams or storm sewers, and some is taken up by plants (Gotkowitz 2010). Estimates of national water reserves vary and are often hard to reconcile. Based on FAO (2013a) data, between 1958 and 2011 the trend in total renewable resources per capita declined at a rate of 1.3 per cent or 231 m3 per year (Figure 5.2). NSCB–UNDP (2000) present a comparable picture of physical changes in the stock of surface water (Table 5.5). On average (for the regions covered), a decline of 6 per cent per year was estimated during 1988–93. The largest decline occurred in Region 9, the Zamboanga Peninsula, where surface water fell at a rate of around 3 million m3 (or 17 per cent) per year. Although the stock of surface water declined in most regions, the closing stock was still substantial as of 1993, at least indicating there was no shortage during these periods. Surface water stock levels in Regions 2, 6, and 8 did indicate that water recharge was smaller than the water inflow. Additional information from PEM (2003) further estimated that, as of 2003, total surface water was likely to be around 126 million m3. Projections combining the NSCB-UNDP (2000) surface water calculations with the PEM (2003) estimates of the stock of surface water indicate a decrease of a further of 2 per cent per year during 1993–2003. PEM (2003) also estimated that Region 10 had the highest surface water potential in 2003 (23 per cent of total surface water potential) and that Region 6 had the lowest (1.6 per cent of total surface water potential). The trend is similar for the stock of groundwater (Table 5.6). Once again, although stocks were declining in most regions, as of 1993, the water balance was still positive — that is, groundwater recharge remained higher than outflows — with the exception of Region 7. In 2003, the national groundwater stock was estimated to be around 20.2 million m3 (PEM 2003), which represents 14 per cent of the country’s total water resource potential that year. Region 2 was estimated to have the highest groundwater potential (14 per cent of national total), whereas Region 6 was estimated to have the lowest groundwater potential (4 per cent of the total). The literature provides several estimates for water abstraction. NSCB– UNDP (2000) estimated surface water requirements to be 43.2 million m3 in 1988 and projected that they would increase to 51 million m3 by 1994.

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Source: Constructed by authors from FAO (Food and Agriculture Organization of the United Nations). AQUASTAT database, 2013a (accessed May 2015).

FIGURE 5.2 Total Renewable Water Resources per capita, 1958–62 to 2008–11

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112,100 139,300 139,900 114,700 111,600 118,700 133,900 117,100 112,400 118,700 122,000 220,400

Beginning Stock, 1988 (million cubic metres) 110,139.06 114,243.64 122,670.58 112,386.01 114,653.74 113,054.57 117,629.36 112,317.80 112,430.56 118,234.08 140,737.17 158,496.57

Ending Stock, 1993 (million cubic metres) 12,(392.19) 1(5,011.27) 1(3,445.88) 12,(462.80) 1(1,389.25) 12,870.91 1(3,254.13) 1(2,956.44) 12,116.11 12,1(93.18) 13,747.43 (12,380.69)

Yearly Change (million cubic metres)

1–3 –13 1–9 1–3 –12 –10 –10 –17 –10 –10 –17 1–6

Yearly Change (%)

Notes: Data are estimates based on the Philippine Economic, Environment and Natural Resources Accounting exercise, which was not updated beyond 1993. Region 3 (Central Luzon) is excluded due to lack of complete data. Source: Constructed by authors from NSCB–UNDP (National Statistical Coordination Board and United Nations Development Programme). Environmental Degradation Due to Selected Economic Activities.> (accessed May 2015).

Region 1 (Ilocos Region) Region 2 (Cagayan Valley) Region 4 (CALABARZON) Region 5 (Bicol Region) Region 6 (Western Visayas) Region 7 (Central Visayas) Region 8 (Eastern Visayas) Region 9 (Zamboanga Peninsula) Region 10 (Northern Mindanao) Region 11 (Davao Region) Region 12 (SOCCSKSARGEN) Total

Region

TABLE 5.5 Estimated Physical Changes in the Regional Surface Water Stock, 1988–93

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114,620 111,850 154,700 137,000 118,625 155,242 112,053 118,400 114,700 115,950 112,635 136,000 261,775

Beginning Stock, 1988 (million cubic metres) 113,869.82 112,482.76 154,061.46 128,684.27 117,688.63 154,150.83 11(2,000.82) 118,615.95 114,615.83 115,925.59 112,148.15 134,899.62 245,142.09

Ending Stock, 1993 (million cubic metres) 1,(150.04) 1,126.55 1,(127.71) (1,663.15) 1,(187.27) 1,(218.23) 1,(810.76) 1,143.19 1,1(16.83) 1,11(4.88) 1,1(97.37) 1,(220.08) (3,326.58)

–3 1 0 –4 –2 0 –39 1 0 0 –1 –1 –1

Yearly Change Yearly (million cubic metres) Change (%)

Source: Constructed by authors from NSCB–UNDP (National Statistical Coordination Board and United Nations Development Programme). Environmental Degradation Due to Selected Economic Activities.> (accessed May 2015).

Region 1 (Ilocos Region) Region 2 (Cagayan Valley) Region 3 (Central Luzon) Region 4 (CALABARZON) Region 5 (Bicol Region) Region 6 (Western Visayas) Region 7 (Central Visayas) Region 8 (Eastern Visayas) Region 9 (Zamboanga Peninsula) Region 10 (Northern Mindanao) Region 11 (Davao Region) Region 12 (SOCCSKSARGEN) Total

Region

TABLE 5.6 Estimated Physical Changes in Regional Groundwater Stock, 1988–93

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The study further projected that agriculture would account for 97 per cent of this amount and that Regions 6, 7, and 8 would account for a combined total of 43 per cent of agricultural water requirements by 1994. Other data cited estimated that agriculture would account for 82 per cent of national water demand in 2013 (FAO 2013a). Looking to the future, projections of water consumption in 2025 have been estimated to range from 62.7 million m3 to 86.5 million m3 (PEM 2003). Agriculture is once again estimated to dominate demand, on average representing around 84 per cent of water consumption in 2025. Water for irrigation is projected to remain the primary source of agricultural water consumption, representing an average of 74 per cent of projected agricultural water consumption in 2025. The rice sector is the heaviest user of irrigation water in the Philippines and, hence, will likely account for the bulk of future water requirements. Such requirements (or consumption) can be deduced from the extent of irrigated rice paddy areas (see Appendix Table 5A.2). Water abstraction for irrigation was estimated to be around 651 million cubic meters in 2014, with the highest abstraction levels in Central Luzon (19.7 per cent of water consumption for irrigated rice). On average, yearly water consumption for irrigated rice was calculated to be around 587 million m3 during 2000–14. Assuming two croppings per year, total water abstraction was around 1.3 billion cubic meters in 2014. Comparing the trends in groundwater stock presented earlier (see Table 5.5 and 5.6) against the trends in water abstraction, water levels for agricultural use would be adequate, particularly for rice irrigation.5 Based on available data, consumption and availability trends do not point to potential water shortages for agricultural use. Region 6 may have a potential problem because it has the lowest total water potential and relatively high projected water consumption. Similarly, Region 7 — which was already showing signs of overextraction of groundwater as of 1993 (at least based on the NSCB–UNDP 2000 study) — is also a region of concern. These results do not, however, mean that the discussion on agricultural water deficit should be ignored. Based on these limited assessments, the more pressing water problem is the efficient and timely delivery of water rather than its limited availability (David 2004). Much of this has to do with efficiency in both water use and conveyance (through irrigation systems). The amount of water needed for irrigation is higher than necessary because of inefficient water use (David 2004).

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These inefficiencies, along with expanding coverage of irrigation services and population growth, can hasten the depletion of future surface and groundwater stocks. Moreover, water availability may be adequate based on aggregated yearly values, but not in terms of its distribution, which can be time-sensitive and variable (Greenpeace 2007). The problem is that water may be abundant when farmers do not need it, and scarce when they do. The spatial and temporal availability and its attendant impacts are largely driven by climatic conditions. Climate change has exacerbated the issue of agricultural water availability, and this issue could become more acute in the future. Because of this the need for timely and accurate data, both spatially and temporally, is a key ingredient in making the agricultural sector more resilient to climatic changes since it is (and should be) a key input in planning.

WATER QUALITY Agriculture is both a cause and a casualty of water pollution (Moxey 2012; OECD 2012). Its potential impacts on water quality include the discharge of sediments that cause siltation of riverbeds, run-off leading to eutrophication, contamination, and salinization; the most common impacts on groundwater are leaching and contamination (Table 5.7). Impacts are often due to poor agricultural practices, such as improper use of fertilizers or poor nutrient management. Nutrient use on arable and permanent crop areas reached a high of 80.79 tons per 1,000 ha of land in 2003; 73 per cent is derived from nitrogen fertilizers and the remainder from phosphate fertilizers (FAO 2013b). In 2010, data from the Food and Agriculture Organization of the United Nations (FAO) showed a nutrient use level of 62.57 tons per thousand ha, almost 90 per cent of which was derived from nitrogen fertilizers. Excessive use of nitrogen fertilizers can lead to surface run-offs and ultimately to water surface pollution or leaching into the groundwater. One common measure of surface water pollution is the level of biological oxygen demand (BOD)6 in bodies of water. High levels of BOD often limit the use of water for domestic purposes, as well as making water uninhabitable by fish and other animals. Greenpeace (2007) showed that the generation of BOD from agriculture accounted between 29 and 37 per cent of total point-source water surface pollution in the Philippines. BOD generation is especially acute in Region 4, which produces 109,000 metric

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Sediments carrying phosphorus and pesticides adsorbed to sediment particles; siltation of river beds; loss of habitat or spawning grounds, and so on

Nutrient runoff, especially of phosphorus, causing eutrophicationa and ultimately taste odor in public water supplies; excess growth of algae causing deoxygenation of water and ultimately fish kills

Spreading on frozen ground causing high levels of water contamination in the form of pathogens, metals, phosphorus and nitrogen, leading to eutrophication and potential contamination

Runoff of pesticides leading to contamination of surface water and loss of the biotab of top predators due to inhibited growth; reproductive failure causing dysfunction in the ecological system in surface waters; public-health impacts from the consumption of contaminated fish; pesticides carried by wind over long distances, contaminating aquatic systems thousands of miles away (for example, tropical/subtropical pesticides found in Arctic mammals)

Contamination of surface water with pathogens (bacteria, viruses, and so on) leading to chronic public-health issues; contamination by metals contained in urine and feces

Fertilizing

Manure spreading

Pesticides

Feedlots/animal corrals

Surface Water

Impacts

Tillage/plowing

Agricultural Activity

TABLE 5.7 Potential Impacts of Agriculture on Water Quality

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Potential leaching of nitrogen, metals, and so on, to groundwater

Leaching of pesticides into groundwater causing public health problems due to contaminated wells

Contamination of groundwater, especially by nitrogen

Leaching of nitrate to groundwater potentially threatening public health

Groundwater

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Multiple issues stemming from pesticide runoff and contamination of surface water and fish; erosion and sedimentation issues

Release of pesticides (for example, Tributyltin and high levels of nutrients to surface water and groundwater through feed and feces, leading to serious eutrophication

Silviculture

Aquaculture

Notes: a. Eutrophication occurs when an overabundance of nutrients in aquatic systems stimulate plant growth and deplete oxygen. b. Biota refers to plant and animal life of a particular region, habitat, or geological period. Source: FAO. FAOSTAT database. 2013b (accessed 20 December 2013).

Disruption of hydrologic regime, often with increased surface runoff and decreased groundwater recharge; affects surface water by decreasing flow in dry periods and concentrating nutrients and contaminants in surface water

Erosion of land, leading to high levels of turbidity in rivers, siltation of the bottom habitat, and so on; disruption and change of the hydrologic regime, often with loss of perennial streams; public-health issues stemming from loss of potable water

Clear cutting

Groundwater Enriching groundwater with salts and nutrients (especially nitrate)

Impacts

Runoff of salts leading to salinization of surface waters; runoff of fertilizers and pesticides to surface waters with ecological damage, bioaccumulation in edible fish species, and so on; High levels of trace elements, such as selenium, causing serious ecological damage and potential human-health impacts

Surface Water

Irrigation

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tons per year (13 per cent of the national total). In contrast, Region 7 produces only 21,000 metric tons of BOD per year. For groundwater pollution, on the other hand, case studies have shown that the nitrate leaching from flooded rice fields is negligible because of rapid denitrification under anaerobic conditions (Bouman, Lampayan, and Trong 2007, citing Buresh and De Datta 1990). Nitrate pollution of groundwater under rice-based cropping systems surpassed the 10 milligrams per litre limit for safe drinking water only when highly fertilized vegetables were included in the cropping system (Bouman, Lampayan, and Trong 2007, citing Bouman et al. 2002). Given that rice cultivation dominates irrigation water use, groundwater leaching may not be an alarming issue for the country. Aside from these assessments, no other data were available to enable the calculation of the extent of other impacts, such as fertilizer and pesticide leaching and run-off.

AGROBIODIVERSITY The Philippines offers diverse agricultural ecosystems, which is reflected in the ex situ germplasm collections held by forty-five government and nongovernment organizations (NGOs). Altoveros and Borromeo (2007) documented around 173,205 accessions by crop (Table 5.8). Scant evidence TABLE 5.8 Number of Documented Accessions of Philippine Germplasm by Crop, 2007 Crop Rice Coconuts Bananas Mangoes Corn Sugarcane Manila hemp

Number of Documented Accessions 5,500 1,224 1,190 1,264 2,099 1,898 1,773

Source: Nestor Altoveros and Teresita Borromeo, “Country Report on the State of Plant and Genetic Resources for Food and Agriculture: Philippines (1997–2006)”, 2007 (accessed May 2015).

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from site-specific studies indicates the occurrence of genetic erosion (that is, loss of genetic diversity within a species) in traditional varieties, the causes of which are crop-specific — including the loss of agricultural land, the impacts of pests and diseases, and adverse environmental conditions. The genetic diversity of papaya, for example, was eradicated by the papaya ringspot virus, and the gene pools of bananas and citrus fruits declined due to the bunchy virus and tristeza virus, respectively. In 2006, flooding caused by typhoon Milenyo destroyed 70 per cent of the genetic material stored at the National Plant Genetic Resources Laboratory in Los Baños, Laguna (DENR–UNDP–ACB–ATENEO 2009, citing Calleja and Aguilar 2006). At the national scale, the extent of genetic erosion is unknown, and no systematic assessment has been done because of funding constraints (Altoveros and Borromeo 2007). DENR/UNDP/ACB/ATENEO (2009) further acknowledged the lack of national indicators or system for monitoring crop varieties. This is not unexpected because agricultural biodiversity rated low among the Philippine biodiversity conservation priorities and was excluded in the identification of key biodiversity areas or sites critical for the conservation of globally important biodiversity. Moreover, as an indication of the complexity of developing measurements of genetic erosion, even the OECD has been unable to determine any concrete indicators (OECD 1999a; Day-Rubenstein et al. 2005). Nonetheless, in simple terms, measures of biodiversity would fundamentally include the number of species, their distribution, and the differences among them within given areas and timeframes. Such concepts can also be applied to crop varieties as a relevant unit of observation rather than species. For the purposes of illustration, the evolution of improved rice varieties is explored below. The Philippines has over 5,500 collected and documented traditional rice varieties found in various agroecological zones, along with many more rare varieties being discovered in ongoing surveys. Prior to the 1970s, an estimated 3,500 traditional rice varieties reportedly existed. Rice germplasm is currently conserved in the genebanks of the Crop Science Cluster of the University of the Philippines, Los Baños, College of Agriculture; the Farmer–Scientist Partnership for Development; the International Rice Research Institute (IRRI); PhilRice; and Southeast Asia Regional Initiatives for Community Empowerment (Altoveros and Borromeo 2007). The National Seed Industry Council released 128 varieties

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by the end of the 1990s (Table 5.9). From these, 50 new rice varieties were added between 2000 and 2015. A total of 178 commercially released rice varieties are currently available, adapted for specific locations and purposes: 111 lowland irrigated varieties; 7 upland varieties; 21 saline varieties; 6 varieties for cool, elevated areas; 25 rain-fed varieties, and 8 glutinous varieties (Pinoy Rice 2015). The introduction of new cultivars may seem to have improved the genetic diversity of rice, and diversifying the number of varieties used in crop production may have strengthened the crop’s resilience to environmental changes (OECD 2001). However, genetic erosion is occurring through situ variety replacements. This means that modern, genetically homogenous, high-yielding rice cultivars have rapidly replaced traditional rice varieties and landraces — a trend that has been attributed to the marketing and promotion of commercial rice varieties. Government investment and policy supported the adoption of modern cultivars as a means of increasing productivity. For example, large investments in irrigation infrastructure propelled the high adoption of modern varieties (MVs) in the 1970s. In 1973, the Marcos regime launched Masagana 99, which provided a complete package of production incentives to farmers, including credit and fertilizer subsidies (Estudillo and Otsuka 2006, citing Esguera 1981). The adoption of modern rice varieties reached 60 per cent in the 1970s and continued to rise in the 1980s. Herdt and Capule (1983) TABLE 5.9 Improved Rice Varieties Released in the Philippines, by Time Period Time Period Pre-1970 1970–80 1981–90 1991–99 2000–15 Total Rice area (million hectares) Number of varieties released per million population

Number of Varieties Released 14 38 20 56 50 178 4.53 39

Sources: Mahabub Hossain et al., “International Research and Genetic Improvement in Rice: Evidence from Asia and Latin America”, in Crop Variety Improvement and Its Effect on Productivity, edited by R. Evenson and D. Gollin (Wallingford, U.K.: CAB International, 2003); PSA (Philippine Statistical Authority). “CountryStat”, 2013 (accessed November 2013).

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documented this rapid expansion. From the 1990s onward, however, limited published data exists on the extent of diffusion of succeeding generations of MVs (Estudillo and Otsuka 2006; Launio, Redondo, and Beltran 2008). Surveys conducted by IRRI in Central Luzon show that at the end of the 1970s, modern rice varieties completely crowded out traditional varieties (Table 5.10). New rice breeds were rapidly diffused without any reversion to traditional cultivars because MVs were proven to be more profitable and sustainable. New breeds were rapidly adopted, such that by the mid-1990s MV3 completely replaced MV2 (Estudillo and Otsuka 2006). Launio, Redondo, and Beltran (2008) estimated the average replacement period for rice varieties to be around 8 to 11 years (the fastest rate being for irrigated agriculture in the dry season). Launio, Redondo, and Beltran (2008) also looked at the diffusion of rice varieties across the Philippines’ major rice-producing areas as an indicator of the genetic diversity cultivated by farmers. The study noted that around ten varieties were grown in 70–80 per cent of the country’s rice area. Indexes of the spatial diversity of rice showed large variation across provinces in terms of richness or number of varieties grown in an area, relative abundance, and “evenness”,7 but trends were ambiguous across time. Findings further suggested that varieties exhibit greater diversity and less dominance during the dry season, although no significant change was observed in spatial diversity overall. This implies that the ex situ genetic diversity of rice is being sustained through ongoing varietal improvements. Data limitations, however, preclude a more detailed analysis of trends in diversity and variety replacements. Further research is needed in this area. Across MVs, the adoption of hybrid rice increased after the 2001 launch and 2002 implementation of the Hybrid Rice Commercialization Project (Sebastian and Bordey 2005). Area harvested grew from 5,371 ha in 2001 to 167,286 ha in 2011. During that time, yields from hybrid seed increased by an average of 38.33 per cent over certified or inbred seed (Table 5.11). One relevant ecological concern on the use of modern and hybrid varieties is cystoplasmic uniformity, which increases the susceptibility of new varieties to mutation in pests and diseases. Around 91 per cent of post-IR8 varieties are genetically related — taken from a single parent that can be traced to a single cytoplasm, Cina (SEARICE 2007, citing de

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100 194 134 118 127 110 110 110 110 110 110 110 110 110 110 110 110 110

Year

1966 1967 1970 1971 1974 1979 1980 1982 1986 1987 1990 1991 1994 1995 1998 1999 2003 2004

10 16 66 92 73 18 19 13 11 12 12 13 10 10 10 10 10 10

MV1 (IR5–IR34) 10 10 10 10 10 92 91 97 38 16 18 15 16 10 10 10 10 10

MV2 (IR36–IR62) 110 110 110 110 110 110 110 110 161 192 190 182 194 100 100 100 100 100

MV3 (IR64–IR72 and PSB Rc2–74) 110 117 129 159 139 162 178 163 167 188 173 103 193 125 150 143 136 165

Nitrogen–Phosphorous– Potassium Fertilizer (kgs per ha per season) 2.3 1.9 2.5 2.4 2.2 3.6 4.3 4.1 3.6 4.3 4.6 4.5 3.9 4.6 4.8 3.4 4.2 4.7

Average Rice Yield (metric tons per ha per season)

Notes: Kgs = kilograms; ha = hectares; MV = modern variety. MV1 includes IR5–IR34 and the C4 series, which were released from the mid-1960s to the mid-1970s; MV2 includes IR36–IR62, released from the mid-1970s to the mid-1980s; and MV3 includes IR64–IR72 and PSB Rc varieties, released from mid-1980s to 2004. Source: Jonna Estudillo and Keijiro Otsuka, “Lessons from Three Decades of Green Revolution in The Philippines”, The Developing Economies 44, no. 2 (2005): 123–48.

Traditional Varieties

Area Planted (%)

TABLE 5.10 Trends in Adoption of Modern Rice Varieties in Central Luzon Provinces, 1996–2004

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11,129,223 11,168,664 11,461,559 11,172,864 12,213,820 11,829,875 11,462,938 11,327,056 11,204,960 11,265,327 11,058,522 12,194,808

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Total

2,115,371 2,127,914 2,177,358 2,203,995 2,368,636 2,313,243 2,238,681 2,218,987 2,197,076 2,203,583 2,167,286 2,022,130

Area Harvested (ha) 5.44 6.04 5.97 5.75 6.01 5.84 6.13 6.06 6.11 6.22 6.33 6.03

Yields (metric tons per ha) 12,568,906 15,688,367 15,517,759 14,952,959 12,959,194 14,547,026 16,963,960 17,627,917 10,810,715 10,382,911 17,797,217 69,816,931

Production (metric tons) 11,599,961 11,283,012 11,233,210 11,083,294 11,641,849 11,016,555 11,600,247 11,714,236 12,614,046 12,524,064 11,703,545 16,014,019

Area Harvested (ha)

Certified Seed

4.28 4.43 4.47 4.57 4.61 4.47 4.35 4.45 4.14 4.11 4.58 4.36

Yields (metric tons per ha)

27.07 36.28 33.35 25.75 30.26 30.60 40.84 36.19 47.84 51.09 38.25 38.33

Yield Difference (metric tons per ha)

Note: ha = hectares. Source: Sergio R. Sebastian and Flordeliza Bordey, “Embracing Hybrid Rice: Impacts and Future Directions”, paper presented at the SEARCA Seminar Series, 19 July 2005, SEARCA, University of the Philippines College, Laguna, 2005.

Production (metric tons)

Year

High-Yielding Varieties

TABLE 5.11 Production and Area Harvested of High-Yielding Varieties and Certified Seeds, 2001–11

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Leon 1994). This problem is more serious in hybrid rice because 95 per cent of the total area in Asia planted to hybrid varieties came from an individual cytoplasm source known as “WA cytoplasm” (SEARICE 2007, citing Brar et al. 1996). The concern over the narrowing of the gene pool of rice grown on farmers’ fields has not been adequately substantiated by existing research.

CLIMATE CHANGE Agriculture contributes to climate change by releasing GHGs. Smith et al. (2007) identified the following GHGs as being associated with the agricultural sector: 1. carbon dioxide (CO2) mainly from microbial decay or the burning of plant material and soil organic matter; 2. methane (CH4) from fermentative digestion by ruminant livestock, stored manure, and rice grown in flooded conditions; and 3. nitrous oxide (N2O) from transformation of nitrogen in soil and manure, which is enhanced under wet conditions by excess nitrogen. The Philippine agricultural sector emitted an estimated 37,000.65 gigagrams CO2 equivalent (Gg CO2 eq) of GHGs in 2000 (DENR–EMB 2011). Emissions from rice cultivation amounted to 16,436.95 Gg CO2 eq or 44 per cent of all agricultural emissions that year. Thus, irrigated rice cultivation is the country’s major source of GHGs from agriculture; this stems from methane, which has a higher carbon potential than other gases. The regional distribution of GHG emissions — which can be estimated based on the share of irrigated rice production across regions — follows a slightly upward trend, with major downward fluctuations occurring in 1988, coinciding with extensive drought that year (Figure 5.3). At the regional level, Central Luzon and the Cagayan Valley were the primary producers of GHG emissions, which is not surprising because the majority of irrigated rice production occurs in these two regions. While its contribution to GHG emissions is not substantial, to date the Philippines has borne the brunt of the adverse impacts of climate change and is, in fact, one of Southeast Asia’s most vulnerable countries. Francisco and Yusof (2009) devised a map showing countries’ relative

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Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Source: FAO. FAOSTAT database, 2013b (accessed 20 December 2013).

FIGURE 5.3 Regional Distribution of Greenhouse Gas Emissions from Rice Cultivation, 1994–2010

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vulnerability to climate change. The Philippines is highly vulnerable to cyclones, landslides, flooding, and drought. The most vulnerable regions are in Northern Luzon because of the frequency with which they experience cyclones. The Cagayan Valley is the most vulnerable region in the north of the country; between 2001 and 2010, it was hit by 156 typhoons (Israel and Briones 2013). The Philippines also ranked fourth on the 2006 Global Risk Index (Virola et al. 2008). Climate-related hazards have profound impact on agriculture. The upward trend in area affected by typhoons and other hazards between 2001 and 2010 resulted in agricultural output losses valued at US$2,234 million (Israel and Briones 2013). Losses to rice production amounted to US$1,218 million or 54 per cent of total commodity losses in agricultural production during 2001–10. The most heavily affected areas were Regions 2 and 3. Thus, the commodity that contributes the most GHGs also bears the greatest losses from climate change. While the impacts of climate-related hazards like floods and droughts can be tangibly measured based on actual losses, the impacts of climate change itself are harder to ascertain because they are based climate variables, such as precipitation, temperature, and CO2 concentration. Longer time-series data are also needed to detect observable changes. As a result, analyses are usually based on predictive projections derived from simulations under a variety of possible scenarios. Such results have shown that the impacts of climate change on specific crops depend on the location, the climate variable deemed to be changing, and the crop’s photosynthetic pathways. Lansigan and Salvacion (2007) examined changes in the yield of corn and rice in response to 10 different combinations of CO2 level and temperature increases in three provinces of the Philippines. Increases in atmospheric CO2 actually led to yield increases, especially for rice, but the increases varied by location. Conversely, temperature increases resulted in yield decreases, although corn was generally more resilient, responding with lower yield reductions and less yield variation. Zhai and Zhang (2009) used a dynamic computable general equilibrium model to look at the impact of climate change on production in several Southeast Asian countries for the period 2004–80. Crop output for the Philippines was projected to decline by 22.5 per cent, and rice output by 11.9 per cent. Imports of rice and other crops were projected to rise because the Philippines is already import-dependent. Agriculture’s contribution to

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GDP was projected to decline, as was the country’s terms of trade. One interesting finding was that the impacts of climate change on agricultural productivity depend on baseline productivity levels, with lower levels indicating greater vulnerability; thus, reversing declining productivity should be an important component of strategies designed to combat the adverse impacts of climate change on agriculture.

MEASURING THE SUSTAINABILITY OF AGRICULTURAL PRODUCTIVITY GROWTH Agricultural performance in the Philippines is often measured by growth in the value or quantity of outputs and yields. A broader and more favoured measure is total factor productivity (TFP),8 which offers a comprehensive indicator of how efficiently a country’s agricultural land, labour, and capital produce agricultural outputs. Sustainability of agricultural growth is often equated with a nonnegative trend in TFP. Teruel (2012) reviewed various estimates of TFP in the Philippines. Despite discrepancies due to methodological differences, estimates showed that the country’s agricultural output growth has been driven by agricultural productivity growth (as opposed to increased use of factor inputs). Although values varied, growth trends were consistent, initially following an erratic pattern, with strong growth during 1970–80 and subsequent declines until the 2000s. Previous studies, however, have failed to address the reality that sustainability goes beyond simply capturing changes in productivity, and that measures should also address the unintended effects of agricultural production on the environment. In particular, TFP measures need to take into account the use of the environment’s “sink” services — that is, its role in absorbing pollution generated by agricultural production. Such sink services are an input to agricultural production that is not accounted for by conventional measures of agricultural productivity. As Ball et al. (2005) found, conventional TFP measures in the United States have overestimated productivity growth during periods where productivity was accompanied by worsening environmental degradation and have underestimated productivity growth in periods when considerable environmental improvements occurred. Thus, failure to account for environmental degradation emanating from agricultural production leads to a misleading picture of agricultural performance or growth.

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Although environmental degradation has been recognized as a possible determinant of TFP growth (Briones 2010b; Teruel 2012), its role has neither been discussed in detail nor incorporated into measurements of agricultural productivity growth in the Philippines. Adding measures of environmental degradation into TFP measures can be used as a sustainability indicator for agricultural growth, as is illustrated in the case study on the Philippine rice sector, given the high levels of GHG emissions resulting from rice cultivation (Box 5.1). It is also interesting to look at the TFP index in terms of a technical change on the one hand, and technical efficiency on the other: technical change measures the change in the production potential between two periods, whereas technical efficiency measures the distance between the actual or observed production level and the potential production level. Although both aspects of the conventional index declined during the periods studied, technical efficiency only declined by 0.4 per cent per year whereas technological change index declined by 2.6 per cent per year (using the conventional Malmquist index). Thus, stagnating levels of technical change largely drove the declining productivity growth in rice. The environmentally adjusted technological change index exhibited the same pattern (see Box 5.1 and Appendix 5A); however, because of the declining GHG emissions from rice (largely attributed to declining production), the average yearly decline was lower, at 1.3 per cent.

SUMMARY AND IMPLICATIONS FOR POLICY The Urgent Need for Data on the Linkages between Agriculture and the Environment In assessing trends in the environmental impacts of agricultural production on land, water, agrobiodiversity, and GHG emissions, it is evident that data on the linkages between agriculture and the environment are not as organized or coherent as those on agricultural production. This is perhaps indicative of the bias of agricultural policy towards increasing production. Data on these environmental variables have been extremely limited and sporadic, both spatially and temporally. Moreover, the data that do exist are often difficult to reconcile based on the many different values found in literature. This situation highlights the urgent need for the collection of comprehensive, up-to-date, comparable data, which are vital to the

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assessment of the effects of agriculture on the environment over time and the effectiveness and efficiency of agricultural and environmental policy measures (Oenema et al. 2011). In addition, such data could identify gaps in current agri-environmental policy.

Convening a Multisectoral Technical Working Group to Build a Set of Agri-Environmental Indicators A step towards achieving sustainability in agricultural productivity, as stressed earlier, is monitoring the effects of agricultural production on land, water, agrobiodiversity, and GHG emissions. Unlike such regions as the European Union and Canada, the Philippines has yet to build a comprehensive set of agri-environmental indicators. The government could convene a multisector technical working group comprising representatives from the Department of Agriculture and its line agencies, the Department of Environment and Natural Resources, environmental NGOs, and academia to identify potential indicators of the state of the four environmental variables. These indicators should be easy to collect, would represent environmental changes, and would be valuable for agrienvironment planning. Such indicators could be fed into the data system of the Philippine Statistics Authority or be part of the compendium of environmental statistics that is published by PSA’s National Statistical Coordinating Board.

Strengthening Institutions Mandated to Collect Agri-Environmental Indicators The capability of institutions that are tasked to collect the basic data for the agri-environmental indicators should likewise be strengthened. The following discussions look at how this could be done for water resources and agrobiodiveristy. Data for these two environmental variables, based on the discussions, were more inadequate compared to the land resources and GHG emissions.

Water Resources The National Water and Resources Board (NWRB) is the primary regulatory agency for water resources. It does not, however, have information on the current quantity, or changes in the quantity, of ground and surface

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BOX 5.1 The Effects of Agricultural Production on the Environment: A Case Study on the Philippine Rice Sector Rice cultivation constitutes a useful case study for measuring the sustainability of agricultural growth because of its impact on the environment. Irrigated rice systems act both as significant sinks for atmospheric CO2 and significant sources of methane and of nitrous oxide (Bouman, Lampayan, and Trong 2007). Current estimates of yearly methane emissions from rice fields range from 20 to 60 teragrams, or 5–10 per cent of total global emissions (Bouman, Lampayan and Trong, 2007 citing Kirk 2004). Closely following Ball et al. (2005), an environmentally sensitive Malmquist TFP index was constructed to facilitate the calculation of an environmental productivity index with which to measure the sustainability of agricultural productivity growth in the Philippine rice sector (see Appendix Table 5A.1 for the data and variables used to construct the indexes). GHG emissions are treated as an “input service” in the sense of being an unaccounted use of the environment’s “sink” services; hence, an input-oriented Malmquist productivity index serves as the baseline for calculations. Both the conventional and the environmentally sensitive Malmquist indexes follow a downward trend during 1994–2010; trends in the average yearly growth rate differ for the three time periods considered (Table A). The highest average yearly decline in TFP productivity occurred during 1994–2000. The decline in agricultural productivity growth rate, however, was lower in succeeding years. The approximate average yearly decline of 2.9 per cent per year is likely to have been driven by extreme climatic events, particularly in 1998 (see Chapter 4, this volume). These events caused rice yields to decline to their lowest levels of the period, and are likely to have caused the decline in the average rate of yearly productivity growth for rice as well. The two versions of the Malmquist index also yielded different rates of decline. Under the conventional measure, TFP growth declined by 2.6 per cent per year, whereas under the modified index it declined by only 1.3 per cent per year. Thus, the conventional index did not account for the rice sector’s use of the environment’s sink services. Except for the erratic dip and subsequent rise between 1996 and 1999, GHG emissions generally followed a declining trend during 1995–2010 (Figure A). Although yields may have decreased, the rice sector’s use of the environment’s sink services also decreased. This also explains the rising environmental productivity index (the ratio between the environmentally sensitive and the conventional Malmquist indexes). The yearly average environmental index being more than one for all time periods indicates, at minimum, that the rice sector is mitigating its impact on the environment to some degree. Source: Authors.

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TABLE A Calculated Measures of Rice Productivity Growth, 1995–2010   Year   1995   1996   1997   1998   1999   2000   2001   2002   2003   2004   2005   2006   2007   2008   2009   2010 Yearly growth rates (%)   1994–2009   1994–2000   1994–2005   2005–2009

Environmentally Sensitive

Conventional

Environmental Productivity

0.966 0.960 1.041 1.197 0.790 0.985 0.979 0.987 1.022 0.957 1.010 0.991 0.959 0.957 1.003 1.035 –1.30 –1.76 –1.02 –0.79

0.961 0.955 0.998 1.078 0.864 0.977 0.977 0.940 0.991 0.966 0.985 0.984 0.958 0.962 0.989 1.007 –2.64 –2.98 –2.74 –1.95

1.005 1.006 1.043 1.110 0.914 1.008 1.001 1.050 1.032 0.991 1.026 1.007 1.001 0.995 1.014 1.028 1.37 1.26 1.78 1.19

Source: Calculated by authors.

Note: While it would be interesting to relate these trends to the evolution of the country’s policy regimes, doing so may not be prudent; FAO uses Tier 1 methodology for GHG calculations; thus, FAO data closely tracks the size of land devoted to rice. Given this, it would be hard to disentangle changes in cultivated rice land with changes in policy regimes. Source: Constructed by authors.

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water through time. A 1998 study funded by the Japanese International Cooperation Agency looked at the Philippines’ actual amount of ground and surface water. One of the study’s outputs was the identification of water deficient “hot spots” and projections of water consumption and availability to 2025, but no follow-up study has been done. To date the closest available information for assessing water resources is the allowed extraction rate (measured in m3 per second) for those who sought water extraction permits from the Board. This, however, represents only a portion of all water extractors. Furthermore, this only measures the extraction rate allowed and not the actual physical quantity of available water. Further hampering the agency from effectively collecting data is their lack of regional offices; the central office based in Manila undertakes all monitoring and granting of permits. An entry point for policy would be to reorganize the Board and allow for additional regional offices. Apart from the NWRB, the NCSB also has developed a physical accounting methodology for water availability. Similar to the JICA study, this study is also outdated and no follow-up has been undertaken because the division that handles this physical accounting is undermanned. The PSA should consider hiring additional personnel to regularly update the physical accounts of available water.

Agrobiodiversity Unlike land and soil resources, no institution has been identified to oversee issues relating to agrobiodiversity in the Philippines. As a result, neither monitoring nor a clear indicator to monitor this environmental variable exists. One agency that could be tasked with monitoring and collecting data on agrobiodiversity would be the Philippine Council for Agriculture and Aquatic Resources and Development (PCAARD), which is one of the science councils under the Department of Science and Technology. PCAARD is strategically located in the Los Baños, in close proximity to, and with support from, relevant branches of academia and national and international agricultural agencies.

Sustainability in Rice Productivity Growth The case study on the Philippine rice sector presented in this chapter demonstrated that Philippine TFP growth has been declining. In

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periods where GHG emissions were falling, the rate of decline in the environmentally adjusted TFP slowed down. Nevertheless, the fact that the environmentally adjusted productivity growth index also remained negative indicates lack of sustainability in productivity growth in the rice sector during 1994–2010. Put another way, the country failed to achieve productivity growth coupled with an improving environment during these periods. The significant downward trend in the environmentally sensitive TFP measure also implies that the declining productivity growth was more dominant than the effect of the improvements in GHG emissions. Also interesting is that these trends can also be linked to the prolonged drought of the mid-1980s, suggesting that climate-related hazards might be a real concern, not only for productivity growth, but also for sustainable growth in rice, and most likely agriculture, in the future. The case study also showed that the primary determinant of the decline in TFP growth was stagnating technical change, which is consistent with two earlier studies focusing on rice TFP in periods prior to those covered here. This ongoing decline in TFP driven by stagnant technological change seems to indicate that, at least for rice, a technological plateau may have been crossed around the mid-1990s. This would not be surprising given the declining intensity of public expenditures in rice research, as well as the declining rate of contribution of rice research to rice TFP (Francisco and Bordey 2013).

Investing in a National R&D Programme for Sustainable Farming Systems Although the case study on rice is just one example and exploration of the sustainability of agricultural productivity growth, it can shed light into an important issue for the broader agricultural sector. The previous discussion points to a need for the Department of Agriculture to invest in a specific national research and development (R&D) programme focusing on identifying or developing farming systems that can achieve increased productivity, while at the same time enhancing lowland and upland ecosystems through land, soil, and water conservation and the mitigation of agricultural GHG emissions. This is not to say, however, that efforts to identify ecofriendly technologies and practices are nascent in the Philippines. Through the years, some promising technologies have emerged. For example, the

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“alternate wetting and drying” irrigation technique has been identified as a primary water-saving technology for rice cultivation. Recent impact assessment of this technology in the Philippines has shown that the “Safe AWD” variant9 has reduced irrigation use by 38 per cent (in hours), without significantly reducing yields or profits (Rejesus et al. 2011). Other benefits include reduced GHG emissions (given that flooded rice fields are one of the main sources of methane gas). Another ecofriendly farming practice gaining attention is the use of site-specific nutrient management. This is an approach that focuses on delivering the optimal amount of nutrients to crops. Inherent in this practice is the use of indigenous nutrients found on-farm, thereby complementing fertilizer use. It has been applied to rice and corn fields, and field studies have shown that its use has resulted in net yearly benefits ranging from US$34 per ha per year in Vietnam to US$168 per ha per year in India (Pampolino et al. 2007). These benefits were largely due to increased yields rather than reductions in the cost of inputs. The Department of Agriculture, for its part, has an ongoing programme on organic agriculture, one of the goals of which is to decrease farmers’ dependence on inorganic chemical pesticides and fertilizers. Undoubtedly, an active science community has been developing ecofriendly technologies and collecting evidence on their impact on the environment. However, these developments have been compartmentalized, in the sense that they have been developed in isolation of other technologies and in pockets of locations. It is important to stress the need for and value of combining these technologies effectively to create integrated, sustainable farming systems that address the multiple goals of productivity, soil and water conservation, and GHG mitigation. Developing such systems could form the framework for the proposed R&D programme of the Department of Agriculture, the mission of which would be to harmonize, connect, and synergize the efforts of the country’s various agricultural research institutions.

Investing in Support Systems for Sustainable Farming The integration of different technologies would obviously occur at the farm level, where sustainability of agricultural productivity begins. But for these farming systems to be widely adopted across villages and beyond, the government would also need to invest in support systems such as the following:

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1. increasing the knowledge of public agricultural extension agents on GHG mitigating technologies and practices, and agents’ capacity to advise farmers on the appropriate practices or combinations of practices and technologies; 2. providing incentives for business or enterprise service providers to develop input and output markets for agricultural products emanating from this farming systems; 3. developing knowledge-sharing and communication platforms to facilitate provincial and regional exchange of success stories; and 4. instituting a monitoring and evaluation mechanism to track the spread of these farming systems.

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APPENDIX 5A An Environment-Adjusted Malmquist Index The Malmquist index was used to derive both the conventional and environmentally sensitive productivity indexes. The theory behind the index is well developed (for a more theoretical description, see Fare et al. 1994; for excellent exposition, see Ball et al. 2004). This discussion outlines the more practical aspects of calculating and implementing the index, in particular, the nonlinear programming problem designed to calculate both indexes.10 (See Appendix Table 5A.1 for a list of the data and variables used.) The conventional Malmquist index is given by the following equation:



Mc =

[(       ) (       )  ] Dtc (xt+1, yt+1)

2

Dtc (xt, yt)

Dct+1 (xt+1, yt+1) Dct+1 (xt, yt)

2

1 2

.

(1)

Given t = 1.., T and I = 1.., I decision making units (in this case the country’s regions), Dtc (xt, yt) is the distance function, which is the solution to the following nonlinear programming function for region “o”: Dtc (xt, yt) = min θ s.t.

λ, θ

Xt λ ≤ θxot ot Yt λ ≥  y θ λ ≥ 0. Here Xt is an (M × I) matrix of purchased input, Yt is a (1 × I) vector of marketed rice output for each region, and λ is a (I × 1) intensity vector. xot, yot on the other hand, are (I × 1) input usage vector and (I × 1) rice output of region “o” for time t. Ball et al. (2004) calls this the within period hyperbolic distance function. At an empirical level, this is actually an inputoriented constant returns to scale data envelopment analysis model. This model can be easily implemented in General Algebraic Modelling System as a nonlinear program and involves solving the problem (I × T) times (that is, one TFP for each region for each time period). However, a more efficient way is to convert the objective function as simply the sum of all θ for the regions. This means that the problem can only be solved T times.

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At the same time the mixed-period hyperbolic distance function needs to be solved Dtc (xt+1, yt+1), which is the solution to the following nonlinear programming problem: Dtc (xt+1, yt+1) = min θ λ, θ

s.t. Xt λ ≤ θxot+1 ot+1 Yt λ ≥ y θ λ ≥ 0.

Note that the set-up is the same as for the previous problem, except that the data for region “o” one period (t + 1) ahead are compared with the previous time period (t) data for the rest of the decision making units. For the mixed distance function, only T – 1 problems are solved. Thus, to derive the conventional Malmquist index, four linear programming problems are solved — one for each of the distance functions defined in equation 1. To account for the effects of greenhouse gas emissions and convert the conventional Malmquist index to an environmentally sensitive index of TFP growth, an additional input constraint is added to the four original nonlinear programmes. Thus, the within-period hyperbolic distance function is now the solution to the following nonlinear programme: DtE (xt, yt, zt) = min θ λ, θ

s.t. Xt λ ≤ θxot Zt λ ≤ θzot ot Yt λ ≥ y θ λ ≥ 0.

Here, Zt is a (1 × I) vector of GHG emission data from rice cultivation for each region, and zot is the GHG emission for region “o” at time t. Again, four programming problems are solved — two within-period and two mixed-period hyperbolic distance functions. With these modifications, the environmentally sensitive Malmquist index is given by:

ME =

[(         )  (         ) ] DEt (xt+1, yt+1, zt+1) DEt (xt, yt, zt)

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2

DEt+1 (xt+1, yt+1, zt+1) Dt+1 (xt, yt, zt) E

2

1 2

.



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Total output for rain-fed and irrigated rice (kg) Area harvested (ha) Fertilizer used (kg) Tractors used (number) Agricultural employment (number of people) Total area harvested for irrigated and rain-fed rice (ha) Methane emissions from rice cultivation, crop residues, and burning of crop residues (gg)

Output Irrigation Fertilizer Capital and machinery Labour Land

Notes: kg = kilograms, ha = hectares, gg = gigagrams. Source: Compiled by authors.

Greenhouse gas emissions

Description

Variable

Regional distribution is based on the share of irrigated land to total irrigated land; national distribution is based on FAO (2013b).

PSA (2013) PSA (2013) PSA (2013); FAO (2013b) PSA (2013); FAO (2013b) PSA (2013)]; FAO (2013b) PSA (2013)

Source

APPENDIX TABLE 5A.1 Data and Variables Used in the Construction of Malmquist Productivity Indexes

250 Asa Jose U. Sajise, Dieldre S. Harder, and Paul Joseph B. Ramirez

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Region

ARMM CAR Caraga Region 1 (Ilocos Region) Region 2 (Cagayan Valley) Region 3 (Central Luzon) Region 4a (CALABARZON) Region 4b (MIMAROPA) Region 5 (Bicol Region) Region 6 (Western Visayas) Region 7 (Central Visayas) Region 8 (Eastern Visayas) Region 9 (Zamboanga Peninsula) Region 10 (Northern Mindanao) Region 11 (Davao Region) Region 12 (SOCCSKSARGEN) Total

43,483 70,427 77,682 229,565 416,441 461,473 93,469 134,619 174,190 294,119 43,457 94,924 90,108 130,047 88,143 261,207 2,703,354

Area (ha)

Area (ha)

Area (ha)

Area (ha)

Area (ha)

Est. Water Consumption (m3)

2004 Area (ha)

Area (ha)

Est. water Consumption (m3)

2006

Area (ha)

Est. water Consumption (m3)

2007

continued on next page

13,113,800 59,611 11,922,200 71,443 14,288,600 17,291,800 90,776 18,155,200 90,817 18,163,400 16,117,400 85,711 17,142,200 89,876 17,975,200 48,941,600 237,958 47,591,600 237,839 47,567,800 85,291,000 433,709 86,741,800 428,617 85,723,400 97,799,400 509,619 101,923,800 556,385 111,277,000 15,381,200 74,458 14,891,600 75,511 15,102,200 26,080,200 134,085 26,817,000 139,477 27,895,400 38,407,400 183,904 36,780,800 196,373 39,274,600 59,868,200 323,802 64,760,400 308,409 61,681,800 9,283,000 49,473 9,894,600 51,618 10,323,600 20,985,400 105,967 21,193,400 122,677 24,535,400 18,341,400 84,348 16,869,600 86,920 17,384,000 22,737,400 111,059 22,211,800 117,720 23,544,000 18,567,600 91,474 18,294,800 83,822 16,764,400 50,137,400 251,932 50,386,400 259,508 51,901,600 558,344,200 2,827,886 565,577,200 2,917,012 583,402,400

Est. Water Consumption (m3)

2005

10,273,600 56,641 11,328,200 65,569 16,427,000 87,227 17,445,400 86,459 15,374,000 80,326 16,065,200 80,587 46,912,200 244,007 48,801,400 244,708 78,889,800 420,641 84,128,200 426,455 96,570,000 480,517 96,103,400 488,997 16,809,800 84,346 16,869,200 76,906 27,797,200 133,273 26,654,600 130,401 34,015,000 188,089 37,617,800 192,037 58,860,400 304,977 60,995,400 299,341 9,447,200 48,548 9,709,600 46,415 20,741,600 104,289 20,857,800 104,927 18,294,600 89,614 17,922,800 91,707 24,349,400 115,615 23,123,000 113,687 18,778,200 93,311 18,662,200 92,838 50,167,600 260,775 52,155,000 250,687 543,707,600 2,792,196 558,439,200 2,791,721

Est. Water Consumption (m3)

2003

11,224,200 51,368 16,630,600 82,135 15,934,200 76,870 46,544,400 234,561 78,971,400 394,449 93,227,600 482,850 16,811,400 84,049 26,810,600 138,986 33,166,600 170,075 58,862,800 294,302 8,450,000 47,236 20,693,200 103,708 18,570,800 91,473 25,497,000 121,747 18,573,000 93,891 51,291,800 250,838 541,259,600 2,718,538

Est. Water Consumption (m3)

2002

9,835,400 56,121 16,913,800 83,153 15,797,800 79,671 46,764,000 232,722 84,720,200 394,857 93,051,600 466,138 17,675,000 84,057 26,522,200 134,053 34,518,600 165,833 56,411,200 294,314 9,222,400 42,250 20,472,600 103,466 17,698,400 92,854 26,042,600 127,485 17,701,600 92,865 52,027,800 256,459 545,375,200 2,706,298

Est. Water Consumption (m3)

2001

8,696,600 49,177 14,085,400 84,569 15,536,400 78,989 45,913,000 233,820 83,288,200 423,601 92,294,600 465,258 18,693,800 88,375 26,923,800 132,611 34,838,000 172,593 58,823,800 282,056 8,691,400 46,112 18,984,800 102,363 18,021,600 88,492 26,009,400 130,213 17,628,600 88,508 52,241,400 260,139 540,670,800 2,726,876

Est. Water Consumption (m3)

2000

APPENDIX TABLE 5A.2 Irrigated Rice Area and Estimated Water Consumption, 2000–14

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Area (ha)

Est. Water Consumption (m3)

2010 Area (ha)

Est. Water Consumption (m3)

2011 Area (ha)

Est. Water Consumption (m3)

2012 Area (ha)

Est. Water Consumption (m3)

2013 Area (ha)

Est. water Consumption (m3)

2014

64,002 12,800,400 52,971 10,594,200 49,488 9,897,600 56,912 11,382,400 49,735 9,947,000 57,826 11,565,200 56,711 11,342,200 93,011 18,602,200 93,265 18,653,000 91,265 18,253,000 93,043 18,608,600 94,068 18,813,600 94,352 18,870,400 93,301 18,660,280 89,273 17,854,600 85,932 17,186,400 84,980 16,996,000 88,264 17,652,800 89,489 17,897,800 99,786 19,957,200 100,072 20,014,400 247,034 49,406,800 255,564 51,112,800 260,757 52,151,400 263,633 52,726,600 273,371 54,674,200 279,749 55,949,800 285,823 57,164,600 454,715 90,943,000 447,183 89,436,600 441,583 88,316,600 479,036 95,807,200 495,500 99,100,140 510,338 102,067,686 511,873 102,374,682 580,358 116,071,600 579,905 115,981,000 599,573 119,914,600 544,198 108,839,600 599,891 119,978,200 631,664 126,332,800 639,952 127,990,400 80,024 16,004,800 75,124 15,024,800 79,494 15,898,800 83,111 16,622,200 80,120 16,024,000 81,415 16,283,000 86,191 17,238,200 145,603 29,120,600 163,113 32,622,600 166,012 33,202,400 174,752 34,950,400 185,166 37,033,200 188,471 37,694,200 189,480 37,896,000 192,774 38,554,800 206,060 41,212,000 216,256 43,251,200 203,609 40,721,800 217,458 43,491,600 222,226 44,445,200 214,082 42,816,400 337,176 67,435,200 336,549 67,309,800 273,564 54,712,800 314,768 62,953,600 305,588 61,117,600 288,187 57,637,400 296,174 59,234,800 54,787 10,957,400 54,083 10,816,600 57,465 11,493,000 59,679 11,935,800 59,759 11,951,800 60,505 12,101,000 59,221 11,844,200 131,398 26,279,600 130,847 26,169,400 129,254 25,850,800 134,126 26,825,200 133,847 26,769,400 129,310 25,862,000 129,728 25,945,600 89,685 17,937,000 93,404 18,680,800 89,362 17,872,400 93,712 18,742,400 91,163 18,232,600 94,776 18,955,200 94,564 18,912,800 122,654 24,530,800 130,154 26,030,800 127,005 25,401,000 132,270 26,454,000 135,103 27,020,600 138,670 27,734,000 140,441 28,088,200 82,975 16,595,000 84,575 16,915,000 84,309 16,861,800 88,219 17,643,800 89,539 17,907,800 88,939 17,787,800 89,243 17,848,600 267,169 53,433,800 267,034 53,406,800 257,958 51,591,600 263,305 52,661,000 263,387 52,677,400 270,123 54,024,600 266,223 53,244,600 3,032,638 606,527,600 3,055,763 611,152,600 3,008,325 601,665,000 3,072,637 614,527,400 3,163,185 632,636,940 3,236,337 647,267,486 3,253,080 650,615,962

Area (ha)

Est. Water Consumption (m3)

2009

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Source: PSA, “CountryStat”, 2014 (accessed April 2014).

ARMM CAR Caraga Region 1 (Ilocos Region) Region 2 (Cagayan Valley) Region 3 (Central Luzon) Region 4a (CALABARZON) Region 4b (MIMAROPA) Region 5 (Bicol Region) Region 6 (Western Visayas) Region 7 (Central Visayas) Region 8 (Eastern Visayas) Region 9 (Zamboanga Peninsula) Region 10 (Northern Mindanao) Region 11 (Davao Region) Region 12 (SOCCSKSARGEN) Total

Region

Area (ha)

Est. Water Consumption (m3)

2008

APPENDIX TABLE 5A.2 — cont’d

11,214,107 17,704,912 17,166,773 50,148,147 89,053,327 107,823,707 16,355,333 30,534,693 38,874,120 60,711,013 10,408,107 23,477,747 18,162,427 25,251,600 17,768,680 52,089,920 586,744,613

Average Annual Water Consumption (m3)

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Notes   1. A waste sink refers to the environment’s ability to absorb waste and pollution; when waste output exceeds the limit of the environment’s sink function, longterm damage occurs.   2. Hotspots of land degradation are determined by overlaying maps identifying moderate to severe erosion with maps indicating slope and land use. Data are currently being validated at the project level (BSWM undated).   3. Soil erosion rates are derived from micro-level studies in the early 1990s that were extrapolated nationwide.  4. Gross value-added (GVA) is a measure of the value of goods and services produced, and in national accounts is calculated as output minus intermediate consumption.   5. By way of context, the FAO estimates total yearly exploitable water resources for the same period to be around 146 trillion m3.   6. BOD measures the quantity of oxygen used by microorganisms in the oxidation of organic matter; high BOD levels in water may indicate increased oxygen consumption, which can potentially threaten aquatic organisms.   7. Evenness compares the similarity of the size of the population of each species in an area.   8. TFP is measured as the ratio of output to an index of production inputs.   9. The “Safe AWD” variant aims to reduce water consumption by 30 per cent, while maintaining production and yields at levels comparable with conventional irrigation techniques. This variant resulted from field trial observations indicating a viable trade-off between regulating irrigation and achieving yields. 10. This discussion closely follows the exposition and notations of Ball et al. (2004).

References Altoveros, Nestor C. and Teresita H. Borromeo. “Country Report on the State of Plant and Genetic Resources for Food and Agriculture: Philippines (1997–2006)”. 2007 (accessed May 2015). Asio, V., R. Jahn, F. Perez, I. Navarrete, and S. Abit, Jr. “A Review of Soil Degradation in the Philippines”. Annals of Tropical Research 31, no. 2 (2009): 69–94. Atienza, Redia N., Jesus Hapal, and Elizalde Morga. “Legislative and Institutional Aspects of Soil and Water Conservation: The Philippine Experience”. 2008 (accessed May 2015).

18-J04349 05 Future of Philippine Agriculture.indd 253

19/11/18 12:04 PM

254

Asa Jose U. Sajise, Dieldre S. Harder, and Paul Joseph B. Ramirez

Ball, V., C. Lovell, H. Luu, and R. Nehring. “Incorporating Environmental Impacts in the Measurement of Agricultural Productivity Growth”. Journal of Agricultural and Resource Economics 29, no. 3 (2004): 436–60. Baluyut, Elvira. “The Agno River Basin (the Philippines) in Inland Fisheries and Multi-Purpose River Basin Planning and Development in Tropical Asian Countries: Three Case Studies”. FAO Fisheries Technical Paper 265. Rome: Food and Agriculture Organization of the United Nations, 1985. Bennagen, Ma. Eugenia C. “The Philippine Experience in Green Accounting and Valuation of Environmental Services: Lessons and Prospects for Public Policy”. 2007 (accessed November 2013). Briones, Roehlano M. “Scenarios and Options for Productivity Growth in Philippine Agriculture: An Application of the AMPLE”. PIDS Discussion Paper Series 2010-05. Manila: Philippine Institute for Development Studies, 2010a. ———. “Addressing Land Degradation: Benefits, Costs, and Policy Directions”. Philippine Journal of Development 68 no. XXXVII/1 (2010b): 41–79. Bouman, B.A.M., P.M. Lampayan, and T.P. Tuong. Water Management in Irrigated Rice: Coping With Water Scarcity. Los Baños: International Rice Research Institute, 2007. Comia, R., E. Paningbatan, and I. Hakanson. “Erosion and Crop Yield Response to Soil Conditions Under Alley Cropping Systems in the Philippines”. Soil and Tillage Research 31 (1994): 249–61. Coxhead, Ian A., and Gerald Shively. “Measuring the Environmental Impacts of Economic Change: The Case of Land Degradation in Philippine Agriculture”. Staff Paper 384. Madison: University of Wisconsin–Madison, Department of Agricultural Economics, 1995. Cruz, W., H. Francisco, and Z. Conway. “The On-Site and Downstream Costs of Soil Erosion in the Magat and Pantabangan Watersheds”. Journal of Philippine Development 26, no. 15/1 (1988): 85–111. DA–DAR–DENR–DOST (Department of Agriculture, Bureau of Soils and Water Management; Department of Agrarian Reform; Department of Environment and Natural Resources; and Department of Science and Technology). The Updated Philippine National Action Plan to Combat Desertification, Land Degradation and Drought (DLDD): FY 2010–2020. 2010 (accessed May 2015). Dano, Antonio M. and David J. Midmore. “Evaluation of Soil and Water Conservation Technologies in Vegetable Based Upland Production System of Manupali Watershed, Southern Philippines”. Paper presented at the 13th International Soil Conservation Organisation Conference, Brisbane, Australia, July 2004.

18-J04349 05 Future of Philippine Agriculture.indd 254

19/11/18 12:04 PM

The Sustainability of Agricultural Growth

255

David, W. 2004. “Water Resources and Irrigation Policy: Issues in Asia”. Asian Journal of Agricultural Development 1, no. 1 (2004): 76–97. David, C.C. and K. Otsuka. “Technological Change, Land Reform, and Income Distribution in the Philippines”. In Modern Rice Technology and Income Distribution in Asia, edited by C. David and K. Otsuka. Boulder and London: Lynne Rienner Publishers, 1994. Day-Rubenstein, K., P. Heisey, R. Shoemaker, J. Sullivan, and G. Frisvold. “Crop Genetic Resources: An Economic Appraisal”. Economic Information Bulletin (EIB-2). Washington, D.C.: United States Department of Agriculture, Economic Research Service, 2005. DENR–UNDP–ACB–ATENEO (Department of Environment and Natural Resources, United Nations Development Programme, ASEAN Center for Biodiversity, and Ateneo de Manila University). Assessing Progress Towards the 2010 Biodiversity Target. The 4th National Report to the Convention on Biological Diversity. 2009 (accessed May 2015). DENR-EMB (Department of Environment and Natural Resources, Environmental Management Bureau). Tracking Greenhouse Gases: An Inventory Manual. 2011

(accessed May 2015). DFID (Department for International Development). Agricultural Sustainability. DFID Working Paper 12. 2004 (accessed May 2015). Estudillo, J. and K. Otsuka. “Lessons from Three Decades of Green Revolution in The Philippines”. The Developing Economies 44, no. 2 (2005): 123–48. Eswaran, H., R. Lal, and P.F. Reich. “Land Degradation: An Overview”. In Responses to Land Degradation, edited by E. Bridges, I. Hannam, L. Oldeman, F. Pening de Vries, S. Scherr, and S. Sompatpanit. Proceedings of the 2nd International Conference on Land Degradation and Desertification, KhonKaen, Thailand. New Delhi: Oxford Press, 2001. FAO (Food and Agriculture Organization of the United Nations). AQUASTAT database. 2013a (accessed May 2015). ———. FAOSTAT database. 2013b (accessed 20 December 2013). Fare, R., S. Grosskopf, M. Norris, and Z. Zhang. “Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries”. American Economic Review 84, no. 1 (2001): 66–83. Francisco, Herminia A. “Upland Soil Resources of the Philippines: Resource Assessment and Accounting for Soil Depreciation. ENRAP Phase II Technical Report No. 6”. In The Philippine Environmental and Resources Accounting Project, Phase II: Reports. Department of Environmental and Natural Resources, United

18-J04349 05 Future of Philippine Agriculture.indd 255

19/11/18 12:04 PM

256

Asa Jose U. Sajise, Dieldre S. Harder, and Paul Joseph B. Ramirez

States Agency for International Development, International Resources Group, Ltd., and Edgevale Associates, 1994. ——— and Marian S. de Los Angeles. “Soil Resource Depreciation and Deforestation: Philippine Case Study in Resource Accounting”. Rome: Food and Agriculture Organization of the United Nations, Planning and Statistics Branch, Policy and Planning Division, Forestry Department, 1998. ——— and Arief Yusof. “Climate Change Vulnerability Mapping for Southeast Asia”. EEPSEA Special and Technical Paper Sp200808s1. 2008 (accessed May 2015). Francisco, Sergio R. and Flordeliza H. Bordey. “Investments in Research, Development, and Extension: Implications on TFP”. Productivity Growth in Philippine Agriculture Monograph Series. Los Baños: Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA), Department of Agriculture-Bureau of Agricultural Research, and Philippine Rice Research Institute (PhilRice), 2013. Gotkowitz, Madeline. ”Groundwater Recharge”. Wisconsin Geological and Natural History Survey, Educational Series 50-2. 2010 (accessed May 2015). Government of the Philippines. “Agriculture and Fisheries Modernization Act”. 1997 (accessed June 2015). Greenpeace. “The State of Water Resources in the Philippines”. Quezon City: Greenpeace Southeast Asia, 2007. Hazell, Peter R. “Agricultural Growth, Poverty, and Environmental Sustainability: Having it All”. 2020 Vision Brief 59. Washington, D.C.: International Food Policy Research Institute, 1999. Herdt, R. and C. Capule. “Adoption, Spread and Production Impact of Modern Rice Varieties in Asia”. Los Baños: International Rice Research Institute, 1983. Hossain, M., D. Gollin, V. Cabanilla, E. Cabrera, N. Johnson, G. Khush, and G. McLaren. “International Research and Genetic Improvement in Rice: Evidence from Asia and Latin America”. In Crop Variety Improvement and Its Effect on Productivity, edited by R. Evenson and D. Gollin. Wallingford, U.K.: CAB International, 2003. Indab, Anabeth L. “The Economic Impact of Soil Erosion on Hydroelectric Power and Irrigation Facilities Operating in the Philippines”. Masters’ Thesis, University of the Philippines, Los Baños, 1999. Israel, Danilo C. and Roehlano M. Briones. “The Impact of Natural Disasters on Agriculture, Food Security and Natural Resources and Environment in the Philippines”. ERIA Discussion Paper 2013-15. 2013 (accessed May 2015).

18-J04349 05 Future of Philippine Agriculture.indd 256

19/11/18 12:04 PM

The Sustainability of Agricultural Growth

257

Lansigan, Felino P. and Arnold R. Salvacion. “Assessing the Effect of Climate Change on Rice and Corn Yields in Selected Provinces in the Philippines”. Paper presented at the 10th National Convention on Statistics, Manila, 1–2 October 2007. Launio, C., G. Redondo, and J. Beltran. “Recent Adoption and Spatial Diversity of Modern Rice Varieties in the Philippines”. Agronomy Journal 100 (2008): 1380–89. Loftas, Tony, Jane Ross, and David Burles. “Dimensions of Need: An Atlas of Food and Agriculture”. Rome: Food and Agriculture Organization of the United Nations, 1995. Low, Pak S., ed. “Economic and Social Impacts of Desertification, Land Degradation and Drought”. White Paper I. UNCCD 2nd Scientific Conference, prepared with the contributions of an international group of scientists. 2013 (accessed May 2015). Moxey, Andrew. Agriculture and Water Quality: Monetary Costs and Benefits across OECD Countries. Paris: Organisation for Economic Co-operation and Development, 2012. NSCB–UNDP (National Statistical Coordination Board and United Nations Development Programme). Environmental Degradation Due to Selected Economic Activities.> (accessed May 2015). Oenema, O., B. Amon, C. van Beek, N. Hutchings, M. Perez-Soba, C. Procter, S. Pietrzak, G. Velthof, F. Vinther, and L. Wilson. “Farm Data Needed for Agri-Environmental Reporting”. EuroStat Methodologies and Working Papers 1977-035. 2011 (accessed May 2015). OECD (Organisation for Economic Co-operation and Development). Environmental Indicators for Agriculture. Volume 1: Concepts and Framework. Paris: OECD, 1999a. ———. Environmental Indicators for Agriculture: Issues and Design. The York Workshop. Paris: OECD, 1999b. ———. Environmental Indicators for Agriculture. Volume 3: Methods and Results. Paris: OECD, 2001. ———. “Water Quality and Agriculture: Meeting the Policy Challenge”. 2012 (accessed 27 December 2013). ———. “Water Use in Agriculture”. Undated (accessed 27 December 2013). Ongley, E. “Chapter 1: Introduction to Agricultural Water Pollution”. In Control of Water Pollution from Agriculture. FAO Irrigation and Drainage Paper 55. 1996 (accessed May 2015). Pampolino, M., I. Manguiat, S. Ramanthan, H. Gines, P. Tan, T. Chi, R. Rajendran, and R. Buresh. “Environmental Impact and Economic Benefits of Site-Specific

18-J04349 05 Future of Philippine Agriculture.indd 257

19/11/18 12:04 PM

258

Asa Jose U. Sajise, Dieldre S. Harder, and Paul Joseph B. Ramirez

Nutrient Management (SSNM) in Irrigated Rice Systems”. Agricultural Systems 93 (2007): 1–24. Paningbatan, E., C. Ciesiolka, K. Coughlan, and C. Rose. “Alley Cropping for Managing Soil Erosion of Hilly Lands in the Philippines”. Soil Technology 8 (1995): 193–204. PEM (Philippine Environmental Monitor). Water Quality. Washington, D.C.: World Bank, 2003. Peskin, Henry M. and Marian S. de Los Angeles. “Accounting for Environmental Services: Contrasting the SEEA and the ENRAP Approaches”. EEPSEA Special Papers. Undated (accessed May 2015). Pimentel, D., C. Harvey, P. Resosudarmo, K. Sinclair, D. Kurz, M. McNair, S. Crist, L. Shpritz, L. Fitton, R. Saffouri, and R. Blair. “Environmental and Economic Costs of Soil Erosion”. Science 267 (1995): 1117–23. Pinoy Rice. “FAQs on Philippine Seedboard (PSB)/ NSIC Rice Varieties”. Pinoy Rice Knowledge Bank. 2015 (accessed June 2015). PSA (Philippine Statistical Authority). “CountryStat”. 2013 (accessed November 2013). ———. “CountryStat”. 2014 (accessed April 2014). Rejesus, R., F. Palis, D. Rodriguez, R. Lampayan, and B. Bouman. “Impact of Alternate Wetting and Drying (AWD) Water Saving Irrigation Technique: Evidence from Rice Producers in the Philippines”. Food Policy 36 (2011): 280–88. Rola, Agnes C., Asa Jose U. Sajise, Dieldre S. Harder, and Joe Alpuerto. “Agricultural Productivity Growth and Environmental Externalities in the Philippines”. In Productivity Growth in Philippine Agriculture. Los Baños: Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA), Philippine Rice Research Institute (PhilRice), and Department of AgricultureBureau of Agricultural Research, 2013. Sajise, P. and A. Sajise. “The Ecological and Economic Aspects of the Multifunctional Role of Agroecosystems”. Asian Journal of Agriculture and Development 4, no. 1 (2011): 59–68. Saastamoinen, Olli. “Off-site Costs of Soil Erosion and Watershed Degradation In The Philippines: Sectoral Impacts and Tentative Results”. Discussion Paper Series 94-18. Manila: Philippine Institute for Development Studies, 1994. SEARICE (Southeast Asia Regional Initiatives for Community Empowerment). “Hybrid Rice in Asia: Sowing the Seeds of Dependency (A Factsheet on Hybrid Rice)”. Penang: Pesticide Action Network, Asia and the Pacific, 2007. Sebastian, Sergio R., and Flordeliza H. Bordey. “Embracing Hybrid Rice: Impacts and Future Directions”. Paper presented at the SEARCA Seminar Series, 19 July 2005, SEARCA, University of the Philippines College, Laguna, 2005.

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The Sustainability of Agricultural Growth

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Smith, P., D. Martino, Z. Cai, D. Wary, H. Janzen, P. Kumar, B. McCarl, S. Ogle, F. O’Mara, C. Rice, B. Scholes, and O. Sirotenko. “Agriculture in Climate Change 2007: Mitigation”. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by B. Metz, O. Davidson, P. Bosch, R. Dave, and L. Meyer. Cambridge, U.K., and New York: Cambridge University Press, 2007. Teruel, Romeo. “Productivity Growth in Philippine Agriculture: An Empirical Exposition”. Paper presented at the 13th International Convention of the East Asian Economic Association, Singapore, 19–20 October 2012. UNEP–FAO–UNDP (United Nations Environment Programme, Food and Agriculture Organization of the United Nations, and United Nations Development Programme). “Land Degradation in South Asia: Its Severity, Causes and Effects Upon the People”. FAO World Soil Resources Report 78. Rome, 1994. Virola, Romulo A., Estrella V. Domingo, Glenita V. Amoranto, and Edward P. Lopez-Dee. “Gearing a National Statistical System towards the Measurement of the Impact of Climate Change: The Case of the Philippines”. Paper presented during the Conference on Climate Change and Official Statistics organized by the United Nations Statistics Division in collaboration with Eurostat, the World Bank, and Statistics Norway at the Oslo Military Society, 14–16 April 2008. WCED (World Commission on Environment and Development). Our Common Future, Report of the World Commission on Environment and Development. Oxford, U.K.: Oxford University Press, 1987. Wiebe, Keith. “Linking Land Quality, Agricultural Productivity, and Food Security”. Agricultural Economic Report AER-823. Washington, D.C.: United States Department of Agriculture, Economic Research Service, 2003. Zhai, Fan and Juzhong Zhuang. “Agricultural Impact of Climate Change: A General Equilibrium Analysis with Special Reference to Southeast Asia”. ADBI Working Paper 131. Tokyo: Asian Development Bank Institute, 2009.

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6 THE GENDERED IMPACTS OF CLIMATE CHANGE Maria Emilinda T. Mendoza

Agriculture and food systems are among the top-ranked sectors deemed most vulnerable to the adverse impacts of climate change (IPCC 2012, p. 235). This is especially so for developing economies, such as the Philippines, where small-scale and subsistence farming predominates, and eradicating poverty is a fundamental development priority. Confronted with all of its challenges within the context of climate change, the sustainability of the agricultural sector is pressed not only in terms of its capacity to meet current and future food security objectives, but also in terms of its ability to provide an appropriate standard of living, including equity, access to productive resources, social protection, and participation in democratic processes. Any analysis of the vulnerability of the agricultural sector would be incomplete without a consideration of the inherent gender dimension. This chapter presents the results of research designed to examine gender dynamics within the Philippine agricultural sector in the context of climate change. Despite the limited existing body of literature on the subject, the findings offer insight into the gender-differentiated impacts of

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climate change, gendered responses to such impacts within the agricultural sector, and opportunities for gendered voices and perspectives within the Philippine’s climate change policy framework. The intent is to promote and guide gender-sensitive decisions that are imperative to equitable and sustainable food and nutrition security, income generation, and improved rural livelihoods.

