Land Acquisition, Industrialization and Livelihoods: A case study on JSW Bengal Steel Plant 3030902439, 9783030902438

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Sumanta Prakash Shee Ramkrishna Maiti

Land Acquisition, Industrialization and Livelihoods A case study on JSW Bengal Steel Plant

Land Acquisition, Industrialization and Livelihoods

Sumanta Prakash Shee • Ramkrishna Maiti

Land Acquisition, Industrialization and Livelihoods A case study on JSW Bengal Steel Plant

Sumanta Prakash Shee Department of Geography Vidyasagar University Medinipure, West Bengal, India

Ramkrishna Maiti Department of Geography Vidyasagar University Medinipure, West Bengal, India

ISBN 978-3-030-90243-8    ISBN 978-3-030-90244-5 (eBook) https://doi.org/10.1007/978-3-030-90244-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

In recent time land acquisition, displacement, resettlement and rehabilitation, and compensation are complex subjects to deal with in highly populated countries like India. Land is not only essential for farmers’ life support, but it is also considered to be the most important asset on which many people build producing systems, commercial activities and livelihood that can be a principal source of wealth and power. It is also evident from Singur of West Bengal that ambiguous implementation processes generally result in the diversion of a large tract of land, often more fertile, than what is required. In India, the colonial Land Acquisition Act of 1894 is applied to acquire private land for “public purpose”. This act recognizes only individual property rights. Under this Act no compensation is payable to landless labourers, forest land users and forest produce collectors, sharecroppers, artisans, etc. because they have no legal right over the land. For the loss of common-pool resources (CPR) (village common lands used for cattle grazing, fuel wood collection, etc.), the Land Acquisition Act has no scope for any kind of compensation. From this point of view, this case study is different from other reviews because we have given a special concentration for those households, which are suffering a lot due to the suspension of the JSW Bengal Steel project. In the present context, the JSW Bengal Steel authority acquired a large parcel of land from local peasants through a compensation package. The peasants protested land acquisition in Singur and Nandigram but not at Salboni, because the assumption was that JSW Bengal Steel project would bring several ancillaries that would make a new phase of industrial development and a large number of employment opportunities would be built up for local villagers in the non-agricultural sectors. After land acquisition JSW authority started to delay the project implementation. Unexpected delay or suspension may immediately impact resource users whose livelihoods are depending on the land and natural resources. There are many cases like as JSW Bengal Steel Plant where a large part of acquired land ends up in the hands after the announcement of suspension or delaying the project establishment. So as a consequence, both whose land was  acquired and who depend on land become sufferers in terms of their livelihoods. The compensation amount was not sufficient to solve the situation. The compensation amount they own, already spent v

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it more in the unproductive head, ultimately a critical economic condition is already started. A sharp regional disparity is observed in agriculture, infrastructure, and overall socio-economic sectors. There are no alternative livelihood opportunities to shift. No policy is developed till now to reduce this vulnerability. After clearance of land use land cover (LULC) for the industrial project causes more runoff, high intensity of soil erosion, and water scarcity all the downstream. This alteration is connected through a systematic interaction of land, water, soil, land use, and livelihoods. The result of the work will be extremely beneficial to policymakers, planners, engineers, hydrologists, and other concerned authorities, working on the regional development and planning. Department of Geography Vidyasagar  University Medinipure, West Bengal, India

Ramkrishna Maiti Sumanta Prakash Shee

Acknowledgements

We would like to express our humble regards and thankfulness to Mr. Arun Mahato and his team, local inhabitants of Salboni, Paschim Medinipur, for assisting in continuous field work and data analysis. Without their active participation, the completion of our research work could have been impossible. We gratefully acknowledge the help rendered in various ways by Dr. Moumita Moitra (Maiti), Dr. Goutam Ghosh, Dr. Pravat Kumar Shit, Dr. Animesh Majee, Dr. Prasenjit Bhunia, Dr. Avijit Manna, Mr. Sumanta Jana, Mr. Sajal Samanta, Mr Debasis Das and Mr. Rabindra Nath Bera, for their untired works and immense support throughout the research work. We also wish to record our sincere gratitude to all the officials of all Departments including BLRO (Salboni), DLRO (Medinipur), MKDA, Meteorological Department (Abas), Irrigation Department, Survey of India, and Government of West Bengal who had provided us valuable information of our study area. We are also grateful to all the Research Fellows, Department of Geography, Vidyasagar University, for their friendly association and innovative assistance. We sincerely express our gratitude to all the members of our family for their encouragement, co-operation and continuous moral and emotional support. Department of Geography Vidyasagar University Medinipure, West Bengal, India

Ramkrishna Maiti Sumanta Prakash Shee

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Contents

1 Introduction����������������������������������������������������������������������������������������������    1 1.1 Introduction��������������������������������������������������������������������������������������    1 1.2 Worldwide Brief Summary of Land Acquisition������������������������������    4 1.3 Land Acquisition, Investment and Implementation��������������������������    6 1.4 India: Land Acquisition, Rural and Implications������������������������������    7 1.5 Objective of the Present Work����������������������������������������������������������   18 1.6 Location of Study Area ��������������������������������������������������������������������   18 1.7 Methodology ������������������������������������������������������������������������������������   18 1.8 Literature Review������������������������������������������������������������������������������   20 References��������������������������������������������������������������������������������������������������   23 2 Study Area������������������������������������������������������������������������������������������������   31 2.1 Introduction and Location����������������������������������������������������������������   31 2.2 Geology��������������������������������������������������������������������������������������������   32 2.3 Climate����������������������������������������������������������������������������������������������   33 2.3.1 Rainfall Characteristics��������������������������������������������������������   36 2.4 Agriculture����������������������������������������������������������������������������������������   40 2.5 Principal Crop ����������������������������������������������������������������������������������   42 2.6 Land Use and Land Cover����������������������������������������������������������������   43 2.7 Soils��������������������������������������������������������������������������������������������������   46 2.8 Ecology ��������������������������������������������������������������������������������������������   47 2.9 Demographic Aspects ����������������������������������������������������������������������   47 2.10 The Growth Rate of Population��������������������������������������������������������   47 2.11 Literacy Structure������������������������������������������������������������������������������   49 2.12 Work Participation and Income��������������������������������������������������������   49 2.13 Road and Traffic Study ��������������������������������������������������������������������   52 2.14 Proposed JSW Steel Plant����������������������������������������������������������������   52 2.15 Major Findings����������������������������������������������������������������������������������   53 2.16 Conclusion����������������������������������������������������������������������������������������   54 References��������������������������������������������������������������������������������������������������   54

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3 Industrial Project ������������������������������������������������������������������������������������   57 3.1 Introduction��������������������������������������������������������������������������������������   57 3.2 Identification of Project and Project Proponent��������������������������������   58 3.3 Nature, Size and Location of the Project������������������������������������������   59 3.4 Brief Description of the Project��������������������������������������������������������   60 3.5 Expected Benefit in the Study Area��������������������������������������������������   62 3.5.1 Compensation Package for Land Acquisition����������������������   63 3.5.2 Construction Phase���������������������������������������������������������������   63 3.5.3 Operation Phase��������������������������������������������������������������������   65 3.5.4 Long-Term Goals������������������������������������������������������������������   67 3.6 Major Findings����������������������������������������������������������������������������������   68 3.7 Conclusion����������������������������������������������������������������������������������������   69 References��������������������������������������������������������������������������������������������������   69 4 Land Acquisition and Landuse Change – A Mouza Level Study��������   73 4.1 Introduction��������������������������������������������������������������������������������������   73 4.2 Data and Methods ����������������������������������������������������������������������������   75 4.3 Stages of Land Acquisition ��������������������������������������������������������������   76 4.4 Land Acquisition and Land Use Changes����������������������������������������   78 4.5 Mouza-Wise Land Acquisition ��������������������������������������������������������   80 4.5.1 Dubrajpur Mouza������������������������������������������������������������������   81 4.5.2 Masru Mouza������������������������������������������������������������������������   81 4.5.3 Natundihi Mouza������������������������������������������������������������������   83 4.5.4 Nitaipur Mouza ��������������������������������������������������������������������   85 4.5.5 Banskopna Mouza����������������������������������������������������������������   87 4.5.6 Chantibandh Mouza��������������������������������������������������������������   88 4.5.7 Ramraidy Mouza������������������������������������������������������������������   89 4.5.8 Asnasuli Mouza��������������������������������������������������������������������   90 4.5.9 Natunbankati Mouza������������������������������������������������������������   92 4.5.10 Chakbhani Mouza ����������������������������������������������������������������   92 4.5.11 Naranchak Mouza ����������������������������������������������������������������   93 4.5.12 Jambediya Mouza ����������������������������������������������������������������   93 4.5.13 Bhalukchati Mouza ��������������������������������������������������������������   95 4.5.14 Kulpheni Mouza��������������������������������������������������������������������   96 4.5.15 Gaighata Mouza��������������������������������������������������������������������   97 4.5.16 Hatimari Mouza��������������������������������������������������������������������   97 4.5.17 Shalika Mouza����������������������������������������������������������������������   98 4.5.18 Kharkasuli Mouza����������������������������������������������������������������   99 4.5.19 Arabari Mouza����������������������������������������������������������������������  100 4.5.20 Kharisol Mouza��������������������������������������������������������������������  102 4.5.21 Barju Mouza�������������������������������������������������������������������������  102 4.5.22 Ghagrasol Mouza������������������������������������������������������������������  103 4.6 Summary of Land Acquisition����������������������������������������������������������  104 4.7 Major Findings����������������������������������������������������������������������������������  105 4.8 Conclusion����������������������������������������������������������������������������������������  107 References��������������������������������������������������������������������������������������������������  108

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5 Hydrological Impact of Landuse Conversion����������������������������������������  111 5.1 Introduction��������������������������������������������������������������������������������������  111 5.2 Data and Methods ����������������������������������������������������������������������������  112 5.3 Relief������������������������������������������������������������������������������������������������  113 5.4 Slope ������������������������������������������������������������������������������������������������  116 5.5 Hydrology ����������������������������������������������������������������������������������������  117 5.6 Drainage��������������������������������������������������������������������������������������������  117 5.7 SCS CN, USDA 1972 Method����������������������������������������������������������  119 5.7.1 Hydrological Soil Group (HSG) ������������������������������������������  120 5.7.2 Land Use-Land Cover (LULC) Classification����������������������  126 5.7.3 Antecedent Moisture Condition��������������������������������������������  128 5.8 Determination of Sub-watershed-Wise Weighted CN����������������������  129 5.8.1 Sub-watershed A ������������������������������������������������������������������  130 5.8.2 Sub-watershed B ������������������������������������������������������������������  131 5.8.3 Sub-watershed C ������������������������������������������������������������������  132 5.8.4 Sub-watershed D������������������������������������������������������������������  133 5.8.5 Sub-watershed E ������������������������������������������������������������������  134 5.8.6 Sub-watershed F��������������������������������������������������������������������  135 5.8.7 Sub-watershed G������������������������������������������������������������������  136 5.8.8 Sub-watershed H������������������������������������������������������������������  137 5.8.9 Sub-watershed I��������������������������������������������������������������������  138 5.8.10 Sub-watershed J��������������������������������������������������������������������  139 5.8.11 Sub-watershed K������������������������������������������������������������������  140 5.8.12 Sub-watershed L ������������������������������������������������������������������  141 5.8.13 Sub-watershed M������������������������������������������������������������������  142 5.8.14 Sub-watershed N������������������������������������������������������������������  143 5.8.15 Change in Curve Number ����������������������������������������������������  144 5.9 Calculation of Discharge (in Million m3)�����������������������������������������  146 5.9.1 Pre-monsoon ������������������������������������������������������������������������  147 5.9.2 Monsoon ������������������������������������������������������������������������������  149 5.9.3 Post-monsoon������������������������������������������������������������������������  154 5.10 Evapotranspiration����������������������������������������������������������������������������  157 5.11 Water Budget������������������������������������������������������������������������������������  159 5.12 Major Findings����������������������������������������������������������������������������������  166 5.13 Conclusion����������������������������������������������������������������������������������������  167 References��������������������������������������������������������������������������������������������������  167 6 Land Acquisition and Livelihood ����������������������������������������������������������  171 6.1 Introduction��������������������������������������������������������������������������������������  171 6.2 Data and Method������������������������������������������������������������������������������  172 6.3 Land Ownership Pattern at the Project Site in 2007 and 2012 ��������  174 6.4 Utilization of Compensation ������������������������������������������������������������  176 6.5 Income and Livelihood Dynamics of the Project Site����������������������  178 6.6 Cope of Livelihood Diversification��������������������������������������������������  180

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6.7 Livelihood Asset Index ��������������������������������������������������������������������  180 6.7.1 Human Asset Index (HAI)����������������������������������������������������  183 6.7.2 Natural Asset Index (NAI)����������������������������������������������������  188 6.7.3 Physical Asset Index (PAI)���������������������������������������������������  189 6.7.4 Financial Asset Index (FAI)��������������������������������������������������  191 6.7.5 Total Livelihood Asset Index (TLAI) ����������������������������������  191 6.8 Livelihood Activity Index ����������������������������������������������������������������  193 6.9 Livelihood Diversification Index������������������������������������������������������  193 6.10 Relationship Between Livelihood Asset Index and Livelihood Diversification Index (Pearson Correlation Coefficient)������������������  194 6.11 Major Findings����������������������������������������������������������������������������������  194 6.12 Conclusion����������������������������������������������������������������������������������������  195 References��������������������������������������������������������������������������������������������������  195 7 Uncertainity and Suffering����������������������������������������������������������������������  199 7.1 Introduction��������������������������������������������������������������������������������������  199 7.2 Data and Method������������������������������������������������������������������������������  200 7.3 A Journey of JSW Bengal Steel Plant 2007–2015����������������������������  200 7.4 Uncertainty and Disappointment������������������������������������������������������  211 7.5 Community Perception on Different Aspects of Industrialization (10-Point Scale) ����������������������������������������������������  226 7.6 Major Findings����������������������������������������������������������������������������������  228 7.7 Conclusion����������������������������������������������������������������������������������������  229 References��������������������������������������������������������������������������������������������������  229 8 Level of Development: A Comparative Study Between Project and Non-project Area ����������������������������������������������������������������  231 8.1 Introduction��������������������������������������������������������������������������������������  231 8.2 Data and Method������������������������������������������������������������������������������  232 8.3 Measuring the Level of Development����������������������������������������������  233 8.4 The Level of Development����������������������������������������������������������������  234 8.4.1 Agricultural Composite Index (C.I) of Development and Rank ������������������������������������������������������������������������������  257 8.4.2 Infrastructural Development ������������������������������������������������  267 8.4.3 Socio-economic Development����������������������������������������������  276 8.5 Major Findings����������������������������������������������������������������������������������  285 8.6 Conclusion����������������������������������������������������������������������������������������  286 References��������������������������������������������������������������������������������������������������  287 9 Environmental Impact Assessment��������������������������������������������������������  289 9.1 Introduction��������������������������������������������������������������������������������������  289 9.2 Data and Method������������������������������������������������������������������������������  291 9.3 Ambient Air Quality Monitoring Method (ISCST3 Model)������������  291 9.3.1 Concentration of SO2 and SPM over the Proposed JSW Industrial Project������������������������������������������������������������������  294

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9.4 Ambient Water Quality Monitoring Method������������������������������������  301 9.4.1 Ambient Water Quality Monitoring over the Proposed JSW Industrial Project����������������������������������������������������������  302 9.5 Noise Level Monitoring Method������������������������������������������������������  306 9.5.1 Observation of Noise Level��������������������������������������������������  306 9.6 Major Findings����������������������������������������������������������������������������������  307 9.7 Conclusion����������������������������������������������������������������������������������������  308 References��������������������������������������������������������������������������������������������������  308 10 Conclusion������������������������������������������������������������������������������������������������  311 Index������������������������������������������������������������������������������������������������������������������  315

List of Figures

Fig. 1.1 Deals status of different proposed project. (Source: Based on Land Matrix database, 2018)���������������������������������������������������������������������������� 5 Fig. 1.2 Land acquisition and project implementation status. (Source: Based on Land Matrix database, 2018)������������������������������������������������������������������ 5 Fig. 1.3 Investment in different sectors in India (2000–2018)����������������������������� 8 Plate 1.1 SEZ story of Indiabulls Infrasturcture. (Source: Times of India 2015)15 Fig. 2.1 Location of study area��������������������������������������������������������������������������� 32 Fig. 2.2 Monthly variation in maximum and minimum temperature (°C) and rainfall (mm)����������������������������������������������������������������������������������������� 34 Fig. 2.3 Variation of annual rainfall in the study area���������������������������������������� 37 Fig. 2.4 Length of a single storm period in days������������������������������������������������ 40 Plate 2.1 Paddy field of the study area����������������������������������������������������������������� 42 Plate 2.2 Double crop land at river bank�������������������������������������������������������������� 44 Plate 2.3 Industrial built up land�������������������������������������������������������������������������� 44 Plate 2.4 Eucalyptus plantations�������������������������������������������������������������������������� 45 Fig. 2.5 LULC of the study area������������������������������������������������������������������������� 45 Plate 2.5 Sal forest at Godapiasal������������������������������������������������������������������������ 46 Plate 2.6 Lateritic exposure���������������������������������������������������������������������������������� 47 Fig. 2.6 Decadal population growth in study area between 1961 and 2011������� 48 Fig. 2.7 Showing the population density (P/km2) of Sundra catchment������������ 48 Fig. 2.8 Showing the rate of literacy in the study area since 1961–2011����������� 49 Plate 2.7 Livelihoods of the study area ��������������������������������������������������������������� 51 Plate 2.8 NH 60 passes through the study area ��������������������������������������������������� 52 Fig. 3.1 Provisional master plan of proposed JSW Bengal Steel project (Source: BLRO, Salboni)���������������������������������������������������������������� 60 Plate 3.1 Local market at entry point of JSW Bengal steel plant (2012)������������� 64 Plate 3.2 New metal road along the JSW project boundary under construction (2018)��������������������������������������������������������������������������������������������� 66 Plate 3.3 Hospital under JSW plant under construction (2012)��������������������������� 67