THE MULTIDIMENSIONAL NATURE OF GENDER EQUALITY In addressing the adverse impacts of climate change on the agricultural sector, success in mainstreaming the gender dimension requires acknowledgement of the multidimensional nature of gender equality. Awareness of the reality that climate change does not affect people equally continues to grow (IPCC 2007, pp. 17, 359; IPCC 2012, p. 7; Mearns and Norton 2010, p. 5; Nellemann, Verma, and Hislop 2011, p. 8; Goh 2012, pp. 1–2). In a special report for policymakers, IPCC (2012, p. 7) noted that people — either as individuals or collectives — are differentially vulnerable to the impacts of climate change on the basis of disparities in economic and social well-being, including factors of health, gender, and class. A number of focused group discussions conducted similarly indicate that even in an agriculture-based community, finer sectoral categorizations can be identified as vulnerable to impacts of climate change (Table 6.1). While inequalities exist even aside from the context of climate change, testimonies of documented cases attest to the reality that climate variability and extreme weather conditions often intensify existing inequalities and vulnerabilities. A study looking into the relative vulnerability of communities indicated that the predominance of agricultural and forestry-based sources of income increases the sensitivity of a community because climate change–related hazards not only affect living conditions within the community, but also affect household productivity and hence income levels (Ballaran et al. 2014, pp. 20, 25). Women and men perceive and experience climate change in diverse ways because of their distinct, socially constructed gender roles, responsibilities, status, and identities. In their rapid response assessment on women and climate change, Nellemann, Verma, and Hislop (2011, pp. 7, 54) reported that women generally have less access to and control of the resources on which they are dependent, as well as limited decision-

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The elderly Widows/widowers

Farmers and fishers

Poor families

Lakeshore residents

Residents in landslide/ erosion prone areas

2. 3.

4.

5.

6.

7.

Fishers

Lakeshore Duck raisers residents Farmers Lakeshore residents The unemployed Residents near bodies of water

Poor, large households

Poor families

Poor, large households Farmers

The elderly Women

Children

The Agricultural Sector

Farmers; those who get income from planting and livestock

The elderly Poor families with numerous children Women

Children

Coastal Areas

The elderly Women

Children

Youth

The sick

Women

The underemployed and unemployed

Poor families with numerous children Households near Those who are bodies of water less educated

The elderly Poor families with more than five children Farmers

Children

Upland Areas

Note: Results are based on focus group discussions conducted in Laguna. Source: Maria Emilinda T. Mendoza, Faces of Vulnerability: Gender, Climate Change and Disaster (Los Baños: Southeast Asian Regional Centre for Graduate Study and Research in Agriculture, 2014), p. 12.

8. 9.

Those with disabilities Children The elderly

Children

1.

Women

Women

Ranking The Elderly

Composition of Focus Group

TABLE 6.1 Focus Group Rankings of Sectors Vulnerable to Climate Change–Related Hazards, 2014

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making power over such resources. Factors such as lack of mobility and social isolation are common contributors in many places, exacerbating vulnerability in the face of extreme events and climate-related disasters (Nellemann, Verma, and Hislop 2011, p. 54). The multidimensionality of gender issues also necessitates a perspective that is sensitive not only to women, but also to other sectors that are often marginalized. In most of Asia, poor people are clearly at high risk from climate change due to constraints in accessing knowledge and financial resources (Ananta, Bauer, and Thant 2013, p. 4). In the study conducted among poor women in flood-prone villages in eastern Uttar Pradesh, India, Ahmed and Fajber (2009, p. 35) report on the difficulty of accessing food after floods, indicating vulnerability to food insecurity. In general, poor people are more vulnerable to the impacts of negative events because they have less money to spend either on preparedness before the fact or on mitigation afterward. In addition, poor people are also more likely to live in houses that are more easily damaged by typhoons.

FEMALE PARTICIPATION IN THE LABOUR FORCE The total number of men and women in the Philippine labour force as of 2012 was 24.6 million and 15.8 million, respectively (Table 6.2). On average, men’s participation in the labour force increased by 2.1 per cent per year between 2008 and 2012, whereas women’s participation rose by 2.9 per cent per year during this time frame. As of 2012, however, women’s participation was only 50.0 per cent, much lower than the 78.5 per cent participation by men. Interestingly, while the data for men registered a yearly average decline in participation of 0.1 per cent, growth in participation of women was 0.7 per cent (BAS 2013). Similarly, while women’s employment in agriculture was only around a third of men’s employed, the yearly rate of growth for men and women was 0.1 and 0.4 per cent, respectively. The slightly faster growth in women’s participation may indicate improvement in the gender balance of the country’s labour force, a trend that also holds for the agricultural sector. Overall, however, both male and female workers are seeking non-agricultural occupations in technical, service, and industrial areas. Evidence indicates that out-migration in rural and agricultural communities is significantly associated with declining yields, and hence incomes. Based on countrywide regional data, Bordey et al. (2013, p. 12)

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Labour force (million) Participation in labour force (%) Employed in agriculture (million) Share of employment in agriculture (%) Nominal daily wage rate of agricultural workers (PhP) Real daily wage rate of agricultural workers (PhP)

114.1 148.6 113.1 139.4 178.9 160.6

Women

Men 124.6 178.5 119.0 123.2 223.5 171.8

115.8 150.0 113.1 121.0 208.3 160.1

Women

2012

–2.1 –0.1 –0.1 –2.1 –4.2 –0.2

Men

–2.9 –0.7 –0.4 –2.5 –3.9 –0.1

Women

Average Growth Rate (%)

Source: BAS (Philippine Bureau of Agricultural Statistics), Agricultural Indicators System Report, Gender-based Indicators of Labor and Employment in Agriculture, Report 2013–08 (Quezon City: BAS, 2013), pp. 10–11.

Men 122.7 178.8 110.0 142.9 189.8 170.4

Variable

2008

TABLE 6.2 Labour and Employment by Gender, 2008 and 2012

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reported that the number of Filipinos working overseas increased by five people per thousand population on average for every metric ton decrease in average rice yields due to climatic variations. Similarly, the number of overseas workers increased to an average of one person per thousand population for every PhP1,000 decline in the average gross revenue per hectare planted to rice. The impact of climate change on the migration of women was noted to be greater, with results indicating an increase of seven women working overseas per thousand female population for every metric ton decrease in average rice yields. One additional female worker per thousand population was also reported to be working overseas for every PhP1,000 decline in gross revenue per year. The study suggests that this gender difference occurs because women are either paid less than men for the same tasks or provide unpaid household labour for activities like weeding. Consequently, women have more to gain from working overseas than do their male counterparts (Bordey et al. 2013, p. 14). Despite these declines, gender-disaggregated labour and employment data support reported improvements in the Philippine global gender inequality index, which measures indicators of the extent of inequality among men and women in terms of basic rights, such as health, education, economic participation, and political empowerment. The World Economic Forum reported that, as of 2013, the Philippines ranked fifth (and was the only Southeast Asian country) among 136 countries determined to have the narrowest gender gaps (WEF 2013, p. 8). Therefore, in the context of changing climatic conditions, gender concerns in Philippine agriculture are not confined to gaps in rights.

FEMALE PARTICIPATION IN AGRICULTURE Crucial to the analysis of women’s roles in Philippine agriculture under climate change is the recognition of women’s extensive involvement in farming activities and along the agricultural supply chain. Female farmers, forestry workers, and fishers were estimated to number 3 million as of 2013 compared with 9 million men (BAS 2013, pp. 26–27), but this does not minimize women’s substantial contribution to Philippine agriculture. Examination of the division of agricultural labour by gender shows that women share most production activities alongside men (Table 6.3). Supplychain studies report that women also participate as traders, wholesalers, and retailers of agricultural crops (Llanto et al. 2013, pp. 19, 32). Women are

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TABLE 6.3 Gendered Division of Labour in Agricultural Production, 2012 Farm Activity Plowing Harrowing Furrowing Leveling Caring for seedlings Preparing land Pulling and bundling seedlings Planting/transplanting Irrigation/watering Caring for crops Mechanical weeding Manual weeding Applying fertilizer Spraying/applying chemicals Picking of snail Off-barring Hilling-up Clearing underbrush Rolling over cover crops Harvesting Threshing Gathering/piling nuts Shelling Husking Splitting nuts Removing coconut meat Hauling Drying

Male

x x

x

x

Female

Both x x x x x x x x x x x x x x x x x x x x x x x

Source: BAS (Philippine Bureau of Agricultural Statistics), Agricultural Indicators System Report, Gender-based Indicators of Labor and Employment in Agriculture, Report 2013–08 (Quezon City: BAS, 2013), pp. 34–35.

active as landless workers, as traders of agricultural and fishery products, and in micro-manufacturing enterprises. Data, however, also show that more agricultural households employ male workers for any single farm activity, indicating either a preference for hiring male workers (Table 6.4) or that female workers provide unpaid agricultural labour. McKay (2005, p. 94) reported that women usually provide unpaid household labour for

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TABLE 6.4 Percentage of Households Employing Men and Women for Selected Farm Activities, 2012 Farm Activity

Men

Women

Plowing Harrowing Furrowing Leveling Caring for seedlings Preparing land Pulling and bundling seedlings Planting/transplanting Irrigation/watering Caring for crops Mechanical weeding Manual weeding Applying fertilizer Spraying/applying chemicals Picking snails Off-barring Hilling-up Clearing underbrush Rolling over cover crops Harvesting Threshing Gathering/piling nuts Shelling Husking Splitting of nuts Removing coconut meat Hauling Drying

46.05 34.68 27.94 14.89 13.70 10.42 48.46 41.31 14.63

10.27 10.11 10.35 10.00 10.00 10.79 33.93 31.11 10.50

12.00 26.32 30.27 17.68 11.22 29.78 33.35 37.23 14.71 70.28 92.63 66.48 50.05 41.94 53.64 52.35 57.07 31.71

10.00 11.32 11.91 10.42 10.07 10.41 10.35 10.29 10.00 24.99 18.73 11.43 17.53 10.34 11.28 13.00 12.48 10.55

Source: BAS (Philippine Bureau of Agricultural Statistics), Agricultural Indicators System Report, Gender-based Indicators of Labor and Employment in Agriculture, Report 2013–08 (Quezon City: BAS, 2013), pp. 34–35.

activities such as planting, weeding, and harvesting. This can also include “exchange” labour, whereby women from different households reciprocate in contributing unpaid labour for farming activities. This supports the earlier suggestion that women in farming communities have more to gain from working overseas or in non-agricultural occupations.

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Analyses of the division of agricultural labour by gender during and after disasters often show that women are over-represented among those negatively affected. In a comprehensive review of literature on the gender-differentiated impacts of climate change on assets and well-being, Goh (2012, p. 7) reported that climate change increases the amount of time and labour farmers need to spend on agricultural production, but that women are more affected. Bynoe (2009, as cited by Goh 2012, pp. 7, 20) reported on a case in Guyana, where both men and women spent more time planting and diversifying crops as a result of droughts and floods, but that women also needed to spend more time providing for household food needs. In the Philippines, in-depth interviews conducted among women in various circumstances within agricultural households indicated that, although male and female household members tended to contribute more work in agricultural production, harvesting, and marketing activities as a result of extreme weather events and disasters, female household members were responsible for marketing products salvaged from damaged crops, whereas men were expected to contribute to rebuilding damaged infrastructure at both the community and household levels. Women’s culturally expected, nonproductive household workload and responsibilities — including caring for children, the sick, and the elderly — pose significant limitations on the time and energy they have available to undertake paid agricultural work (Mendoza 2014, pp. 26, 60). This reality may also perpetuate a preference for male labour. The amount of time women are required to devote to unpaid labour also contributes to the different gender-based difficulties and challenges they face in times of climate-related disasters.

INCOME AND EXPENDITURE LEVELS The growing rate of participation by women in agriculture does not translate into higher levels of paid labour or equal wage rates. Data on income and expenditure levels indicate significant gender differentials. The nominal wage rate of female agricultural workers in 2012 averaged PhP208.3 compared with PhP223.5 for male workers. For the 2008–12 period, average yearly growth in the nominal wage was slightly lower for female than for male workers — 3.9 per cent compared with 4.2 per cent (BAS 2013, pp. 30–31). Furthermore, men’s real wage rates increased,

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whereas women’s real wage rates declined; hence, the increase in women’s nominal wages by 2012 was insufficient to maintain women’s purchasing power in 2008. Reduced income levels, largely stemming from reduced agricultural productivity, were reported by women in case studies conducted in Laguna (Mendoza 2014, pp. 27–56). This has significant implications for women, especially in female-headed households, in terms of their ability to provide sufficient food during and after the incidence of disasters. This impact could be exacerbated by the fact that: (1) as previously discussed, women have less time to undertake paid employment or engage in enterprises based on the time they are required to spend on family and household responsibilities; and (2) opportunities for paid agricultural employment are often disrupted during extreme weather events, and non-farm activities tend to be less profitable because households undergoing rehabilitation and recovery from negative events have less disposable income.

LANDHOLDING AND ACCESS TO CREDIT Evidence indicates that women’s contribution to agricultural production in the Philippines is recognized but undervalued. Women are found to perform productive work side by side with their male counterparts. In addition to having fewer opportunities to undertake paid work, women have less access to productive resources compared with men. Access to land, technology, extension services, capital, and infrastructure support tend to favour male farmers, subtly marginalizing women. This gender difference is often hidden in household-level analyses that do not disaggregate women’s and men’s engagement in agriculture. The 2010 report of the Department of Agrarian Reform (DAR) indicated that women comprised only 29 per cent of all agrarian reform beneficiaries. As of 2013, the government has acquired and distributed 6.9 million hectares or 88 per cent of the estimated 7.8 million hectares of land covered by the country’s Comprehensive Agrarian Reform Program (CARP), either through DAR or the Department of Environment and Natural Resources (DENR). Much is to be done in monitoring and transparently reporting on the extent of land ownership, or co-ownership with spouses, by female beneficiaries. Ownership and control over land in the Philippines generally favours men. This can be explained, in part, by the preferential inheritance of land by sons rather than daughters

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(Quisumbing, Estudillo, and Otsuka 2004, p. 2). Men’s dominant ownership of land also skews their access to formal sources of credit, restricting women’s access to credit to more informal and exploitative sources. In turn, this inequality has the potential to affect women’s financial ability to adapt to the impacts of climate change. The control and ownership of other household assets are equally important in Philippine agriculture. Studies of the gender-differentiated impacts of shocks on asset dynamics in Bangladesh indicate that husbands in male-headed households tend to own and control more assets, especially in the form of land, but that such assets were negatively affected by the impacts of extreme climatic events; assets owned by wives or held jointly by husbands and wives in such households were not found to be affected in the same way (Rakib and Matz 2014, p. 13; Quisumbing et al. 2011, as cited by Goh 2012, p. 18). Overall, assets controlled by women were found to benefit the well-being of other members of households, especially children, in terms of health, education, and nutrition (Rakib and Matz 2014, p. 18). This lends support for policies and programs aimed at protecting — and increasing — women’s ownership and control of assets.

GENDER-DIFFERENTIATED RESPONSES TO CLIMATE CHANGE The Philippine Magna Carta of Women (Republic Act 9710) is the best indicator of the country’s gender-related goals (PCW 2010). The Act provides the legal framework for the eradication of all forms of discrimination against women, ensuring their social protection and the promotion of all forms of human rights and fundamental freedoms. In relation to agriculture, the Act seeks to strengthen equal rights to land ownership. For example, the law mandates DAR to issue emancipation patents and certificates of land ownership to all qualified beneficiaries regardless of sex, civil status, or physical condition. It also requires DENR to ensure co-stewardship of spouses, and equal status in the issuing of lease agreements and other fishery rights. National policy on climate change has clearly identified gender mainstreaming as a cross-cutting theme, stipulating that it should be highlighted in research and development, planning and policy-making, capacity development, and climate change adaptation (CCC 2011, p. 2). Mainstreaming gender in climate change action planning is intended to ensure that the concerns, issues, and experiences of men and women are

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integrated into the design, implementation, and monitoring and evaluation of policies and programs (CCC 2011, pp. 10, 22). What remains lacking is the translation of this formal national commitment into local climate change action plans. To date, all local government agencies have a representative or office for gender and development, at the very least, as an institutional mechanism to ensure the incorporation of gender issues within local governance and development efforts; nevertheless, mainstreaming of gender in ongoing climate change action planning remains weak, as is indicated by the lack of participation of gender and development officers in any local climate change action planning. Work is ongoing to clarify how gender mainstreaming can be actualized in local government responses to climate change. Progress has, however, been made in the form of vulnerability assessments as a precursor to needs-based climate change adaptation and disaster risk reduction planning. One of the identified paths is ensuring that women are involved in all stages of the assessment process; another is capturing the vulnerability of women in local communities, which remains a considerable challenge due to the lack of local-level sex-disaggregated data and could lead to the recommendation and adoption of inappropriate climate change practices as far as gender is concerned.

GENDERED ADAPTATIONS TO CLIMATE CHANGE Some studies point at the significant role women play in climate change adaptation. In their study on demand elasticities of rice, Lantican, Sombilla, and Quilloy (2013, pp. 12–13) reported that women consume less rice. Their study also indicated that the family food basket was more diverse in female-headed households and that women pay more for rice per kilogram on average but spend less on rice overall per year because they consume less. These findings indicate that female-led food consumption practices merit further examination because they may be more open to adaption in light of climate-related threats to rice production. Diversifying sources of income is also seen as a viable strategy for adapting to the adverse impacts of clime change in the agricultural sector, and, once again, women seem to play an important role. In studying households engaged in producing and selling flower garlands in peri-urban metropolitan Manila, Boncodin et al. (2009, pp. 57–59) noted that even though men control most of the farm assets, women perform significant

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roles in harvesting and marketing the flowers and have significant control of the daily cash income, as well as market information. Analysis by Bordey et al. (2013, p. 14) of labour migration indicates that, in choosing to travel overseas to obtain work, women have more to gain compared with men. While the migration of women for work remains controversial, the practice offers significant potential benefits to households confronted by climate-related risks and events. Mendoza (2014, p. 20) reported that many families depend on the remittances from family members working abroad, especially when natural disasters occur. Thus, although migration depletes agricultural labour at home, family members working abroad provide significant social capital, especially for disasterprone families. Family becomes more important in the absence of strong social linkages — such as those with local government, especially with clear and well-planned climate change adaptation programmes (Table 6.5). Discussions consistently confirmed the accessibility and hence importance of family linkages, especially during and after disaster situations when other types of social linkages may be least accessible.

CONCLUSION AND POLICY IMPLICATIONS In order for the principles of equity and sustainable development to be upheld, agriculture-related climate change policies and programmes must be gender responsive. This in turn necessitates gender-sensitive processes in the crafting of policies and programs, which demands that women be included at all levels of decision-making. The active participation of the Philippine Commission on Women in crafting the National Framework Strategy on Climate Change and the subsequent National Climate Change Action Plan is a clear example; nevertheless, women’s participation in the implementation of various components of the action plan and mainstreaming of issues benefiting women and other marginalized sectors have yet to be realized. While local gender and development activities are constant and ongoing, institutional mechanisms are conspicuously absent from local decision-making and planning related to climate change responses. Representation by gender and development officers in the formation of local government committees and technical working groups is often neglected, indicating a lack of appreciation for the importance of gender issues in actions related to climate change. Consequently, gender issues

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Municipal government Other organizations in the area (senior citizens and youth) NGOs/POs

Universities/ academia

8.

Universities/ academia

Municipal government NGOs/POs

Universities/ academia

Municipal government NGOs/POs

Local government

Working members of the family (at home or in abroad) Friends and neighbors Credit cooperatives

Highland Areas

Municipal government NGOs/POs

Church-based organizations Local government

Municipal government NGOs/POs

Youth organizations Local government

Friends and neighbors Universities/ academia

Parents/siblings working abroad

Youth

Note: Results are based on focus group discussions conducted in Laguna. NGOs = nongovernmental organizations; POs = people’s organizations. Source: Maria Emilinda T. Mendoza, Faces of Vulnerability: Gender, Climate Change and Disaster (Los Baños: Southeast Asian Regional Centre for Graduate Study and Research in Agriculture, 2014), p. 21.

9.

7.

6.

Municipal government Other organizations NGOs/POs in the area (youth, women, and men)

Local government

Credit from 5–6 sources Credit cooperatives

Friends and neighbors

Relatives

5.

Credit cooperatives

Friends and neighbors

Relatives

Local government

Friends, relatives, and neighbors Internal revenue allocation of the Local government agency Credit cooperatives

4.

3.

2.

Children working Working members Working members locally or in abroad) of the family (locally of the family (at or in abroad) home or abroad)

Working members of the family (locally or in abroad) Friends and neighbors Credit cooperatives

1.

Coastal Areas

The Elderly

Ranking Women

The Agricultural Sector

Composition of Focus Group

TABLE 6.5 Focus Group Rankings of the Importance of Different Forms of Social Capital, 2014 The Gendered Impacts of Climate Change

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have yet to be mainstreamed into local government planning, disaster risk and reduction management planning, or climate change planning. Informal discussions with twenty-one local government agencies during 2011–14 indicate that confusion remains regarding how to progress local climate change action plans, and very little effort has been invested in involving gender and development officers in climate change–related activities. Of concern, both gender and development officers and members of existing technical working groups lack knowledge on how to mainstream gender issues within climate change action planning. In contrast, at the household and community levels, women are prominent and active in participating in community discussions and meetings. Studies indicate that women are actively involved in agricultural activities and in spontaneous adaptation to climate change and disaster risk management. More studies are needed, however, to determine the specifics of women’s vulnerabilities and knowledge levels, and the roles they play in adapting to climate change. Missing the opportunity to incorporate such information into local and national adaptation strategies is likely to result in lost opportunities to reduce the negative impacts of climate change, and may undermine the viability of projects intended to ensure the adoption of appropriate adaptation measures. National policies are needed to ensure that women’s knowledge, opinions, and influence are including in local climate change adaptation strategies for the agricultural sector. Government policy targeting adaptation to and mitigation of the adverse impacts of climate change should be targeted to vulnerable sectors. It is imperative that such vulnerabilities be ascertained, including those of women in various circumstances and in the agricultural sector. Ascertaining their capacity to contribute to viable and effective adaptations should be part of this process in order to ensure that any adaptations endorsed and pursued, nationally and down to local level, are responsive to the vulnerabilities of women. At the very least, adaptation measures adopted and implemented should not be maladaptive as far as gender is concerned. Well-intended measures aimed at reducing poverty and developing the economy must not put women at a disadvantage, nor should they omit the unique needs of women. More agricultural research is needed incorporating a gender perspective and gender-based analyses of the division of labour in agricultural production and marketing, as well as in the consumption of food. This highlights the importance of sex-disaggregated data in agriculture. The

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Women’s Empowerment, Development and Gender Equality Plan for 2013–16 (PCW 2014), which was formulated through a multi-agency effort, specifies this research agenda as requiring • an assessment of the current state of gender mainstreaming in the agricultural, environmental, biodiversity, climate change, and disaster risk sectors; • an analysis of the differing needs of women and men in promoting more efficient agricultural, environmental, and natural resources management; and • an analysis of the effectiveness of gender policies, activities, and programmes in achieving sector outcomes. Gender-based analysis of climate change in the context of Philippine agriculture may partly be undertaken using national-level sex-disaggregated data; however — because much can be learned from what women actually do in their homes and on their farms — more context-specific participatory research is also needed to highlight local vulnerabilities, ethnicities, knowledge, and spontaneous adaptation strategies used by men and women. Needless to say, the diversity of these voices must be heard and considered in the formulation of national policy. A related point of intervention is promoting men’s and women’s capacity to participate in discussions, meetings, and other activities relating to climate change and disaster management planning as they relate to agriculture. This need not be only in terms of raising awareness in the areas of agricultural technology, the science of climate change, and the development of livelihood enterprises to reduce poverty. It may be equally important to target confidence-building for the purpose of advocacy, teambuilding, and other cooperative activities. To support the development of a more gender-balanced and resilient agricultural sector in the light of climate change, both men and women need the confidence to contribute their opinions and allow their voices to be heard.

References Ahmed, Sara and Elizabeth Fajber. “Engendering Adaptation to Climate Variability in Gujarat India”. Gender and Development 17, no. 1 (2009): 33–50. Ananta, Aris, Armin Bauer, and Myo Thant. The Environments of the Poor in Southeast

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Asia, East Asia and the Pacific. Singapore: Institute of Southeast Asian Studies and Asian Development Bank, 2013. Ballaran Jr., Vicente G., Maria Emilinda T. Mendoza, Jaimie Kim Bayani-Arias, Rowena A. Dorado, and Bessie M. Burgos. Climate Change Vulnerability Mapping of Selected Municipalities in Laguna, Philippines. Los Baños: Southeast Asian Regional Centre for Graduate Study and Research in Agriculture, 2014. BAS (Philippine Bureau of Agricultural Statistics). Agricultural Indicators System Report, Gender-based Indicators of Labor and Employment in Agriculture (Report 2013–08). Quezon City: BAS, 2013. Boncodin, Raul et al. “Gender in Jasmine Flower-Garland Livelihoods in Peri-Urban Metro Manila, Philippine”. In Women Feeding Cities: Mainstreaming Gender in Urban Agriculture and Food Security, edited by Alice Hovorka, H. de Zeeuw, and M. Njenga, ed. Warwickshire. U.K.: Practical Action Publishing Ltd., 2009. Bordey, Flordeliza H. et al. Linking Climate Change, Rice Yield and Migration: The Philippine Experience. EEPSEA Research Report. Los Baños: International Development Research Centre (Canada), Economy and Environment Program for Southeast Asia, 2013. CCC (Climate Change Commission). National Climate Change Action Plan 2011–2028. Manila: CCC, 2011. Goh, Amelia H.X. A Literature Review of the Gender-Differentiated Impacts of Climate Change on Women’s and Men’s Assets and Well-Being in Developing Countries. CAPRi Working Paper 106. Washington, D.C.: International Food Policy Research Institute, 2012. IPCC (Intergovernmental Panel on Climate Change). Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by M. Parry, O. Canziani, J. Palutikof, P. van der Linden and C. Hanson. Cambridge: Cambridge University Press, 2007. ———. “Summary for Policymakers”. In Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, edited by C. Field, V. Barros, T. Stocker, D. Qin, D. Dokken, K. Ebi, M. Mastrandrea, et al. A special report of working groups I and II of the Intergovernmental Panel on Climate Change. Cambridge and New York: Cambridge University Press, 2012. Lantican, F., M. Sombilla, and K. Quilloy. Estimating the Demand Elasticities of Rice in the Philippines. Los Baños: Southeast Asian Regional Centre for Graduate Study and Research in Agriculture, 2013. Llanto, Gilberto M. et al. Strengthening Markets of High Value Fruits and Vegetables in Mindanao: The Case of Transport and Shipping Service Improvement. Los Baños: Southeast Asian Regional Centre for Graduate Study and Research in Agriculture, 2013.

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McKay, Deirdre. “Reading Remittance Landscapes: Female Migration and Agricultural Transition in the Philippines”. Danish Journal of Geography 105, no. 1 (2005): 89–99. Mearns, Robin and Andrew Norton, eds. Social Dimensions of Climate Change: Equity and Vulnerability in a Warming World. Washington, D.C.: World Bank, 2010. Mendoza, Maria Emilinda T. Faces of Vulnerability: Gender, Climate Change and Disaster. Los Baños: Southeast Asian Regional Centre for Graduate Study and Research in Agriculture, 2014. Nellemann, C., R. Verma, and L. Hislop, eds. Women at the Frontline of Climate Change: Gender Risks and Hopes. A Rapid Response Assessment. New York: United Nations Environment Programme; Arendal, Norway: GRID-Arendal, 2011. PCW (Philippine Commission on Women). Magna Carta of Women: Implementing Rules and Regulations. Manila: PCW, 2010. ———. Women’s Empowerment, Development and Gender Equality Plan 2013–2016. Manila: PCW, 2014. Quisumbing, Agnes R., Jonna P. Estudillo, and Keijiro Otsuka. Land and Schooling: Transferring Wealth Across Generations. Baltimore: Johns Hopkins University Press for the International Food Policy Research Institute, 2004. Rakib, Muntaha and Julia Anna Matz. The Impact of Shocks on Gender-Differentiated Asset Dynamics in Bangladesh. IFPRI Discussion Paper 1356. Washington, D.C.: International Food Policy and Research Institute, 2014. WEF (World Economic Forum). The Global Gender Gap Report 2013, Insight Report. Geneva: World Economic Forum, 2013.

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7 ADAPTATION AND MITIGATION STRATEGIES Marites M. Tiongco

The United Nations Environment Programme has indicated that the most dangerous impacts of climate change could still be averted if rational and adequately financed adaptation and mitigation strategies are initiated to forestall disasters and migrations at unprecedented scales — but they must be applied immediately and aggressively. In the agricultural sector, adaptation and mitigation strategies primarily involve responses to higher temperatures, excessive precipitation, extreme weather events, rising sea levels, and the evolution of minor diseases and pest infestations into major ones. Resulting challenges include landslides, severe soil erosion, flooding, and drought, which in turn cause losses of crops, livestock, and fisheries; reductions in yields; shortages of water; and destruction of infrastructure (Table 7.1). Numerous strategies, technologies, and tools are available, but they need to be applied through coordinated and targeted approaches that respond to a complexity of locally specific conditions. Importantly, mainstreaming adaptation and mitigation strategies in agricultural development planning requires increased information and understanding about climate change and its implications, greater advocacy in the use of

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Changing weather patterns

Risk

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Increased energy costs and reduced poultry and hog harvests

Poor crop yields

Failure to establish crops

Possible Impacts on Agriculture Highly likely based on warm spells and heat waves, with increased frequency over most land areasa Virtually certain based on warmer days and nights, fewer cold days and nights, and warmer and more frequent hot days and nights over most land areas (and on warming of the most extreme days and nights each year)

Direction and Likelihood of Future Trends Breeding and screening crops resilient to changing weather patterns

• Adaptation strategies: Making available and adopting resilient crops • Mitigation strategies: Reducing methane and nitrous oxide production in agriculture • Adaptation strategies: Ensuring efficient weather forecasting and cultural management strategies; making adjustments to cropping calendars; and modifying crop-establishment practices • Mitigation strategies: Using organic fertilizers and pesticides, pesticides derived from nonfossil fuels, and plant incorporated pesticides; and using mulching and zero to minimal tillage • Adaptation strategies: Adopting energyefficient poultry- and hog-raising systems; adopting feed formulation and a feeding strategy for ruminants; and constructing energy-efficient buildings • Mitigation strategies: Harvesting methane from animal manure; adopting energyefficient or green machinery; delivering timely, location-specific weather information to farmers; and harvesting methane for selfcontained energy use

continued on next page

Breeding and screening crops resilient to changing weather patterns; conducting onfarm testing; providing information, education, and communication; and making nonfossil fuel–based pesticides and plantincorporated pesticides available Making energy-efficient or green machines available; conducting energy audits of postharvest facilities, and constructing energyefficient infrastructure; delivering timely, reliable, location-specific weather information; making appropriate technologies available for harvesting; and using methane from livestock wastes

Further Action

Adaptation/Mitigation Strategy

TABLE 7.1 Examples of Projected Climate Change Impacts on Philippine Agriculture, Forestry, and Fisheries

Adaptation and Mitigation Strategies

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Floods

Severe soil erosion

Landslides

Risk

Destruction of crops and fisheries in floodprone areas; destruction of postharvest facilities and farm-to-market roads; destruction of livestock houses in flood-prone areas;

Likely based on increased incidence of extremely high sea levels (excluding tsunamis)b

Highly likely based on increased frequency of heavy precipitation events over most areas

Further Action

Providing planting materials for agro-reforestation and cover crops; screening crop species that could minimize soil erosion Ensuring planting materials are made available on time, coupled with providing information, education, and communication; establishing early warning systems to harvest fish earlier;

• Adaptation strategies: Submerging and using flood-tolerant rice and corn varieties; using early maturing varieties to avoid floods during the first cropping; using weather-resilient infrastructure; situating livestock housing appropriately; using appropriate feed formulations; establishing

Providing reliable and accurate weather forecasting; using backyard seed nurseries for indigenous agroforestry species; implementing communitybased, integrated watershed management; and supporting community-based organizing

• Adaptation strategies: Stabilizing slopes using engineering solutions and vegetative strip technology; covering crop using legumes in denuded landscapes • Mitigation strategies: Sequestering carbon dioxide through agro-reforestation

Likely based on increased incidence of extremely high sea level (excluding tsunamis)b

Collateral damage to lowland agriculture, aquaculture, coastal fishery resources, settlements, and infrastructure Soil-nutrient depletion; siltation of irrigation systems, rivers, and streams; increased occurrence of dust storms, especially during El Niño events

Adaptation/Mitigation Strategy

Highly likely based on • Adaptation strategies: adopting soil and increased frequency over most water conservation practices and agroareas of heavy precipitation reforestation of denuded landscapes; events and providing information, education, communication, and early warning to downstream inhabitants • Mitigation strategies: sequestering carbon dioxide through agro-reforestation

Direction and Likelihood of Future Trends

Destruction of upland agricultural systems

Possible Impacts on Agriculture

TABLE 7.1 — cont’d

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Drought

Significant reduction in yields and loss of crops; water shortages; heat stress on farm animals; increased energy costs to poultry and hog raisers; and loss of capital among farmers

destruction of farms; loss of livestock; loss of farm inputs, machinery, and implements; and loss of farm capital

Likely based on increased areas affected by drought

• Adaptation strategies: Use of droughttolerant crops; efficient water use in irrigation systems (drip irrigation); use of early maturing varieties to escape drought; use of crop-establishment technology to shorten turn-around time between crops; construction of well-ventilated buildings and dwellings; choice of poultry and hogs that are tolerant to higher temperatures; construction of energy-efficient buildings; and use of water-conservation practices • Mitigation strategies: use of integrated sustainable practices (for example, no tillage) that reduce/capture greenhouse gas emissions; use of organic fertilizers to increase soil capacity to capture carbon dioxide; and use of special planting programmes in drought-prone areas

evacuation protocols and locating centers above flood levels; locating storage sheds above flood levels; and establishing savings and seed banks in flood-free areas • Mitigation strategies: Converting areas that are not economically viable for fish production to wetlands or other uses; ensuring conversion strategies have mitigation potential; capturing methane; and reducing methane output

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continued on next page

making capital available to replace destroyed properties; facilitating timely delivery of reliable, location-specific, and reliable weather information to farmers; establishing early warning systems; making information available on flood-prone areas; making advisories available on emergency procedures during floods; providing subsistence subsidiesc Making water available at the right time or when crops need it; using efficient irrigation and drainage systems; providing planting materials; making water use efficient/drought-tolerant crops available; using watershed-management approaches to agriculture and fishery establishment; making water conservation practices available; providing advisories in drought-prone areas; facilitating timely delivery of reliable, locationspecific weather information to farmers; making poultry and livestock breeds that are tolerant to higher temperatures available; and providing subsistence subsidiesd

Adaptation and Mitigation Strategies

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Direction and Likelihood of Future Trends Adaptation/Mitigation Strategy

• Adaptation strategies: Use of short and early maturing rice and corn varieties and other food crops; use of early maturing, shorter, and sturdy bananas, fruit trees, and coconuts; designing and situating poultry and pig housing to resist gale-force winds; building wind-resistant infrastructure; planting windbreaks; ensuring reliable and localized weather forecasting; and establishing evacuation protocols and centers in the event of strong typhoons • Mitigation strategies: Shifting crops from irrigated rice to dry/wet seeded varieties that minimize carbon dioxide, ammonium, and nitrogen dioxide generation; using fruit trees and windbreaks as carbon sinks

Loss of crops, livestock, Virtually certain based on • Adaptation strategies: Use of pestand aquaculture warmer days and nights, fewer resistant crops, livestock, and fish; use cold days and nights, and of environmentally friendly pest-control warmer and more frequent strategies; and bio-control of pests and hot days and nights over most diseases land areas (and on warming • Mitigation strategies: Establishing bioof the most extreme days and pesticides to serve as carbon sinks (for nights each year) example, neem trees)

Possible Impacts on Agriculture

Strong winds Lodging of rice and Likely based on increased corn, fruit trees, incidence of intense tropical plantation crops, and cyclone activity others; destruction of poultry and pig pens; destruction of residences and fishing vessels (see Appendix Tables 7A.1 and 7A.2 for production losses and economic damages due to climate-related natural disasters such as tropical storms)

Increased pest pressure

Risk

TABLE 7.1 — cont’d Further Action

Making planting materials available; providing early warning systems and advisories; providing risk maps; making early maturing, shorter, sturdy bananas, fruit trees and coconuts available; ensuring the availability of high-quality construction materials; screening plant materials resistant to strong winds; making advisories on emergency procedures available during typhoons; making capital available to replace fishing boats; making information on typhoon paths available; and providing subsistence subsidies

Making pest-resistant crops and environmentally friendly, nonfossil fuel–based pesticides available

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Notes: a. Extremely high sea levels are based on average sea levels and regional weather systems and are defined as the highest 1 per cent of hourly values of observed sea levels at a station for a given reference period. b. In all scenarios, the projected global average sea level in 2100 is higher than in the reference period; the effect of changes in all regional weather systems on sea level extremes has not been assessed. c. Damages to agriculture and property between 1975 and 2002 were estimated to be around $55 million and $83 million, respectively (Amadore 2005, cited in Asian Development Bank 2009). d. The sharpest fall in gross value-added and in volume of production was recorded during the El Niño years of 1982/83 and 1997/98 (Amadore 2005, cited in Asian Development Bank 2009). Sources: Compiled by authors from IPCC (Intergovernmental Panel on Climate Change), Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change, edited by N. Nakićenović and R. Swart. 2000 , “Climate Change 2007: Synthesis Report”, Fourth Assessment Report of the Intergovernmental Panel of Climate Change. 2007 (accessed 29 December 2013) and DA (Department of Agriculture), “DA-Policy and Implementation Program on Climate Change”, Attachment to the “Memorandum: Mainstreaming Climate Change in the DA Programs, Plans & Budget”, 2013a (accessed 19 April 2014).

Adaptation and Mitigation Strategies

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climate-smart practices, and intensified development and dissemination of risk-resilient technologies. On this basis, this chapter examines the Philippines’ level of resilience to the adverse impacts of climate change, primarily focusing on agricultural policies, programmes, and activities currently in place. More specifically, the chapter assesses the alignment of climate change policies and priorities with the intended outcomes of the National Climate Change Action Plan (NCCAP) and Philippine Development Plan. The chapter presents evidence of the effectiveness of the country’s agricultural adaptation and mitigation policies and initiatives, including recent trends in government budget allocations, and identifies constraints and challenges, proposing recommendations to overcome them. The primary methodology used is a review of the available literature, roadmaps, action plans, and project reports. Supporting information was also gathered through key informant interviews and consultations with key officials from the Philippine Department of Agriculture and local government agencies in Albay, Camarines Sur, Northern Samar, Davao City, and Benguet, among others. Some additional case study information was also collected for illustrative purposes.

I. THE CURRENT GOVERNMENT FRAMEWORK IN SUPPORT OF ADAPTATION AND MITIGATION Recognizing the importance of managing greenhouse gases (GHGs), the Philippines has been proactive in responding to the negative impacts of climate change, including reducing atmospheric GHG emissions. As early as 1991, and even prior to the 1994 signing the United Nations Framework Convention on Climate Change, the Philippine government created the Inter-Agency Committee on Climate Change to coordinate and monitor the country’s climate change–related challenges and initiatives. The government signed the Kyoto Protocol in 2003, after which various laws and government issuances were enacted (Table 7.2). The key legislation is discussed in more detail below.

Enactment of Climate Change Act (Republic Act 9729) The Climate Change Act, signed in October 2009, calls for the State to integrate the concept of climate change into various phases of policy

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2014

2013 2013

2011 2012 2012 2013

1991 1994 2003 2003 2007 2009 2009 2010 2010 2010

Year of Implementation

Sources: SEPO (Senate Economic Planning Office), “Philipine Agricultural Exports At A Glance. Senate of the Philippines, 16th Congress”, 2012 (accessed 3 February 2014); DA (Department of Agriculture), “DAPolicy and Implementation Program on Climate Change”, Attachment to the “Memorandum: Mainstreaming Climate Change in the DA Programs, Plans & Budget”, 2013a (accessed 19 April 2014).

Creation of the Inter-Agency Committee on Climate Change Signing of the United Nations Framework Convention on Climate Change Ratification of the United Nations Framework Convention on Climate Change Signing of the Kyoto Protocol Creation of the Presidential Task Force on Climate Change Enactment of the Climate Change Act (RA 9729) Creation of the Climate Change Commission (CCC) Formulation of the National Framework Strategy on Climate Change (NFSCC), 2011–2028 Mainstreaming climate change in the Philippine Development Plan 2011–2016 Enactment of the Philippine Strategy on Climate Change Adaptation; Disaster Risk Reduction and Management (DRRM) Act (RA 10121) Formulation of the National Climate Change Action Plan (NCCAP), 2011–2028 Creation of Cabinet Cluster on Climate Change Adaptation and Mitigation Enactment of the People’s Survival Fund Act (RA 10174) Mainstreaming guidelines on integrating disaster risk reduction and climate change adaptation concerns into the Environmental Impact Statement systems Mainstreaming climate change in the Department of Agriculture programmes, plans, and budgets Development and implementation of guidelines in tagging/tracking government expenditures on climate change in the budget process Institutionalizing Philippine Greenhouse Gas Inventory Management and Reporting System (Executive Order No. 174)

Legislation/Policy/Plan

TABLE 7.2 Government Policies and Strategies in Response to Climate Change, 1991–2014 Adaptation and Mitigation Strategies

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formulation, development plans, poverty reduction strategies, and other government development initiatives. The Act encourages a collective approach to climate change by all stakeholders, including national and local government, private enterprise, nongovernmental organizations, local communities, and the public. The Act’s implementing body is the Climate Change Commission, which spearheaded the formulation of the National Framework Strategy for Climate Change (Climate Change Commission 2009) and the aforementioned NCCAP (Climate Change Commission 2010). The NFSCC 2010–2022 is the government’s national roadmap for ensuring the country becomes more resilient to climate risks. The framework strategy’s main goals are to build the adaptive capacity of communities, increase the resilience of natural ecosystems, and optimize sustainable mitigation opportunities. The framework comprises seven key results areas: (1) enhanced vulnerability and adaptation assessments; (2) integrated ecosystem-based management; (3) water governance and management; (4) a climate-responsive agricultural sector; (5) a climateresponsive health sector; (6) climate-proof infrastructure; and (7) disaster risk reduction (Figure 7.1). The pursuit of a climate-responsive agricultural sector is primarily intended to protect and enhance ecosystems and ecosystem services to ensure greater food security and increase livelihood opportunities. Priority strategies include the following: • Reducing climate change risks and the vulnerability of natural ecosystems and biodiversity through ecosystem-based management approaches, conservation efforts, and sustainable environmental and natural resources–based economic endeavors, such as ecotourism • Increasing the resilience of agricultural communities by developing climate change–sensitive technologies, establishing climate-proof agricultural infrastructure and climate-responsive food production systems, and providing support services to the most vulnerable communities • Strengthening the resilience of fisheries by restoring fishing grounds, stocks, and habitats and investing in sustainable and climate change– responsive fishing technologies and products • Expanding investments in aquaculture and other food-production areas • Strengthening the crop-insurance system as an important risk-sharing mechanism to introduce innovative risk transfer mechanism such as a weather-based insurance system

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•

•

•

•

•

•

• •

• • •

•

MITIGATION Energy efficiency and conservation Sustainable infrastructure Renewable energy Environmentally sustainable transport National REDD+ strategy Waste management

THE GOAL To build the adaptive capacity of communities, increase the resilience of natural ecosystems to climate change, and optimize mitigation opportunities for sustainable development

ADAPTATION Enhanced vulnerability and adaptation assessments Integrated ecosystem-based management A climate-responsive agricultural sector Water governance and management A climate-responsive health sector Disaster risk reduction

Policy, planning, and mainstreaming

Valuations

Financing

Multistakeholder partnerships

MEANS OF IMPLEMENTATION

Note: REDD+ = reducing emissions from deforestation and forest degradation plus. Source: CCC (Climate Change Commission), “National Framework Strategy on Climate Change 2010–2022”, 2009 (accessed 19 December 2013).

Gender mainstreaming

Research and development and technology transfer

Information, education, communication, and advocacy

Knowledge management

Capacity development

CROSS-CUTTING AREAS

CLIMATE CHANGE IMPACTS AND VULNERABILITIES Ecosystems Energy Food Water Health Communities Infrastructure

FIGURE 7.1 FIGURE 7.1 Operating Structure of the National Framework Strategy for Climate Change, 2010–22 Operating Structure of the National Framework Strategy for Climate Change, 2010–22 Adaptation and Mitigation Strategies

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• Strengthening sustainable, multisectoral, and community-based resource management mechanism The framework strategy is put into effect through the action plan, which in turn focuses on seven thematic areas: (1) food security; (2) water sufficiency; (3) ecosystems and environmental stability; (4) human security; (5) climate-smart industries and services; (6) sustainable energy; and (7) knowledge and capacity development. The plan also identifies actions to ensure greater food security and resilience in agriculture (Figure 7.2). Specific agricultural policy reforms include the land-use bill and regulations to shift commodities and convert agricultural land to nonagricultural uses.1

The Philippine Disaster Risk Reduction and Management Act of 2010 (RA 10121) The government’s climate change agenda also emphasizes the convergence of adaption and disaster risk reduction management (DRRM). Consequently, the Disaster Risk Reduction Management Act of 2010 defines the country’s National Disaster Risk Reduction and Management Framework, under which initiatives are institutionalized and funds appropriated for training, supplies and equipment, post disaster activities, insurance, and other purposes. The Act specifies that at least 5 per cent of the estimated total standard revenues of local government agencies be set aside as the Local Disaster Risk Reduction and Management Fund to support both climate change adaptation and disaster risk reduction activities. Of the allocated funds, 70 per cent can be used for DRRM activities, such as (but not limited to) pre-disaster preparedness programme training, life-saving rescue equipment, supplies, and medicines; post-disaster activities; and disaster insurance premiums. The remaining 30 per cent is allocated to a Quick Response Fund, as a standby fund for immediate use by implementing agencies — for example, the Department of Agriculture, Department of National Defense, Department of Social Welfare and Development, Department of Education, and Department of Public Works and Highways — for disaster response activities, such as relief and recovery programmes. In 2013 and 2014, PhP500 million was allocated to the Department of Agriculture for the provision of seed and planting materials to households and communities affected by natural disasters, including fingerlings/fries,

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IMMEDIATE OUTCOME

OUTPUTS

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1.1.3. Establish kn owledge management on climate change information for agriculture and fisheries

1.2.3. Monitor and evaluate the implementation of climate change adaptation and disaster risk reduction plans in agriculture

1.2.2. Scale up implementation of best practices

2.1.2. Integrate climate change adaptation and disaster risk reduction into agricultural and fisheries curriculums and training programmes

2.1.1. Build the adaptation and disaster risk reduction capacity of farming and fishing communities

1.2.1. Integrate and harmonize climate change adaptation and disaster risk reduction into national and local agricultural and fisheries policies and plans, including the Philippine Development Plan

1.1.1. Enhance site-specific knowledge on the vulnerability of agriculture and fisheries to the impacts of climate change

1.1.2. Conduct research and disseminate knowledge and technologies on climate change adaptation to reduce the vulnerability of the sector

2.1. Enhanced capacity of government, farming and fishing communities, and industry for climate change adaptation and disaster risk reduction

1.2. The formulation of climate sensitive agriculture and fisheries policies, plans, and programmes

2.2.1. Implement risk transfer and social protection mechanims for agriculture and fisheries

2.2. Enhanced social protection for farming and fishing communities

2. Enhanced climate change resilience of agricultural and fishing communities

1.1. Enhanced knowledge on the vulnerability of agriculture and fisheries to the impacts of climate change

1. Enhanced resilience to climate change in agriculture and fisheries production and distribution systems

Strategic Actions on Food Security under the National Climate Change Action Plan, 2011–28

Source: CCC (Climate Change Commission), “Philippines Climate Change Adaptation Policy Initiatives National Climate Change Action Plan. Local Government Academy”, 2011 (accessed 29 December 2013).

ACTIVITIES

FIGURE 7.2 FIGURE 7.2 Strategic Actions on Food Security under the National Climate Change Action Plan, 2011–28 Adaptation and Mitigation Strategies

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livestock, fishing nets, and small boats, as well as the repair of irrigation systems (Department of Agriculture 2014). The Act is founded on the principle of proactivity and facilitated action, but the Quick Response Fund, which is limited to post-disaster relief and rehabilitation, would indicate that this principle is not being instituted (Domingo 2014). Restricting access to the funds for pre-disaster expenses puts unnecessary pressure on relief and rehabilitation operations in the event of disasters, the timing and magnitude of which is always uncertain.

People’s Survival Fund Act of 2012 (RA 10174) The need to have a sustained source of funding for climate change–related projects prompted an amendment of the Climate Change Act to establish the People’s Survival Fund, which is a special fund within the national treasury to finance adaptation initiatives based on the NFSCC. The fund had an opening balance of PhP1 billion under the General Appropriation Act, which can be augmented by donations, endowments, grants, and other contributions. Another PhP1 billion has been allocated in 2016 to finance climate change adaptation projects. The fund is managed by a board that is chaired by the Climate Change Commission and comprises representatives from government agencies, academia, private enterprise, and nongovernmental organizations. The fund is intended to support the adaptation activities of local governments and communities. It is allocated to projects based on, but not limited to, the following criteria: (1) the community having a relatively high level of vulnerability to climate change; (2) the participation of the community in the design of the project; (3) the potential of the project for reducing poverty; (4) the potential of the project to be cost-effective and sustainable; (5) the potential of the project to respond to genderdifferentiated vulnerabilities; and (6) participation in the project by local government agencies with a climate change action plan in place.

II. MAINSTREAMING ADAPTATION AND MITIGATION STRATEGIES The Philippine Development Plan 2011–16 The Philippine Development Plan 2011–16 is the primary document outlining national development programmes and strategies to guide the

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current leadership. The plan focuses on developing capacities for coping with and countering immediate climate threats, emphasizing climate change adaptation in the areas of agriculture, infrastructure, water governance, flood management, and housing. The plan emphasizes ecosystem-based management, climate-sensitive technologies, and climate-resilient systems to reduce the risks posed by the changing climate. For agriculture, the target for the end of 2016 is to achieve higher incomes and improved food security, especially for farm households, by increasing agricultural and fisheries production through more prudent use of resources; building greater linkages with manufacturing and industry, including increasing the production of raw materials; and enhancing the sector’s resilience to the risks of climate change. Recognizing that effective DRRM will enhance adaptive capacity to climate change, climate variability, and extreme climate events, the Philippine Development Plan was updated to integrate policies and measures that address climate change in agricultural development planning and decision making. Strategies in the area of “Competitive and Sustainable Agriculture and Fisheries” include: (1) encouraging the diversification of production and livelihood options; (2) reducing the degradation of and improving the quality of the environmental resources; and (3) increasing the resilience of agricultural communities and their capacity to respond effectively to climate change risks and natural hazards. One item is crop or agricultural insurance protection. The development plan targets over 2 million farmer beneficiaries by 2016 — a 60 per cent increase per year since 2012. This target is to be achieved by providing insurance to farmers, including training in crop diversification and the impact of weather on crops; promoting the adoption of climate-responsive technologies and innovations in crop production; and processing and distributing agricultural products.2 Farmers are to be provided with credit assistance and appropriate incentive mechanisms to undertake climate change–adaptation measures. These strategies are to be implemented so that farmers simultaneously increase their resilience to the risks of climate change and their agricultural productivity. In addition to the guidelines for mainstreaming climate change adaptation, the public investment programme and results matrix outline the goals, outcomes, and outputs to be achieved, along with the corresponding indicators, baseline information, end-of-plan targets, and responsible agencies.

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Agriculture and Fisheries Modernization Plan 2011–17 The roadmap for developing the agricultural sector is laid out in the Agriculture and Fisheries Modernization Plan 2011–17. This plan recognizes climate change as the “new normal” environment that adds greater pressure to the goal of increasing agricultural productivity. In response, the plan includes an entire chapter on medium- and long-term responses to climate change in agriculture. In particular, the plan states that policies and programmes that aim to build the adaptive capacity of farming and fishing communities will be further reinforced through seven strategic areas: (1) the development of a climate information system; (2) the conduct of research and development for adaptive tools, technologies, and practices; (3) the repair and improvement of irrigation systems and the establishment of small water-impounding projects and small-farm reservoirs; (4) the development of climate change–adaptive infrastructure; (5) the introduction of financing mechanisms and instruments for transferring risk; (6) the institution of regulations to ensure effectiveness and safety; and (7) the development of a fully engaged extension system. The required funding for climate change initiatives during the period of the plan was PhP7.6 billion.

Synergies between National and Local Action Plans and Adaptation Policies The Climate Change Act directs local government agencies to prioritize climate change issues and set local climate change action plans using the NCCAP as a guide. Being on the frontline in responding to the impacts of climate change, local government agencies are intended to take the initiative in climate-proofing vulnerable provinces and municipalities via their respective comprehensive development and land-use plans. For example, users’ manuals have been formulated to guide provincial governments in incorporating climate change adaptation and disaster risk reduction concerns into their development and land-use plans. The manuals are based on comprehensive and scientifically sound vulnerability assessments of the respective municipalities. They provide their municipalities with the necessary technical guidance to formulate risk-resilient land-use and development action plans, while also providing guidance on possible sources of financial assistance to implement the plans. The vulnerability assessment of municipalities is done by the local

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community in collaboration with scientists and researchers. As of 2011, this exercise had been undertaken in Caraga, Mindanao (Region 13), which has produced, completed, and pilot-tested its users’ manual. Another example of successful synergies between national and local climate change action plans is the climate change adaptation and disaster risk reduction strategy pioneered in the province of Albay, Luzon, which focuses on pre-emptive evacuation to achieve zero casualties. In mainstreaming climate change adaptation into the province’s local development planning processes, this strategy involved building a climate change adaptation roadmap around the Millennium Development Goal target of achieving greater environmental sustainability (Espinas 2012). Policies were formulated, programmes developed, and budgets allocated for the purpose of achieving greater environmental sustainability within the province. Additionally, the capacity of select institutions was strengthened either in implementing or guiding the implementation of programmes and projects that would ensure the successful achievement of specific goals. This strategy is considered one of the best practices for climate change initiatives and replication in provinces with similar geographic and climatic characteristics.

Institutional Capacity to Address Climate Change Risks in Agriculture The institutions directly involved in addressing and coordinating climate change actions are the Cabinet Cluster on Climate Change, Climate Change Council, People’s Survival Fund Board, National Disaster Risk Reduction and Management Council, National Economic and Development Authority (Climate Change Commission), Department of Budget and Management, Department of Finance, local government agencies, House of Representatives Ecological Committee, and National Council on Sustainable Development. The Climate Change Council is the lead policymaking body tasked with coordinating, monitoring, and evaluating the climate change agenda.3 The Council has effectively formulated the national climate change strategy and action plan and led the preparation of the People’s Survival Fund. The Council has a broad mandate to be carried out with counterpart departments and agencies. Responsibilities include implementing regional and local actions; building local adaptation capacity with the Department of Interior and Local Government; mainstreaming climate issues into

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development and planning processes with Climate Change Commission; aligning climate change adaptation and DRRM actions with the National Disaster Risk Reduction and Management Framework; and putting the People’s Survival Fund into operation in cooperation with the Fund’s Board, whose role is mobilizing resources. The Climate Change Council’s role also includes setting strategic directions for local government agencies to improve their integration of climate-related objectives into their local climate change adaptation and DRRM programmes. Most local government agencies have limited technical capacity, so developing such capacity is among the key areas prioritized in the National Framework Strategy for Climate Change. Among the strategic priorities for capacity development is strengthening institutional arrangements for adaptation and mitigation (Climate Change Commission 2009). Consequently, World Bank (2013) notes that the key challenges in responding to climate change issues include “lack of institutional capacity, knowledge generation and management, and monitoring and evaluation” across government levels and departments involved. This is where cooperative efforts are required between from the Climate Change Council, Climate Change Commission, and the Department of the Interior and Local Government to deliver this much-needed support to local government agencies. Similarly, capacity for mainstreaming climate change in local development plans needs to be built through training and incentives for knowledge generation, facilitation, and sharing — particularly to overcome the significant capacity gap in oversight agencies, departments and local government agencies — and to enhance public awareness of the integration of climate change adaptation and disaster risk management (World Bank 2013). Building on the recommendation of World Bank (2013), the Department of Agriculture developed a comprehensive climate change action plan that has been mainstreamed across all units, and also embarked on a national initiative and communication strategy “Adaptation and Mitigation Initiatives in Agriculture”. The strategy aims to provide efficient and resilient support services to address climate challenges in agriculture effectively. It also addresses the importance of integrated agricultural development planning by focusing on the “strategic agricultural development zone” as the primary domain for climate change planning. In 2013, the Department of Agriculture began mainstreaming climate change in its programmes, plans, and budget (Department of Agriculture

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2013b). The goals were to build the knowledge, attitudes, and skills of its staff; to develop a culture with a deep consideration of climate change and sustainable development issues; and to mainstream climate change systems wide, such that all agencies be wholly climate responsive in their decision-making processes from the establishment of priorities, to the setting of policy, to the execution of monitoring and evaluation (M&E) (Department of Agriculture 2013b). The initiatives of the Department of Agriculture’s System-Wide Climate Change Office include mainstreaming climate change, the climate information system, the Philippine adaptation and mitigation in agriculture knowledge toolbox, climate-resilient agricultural infrastructure, financing and risk transfer instruments on climate change, climate-resilient agriculture and fisheries regulations, and the climate-resilient agricultural extension system. Implementing climate change adaptation and mitigation measures requires investment, technologies, and know-how, together with financial resources. Considering the country’s limited resources, it is important that they be invested in highly targeted and integrated ways to maximize results.

III. THE EFFECTIVENESS OF AGRICULTURAL ADAPTATION AND MITIGATION INITIATIVES The Alignment of Ongoing Initiatives with the National Climate Change Action Plan and the Philippine Development Plan’s Target Outputs To date, the Department of Agriculture has aligned some of its climate change programmes, activities, and projects with strategies defined by NCCAP on climate-smart industry and services, sustainable energy, and outcomes relating to food security. Establishing systematically consistent targets and outputs between NCCAP and the Department of Agriculture will determine the extent to which activities can be carried out effectively. The alignment of strategies and outcomes between NCCAP and the Philippine Development Plan (National Economic and Development Authority 2011a) has been improved through the updated development plan. A commonly specified outcome — supported by outputs, indicators, and targets — is increasing climate change resilience in agriculture, fisheries, and environmental and natural resources.4

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Knowledge on the Vulnerability of Agriculture and Fisheries to the Impacts of Climate Change It has been said that climate change is a global issue that requires local solutions. Thus, in order to be able to respond appropriately, it is extremely important that decision-makers and farmers understand the types and levels of vulnerability they face in different locations. While global projections and scenarios have been provided by international organizations and institutions, the challenge for the Philippines is to interpret this body of information within the local context so that farmers can put appropriate adaptation measures into practice. Some of the outputs produced include the following:

Flood Hazard Maps The project “Climate Twin Phoenix” included the generation of flood hazard maps for different rainfall scenarios for integration into the country’s rain monitoring system under the “Nationwide Operational Assessment of Hazards” project (Climate Change Commission 2013). This work is intended to serve as the basis for early warning systems in the identified locations. The maps will also be used as a foundation for local government contingency plans and development programmes (Climate Change Commission 2013). The work is also intended to enhance the competencies of the local government agencies concerned in mainstraining adaptation and DRRM into their comprehensive land-use plans. The Climate Twin Phoenix project also uses knowledge and education to raise awareness about disasters, to educate the community, and to assist local governments in integrating climate change adaptation and disaster risk reduction in planning their cities and municipalities and in advancing policies that support strategies and actions towards sustainable development (UNDP 2013a). The project was implemented in Cagayan de Oro, Iligan City, Davao Oriental, and Compostela Valley Province.

Provincial Agricultural Models A provincial agricultural model was developed from the assessment of the impacts of climate change on the agricultural sector through the project “Assessments of Climate Change Impacts and Mapping of Vulnerability

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to Food Insecurity under Climate Change to Strengthen Household Food Security with Livelihoods’ Adaptation Approaches”, which was led the by NEDA. In order to generate projections, the provincial agricultural model depends on output data from partner agencies derived from climate, crop, and hydrological models (Department of Agriculture 2013a).

Climate-Sensitive Agriculture and Fisheries Policies, Plans, and Programmes Consistent with the agenda of mainstreaming climate change adaptation and mitigation in the country’s development, local, and sectoral plans, certain programmes and projects, discussed below, produced necessary outputs for climate-proofing development plans relating to agriculture and fisheries.

Systemwide Climate Change Programme Instigated in early 2013, this programme was a strategic move on the part of the Department of Agriculture to more directly address climate change considerations, including vulnerabilities and risks in agriculture. The programme cut across the department’s policy instruments and agencies, focusing on seven key components: (1) mainstreaming climate change adaptation and mitigation initiatives in agriculture; (2) a climate information system; (3) the Philippine adaptation and mitigation in agriculture knowledge toolbox; (4) climate-smart agricultural infrastructure; (5) financing and risk transfer instruments on climate change; (6) climatesmart agriculture and fisheries regulation; and (7) a climate-smart agricultural extension system. A comprehensive M&E system is needed to facilitate a better assessment of the programme.

Low-emission Capacity Building With a focus on agriculture, this project was designed to produce: (1) a robust national system for preparing inventories of GHG emissions; (2) the formulation of nationally appropriate mitigation action roadmaps within the context of national development priorities; and (3) the design of the monitoring, reporting, and verification systems to support the implementation and evaluation of the roadmaps (UNDP 2013b). The project

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also focused on increasing awareness of climate change adaptation and mitigation within industry, and encouraging the private sector to develop climate-resilient, low-carbon initiatives.

The Capacity for Climate Change Adaptation and Disaster Risk Reduction Management The following actions have been undertaken to enhance the capacity of government, industry, and the farming and fishing communities in the areas of climate change adaptation and DRRM.

Capacity Needs Assessments The capacity development needs of institutions were initially assessed in 2011 under a Millennium Development Goals initiative on climate change and the environment implemented by NEDA. The assessments looked at institutions’ functional and technical capacities in relation to climate change adaptation and mitigation, particularly in terms of their ability to: (1) engage in multistakeholder dialogues; (2) develop mandates and vision statements based on assessment results; (3) formulate policy and strategy; (4) budget, manage, and implement strategies; and (5) monitor and evaluate progress. The assessments were only conducted on a limited number of institutions, including the Climate Change Council and local government agencies in ten municipalities (Cavite, Ifugao, Pangasinan, Sorsogon, Antique, Biliran, Bohol, Agusan del Norte, Bukidnon, and Surigao del Norte). According to the project’s capacity assessment results (National Economic and Development Authority 2012a), the participating agencies had high capacity in areas 1, 2, and 3, but low capacity in areas 4 and 5. The findings also indicated high capacity among the participating agencies’ leadership and personnel, which could be attributed to the government’s efforts to prioritize climate change adaptation and DRRM. Moreover, by enacting the 2009 Climate Change Act and 2010 DRRM Act, the government has established the necessary policy environment within which climate and disaster risks can be addressed. In turn, these actions have increased the country’s access to international funding for climate change adaptation and created active partnerships between funding agencies and institutions to strengthen the institutions’ adaptive capacities.

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The assessment did, however, determine that the institutions’ M&E capacity was low, which could be because the country’s system of M&E for climate change adaptation is relatively new, as are the policies to mainstream climate change adaptation into the planning and budgeting processes of local government agencies. A customized M&E system targeting climate change adaptation may be needed. In contrast, a bottom-up approach seemed to work in the project “Enhanced Climate Change Adaptation Capacity of Communities in Contiguous Fragile Ecosystems in the Cordilleras”. The thematic approaches used to enhance local stakeholder capacity involved enhancing: (1) knowledge about climate change; (2) planning capacity in local government agencies; (3) data management for climate change planning and monitoring; and (4) capacities for implementing climate change adaptation options at the farm level (Sandoval, Jr. and Baas 2013).

Conservation Farming Villages In 2011, conservation farming villages were established in the upland communities of La Libertad, Negros Oriental, and Ligao, Albay, as a means of introducing climate change adaptation and mitigation technologies to farmers. Under this type of scheme, farmers are encouraged to adopt conservation farming from a wide range of technologies to combat the effects of climate change, loss of biodiversity, land degradation, and drought, while at the same time increasing land productivity and promoting land conservation and rehabilitation (National Economic and Development Authority 2012a; Utzurrum and Ablan 2013). These technologies include sloping landscapes, contour farming, rapid composting, mulching, multispecies cropping, conservation tillage, water-saving technologies and water management, sowing hedgerow seeds, constructing physical barriers/rock walls to erosion, and shifting crops and cropping patterns. One of the lessons learned through this programme is the benefit of the strong partnership developed between the institutions involved, such as the Philippine Council for Agriculture, Forestry and Natural Resources Research and Development; Silliman University; La Libertad, Negros Oriental; and the conservation farming village community. This has contributed to the accomplishment of the expected outputs, one of which was to empower the community to solve current and future issues.

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Smart Agricultural Approaches The project “Smarter Approaches to Reinvigorate Agriculture as an Industry” was initiated to develop and implement science-based crop and cropping-system technologies and protocols, as well as long-term strategies geared towards maximizing crop yields while minimizing any adverse environmental or climate impacts on six priority crops: rice, corn, bananas, coconuts, coffee, and cacao. The project adopted the strategy of “smart agriculture” by employing a combination of technological innovations in information, cropping systems modelling, geographic information systems, and field sensors to develop decision-support models and an early warning system to help farmers and policymakers make sound, science-based judgments relating to climate change. Orientation seminars and training on farming the six priority crops were also conducted to build capacity among the different stakeholders in the provinces of Isabela and Panay Island, and the municipality of Catarman in Northern Samar. The project comprised five integrated components: (1) developing and evaluating crop models with a view to launching a nationwide cropforecasting platform capable of producing crop advisories and forecasts for the six priority crops; (2) developing environmentally targeted integrated crop management protocols for the purpose of establishing an automatic weather station and ground-sensor network and updating soil, land suitability, and environmental information for an integrated crop management database; (3) establishing an online knowledge portal for climate-resilient and sustainable crop production in the Philippines that includes the programme’s crop advisories and forecasting tools; (4) building the knowledge and capacity to diagnose learning needs, developing a learning framework for stakeholder communities, developing training modules, and conducting capacity-building workshops and technical training on the use of the online platform; and (5) mainstreaming the results of the project by crafting policy recommendations, publishing research outputs in peer-reviewed journals, and establishing a crop-climate forecasting and modeling laboratory within University of the Philippines, Los Baños. The project was launched in November 2013, and assessments have yet to be conducted. An important contribution of this initiative is the evidence-based planning and identification of management mechanisms that could help local government agencies address climate risks and natural disasters.

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Institutionalizing Best Practices in Farming Another project in the Bicol region, “Strengthening Capacities for Climate Risk Management and Disaster Preparedness in Selected Provinces of the Philippines”, initiated institutionalized, community-based adaptation practices. Agricultural universities and extension services facilitated the selection and field-testing of a set of best practices in cropping, livestock, and fisheries management. Enhanced climate information products were jointly produced by the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) and the Department of Agriculture to meet the needs of farmers and inform the seasonal selection of crop varieties for field demonstrations (Baas and Ricoy 2013).

Information Dissemination The Department of Agriculture has developed techniques and strategies to make crops more tolerant to the adverse impacts of climate change (see Lansigan, this volume) and has disseminated them to farmers through a variety of projects. Improvements in agricultural extension and support services have made farmers aware of climate change–adaptation technologies, enabling them to make better decisions in the areas of adaptation, mitigation, and preparedness. Nevertheless, financial and technical support to local government agencies involved in disseminating information and providing extension services needs to be strengthened, particularly in provinces categorized as having low adaptation and mitigation capacity, a high incidence of poverty, and a high risk of exposure to climate hazards like flooding and landslides (Figure 7.3).

Enhanced Social Protection for Farming and Fishing Communities Climate change greatly affects individuals, and particularly those whose livelihoods depend on natural resources. While climate change impacts are inevitable, governments seek to protect farmers and fishers through initiatives such as those described below.

Climate-Proof Livelihood Options Another project associated with the Millennium Development Goals

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FIGURE 7.3 Provinces with the Highest Incidence of Poverty, Number of Poor Households, and Risk of Climate Hazards

Source: NEDA (National Economic and Development Authority), “Philippine Development Plan 2011–2016: Revalidated Public Investment Program”, 2014 (accessed 15 December 2014).

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involved field testing indigenous or location-specific adaptation measures in contiguous fragile ecosystems.5 The information and data generated from field demonstration tests of adaptation options will enable the development of a climate change–adaptation strategy for the Cordillera region, including effective and efficient coping mechanisms and possible alternative, climate-proof livelihoods. The dispersed location of the field plots made this project difficult to monitor, however. Additional tests were undertaken at a pilot site located in the province of Camarines Sur in attempts to climate-proof the community’s livelihood and develop a replicable template for use at similar sites in the Philippines. Lack of impact monitoring data meant that the effectiveness of the adaptation options and level of benefits could not be determined, once again highlighting the importance of impact evaluation.

Financial Risk Management Schemes Another project introduced financial risk management mechanisms to vulnerable farming populations in the province of Agusan Del Norte to assist them in adapting to climate change by augmenting their productivity levels. In proposing both financial and productive resources, such as innovative financial schemes and a revolving fund that is self-replenishing from the principal and interest payments of borrowers, it was expected that the affected populations would have greater opportunity to diversify their livelihoods and hence become more resilient to the impacts of climate change (ILO 2012). The finance schemes benefitted around 837 farmers through weather index–based insurance issued to members of a local cooperative by a rural bank in coordination with municipal governments. A further three schemes — Rural Bank, Cooperative, and Local Government Loan Facility — served as forms of credit deliveries to 753 farmers. In addition, forty-one early warning systems and weather monitoring devices were installed in priority areas, and five indexed-based weather-insurance products were established in two priority municipalities for rice and corn. Knowledge-based products were also made available, together with the creation of a focal team and other partnerships, which were expected to maintain project gains and facilitate the duplication and upscaling of mechanisms. In 2013, the Department of Trade and Industry in the Caraga region renewed its partnership with the Beneficiaries Multi-Purpose Cooperative

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of the Bauag Comprehensive Agrarian Reform Program as part of the “Climate Change Adaptation Support Program”. The partnership aimed to extend the cooperative model to the towns of Buenavista, Jabonga, Kitcharao, and Cabadbaran City and was expected to benefit at least 100 farmers in vulnerable sites in Agusan Del Norte (PIA 2013). The Land Bank also developed a credit window through which agricultural enterprises, particularly hog farms, could obtain credit for biogas facilities to capture methane from hog manure, convert the gas to electrical energy, channel the power to the local grid, and — at the same time — apply for credits in the carbon market through the World Bank’s Clean Development Mechanism programme (Calado 2012). A similar credit window is needed for farmers who decide to shift from conventional to organic farming systems (NOAB 2011).6

The Effectiveness of Climate Change Adaptation and Mitigation Initiatives The Department of Agriculture requires additional resources and skills in order to align it plans and programmes with NCCAP’s outcome on food security. Most initiatives fall under the area of strengthening local adaptive capacity (such as providing better climate information, conducting research and development, building early warning systems, ensuring efficient irrigation systems, and so on). A few, however, focus on mitigation (for example, developing farming practices that reduce GHG emissions or promoting sustainable land management to addresses land degradation). To measure the effectiveness of these programmes and projects, these programmes require M&E, including financial tracking. In the absence of official M&E systems, initiatives are assumed to have achieved their full potential. Based on the target outcomes for climate-resilient agriculture defined by NCCAP and in the Philippine Development Plan, the following gaps were identified. 1. Field officers employed by the Department of Agriculture and local government agencies lack the technical skills needed to assist farmers in adopting organic farming practices or converting from conventional to organic farming systems. 2. Organic farming practices and systems are not integrated into the

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4. 5. 6.

7.

8.

305

science and technology research agenda or government agricultural plans and programmes. Farmers lack both the capacity to take up innovative and “smart” technologies, as well as the necessary level of empowerment to access sustained sources of credit to meet production needs and to connect with markets. The question of households’ willingness to pay for organic products has not been addressed. The vulnerability of soil, water, and human and environmental resources in the context of climate change need to be assessed. Climate change preparedness and adaptation have not been mainstreamed at the local level in areas with varying vulnerability potentials. Comprehensive and systematic mapping is needed at all levels — that is, barangay, town, and province — in order to determine which areas are highly vulnerable to specific climate change impacts. Naturally suited adaptation measures need to be identified for specific areas (for example, crop varieties and rotation plans).

IV. AGRICULTURAL FINANCE TO BUILD RESILIENCE TO CLIMATE CHANGE World Bank (2013) examined the climate expenditures of five Philippine departments: Department of Agriculture, Department of Energy, Department of Environment and Natural Resources, Department of Public Works and Highways, and PAGASA. The study showed that climate-related budget appropriations had increased steadily from PhP12 billion in 2008 to PhP35 billion in 2012. However, climate appropriations represent a small part of the national budget — 0.9 to 1.9 per cent between 2008 and 2012. During this period, nearly 72 per cent of the appropriations was directed to adaptation-related initiatives, whereas 18 per cent was allocated to mitigation-related initiatives. In 2013, a total of twenty-three ongoing programmes and projects addressed climate change adaptation and mitigation at a cost of PhP95.971 billion — almost three times higher than the 2012 figure of PhP34.710 billion, which covered thirty-seven projects (Table 7.3). The Official Development Assistance Portfolio Review (National Economic and Development Authority 2012b, 2013) indicates that a total of PhP56.363 billion in loans and

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43 13 22 — 78

73.87 77.38 77.51 — 88.69

Cost (billion PhP)

2011

26 11 12 12 61

No. of Projects 21.96 78.04 74.72 18.21 52.92

Cost (billion PhP)

2012

10 12 — 71 23

No. of Projects

17.564 56.363 — 70.001 73.928

Cost (billion PhP)

2013

Notes: Projects may contribute to more than one component. Cost refers to the total project cost, not the project component that specifically addresses climate change. Moreover, the total costs of some projects were not documented, so the total amount may also not reflect the total investment in climate change inititives. Sources: NEDA(National Economic and Development Authority), “2011 ODA Portfolio Review”, 2011 (accessed 10 November 2015); NEDA (National Economic and Development Authority), “2012 ODA Portfolio Review”, 2012b (accessed 10 November 2015); NEDA(National Economic and Development Authority), “CY 2013 ODA Portfolio Review”, 2013 (accessed 10 November 2015).

Adaptation Mitigation Adaptation and mitigation Disaster risk reduction Total

Component

No. of Projects

TABLE 7.3 Official Development Assistance Initiatives with Climate Change Adaptation, Mitigation, and Disaster Risk Reduction Components

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grants was invested in mitigation-related initiatives in 2013, representing an increase of PhP13.924 billion over the 2012 figure; PhP17.56 billion was invested in adaptation-related initiatives. Some climate change investments were not included in the report, which explains why the 2012 figures are so much lower than those for 2013. Most of these ODA funds are still directed towards building capacity, strengthening institutions, enhancing national and local plans, and post-disaster efforts (such as response, recovery, and rehabilitation) rather than prevention. Further analysis revealed that most of the climate expenditures and appropriations fall under the NCCAP priority on Water Sufficiency, Ecosystem and Environmental Stability, and Food Security. World Bank (2013) concluded that the increased budget appropriation indicated enhanced leadership and growing awareness of climate change adaptation and mitigation. It is also important, however, to determine the efficiency of budget increases in terms of improving the resilience of each sector. The Asian Development Bank study, “Economics of Climate Change in Southeast Asia” (ADB 2009), determined that the avoided damage in agricultural and the coastal zones of Indonesia, the Philippines, Thailand, and Vietnam could reach 1.9 per cent of GDP by 2100, with investment in adaptation measures amounting to 0.2 per cent of GDP. While almost all sectors have adaptation needs, the report noted that the water, agricultural, forestry, coastal and marine, and health sectors required detailed attention. Agriculture, in particular, required several priority actions, including: (1) strengthening local adaptive capacity through better climate information; (2) conducting research and development on heat-resistant crop varieties; (3) developing early warning systems; and (4) developing efficient irrigation systems. It was also suggested that innovative risk-sharing instruments be explored, such as index-based insurance schemes. Trends in the share of the budget appropriated for adaptation activities as a share of GDP and climate change–related damage to agriculture indicate that the Philippines has allocated the required investment in adaptation to protect the sectors from the impacts of climate change (Figure 7.4). It is important to note that 75 per cent of the appropriations allocated to climate adaptation were earmarked for flood control protection. Data on agricultural losses caused by climate-related events also indicate an increasing trend. While it has not yet reached the projected 1.9 per cent of GDP, agricultural losses due to natural disasters have significantly increased, with damage amounting to 0.55 per cent of GDP in 2012.

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FIGURE 7.4 Adaptation Cost and Climate Change–Related Damage to FIGURE 7.4 Agriculture as Percentage of GDP

Adaptation Cost and Climate Change–Related Damage to Agriculture as Percentage of GD Share of gross domestic product (%) 0.6

0.5

Climate change–related damage to agriculture

0.4 0.3 0.2

Adaptation cost

0.1 0

2008

2009

2010

2011

2012

Source: GoP (Government of the Philippines), “National data portal”, no date (accessed 19 December 2013 and 15 September 2015).

The Department of Agriculture secured funding to support the development of mitigation and adaptation projects by PhilRice, including the promotion of location-specific, rice-based technologies suitable for different conditions (Appendix Table 7A.3). The following agricultural programmes, activities, and projects correlate with investment targets of PhP27.7 billion for the 2013–16 period and PhP5.4 billion for ongoing targets (National Economic and Development Authority 2014): • Mechanization of Philippine Sugarcane Farms • Philippine Rural Development Program • Balintingon Reservoir Multipurpose Project • Ilaguen Multipurpose Project • Chico River Pump Irrigation Project • Tumauini Reservoir Project • Fisheries, Coastal Resources, and Livelihood Project

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• Balog-Balog Multi-purpose Irrigation Project Phase II • Public–Private Partnership Program: Logistics Support in the AgriFishery Products Supply Chain (Transportation of Agri-Fishery Products Utilizing the Southrail Main Line) Data indicate that the increased budget for climate change adaptation and mitigation activities cannot automatically be equated with greater agricultural resilience to the impacts of climate change. Given the Philippines’ level of vulnerability and the role that the agricultural sector plays in the national economy, it would seem important to assess the allocation of funding for climate change adaptation across and within government departments and agencies. The shift from a fragmented to a coordinated agenda for achieving resilience provided unique opportunities to enhance climate-related planning and prioritization. Issuances of more coherent policies call for the systematic integration of climate change in various phases of policy formulation, development planning, and prioritization across all agencies and departments. For instance, through the government’s public financial management reforms and new budgeting approach were adopted to support activities responding to climate change. In the approved 2015 budget, twenty-four government agencies used the climate change expenditure tagging guidelines and procedures to report and track activities related to climate change adaptation and mitigation (Department of Budget and Management – Climate Change Commission – Department of Interior and Local Government 2014).7 Data indicated that PhP176.6 million or 20 per cent of the total budget included components addressing climate change adaptation and mitigation (GOP 2015). About 98 per cent of the tagged expenditures were for adaptation measures, such as flood control, reforestation, sector-specific research and development on climate change, and disaster risk reduction. Of this, PhP38.3 million or 21.7 per cent was allocated to departments and agencies working directly for the agricultural sector and whose work programme reflects an increased focus on outputs for market development services; extension support, education, and training services; research and development; and credit facilitation services. Evidently, policies and measures to ensure that the goal of achieving national climate resilience have been effective in influencing the country’s planning and investment processes. It should be noted, however, that

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the complete planning and budgeting cycle includes M&E processes in support of a more coherent set of programmes and projects targeting climate resilience. These processes include a core list of indicators that define performance. Currently, the Philippines maintains a results matrix that measures the country’s overall performance against its development targets. As of 2013, two specific outcome indicators in the matrix gauged the country’s resilience to climate change adaptation and DRRM: (1) a decrease in yearly damage and losses due to natural disasters, environmental hazards, and human-induced and hydrometeorological events; and (2) an increased budget for climate change adaptation and DRRM. While these indicators measure the country’s outcome targets, they cannot capture the direct impact of specific climate change–related initiatives. Consequently, it is suggested that a set of measurable indicators for climate actions need to be defined, especially for the agricultural sector. Such indicators must be consistent and supported by measurable targets to monitor progress, in order to encourage the proposal and implementation of more focused and aligned activities within and across government agencies and sectors.

V. SUMMARY AND POLICY IMPLICATIONS In general, national strategies for adapting to and mitigating the impacts of climate change complement the international framework for effective action, as defined by the United Nations Framework Convention for Climate Change, including an enabling environment and mechanisms for the transfer of technologies. In particular, the alignment of the major outputs of agricultural agencies, such as the Department of Agriculture, aligns with NCCAP’s food security goals and the Philippine Development Plan’s 2011–2016 target on climate-resilient agriculture. Nevertheless, the implementation and execution of policy remains weak for numerous reasons, including limited institutional capacity and limited resources of the implementing agencies. The nature of the current adaptation and mitigation initiatives indicates that the preparedness of the agricultural sector is still in its early stages and is focused on building capacity and understanding the impacts of climate change in specific locations. As a result, the effectiveness of these projects cannot yet be measured in terms of improved food security of higher incomes. Observations of particular note are discussed further below.

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1. Different projects have been initiated in different regions, but their outputs have not been replicated in other parts of the country. While relevant outputs have been produced to enhance agricultural resilience, they often only focus on the Bicol Region. For example, vulnerability assessments were useful in prioritizing initiatives and identifying coping strategies within the context of comprehensive development and land-use plans; however, the process of integrating climate risk in the prioritization of programmes, activities, and projects was not practised in most local government agencies because maps and risk assessments were not available. In the absence of records from national agencies, such as the Housing and Land Use Regulatory Board and Department of the Interior and Local Government, and aside from pilot sites, the number of local government agencies that had mainstreamed climate change adaptation and mitigation in their comprehensive land-use plans and comprehensive development plans could not be determined. In particular — with the exception of areas covered by Millennium Development Goal Funds 1656 Joint Programme and related projects — community-based agricultural adaptation strategies have not yet been established. 2. Downscaling vulnerability and risk assessment to the farm level remains a challenge. Projecting climate change impacts could be one way of informing decisions, but the current drawback is that many projections have coarse spatial resolutions and hence are not useful in informing decisions about smaller geographic areas. 3. Measuring the impacts of programmes and projects is often neglected. Given that policies and programmes on climate change adaptation and mitigation are relatively new, evaluation studies and determinations of the costs and benefits of projects are lacking. A review of the design of programmes and projects indicates that impact assessments were either not included in projects or not funded. The importance of impact evaluation studies may be acknowledged, but this has yet to result in actual studies and data. 4. Action on ensuring the transparency of the planning and implementation of programmes and projects has been initiated but needs further enhancement. Aside from the formulation of climate change policies and legislation, governance and accountability issues have also been addressed, including tagging government

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expenditures on climate change in the budget process. As a result, it is now easier to track funding to the agricultural sector, but the typologies and indicators used for climate change expenditures in the tagging system need to be refined. In terms of policy implications, the observations discussed below are of particular note. 1. Community-based adaptation strategies must be locationspecific. While various projects suggest different tools for local implementation, greater understanding of the need for locationspecific responses to climate change is needed. The provinces in Bicol and Cordillera have already produced notable results in the implementation of community-based adaptation strategies. A framework can be drawn from these results so that other regions can replicate the process and promote integration within relevant government institutions. Such a framework would guide other local government agencies in designing locally appropriate adaptation strategies. 2. Agricultural extension and support services need to be strengthened to develop farmers’ awareness of climate risks and understanding of climate-sensitive farming technologies. Local government capacity to conduct farmer training and extension and provide better communication networks needs to be enhanced so that farmers can make better decisions about climate change and adaptation strategies and disaster risk preparedness and mitigation. As the updated the Philippine Development Plan 2011–16 states, local government agencies should prioritize strategies focusing on disaster risk reduction and mitigation, income diversification, and social insurance and protection. In order to support the above strategies, local government agencies require financial and technical support, particularly in provinces categorized as having low capacity, a high incidence of poverty, and a high risk of exposure to natural disasters like flooding and landslides (see Figure 7.3). A lot of calls have been made for additional funding, both for national and local government initiatives. It is important, however, that any additional funding be directed to activities relevant to the needs and gaps of the location involved. Considering the number of initiatives

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already in place, an inventory should be conducted of the skills and resources available to accomplish specific targets. Further, the provinces identified in the updated Philippine Development Plan 2011–16 as being vulnerable to climate change and having a high incidence of poverty should be given priority. 3. Data and information related to climate change should be made readily available and accessible. State-of-the-art adaptation and mitigation tools will be of no use unless they are readily available and accessible to users, and accompanied by relevant information. An institutionalized system of data gathering and management is needed to ensure the sustainability of practices promoting climate resilience. It is equally important that an information and communications campaign be targeted to poor farmers to increase their adaptive capacity. Information about the impacts of climate change and different adaptation measures must be made available in a timely manner. This means that all climate change data, information, and literature (including climate change projection studies) should be collected, compiled, and made available at the local level. Moreover, the government must increase its investments in methods of disseminating seasonal forecast information in terms of its agronomic and economic implications to enable farmers to understand the importance of adaptation and mitigation strategies in crop and livestock production and their effectiveness in improving resource use efficiency. 4. Research and development need to be strengthened so the Philippines can fully tap its potential in developing and use of climate-friendly technologies. Creative financial and economic mechanisms and instruments are needed to facilitate technology development and transfer (examples include incentives for patents and protection of intellectual property rights). 5. Private-sector participation in financing and investing in adaptation and mitigation technologies needs to be promoted. While the trend towards agricultural climate change–initiatives is increasing, World Bank (2013) showed that there are still unfunded programmes and projects in the Department of Agriculture alone. Assistance from bilateral and multilateral donor agencies has been insufficient to meet the increasing demand for funding to address climate change–related challenges. It is highly recommended

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that the government fast track the roadmap for climate action on agriculture, which aims to integrate the participation and support of the private sector in financing and investing in adaptation and mitigation technologies. This initiative would not only complement funding from other sectors, but also encourage ownership and accountability of private individuals and organizations. 6. A monitoring and evaluation system for the country’s climate change initiatives is needed, including but not limited to enhancing resilience in the agricultural sector. Currently, indicators of adaptive capacity only include decreased damage and losses due to natural disasters, environmental hazards, and human induced and hydrometeorological events, which were used as a proxy to monitor improvements in adaptive capacity at the community level. A set of measurable indicators of climate-related actions in agriculture needs to be defined and an M&E system institutionalized. Such indicators must be consistent and supported by measurable targets for monitoring progress and to encourage the proposal and implementation of more focused and aligned activities within government agencies and across sectors.

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Region ARMM CAR Region 1 Region 2 Region 3 Region 4a Region 4b Region 5 Region 6 Region 7 Region 8 Region 9 Region 10 Region 11 Region 12 Caraga Philippines

2008

2009

2010

2011

continued on next page

Production Loss Production Loss Production Loss Production Loss Affected Affected Affected Affected Affected Volume Value Volume Value Volume Value Volume Value Volume Value Area Area Area Area Area (thousand (thousand (thousand (thousand (thousand (thousand (thousand (thousand (thousand (thousand (thousand (thousand (thousand (thousand (thousand ha) metric tons) PhP) ha) metric tons) PhP) ha) metric tons) PhP) ha) metric tons) PhP) ha) metric tons) PhP) – – – 21.6 1.0 280,407.1 5.8 5.7 125,717.8 1.5 3.1 52,931.2 10.0 7.1 222,787.1 32.7 18.4 254,054.8 2.5 0.8 15,195.3 49.4 74.6 1,280,433.3 28.2 42.2 722,936.1 27.7 18.6 343,790.4 21.0 7.8 144,754.1 18.9 10.3 197,301.5 136.2 432.7 7,369,726.6 27.5 20.1 340,901.0 62.8 31.6 603,089.2 119.0 101.8 1,299,828.7 92.9 55.8 1,036,724.6 129.1 181.1 3,118,435.4 324.3 426.8 6,780,831.6 227.2 192.9 3,123,052.8 87.6 9.4 617,354.7 25.4 10.1 238,467.2 265.9 508.2 8,403,868.0 132.3 153.2 2,619,259.6 380.7 673.9 9,730,560.8 – – – 7.8 1.0 22,271.2 18.0 52.7 919,497.8 9 29.9 515,162.2 5.7 10.2 136,494.3 18.7 9.3 135,181.2 9.7 6.6 131,800.7 33.3 8.0 413,313.8 35.6 69.3 1,211,069.2 32.9 39.2 696,328.1 17.6 18.6 236,219.4 54.7 54.1 780,593.8 85.6 91.6 1,689,992.6 18.4 45.7 780,098.9 111.6 157.8 1,981,307.9 10.0 15.0 157,256.7 89.8 60.8 1,288,694.1 17.0 13.1 85,986.9 62.2 91.3 1,580,504.8 5.0 4.1 57,546.3 0.9 2.3 26,190.0 0.2 0 1,339.8 0 0 125.6 – – – – – – 8.5 – 56,851.0 27.4 12.3 285,045.7 2.9 0 8,746.8 – – – 23.8 11.3 198,887.2 0.0 – 611.5 7.7 18.2 280,962.8 0 0 374.0 1.1 1.3 21,522.0 3.4 – 68,664.5 0.1 0.1 2,721.9 0 – 292.1 0.6 0.3 9,057.4 4.4 12 203,609.0 2.8 1.4 43,396.4 9.1 – 91,909.2 0.1 0.1 1,229.2 2.7 – 21,344.8 1.2 4.7 80,308.0 6.0 – 41,420.2 8.3 3.1 53,772.7 13.9 9.7 212,947.6 7.5 12.0 239,152.3 13.6 37.4 636,463.0 7.7 14.3 318,225.2 10.5 0.2 48,251.7 2.8 6.4 78,342.2 15.6 0.3 145,660.8 – – – 15.2 – 35,379.5 258.0 160.4 2,486,162.1 375.3 247.4 4,851,614.8 769.8 1,380.4 23,831,434.1 365.9 396.4 6,357,630.4 922.5 1,162.5 17,600,929.9

2007 Production Loss

APPENDIX TABLE 7A.1 Palay Production Losses Due to Climate-Related Natural Disasters, 2007–15

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2014

2015

Production Loss Production Loss Affected Affected Affected Volume Value Volume Value Volume Value Area Area Area (thousand (thousand (thousand (thousand (thousand (thousand (thousand (thousand (thousand ha) ha) metric tons) PhP) metric tons) PhP) ha) metric tons) PhP) – – – – – – – – – 0.9 0.3 6,162.5 15.3 18.1 304,622.2 25.0 13.7 335,242.8 25.3 6.0 262,262.7 49.1 42.8 727,930.4 121.9 88.0 1,268,396.5 44.6 22.9 430,511.4 33.4 23.6 414,818.4 208.4 107.3 1,863,380.3 259.9 295.4 4,506,866.3 52.5 59.6 1,035,366.7 271.1 399.5 7,052,330.7 1.8 2.6 41,965.6 10.2 16.7 308,737.2 5.7 14.7 256,346.6 16.4 23.6 374,828.5 28.5 22.1 469,206.8 24.0 11.9 191,898.6 13.8 18.9 259,119.1 63.7 99.2 1,912,099.4 61.4 53.6 1,081,559.2 52.6 48.5 820,122.0 17.7 30.0 513,005.6 – – – 0.1 0.2 4,029.0 3.3 8.6 152,145.7 – – – 31.3 34.2 232,409.4 60.8 17.8 327,213.6 15.4 22.6 392,481.8 0.6 0.3 6,617.5 4.0 2.3 83,907.7 0.3 1.2 20,383.0 0.3 0.3 11,962.8 14.8 1.3 80,679.8 4.6 17.4 296,344.0 4.6 5.8 182,068.0 24.1 1.1 169,064.7 1.4 3.7 47,905.2 – – – 49.1 42.8 727,930.4 11.9 17.6 409,695.8 – – – – – 7.5 8.9 150,654.0 7.5 452.2 459.3 7,138,925.0 377.4 343.3 6,498,798.1 758.5 760.0 13,366,618.4

2013 Production Loss

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Sources: Compiled by author based on Danilo Israel and Roehlano Briones, Impacts of Natural Disasters on Agriculture: Food Security, and Natural Resources and Environment in the Philippines (Makati City: Philippine Institute for Development Studies, 2012); and DA (Department of Agriculture), Reports on damages and losses due to climate-related natural disasters, Quezon City, various years.

Region ARMM CAR Region 1 Region 2 Region 3 Region 4a Region 4b Region 5 Region 6 Region 7 Region 8 Region 9 Region 10 Region 11 Region 12 Caraga Philippines

Affected Volume Value Area (thousand (thousand (thousand ha) metric tons) PhP) – – – 0.3 0.1 4,220.6 9.0 3.2 109,030.1 17.8 3.1 99,979.2 89.2 54.4 1,605,626.3 8.6 3.7 145,376.6 15.6 25.3 500,883.2 22.3 25.6 471,600.7 23.2 22.2 311,670.7 – – – 11.1 7.1 44,488.3 5.4 6.0 112,885.3 4.3 10.8 196,870.0 18.3 3.1 196,062.4 0.2 0.3 7,306.6 2.2 5.4 72,438.2 3,878,438.1 227.6 170.3

2012 Production Loss

Appendix Table 7A.1 — cont’d

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1990 1995 1993 2004 1998 2009 2009 2011 2012 2012 2013 2013 2014 2014 2015 2015

Ruping (Mike) Rosing (Angela) Kadiang (flo) Winnie Loleng (Babs) Ondoy (Ketsana) Parma (Peping) Sendong Southwast Monsoon (Habagat) Bopha (Pablo) Tropical Depression Crising Haiyan (Yolanda) Rammasun (Glenda) Ruby (Hagupit) Koppu (Lando) Melor (Nona)

NCR and Regions 3 to 12 NCR, CAR, Regions 1 to 5, and Region 8 NCR, CAR, and Regions 1 to 4 Eastern Luzon CAR, Regions 1 to 6, and Region 8 NCR and Regions 3 and 4a CAR and Regions 1 and 3 Region 10 NCR and Regions 3 and 4a Region 4b, Regions 6 to 12, and Caraga Region 4b, Regions 6 to 12, and ARMM Regions 4 to 8, Regions 10 and 11, and Caraga Regions 1, 3, 4a, 4b, 5, 8, NCR and CAR Regions 4a, 4b, 5,6,7, and 8 Regions 1,2,3,4a, 4, and CAR Regions 2,3,4a,5, and 8

Areas Affected 28.512 29.037 27.193 20.185 23.695 26.670 20.500 20.309 22.400 26.530 20.011 10.480 33.8 23.65 29.69 24.33

Agriculture (billion PhP)

10.846 10.799 28.756 20.188 26.787 10.970 27.300 22.070 23.060 36.950 20.011 12.450 38.62 25.09 11.0 26.46

Total (billion PhP)

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region; NCR = National Capital Region. Source: NDRRMC (National Disaster Risk Reduction and Management Council), “SitRep No. 59”, 2013 (accessed 11 December 2013).

Year

Typhoon/Monsoon

APPENDIX TABLE 7A.2 Typhoons Causing the Most Economic Damage, 1990–2015

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Expected Outputs

Provision of hauling trucks and highpowered tractors

Construction of rural infrastructure: 2,346km of (farm-market roads; 775 linear meters of bridges; 30,205 ha of irrigation; and 294 potable water supply

Development of agricultural enterprises: Generation of 14,900 ha

Development of agricultural enterprises: Generation of 8,700 ha

Development of agricultural enterprises: Generation of 30,000 ha

Programme, Activity, Project

Mechanization of Philippine Sugarcane Farms

Philippine Rural Development Program

Balintingon Reservoir Multipurpose Project

Chico River Pump Irrigation Project

Ilaguen Multipurpose Project

Increase in yields of major commodities; decrease in yearly proportion of farm household income to total income Increase in yields of major commodities increased; decrease in yearly proportion of farm household income to total income Increase in yield and volume of major commodities Increase in yield and volume of major commodities Increase in yield and volume of major commodities

PDP-RM Critical Indicators Addressed

23,907.8

Regions 1, 2, 3, 4a, 4b, 5, 6, 7, 8, 9, 10, 11, 12, 13, ARMM, and CAR Region 3

Region 2

Region 2

3,627.5

21,100.0

Regions 2, 3, 4a, 5, 6, 7, 8, 10, 11, and 12

23,500.0a

600.0

Subtotal 2013–16

Regions Covered

Total for Continuing Investment Targets

Total

Investment Targets (PhP millions)

APPENDIX TABLE 7A.3 Major Department of Agriculture Programmes, Activities, and Projects on Productivity and Climate Change Resilience, 2013–16

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Logistics centres equipped with cold chain equipment and warehouses; establishment of other needed facilities

Development of agricultural enterprises: Generation of 2,385 ha; rehabilitation of 3,615 ha Development of agricultural enterprises: Generation of 24,849 has generated; restoration of 2,000 ha; rehabilitation of 14,301 ha Protection and rehabilitation of coastal communities Reduction in level of postharvest losses

Increase in volume of production

Increase in yield and volume of major commodities Increase in yield and volume of major commodities Regions 4b, 5, 8, 13, and ARMM Regions 4a, 4b, and 5

Region 3

Region 2

–

5,348.7

27,694.4

1,121.2

21,000.0

21,686.6

33,043.1

1,000.0

2,807.7

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region; and ha = hectares. a. This is reflected in Chapter 10 of the Philippine Development Plan 2011–16 (National Economic and Development Authority 2013). Source: NEDA (National Economic and Development Authority), “Philippine Development Plan 2011–2016: Revalidated Public Investment Program”, 2014 (accessed 15 December 2014).

Public–Private Partnership Program: Logistics Support on the Agri-Fishery Products Supply Chain (Transportation of Agri-Fishery Products Utilizing the Southrail Main Line) Total

Fisheries, Coastal Resources and Livelihood Project

Balog-Balog Multi-Purpose Irrigation Project Phase 2

Tumauini Reservoir Project

320

Marites M. Tiongco

Notes 1. The National Land Use Act is intended to guide the optimum allocation of land among competing uses within the framework of sustainable development. It also provides a mechanism for resolving land-use policy conflicts, taking into consideration the principles of social equity and economic efficiency. In the case of land conversion, a two-year moratorium (2017–19) on the conversion of agricultural lands into nonagricultural uses such as industrial parks, has been imposed to ensure food security. 2. For more information, see strategies 14–16 in Chapter 4 of the midterm update of the Philippine Development Plan 2011–16. 3. The Climate Change Council also acts as secretariat to the Cabinet Cluster on Climate Change. 4. In the updated Philippine Development Plan, the chapters on competitive and innovative industry and services, competitive and sustainable agriculture and fisheries, social development, good governance and the rule of law, sustainable and climate-resilient environment and natural resources, and accelerating infrastructure development all include extensive discussions on climate change, particularly in relation to adaptation and DRRM (Chapters 3, 4, 6, 7, 9, and 10, respectively). 5. In particular, the project focused on the two land-locked provinces of Benguet and Ifugao in the Cordilleras. 6. Note that conversion to organic farming is not well planned under the Organic Agriculture Act 2010 (RA 10068). No part of the Act or its implementing rules and regulations tackles the process of converting from conventional to organic farming. 7. The twenty-four agencies were the Climate Change Commission, Department of Agriculture, Department of Education, Department of Energy, Department of Environment and Natural Resources, Department of Foreign Affairs, Department of the Interior and Local Government, Department of Labor and Employment, Department of Public Works and Highways, Department of Science and Technology, Department of Social Welfare and Development, Department of Tourism, Department of Trade and Industry, Department of Transportation and Communications, Housing and Land Use Regulatory Board, Housing and Urban Development Coordinating Council, Metropolitan Manila Development Authority, Mindanao Development Authority, National AntiPoverty Commission, National Commission on Indigenous Peoples, National Commission on Muslim Filipinos, National Economic and Development Authority, Pasig River Rehabilitation Commission, and Presidential Communications Operations Office.

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References Asian Development Bank. The Economics of Climate Change in Southeast Asia: A Regional Review. Manila: Asian Development Bank, 2009 (accessed October 2014). Baas, S. and A. Ricoy. “Enhancing Community Based Adaptation through Institutionalization of Good Practices in Bicol Region, Philippines”. Adapted in “How does climate change alter agricultural strategies to support food security? Draft”, 11 March 2013 (accessed 10 November 2014). Calado, Prudencio III. “Carbon Finance Support Facility for Clean Development Mechanism Projects”. Presentation to Capacity Building Seminar on “Post 2012: Carbon Market”, July 2012. CCC (Climate Change Commission). “National Framework Strategy on Climate Change 2010–2022”. 2009 (accessed 19 December 2013). ———. National Climate Change Action Plan: 2011–2028. 2010 (accessed 29 December 2013). ———. “Philippines Climate Change Adaptation Policy Initiatives National Climate Change Action Plan. Local Government Academy”. 2011 (accessed 29 December 2013). ———. “Project Climate Twin Phoenix”. 2013 (accessed 29 December 2013). DA (Department of Agriculture). “DA-Policy and Implementation Program on Climate Change”. Attachment to the “Memorandum: Mainstreaming Climate Change in the DA Programs, Plans & Budget”. 2013a (accessed 19 April 2014). ———. “Memorandum: Mainstreaming Climate Change in the DA Programs, Plans & Budget”. 2013b (accessed 19 April 2014). ———. “General Appropriations Act”. 2014 (accessed 16 November 2014). ———. Reports on damages and losses due to climate-related natural disasters. Quezon City, various years.

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Department of Budget and Management, Climate Change Council, and Department of the Interior and Local Government. “Joint Memorandum Circular 2014-01”. 2014 (accessed 14 December 2015). Domingo, Sonny. “Presentation to the House of Representatives on ZBB Study: DRRM and Quick Response Funds”. 2014 (accessed 15 November 2014). Espinas, Agnes. Geography and Public Planning: Albay and Disaster Risk Management. Human Development Network Discussion Paper Series 4. 2012 (accessed 19 December 2013). GoP (Government of the Philippines). “National data portal”. No date (accessed 19 December 2013, and 15 September 2015). ILO (International Labour Organization). “MDG-F 1656 Joint Programme on Climate Change Adaptation: Outcome 3.4 Climate Resilient Farming Communities in Agusan del Norte through Innovative Risk Transfer Mechanisms”. 2012 (accessed 16 March 2014). IPCC (Intergovernmental Panel on Climate Change). Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change, edited by N. Nakic´ enovicć´ and R. Swart. 2000 (accessed 29 December 2013). ———. “Climate Change 2007: Synthesis Report”. Fourth Assessment Report of the Intergovernmental Panel of Climate Change. 2007 (accessed 29 December 2013). Israel, Danilo and R. Briones. Impacts of Natural Disasters on Agriculture: Food Security, and Natural Resources and Environment in the Philippines. Makati City: Philippine Institute for Development Studies, 2012. NDRRMC (National Disaster Risk Reduction and Management Council). “SitRep No. 59”. 2013 (accessed 11 December 2013). NEDA (National Economic and Development Authority). “Philippine Development Plan 2011–2016”. 2011a (accessed 20 January 2014). ———. “2011 ODA Portfolio Review”. 2011 (accessed 10 November 2015). ———. “Compendium of Good Practices on Climate Change Adaptation”. 2012a

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(accessed 15 December 2014). ———. “2012 ODA Portfolio Review”. 2012b (accessed 10 November 2015). ———. “CY 2013 ODA Portfolio Review”. 2013 (accessed 10 November 2015). ———. “Philippine Development Plan 2011–2016: Revalidated Public Investment Program”. 2014 (accessed 15 December 2014). NOAB (National Organic Agriculture Board). “National Organic Agriculture Program 2012–2016”. Department of Agriculture. 2011 (accessed 10 April 2014). PIA (Philippine Information Agency). “Coop Model Climate Change Adaptation Financing Continues via DTI-Baug Coop Partnership”, 25 September 2013 (accessed 16 March 2014). Sandoval, Roberto and S. Baas. “Adapting to Climate Change: The Cordillera Experience”. 2013 (accessed 30 January 2014). SEPO (Senate Economic Planning Office). “Philipine Agricultural Exports At A Glance. Senate of the Philippines, 16th Congress”. 2012 (accessed 3 February 2014). UNDP (United Nations Development Program). “Enabling Regions 10 and 11 to Cope with Climate Change (Project Climate Twin Phoenix)”. 2013a (accessed 29 December 2013). ———. “Low Emission Capacity Building Programme Philippine Project”. 2013b (accessed 29 December 2013). Utzurrum, Jr., Santiago and Christopher Ablan. “Sustainable Upland Farming through the Establishment of Barangay Sagip-Saka Conservation Farming Villages (CFV): A Modality for Climate Change Adaptation”. Dumaguete City: Silliman University, 2013. World Bank. “Getting a Grip on Climate Change in the Philippines”. 2013 (accessed 9 December 2013).

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8 RISK MANAGEMENT AND COPING STRATEGIES Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc

The Philippines is inherently vulnerable to adverse natural events of extreme intensity purely based on its geographic location.1 The warm western Pacific waters, normally around 28°C, contribute to the formation of typhoons, 18–20 of which reach the Philippines each year on average. Cagayan Valley (Region 2), Central Luzon (Region 3), and the Cordillera Administrative Region (CAR) are particularly vulnerable, averaging about seven to nine typhoons per year (Figure 8.1). Flooding occurs in a number of regions, the Western Visayas registering the highest incidence. The Philippines also rests on the Pacific “Ring of Fire”, where most of the earth’s volcanic eruptions and earthquakes occur. Geophysical events, such as earthquakes and tsunamis, occur with regularity, albeit at long intervals. The Bicol Region, home of the active Mayon Volcano, experienced the greatest number of volcanic eruptions during 1991–2006. Earthquakes of moderate and high magnitude occur most frequently in the Central Visayas and Bicol regions (Figure 8.1). Climate projections for the Philippines are similar to those in many other parts of the world (Chapters 2 and 4, this volume). Using the

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FIGURE 8.1 The Incidence of Natural Disasters

Notes: CAR = Cordillera Administrative Region; Region 4a = CALABARZON; Region 4b = MIMAROPA; Region 9 = Western Mindanao; Region 11 = Davao Region; Region 12 = SOCCSKSARGEN; Region 13 = Caraga. For volcanic events, data are for 1991–2006; for earthquakes, data are from 2003–2013; for typhoons, data are for 2001–2010, for floods, data are for 2007–2011; and for droughts, data are for 2007–2011. Sources: Data on typhoons are from PAGASA (Philippine Atmospheric, Geophysical and Astronomical Services Administration). Unpublished climate data. Quezon City, 2014.; data on floods and droughts are from the DA–MAD (Department of Agriculture, Management Audit Division). Unpublished flood and drought data. Quezon City, 2014; data on volcanic events are PHIVOLCS–VD (Philippine Institute of Volcanology and Seismology, Volcanology Division). Unpublished data on volcanic events. Quezon City, 2014; data on earthquakes are from PHIVOLCS–SD (Philippine Institute of Volcanology and Seismology, Seismology Division). Unpublished data on earthquakes. Quezon City, 2014.

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326 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc Intergovernmental Panel on Climate Change (IPCC) for the “A1B scenario” most relevant to the Philippines, Cinco et al. (2013) projected that mean yearly temperatures will rise between 1.9°C and 2.2°C by 2050, over baseline levels of between 25.5°C and 27.6°C (derived as averages of minimum and maximum temperatures for the 1971–2000 period). Increasing rainfall concentration and mean rainfall levels indicate that the wet seasons of June–August and September–November will become wetter in Luzon and Visayas towards 2050, yet higher rainfall concentrations combined with higher temperatures are likely to increase moisture stress in the dry season. In particular, it is expected that the frequency of damaging storms will increase. Although disputed by some (Cruz et al. 2007), evidence also suggests that the frequency of droughts will increase (Miyan 2015). One implication of these changes is that farmers’ experience of the frequency, duration, strength, and timing of rainfall and the frequency of droughts will be less reliable than previously; hence, the accuracy of their subjective decision-making processes will decline, causing their level of risk to rise. Past experience will become — and is already becoming — less useful as a predictor of future experience. The bottom line is that risk and uncertainty facing farmers are increasing. Farm households have limited means of reducing their risk, and available strategies may be too costly in terms of foregone profits (Roumasset 1976, 1979, 2015; Walker and Jodha 1986; Walker and Ryan 1990; and Duflo, Kremer, and Robinson 2008). Similarly, the increased risk associated with climate change may decrease farmers’ welfare by forcing them to reduce household expenditures (for example, by removing children from school or foregoing health care services) and by injecting greater variability into their spending patterns (Chetty and Looney 2006). This chapter provides a conceptual framework for understanding risk management and resilience at farm-household and national levels. At the household level, data from the Social Protection Survey of the Philippine Center for Economic Development (PCED) are used to explore how farm households cope with natural disasters (Ravago et al. 2016a). At the national level, the discussion focuses on how public policy can be designed to balance available controls to maximize economic welfare both ex ante and ex post (that is, before and after a disaster occurs). At the farm level, the issue is how farmers can take advantage of available risk-reducing and coping mechanisms to maximize their well-being. The next sections present a review of overall and agricultural vulnerability to

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climate change and natural disasters in the Philippines, an overview of the country’s disaster-management capacity over time, and a conceptual framework for understanding household-level resilience to shed light on the pros and cons of alternative public policies designed to reduce household vulnerability. Thereafter, the chapter presents the results of the PCED’s Social Protection Survey of household responses to the seven mostfrequently occurring natural events and results of a modelling exercise that used the survey data to explore the key factors that determine whether farm households recover from shocks.

VULNERABILITY OF THE PHILIPPINES TO CLIMATE CHANGE According to the World Risk Report 2013, the Philippines ranks third in world risk index of global disaster hotspots (scoring 27.52 per cent) after Vanuatu, at 36.43 per cent, and Tonga, at 28.71 per cent (UNU-EHS 2013). The same source defines exposure according to the population at risk and vulnerability based on susceptibility to the adverse impacts of climate change combined with the ability to cope and to adapt. “Susceptibility” is the likelihood of being harmed if a natural hazard occurs; “coping” is the ability to lessen the negative consequences of natural hazards; and “adaptation” involves the long-term societal processes whereby structural changes and strategies are introduced to deal more effectively with the impacts of natural hazards. As the risks of such hazards increase, exposure, vulnerability, and susceptibility all tend to increase as well.2 Naturally occurring events reach disaster status when they overwhelm local response capacity and cause great damage and human suffering. The Centre for Research on the Epidemiology of Disasters (CRED) maintains the Emergency Events Database (EM-DAT), the largest database of natural disasters at the country level. For a natural hazard to be counted as a disaster by CRED, the following criteria must be satisfied: 10 or more people were killed; 100 or more people were injured or suffered losses; a state of emergency was declared; and a call for international assistance was issued. On average, of twenty storms passing through the Philippines each year, seven reach disaster status (Figure 8.2). Data suggest a slight upward trend, especially from 2005, which would be more pronounced with the inclusion of typhoon Haiyan (Yolanda) in November of 2013. Disastrous

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328 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc FIGURE 8.2 Frequency of Natural Disasters, Philippines, 2000–15

FIGURE 8.2 Frequency of natural disasters, Philippines, 2000–2015 a. Droughts, floods, and storms Frequency 18 16

Droughts Floods

14

Storms

12 10 8 6 4 2 0 2000

2002

2004

2006

2008

2010

2012

2014 2015

b. Earthquakes, mass water movements, and volcanic eruptions Frequency 5

Earthquakes Mass water movement

4

Volcanic eruptions

3 2 1 0 2000

2002

2004

2006

2008

2010

2012

2014 2015

Source: Constructed by authors from CRED (Centre for Research on the Epidemiology of Disasters). EM-DAT: The International Disaster Database, no date. (accessed 11 June 2014)

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flooding has shown an increasing trend over the same period, but the data do not show any significant increase in droughts. No pronounced trend is evident for the incidence of geophysical events, including earthquakes and volcanic eruptions that have reached disaster status. The slight upward trend since 2005 may be partly a consequence of there being fewer adverse events that year and increased exposure thereafter. Even if the incidence of adverse events is not increasing, more events may reach disaster status due to greater populations in harm’s way. At high levels of per capita income, the reverse is likely because vulnerability is reduced by various avoidance measures. A disaster Kuznets curve can thus be hypothesized, with disasters first increasing with per capita income (and population) and then declining. Put another way, at lower levels of income the impact of population dominates, whereas at higher levels, the impact of greater spending on disaster avoidance becomes greater. Studies have shown that these natural disasters can potentially adversely affect different facets of an economy, from the long-run growth rates to natural resource prices (Prestemon and Holmes 2002; Skidmore and Toya 2002; Cavallo and Noy 2010; Cavallo et al. 2010). Das et al. (2003) examined both direct and indirect impacts on the agricultural sector. Direct impacts are more immediate, including the destruction of crops, farm buildings, installations, machinery, equipment, means of transport, stored commodities, cropland, irrigation works, and dams. Indirect impacts of disasters include the loss of potential production due to interrupted flow of goods and services, lost production capacities, and increased production costs.

VULNERABILITY OF THE PHILIPPINE AGRICULTURAL SECTOR TO CLIMATE CHANGE The agricultural sector of the Philippine economy contributes about 10 per cent of the country’s total output and employs nearly one-third of the total labour force (see Chapter 1, this volume). The growth performance of the sector was lacklustre during 2000–10, which may partly be attributable to the vulnerability of the sector to weather-related shocks. The sector has always been heavily affected by natural disasters. During 2000–13, the total damage to agricultural commodities due to typhoons, floods, and droughts amounted to PhP195 billion (Table 8.1). The crops typically

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1,595 805 548 1,320 1,698 1,942 3,401 1,882 5,015 23,842 15,559 17,842 3,878 7,139 86,468 6,176

Rice

58 546 330 1,696 1,436 2,446 1,179 2,783 1,806 1,418 8,486 2,752 1,719 2,770 29,426 2,102

Corn 352 359 115 424 1,155 32 3,178 376 2,283 2,504 1,108 1,185 2,036 n.a. 15,109 1,079

HVCC n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. 22,232 1,493 23,725 1,695

Banana 91 65 12 124 738 20 233 178 n.a. n.a. n.a. n.a. n.a. 435 1,895 135

Vegetables

Coconut 47 0 0 1 439 n.a. 1,115 0 1,133 n.a. n.a. n.a. 1,122 17,746 21,604 1,543

PhP million 41 74 n.a. n.a. n.a. n.a. n.a. n.a. 36 n.a. n.a. n.a. n.a. 1,211 1,362 97

Sugarcane 95 0 n.a. 0 159 n.a. 602 n.a. 12 69 n.a. n.a. 20 542 1,500 107

Other 358 255 127 242 1,906 6 1,081 89 3,152 1,597 303 859 723 1,552 12,250 875

8 95 16 49 44 0 223 3 246 88 28 165 369 828 2,162 154

Fisheries Livestock

2,644 2,200 1,150 3,857 7,576 4,447 11,012 5,311 13,683 29,519 25,484 22,804 32,099 33,716 195,501 13,964

Total

Notes: “HVCC” = high-value commercial crops; “Other” includes abaca, tobacco, cassava, mangoes, root crops, other crops, and National Food Authority (NFA). The averages for bananas and other crops are based only on one or two years; n.a. indicates that data were not available. Source: Compiled by authors from DA–MAD (Department of Agriculture, Management Audit Division). Unpublished flood and drought data. Quezon City, 2014.

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Total Average

Year

TABLE 8.1 Total Value of Damage to Agricultural Commodities Due to Typhoons, Floods, and Droughts, 2000–13

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damaged were rice and corn, with total damage amounting to PhP86 billion and PhP29 billion, respectively. Fisheries products also recorded significant damages, to a total value of PhP12 million for the same period. The early value of damages rose from 2009 to 2013. Typhoons Ondoy (Ketsana) and Pepeng (Parma) hit several parts of the Philippines in late September and early October 2009, which brought the total yearly damage to agriculture to PhP29.5 billion. In 2012, Typhoon Pablo significantly destroyed the banana-producing areas in the southern part of the Philippines, leaving damage valued at PhP22.2 billion. Total damages to irrigation and other agricultural facilities were estimated to be PhP8.9 billion and PhP15.7 billion, respectively (Table 8.2). On 8 November 2013, the Philippines endured another typhoon, which is considered to be the most destructive to occur since the turn of the TABLE 8.2 Value of Damage to Agricultural Facilities and Irrigation Due to Typhoons, Floods, and Droughts, 2000–13 Agricultural Facilities, Infrastructure, and Equipment Year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Total Average

Irrigation

PhP million 0.23 880.21 31.35 11.66 636.13 n.a. 1,287.17 2.62 1,865.86 190.01 167.92 241.72 82.72 3,508.41 8,905.99 636.14

0.23 880.21 31.35 11.66 636.13 n.a. 1,287.17 2.25 1,697.50 3,860.40 1,279.99 2,143.55 1,735.98 2,181.15 15,747.56 1,124.83

Note: n.a. indicates that data were not available. Source: Compiled by authors from DA–MAD (Department of Agriculture, Management Audit Division). Unpublished flood and drought data. Quezon City. 2014.

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332 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc millennium. Typhoon Haiyan (Yolanda) devastated the Visayan region the most and damaged the national economy to the value of PhP571 billion, including the agriculture sector to the tune of PhP62 billion (Table 8.3). Israel and Briones (2012) estimated the impacts of typhoons, floods, and droughts on agriculture using the Agricultural Multi-Market Model for Policy Evaluation (AMPLE). AMPLE is an eighteen production-sector, partial-equilibrium model suitable for understanding the evolution of underlying economic fundamentals, in contrast to actually predicting movements of the market. The main finding of the study is that typhoons have significant negative impacts on rice production at the local level.

PHILIPPINE DISASTER-MANAGEMENT CAPACITY Given the Philippines’ history of natural disasters, it is not surprising that disaster risk management can be traced to the 1930s during the Commonwealth period. The principal office in-charge was the Civilian Emergency Administration, created by Executive Order (EO) 355. The National Emergency Commission, mandated the Administration to formulate and execute policies and plans for the protection and welfare of the civilian population under extraordinary and emergency conditions. From thereon, other laws were passed creating — or renaming — the agency in charge of disaster risk management (Figure 8.3). The National Disaster Coordinating Council (NDCC) was created by Presidential Decree 1566 in 1978 to coordinate and supervise the country’s disaster management. NDCC comprised secretaries of various national agencies and was chaired by the Secretary of National Defense. In its three decades of existence, NDCC shifted from reactive emergency management to more proactive and comprehensive disaster risk management, and to disaster risk management being integrated into the country’s development agenda. In July 2009, Congress passed Republic Act (RA) 9729, also known as the Climate Change Act. The objective was to mainstream climate change into the formulation of government policy by establishing a National Framework Strategy and Program on climate change. Concomitantly, the law also created the Climate Change Commission (CCC), mandated to coordinate, monitor, and evaluate government programs and action plans relating to climate change. CCC has the status of a national government agency and is attached to the Office of the President. EO 888 was signed

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16,024.30 5,329.30 4,255.20 6,010.80 429.00 3,743.50 3,743.50 – 23,175.30 17,953.50 1,170.80 4,051.00 4,000.00 4,000.00 46,943.10 1,063.60

Public 4,285.00 1,500.00 – 216.00 2,569.00 67,560.00 27,560.00 40,000.00 305,472.10 3,726.20 1,959.90 299,786.00 – – 377,317.10 8,549.20

Private 7,108.40 4,575.20 322.90 24.30 2,186.00 87.00 87.00 – 3,442.30 1,303.90 1,932.40 206.00 300.00 300.00 10,937.70 247.80

Public

Private 6,565.40 4,126.40 – – 2,439.00 106,716.60 30,716.60 76,000.00 22,628.80 916.30 510.50 21,202.00 – – 135,910.80 3,079.40

Losses 33,983.10 15,530.90 4,578.10 6,251.10 7,623.00 178,107.10 62,107.10 116,000.00 354,718.50 23,899.90 5,573.60 325,245.00 4,300.00 4,300.00 571,108.70 12,940.00

Total

Note: Dashes indicate lack of data due to ongoing field assessments at the time of publication. Source: NEDA (National Economic and Development Authority). “Reconstruction Assistance on Yolanda (RAY)”. 2013a. (accessed 16 June 2014).

Infrastructure sectors Electricity Roads, bridges, flood control, and public buildings Transport Water and sanitation Economic sectors Agriculture Industry and services Social sectors Education Health Housing Cross-sectoral Local government Total (PhP million) Total (US$ million)

Sector

Damages

Economic Loss and Damage (PhP million)

TABLE 8.3 Total Value of Economic Loss and Damages Due to Typhoon Haiyan (Yolanda)

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Japanese Occupation

Presidential Decree 1566: In 1973, the Office of Civil Defense (OCD) In 1978, The National Disaster Coordinating Council (NDCC) was created.

1954–1968

1970s

Source: Constructed by authors from Danilo Israel, and Roehlano Briones “Impacts of Natural Disasters on Agriculture, Food Security, and Natural Resources and Environment in the Philippines”. PIDS Discussion Paper Series 2012-36. Makati: Philippine Institute for Development Studies, 2012.

Commonwealth to Post-Commonwealth

EO No. 355: Created the Civilian Emergency Administration (CEA)

EO No. 36: Created the Civilian Protection Services (CPS)

2000s

RA 10121 or the Philippine Disaster Risk Reduction and Management Act of 2010 EO 888 SNAP RA 9729 The Climate Change Act of 2009

RA 1190, otherwise known as the Civil Defence Act of 1954: Created the National Civil Defence Administration (NCDA)

FIGURE 8.3 A snapshot of Philippine disaster risk management over time

FIGURE 8.3 The Evolution of Philippine Disaster Risk Management over Time

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in 2010, adopting the Strategic National Action Plan (SNAP) on Disaster Risk Reduction (DRR) through 2019. SNAP is charged with providing a roadmap for sustaining DRR initiatives and promoting good practices by individuals, organizations, local government, and the private sector. EO 888 also institutionalizes DRR planning by all government agencies. Shortly after the signing of EO 888, NDCC was restructured into the National Disaster Risk Reduction and Management Council (NDRRMC) through the passage of RA 10121. This law empowered NDRRMC with policymaking, coordination, integration, supervision, monitoring, and evaluation functions related to disaster risk management. The Secretary of the Department of National Defense is the Chair. In contrast to NDCC, where Secretaries of other selected departments only served as members, the Secretaries of the Department of Interior and Local Government, the Department of Social Welfare and Development, and the Department of Science and Technology, along with the Economic Planning Secretary/ director general of the National Economic and Development Authority (NEDA), serve as NDRRMC’s Vice-Chairs. With the institutionalization of DRR and creation of NDRRMC, Administrative Order No. 1 was issued directing local government agencies to adopt and use DRR guidelines. NDRRMC and CCC have coordinated their activities by signing a memorandum of understanding to harmonize Local Climate Change Action Plans and Local Disaster Risk Reduction Management Plans at the local government level. NEDA was tasked with conducting capacity-building activities to integrate DRR into planning by local, regional, and national level government offices. Cognizant that geographical considerations matter in improving the quality of human life, the government included spatial considerations in the mid-term update of the Philippine Development Plan, 2011–16 (NEDA 2013b). The mid-term update categorized the focus of government interventions according to the following: Category 1: the number or magnitude of poor households in the province Category 2: the provincial incidence of poverty (proportion of poor individuals in the provincial population) Category 3: the province’s vulnerability to natural disasters (in particular, floods and landslides)

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336 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc Many of the most vulnerable provinces lie along the country’s eastern seaboard facing the Pacific Ocean. When natural disasters hit, the marginally nonpoor can easily slide into poverty (Table 8.4). Despite the Philippines’ history of disaster management, its ability to efficiently and systematically respond to disaster is still a work in progress. Several constraints and issues hinder disaster risk management (NDRRMC 2011): (1) ineffective vertical and horizontal coordination among member agencies; (2) limited coverage by governmental and partner organizations due to resource constraints; (3) ineffective local government capacity, such as lack of managerial and technical competencies; (4) limited funds, equipment, and facilities for monitoring and early warning; (5) insufficient hazard and disaster risk data and information; (6) inadequate mainstreaming of disaster risk management into development planning and implementation; (7) poor enforcement of environmental management laws and other relevant regulations; and (8) inadequate socioeconomic and environmental management programmes to reduce the vulnerability of marginalized communities. In November 2013, the country’s disaster management and response capacity were again tested when typhoon Haiyan (Yolanda) hit. The TABLE 8.4 Category 3 Provinces Exposed to Multiple Hazards Region

Province

Region 1: Ilocos Cordillera Administrative Region Region 2: Cagayan Valley Region 3: Central Luzon Region 4a: CALABARZON Region 5: Bicol Region 6: Western Visayas Region 7: Central Visayas Region 8: Eastern Visayas

Ilocos Norte and Ilocos Sur Abra and Benguet Cagayan, Quirino, Isabela, and Nueva Vizcaya Zambales, Pampanga, and Aurora Cavite, Laguna, Rizal, and Quezon Albay and Catanduanes Antique and Iloilo Bohol Eastern Samar, Leyte, Northern Samar, and Southern Leyte Zamboanga del Sur and Zamboanga Sibugay Dinagat Islands, Agusan del Sur, Surigao del Norte, and Surigao del Sur

Region 9: Western Mindanao Region 13: Caraga

Source: NEDA (National Economic and Development Authority). “Reconstruction Assistance on Yolanda (RAY)”. 2013a. (accessed 16 June 2014).

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protocol calls for a post-disaster needs assessment (PDNA) before the formulation of a recovery plan. Given the extent of the damage (Table 8.3) and the affected area, however, completing the reconstruction and recovery according to the protocol plan would have taken six months and involved unacceptable expense and adverse impact on people’s lives. In its capacity as Vice-Chair for Rehabilitation and Recovery under NDRRMC, NEDA, with assistance from the Australian Embassy, the Asian Development Bank, and the World Bank, led the preparation of the document, “Reconstruction Assistance for Yolanda (RAY): Build Back Better” (NEDA 2013a). RAY is an organized framework that aims to restore the economic and social conditions of the affected areas to their pre-Yolanda levels, while strengthening resilience to disaster. The importance of disaster preparedness cannot be understated. Studies have shown that such investments yield a very high rate of return. Dedeurwaerdere (1998) estimates that a benefit–cost ratio of 3.5–30 can be potentially realized from investments in disaster preparedness.3 The country’s disaster preparedness is wanting, given the projected increase in both the occurrence and intensity of extreme natural events. Improving national policies requires a framework for natural disaster risk management at the national level, as well as for risk management at the farm level.

A FRAMEWORK FOR MANAGING THE RISK ASSOCIATED WITH NATURAL DISASTERS The theory and practice of managing disaster-associated risk appear to economists and many others to be ad hoc and full and ambiguities relative to the theory of decision-making under uncertainty (Alexander 2013). For example, some approaches to managing disaster-related risk involve reducing vulnerabilities without considering the full range of possible outcomes and their likelihoods. In managing risk, farmers must optimize use of their available resources, weighing their perceived risk and the cost of risk-management options against the opportunity costs of alternative resource uses. Ad hoc approaches can only lead to suboptimal strategies. On the other hand, the standard theory of decision-making under uncertainty typically relates to a single decision with a spectrum of potential outcomes. In contrast, the objective of disaster management is to select a sequential portfolio of management strategies (Figure 8.4).

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338 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc FIGURE 8.4 A Conceptual Framework for Managing the Risk of Natural Disasters

FIGURE 8.4 Aconceptual framework for managing the risk of natural disasters Event Mitigation

Risk (event probabilities)

Controls (zoning requirements, seawalls, building codes, and so on)

Potential exposure (the extent of the population in harm’s way)

Ex ante loss reduction (early warning and response)

Coping (rebuilding and rehabilitation)

Resilience (freedom from long-term losses)

Vulnerability (distribution of initial losses) 1. Expected loss 2. Maximum probable loss

Ex post loss reduction (relief, drainage, evacuation hospitalization, and healthcare)

Exposure (damages)

Notes: The oval items depict specific actions to be taken, whereas each rectangle provides the corresponding statistical depiction of the indicated disaster characteristic. Ex ante responses are those that occur prior to a disaster, whereas ex post responses occur after the disaster has struck. Source: Devised by authors.

To provide a well-defined framework, some assumptions must be made about the likelihoods of particular events. The challenge for national policymaking is to develop a strategy for choosing specific actions (as depicted by the oval items in Figure 8.4). To avoid the usual confusion associated with defining terms, it is helpful to note that the “mitigation” ovals in Figure 8.4 are actions that can be taken. While the official government definition lumps all mitigation actions together, Figure 8.4 distinguishes them according to the stage at which they are taken. Vulnerability, resilience and the other rectangles depict characteristics of the resulting probability distributions.

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The figure should not be understood to mean that actions at different stages can be recursively determined. Rather, a complete risk-management strategy determines actions simultaneously. For example, the extent of preventive zoning and the strictness of building codes depend on the distribution of event risks and the costs of subsequent coping and other actions that can be taken. For the purposes of this discussion, event risk is defined as the exogenous probability that an event will exceed a critical level (for example, of rainfall, wind speed, or Richter level). When an event of a particular severity is characterized as a one-in-100-year event, for example, this is simply an informal way of saying that its probabililty is .01. Given the probabilities of various events and “controls”, such as seawalls, building codes, and zoning requirements, a distribution of the associated damages can be estimated. Potential exposure is then a characteristic of that distribution. For example, potential exposure might be represented by expected deaths — the sum of the number of people killed in each adverse state multiplied by their respective probabilities, should no further action be taken such as early warning and evacuation. The next level of controls, such as early warning technology and protocols, converts the distribution of potential exposures to one of actual exposures. The subsequent level deals with evasive actions that may be taken after the event has occurred, including emergency dredging, repairs, and additional evacuation. Vulnerability refers to the distribution of initial losses, given the aforementioned controls. The “risk of loss” is the probability that loss will exceed a critical level. Resilience can then be estimated as “one minus the probability of sustaining losses above a particular threshold”. “Coping” comprises a set of intervening actions between vulnerability and resilience — such as borrowing, relief, and rehabilitation — that reduce sustained losses and increase resilience by enabling households to stabilize their consumption patterns over time.4 Optimal risk management involves simultaneously finding the leastcostly combination of actions at each of the levels described. The difficulty results from the interdependence of the various levels; hence, the extent of risk reduction at one level depends on how much risk has been reduced at prior levels. For example, the ability of governments or farm households to cope with risks depends on their prior decisions, such as having accrued savings for use in the event of a disaster. In this sense, optimal coping strategies involve both planning for contingencies and execution after the

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340 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc event has occurred. Given the huge number of potential strategies and the difficulty of establishing all the consequences, this is a hugely complex problem, and may explain why both the theory and practice of disaster risk management appear to economists to be ad hoc. At the farm level, disaster risk management can be conceptualized as two sets of controls: those for managing risks and those for coping with risks (Figure 8.5). Risk management encompasses all aspects of farming practices. By their choice of techniques, including capital formation and diversification, farmers implicitly choose a distribution of outcomes along with their probabilities. Risk-reduction strategies may include what and when to plant and both crop and employment diversification. FIGURE 8.5

FIGURE 8.5 Farm-level management Farm-Level Riskrisk Management Expected utility (expected household welfare)

“Ex post coping strategies : (reduced consumption, use of savings, and insurance claims)

Income distribution

Ex ante risk-reduction strategies (investment in farm capital, choice of crops, farming techniques, and so on)

Asset distribution

Ex ante coping strategies : (savings, investments, and “self-insurance”)

Event distribution (based on exogenous weather and other conditions)

Notes: Ex ante responses are those that occur prior to a disaster, whereas ex post responses are those that occur afterward. “Self-insurance” comprises actions that allow an alternative source of income in the event of adverse shocks. Source: Devised by authors.

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These strategies can be reduced to a relationship between a “premium”, that is, the sacrifice in expected income and the amount by which risk is reduced. The choice of coping strategy in this context is an ex ante decision involving precautionary mechanisms, such as saving and insurance, that can be used to “smooth” (stabilize) consumption in the face of adverse events. Accruing savings includes the purchase of durables (such as jewellery) that can be sold. Insurance includes the cultivation of relationships (social capital) that can be drawn on in hard times (Walker and Jodha 1986). Ex post coping actions involve the execution of precautionary strategies, such as cashing in savings or insurance and borrowing from relatives and friends. Despite the necessary simplifications, this remains a complex problem based on the number of potential actions and the levels at which they may be applied. The problem needs to be formalized so that the many inherent ambiguities can be theoretically resolved. Box 8.1 presents the results of the economic modelling of farm-household risk management and coping strategies (see Ravago et al. 2016b, for additional mathematical details). The model informs the empirical investigation presented in the next section, which investigates the extent to which farm households experience different shocks and their responses to these shocks in terms of their choices of risk-management and other coping strategies, which heavily depend on their economic status.

EMPIRICAL EVIDENCE OF FARM-LEVEL RISK MANAGEMENT PCED conducted its baseline Social Protection Survey in May–June 2014 for the purpose of investigating the full spectrum of adverse “shocks”, including health and economic shocks and those caused by naturally occurring events, and the stops farm households took to cope with them (see Ravago et al. 2016a). The survey covered thirty-two types of shocks under the category “adverse events that reduce welfare”. Data collected included information on the sample households’ demographics, income and expenditure levels, assets and housing characteristics, vulnerability to shocks, coping mechanisms employed, and participation in and use of social protection programmes. The survey employed a multistage cluster-sampling design with a nationally representative sample of 3,100 households randomly drawn from fifty-seven of the Philippines’ eighty

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342 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc

BOX 8.1 Results of Economic Modelling of Farm-Household Risk Management and Coping Strategies A farm household is assumed to be aware of the likelihood of a disaster occurring in the next period and must determine how much of its “endowment” to allocate to current consumption, on- and off-farm investments, and “self-insuring” farm management techniques that limit the variability of output. On-farm capital investments are vulnerable to natural disasters, whereas off-farm investments are assumed to be “safe”. The household’s ex ante problem is to choose whether to invest in on-farm capital (K), “insurance” (I), and off-farm capital (N) to maximize its expected welfare, given its preferences regarding consumption in the current period, as well as in one “good” and one “bad” future state. The formal representation of the household’s welfare is the value function, U(C0) + bEU(C1) , known as expected utility (V). The utility functions U(C), capture the extent to which the household prefers a more even distribution of consumption. Expected utility in the second period, EU(C1), is the weighted average of the utilities in the good and the bad state, where the weight of the bad state is given by its probability, p, and that of the good state by its probability, (1-p). Consumption and investment must also satisfy the budget constraint such that the household’s total expenditure on current consumption (C0), K, I, and N equals its endowment (W) plus any borrowing (B). The mathematical details of the economic model are provided in Ravago et al. 2016b. In order to gain more insight into the interplay of all these factors in the farm household’s allocation decision and to illustrate the interaction of coping and risk management, a numerical simulation is provided based on the economic model just described. Scenarios considered include whether a household is relatively well off (high endowment) or poor (low endowment) and whether it has a high or low preference for consumption smoothing. Cases are also illustrated where the probability of disaster is either low or high. How a farm household determines its portfolio mix between on-farm capital, off-farm capital, and “insurance” depends on the respective returns and on consumption smoothing preferences (the degree of risk aversion). In addition, better endowed households respond to an increased risk of experiencing a disaster by investing more in safer, off-farm capital. Poorer households may not have this option, either because they are unable to secure a loan or because (as in this example) borrowing rates are higher than the net returns from the safe asset, for example, lending. Under the high-endowment/risk-neutral scenario, current consumption (C0) and consumption in the bad state (C11) are very low (Box Table 8.1,

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panel a). Without the need for smoothing, the household’s best strategy is to “put its eggs into the future good-state (C11) basket”. They do this by saving and investing in off-farm capital, K, and off-farm capital, N, but especially in on-farm capital. As the preference for smoothing (η) increases, the sum of K, “insurance” I, and N decreases in order to increase current consumption. In addition, increasing the proportion of safe and risk-reducing assets (N and I), relative to K, increases bad-state consumption. As the probability of disaster increases, K becomes more vulnerable to damage, such that agents increase the allocation of savings to the safe asset, because of its higher expected rate of return. Agents with higher risk aversion increase the proportion of their portfolios in N even more than risk-neutral agents in order to smooth consumption towards the bad state. BOX TABLE 8.1 a. The high-endowment scenario

Probability of a Bad State

Risk Neutral (η=0) Low High (1/3) (1/2)

b. The low-endowment scenario Risk Averse (η=2) Low High (1/3) (1/2)

Probability of a Good State

Risk Neutral (η=0) Low High (1/3) (1/2)

Risk Averse (η=2) Low High (1/3) (1/2)

K 6.91 2.92 1.09 0.71 K 1.0000 1.0000 0.254 0.263 I 0.00 0.00 0.13 0.14 I 0.0000 0.0000 0.195 0.223 N 3.09 7.08 2.93 3.49 B Negligiblea Negligiblea 0.00 0.00 K/(K+I+N) 0.69 0.29 0.26 0.16 K/K+I 1.0000 1.0000 0.57 0.54 I/(K+I+N) 0.00 0.00 0.03 0.03 I/K+I 0.0000 0.0000 0.43 0.46 a a N/(K+I+N) 0.41 0.71 0.71 0.80 C0 0.0001 0.0001 0.55 0.51 C0 Negligible Negligible 5.84 5.65 C11 0.0000 0.0001 0.34 0.38 C11 4.33 9.92 4.78 5.42 C12 5.9999 5.9995 2.071 2.086 C12 26.09 22.16 9.79 9.15 Coefficient Coefficient of variation of variation (consumption) 1.73 1.73 0.96 0.95 (consumption) 1.38 1.04 0.26 0.31 V 4.0414 3.523 –3.09 –3.45 V 18.27 15.56 –0.30 –0.32 Note: a. Although the value of borrowing for both high and low probability is a very small positive number, the value in the high probability case is higher than the value in the low probability case.

Hence, without a preference for smoothing, the expected utility-maximizing solution is to invest heavily in high-payoff farm capital, which results in large consumption in the good state and low consumption in the current period and in the bad state. As preference for smoothing (risk aversion) increases, the household adjusts its portfolio towards safer investments. The result of an increased probability of disaster is to decrease current consumption, affording an increase in savings and an increased percentage of savings going to off-farm investments. The second effect dominates the first such

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344 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc

that investment in vulnerable capital decreases. Risk-reducing techniques (represented by I) increase with risk, but only slightly. Under the low-endowment, risk-neutral scenario, the household borrows a negligible amount to finance the possibility of high consumption in the good, future state (Box Table 8.1, panel b). However, risk-averse households in this particular example do not borrow at all. These households put almost half of their endowment into savings for K and I, and just over half for current consumption. This leaves future consumption in the bad state to be more than half of the amount of current consumption, with future consumption in the good state being much higher. The composition of savings also changes in favour of risk reduction, I. Increasing the probability of disaster has surprisingly little effect on a household’s choices because of offsetting effects. On the one hand, the household is tempted to decrease investment in K in response to its increased vulnerability. On the other hand, the household needs K in order to provide for consumption in the future bad state. In this simulation, the second effect outweighs the first and thus K increases slightly. As expected, the increased risk of disaster is reflected in lower expected utility for both the risk-neutral and risk-averse households. Low-income households that have lower discount factors have higher current consumption and allocate less to capital formation. This poses an additional barrier to climbing out of poverty. In summary, as the smoothing parameter η increases, high-endowment households will invest less in on farm-capital and more in off-farm capital to help smooth consumption between the good and bad states. The ability to undertake off-farm investments lowers the need to employ risk-reducing measures on-farm. In contrast, low-endowment, risk-averse households have limited means to smooth consumption. Borrowing for off-farm capital is either unavailable for (unsecured loans) or carries interest rates that are higher than the expected returns. Borrowing for farm capital is unattractive in the face of high interest rates and the fact that more farm capital renders the households even more vulnerable by increasing assets at risk. The only remaining strategies are to reduce current consumption to finance small, additional amounts of farm capital and to choose farming techniques that sacrifice expected returns in order to reduce the variation of returns. These prospects leave the poor with low consumption in two of the three states and extensive exposure to disaster risks. This does not imply, as suggested by Chetty and Looney (2006), that there is a strong case for government-subsidized social insurance. Resources may be better spent in removing the underlying causes of poverty, such

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as low agricultural productivity and transaction costs that tend to isolate disadvantaged areas. Even the ex ante risk management considered here involves nontrivial computations. A more complete model would allow for both ex ante and ex post coping strategies. This could be done in a three-period model, whereby households make consumption and investment decisions at both times 0 and 1. If the adverse event occurs, the household engages in some belt-tightening by cutting back on consumption and investment, augments income by giving up some leisure, and borrows or sells durable assets. Alternatively, an augmented two-period model could be used. The main takeaway of the modelling exercise is that wealthier farmers, who are less inclined to smooth consumption, will invest heavily in highpayoff farm capital, which will allow them to consume more in the future good state but will consequently make them consume less in the current period and the future bad state. The more these farmers prefer to smooth consumption, the more they adjust their portfolios towards safer off-farm investments and away from farm capital (which can be damaged if a disaster occurs). When the probability of disaster increases, rich farmers likewise allocate more to safer investments and also invest more in risk-reducing techniques. Poorer farmers borrow and invest in farm capital until its return is equal to the cost of borrowing. Since they face a borrowing rate that is higher than returns to off-farm investments, these farmers do not invest off-farm. In other words, there may be little that low-income households can do in response to increased vulnerability. Policy and resources should be directed towards removing the underlying causes of poverty, such as low agricultural productivity and transaction costs that tend to isolate disadvantaged people and areas. Source: Ravago, Roumasset, Jandoc (2016b).

provinces. The provinces were chosen to represent both high- and lowrisk areas in terms of weather conditions, population density, and security issues and to include at least one province per region. The next sections present (1) the results of the PCED Social Protection Survey of household responses to the seven most-frequently occurring natural events in the Philippines, and (2) results of a modelling exercise utilizing the survey data to explore the key factors determining whether farm households recover from shocks.

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346 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc

Synthesis of Social Protection Survey Data Of the 3,100 households surveyed, 834 farm households were identified (Table 8.5). An agricultural or farm household is defined as one whose owner/nonowner: (1) is responsible for making the day-to-day decisions required to operate the holding, including the management and supervision of hired labour; or (2) works on the land alone or with members of the household; or (3) does not work on the land but employs others to do so; or (4) is self-employed working on a farm; or (5) is an employer in his/her own family-operated farm, either receiving or not receiving cash or a share of farm output; or (6) is an entrepreneur engaged in crop farming or gardening and livestock and poultry raising. About 31 per cent of the farm households sampled were in the poorest quintile. Natural events of extreme intensity that cause shocks are classified as (1) frequently occurring natural events, where the reference period of recall is January 2009 until the time of the survey and (2) less frequently occurring events, where the reference period is 1980 until the time of the survey. These events include strong winds and rain, flooding, landslides, drought, extreme heat, big waves (including tsunamis and storm surges), biological hazards, and crop losses from pests and disease. Earthquakes and volcanic eruptions are classified as less frequently occurring natural events. The nature of the coping mechanisms used for frequently versus less frequently occurring events differs markedly, as do policy actions. Charveriat (2000) noted that public investments in preparedness for the more frequent events are typically undertaken because the realization of benefits accrues while those in power are still serving. This discussion TABLE 8.5 Economic Profile of Sample Households Poverty Ranking (Poorest to Wealthiest) 1 2 3 4 5 Total

Total No. of Households 3,620 3,620 3,620 3,620 3,620 3,100

Average Per Capita No. of Farm Share of Farm Expenditure Per Year Households Households (%) 10,079 18,191 26,385 38,888 75,756 33,860

190 173 174 148 149 834

31 28 28 24 24

Source: Calculated by authors.