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Plate 4.1 Plate 4.2 Plate 4.3 Plate 4.4 Fig. 4.1 Plate 4.5 Fig. 4.2 Fig. 4.3 Fig. 4.4 Plate 4.6 Fig. 4.5 Fig. 4.6 Fig. 4.7 Fig. 4.8 Fig. 4.9 Fig. 4.10 Fig. 4.11 Fig. 4.12 Fig. 4.13 Fig. 4.14 Fig. 4.15 Fig. 4.16 Fig. 4.17 Fig. 4.18 Fig. 4.19 Fig. 4.20

List of Figures

Paddy and industrial land���������������������������������������������������������������������� 76 Road surrounding the project boundary������������������������������������������������ 77 Proposed green belt������������������������������������������������������������������������������� 77 Under construction of the proposed plant��������������������������������������������� 78 Mouza-wise land acquisition and land use. (Source: BLRO, Salboni)� 79 HH survey��������������������������������������������������������������������������������������������� 80 Land acquisition and land use change of Dubrajpur mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 81 Land acquisition and land use change of Mashru mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 83 Land acquisition and land use change of Natundihi mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 84 Acqired agricultural land���������������������������������������������������������������������� 85 Land acquisition and land use change of Nitaipur mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 86 Land acquisition and land use change of Banskopna mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 87 Land acquisition and land use change of Chantibandh mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 89 Land acquisition and land use change of Ramraidy mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 90 Land acquisition and land use change of Asnasuli mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 91 Land acquisition and land use change of Natunbankati mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 92 Land acquisition and land use change of Chakbhani mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 93 Land acquisition and land use change of Naranchak mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 94 Land acquisition and land use change of Jambedya mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 94 Land acquisition and land use change of Bhalukchati mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 95 Land acquisition and land use change of Kulpheni mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 96 Land acquisition and land use change of Gaighata mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 97 Land acquisition and land use change of Hatimari mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 98 Land acquisition and land use change of Shalika mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)����������������������������������� 99 Land acquisition and land use change of Kharkasuli mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)��������������������������������� 100 Land acquisition and land use change of Arabari mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)��������������������������������� 101

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Fig. 4.21 Land acquisition and land use change of Kharisol mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)��������������������������������� 102 Fig. 4.22 Land acquisition and land use change of Barju mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)��������������������������������������������� 103 Fig. 4.23 Schematic diagram showing the accompanying events of family fragmentation triggered by land acquisition. (After Guha 2007)������� 107 Fig. 5.1 DEM of the Sundra catchment. (Source: SRTM DEM, 2010)����������� 114 Plate 5.1 Rocky out crop along the channel������������������������������������������������������� 114 Plate 5.2 Lateritic patches���������������������������������������������������������������������������������� 115 Fig. 5.2 Long profile of the Sundra catchment������������������������������������������������� 115 Fig. 5.3 Distribution of Slope within the study area���������������������������������������� 116 Fig. 5.4 Sundra river is the tributary of river Tamal. (Source: SRTM, DEM 2010)��������������������������������������������������������������������������������������������������� 117 Plate 5.3 Natural depression on study area�������������������������������������������������������� 118 Plate 5.4 Cross-profile survey on river Sundra�������������������������������������������������� 119 Fig. 5.5 Showing the relationship between HSG, LULC and AMC in CN estimation�������������������������������������������������������������������������������������������� 120 Fig. 5.6 Showing the location of soil sample along with sieve analysis results. (Source: Field survey 2012)���������������������������������������������������������������� 121 Fig. 5.7 Infiltration curve through field measurement (Banskopna)���������������� 122 Plate 5.5 Field measurement of infiltration rate at Banskopna�������������������������� 123 Fig. 5.8 HSG distribution of Sundra catchment. (Source: Department of agriculture; soil survey and landuse planning, field survey, 2012)����� 126 Fig. 5.9 LULC map of the Sundra catchment before land acquisition (2007). (Source: Shee and Maiti, 2019; Google earth 2007 and IRS P6 2007 (OCT))������������������������������������������������������������������������������������������������� 127 Fig. 5.10 Expected LULC map (Source: Google earth 2014 and JSW master plan)���������������������������������������������������������������������������������������������������� 127 Fig. 5.11 Fourteen (14) sub-watersheds of Sundra catchment. (Source: Based on SWAT analysis tool, 2009)������������������������������������������������������������������ 130 Fig. 5.12 LULC classification according to HSG in sub-watershed A. (Source: Based on Google earth 2014; Department of Agriculture soil classification map)��������������������������������������������������������������������������������������������� 131 Fig. 5.13 LULC classification according to HSG in sub-watershed B. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)����������������������������������������������������������������������������������������� 132 Fig. 5.14 LULC classification according to HSG in sub-watershed C. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)����������������������������������������������������������������������������������������� 133 Fig. 5.15 LULC classification according to HSG in sub-watershed D. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)����������������������������������������������������������������������������������������� 134 Fig. 5.16 LULC classification according to HSG in sub-watershed E. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)����������������������������������������������������������������������������������������� 135

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Fig. 5.17 LULC classification according to HSG in sub-watershed F. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)����������������������������������������������������������������������������������������� 136 Fig. 5.18 LULC classification according to HSG in sub-watershed G. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)����������������������������������������������������������������������������������������� 137 Fig. 5.19 LULC classification according to HSG in sub-watershed H. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)����������������������������������������������������������������������������������������� 138 Fig. 5.20 LULC classification according to HSG in sub-watershed I. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)����������������������������������������������������������������������������������������� 139 Fig. 5.21 LULC classification according to HSG in sub-watershed J. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)����������������������������������������������������������������������������������������� 140 Fig. 5.22 LULC classification according to HSG in sub-watershed K. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)����������������������������������������������������������������������������������������� 141 Fig. 5.23 LULC classification according to HSG in sub-watershed L. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)����������������������������������������������������������������� 142 Fig. 5.24 LULC classification according to HSG in sub-watershed M. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)����������������������������������������������������������������������������������������� 143 Fig. 5.25 LULC classification according to HSG in sub-watershed N. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)����������������������������������������������������������������������������������������� 144 Fig. 5.26 Sub-watershed-wise weighted CN based on existing (2014) LULC and soil condition (AMC II). (Source: Based on SCS CN USDA, 1972 method)����������������������������������������������������������������������������������������������� 145 Fig. 5.27 Sub-watershed-wise weighted CN after implementation of JSW industrial work (AMC II). (Source: Based on SCS CN USDA, 1972 method)����������������������������������������������������������������������������������������������� 145 Fig. 5.28 Sub-watershed-wise runoff variation (Mm3) in pre-monsoon. (Source: Based on SCS CN USDA, 1972 method)������������������������������������������� 148 Fig. 5.29 Sub-watershed-wise runoff variation (Mm3) in pre-monsoon. (Source: Based on SCS CN USDA, 1972 method)������������������������������������������� 148 Fig. 5.30 Showing sub watershed wise runoff variation (Mm3) in monsoon after land acquisition in 2014. (Source: Based on SCS CN USDA, 1972 method)����������������������������������������������������������������������������������������������� 149 Fig. 5.31 Sub-watershed-wise runoff variation (Mm3) in monsoon in expected condition based on JSW master plan. (Source: Based on SCS CN USDA, 1972 method)������������������������������������������������������������������������� 150 Plate 5.6 JSW project boundary collapsed at Arabari���������������������������������������� 150 Plate 5.7 JSW project boundary collapsed at Barju������������������������������������������� 151

List of Figures

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Plate 5.8 JSW project boundary collapsed at Asnasuli�������������������������������������� 151 Plate 5.9 JSW project boundary collapsed at Banskopna���������������������������������� 152 Plate 5.10 Disrupted local transport network near Banskopna���������������������������� 153 Plate 5.11 Damaged agricultural field at Banskopna������������������������������������������� 153 Plate 5.12 Damaged local houses at Natundihi���������������������������������������������������� 154 Plate 5.13 Water scarcity at an agriculture field��������������������������������������������������� 155 Fig. 5.32 Expected change in runoff volume (Mm3)������������������������������������������ 156 Plate 5.14 Water storage for future use���������������������������������������������������������������� 161 Fig. 5.33 Location of proposed farm ponds at upper and middle catchment of Sundra watershed�������������������������������������������������������������������������������� 161 Fig. 5.34 Mass curve of proposed farm pond at sub-watershed A��������������������� 162 Fig. 5.35 Mass curve of proposed farm pond at sub-watershed C��������������������� 162 Fig. 5.36 Mass curve of proposed farm pond at sub-watershed D��������������������� 163 Fig. 5.37 Mass curve of proposed farm pond at sub-watershed E��������������������� 163 Fig. 5.38 Mass curve of proposed farm pond at sub-watershed F���������������������� 164 Fig. 5.39 Mass curve of proposed farm pond at sub-watershed M�������������������� 164 Fig. 5.40 Mass curve of proposed farm pond at sub-watershed N��������������������� 165 Fig. 6.1 The changes in farm income in between 2007 and 2018�������������������� 178 Fig. 6.2 The HAI in Sundra catchment after land acquistion (2018)��������������� 184 Fig. 6.3 The HAI in Sundra catchment before land acquisition (2007)����������� 187 Fig. 6.4 The NAI in Sundra catchment after land acquistion (2018)��������������� 188 Fig. 6.5 The NAI in Sundra catchment before land acquisition (2007)����������� 189 Fig. 6.6 The PAI in Sundra catchment after land acquistion (2018)���������������� 190 Fig. 6.7 The PAI in Sundra catchment before land acquisition (2007)������������ 190 Fig. 6.8 The TLAI in Sundra catchment after land acquisition (2018)������������ 192 Fig. 6.9 The TLAI in Sundra catchment before land acquisition (2007)��������� 192 Plate 7.1 Mining given to JSW. (Source: The Telegraph, 30th November 2014)��������������������������������������������������������������������������������� 201 Plate 7.2 JSW Bengal Steel plant, Salboni block of Paschim Medinipur (2011)�������������������������������������������������������������������������������������������������� 201 Plate 7.3 “Ankur” residential complex of JSW Bengal Steel plant under construction (2012)����������������������������������������������������������������������������� 202 Plate 7.4 Lease agreement with Government of W.B of 189 acres ceiling exceed private land for JSW Bengal steel project. (Source- Anadabazar Patrika, 5th May, 2012)������������������������������������� 203 Plate 7.5 Cancellation of coal block allotment in Bengal. SourceThe Telegraph, 21st Sep, 2012������������������������������������������������������������� 204 Plate 7.6 After coal block, iron ore fix makes Salboni project on hold (2013) (Source: The Telegraph, 28th April 2013)�������������������������������������������� 205 Plate 7.7 Official announcement by JSW chairperson come managing director to put the proposed JSW Bengal Steel project on hold (Anadabazar patrika 1st December, 2014)������������������������������������������� 206 Plate 7.8 At Arabari�������������������������������������������������������������������������������������������� 207 Plate 7.9 Link road between Godapiasal and JSW (2013)��������������������������������� 207 Plate 7.10 Link road under construction at Barju (2013)������������������������������������� 208 Plate 7.11 Same link road at Barju (2015)����������������������������������������������������������� 208

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List of Figures

Plate 7.12 Setting up new market (2012)������������������������������������������������������������� 209 Plate 7.13 Closed market complex (2018)����������������������������������������������������������� 209 Plate 7.14 JSW nursery school (2014)����������������������������������������������������������������� 209 Plate 7.15 JSW nursery school (2016)����������������������������������������������������������������� 209 Plate 7.16 Ramu Sing from Mashru (2009)��������������������������������������������������������� 210 Plate 7.17 Mukulda-r canteen at Jambedya (2013)���������������������������������������������� 210 Plate 7.18 Mukulda-r canteen at Jambedya (2015)���������������������������������������������� 210 Plate 7.19 JSW Bengal Steel plant on hold, Salboni block of Paschim Medinipur (2015)�������������������������������������������������������������������������������� 225 Plate 7.20 Uncertainty and disappointment by land loser (2014)������������������������ 226 Fig. 8.1 Positive and negative changes in net sown area between 2007 and 2018���������������������������������������������������������������������������������������������������� 236 Fig. 8.2 The changes of marginal workers between 2001 and 2011���������������� 237 Fig. 8.3 The changes in main cultivators between 2001 and 2011������������������ 238 Plate 8.1 Boring for Submersible����������������������������������������������������������������������� 239 Fig. 8.4 The changes in irrigated area between 2007 and 2018����������������������� 239 Plate 8.2 Crop rotation��������������������������������������������������������������������������������������� 240 Fig. 8.5 The variation of crop income (%) at the project site��������������������������� 241 Fig. 8.6 The changes in crop income��������������������������������������������������������������� 241 Fig. 8.7 The location of educational institution at Sundra catchment in 2014������������������������������������������������������������������������������������������������ 242 Plate 8.3 Free education sponsored by JSW������������������������������������������������������ 243 Plate 8.4 Drying pond at Bagasol in summer���������������������������������������������������� 244 Plate 8.5 Monitoring ground water level during winter season������������������������� 244 Plate 8.6 Monitoring ground water level during dry season������������������������������ 245 Plate 8.7 Reservoir at the JSW project site�������������������������������������������������������� 246 Fig. 8.8 Availability of drinking water before land acquisition (2010)������������ 246 Fig. 8.9 Availability of drinking water after suspension (2017)����������������������� 247 Fig. 8.10 The medical facility before land acquistion���������������������������������������� 248 Fig. 8.11 The medical facility during construction�������������������������������������������� 249 Plate 8.8 Link road between NH 60 to Ashnabani, 2012����������������������������������� 250 Plate 8.9 Link road between Sundra to Annandapur, 2012�������������������������������� 251 Fig. 8.12 Availability of transport facility before land acquisition�������������������� 251 Fig. 8.13 Availability of transport facility after suspension (2016)������������������� 252 Plate 8.10 Local market installation along the NH 60 (2014)����������������������������� 253 Fig. 8.14 The population distribution of the study area������������������������������������� 254 Fig. 8.15 The population distribution of study area (2011)������������������������������� 255 Fig. 8.16 The literacy rate in the study area (2001)������������������������������������������� 256 Fig. 8.17 The literacy rate in the study area (2011)������������������������������������������� 256 Fig. 8.18 The monthly family income before (2007) and after suspension (2018)�������������������������������������������������������������������������������������������������� 257 Fig. 8.19 Level of development in agricultural sector after suspension (2018)������������������������������������������������������������������������������� 266 Fig. 8.20 Level of development in agricultural sector before land acquisition (2007)�������������������������������������������������������������������������������������������������� 266

List of Figures

Fig. 8.21 Fig. 8.22 Fig. 8.23 Fig. 8.24 Fig. 9.1 Fig. 9.2a Fig. 9.2b Fig. 9.2c Fig. 9.3 Fig. 9.4 Fig. 9.5 Fig. 9.6 Fig. 9.7 Fig. 9.8 Fig. 9.9

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The level of infrastructural development after suspension (2018)������ 275 The level of infrastructural development before land acquisition������� 275 The level of socio-economic development after suspension��������������� 284 The level of socio-economic development before land acquisition���� 284 Seasonal variation of mixing height (in m)����������������������������������������� 294 Seasonal variation of wind speed (m/s) in pre-monsoon�������������������� 295 Seasonal variation of wind speed (m/s) in monsoon�������������������������� 296 Seasonal variation of wind speed (m/s) in post-monsoon������������������ 297 Seasonal hourly variation of SO2 concentration (μg/m3)�������������������� 298 Concentration of SO2 (μg/m3) in post-monsoon��������������������������������� 298 Concentration of SO2 (μg/m3) in pre-monsoon����������������������������������� 299 Concentration of SO2 (μg/m3) in Monsoon����������������������������������������� 299 Concentration of SPM (μg/m3 ) in post-monsoon������������������������������� 300 Concentration of SPM (μg/m3) in pre-monsoon��������������������������������� 300 Concentration of SPM (μg/m3) in monsoon���������������������������������������� 301

List of Tables

Table 1.1 Table 1.2 Table 1.3 Table 1.4 Table 1.5 Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 2.6 Table 2.7 Table 3.1 Table 3.2 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 4.6 Table 4.7 Table 4.8 Table 5.1 Table 5.2

Land investment in different sectors����������������������������������������������������� 6 State-wise investment (2000–2018)����������������������������������������������������� 9 List of few abandoned projects in India (2000–2018)������������������������ 10 Negotiation status������������������������������������������������������������������������������� 12 List of few projects in India not started���������������������������������������������� 12 Showing the average monthly distribution of meteorological parameters������������������������������������������������������������������������������������������ 35 Analysis of average wind speeds�������������������������������������������������������� 36 Monthly rainfall distribution in mm since 1969 to 2009�������������������� 38 Major storm periods and rainfall received in mm������������������������������ 39 Rainfall distribution in wet and dry session��������������������������������������� 41 Harvest period of staple crop in study area���������������������������������������� 42 Showing the work force in a different category of the study area (1961–2011)��������������������������������������������������������������������������������������� 50 Brief description of the JSW Bengal Steel project plant at Salboni��� 61 Summary of land rates according to official land acquisition records���������������������������������������������������������������������� 63 Land acquisition and land use change������������������������������������������������ 79 A short summary of KALACHAND HEMBRAM (Land acquired from Dubrajpur Mouza but living at Mashru mouza)������������������������ 82 A short summary of Gorachand Saren (Land acquired from Dubrajpur Mouza but living at Mashru mouza)��������������������������������� 82 A short summary of Nakul Murmu���������������������������������������������������� 84 A short summary of Adhir Singh������������������������������������������������������� 86 A short summary of Pradip Singh������������������������������������������������������ 88 Percentage of land acquired from affected mouzas�������������������������� 104 Land acquisition scenario of JSW Bengal Steel plant, Salboni������� 106 Record book for measuring infiltration by constant head method at Banskopna village����������������������������������������������������������� 122 Showing the location of soil sample along with sieve analysis result and rate of infiltration����������������������������������������������� 124 xxiii

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List of Tables

Table 5.3 LULC conversion of the Sundra catchment������������������������������������� 128 Table 5.4 Sub-watershed-wise runoff (Mm3) estimation in 2007, 2014 and expected condition���������������������������������������������������������������������������� 147 Table 5.5 The average rainfall and runoff volume in the study area���������������� 156 Table 5.6 Water budget 2019 for proposed crop water requirement (“Rabi” crop)�������������������������������������������������������������������������������������������������� 160 Table 5.7 Reservoir volume (Mm3) of proposed farm ponds��������������������������� 165 Table 6.1 Land ownership pattern of affected households at the project site in 2007 (before acquisition)������������������������������������������������������������������ 174 Table 6.2 Land ownership pattern of the affected household at the project site in 2012 (after acquisition)�������������������������������������������������������������������� 175 Table 6.3 Profile of utilization of compensation money in percentage by land loser HH in the study area���������������������������������������������������������������� 177 Table 6.4 Percentage of farm and non-farm income at project affected mouzas in 2007, 2014 and 2018������������������������������������������������������������������������ 179 Table 6.5 Livelihoods of the affected families������������������������������������������������� 180 Table 6.6 Index of livelihood assets (after Yan et al., 2010)����������������������������� 181 Table 6.7 The different livelihood asset index������������������������������������������������� 184 Table 7.1 A scheduled socio-economic initiation of JSW Bengal Steel plant at Salboni���������������������������������������������������������������������������������������������� 207 Table 7.2 A short summary of interview with Sukal Tudu (Khairisol mouza) 211 Table 7.3 A short summary of interview with Rani Mahato (Ashnasuli mouza)���������������������������������������������������������������������������������������������� 212 Table 7.4 A short summary of interview with Hari Hemram (Arabari mouza)213 Table 7.5 A short summary of interview with Biswanath Hemram (Chatibandh mouza)���������������������������������������������������������������������������������������������� 214 Table 7.6 A short summary of interview with Satis Patra (Ashnasuli mouza)� 215 Table 7.7 A short summary of interview with Laxmikanta Chalok (Kulpheni mouza)���������������������������������������������������������������������������������������������� 216 Table 7.8 A short summary of interview with Sushila Chalok (Kulpheni mouza)���������������������������������������������������������������������������������������������� 217 Table 7.9 A short summary of interview with Kalipada Mahato (Ramrydi mouza)���������������������������������������������������������������������������������������������� 218 Table 7.10 A short summary of interview with Charan Baskey (Ramrydi mouza)���������������������������������������������������������������������������������������������� 219 Table 7.11 A short summary of interview with Turi Hemram (Kharkasuli mouza)���������������������������������������������������������������������������������������������� 220 Table 7.12 A short summary of interview with Banshidhar Sing (Banskopna mouza)���������������������������������������������������������������������������������������������� 221 Table 7.13 A short summary of interview with Ramu Sing (Mashru mouza)��� 222 Table 7.14 A short summary of interview with Nishit Mahato (Nitaipur mouza)���������������������������������������������������������������������������������������������� 223 Table 7.15 A short summary of interview with Samir Mahato (Ashnasuli mouza)���������������������������������������������������������������������������������������������� 224

List of Tables

xxv

Table 7.16 Community perception of project affected people on different aspects of industrialization (2018)���������������������������������������������������������������� 227 Table 8.1 Showing the drinking water facility in the study area between 2007 and 2017������������������������������������������������������������������������������������������� 247 Table 8.2 The demographic changes of the study area������������������������������������ 254 Table 8.3 The changes of literacy rate at the study area����������������������������������� 255 Table 8.4 Agricultural composite index (C.I) of development and rank of the mouzas between 2007 and 2018������������������������������������������������������� 258 Table 8.5 Number of mouzas with percentage of population and area under different levels of agricultural development in 2018������������������������ 264 Table 8.6 Number of mouzas with percentage of population and area under different levels of agricultural development in 2007������������������������ 265 Table 8.7 Number of project affected mouzas with percentage of population and area under different levels of agricultural development in 2018 and 2007�������������������������������������������������������������������������������������������������� 267 Table 8.8 Infrastructural composite index (C.I) of development and rank of the mouzas in 2007 and 2018����������������������������������������������������������������� 268 Table 8.9 Number of mouzas with percentage of population and area under different levels of infrastructural development in 2018�������������������� 273 Table 8.10 Number of mouzas with percentage of population and area under different levels of infrastructural development in 2007�������������������� 274 Table 8.11 Number of project affected mouzas with percentage of area and population under different levels of infrastructure development in 2018 and 2007���������������������������������������������������������������������������������� 276 Table 8.12 Socio-economic composite index of development (C.I) and rank of the mouzas in 2007 and 2018����������������������������������������������������������������� 277 Table 8.13 Number of mouzas with percentage of population and area under different levels of socio-economic development in 2018����������������� 282 Table 8.14 Number of mouzas with percentage of population and area under different levels of socio-economic development in 2007����������������� 283 Table 9.1 Details of industrial unit of JSW iron and steel plant at Salboni block, Paschim Medinipur��������������������������������������������������������������������������� 292 Table 9.2 Analytical techniques for water sampling analysis�������������������������� 302 Table 9.3 Surface water quality������������������������������������������������������������������������ 303 Table 9.4 Ground water quality������������������������������������������������������������������������ 304 Table 9.5 Noise levels [dB(A)] in the study area��������������������������������������������� 307

About the Editors

Sumanta  Prakash  Shee  Dr. Sumanta Prakash Shee is Assistant Teacher of Geography at Golar Sushila Vidyapith, Paschim Medinipur, West Bengal, India. He completed his doctoral degree at Vidyasagar University, and he has 5 years of teaching experience at the higher secondary level in geography and 1 year at the undergraduate level. His teaching interests include Hydrology, Geomorphology, and Climatology including Remote Sensing and GIS. He has published several research articles in renowned national and international journals. He is a life member of the Indian Institute of Geomorphologists and the Geographical Society of India. Ramkrishna  Maiti  Dr. Ramkrishna Maiti is Professor in the Department of Geography at Vidyasagar University, West Bengal, India. His significant contributions have been in the area of Geomorphology, Hydrology, Environmental Hazards, and Geographical Philosophy. He leads several research projects funded by the UGC and ICSSR. He has over 90 publications to his credit in the national and international journals of repute. He authored several books like Semi-Quantitative Approach for Landslide Assessment and Prediction (Springer), Sedimentation in the Rupnarayan River (2 volumes)(Springer), Management Techniques of Rill-Gully Erosion in Badland Topography (LAMBERT Academic Press, Germany), Modern Approaches to Fluvial Geomorphology (Primus Books), and Development of Geographical Thought (Nabodaya Publishers).

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Abbreviations

HH Household ha Hectares IMD Indian Meteorological Department LULC Landuse land cover Mtpa Million ton per annual INR Indian Rupees HSG Hydrological soil group CN Curve number ET Evapotranspiration CPR Common pool resource HAI Human Asset Index PAI Physical Asset Index NAI Natural Asset Index FAI Financial Asset Index TLAI Total Livelihood Asset Index GDP Gross Domestic Product CEPI Comprehensive Environmental Pollution Index CPP Captive Power Plant BF Blast Furnace BLRO Block land reform office DLRO District land reform office UNDP United Nation Development Program AMC Antecedent moisture condition SCS Soil Conservation Service RWH Rain Water Harvesting BDO Block Development Officer DLRO District land reform officer MKDA Medinipur Khargapur Development Authority EIA Environmental Impact Assessment

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VC fp k fc fo Kc

Abbreviations

Ventilation coefficient The infiltration capacity (depth/time) at some time t A constant indicating the rate of decrease in f capacity A final and equilibrium capacity The initial infiltration capacity The crop coefficient

Chapter 1

Introduction

Abstract  This chapter is focused on different developmental strategies and a brief review of worldwide land acquisition, investment and implementation. India is one of the fastest growing country in service sectors with an annual growth rate of above 9% (since 2001). We try to give a brief discussion on land acquisition scenarios in different states in India like Maharashtra, Karnataka, Andhra Pradesh, Gujrat, etc. and also include agriculture-based state West Bengal. Land acquisition at Singur and Nandigram of West Bengal become under limelight due to a big political movement from local farmers in 2007, and both projects are now abandoned. At the same time at West Bengal, JSW Bengal Steel Ltd. acquired an area of 4225 acre of land without any protection from local villagers to set up a 10.0  mtpa (million tonne per annum) integrated steel plant, but after a decade, the proposed project is suspended. We also added different literature reviews on land acquisition and its consequent impact on the socio-economic condition; this case study differs from other reviews because we have given special focus on those households, who are suffering a lot due to the suspension of the JSW Bengal Steel project. Keywords  Land acquisition · West Bengal · Singur · Nandigram · Salboni · Political movement · Case study · JSW Bengal steel plant

1.1  Introduction Development is a multidimensional process that refers to the social, economical, political and cultural changes in human societies. Development also leads to economic growth through the establishment of big industries, major hydro projects, irrigation projects, highways, etc. and becomes the lifeline of all economy. Every Government from every nation aims to accelerate the process of development projects to improve the quality of life of the nation’s people. But it’s not an easy one; it can never be deal with single-handedly. At the initial stage of large-scale development, it might be necessary to acquire land from individuals in that locality where the project is going to be done, and as a consequence, a sizable number of people © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. P. Shee, R. Maiti, Land Acquisition, Industrialization and Livelihoods, https://doi.org/10.1007/978-3-030-90244-5_1

1

2

1 Introduction

become affected both physically and economically (Robinson 2003; Bhattacharya et al. 2013). Industrialization is the key indicator of economic development. There is a close relationship between industrialization and human development through contribution to the GDP (Gross Domestic Product) and other sectors of the economy. These indicators are related to wage employment, agricultural development, improved livelihood, skill information, entrepreneurship, etc. While the benefit of industrialization to human development is significant in its contribution, there are also a lot of negative outcomes throughout the years. The major negative outcome is reported due to industry-induced displacement. Displacement is the factor responsible for impoverishment and deprivation. It creates a vicious circle of displacement, beginning with the acquisition of land for the establishment of industry (Bhattacharya et  al. 2013). Economic growth increases trade and investment and technological advance of a society, but any unplanned development program creates a lot of socio-­ economic vulnerability in the future. Land is one of the most valuable resources in the world, but in the last few years, a surge of large-scale land acquisition in developing countries has been observed due to rapid population growth and urbanization (Anseeuw et al. 2012; Deininger and Byerlee 2011; Ding 2007). Most of the countries have some laws and procedures to protest the acquisition of privately owned land. Land is not only essential to improve farmers’ quality of life, but it is also considered to be the most important asset on which many people build producing systems, commercial activities and livelihoods that can be a principal source of wealth and power (Mattingly 1993). Developing countries are particularly facing several challenges in land management and how best to provide land to accommodate urbanization in ways that accelerate economic growth and promote human settlement (Ding 2007). To accelerate urbanization, land is necessary for employment, placement, housing and urban infrastructure which is often met through urban encroachment into rural areas. China is one of the fastest growing urbanizing country in the world in the first decade of the twenty-first century and is projected to have met 10–15 million new urban residents annually (Ding 2004). Since 2008, large-scale land acquisitions have been spreading rapidly worldwide (Van der Ploeg et al. 2015), after the successful implementation of a proposed project which can increase livelihood sustainability through employment creation and technological spillovers (World Bank and UNCTAD 2014; Smaller et al. 2015) but in case of unexpected delay or suspension may have immediate impacts on resource users whose livelihoods depend on the land and natural resources acquired (Boamah and Overå 2016; Oberlack et al. 2016; Yengoh et al. 2016; Zoomers et al. 2016). Land is naturally fixed and the most valuable basic and long-lasting source of livelihood in the agrarian economy as well as for human inhabitation but is one of the most disputed issues in public policies in India. To encourage industrialization with the hope that it would solve the prevailing problem of unemployment, by gifting thousands of acres of valuable land to a developer in a significant amount to conduct business including relaxation in laws of the land, including those meant for the welfare of labor, environmental protection, taxation, etc. (Chandrasekhar 2006;

1.1 Introduction

3

Parwez 2016). Land acquisition, displacement, resettlement and rehabilitation and compensation are complex subjects to deal with. At the same time, project development and land speculation are becoming a threat (Kurian 2000). Land acquisition and diversion of land from agriculture to non-agriculture is predominantly important in large agrarian population countries like India because it should be preceded by a considerable shift of workforce and local population (Bhagwati and Panagriya 2013; Patnaik 2008; Shah et al. 2012). In the absence of such provision, land acquisition of agricultural land is most likely to invite resistance from farmers and local communities (Parwez 2016); the situation gets further stressed when the Government plays the key role of trader (Chandrasekhar 2006). From the history of land acquisition, farmers and peasants in various states such as West Bengal, Uttar Pradesh, Maharashtra, Uttarakhand, Odisha, Punjab and many more strictly opposed to land acquisition (Ghosh 2006; Aggarwal 2006). Ultimately a question arises on the process of obtaining the land rather than the need of acquiring agricultural land to promote built-up land for industrialization (World Bank 2008; Shah et al. 2012). It is also evident from Singur of West Bengal that ambiguous implementation processes generally result in the diversion of a large tract of land, often more fertile, than what is required. It is also observed in many cases like JSW Bengal Steel Plant at Salboni, West Bengal, that a large part of acquired land ends up in the hands after the announcement of suspension or delaying the project establishment, leading to livelihood losses for farmers and dependent, whose land was acquired and leads to destroying of livelihood and large scale displacement of the local population; in fact largely both of these form a nexus to indulge in “primitive accumulation” by state (Chandrasekhar 2006; Aggarwal 2006; Patnaik 2008). In this kind of developmental process, beneficiaries are always the big corporations, but the benefits do not percolate to the land-lost peasants, ordinary people, tenants, farm labourers and tribal and farmer communities, dependent on land in several ways (Banerjee 2006; Nielsen 2010; Roy 2007; Shah et al. 2012). In India, the colonial Land Acquisition Act of 1894 is applied to acquire private land for “public purposes”. This act is only recognized for individual property rights. Under this Act, no compensation is payable to landless labourers, forest land users and forest produce collectors, sharecroppers, artisans, etc. because they have no legal right over the land. For the loss of common pool resources (CPR) (village common lands used for cattle grazing, fuel wood collection, etc.), the Land Acquisition Act has no scope for any kind of compensation (Guha 2004a, b). In India, the Land Acquisition Act of 1894 allows displacement for undefined public purposes without the consent of people affected. But the reality is that in our county, most of the poor people are cultivating the Government land for generation without any written evidence (patta) for that which makes an accelerated displacement work without any compensation. Those are leading to deprivation of land, and most of the small and marginal farmers become landless, thereby affecting human development (Bhattacharya et al. 2013). The Indian Government also passed the SEZ Act, 2005 in the parliament for ensuring resettlement and rehabitation for people who would be severely affected by land acquisition and development projects (Guha 2011). Land is primarily a state subject. There is no systematic and comprehensive work on land

4

1 Introduction

acquisition policy until today. The faulty conversation or diversion of land from the agriculture to the allied sector, the most critical issue is that of the nexus between the state and the private sector (Banerjee 2006; Patnaik 2008). Each case of land acquisition in India has its own peculiarities and historical antecedents that depend on the nature of the socio-economic and political composition of a particular area (Guha 2007). In India the lack of clear environmental policies also provides a new dimension to the debate. As a consequence, sadly, concerns are only reflected when land is diverted out of the primary sector and serves a threat to the livelihood of the farmers (Parwez 2016). Andhra Pradesh is one of the most active state of India for spreading industrialization. Land acquisition is very interesting in Andhra Pradesh, and the state Government has managed to divert major areas of land for SEZ’s largely without the opposition movements, that have occurred in other states, such as Maharashtra or West Bengal. In Andhra Pradesh, land uses are classified as agricultural, waste land or Government land. Government land is basically used for agriculture or at least provides support to livestock or other livelihood supporting activities; no vacant land is waiting for an investor to construct a plant on it. For acquisition, preferance is given to the non-agricultural land, sometimes including patches of forest but usually avoiding village house sites and the Government strategy is very inventive in case of finding land for SEZ (Oskarsson 2009).

1.2  Worldwide Brief Summary of Land Acquisition Large-scale land acquisition in recent times in Africa, Asia and Latin America is under the limelight. Different media reports reveal these acquisitions involve outright land purchases or more commonly long-term land lease especially on government-­owned land (Cotula 2012). Ongoing research by different organization shows that commercial pressure on land is increasing rapidly worldwide in different sectors like agriculture including extractive industries, tourism, natural parks and renewable energy. So a holistic approach is crucial to understanding the land pressures faced by the rural poor worldwide (Woodhouse 2012). Today, media are playing a key role in raising public awareness about large-scale land acquisition worldwide and generate an impressive amount of online databases such as GRAIN (farmlandgrab.org), LAND MATRIX, International Land Coalition (ILC), International Institute for Environment and Development (IIED) and Center for International Forestry Research (CIFOR) (Cotula 2011). The online database of Land Matrix contains extensive information on publicly known deals and its implementation status that provides useful insight on trends in the global land rush. From the available database of Land Matrix (2018), globally, 2749 deal information are available in which 109 deals are in oral agreement, 2233 deal contracts are signed, 77 deals are intended, 189 deals are under negotiation, 86 deal negotiations have failed and 55 deal contracts are cancelled. The amount of negotiation land distribution of 87,249,770 hectares (ha) is shown in Fig. 1.1.