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focuses on shocks arising from frequently occurring natural events and the coping mechanisms households used. Of the 834 farm households, 779 reported having experienced at least one of the seven frequently occurring natural events since January 2009 (Table 8.6). Among the farm households, 355 (43 per cent) reported having experienced strong winds and rain; 200 (24 per cent) experienced flooding. The respondents were also asked to rank the severity of the shocks they experienced, with 1 being the most severe. Of the 779 households reporting to have experienced the 32 types of shocks covered in the survey, 445 households ranked these shocks as the “most severe” of the shocks they had experienced within the survey’s timeframe specified. Among the 355 farm households that experienced strong winds and rain, 233 (66 per cent) ranked this shock as most severe. Among the 200 farm households that reported having experienced flooding due to continuous rain and storms, 58 per cent rank this shock as the most severe. For the 128 farm households that experienced drought, 44 per cent rank it as the most severe. The last column of Table 8.6 indicates the number of households ranking each event among their “top-five most-severe” shocks. Of the 834 farm household sample, 523 identified the seven shocks arising from frequently occurring natural events among their top-five most-severe. Survey questions went on to elicit information about the farm households’ losses and damages, adjustments to spending and investments, coping measures, and assistance sought from public and private institutions with each shock. Households lost some of their assets and incurred medical and other recovery-oriented expenses (Table 8.7) and suffered damages of crops, livestock, and farming equipment (Table 8.8). Of the 355 farm households that experienced strong winds and rains, 67 per cent lost all or part of their crops, 6 per cent lost livestock, and 2 per cent lost farming equipment. Of the 200 farm households that experienced flooding, losses to crops, livestock, and farming equipment were experienced by 61, 8, and 1 per cent, respectively. Respondents were also asked about the effect of particular shocks on household well-being. Among these, strong winds and rain and flooding affected the greatest number of farm households, with more than 50 per cent of those experiencing shocks reporting that their family’s well-being had been greatly affected (Table 8.9). Respondents were then asked to rate the extent of their recovery based on the scale of (1) no recovery, (2) little recovery, (3) significant recovery,

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355 (100) 200 (100) 110 (100) 128 (100) 132 (100) 115 (100) 149 (100) 779

233 1(66) 116 1(58) 114 1(40) 156 1(44) 114 1(13) 113 1(60) 129 1(59) 445

Note: Figures in parenthesis indicate the share of households reporting a result among the total number experiencing the shock. Source: Calculated by authors from survey data.

Total

Pest infestations and crop diseases

Big waves (tsunamis and storm surges)

Extreme heat

Drought

Landslides/mudslides

Flooding due to continuous rain, storms, and so on

Strong winds and rain

Type of Shock (Frequently Occurring Natural Events)

268 1(75) 137 1(69) 114 1(40) 168 1(53) 111 1(34) 113 1(60) 132 1(65) 523

No. of Households Ranking the Shock No. of Households No. of Households Among Their Experiencing the Ranking the Shock as Specified Shock the “Most Severe” “Top-Five Most Severe”

TABLE 8.6 Incidence and Severity of Shocks Experienced

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135 1(38) 167 1(34) 113 1(30) 129 1(23) 116 1(19) 112 1(40) 118 1(37)

Loss/Destruction of Assets 17 1(5) 13 1(7) 10 10 13 1(2) 13 1(9) 10 10 10 10

Unplanned Medical Expenses 32 1(9) 18 1(9) 12 (20) 17 1(5) 12 1(6) 10 10 16 (12)

Other Expenses

180 1(51) 106 1(53) 115 1(50) 189 1(70) 121 1(66) 113 1(60) 126 1(53)

No Impact

Notes: Figures in parenthesis indicate the share of households reporting a result among the total number experiencing the shock. See Column 1 of Table 8.6 for the total number of households experiencing each shock. Source: Calculated by authors from survey data.

Pest infestations and crop diseases

Tsunamis and storm surges

Extreme heat

Drought

Landslides/mudslides

Flooding due to continuous rain, storms, and so on

Strong winds and rain

Type of Shock (Frequently Occurring Natural Events)

TABLE 8.7 Impact of Shocks Experienced

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238 1(67) 122 1(61) 119 1(90) 188 1(69) 117 1(53) 111 1(20) 146 1(94)

Crop Loss 20 1(6) 16 1(8) 10 1(0) 15 1(4) 10 1(0) 10 1(0) 10 1(0)

Livestock Loss 7 (2) 2 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

Loss of Farming Equipment

106 1(30) 168 1(34) 111 1(10) 138 1(30) 115 1(47) 114 1(80) 113 11(6)

No Loss

Notes: Figures in parenthesis indicate the share of households reporting a result among the total number experiencing the shock. See Column 1 of Table 8.6 for the total number of households experiencing each shock. Source: Calculated by authors based on survey data.

Pest infestations and crop diseases

Tsunamis and storm surges

Extreme heat

Drought

Landslides/mudslides

Flooding due to continuous rain, storms, and so on

Strong winds and rain

Type of Shock (Frequently Occurring Natural Events)

TABLE 8.8 Farm-Related Damages Experienced

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Note: Figures in parentheses indicate the share of households in each category. Source: Calculated by authors from survey data.

Pest infestations and crop diseases

Tsunamis and storm surges

Extreme heat

Drought

Landslides/mudslides

Flooding due to continuous rain, storms, and so on

Strong winds and rain

Type of Shock (Frequently Occurring Natural Events) 41 (12) 18 1(9) 12 (20) 29 (23) 12 1(6) 11 (20) 12 1(4)

No Impact 133 1(37) 160 1(30) 111 1(10) 148 1(38) 114 1(44) 112 1(40) 113 1(27)

Some Impact

TABLE 8.9 Effect of Shocks on the Family’s Well-Being

116 1(33) 174 1(37) 115 1(50) 137 1(29) 113 1(41) 111 1(20) 121 1(43)

Much Impact

65 (18) 48 (24) 12 (20) 14 (11) 13 1(9) 11 (20) 13 (27)

Extreme Impact

355 (100) 200 (100) 110 (100) 128 (100) 132 (100) 115 (100) 149 (100)

Total

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352 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc and (4) complete recovery. About 20 to 50 per cent of the farm households reported having fully recovered from the shock (Table 8.10). The remaining households were still experiencing various levels of welfare loss. In the context of the survey, recovery is understood to be in terms of financial well-being (respondents were asked how much money they would need to reinstate their family’s well-being to pre-shock levels). Unsurprisingly, the amount of money required by those who had not recovered at all was higher, at a median of PhP15,000, than those who had experienced partial or full recovery, at a median of PhP10,000 (Table 8.11). Turning to an examination of the various coping strategies employed by the farm households, across all shocks, farm households primarily depended on loans and savings to cope (Table 8.12). About 7 per cent of households that experienced shocks from frequently occurring natural events reported selling goods, including crops that they might otherwise have consumed. About one-third of households that experienced shocks reduced their expenditures to cope with shocks, and many of these households requested assistance from the government, from individuals or groups, or from nongovernment organizations (see Appendix  8.1 through 8.5 for additional information about coping strategies). Respondents indicated that using cash savings, reducing spending, and borrowing from others were their most important coping strategies (Table 8.13). Among the 523 farm households that identified shocks from frequently occurring natural events among their top-five most-severe, 30 per cent reported using cash savings as their most-important coping strategy, whereas 18 per cent specified reducing spending, and 14 per cent indicated borrowing from others as their most important coping strategies. Many farm households that experienced shocks took precautionary measures at the start of planting season to lower their risk of loss. These measures included adjusting or delaying planting time, adjusting the choice of crop variety, increasing the use of fertilizer, building better farm infrastructure, building dikes to improve water flow, and cleaning streams and canals of sediments and other impediments to flow (see Appendix Table 8.6 for further details). Data show that farm households invest in risk-management measures when the benefits of such measures accrue only to them. Adjusting planting time and choosing crop varieties are the most common measures; however, as with other public goods, households seldom invest in cleaning canals and building dikes because

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Note: Figures in parentheses indicate the share of households in each category. Source: Calculated by authors from survey data.

Pest infestations and crop diseases

Tsunamis and storm surges

Extreme heat

Drought

Landslides/mudslides

Flooding due to continuous rain, storms, and so on

Strong winds and rain

Type of Shock (Frequently Occurring Natural Events) 72 (20) 41 (21) 12 (20) 44 (34) 15 (16) 11 (20) 18 (16)

No Recovery 97 (27) 40 (20) 11 (10) 27 (21) 18 (25) 12 (40) 15 (10)

69 (19) 52 (26) 12 (20) 15 (12) 14 (13) 11 (20) 17 (35)

Significant Recovery

117 1(33) 167 1(34) 115 1(50) 142 1(33) 115 1(47) 111 1(20) 119 1(39)

Complete Recovery

Partial or Full Recovery Little Recovery

TABLE 8.10 Perception of Recovery from Shocks

355 (100) 200 (100) 110 (100) 128 (100) 132 (100) 115 (100) 149 (100)

Total

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22,501

15,000 10,000

10,000

188 1(17) 171 1(33) 142 11(8) 140 11(8) 231 1(44)

No. of Households That Adopted the Mechanism

Note: “Other” includes borrowing from friends or family, pawning items, and so on. Source: Calculated by authors from survey data.

None

Other

Sold farm goods and equipment

Used cash savings

Took out a loan

Financial Coping Mechanism

21,563 22,778

1.12 1.29

1.33 111

111

500

435 1(83) 352 1(67) 481 1(92) 483 1(92) 292 1(56)

No. of Households That Did Not Adopt the Mechanism

28,974

30,280

24,121

Standard Coefficient Minimum Deviation of Variation (PhP)

TABLE 8.12 Adoption of Financial Coping Mechanisms

522

Total

Source: Calculated by authors from survey data.

119 403

No recovery

No. of Mean No. Median No. Households of Households of Households

Partial or full recovery

Type of Recovery

TABLE 8.11 Average Degree of Recovery Versus Type of Recovery

523 (100) 523 (100) 523 (100) 523 (100) 523 (100)

Total

200,000

200,000

150,000

Maximum (PhP)

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TABLE 8.13 Most Important Coping Strategies Strategy Borrowing from others Using cash savings Delaying investment Selling assets Selling harvest/products Pawning property Stopping school/changing schools Reducing consumption Temporarily migrating Receiving help from a politician Taking on extra work Asking relatives for help None Total

Frequency (no.)

Share (%)

174 156 112 112 116 111 113 192 113 111 113 111 159 523

1(14) 1(30) 11(0) 11(2) 11(3) 11(0) 11(1) 1(18) 11(1) 11(0) 11(1) 11(0) 1(30) (100)

Source: Calculated by authors from survey data.

the whole community benefits from these activities. This is where local governments can fill in the gap. About 77 per cent of the 523 farm households that experienced shocks actually undertook risk management (that is, preventive) measures (Table 8.14). Of these, relatively more were from the upper 60 per cent of the sample, which is to be expected. Poorer households have less access to risk management measures. Datt and Hoogeveen (2003) also found that, relative to nonpoor households, poor Philippine households have limited ability to cushion the impact of shocks on their consumption patterns. The analysis also addressed whether prior experience of a shock prompted households to take precautionary measures to manage risk. Of households that previously experienced a similar shock, 77 per cent undertook long-term risk management measures (Table 8.15). Prior experience may also influence household responses when a natural event is imminent; these responses include precautionary measures typically undertaken right after receiving a warning, such as securing the dwelling with ropes, stockpiling food and other essentials, moving to evacuation areas, going to relatives’ or friends’ houses, and moving productive assets to safer places.

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356 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc TABLE 8.14 Adoption of Risk-Management Measures, by Economic Profile

Economic Profile Lower 40 per cent Upper 60 per cent Total

No. of Households That Adopted the Measure

No. of Households That Did Not Adopt the Measure

Total

156 1(80) 249 1(76) 405 1(77)

139 1(20) 179 1(24) 118 1(23)

195 (100) 328 (100) 523 (100)

Notes: Figures in parentheses indicate the share of adopting and nonadopting households. (Pearson chi2(1) = 1.1682; Pr = 0.280.) Source: Calculated by authors from survey data.

TABLE 8.15 Adoption of Precautionary Measures

Experienced Shock Before No Yes Total

No. of Households That Adopted the Measure

No. of Households That Did Not Adopt the Measure

Total

171 1(76) 234 1(77) 405 1(77)

153 1(24) 165 1(23) 118 1(23)

224 (100) 299 (100) 523 (100)

Notes: Figures in parentheses indicate the share of adopting and nonadopting households. (Pearson chi2(1) = 0.2706; Pr = 0.603.) Source: Calculated by authors from survey data.

A primary motivation for undertaking precautionary measures is the risk of losing crops and other farm assets. Consistent with the riskmanagement measures of adjusting planting time and choosing a different crop variety, about two-thirds of the farm households that experienced shocks worried about losing their crops (Table 8.16). In summary, floods and strong winds and rain were the most commonly experienced shocks that caused damages to assets and crops. About half the farm households that experienced these events recovered financially

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258 1(73) 135 1(68) 110 (100) 196 1(75) 121 1(66) 112 1(40) 147 1(96)

Crop Loss 29 1(8) 29 (15) 11 (10) 11 (9) 14 (13) 10 1(0) 12 1(4)

Livestock Loss 15 1(4) 18 1(4) 10 1(0) 12 1(2) 10 1(0) 10 1(0) 10 1(0)

Loss of Farming Equipment

82 (23) 47 (24) 10 1(0) 26 (20) 19 (28) 13 (60) 12 1(4)

No Loss

Notes: Figures in parentheses indicate the share of households reporting a result among the total number experiencing the shock. See Column 1 of Table 8.6 for the total number of households experiencing each shock. Source: Calculated by authors based on survey data.

Pest infestations and crop diseases

Tsunamis and storm surges

Extreme heat

Drought

Landslides/mudslides

Flooding due to continuous rain, storms, and so on

Strong winds and rain

Type of Shock (of Frequently Occurring Natural Events)

TABLE 8.16 Damages of Concern to Farm Households

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358 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc — either fully or partially — by using savings, obtaining loans, or selling farm goods and equipment.

Factors Determining Farm Household Recovery To investigate the factors that determine whether or not farm households (either fully or partially) recover from frequently occurring natural events, a logit model was used, following Ravago and Mapa (2014). The sample comprised the 523 farm households that ranked the shocks they experienced from frequently occurring natural events among their top-five most-severe shocks experienced within the timeframe specified under the survey (relative to thirty-two shocks overall). Of the full range of farm households’ coping mechanisms, precautionary measures primarily involved risk management (Appendix Table 8.7). Measures used to cope with shocks included using cash savings; taking out loans; selling crops and farm equipment; and reducing expenses on education, utilities, and recreation (as well as stopping schooling altogether). Other forms of coping with shocks included temporary migration and seeking assistance from the government and private groups. The model included initial household conditions, such as the educational attainment, age, and gender of the household head, and whether the household received conditional cash transfers (CCTs) from the government. The model also looked at the interaction of CCTs with the two other prominent coping mechanisms: using cash savings and selling farm goods. Additional variables were included to identify whether households (1) had prior experience of the same shock (to test whether learning from past experience affected recovery); (2) were located in Region 8 (because that region was the location of Typhoon Haiyan (Yolanda), the most recent natural event of extreme magnitude); and (3) had ranked any of the frequently occurring natural events as the most severe they had experienced within the survey’s specified timeframes. The final sample comprised 445 farm households (Column 2 of Table 8.7; results of full and reduced versions of the logit model are presented in Appendix Tables 8.7 and 8.8). Even though expenditures on education and discretionary consumption were not significant factors in household recovery, it may be that reducing educational expenditures keeps families in poverty. For example, Chetty and Looney (2007) examined the effect of household unemployment shocks on the distribution of growth rates in food consumption and education

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expenditures in Indonesia. For households in the top decile, consumption and education expenditures grew at the same rate as before the shock, whereas for households in the bottom 40–50 per cent, expenditure levels decreased after the shock. Reduced spending on education and other investments by these households would lessen their prospects of eventually climbing out of poverty. Results from the reduced model using data for the Philippines indicate that the dominant coping strategies adopted are using cash savings and selling farm goods and equipment (Appendix Table 8.8). For households that used cash savings, the probability of partial to full recovery increased by about 13 percentage points (marginal effect) relative to households that did not have savings (controlling for other factors). For households that sold farm goods to cope with shocks, the probability of partial to full recovery increased by about 12 percentage points relative to households that did not sell goods (again controlling for other factors). Unsurprisingly, for farm households located in Region 8, the probability of partial to full recovery decreased by about 8 percentage points. The results also show that education substantially increased the probability of recovery. When the household head had one or more years of high-school education, the probability of partial to full recovery increased by 14 percentage points; if the household head had graduated from high school, the probability increased by 9 percentage points; if the head had some college education, the probability of partial to full recovery increased by 12 percentage points; and when household heads had graduated college, the probability increased by 13 percentage points. The gender of the household head also influenced recovery (as the result in Appendix Table 8.8 shows). For female-headed households, the probability of partial to full recovery increased by about 10 percentage points relative to male-headed households.

CONCLUDING REMARKS Empirical evidence from the Social Protection Survey shows that farm households employ various strategies to cope with adverse shocks and avoid severe disruption of their consumption levels. Examples of these coping strategies include borrowing; drawing on savings; selling household assets; harvesting early; selling harvests they might otherwise have consumed; and asking for assistance from individuals, groups, the

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360 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc government, and nongovernment organizations. Using cash savings, reducing consumption, and borrowing were the most important and most frequently employed strategies. Empirical data also shows that farm households that experienced shocks also took risk-reducing measures at the start of the planting season. Adjusting planting time and choosing a different crop variety were the most common and important of these measures. As for any public goods, however, households seldom invest in cleaning streams and canals or building dikes because these activities benefit the whole community. Further research is needed to determine priorities among the risk management and coping strategies represented in the conceptual framework provided. Our simple theoretical exercises help to generalize these lessons. As either risk aversion or the probability of disaster increases, wealthier households can substitute less vulnerable, off-farm capital for farm capital, including human capital. As Benjamin Franklin put it, “If a man empties his purse into his head, no one [including nature] can take it from him.” Another risk management strategy is to invest in risk-reducing assets such as drainage and sturdier buildings. Poorer households, however, are typically unable to borrow for off-farm investments and have little incentive to borrow more than negligible amounts for farm capital because of high borrowing rates and the fact that farm capital is vulnerable to natural disasters. They have only slightly more capacity to invest in diversification and preventative capital. Given these limitations, there may be little that low-income households can do in response to increased vulnerability from climate change. The fact that low-income households are largely unable to cope with shocks does not, however, imply a strong case for government-subsidized social insurance. Crop insurance, for example, is likely to be already oversubsidized (Wright 2014, 2015). Risk management interventions are more appropriately directed towards the sources of risk aversion. A major reason behind the appearance of risk aversion is transaction costs (Roumasset 1979, 2015). For example, buying prices are higher than selling prices because of transportation, communication, and the costs of contracting. Borrowing costs are typically higher than the returns to saving for the same reasons. Government policies that decrease unit transaction costs (such as the cost of transporting 1 kilogram of produce for 1 kilometre) thus decrease the costs of risk. Insofar as climate change increases the costs of risk, it also increases the benefits of transportation

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and communications infrastructure. Just as households find that costly mechanisms to maintain consumption levels in times of crisis may be worthwhile, countries can ameliorate scarcity across locations through better infrastructure, thereby lowering the consequences of adverse events. Similarly, policies that improve the rule of law in commercial transactions (such as enforcing standards and measures) decrease the costs of risk. Undertaking costly measures of managing risks without commensurate attention to the artificial creation of risk aversion is clearly inefficient. Climate change exacerbates burden of agricultural policies that increase transaction costs. For example, the policies of the National Food Authority both increase consumer prices and displace private investments in transportation and storage that would decrease the associated transaction costs (Roumasset 2000). Similarly, land-reform policies have increased transaction costs, most notably in the agricultural land market, to the point that legal transactions are uncommon (Sicat 2014). Reforming these risk-increasing policies should be the first priority in helping farmers adapt to climate change. Country options are even greater than those of wealthy households and are illustrated in the conceptual framework depicted in Figure 8.4. The Fukushima earthquake and tsunami illustrate the importance of precautionary capital, which can be increased by shoreline hardening or decreased by putting vulnerable facilities in harm’s way. Early warning systems, which would have done a great deal to reduce the loss of life from the tsunami that devastated Aceh, Indonesia, exemplify another type of precautionary capital. Other examples include zoning, flood control inlets, and structures that absorb coastal wave energy. The same framework also illustrates the daunting research challenges of comprehensive disasterrisk management, inasmuch as the ideal system requires estimating interdependent probability distributions at multiple stages before and after the disaster and how these distributions would change with different combinations of mitigation and coping actions. There is a need for research that simplifies the needed computations while not losing sight of what is being approximated.

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40 (11) 37 (19) 12 (20) 19 (15) 13 1(9) 11 (20) 17 (35)

121 1(34) 168 1(34) 117 1(70) 140 1(31) 110 1(31) 111 1(20) 110 1(20)

7 (2) 5 (3) 0 0 7 (5) 3 (9) 0 0 3 (6)

16 1(5) 115 1(3) 12 (20) 18 1(6) 10 10 10 10 15 (10)

1 (0) 1 (1) 0 0 1 (1) 0 0 0 0 0 0

1 (0) 0 0 0 0 0 0 0 0 0 0 0 0

173 1(49) 1187 1(44) 110 110 155 1(43) 116 1(50) 113 1(60) 114 1(29)

Notes: Figures in parenthesis indicate the share of households reporting a result among the total number experiencing the shock. See Column 1 of Table 8.6 for the total number of households experiencing each shock. Source: Calculated by authors from survey data.

Pest infestations and crop diseases

Tsunamis and storm surges

Extreme heat

Drought

Landslides/mudslides

Flood due to continuous rain, storms, and so on

Strong winds and rain

Harvested/ Mortgaged/ Manufactured Sold Pawned Delayed/ Products Used Household Assets/ Forewent or Crops in Assets/ Took out Cash Goods None Investments Advance Goods a Loan Savings (6) (7) (5) (4) (3) (2) (1)

APPENDIX TABLE 8.1 Number of Farm Households Resorting to Financial Coping Strategies

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26 1(7) 14 1(7) 12 (20) 11 1(9) 13 1(9) 10 1(0) 14 1(8)

Strong winds and rain

Note: Figures in parentheses indicate the share of households in each category. Source: Calculated by authors from survey data.

Pest infestations and crop diseases

Tsunamis and storm surges

Extreme heat

Drought

Landslides/mudslides

Flood due to continuous rain, storms, and so on

Yes

Type of Shock (Frequently Occurring Natural Events) 329 1(93) 186 1(93) 118 1(80) 117 1(91) 129 1(91) 115 (100) 145 1(92)

No 355 (100) 200 (100) 110 (100) 128 (100) 132 (100) 115 (100) 149 (100)

Total

Sold Goods to Cope

APPENDIX TABLE 8.2 Incidence of Selling Goods to Cope from Shock

Livestock 14 (15) 16 (43) 10 1(0) 14 (36) 10 1(0) 10 1(0) 11 (25)

Crops 123 1(88) 119 1(64) 112 (100) 117 1(64) 113 (100) 110 11(0) 113 1(75)

Items Sold

0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

Farm Equipment

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Note: Figures in parentheses indicate the share of households in each category. Source: Calculated by authors from survey data.

Pest infestations and crop diseases

Tsunamis and storm surges

Extreme heat

Drought

Landslides/mudslides

Flood due to continuous rain, storms, and so on

Strong winds and rain

Type of Shock (Frequently Occurring Natural Events) 121 1(34) 174 1(37) 113 1(30) 127 1(21) 118 1(25) 112 1(40) 116 1(33)

Reduced Consumption

APPENDIX TABLE 8.3 Households Reducing Consumption Due to Shocks

234 1(66) 126 1(63) 117 1(70) 101 1(79) 124 1(75) 113 1(60) 133 1(67)

Did Not Reduce Consumption

355 (100) 200 (100) 110 (100) 128 (100) 132 (100) 115 (100) 149 (100)

Total

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41 (34) 32 (43) 11 (33) 14 (52) 14 (50) 10 1(0) 11 (69)

Food Only

Note: Figures in parentheses indicate the share of households in each category. Source: Calculated by authors from survey data.

Pest infestations and crop diseases

Tsunamis and storm surges

Extreme heat

Drought

Landslides/mudslides

Flood due to continuous rain, storms, and so on

Strong winds and rain

Type of Shock (Frequently Occurring Natural Events) 19 (16) 16 1(8) 10 1(0) 13 (11) 10 1(0) 10 1(0) 10 1(0)

Education 38 (31) 21 (28) 10 1(0) 17 (26) 11 (13) 10 1(0) 13 (19)

Utilities 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

Medical Care

APPENDIX TABLE 8.4 Households Reducing Consumption Due to Shocks, by Type of Consumption

53 (44) 31 (42) 12 (67) 18 (30) 13 (38) 11 (50) 13 (19)

Recreation

15 1(4) 10 1(0) 10 1(0) 11 1(4) 11 (13) 10 1(0) 10 1(0)

Other Nonfood

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45 (13) 25 (13) 11 (10) 13 1(2) 10 10 10 10 11 1(2)

No 310 1(87) 175 1(88) 119 1(90) 125 1(98) 132 (100) 115 (100) 148 1(98) 10

16 1(5) 15 1(3) 10 10 14 1(3) 11 1(3) 10 10

Yes 339 1(95) 195 1(98) 110 (100) 124 1(97) 131 1(97) 115 (100) 149 (100)

No 30 1(8) 10 1(5) 10 10 11 1(1) 10 10 10 10 10 10

Yes 325 1(92) 190 1(95) 110 (100) 127 (99) 132 (100) 115 (100) 149 (100)

No

Received Nongovernmental Individual/Group Organization/ Assistance Charity Assistance 279 1(79) 163 1(82) 119 1(90) 121 1(95) 131 1(97) 115 (100) 148 (98)

Yes

76 (21) 37 (19) 11 (10) 17 1(5) 11 1(3) 10 1(0) 11 1(2)

No

Received or Asked for Assistance

Notes: Figures in parenthesis indicate the share of households reporting a result among the total number experiencing the shock. See Column 1 of Table 8.6 for the total number of households experiencing each shock. Source: Calculated by authors from survey data.

Pest infestations and crop diseases

Tsunamis and storm surges

Extreme heat

Drought

Landslides/mudslides

Flood due to continuous rain, storms, and so on

Yes

Type of Shock (Frequently Occurring Natural Events)

Strong winds and rain

Received Government Assistance

APPENDIX TABLE 8.5 Incidence of Asking for Assistance

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172 1(48) 183 1(42) 114 1(40) 159 1(46) 110 1(31) 113 1(60) 130 1(61)

75 (21) 34 (17) 14 (40) 38 (30) 17 (22) 10 1(0) 17 (35)

24 1(7) 13 1(7) 14 (40) 14 (11) 13 1(9) 10 1(0) 18 (16)

22 1(6) 16 1(8) 12 (20) 10 1(8) 14 (13) 10 1(0) 12 1(4)

0 (0) 1 (1) 0 (0) 1 (1) 0 (0) 0 (0) 0 (0)

0 (0) 1 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

84 (24) 56 (28) 10 1(0) 24 (19) 19 (28) 12 (40) 12 1(4)

Notes: Figures in parenthesis indicate the share of households reporting a result among the total number experiencing the shock. See Column 1 of Table 8.6 for the total number of households experiencing each shock. Source: Calculated by authors from survey data.

Pest infestations and crop diseases

Tsunamis and storm surges

Extreme heat

Drought

Landslides/mudslides

Flood due to continuous rain, storms, and so on

Strong winds and rain

Type of Shock (Frequently Occurring Natural Events)

Adjusted/ Chose Crop Variety Increased Built Better Built Dikes Cleaned Delayed Farm for Better Streams, Planting Suited for the Use of the Shock Fertilizer Infrastructure Water Flow Canals None Time

APPENDIX TABLE 8.6 Long-Term Risk-Management/Precautionary Measures

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0.28 0.31 0.29 0.66 0.27 0.78 0.28 0.34 0.39 0.33 0.40 0.54 0.01 0.43 0.33 0.30 0.24 0.68 1.27 0.24 0.35 0.64

0.39 1.02 0.57 0.85 0.97 –0.01 0.70 0.33 0.06 –0.01 0.10 0.19 –0.64 –0.53 1.39

Robust Standard Error

0.04 0.02 0.81 0.87 0.03 –0.09 –0.08

Coefficient

0.84 0.85 0.01 0.13 0.01

0.22 0.01 0.09 0.03 0.08 0.24 0.10 0.28 0.87 0.16

0.84 0.91 0.01 0.18 0.92 0.89 0.85

p-value

Notes: The dependent variable is whether households partially or completely recovered. No. of observations = 523. Log pseudolikelihood = –256.78874. Wald chi2 = 44.35 (p-value = 0.0021); McFadden R-squared = 0.0841 and * indicates significant variables at the 95 per cent confidence level. Source: Calculated by authors from survey data.

Adopted precautionary measures Took out a loan Used cash savings* Sold farm goods and equipment Reduced consumption (education, utilities, and recreation) Moved to another area Received assistance (government and private) Education of household head Elementary graduate High-school undergraduate* High-school graduate* College undergraduate* College graduate* Age of household head Sex of household head (female = 1)* Household has other sources of income Household receives conditional cash transfers (CCTs) Household experienced similar shock before Interactions Spent cash savings and household receives CCTs Household sold goods and household receives CCTs Region 8 = 1* Household ranked shock as “most severe” = 1 Constant*

Explanatory Variable

APPENDIX TABLE 8.7 Factors Influencing Recovery, Based on Full Model

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0.26 0.58 0.33 0.39 0.32 0.40 0.53 0.01 0.43 0.26 0.23 0.34 0.61

0.40 1.03 0.59 0.86 1.00 –0.01 0.73 0.11 –0.67 –0.52 1.36

Robust Standard Error

0.86 0.93

Coefficient

0.22 0.01 0.07 0.03 0.06 0.36 0.09 0.68 0.01 0.13 0.03

0.00 0.11

0.06 0.14 0.09 0.12 0.13 0.00 0.10 0.02 –0.12 –0.08

0.13 0.12

p-value Marginal Effects

Notes: The dependent variable is whether households partially or completely recovered. No. of observations = 523. Log pseudolikelihood = –257.38184; Wald chi2 = 41.83 (p-value = 0.0001); McFadden R-squared = 0.0823; and * indicates significant variables at the 95 per cent confidence level. Source: Calculated by authors from survey data.

Used cash savings* Sold farm goods and equipment* Education of household head Elementary graduate High-school undergraduate* High-school graduate* College undergraduate* College graduate* Age of household head Sex of household head (female = 1)* Household receives conditional cash transfers Region 8 household = 1* Household ranked shock as “most severe” Constant

Explanatory Variable

APPENDIX TABLE 8.8 Factors Influencing Recovery, Based on Reduced Model

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Notes The authors gratefully acknowledge the excellent research assistance of Pia Medrano. They are also grateful for the comments and suggestions of participants at the IFPRI–NEDA project workshop held in Tagaytay, 16–17 November 2015, and at the Western Economic Association International conference held in Wellington, New Zealand, 8–10 January 2015. Any errors of commission or omission are the sole responsibility of the authors. 1. Storms that develop over the northwestern Pacific Ocean are called typhoons; those that originate in the South Pacific and over the Indian Ocean are called cyclones; and those that form over the eastern Pacific Ocean and Atlantic Ocean are called hurricanes. 2. Given that these definitions are somewhat vague, as are the official definitions adopted by the Philippine government, an alternative is suggested below to distinguish between the characteristics of vulnerability and the various actions that can be taken to avoid it. 3. Appropriate methodology for ex ante benefit–cost analysis of disaster preparedness is still evolving. See, for example, Mechler and Reinhard (2003); Jonkman et al. (2004); World Bank (2007). 4. The official Philippine definitions do not distinguish between “vulnerability” and “resilience” other than to indicate that the two are complements — that is, that low vulnerability implies high resilience. Under our conceptual framework, resilience subsumes vulnerability but the reverse does not hold (Figure 8.4).

References Alexander, D.E. “Resilience and Disaster Risk Reduction: An Etymological Journey”. Natural Hazards Earth System Science 13 (2013): 2707–16. Cavallo, Eduardo and Ilan Noy. “The Economics of Natural Disasters: A Survey”. Inter-American Development Bank, 2010 (accessed 30 July 2014). ———, Sebastian Galiani, Ilan Noy, and Juan Pantano. “Catastrophic Natural Disasters and Economic Growth”. Inter- American Development Bank, 2010

(accessed 30 July 2014). Charveriat, Celine. “Natural Disasters in Latin America and the Caribbean: An Overview of Risk”. Inter-American Development Bank, 2000 (accessed 15 October 2014). Chetty, Raj, and Adam Looney. “Consumption Smoothing and the Welfare Consequences of Social Insurance in Developing Economies”. Journal of Public Economics 90, no. 12 (2006): 2351–56.

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Risk Management and Coping Strategies

371

——— and Adam Looney. “Income Risk and the Benefits of Social Insurance: Evidence from Indonesia and the United States”. In Fiscal Policy and Management in East Asia, edited by T. Ito and A. Rose. MA, USA: National Bureau of Economic Research, 2007. Cinco, Thelma, Flaviana Hilario, Rosalina de Guzman, and Emma Ares. “Climate Trends and Projection in the Philippines”. In Philippine Atmospheric, Geophysical and Astronomical Services Administration PAGASA, 2013 (accessed 15 September 2014). CRED (Centre for Research on the Epidemiology of Disasters). EM-DAT: The International Disaster Database, no date. (accessed 11 June 2014). Cruz, Rex, Hideo Harasawa, Murari Lal, Shaohonng Wu, Yurij Anokhin, Batima Punsalmaa, Yasushi Honda, et al. “Asian Climate Change 2007: Impacts, Adaptation and Vulnerability”. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by M. Parry, O. Canziani, J. Palutikof, P. van der Linden, and C. Hanson. Cambridge, UK: Cambridge University Press, 2007. DA–MAD (Department of Agriculture, Management Audit Division). Unpublished flood and drought data. Quezon City. 2014. Das, Haripada, T.I. Adamenko, K.A. Anaman, R.G. Gommes, and G. Johnson. “Agrometeorology Related to Extreme Events”. WMO 943. Geneva: World Meteorological Organization. 2003. Datt, Gaurav, and Hans Hoogeveen. “El Niño or El Peso? Crisis, Poverty and Income Distribution in the Philippines”. World Development 31, no. 7 (2003): 1103–24. Dedeurwaerdere, Ann. “Cost-Benefit Analysis for Natural Disaster Management: A Case-study in the Philippines”. Brussels: Centre for Research on the Epidemiology of Disasters, 1998. Duflo, Esther, Miachel Kremer, and Jonathan Robinson. “How High Are Rates of Return to Fertilizer? Evidence from Field Experiments in Kenya”. American Economic Review 98, no. 2 (2008): 482–88. Israel, Danilo, and Roehlano Briones. “Impacts of Natural Disasters on Agriculture, Food Security, and Natural Resources and Environment in the Philippines”. PIDS Discussion Paper Series 2012-36. Makati: Philippine Institute for Development Studies, 2012. Jonkman, S.N., M. Brinkhuis-Jak, and Matthijis Kok. “Cost Benefit Analysis and Flood Damage Mitigation in the Netherlands”. HERON 49, no. 1 (2004): 95–111. Mechler, Reinhard. “Natural Disaster Risk and Cost-Benefit Analysis”. In Building Safer Cities: The Future of Disaster Risk, edited by A. Kreimer, M. Arnold, and A. Carlin, Chapter 3. Washington, D.C.: World Bank, 2003.

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372 Majah-Leah V. Ravago, James A. Roumasset, and Karl Robert L. Jandoc Miyan, M. Alimullah. “Droughts in Asian Least Developed Countries: Vulnerability and Sustainability”. Weather and Climate Extremes 7 (2015): 8–23. NDRRMC (National Disaster Risk Reduction and Management Council). “National Disaster Risk Reduction and Management Framework”. 2011 (accessed 14 June 2014). NEDA (National Economic and Development Authority). “Reconstruction Assistance on Yolanda (RAY)”. 2013a (accessed 16 June 2014). ———. “Philippine Development Plan 2011–2016 Midterm Update”. 2013b (accessed 22 January 2014). PAGASA (Philippine Atmospheric, Geophysical and Astronomical Services Administration). Unpublished climate data. Quezon City. 2014. PHIVOLCS–SD (Philippine Institute of Volcanology and Seismology, Seismology Division). Unpublished data on earthquakes. Quezon City. 2014. PHIVOLCS–VD (Philippine Institute of Volcanology and Seismology, Volcanology Division). Unpublished data on volcanic events. Quezon City. 2014. Prestemon, Jeffrey and Thomas Holmes. “Timber Price Dynamic Following a Natural Catastrophe”. American Journal of Agricultural Economics 82, no. 1 (2002): 145–60. Ravago, Majah-Leah and Dennis Mapa. “Eastern Visayas after Yolanda: Evidence from Household Survey”. Philippine Center for Economic Development, 2014 (accessed 18 December 2014). ———, Stella Quimbo, Dennis Mapa, Aleli Kraft, and Joseph Capuno. “Technical report on social protection survey of the Philippine Center for Economic Development”, University of the Philippines School of Economics. 2016a. ———, James Roumasset, and Karl Jandoc. “Risk Management and Coping Strategies: Climate Change and Agriculture in the Philippines”. Philippine Review of Economics 53, no. 2 (2016b): 64–103. Roumasset, James. Rice and Risk: Decision-Making among Low-Income Farmers in Theory and Practice. Amsterdam: North-Holland Publishing, 1976. ———. “Risk Aversion, Agricultural Development and the Indirect Utility Function”. In Risk, Uncertainty and Agricultural Development, edited by J. Roumasset, J. Boussard, and I. Singh. Los Baños: SEARCA/ADC, 1979. ———. Black-Hole Security. Honolulu: Economics Department, University of Hawaii, 2000. ———. “Reflections on the Foundations of Development Policy Analysis”. In Sustainable Economic Development: Resources, Environment, and Institutions, edited by A. Balisacan, U. Chakravorty, and M. Ravago, Oxford and San Diego: Elsevier Academic Press, 2015.

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Sicat, Gerardo. 2014. “Agrarian Reform and Economic Development: ‘Equity’ with Efficiency”. Philippine Star, 26 February 2014 (accessed 11 December 2014). Skidmore, Mark and Hideki Toya. “Do Natural Disasters Promote Long-Run Growth?”. Economic Inquiry 40, no. 4 (2002): 664–87. UNU-EHS (United Nations University, Institute for Environmental and Human Security). 2013. World Risk Report 2013. 2013 (accessed 15 January 2014). Walker, Thomas and N.S. Jodha. “How Small Farm Households Adapt to Risk”. In Crop Insurance for Agricultural Development, edited by P. Hazell, C. Pomareda, and A. Valdes. Baltimore: Johns Hopkins University Press, 1986. ——— and James Ryan. Village and Household Economies in India's Semi-Arid Tropics. Baltimore: John Hopkins University Press, 1990. Wright, Brian. “Multiple Peril Crop Insurance”. Choices: The Magazine of Food, Farm, and Resources Issues, 2014 (accessed 30 July 2014). ———. “The Role of Agricultural Economists in Sustaining Bad Programs”. In Sustainable Economic Development: Resources, Environment and Institutions, edited by A. Balisacan, U. Chakravorty, and M. Ravago. Oxford and San Diego: Elsevier Academic Press, 2015. World Bank. “Western Kenya Community Driven Development and Flood Mitigation Project.” Project Appraisal Document. Washington, D.C., 2007.

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PART III Investments and Supporting Policies to Alleviate Climate Change Impacts to Philippine Agriculture

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9 A BIOPHYSICAL APPROACH TO MODELLING ALTERNATIVE AGRICULTURAL FUTURES UNDER CLIMATE CHANGE Timothy S. Thomas, Vijay Nazareth, and Renato A. Folledo, Jr.

The Philippines is noted for its climatic and ecological diversity, being a nation of many islands and spanning a large number of latitudes; hence, the impact of climate change would not be expected to be uniform. Yet, even if the changes were to have negative impacts on crop yields in every location, the question remains as to how bad such impacts may be. Will the impact be unequivocally negative, or might some parts of the country experience positive agricultural impacts? And could identifying places likely to experience negative outcomes ahead of time help people to plan and adapt? A 5 per cent yield loss, for example, is not so serious when compared with, say, a 40 per cent loss, and preparations would certainly differ depending on the extremes anticipated. There is also the question of climate change having differential impacts on crop yields. What if climate change negatively affects one crop in a given location, but positively affects

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another in the same location? Could this information help farmers and policymakers to prepare for what the future may bring? This chapter presents the results of research designed to fill gaps in the knowledge base about the intensity of the impacts of climate change on agriculture in the Philippines, taking a spatially disaggregated approach intended to differentiate the impacts across regions. The research employed crop-modelling software together with the latest climate models to evaluate the impact of climate change on key crops. With this knowledge in hand, policymakers, researchers, and farmers should be better equipped to both prepare for and adapt to the impacts of climate change. To date, most of what has been published about the impact of climate change on agriculture has been based on climate models from the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), or even earlier. The research in this chapter is based on climate models from the IPCC’s Fifth Assessment Report (AR5), which was published beginning in 2013 and provides the most accurate, up-to-date predictions available. The analysis presented in this chapter is based on the regional divisions presented in Figure 9.1 The main land cover in each (approximately) 500-metre grid-cell is presented in Figure 9.2. The predominant land cover is evergreen broadleaf forest, with a significant portion of the country under a mosaic of cropland and natural vegetation. Comparing the elevation and land cover maps (Figures 9.2 and 9.3), it is evident that many of the forested areas are mountainous, whereas many of the agricultural areas are in the flatter lowlands. Parks, reserves, and other areas with protected status (Figure 9.4) to some extent help explain areas that restrict agricultural use and expansion. Results from the biophysical models from this chapter are used as input data to inform the models of both Chapter 10 and Chapter 11. Chapter 10 takes the direct climate impact on productivity from this chapter and from similar modelling efforts using one of the same biophysical models, and uses them as inputs to an economic model which computes the fuller impact of climate change on production, once changes in technological growth, on the supply side, and changes in GDP and population, on the demand side, are used to calculate equilibrium global prices. In finding the equilibrium, the model considers how consumers might change their demand for the type of food they eat (and give to livestock and use for energy) in response to prices. It also considers how farmers optimize their inputs and their crop choices in response to the same prices.

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FIGURE 9.1 Regional Divisions Underlying the Analysis

Notes: The Autonomous Region in Muslim Mindanao (ARMM) and the Cordillera Administrative Region (CAR) are treated separately in the analysis presented, but they are also included within the Luzon and Mindanao regions, respectively. Source: Constructed by authors based on GADM, GADM database of Global Administrative Areas, Version 1.0, 2010 (accessed 11 March 2010).

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Timothy S. Thomas, Vijay Nazareth, and Renato A. Folledo, Jr. FIGURE 9.2 Land Cover, 2009

Source: Mark A. Friedl, Damien Sulla-Menashe, Bin Tan, et al., “MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of New Datasets”, Remote Sensing of Environment 114 (2010): 168–82.

Chapter 11 takes inputs from both this chapter and Chapter 10 to consider an even richer model, in which labor shifts between agriculture and other sectors in response to prices. Each model makes its own contribution to understanding how people will be affected by climate change.

CURRENT CLIMATE The climate during the 1950–2000 period was used as the baseline from which to project changes during 2000–50. The wettest parts of the country

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FIGURE 9.3 Elevation

Note: Elevation is measured in meters above sea level. Source: GLOBE Task Team and others, including David A. Hastings, Paula K. Dunbar, Gerald M. Elphingstone, et al., “The Global Land One–kilometer Base Elevation (GLOBE) Digital Elevation Model”, Version 1.0. National Oceanic and Atmospheric Administration, National Geophysical Data Center, 1999 (accessed 28 January 2005).

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Timothy S. Thomas, Vijay Nazareth, and Renato A. Folledo, Jr. FIGURE 9.4 Protected Areas

Source: IUCN (International Union for the Conservation of Nature) and UNEP-WCMC (United Nations Environment - World Conservation Monitoring Center), The World Database on Protected Areas, Cambridge, UK: UNEP-WCMC, 2014 (accessed 1 June 2014).

appear to be in eastern Mindanao, although high rainfall is also found in eastern Visayas and in the mountains where the Cordillera Administrative Region (CAR) is located (Figure 9.5; Table 9.1). The main agricultural areas of Luzon are among the nation’s driest but still have considerable rainfall levels of 1,400 to 1,900 millimetres per year. While it is not universally true that the driest parts of the other major regions (Visayas and Mindanao) are the most densely cultivated, by Philippine standards relatively low rainfall levels are generally preferred for agriculture, although these levels

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FIGURE 9.5 Mean Yearly Rainfall, 1950–2000

Note: Rainfall is measured in millimetres. Source: Robert J. Hijmans, Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis, “Very High Resolution Interpolated Climate Surfaces for Global Land Areas”, International Journal of Climatology 25 (2005): 1965–78.

would be considered high by many of the world’s countries (Figure 9.6 and Table 9.2). The general distribution of rainfall in the wettest three consecutive months (calculated at each pixel, so the actual three-month period varies) follows a similar geographic distribution as that of yearly rainfall. CAR

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TABLE 9.1 Yearly Rainfall by Region and Percentile, 1950–2000 Yearly Rainfall (millimetres) Region 1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

5th 25th 50th 75th 95th percentile percentile percentile percentile percentile 1,751 1,737 1,855 1,707 1,667 1,636 1,785 1,719

2,060 2,039 2,246 2,133 2,129 2,182 1,979 2,096

2,465 2,432 2,698 2,431 2,585 2,616 2,290 2,491

2,897 2,862 3,059 2,975 2,863 2,904 2,717 2,892

3,367 3,320 3,545 3,466 4,024 4,080 2,923 3,561

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Data were aggregated to five arc minutes. Source: Calculated by the authors based on data from Robert J. Hijmans, Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis, “Very High Resolution Interpolated Climate Surfaces for Global Land Areas”, International Journal of Climatology 25 (2005): 1965–78.

recorded the highest median rainfall in the wettest three months, followed by Luzon (excluding CAR). Rainfall in Mindanao is generally lower than in the other areas during the wettest three months, yet its easternmost area has very high rainfall levels in the wettest three months. Rainfall in the driest three consecutive months of the year follows strikingly different patterns (Figure 9.7; Table 9.3). While Mindanao clearly gets rainfall throughout the year, most of Luzon has a distinct dry season. Visayas falls between the two extremes. This general trend towards a distinct dry period is most pronounced on the westward part of each grouping, with the easternmost part experiencing a fairly high level of rainfall, similar to that noted for most of Mindanao. The mean daily maximum temperature for the warmest month of the year is shown in Figure 9.8, with the companion data aggregated in Table 9.4. As expected, higher elevations are much cooler than lower elevations, but the distribution differs little across the major groupings or by north-south or east-west directions. It should be noted that high elevations have cold enough temperatures to preclude the planting of many annual crops.

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FIGURE 9.6 Rainfall in the Wettest Three Months, 1950–2000

Source: Robert J. Hijmans, Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis, “Very High Resolution Interpolated Climate Surfaces for Global Land Areas”, International Journal of Climatology 25 (2005): 1965–78.

FUTURE CLIMATE General circulation models (GCMs), often referred to as “climate models”, are developed by climate scientists to determine how a climate might change in response to greenhouse gas (GHG) accumulation in the upper atmosphere. The IPCC has a process by which teams submit models for use in their assessment reports. AR4 included twenty-four models, whereas

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TABLE 9.2 Rainfall in the Wettest Three Months by Region and Percentile, 1950–2000 Rainfall in the Wettest Three Months (millimetres) Region 1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

5th 25th 50th 75th 95th percentile percentile percentile percentile percentile 692 684 762 611 528 528 538 616

917 916 935 769 697 718 660 806

1,134 1,123 1,209 1,950 1,883 1,888 1,783 1,982

1,382 1,382 1,384 1,185 1,983 1,999 1,947 1,276

1,885 1,925 1,614 1,469 1,752 1,784 1,008 1,739

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Data were aggregated to five arc minutes. Source: Calculated by the authors based on data from Robert J. Hijmans, Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis, “Very High Resolution Interpolated Climate Surfaces for Global Land Areas”, International Journal of Climatology 25 (2005): 1965–78.

AR5 included sixty-one models. The analysis presented in this chapter is based on four of the GCMs developed for the IPCC’s AR5 report: • GFDL-ESM2M, which was produced by the National Oceanographic and Atmosphere Administration General Fluid Dynamics Laboratory (GFDL) (Dunne et al. 2012, 2013); • HadGEM2-ES, the Hadley Centre Global Environmental Model (HadGEM), from the Met Office Hadley Centre (Collins et al. 2011; Martin et al. 2011); • IPSL-CM5A-LR, generated by Institut Pierre-Simon Laplace (IPSL) (Dufresne et al. 2013); and • MIROC-ESM-CHEM (MIROC), from the Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (University of Tokyo), and National Institute for Environmental Studies (Sakamoto et al. 2012). In IPCC’s AR5, each GCM was used with five different representative concentration pathways (RCPs), which provide modellers with the pattern

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FIGURE 9.7 Rainfall in the Driest Three Months, 1950–2000

Source: Robert J. Hijmans, Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis, “Very High Resolution Interpolated Climate Surfaces for Global Land Areas”, International Journal of Climatology 25 (2005): 1965–78.

of emissions and accumulation of GHGs (Stocker et al. 2014). Among the RCPs, one is historical (no change), and the remaining four reveal the amount of “radiative forcing” they yield by 2100 (that is, the ability of the atmosphere to retain heat instead of radiating it back to space). RCP2.6, for example, has around 2.6 watts per metre squared in 2100. Of the remaining RCPs (RCP4.5, RCP6.0, and RCP8.5), RCP8.5 represents the

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TABLE 9.3 Rainfall in the Driest Three Months by Region and Percentile, 1950–2000 Rainfall in the Driest Three Months (millimetres) Region 1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

5th 25th 50th 75th 95th percentile percentile percentile percentile percentile 127 127 132 110 235 229 257 132

160 155 106 193 300 303 284 129

135 130 156 251 374 386 315 256

244 248 226 444 473 492 388 383

410 416 326 571 619 624 422 562

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Data were aggregated to five arc minutes. Source: Calculated by the authors based on data from Robert J. Hijmans, Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis, “Very High Resolution Interpolated Climate Surfaces for Global Land Areas”, International Journal of Climatology 25 (2005): 1965–78.

highest amount of GHG accumulation modeled; it is also the one most used by researchers. Changes in yearly precipitation between the 1950–2000 period and 2050 under four GCMs and based on the assumption of RCP8.5 are shown in Figure 9.9 and quantified in Table 9.5. All of the models indicate that the Philippines will generally receive more rainfall, with the median increase averaged across the cells being 256 millimetres. While the averages from the climate models differ little for the country as a whole, there are significant regional differences. The most obvious of these are the significantly drier portion of CAR and western Luzon in the GFDL climate model, and the significantly drier portion of Mindanao (most of central and south) and ARMM in the HadGEM climate model (which also has a modestly drier portion in western Luzon). It should be noted that the significantly drier areas are likely to influence crop model results in their respective regions. What is not as easy to distinguish from the maps but can be discovered in the tables is that the HadGEM climate model projects much higher rainfall for Visayas — on average, an increase of more than 500 millimetres. This trend is supported in all of the models, but especially by GFDL. Furthermore,

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FIGURE 9.8 Mean Daily Maximum Temperature in the Warmest Month, 1950–2000

Source: Robert J. Hijmans, Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis, “Very High Resolution Interpolated Climate Surfaces for Global Land Areas”, International Journal of Climatology 25 (2005): 1965–78.

the general trend is for much higher rainfall in the already wet eastern (especially coastal) areas, which holds for all four GCMs. The projected changes in the mean daily maximum temperature for the warmest month of the year are shown in Figure 9.10 and aggregated in Table 9.6. The HadGEM climate model predicts the largest temperature changes of the four models reasonably consistently across the country, although northern Luzon has noticeably higher temperature changes than

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TABLE 9.4 Mean Daily Maximum Temperature in the Warmest Month by Region, 1950–2000 Mean Daily Maximum Temperature (°C) Region 1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

5th 25th 50th 75th 95th percentile percentile percentile percentile percentile 25.3 26.5 21.4 27.9 25.5 25.6 25.2 25.7

28.7 29.2 24.5 29.5 28.4 28.4 28.1 28.9

30.2 30.3 27.6 30.2 30.0 30.0 30.5 30.1

30.7 30.8 29.9 30.6 30.8 30.7 31.5 30.7

31.5 31.5 30.8 31.3 31.8 31.7 32.1 31.5

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Data were aggregated to five arc minutes. Source: Calculated by the authors based on data from Robert J. Hijmans, Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis, “Very High Resolution Interpolated Climate Surfaces for Global Land Areas”, International Journal of Climatology 25 (2005): 1965–78.

elsewhere. Note also that there is an area of relatively low temperature increase in Central Luzon in the MIROC model. Overall, the temperature increases in the MIROC and GFDL climate models are modest; the IPSL increase could result in lower crop yields than in the other models due to heat stress. A comparison of climate indicators from the IPCC’s AR5 analysis show changes in 2050 from the 1950–2000 baseline period, assuming projected emissions under RCP8.5 (Table 9.7). This repeats the yearly precipitation and daily maximum temperature for the warmest month from the previous two tables, and adds values for rainfall during the wettest and driest three months. Overall, the HadGEM model suggests the highest yearly rainfall levels, the highest wet-season rainfall, the least increase in dry-season rainfall, and the hottest temperature for the hottest month. The other three GCMs are very similar to each other across all four indicators. For wet-season rainfall, the IPSL and MIROC results are very close, whereas the GFDL results fall fairly evenly between IPSL and MIROC on the low end, and HadGEM on the high end. Note that rainfall levels increase for all four GCMs in both the wet and the dry seasons.

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FIGURE 9.9 Projected Changes in Mean Yearly Precipitation from Four AR5 General Circulation Models, 2000–50 a. General Fluid Dynamics Laboratory (GFDL)

b. Hadley Centre Global Environmental Model (HadGEM)

c. Institut Pierre-Simon Laplace (ISPL)

d. Model for Interdisciplinary Research on Climate (MIROC)

Notes: All results are from the Coupled Model Intercomparison Project, and are based on the representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). Source: Christoph Müller and Richard Robertson, “Projecting Future Crop Productivity for Global Economic Modeling”, Agricultural Economics 45 (2014): 37–50.

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Timothy S. Thomas, Vijay Nazareth, and Renato A. Folledo, Jr. TABLE 9.5 Projected Change in Yearly Rainfall from Four General Circulation Models, 2000–50 Projected Change in Rainfall (millimetres)

Region 1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

GFDL

HadGEM

IPSL

MIROC

167 172 133 309 329 346 210 247

252 253 248 531 240 262 286 298

235 240 204 297 200 203 181 235

250 245 280 261 290 295 254 265

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region; GFDL = General Fluid Dynamics Laboratory; HadGEM = Hadley Centre Global Environmental Model; IPSL = Institut Pierre-Simon Laplace; MIROC = Model for Interdisciplinary Research on Climate. Data were aggregated to five arc minutes. Source: Calculated by the authors based on data from Christoph Müller and Richard Robertson, “Projecting Future Crop Productivity for Global Economic Modeling”, Agricultural Economics 45 (2014): 37–50.

Table 9.8 presents the same information as Table 9.7, but assuming an A1B scenario from IPCC’s AR4 analysis (which is similar to the AR5 pathway). Compared with the GCMs used in IPCC’s AR5, the A1B results are more mixed. Two of the models actually predict lower yearly rainfall: the Centre National de Recherches Météorologiques (CNRM) model predicts a small reduction, whereas the Model for Interdisciplinary Research on Climate (MIROC) predicts a large reduction. The European Centre Hamburg (ECHAM) model, on the other hand, predicts a large increase, although not as large as the HadGEM results from IPCC’s AR5. The Commonwealth Scientific and Industrial Research Organisation (CSIRO) model shows only a modest rise in temperature, while the other three models predict an approximately 70-per cent higher rise in temperature. Of the four AR5 models considered, the three with the lowest temperature increases have similar temperature projections to the three of the four AR4 models that have the highest temperature increases. However, the fourth AR5 model predicts a much hotter climate for the Philippines, whereas the fourth AR4 model predicts one that is only modestly warmer than the present climate. IPCC AR4 models shown in Table 9.8 generally predict an increase in rainfall in the wet season, although MIROC suggests

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FIGURE 9.10 Projected Change in Mean Daily Maximum Temperature in the Warmest Month from Four AR5 General Circulation Models, 2000–50 a. General Fluid Dynamics Laboratory (GFDL)

b. Hadley Centre Global Environmental Model (HadGEM)

c. Institut Pierre-Simon Laplace (ISPL)

d. Model for Interdisciplinary Research on Climate (MIROC)

Notes: All are from the CMIP project and are based on the representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). Source: Christoph Müller and Richard Robertson, “Projecting Future Crop Productivity for Global Economic Modeling”, Agricultural Economics 45 (2014): 37–50.

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TABLE 9.6 Projected Change in Mean Daily Maximum Temperature in the Warmest Month from Four AR5 General Circulation Models, 2000–50 Temperature (°C) Region 1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

GFDL

HadGEM

IPSL

MIROC

1.96 1.95 1.98 1.77 1.46 1.47 1.43 1.76

2.58 2.53 2.90 2.31 2.30 2.30 2.31 2.44

1.86 1.86 1.84 1.95 2.10 2.10 2.13 1.96

1.43 1.45 1.25 2.16 1.75 1.77 1.57 1.67

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region; GFDL = General Fluid Dynamics Laboratory; HadGEM = Hadley Centre Global Environmental Model; IPSL = Institut Pierre-Simon Laplace; MIROC = Model for Interdisciplinary Research on Climate. Data were aggregated to five arc minutes. Source: Calculated by the authors based on data from Christoph Müller and Richard Robertson, “Projecting Future Crop Productivity for Global Economic Modeling”, Agricultural Economics 45 (2014): 37–50.

TABLE 9.7 Comparison of Rainfall and Temperature Changes from Four AR5 General Circulation Models, 2000–50 Rainfall or temperature change

GFDL HadGEM IPSL MIROC

Change in yearly precipitation (mm) Change in precipitation for wettest three months (mm) Change in precipitation for driest three months (mm) Change in maximum daily temp­erature for warmest month (°C)

.247 .142

.298 .176

.235 .105

.265 .102

.127 1.76

.117 2.44

.131 1.96

.136 1.67

Notes: This scenario is based on the representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). GFDL = General Fluid Dynamics Laboratory; HadGEM = Hadley Centre Global Environmental Model; IPSL = Institut Pierre-Simon Laplace; MIROC = Model for Interdisciplinary Research on Climate. Source: Calculated by the authors based on data from Christoph Müller and Richard Robertson, “Projecting Future Crop Productivity for Global Economic Modeling”, Agricultural Economics 45 (2014): 37–50.

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TABLE 9.8 Comparison of Rainfall and Temperature Changes from Four AR4 General Circulation Models, 2000–50 Rainfall or temperature change Change in yearly precipitation (mm) Change in precipitation for wettest three months (mm) Change in precipitation for driest three months (mm) Change in maximum daily temp­erature for warmest month (°C)

CNRM CSIRO ECHAM MIROC –8 25

.196 .133

.227 .182

–194 –20

–39 1.70

.117 0.99

.131 1.63

–77 1.85

Notes: CNRM = Centre National de Recherches Météorologiques; CSIRO = Commonwealth Scientific and Industrial Research; ECHAM = European Centre Hamburg; MIROC = Model for Interdisciplinary Research on Climate. Source: Calculated by the authors based on data from Robert J. Hijmans, Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis, “Very High Resolution Interpolated Climate Surfaces for Global Land Areas”, International Journal of Climatology 25 (2005): 1965–78; and from Peter G. Jones, Philip K. Thornton, and Jens Heinke, “Generating Characteristic Daily Weather Data Using Downscaled Climate Model Data from the IPCC’s Fourth Assessment”, Project report for the International Livestock Research Institute (Geneva: Intergovernmental Panel on Climate Change, 2009).

a small decrease. AR4 also shows an average decrease in rainfall in the dry season, although CSIRO and ECHAM show small increases. As previously noted, the four AR5 models used in this chapter report increases in all three rainfall categories (that is, yearly, in the wettest three months, and in the driest three months).

THE IMPACT OF CLIMATE CHANGE ON AGRICULTURE, FORESTS, AND LAND USE While cropland is distributed throughout the country, concentrations are especially high in Central Luzon and the Cagayan Valley, although at lower densities in the most mountainous regions (Figure 9.11). In determining the relative importance of different crops, it is always difficult to know which metric to use. Candidates include harvest area, value of production, and export value. The logic in using harvested area is that whatever farmers value enough to devote their land to must be the item of greatest importance to them, and in a similar sense is perhaps of greatest agricultural value to the nation (Table 9.9). The five leading crops by harvested area are

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Timothy S. Thomas, Vijay Nazareth, and Renato A. Folledo, Jr. FIGURE 9.11 Share of Cropland, 2010

Source: John Latham, Renato Cumani, Iiaria Rosati, and Mario Bloise, Global Land Cover SHARE (GLC–SHARE), 2014 (accessed 3 March 2014).

rice, coconuts, maize, vegetables, and bananas. Apart from maize, all of these increased in area harvested between 2001 and 2012, with vegetables and bananas expanding more rapidly than rice and coconuts. Notably, harvested area of natural rubber almost doubled in that short period. While maize area did not expand during the period, its productivity did, with yields increasing by a little more than 50 per cent. The increase in banana

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11 12 13 14 15 16 17 18 19 10

4,526,921 3,570,577 2,545,825 2,586,933 2,451,249 2,409,292 2,377,300 2,218,694 2,196,755 2,158,840

12.2 13.6 10.0 22.3 18.6 12.0 14.4 12.2 46.4 90.1

16,829,600 15,539,093 16,918,283 14,880,734 19,164,127 29,333,334 13,313,867 12,178,094 29,809,095 29,145,043

36.0 23.6 52.4 18.8 88.8 16.9 11.3 23.1 –6.5 89.7

13.72 14.35 12.72 18.32 20.31 71.67 18.78 19.96 14.11 10.91

Yield, 2010–12 (Tons per Hectare)

21.2 8.7 52.8 –2.8 59.2 4.3 –2.8 20.4 –36.2 0.0

Percentage Change in Yield (%)

Notes: Percentage change was calculated by comparing the three-year average for 1999–2001 with the three-year average for 2010–12. Source: Calculated by the authors based on data from FAO (Food and Agriculture Organization of the United Nations), FAOSTAT database, 2014 (accessed 17 February 2014).

Rice (paddy) Coconuts Maize Other vegetables Bananas Sugarcane Other tropical fruit Cassava Mangoes, mangosteens, and guavas Rubber (natural)

Commodity

Percentage Percentage Change Tons Harvested Change in in Tons Hectares, Harvested Produced, Rank Produced, 2010–12 Hectares, 2010–12 by Area Harvested (Mean) 2001–12 (%) (Mean) 2001–12 (%)

TABLE 9.9 Harvested Area, Production, and Yield of Leading Agricultural Commodities, 2010–12, and Percent Change, 1999–2001

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productivity was even higher, at just under 60 per cent. Rice productivity rose at just over 20 per cent, whereas coconuts expanded more modestly, by just under 9 per cent. Trends in harvested area for the five leading crops that were not in aggregated categories were relatively constant during 1990–2012, showing modest overall growth rates (Figure 9.12). Yield trends also showed modest increases over the same period, although maize yields more than doubled, and banana yields increased by around 80 per cent during 1990–2012 (Figure 9.13).

CROP MODELLING RESULTS USING CLIMATE MODELS Overview of Modelling Climate Impacts on Agriculture Using DSSAT Crop Modelling Software The Decision Support System for Agrotechnology Transfer (DSSAT) is crop simulation software package consisting of multiple mathematical models (Jones et al. 2003). The developers keep adding to the number of crops that can be modelled (twenty-eight as of 2016). Some crops can be simulated in more than one model, providing complementary perspectives. The analysis in this chapter utilized DSSAT for rice, maize, and sugarcane (the leading crops included in DSSAT). The models “grow” the crop in daily time increments using DSSAT’s weather simulator and the weather data the GCMs provide. In the analysis, the weather was simulated 100 different times so that a meaningful average impact of climate could be calculated. The soil data used were adapted by Koo and Dimes (2010) from the Harmonized World Soil Data Base by Batjes et al. (2009). They were then further simplified into twenty-seven types, each with high, medium, or low soil organic carbon; deep, medium, or shallow rooting depth; and a major component of sand, loam, or clay. When grid-cells had more than one soil type represented, the dominant type was used. Since the climate data were available at the five arc-minute level, DSSAT software was used to predict yields in each five arc-minute grid-cell (approximately 9–10 kilometres) — that is, yields were computed in each cell, considering the specific impact of soil and climate on production. Procedures were also developed to overcome some limitations related to crop varieties. Given

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Source: Authors, based on data from FAO (Food and Agriculture Organization of the United Nations), FAOSTAT database, 2014 (accessed 17 February 2014).

FIGURE 9.12 Harvested Area Trends, 1990–2012

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Source: Authors, based on data from FAO (Food and Agriculture Organization of the United Nations), FAOSTAT database, 2014 (accessed 17 February 2014).

FIGURE 9.13 Yield Trends, 1990–2012

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that many different varieties are used throughout the Philippines, as well as multiple varieties in most locations, it would take extensive surveying of farmers to find out which varieties they actually use. Moreover, since none of the local varieties, nor a large share of the commercial varieties, are preprogrammed in DSSAT, computing the parameters of each variety would be impractical. Instead, experts were consulted as to which varieties already programmed in DSSAT were appropriate for Philippine climates. For example, ten varieties of rice were tested, and the assumption was made that whatever variety was best in a given location for the baseline climate during 1950–2000 would be the variety used as the one grown in that location. A similar exercise was used to determine the planting month, allowing DSSAT to range over a number of possible months; whichever produced the highest yield was chosen as the planting month in that particular location. The models solved for the yield at each grid-cell based on the optimal cultivar and optimal month, enabling the production of the detailed maps of predicted potential yields presented in this chapter. For a different perspective, results are also summarized in tables; however, to make sense, the data had to be aggregated into meaningful geographic areas (that is, regions or groupings of regions). This presented a certain dilemma, given the various methods of aggregating yields. The ideal way is to use weighted equivalents of harvested areas averaged across the areas where the crop is (or will be) grown. This was done by calculating the total national (or regional) production and dividing the results by the corresponding area harvested. Since MapSPAM datasets (You and Wood 2006; You, Wood, and Wood–Sichra 2006, 2009; and You, Wood, Wood–Sichra, and Wu 2014) provide the modeled harvested area for 2005 in each grid-cell, they were used to determine the weights.

Overview of Modelling Climate Impacts on Bananas and Coconuts Using WaNuLCAS Crop Modelling Software Coconuts and, to a slightly lesser extent, bananas are very important crops in the Philippines, but they are not included in the DSSAT software, so the Water, Nutrient and Light Capture in Agroforestry Systems (WaNuLCAS) crop modelling package was used to evaluate the impacts of climate change on these two crops. The WaNuLCAS program, which was developed for the World Agroforestry Centre (van Noordwijk, Lusiana, and Khasanah 2004)

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runs under STELLA modelling software. Just as with DSSAT, WaNuLCAS “grows” the plant each day, accounting for water, nutrients, and light, as well as soil properties. What is more challenging with WaNuLCAS is dealing with perennials, which are grown over multiple years and require a much longer stream of weather data for the analysis, allowing a longer period of time for things to go wrong. WaNuLCAS does not take into consideration the direct impact of temperature on plants — that is, the model is unable to account for heat stress. Rather, temperature affects the growth of the plant through increased evapotranspiration, meaning the main climate variable it accounts for is rainfall, with temperature playing only a secondary role. For the purpose of the model simulation, bananas were grown over a seven-year cycle across twenty-four locations, and coconuts were grown continuously after planting across twentythree locations. Ultimately, areas with extended periods of low yields were dropped from the analysis, leaving sixteen locations for bananas and seventeen for coconuts (on a real farm, farmers would likely have replanted in such locations). Regressions were run using the data in combination with baseline (2000) climate values for each of the four GCMs in 2050. Regression results were then used to project yields for each GCM over the entire surface of 3,473 locations in the Philippines. The analysis was only run with water stress, rather than both water and nutrient stress. Statistically, rainfall in the driest three months seemed to explain most of the yield variation for bananas, but for coconuts the key factors determining yields were rainfall in the driest and wettest three months, as well as mean maximum daily temperature for the warmest month.

Rice While irrigated rice is grown throughout the Philippines, it is obviously limited to areas where irrigation is technically feasible (Figure 9.14). There are large, very dense concentrations of irrigated rice in Central Luzon and Cagayan Valley, and smaller dense areas in southern Mindanao (SOCCSKSARGEN) and in eastern Visayas. The production of rain-fed rice is more widely distributed throughout the country because it is not constrained by the need for infrastructure (Figure 9.15). The highest concentration of rain-fed production is in the western portion of Visayas (Figure 9.15a). Compared with irrigated rice, much greater variation exists

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b. Productivity

Notes: Intensity is based on hectares per pixel, and a pixel at the equator represents approximately 8,500 hectares. Productivity is based on kilograms per hectare. Sources: Calculated by authors from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood–Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

a. Intensity

FIGURE 9.14 Intensity and Productivity of Irrigated Rice, Circa 2005

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b. Productivity

Notes: Intensity is based on hectares per pixel, and a pixel at the equator represents approximately 8,500 hectares. Productivity is based on kilograms per hectare. Source: Calculated by authors from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood–Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

a. Intensity

FIGURE 9.15 Intensity and Productivity of Rain-Fed Rice, Circa 2005

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in rain-fed rice yields, from the 500–1,000 kilograms per hectare (kg per ha) range, to the 2–3 metric ton range (Figure 9.15b). Looking at results aggregated by region, rice production in Luzon is dominated by irrigated production; Visayas is dominated by rain-fed production; and Mindanao is relatively even, with 57 per cent of the rice production being rain-fed (Table 9.10). For much of the Philippines, climate change will have very little impact on the productivity of irrigated rice (Figure 9.16a).1 The analysis was run with a baseline of low fertilizer use, which was assumed to be 30 kg of nitrogen per ha. The change from adjusting planting month is shown in Figure 9.16b. Some readers may object to the inclusion of planting month as an adaptation mechanism, but this is an important variable not always taken into consideration in studies on the impact of climate change agriculture. Farmers will not immediately perceive the need to change their historic planting patterns, so this measure can be seen as a gain that will come with some delay — that is, once it is either discovered by farmers or is communicated to them through extension efforts based on the results of agricultural research. Looking at the added yield from changing cultivars, it is important to understand what this truly represents. It is the value of changing to a new cultivar that currently exists, not to TABLE 9.10 Harvested Hectares of Irrigated and Rain-Fed Rice by Region, Circa 2005 Region 1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

Harvested Hectares of Irrigated Rice

Harvested Hectares of Rain-Fed Rice

1,418,481 1,370,645 47,836 165,923 427,987 367,206 60,781 2,012,391

740,577 683,093 57,484 716,680 562,828 445,720 117,108 2,020,086

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Sources: Calculated by authors from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

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Timothy S. Thomas, Vijay Nazareth, and Renato A. Folledo, Jr. FIGURE 9.16 Median Percentage Change in Irrigated Rice Yields under Climate Change, and the Projected Effect of Adaptation Measures under a Baseline Scenario of Low Fertilizer Use, 2000–50

a. Yield change without adaptation

b. Yield change from adjusting the planting month

c. Yield change from adjusting the cultivar

d. Yield change from adjusting the planting month and cultivar

Notes: This scenario is based on the representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). Off-season rice restricted the planting date to five, six, or seven months beyond the planting date for rice. Source: Authors.

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one that may exist in the future. It will be extremely important to develop new, higher yielding cultivars that are resistant to critical abiotic stresses from climate change, but these results are simply a measure of what could result from farmers experimenting with different existing cultivars, or from agricultural research informing farmers and extension agents of the best cultivars to use under a changing climate (Figure 9.16c). The combined effect of changing both the cultivar and planting month is not always the simple product of each effect; synergies often occur. Although the median change is relatively modest, in some areas the impact of combining these two simple measures is well over 20 per cent (Figure 9.16d). As noted in the discussion of the results presented in Figure 9.16, the impact of climate change on irrigated rice is virtually nonexistent; yet, surprisingly, yield gains are possible for irrigated rice under climate change (Table 9.11). Since these results exclude the development of new varieties, the point being made is that farmers can reoptimize their production techniques in response to the changing climate. Yield increases averaging 2.6 per cent are possible in the Philippines as a whole, and slightly larger gains are possible in Luzon. Furthermore, as would be expected, large yield gains are possible through increased use of fertilizer. It is important to note, however, that the cost of fertilizer to the farmer has not been taken into consideration, so that it may not be economically viable to increase yields through increased fertilizer use. Interestingly, when increased fertilizer use is coupled with changing the variety cultivated — and, as necessary, the planting month — yields are dramatically larger. Put simply, when farmers are planning to use more fertilizer, they should choose a variety that is more responsive to higher fertilizer levels. Surprisingly, the patterns of impact on irrigated rice with high fertilizer use are similar to those for irrigated rice with low fertilizer use, although the negative climate impact in eastern Mindanao is not as pronounced (Figure 9.17). It is more difficult to visually compare adaptation effects across low and high fertilizer-use scenarios, so in this case the tabulated aggregate results are preferable (Table 9.12). National level results are very similar for both the low and high fertilizer use. Regionally, a key difference is that Mindanao shows a larger positive yield effect from changing varieties compared with the other regions, but when both the variety and planting month are changed, Luzon seems to produce the largest synergies. For rain-fed rice with low fertilizer use, results indicate the impact in Luzon to be highly negative in many locations, sometimes exceeding 20 per cent

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–0.2 –0.2 –0.2 –1.1 –0.8 –0.8 –0.9 –0.4

1.3 1.3 1.3 1.9 1.0 1.0 1.2 1.2

0.7 0.8 0.2 0.3 0.8 0.7 1.6 0.7

Change in Variety (%) 3.0 3.0 3.2 0.1 2.4 2.2 3.7 2.6

Change in Planting Month and Variety (%)

Improvement from Adaptation Measure Change in Planting Month (%) 192.0 193.2 171.7 196.4 105.8 102.5 122.1 198.6

140.9 142.1 119.4 142.1 147.9 143.7 169.2 144.6

Combined with Change in Planting On Its Own Date and Variety (%) (%)

Improvement from Fertilizer Use

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. This scenario is based on the assumption of representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). Fertilizer impacts are based on an increase from 30 to 90 kilograms of nitrogen per hectare. Optimal planting month for 2000 was determined by looking at the month with the highest mean yields. Sources: Calculated by authors at the grid-cell level from their own analysis, then aggregated using weights for irrigated rice from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

Region

Direct Impact of Climate Change (%)

TABLE 9.11 Projected Climate Impacts on Irrigated Rice Yields under a Baseline Scenario of Low Fertilizer Use, 2000–50

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FIGURE 9.17 Median Percentage Change in Irrigated Rice Yields under Climate Change, and the Projected Effect of Adaptation Measures under a Baseline Scenario of High Fertilizer Use, 2010–50 a. Yield change without adaptation

b. Yield change from adjusting the planting month

c. Yield change from adjusting the cultivar

d. Yield change from adjusting the planting month and cultivar

Notes: This scenario is based on the representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). Off-season rice restricted the planting date to five, six, or seven months past the planting date for rice. Source: Authors.

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TABLE 9.12 Projected Climate Impacts on Irrigated Rice Yields under a Baseline Scenario of High Fertilizer Use, 2000–50 Improvement from Adaptation Measure

Region

Direct Impact of Climate Change (%)

Change in Planting Month (%)

Change in Variety (%)

Change in Both Planting Month and Variety (%)

–0.1 –0.2 –2.0 –0.6 –0.7 –1.0 –1.5 –0.0

1.7 1.7 1.8 1.6 1.4 1.4 1.0 1.6

0.5 0.5 0.2 0.7 1.6 1.6 1.2 0.8

5.0 5.1 2.6 0.0 3.2 3.2 2.7 4.2

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Fertilizer impacts are based on an increase from 30 to 90 kilograms of nitrogen per hectare. Optimal planting month for 2000 was determined by looking at the month with the highest mean yields. Sources: Calculated by authors at the grid-cell level from their own analysis, then aggregated using weights for irrigated rice from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

(Figure 9.18). Whereas yield changes in Cagayan Valley are minimal and mostly positive, Central Luzon and Mimaropa experience large negative impacts. In contrast, Mindanao experiences yield increases, some areas achieving up to 20 per cent higher productivity. Visayas experiences mostly neutral climate impacts on rain-fed rice, with areas of increase being offset by areas of decrease. The tabulated data indicate that losses of around 7.1 per cent without adaptation or technological improvement (Table 9.13). CAR experiences a smaller loss of 6.0 per cent, Mindanao experiences a 5.0 per cent rise, and ARMM gains by 5.9 per cent. Visayas is mostly neutral (a 0.1-per cent change). Changing the planting month will be a critical adaptation option in Luzon, whereas changing the variety would be more important in Mindanao. The option of changing both the cultivar and the planting month could boost the country’s overall rain-fed rice yields by 5.1 per

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FIGURE 9.18 Median Percentage Change in Rain-Fed Rice Yields under Climate Change, and the Projected Effect of Adaptation Measures under a Baseline Scenario of Low Fertilizer Use, 2000–50 a. Yield change without adaptation

b. Yield change from adjusting the planting month

c. Yield change from adjusting the cultivar

d. Yield change from adjusting the planting month and cultivar

Note: This scenario is based on the representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). Source: Authors.

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–7.1 –7.2 –6.0 –0.1 –5.0 –4.8 –5.9 –1.2

5.7 5.7 5.1 3.4 1.8 1.7 2.1 3.7

Change in Planting Month (%) 1.3 1.4 0.6 2.5 2.5 2.9 1.4 2.1

Change in Variety (%) 7.3 7.4 5.9 6.3 5.1 5.3 4.5 6.3

Change in Both Planting Month and Variety (%) 62.0 61.1 73.4 57.9 43.2 43.5 42.2 55.0

73.6 73.0 80.5 71.0 49.6 50.8 45.0 65.6

Combined with Change in Planting On Its Own Date and Variety (%) (%)

Improvement from Fertilizer Use

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Fertilizer impacts are based on an increase from 30 to 90 kilograms of nitrogen per hectare. Optimal planting month for 2000 was determined by looking at the month with the highest mean yields. Sources: Calculated by authors at the grid-cell level from their own analysis, then aggregated using weights for rain-fed rice from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

Region

Direct Impact of Climate Change (%)

Improvement from Adaptation

TABLE 9.13 Projected Climate Impacts on Rain-Fed Rice Yields under a Baseline Scenario of Low Fertilizer Use, 2000–50

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cent (found by adding the direct effect of climate change to the effect of changing both cultivar and planting month in Table 9.13). The possible gains are around 10 per cent in Mindanao and 6 per cent in Visayas. Comparing increases under rain-fed irrigated rice, it is interesting to note that the potential impact of fertilizer on yields is much more modest for rain-fed rice, and this is especially so when the planting date and cultivar are changed. A similar pattern is evident in comparing the results for rain-fed rice with high and low fertilizer use (Figure 9.19). The changes are still in the same general area as before, with Luzon losing, Mindanao gaining, and Visayas remaining mostly neutral. Losses in Luzon are projected to be more than 7 per cent, but they are balanced by losses of around 4 per cent in Visayas, and only half a per cent in Mindanao (Table 9.14). Incorporating the simple adaptation options, however, rain-fed rice does better in Luzon than in Visayas, and almost as well as in Mindanao. TABLE 9.14 Projected Climate Impacts on Rain-Fed Rice Yields under a Baseline Scenario of High Fertilizer Use, 2000–50 Improvement from Adaptation Measure

Region

Direct Impact Change in of Climate Planting Month Change (%) (%)

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

–7.4 –7.5 –6.7 –4.1 –0.5 –0.4 –0.8 –4.5

4.4 4.5 3.7 3.6 2.8 2.7 3.0 3.7

Change in Variety (%)

Change in Both Planting Month and Variety (%)

0.3 0.3 0.5 0.6 1.8 1.9 1.5 0.8

4.7 4.7 4.3 4.4 5.4 5.4 5.1 4.8

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Fertilizer impacts are based on an increase from 30 to 90 kilograms of nitrogen per hectare. Optimal planting month for 2000 was determined by looking at the month with the highest mean yields. Sources: Calculated by authors at the grid-cell level from their own analysis, then aggregated using weights for rain-fed rice from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

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Timothy S. Thomas, Vijay Nazareth, and Renato A. Folledo, Jr. FIGURE 9.19 Median Percentage Change in Rain-Fed Rice Yields under Climate Change, and the Projected Effect of Adaptation Measures under a Baseline Scenario of High Fertilizer Use, 2010–50

a. Yield change without adaptation

b. Yield change from adjusting the planting month

c. Yield change from adjusting the cultivar

d. Yield change from adjusting the planting month and cultivar

Note: This scenario is based on the representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). Source: Authors.

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Maize Although rain-fed maize is grown throughout the Philippines, the area with the densest maize production is Mindanao (Figure 9.20a). In Luzon, the highest concentration is in Cagayan Valley, whereas the greatest concentration in Visayas is in Central Visayas. Yields vary across the Philippines, ranging from nearly 500 kg per ha to over 3 metric tons per ha (Figure 9.20b). Around 60 per cent of rain-fed maize is produced in Mindanao, of which almost 20 per cent is grown in ARMM (Table 9.15). Luzon accounts for almost a quarter of the area devoted to rain-fed maize, and Visayas accounts for almost a sixth. Climate change is likely to have large, negative, and comparatively universal impacts on rain-fed maize in the Philippines (Figure 9.21a), although some diversity is evident. For example, in north-central Mindanao and extending into ARMM the impact of climate change on rain-fed maize will be negligible. In quantifying the effects, yield losses due to climate change are projected to be around 19 per cent for the Philippines as a whole, averaging slightly higher for Visayas, at around 22 per cent (Table 9.16). Changing cultivars only increases production by an average of about half a per cent (Figure 9.21b). In most places, the optimal cultivar in 2000 will also be the optimal cultivar of 2050, and in the few places where the TABLE 9.15 Harvested Hectares of Rain-Fed Maize by Region, Circa 2005 Region 1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

Harvested Hectares of Rain-Fed Maize 1,597,934 1,555,485 1,142,449 1,381,781 1,462,556 1,200,458 1,262,098 2,442,271

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Sources: Calculated by authors from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

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b. Productivity

Notes: Intensity is based on hectares per pixel, and a pixel at the equator has approximately 8,500 hectares in it. Productivity is based on kilograms per hectares. Sources: Calculated by authors from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

a. Intensity

FIGURE 9.20 Intensity and Productivity of Rain-Fed Maize, Circa 2005

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FIGURE 9.21 Median Percentage Change in Rain-Fed Maize Yields under Climate Change, and the Projected Effect of Adaptation Measures under a Scenario of Low Fertilizer Use, 2000–50 a. Yield change without adaptation

b. Yield change from adjusting the planting month

c. Yield change from adjusting the cultivar

d. Yield change from adjusting the planting month and cultivar

Note: This scenario is based on the representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). Source: Authors.

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–18.8 –18.9 –17.1 –22.4 –19.1 –18.7 –21.4 –19.5

6.4 6.2 9.1 4.8 4.3 4.3 4.6 4.9

Change in Planting Month (%) 0.8 0.7 1.2 0.2 0.4 0.4 0.4 0.5

Change in Variety (%) 7.3 7.1 9.6 5.2 4.9 4.9 4.9 5.5

Change in Planting Month and Variety (%) 48.6 48.3 52.5 50.3 48.2 48.2 48.3 48.6

56.5 55.6 68.1 52.5 49.6 49.8 48.3 51.6

Combined with Change in Planting On Its Own Date and Variety (%) (%)

Improvement from Fertilizer Use

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Fertilizer impacts are based on an increase from 30 to 90 kilograms of nitrogen per hectare. Optimal planting month for 2000 was determined by looking at the month with the highest mean yields. Sources: Calculated by authors at the grid-cell level from their own analysis, then aggregated using weights for rain-fed maize from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

Region

Direct Impact of Climate Change (%)

Improvement from Adaptation

TABLE 9.16 Projected Climate Impacts on Rain-Fed Maize Yields under a Baseline Scenario of Low Fertilizer Use, 2000–50

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cultivar is not already optimal, improvements from changing cultivars will be minor (Figure 9.21c). Luzon benefits most from adaptation: changing the variety, the planting month, and both the variety and planting month all net higher results than in the other two regions (Figure 9.21d). Within Luzon, CAR benefits most from adaptation, with a combined benefit of 9.6 per cent, compared with 7.1 per cent for the rest of Luzon, and 5.5 per cent for the entire country. Nevertheless, even with changing the planting month and cultivar, the direct climate impact on rain-fed maize is still around a 14 per cent reduction in yields, implying that special care will be needed in devising adaptation strategies for maize. It is important to note that the impact of fertilizer on yields is reasonably high where relatively low levels are currently in use. For the purposes of the analysis, low-levels of fertilizer use were assumed to be 30 kg of nitrogen per ha, whereas high levels were assumed to be 90 kg of nitrogen per ha. It can generally be said that for each additional kg of nitrogen per ha applied, yields will increase by 0.8 per cent. Responses are slightly higher in Visayas and CAR, and particularly higher in Luzon when the optimal variety and planting month are used in 2050. This means that if farmers don’t want to change from maize to another crop, and they can afford to apply fertilizer, they can choose to compensate for the adverse impacts of climate change by increasing fertilizer use. It appears that little would be gained by switching to a different cultivar when using higher levels of fertilizer. The impact of climate change on rain-fed maize at high fertilizer levels is similar to that of maize at low fertilizer levels: negative and quantitatively large almost across the entire country (Figure 9.22). Confirming the results from the figure, the direct climate impact on maize at higher levels of fertilizer is likely to be almost a 22-per cent reduction in yield (Table 9.17). This can be partially compensated for by changing planting month and, in a few cases, by changing crop variety, but a yield decline of almost 16 per cent remains. Additional fertilizer could perhaps be used to compensate in cases where the maximum recommended fertilizer levels have not already been reached, but in general this adaptation measure won’t compensate for the yield reduction under this scenario. In addition to considering the implications of the impact of climate change on rain-fed maize, it is important to consider whether results are credible. Losses of around 20 per cent are quite significant, and the fact that they are relatively uniform across the country (with the exception of

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Timothy S. Thomas, Vijay Nazareth, and Renato A. Folledo, Jr. FIGURE 9.22 Median Percentage Change in Rain-Fed Maize Yields under Climate Change, and the Projected Effect of Adaptation Measures under a Scenario of High Fertilizer Use, 2000–50

a. Yield change without adaptation

b. Yield change from adjusting the planting month

c. Yield change from adjusting the cultivar

d. Yield change from adjusting the planting month and cultivar

Notes: This scenario is based on the representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). The stars in the top left map indicate the sights for which more detailed analysis was conducted. Source: Authors.

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TABLE 9.17 Projected Climate Impacts on Rain-Fed Maize Yields under a Baseline Scenario of High Fertilizer Use, 2000–50 Improvement from Adaptation Measure

Region

Direct Impact Change in Change in of Climate Planting Month Variety Change (%) (%) (%)

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

–20.6 –20.7 –18.6 –25.0 –21.2 –20.6 –24.1 –21.6

6.8 6.8 7.2 4.9 4.8 4.5 6.2 5.3

0.4 0.4 0.9 0.2 0.9 0.8 1.2 0.7

Change in Both Planting Month and Variety (%) 7.1 7.0 7.5 5.1 5.8 5.5 7.3 6.0

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Fertilizer impacts are based on an increase from 30 to 90 kilograms of nitrogen per hectare. Optimal planting month for 2000 was determined by looking at the month with the highest mean yields. Sources: Calculated by authors at the grid-cell level from their own analysis, then aggregated using weights for rain-fed maize from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

highlands) — especially in such a climatically diverse country with differing rainfall patterns — makes further investigation prudent. Because elevated areas are cooler and generally have more positive yield effects compared with the lower elevations, the large yield reductions might be driven by temperature changes. With this in mind, two areas were investigated in greater detail (denoted by stars in Figure 9.22a). These locations were chosen because they had some of the densest concentrations of the country’s maize production and because they are geographically distant from each other in diverse climates (Figure 9.20a). The rainfall and temperature distribution for Cagayan Valley is shown in Figure 9.23, under the 2000 baseline climate and under the four GCMs used to project results in 2050. Rainfall peaks in November, and levels are much lower from January through July. The DSSAT analysis indicates that peak production under the 1950–2000 climate would occur if maize were

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Notes: GFDL = General Fluid Dynamics Laboratory; HadGEM = Hadley Centre Global Environmental Model; IPSL = Institut Pierre-Simon Laplace; MIROC = Model for Interdisciplinary Research on Climate. Source: Calculated by the authors based on data from Robert J. Hijmans, Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis, “Very High Resolution Interpolated Climate Surfaces for Global Land Areas”, International Journal of Climatology 25 (2005): 1965–78; and from Peter G. Jones, Philip K. Thornton, and Jens Heinke, “Generating Characteristic Daily Weather Data Using Downscaled Climate Model Data from the IPCC’s Fourth Assessment”, Project report for the International Livestock Research Institute (Geneva: Intergovernmental Panel on Climate Change, 2009).

FIGURE 9.23 Rainfall and Temperature Profiles by Month and Model for a Location in Cagayan Valley, 2000 and 2050

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planted in August. Looking at temperatures for the same time period, daily maximums in August average nearly 32°C, which represents a decline from a peak of near 34°C in May, before a further decline to less than 28°C in December and January. With climate change, however, August temperatures appear to be 1.5°C to 2°C higher, depending on the GCM used. One study (Lobell et al. 2011, p. 42) found that “each degree day spent above 30°C reduced the final yield by 1 per cent under optimal rain-fed conditions, and by 1.7 per cent under drought conditions.” That study’s research supports the idea that the temperatures in this location in Cagayan Valley are already at the threshold of producing heat stress. Climate change will simply kick the area over the edge, adversely affecting maize yields. The “degree day” concept is not well known; it is based on the concept of the number of degrees that are recorded beyond a baseline level. Using the 30°C limit put forward in the Lobell et al. (2011) study, if for example the temperature were 31°C for an entire day, it would represent a one-degree day over 30°C, but if the temperature were 34°C all day, that would represent a four-degree day over 30°C. If, on the other hand, the temperature only reached 31°C for 6 hours but remained at 30°C or below for the rest of the day, that would represent a 0.25-degree day over 30°C, but if it reached 31°C for 12 hours, and remained at or below 30°C for the rest of the day, that would represent a 0.5-degree day over 30°C. So, the longer the time the temperature remains over the baseline level, the higher the number of degree days. Butler and Huybers (2012) suggest that maize adapts locally to high temperatures and that the estimated yield reduction due to a 2°C change might be less than half of the levels expected were this finding not taken into consideration. Furthermore, Davin et al. (2014) suggests that temperatures near the ground are cooler than the general outdoor air temperature when using no-till maize. While they do not examine the yield impacts of lower temperatures in this micro-environment, they imply that yield-damaging heat would be mitigated by using no-till. Optimal planting months in 2050 were investigated using DSSAT under the four GCMs. Three of the models solved the problem by delaying planting until September; the IPSL model — which has the highest daily maximum temperature of the four GCMs for October through February — suggested December as the optimal planting month, meaning it determined it to be more optimal to grow maize during the much lower rainfall months of January through May than to endure the heat from August to November.

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The second location chosen for further investigation is Cotabato, located in the western portion of Mindanao (Figure 9.24). In Cagayan Valley, rainfall peaks relatively sharply around November, but is not as distinct in Cotabato, where rainfall levels are higher from May until October/November and lower from December until April. Surprisingly, DSSAT chose a 2000 baseline planting month of April despite its having the highest yearly temperatures (a mean daily maximum of around 33°C). It may be that in the early phase of growth plants are not as sensitive to high temperatures compared with later phases. The optimal solution for all four GCMs was to change the planting month to a climate more suitable for maize. The HadGEM model shifted planting to January, whereas the MIROC model changed it to May. In Cotabato, the optimal planting month as determined by computing highest yields in the DSSAT biophysical model is different for each GCM because both precipitation and temperature shift differently based on the climate model. Also complicating matters is a relatively uniform mean daily maximum temperature profile, whereby the temperature is higher than 30°C throughout the year. Nonetheless, given the findings of Lobell et al. (2011), it seems clear that on any given day in any month in Cotabato, part of the day will be hotter than 30°C. So, when this temperature is increased by around 2°C due to climate change, the number of degree days over 30°C will rise significantly, regardless of when farmers choose to plant. So, what does this finding imply for researchers, policymakers, donors, and farmers? It is possible that investment in developing heat-resilient varieties will bear fruit. It is not clear that the Lobell et al. (2011) 30°C limit for maize can never be surpassed — rather, it may simply be the barrier that the current generation of crop scientists are challenges to overcome. Even if heat-resilient maize cannot be developed, shorter-duration varieties make it easier to grow maize during the optimal rainfall months, while at the same time avoiding the worst of the hottest months. Apart from the hope of new varieties, farmers may still have some options. For those with relatively low fertilizer input levels, increasing fertilizer use can compensate for yield losses due to climate change. This, of course, requires capital, so there will always be the tradeoff between the cost of adding fertilizer and the benefits of increasing yields. In the case of Cagayan Valley, it seems that if supplemental irrigation is available — or could be made available with some investment — farmers

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Notes: GFDL = General Fluid Dynamics Laboratory; HadGEM = Hadley Centre Global Environmental Model; IPSL = Institut Pierre-Simon Laplace; MIROC = Model for Interdisciplinary Research on Climate. Source: Calculated by the authors based on data from Robert J. Hijmans, Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis, “Very High Resolution Interpolated Climate Surfaces for Global Land Areas”, International Journal of Climatology 25 (2005): 1965–78; and from Peter G. Jones, Philip K. Thornton, and Jens Heinke, “Generating Characteristic Daily Weather Data Using Downscaled Climate Model Data from the IPCC’s Fourth Assessment”, Project report for the International Livestock Research Institute (Geneva: Intergovernmental Panel on Climate Change, 2009).

FIGURE 9.24 Rainfall and Temperature Profiles by Month and Model for a Location in Cotabato, 2000 and 2050 A Biophysical Approach to Modelling Alternative Agricultural Futures

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could delay planting until the cooler months, and compensate for lost rainfall with irrigation. The constraint on this approach is whether an irrigated crop is already being grown in the dry season, and whether the irrigated crop’s planting date could be moved to allow maize to be grown at least during some part of the second crop’s normal growing season. Using irrigation to solve the problem would not work in Cotabato, however, because the limitations there are not so much rainfall levels but uniformly high temperatures (above 30°C) throughout the year. Another option for consideration is whether farmers might be able to shift into other crops that are more heat-resilient. This might entail additional investment in agricultural research and extension to enable crops not well known by farmers to be developed and tested, and for farmers to be trained in their cultivation. It is possible that the substitution could be to a crop that is already well known. Since rice yields will either modestly or not adversely be affected by climate change, farmers could potentially switch from cultivating maize to cultivating rice. This could have an additional positive benefit of increasing the probability of establishing national selfsufficiency in rice cultivation.

Sugarcane Most irrigated sugarcane in the Philippines is grown in the western part of Visayas, and a lesser but important area of concentration in Central Luzon (Figure 9.25). Yields appear uniform throughout the country, and are generally above 100 tons per ha. A large share of rain-fed sugarcane production is undertaken in the western part of Visayas (Figure 9.26). Production also occurs in Central Luzon, but in lower concentrated than irrigated sugarcane. An important cluster is also located in northern Mindanao. Yields for rain-fed sugarcane seem to be much more variable than for irrigated sugarcane, and average productivity is much lower. It seems that yields range from 20 to 90 tons per hectare. Areas planted to rain-fed and irrigated sugarcane are about equal, even within each regional grouping (Table 9.18). By far, Visayas is the main producing area, with just under two-thirds of all of the nation’s sugarcane production located there. In the main irrigated sugarcane producing areas, expected losses appear to be in the range of 2–10 per cent, with Luzon and Mindanao at the lower end of the range, and Visayas at the upper end (Figure 9.27).

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b. Productivity

Notes: Intensity is based on hectares per pixel, and a pixel at the equator represents approximately 8,500 hectares. Productivity is based on kilograms per hectare. Sources: Calculated by authors from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

a. Intensity

FIGURE 9.25 Intensity and Productivity of Irrigated Sugarcane, Circa 2005 A Biophysical Approach to Modelling Alternative Agricultural Futures

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b. Productivity

Notes: Intensity is based on hectares per pixel, and a pixel at the equator represents approximately 8,500 hectares. Productivity is based on kilograms per hectare. Sources: Calculated by authors from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

a. Intensity

FIGURE 9.26 Intensity and Productivity of Rain-Fed Sugarcane, Circa 2005

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TABLE 9.18 Harvested Hectares of Irrigated and Rain-Fed Sugarcane by Region, Circa 2005 Region 1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

Harvested Hectares of Irrigated Sugarcane

Harvested Hectares of Rain-Fed Sugarcane

30,437 30,418 19 119,417 34,647 34,644 3 184,501

32,976 32,855 121 117,665 45,150 43,867 1,283 195,791

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Sources: Calculated by authors from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

The tabulated yield changes confirm the observation that losses are a modest 4.3 per cent averaged across the country (Table 9.19). The largest losses — still at only 5.6 per cent — are in Visayas, followed by Luzon, and then Mindanao. Unfortunately, little gain seems to result from the adaptation measures considered. For rain-fed sugarcane, the losses seem larger than for irrigated sugarcane, although it is difficult to see precisely by how much (Figure 9.28). The tabulations show that losses are slightly more for rain-fed than for irrigated sugarcane, but that the distribution of losses differs (Table 9.20). Yield losses in Luzon are projected to be about 8.6 per cent; in Visayas they are slightly less (5.8 per cent), and in Mindanao they are small (only 0.5 per cent).

Coconuts Most coconut production in the Philippines takes place south of 15° North (Figure 9.29a). Yields vary, with some area yielding as low as 2 or 3 tons per ha and others yielding as high as 6 tons per ha (Figure 9.29b). In terms of area, about half the country’s coconut cultivation occurs in Mindanao, 30 per cent occurs in Luzon, and around 18 per cent occurs in Visayas (Table 9.21). It should be noted that it is always difficult to interpret the

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FIGURE 9.27 Median Percentage Change in Irrigated Sugarcane Yields under Climate Change, and the Projected Effect of Adaptation Measures, 2010–50 a. Yield change without adaptation

b. Yield change from adjusting the planting month

c. Yield change from adjusting the cultivar

d. Yield change from adjusting the planting month and cultivar

Notes: This scenario is based on the representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). Source: Authors.

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TABLE 9.19 Projected Climate Impacts on Irrigated Sugarcane Yields, 2000–50 Improvement from Adaptation Measure

Region

Direct Impact Change in Change in of Climate Planting Month Variety Change (%) (%) (%)

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

–3.6 –3.6 –2.2 –5.6 –1.2 –1.2 –0.4 –4.3

0.5 0.5 0.0 0.9 0.2 0.2 0.4 0.7

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Change in Both Planting Month and Variety (%) 0.7 0.7 0.0 0.9 0.3 0.3 0.4 0.7

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Sources: Calculated by authors at the grid-cell level from their own analysis, then aggregated using weights for irrigated sugarcane from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

maps and make conclusions about the geographical impact of climate change on productivity because there may be few coconuts where losses are expected to be large and many where gains are expected to be modest (Figure 9.30). GFDL and HadGEM project modest increases and decreases, whereas IPSL and MIROC project both stronger gains and losses. The aggregated results are clearer (Table 9.22). More than any other region, Mindanao is favoured by climate change in terms of increased coconut productivity according to three of the four GCMs; however, the IPSL results indicate that Luzon will benefit more than the other regions. In addition, on average, climate change appears to be favourable for coconut production across the country. This is not always the case; for example, GFDL indicates reduced coconut productivity in both Luzon and Visayas. Results also project negative impacts on coconut productivity in CAR, but it is important to note that, since so few coconuts are grown in CAR, this result can be overlooked.

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FIGURE 9.28 Median Percentage Change in Rain-Fed Sugarcane Yields under Climate Change, and the Projected Effect of Adaptation Measures, 2010–50 a. Yield change without adaptation

b. Yield change from adjusting the planting month

c. Yield change from adjusting the cultivar

d. Yield change from adjusting the planting month and cultivar

Note: This scenario is based on the representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). Source: Authors.

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–8.6 –8.7 –6.6 –5.8 –0.5 –0.4 –3.9 –4.7

1.8 1.8 0.9 1.6 1.4 1.4 1.9 1.6

Change in Planting Month (%) 0.1 0.2 0.1 0.1 0.1 0.1 0.0 0.1

Change in Variety (%) 2.2 2.3 1.2 1.9 1.5 1.5 1.9 1.8

Change in Planting Month and Variety (%) 3.1 3.1 4.4 2.4 6.3 6.4 2.9 3.7

10.6 10.6 16.9 18.7 19.7 19.8 16.4 19.3

Combined with Change in Planting On Its Own Date and Variety (%) (%)

Improvement from Irrigation

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Sources: Calculated by authors at the grid-cell level from their own analysis, then aggregated using weights for rain-fed sugarcane from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

Region

Direct Impact of Climate Change (%)

Improvement from Adaptation Measure

TABLE 9.20 Projected Climate Impacts on Rain-Fed Sugarcane Yields, 2000–50

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b. Productivity

Notes: Intensity is based on hectares per pixel, and a pixel at the equator represents approximately 8,500 hectares. Productivity is based on kilograms per hectare. Sources: Calculated by authors from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

a. Intensity

FIGURE 9.29 Intensity and Productivity of Rain-Fed Coconuts, Circa 2005

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TABLE 9.21 Harvested Hectares of Coconuts by Region, Circa 2005 Region

Harvested Hectares of Coconuts

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

950,292 949,958 334 576,630 1,640,981 1,370,974 270,007 3,167,903

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Sources: Calculated by authors from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

TABLE 9.22 Climate Impacts on Rain-Fed Coconut Yields from Four AR5 General Circulation Models, 2000–50 Yield Changes (%) Region 1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

GFDL

HadGEM

IPSL

MIROC

–0.5 –0.5 –0.7 –1.7 –2.7 –2.4 –4.0 –1.0

–0.4 –0.4 –7.0 –0.6 –2.3 –2.3 –2.6 –1.5

4.0 4.0 0.3 1.1 1.1 1.0 2.0 1.9

–2.3 –2.3 –2.7 –2.6 –3.8 –3.9 –3.0 –3.2

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region; GFDL = General Fluid Dynamics Laboratory; HadGEM = Hadley Centre Global Environmental Model; IPSL = Institut Pierre-Simon Laplace; MIROC = Model for Interdisciplinary Research on Climate. Sources: Calculated by authors at the grid-cell level from their own analysis, then aggregated using weights for rain-fed coconut from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

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Timothy S. Thomas, Vijay Nazareth, and Renato A. Folledo, Jr. FIGURE 9.30 Median Percentage Change in Rain-Fed Coconut Yields from Four AR5 General Circulation Models, 2000–50

a. General Fluid Dynamics Laboratory (GFDL)

b. Hadley Centre Global Environmental Model (HadGEM)

c. Institut Pierre-Simon Laplace (ISPL)

d. Model for Interdisciplinary Research on Climate (MIROC)

Notes: This scenario is based on the representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). Source: Authors.

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Bananas Similar to the distribution of coconuts, bananas tend to be more concentrated below 15° North; south of that latitude, however, they seem to be grown almost everywhere (Figure 9.31a). Yields seem to vary from 2 to 30 tons per hectare (Figure 9.31b). Slightly more than half of the country’s banana area is located in Mindanao, almost 30 per cent is located in Luzon, and around 20 per cent is located in Visayas (Table 9.23). Figure 9.32 shows the spatially differentiated climate effects on rain-fed banana yields. Except for the IPSL model, the maps show yield losses throughout most of Luzon, with the GFDL and HadGEM models indicating the largest losses (Figure 9.32). The GFDL and HadGEM models also indicate negative results for banana productivity in Visayas, although MIROC and IPSL indicate somewhat mixed results in Visayas. Only the HadGEM model is pessimistic about production in Mindanao; the MIROC model appears optimistic. Depending on the GCM used, national productivity losses are projected to range from 0.9 to 7.3 per cent (Table 9.24). Luzon is likely to be most adversely affected, with median losses projected to be higher than 11 per cent.

The Potential for Various Agricultural Technologies Rosegrant et al. (2014) analysed the potential benefits of developing a variety of agricultural technologies. Their analysis was done globally at a halfdegree resolution using a similar methodology to that used in this chapter, but the analysis relied on two GCMs from the IPCC’s AR4–CSIRO and MIROC — using the A1B scenario. While the resolution they used is thirtysix times coarser than the resolution used in the DSSAT crop modelling analysis earlier in this chapter, the results are nonetheless useful, not only in terms of the magnitude of potential technology interventions, but also because they illuminate the regional differences. One of the drawbacks of their analysis, which limits our ability to fully apply the results, is that while they successfully calculate benefits from each technology, they do not calculate costs. Consequently, it is more difficult to determine whether a particular technology is more economically beneficial. The technology that leads to the highest rain-fed maize productivity increase is integrated soil fertility management (ISFM), indicating a little more than a 32-per cent increase for the country, followed by no-

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b. Productivity

Notes: Intensity is based on hectares per pixel, and a pixel at the equator represents approximately 8,500 hectares. Productivity is based on kilograms per hectare. Sources: Calculated by authors from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

a. Intensity

FIGURE 9.31 Intensity and Productivity of Rain-Fed Bananas, Circa 2005

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FIGURE 9.32 Projected Yield Change in Rain-Fed Bananas from Four AR5 General Circulation Models, 2000–50 a. General Fluid Dynamics Laboratory (GFDL)

b. Hadley Centre Global Environmental Model (HadGEM)

c. Institut Pierre-Simon Laplace (ISPL)

d. Model for Interdisciplinary Research on Climate (MIROC)

Note: This scenario is based on the representative concentration pathway 8.5 for greenhouse gas emissions as outlined in the Fifth Assessment Report of Intergovernmental Panel on Climate Change (Stocker et al. 2014). Source: Authors.

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TABLE 9.23 Harvested Hectares of Bananas by Region, Circa 2005 Region

Harvested Hectares of Bananas

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

120,679 116,953 113,726 184,272 210,231 180,065 130,166 415,182

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Sources: Calculated by authors from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

TABLE 9.24 Projected Climate Impacts on Rain-Fed Banana Yields from Four AR5 General Circulation Models, 2000–50 Region

GFDL

HadGEM

IPSL

MIROC

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

–12.7 –12.6 –14.5 1–9.3 1–1.1 1–1.6 1–1.9 1–5.6

–16.3 –16.0 –24.8 1–6.5 1–3.6 1–3.8 1–2.6 1–7.3

–0.3 –0.3 –0.0 –0.6 –1.6 –1.6 –1.8 –0.9

1–9.6 1–9.5 –10.0 1–3.0 –12.0 –12.1 –11.6 1–1.8

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region; GFDL = General Fluid Dynamics Laboratory; HadGEM = Hadley Centre Global Environmental Model; IPSL = Institut Pierre-Simon Laplace; MIROC = Model for Interdisciplinary Research on Climate. Sources: Calculated by authors at the grid-cell level from their own analysis, then aggregated using weights for rain-fed bananas from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

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till agriculture, with a 24-per cent increase (Table 9.25). ISFM includes the use of both organic inputs and synthetic fertilizers to maximize soil fertility characteristics beneficial to crop productivity. Both technologies share an outcome of increasing soil organic matter (Rosegrant et al. 2014), which in turn is linked to enhancing other soil fertility indicators, such as nutrient and water retention. Other technologies appear to be promising, with pest protection providing nearly 17 per cent yield improvement, and several others offering improvements of between 9 and 14 per cent, including (from highest to lowest) weed protection, nitrogen-efficient varieties, crop-disease protection, and heat-tolerant varieties. In the Philippine context, water harvesting and drought-tolerant varieties give little benefit. Unlike the case for rain-fed maize, nitrogen-efficient varieties seem to offer large potential benefits for irrigated rice (Table 9.26). Nationally, improvements were projected to be above 53 per cent, with CAR benefiting the most (around 61 per cent). The simulation was undertaken based on developing crops that do not currently exist, so investment decisions should not be based on these statistics alone, although it is very encouraging to think that future plant breeding could offer an important yield breakthrough. As for rain-fed maize, ISFM appears to offer sizeable potential for productivity growth in irrigated rice in the Philippines, averaging more than 27 per cent nationwide, and ranging from a low of around 23 per cent in Luzon to a high of almost 41 per cent in ARMM. The results for precision agriculture are almost as high, indicating a near 26 per cent increase in productivity nationwide. For the purpose of the Rosegrant et al. (2014) study, this involved a more optimum planting density and schedule of fertilizer application. Protection against crop diseases, pests, and weeds also offered potential yield improvements. The potential for improvement for rain-fed rice is much more limited than for irrigated rice or rain-fed maize (Table 9.27). As for irrigated rice, nitrogen-efficient varieties show the most potential across the country, but the gain is limited to 12 per cent (much less than the 53 per cent for irrigated varieties). It is not clear why there should be such a large difference, but it may have to do with the variety chosen within the crop modelling software, and the variety’s ability to use the nitrogen. Other potential technologies, ranging from just under 7 per cent to just under 10 per cent in improved rain-fed rice yields, include crop disease protection, pest protection, ISFM, and weed protection.

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5.1 5.3 4.6 4.1 2.6 2.9 1.1 3.2

13.2 16.2 12.9 11.3 18.2 18.2 18.2 19.4

HeatTolerant Varieties (%) 11.9 10.8 15.7 12.7 14.2 14.2 13.9 13.7

25.4 26.4 21.9 24.7 23.7 24.6 19.1 24.1

11.3 11.4 11.2 10.0 12.4 12.2 13.5 12.0

16.3 16.4 15.7 13.1 17.1 17.1 17.3 16.7

12.5 12.8 11.3 10.7 14.4 14.1 16.3 13.9

34.7 32.6 42.1 32.1 31.8 31.4 34.2 32.4

0.5 0.7 0.1 0.0 0.9 1.1 0.2 0.8

Integrated CropNitrogenSoil Fertility Water Disease Pest Weed Efficient No-Till Varieties Agriculture Protection Protection Protection Management Harvesting (%) (%) (%) (%) (%) (%) (%)

Source: Mark W. Rosegrant, Jawoo Koo, Nicola Cenacchi, et al, Food Security in a World of Natural Resource Scarcity: The Role of Agricultural Technologies (Washington, D.C.: International Food Policy Research Institute, 2014).

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

Region

DroughtTolerant Varieties (%)

TABLE 9.25 Projected Improvements in Rain-Fed Maize Yields from Various Technologies, 2050

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1.8 1.7 2.7 1.7 1.5 1.7 0.1 1.7

52.1 51.3 61.1 49.1 56.7 56.4 59.0 53.1

22.6 21.5 35.0 31.9 32.8 32.0 39.1 25.6

10.2 10.2 10.3 18.9 11.7 11.6 11.9 10.6

18.1 18.1 17.2 10.4 19.6 19.5 10.2 18.6

5.1 5.1 4.7 7.3 7.6 7.5 8.4 5.8

NitrogenEfficient Precision Crop-Disease Pest Weed Varieties Agriculture Protection Protection Protection (%) (%) (%) (%) (%)

24.4 22.8 40.5 33.7 34.4 33.6 40.6 27.4

Integrated Soil Fertility Management (%)

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Source: Mark W. Rosegrant, Jawoo Koo, Nicola Cenacchi, et al, Food Security in a World of Natural Resource Scarcity: The Role of Agricultural Technologies (Washington, D.C.: International Food Policy Research Institute, 2014).

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

Region

HeatTolerant Varieties (%)

TABLE 9.26 Projected Improvements in Irrigated Rice Yields from Various Technologies, 2050

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1.8 1.9 1.5 1.2 1.3 1.4 0.6 1.4

1.9 2.0 1.2 0.4 0.4 0.3 1.0 0.9

HeatTolerant Varieties (%) 11.2 11.0 11.9 13.3 11.6 11.2 13.4 12.0

3.0 3.2 2.4 2.2 0.6 0.5 0.8 1.9

NitrogenEfficient Precision Varieties Agriculture (%) (%) 19.8 19.9 19.3 19.8 11.6 11.3 12.8 10.4

18.1 18.2 17.9 19.5 19.7 19.5 10.5 19.1

5.4 5.5 5.0 5.9 7.8 7.4 9.5 6.4

8.0 8.1 7.8 6.8 4.9 3.9 9.0 6.6

Integrated Soil Fertility Crop-Disease Pest Weed Protection Protection Protection Management (%) (%) (%) (%)

Notes: ARMM = Autonomous Region in Muslim Mindanao; CAR = Cordillera Administrative Region. Source: Mark W. Rosegrant, Jawoo Koo, Nicola Cenacchi, et al, Food Security in a World of Natural Resource Scarcity: The Role of Agricultural Technologies (Washington, D.C.: International Food Policy Research Institute, 2014).

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

Region

DroughtTolerant Varieties (%)

TABLE 9.27 Projected Improvements in Rain-Fed Rice Yields from Various Technologies, 2050

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CONCLUSION AND IMPLICATIONS FOR POLICY The crop modelling results from the previous sections of this chapter are summarized in Table 9.28. Results for rain-fed rice and rain-fed sugarcane generally indicate modest negative impacts of climate change on agriculture in the Philippines. They also indicate geographic differences in climate change impacts on agriculture, with Luzon being more negatively affected than Mindanao, and Visayas falling between the other two regions. The impact of climate change on irrigated crops is less than on rain-fed crops. Maize differs from sugarcane in that it is projected to undergo large, negative impacts from climate change that are fairly similar across the country, except that the impact in Visayas is slightly more negative. After careful analysis of monthly rainfall and temperature patterns, with and without climate change, it was concluded that these results are consistent with documented yield impacts on maize at higher temperatures. Because maize is such an important crop for farmers in the Philippines, these results indicate that careful consideration should be given to adaptation strategies for maize. Furthermore, because yield losses are reasonably high for both rain-fed rice and rain-fed sugarcane in Luzon, it is important to note the need for special attention to adaptation strategies in Luzon, because all major crops there are negatively affected. This also limits the options for maize adaptation in Luzon because one of the strategies that might be used in the rest of the country is to cultivate an alternative crop, such as rice or sugarcane. There are adaptation possibilities, however. Investment in agricultural research could result in heat-tolerant varieties of maize, rice, and sugarcane. The Rosegrant et al. (2014) study suggested that heat-tolerant varieties would only help modestly, but that study used the older AR4 GCMs, and perhaps did not project the same kind of losses as have been noted in this chapter using the newer AR5 GCMs. An alternative to heat-tolerant varieties that would nonetheless help with losses due to hotter temperatures would be the development of shortduration varieties that would allow farmers to plant in cooler months, yet not miss out on months with sufficient rainfall for good yields. For farmers who currently under-use fertilizer, increasing use could offer an effective means of adapting to climate change. In light of rising food prices — which may rise faster than fertilizer prices — this strategy could be beneficial. Of course, the fertilizer does not need to be a chemical one; better use of manures or nitrogen-fixing plants (as cover crops, intercropped, or in

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–7.1 –7.2 –6.0 –0.1 –5.0 –4.8 –5.9 –1.2

1. Luzon 1.1 Luzon 1.2 CAR 2. Visayas 3. Mindanao 3.1 Mindanao 3.2 ARMM Total

–7.4 –7.5 –6.7 –4.1 –0.5 –0.4 –0.8 –4.5

High –0.2 –0.2 –0.2 –1.1 –0.8 –0.8 –0.9 –0.4

Low –0.1 –0.2 –2.0 –0.6 –0.7 –1.0 –1.5 –0.0

High

Irrigated –18.8 –18.9 –17.1 –22.4 –19.1 –18.7 –21.4 –19.5

Low –20.6 –20.7 –18.6 –25.0 –21.2 –20.6 –24.1 –21.6

High

Rain-Fed

Maize

–8.6 –8.7 –6.6 –5.8 –0.5 –0.4 –3.9 –4.7

–

Rain-Fed –3.6 –3.6 –2.2 –5.6 –1.2 –1.2 –0.4 –4.3

–

Irrigated

Sugarcane

–1.4 –1.4 –1.7 –0.9 –2.5 –2.4 –2.8 –1.7

–

Rain-Fed

Coconuts

–11.2 –11.1 –12.3 1–4.8 1–1.4 1–1.6 1–0.1 1–3.7

–

Rain-Fed

Bananas

Notes: “Low” and “High” refer to fertilizer levels of 30 and 90 kilograms of nitrogen per hectare, respectively. The sugarcane model did not respond to changing nitrogen levels and hence was run without this variable. Values for bananas and coconuts are the median values across the four climate models used. Sources: Calculated by authors at the grid-cell level, then aggregated using weights for the respective crops from MapSPAM, which is described in Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu, “Generating Global Crop Distribution Maps: From Census to Grid”, Agricultural Systems 127 (May 2014): 53–60.

Low

Region

Rain-Fed

Rice

TABLE 9.28 Summary of Crop Model Results for Yield Changes Due to Climate Change, 2000–50

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rotation) might be an effective solution. The Rosegrant et al. (2014) study pointed to potentially large increases in productivity for rain-fed maize and irrigated rice using ISFM, which uses both synthetic fertilizer and organic additions to the soil. For irrigated areas, the strategy of slightly shifting the growing season for rain-fed crops to avoid the hottest months, supplemented by irrigation, offers benefits, and the irrigation could still be used for off-season crops. This often requires careful consideration of the impact on both crops, and may be greatly enhanced by using shorter duration varieties for both crops. In areas that do not currently have irrigation but have that potential, investment in irrigation infrastructure might be highly beneficial to overcome the limitations of rain-fed agriculture.

Note 1. Note that results exclude damage from tropical cyclone winds and flooding, which were not estimated in the analysis presented in this chapter.

References Batjes, N., K. Dijkshoorn, V. van Engelen, et al. Harmonized World Soil Database. Laxenburg, Austria: International Institute for Applied Systems Analysis, 2009. Butler, E. and P. Huybers. “Adaptation of US Maize to Temperature Variations”. Nature Climate Change 3, no. 1 (18 November 2012): 68–72 (accessed 16 July 2013). Collins, W., N. Bellouin, M. Doutriaux-Boucher, N. Gedney, P. Halloran, T. Hinton, J. Hughes, et al. “Development and Evaluation of an Earth–System Model — HadGEM2”. Geoscience Model Development 4, no. 4 (2011): 1051–75. Davin, E., S. Seneviratne, P. Ciais, A. Olioso, and T. Wang. “Preferential Cooling of Hot Extremes from Cropland Albedo Management”. Proceedings of the National Academy of Sciences of the United States of America 111, no. 27 (2014): 9757–61 (accessed 26 June 2014). Dufresne, J.-L., M.-A. Foujols, S. Denvil, A. Caubel, O. Marti, O. Aumont, Y. Balkanski, et al. “Climate Change Projections Using the IPSL–CM5 Earth System Model: From CMIP3 to CMIP5”. Climate Dynamics 40, nos. 9/10 (2013): 2123–65. Dunne, J., J. John, A. Adcroft, S. Griffies, R. Hallberg, E. Shevliakova, R. Stouffer, et al. “GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models

18-J04349 09 Future of Philippine Agriculture.indd 447

19/11/18 12:06 PM

448

Timothy S. Thomas, Vijay Nazareth, and Renato A. Folledo, Jr.

Part I: Physical Formulation and Baseline Simulation Characteristics”. Journal of Climate 25, no. 19 (2012): 6646–65. ———, J. John, E. Shevliakova, R. Stouffer, J. Krasting, S. Malyshev, P. Milly, et al. “GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part II: Carbon System Formation and Baseline Simulation Characteristics”. Journal of Climate 26, no. 7 (2013): 2247–67. FAO (Food and Agriculture Organization of the United Nations). FAOSTAT database, 2014 (accessed 17 February 2014). Friedl, M., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. “MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of New Datasets”. Remote Sensing of Environment 114 (2010): 168–82. GADM. GADM database of Global Administrative Areas. Version 1.0, 2010 (accessed 11 March 2010). GLOBE (Task Team and others, including D. Hastings, P. Dunbar, G. Elphingstone, et al.). The Global Land One–kilometer Base Elevation (GLOBE) Digital Elevation Model, Version 1.0. National Oceanic and Atmospheric Administration, National Geophysical Data Center, 1999 (accessed 28 January 2005). Hijmans, R., S. Cameron, J. Parra, P. Jones, and A. Jarvis. “Very High Resolution Interpolated Climate Surfaces for Global Land Areas”. International Journal of Climatology 25 (2005): 1965–78. IUCN (International Union for the Conservation of Nature) and UNEP-WCMC (United Nations Environment — World Conservation Monitoring Center). The World Database on Protected Areas. Cambridge, UK: UNEP-WCMC, 2014 (accessed 1 June 2014). Jones, P., P. Thornton, and J. Heinke. “Generating Characteristic Daily Weather Data Using Downscaled Climate Model Data from the IPCC’s Fourth Assessment”. Project report for the International Livestock Research Institute. Geneva: Intergovernmental Panel on Climate Change, 2009. Jones, J., G. Hoogenboom, C. Porter, et al. “The DSSAT Cropping System Model”. European Journal of Agronomy 18, nos. 3–4 (2003): 235–65. Koo, J., and J. Dimes. HC27 Generic Soil Profile Database, Version 1. Washington, D.C.: International Food Policy Research Institute, July 2010. Latham, J., R. Cumani, I. Rosati, and M. Bloise. 2014. Global Land Cover SHARE (GLC–SHARE), 2014 (accessed 3 March 2014). Lobell, D., M. Bänziger, C. Magorokosho, and B. Vivek. “Nonlinear Heat Effects on African Maize as Evidenced by Historical Yield Trials”. Nature Climate Change 1 (April 2011): 42–45. Martin, G., N. Bellouin, W. Collins, et al. “The HadGEM2 Family of Met Office

18-J04349 09 Future of Philippine Agriculture.indd 448

19/11/18 12:06 PM

A Biophysical Approach to Modelling Alternative Agricultural Futures

449

Unified Model Climate Configurations”. Geophysical Model Development 4 (2011): 723–57. Müller, C. and R. Robertson. “Projecting Future Crop Productivity for Global Economic Modeling”. Agricultural Economics 45 (2014): 37–50. Rosegrant, M., J. Koo, N. Cenacchi, et al. Food Security in a World of Natural Resource Scarcity: The Role of Agricultural Technologies. Washington, D.C.: International Food Policy Research Institute, 2014. Rosenzweig, C., J. Elliott, D. Deryng, et al. “Assessing Agricultural Risks of Climate Change in the 21st Century in a Global Gridded Crop Model Intercomparison”. Proceedings of the National Academy of Sciences of the United States of America 111, no. 9 (2014): 3268–73. Sakamoto, T., Y. Komuro, T. Nishimura, et al. “MIROC4h: A New High-Resolution Atmosphere–Ocean Coupled General Circulation Model”. Journal of Meteorology Society of Japan 90, no. 3 (2012): 325–59. Stocker, T., D. Qin, G.-K. Plattner, et al. “Technical Summary”. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by T. Stocker, D. Qin, G.-K. Plattner, et al. Cambridge: Cambridge University Press, 2013. van Noordwijk, M., B. Lusiana, and N. Khasanah. WaNuLCAS Version 3.1, Background on a Model of Water Nutrient and Light Capture in Agroforestry Systems. Bogor, Indonesia: International Centre for Research in Agroforestry, 2004. You, Liangzhi and Stanley Wood. “An Entropy Approach to Spatial Disaggregation of Agricultural Production”. Agricultural Systems 90, nos. 1–3 (2006): 329–47. ———, Stanley Wood, and Ulrike Wood-Sichra. “Generating Global Crop Distribution Maps: From Census to Grid”. Paper presented at the International Association of Agricultural Economists Conference, Brisbane, Australia, 11–18 August 2006. ———, Stanley Wood, and Ulrike Wood-Sichra. “Generating Plausible Crop Distribution and Performance Maps for Sub-Saharan Africa Using a Spatially Disaggregated Data Fusion and Optimization Approach”. Agricultural Systems 99, nos. 2–3 (2009): 126–40. ———, Stanley Wood, Ulrike Wood-Sichra and Wenbin Wu. “Generating Global Crop Distribution Maps: From Census to Grid”. Agricultural Systems 127 (May 2014): 53–60.

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10 A PARTIAL EQUILIBRIUM APPROACH TO MODELLING ALTERNATIVE AGRICULTURAL FUTURES UNDER CLIMATE CHANGE Nicostrato D. Perez and Mark W. Rosegrant

The future of Philippine agriculture ultimately depends on the extent of climate change’s impact and the choices governments, agricultural producers, and consumers make in efforts to adapt to these impacts. This chapter focuses on modelling the economic impacts of climate change on Philippine agriculture, emphasizing the potential for adaptation technologies and government investment policies not only to assist the country in proactively preparing for and mitigating these impacts, but also to achieve its food security objectives. The main instrument underlying the modelling and analyses in this chapter is the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT), a partial equilibrium economic model developed and maintained by the International Food Policy Research Institute. The model facilitates the simulation of the impacts and costs of climate change; the effectiveness of existing and emerging production technologies and strategies; and the

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contribution of demographic, development, and investment policies to food security and climate change adaptation efforts (for details of the model, see Rosegrant and the IMPACT Development Team 2012). The analyses presented in this chapter are based on model simulations undertaken using an interlinked combination of modelling tools: the Decision Support System for Agrotechnology Transfer (DSSAT), a biophysical crop-model described in Chapter 9 (this volume), the IMPACT model (briefly described above and in more detail in Rosegrant and the IMPACT Development Team (2012)), including the IMPACT-WATER model (described in Rosegrant, Cai, and Cline (2002) and updated and expanded since then), and a dynamic computable general equilibrium (DCGE) model for the Philippines described in Chapter 11 (this volume). The technical linkages among these models were earlier described in Chapter 9. Simulations were undertaken under four future climate scenarios derived from the following general circulation/climate models: the General Fluid Dynamics Laboratory (GDFL), Hadley Centre Global Environmental Model (HadGEM), Institut Pierre-Simon Laplace (IPSL), and the Model for Interdisciplinary Research on Climate (MIROC). Two primary scenarios were modelled for the 2011–50 period, one being the baseline scenario without climate change, and one a scenario with climate change based on the average results from the GDFL, HadGEM, IPSL, and MIROC simulations. In detailed technology assessments the MIROC model was used because it was deemed to be the most accurate simulation of the future Philippine climate. Similar to Chapter 9 (this volume), the analyses utilize the IPCC’s representative concentration pathways (RCPs), which reflect the causes of climate change in terms of the amount of “radiative forcing” they yield by 2100 (that is, the ability of the atmosphere to retain heat instead of radiating it back to space). The results reported here, as in Chapter 9 (this volume), are from RCP8.5, which represents the highest amount of GHG emissions, and appears to be the most suitable based on current emissions trajectories. Subsequent sections of the chapter estimate the impact of climate change on Philippine agriculture using indicators, such as changes in production and yield levels; changes in food security levels (measured in terms of food availability, food consumption, per capita energy intake, child malnutrition, and population at risk of hunger); and economic indicators, such as changes in economic surplus (as an indicator of social welfare), loss of earnings (for the effect of malnutrition), and damage estimations (for the

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impact of extreme events). Thereafter, the focus shifts to an analysis of the potential benefits of a range of climate change adaptation technologies and irrigation investment policies. DSSAT simulation results from Chapter 9 of climate change adaptation technologies (change of planting month, change in variety, and increased fertilizer use) were used as inputs for IMPACT simulation of existing rice and corn adaptation technologies. Inputs for new and emerging adaptation technologies, also simulated by DSSAT simulations, were adopted from previous study of Rosegrant et al. (2014). Finally, the chapter summarizes the policy implications of the analyses presented.

THE ECONOMIC IMPACTS OF CLIMATE CHANGE ON AGRICULTURE The Impact of Climate Change on Production and Yields The direct impacts of climate change on agriculture predominantly occur through changes in crop productivity as a result of heat and water stress (IPCC 2014). IMPACT’s projections of the impact of climate change to 2050 show that, on average, the world’s total agricultural crop yields fall by 4.5 per cent compared with baseline levels reflecting no climate change (Figure 10.1). The yield effects of climate change to Philippine agriculture, however, are in general less severe than for the world as whole, at 2.9 per cent decline in yields. Globally and by major crop group, average crop yields are projected to decline in response to climate change by 2050 (Table 10.1). The largest impact is on sugarcane (11.2 per cent) and cereals (6.9 per cent), followed by roots and tubers (2.6 per cent) and pulses (1.9 per cent). Yields of rice and corn — the country’s first and third most important crops by cultivated area (the second being coconuts) — are projected to decline by 7.7 and 18.8 per cent, respectively. Domestically for the Philippines, on average, climate change’s overall impact on yields is negative (–2.9 per cent), despite its positive yield effect on fruit and vegetables, pulses, and roots and tubers (Table 10.1). For rice and corn, the negative yield impacts are lower for the Philippines than for the rest of the world, at 4.1 and 7.7 per cent for rice, respectively, and 15.7 and 18.8 per cent for corn, respectively. Worldwide, agricultural crop production is projected to decline by an average of 3.4 per cent to 2050 (Table 10.1), which is less than the change

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1.1 1.0 0.9

0.8 0.7

1.1

1.0

0.9

0.8

0.7

0.5 1980 1990 2000 2010 2020 2030 2040 2050

Historical Without climate change With climate change

Note: Data are based on average values from the four climate models underlying the analyses. Source: Constructed by authors based on model simulation results.

0.5 1980 1990 2000 2010 2020 2030 2040 2050

0.6

1.2

1.2

Historical data Without climate change With climate change

1.3

1.3

0.6

1.4

1.4

1.5

2010 = 1.0

2010 = 1.0

1.5

b. The Philippines

a. The World

Historical and Averageted Agricultural Crop Yields, with and without Climate Change, 1970–2050

FIGURE 10.1 Historical and Average Projected Agricultural Crop Yields, FIGURE 10.1 with and without Climate Change, 1970–2050 Partial Equilibrium Approach to Modelling Alternative Agricultural Futures

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454

TABLE 10.1 Average Projected Impact of Climate Change on Agriculture Globally and in the Philippines, 2030 and 2050

Impact of Climate Change Crop yields All crops Cereals Corn Rice Fruit and vegetables Pulses Roots and tubers Sugarcane Crop production All crops Cereals Corn Rice Fruit and vegetables Pulses Roots and tubers Sugarcane World/consumer prices Cereals Corn Rice Wheat Fruit and vegetables Pulses Roots and tubers Sugar Consumption Cereals Corn, as food Corn, as feed Rice Wheat Fruit and vegetables Pulses Roots and tubers Sugar

The World The Philippines 2030 2050 2030 2050 Percentage Change from Baseline Levels (%) –2.4 –3.7 –10.4 –4.2 –0.3 –1.2 –1.5 –5.8

–4.5 –6.9 –18.8 –7.7 –0.0 –1.9 –2.6 –11.2

–1.7 –3.7 –7.6 –1.7 1.4 0.3 0.0 –4.1

–2.9 –7.6 –15.7 –4.1 1.9 0.7 0.2 –8.3

–1.9 –3.4 –8.3 –2.9 –0.8 –1.3 –1.7 –1.7

–3.4 –6.0 –17.0 –5.5 –1.7 –2.4 –2.9 –3.2

–1.0 –2.3 –5.8 –0.6 2.0 –0.3 –0.5 –1.8

–1.7 –6.1 –13.0 –3.2 3.9 0.5 –0.5 –3.0

10.6 22.7 12.0 2.3 4.5 6.0 4.2 3.9

23.9 44.4 25.7 11.1 9.6 12.0 8.3 7.5

14.0 22.7 12.0 2.3 6.1 6.0 2.5 3.9

24.3 44.4 17.2 11.1 12.7 11.6 5.8 7.5

–1.8 –4.9 –10.6 –2.7 –0.6 –0.8 –0.7 –1.5 –1.1

–4.2 –8.6 –21.5 –5.4 –2.8 –1.7 –1.3 –2.5 –2.1

–2.0 –3.2 –8.7 –2.2 –0.7 –1.2 –0.2 –0.5 –1.3

–3.1 –5.6 –18.2 –2.9 –3.4 –2.3 –0.4 –0.9 –2.4

 

Note: Data compare average results of climate change simulations from the four general circulation models underlying the analyses in this chapter with baseline results under a scenario of no climate change. Source: Constructed by authors based on model simulation results.

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in yields given some offsetting increases in agricultural land area due to the price increases induced by climate change, as shown below. In terms of commodity groups, projected declines to 2050 are highest for cereals (6.0 per cent) and sugarcane (3.2 per cent), followed by roots and tubers (2.9 per cent) and pulses (2.4 per cent). Given extensive cultivation of cereals (in this case, rice and corn), sugarcane (planted to more than 50 per cent of agricultural land), tree crops (such as coconuts and fruit and nut trees), and perennials (such as bananas and coffee), the overall impact of climate change on Philippine production is projected to decline by 1.7 per cent, despite positive average impacts on all other crops. Cereal production is projected to decline by 6.1 per cent to 2050 compared with baseline levels, whereas fruit and vegetable production is projected to rise by 3.9 per cent. These results further emphasize the lower contraction in the production of rice and corn in the Philippines compared with the rest of the world.

The Impact of Climate Change on Prices and Consumption The effects of lower production and yields on the accessibility of agricultural commodities are projected to be exacerbated by substantially higher food commodity prices due to climate change both in 2030 and 2050 (Table 10.1). World cereal prices are projected to rise by 23.9 per cent in 2050 compared with baseline levels, followed by pulses (12.0 per cent) and fruit and vegetable (9.6 per cent). Among cereal commodities, corn prices are projected to increase the most to 2050 (44.4 per cent compared with baseline levels), followed by rice (25.7 per cent) and wheat (11.1 per cent). In the Philippines, substantial increases in consumer prices are also projected to 2050 for cereals (24.3 per cent), fruit and vegetables (12.7 per cent), pulses (11.6 per cent), and sugar (7.5 per cent). Among cereals, rice prices are projected to rise by 17.2 per cent, corn prices by 44.4 per cent, and wheat prices by 11.1 per cent.1 Increased food prices mean less access to food and hence reduced consumption, especially by poor people. Average global consumption to 2050 is projected to decline by 4.2 per cent for cereals, 2.5 per cent for roots and tubers, 2.1 per cent for sugar, 1.7 per cent for fruit and vegetables, and 1.3 per cent for pulses (Table 10.1). In the Philippines, declines in average food consumption to 2050 are similarly projected for cereals (3.1 per cent), sugar (2.4 per cent), fruit and vegetables (2.3 per cent), and roots and tubers (0.9 per cent). Among cereals, consumption of rice is projected to

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decline by 2.9 per cent, corn by 5.6 per cent as food and 18.2 per cent as feed, and wheat by 3.4 per cent.

The Impact of Climate Change on Food Security Another impact of climate change through shifts in the agricultural sector is reduced food security, as indicated by the availability of food, the prevalence of child malnutrition, and the number of people at risk of hunger (Table 10.2). Access to food — based on the projected changes in consumption, discussed above, and quantified as reduced energy consumption per day — is projected to decline by 2.7 per cent in developing countries on average by 2050. Access is also projected to decline in developed countries but only by 1.4 per cent. Declines in the Philippines are comparable with those in developing countries as a group (2.2 per cent). Malnutrition is a direct consequence of inadequate food intake/access. As of 2010, 149.5 million children were estimated to be malnourished in developing countries (Table 10.2). Under a baseline scenario without climate change, this number is projected to decline to 97.7 million in 2050, but with climate change this number is projected to be 3.5 per cent higher (101.1 million). For the Philippines, climate change is projected to increase the number of malnourished children by 2.7 per cent in 2050 (2.15 million without climate change, versus 2.20 million with climate change). The impact of climate change on the number of people at risk of hunger is estimated to be more severe (Table 10.2). In developing countries, 872.4 million people were estimated to be at risk of hunger in 2010. This number is projected to fall to 407.2 million in 2050 under a baseline scenario of without climate change, but with climate change this number is projected to be 13.9 per cent higher (463.8 million). Similarly, for the Philippines, climate change is projected to increase the number of people at risk of hunger by 12.8 per cent in 2050 (from 15.2 to 17.1 million).

The Impact of Climate Change on Producers and Consumers The impact of climate change on agricultural producers and consumers, and hence on society as a whole, can be measured in terms of the costs and benefits that accrue from shifts in supply and demand over time, using an economic surplus framework as described by Alston, Norton, and Pardey (1995). Note that this framework of welfare measure is

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TABLE 10.2 Projected Impact of Climate Change on Food Security, 2011–30 and 2011–50 Food Security Indicators Food availability Developed countries Without climate change With climate change Developing countries Without climate change With climate change The Philippines Without climate change With climate change Malnourished children Developing countries Without climate change With climate change The Philippines Without climate change With climate change Population at risk of hunger Developing countries Without climate change With climate change The Philippines Without climate change With climate change

2010

2030

2050

Kilocalories per capita per day

2030

2050

Percentage Change (%)

3,386 3,386

3,442 3,422

3,518 3,468

–0.6

–1.4

2,685 2,685

2,973 2,934

3,158 3,072

–1.3

–2.7

2,510 2,510

2,652 2,767 2,615 2,707 Millions

–1.4

–2.2

149.48 149.48

123.63 125.65

97.66 101.08

1.6

3.5

3.07 3.07

2.70 2.74

2.15 2.20

1.5

2.7

872.38 872.38

545.34 595.10

407.16 463.76

9.1

13.9

16.09 16.09

16.11 17.40

15.16 17.11

8.0

12.8

Notes: Data compare average results of climate change simulations from the four general circulation models underlying the analyses in this chapter with baseline results under a scenario of no climate change. Source: Constructed by authors based on model simulation results.

conceptually different with what is used and described in Chapter 11 (this volume) that calculates changes in consumers’ equivalent variation (EV), and thus may give dissimilar values. Within this economic surplus framework, positive values indicate net benefits and negative values indicate net costs. On this basis, the economic cost of climate change based on changes in the supply and demand of agricultural commodities worldwide is estimated to be US$2.18 trillion for the forty-year period from 2011 to 2050, or around US$54.5 billion per

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year (Table 10.3). The HadGEM model projects the highest negative net impact (US$2.92 trillion), whereas the GFDL model projects the lowest (US$1.41 trillion). In this way, consumers bear the costs of climate change by paying higher prices for food and other agricultural commodities, ultimately incurring overall welfare losses of US$4.38 trillion on average for the forty-year period to 2050, or US$110 billion per year. Producers, on the other hand, register net gains of US$2.2 trillion, on average, overall or US$55.0 billion per year because the higher prices for their produce, generated by the global effects of climate change, offset the projected declines in production (Table 10.3). However, many farmers — especially smallholder farmers — are net consumers of food, so may suffer overall economic losses from the combined producer and consumer effects. Trends in the Philippines are similar, with overall net losses of US$16.7 billion on average, or US$418 million per year (PhP19 billion per year). The TABLE 10.3 Changes in Welfare Due to Climate Change, 2011–50 Welfare Measure Climate Model

Producer Surplus

Consumer Surplus Economic Surplus Trillion U.S. dollars

World GFDL HadGEM IPSL MIROC Average

1.26 3.08 2.22 2.23 2.20

2–1.41 2–2.92 2–2.20 2–2.18 2–2.18

The Philippines GFDL HadGEM IPSL MIROC Average

–2.67 –6.01 –4.42 –4.42 –4.38 Billion U.S. dollars

39.39 69.13 50.62 60.77 54.98

–48.26 –91.78 –74.23 –72.50 –71.69

2–8.87 –22.65 –23.61 –11.74 –16.72

Notes: Costs are based on net present value estimates using a real discount rate of 3 per cent. GFDL = General Fluid Dynamics Laboratory; HadGEM = Hadley Centre Global Environmental Model; IPSL = Institut Pierre-Simon Laplace; MIROC = Model for Interdisciplinary Research on Climate. Source: Constructed by authors based on model simulation results.

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IPSL model registers the highest estimates (US$22.6 billion) and the GFDL model the lowest (US$8.9 billion). Costs to Philippine consumers average US$71.7 billion for the forty-year period or US$1.8 billion per year (PhP80.7 billion per year), while producers gain an average of US$55.0 billion overall or US$1.4 billion per year (PhP61.8 billion per year).

The Impact of Climate Change through Productivity Losses and Healthcare Costs due to Malnutrition The number of malnourished children in the Philippines in 2050 is projected to be 2.2 million, and the number of hungry people 17.1 million. Together, the increase in malnourished people due to the indirect effect of climate change on the food supply system is projected to be 2.0 million in 2050 alone. Malnutrition imposes high costs to society both through the loss in productivity due to poor health, and through the additional costs of healthcare. The Food and Agricultural Organization of the United Nations (FAO 2013) estimated the global economic cost of malnutrition due to lost productivity and additional healthcare to be as much as 5 per cent of global GDP (around US$3.5 trillion per year or US$500 per person). Similarly, World Bank (2006) estimates that the economic costs of productivity losses to individuals due to malnutrition are more than 10 per cent of lifetime earnings, and that lost GDP resulting from malnutrition runs as high as 2–3 per cent. Applying these two estimation procedures to the Philippines, the cost of malnutrition due to climate change is projected to be as high as US$646 million per year (PhP29 billion per year) and as much as US$26 billion (net present value equivalent) for the 2011–50 period (based on the FAO 2013 data). Using the World Bank data, the values are lower, at US$470 million per year (PhP21 billion per year) and US$19 billion in net present value for the forty-year period (Table 10.4).