1.2 Worldwide Brief Summary of Land Acquisition

5

80000000 70000000 60000000

Land in ha

50000000 40000000 30000000 20000000 10000000 0 Concluded (oral Concluded Intendent Intendend (Under aggrement) (Contract signed) (Expression of negotiation) interest)

Failed (Negotiation failed)

Failed (Contract Cancelled)

Fig. 1.1  Deals status of different proposed project. (Source: Based on Land Matrix database, 2018)

For the above proposed deal, 63,369,674 ha (63%) land information is available on the online database, and 37,076,669  ha (37%) land record is unavailable (December, 2018). The distribution of acquired land is shown in Fig. 1.2. 50000000 45000000 40000000

Land in ha

35000000 30000000 25000000 20000000 15000000 10000000 5000000 0 Project not started

Startup phase (no production)

In operation (production)

Project abandoned

Fig. 1.2  Land acquisition and project implementation status. (Source: Based on Land Matrix database, 2018)

6

1 Introduction

From the Land Matrix database, it is observed that roughly 87–90 million ha of land were acquired worldwide. According to this inventory, 9.5 million ha, 9.0 million ha and 8.9 million ha of land area was transacted in Central Africa, Eastern Africa and Western Africa, respectively. SE Asia also received 366 deals that covered 2.3 million ha of land area. A media report suggest that Sudan (4.9 million ha), Ethiopia (4.0 million ha), Madagascar (3.6 million ha) and Mozambique (4.7 million ha) are among the key recipients of land-based investment in Africa. Outside Africa, SE Asia (Cambodia 0.24  million ha; Laos 0.80  million ha; Philippines 5.0 million ha; Indonesia 1.5 million ha) and parts of Eurasia (e.g. Ukraine 6.8 million ha and Russia 2.1 million ha) appear to be significant recipient countries. In Southern Asia, India, Pakistan, Sri Lanka and Bangladesh make a significant role in investment with nearly 0.95  million ha of land acquired for 81 deals. Visser and Spoor (2011) analysed that Argentina and Brazil are one of the relevant countries in Latin America where companies are not buying land directly but acquisition here may more commonly involve buying shares in companies that hold land. Investment is not only focused on agriculture for food or fuel, but pressure on land is also now basically on developing countries like India; our country is growing a wider set of factors both endogenous, such as strong demographic growth, and exogenous like investment in petroleum, mining and tourism (Cotula 2012).

1.3  Land Acquisition, Investment and Implementation Based on 2603 deal information (2018), it is observed that maximum land investment is in the agriculture sector (50,278,803 ha). In forestry and industry, the minimum investment is allotted as 23.8% and 0.1%, respectively. To spread industry 93 deals are intended, covering 1,12,328 ha of land worldwide (Table 1.1). It can be noted that 7,344,649  ha of land in the agriculture sector failed to implement 101 deals. Table 1.1  Land investment in different sectors Sectors Agriculture Forestry Tourism Industry Conservation Renewable energy Other Multiple Intention Total

No of deals 1859 232 21 102 5 16

Land investment (ha) 50,278,803 22,032,540 626,150 118,600 51,644 553,237

Failure % deals 54.4 101 23.8 6 0.7 3 0.1 9 0.1 0 0.6 0

19 349

337,137 18,407,684

0.4 19.9

0 22

0 407,398

2603

92,405,795

100 141

8,025,276

Source: Author’s calculation based on Land Matrix 2018

Land investment (ha) 7,344,649 255,900 11,057 6272 0 0

% 91.51 3.19 0.14 0.08 0.00 0.00 0.00 5.08 100

1.4 India: Land Acquisition, Rural and Implications

7

In Liberia, 1,602,000 ha of land was acquired for farmland, and later 1,195,894 ha of land was granted for mining exploration or development concessions since 2004. And in the case of failed 30,000  ha biofuel project in an area of Mozambique, affected villagers are resettled from the newly established Natural Park (FIAN 2010; Nhantumbo and Salomão 2010). In Ethiopia, local investors acquired more than 60% of land area in the period of 2004–2009, and according to the World Bank study 97% of the land area was acquired in Nigeria and for about half or more in Sudan (78%), Cambodia (70%), Mozambique (53%) and Ehiopia (49%) only 7% allotted for Liberia (Deininger and Byerlee 2011; Cotula 2012). In Southern Asia, among the other countries, India’s role in land acquisition is very active. In Madagascar, a 230,000 ha deal was done by an Indian company (Üllenberg 2009). To buy or leasing plantations in Africa, Ethiopia, Kenya, Madagascar, Senegal and Mozambique, 80 Indian companies have invested more than US$ 2.4  billion (Rowden 2011). Large-scale land acquisitions are not new; in the nineteenth and twentieth century, key investors were Europe, the United States and Japan, basically involved in large-scale plantations. From the 1960s onwards, increasing unionization of estate labour forces and stricter labour legislation also encouraged a move away from the plantation and shifted towards developing long-term contractual relationships with local supplies (Tiffen and Mortimore 1990; UNCTAD 2009). At China, it is also observed that the urbanization rate was increasing rapidly at 18%, 30% and 36% in 1978, 1995 and 2000, respectively; it reached >40% in 2004 (Ding 2007). In the last 20-year period from 1979 to 1999, 5.2 billion m2 of residential construction was completed. Housing consumption per capita in cities and towns increased from 3.6 m2 in 1979 to 9.8 m2 in 1992. This statistic reveals urban spatial expansion in an enormous way. Urban built-up areas also increased by 46.7% in the period of 1989–1997 in Zhujiang delta (Weng 2001).

1.4  India: Land Acquisition, Rural and Implications Management and uncertainty are linked to each other. In this context, uncertainty is an obstacle or even a threat to successful management. In general trends of science, technology and organization make new challenges for dealing with the uncertainty of projects (Böhle et al. 2016). Statistical information on the amount of land acquisition as well as data  of the number of investors are  incompleted or non-exist in most of the developing countries (Uzun et al. 2009). Statistics on land holding and land deals are typically collected at the enterprise level from web based and media research; the method used here has some limitations. Increasing population mass in developing countries like India heavily depends on land in terms of livelihoods and settlement. Since the first decade of industrial development is marked by a struggle for assets in industrial and energy sectors, even state policy still now has a strong urban bias. As a consequence, deterioration is observed in livelihoods of rural areas and the related rural-urban migration due to the withdrawal of large tracts of land from production (Visser and Spoor 2011). The

8

1 Introduction

economic history of India is a mixed economy of manufacturing and services, but the majority still survives on agriculture. India’s economy is a developing mixed economy and the world’s fifth largest economy by nominal GDP. India is the source of huge raw materials of industrial resources, and after independence, a large number of industries are established in private, public and joint sectors. But the level of development is very little compared to advanced countries, and about 10% of the total workers are employed in the organized industrial sector (Ganguly and Mukherji 2011; World Development Indicator 2017). India is one of the fastest growing country in the service sector with an annual growth rate of above 9% since 2001, which contributed 57% of GDP in 2012–2013 (The Hindu 2016). The development of human society is depending on the understanding of the complex interworking of a series of natural and social activities. In many of the developing countries like India, 70% of the total population is still now depending on natural resource-based livelihood support systems (Reddy et  al. 2003). In our    country, agriculture sectors are continuing to play a crucial role in development because it is a large sector of aggregate income and total labour force (De Gorter and Swinnen 2002; Dethier and Effenberger 2012). The agricultural sector of India is offering largest employment opportunities but contributes to a declining share of its GDP (17% in 2013–2014), and India ranks second worldwide in farm output (The Economic Times 2016). Industrialization may provide great employment opportunities, which may accelerate the economic development of India (Cypher and Dietz 2008); statistics reveals that contribution in this sector was 26% GDP in 2013–2014 (The Economic Times 2016). Sustained media reporting always makes a key role in public awareness on large-­ scale land acquisition. In India, from the online database of DATA MATRIX (2000–2018), it is observed that 69 out of 85 deals are on heavy industries like steel, aluminium, power, infrastructure, SEZ, etc. (Fig.  1.3), and the rest of the deals 80 70

No of deals

60 50 40 30 20 10 0

Bio- fuels

Biofuels, Biofuels, Food crops, Industry Non-food agricultural commodities

Biofuels, Food crops Food crops, Renewable Industry Energy

Fig. 1.3  Investment in different sectors in India (2000–2018)

Industry

Industry (Other)

Tourism

Industry, Renewable Energy

1.4 India: Land Acquisition, Rural and Implications

9

belong to bio-fuels, food and tourism. From the MATRIX database, it is also observed that 73 contracts were  signed/have negotiation from a domestic investment like JSW, TATA, Jindal, Videocon Ltd., etc.; foreign invest is observed mostly in bio-fuel, food crops and renewable energy sector. Media reports also suggest that Andhra Pradesh, Karnataka, Maharashtra and Gujrat are key recipients of land-based investment in India. Apart from this, Tamil Nadu, Odisha and Chhattisgarh appear to be significant recipient states of India. From the database (Table 1.2), it is observed that 84 investment deals are distributed among 18 states, in which only 33 industrial projects are in operation and 19 investment projects are under construction phase, and expected to start shortly, a considerable percentage (32%) of proposed projects have been delaying for a long time in which 8  projects are already declared to be abandoned (Table 1.3).

Table 1.2  State-wise investment (2000–2018)

State Andhra Pradesh Chhattisgarh Tamil Nadu Karnataka Uttarakhand Uttar Pradesh Telangana Madhya Pradesh Puducherry Haryana Himachal Pradesh Rajasthan Uttaranchal Nagaland Maharashtra Odisha Gujrat West Bengal Total

Start-up phase (no Deal operation) 13 3 5 8 11 1 1

1 1 5 1

1 1

1

Project Project No In operation not (production) started abandoned information 5 3 1 4 6 2

1 1

1

1

1 1 1 1 1 1 18 5 11 3 84

3

Contact cancelled/ under negotiation 1

1 1 1 1 1 3 2 1

5 3 6

19

33

Source: Land Matrix 2018

1 6

1

1

2

2 2 19

2 1 8

2

3

Investor country India

Videocon Industries Limited

India

India Pondicherry SEZ Co Ltd., SUBHASH PROJECTS AND MARKETING LIMITED (SPML), OM METALS INFRAPROJECTS LIMITED (OMIL), PONDICHERRY INDUSTRIAL PROMOTION DEVELOPMENT AND INVESTMENT CORPORATION LIMITED(PIPDIC) HSIIDC Limited, Reliance Industries Ltd. India (RIL)

Investor name Essar Group [2013] Failed (contract cancelled)

Negotiation status

[2012] Failed (contract cancelled) Gandheli, Maharashtra [2012] Failed (contract 431,007, India cancelled) Adgaon, Nashik, Maharashtra, India Deolali, Maharashtra 413,716, India Balapur, Maharashtra 431,701, India Zalta, Maharashtra 431,007, India Chincholi, Maharashtra 431,007, India Parvati Nagar, Aurangabad, Maharashtra 431,004, India

Haryana, Gurgaon, Jhajjar

Krishna Nagar, Puducherry, India

Location Surat, Gujarat, India

Table 1.3  List of few abandoned projects in India (2000–2018)

10,117

2763

[2011] Project abandoned

346

202

2799

121

Intended Contract size in acre size 248

[2012] Project abandoned

Implementation status [2010] Project abandoned [2010] Project abandoned

Outright purchase

Outright purchase

Nature of the deal Outright purchase Lease/ concession

10 1 Introduction

India

India

Essar Group

Defence Research and Development Organisation

Source: Land Matrix 2018

India

Tata Sons Ltd.

Varavoo Kaval, Karnataka, India Kundapura, Karnataka, India

Gujarat, Jamnagar

West Bengal, Singur

[2006] Concluded (oral agreement) [2008] Failed (contract cancelled) [2011] Concluded (contract signed) 1125

[2008] Project abandoned [2014] Project abandoned

997

[2008] Project abandoned

1736

405

Lease/ concession

1.4 India: Land Acquisition, Rural and Implications 11

12

1 Introduction

It is also to be noted that nearly 15,000 acres of land for the abandoned project was purchased/leased from local farmers and have become useless. As a token example, some projects of different states of India are discussed briefly here. In 2012, the state of Haryana Reliance Industrial Ltd. (RIL) was going to set up SEZ, and as part of a larger plan, RIL acquired an additional 5000 acres adjacent to this land. From market sources, RIL spent an amount of INR 200 billion to purchase 1400 acres of land as the price of land in the region was roughly INR 0.15 billion an acre. They planned to set up a cargo airport and a 2000  MW power plant and expected to bring in an investment of around INR 250 billion. Due to the global economic meltdown, there was uncertainty among investors, and the demand for commercial space shrunk. As a consequence, RIL slow down the project. Haryana chief minister Bhupinder Singh Hooda announced that SEZs turned out to be a failure in the state as they failed to generate jobs (Business Today 2012). From a review of media reports since the last decade, 19 deals (Tables 1.4 and 1.5) were delayed or suspended mostly due to unavailability of raw materials or fiscal deficit. In Andhra Pradesh, Karnatak and Maharashtra, respectively,  3, 3  and 6 deals were recoded under suspension, and acquired land of nearly 20,000 acre is now under thread. Table 1.4  Negotiation status Implementation status Start-up phase (no operation) In operation (production) Project not started Project abandoned

Before Deal 2007 19 1

2007– 2010 3

2011– 2013 7

2014– 2016 5

Data unavailable 3

33 19 8

8 4 4

10 7 2

2 3 2

12 2

1 3 0

Source: Land Matrix 2018 Table 1.5  List of few projects in India not started

Category Food crops, Industry Industry

Industry

Investor name United Infrastructure Pvt. Ltd Indiabulls Infrastructure, Maharashtra Industrial Development Corporation Ltd JSW Steel

Intended size in Contract Production Nature of acre size size the deal Location Madhya 223 Outright Pradesh, Indore purchase Maharashtra, Nashik, Sinner

1023

600

Salboni, West Bengal 721,518, India

2023

1740

Outright purchase

Outright purchase, lease/ concession (continued)

1.4 India: Land Acquisition, Rural and Implications

13

Table 1.5 (continued)

Category Industry

Industry

Industry

Industry

Industry

Industry Industry

Industry

Industry

Industry

Investor name Mahindra Reality developers, Maharashtra Industrial Development Corporation Ltd Areva S.A., Nuclear Power Corporation of India Ltd. (NPCIL) West Bengal Industrial Development Corporation (WBIDC), New Kolkata International Development Pvt. Ltd. (NKID) SKIL Infrastructure Limited

Intended size in Contract Production Nature of acre size size the deal Location Maharashtra, 1214 Outright Pune, Lonavala purchase

Juve Jaitapur, Maharashtra, India

1000

Nayachar

5400

Lease/ concession

3230

Outright purchase

4998

Outright purchase

Gagret, Himachal Pradesh 177,201, India Karnataka, SKIL Bangalore Infrastructure (rural), Limited Nandagudi Reliance Industries Maharashtra, Ltd. (RIL) Raigad Andhra Rassai Properties Pradesh, & Industries Anantapur, Limited location: Hindupur JSW Aluminium Andhra Limited Pradesh, Vizianagaram, location: S. Kota N.G. Realty Pvt. Gujarat, Ltd. Ahmedabad, location: Rajoda Gujarat, Chervil Vadodara, Infrastructure location: Gram Private Limited Nimeta, Taluka Waghodia

10,000

938

1874

1010

366

486

240

222

217

220

220

Outright purchase

(continued)

14

1 Introduction

Table 1.5 (continued)

Category Industry

Investor name Karnataka Industrial Areas Development Board (KIADB)

Industry

Maharashtra Industrial Development Corporation Ltd Andhra Pradesh Industrial Infrastructure Corporation Ltd. (APIIC), GMR Group, Infrastructure Leasing & Financial Services Limited (IL&FS) Sagitaur Ventures India Pvt. Ltd.

Industry

Industry

Industry, others (please specify) Industry, others (please specify)

H.N.Company

Marathon Pachin Infrastructure

Intended size in Contract Production Nature of acre size size the deal 405 Lease/ concession

Location Kadechur, Karnataka, India, Yadgir, Karnataka, India 1010 Maharashtra, Amravati, location: Nandgaon Peth 4249 Mulapeta, Andhra Pradesh 532,195, India

Challakere, Karnataka, India Nagaland, Dimapur

Maharashtra, Raigad

1010

3359

404

400

Outright purchase

Lease/ concession Outright purchase

400

Source: Land Matrix 2018

In 2006 Indiabulls Infrastructure, Maharashtra Industrial Development Corporation Ltd. was going to set up a multi-product SEZ for growth sectors like textiles, electronics/electrical, automobile and auto component, pharmaceuticals and bio-tech, food processing at Sinnar of Nasik district (Plate 1.1). For the proposed project of nearly 10 years, the company purchased 2500 acres of prime land and received sops worth nearly INR 3.0 billion. But since 2007, after approving of SEZ, up to now, the investment amount is zero. Now, to take back the land, the state has issued two warning notices to the company (Times of India 2015).