The Economic Impact of Extreme Climate Events on the Agricultural Sector Studies project that the frequency and intensity of extreme weather events such as typhoons, floods, and droughts, is likely to increase under climate change. The frequency of intense tropical cyclones and hurricanes is projected to increase by around 10 per cent in 2050, and

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TABLE 10.4 Economic Cost of Malnutrition Due to Climate Change, 2011–50

Estimation Approach Loss of productivity and increased healthcare costs Loss of lifetime earnings

Average Number of Additional Malnourished People per Year

Economic Costs Average Yearly Cost per Person

1.29 million US$500

a

1.29 million US$361a

Cost per Year

Total Cost

US$646.15 million US$25.85 billion PhP29.08 billion US$469.67 million US$18.79 billion PhP21.14 billion

Notes: The total cost is based on net present value estimates using a real discount rate of 3 per cent. Values in Philippine pesos are calculated based on a foreign exchange rate of US$1 = PhP45. a 10 per cent of GDP per capita, which serves as proxy for earnings per capita (2010 purchasing power parity dollars). Source: Constructed by authors based on model simulation results.

their intensity by around 23 per cent (Emanuel 2013). Extreme El Niño events are also projected to double in response to increased greenhouse gas emissions (Cai et al. 2014). Damage to Philippine agriculture due to typhoons, floods, and droughts during 2000–10 has been estimated at around US$3,226 million, representing US$2,837 million in damage to crops, livestock, and fisheries; US$130 million in damage to agricultural facilities; and US$258 million in damage to irrigation systems (Israel and Briones 2013). This is equivalent to an average of US$297 million or PhP13.36 billion per year during 2000–10 (Table 10.5). The estimates of damage to agriculture during 2000–10, discussed above, combined with projected changes in producer prices of cereals due to climate change and the projected increase in the frequency and intensity of extreme events to 2050 can be used to estimate the cost of future damage to Philippine agriculture due to these extreme events (Table 10.6). Even in the absence of climate change, extreme events are estimated to cause severe damage to Philippine agriculture, at an estimated US$13.7 billion (PhP615 billion) during 2011–50, or US$342 million (PhP15 billion) per year. Under climate change, these estimates are projected to increase to US$18.7 billion (PhP840 billion) overall, or US$467 million (PhP21 billion) per year. The costs attributable

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TABLE 10.5 Total Value of Damage to Philippine Agriculture Due to Typhoons, Floods, and Droughts, 2000–10 Crops, Livestock, and Fisheries Year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Total Average (million U.S. dollars) Average (billion PhP)

Agricultural Facilities

Irrigation

Million U.S. dollars 76.00 54.79 28.28 90.37 171.69 102.53 272.57 146.14 390.69 786.92 717.47 2,837.45 257.95 11.61

0.01 21.92 0.77 0.28 14.41 n.a. 31.86 0.14 53.26 5.19 2.20 130.06 13.01 0.59

0.01 21.92 0.77 0.28 14.41 n.a. 31.86 0.14 48.46 102.78 37.58 258.23 25.82 1.16

Notes: Values were converted to current 2010 U.S. dollars; n.a. = data were not available. Source: Constructed by authors from Israel, D.C. and R.M., Briones, Impacts of Natural Disasters on Agriculture, Food Security, and Natural Resources and Environment in the Philippines (ERIA, 2013).

TABLE 10.6 Projected Value of Damage to Philippine Agriculture Due to Extreme Events, with and without Climate Change, 2011–50 Total Costs Damages due to Extreme Events 2000–2010 2011–2050 With climate change Without climate change Damage due to climate change

Costs per Year

Million U.S. Billion Million U.S. Billion dollars PhP dollars PhP 13,226

145

297

13

18,673 13,677 14,996

840 615 225

467 342 125

21 15 16

Notes: Due to lack of available data on crop and physical damages and prices, these projections were based on increasing the estimated value of damages with the frequency and intensity of extreme events, not by assessing increases in physical damages and then multiplying by their prices. Costs are based on net present value estimates using a real discount rate of 3 per cent. Values in Philippine pesos are calculated based on a foreign exchange rate of US$1 = PhP45. Source: Constructed by authors based on model simulation results.

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to climate change are therefore US$5.0 billion (PhP225 billion) in total, or US$125 million (PhP6 billion) per year.

The Total Impact of Climate Change on the Philippine Economy Estimates of the total economic cost of climate change to Philippine agriculture through its impact on producer and consumer welfare, lost productivity and lifetime earnings of malnourished people, and damage due to extreme climate events are estimated to be around US$40.5 billion (PhP1,822 billion) for the 2011–50 period, or around US$1.0 billion (PhP46 billion) per year (Table 10.7). The direct impact of climate change on crop productivity through changes in rainfall and temperature patterns is estimated to be as much as US$17 billion for the 2011–50 period, or US$418 million per year. The impact of climate change is much higher through the loss of human productivity and hence lifetime earnings due to malnutrition, which is estimated to total as much as US$19 billion for the 2011–50 period or about US$470 million per year. Agricultural damage due to extreme climate events has an estimated total of as much as US$19 billion during the 2011–50 period, but of this amount only US$5 billion in total or US$125 million per year can be attributed to climate change.

THE POTENTIAL IMPACT OF ADAPTATION STRATEGIES The economic impact of climate change on agriculture can be compensated through the implementation of adaptation strategies and investment policies. Adaptation strategies will, and should, differ across locations (Chapter 7, this volume). Some countries, for example, may be more selective in their choice of strategies — for example, implementing adaptation for selected crops or in specific areas only — whereas other countries may choose a more comprehensive approach, and still others may choose not to implement any adaptation measures at all (Table 10.8). The following scenarios reflect this reality by simulating a mix of strategies equivalent to compensating for only about half of the impact of climate change on agricultural productivity: 1. No one adapts to climate change. Without adaptation, the Philippines and the rest of the world are projected to incur the full impact of

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Changes in supply and demand

Additional number of malnourished

Increased frequency and intensity of extreme events

Producer and consumer welfare

Malnutrition

Extreme events

1,125 1,012

Total

1,470

1,418

Estimated damage to agriculture

Loss of lifetime earnings

Economic surplus

Estimation Approach

46

16

21

19

Billion PhP

40,498

14,996

18,787

16,715

1,822

1,225

1,845

1,752

Billion PhP

Total Cost Million U.S. dollars

Notes: Costs are based on net present value estimates for forty years using a real discount rate of 3 per cent. Values in Philippine pesos are calculated based on a foreign exchange rate of US$1 = PhP45. Source: Constructed by authors based on model simulation results.

Description

Cost Category

Million U.S. dollars

Cost per Year

Economic Costs

TABLE 10.7 The Economic Cost of Climate Change to Philippine Agriculture, 2010–50

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55.0 –71.7 –16.7

2,144.4 –4,306.4 –2,162.0 36.6 –39.3 –2.7

1,173.6 –2,376.5 –1,202.9

All Adapt

29.2 –40.2 –11.0

1,189.6 –2,397.5 –1,207.9

Billion U.S. dollars

62.2 –69.4 –7.2

2,127.7 –4,284.7 –2,157.0

The Philippines Adapts, but the Rest of the World Does Not

Notes: Data are based on net present values of annual changes in consumer, producer, and total surpluses for the 2011–50 period compared with baseline levels (without climate change). The underlying data are averages from the four general circulation models underlying the analyses. Adaptation strategies are only assumed to mitigate up to 50 per cent of the impact of climate change. Source: Constructed by authors based on model simulation results.

The Philippines Producer surplus Consumer surplus Total surplus

The world, excluding the Philippines Producer surplus Consumer surplus Total surplus

Welfare Measure

None Adapt

The Rest of the World Adapts, but the Philippines Does Not

Adaptation Scenarios

TABLE 10.8 Changes in Social Welfare Due to Climate Change, with and without Adaptation, 2011–50

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climate change. This is equivalent to a US$16.7 billion loss to the Philippines and a US$2.2 trillion to the rest of the world. 2. All adapt to climate change. The full costs of climate change can be drastically reduced if everyone adapts. Under this scenario, the net loss to the Philippines is US$2.7 billion — or an effective gain of US$14 billion, which is equivalent to an 84 per cent recovery — and the net loss to the rest of the world is US$1.2 trillion — an effective gain of US$959 billion, which is equivalent to a 44 per cent recovery. 3. The rest of world adapts, but the Philippines does not. When the Philippines does not implement adaptation, but the rest of the world does, the Philippines still benefits as a result of declining world agricultural crop prices. Under this scenario, the Philippines effectively gains US$5.7 billion, which is equivalent to a 34 per cent recovery. The rest of the world can benefit from net gains of US$954 billion or a 44 per cent recovery. 4. The Philippines adapts, but the rest of the world does not. When the Philippines adapts but the rest of the world does not, higher gains of around US$10 billion result for the country, which is equivalent to a 57 per cent recovery; the gain for the rest of the world is minimal, at US$5 billion or a recovery of only 0.2 per cent. The general effects of adaptation strategies on the impact of climate change are to mitigate the loss of agricultural productivity, rein in the increase in food prices, increase the availability of food to consumers, and improve nutrition and food security. Producer welfare gains are lower because price increases are mitigated, but consumer losses are much lower. Smaller producers will mostly incur net gains because of their reliance on the market for food purchases. The implementation of adaptation strategies and investment policies in the Philippines offers a tremendous opportunity to counteract the impact of climate change (by up to 84 per cent), whether the rest of the world implements adaptation strategies or not.

Potential Adaptation Technologies and Investment Policies Although the yield effects of climate change are not always negative, the majority of reported studies point to declines in productivity. As previously discussed, the productivity of cereals is projected to decline

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the most, both globally and domestically. In the Philippines, climate change is projected to cause rice yields to decline by 1.7 per cent in 2030 and 4.1 per cent in 2050, and corn yields to decline by 7.6 per cent in 2030 and 15.7 per cent in 2050 (Table 10.1). The ensuing analysis therefore focuses on the adaptation strategies recommended for these two crops, and comprises three categories of technologies: (1) the application of existing crop production technologies; (2) the support, promotion, and dissemination of new and emerging crop production technologies; and (3) infrastructure investment, in particular, the development of irrigation systems (Table 10.9). It should be noted that the results presented in Table 10.10 assume 100 per cent adoption of fully developed adaptation technologies in farmers’ fields (meaning not only those already in existence but those yet to be developed, tested, and disseminated, and adopted). Thus, they represent the biophysical yield potential for these technologies. The economic analysis of these technologies presented below utilizes this yield potential together with the estimated adoption rates and probability of successful development of each technology.

Applying Existing Technologies The first category of adaptation technologies is derived from the analysis presented in Chapter 9 (this volume) on biophysical crop modelling under MIROC climate only. These technologies are readily available for implementation (Tables 10.9 and 10.10): 1. Adding fertilizer. The application of fertilizer is part of a package of modern agricultural technologies. Since the current average level of fertilizer application in the Philippines is somewhat below economically optimal levels, opportunities exist for yield increases through this strategy. This technology assumes the addition of 10 kilograms (kg) of urea fertilizer per hectare (ha), or equivalent nitrogen content to regions with fertilizer input of less than 90 kg per ha of nitrogen fertilizer. Assuming full implementation on irrigated rice farms, this strategy is estimated to have the biophysical potential to increase yields of irrigated rice in the Philippines by as much as 10 per cent, thereby raising average national rice yields by as much as 2.6 per cent. 2. Changing the planting date. Given changing rainfall and temperature

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patterns under climate change, productivity can be increased by simply altering the planting date or month, for example, to avoid heat stress at times when plants are most vulnerable. With full implementation of this strategy, average rain-fed rice yields could be increased by as much as 3.0 per cent. In order for this strategy to be implemented, however, institutions need to be in place to monitor and disseminate short-term rainfall and temperature forecasts to farmers. 3. Changing seed variety. This strategy involves shifting to alternative existing varieties, not newly developed ones (which are discussed separately in the next section). Prolonged use of same varieties on the same fields lowers the quality of seeds, affects their resistance to the elements and to existing pests and diseases, and reduces their productivity. So, when farmers choose varieties of rice and corn not recently planted in their fields — but readily available from certified seed growers or government extension agencies — they can increase their yields. For example, full adoption of this strategy by all rice and corn farmers could increase their yields by as much as 1.5 per cent. 4. Combining all three strategies. The effects of the three strategies described above are complementary, so when all three are implemented simultaneously, and assuming full adoption at the national level, potential yield gains can be as much as 4–6 per cent. Yield increases of 2 to 3 per cent may not seem that significant, but production data for 2010 indicate that a 1 per cent increase in rice yields represents an additional 105,201 metric tons (mt) of rice or 63,768 mt of corn, PSA (2015). Considering that the country’s rice imports averaged 1,505,500 mt per year during 2004–13, a 3 per cent yield increase would amount to 315,603 mt or around 21 per cent of yearly imports. Note that, although the above technologies are currently available, actual adoption is not widespread. Although average fertilizer use is relatively high for rice, suboptimal fertilizer application continues to persist on many farms, even on irrigated rice farms. Planting dates are usually determined by the onset of the rainy season, whereas the concept of changing planting dates focuses more on minimizing heat stress rather than water stress. The optimal planting window minimizes both heat and water stress, so the successful implementation of this strategy requires

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a

Addition of fertilizer

Change of variety

Change of planting date

Type of Technology

Enhanced nutrient-use efficiency

Drought tolerance

Combination of the above three strategies 2. New and emerging technologiesb Varietal-trait/Seed technologies Heat tolerance

1. Existing technologies

Category of Technology

These improved varieties allow the plant to maintain better yields than regular varieties at higher temperatures. These improved varieties allow the plant to maintain better yields than regular varieties through enhanced soil-moisture uptake capabilities and reduced vulnerability to water deficiency. Thee varieties have an enhanced yield response to soil nutrients (such as but not limited to nitrogen, phosphorous, and potassium), which are found in inorganic fertilizers.

Given changing rainfall and temperature patterns under climate change, yields can be increased by simply altering planting dates. Prolonged use of same varieties in the same fields lowers seed quality and affects the resistance of plants both to the elements and to existing pests and diseases; hence, a change to varieties not recently planted (but still readily available) can increase productivity. The current level of fertilizer application in the country still offers opportunities for yield increases especially to low-fertilizer farms. This adaptation involves the application of additional 10 kg of urea fertilizer (or equivalent nitrogen content) per hectare on low-fertilizer input farms (less than 90 kg of Nitrogen fertilizer per ha). This strategy involves the simultaneous implementation of all three of the preceding strategies in the same farm.

Description of Technology

TABLE 10.9 Description of Adaptation Technologies and Irrigation Development

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90 per cent development

70 per cent development

This involves the construction of new irrigation systems to a level of 70 per cent of the country’s potential irrigable area. This involves the construction of new irrigation systems to a level of 90 per cent of the country’s potential irrigable area.

This technology involves chemical treatment to protect crops against weeds. This technology involves chemical treatment to protect crops against insects (arthropod pests). This technology involves chemical treatment to protect crops against diseases (pathogens). This approach involves combining the application of three technologies, one each from varietal trait, farm management, and crop protection categories.

This approach involves minimum or no soil disturbance, often in combination with residue retention, crop rotation, and use of cover crops. This approach combines the use of chemical fertilizers, crop residues, and manure/compost. This approach involves channeling water towards crop fields through macroor micro-catchment systems or by using earth dams, ridges, or graded contours. This approach involves global positioning system (GPS)–assisted delivery of agricultural inputs, as well as low-technology agricultural practices aimed to optimize crop management (including effective plant spacing and use of appropriate planting windows).

Notes: abased on Chapter 9 (this volume); bbased on Rosegrant et al., Food Security in a World of Natural Resource Scarcity: The Role of Agricultural Technologies (Washington, D.C.: International Food Policy Research Institute, 2014). Source: Constructed by authors.

3. Irrigation development

Combined technology

Disease protection

Crop-protection technologies Weed protection Insect protection

Precision agriculture

Integrated soil fertility management Water harvesting

Farm-management technologies No-till

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470

TABLE 10.10 The Potential Impact of Adaptation Strategies on Rice and Corn Productivity, 2050 Luzon Visayas Mindanao The Philippines Technology Yield improvement Rice, irrigated Existing technologiesa Change planting date Change variety Add fertilizer All three technologies New and emerging technologiesb Varietal traits Heat tolerance Nutrient-use efficiency Farm management Integrated soil fertility management Precision agriculture Crop protection Against disease Against pests Against weeds Combined technology Rice, rain-fed Existing technologiesa Change planting date Change variety Add fertilizer All three technologies New and emerging technologiesb Varietal traits Heat tolerance Drought tolerance Nutrient-use efficiency Farm management Integrated soil fertility management Precision agriculture Crop protection Against disease Against pests Against weeds Combined technology

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Per cent

1.0 1.4 2.6 5.1

1.2 1.2 2.6 5.0

1.0 2.1 2.5 5.6

1.0 1.5 2.6 5.2

1.9 50.3

0.3 45.2

1.8 54.8

1.9 51.3

23.6 20.9

30.9 28.7

30.5 29.2

25.7 23.4

10.2 8.2 5.4 56.3

8.4 11.0 7.6 61.5

11.8 10.1 8.0 68.1

10.6 8.8 6.1 59.6

4.1 0.9 1.4 6.5

2.8 1.7 1.3 5.9

1.9 2.2 1.0 5.2

3.0 1.5 1.3 5.9

2.8 2.0 10.8

0.03 0.7 13.0

0.03 1.4 11.4

1.0 1.4 11.7

7.7 3.7

7.0 1.4

4.5 0.7

6.4 2.0

9.5 8.3 5.7 18.8

9.5 9.8 6.3 17.3

11.7 10.3 8.0 16.9

10.3 9.5 6.7 17.7

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Partial Equilibrium Approach to Modelling Alternative Agricultural Futures Corn Existing technologiesa Change planting date Change variety Add fertilizer All three technologies New and emerging technologiesb Varietal traits Heat tolerance Drought tolerance Nutrient-use efficiency Farm management No till Integrated soil fertility management Water harvesting Crop protection Against disease Against pests Against weeds Combined technology Area growth Irrigation development 70 per cent of potential irrigable area 90 per cent of potential irrigable area Rehabilitation

3.8 0.7 1.5 6.1

2.2 0.3 1.5 4.1

1.7 0.7 1.5 3.8

2.2 0.6 1.5 4.3

17.8 6.0 11.1

17.3 4.5 12.0

11.1 2.6 13.3

13.3 3.4 12.8

26.8 31.5 0.6

23.3 30.3 0.1

23.2 30.7 0.9

24.0 30.8 0.8

11.2 16.2 12.5 44.6

9.8 13.0 11.2 40.5

12.0 16.9 14.5 41.7

11.7 16.5 13.9 42.4

15.6 48.6 3.9

1.5 30.5 4.5

67.9 115.9 2.7

25.9 61.9 3.6

471

Notes: a based on Chapter 9 (this volume) and b based on Rosegrant et al. (2014), but simulated only under MIROC climate scenario resulting to slight differences from Chapter 9 values. Data shown in this table are from the DSSAT biophysical crop model and assume a 100 per cent adoption rate and a 100 per cent probability of success in developing the specified technologies. As inputs for the IMPACT economic simulations, these data were adjusted according to the estimated adoption rates and probability of successful development presented in Table 10.12. Source: Constructed by authors based on simulation results under MIROC climate scenario

an agency capable of monitoring and disseminating information on temperature and rainfall trends. Farmers are also used to saving seed for replanting, which perpetuates the use of the same varieties, so the presence of a seed industry offering a range of varieties accessible to farmers can help promote the adoption of this practice.

Promoting New and Emerging Technologies New and innovative agricultural technologies are continually being developed to increase agricultural productivity and improve food security

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and nutrition. Agricultural research and development (R&D) is also focusing on technologies and practices to combat the effects of climate change. Significant advances in climate-resilient agriculture have already occurred, and technologies are being tested in the field and disseminated to and adopted by farmers on a limited scale. Other technologies are still in their initial stages of development or are undergoing rigorous field testing and evaluation before being released for adoption. Prior to the application of some of these technologies, crop modelling — such as the analysis presented in this chapter — facilitates the assessment of these technologies and their potential to alleviate the impacts of climate change. This category of technologies includes the following approaches (Table 10.10): 1. Varietal trait/Seed technologies. These technologies are designed to address climate change through heat-tolerance, drought-tolerance, and nutrient-use efficiency improvements in seed varieties. Assuming their successful development and full adoption, as discussed above, nutrient-use efficient varieties are estimated to have the potential to increase average irrigated rice yields by as much as 51 per cent. 2. Farm management technologies. These technologies include farmmanagement practices, such as no-till, integrated soil fertility management (ISFM), water harvesting, and precision agriculture, which involves applying the right treatment in the right place at the right time of agricultural inputs (Gebbers and Adamchuck 2010). With precision agriculture, farmers use real-time information on the status of fertilizer, soil, and water in their fields to assist in making the best planting, fertilizing, irrigation, and harvesting decisions. When fully adopted, ISFM has the potential to increase irrigated rice yields in the Philippines by as much as 26 per cent, and corn yields by 31 per cent, while precision agriculture can increase irrigated rice yields by 23 per cent. 3. Crop protection technologies. Under climate change, new kinds of pests, diseases, and weeds are anticipated to arise. New and emerging crop protection technologies designed to address these vulnerabilities have the potential to increase Philippine rice yields by 11 per cent and corn yields by 17 per cent, on average. 4. Combined technology. New and emerging technologies have

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varying degrees of complementarity or non-complementarity in terms of their effect on crop yields. They have high degrees of complementarity across technology groups, but are less complementary within the same groups. The combined effects of these cross-category technologies cannot be directly simulated with existing models, but indicative of relatively low estimates are assumed to be the sum of the average impact of each technology group. Combined technology is estimated, for example, to have the potential to increase irrigated rice yields in the Philippines by 60 per cent.

Investing in Irrigation Development Climate change is projected to reduce Philippine rice production by 3.2 per cent and corn production by 13.0 per cent between 2011 and 2050. Since crop production is a function of yields and area, irrigation development is an important strategy for increasing production by expanding the area available for cultivation. Unlike existing and emerging adaptation technologies that primarily work by increasing yields, irrigation development not only increases crop yields, but also expands the available cultivation area both by opening up new lands to agriculture and by making (formerly) rain-fed land more productive. Crop yields are also increased by minimizing constraints to crop growth and productivity due to water stress. So that, as irrigated areas increase, average yields of both irrigated and rain-fed areas also increase. As of December 2013, an estimated total of 3.02 million ha of irrigable area have been developed in the Philippines, representing 55.6 per cent of potential irrigable area (Table 10.11). The average cropping intensity2 of these irrigated areas was estimated to be around 145 per cent for the same period, NIA (2014). The IMPACT simulations underlying this analysis modelled three irrigation development scenarios to explore the impact of adaptation investment policies (Table 10.10): 1. Irrigation development at a rate of 70 per cent of potential irrigable area by 2030, applied by major regional grouping (that is, Luzon, Visayas, and Mindanao), meaning that Mindanao — which has the lowest share of irrigated land of the three groupings — requires a 68 per cent expansion of existing irrigated area.

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97,310 262,744 456,898 480,783 85,929 138,719 239,440 189,934 46,159 84,081 156,205 74,952 113,631 147,313 286,263 159,249 3,019,609

13,688 46,095 148,315 193,707 20,450 18,938 23,189 47,020 11,182 20,883 25,643 16,161 25,758 35,345 63,677 30,164 740,214

Notes: ARMM = Autonomous Region of Muslim Mindanao; CAR = Cordillera Administrative Region. Firmed-up service area is equivalent to the service area less any land either converted from agricultural to nonagricultural uses or considered permanently “nonrestorable” (that is, having either insufficient water or irrigation facilities that can no longer be completed for technical reasons). Data for estimated total irrigable area are based on the 3 per cent slope criteria, the minimum slope of land for which irrigation is feasible. Source: NIA, Annual Report (Quezon City, 2014).

Region CAR Region 1 (Ilocos Region) Region 2 (Cagayan Valley) Region 3 (Central Luzon) Region 4a (CALABARZON) Region 4b (MIMAROPA) Region 5 (Bicol Region) Region 6 (Western Visayas) Region 7 (Central Visayas) Region 8 (Eastern Visayas) ARMM Region 9 (Zamboanga Peninsula) Region 10 (Northern Mindanao) Region 11 (Davao Region) Region 12 (SOCCSKSARGEN) Region 13 (Caraga) Total

Estimated National Total Irrigable Irrigation System Area

“Firmed-up” Service Area Other Government Remaining Assisted Communal Private Potential Irrigation Irrigation Irrigation Irrigation Area to be System Total System System Development Developed Hectares Per Cent Hectares 46,355 27,073 1,667 88,783 91.24 8,527 50,544 21,232 50,722 168,592 64.17 94,152 50,987 49,499 23,811 272,611 59.67 184,286 66,594 7,792 22,357 290,450 60.41 190,333 18,100 6,334 2,578 47,462 55.23 38,467 31,379 14,469 4,201 68,986 49.73 69,732 68,810 25,059 13,035 130,093 54.33 109,347 34,866 15,053 15,459 112,397 59.18 77,537 23,761 4,702 1,346 40,991 88.80 5,168 37,031 6,197 3,286 67,397 80.16 16,684 19,278 90 295 45,306 29.00 110,900 22,344 1,787 3,509 43,801 58.44 31,151 25,205 6,247 3,659 60,869 53.57 52,762 23,326 1,636 2,812 63,119 42.85 84,194 34,297 3,315 10,823 112,112 39.16 174,150 23,543 4,137 7,782 65,626 41.21 93,623 576,419 194,620 167,342 1,678,595 55.59 1,341,014

TABLE 10.11 Status of Irrigation Development in the Philippines as of 31 December 2013

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2. Irrigation development to meet a target of 90 per cent of potential irrigable area by 2030, which requires a 116 per cent expansion of existing irrigated area in Mindanao. 3. Rehabilitation of existing irrigation systems to raise the average cropping intensity from 145 to 160 per cent by 2030.

The Potential for Yield Improvement through Adaptation Technologies As with the impacts of climate change, the impacts of the adaptation technologies and irrigation development are location-specific. As previously discussed, the yield improvement results presented so far in this chapter assume 100 per cent adoption of fully developed adaptation technologies (even the new and emerging ones) in farmers’ fields. For the analysis in this section, however, results are based on estimated adoption rates and probabilities of successful development of technologies simulated (Table 10.12).

Comparing Yield Improvements from Adaptation Technologies across Crops Simulation results for the 2011–50 period indicate that, for irrigated rice, nutrient-use efficient varieties, ISFM, and precision agriculture offer the highest yield increase potentials, at 51.3, 25.7, and 23.4 per cent, respectively, over baseline levels without climate change (Table 10.10). Changing planting dates and varieties, and shifting to heat-tolerant varieties are projected to be least effective in improving yields (at 1.0, 1.5, and 1.9 per cent, respectively). For rain-fed rice, nutrient-use efficient varieties are projected to offer the highest yield improvement (11.7 per cent), followed by crop protection technologies against diseases (10.3 per cent) and against pests (9.5 per cent). The lowest yield improvements are projected to result from heattolerant and drought-tolerant varieties (1.0 and 1.4 per cent, respectively). For corn, the highest yield improvements are projected to result from the application of ISFM (30.8 per cent), no-till farming (24.0 per cent), and crop protection against pests (16.5 per cent), whereas the least impact results from changing varieties (0.6 per cent) and water harvesting (0.8 per cent). The approaches of combining existing complementary technologies (that is, changing date and variety and adding fertilizer) and combined

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TABLE 10.12 Assumptions on Adoption, Development, and Application of Selected Climate Change Adaptation Technologies Technology Existing technologies Change planting date Change variety Add fertilizer All three technologies Emerging technologies Varietal traits Heat tolerance Drought tolerance Nutrient-use efficiency Farm management No till Integrated soil fertility management Water harvesting Precision agriculture Crop protection Against disease Against pests Against weeds Combined technology

Probability of Adoption Expected Rate of Development (%) Rate (%) Application (%) 100 100 100 100

60 60 60 60

60 60 60 60

100 100 135

60 60 60

60 60 21

100 100 100 100

60 40 40 40

60 40 40 40

160 160 160 135

60 60 60 40

36 36 36 14

Source: Estimated by authors and based on Launio, C., G. Redondo, J. Beltran, and Y. Morooka, “Adoption and Spatial Diversity of Later Generation Modern Rice Varieties in the Philippines”, Agronomy Journal 100 (2008): 1380–89.

technology (that is, varietal trait, farm-management, and crop protection technologies) have the potential to further increase yield improvements for both rice and corn. Combined technology can potentially increase irrigated rice yields by as much as 59.6 per cent, rain-fed rice yields by 17.7 per cent, and corn yields by 42.4 per cent in 2050.

Comparing Yield Improvements from Adaptation Technologies across Regions Cross-regional comparisons of the projected results of yield improvements from adaptation technologies are useful in determining where to

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concentrate the implementation of selected adaptation technologies (Table 10.10). Nutrient-use efficient varieties, ISFM, and precision agriculture are projected to offer the highest yield improvement potentials for irrigated rice. Two of these technologies, nutrient-use efficient varieties and precision agriculture, are more effective in Mindanao than in the other two regions, offering 54.8 and 29.2 per cent yield improvements, respectively. ISFM is most suited to Visayas (30.9 per cent improvement) and Mindanao (30.5 per cent), but still offers substantial yield improvement potential in Luzon (23.6 per cent). For rain-fed rice, nutrient-use efficient varieties offer the best results in Visayas (13.0 per cent), whereas crop protection against diseases and pests offer the best results in Mindanao (11.7 and 10.3 per cent, respectively). For corn, ISFM and no-till farming offer marginally higher yield potentials in Luzon (31.5 and 26.8 per cent, respectively) than in either of the other regions, and crop protection against pests offer greater potential in Mindanao and Luzon (16.9 and 16.2 per cent, respectively) than in Visayas. Combined technology is best suited to Mindanao and Luzon; it offers the best results for irrigated rice in Mindanao (68.1 per cent) and for rain-fed rice and corn in Luzon (18.8 and 44.6 per cent, respectively). Combining existing technologies works best for irrigated rice in Mindanao (5.6 per cent) and for rain-fed rice and corn in Luzon (6.5 and 6.1 per cent, respectively). The simulations of area growth through irrigation development are based on the assumption of the availability of the necessary government and private funding, and that new construction would be completed by 2030 and fully operational by 2035 (Table 10.10). The initial levels of irrigation development are 60.6 per cent of potential irrigable area in Luzon, 69.0 per cent in Visayas, and 41.7 per cent in Mindanao (calculated by the authors from Table 10.11). When applied uniformly across the three regional groupings, the strategy of developing 70 per cent of potential irrigable area results in Mindanao gaining an additional 265,500 ha of irrigated land, equivalent to a 68 per cent increase over 2013 levels. Equivalent results for Luzon increase irrigated area by 166,300 ha land or 16 per cent over 2013 levels. For Visayas, the increase is only marginal (3,300 ha or 1.5 per cent) due to the existing high degree of irrigation development within this grouping. The strategy of developing 90 per cent irrigation expansion follows a similar pattern across the three major regional groupings, but (unsurprisingly) at a higher rate of increase (116 per cent for Mindanao, 49 per cent for Luzon, and 31 per cent for Visayas). Rehabilitation of

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irrigation facilities improves water-use efficiency and the availability of water for irrigation. As an alternative form of irrigation development, rehabilitation aims to raise cropping intensity from the existing average of 145 per cent, to 160 per cent, which represents regional increases in irrigated land of 4.5 per cent in Visayas, 3.9 per cent in Luzon, and 2.7 per cent in Mindanao.

The Impact of Existing Adaptation Technologies on Production and Consumption The following analysis explores the potential impact on production and consumption of corn and rice of existing technologies, new and emerging technologies, and investment in irrigation development (Table 10.13).3 The three existing technologies explored in the analysis are all projected to have only modest impacts on production and consumption. The application of additional fertilizer has the highest yield impact on average rice yields in 2050 (1.1 per cent). This shows that, although the application of additional fertilizer on farms with low-fertilizer use can increase yields, the effect on national average yields is minimal because, on average, rice farms already use high levels of fertilizer, at around 4.5 bags per ha as of 2009–11 (PSA 2015). For corn, changing the planting dates to take advantage of better rainfall and temperature patterns has the best effect of both yields (1.3 per cent) and production (1.7 per cent). Simultaneous adoption of all three existing technologies, however, does have a significant impact on both rice yields (2.5 per cent) and production (1.5 per cent) — considering that the average impact of climate change on Philippine rice is a decline of about 4.1 per cent in yields and 3.2 per cent in production (Table 10.13). Consumer rice prices are also projected to decline by 6.5 per cent, with a corresponding increase in consumption (1.4 per cent), although these may be insufficient to compensate for the price increases and consumption declines caused by climate change.

The Impact of New and Emerging Adaptation Technologies on Production and Consumption Production and Yields Most of the new and emerging adaptation technologies under study are more effective for use with corn than with rice because corn is much

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more negatively affected by climate change, so adaptation technologies have more capacity to improve production and yields. Heat-tolerant corn varieties are projected to increase yields by as much as 7.9 per cent and production by 10.2 per cent in 2050. Drought-tolerant corn varieties are projected to have more modest impact of yields and production, at 2.0 and 2.6 per cent, respectively. Nutrient-use efficient corn varieties are projected to increase yields by 3.1 per cent and production by 4.4 per cent. None of these corn varieties, however, can compensate for the full impact of climate change on corn production (Table 10.13). For rice, however, nutrient-use efficient varieties are more likely to compensate for the full effect of climate change, given that they are projected to increase yields by 6.1 per cent and production by 3.5 per cent. Farm management technologies have similar effects (higher for corn than for rice), but in this case, they may be capable of compensating for the full effect of climate change. No-till farming is projected to increase yields by 14.2 per cent and production by 18.4 per cent in 2050, which would fully compensate for production declines stemming from climate change. A minimum 16.0 per cent increase in production is needed to compensate for the 13.0 per cent decline due to climate change. ISFM technology is also projected to increase yields by 12.2 per cent and production by 16.3 per cent, which would be sufficient to compensate for the production shortfall stemming from climate change (Table 10.13). For rice, ISFM is projected to increase yields by 6.9 per cent and production by 3.8 per cent in 2050 (Table 10.13). Similarly, precision agriculture is projected to increase yields by 6.0 per cent and production by 3.4 per cent. In both cases, these increases are sufficiently high to compensate for production losses due to climate change. Modest increases in yields and production are simulated for the three crop protection technologies, with protection against pests highest for corn yields (5.9 per cent) and protection against disease highest for rice yields (3.0 per cent). Combined technology has the highest impact in 2050 both on rice (up to 12.9 and 7.0 per cent for rice yields and production, respectively) and on corn (21.4 and 29.0 per cent for corn yields and production, respectively) and can also more than compensate for production declines in both crops caused by climate change. Notably, however, although combined technology can potentially provide higher yield and production benefits, these technologies are the least adopted and the more difficult to apply because they include new practices for every major aspect of farm activities.

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Adaptation Technology

Rice Change planting date Change variety Add fertilizer All three technologies Varietal traits Heat tolerance Drought tolerance Nutrient-use efficiency Farm management Integrated soil fertility management Precision agriculture Crop protection Against disease Against pests Against weeds Combined technology Irrigation development 70 per cent of potential irrigable area 90 per cent of potential irrigable area Rehabilitation

Consumer Price

0.42 0.43 0.64 1.48 0.41 0.09 3.45 3.78 3.38 1.73 1.47 1.02 7.04 10.41 23.47 1.18

0.84 0.15 6.13

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6.90 6.01 2.98 2.55 1.79 12.93 11.63 26.61 1.34

–36.57 –55.01 –5.16

–7.56 –6.44 –4.51 –26.88

–15.67 –14.16

–1.75 –0.34 –14.43

–1.85 –1.89 –2.82 –6.51

Percentage Change

Production

0.67 0.71 1.10 2.51

Yield or Area

Indicator of Impact

9.55 17.32 1.10

1.61 1.37 0.95 6.48

3.50 3.13

0.39 0.09 3.19

0.39 0.40 0.60 1.38

Consumption

TABLE 10.13 The Projected Impact of Adaptation Technologies and Irrigation Investment on Rice and Corn Indicators, 2050

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1.69 0.50 1.21 3.45 10.19 2.55 4.37 18.36 16.31 0.41 5.54 7.70 6.45 29.00 –3.17 –3.27 0.37

1.30 0.36 0.89 2.58 7.91 2.00 3.05 14.24 12.21 0.33 4.18 5.89 4.96 21.39 –3.18 –3.27 0.37

. . . .

. . . .

.. .. .. ..

... ... ... ... ... ... ...

... ... ...

... ... ...

... ... ...

. . . .

... ... ... ...

... ... ...

... ... ...

. . . .

Notes: Data reflect the percentage change from the Model for Interdisciplinary Research on Climate (MIROC) climate change scenario (unlike the previous climate change scenario results that averaged the results from the four climate models). Yield changes stem from the application of adaptation technologies, whereas area changes stem from irrigation investment. The data on yield changes in this table differ from those presented in Table 10.9 because they reflect the effective change due to adaptation technology, factoring in adoption rates, the probability of the R&D being successful, and demand and supply responses to prices and other economic variables. These data are also not directly comparable with those presented in Table 10.1 because those comparisons were derived from the baseline scenario of no climate change, whereas these comparisons are with the MIROC model’s baseline scenario, which includes climate change. The data on irrigation investment are also not comparable with those presented in Table 10.1 because those comparisons pertain to increases in the total irrigation area, whereas these comparisons are with the total (irrigated and rain-fed) harvested area, including changes in cropping intensity, irrigated-rain-fed ratios, and nonrice crop use of irrigated lands. Ellipses indicate that changes were not significant. Source: Constructed by authors based on simulation results under MIROC climate scenario.

Corn Change planting date Change variety Add fertilizer All three technologies Varietal traits Heat tolerance Drought tolerance Nutrient-use efficiency Farm management No till Integrated soil fertility management Water harvesting Crop protection Against disease Against pests Against weeds Combined technology Irrigation development 70 per cent of potential irrigable area 90 per cent of potential irrigable area Rehabilitation

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Prices and Consumption The same adaptation technologies that can compensate for the full effect of climate change-induced production losses (that is, nutrient-use efficient varieties, ISFM, precision agriculture, and combined technology) can also compensate for the full effect of consumption declines. Combined technology is projected to result in the highest increase in rice consumption in 2050 (6.5 per cent), followed by ISFM (3.5 per cent), nutrient-use efficient varieties (3.2 per cent), and precision agriculture (3.1 per cent) (Table 10.13). In contrast to rice, adaptation technologies for corn generate significant yield or production gains, but do not have an impact on lowering domestic corn prices or increasing the consumption of corn. The Philippines is a small corn producer with no influence on international corn prices; it has been a consistent net importer of corn, and the equilibrium domestic prices have consistently been above world prices. Consequently, increased production does not influence the price of corn, but rather reduces the country’s reliance on corn imports. The situation is different for rice, however. Although the Philippines is a small rice producer and world price transmission is also assumed to be perfect, the changes in domestic prices simulated here are within the import–export price wedge. Within this price range, equilibrium domestic rice market prices move independently of world price movements in response to changes in domestic supply and demand.

Irrigation Development Simulation results indicate that, with a 11.6 per cent increase in harvested area, rice production is projected to increase by as much 10.4 per cent in 2050 through the development of up to 70 per cent of the country’s potential irrigable lands; for the more aggressive irrigation development scenario (90 per cent development of potential irrigable lands), the projected increase in effective harvested rice area is 26.6 per cent and the projected increase in rice production is 23.5 per cent. Both of these scenarios compensate for the full effect of production and consumption losses due to climate change. Rehabilitation of irrigation systems to increase cropping intensity and the effective rice area, while less expensive than irrigation development, does not increase production sufficiently to compensate for the negative impacts of climate change. Irrigation development, on the other hand, reduces the land area available for corn production (by converting rain-fed lands

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traditionally devoted to corn, to irrigated lands suitable for rice production). Declines under the 70 and 90 per cent irrigation development strategies are projected to be 3.2 and 3.3 per cent, respectively, for corn area and 3.2 and 3.3 per cent, respectively, for corn production.

The Impact of Existing Adaptation Technologies on Food Security Of the adaptation technologies studied, combined technology has the greatest impact in increasing food availability (2.9 per cent) and reducing the number of malnourished children (3.4 per cent) and hungry people (14.3 per cent) in 2050. However, by increasing the area available for production, irrigation development to 70 per cent of the country’s potential has an even higher impact — increasing food availability by 4.1 per cent and reducing the number of malnourished children by 4.8 per cent and the number of hungry people by 19.6 per cent (Table 10.14). While the 90 per cent irrigation development strategy has even higher impacts on these indicators — 7.3, 8.4, and 30.3 per cent, respectively — its associated costs and logistical challenges make it implausible. All of these strategies have the potential to compensate for the full impact of climate change on these indicators (see Table 10.2 for comparisons). Three other adaptation technologies are also projected to have strong impact in counteracting the effects of climate change on food security: ISFM, precision agriculture, and nutrient-use efficient technologies. Of these, ISFM has the highest impact on food availability (1.6 per cent), malnutrition (–1.9 per cent), and hunger (–8.6 per cent) in 2050. Notably, these are the same technologies projected to fully compensate for losses in rice production and consumption due to climate change.

The Impact of Existing Adaptation Technologies on Producers and Consumers It should be noted that this analysis only focuses on rice and corn, whereas climate change affects all agricultural production systems. Nevertheless, these results are indicative of the welfare gains or losses to producers and consumers, and hence to society, and their ability to compensate for the economic impact of climate change on the sector. The net economic gains are highest for the development of new irrigation systems and

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0.3 0.3 0.4 0.7

0.4 0.1 1.5 0.2 1.6 1.5 0.1 0.8 0.7 0.5 2.9 4.1 7.3 0.7

2,718 2,718 2,721 2,730

2,720 2,714 2,752 2,715 2,755 2,751 2,713 2,733 2,730 2,725 2,789 2,823 2,910 2,728 2,711

2.10 2.02 2.18 2.20

2.18 2.18 2.19 2.13

2.20 2.16 2.16 2.20

2.19 2.20 2.16

2.19 2.19 2.19 2.18

Millions

–4.8 –8.4 –0.8

–1.0 –0.8 –0.6 –3.4

–0.2 –1.9 –1.7 –0.1

–0.4 –0.2 –1.8

–0.3 –0.3 –0.5 –0.8

Percentage Change

13.65 11.83 16.37 16.97

16.23 16.32 16.47 14.54

16.82 15.52 15.65 16.88

16.64 16.85 15.62

16.73 16.73 16.61 16.32

Millions

–19.6 –30.3 1–3.5

1–4.4 1–3.8 1–2.9 –14.3

1–0.9 1–8.6 1–7.8 1–0.6

1–1.9 1–0.7 1–7.9

1–1.4 1–1.4 1–2.1 1–3.9

Percentage Change

Note: Data reflect the percentage change from the Model for Interdisciplinary Research on Climate (MIROC) baseline climate change scenario. Source: Constructed by authors based on MIROC simulation results.

Change planting date Change variety Add fertilizer All three technologies Varietal traits Heat tolerance Drought tolerance Nutrient-use efficiency Farm management No till Integrated soil fertility management Precision agriculture Water harvesting Crop protection Against disease Against pests Against weeds Combined technology Irrigation Development 70 per cent of potential irrigable area 90 per cent of potential irrigable area Rehabilitation MIROC’s climate change scenario

Adaptation Technology

Food Availability Kilocalories per capita Percentage per day Change

Indicator of Food Security Malnourished Children Population at Risk of Hunger

TABLE 10.14 The Projected Impact of Adaptation Technologies and Irrigation Investment on Food Security, 2050

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the implementation of combined technology. Of the single adaptation technologies, ISFM, nutrient-use efficient varieties, and precision agriculture are also projected to have high economic impacts. In general, gains are achieved through production growth that reduces the domestic price of rice. Producers lose because of the lower prices, but consumers gain more. The highest overall economic gains result from 90 per cent irrigation development (US$41.1 billion for the forty-year period from 2011 until 2050), followed by combined technology (US$19.0 billion), and 70 per cent irrigation development (US$13.9 billion) (Table 10.15). Of the single technologies, ISFM is projected to have the highest economic impact (US$10.2 billion in total), followed by nutrient-use efficient varieties (US$8.4 billion), and precision agriculture (US$7.8 billion). Despite being limited to rice and corn in this analysis, when fully implemented on farms, combined technology more than compensates for welfare losses due to climate change. Gains from other technologies, although not sufficient to fully compensate for the effects of climate change, also offer considerable benefits. Gains from ISFM, nutrient-use efficient varieties, and precision agriculture are projected to be equivalent to US$254.4 million, US$208.9 million, and US$196.0 million per year for the forty-year period projected.

The Effectiveness of Adaptation Strategies in Countering the Impact of Climate Change The simulation results presented in this section indicate which of the potential adaptation strategies are most successful in countering the negative impacts of climate change (Table 10.16). As discussed earlier in this chapter, climate change is projected to reduce Philippine rice production by as much as 3.2 per cent, rice consumption by 2.9 per cent, and available food for consumption by 2.2 per cent in the 40-year period to 2050. Child malnutrition is projected to increase by 2.7 per cent, the incidence of hunger by 12.8 per cent, and welfare losses are estimated to total US$16.72 billion. To fully compensate for the effects of climate change on these indicators, adaptation strategies must increase production by a minimum of 3.3 per cent, consumption by a minimum of 3.0 per cent, and the availability of food by a minimum of 2.2 per cent. Strategies must also reduce the number of malnourished children by at least 2.6 per cent, and the number of hungry people by 11.4 per cent. The agricultural market is

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TABLE 10.15 Changes in Welfare, by Adaptation Technology and Irrigation Investment, 2050 Welfare Measure (Net Present Value) Producer Surplus Adaptation Technology Change planting date Change variety Add fertilizer All three technologies Varietal traits Heat tolerance Drought tolerance Nutrient-use efficiency Farm management No till Integrated soil fertility management Precision agriculture Water harvesting Crop protection Against diseases Against pests Against weeds Combined technology Irrigation development 70 per cent of potential irrigable area 90 per cent of potential irrigable area Rehabilitation

Consumer Surplus

Economic Surplus

(Billion U.S. dollars) 0.21 0.11 0.19 0.29

1.10 1.11 1.70 3.53

1.32 1.22 1.89 3.81

1.01 0.27 –0.25

1.42 0.49 8.60

2.42 0.76 8.36

1.70 0.65 –0.55 0.08

0.51 9.52 8.39 0.33

2.20 10.18 7.84 0.41

0.42 0.67 0.62 –0.21

4.11 3.50 2.50 19.24

4.52 4.16 3.13 19.03

–26.90 –38.27 0.98

40.82 79.40 3.52

13.92 41.13 4.50

Notes: These values are changes from MIROC baseline only, which includes climate change, and, given that the data are in monetary terms, they can be compared with all the model results presented in Table 10.3. Source: Constructed by authors.

also required to generate an economic surplus totaling the equivalent of US$16.72 billion (Table 10.16). Results indicate that only two adaptation strategies compensate for the full impact of climate change — 90 per cent irrigation development and combined technology — which are also the most expensive to implement or apply (Table 10.16). Developing irrigation to 90 per cent irrigation would require construction of irrigation systems to cover more than 1 million ha

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–2.9 3.0 0.6 1.4 3.2 3.5 3.1 1.6 6.5 9.6 17.3

–3.2 3.3 1.1 2.5 6.1 6.9 6.0 3.0 12.9 11.6 26.6

4.1 7.3

1.5 1.6 1.5 0.8 2.9

0.4 0.7

–2.2 2.2

Percentage Change

–4.8 –8.4

–1.8 –1.9 –1.7 –1.0 –3.4

–0.5 –0.9

–2.7 –2.6

Malnourished Children

–19.6 –30.3

–7.9 –8.6 –7.8 –4.4 –14.3

–2.1 –3.9

12.8 –11.4

At Risk of Hunger

13.92 41.13

8.36 10.18 7.84 4.52 19.03

1.89 3.81

–16.72 16.72

Billion U.S. dollars

Total Economic Surplus

Note: Data on the impact of climate change are based on a comparison of average results from the four climate models with baseline results under a scenario of no climate change (from Tables 10.1, 10.2, and 10.3). Data on the minimum requirement to compensate for the full effect of climate change are based on average results from the four climate models. In order to compensate the full effect of climate change, results must be equal to or higher than positive values or equal to or lower than negative values. Source: Constructed by authors from model simulation results.

Impact of climate change Minimum requirement to compensate the full effect of climate change Existing technologies Add fertilizer Combination of changing date and variety and adding fertilizer Emerging technologies Nutrient-efficient varieties Integrated soil fertility management Precision agriculture Crop protection Combined technology Irrigation development 70 per cent of potential irrigable area 90 per cent of potential irrigable area

Adaptation Technology

Rice Rice Food Production Consumption Availability

TABLE 10.16 Effectiveness of Adaptation Strategies in Compensating for the Impact of Climate Change on Production, Consumption, and Food Security, 2050

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of land, and implementing combined technology would require significant new skills and additional resources on the part of farmers. The next most-promising strategy is 70 per cent irrigation development, which exceeded the minimum required values for the majority of indicators. As an alternative to 90 per cent irrigation development, this strategy would require the construction of irrigation systems to cover around 430,000 ha of new land. Looking at individual technologies, the three most promising are ISFM, nutrient-use efficient varieties, and precision agriculture. Crop protection, adding fertilizer, and combining existing technologies are still worthy of consideration, however. Crop protection is relatively easier to implement than ISFM or precision agriculture and has higher likelihood of successful development than nutrient-use efficient varieties, whereas the remaining two existing technologies are readily available for immediate implementation.

POLICY IMPLICATIONS The analysis presented in this chapter indicates that the agricultural sector will incur high economic costs and deterioration of food and nutrition security because of climate change. Addressing the agriculturerelated challenges presented by climate change requires three adaptation initiatives: increasing crop productivity through enhanced investment in agricultural research for improved crop varieties; developing and promoting the adoption of resource-conserving management practices; and increasing investment in cost-effective irrigation. In responding to the impacts of climate change, the most promising adaptation technologies and strategies are nutrient-use efficient varieties, ISFM, precision agriculture, combined technology, and developing irrigation to 70 per cent of potential irrigable lands. By 2050, climate change is projected to reduce Philippine rice production by as much as 3.2 per cent, consumption by 2.9 per cent, and food availability by 2.2 per cent; to increase child malnutrition by 2.7 per cent and the incidence of hunger by 12.8 per cent; and to cost an estimated US$16.72 billion in social welfare losses in the agricultural sector. On this basis, to compensate for the full impact of climate change on agriculture, at minimum, adaptation strategies must increase production by 3.3 per cent, consumption by 3.0 per cent, and food availability by 2.2 per cent; must reduce the number of malnourished children by 2.6 per cent and

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the number of hungry people by 11.4 per cent; and must generate the US$16.72 billion in social welfare gains. Only two of the adaptation strategies explored in the analysis in this chapter meet all of the requirements: combined technology, indicating broad-based adoption of a range of agricultural technologies, and irrigation development to 90 per cent of potential irrigable lands. As previously discussed, 90 per cent irrigation development would present financial and logistical impediments, but 70 per cent irrigation development is a viable adaptation option, requiring less than half the resources of the 90 per cent irrigation development strategy and meeting all but one of the minimum required improvements noted above (that is, failing only to meet requirements for increased economic surplus). Nutrient-use efficient varieties, ISFM, and precision agriculture are the next most-promising approaches after irrigation development. Other strategies worth considering are crop protection technologies, adding fertilizer, and the simultaneous implementation of the existing technologies presented in this chapter (that is, changing planting date and variety, and adding fertilizer). Crop protection is comparatively easier to implement than ISFM and precision agriculture, and agricultural R&D is more likely to succeed in developing these technologies than nutrient-use efficient varieties. There are fewer impediments to the immediate implementation of strategies using existing technologies, but the biggest benefits will be from new or currently underutilized technologies. Implementing these technologies on the ground will require institutional, policy, and investment advances in many areas. Since many of the technologies are knowledge intensive, it will be important for extension systems to increase their knowledge capacity and for innovative forms of extension — for example, through information and communication technologies — be implemented. To further support adoption of these technologies, improved governance, legal, and regulatory systems that do not hinder the development and uptake of new technologies will be important, as well as investments in rural infrastructure.

Notes 1. Although wheat is not produced in the Philippines, it is an important imported commodity, with yearly per capita consumption of about 23 kilograms in 2010, representing about 20 per cent of per capita rice consumption.

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2. Cropping intensity indicates the number of times a hectare of land is cropped in one year, with 100 per cent signifying one crop per year. 3. For this aspect of the analysis, rather than averaging results of the four climate models, the MIROC model results were used as climate inputs into the IMPACT model because they were deemed to be the most accurate simulation of the future Philippine climate. In addition, the simulations of economic benefits factor in the estimated probability of successful development of the various technologies and estimated rates of adoption of the technologies.

References Alston, J., G. Norton, and P. Pardey. Science under Scarcity: Principles and Practice of Agricultural Research Evaluation and Priority Setting. Ithaca, NY: Cornell University Press, 1955. Cai, W., S. Borlace, M. Lengaigne, P. van Rensch, M. Collins, G. Vecchi, A. Timmermann, et al. “Increasing Frequency of Extreme El Niño Events Due to Greenhouse Warming”. Nature Climate Change 4 (2014): 111–16. Emanuel, K. “Downscaling CMIP5 Climate Models Shows Increased Tropical Cyclone Activity over the 21st Century”. Proceedings of the National Academy of Sciences of the United States of America 110, no. 30 (2013): 12219–24. FAO (Food and Agriculture Organization of the United Nations). The State of Food and Agriculture 2013: Food Systems for Better Nutrition. Rome, 2013. Gebbers, R. and V. Adamchuck “Precision Agriculture and Food Security”. Science 327 (2010): 828–31. IPCC (Intergovernmental Panel on Climate Change). Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, [edited by Field, V. Barros, D. Dokken, K. Mach, M. Mastrandrea, T. Bilir, M. Chatterjee, et al.] Cambridge, UK and New York: Cambridge University Press, 2014. Israel, D.C. and R.M. Briones. Impacts of Natural Disasters on Agriculture, Food Security, and Natural Resources and Environment in the Philippines. Economic Research Institute for ASEAN and East Asia (ERIA) Discussion Paper, 2013. Launio, C., G. Redondo, J. Beltran, and Y. Morooka. “Adoption and Spatial Diversity of Later Generation Modern Rice Varieties in the Philippines”. Agronomy Journal 100 (2008): 1380–89. NIA (National Irrigation Authority). Annual Report. Quezon City, 2014. PSA (Philippine Statistics Authority). Philippine CountrySTAT. (accessed March 2015). Rosegrant, M., X. Cai, and S. Cline. World Water and Food to 2025: Dealing with Scarcity. Washington, D.C.: International Food Policy Research Institute, 2002.

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——— and the IMPACT Development Team. International Model for Policy analysis of Agricultural Commodities and Trade (IMPACT): Model description, July 2012 (accessed March 2015). ———, J. Koo, N. Cenacchi, C. Ringler, R. Robertson, M. Fisher, C. Cox, et al. Food Security in a World of Natural Resource Scarcity: The Role of Agricultural Technologies. Washington, D.C.: International Food Policy Research Institute, 2014. World Bank. Repositioning Nutrition as Central to Development: A Strategy for LargeScale Action. Washington, D.C.: World Bank, 2006.

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11 A GENERAL EQUILIBRIUM APPROACH TO MODELLING ALTERNATIVE AGRICULTURAL FUTURES UNDER CLIMATE CHANGE Angga Pradesha and Sherman Robinson

High agricultural productivity is the key driver of successful structural transformation to promote long-term economic growth (see Chapter 1, this volume). Climate change, however, has the potential to impede the structural transformation process by introducing new and complex agricultural production risks at both national and global levels. Nationally — and especially in an already vulnerable archipelagic country like the Philippines — the adverse impacts of climate change could directly reduce agricultural productivity, while the indirect impact on global trade, and hence world food prices, could have negative impacts on the economy’s international terms of trade. Both the direct and indirect effects of climate change could potentially damage the Philippine economy, as more resources are needed in the future to produce the same level of outputs. Projections indicate that lower crop productivity induces the agricultural sector to retain less productive labour and capital,

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thereby absorbing resources that could be used more productively in the nonagricultural sectors and slowing the process of structural transformation. Higher world crop prices induced by climate change cause the country to pay more for imported food. On the other hand, the transmission of higher agricultural prices to domestic markets benefit farmers and rural households because they receive more income from agricultural activities. As incomes increase, farmers are motivated to invest in improving crop productivity and increasing production, which potentially helps mitigate the high word prices induced by global climate change. Estimating how these drivers interact in the economy is critical to determining their direct and indirect effects, and hence the net impact of climate change on the Philippine economy. This chapter presents results of research designed to assess the impacts of climate change by considering both crop productivity and global agricultural trade effects that provide the main channels through which climate change directly affects the agricultural sector and indirectly affects the rest of the economy. An economywide computable general equilibrium (CGE) model was used to capture direct and indirect linkages, focusing on macroeconomic performance and the effects on long-term growth and structural change. The analysis also explores scenarios that include alternative policies intended to bring about the most advantageous outcomes in terms of higher economic growth and better income distribution. The country’s long-held policy for achieving food self-sufficiency is also examined, highlighting the government’s strategic role not only in helping to mitigate climate impacts, but also in achieving higher economic growth based on different policy interventions. A dynamic computable general equilibrium (DCGE) model for the Philippines was also used to capture the processes of growth and structural change, including shifts in factor inputs (such as labour and land), and changes in the distribution of income. Additionally, since the model disaggregates agricultural activities at the subnational level, the analyses include socioeconomic differences in impacts across the country’s three major subregions: Luzon, Visayas, and Mindanao. In order to isolate the impact of climate change on agricultural productivity and world prices, the DCGE model was linked with two other models: (1) the global International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT), which captures long-term world market interactions, and (2) the Decision Support System for Agro-technology Transfer (DSSAT),

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a micro-focused, biophysical crop simulation model that operates at the pixel level. Analytical results on the impacts of climate change using IMPACT and DSSAT are presented in Chapter 8 and 9 respectively. Some key parameters from those chapters are then used to set the exogenous climate shocks to the DCGE model. The next section provides an overview of the modelling scenarios undertaken. Thereafter, simulation results are presented exploring potential local and global climate effects, and various policy options and their potential as strategies for adapting to the impacts of climate change. An economywide benefit–cost analysis is included to estimate the net impact of each policy option in mitigating climate change effects. The chapter concludes with a summary of the findings and their associated policy implications.

OVERVIEW OF THE MODEL SCENARIOS The Experimental Scenarios The model simulations undertaken for this study explore three climate scenarios and six policy options (Table 11.1). Scenario 1 assesses the local climate effect originating from changes in national productivity, whereas Scenario 2 captures the global trade effect by looking at changes in international agricultural prices due to global climate effects. Scenario 3 combines both the local and global effects in order to assess the total effect of climate change on the country. (See Appendix 11A for a description of the modelling framework.) Price interventions and trade policy — in the form of rice subsidies and agricultural tariffs — have played a major role in shaping the performance of the Philippine agricultural sector by creating an incentive structure that leads to a misallocation of resources and significant costs to the economy (David 2003). For this reason, as part of the baseline scenario, these two policies were included in the analysis of the impacts of climate change on the Philippine economy to quantify the interaction between the cost of market intervention policies and climate change effects. The main objective of the Philippine government’s rice subsidy policy is to stimulate domestic rice production to promote rice self-sufficiency and less dependence on imported rice. The National Food Authority (NFA) provides subsidies to producers and consumers, and restricts rice

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General Equilibrium Approach to Modelling Alternative Agricultural Futures 495 TABLE 11.1 List of Climate and Policy Scenarios No.

Scenario

Climate scenarios 1. Crop-yield effect 2.

World-price effect

3.

Combined climate effect

Policy options 4. 5

6.

7. 8.

9.

Higher rice yields with NFA subsidies Irrigation development with NFA subsidies Tariff cut of 50 per cent on agricultural and food commodities with NFA subsidies Higher rice yield without NFA subsidies Irrigation development without NFA subsidies Tariff cut of 50 per cent on agricultural and food commodities without NFA subsidies

Description Changes in productivity for all agricultural commodities (from DSSAT crop model) Changes in international agricultural and food commodity prices (from IMPACT global model) Combination of results from the crop-yield and world-price scenarios All policy simulations are based on the combined climate effect (Scenario 3) Increase rice yields to close the gap by 30 per cent, while retaining the rice subsidy policy Increase irrigated area following NIA’s master plan to around 90 per cent of the country’s potential irrigable area in 2028, while retaining the rice subsidy policy Reduce the import tariff on agriculture and food commodities by 50 per cent of the current rate, while retaining the rice subsidy policy Increase rice yields to close the yield gap by 30 per cent while eliminating the rice subsidy policy Increase irrigated area following NIA’s master plan to around 90 per cent of the country’s potential irrigable area in 2028, while eliminating the rice subsidy policy Reduce the import tariff on agriculture and food commodities by 50 per cent of the current rate while eliminating the rice subsidy policy

Notes: NFA = National Food Authority; NIA = National Irrigation Agency. Source: Devised by authors.

imports. Previous reviews of this program indicate that it is extremely costly because the government buys rice at high prices from farmers and sells at low prices to consumers. The average cost of the NFA operation between 2005 and 2008 was about PhP20 billion per year (Fernandez and Velarde 2012). This value is in line with figures reported by the NFA, showing a net loss of PhP21.5 billion per year during the same period (SEPO 2010).

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The baseline model captures this economic cost by imposing a quota on rice imports set to target a high rice self-sufficiency rate (94 per cent). The tariff-equivalent rent from the rice quota is treated as additional tariff revenue received by the government. This revenue largely goes to consumer and producer subsidies. Most of it goes to consumers given that NFA rice is mainly procured from imports. Roumasset (2000) calculated that 70 per cent of the NFA’s costs from forgone tax revenue can be attributed to the consumer tax. Therefore, in the model, the producer tax is set at –2 per cent, while the consumer tax is set at –4 per cent (both subsidies). Data on official tariff rates for other commodities came from the GTAP 7 database (Badri, Dimaran, and McDougall 2008) and official tariff data from Philippine Tariff Finder (DOF 2014). Experiments with policy options were designed to leverage the government’s potential to institute policies to mitigate the adverse impacts of climate change, while taking advantage of future opportunities. Three adaptation policies are proposed in this study, which can be seen as alternative future investments that the government could make to mitigate climate change. The first represents investment in research and development (R&D) to achieve improvements in rice productivity. The second is investment in developing new irrigation infrastructure as another way to improve the productivity of rice, as well as other irrigated crops. The third strategy pushes the country to become more open by reducing agricultural and food import tariffs to potentially mitigate the impact of rising world prices due to climate change. Under these alternative beneficial investments and trade policy scenarios, we consider whether it is necessary for the government to continue subsidizing the rice sector through the NFA programme. To examine this policy alternative, we set up three additional scenarios by eliminating the rice programme under each adaptation policies. Scenarios 4 through 9 combine the total climate effect (Scenario 3) with the proposed adaptation strategies, as well as considering two different policy environments reflecting whether the government maintains or eliminates the NFA’s rice subsidy (Table 11.1). Scenarios 4 and 7 explore the first adaptation strategy, simulating higher national rice productivity through investment in R&D targeting the country’s existing potential. These scenarios use the rate of yield change for Philippine rice presented in the most recent global crop yield study (Fischer, Byerlee, and Edmeades 2014), which looked at the possibility of increasing global crop

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productivity based on the yield gap between actual (farm) and potential (experimental) yields. In the Philippines, the productivity gap in rice is at least 53 per cent after taking into account cumulative growth in farm yields of around 0.6 per cent per year. The research presented in this chapter incorporates the reasonable assumption that investment could close the yield gap by around 30 per cent by 2050, which implies that rice productivity would increase by 0.37 per cent per year throughout the simulation period (2012–50). This yield increase is much higher than the negative impact on rice productivity caused by climate change, so a positive impact on rice production is expected. Furthermore, the high value-added share for rice implies that production impacts would generate spillover effects to the rest of the economy. Scenarios 4 and 7 explore the impact of higher rice yields, with and without the NFA rice subsidy, respectively. Scenarios 5 and 8 consider an adaptation strategy through investment in new irrigation facilities. The Philippines has about 3 million hectares of potential irrigable land, but only about half this amount is actually irrigated. This study analyses the impact of expanding irrigation by one million hectares, following the National Irrigation Administration (NIA) master plan as part of the strategy to increase rice production and mitigate the negative impacts of climate change (NIA 2014). In the IMPACT model, productivity increases due to the expansion of irrigated land include rice and other crops (see Appendix Table 11A.3). Simulation results indicate that higher productivity gains in coconuts, sugar, and other crops are mainly influenced by the yield gap between irrigated and non-irrigated crops, as well as reductions in total rain-fed land converted to irrigated land. Scenarios 5 and 8 explore the impact of irrigation expansion, with and without the NFA rice subsidy, respectively. Scenarios 6 and 9 incorporate lower import tariffs. Past studies show that reducing import tariffs in the Philippines would induce consumers to substitute food imports for domestic commodities, which would marginally reduce the poverty rate (Corroraton, Cockburn, and Corong 2006). Even though this strategy may reduce the output of agricultural commodities, it helps to correct the misallocation of resources. Scenarios 6 and 9 imposes a 50 per cent reduction in agricultural and food import tariffs from 2020 onward (with and without the NFA price subsidy), which is a moderate reduction given the low tariff already applied on most agricultural commodities in the Philippines (see Appendix Table 11A.4).

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This trade policy scenario is also in line with the Association of Southeast Asian Nations (ASEAN) economic community’s agenda of promoting tariff reduction among its member countries, with 2015 as the milestone.1

The Baseline Scenario Credible analysis of the impact of climate change over time depends on the establishment of a benchmark “baseline scenario” against which projections under the various experimental scenarios can be measured. Key to this process is defining a long-term GDP growth rate that reflects a plausible future pathway for the country based on historical trends and climate and socioeconomic factors. For this study, the GDP growth projection adopted is based on the Shared Socioeconomic Pathways (SSP) framework, which is widely used by climate change researchers to facilitate comparative estimation of future climate impacts, as well as adaptation and mitigation scenarios. The SSP framework delineates five GDP growth rates based on different climate and socioeconomic challenges a country faces (Kriegler et al. 2014). This study uses SSP2, which represents a middle path, whereby the economy grows moderately fast with medium-level climate and socioeconomic challenges. Given that this is a national GDP growth rate, more information is needed to calibrate the growth rate at the sectoral level. Historical growth rates for agriculture, industry, and services aggregates during 1960–2013 were used to project structural change. As a result, in the baseline scenario the services sector grows more rapidly than the other two sectors (Table 11.2). Structural change is indicated by the reduction in the agricultural share of GDP from 11.9 per cent in 2011 to about 8.2 per cent in 2050. In contrast, the manufacturing share of GDP only decreases slightly, whereas the services sector strengthens considerably. The next process establishes more detailed growth projections for the agricultural sector based on historical data. Under the baseline scenario, four agricultural subsectors — palay (rice), bananas, poultry, and fisheries — grow at higher rates than average, increasing their shares into the future (Table 11.3). These subsectors account for more than half of the country’s agricultural GDP. In contrast, the shares of coconuts, sugarcane, and other crops are projected to decline over time based on historical data. At the regional level, most staple crops are grown in Luzon, whereas corn is mostly grown in Mindanao. Luzon is also the primary location for

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General Equilibrium Approach to Modelling Alternative Agricultural Futures 499 TABLE 11.2 Baseline Projections of GDP by Major Sector, 2011 and 2050

2011 GDP (billion PhP)

Sector Agriculture Industry Services Total GDP

1,091 2,776 5,284 9,150

Yearly Growth Rate GDP Share (%) (%)

Historical Growth Rate (%)

.2011

.2050

2011–50

1960–2013

111.9 130.3 157.7 100.0

118.2 126.8 165.0 100.0

3.3 4.0 4.6 4.3

3.2 3.5 4.2 3.8

Notes: Historical data are from numerous sources. GDP = gross domestic product. Source: Constructed by authors from DCGE model simulation results.

TABLE 11.3 Baseline Projections of Agricultural GDP by Commodity, 2011–50 GDP Share (%) Commodity Palay (rice) Corn Coconuts Sugarcane Bananas Other crops Livestock Poultry Fisheries Other agriculture Agricultural total

Yearly Growth Rate (%)

Historical Growth Rate (%)

2011

2050

2011–50

1960–2013

22.8 6.2 7.2 3.0 3.7 12.6 12.3 7.9 16.1 8.1 100.1

23.2 5.6 4.9 2.3 6.2 10.3 9.6 8.3 22.0 7.6 100.1

3.4 3.1 2.3 2.6 4.9 2.8 2.7 3.4 4.2 3.2 3.3

3.3 3.2 0.1 1.3 4.7 2.9 3.1 6.0 3.9 2.7 3.2

Notes: Historical data are from numerous sources. GDP = gross domestic product. Source: Constructed by authors from DCGE model simulation results.

livestock and poultry production (Table 11.4). Export and other crops that are more tradable are mainly produced in Mindanao, which therefore has stronger linkages with international markets and might be more affected than other regions by changes in international food prices resulting from climate change. Different production patterns across the three major regions

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TABLE 11.4 Baseline Agricultural Production Shares by Region, 2011 Production Share (%) Commodities Palay Corn Export crops Other crops Livestock Total agricultural production

Luzon

Visayas

Mindanao

The Philippines

55.3 39.3 17.0 17.1 54.6 42.9

21.2 18.2 24.9 10.1 19.6 19.2

23.5 52.5 58.1 72.7 25.8 37.8

100 100 100 100 100 100

Source: Constructed by authors from DCGE model simulation results.

also imply different responses to structural change, as well as to changes in climate and crop yields. Structural change is also observed in the labour market, where the share of agricultural labour is projected to decrease from 33.6 to 23.5 per cent (Table 11.5). This rate of reduction is matched by an increase in the share of labour in the services sector (while the share of labour in industry remains relatively constant). While an increasing share of labour in industry would potentially offer both higher levels of productivity and better returns, the projected unchanging share is in line with past trends (Chapter 1, this volume). Structural change within the agricultural sector has further implications for international trade flows in the Philippines, mainly in the form of processed foods through the manufacturing sector (Figure 11.1). Coconut oil, canned fish, and canned fruit are the country’s main export commodities, representing half of total agricultural and food exports. However, structural change is projected to shift the trade pattern such that, by 2050, bananas and canned fish become the dominant export commodities, largely based on high production growth. At the same time, lower domestic coconut production is projected to cause a significant decline in the export of coconut oil. Similarly, the bulk of imported food commodities are processed foods, with the exception of other crops (Figure 11.2). Rice was among the top imported commodities in 2011, but its share of imports is projected to decline by 2050, indicating the impact of the rice subsidy in this baseline scenario, which helps to increase domestic rice production and limit import demand. The trend of higher shares of

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General Equilibrium Approach to Modelling Alternative Agricultural Futures 501 TABLE 11.5 Baseline Projections of Sectoral Shares of Labour Force, 2011 and 2050 Labour Force (thousands)

Sector Agriculture Industry Services All sectors

Share of Labour Force (%)

12,383 15,509 19,322 37,214

2011

2050

33.6 14.7 51.7 100

23.5 14.7 61.9 100

Source: Constructed by authors from DCGE model simulation results.

FIGURE 11.1 FIGURE 11.1 Baseline Changes in Shares of Major Food Commodity Exports, 2011 and 2050

Baseline Changes in Shares of Major Food Commodity Exports, 2011 and 2050

% 90 80

70 60 50

Sugar

40

Coconut oil

30

Fish, canned

20

Fruit, canned

10 0

Bananas 2011

2050

Source: Constructed by authors from DCGE model simulation results.

dairy and processed meat in food imports reflects projected high growth in demand, even with high domestic production growth. This trend is a common pattern in developing countries — the high-income elasticity of demand for these commodities is matched by higher household income levels as the economy grows.

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FIGURE FIGURE11.2 11.2 Baseline Changes in Shares of Major Food Commodity Imports, 2011 and 2050

Baseline Changes in Shares of Major Food Commodity Imports, 2011 and 2050

% 90 80 70 60

Other food

50

Flour and milled grain

40

Animal feed

30

Rice

20

Meat

10

Dairy

0

Other crops 2011

2050

Source: Constructed by authors from DCGE model simulation results.