1.4 India: Land Acquisition, Rural and Implications

15

Plate 1.1  SEZ story of Indiabulls Infrasturcture. (Source: Times of India 2015)

The Nuclear Power Corporation of India Limited (NPCIL) plant planned to set up a 9900 MW nuclear power plant at Jaitapur, Maharashtra. The agreement was signed between a French nuclear engineering company and NPCIL on December 6, 2010. The state Government of Maharashtra approved a hefty revised compensation package for farmers opposing the Jaitapur nuclear power plant. The compensation package was offered nearly INR 0.1  million for barren land and between INR 0.2 million and INR 0.5 million for the grazing land and the irrigated plots. The ambitious project would be set up at Madban village of Ratnagiri district, but now, it was abandoned due to protest by local farmers, fishermen, NGOs, environmental activists and also the opposition (Times of India 2013). In 2013, Reliance Industries chief Mukesh Ambani and Jai Corp made an agreement with state Government to set up SEZ at Mahamumbai, in Raigarh district, spreading over 10,000 acres of land. Local villagers of Uran, Pen and Panvel made a stiff resistance during the land acquisition. So far, only 4600 acres were acquired. Now Ambani is looking at an exit from the Mumbai SEZ project, and Jai Corp blamed the global financial crisis and the withdrawal of fiscal incentives by the Centre for the project turning unviable. Jai Corp said, “the new land acquisition Act,

16

1 Introduction

notified in January 2014, made it difficult to buy land in contiguity, vital for SEZ” (Business Standard 2015). In 2007, SKIL Infrastructure Ltd. was going to set up a SEZ, and State High Level Clearance Committee (SHLCC) cleared the INR 150  billion proposals to come up on 12,350 acres spread over 36 villages at Nandagudi in Hoskote taluk, Karnataka. The Revenue Department however voiced against handing over that such “prime land” around Bangalore to a private developer. Urban development authority also pointed out that the BMRDA (Bangalore Metropolitan Region Development Authority) planned to build a township, and it is not advisable for a separate SEZ there (Times of India 2008). The Hindupur SEZ (HAPSEZ) was going to invest on Rassai Properties & Industries Ltd. at Parigi in Hindupur in Anantapur district of Andhra Pradesh. This is a multi-product and multi services SEZ, planned to develop such as Modern township, Infotech Park, Hospitality, Media City, etc. and it would spread over of 2500 acres. Out of this, the company planned to develop a SEZ land of 2250 acres and already acquired 1418 acres of land (Expert Properties 2009) but still now it is not started. JSW Aluminum Ltd., which belongs to Sajjan Jindal Group, signed MoU with Andhra Pradesh Government on July 1, 2005, to set up a 1.4 million tonne alumina refinery and 0.25 million tonne smelter that would spread over 1086 acres with an investment of INR 50.0  billion at Boddavara in Vizianagaram district, Andhra Pradesh. During land acquisition, it faced a strong protest from local farmers, and after acquiring the land, it faced other difficulties from mines ore; finally, Anrak Aluminium Ltd., which came much later, could manage to get mining leases from the Centre through APMDC (Andhra Pradesh Mineral Development Corporation). Now, when the company failed to ground its alumina refinery project at Boddavara, fresh trouble was brewing for JSW Aluminium Ltd. with the project-affected people staking claim to their lands and undertaking farming operations. They claimed that “As the Jindal management failed to implement the package it promised in lieu of lands taken from us, we have decided to cultivate in our erstwhile lands”, Sunkari Eswara Rao addressed (The Hindu 2012). N G Realty Pvt. Ltd. planned to set up an industrial park, spread over 230 ha at Rajoda, Ahmedabad district of Gujrat. For the proposed SEZ, N G Group invested INR 3.50 billion in this project, including INR 1.0 billion in development and INR 2.5  billion in acquiring land. After 4  years of continuous struggle to develop an engineering special economic zone (SEZ), city-based developer NG Realty now finally decided to abandon the plan and de-notify its Gallops SEZ (Business Standard 2013). In India, when various states are competing to transform dusty farmlands into shiny, new-age factories that would create new employment opportunities, Singur (West Bengal) seems the pronounced leitmotif of industrial mobility in the reverse. Tata Nano project was laid down on May 2006; for the said plant, 997 acres including 330 acres of highly fertile land (Boro paddy on 230 acre; Sesame seeds on 40 acres; Maize, Green grams, Black grams on 60 acres; Potato on 20 acres) that supported hundreds of families with tiny land holdings was acquired. But majority of

1.4 India: Land Acquisition, Rural and Implications

17

the landowners in Singur were anxious about the fertility of their land, and many believed that the Tata factory infrastructure may  destroy the farmland, and even though the Supreme Court verdict came in their favour in 2008, it is a near-­ impossible proposition to resume agricultural activity in Singur (Times of India 2017). Industrialization in the state of West Bengal, India, hangs uncertain after protests and agitations over the issue of land acquisition have resulted in Nandigram and Singur. In July 2006, Salim Group signed an agreement with West Bengal Government to build a chemical hub in a special economic zone; a large and a few small townships; a 100-km-long, 100-m-wide super highway; and other projects. For the biggest FDI (Foreign Direct investment) deal in the history of India, Salim Group was going to invest some US$ 4.2 billion in West Bengal on 40,000 acres of land proposed  to acquire from farmers and other landowners. The chemical hub would be built in Nandigram, across the Haldi River from the industrial city of Haldia. For the proposed hub, some 15,000 acres of land would be needed, which would be taken from 29 mouzas (smallest revenue units). In the case of land acquisition of Nandigram that sparked the bloodshed, a dozen died. As a result, protests and riots sprang up all over West Bengal. Buses were burnt, schools closed and traffic ground to a halt. Nearly 1000 were arrested, but the rioting went on. It stopped only after the Chief Minister of West Bengal, Buddhadeb Bhattacharya, symbolically tore up the acquisition notice and put a 3-month stop to all land acquisition (The Asia Magazine 2009). In 2006, M/S JSW Bengal Steel Ltd. signed an agreement with West Bengal Government to invest INR 350.0 billion in a 10-million-tonne steel and a 1680 MW power plant at Salboni block of the Paschim Medinipure, West Bengal, which was started to delay and ultimately suspended due to global recession and insufficiency of raw materials in 2015. The industrial plant already acquired 4225 acres of land spreading over 22 mouzas; almost 700 household (HH) lost their farm land, fodder farm and grazing land due to land acquisition, and now they become jobless (The Telegraph 2015; Shee and Maiti 2018). In West Bengal since the last decade, three big project agreements were signed at Singur, Nandigram and Salboni. The first two projects became abandoned due to strong protests from local villagers. In this circumstance, the present study is focused on JSW Bengal Steel plant in the Salboni block of Paschim Medinipur district, West Bengal, India. In spite of all the favourable atmosphere from local villagers, after land acquisition, the start of the project was delayed and finally suspension announced on August 16, 2015. The foundation stone of the JSW Bengal steel project at Salboni block of Paschim Medinipur, India, was laid on November 2, 2008. Only a part of the construction work was finished after completion of land acquisition. An unprecedented delay in the project work puts a question mark on the future of the project. These problem of land acquisition and delay in execution are varying connotations that depend on the specific geographical as well as sociopolitical contexts in which they occur. Those who have lost their productive lands are still suffering from the loss of their livelihood. Unprecedented delays in work will lead to economic uncertainty.

18

1 Introduction

1.5  Objective of the Present Work The general objective of the present work is to assess the impacts of industrialization and associated acquisition on the physical, economic and social environment. The detailed objectives are written below. 1. Make a modest attempt to generate a database on the different aspects of land acquisition and its consequences on geomorphology and hydrological impact. 2. Possible changes in agriculture and livelihood activities. 3. To study the impacts of delay in project implementation through a comparative analysis between projects affected area and non-project area. 4. The policy dimensions of land acquisition.

1.6  Location of Study Area M/S JSW Bengal Steel Ltd. had a plan to set up a 10.0 million tonne per annum integrated steel plant at the upper catchment of Sundara basin, the tributary of the Shilabati that ultimately pours to the river Rupnarayan, located at Salboni Block of Paschim Medinipur, West Bengal, India. For the proposed project, 4225 acres of land was acquired including 2362.34 acres of forest land and 311.08 acres of crop land; as a consequence, 700 households have lost their farm land from 22 mouzas. Those mouzas are treated as project-affected mouzas, and the rest of the mouzas are termed as non-project-affected mouzas. From the source of the BLRO (Block land reform officer), land acquisition was started in 2007. But due to some litigation, the acquisition process was taking too long. Finally, the company has selected 4225.4 acres of land for the proposed plant. A total of 1222 households are residing in the 22 project-affected mouzas; out of which, 700 peasant families are directly affected in the sense that private land (“Raity” and “Patta”) they owned is acquired (Anandabazar Patrika, 11th July, 2012). The study area covers 111 mouzas with an area of 122 km2 and extends between 22°32′10”N–22°37′40”N latitude and 87°13′10″ E–87°23′30″E longitude (Chap. 2).

1.7  Methodology The present study is based on intensive fieldwork conducted in villages of Salboni and Keshpur block. The methodology in the study covers finding the data sources, the procedure of soil, water sampling collecting and data analysis. The deductive methodology of investigation is followed to establish the impact of industrialization by field investigation and measurements supported with some secondary information. Reports and publications of the various departments of the West Bengal Govt. are the main source of secondary data. The research instrument used for the study is

1.7 Methodology

19

questionnaires. Land acquisition is studied from two different angles. First, the acquisition process is described from an administrative perspective and, second, from the aspect of its various socio-economic consequences. Out of 700 household from project affected mouzas, 538 households’ information were collected by non-­ random opportunity and snowball sampling method through field survey. A stratified random sampling survey was applied for non-project area covering 1604 households. The investigation of the first phase was conducted in 2009–2010 during land acquisition, and repeated investigation on the same household was held in 2012 to 2014 after acquisition. JSW authority announced on November 30, 2014, to hold the proposed project due to lack of raw materials, and finally suspension announcement was done on August 16, 2015. The third phase of the investigation was conducted in 2016 to 2017 after the suspension of the project. During the second (2012 to 2014) and third phase (2016 to 2017) of the investigation, we tried to understand the difficulties they are facing in the source of income, the option of livelihoods adjustment with altered situation in terms of income, profession and labor potentially and what they are expecting from JSW authority. It is observed that no settlement land was encroached or moved away that made easier to deal with the same household after first wave of interviews. Household interview at the first phase is mainly concentrated on land acquisition, land uses, utilization of compensation and expectation from the proposed project. Before field observation, we collected all project-affected mouza maps including details of plot-wise land ownership records and land uses from BLRO (block land reform officer) and DLRO (district land reform officer). We also collected a sample list of few affected households including a provisional master plan of the proposed project from MKDA (Medinipur Khargapur Development Authority). Land use and Land cover map (LULC) of the study area is prepared with the help of satellite and recent google imagery. The analysis of the collected secondary, as well as primary information and laboratory analysis of soil and water, was done with great care to make a synthesis of all the studied parameters in a holistic manner. Recent technology and software as arc GIS with extension tools, Erdas Imagine, e-water tool kits, arc Info models, etc. were used to prepare a required tables, diagrams and maps for detailed analysis. The quality of life index was computed to understand the different levels of quality of life through analysing the relationship between education level and quality of life and material status and life of the sample respondents. The impact at every stage of development of this industrial project is estimated. The amount of runoff discharge available at target points of the sub-basin is calculated by using SCS CN, after USDA, 1972 method. The amount of evapotranspiration loss is accounted after Jensen et  al. (1990) and Penman (1963). Different livelihood asset indexes like human, natural, physical, social and financial are prepared by following Yan et al. (2010). Then a different livelihood asset index is then correlated with livelihood diversification index through Pearson correlation coefficient to know the probability of livelihood diversification. After the suspension of the project, the local people became frustrated. A survey was conducted on different aspects of industrialization such as living standards, major income sources, labour input orientation, medical facility and daily life problems they are facing such as collecting fodder and fuel,

20

1 Introduction

food insecurities, family income and livestock farming and overall assessment of the proposed project at a 10-point scale. Finally, a level of development study was  conducted separately for agriculture, infrastructural facilities and socio-­ economic fields at mouza level that will help identify where a given mouza of the entire study area is standing in relation to others within the study area. A huge amount of primary and secondary information like mouza-wise crop land, yield rate, fertilizer consumption, household size, education, livestock information, family income sources, demographic information (total population, marginal worker, no of main cultivators, literacy rate, agricultural labor, etc.), amenities, loan information, etc. were  used as input in Wroclaw taxonomic method (Ohlan 2013). Primary data were collected through a continuous stratified random sampling method, and Census of India, district statistical handbook, local panchayet office and Government office served as sources of secondary information. An attempt was made to determine the assimilative capacity of the ambient atmosphere, water quality and noise of the study area. Hourly meteorological information like wind direction, wind speed, air pressure and sunshine were  used as input in ISCST3 air dispersion model to predict the concentration distribution pattern of primary pollutants, namely, SO2 and SPM (suspended particulate matter), on the local environment. Water samples were also collected from different places and tested at the laboratory for basic chemical analysis. A preliminary reconnaissance was conducted by JSW authorities using an integrated sound level meter manufactured by Quest technology, USA, to identify the major noise-generating sources in the project site.

1.8  Literature Review Literature review is attempted to assess the national and international studies of development in the concerned field. Saha and De (1976) studied the trend of population in post-Independence India. Census of India (1961, 1971, 1981, 1991, 2001, 2011), Hunter (1876) and Bengal District Gazetteer Medinipur (1911, 1923) presented historical records about the trend of population, agriculture, communication, common resources and history of human occupancy of Medinipur. O’Malley (1911) discussed the physical and cultural background of the district. Tucker et al. (2001) discussed on technical analysis of drainage basin from digital terrain data. Ascoli (1910), Bagchi (1944) and Vredenburg (1908) studied on geological history of the alluvial plain of Bengal. Panda and Guha (2009) made some recommendations for the development on the socio-economic life of Lodhas in some villages of Medinipur subdivisions. Bhowmick (1994) and Danda (2002) and Roy (2009) conducted an applied research and development work among the backward region of Medinipur district. Basu (1949) discussed about the ancient society of Bengal. Manju et  al. (2002) analysed the conditions and growth of the handicraft industries in the medieval and early British period. Chaudhuri (1974) observed how Bengal developed as a great industrial region in India during the seventeenth and eighteenth centuries through specializing in the manufacture of cotton goods for exports. Usually

1.8 Literature Review

21

planned projects are setting up in rural and semi-rural areas, where agriculture is the main occupation according to Panda and Behera (2008). Birdar (2011) said that land is the main source of income where people depend on agriculture, but on the other hand, land is fixed by nature. Sarkar (2007) told that development needs land acquisition. Fahimuddin (2011), Kombe (2010) and Kusiluka et al. (2011) examined the negative impacts of land acquisition on local livelihoods. Von Braun and Meinzen-Dick (2009) discussed how to bring balance between land acquisition and development. Choudhury (1992) studied on Government policy and strategy of displacement and resettlement of land-lost families. Majumder and Guha (2009) conducted a field-based study to analyse the impact of land acquisition among peasants in rural areas of Kharagpur division in Paschim Medinipur. Guha (2006) worked on impacts of land acquisition in Paschim Medinipur district. Dunne and Leopold (1978) observed the nature of land use changes and their consequent impacts on runoff at basin scale. Chang (2007) explained that topography, soil characters, rock types, infiltration, evaporation, rainfall intensity, land use, etc. are unique site-­ specific factors of each watershed. Brath et al. (2006), Crooks and Davies (2001), Costa et al. (2003), Franczyk and Chang (2009), and Miller et al. (2002) developed a model to assess the effects on flood frequency after land use alteration. Kumar et al. (1991), Moitra Maiti and Maiti (2009), Schwab et al. (1993) and Shi et al. (2009) applied SCS CN method to predict runoff volumes from agricultural fields and small watershed. Chow (1951, 1954) and Hewitt et al. (1954) observed the spatial and temporal variability of rainfall as an important factor in water management. Horton (1939) worked on infiltration leakage. Evapotranspiration loss was estimated by Jensen et al. (1990) and Penman (1948. 1956). Keating et al. (2003) and Schwab et al. (1993) pointed out on the different methods to estimate actual usable amount of water. De Roo et al. (2001), De Souza (2010), Graham and Butts (2005) and Hastenrath and Greischar (1993) advised that extra care would be needed for watershed management and treatment. Mustafa et al. (2012), Mohan and Shrestha (2000), Schwab et al. (1993) and Seth et al. (1999) applied different hydrological models for better management. Athavale (2003), Jana et  al. (1997), Kumar et  al. (1991) and Pandey (2002) worked on watershed management and water harvesting on small catchment. Industrialization makes rapid development in society. Proper development should improve people’s quality of life (Ohlan 2013). Dasgupta (1971) discussed on different, unanticipated adverse impacts, which came through the development process. Rama Krishna et  al. (2005) analysed that adverse impacts may also bring considerable changes in the atmosphere, human and animal health, land uses, water resources, etc. Bhanarkar et al. (2005) worked on atmospheric impact on Jamshedpur region, India. Goyal and Nebebe (2000), Lorber et al. (2000), Manju et al. (2002), Nandankar (1999) and Sax and Isakov (2003) used ISCST3, Gaussian Plume, AERMOD, etc. the popular models for air quality treatment. Delays of any proposed project lead to the sufferings due to loss of livelihood by land acquisition. Chambers and Conway (1992) and Yan et al. (2010) observed that in this situation, livelihood diversification is an important strategy that reduces the livelihood vulnerability and ensures food security. Block and Webb (2001), Ellis

22

1 Introduction

(2005), Glavovic and Boonzaier (2007), Khatun and Roy (2012) and Yan et  al. (2010) observed in different developing countries in the world how people diverse their livelihood to reduce vulnerability of life, secure food and adjust with nature. De Janvry and Sadoulet (2001) worked with the Mexican Indian; Yan et al. (2010) focused on “Nomades” of Tibetan plateau to study how they are suffering in long-­ term vulnerable condition. Ouyang et al. (2010) gave a view of landscape change and its consequence on livelihood in China. Saha and Bahal (2010) prepared a short story on rural livelihood diversification in West Bengal. Yan et al. (2010), Saha and Bahal (2010) and Shiyani and Pandya (1998) developed various livelihood diversification indexes based on physical, social, human and economic assets. Barrow and Hicham (2000), Bradstock (2006), Ellis and Allison (2004), Holden et al. (2004), Koczberski and Curry (2005), McCusker and Carr (2006) and Soini (2005) focused on the relationship between livelihood strategy and land use change. Development without proper management in different development sectors brings regional disparities. Choudhury (1992) and Sarkar (1994) developed a socioeconomic indicator to bring uniformity in regional development. Arief (1982), Bhatia and Rai (2004), Ewusi (1976) and Ohlan (2013) applied the Wroclaw taxonomic method to find out the disparities in levels of regional development for better management. Arief (1982) and Ohlan (2013) applied this method in Malaysia and West Bengal, respectively. Ewusi (1976) found out the socioeconomic disparities in levels of regional development in Ghana. Desai et al. (2007) observed that poor design and implementation of the rehabilitation and resettlement policy by Indira Sagar Pariyojna in Madhya Pradesh was the main cause to deteriorating living standards of affected families. Sarkar (1994) and Sarkar (2007) analysed the land acquisition and regional disparities in West Bengal. At the macroeconomic level, probably, the most important relationship in the proposed field of the study is the process of interaction between industry and agriculture (Das 1999). Decentralization of industry may be more conducive to stimulating local initiative and allowing more balanced development in relation to the emergence of the new economic source. Economic activity tends to be concentrated in a comparatively few urbanized region in both developed and developing countries due to its uneven distribution over the space, and this paves the way for regional inequality (Piore and Sabel 1984). Land acquisition becomes a crucial political issue in India. Ali and Guha (2003); Guha et  al. (1996), Guha (2004a, b, 2006, 2007) and Majumder and Guha (2008) focused on various aspects of land acquisition at Medinipur District, problem arising to resettlement and rehabilitation policies (Gazette of India 2004; Govt. of India 1985) and its consequent impact on peasant families in the Medinipur district of West Bengal. In the case of developing countries, historical factors, combined with an attempt to achieve rapid growth through industrialization, have led to the development of the few isolated pockets of development at the cost of continuing and often increasing poverty of the vast hinterland.

References

23

References Aggarwal A., 2006. Special Economic Zones: Revisiting the Policy Debate. Economic and Political Weekly, 4143/44, pp 4533–4536. Ali, B. and Guha, A., 2003. Perception and Cognition about Ageing: Elderly Persons in Displaced Families in Medinipur, West Bengal, in http://www.anthrobase.com/txt/A/Ali_Guha_01.htm Retrieved on 6/16/2003. Anseeuw, W., Wily, L.A., Cotula, L. and Taylor, M., 2012. Land rights and the rush for land: Findings of the global commercial. ILC: Rome, Italy. Arief, S., 1982. Regional disparities in Malaysia. Social Indicators Research, 11(3), pp. 259–267. Ascoli, F.D., 1910. The rivers of the delta. J. Asiat. Soc. Bengal, NS, 6, pp.543–556. Athavale, R.N., 2003. Water harvesting and sustainable supply in India. Centre for Environment Education; Rawat Publications. Bagchi, K., 1944. The Ganges delta: Calcutta. Banerjee, P., 2006. Land Acquisition and Peasant Resistance at Singur. Economic and Political Weekly, pp 4718–4720. Barrow, C.J. and Hicham, H., 2000. Two complimentary and integrated land uses of the western High Atlas Mountains, Morocco: the potential for sustainable rural livelihoods. Applied Geography, 20(4), pp. 369–394. Basu, N.K., 1949. Hindusamajer Garan. Vishwabharati Publication, Calcutta. Bengal District Gazetteer Medinipur (1911) Bengal secretariat Book Depot, Calcutta. Bengal District Gazetteer Medinipur (1923) Bengal secretariat Book Depot, Calcutta. Bhagwati, J., and Panagriya, A., 2013. India’s tryst with destiny: Debunking myths that undermine progress and addressing new challenges. Harper Collins. Bhanarkar, A.D., Goyal, S.K., Sivacoumar, R. and Rao, C.C., 2005. Assessment of contribution of SO 2 and NO 2 from different sources in Jamshedpur region, India, Atmospheric Environment, 39(40), pp.7745–7760. Bhatia, V.K. and Rai, S.C., 2004. Evaluation of socio-economic development in small areas. New Delhi, India. Bhattacharya, P., Mal, S., Tripathy, S., Mandal, S. and Ghosh, B., 2013. Development-induced displacement and human development through industrialisation in India. African Journal of Geo-Science Research, 1(3), pp.01–05. Bhowmick, P.K., 1994. The Lodhas of West Bengal, Kolkata. Institute of Social Research and Applied Anthropology, (ISRAA). Birdar, S.G., 2011. Land acquisition rehabilitation and resettlement paradigm shift and emerging scenario. Southern Economist, 50(7), pp. 19–26. Block, S. and Webb, P., 2001. The dynamics of livelihood diversification in post-famine Ethiopia. Food policy, 26(4), pp.333–350. Boamah, F. and Overå, R., 2016. Rethinking livelihood impacts of biofuel land deals in Ghana. Development and Change, 47(1), pp.98–129. Böhle, F., Heidling, E. and Schoper, Y., 2016. A new orientation to deal with uncertainty in projects. International Journal of Project Management, 34(7), pp.1384–1392. Bradstock, A., 2006. Land reform and livelihoods in South Africa’s Northern Cape province. Land Use Policy, 23(3), pp.247–259. Brath, A., Montanari, A. and Moretti, G., 2006. Assessing the effect on flood frequency of land use change via hydrological simulation (with uncertainty). Journal of Hydrology, 324(1), pp.141–153. Business Standard, 2013. Private firms set up industrial parks to meet SMEs’ demand. https:// www.business-­standard.com/article/sme/private-­firms-­set-­up-­industrial-­parks-­to-­meet-­smes-­ demand-­113030400483_1.html. Retrieve on 22 May, 2018. Business Standard, 2015. Mukesh Ambani likely to exit Navi Mumbai SEZ. https://www. business-­s tandard.com/article/companies/mukesh-­a mbani-­l ikely-­t o-­exit-­n avi-­m umbai-­ sez-­115090700801_1.html. Retrieve on 28 May, 2018.

24

1 Introduction

Business Today, 2012. Reliance Industries mulling to exit Haryana SEZ project, https://www. businesstoday.in/current/corporate/reliance-­set-­to-­exit-­gurgaon-­sez-­project/story/21709.html, Retrieve on 22 May, 2018. Census of India., 1971. India General Population Tables. Controller of Publication, Delhi. Census of India., 1981. India General Population Tables. Series 1, Part 2A (i), Table A-1 to A-3, Controller of Publications, Delhi. Census of India., 1991. India General Population Tables, Controller of Publication, Delhi. Census of India., 2011. India General Population Tables, Controller of Publication, Delhi. Census of India., 1961. West Bengal District Census Handbooks: Medinipur. Vol-1 Superintendent, Government Printing, Calcutta. Census of India.,2001. India General Population Tables, Controller of Publication, Delhi. Chambers, R. and Conway, G., 1992. Sustainable rural livelihoods: practical concepts for the 21st century. Institute of Development Studies (UK). Chandrasekhar, C. P., 2006. Primitive Accumulation. Frontline, 2320, pp 24–26. Chang, H., 2007. Comparative streamflow characteristics in urbanizing basins in the Portland Metropolitan Area, Oregon, USA. Hydrological Processes, 21(2), pp.211–222. Chaudhuri, K.N., 1974. The Structure of Indian Textile Industry in the Seventeenth and Eighteenth Centuries. Indian Economic and Social History Review, 11(2–3), pp.127–182. Choudhury, U.D.R., 1992. Inter-state and intra-state variations in economic development and standard of living. Economic and Political Weekly, pp.2651–2655. Chow, V.T., 1951. A general formula for hydrologic frequency analysis. Eos, Transactions American Geophysical Union, 32(2), pp.231–237. Chow, V.T., 1954. Log-probability law and its engineering applications. Proceedings (American Society of Civil Engineers); v. 80, separate no. 536. Costa, M.H., Botta, A. and Cardille, J.A., 2003. Effects of large-scale changes in land cover on the discharge of the Tocantins River, Southeastern Amazonia. Journal of Hydrology, 283(1), pp.206–217. Cotula, L., 2011. Land deals in Africa: What is in the contracts?. IIED. Cotula, L., 2012. The international political economy of the global land rush: A critical appraisal of trends, scale, geography and drivers. The journal of peasant studies, 39(3–4), pp.649–680. Crooks, S. and Davies, H., 2001. Assessment of land use change in the Thames catchment and its effect on the flood regime of the river. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(7), pp.583–591. Cypher, J.M. and Dietz, J.L., 2008. The process of economic development. Routledge. Danda, A.K., 2002. Predicaments of a marginalized community: The Lodha. Journal of Anthropological 51, pp.103–11. Das, K., 1999. Flexible specialization in industrialization in the context of urban development: A study of Tiruppur, Kannur and Morbi towns. Ph.D. thesis, Jawaharlal Nehru University, New Delhi. Dasgupta, B., 1971. Socio-economic classification of districts: a statistical approach. Economic and Political Weekly, pp.1763–1774. De Gorter, H. and Swinnen, J., 2002. Political economy of agricultural policy. Handbook of agricultural economics, 2, pp.1893–1943. De Janvry, A. and Sadoulet, E., 2001. Income strategies among rural households in Mexico: The role of off-farm activities. World development, 29(3), pp.467–480. De Roo, A., Odijk, M., Schmuck, G., Koster, E. and Lucieer, A., 2001. Assessing the effects of land use changes on floods in the Meuse and Oder catchment. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(7), pp.593–599. De Souza, A., 2010. Water and Development: Forging Green Communities for Watersheds. Orient Blackswan. Deininger, K. and Byerlee, D., 2011. Rising global interest in farmland: can it yield sustainable and equitable benefits?. World Bank Publications. Desai, K., Jain, V., Pandey, R., Srikant, P. and Trivedi, U., 2007. Rehabilitation of the Indira Sagar Pariyojana Displaced. Economic and Political Weekly, pp.27–36.

References

25

Dethier, J.J. and Effenberger, A., 2012. Agriculture and development: A brief review of the literature. Economic Systems, 36(2), pp.175–205. Ding, C., 2004. Farmland preservation in China. Landline, June. Ding, C., 2007. Policy and praxis of land acquisition in China. Land use policy, 24(1), pp.1–13. Dunne, T. and Leopold, L., 1978. Water in environmental planning WH Freeman. San Francisco, CA, 50. Ellis, F. and Allison, E., 2004. Livelihood diversification and natural resource access. Overseas Development Group, University of East Anglia. Ellis, F., 2005. Small farms, livelihood diversification, and rural-urban transitions: Strategic issues in Sub-Saharan Africa. The future of small farms, 135. Ewusi, K., 1976. Disparities in levels of regional development in Ghana. Social indicators research, 3(1), pp.75–100. Expert Properties, 2009. Rashmika – an interesting proposition due to the Special Economic Zone. http://www.expat-­properties.com/media/archives/rashmika-­sez.php. Retrieve on 29 May, 2018. Fahimuddin., 2011. Land acquisition and payment: Some ground realities in Uttar Pradesh. Man and Development, 23(2), pp. 43–58. FIAN. 2010.Land grabbing in Kenya and Mozambique.Heidelberg: FIAN. Available from: http:// www.fian.org/resources/documents/others/land-­grabbing-­in-­kenyaand-­mozambique. Franczyk, J. and Chang, H., 2009. The effects of climate change and urbanization on the runoff of the Rock Creek basin in the Portland metropolitan area, Oregon, USA. Hydrological Processes, 23(6), pp.805–815. Ganguly, S. and Mukherji, R., 2011. India since 1980. Cambridge University Press. Gazette of India., 2004. National Policy on Resettlement and Rehabilitation, Gazette of India, Extraordinary, Part I, Section I. Ghosh, M., 2006. Economic Growth and Human Development in Indian States. Economic and Political Weekly, pp. 3321–3327. Glavovic, B.C. and Boonzaier, S., 2007. Confronting coastal poverty: Building sustainable coastal livelihoods in South Africa. Ocean and Coastal Management, 50(1), pp.1–23. Govt. of India, 1985. Report of the Committee on Rehabilitation of Displaced Tribals due to Development Projects, Ministry of Home Affairs, New Delhi. Goyal, S.K. and Nebebe, F., 2000. Determination of economic production-shipment policy for a single-vendor-single-buyer system. European Journal of Operational Research, 121(1), pp.175–178. Graham, D.N. and Butts, M.B., 2005. Flexible, integrated watershed modelling with MIKE SHE. Watershed models, 849336090, pp.245–272. Guha, A., 2004a. Dispossession of Peasants from Agricultural Land in West Medinipur: A Need for radical changes in the Policy of the Government of West Bengal. Society and Change, Vol. XII, No. 4. Guha, A., 2004b. Land acquisition in a West Bengal district, Economic and Political Weekly, October 16–22, Vol.XXXIX, No.46. Guha, A., 2006. Giving Tiller’s Land to The Capitalists: An Ethnography of Land Acquisition in West Bengal’. Journal of the Indian Anthropological Society, Soc, 41, pp.223–241. Guha, A., 2007. Land, Law, and the Left: The Saga of Disempowerment of the Peasantry in the Era of Globalization. Concept Publishing Company. Guha, A., Jana, L., Barman, T. and Mondal, R., 1996. Plough, politics, pig iron and displacement in Medinipur in the wake of liberalisation. Journal of the Indian Geographical Foundation, 3, pp.59–79. Guha, A., 2011. Special Economic Zones, land acquisition and civil society in West Bengal in Hari Mohan Mathur (ed.), Resettling Displaced People: Policy and Practice in India. New Delhi, Routledge and Council for Social Development, pp. 336–357. Hastenrath, S. and Greischar, L., 1993. Circulation mechanisms related to northeast Brazil rainfall anomalies. Journal of Geophysical Research: Atmospheres, 98(D3), pp.5093–5102. Hewitt, R.I., Gumble, A., Wallace, W.S. and Williams, J.H., 1954. Experimental Chemotherapy of Trypanosomiasis. IV. Reversal by Purlnes of the in vivo Activity of Puromyoln, and an Amino

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1 Introduction

Nuoleoside Analog, against Trypanosoma equiperdum. Antibiotics and chemotherapy, 4(12), pp.1222–7. Holden, S., Shiferaw, B. and Pender, J., 2004. Non-farm income, household welfare, and sustainable land management in a less-favoured area in the Ethiopian highlands. Food Policy, 29(4), pp.369–392. Horton, R.E., 1939. Analysis of runoff-plat experiments with varying infiltration-capacity. Eos, Transactions American Geophysical Union, 20(4), pp.693–711. Hunter, W.W., 1876. Statistical Account of Medinipur, Trubner and Co., London; reprint (1997) West Bengal District Gazetteers. Govt. of W.B, Calcutta, pp. 1–295. The Hindu (2016). India has second fastest growing services sector, http://www.thehindu.com/ business/budget/india-­h as-­s econd-­fastest-­g rowing-­s ervices-­s ector/article6193500.ece. Retrieve on 10th Aug, 2016. Jana, L., Barman, T. and Guha, A., 1997. Dispossession Of The Tribal People From Their Agricultural Land: A Case Study Of Koras In Medinipur. Man and Life, 23(1–2), pp.85–89. Jensen, M.E., Burman, R.D. and Allen, R.G., 1990. Evapotranspiration and irrigation water requirements. ASCE. Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N., Meinke, H., Hochman, Z. and McLean, G., 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18(3), pp.267–288. Khatun, D. and Roy, B.C., 2012. Rural livelihood diversification in west Bengal: determinants and constraints. Agricultural Economics Research Review, 25(1), pp.115–124. Koczberski, G. and Curry, G.N., 2005. Making a living: Land pressures and changing livelihood strategies among oil palm settlers in Papua New Guinea. Agricultural Systems, 85(3), pp.324–339. Kombe, W., 2010. Land Conflicts in Dar es Salaam: who gains? Who loses?. Cities and Fragile States Working Paper, 82. Kumar, P., Tiwart, K.N. and Pal, D.K., 1991. Establishing SCS runoff curve number from IRS digital data base. Journal of the Indian Society of Remote Sensing, 19(4), pp.245–252. Kurian, N.J., 2000. Widening Regional Disparities in India: Some Indicators. Economic and Political Weekly 357, pp 538–550. Kusiluka, M.M., Kongela, S., Kusiluka, M.A., Karimuribo, E.D. and Kusiluka, L.J., 2011. The negative impact of land acquisition on indigenous communities’ livelihood and environment in Tanzania. Habitat International, 35(1), pp.66–73. Lorber, M., Eschenroeder, A. and Robinson, R., 2000. Testing the USA EPA’s ISCST-Version 3 model on dioxins: a comparison of predicted and observed air and soil concentrations. Atmospheric Environment, 34(23), pp.3995–4010. Majumder, A. and Guha, A., 2008. A decade after land acquisition in Paschim Medinipur, West Bengal. Journal of the Indian Anthropological Society, 43(2), pp.121–33. Majumder, A.R.U.P. and Guha, A., 2009. The impact of land acquisition on joint-extended family among the peasants of Gokulpur, Passchim Medinipur. Journal of the Indian Anthropological Society, 44(1), pp.77–84. Manju, N., Balakrishnan, R. and Mani, N., 2002. Assimilative capacity and pollutant dispersion studies for the industrial zone of Manali. Atmospheric Environment, 36(21), pp.3461–3471. Mattingly, M., 1993. Urban management intervention in land markets. McCusker, B. and Carr, E.R., 2006. The co-production of livelihoods and land use change: Case studies from South Africa and Ghana. Geoforum, 37(5), pp.790–804. Miller, S.N., Kepner, W.G., Mehaffey, M.H., Hernandez, M., Miller, R.C., Goodrich, D.C., Devonald, K., Heggem, D.T. and Miller, W.P., 2002. Integrating Landscape Assessment And Hydrologic Modeling For Land Cover Change Analysis1. Mohan, S. and Shrestha, M.N., 2000, November. A GIS based integrated model for assessment of hydrological changes due to land-use modifications. In Symposium on restoration of lakes and wetlands (pp. 27–29).