THE IMPACT OF CLIMATE CHANGE In this section, projection results for the scenarios described earlier are compared with results for the baseline scenario. Results are presented as percentage deviations from baseline values for each variable of interest in 2050, unless specified otherwise. Macro-level changes are discussed first, followed by micro-level changes, which include shifts in national and subnational agricultural production levels and their effect on income distribution across different types of households. Finally, the welfare cost of climate change is estimated through an assessment of the impact on the economy as a whole, including both direct and indirect effects. Under Scenario 1, the impact of climate change on GDP growth in the Philippines is modest, but under Scenario 2, the adverse effect through changes in global trade and world prices (from IMPACT model projections) is more significant (Table 11.6). Overall, climate change impedes the country’s long-term economic growth, lowering GDP in 2050 by 0.9 per cent from baseline levels. At the sectoral level, the global effect of climate

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General Equilibrium Approach to Modelling Alternative Agricultural Futures 503 TABLE 11.6 Projected Impact of Climate Change on Real Value-Added by Sector, 2050 Change from Baseline Levels (%) Sector Agriculture Industry Service Total GDP

Scenario 1 Crop-Yield Effect

Scenario 2 World Price Effect

Scenario 3 Combined Effect

–2.1 –0.3 –0.3 –0.4

–6.9 –2.6 –0.7 –0.6

–3.1 –2.4 –0.8 –0.9

Source: Constructed by authors from DCGE model simulation results.

change actually stimulates higher production of agricultural commodities, while the local effect is to deter agricultural growth. This is mainly due to the nature of the shock, whereby, under Scenario 1, the negative change in productivity affects almost all Philippine agricultural commodities, whereas the higher international agricultural prices projected under Scenario 2 prompt farmers to increase their production levels. The shifts in supply and demand for agricultural commodities due to climate change creates adjustments in factor markets that drive the country’s allocation of resources. The main indicator of how input factors are being mobilized is factor returns. Projections indicate that the real returns to factors that are heavily utilized in agriculture are higher with climate shocks, mainly as a result of higher international agricultural prices (Table 11.8). For example, the returns to land and less skilled labour, which constitute most of the value-added in agricultural sector, are higher under climate change, once again due to higher international agricultural commodities prices, as reflected in Scenario 2. On the other hand, returns are lower for more highly skilled labour and nonagricultural capital, which are mainly used in industry and services. Changes in factor prices stimulate all factor inputs — especially labour, which is assumed in the model to be mobile across sectors. Consequently, nonagricultural production levels decline in response to the reduced availability of unskilled labour. This trend hinders the structural transformation process, which would otherwise attract unskilled labour out of agriculture and into higher productivity sectors. Given the significant size of the country’s industry and services sectors, higher agricultural growth cannot compensate for the negative spillover

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effect from reduced nonagricultural output due to factor movements. Under Scenario 3, even though the rate of agricultural GDP growth increases, the spillover effect through factor markets affects production in other sectors and causes the economy to shrink. This result highlights the need to continue to improve agricultural productivity in the long run to promote the structural transformation process, so that labour can move out of agriculture and into more productive sectors. Given agriculture’s low productivity relative to other sectors, a contraction of labour in nonagricultural sectors translates into lower economic growth (Figure 11.3). The upward trendline in the figure indicates higher demand for agricultural labour in response to climate change, whereas the downward trendline shows reduced GDP as a consequence of labour (especially unskilled labour) remaining in the agricultural sector. The less labour that moves out of agriculture, the smaller the value-added generated by that labour, given the high productivity gap. Labour in agriculture is one-fifth as productive as labour in the nonagricultural sectors. Climate change thus creates the wrong incentives for the reallocation of resources, deterring the structural transformation process and slowing long-term economic growth. Agricultural production declines across all regions under Scenario 1 (Table 11.7). Under Scenario 2, however, an interesting pattern emerges whereby Mindanao reaps much greater benefits than the other two regions, mainly due to the increased production of export crops stimulated by higher international agricultural prices. Exports drive the process, increasing total agricultural output by 14 per cent, despite reduced output of nonexport crops and livestock. The production of staple crops (dominated by rice) is projected to decline under climate change. Under Scenarios 1 and 2, staple crop production decreases by 1.9 and 1.8 per cent, respectively, in 2050, whereas, under Scenario 3, the total impact of climate change is projected to reduce staple crop production by 2.9 per cent. Declining staple crop production occurs across all three regions, although the magnitude is not large. Smaller percentage changes in rice production in Luzon would still result in higher value compared with reductions in other regions, given that more than half of production occurs in Luzon. Overall, production changes in staple crops are mainly driven by productivity or local climate shocks rather than the global price shock. On the other hand, export crop production is mainly driven by global price changes. The world price effect is projected to stimulate higher

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-1

-0.8

-0.6

-0.4

-0.2

0

0.2

Change in total GDP (%)

2011 2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050

Total GDP

Source: Constructed by authors from DCGE model simulation results.

-0.5

0.5

1.5

2.5

3.5

4.5

5.5

Change in demand for agricultural labor (%) 6.5 Agricultural labor demand

Change in the Demand for Agricultural Labour and in GDP under Climate Change, 2011–50

FIGURE 11.3 FIGURE 11.3 Change in the Demand for Agricultural Labour and in GDP under Climate Change, 2011–50

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TABLE 11.7 Projected Impact of Climate Change on Agricultural Production by Region, 2050 Change from Baseline Levels (%) Commodity/region All agriculture Luzon Visayas Mindanao Staple crops Luzon Visayas Mindanao Export crops Luzon Visayas Mindanao Other crops Luzon Visayas Mindanao Livestock Luzon Visayas Mindanao

Scenario 1 Scenario 2 Crop-Yield Effect World-Price Effect –2.0 –1.8 –2.0 –2.9 –1.9 –1.9 –2.3 –1.6 –5.2 –5.1 –3.5 –5.7 –3.1 –7.1 –3.5 –2.0 –0.8 –0.8 –0.9 –0.8

–6.7 –1.2 –3.7 21.5 –1.8 –0.7 –1.9 –3.9 55.5 34.1 24.8 70.3 –1.7 –3.2 –1.5 –3.4 –2.5 –2.4 –2.5 –2.5

Scenario 3 Combined Effect –3.0 –1.0 –0.8 13.1 –2.9 –2.1 –3.5 –4.2 35.6 19.2 14.9 46.1 –3.7 –4.1 –1.7 –3.8 –2.9 –2.8 –2.8 –2.8

Notes: Staple crops include rice and corn; export crops include coconuts, sugar, bananas, other fruit, and coffee. Source: Constructed by authors from DCGE model simulation results.

production of export crop with Mindanao dominating, given the high share of exported crops produced in that region. Despite experiencing lower productivity from local climate shock, the net effect of climate change under Scenario 3 is a 36 per cent increase in the production of export crops. The different impacts of climate change are based not only on the source of the climate shock, but also on the agricultural characteristics of the different regions. This variation ultimately determines the regional differences in household income distribution under climate change. The impact of climate change on income distribution can be captured by looking at changes in returns to factor inputs — that is, labour, land,

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livestock, and capital. Changes in wage rates have a bigger impact on lower income households, which derive the majority of their livelihoods from unskilled labour, mostly in the agricultural sector. Individuals with higher education levels are assumed to be more highly skilled and are mainly employed in industry and services. Climate change is projected to have a positive impact on the wage rates of unskilled labourers, as discussed earlier, mainly caused by higher international food commodity prices (Table 11.8). In contrast, highly skilled labour and nonagricultural capital yield lower returns under climate change in 2050 (in the range of 2 to 3 per cent). Overall, even though the economy shrinks in response to climate change, unskilled workers, who are mainly employed in the agricultural sector, fare better than more highly skilled workers, who are mainly engaged in nonagricultural activities. Looking at income-levels across regions, households are categorized as either lower- or upper-income based on per capita income quintiles. The first and second quintiles (the bottom 40 per cent) are categorized as lower-income households, and the top three quintiles (the remaining 60 per cent) are classified as upper-income households (Table 11.9). As expected, the positive effect of climate change on household income mainly originates through world-price effects. Higher returns to land and unskilled labour cause income levels to rise in rural households across all regions, especially among households located in Mindanao (based on the higher gains from agriculture previously discussed). In addition, because rural upper-income households own much of the land in rural areas, they gain more than rural lower-income households, which only gain from increased wages. For urban households, the results are mixed. The contraction in income for urban upper-income households stems from lower returns to their capital and skilled labour. In contrast, the increase of income for urban lower-income households mainly stems from higher returns to unskilled labour. Overall, rural households fare better than urban households under climate change, and households in Mindanao are the least affected. The pattern of these income changes across households will also be reflected in welfare results (discussed below). The net benefits or costs of climate change estimated under this study refer to the changes in welfare parameters, whereby positive values are benefits and negative values are costs. In addition to calculating the net economic benefits or costs at the national level, welfare is also assessed at the household level based on changes in real household incomes across

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Luzon Visayas Mindanao

Luzon Visayas Mindanao

Agriculture

Land

Livestock

Capital

Source: Constructed by authors from DCGE model simulation results.

Nonagriculture

No education Primary education Secondary education Tertiary education

Type of labour

Input Factors

–0.6

–0.3

–0.4 –0.4 –0.4

–1.5 –1.3 –2.4

–0.2 –0.3 –0.7 –0.7

Scenario 1 Crop-Yield Effect

–2.6

–0.1

–1.8 –1.7 –1.7

13.2 14.3 21.3

–4.8 –3.3 –2.1 –2.5

Scenario 2 World-Price Effect

–2.7

–1.1

–1.3 –1.3 –1.3

12.6 13.0 19.6

–4.0 –2.2 –2.6 –2.9

Scenario 3 Combined Effect

Change from Baseline Levels (%)

TABLE 11.8 Projected Impact of Climate Change on Real Returns to Factor Inputs, 2050

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General Equilibrium Approach to Modelling Alternative Agricultural Futures 509 TABLE 11.9 Projected Impact of Climate Change on Real Household Incomes, 2050 Change from Baseline Levels (%) Region/Income Group Luzon Rural Lower income Upper income Urban Lower income Upper income Visayas Rural Lower income Upper income Urban Lower income Upper income Mindanao Rural Lower income Upper income Urban Lower income Upper income

Scenario 1 Scenario 2 Crop-Yield Effect World-Price Effect –0.7 –0.4 –0.3 –0.4 –0.7 –0.4 –0.7 –0.6 –0.3 –0.2 –0.4 –0.7 –0.3 –0.7 –0.4 –0.2 –0.3 –0.3 –0.7 –0.4 –0.7

–2.0 –1.6 –1.3 –1.7 –2.5 –0.8 –2.6 –0.9 –2.3 –2.0 –2.4 –2.5 –1.2 –2.7 –0.6 –5.7 –1.6 –7.0 –2.5 –0.8 –2.8

Scenario 3 Combined Effect –2.4 –0.8 –0.6 –0.8 –2.9 –0.1 –3.0 –1.5 –1.4 –1.2 –1.5 –2.8 –0.5 –3.1 –0.1 –4.6 –0.9 –5.8 –2.9 –0.1 –3.1

Source: Constructed by authors from DCGE model simulation results.

geographic regions. The economic benefit or cost of a climate shock is measured by the change in total real absorption (that is, total final demand, comprising aggregate consumption, investment, and government) from the baseline values. Under climate change, the economy contracts by an average of PhP145 billion per year, mainly due to a reduction in private consumption and total investment caused by higher commodity prices and lower income levels among urban upper income households, as previously discussed (Table 11.10). The world- price effect is significantly larger than the crop-yield effect. Following Blonigen, Flynn, and Reinert (1997), the welfare cost of climate change at the household level is calculated in terms of changes

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TABLE 11.10 Projected Impact of Climate Change on Total Absorption, 2011–50 Average Yearly Change from Baseline Levels (billion PhP) Variable Yearly absorption Private consumption Investment Government consumption

Scenario 1 Crop-Yield Effect

Scenario 2 World-Price Effect

Scenario 3 Combined Effect

–53.26 –42.54 –10.10 1–0.62

–91.59 –77.33 –19.78 –15.52

–145.09 –119.48 1–29.85 –114.24

Notes: Positive values indicate benefits, whereas negative values indicate costs. Source: Constructed by authors from DCGE model simulation results.

in equivalent variation (EV) from baseline values. EV is the amount of money a consumer would have to receive after a price change to maintain his new utility level if prices had not changed — hence, changes in welfare are measure in baseline prices. The pattern of welfare change at the household and regional levels follows the pattern of income changes previously discussed: urban upper-income households are negatively affected by climate change, and Mindanao is the only region that benefits (Table 11.11). This result potentially offers guidance to policymakers in considering the need to compensate vulnerable groups for their losses. Urban upper-income households are the only category of households to incur a cost due to climate change in the amount of around PhP220 billion per year. At the regional level, urban upper-income households in Luzon are the most affected, bearing losses of PhP158 billion per year due to higher commodity prices and reduced incomes due to slow growth in nonagricultural sectors. In contrast, rural upper-income households in Mindanao gain most due to the effects of climate change, with incomes increasing by around PhP50 billion per year. In total, the gain accruing to rural households is much less than the loss borne by urban households, ultimately leading to total household welfare losses of around PhP120 billion per year, compared with baseline levels.

THE IMPACT OF POLICY OPTIONS The analysis presented in this chapter also explored scenarios of policy intervention intended to mitigate the adverse impacts of climate change

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General Equilibrium Approach to Modelling Alternative Agricultural Futures 511 TABLE 11.11 Projected Impact of Climate Change on Household Welfare, 2011–50 Average Yearly Change from Baseline Levels (billion PhP) Region/Income Group Luzon Rural Lower income Upper income Urban Lower income Upper income Visayas Rural Lower income Upper income Urban Lower income Upper income Mindanao Rural Lower income Upper income Urban Lower income Upper income

Scenario 1 Crop-Yield Effect

Scenario 2 World-Price Effect

Scenario 3 Combined Effect

–26.91 –3.59 –1.04 –2.55 –23.32 –0.95 –22.37 –6.76 –2.58 –1.21 –1.37 –4.19 –0.35 –3.84 –8.05 –3.46 –1.74 –1.71 –4.59 –0.46 –4.13

–108.94 34.34 5.61 28.73 –143.27 4.32 –147.59 –2.69 23.15 5.31 17.85 –25.84 1.94 –27.78 33.22 62.05 3.98 58.06 –28.83 1.56 –30.39

–129.17 26.25 3.67 22.58 –155.42 2.66 –158.08 –10.51 17.52 3.26 14.26 –28.02 1.29 –29.31 19.81 50.96 1.50 49.45 –31.15 0.82 –31.97

Notes: Positive values indicate benefits, whereas negative values indicate costs. Source: Constructed by authors from DCGE model simulation results.

by promoting higher economic growth and better income distribution. The policy options include higher rice yields through investment in agricultural R&D, irrigation expansion as part of infrastructure development, and trade liberalization by reducing tariff levels. These scenarios, if successful, raise the question of whether the current NFA rice subsidy programme would then be needed. If these programmes successfully promote adaptation to climate change and productivity growth, they would result in less need for the current subsidy programme. By maintaining the current rice subsidy, the Philippines could lose opportunities to mitigate the adverse effects of climate change. Under Scenarios 4, 5, and 6, when a positive stimulus is introduced to the

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economy, improvement in GDP growth is only slight. While agricultural GDP improves, the change is too small to have sufficient impact on the whole economy (Table 11.12). This finding echoes the recommendation put forward in other studies that the current rice subsidy policy should be reconsidered based on its cost to the Philippine economy (Jha and Mehta 2008; SEPO 2010). Eliminating the rice subsidy would create a better overall economic environment and facilitate the success of the proposed adaptation strategies. Overall, the simulation results show that there will be less need of government support on the rice sector, when productivity of rice and other crops increase through investment in agricultural R&D and irrigation expansion. The results indicate that these adaptation and investment programs actually work better without the price distortions arising from the NFA programme. Even though the results indicate little difference in agricultural growth in response to these policy interventions, the rate of crop production growth does differ (Table 11.13). Rice production is much higher with the NFA subsidy in place, whereas export crop and other crop production is higher without the NFA subsidy. This indicates that the rice subsidy disrupts optimal resource allocation by diverting agricultural resources from higher to lower value crops. In addition, by eliminating the rice subsidy, the government could improve its fiscal balance (Table 11.14). The main difference is derived not only from higher producer and consumer tax revenues, but also from slightly higher income taxes. To assess the net benefit or cost of each adaptation policy, the average yearly absorption value of each strategy was compared with the absorption value under the combined climate effect (Scenario 3), which is estimated to be around PhP145 billion per year (Table 11.15). The higher the value given by each strategy, the more successful it is in reducing the combined climate effect. The adaptation strategies are uniformly more successful when the NFA subsidy is left behind (Scenarios 7, 8, and 9) than when it is maintained (Scenarios 4, 5, and 6). The total cost of climate change is only reduced by PhP56 billion per year under Scenario 4 and by PhP42 billion per year under Scenario 5 (Table 11.15). The worst case occurs under Scenario 6, with costs increasing by PhP3 billion per year. In contrast, the value of average yearly absorption rises when the NFA subsidy is eliminated, increasing total welfare by PhP128 billion, PhP118 billion, and PhP81 billion per year (Scenarios 7, 8, and 9, respectively) compared with the combined climate effect (Scenario 3). These results demonstrate

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–3.1 –2.4 –0.8 –0.9

–5.6 –2.1 –0.7 –0.5

Note: NFA = National Food Authority. Source: Constructed by authors from DCGE model simulation results.

Agriculture Industry Service GDP

Sector/Rate –5.3 –2.3 –0.7 –0.6

–3.4 –2.4 –0.8 –0.9

–5.2 –1.0 –0.2 –0.3

Change from Baseline Levels (%)

Increased Rice Agricultural Irrigation Expansion Tariff Reduction Productivity without NFA with NFA with NFA Subsidy Subsidy Subsidy

Scenario 7

Increased Rice Combined Productivity with NFA Climate Subsidy Effect

Scenario 6

Scenario 5

Scenario 3 Scenario 4

Scenario 9

–5.1 –2.1 –0.2 –0.3

–3.2 –2.2 –0.4 –0.6

Agricultural Tariff Irrigation Reduction Expansion without NFA without NFA Subsidy Subsidy

Scenario 8

TABLE 11.12 Projected Impact of Policy Responses to Climate Change on Macroeconomic Variables, 2050

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Commodity Group –2.9 35.6 –3.7 –2.9

–1.1 41.4 –2.9 –2.2

–3.1 38.3 –5.0 –2.9

–2.6 44.7 –2.4 –1.8

Change from Baseline Levels (%) –1.3 43.7 –1.5 –2.3

Scenario 9

–4.7 47.4 –1.0 –1.9

–7.6 43.6 –4.3 –2.4

Agricultural Tariff Irrigation Reduction Expansion without NFA without NFA Subsidy Subsidy

Scenario 8

Notes: Staple crops include rice and corn; rice constitutes 80 per cent and corn 20 per cent. NFA = National Food Authority. Source: Constructed by authors from DCGE model simulation results.

Staple crops Export crops Other crops Livestock

Increased Rice Agricultural Irrigation Expansion Tariff Reduction Productivity without NFA with NFA with NFA Subsidy Subsidy Subsidy

Scenario 7

Increased Rice Combined Productivity with NFA Climate Subsidy Effect

Scenario 6

Scenario 5

Scenario 3 Scenario 4

TABLE 11.13 Projected Impact of Policy Responses to Climate Change on Agricultural Production, 2050

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–0.06 –0.10 –0.06 –0.08 –0.08

–0.05 –0.09 –0.03 –0.06 –0.06

–0.05 –0.09 –0.04 –0.07 –0.07

–0.06 –0.19 –0.06 –0.07 –0.09

–0.04 –0.10 –3.10 –0.06 –0.02

Change from Baseline Levels (%)

Notes: The activity tax applies to palay; the commodity tax applies to rice. NFA = National Food Authority. Source: Constructed by authors from DCGE model simulation results.

Income taxes Import taxes Activity tax Commodity tax Total revenues

Revenue Stream

Increased Rice Agricultural Irrigation Expansion Tariff Reduction Productivity without NFA with NFA with NFA Subsidy Subsidy Subsidy

Scenario 7

Increased Rice Combined Productivity with NFA Climate Subsidy Effect

Scenario 6

Scenario 5

Scenario 3 Scenario 4

Scenario 9

–0.04 –0.10 –3.10 –0.06 –0.02

–0.05 –0.22 –3.10 –0.05 –0.05

Agricultural Tariff Irrigation Reduction Expansion without NFA without NFA Subsidy Subsidy

Scenario 8

TABLE 11.14 Projected Impact of Policy Responses to Climate Change on Government Revenues, 2050

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–145.1 –119.5 1–29.8 –114.2

–102.7 1–85.0 1–22.5 –114.8

–147.8 –118.7 1–32.0 –112.8

–17.3 –53.7 –10.6 –35.8

Scenario 9

–26.9 –64.8 –12.2 –35.8

–63.8 –89.4 1–7.7 –33.4

Agricultural Tariff Irrigation Reduction Expansion without NFA without NFA Subsidy Subsidy

Scenario 8

Average Yearly Change from Baseline Levels (Billion PhP) –88.6 –70.1 –23.4 –14.9

Notes: Positive values indicate benefits; negative values indicate costs. Source: Constructed by authors from DCGE model simulation results.

Yearly absorption Private consumption Investment Government consumption

Variable

Increased Rice Agricultural Irrigation Expansion Tariff Reduction Productivity without NFA with NFA with NFA Subsidy Subsidy Subsidy

Scenario 7

Increased Rice Combined Productivity with NFA Climate Subsidy Effect

Scenario 6

Scenario 5

Scenario 3 Scenario 4

TABLE 11.15 Projected Impact of Policy Responses to Climate Change on Total Absorption, 2011–50

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that the adaptation strategies work better under a more favourable policy environment, but the strategy of increasing rice yields and expanding irrigation are far more successful in mitigating the impacts of climate change compared with the strategy of reducing agricultural tariffs. Results were similar to those above for the impact of climate change and adaptation strategies on income distribution across households and regions (Table 11.16). Once again, the welfare gain from adaptation strategies is lower under Scenarios 4, 5, and 6 than under Scenarios 7, 8, and 9. Urban upper-income households, which are the group most adversely affected by climate change, are significantly better off when adaptation strategies are introduced without the NFA subsidy. This benefit mainly accrues to households located in Luzon. Urban upper-income households (the majority of which are middle-income households) benefit more through the elimination of rice subsidy, which opens the potential for government spending and investment in the nonagricultural sector. Higher investment and government spending stimulates increased production of industrial goods and public services that eventually increase the demand for skilled labour. For these reasons, urban upper-income household benefit the most, especially in Luzon.

BENEFIT-COST ANALYSIS The impact of climate change is not linear over time. Climate shocks at local and global levels show similar trends, with the total cost of climate change growing from about PhP90 billion in 2030 to PhP500 billon in 2050 (Figure 11.4). The steady trend of climate-related costs as a share of GDP illustrates how these costs accumulate — reaching nearly 1 per cent of GDP in 2050 — despite positive economic growth. The net benefit of adopting three adaptation strategies (increasing rice productivity, expanding irrigation infrastructure, and reducing agricultural tariffs) with and without the NFA rice subsidy is measured as the change in total absorption from the combined climate effect (Scenario 3) to 2050. Once again, the trend emphasizes the drawback of maintaining the NFA subsidy (Figure 11.5). The worst results occur when a policy of trade liberalization is implemented, whereby the Philippines opens its economy for all agricultural commodities, except for rice. This strategy exacerbates the impact of climate change, further reducing total welfare by almost PhP20 billion in 2050. When the NFA subsidy is eliminated, however, the

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Luzon Rural Lower income Upper income Urban Lower income Upper income Visayas Rural Lower income Upper income Urban Lower income Upper income Mindanao Rural Lower income Upper income Urban Lower income Upper income

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–129.2 26.3 3.7 22.6 –155.4 2.7 –158.1 –10.5 17.5 3.3 14.3 –28.0 1.3 –29.3 19.8 51.0 1.5 49.5 –31.1 0.8 –32.0

–100.1 26.3 4.9 21.4 –126.4 3.7 –130.1 –3.5 18.6 4.2 14.4 –22.0 1.8 –23.8 29.5 54.1 2.4 51.7 –24.6 1.4 –26.0

–106.9 28.4 4.7 23.7 –135.4 3.7 –139.0 –5.5 18.8 4.0 14.7 –24.3 1.7 –25.9 26.7 53.8 2.3 51.5 –27.1 1.3 –28.4

–127.1 25.2 3.6 21.7 –152.3 2.7 –155.1 –10.7 16.9 3.1 13.8 –27.6 1.3 –28.9 19.8 50.5 1.5 49.0 –30.7 0.8 –31.5

–83.4 23.6 3.7 19.9 –107 3.1 –110.1 –2.9 17.0 3.2 13.8 –19.9 1.4 –21.3 31.6 53.6 1.6 52.0 –22.0 1.1 –23.1

Notes: Positive values indicate benefits, whereas negative values indicate costs. NFA = National Food Authority. Source: Constructed by authors from DCGE model simulation results.

Region/Income Level –88.2 25.6 3.6 22.1 –113.8 3.1 –116.9 –4.5 17.2 3.0 14.2 –21.7 1.3 –23.1 29.4 53.5 1.6 51.9 –24.1 1.0 –25.0

–103.8 21.9 2.6 19.2 –125.7 2.3 –128.0 –8.4 15.3 2.3 13.0 –23.8 1.0 –24.8 24.6 50.9 0.9 50.0 –26.3 0.6 –26.9

Scenario 3 Scenario 4 Scenario 5 Scenario 6 Scenario 7 Scenario 8 Scenario 9 Increased Agricultural Increased Tariff Rice Irrigation Agricultural Rice Irrigation Expansion Reduction Combined Productivity Expansion Tariff Reduction Productivity Climate with NFA with NFA with NFA without NFA without NFA without NFA Subsidy Effect Subsidy Subsidy Subsidy Subsidy Subsidy Average Yearly Change from Baseline Levels (Billion PhP)

TABLE 11.16 Projected Impact of Policy Responses to Climate Change on Household Welfare, 2011–50

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FIGURE 11.4

FIGURE 11.4 Projected Cost Change and Its of Share of Total GDP, Projected Cost of of Climate Climate Change and Its Share Total GDP, 2011–50

2011–50

Billion Php 600

1.2%

Total impact of climate change (left axis) Global impact of climate change (left axis)

500

1.0%

Impact of climate change on productivity (left axis) Impact of climate change as a share of GDP (right axis)

400

0.8%

300

0.6%

200

0.4%

100

0.2%

0

0.0% 2011

2016

2021

2026

2031

2036

2041

2046

Notes: Costs are measured as changes from results of the combined climate effect (Scenario 3). GDP = gross domestic product. Source: Constructed by authors from DCGE model simulation results.

net benefits of all three strategies are improved. Among all the investment strategies, the highest benefits are derived from improving rice productivity, followed by expanding irrigation infrastructure. It is not possible to undertake a benefit–cost analysis of increasing productivity-enhancing agricultural research and dissemination due to lack of data, but such analysis is possible for investing in irrigation development. The benefit–cost ratio is calculated by comparing the discounted economic benefits of developing irrigation to mitigate the adverse impacts of climate change with the discounted total cost of building new dams according to the NIA’s master plan. The estimated cost of expanding irrigation to about 90 per cent of the country’s potential irrigable area is around PhP451 billion (NIA 2014), and the government plans to meet this goal in 2028 (beginning in 2014). This cost includes both building new dams and improving the condition of old ones (see Appendix Table 11A.6 for the cost breakdown). Consequently, the analysis

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520

FIGURE 11.5

FIGURE 11.5 Net Benefit of Adaptation Strategies in Mitigating Climate Change, 2011–50 Net Benefit of Adaptation Strategies in Mitigating Climate Change, 2011–50

Billion Php 380

Scenario 4

330

Scenario 5 Scenario 6

280

Scenario 7

230

Scenario 8 Scenario 9

180 130 80 30 -20

2012

2017

2022

2027

2032

2037

2042

2047

Note: Net costs and benefits are measured as changes from results of the combined climate effect (Scenario 3). Source: Constructed by authors from DCGE model simulation results.

assumes that no significant costs are incurred to maintain the old dams and build ones to 2050. The net benefits or costs were calculated based on the difference between the total building costs and the total mitigation benefits per year, measured in net present value, using a discount rate of 5 per cent and 2014 as the base year (Figure 11.6). The impact of irrigation development increases overtime, mainly due to higher crop productivity as the area of irrigated land increases (Figure 11.6). The analysis assumes that the real costs of building new dams are the same, even when the investment is delayed until 2025. The total welfare gain from investing in building new dams to mitigate climate change is PhP467 billion when investment begins in 2014, and the total cost is PhP339 billion (both in net present value). This equates to a cost–benefit ratio of 1.38, which is a very high ratio indicating that an investment of PhP100 billion would result in benefits of PhP38 billion. When investment in irrigation expansion is postponed until 2025, the total cost is lower (PhP198 billion), but the total benefits are also lower

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FIGURE 11.6 FIGURE 11.6

Benefits andCosts Costs of of Irrigation Irrigation Expansion, 2014–50 and 2025–50 NetNet Benefits and Expansion, 2014–50 and 2025–50 Net present value 40

30

Immediate investment (2014–2050) Delayed investment (2025–2050)

20

10 0 -10 -20 -30 -40 -50 -60

2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050

Notes: Cost and benefits were calculated in net present value using a discount rate of 5 per cent. Source: Constructed by authors from DCGE model simulation results.

(PhP249 billion), resulting in a cost–benefit ratio of 1.26. This difference emphasizes the significant benefit of taking early action to mitigate the adverse impacts of climate change.

SUMMARY AND POLICY IMPLICATIONS The Philippine economy has yet to undergo the challenges associated with structural transformation. High agricultural productivity is essential to this process to ease the movement of labour out of agriculture and provide affordable domestic food commodities. Results of the model simulations presented in this chapter indicate that climate change suppresses longterm economic growth, causing welfare losses of PhP145 billion per year, on average, to 2050. It also reduces the size of economy, whereby GDP is estimated to be reduced by almost 1 per cent in 2050. Locally, the climate shock reduces crop productivity, thereby lowering national agricultural production. Even more damaging, however, are the negative impacts on

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world prices and global trade flows, which affect the country’s whole economy. Lower levels of global food production due to climate change cause the prices of international agricultural commodities to rise, with both positive and negative consequences for the Philippine economy. Higher international agricultural commodity prices stimulate higher agricultural production but have negative effects in the overall economy by keeping unskilled labour in the agricultural sector, thereby inhibiting the process of structural change. Reducing the growth in the industrial and service sectors hinders long-term economic growth, and the associated agricultural growth is insufficient to offset this trend. As a result, factor inputs used in the industry and services sectors, such as capital and skilled labour, yield lower returns, whereas land, unskilled labour, and livestock yield higher returns in response to higher commodity prices. Rural households generally benefit from higher income levels, mainly from the agricultural sector, whereas urban upper-income households, especially in Luzon, experience welfare losses. Overall, even though climate change could stimulate higher agricultural production in the Philippines, especially in terms of export crops, the reallocation of labour due to international price shocks disturbs the structural transformation process and causes much greater economic losses. This result indicates how spillover effects from climate change could slow the process of economic development in the Philippines. The three adaptation strategies analysed under this study mainly focus on improving agricultural productivity in efforts to mitigate the high costs of climate change and take advantage of higher international agricultural prices. All three adaptation strategies have the potential to mitigate the effects of climate change. The results of the analyses presented in this chapter indicate that investment in improving rice productivity and expanding irrigation infrastructure generate the highest benefits in mitigating the adverse impacts of climate change. In addition, with these productivity-enhancing investments, we find that the rice support program provided by the NFA is no longer needed. In this case, phasing out the NFA policy will result in higher benefit to the country by providing more fiscal space and resources to support other investment programmes. In addition, results of cost–benefit analyses of investment in irrigation infrastructure based on early (2014–50) versus later (2025–50) implementation, indicate clear gains from early investment, with benefit–cost ratios of 1.38 versus 1.26, respectively.

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General Equilibrium Approach to Modelling Alternative Agricultural Futures 523

Appendix 11A Modelling Framework The DCGE model developed for this analysis follows the standard International Food Policy Research Institute (IFPRI) CGE model (Lofgren, Harris, and Robinson 2002), but extends it by incorporating the interperiod solution to capture the effect of changes in investment and capital accumulation as documented in Diao and Thurlow (2012). It is solved recursively, meaning that subsequent years are solved based on past information acquired by agents, who are assumed to have no information about the future. This type of model has been used to assess the economic impacts of climate change at country-level in a number of cases (for example, Pauw, Thurlow, and Van-Seventer 2010; World Bank 2010; Arndt, Robinson, and Willenbockel 2011; and Wiebelt et al. 2013). In this study, the Philippine economy is portrayed in the model using social accounting matrix data based on the most recent input-output table, and supported by various other macro- and micro-level datasets (PSA 2006). The model includes fourteen agricultural subsectors, two mining subsectors, fourteen food-industry subsectors, seven other manufacturing subsectors, and two service subsectors (Appendix Table 11A.1). The more detailed disaggregation of the food industry is intended to facilitate an understanding of agricultural and food value chains, as well as their linkages with international trade. All production activities in the agricultural sector are disaggregated by the three major subregions, with the exception of industrial activities and services, forestry, and fisheries. All production activities in the model combine intermediate and factor inputs in generating sectoral outputs, whereby capital and labour are the main factor inputs used by all sectors, but land and livestock are region-specific inputs only used in the agricultural sector. Factors are assumed to be mobile across sectors, with the exception of capital, which is differentiated as either agricultural or nonagricultural. Labour is categorized by four levels of education to represent unskilled labour at one extreme, and highly skilled labour at the other. The model categorizes households based on income levels and location, according to the country’s three major subregions and whether households are located in rural or urban areas. In total, thirty specific household types are included to provide information on income distribution at both national and subnational levels. The DCGE model is linked with both the DSSAT and the IMPACT models, where each provides parameters to the DCGE model that

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are solved endogenously within the linked models (Figure 11.7). As a biophysical model, DSSAT simulates the impact of climate change on crop productivity by considering changes in various climate variables, such as precipitation, temperature, and solar radiation. This information is taken from four different climate models.2 Based on these parameters, the model then estimates the impact of climate change on crop yields across time and space. The DSSAT model is solved at the pixel level, and the DCGE model takes yield parameters by aggregating the net impact at the regional level (Appendix Table 11A.2). Changes in crop productivity based on the DSSAT results are taken as exogenous shocks to total factor productivity in the DCGE model, defining the crop-yield effect for the DCGE model. The solution of the global IMPACT model generates estimates of world prices under different climate scenarios. These are provided to the DCGE model as exogenous world prices and define the world-price effect of a climate shock (Figure 11.7). We also introduce a feedback linkage by passing Philippine GDP growth projection from DCGE to the IMPACT model, considering the important factor of economic growth in driving the agricultural commodities demand in the IMPACT model. Final iteration on the world price effect of a climate shock is presented in Appendix Table 11A.3. The IMPACT and DCGE models both include agriculture, and it is necessary to ensure that the two models are linked by targeting similar baseline parameters. The common parameters used to drive the new baseline levels in the two models are projected income levels and population growth, based on the SSP framework, which delineates five GDP growth rates according to the different climate and socioeconomic challenges the country faces (Kriegler et al. 2014). As previously mentioned, this study uses SSP2, representing a middle path whereby the economy grows moderately with medium-level climate and socioeconomic challenges. All simulations are compared against this baseline scenario, whereby crop yield and world-price effects are measured as changes from baseline levels. Combining these two shocks provides an estimation of the total impact of climate change on the Philippine economy and agricultural sector at both national and regional levels. For the transmission of global prices, the model assumes that the Philippines is a small open economy, where import and export prices are assumed to be exogenous, meaning that global price changes can be directly transmitted to the Philippines by altering these

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Source: Adopted from Rosegrant et al. (2016).

The Interlinked Modelling System Used to Assess Climate Change Impacts in the DCGE Model

FIGURE 11.7 FIGURE 11.7 The Interlinked Modelling System Used to Assess Climate Change Impacts in the DCGE Model

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two parameters. Changes in world market prices affect both the domestic market and the country’s total trade balance. The model does, however, allow imperfect price transmission between world and domestic prices, which provides flexibility for the domestic market to adjust. Eventually, the spillover effects are captured in the market as all agents adapt to the changes with new decisions following their best interests. All the scenarios specified in this study are solved under the same micro-closures, whereby the wage rate is allowed to adjust, and full employment for all factors is assumed. Factors are also assumed to be mobile across sectors, given the long-run focus of the analysis. A “balanced” macro closure is specified, which assumes that the macro demand aggregates (consumption, investment, and government) are fixed shares of total absorption. Savings rates (including by government) are assumed to adjust to achieve macro balance. Foreign savings are fixed and the real exchange rate adjusts to maintain the fixed trade balance.

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General Equilibrium Approach to Modelling Alternative Agricultural Futures 527 APPENDIX TABLE 11A.1 Commodities Included in the Model No.

Commodity

11 12 13 14 15 16 17 18 19 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

Palay Corn Coconuts, including copra Sugarcane Bananas Fruit Coffee Cassava Other crops Livestock Poultry Agricultural activities and services Forestry Fishing Mining Crude oil, natural gas, and condensate Slaughtering, meat processing, and dairy products Dairy products Fruit and vegetable canning Fish canning and processing Coconut/vegetable oil Rice and corn milling Flour, grain milling, and starch products Bakery and noodle manufacturing Sugar milling and refining Manufacturing of cocoa and coffee processing Manufacturing of animal feed Other food products Beverage industries Tobacco manufacturing Final goods manufacturing Intermediate goods manufacturing Petroleum and other fuel products Chemicals and chemical products Heavy industrial manufacturing Construction Utilities Trade services Government services

Source: Compiled by authors.

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APPENDIX TABLE 11A.2 Productivity Shock Introduced in the Model Crop yield shock Commodity

Region

Palay (irrigated)

Luzon Visayas Mindanao Luzon Visayas Mindanao Luzon Visayas Mindanao Luzon Visayas Mindanao Luzon Visayas Mindanao Luzon Visayas Mindanao Luzon Visayas Mindanao Luzon Visayas Mindanao Luzon Visayas Mindanao Luzon Visayas Mindanao

Palay (rain-fed) Corn Coconuts Sugar Bananas Fruit Coffee Cassava Other crops

Yearly change from baseline levels (%) –0.003 –0.017 –0.001 –0.151 –0.041 –0.044 –0.502 –0.599 –0.462 –0.031 –0.013 –0.049 –0.127 –0.118 –0.017 –0.201 –0.099 –0.099 –0.201 –0.099 –0.099 –0.201 –0.099 –0.099 –1.187 –1.147 –0.607 –0.200 –0.131 –0.087

Source: Constructed by authors from DSSAT and WaNulCas simulation results.

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General Equilibrium Approach to Modelling Alternative Agricultural Futures 529 APPENDIX TABLE 11A.3 World Price Shock Introduced in the Model World Price Shock Commodity

Yearly Change from Baseline Levels (%)

Palay Corn Coconuts Sugar Bananas Fruit Coffee Cassava Other crops Livestock Poultry Meat, processed Dairy Fruit, canned Coconut oil Rice, milled Sugar, processed Coffee, processed

0.724 1.060 0.197 0.351 0.504 0.494 0.529 0.313 0.235 0.257 0.282 0.257 0.257 0.494 0.197 0.724 0.351 0.529

Source: Constructed by authors from IMPACT model simulation results.

APPENDIX TABLE 11A.4 Projected Productivity Gain from New Irrigation Infrastructure Yield Change Commodity

Yearly Change from Baseline Levels (%)

Palay Corn Coconuts Sugar Bananas Fruit Coffee Cassava Other crops

0.117 0.069 0.069 0.110 0.020 0.038 0.009 0.000 0.118

Source: Constructed by authors from IMPACT model simulation results.

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Angga Pradesha and Sherman Robinson APPENDIX TABLE 11A.5 Import Tariff Rates

Sector Palay (rain-fed) Corn Coconuts, including copra Coffee Other crops Livestock Poultry Forestry Mining Crude oil, natural gas, and condensate Slaughtering, meat processing, and dairy products Dairy products Fruit and vegetable canning Fish canning and processing Coconut/vegetable oil Rice and corn milling Flour, grain milling, and starch products Bakery and noodle manufacturing Sugar milling and refining Manufacturing of cocoa and coffee processing Manufacturing of animal feed Other food products Beverage industries Tobacco manufacturing Final goods manufacturing intermediate good manufactures Petroleum and other fuel products Chemicals and chemical products Heavy industrial manufacturing

Tariff Rate (%) 51.4 49.9 15.3 14.7 17.2 16.7 13.7 10.4 15.6 16.8 14.1 14.1 16.6 16.5 12.3 49.5 15.1 16.4 26.8 16.5 18.0 15.2 11.5 17.4 15.1 13.0 17.6 15.1 18.4

Source: Constructed by authors based on baseline model results.

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General Equilibrium Approach to Modelling Alternative Agricultural Futures 531 APPENDIX TABLE 11A.6 Cost Structure of Building Dams Following the National Irrigation Authority’s Masterplan Year

Cost (thousand PhP)

New Irrigated Area (ha)

Restoration (ha)

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 Total

21,111,849 52,928,733 48,105,913 20,216,520 28,842,361 32,291,140 34,405,282 33,371,413 33,864,495 31,242,846 28,394,359 26,825,032 21,861,222 21,186,012 16,474,229 451,121,406

41,158 65,363 72,120 50,980 48,004 50,419 56,100 56,096 67,032 60,313 64,171 97,154 79,835 48,048 83,515 940,308

7,468 90,000 70,000 41,100 43,234 43,228 43,228 43,628 59,835 2,100 2,647 1,900 1,800 1,700 1,600 453,468

Source: NIA (National Irrigation Authority). NIA’s Irrigation Masterplan 2014–2028. Quezon City, 2014.

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Notes 1. Even though 2015 was the self-imposed deadline for reducing import tariffs among ASEAN countries, it can be seen as a period of preparation towards reaching the targeted goal (Das et al. 2013). More active tariff reductions would be anticipated in 2020. 2. Chapter 9 provides more information on the climate models used and how it is linked with DSSAT model.

References Arndt, C., S. Robinson, and D. Willenbockel. “Ethiopia’s Growth Prospects in a Changing Climate: A Stochastic General Equilibrium Approach”. Global Environmental Change 21, no. 2 (2011): 701–10. Badri, N., B. Dimaran, and R. McDougall. GTAP 7 Data Base Documentation. West Lafayette, IN: Purdue University, Center for Global Trade Analysis, 2008. Blonigen, B., J. Flynn, and K. Reinert. “Sector-Focused General Equilibrium Modeling”. In Applied Methods for Trade Policy Analysis, edited by J. Francois and K. Reinert. Cambridge: Cambridge University Press, 1997. Corroraton, C., J. Cockburn, and E. Corong. “Philippines: Doha Scenarios, Trade Reforms, and Poverty in the Philippines”. In Poverty and WTO: Impact of the DOHA Development Agenda, edited by H. Thomas and A. Winters. Washington, D.C.: World Bank and Palgrave Macmillan, 2006. Das, S. B., J. Menon, R. Severino, and O. Shrestha. The ASEAN Economic Community: A Work in Progress. Singapore: Institute of Southeast Asian Studies, 2013. David, C. “Agriculture”. In The Philippine Economy: Development, Policies, and Challenges, edited by A. Balisacan and H. Hill. New York: Oxford University Press, 2003. Diao, X. and J. Thurlow. “A Recursive Dynamic Computable General Equilibrium Model.” In Strategies and Priorities for African Agriculture, edited by X. Diao, J. Thurlow, S. Benin, and S. Fan. Washington, D.C.: International Food Policy Research Institute, 2012. DOF (Department of Finance). “Philippine Tariff Finder”. 2014 (accessed 7 July 2014). Fernandez, L. and E. Velarde. Who Benefits from Social Assistance in the Philippine? Evidence from the Latest National Household Surveys. Philippine Social Protection Note 4. Manila: World Bank, 2012. Fischer, T., D. Byerlee, and G. Edmeades. Crop Yields and Global Food Security: Will Yield Increase Continue to Feed the World? ACIAR Monograph 158. Canberra: Australian Centre for International Agricultural Research, 2014. Kriegler, E., J. Edmonds, S. Hallegatte, L. Ebi, T. Kram, K. Riahi, A. Winkler, and

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General Equilibrium Approach to Modelling Alternative Agricultural Futures 533 D. van Vuuren. “A New Scenario Framework for Climate Change Research: The Concept of Share Climate Policy Assumptions”. Climatic Change 122 (2014): 401–14. Jha, S., and A. Mehta. Effectiveness of Public Spending: The Case of Rice Subsidies in the Philippines. ADB Economics Working Paper Series 138. Manila: Asian Development Bank, 2008. Lofgren, H., R. Harris, and S. Robinson. A Standard Computable General Equilibrium (CGE) Model in GAMS. Trade and Macroeconomics Discussion Paper 75. Washington, D.C.: International Food Policy Research Institute, 2002. NIA (National Irrigation Authority). NIA’s Irrigation Masterplan 2014–2028. Quezon City: 2014. Pauw, K., J. Thurlow, and D. Van-Seventer. Drought and Floods in Malawi. IFPRI Discussion Paper 962. Washington, D.C.: International Food Policy Research Institute, 2010. PSA (Philippine Statistics Authority). “Input-Output Table”. 2006 (accessed June 2014). Roumasset, J. Black-Hole Security. Working Paper 5. Honolulu: University of Hawaii, 2000. Rosegrant, M., N. Perez., A. Pradesha, and T. Thomas. “The Economywide Impacts of Climate Change on Philippine Agriculture”. Climate Change Policy Note 1. International Food Policy Research Institute. Washington, D.C., 2016. SEPO (Senate of the Philippines). Subsidizing National Food Authority: Is it a Good Policy? Policy Brief PB-10-04. Manila, 2010. Wiebelt, M., C. Breisinger, O. Ecker, P. Al-Riffai, R. Robertson, and R. Thiele. “Compounding Food and Income Insecurity in Yemen: Challenges from Climate Change”. Food Policy 43 (2013): 77–89. World Bank. Economics of Adaptation to Climate Change. Main Report Vol. 1 of Ethiopia. Washington, D.C., 2010.

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PART IV Conclusion

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12 SUMMARY AND POLICY RECOMMENDATIONS Mercedita A. Sombilla and Mark W. Rosegrant

Agriculture’s direct share of the Philippines’ gross domestic product (GDP) has fallen over time, from around from 20 per cent in the 1960s to 10 per cent as of 2014 (Chapter 1, this volume). Nevertheless, one-third of the labour force is still employed in agriculture, and 70 per cent of rural poor people depend on the sector and related activities for major shares of their income (Chapter 1, this volume). For these reasons, the medium-term development plans of successive Philippine administrations have consistently acknowledged the need to energize and modernize the agricultural sector in the pursuit of vigorous, broad-based economic growth and development that will generate employment and reduce poverty and inequality. Success in attaining this goal, however, has continued to be elusive. In fact, it has become more challenging due to climate change and the associated increase in the number and severity of natural disasters. This volume has identified key contributing factors to the complex challenge of developing and modernizing the Philippine agricultural sector, examined how changes in key input resources have affected agricultural growth, discussed the range of additional threats that climate

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change poses to the sector, quantified recent agricultural losses due to the impacts of climate change, and identified measures to help mitigate these losses under a variety of potential policy scenarios. The purpose of this final chapter is to summarize the key findings of the preceding chapters, and synthesize recommendations on the policy and institutional changes needed to strengthen the sector and enhance its resilience to climate change.

AGRICULTURE’S POTENTIAL AS AN ENGINE OF GROWTH AND POVERTY REDUCTION Past studies analysing the Philippine agricultural sector have noted its weak growth performance despite numerous programmes to invigorate and modernize the sector. Productivity growth — measured in terms of land productivity, commodity yield per hectare (ha), or total factor productivity (TFP) — has slowed over time (Chapters 1 and 5, this volume). This has contributed to the sector’s low levels of labour productivity and the high incidence of poverty in rural areas, where 69 per cent of the agricultural labour force remain either unpaid or self-employed (Chapter 1, this volume). The needed structural change that should have facilitated income diversification, increased agricultural wage rates, and reduced poverty and income inequality has not been achieved. This is in significant part due to weak policies and poor implementation of plans and programmes (Table 12.1). The question remains as to whether agricultural transformation could still be promoted in the Philippines to spur more sustained growth and reduce poverty. Studies show that a successful transformation can be achieved with the right mix of policies and programmes that boost production of areas with high potential for agricultural productivity growth and, at the same time, strengthen the nonfarm sector (Chapter 1, this volume). Identifying the right mix of policies and programmes requires an accurate assessment of the key drivers of production growth, especially in the light of the emerging “new normal” environment brought about by climate change.

CHANGING LAND AND WATER USE Land and water are essential inputs to agricultural production. With population growth, the demand for food also rises and, hence, the demand

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Development of value-adding and agribusiness activities

Implementation and support services delivery

Level and allocation of financing

GOV, POL

CAP, GOV POL, IMP CAP, GOV, IMP IMP, CAP

MKT, CAP

CAP, IMP

GOV, IMP POL, GOV

POL, GOV, IMP

POL, GOV, IMP

CAP CAP, IMP

continued on next page

• Lack of skills to disseminate technologies/interventions, or to evaluate their impact; need for comprehensive, up-to-date, comparable data and indicators • Weak safeguards against disasters; low rate of participation to insurance; lack of early warning systems • Difficulty in meeting government requirements to receive technical assistance • Inadequate mechanisms for technology transfer, especially imported technology • Weak monitoring and evaluation of plan/programme/project implementation/ performance • Instability/unreliability of raw material supplies, including low productivity, weak production diversification, and weak schemes for product consolidation • The high cost of raw materials based on poor transport, communications, power infrastructure, and tariffs for raw material imports

CAP

• Limited development outlook/vision to guide planning and programming; poor prioritization of strategies • Weak understanding of the needs of the sector; R&D out of sync with needs; lack of perceived need for technology, credit, insurance, and so on • Poor programme/project design • Lack/insufficient data to support planning/programme and project design, available data of poor quality • Insufficient funds for the development and maintenance of needed infrastructure, including irrigation, water supply, reliable and accessible power, and roads — particularly farm-to-market roads and bridges connecting production areas with markets • Insufficient funds for other productivity-enhancing activities, such as R&D, extension, credit, insurance, market development, and information and communication technologies • Difficult/cumbersome government procurement procedures • Subsidies that distort technology choices, such as on key farm inputs, and so on

Development planning and program/project design CAP, MKT

Focus

Description

Problem

TABLE 12.1 Key Impediments to the Success of the Agricultural Plans and Programmes Summary and Policy Recommendations

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GOV, MKT

• Availability and accessibility to affordable credit, including credit for production involving long-gestating crops • Limited inclusion of farmers in commodity value chains • Weak private-sector participation in agribusiness development, including development of agribusiness schemes based, for example, of contract-based production • Lack of access to markets and ports based on poor transportation and communications infrastructure and the high cost of power • Tariff barriers on the export of some commodities to some countries (for example, mangoes, bananas, pineapples) • Inability to respond to increased demand due to low productivity and lack of willingness to collaborate to maximize potential market volume • Lack of quality standards due to absence of systems and institutions to assess and maintain them, resulting in poor product handling, storage, and so on • Theft of agricultural inputs and produce • Tolerance by local officials of lawlessness causing disruption to development efforts • Weak coordination and overlapping functions of institutions • Weak capacity of institutions to effectively and efficiently implement • Proliferation of conflicting laws and regulations, exacerbated by weak enforcement, including those related to property rights

MKT, CAP, IMP GOV GOV IMP, GOV CAP IMP, GOV

IMP, CAP

EXT

GOV

CAP, MKT POL, MKT

Focus

Description

Notes: CAP = weak capacities especially on the part of the government institutions concerned; EXT = external factors beyond national control, such as the policies of trading partners; GOV = weak governance, including graft and corruption; IMP = weak policy implementation, including inadequate budgetary allocations of key support services; MKT = unfavorable market realities; POL = inappropriate policies; and R&D = research and development. Source: Constructed by authors based on Rolando T. Dy, “Private-Sector Investment and Rural Growth”, Report submitted to the World Bank Office Manila (2005) and Cielito F. Habito and Roehlano M. Briones, “Philippine Agriculture Over the Years: Performance, Policies and Pitfalls”, paper presented at the conference Policies to Strengthen Productivity in the Philippines, sponsored by the Asia-Europe Meeting (ASEM) Trust Fund, Asian Institute of Management Policy Center, Foreign Investment Advisory Service, Philippines Institute of Development Studies, and the World Bank, held in Makati City, 27 June 2005 (Table 6).

Institutions and governance

Issues of law and order

Access to local and global markets

Problem

TABLE 12.1 — cont’d

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for more agricultural land and water to enhance production. Yet the availability of productive agricultural lands remains static or declines. The situation with water, also a finite resource, is the same, not only because of the growing competition for its use in the agricultural, industrial, and service sectors, but also because of seasonal variations, uneven geographic distribution, and a changing climate. As in many developing countries, expansion of agricultural area to enhance food production has come at the expense of forest cover, which declined from approximately 27 million ha in 1521 when the Spaniards arrived, to 21 million ha in the 1900, to only 6.1 million ha as of 1996 (Chapter 2, this volume). Agricultural land expansion has been identified as one of principal direct drivers of the loss of tropical forest. The other direct drivers include timber extraction and infrastructure development. There are also underlying indirect drivers of forest destruction and secondary forest formation that are associated with systemic issues, such as corruption, poverty, high population density, and migration to marginal upland areas where conventional agricultural practices are unsuitable. But the deforestation trend was reversed in the late 1990s, reaching 7.7 million ha in 2010, even though agricultural land continued to expand. This recovery occurred in part in response to deliberate policies, such as a moratorium on logging old growth and mossy forests at more than 1,000 metres above sea level, as well as reforestation efforts by government agencies, nongovernmental organizations (NGOs), the private sector, and civil society organizations. The emerging trends show that both forest cover and agricultural land are expanding, an indication that the Philippines is undergoing a state of land-use transition (Chapter 2, this volume). Agricultural water use has increased over time and has primarily come from national irrigation projects, which for many years were the country’s largest public agricultural expense (Chapter 3, this volume). In the 1970s and 1980s, public expenditures on irrigation represented about 45 per cent of all public agricultural spending and 12 per cent of spending on infrastructure development. The relative importance of irrigation in public agricultural spending has fluctuated since the late 1980s, declining by more than half of public agricultural spending throughout the 2000s (or to 6 per cent of total spending on infrastructure), but rebounding to nearly 30 per cent of total public agricultural spending in recent years, (or to about 10 per cent of total spending on infrastructure). This gradual

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increase was partly in response to the country’s heightened aspiration to achieve rice self-sufficiency. Supplementing the country’s national irrigation systems are communal irrigation systems. Their share of total irrigation investment rose from 5 per cent in the 1970s to more than 40 per cent in the early 1990s. As of 2015, the country’s total irrigable area was slightly more than 3 million ha, of which about 25 per cent was serviced by national irrigation systems and 20 per cent by communal irrigation systems. The remaining areas were serviced by private irrigation systems and systems operated and maintained by other government agencies. The remaining area with potential for irrigation development is still about 1.3 million ha. With the availability of more water for agriculture and the adoption of other productivity-enhancing technologies, land intensification has enabled increased food production with less pressure to open new lands. It appears that the Philippines has begun to expand forest protection, while at the same time boosting the productivity of steadily increasing agricultural lands. This progress is being achieved through a combination of alternative economic activities that have the potential to strike a balance between natural resource use and agricultural production, meeting demand for food and avoiding environmental degradation. This is especially encouraging given the potential future impacts of a changing climate (Chapters 2 and 4, this volume).

SUSTAINING AGRICULTURAL PRODUCTIVITY The issue of whether this potential new balance between natural resource use and agricultural production will continue is addressed in Chapter 5 (this volume), which assesses the effect of agricultural productivity growth on the environment. The analysis examines trends in agricultural productivity growth to determine whether four negative externalities — land degradation, limited water availability, loss of biodiversity, and climate change — could threaten the sustainability of further production increases and, hence, have negative impacts on food security. In addition, a case study analysed greenhouse gas (GHG) emissions from the rice sector to assess the sustainability of rice productivity growth during 1994–2010 using an environmentally adjusted Malmquist TFP Index. The environment-adjusted productivity was found to be associated with stagnating technical change, consistent with two earlier studies focusing

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on rice TFP. This ongoing decline in environmentally adjusted TFP was shown to be driven by stagnant technological change, rather than by growth. To address environmental productivity decline, it is therefore recommended that more research and development (R&D) resources be specifically allocated to developing sustainable farming systems, along with support systems to promote them. Additional targeting of research to sustainable farming systems is essential for sustainable agricultural productivity growth in the Philippines. The country’s ability to support further increases in production is limited by the reality that nearly half of its arable land is categorized as moderately to severely degraded — representing 8.5 and 5.2 million ha, respectively — causing significant declines in soil productivity and water retention (Chapter 5, this volume). This makes degraded lands highly vulnerable to drought, which ultimately could threaten food security. Accompanying land degradation is the rise in the volume of soil loss due to erosion from an estimated 340 million tons per year in the late-1980s to around 350 million tons per year by 2000. Economic losses from soil degradation can be significantly mitigated by the application of organic and inorganic fertilizer and by the adoption of soil conservation and better land-management practices. Despite the increasing availability of agricultural water from irrigation, competition for water use has been increasing at more than double the rate of population growth (Chapter 3, this volume). Increased demand for water stems from population growth, as well as from agricultural, industrial, and other uses as income increases. The efficient and timely delivery of irrigation is the single biggest issue in agriculture. Data show that the amount needed for irrigation has been higher than necessary due to inefficient water use. With the expanding coverage of irrigation services and population growth, depletion of future surface and groundwater stocks could indeed occur (Chapters 3 and 5, this volume). The possibility of water deficits also has a spatial and temporal dimension because of climatic variability that could become more acute in the future. Declining water quality is another growing problem. Groundwater pollution is occurring in some areas due to discharge of sediments that cause siltation of riverbeds and run-off leading to eutrophication, salinization, leaching, and contamination. Poor agricultural practices, such as improper use of fertilizer or poor nutrient management, have also contributed to declining water quality. The loss of crop biodiversity could also influence

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long-term productivity, with genetically homogeneous modern varieties potentially becoming more susceptible to evolving pests and diseases. There is, however, little evidence that this is happening in the Philippines (Chapter 5, this volume). The last of the negative externalities discussed relates to climate change. Extreme climate variations, which are likely to increase in frequency and severity due to climate change have a profound impact on agriculture (Chapters 4 and 7, this volume). The extent of damages due to climate change depends both on the timing and duration of the event in question, and on the crop’s stage of growth at the time of the event. Crop yields and area harvested fell significantly during El Niño Southern Oscillation episodes such as those observed in 1982/83, 1997/98, and 2014/15. Rising sea levels caused by increased sea surface temperatures associated with global warming lead to saltwater intrusion, resulting in salinity problems in low-lying coastal areas used for agricultural production. Salinity stress is expected to become even more serious in coastal and deltaic areas vulnerable to rising sea levels. Coastal areas in the Philippines cover about 34,000 square kilometres, encompassing 804 municipalities and cities, and 23,492 barangays (that is, administrative divisions). Exposed areas are planted to such crops as rice and corn, as well as fruit trees. Rising sea levels are expected not only to reduce crop yields, but also to reduce the area of crop production due to the inundation of coastal areas.

IMPACTS OF CLIMATE CHANGE ON AGRICULTURE AND THE ECONOMY Historical evidence of the changes in climate variables at local and national levels clearly show that climate change is already occurring in the Philippines (Chapter 4, this volume). These variables include an increase in temperature by an average of 0.65°C during 1951–2010;1 statistically significant increases in both the frequency and intensity of extreme daily rainfall events (from a fairly uniform rainfall distribution during 1959–78 to greater variation in 1979–2006, including a peak frequency of 162 millimetre (mm) and an observed extreme event of about 348 mm); and rising sea levels stemming from increased surface sea temperatures ranging from 0.20°C per decade in southern Mindanao to 0.33°C per decade in the north of the archipelago during 1985–2006. Other evidence of climate change includes the frequency and intensity of extreme weather and climatic events, the number and sequence of wet and dry days, and an increase in

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the number of warm nights and hot days. Historical records indicate that extreme events have become more intense over time, and areas previously not affected by such events have become affected in recent years. Climate change has profound adverse impacts on the agricultural sector because of its negative impact on yields and productivity. During extreme weather events, farm structures and equipment are damaged or lost, and infrastructure, such as roads, bridges, and irrigation systems, can be washed out.

The Direct Impacts of Climate Change on Agriculture The simulations undertaken for this study utilized a series of interlinked models, whereby the results of the initial modelling exercises could be used as inputs into subsequent simulations (Chapters 9 through 11, this volume). The Decision Support System for Agrotechnology Transfer (DSSAT) suite of crop models were used to simulate crop growth in daily time increments. Projection results indicate that the impact of climate change on irrigated rice is virtually nonexistent because irrigation provides protection against droughts. The impact on rain-fed rice in Luzon, however, is projected to be highly negative in many locations, sometimes exceeding 20 per cent. Whereas yield changes in the Cagayan Valley are projected to be minimal and mostly positive, Central Luzon and Mindoro Occidental and Oriental, Marinduque, Romblon and Palawan are projected to incur large, negative impacts. In contrast, yields are projected to increase in Mindanao under climate change, with some areas having up to 20 per cent higher productivity. The impacts on rain-fed rice in Visayas are projected to be mostly neutral overall, with areas of increase being offset by areas of decrease. Climate change is likely to have large, negative, and comparatively universal impacts on rain-fed maize in the Philippines, although some diversity is also evident across regions. Yield losses due to climate change are projected to be around 19 per cent for the whole Philippines, and slightly higher on average for Visayas (around 22 per cent). In north-central Mindanao and extending into the Autonomous Region in Muslim Mindanao, the impact of climate change on rain-fed maize is projected to be negligible. As in the case of rice, yield losses of irrigated sugarcane are projected to be much less than losses for rain-fed sugarcane. The same geographic differences arise, with Luzon experiencing the largest negative impact, followed by Visayas, and then Mindanao. Cassava is projected to incur the largest yield losses. (Chapter 9, this volume)

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Various measures could compensate for yield losses due to climate change. Increased use of fertilizer, changing cultivars, and changing planting periods are examples of these measures. A combination of any of them has been shown to yield even more positive results. For example, yield gains in irrigated rice averaging 2.6 per cent are possible in the Philippines, and slightly larger gains are possible in Luzon through increased use of fertilizer coupled with changing the variety cultivated (and, as necessary, the planting month). The same measures could work for the other commodities. The need for special attention to adaptation strategies in Luzon should be noted because all major crops in that region are projected to be negatively affected by climate change. In addition, maize is projected to undergo similarly large, negative impacts across the country (20 per cent), with the exception that the impact in Visayas is projected to be slightly more negative. Losses for cassava are projected to be more than 30 per cent in Luzon, and more than 40 per cent in Mindanao.

The Impact of Climate Change on Commodity Production, Prices, and Economic Welfare DSSAT projection results for the Philippines and globally were used as inputs into further simulations using the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) of the International Food Policy Research Institute (IFPRI). The yield variations due to climate change are projected to lead to reduced supply of crop and livestock commodities, higher world commodity prices, and reduced food consumption (Chapter 10, this volume). At the same time, higher commodity prices are projected to induce higher levels of farm production, partially offsetting the negative impact of climate change on yields. Taking these positive and negative effects into account — and assuming the full transmission of higher world prices to domestic markets in the long run — average results from four general circulation models (GCMs) project a 1.7 per cent contraction in total crop production in the Philippines in 2050 compared with baseline levels (that is, without climate change), but at significantly higher prices. Cereal production is projected to fall by 6.1 per cent in 2050 compared with baseline levels. The negative impact of climate change on corn production (a decline of 13.0 per cent) is projected to be significantly higher than for rice production (a decline of only 3.2 per

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cent). Due to the higher prices of cereals used as feed, meat production is also projected to undergo a decline of about 0.9 per cent. The resulting effects of reduced productivity and production on the accessibility of agricultural commodities for consumption are substantial (Chapter 10, this volume). Agricultural food commodity prices are projected to increase due to climate change, making food commodities less accessible, especially for poor people. Substantial increases in consumer prices by 2050 are projected for cereals (24 per cent), fruits and vegetables (13 per cent), and pulses (12 per cent) compared with baseline values. Meat prices are projected to increase by 4 per cent, and among cereals, rice prices are projected to increase by 17 per cent, corn prices by 44 per cent, and wheat prices by 11 per cent. The decline in average per capita consumption in 2050 is projected to be 3.1 per cent for cereals, 2.3 per cent for fruits and vegetables, 2.4 per cent for sugar, 0.9 per cent for roots and tubers, 0.4 per cent for pulses, and 0.3 per cent for meat. Among cereals, per capita consumption of corn is projected to decline by an average of 5.6 per cent, wheat by an average of 3.4 per cent, and rice by an average of 2.9 per cent in 2050.

The Impact of Climate Change on Food Security, Childhood Malnutrition, and Hunger Another impact of climate change is the effect of agricultural changes on food security, which in this analysis is measured as the prevalence of childhood malnutrition and the number of people experiencing hunger or at risk of hunger (Chapter 10, this volume). Three million children in the Philippines were classified as malnourished in 2010. Under a baseline scenario (that is, without climate change), this number is projected to decline to 2.7 million in 2030 and to 2.15 million in 2050, based on average results from the four GCMs; with climate change the number is projected to increase by 40,000 (2 per cent) in 2030 and 50,000 (3 per cent) in 2050. The impact of climate change on the number of people at risk of hunger is estimated to be even more severe. Results averaged across the four GCMs project the number of people at risk of hunger to increase by 1.3 million in 2030 (8 per cent) and 2.0 million in 2050 (13 per cent). An indirect economic cost of climate change is loss of productivity among the Philippine population, in terms of income generation, due to escalating levels of malnutrition. With an average of 1.29 million additional

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malnourished people per year resulting from climate change, the estimated yearly cost is US$470 million for the forty-year period to 2050, again in 2010 PPP dollars, which is equivalent to 21 billion Philippine pesos (PhP) (Chapter 10, this volume).

The Economywide Consequences from Climate Change Impacts on Agriculture The final phase of the modelling work examined the economywide impact of climate change in the Philippine agricultural sector by focusing on movements in labour markets and their long-term impact on economic growth and income distribution (Chapter 11, this volume). This analysis utilized the Philippine Dynamic Computable General Equilibrium (PhilDCGE) model, linked with the DSSAT and IMPACT models to capture both the local and global effects of climate change. The first shock on yield changes was derived from the DSSAT model to show the local climate effect on Philippine agriculture. The world price shocks for agriculture, derived from IMPACT, were used to model international commodity price changes in the Phil-DCGE model as part of the global climate shock. Results indicate that climate change is projected to reduce GDP in the Philippines by 0.9 per cent in 2050. At the sectoral level, the global impact of climate change on trade — which results in higher prices — creates an incentive for farmers to increase their production of agricultural export commodities. However, the local productivity effect reduces the production of agricultural commodities due to yield reductions for most of the crops, including rice. Given interlinkages among the different sectors in the economy, the net productivity gains to the agricultural sector due to the increased demand for, and prices of, agricultural exports under climate change are insufficient to compensate for the negative impacts on productivity in the nonagricultural sectors. The change in demand for all agricultural commodities due to climate change causes shifts in input markets that eventually drive a reallocation of resources. Key among these shifts is the movement of labour (especially unskilled labour) from agriculture to nonagricultural sectors. The climate change effect, mainly driven by global trade adjustments, creates incentives for labour to remain in agriculture. This is reflected by higher demand for labour in the sector, which directly affects labour markets in the nonagricultural sectors. Climate change is

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projected to increase the demand for unskilled labour in the agricultural sector by 4 per cent, consequently reducing the absorption level of labour by the nonagricultural sectors by an average of by 6.5 per cent in 2050. As a result, value-added in both industry and services declines in response to the contraction of the available workforce to support production. The economic cost of climate change can be calculated based on the “real absorption value”, which reflects consumption and investment levels in the Philippines. On this basis, climate change is projected to cost about PhP145 billion per year, mainly due to reduced levels of private consumption and total investment. The impact of global trade dominates this result, indicating the importance of international agricultural prices as drivers of resource allocation. The adverse impacts of climate change that drive labour to remain in agriculture are the main reason for the decline in national income, and ultimately the decline in consumption and investment levels. Further simulations indicate that adaptation policies can overcome the negative impacts of climate (Chapter 11, this volume). Three adaptation strategies have the potential to promote higher agricultural production into the future. The first strategy focuses on rice productivity by increasing investments in R&D, the second targets investment to expand irrigation infrastructure, and the third involves reducing agricultural tariffs. Each of these three options was simulated under climate change, with and without the country’s existing National Food Authority (NFA) rice self-sufficiency policy. Modelling results show that all three adaptation strategies have a positive effect on the economy, reflected in higher GDP levels compared with those projected under climate change without the introduction of adaptation strategies. Increasing rice productivity has the largest impact, followed by expanding irrigation area and reducing agricultural tariffs. Similarly, by mitigating the adverse impacts of climate change, all three adaptation strategies are projected to have significant net welfare benefits. No single strategy is projected to mitigate the full financial cost of climate change (estimated to be about PhP145 billion per year), but gains of PhP128 billion per year are projected for rice productivity investments, PhP118 billion for irrigation investments, and PhP81 billion for elimination of rice tariffs when the NFA rice subsidy is eliminated. Of the three adaptation strategies, increasing rice productivity provides the greatest benefits, followed by expanding irrigation area.

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In summary, the impact of climate change on agriculture is projected to cost the Philippine economy about PhP145 billion per year through 2050. The three adaptation strategies analysed under this study have the potential to significantly reduce the high costs of climate change, and their impact is projected to be much higher if the Philippines abandons its current rice subsidy policy, which introduces economic incentives that impede the process of structural transformation. The rice subsidy is also extremely costly and, as many studies have indicated, has been inefficiently implemented. The results of cost–benefit analyses indicate that investments in increasing rice productivity and expanding irrigation infrastructure have the highest impact in mitigating climate change effects. Estimates of the return to investments in irrigation infrastructure indicate a cost–benefit ratio of 1.38, but only if the government acts quickly. Delaying investment not only reduces the overall cost of this adaptation strategy, but also reduces its benefits. Additional strategies to further mitigate the impact of climate change include developing real-time weather information systems to support farmers’ decision-making processes; improving the provision of agricultural extension services through innovative methods, such as information and communication technologies; and supporting a stronger seed industry to facilitate the adoption of new varieties (Chapter 10, this volume). To achieve food security under climate change, the focus of policy needs to include R&D on productivity- and efficiency-enhancing measures, together with investments to develop appropriate technologies for local conditions, irrigation infrastructure, and farming systems for crop diversification.

RISK MANAGEMENT AND COPING STRATEGIES FOR FARM HOUSEHOLDS In addition to these long-term impacts of climate change, the projected increase in the frequency of damaging storms and droughts can directly affect farm households, particularly their ability to predict the frequency, duration, strength, and timing of rainfall and the frequency of droughts. With the decline in the accuracy of farmers’ subjective decision-making processes, their level of risk to natural disasters increases. Experience will become — and is already becoming — less useful as a predictor of future experience. This situation threatens farm households that

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are usually limited in their ability to reduce risk. Moreover, available strategies to manage risk may be too costly in terms of foregone profits (Chapter 8, this volume). The extent to which farm households experience different shocks, and their responses to such shocks in terms of their decisions about riskmanagement and other coping strategies heavily depend on their economic status and attitudes towards risk as investigated in Chapter 8. When the probability of disaster increases, wealthier farmers allocate more of their resources to safer investments and invest more in risk-reducing techniques. Poorer farmers borrow and invest in farm capital until its return is equal to the cost of borrowing. Since they face a borrowing rate that is higher than returns to off-farm investments, these farmers do not invest off-farm. In other words, there may be little that low-income households can do in response to increased climate vulnerability. Results from the survey indicate that about 77 per cent of the sample of 523 farm households that experienced shocks actually undertook riskmanagement (that is, preventive measures). In identifying their most important strategy for coping with climate-related shocks, 30 per cent of households reported using cash savings, 18 per cent specified reducing their spending, and 14 per cent indicated borrowing from others. Many farm households that experienced shocks took precautionary measures at the start of the planting season to lower their risk of loss. These measures included adjusting or delaying planting time, adjusting the choice of crop variety, increasing the use of fertilizer, building better farm infrastructure, building dikes to improve water flow, and cleaning streams and canals of sediments and other impediments to flow. Adjusting planting time and choosing crop varieties are the most common measures; however, as with other public goods, households seldom invest in cleaning canals and building dikes because the whole community benefits from these activities.