References

27

Moitra Maiti, M. Maiti, R. K., 2009. Sub watersheds for Rational Management of Water Crisis of Darjilling Town, W.B., Geographical Review of India, 71(2), pp. 117–129. Mustafa, Y.M., Amin, M.S.M., Lee, T.S. and Shariff, A.R.M., 2012. Evaluation of land development impact on a tropical watershed hydrology using remote sensing and GIS. Journal of spatial hydrology, 5(2). Nandankar, P.K., 1999. ENVIRONMENTAL METEOROLOGY Assimilative capacity of the atmosphere at Lucknow with respect to air pollution. Mausam, 50(3), pp.263–268. Nhantumbo, I. and Salomão, A., 2010. Biofuels, land access and rural livelihoods in Mozambique. London: IIED Nielsen, K.  B., 2010. Contesting India’s Development? Industrialisation, Land Acquisition and Protest in West Bengal. Forum for Development Studies, pp 145–170. Oberlack, C., Tejada, L., Messerli, P., Rist, S. and Giger, M., 2016. Sustainable livelihoods in the global land rush? Archetypes of livelihood vulnerability and sustainability potentials. Global environmental change, 41, pp.153–171. Ohlan, R., 2013. Pattern of regional disparities in socio-economic development in India: District level analysis. Social indicators research, 114(3), pp.841–873. O’Malley, L.S.S., 1911. Bengal District Gazetteers: Medinipur. Bengal Secretariat Book Depot. Oskarsson, P., 2009. Zoning Andhra Pradesh: Land for SEZs via a land for the poor program. Indian Development Review. Ouyang, W., Skidmore, A.K., Hao, F. and Wang, T., 2010. Soil erosion dynamics response to landscape pattern. Science of the Total Environment, 408(6), pp.1358–1366. Panda, D.K. and Behera, N.K., 2008. Development and displacement: An Orissa experience. Journal of Rural Development, 27(1), pp. 91–109. Panda, S. and Guha, A., 2009. Development Inputs among the Lodhas in a District of West Bengal: Problems of Implementation and Recommendations. Journal of the Indian Anthropological Society, 44, pp.69–75. Pandey, R., 2002. Development of Roof Top Water Harvesting System in Darjeeling. The Administrator, vol–45 Dec, pp. 97–105. Parwez, S., 2016. b. A study on Special Economic Zone implicated land acquisition and utilisation. International Journal of Development and Conflict, 6(2), pp.136–153. Patnaik, U., 2008. Imperialism, Resources and Food Security with reference to the Indian Experience. Human Geography, 11, pp. 40–53. Penman, H.L., 1948, April. Natural evaporation from open water, bare soil and grass. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences (Vol. 193, No. 1032, pp. 120–145). The Royal Society. Penman, H.L., 1956. Estimating evaporation. Eos, Transactions American Geophysical Union, 37(1), pp.43–50. Penman, H.L., 1963. Vegetation and hydrology. Soil Science, 96(5), p.357. Piore, M.J. and Sabel, C.F., 1984. The second industrial divide: possibilities for prosperity. Basic books. Rama Krishna, T.V.B.P.S. Reddy, M.K. Reddy, R.C. and Sing, R.N., 2005. Impact of an industrial complex on the ambient air quality: Case study using dispersion model, Atmospheric Environment, 39, pp. 5395–5407. Reddy, G.P., Rao, M.S.R., Math, N.S.K. and Adhikari, R.N., 2003. Environmental sustain ability through watershed programme in semi-arid region of Andhra Pradesh. Indian Journal of Soil Conservation (India). Robinson, W.C., 2003. Risks and rights: The causes, consequences, and challenges of development-­ induced displacement. Occasional Paper. Rowden, R., 2011. India’s Role in the New Global Farmland Grab An Examination of the Role of the Indian Government and Indian Companies Engaged in Overseas Agricultural Land Acquisitions in Developing Countries By. Roy, D., 2007. Politics at the Margin: A Tale of Two Villages. Economic and Political Weekly, pp 3323–3329. Roy, S.S. 2009. What good is Independent India for the Lodhas? The Statesman, 4 December, pp.15.

28

1 Introduction

Saha, B. and Bahal, R., 2010. Livelihood Diversification Pursued by Farmers in West Bengal. Indian Research Journal of Extension Education, 10(2), pp.1–9. Saha, R.P. and De, A.K., 1976. Population trend in Post-Independence India. In Banerjee, A.R. and Das, A.K. (ed) Biosocial Studies in India, Editions Indian, Calcutta, pp. 515–30. Sarkar, A., 2007. Development and displacement: land acquisition in West Bengal. Economic and Political Weekly, pp.1435–1442. Sarkar, P.C., 1994. Regional imbalances in Indian economy over plan periods. Economic and Political Weekly, pp.621–633 Sax, T. and Isakov, V., 2003. A case study for assessing uncertainty in local-scale regulatory air quality modeling applications. Atmospheric Environment, 37(25), pp.3481–3489. Schwab, G.O., Fangmeier, D.D., Elliot, W.J. and Frevert, R.K., 1993. Soil and water conservation engineering. John Wiley and Sons, Inc.. Seth, S.M.  Jain, S.K. and Jain, M.K., 1999. Remote Sensing and GIS application studies at National Institute of Hydrology, Map India. Shah, A., Nandani, D. and Joshi, H., 2012. Marginalisation Or Mainstreaming? Evidence from Special Economic Zones in Gujarat. Gujarat Institute of Development Research. Shi, Z.H., Chen, L.D., Fang, N.F., Qin, D.F. and Cai, C.F., 2009. Research on the SCS-CN initial abstraction ratio using rainfall-runoff event analysis in the Three Gorges Area, China, Catena, 77(1), pp.1–7. Shiyani, R.L. and Pandya, H.R., 1998. Diversification of agriculture in Gujarat: A spatio-temporal analysis. Indian journal of agricultural economics, 53(4), p.627. Smaller, C., Speller, W., Mirza, H., Bernasconi-Osterwalder, N. and Dixie, G., 2015. Investment Contracts for Agriculture: Maximizing Gains and Minimizing Risks. Soini, E., 2005. Land use change patterns and livelihood dynamics on the slopes of Mt. Kilimanjaro, Tanzania. Agricultural Systems, 85(3), pp.306–323. The Asia Magazine, 2009. Liem Sioe Liong: Bringer of Strife. http://www.theasiamag.com/people/liem-­sioe-­liong-­bringer-­of-­strife. Retriev on 26 May, 2018. The Telegraph, 2015. Salboni start drill with cement. https://www.telegraphindia.com/1150817/ jsp/business/story_37529.jsp#.Vmqfvkp97Dc. Retrive on 21 March 2017. Shee, S.P. and Maiti, R., 2018. Land acquisition, livelihood and income: the case of JSW Bengal Steel Plant at Salboni Block, Paschim Medinipur, West Bengal, India. Environment, Development and Sustainability, pp.1–18. The Economic Times (2016) Agriculture’s share in GDP declines to 13.7% in 2012-13, http://articles.economictimes.indiatimes.com/2013-­08-­30/news/41618996_1_gdp-­foodgrains-­allied-­ sectors, retieved on 22.05.2017. The Hindu, 2012. Jindal alumina project in a limbo. http://www.thehindu.com/todays-­paper/tp-­ national/tp-­andhrapradesh/jindal-­alumina-­project-­in-­a-­limbo/article4018774.ece. Retrieve 24 May, 2018. Tiffen, M. and M. Mortimore. 1990.Theory and practice in plantation agriculture: an economic review.London: ODI Times of India, 2017. Singur - site of the mothballed Tata Nano factory - is now returning back to its agricultural roots, https://economictimes.indiatimes.com/news/politics-­and-­nation/singur-­ site-­of-­the-­mothballed-­tata-­nano-­factory-­is-­now-­returning-­back-­to-­its-­agricultural-­roots/articleshow/57583783.cms, Retrieve on 24 May, 2018. Times of India, 2008. Things fishy at Nandagudi SEZ. https://timesofindia.indiatimes.com/city/ bangalore/Things-­fishy-­at-­Nandagudi-­SEZ/articleshow/3710921.cms?referral=PM, Retrieve on 26 May, 2018. Times of India, 2013. Jaitapur payout: Rs 22.5L per hectare. https://timesofindia.indiatimes.com/ city/mumbai/Jaitapur-­payout-­Rs-­22-­5L-­per-­hectare/articleshow/18474280.cms?referral=PM, Retrieve on 26 May, 2018. Times of India, 2015. MIDC issues notices to company for not building SEZ. https://timesofindia. indiatimes.com/city/nashik/MIDC-­issues-­notices-­to-­company-­for-­not-­building-­SEZ/articleshow/46225577.cms. Retrieve on 26 May, 2018.

References

29

Tucker, G.E. Catani, F. Rinaldo, A. Bras, R.L., 2001. Statistical analysis of drainage density from digital terrain data. Geomorphology, 36, pp. 187–202 Üllenberg, A., 2009. Foreign direct investment (FDI) in land in Madagascar. GTZ. UNCTAD. 2009.World investment report 2009: transnational corporations, agricultural production and development. Geneva: United Nations Conference on Trade and Development Uzun, V., Gataulina, E.A., Saraikin, V.A., Bashmachnikov, V., Pavlushkina, O. and Rodionova, O., 2009. Tendentsii razvitiya i mekhanizmy vzaimodeistviya kruphogo i malogo biznesa v agropromyshlennom komplekse. Moscow: VIAPI. Van der Ploeg, J.D., Franco, J.C. and Borras Jr, S.M., 2015. Land concentration and land grabbing in Europe: a preliminary analysis. Canadian Journal of Development Studies/Revue canadienne d’études du développement, 36(2), pp.147–162. Visser, O. and Spoor, M., 2011. Land grabbing in post-Soviet Eurasia: the world’s largest agricultural land reserves at stake. The Journal of Peasant Studies, 38(2), pp.299–323. Von Braun, J. and Meinzen-Dick, R.S., 2009. Land grabbing” by foreign investors in developing countries: risks and opportunities. Washington, DC: International Food Policy Research Institute. Vredenburg, E., 1908. The geological history of the alluvial plain of Bengal: Hemchandra Memorial Series. Part I, pp.24–59. Weng, Q., 2001. A remote sensing? GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. International journal of remote sensing, 22(10), pp.1999–2014. Woodhouse, P., 2012. New investment, old challenges. Land deals and the water constraint in African agriculture. The Journal of Peasant Studies, 39(3–4), pp.777–794. World Bank, UNCTAD, 2014. The practice of responsible investment principles in larger scale agricultural investments. Implications for corporate performance and impact on local communities. United Nations Conference on Trade and Development, World Bank, Washington World Bank Report no. 86175-GLB World Bank, 2008. Special Economic Zones, Performance, Lessons learned and Implications for Zone Development. FIAS Occasional Paper. Washington DC. World Development Indicators database, World Bank. Database. Accessed on 2 July 2017. https:// en.wikipedia.org/wiki/Economy_of_India#cite_note-­36 Yan, J., Wu, Y., Zhang, Y. and Zhou, S., 2010. Livelihood diversification of farmers and nomads of eastern transect in Tibetan Plateau. Journal of Geographical Sciences, 20(5), pp.757–770. Yengoh, G.T., Steen, K., Armah, F.A. and Ness, B., 2016. Factors of vulnerability: How large-scale land acquisitions take advantage of local and national weaknesses in Sierra Leone. Land Use Policy, 50, pp.328–340. Zoomers, A., Gekker, A. and Schäfer, M.T., 2016. Between two hypes: Will “big data” help unravel blind spots in understanding the “global land rush?”. Geoforum, 69, pp.147–159.

Website Land Matrix https://landmatrix.org

Chapter 2

Study Area

Abstract  The proposed JSW steel plant is located at the upper part of the Sundra catchment, interfluve between river Tamal and Parang. The environment of the study area is typical type of monsoonal tropic and receives a periodic high magnitude of rainfall. The majority of the people depend on land and forest, and most of these people are poor. A brief note on the landscape like geology, soil characteristic, the intensity of agricultural production, demographic distribution, occupational structure, economic condition, etc. is included here and also an overview on climatic condition and land uses of the study area. Keywords  Sundra catchment · Geology · Agriculture · Demography · Livelihoods · Climate · Land use

2.1  Introduction and Location Paschim Medinipur is the south-west (SW) district of the state West Bengal, India, that comprises 4 subdivisions and 30 development blocks (District Census 2011), and it is one of the country’s 250 most backward districts out of a total of 640 (Directory of District 2008). Paschim Medinipur is one of the eleven district in West Bengal receiving funds from the Backward Regions Grant Fund Program (Ministry of Panchayati Raj 2020). Jindal Group of Companies’ M/S JSW Bengal Steel Ltd. announced to set up a 10.0 mtpa (million tonne per annum) integrated steel plant (The Telegraph 2006) within the Sundra catchment, located at Godapiasal of Salboni block, Paschim Medinipur district, West Bengal, India. The catchment is located interfluve between river Tamal and Parang. Sundra is a non-perennial tributary of the Shilabati that ultimately pours to the river Rupnarayan. The study area covers almost 122 km2 area and stretches between 22°32′10″N to 22°37’40″N latitude and 87°13′10″ E to 87°23’30″E longitude (Fig. 2.1). There are 111 mouzas in which 25 mouzas are uninhabited. The western part of the catchment is the extension part of Chota Nagpur plateau. The study area is

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. P. Shee, R. Maiti, Land Acquisition, Industrialization and Livelihoods, https://doi.org/10.1007/978-3-030-90244-5_2

31

32

2  Study Area

Fig. 2.1  Location of study area

located in an environment, typical of monsoonal tropics, with periodic high magnitude of rainfall. The study area contains a total population, as ascertained by Census 2011, of 32,689 inhabitants. JSW authority made a proposal to set up a township here, and more than 10,000 people are expected to get work directly after implementation of project work (JSW EIA 2007). The area falls under typical monsoonal tropics, and rainfall is sufficient, but the moisture holding capacity of the soil is very poor due to coarse grain size; as a consequence, the land becomes dry after rainfall and results in very low agricultural yield rate (Dazzi et al. 1991; District Statistical Handbook 2006; Saxena et al. 1995). The present chapter deals with the physical background of geology, relief, climate, soil, vegetation, livelihood, demography, etc. to understand the interaction of the dominant processes in the study area.

2.2  Geology The study area consists primarily of large alluvial floored with Quaternary sediments (Ascoli 1910; Bagchi 1944; Colebrooke 1803; Vredenburg 1908; Willcocks 1930). The geological map of India indicates that the study area is considered to be lower Jurassic of the upper Gondwana system (Arnett 1971; Ball 1877; Kirkby 1971; West 1949). Reconnaissance is not sufficient to establish that whether both recent and Pleistocene were deposited or not, but in terms of lithological characters, both groups of sediments are remarkably similar (Morgan and McIntire 1959).

2.2 Geology

33

Pleistocene sediments are well oxidized, and the surface is reddish brown or mottled (Barbour 1935; DeTerra 1943; Russell 1940). Chotanagpur Genesis contributes in minimum areas of 1.2% occupying the NW (North West) of the study area. In the Paschim Medinipur district, the surface is detrital, and laterite contains in more or less abundance, small rounded fragments of other rocks. Occasionally, it constitutes the mass of the rock, and the laterite then becomes coarse gritty sandstone of red colour, which does not differ in lithological character much from the sandstone of very different geological data (Bloom 1998; Carter and Chorley 1961). Often the rock becomes conglomerates; pebbles of quartz and rounded fragments of other rocks are embedded in it. In and around Salboni, these pebbles are coated as in other ferruginous conglomerates with oxide of iron (O’Malley 1911; Schumm 1956). The Western part of the study area is principally composed of hard gray and greyish-­ white gritty quartzite associated with large masses or irregular veins of quartz; ultimately the rocks are much twisted and contorted. Bands of quartzose grits are generally from the precipitous peaks which are dotted over this area (O’Malley 1911; Deb 1956). The lateritic rocks cover a 31.3% of the area, but in the majority of cases, the only variety visible at the surface is a gravelly, pisolitic and nodular exposed rock and a continuous layer spread over the country; swelling is observed here and there with a gently undulating surface (Horton 1945; Howard 1997; Morisawa 1985). The rise in the ground is so gradual; in fact level difference is only noticed when seen from a little distance. These long, low swells of lateritic gravels and laterite are chiefly covered with low copies, with occasional patches of grassland, but their dry parched and stony soil is ill adapted for cultivation. Frequently, the detrital or nodular laterite is like loose gravel; each nodules are separate, but not uncommonly, it is cemented to a solid mass, which can be quarried like any other rock; many places may be observed in pits along the roadsides, and in these pits, the collection of more solid variety with the more loosely coherent may be traced. In all cases, it seems to have a result from a reconsolidation or subsequent cohesion of previously free particle or nodular. Through continuous infiltration, process mineral and organic matter are decomposing and partially taking up the iron and then again redepositing it, finally forming a cement between the nodules. This re-cementing is always seen along lines of joining of carks, by which such water has trickled through the rocks and solid portions are seen irregularly disposed along the irregular direction of such infiltration (Cluff 1974). There re-cemented masses of nodular lateritic (kankar), formed from the already dried up and exposed particles, generally fall to pieces on exposure. In this respect, as in others, they differ from the moist and clayey varieties of laterite, the peculiar character of which is that it becomes harder on exposure and desiccation (O’Malley 1911).