GENDERED ADAPTATION TO CLIMATE CHANGE Female farmers, forestry workers, and fishers in the Philippines were estimated to number 3 million as of 2013, compared with 9 million men; women also share most of the production activities alongside men (Chapter 6, this volume). Supply-chain studies report that Philippine women also participate as traders, wholesalers, and retailers of agricultural

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crops. Women are active as landless workers, as traders of agricultural and fishery products, and in micro-manufacturing enterprises. The high rate of participation by women in agriculture, however, does not translate into higher levels of paid labour or equal wage rates; not only are women paid less than their male counterparts, but their wages also have grown at a much slower rate. Furthermore, women do not have equal access to productive resources compared with men. Access to land, technology, extension services, capital, and infrastructure support tend to favour male farmers, marginalizing women. This gender difference is often hidden in household-level analyses that do not disaggregate women’s and men’s engagement in agriculture. This gender disparity is made worse during and after disasters. In addition to their continued responsibility to provide for household food needs and care for children, the elderly, and the sick, women are over-represented among those negatively affected by climate change, and they spend more of their time and labour on agricultural production when coping with extreme weather events.

Climate Change Adaptation by Women Women play a significant role in climate change adaptation (Chapter 6, this volume). The family food basket can be more diverse in female-headed households and women pay more for rice per kilogram on average but spend less on rice overall per year because they consume less. These findings indicate that female-led food consumption practices merit further examination because they may be more open to adaptation to climaterelated threats to rice production. Diversifying sources of income may be a viable strategy for adapting to the adverse impacts of climate change in the agricultural sector, and, once again, women play an important role. For example, in households engaged in producing and selling flower garlands in peri-urban metropolitan Manila, even though men control most of the farm assets, women perform significant roles in harvesting and marketing the flowers and have significant control of the daily cash income, as well as market information. Analysis of labour migration indicates that, in choosing to travel overseas to obtain work, women may gain more than men. While the migration of women for work remains controversial, the practice offers significant potential benefits to households confronted by climate-related

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risks and events. Many families depend on remittances from family members working abroad, especially when natural disasters occur. Thus, although migration depletes agricultural labour at home, family members working abroad provide significant social capital, especially for disasterprone families. These adaptation strategies are inadequate compared with what women could potentially contribute to climate change adaptation. In this regard, women’s specific vulnerabilities, knowledge levels, and the roles they play in adapting to climate change should be fully understood and accounted for in policy. Without this policy attention, opportunities to reduce the negative impacts of climate change will be lost, and the viability of projects intended to ensure the adoption of appropriate adaptation measures will be undermined.

THE CURRENT GOVERNMENT FRAMEWORK IN SUPPORT OF ADAPTATION AND MITIGATION Recognizing the importance of managing GHG emissions, the Philippines has proactively responded to the negative impacts of climate change, including reducing atmospheric GHG emissions. As early as 1991, and even prior to the 1994 signing the United Nations Framework Convention on Climate Change, the Philippine government created the Inter-Agency Committee on Climate Change to coordinate and monitor the country’s climate change–related challenges and initiatives. The government signed the Kyoto Protocol in 2003, after which various laws and government issuances were enacted (Chapter 7, this volume). National strategies designed to facilitate adaptation and mitigation of the impacts of climate change have also been established to complement the international and local framework for effective action, including an enabling environment and mechanisms for the transfer of technologies. Among these is the alignment of the major outputs of agricultural agencies, such as the Department of Agriculture, with the food security goals of the National Climate Change Action Plan (NCCAP) and the Philippine Development Plan’s 2011–16 target on climate-resilient agriculture. Nevertheless, as noted above the implementation and execution of policy and programs have remained weak for numerous reasons that need prompt attention.

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GAPS IN RESPONDING TO THE THREAT OF CLIMATE CHANGE The multifaceted challenges of responding to the threat of climate change require that more be done to ensure the Philippines’ ability to respond effectively and efficiently. The key gaps are discussed below.

Weak Alignment of Climate Change Strategies with Other Plans The NCCAP is the Philippines long-term agenda to build the adaptive capacity of men and women in their communities, increase the resilience of vulnerable sectors and natural ecosystems to climate change, and optimize mitigation opportunities towards gender-sensitive and rightsbased sustainable development. The plan identifies specific actions to address and mainstream seven thematic areas in existing national, local, and sectoral plans. To date, however, only partial alignment has been achieved, which has hindered the country’s ability to coordinate and implement its climate change strategies and actions. An example is the slow replication of successful pilot projects — such as the Ecotown projects of the Climate Change Commission — in other regions. The relevant agencies, especially those with local offices, have not facilitated the implementation of similar projects in other areas. Lack of alignment has also limited convergence of efforts to strengthen agencies’ understanding of climate and disaster risk and, hence, to enhance the capacity of local government agencies to effect appropriate development planning. Weak alignment of climate change strategies with sectoral plans also explains why some government departments are only beginning to recognize the impacts of climate change. The limited understanding of these impacts explains their absence in the design of infrastructure, such as irrigation, roads, bridges, and other facilities. This increased recognition needs to be translated into better preparation in response to the potential negative impacts of climate change.

Lack of Capacity and Technical Skills The partial alignment of development plans is exacerbated by the weak institutional capacity and technical skills in implementing the climate

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agenda from the national level, all the way down to local government agencies (Chapter 7, this volume). The critical role of an efficient extension services to facilitate the promotion and out-scaling of best practices and “smart” adaptation and mitigation measures to enhance agriculture’s resilience to climate change needs to be emphasized. Partly contributing to the weak institutional capacity is a lack of consistent and effective climate change knowledge management and dissemination to guide planning and budgeting. This includes the total lack of knowledge-sharing and communication platforms to facilitate local and national exchange of success stories. Downscaling vulnerability and risk assessments, as well as climate change projections for use in local planning, also remain a challenge. Added to this is the consolidation of all learning, methodologies, and tools acquired through special projects and their promulgation among local government agencies and village communities.

Inadequate Consideration of Gender Sensitivities in the Implementation of Climate Change Action As just noted, women’s extensive involvement in farming activities and along the agricultural supply chain is well documented in the literature (Chapter 6, this volume). While gender issues are well recognized in government and other plans, such as the disaster risk reduction management and climate change plans, gender issues have only partially been incorporated into their implementation. While the Philippine Commission on Women actively participated in crafting the National Framework Strategy on Climate Change and NCCAP, women’s participation in the implementation of various components of the action plan, and the mainstreaming of issues benefiting women and other marginalized groups, have yet to be realized. This neglect of women in local decision-making and planning, including climate change–related responses, has persisted despite the recognition of women’s active involvement in agricultural activities and in spontaneous adaptation to climate change and disaster risk management. Despite legal frameworks that eradicate all forms of discrimination against women and seek to strengthen equal rights to land ownership, as well as plans that stipulate that gender should be highlighted in R&D, planning and policymaking, capacity development, and climate change adaptation, little has been done to translate formal national commitments into local climate change action plans.

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The Need to Strengthen Environmental and Climate Data and Information The urgent need to collect and establish a comprehensive, up-to-date, and comparable environmental and climate data has been strongly expressed both in this volume and in various other studies. The available data are limited and sporadic, both spatially and temporally, making the information difficult to reconcile for meaningful use in much-needed assessment studies (Chapter 5, this volume). Disaggregated data are increasingly being sought (by location, gender, and so on). The establishment of such a database would require the development and institutionalization of an efficient monitoring system, including a comprehensive set of agri-environment and climate indicators supported by measurable targets.

The Need for Stronger Climate Change–Related R&D Although a body of climate change–related research is growing in the Philippines, considerable gaps remain. Impact studies are critically needed to determine the country’s vulnerability and adaptive capacity; to identify potential positive and negative, and direct and indirect impacts on key sectors (water, biodiversity, forestry, coastal and marine fisheries, agriculture, health, energy, and infrastructure); and to determine how existing and future policies can influence changes in resource use and human behaviour, especially considering increasing vulnerability to the impacts of climate change. Science-based knowledge on adaptation and mitigation approaches, best practices, and technologies is still inadequate. For agriculture, an R&D programme is needed focusing on identifying and developing farming systems to achieve increased productivity, while enhancing lowland and upland ecosystems through land, soil, and water conservation and the mitigation of agricultural GHG emissions. The Intergovernmental Panel on Climate Change has identified research areas on genetic and resource management that need to be undertaken to determine appropriate and sustainable measures to strengthen the resilience of the agricultural sector against climate change and, thus, protect smallholder and other vulnerable farmers (Table 12.2). Recommended activities include research on enhanced breeding and improved farm and production practices, information dissemination and public awareness campaigns, and the adoption of effective regulatory measures and policies to correct human- and industry-induced malpractice.

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• Identifying appropriate genes • Generating necessary resources to develop varieties • Time required to develop, field test, and successfully disseminate/promote the adoption of varieties • Prevalence of new pests and diseases • Need for extensive research on nutrients and fertilizer requirements of new crop varieties

Choice of crop and cultivar: • Using more heat- and drought-tolerant crop varieties in areas under water stress • Using more disease- and pest- tolerant crop varieties • Using salt-tolerant crop varieties • Using higher yielding and earlier maturing crop varieties in cold regions Farm management: • Modifying the application of nutrients/fertilizers • Modifying the application of insecticides/pesticides • Changing planting date to take advantage of the prolonged growing season and irrigation • Developing adaptive farm-level management strategies

• Breeding livestock for greater tolerance and productivity • Increasing forage stocks for use during unfavorable time periods • Improving the management of pastures and grazing, including grasslands • Improving the management of stocking rates and rotation of pastures • Increasing the quantity of forage used for grazing animals

Agricultural cropping

Livestock production

continued on next page

• Successfully breeding less climate-sensitive livestock • Successfully developing less climate-sensitive grass and pasture varieties • Poor nutrition of many native grassland species for animals • Need for resources and advanced technologies for feed and veterinary service

• Potential impact on yields of shifting planting date • Grass-level resources and technologies required

Agricultural Knowledge and Technology Challenges

Adaptation Measures

TABLE 12.2 Climate Change Adaptation Measures in the Agricultural Sector

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• Developing and distributing more drought-, disease-, pest-, and salt-tolerant crop varieties • Developing improved processing and conservation technologies in livestock production • Improving crossbreeds of high-productivity animals

Development of agricultural biotechnologies

• Technological challenge for poor countries • Need for faster technological transfer • Potential need for nexus among technology owners

• Potential to cross-breed local varieties with fish from arid regions; uncertainty of long-term effects • Generating necessary resources and technology

Agricultural Knowledge and Technology Challenges

Source: Intergovernmental Panel on Climate Change (IPCC), Synthesis Report, Contribution of Working Groups I, II, and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by Core Writing Team, R. Pachauri, and A. Reisinger (Geneva, 2007).

• Breeding fish tolerant to high water temperatures • Improving fisheries management to address climate change–related challenges

Fisheries

• Planting native grassland species • Increasing plant coverage per hectare • Providing local specific support in supplementary feed and veterinary service

Adaptation Measures

TABLE 12.2 — cont’d

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The Need for a More Strategic Climate Financing Scheme While funding for climate initiatives continues to rise, especially with the implementation of more foreign-assisted projects, the level still seems inadequate for the necessary actions and preparations to enable the country to achieve its climate change–related goals and enhance its resilience to projected threats (Chapter 7, this volume). Current funding has been skewed in favour of a few major projects, such as flood control concentrated within a few departments and agencies. Additional funding for agriculture-related climate initiatives is needed, particularly to strengthen adaptive capacity at local levels by improving climate information; conducting R&D on heatresistant crop varieties; developing early warning systems; developing appropriately designed and located irrigation systems; taking the impacts of climate change into consideration; and exploring and developing innovative risk-sharing instruments. Lack of climate-related funding among local government agencies needs to be addressed. Regardless of their high level of vulnerability, with numerous urgent needs, local agencies may be unable to allocate resources to climate change initiatives. Although funding for special projects is available, it is fragmented and subject to intensive competition. Creative financial mechanisms are needed to facilitate technology development and transfer. Private-sector participation in climate financing needs to be promoted. The NCCAP envisions that public funding for climate change initiatives prioritizes adaptation efforts, while at the same time creating an enabling environment to encourage private-sector participation to optimize mitigation opportunities for sustainable development. To date, private participation has been extremely limited.

KEY POLICY RECOMMENDATIONS Important policy recommendations are embedded throughout this concluding chapter, but an overview of key priorities is provided below. If the Philippines is to succeed in achieving its long-held agricultural and development goals and address the complex threat of climate change, corrective action and policy reforms are needed without delay. At the implementation level, overcoming the gaps and weaknesses within key agricultural programmes will depend on the following:

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1. formulating plans that include appropriate additional interventions to strengthen the implementation of existing ones, along with designing new “fit-for-purpose” programmes to enhance and sustain agricultural productivity and production with a much narrower margin of error under climate change; 2. nurturing the recovery of the fragile natural forest resource base and environment, given finite, degraded, and underproductive land; 3. allocating sufficient budgets, refining allocations to improve cost effectiveness and diversification, and ensuring timely disbursements; 4. rectifying programme design and execution, including the streamlining and restructuring coordination across agencies; 5. strengthening human resource capacity, especially within local government agencies, to improve the efficiency of delivery of rural services and other extension support; and 6. strengthening governance, especially in advocating greater transparency to put a stop to corruption. Unchecked, climate change will drive up economic costs in the agricultural sector and cause a deterioration of the country’s food and nutrition security. Addressing the direct challenges of climate change to the agricultural sector requires developing and promoting the adoption of resource-conserving management practices and increasing investment in cost-effective irrigation. In responding to the impacts of climate change, the most promising adaptation technologies and strategies are nutrientuse efficient varieties, integrated soil fertility management, precision agriculture, combined technology, and further cost-effective expansion of irrigation. Critical policy interventions of relevance to the challenges inherent in climate change are outlined below.

Enhanced Investment in Irrigation, Fertilizer and Rural Infrastructure Development The findings of the modelling work presented in this volume emphasize the strongly positive impact of expanding the country’s irrigated area, and the vast undeveloped potential irrigated area that remains. Selective new investment in irrigation expansion and rehabilitation of existing

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systems would be beneficial in adapting to climate change and improving food security (Chapters 3 and 10, this volume). In areas that do not currently have irrigation but have that potential, investment in irrigation infrastructure might be highly beneficial to overcome the limitations of rain-fed agriculture. In addition to boosting crop yields, it can allow two growing seasons in some locations which only have one now. For existing irrigated areas, the strategy of slightly shifting the growing season for rain-fed crops to avoid the hottest months, supplemented by irrigation, offers benefits, and the irrigation could still be used for off-season crops. This often requires careful consideration of the impact on both crops, and may be greatly enhanced using shorter-duration varieties for both crops. Developing the transportation network is another priority to improve farmers’ access to input and output markets. Importantly, gaps and weaknesses hindering greater investment in infrastructure development must be overcome. In addition, especially with climate change, proper identification of the location of the irrigation and transportation facilities is essential, including ensuring that their structural and engineering design is suitably resilient to the adverse impacts of climate change.

Strengthening Climate Risk Management, Risk Transfer, and Risk Sharing Mechanisms The Philippine Crop Insurance Corporation (PCIC) should explore the potential for developing and offering more objective and attractive agricultural insurance products to farmers, such as the weather index–based insurance (WIBI), which has been successfully piloted in several areas of the Philippines. Because WIBI relies on publicly available information, is standardized, is more transparent, and cannot be manipulated by the insured, it is less costly to administer compared with general insurance, making it a more viable option in instances of mass exposure, such as weather events involving entire communities. Nevertheless, given that WIBI coverage is based on a predetermined index of damages, it involves “basis risk”, whereby a household’s insurance coverage has the potential to be either higher or lower than the losses the household actually incurred. As a result, in order to be sustainable, index-based insurance has required subsidies. The implementation of WIBI in the Philippines has been beset with operational issues and challenges, such

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as the developing the necessary location- and crop-specific weather-based indexes, the availability of climate-based risk profiles for specific locations, and need to minimize subsidies. These issues should be resolved through collaborative R&D activities involving PCIC, academic institutions, and the relevant development agencies.

Increased Investment in Agricultural R&D and Extension The importance of R&D in facilitating agricultural modernization cannot be overemphasized, especially in developing sustainable farming systems, along with support systems to promote them. Increasing crop productivity through enhancing investment in agricultural research will be essential to ensure the development of improved crop varieties that are more tolerant to the stresses caused by climate change. New approaches in agricultural research and extension should be developed and implemented to enhance the adaptive capacity of vulnerable farmers, and to reduce the exposure and sensitivity of farmers and farming communities. A number of technologies are in advanced stages of development, including climatesmart technologies that are more resilient to severe and extreme climate conditions. Increased and sustained investment is needed to ensure the development, availability and accessibility of stream of these technologies and practices. In addition to these “new and emerging” technologies, R&D is also needed in response to fundamental problems, such as lowering the production costs of commodities to make them more competitive. Farming systems and varieties that would lower labour costs in rice production, for example, are very much needed given that they account for the largest share of the commodity’s total production cost. The rapid dissemination of technologies and their proper adoption and use is another critical area that goes together with R&D. Extension and seed industry reforms need to address bottlenecks in the adoption of improved technologies. Since many of the technologies are knowledge-intensive, it will be important for extension systems to increase their knowledge capacity, and for innovative forms of extension — for example, through information and communication technologies — to be implemented. To further support adoption of these technologies, improved governance, legal, and regulatory systems that do not hinder the development and uptake of new technologies will be important, as well as investments in rural infrastructure. The reach of public extension should be expanded

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through better coordination with NGOs and private companies engaged in delivering extension advice to farmers. In addition, for agricultural extension programmes to be effective and efficient, local government agencies should be given active roles in increasing the adaptive capacity of vulnerable farmers through training and other capacity building activities, the promotion of climate-smart technologies, and the dissemination of advisories on climate-related issues and concerns, such as forecasts of the seasonal climate, episodes of wet or dry spells; outbreaks of pests and diseases, and so on. The mandate of the Department of Agriculture’s Agricultural Training Institute should include training of farmers and farm community leaders.

Enhanced Investment in the Development of High-Quality Databases An institutionalized system of data gathering and management is a widely expressed need to support programme evaluation, assess policy impact, and guide effective planning and programming. As previously indicated, data should be disaggregated to support richer analyses. For example, the system should be able to disseminate seasonal weather information in terms of its agronomic and economic implications so that farmers can make decisions on the use of various production strategies to enhance the resilience of their crop, livestock, and fish production, while also improving resource-use efficiency. Considering the Philippine Atmospheric Geophysical and Astronomical Services Administration’s (PAGASA) limited financial resources and its inadequate network of weather gauging stations, investment in establishing gauging stations by local government agencies in the most vulnerable areas could have significant benefits. Data from these stations could be used in many practical applications, such as location-specific flood early warning systems; scheduling irrigation, fertilization, and pest management; and as an information source for WIBI products. PAGASA could help local government agencies and the Department of Agriculture in calibrating equipment, training personnel to collect and process data, and using weather and climate information for field operations and planning. Thus, generating, processing, and disseminating climate-related data could become a shared responsibility across different agencies.

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Greater Private Sector Participation in Agricultural Development Research and technology development would benefit from greater private sector support and participation. Private sector research and seed development have social benefits above the private returns generated and would be facilitated by the improved infrastructure and regulatory systems noted above. Private sector participation in agribusiness and other rural industries is also needed to create employment opportunities, especially for farm labourers. Public–private partnerships, along with improved rural infrastructure, would provide opportunities to advance the development of more effective values chains. Importantly, higher household income from nonfarm activities would improve food security. Another area of public– private partnership that could be explored is the provision of WIBI products. Finally, broader macroeconomic, trade, and food-security policy should be reoriented towards facilitating rather than inhibiting trade, competition, and crop diversification. Quantitative restrictions combined with high rice tariffs are inconsistent with the paramount development objectives of reducing poverty and generating long-term sources of productivity and income growth in rural areas. Fertilizer and water subsidies should also be removed because they weaken farmer decisions, lead to overuse of inputs, and are a financial burden to the government, potentially crowding out public investments in research and irrigation. Trade and macroeconomic policies are important determinants of overall economic growth. Broad economic growth — based on favourable and even-handed trade, macroeconomic, and price policies that create a level playing field across sectors and commodities — can in turn generate substantial benefits for agriculture and food security, including the creation or strengthening of domestic markets for agricultural commodities and the generation of capital for investment in agriculture.

CONCLUSION Successfully addressing existing weaknesses and gaps in Philippine agricultural programmes and policies is daunting; the additional task of fortifying them against the threat of climate change and other emerging challenges is even more difficult. The findings of this volume as presented in the different chapters provide a road map for prioritizing policies and

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programmes to generate more rapid agricultural and economic growth under climate change. The agenda to promote growth, employment, and food security under climate change urgently needs to be moved forward. The Philippine government needs to engage all sectors concerned, including the private sector, civil society organizations, and the people. In the Philippines, as in most counties, the menu of management, technology, and investment options for climate adaptation and mitigation is in many ways the same as has been developed for agricultural productivity growth. Moreover, the constraints that need to be overcome with (and without) climate change are essentially the same: risks, uncertainty, imperfect markets, lack of credit, and insurance. So, what difference does climate change make? Fundamentally, climate change policy is the same as good agricultural policy, but climate change increases the cost of policy failure and changes policies, investments, and appropriate technology on the margin. As shown above, both agricultural growth and climate adaptation require increased investments in agricultural R&D. Under climate change, R&D investment in crop breeding should give higher priority to traits that improve adaptation or help in GHG mitigation, such as nitrogen use efficiency and heat and drought tolerance, and for livestock breeding the need to shift to more efficient production technologies that reduce GHG emissions. Increased irrigation investments are another priority both for growth and for climate adaptation. In some regions, climate change will make large dams more valuable to handle increased variability in precipitation and run-off, but in more cases greater emphasis should be given to small-scale irrigation for flexibility. Removing subsidies for fertilizer, water, and energy, and putting the financial savings into productivity-enhancing investments will boost agricultural growth and simultaneously reduce GHG emissions. Finally, the increase in production variability due to climate change over time can increase the benefits derived from removing agricultural trade and macroeconomic distortions. Open trade becomes even more important because climate change will increase the reliance of many developing countries on food imports.

Note 1. Note that the yearly average increase was faster during 1981–2010 than during 1951–2010: 0.0164°C compared with 0.0108°C.

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References Briones, Roehlano M., Mercedita Sombilla, and Arsenio Balisacan, eds. Productivity Growth in Philippine Agriculture. Los Baños: Southeast Asian Regional Center for Graduate Study and Research in Agriculture, Bureau of Agricultural Research of the Philippine Department of Agriculture, and Philippine Rice Research Institute, 2014. Dy, Rolando T. Private-Sector Investment and Rural Growth. Report submitted to the World Bank Office Manila, 2005 (accessed 25 May 2016). Habito, Cielito F. and Roehlano M. Briones. “Philippine Agriculture Over the Years: Performance, Policies and Pitfalls”. Paper presented at the conference, Policies to Strengthen Productivity in the Philippines, Makati City, Philippines, 27 June 2005. IPCC (Intergovernmental Panel on Climate Change). Synthesis Report. Contribution of Working Groups I, II, and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by Core Writing Team, R. Pachauri, and A. Reisinger. Geneva, 2007.

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INDEX

Note: Page numbers followed by “n” refer to Notes

A Abra River, 147 adaptation and mitigation strategies, 278, 549, 553, 557–58 agricultural finance, 305–10 Agriculture and Fisheries Modernization Plan (2011–17), 292 best practices in farming, 301 capacity needs assessments, 298–99 Climate Change Act, 284, 286, 288 climate-proof livelihood options, 301, 303 conservation farming villages, 299 Disaster Risk Reduction Management Act (2010), 288, 290 effectiveness of climate change, 304–5 financial risk management schemes, 303–4 fisheries, vulnerability of, 296–97 government framework in support, 284 gross domestic product, 307 information dissemination, 301 institutional capacity, 293–95 low-emission capacity building, 297–98

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monitoring and evaluation, 314 national and local action plans, 292–93 National Climate Change Action Plan, 295 People’s Survival Fund Act (2012), 290 Philippine Development Plan (2011–16), 290–91 private-sector participation, 313–14 smart agricultural approaches, 300 systemwide climate change programme, 297 vulnerability assessments, 311 adaptation strategies, potential impact of, 462–65 changes in welfare, 486 changing seed variety, 467 countering, 485–88 effectiveness of, 485–88 existing technologies, 466–71 fertilizer, 466, 467 food security, 483, 484 innovative agricultural technologies, 471–73 irrigation development. See irrigation development planting date, 466–67 prices and consumption, 482

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568 producers and consumers, 483–85 production and yields, 478–81 rice and corn productivity (2050), 470–71 social welfare, 464 technologies and investment policies, 465–66 yield improvement, 475–78 ADB. See Asian Development Bank (ADB) additional fertilizer, 478 additional training modules, 202 adjusting planting time, 551 Administrative Order No. 1, 335 AFMA. See Agriculture and Fisheries Modernization Act (AFMA) AFMP. See Agricultural and Fisheries Modernization Plan (AFMP) Agno River water, 148 agrarian reform communities (ARCs), 55 Agrarian Reform Funds, 143 Agricultural and Fisheries Modernization Plan (AFMP), 40 actual vs. mandated budget (2000–05), 41 budget, 40–41 agricultural commodities, 457, 497 resilience of, 286 supply and demand, 503 agricultural damage, 462 agricultural extension, 312–13 agricultural households, 266, 268 agricultural intensification, 74–76 agricultural lands, 72 Agricultural Multi-Market Model for Policy Evaluation (AMPLE), 332 agricultural policy, 113, 240, 288 agricultural producers and consumers, 456–59 agricultural productivity, 492

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Index agricultural programmes, performance, 47 agricultural research, 274 agricultural resilience, 309, 311 agricultural sector, 538 technologies, 437, 441 water supply, 221 weather-related shocks, 57–58 Agricultural Training Institute (ATI), 200–202 agricultural value-added, 220 agricultural workers characteristics, 11 male to female ratio, 10 Agriculture and Fisheries Modernization Act (AFMA), 40–41, 113–14, 212 National Banner Programmes, 41–43 Agriculture and Fisheries Modernization Plan (2011–17), 292 agriculture, change impacts on, 279–82 agri-environmental indicators, 241 agrifuels, 85 agri-insurance coverage, 198, 203 agrobiodiversity, 230–36, 244 agroecosystems, 72 agroforestry, 83 Ahmed, Sara, 263 alienable and disposable lands, 78 Alston, J., 456 Altoveros, Nestor C., 230 AMPLE. See Agricultural MultiMarket Model for Policy Evaluation (AMPLE) AMRIS. See Angat-Maasim Rivers Irrigation System (AMRIS) Anderson, Kym, 4, 25 Angat-Maasim River Irrigation

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Index System (AMRIS), 137, 151–53, 161 Angat-Magat Integrated Agricultural Development Project, 159 Angat Reservoir, 152 Annual Poverty Indicators Survey, 33 aquaculture production, 22 Aquino administration, 3, 54 ARCs. See agrarian reform communities (ARCs) AR4 GCMs, 445 ASEAN Economic Community, 60 Asia, forest cover in, 73 Asian Development Bank (ADB), 153, 307 Association of Southeast Asian Nations (ASEAN), 13, 498 Ateneo de Manila University, 63 ATI. See Agricultural Training Institute (ATI) Autonomous Region in Muslim Mindanao, 545 B Balisacan, Arsenio, 33, 37 Ball, V., 239 bananas, 396, 402, 437, 440 banner programmes, 44 baseline scenario, 496, 498–502 of agricultural GDP (2011–50), 499 domestic production growth, 501 exports, food commodity, 501 GDP growth rate, 498, 499 imports, food commodity, 502 production shares (2011), 500 sectoral shares of labour force, 501 structural change, 500 Batjes, N., 398 Beltran, J., 233 benefit–cost analysis, 494, 517–21

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569 Bicol Region, 324 biodiversity, 108–9, 231 biological oxygen demand (BOD), 227, 253n6 biophysical models, 377 Blonigen, B., 509 Boncodin, Raul, 261 Bordey, Flordeliza H., 45, 272 Borlaug hypothesis, 75–76 Borras, Saturnino, 114 Borromeo, Teresita H., 230 Bresciani, Fabrizio, 37 Briones, Roehlano M., 216, 220, 221, 238, 332 Bruntland Commission, 212 budget, 62 Agricultural and Fisheries Modernization Plan, 40–41 constraint, 343 Department of Agriculture (1998– 2015), 40, 41–42 irrigation, 63n2 Buenaventura, E., 154, 155 Bureau of Soils and Water Management, 141 “buy high, sell low” policy, 59 C Cagayan Valley, 238, 424, 426 capacity needs assessments, 298–99 capital accumulation, 5 Capule, C., 232 CAR. See Cordillera Administrative Region (CAR) carabao production, 22 carbon dioxide (CO2), 236, 238 carbon dynamics, 108 CARP. See Comprehensive Agrarian Reform Program (CARP) CARP Extension with Reforms (CARPer), 55, 57, 114–15

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570 Casecnan Irrigation Component Project, 153 cash crops, 92 CCC. See Climate Change Commission (CCC) Centre for Research on the Epidemiology of Disasters (CRED), 327 Centre National de Recherches Météorologiques (CNRM) model, 392 cereal crops, 90, 92, 97 prices, 455 production, 455, 465–66, 546 certified seeds production, 235 charcoal, 86–87 Charveriat, Celine, 346 Chenery, Hollis, 4 Chetty, Raj, 344, 358 child malnutrition, 456, 485, 488, 547–48 China forest cover, 72 poverty-reducing effects, 36 Cinco, T., 175 CIS. See communal irrigation system (CIS) CISPER database. See Communal Irrigation System Performance (CISPER) database Civilian Emergency Administration, 332 Clark, Colin, 4 Clean Development Mechanism programme, 304 climate change, 57–59, 121, 227, 236–39, 544–45 action, gender issues, 555 adaptation and mitigation strategies, 517, 549, 553, 557–58 adverse impacts, 496, 549

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Index on agricultural production, 506 biodiversity and site-species suitability, 108–9 childhood malnutrition and hunger, 547–48 cost estimation, 507 cross-cutting land-use and, 115–18 demand for agricultural labour, 505 direct impacts, 545–46 economic cost, 549 economy contracts, 509 economywide consequences, 548–50 environmental and climate data, 556 extension systems, 562–63 financing scheme, 559 on food commodity production, 546–47 food security, 547–48 GDP growth rates, 498, 502 gendered adaptation to, 551–53 high-quality databases, 563 on household welfare, 511, 518 on income distribution, 506 indirect economic cost, 547 institutional capacity, 554–55 investment in irrigation, 560–61 long-term impacts, 550 National Climate Change Action Plan, 553, 554 negative impact, 546, 549 net benefit of, 520 policy recommendations, 559–60 and policy scenarios, 495 prices and economic welfare, 546–47 private sector participation, 564 on real household incomes, 509 on real value-added, 503 research and development investment, 556, 562–63

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Index returns to factor inputs, 508 scenarios, 102 technical skills, 554–55 on total absorption, 510 transportation network, 561 wage rates, 507 weak alignment, 554 welfare cost, 509 Climate Change Act, 140, 284, 286, 288, 290, 292, 298, 332 climate change adaptation, 270–72, 274, 293, 298–301 Climate Change Adaptation Support Program, 304 climate change–adaptive infrastructure, 292 Climate Change Commission (CCC), 286, 290, 294, 332 Climate Change Council, 293, 294, 298, 320n3 climate change risks, 286 climate-forecasting options, 196 climate hazards, 238, 302 climate information system, 292 climate models. See general circulation models (GCMs) climate-proof infrastructure, 286 climate-resilient systems, 291, 472 climate-responsive agricultural sector, 286 climate-sensitive agriculture policies, 297–98 climate-sensitive farming technologies, 291, 312–13 climate shock, cost of, 509, 517 climate-smart agriculture, 192 Climate Twin Phoenix project, 296 climate variability, changes in agricultural extension services, 200–1 agri-insurance coverage, 198

18-J04349 13 Future of Philippine Agriculture.indd 571

571 climate forecasting options, 196 climate-related indicators, 181 climate-smart agriculture, 192 crop management options, 194, 195 on crops. See crop production, climate variability on disaster funds and subsidies, 198–201 extreme weather events, 181–85 good agricultural practices, 192, 193 increased seasonal, 184–85 institutionalized strategies, 196 intense rainfall-related events, 183 knowledge-based crop-forecasting system, 197 land-cover change and, 101–3 Legazpi mean temperature, 180 mean temperature anomalies (1951–2010), 176 meteorological rainy days in Legazpi, 182 Muñoz, Nueva Ecija mean temperature, 179 nutrient management options, 196 overview, 174–75 probability distribution, 178 risk transfer mechanism, 203 sea level rises, 181 sea surface temperatures, 181 seasonal climate and crop forecasts, 204 temperature and precipitation, 175–78 temperature-related events, 184 water management options, 194, 196 weather gauging stations (1951– 2008), 177, 178, 201–2 weather index-based insurance products, 198, 203 wet and dry days sequence, 178–81 worst dry episode, 184

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572 CNRM model. See Centre National de Recherches Météorologiques (CNRM) model coconuts, 401, 431. See also rain-fed coconuts cultivation, 429 harvested hectares of, 435 Combalicer, Edwin, 107 commercial agricultural expansion, 85–86 commercial fishing, 25 commodities, 44 Commonwealth Scientific and Industrial Research Organisation (CSIRO) model, 392 communal irrigation system (CIS), 137, 542 expansion and rehabilitation, 141 expenditures on, 148 rehabilitation projects, 148 share of irrigation investments, 143 Communal Irrigation System Performance (CISPER) database, 135 community-based adaptation strategies, 312 Comprehensive Agrarian Reform Program (CARP), 55–57, 114–15, 119, 143, 269 conditional cash transfer programme, 52 Cong Dadong dam, 162 conservation farming villages, 299 Consumer Price Index (CPI), 28, 31 coping strategies, 341–45 ex post, 341 for farm households, 550–51 important, 355 Cordillera Administrative Region (CAR), 145, 324, 382

18-J04349 13 Future of Philippine Agriculture.indd 572

Index corn prices, 455, 482 production, 25, 452, 473, 546 cost–benefit analyses, 550 Cotabato, 424, 425 Coxhead, Ian A., 221 CPI. See Consumer Price Index (CPI) CRED. See Centre for Research on the Epidemiology of Disasters (CRED) credit policy reforms, 45–46 crop biodiversity, 543 crop insurance programmes, 45–46, 286, 360 crop management options, 194, 195, 300 crop modelling, 300, 445, 446 bananas, 437 coconuts. See coconuts Decision Support System for Agrotechnology Transfer, 398, 401 irrigated rice. See irrigated rice maize. See rain-fed maize software, 378 sugarcane, 426–29 Water, Nutrient and Light Capture in Agroforestry Systems, 401–2 crop production, climate variability on, 16, 17–18, 492 export, 504 growth and development, 185–90 impacts, 185 imports, 238 natural disasters, crop losses due to, 190 output, 238 practices and strategies, 204–5 process-based crop model, 187 rice production (1987–2014), 186

19/11/18 12:07 PM

Index technologies, 466 watersheds, 190–91 and yields, 452–55, 462, 473 crop protection, 472, 488, 489 Crop Science Cluster of the University of the Philippines, 231 crop yields, 473 cropland, 395, 396 cropping intensity, 490n2 cross-cutting land-use, policies and, 115–18 CSIRO model. See Commonwealth Scientific and Industrial Research Organisation (CSIRO) model cultivars, 232 cytoplasm, 233 D DAR. See Department of Agrarian Reform (DAR) Datt, Gaurav, 355 David, C., 161, 162 DCGE model. See dynamic computable general equilibrium (DCGE) model de Guzman, R., 175 de Los Angeles, Marian S., 220 decision-making theory, 337 Decision Support System for Agrotechnology Transfer (DSSAT), 398, 401, 423, 437, 451, 452, 493, 494, 523–24, 545, 546, 548 Dedeurwaerdere, Ann, 337 deforestation, 83–84 agricultural land-cover change, 89–97 commercial agricultural expansion, 85–86 demand for timber, 84–85 direct drivers, 84–87 fuelwood and charcoal, 86–87

18-J04349 13 Future of Philippine Agriculture.indd 573

573 increasing population and upland migration, 89 indirect drivers, 87–101 land-use transition, 97–101 mining and infrastructure projects, 86 policies, governance, and institutions, 87–88 shifting cultivation, 85 trend, 541 urbanization, 87 degradation, 83–84, 543 commercial agricultural expansion, 85–86 demand for timber, 84–85 fuelwood and charcoal, 86–87 mining and infrastructure projects, 86 shifting cultivation, 85 urbanization, 87 “degree day” concept, 423 “deindustrialization”, 5 DENR. See Department of Environment and Natural Resources (DENR) Department of Agrarian Reform (DAR), 269, 270 Department of Agriculture, 114, 185, 241, 246, 294, 301, 318–19, 563 Agricultural Training Institute, 200–2 budget, 40, 41–42, 62 collaboration with, 202 for commodity programmes, 43 and International Rice Research Institute, 204 and local government agencies, 202 Department of Environment and Natural Resources (DENR), 114, 241, 269, 270

19/11/18 12:07 PM

574 Department of the Interior and Local Government, 311 Department of Trade and Industry, 303 Diao, X., 523 Dimes, J., 398 disaggregates agricultural activities, 493 disaster Kuznets curve, 329 Disaster Risk Reduction (DRR), 335 disaster risk reduction management (DRRM), 286, 288, 291, 293, 332–37, 340 capacity for, 298–301 Disaster Risk Reduction Management Act (2010), 288, 290, 298 downscaling vulnerability, 555 drip irrigation/sprinklers, 135 “Driving Force–State–Response” framework, 212, 213 droughts, 238, 459–61 drought-tolerant corn, 479 Dumagan, Jesus, 25 dynamic computable general equilibrium (DCGE) model, 451, 493, 494, 523–25 E East Asia, 36 ECHAM model. See European Centre Hamburg (ECHAM) model economic internal rate of return (EIRR), 159 economic welfare, 546–47 ecosystem-based management, 286, 291 ecosystems, 288 agriculture, 230 terrestrial, 108 ecosystem services, land cover and climate change, 105

18-J04349 13 Future of Philippine Agriculture.indd 574

Index biodiversity and site-species, 108–9 carbon dynamics, 108 soil quality and stability, 105–6 water quantity, 107 Ecotown projects, 554 El Niño events, 107, 460 El Niño–induced drought, 115 El Niño Southern Oscillation (ENSO) episodes, 184, 544 elevation, 381 Emergency Events Database (EM-DAT), 327 employment agricultural, 5–6 composition (2000–15), 9 by gender, 264 opportunities for productive, 10 structural transformation in, 8–12 “Engel value”, 28 enhanced vulnerability, 286 ENSO episodes. See El Niño Southern Oscillation (ENSO) episodes environment-adjusted Malmquist index, 248–52 environmental degradation, 240 environmental Kuznet’s curve. See forest transition theory environmental policy, 241 environmental services, 122n4 environmental stability, 288 equity modifiers, 211 equivalent variation (EV), 510 eroded soil, 221 European Centre Hamburg (ECHAM) model, 392 evapotranspiration, 181 event risk, 339 ex ante risk management, 341, 345 ex post coping strategy, 341, 345 Executive Order (EO) 355, 332 Executive Order (EO) 888, 332, 335

19/11/18 12:07 PM

Index expenditure levels, female agricultural workers, 268–69 exports, 13 agricultural products, 5 value of major (2001–14), 15, 16 extreme intensity, natural events of, 346 extreme weather events, 459–62, 544–45 F Fajber, Elizabeth, 263 family food basket, 271 Family Income and Expenditure Survey, 28, 33, 49 FAO. See Food and Agriculture Organization (FAO) farm-household risk management, 340–45 assistance incidence, 366 coping strategies for, 550–51 damages, 357 definition, 346 economic profile, 346 extreme intensity, natural events of, 346 family’s well-being, 351 farm-related damages experienced, 350 financial coping mechanisms, 354 precautionary measures, 356 recovery, 347, 358–59 reducing consumption, 364, 365 resorting to coping strategies, 362 selling goods, incidence of, 363 shocks experienced, impact of, 349 “top-five most-severe” shocks, 347, 348 farm inputs, 44 farm management technologies, 472, 479 farm rehabilitation programme, 198

18-J04349 13 Future of Philippine Agriculture.indd 575

575 farmers’ awareness of climate risks, 312–13 farming, social protection for climate-proof livelihood options, 301–3 financial risk management schemes, 303–4 female-led food consumption, 271 female participation in agriculture, 265–68 in labour force, 263, 265 fertilizer, 445, 466 irrigated rice, 406–10 rain-fed maize, 417–21 rain-fed rice, 411–14 Fifth Assessment Report (AR5), 378, 386 Filipinos household expenditures, 48 workforce, 10 finance, of agriculture, 305–10 financial risk management schemes, 303–4 fiscal reform, 39 fishery, 16 aquaculture, 22 change impacts on, 279–82 commercial fishing, 25 municipal fishing, 22, 25 policies, 297–98 production trends (2000–15), 24 products, 331 resilience, 286 fishing communities, social protection for climate-proof livelihood options, 301–3 financial risk management schemes, 303–4 flood, 238, 324, 329, 459–61 hazard maps, 296 management, 291

19/11/18 12:07 PM

576 “flying geese” metaphor, 4 Flynn, J., 509 Food and Agriculture Organization (FAO), 105–6, 459 2010 Global Forest Resource Assessment for 1990–2000, 79 of United Nations, 78, 227 food and consumption patterns, 25 food expenditures, 28 household preferences, 25, 28 per capita income vs. household expenditures share, 30 shares of household expenditures, 29 food security, 291, 547–48 NCCAP’s outcome on, 304 policy, 59–60 strategic actions on, 289 food systems, 260 commodity production, 546–47 consumption, 455–56 prices, 48–49, 455–56 security, 456, 483, 484 self-sufficiency, 493, 494, 496 sufficiency policy, 47–50 foreign-assisted projects, 143 economic performance, 155–59, 168–70 irrigation projects, 164–67 forest, 72, 395–98 agroforestry, 83 fallows and shifting cultivation, 83 grasslands, 82 industrial tree plantations, 82–83 land policies and programmes, 109–13 net loss, 72 old-growth, 82 secondary, 82 shrublands, 82 typologies, 80–83

18-J04349 13 Future of Philippine Agriculture.indd 576

Index forest cover agricultural land and, 99 in Asia, 73 changes in, 79–80 in China, 72 definition, 79 global net loss, 72 remote-sensing data, 80 forest transition indicators (1961– 2011), 101, 102 forest transition theory, 97 forestlands, 78 forestry, change impacts on, 279–82 Forestry Management Bureau of the Department for Environment and Natural Resources (DENRFMB), 78, 80 forestry policies, 109, 111–12 agricultural policies, 113 land policies and programmes, 109–13 forestry subsector, 16 Fourth Assessment Report (AR4), 378, 385 Francisco, Herminia A., 220, 236 Francisco, Sergio, 45 Franklin, Benjamin, 360 freshwater ecosystems, 133 fruits and vegetables prices, 455 production, 455 fuelwood, 86–87 Fukushima earthquake, 361 Fuwa, Nobuhiko, 37 G GCMs. See general circulation models (GCMs) GDFL model. See General Fluid Dynamics Laboratory (GFDL) model

19/11/18 12:07 PM

Index GDP. See gross domestic product (GDP) gender-disaggregated labour data, 265 gender issues, 263 gender-sensitive decisions, 261 gendered adaptations to climate change, 271–72, 551–53 gendered impacts of climate change, 260 division of labour, 266 female participation. See female participation gender-differentiated responses, 270–71 gendered adaptations, 271–72, 551–53 income and expenditure levels, 268–69 multidimensional nature of gender equality, 261–63 General Appropriation Act, 155, 290 general circulation models (GCMs), 385, 390, 402, 546 General Fluid Dynamics Laboratory (GFDL) model, 388, 390, 431, 437, 451, 458 genetic diversity of papaya, 231 genetic erosion, 231, 232 geophysical events, 324, 329 germplasm, 230 GFDL-ESM2M, 386 GFDL model. See General Fluid Dynamics Laboratory (GFDL) model GHG emissions. See greenhouse gas (GHG) emissions global gender inequality index, 265 Global Risk Index, 238 Goh, Amelia H.X., 268 Government of India (GOI), 120

18-J04349 13 Future of Philippine Agriculture.indd 577

577 government policies mitigation, 274 and strategies, climate change, 285 grasslands, 82 Green Revolution, 90 greenhouse gas (GHG) emissions, 71, 74, 214, 236, 245, 284, 385, 553 from forestry/agriculture sector, 104 land-cover change and, 103–4 levels of, 240 from rice cultivation, 237 Greenpeace, 227 gross domestic product (GDP), 6, 7, 39, 97, 101, 215, 220, 239, 307, 537 composition of (1960–2015), 7 growth rate, 498 gross value-added (GVA), 253n4 groundwater, 133, 222 nitrate pollution of, 230 overextraction of, 226 pollution, 221, 230, 543 stock, 222, 225, 226 GVA. See gross value-added (GVA) H HadGEM2-ES, 386 Hadley Centre Global Environmental Model (HadGEM) model, 388–90, 424, 431, 437, 451, 458 Haiyan (Yolanda) typhoon, 57, 76, 102 Harmonized World Soil Data Base, 398 harvested area, 395, 397–99 Hazell, Peter R., 211 heat-tolerant corn, 479 Herdt, R., 232 high-value crops, 42 high-yielding varieties production, 235 Hilario, F., 175

19/11/18 12:07 PM

578 Hislop, L., 261 hog production, 22 Holdridge Lifezones model, 109 Hoogeveen, Hans, 355 Housing and Land Use Regulatory Board, 311 human agricultural activity, 212 human productivity, 462 human resource capacity, 212 human security, 288 hunger, 456, 485 hurricanes, 459 hybrid rice, 233, 236 Hybrid Rice Commercialization Project, 233 hydropower generation, for water, 151 I ICI. See Interdepartmental Convergence Initiative (ICI) IFPRI–NEDA project, 63, 370 Ilocos Norte Irrigation Project I, 159 Ilocos provinces, 37, 52 IMPACT model. See International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) model imports, 5, 13, 47, 500 crop production, 238 food commodity, 502 rice, 238, 467, 496 value of agricultural imports (2001–14), 17 volume of major imports (2001–14), 18 Im, Sangjun, 107 incentive schemes, 38, 120 income-based poverty, 33, 35 income levels, female agricultural workers, 268–69

18-J04349 13 Future of Philippine Agriculture.indd 578

Index industrial capital, accumulation of, 5 industrial tree plantations, 82–83 inequity, access to social services, 52 inflation rate, 31, 39, 49 information, climate change, 313 information dissemination, 301 infrastructure, 50–51 development, 37 investment, 466 projects, 86 public investment in, 51 transport, 51–52 innovative agricultural technologies, 471–72 crop protection, 472 farm management, 472 varietal trait/seed technologies, 472 institutional capacity, climate change risks, 293–95 institutionalized strategies, climate variability on, 196 Institute Pierre-Simon Laplace (IPSL) model, 390, 423, 431, 437, 451, 459 insurance coverage, 46, 198, 203, 561 insurance programme, agriculture, 46 integrated soil fertility management (ISFM), 437, 441, 447, 472, 475, 477, 479, 482, 489 Inter-Agency Committee on Climate Change, 284, 553 Interdepartmental Convergence Initiative (ICI), 110 Intergovernmental Panel on Climate Change (IPCC), 101, 261, 326, 378, 385, 556 Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC-AR5), 71, 109 interlinked modelling system, 525

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Index International Food Policy Research Institute (IFPRI), 450, 546 International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) model, 450, 452, 490n2, 493, 494, 497, 502, 523–24, 546, 548 International Rice Research Institute (IRRI), 204, 231, 233 investment credit, 46 IPSL-CM5A-LR, 386 irrigated agriculture, 134, 220, 233 irrigated rice, 402 area, 251–52 harvested hectares, 405 high fertilizer use, 409, 410 intensity and productivity, 403 low fertilizer use, 406–8 projected improvements in, 443 irrigated sugarcane, 426 climate impacts on (2000–50), 431 harvested hectares, 429 intensity and productivity, 427 median percentage change in, 430 irrigation development, 468–69, 473, 475, 482–83, 489 budget, 63n2 government and private funding, 477 infrastructure investments, 232 investment in, 473–75 programme, 44–45 irrigation systems, 475. See also National Irrigation Administration (NIA) climate change adaptation in, 134–36 design, 161 distribution of, 149–50 for domestic water supply, 153

18-J04349 13 Future of Philippine Agriculture.indd 579

579 firmed-up service area, 143, 144 investments by use (1965–2015), 142, 147 operation, 137 as policy instrument, 139 in public agricultural spending, 139–40, 143 public investments in, 134, 135, 141 rehabilitation projects, 148 repair and improvement of, 292 and water management, 136, 226 Israel, Danilo, 332 J Japan, 13 rehabilitation projects, 148 structural transformation, 4 Japanese International Cooperation Agency (JICA), 244 K Kastner, Thomas, 90 knowledge-based crop-forecasting system, 196, 197 Koo, J., 398 Korean International Cooperation Agency, 138 Kuznets, Simon, 4 Kyoto Protocol (2003), 284, 553 L labour force expansion of, 11 female participation in, 263, 265 labour-intensive manufacturing industries, 5 labour migration, 272, 552 labour productivity (1987–2015), 8, 10 labour trends (1990–2014), 12 Lambin, Eric, 99 Land Bank, 63n8, 304

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580 land classification, 78 alienable and disposable lands, 78 conflicting forest classification, 78–79 forestlands, 78 land-cover changes, 71, 89, 378, 380 by cash crops, 95 cereal crops, 90, 97 changes in forest cover, 79–80 climate variability and, 101–3 drivers of forest cover change, 83–84 forest cover and, 99 forest typologies, 80–83 global, regional, and national level, 73 greenhouse gas dynamics, 103–4 harvested area of primary crops, 91 land classification, 78–79 by major product, 94 remote-sensing, 81 selected regions, 90 share of agricultural land (1990– 2011), 93 transformations, 85 tree and shrub-based products, 96 by type, 80 land degradation, 215–21, 253n2 land expansion, agricultural production, 538, 541–42 land ownership, 57, 269–70 land productivity, crops (2000–15), 26 land-reform policies, 55, 361 “land sharing”, multifunctionality and, 76–77 land suitability, climate extremes on, 102–3 land use, 395 current discourse on, 74–75 impact of climate change, 395–98 policy recommendations, 118–21

18-J04349 13 Future of Philippine Agriculture.indd 580

Index land-use transition, 74, 97 forest transition indicators, 101, 102 forest transition theory, 97 indicators (1961–2011), 100 pathways, 99 phases, 98 Lansigan, Felino P., 238 Lantican, F., 271 Launio, C., 233 Levine, G., 155 livestock subsector, 16, 18 chicken production, 22 gross value-added (2001–15), 21 hog and carabao production, 22 production trends (2000–15), 23 Local Climate Change Action Plans, 335 Local Disaster Risk Reduction Management Plans, 335 logging activities, 84–85 long-term risk management, 355, 367 Looney, Adam, 344, 358 low-emission capacity building, 297–98 Low, Pak S., 220 Lucas, Robert, 8 M macroeconomic constraints, 38–40 Magat River Integrated Irrigation System (MRIIS), 137 Magna Carta of Women, 270 maize, 92, 396, 423. See also rain-fed maize adaptation strategies for, 445 heat-resilient, 424 Malmquist productivity indexes, 248–50 malnutrition, 456, 459, 460, 462, 483 Manasan, Rosario, 54 Manila Water Company (MWC), 151

19/11/18 12:07 PM

Index manufacturing sector, 8, 500 MapSPAM datasets, 401 Mather, A., 97 Maynilad Water Services (MWS), 151 McKay, Deirdre, 266 mean daily maximum temperature from AR5 general circulation models, 393, 394 in warmest month, 389, 390 Mendelsohn, R., 103 Mendoza, Maria Emilinda T., 272 methane (CH4), 236, 242, 246 Metropolitan Waterworks and Sewerage System (MWSS), 151–53 Meyfroidt, Patrick, 99 microfinance, for farmers, 198–200 micro-level agricultural activity, 212 migration of women, 265, 272, 552 Millennium Development Goal Funds 1656 Joint Programme, 311 Millennium Development Goals, 48, 293, 298 mining projects, 86 MIROC-ESM-CHEM (MIROC), 386 Model for Interdisciplinary Research on Climate (MIROC) model, 392, 424, 431, 437, 451, 490n2 model simulations baseline scenario, 496, 498–502 commodities, 527 computable general equilibrium model, 493 Decision Support System for Agrotechnology Transfer, 493, 494, 523–24 dynamic computable general equilibrium model, 493, 494, 523–25 experimental, 494–98 import tariff rates, 530

18-J04349 13 Future of Philippine Agriculture.indd 581

581 International Model for Policy Analysis of Agricultural Commodities and Trade model, 493, 494, 497, 502, 523–24 productivity shock in, 528 world price shock in, 529 monoculture production, 76–77 Moya, T., 160 multidimensional nature of gender equality, 261–63 multidimensional poverty, 33, 35 multifunctional landscapes, 77 municipal fishing, 22, 25 N national and local action plans, 292–93 National Climate Change Action Plan (NCCAP), 272, 284, 289, 295, 553–55, 559 National Demographic and Health Survey, 33 National Disaster Coordinating Council (NDCC), 332, 335 National Disaster Risk Reduction and Management Council (NDRRMC), 335, 336 National Disaster Risk Reduction and Management Framework, 288, 294 National Economic and Development Authority (NEDA), 63, 138, 335 National Emergency Commission, 332 National Food Authority (NFA), 47, 63n5, 361, 495, 549 accumulated debt, 50 operations, 49 rice subsidy policy, 494, 496, 511, 512, 517

19/11/18 12:07 PM

582 National Framework Strategy and Program, 332 National Framework Strategy on Climate Change (NFSCC), 286, 287, 294 national government borrowing programme, 39 National Greening Program (NGP), 110, 113 National Integrated Protected Area System, 82 National Irrigation Administration (NIA), 134, 135, 497, 519, 531 climate change strategy, 136–39 corporate structure, 153 five-year rationalization programme, 143 irrigation water, 152 Management Information Division, 135 master irrigation plan (2014–28), 136, 138–39 and Metropolitan Waterworks and Sewerage System (1968–2015), 151–53 recalculated service area, 143 short- to medium-term strategy, 139 Systems Management Division, 135 water rights for AMRIS, 151 National Irrigation System Performance (NISPER) databases, 135 national irrigation systems (NIS), 137, 541–42 diversion schemes, 145 expenditures on during (1965– 2015), 148 operation and maintenance, 153–58 pump irrigation schemes, 145

18-J04349 13 Future of Philippine Agriculture.indd 582

Index with recorded information (1965– 2008), 154 rehabilitation projects, 148 reservoir schemes, 145 service area of, 146 share of irrigation investments, 143 storage schemes, 145 National Land Use Act, 320n1 National Land-Use and Management Act, 115, 120–21 National Plant Genetic Resources Laboratory, 231 national policy on climate change, 270 National Power Corporation (NPC), 151 National Seed Industry Council, 231 National Statistical Coordination Board and United Nations Development Programme (NSCB–UNDP), 222 National Water and Resources Board (NWRB), 241, 244 national water reserves, 222 Nationwide Operational Assessment of Hazards project, 296 natural disasters, risk management, 57–59, 327 climate change and, 327 database, 327 direct impacts, 329 framework for, 337–41 frequency of, 328 incidence, 325 indirect impacts, 329 objective, 337 potential exposure, 339 preparedness, 337 provinces exposed to multiple, 336 rainfall concentrations, 326

19/11/18 12:07 PM

Index NEDA. See National Economic and Development Authority (NEDA) Nellemann, C., 261 Network of Protected Areas for Agricultural and Agro-industrial Development (NPAAAD), 113 NISPER databases. See National Irrigation System Performance (NISPER) databases nitrate pollution, of groundwater, 230 nitrogen fertilizers, 227, 466 nitrous oxide (N2O), 236 nonagricultural income, agricultural vs., 61 nonagricultural sectors, 37, 493, 503 absorption level of labour, 549 labour markets in, 548 nongovernmental organizations (NGOs), 79, 230 Nonhebel, Sanderine, 90 Norton, G., 456 no-till farming, 472, 479 nutrient-management options, 196 nutrient-use efficient corn, 479 varieties, 475, 477, 479, 482, 489 O OECD. See Organization for Economic Co-operation and Development (OECD) off-farm capital investments, 342 Office of the Presidential Assistant for Food Security and Agricultural Modernization (OPAFSAM), 63 oil palm plantations, 85 old-growth forests, 82 on-farm capital investments, 342 Ongley, E., 212 online knowledge portal, 300

18-J04349 13 Future of Philippine Agriculture.indd 583

583 operations and management (O&M), 44, 135, 137, 141, 143, 154–57, 160 Organic Agriculture Act (2010), 320n6 Organization for Economic Co-operation and Development (OECD), 212, 231 out-migration, rural and agricultural communities, 263 output growth, 15–17 structural transformation in, 6–8 overseas workers, 265 ownership, land, 269–70 ownership transfer, 56 P paddy irrigation, 226 PAGASA. See Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) Palawan Integrated Area Development I, 159 palay (unmilled rice grain), 17, 18, 25, 315–16 Pampanga Delta irrigation system, 162 Pantawid Kuryente programme, 54 Pantawid Pamilya programme, 52, 54 Pardey, P., 456 partial equilibrium approach agricultural producers and consumers, 456–59 crop production and yields, 452–55 extreme weather events, 459–62 food prices and consumption, 455–56 food security, 456 healthcare costs, 459 productivity losses, 459 Participatory Irrigation Development Program (PIDP), 136

19/11/18 12:07 PM

584 Payment for Ecosystem Services (PES) schemes, 119, 120 People’s Survival Fund Act (2012), 290, 294 Pepeng (Parma), 331 perennials, 455 PES schemes. See Payment for Ecosystem Services (PES) schemes Phil-DCGE model. See Philippine Dynamic Computable General Equilibrium (Phil-DCGE) model Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), 174, 183, 200, 204, 301, 563 Philippine Bureau of Soils and Water Management, 215 Philippine Center for Economic Development (PCED) Social Protection Survey, 326, 327, 341, 345, 359 Philippine Climate Change Adaptation Project, 204 Philippine Competition Commission, 63 Philippine Council for Agriculture and Aquatic Resources and Development (PCAARD), 244 Philippine Crop Insurance Corporation (PCIC), 46, 198, 561 Philippine Department of Agriculture, 284 Philippine Development Plan (2011– 16), 47, 284, 290–91, 295, 310, 312–13, 320n4, 335, 553 Philippine Dynamic Computable General Equilibrium (PhilDCGE) model, 548 Philippine irrigation systems, 137 Philippine National REDD-Plus Strategy (PNRPS), 18

18-J04349 13 Future of Philippine Agriculture.indd 584

Index Philippine Statistics Authority (PSA), 11, 241, 244 phosphate fertilizers, 227 plans and programmes, agriculture, 538–40 planting date, 466–67, 475 policy options, impact of, 510–17 agricultural production, 514 government revenues, 515 irrigation expansion, 520, 521 macroeconomic variables, 513 total absorption, 516 policy recommendations, 118 forestry and agriculture policies and programmes, 118–19 governance, and institutions, 87–88 incentivizing multifunctional agriculture, 119–20 National Land Use and Management Act, 120–21 population density, 92 growth, 5 increasing, deforestation and, 89 share of urban, rural, and agricultural, 32 post-disaster needs assessment (PDNA), 337 poultry subsector growth in, 18 production trends, 23 poverty, 30, 33, 49, 260, 302 health deprivation indicators and, 52 incidence of, 34, 61 multidimensional vs. income-based, 33, 35 trends, 30–32 poverty reduction, 6, 33, 61, 538 agricultural growth as engine, 35–37

19/11/18 12:07 PM

Index China as contributor to, 36 income growth and, 36–37 programmes, 54 precipitation, 222 precision agriculture, 472, 475, 477, 479, 489 private irrigation systems (PIS), 137 private lands, redistribution of, 55–56 process-based crop model, 187 producers lose, 485 productivity growth, agriculture, 25, 538, 542–44 budget, 40 climate change, 57–59 crops, 17–18 elasticity, 37 fisheries, 22–25 food and consumption patterns, 25–30 food sufficiency policy, 47–50 gross value-added (2000–15), 20–21 high transaction costs, 50–52 livestock and poultry, 18–22 as local poverty reduction, 35–37 macroeconomic constraints, 38–40 modernization of sector, 40–47 natural disasters, 57–59 output growth, 15–17 policy and governance issues, 38–59 poverty and, 30–35 productivity growth, 25 property rights reform, 55–57 structural transformation. See structural transformation unequal access to social services, 52–54 productivity losses, 459 “productivity–size inverse relationship”, 77 project SARAI, 192

18-J04349 13 Future of Philippine Agriculture.indd 585

585 property rights reform, 55–57 provincial agricultural model, 296–97 public agricultural spending, 541 public institutions, 50–51 public–private partnerships, 564 public-sector debt, 39 pulses, 455 Q Quick Response Fund, 288, 290 Quilloy, K., 45, 271 R RA 6657, 56 RA 9700, 56 radiative forcing, 387 rainfall, 382, 383 from AR4 general circulation models, 395 from AR5 general circulation models, 392, 394 in Cagayan Valley, 422 in driest three months, 387, 388 in Mindanao, 384 by region and percentile, 384 in Visayas, 384 in wettest three months, 384–86 rain-fed bananas from AR5 general circulation models, 439, 440 intensity and productivity of, 438 rain-fed coconuts from AR5 general circulation models (2000–50), 435, 436 climate impacts on, 435 intensity and productivity of, 434 median percentage change in, 436 rain-fed maize harvested hectares, 415 high fertilizer use, 420, 421 intensity and productivity, 416

19/11/18 12:07 PM

586 low fertilizer use, 417–19 in Mindanao, 415 nitrogen-efficient varieties, 441 projected improvements in, 442 in Visayas, 415 rain-fed rice, 475, 477 harvested hectares, 405 high fertilizer use, 413, 414 intensity and productivity, 404 low fertilizer use, 411, 412 production, 402 projected improvements in, 444 in Visayas, 405, 545 rain-fed sugarcane, 426 climate impacts on, 433 harvested hectares of, 429 intensity and productivity of, 428 losses, 429 median percentage change in, 432 yields for, 426 rainwater harvesting, 194 “real absorption value”, 549 real wage rates, 268–69 “rebound effect”, 75 Reconstruction Assistance for Yolanda (RAY), 337 Redondo, G., 233 Reducing Emissions from Deforestation and Forest Degradation (REDD+), 75, 115–18 Reinert, K., 509 renewable resources, 221, 222 renewable water resources per capita, 223 representative concentration pathways (RCPs), 386–87, 451 Republic Act 6657 (1988), 114–15 Republic Act 8435 (1997), 113–14 Republic Act 9700 (2009), 114 Republic Act 9729, 140, 332

18-J04349 13 Future of Philippine Agriculture.indd 586

Index research and development (R&D), 313, 472, 496, 511, 512 Research Development and Extension Programme, 45 “Revised Forestry Code of 1975”, 78 Revised Management Plan for Forestry Development, 110 Revised National Action Plan to Combat Desertification, Land Degradation, and Drought (DA–DENR–DST–DAR 2010), 106 Reyes, Celia, 46 rice. See also irrigated rice; rain-fed rice in agriculture’s budget, 41–42 breeds, 233 consumption, 455–56, 482 cultivation, greenhouse gas emissions, 237 genetic diversity, 232 germplasm, 231 imports, 238, 467 modern rice varieties, 232–34 post-IR8 varieties, 233 prices, 48, 455, 478, 482 production, 238, 243–45, 398, 405, 452, 466, 467, 473, 482, 497, 542 self-sufficiency in, 542 spatial diversity, 233 subsidy policy, 494, 496, 511, 512, 549–50 varieties, 231–32 Ringler, C., 136 risk assessment, 311, 555 risk management, 550–51 and coping strategy, 341–43 disaster-management capacity, 332–37, 340 economic loss and damages, 333 ex ante, 345

19/11/18 12:07 PM

Index extreme intensity, natural events of, 346 factors influencing recovery, 368–69 farm-household. See farmhousehold risk management Haiyan (Yolanda) typhoon, 332, 333 interventions, 360 long-term, 355, 367 measures by economic profile, 356 National Disaster Risk Reduction and Management Council, 335, 336 natural disasters. See natural disasters, risk management optimal, 339 precautionary measures, 356, 367 and resilience, 326 strategy, 360 total value of damage, 330 typhoon, damages to irrigation, 331–32 and vulnerability. See vulnerability World Risk Report (2013), 327 river basin approach, 139 road networks, 37 Roca, D., 154, 155 Rola, Agnes C., 221 Rosegrant, M., 136, 451, 452 rubber plantations, 86 rural upper-income households, 507, 510 S Safe AWD, 246, 253n9 salinity stress, 190, 544 Salvacion, Arnold R., 238 Samar Island Rural Development Project, 159 science-based decision support systems, 196 science-based knowledge, 556

18-J04349 13 Future of Philippine Agriculture.indd 587

587 seasonal climate variability, 184–85 sedimentation, 220 seed industry reforms, 562 seed variety, 467 Sen’s theory, 122n1 sex-disaggregated data, 271, 274, 275 Shared Socioeconomic Pathways (SSP), 498 Shepley, S., 154, 155 shifting cultivation, 122n3 deforestation and, 85 forest fallows and, 83 Shively, Gerald, 221 shrublands, 82 sink services, 239 site-species suitability, 108–9 small-farm reservoirs, 292 “smart agriculture”, 300 Smarter Approaches to Reinvigorate Agriculture as an Industry project, 300 social isolation, 263 social protection, farming and fishing communities, 301–4 social services, unequal access to basic, 52–54 social welfare, 464 soil degradation, 215–21 depletion, 221 deposition, 221 fertility, 220 nutrients, 220 productivity, 215, 543 quality, 105–6, 215 resources, 220, 221 Soil and Terrain Database (SOTER), 215 Soil Degradation in South and Southeast Asia (ASSOD), 215 soil erosion, 106, 220–21, 253n3

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588 gross and average, 219 severity of, 216, 218 Sombilla, M., 45, 271 sources of income, 261, 271, 552 Southern Philippines Irrigation Sector Project, 159 staple crop production, 504 Strategic Agricultural and Fisheries Development Zones (SAFDZ), 113, 114 Strategic National Action Plan (SNAP), 335 structural transformation, 3–4 agriculture context of, 4–6 “deindustrialization”, 5 economic forces behind, 4–5 in employment, 8–12 in output, 6–8 poverty reduction to, 6 process, 492–93, 503–4 in trade, 13–15 sugarcane, 92, 426, 455. See also irrigated sugarcane; rain-fed sugarcane “superlative index number” procedure, 25 Super Typhoon Haiyan (Yolanda), 57, 76, 102 surface water pollution, 227 stock, 222, 224 susceptibility, 327 sustainability of agricultural growth, 212 agri-environmental indicators, 241 agrobiodiversity, 230–36, 244 climate change, 236–39 incentives, 211 infrastructure, 211 innovation, 211 input-use efficiency, 211

18-J04349 13 Future of Philippine Agriculture.indd 588

Index institutions, 211 investing for sustainable farming systems, 245–47 land degradation, 215–21 linkages between agriculture and the environment, 240–41 measuring, 239–40 rice productivity, 244–45 soil degradation, 215–21 water availability, 221–27 water quality, 227–30 water resources, 241, 244 sustainable farming systems, 543 national R&D programme, 245–46 support systems, 246–47 swidden agriculture, 83, 122n3 Syrquin, Moises, 4 systemwide climate change programme, 297 T Tabios, G., 161, 162 temperature-related events, 184 Teruel, Romeo, 25, 239 Thurlow, J., 523 timber licence agreement (TLA), 84–85 timber, logging and demand for, 84–85 total factor productivity (TFP), 25, 27, 239–40, 244–45, 253n8, 542–43 trade with major partners (2008–15), 14 policy scenarios, 496, 498 structural transformation in, 13–15 value and share of (1980–2015), 14 transaction costs, 50–52 transportation network, 561 transport infrastructure, investment in, 51–52 tree crops, 455

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Index tropical cyclones, 459 tubers, 455 Tulong para kay Lolo at Lola programmes, 54 typhoons, 57, 238, 317, 459–61 Haiyan (Yolanda), 332, 333, 336 Milenyo (2006), 231 Ondoy, 331 Pablo, 331 U “underemployed”, 11 “unemployed”, 11 United Nations Convention to Combat Desertification, 215 United Nations Environment Programme, 278 United Nations Framework Convention for Climate Change, 310, 553 University of the Philippines Marine Science Institute (UP-MSI 2012), 181 upland migration, 89 Upper Pampanga River Integrated Irrigation System (UPRIIS), 137, 153 urban upper-income households, 510, 517 urbanization, 36, 37, 87 urea fertilizer, 466 V varietal trait/seed technologies, 472 Verma, R., 261 Visayan earthquake, 57 vulnerability agricultural sector of Philippine economy, 329–32 direct and indirect impacts, 329 growth performance, 329

18-J04349 13 Future of Philippine Agriculture.indd 589

589 impacts, of climate change, 262 natural disasters. See natural disasters, risk management Philippines to climate change, 327–29 and resilience, 339, 370n4 W WA cytoplasm, 236 waste sink, 214, 253n1 water abstraction, for irrigation, 226 agricultural production, 538, 541–42 availability, 221–27 consumption, 226, 251–52 demand for, 221, 226 governance, 286, 291 for irrigation, 226 management, 194, 196, 286 pollution, 227 quality, 221, 227–30, 543 quantity, 107 resources, 241, 244 retention, 215 sufficiency, 288 use, 221 water harvesting, 136, 472, 475 water-impounding projects, 292 Water, Nutrient and Light Capture in Agroforestry Systems (WaNuLCAS), 401–2 Water Resources Development Project, 159 water resources, for agriculture agenda for, 163 competition for, 151–53 conventional interventions, 163 distribution of, 147, 149–50 during 1965–2015, 145 foreign-assisted projects, economic performance of, 155–59

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Index

590 hydropower generation, 151 investment projects, 147 irrigation sector. See irrigation systems National Irrigation Administration. See National Irrigation Administration operation and maintenance, 153–55 policy implications, 161–63 preparedness of, 134 projects types, 140–45 public investment, 139–40, 145 system types, 140–45 water security challenges, 133 watersheds, climate variability on, 190–91 weather index-based insurance (WIBI) products, 198, 203, 561 welfare gains, 465 losses, 458 wheat, 455, 489n1 Wilson, D., 175

18-J04349 13 Future of Philippine Agriculture.indd 590

women, climate change adaptation, 552–53 Women’s Empowerment, Development and Gender Equality Plan, 275 World Agroforestry Centre, 401 World Bank, 32, 50, 54, 120, 220, 294, 305, 307, 313, 459 World Economic Forum, 265 Global Competitiveness Report, 50 Y yearly precipitation, 388, 390–91 yield improvement, 475–78 yield trends, 398, 400 Yolanda (or Haiyan) typhoon, 57, 76, 102, 332, 333, 336 Yusof, Arief, 236 Z Zhai, Fan, 238 Zhuang, Juzhong, 238 Zhu, T., 136

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