2.3  Climate Data for the analysis of daily temperature and rainfall was collected from the Indian Meteorological Department (IMD), Pune, for the last forty years; the study area is located in an environment, typical of monsoonal tropics, with an average

34

2  Study Area

40.0

400.0

35.0

350.0

30.0

300.0

25.0

250.0

20.0

200.0

15.0

150.0

10.0

100.0

5.0

50.0

0.0

0.0

Rain fall

Max temp(°C)

Rain fall in mm

Temperature in °C

temperature varying from 19 °C in January to a maximum of 32 °C in July (Fig. 2.2). From monthly temperature data analysis, it is observed that maximum temperature ranges between 25.6 °C to 37.2 °C, where the minimum temperature ranges from 13.3 °C to 26.1 °C. The temperature sometimes goes down to 5 °C in the month of January in the winter and may also rise to 47 °C in summer (Table 2.1). This high temperature is responsible for greater evaporation loss, and thus the wilting point is reached. The first half of summer is dominated by hot dry wind and high temperature. Diurnal variation of temperature is gradually increased in day time and reaches maximum at noon (14:00–16:00 h) and then gradually decreases at night. Each year there is an average of 103 rainy days. In most years, there are 232 to 283 sunny days. There occur occasionally during summer months periods of atmospheric disturbance, the most important feature of which is the occurrence of a local hot weather storm usually called norwesters. The thunderstorm is generally accompanied by heavy showers, but rainfall in March and April is only 47–65 mm/month. In May there is a rapid increase in rain owing to the occasional incursion of cyclonic storm, and the rainfall consequently rises to over 134 mm. In Paschim Medinipur, as in some of the more westerly districts of south-west Bengal, where the surface soil is composed of red laterite, the hot westerly winds from central India penetrate during, exceptionally high day temperature. The wind speed is relatively higher in day time and reaching maximum at noon and gradually decreasing with night fall and reaches a minimum at midnight. In pre-­ monsoon, wind speed is highly variable (Table 2.2) from 0 to 11.2 m/s, while the variation is less in post-monsoon (0–4.4 m/s). Variation in wind speed is observed as 1.08, 0.94 and 0.54, respectively, in pre-monsoon, monsoon and post-monsoon,

Min temp(°C)

Fig. 2.2  Monthly variation in maximum and minimum temperature (°C) and rainfall (mm)

Source: IMD, Pune

Station Min Max level temp temp pressure Month (°c) (°c) (in hpa) JAN 13.3 25.6 1011.8 FEB 16.6 29.1 1009.6 MAR 21.1 34.1 1005.4 APR 24.3 37.2 1003.0 MAY 25.6 36.9 999.7 JUN 26.1 34.6 995.8 JUL 25.8 32.4 996.1 AUG 25.7 31.9 997.6 SEP 25.3 31.9 1001.6 OCT 23.1 31.5 1006.6 NOV 18.4 29.2 1009.9 DEC 13.8 26.1 1012.3

Mean seal level pressure (in hpa) 1017.2 1014.9 1010.7 1008.0 1004.6 1001.0 1001.4 1002.7 1006.7 1011.7 1015.1 1017.6 Relative humidity (in %) 62.7 60.0 58.5 63.6 68.9 76.8 81.7 82.3 81.1 74.2 65.6 61.0

Wind direction (in 16 points of compass) 12.1 10.9 11.5 12.0 13.5 12.2 12.5 10.7 10.6 8.4 10.5 12.1 Wind speed (in Kmph) 2.5 3.0 2.8 3.2 3.9 3.5 3.1 3.1 2.6 2.0 2.3 2.3

Table 2.1  Showing the average monthly distribution of meteorological parameters Average wind speed (in kmph) 1.3 1.7 2.3 3.3 3.7 2.8 2.2 2.1 1.7 1.2 1.3 1.3

Average wind speed (in m/s) 0.4 0.5 0.6 0.9 1.0 0.8 0.6 0.6 0.5 0.3 0.4 0.4

Avg daily rainfall in mm 0.5 0.9 1.6 2.1 4.4 8.9 11.6 10.7 9.4 3.5 0.5 0.2

Dew point tem (°c) 12.9 16.1 20.4 23.6 25.0 25.7 25.4 25.4 25.0 22.7 17.9 13.4 Total rainfall in mm 11.0 22.5 47.3 65.6 137.0 267.0 362.5 333.9 282.6 106.9 15.6 6.2

Cardinal direction degree direction of Avg tem(°c) wind 19.4 0.0 22.9 11.2 27.6 33.8 30.8 56.4 31.3 78.8 30.4 101.3 29.1 123.8 28.8 146.3 28.6 168.8 27.3 191.3 23.8 213.8 19.9 236.3

2.3 Climate 35

36

2  Study Area

while the coefficient of variation is less in pre-monsoon (0.577) and is peak in the post-monsoon (0.76). That means maximum frequency of calm condition is observed in post-monsoon season. Different stability classes of atmosphere are considered as per Pasquill’s (1961) classification as class 1 (very unstable), class 2 (moderately unstable), class 3 (slightly unstable), class 4 (neutral), class 5 (slightly stable) and class 6 (highly stable), compiled from hourly wind speed, cloud cover and solar radiation. Atmospheric stability is higher in early morning and then gradually changes due to sunshine and rise in temperature. Air mass reaches highly unstable class during midday when the wind speed is at maximum.

2.3.1  Rainfall Characteristics Rainfall data was collected from IMD, Pune, and for better accuracy, day-wise data was collected from the meteorological department of West Bengal at Abash, Paschim Medinipur. The analysis shows that the entire study area falls within the Medinipur gauge station. The rainfall record since 1969 is used for estimating the rainfall input analysis. Present analysis includes the variation of annual rain, seasonal distribution of rain and nature of storm rainfall. • Annual Rainfall Characteristics of the Study Area From rainfall data analysis, it is observed that annual rainfall varies from 1086.1 mm to 2376.3 mm with an average of 1650.3 mm (Fig. 2.3); this amount is quite sufficient to support agriculture and related water activities, but availability is less in non-monsoon season due to less retention.

Table 2.2  Analysis of average wind speeds Minimum wind speed (m/s) Maximum wind speed (m/s) Range Mean Median First quartile Third quartile Standard error 95% confidence interval 99% confidence interval Variance Standard deviation Coefficient of variation Source: IMD, Pune

Pre-monsoon 0 11.17 11.17 1.80 1.78 0.89 2.22 0.01 0.02 0.03 1.08 1.04 0.577

Monsoon 0 6.69 6.69 1.55 1.33 0.89 2.22 0.01 0.02 0.02 0.94 0.97 0.624

Post-monsoon 0 4.47 4.47 0.96 0.89 0.44 1.33 0.01 0.02 0.02 0.54 0.73 0.764

2.3 Climate

37

2500

Annual rainfall (mm) distribuon 1969-2009

in mm

2000 1500 1000 500 0 1969

1976

1983

1990 Year

1997

2004

Fig. 2.3  Variation of annual rainfall in the study area

• Month-Wise Rainfall Characteristics The area receives (Table 2.3) an average annual rainfall of 1650 mm, with a standard deviation of 158.51 mm. July is the wettest (mean 360 mm) month, accounting for nearly 25% of total annual precipitation, although rainfall varies substantially from year to year depending upon monsoon. On average, nearly 81.7% of the total annual rainfall occurs between June and the end of October. In cold weather months of November and December, there are only a few mm rainfalls, and such rains are due to the northwards movement of cyclonic storm from the south of the Bay of Bengal (Hunter 1876). From about the end of December, when the northerly trade wind becomes established, cold season storms are caused by shallow depressions, which originate in the NW (north-west) of the Bay of Bengal and moves eastwards. Then, a general cloudy weather with light rainfall is observed. These depressions continue during the hot weather month, but after, the southerly winds commence. Thunderstorms are frequent features as they are the reverse in January and February. Rain during the storm periods does not retain for a long time due to coarse grain size; as a consequence, moisture-retaining capacity of the soil is very low, and so no water is not available in prolonged dry period. • Concentration of Rainfall in Few Intensive Storm Days Concentrated rainfall in a short duration is the main characteristics of rain in the study area (Table 2.4). It is observed that maximum 630.8 mm (29.2%) rainfall was received in 2008 a single storm (June 15–22). Almost every year since 1969, it experiences 16% of annual rain, received within a single storm of 8–10 days. • Duration of Storm Period The storm period may last for 6–8  days, even it may extend to 19  days (July 7–25, 2001) (Fig. 2.4). Almost all the rain in the study area is by cyclonic nature. Effective rainwater management should retain the water received in those days to make it available in dry periods.

38

2  Study Area

Table 2.3  Monthly rainfall distribution in mm since 1969 to 2009 Year

JAN FEB

MAR APR

MAY JUN

JUL

AUG SEP

OCT

NOV DEC Total

1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Avg

7.4 10 24 0 0 0 14.8 0 21.1 3.6 17.9 5.2 15.8 4.4 0.2 15 4.4 16.2 0 0 0.9 0 20 8.6 0 0 34.4 19.8 20.6 44.6 0 12 0 38.7 0.4 0 10.4 0 2 80.5 0 11.05

3.6 62.4 24 7.3 39.6 45.4 11.4 60.3 0 85.6 6.3 108.8 157.8 162.4 51.3 0.8 12.1 15.4 18.4 68 4.8 152.6 91.4 0 21.8 0.4 8 23 64.6 153.2 0 12.6 55 10 99.1 11.6 86.7 4.6 146.5 8.1 35 47.07

159.8 94.8 152 10.4 92 92.7 54.8 84.2 141.6 138.7 68.2 155.3 195.1 11.8 85.5 43.2 82.2 201.2 121 155.4 284.4 220.8 112.6 231.3 173.8 124.5 187.4 67.3 64.4 67.4 306.3 171.7 243.5 141.3 88.6 84.7 140.6 111 43 86.6 403.6 134.0

350.4 153.8 562.6 220.2 249 578.6 209 275.8 582 279.3 300 474.4 420 323.4 67.5 477.4 153.8 308.9 517.2 328.2 364.4 563.4 440.8 408.2 356.5 285.1 221.6 331.8 410.5 251.1 453.2 404.3 345.9 376.1 201.5 214.6 299 414.1 782 468.4 357 360.5

345.2 181.4 534.1 762.6 216.6 715 232.9 309 176.7 300.4 193.7 226.3 348.5 260.3 318.6 435.9 466.5 240.8 377.8 291.3 341.2 262.4 287.9 238 550.4 386.3 402.4 352.1 430.6 113 316.4 268.6 210.2 223.2 297.3 406.5 243.3 467.5 346.6 314.2 288 333.6

23.4 82 395.6 31 271.4 153.4 71.8 30.8 78.2 199.5 10.9 31.4 5.2 14.4 164.4 67.9 212.6 202.4 46 96 136 171.8 146.2 20.6 45.6 44.2 95.8 51.6 30 142.2 174.6 24.2 199.8 41.5 449.3 102.9 250.8 35.4 10.1 16.5 88.1 108.9

2.6 12.2 9.2 0 10 0.8 26 0.5 46.1 0 0.8 2.6 0 5.3 0 0 0 48.8 82 14.6 0 29.2 14.8 1.2 15.8 0.8 172 0 22.7 29.4 3.4 0 6.5 38.2 1.2 0 0 1.7 14.2 0 5.8 15.0

0 13.4 2.8 55.4 18.4 0 36 59.2 8.7 26.5 3.9 29 67.2 155.7 91.7 0.8 27 10.4 37.7 6.7 0 35 0 1 1 65 8.6 10.4 22 51.6 0 0 0 2.7 0.8 2.4 1.1 0 65.9 0 0 22.3

Source: IMD, Pune

127.8 6.8 197.4 39 19 66.8 115.8 38.5 64.9 78.2 29.6 19.4 92.3 83.9 78.8 122.5 35.6 74.8 61.2 68.6 2 105.2 71.6 24.6 117.2 151.2 7 67.8 63.7 65 17.6 47.8 56.4 132 10.3 56.1 45.9 68.7 43.6 108.4 0.8 65.46

125.4 176.7 168 205.2 160 87.4 200.4 53.5 328.5 189 341.7 243.9 211.6 226.5 141.5 465.9 318.4 168.4 135.8 332 488.6 406.6 169.6 360.2 435.9 379.8 206.4 465.6 357.7 168.4 194.4 181.6 352.9 374 294.5 239.6 180.1 111.9 335.2 798 44.2 264.0

410.8 453.2 306.6 335.3 388.6 270.5 364.2 217.3 128.6 850.2 204 125.7 174.4 166.1 81.2 238.5 228.4 511 251 229.9 181.4 375.2 219.9 123.8 554.7 243 346.4 125.7 185.2 215.6 478.6 168.8 145.6 384.2 76 164.1 362.7 308.2 438.6 279.6 256.3 282.17

2.4 0 0 0 12.2 0 0 0 36 6.3 20.6 0 54 0 5.4 0 0 5.2 6.8 0 3.2 13.6 19.6 2.4 0 0 0 0 35.1 0 0 0 0 0 14 0 3.4 0 0 0 7.5 6.0

1558.8 1246.7 2376.3 1666.4 1476.8 2010.6 1337.1 1129.1 1612.4 2157.3 1197.6 1422 1741.9 1414.2 1086.1 1867.9 1541 1803.5 1654.9 1590.7 1806.9 2335.8 1594.4 1419.9 2272.7 1680.3 1690.2 1515.1 1707.1 1301.5 1944.5 1291.6 1615.8 1761.9 1533 1282.5 1624 1523.1 2227.7 2160.3 1486.3 1650.3

2.3 Climate

39

Table 2.4  Major storm periods and rainfall received in mm Major storm period 5-Sep-1969 13-Sep-1969 5-Sep-1970 10-Sep-1970 25-Aug-1971 31-Aug-1971 9-Aug-1972 15-Aug-1972 29-Sep-1973 4-Oct-1973 6-Aug-1974 16-Aug-1974 5-Aug-1975 21-Aug-1975 25-Jul-1976 5-Aug-1976 25-Jul-1977 1-Aug-1977 27-Sep-1978 7-Oct-1978 3-Aug-1979 10-Aug-1979 15-Jun-1980 23-Jun-1980 17-Jun-1981 25-Jun-1981 28-Jul-1982 7-Aug-1982 11-Aug-1983 27-Aug-1983 7-Jul-1984 19-Jul-1984 14-Aug-1985 21-Aug-1985 23-Sep-1986 9-Oct-1986 18-Aug-1987 28-Aug-1987 15-Sep-1988 24-Sep-1988 28-Jul-1989 6-Aug-1989 30-Jun-1990 13-Jul-1990 5-Jun-1991 10-Jun-1991 16-Jul-1992 29-Jul-1992 9-Aug-1993 15-Aug-1993 25-Jun-1994 29-Jun-1994 10-May-1995 17-May-1995 30-Jul-1996 9-Aug-1996 22-Jun-1997 29-Jun-1997 25-Jun-1998 5-Jul-1998 20-Jul-1999 29-Jul-1999 10-Jul-2000 19-Jul-2000 7-Jul-2001 25-Jul-2001 22-Jun-2002 30-Jun-2002 5-Oct-2003 10-Oct-2003 10-Aug-2004 24-Aug-2004 9-Sep-2005 15-Sep-2005 15-Aug-2006 23-Aug-2006 2-Jul-2007 7-Jul-2007 15-Jun-2008 22-Jun-2008 5-Sep-2009 10-Sep-2009 Source: IMD, Pune

Total days 9 6 7 7 6 11 17 12 8 11 8 9 9 11 17 13 8 17 11 10 10 14 6 14 7 5 8 11 8 11 10 10 19 9 6 15 7 9 6 8 6

Amount of rainfall (mm) 177 183 321.2 253 190.1 479.6 187.4 253 347.5 478.7 111.1 129.6 136.2 236.2 220.1 260.2 212.1 560.8 330 171.2 302 395.2 124.4 290.6 407.2 179.8 186.6 179.3 269.8 170.6 295.8 256.4 198.9 196.5 325 220.2 258.4 349 445.6 630.8 191.8

Rainfall received (%) 11.4 14.7 13.5 15.2 12.9 23.9 14.0 22.4 21.6 22.2 9.3 9.1 7.8 16.7 20.3 13.9 13.8 31.1 19.9 10.8 16.7 16.9 7.8 20.5 17.9 10.7 11.0 11.8 15.8 13.1 15.2 19.9 12.3 11.2 21.2 17.2 15.9 22.9 20.0 29.2 12.9

40

2  Study Area 20 18 16

in days

14 12 10 8 6 4 2 0 Year

Fig. 2.4  Length of a single storm period in days

• Distribution of Rainfall in Wet and Dry Days The period from June to October is considered as the wet period, and on an average, 81.5% of the total rainfall is received during this period (Table  2.5). From analysis it is observed that 20–30% of the total annual rainfall is concentrated to a single storm of 8–10 days. The prolonged dry period of 7 months only receives very meagre amount of rain that may be as low as 18% of the annual total. Rain in the dry period is so less that in maximum cases almost all the rain received are either evaporated or absorbed by vegetation or soil and is dried down in the next day. So, the rain water in the wet season is the effective input that is to be retained in situ for availability in a longer dry period.

2.4  Agriculture The study area consists mainly of a lateritic upland tract that is extensively dissected and undulated and largely covered with Sal jungle, in which the best lands are found at the bottom of the depression between successive ridges. These low lands are highly valued for retaining moisture and also because the soil is enriched by the materials washed down from the slopes. Manure is in general used for preventing the exhaustion of the land. In case of double cropped land (locally known as Kala), after the “Aus” or autumn rice is harvested, pulse or oil seeds are cultivated in cold weather. This is continued year by year, but cannot be called a “rotation of crops” (O’Malley 1911). Irrigation is basically used for winter crops in addition to the canal, tanks, dammed-up streams and small natural water courses. Agriculture and related activity are the only source of income in this region. Agriculture is the provider of employment for 72% of the concerned area’s workforce in 2011 (Census of

2.3 Climate

41

Table 2.5  Rainfall distribution in wet and dry session Year 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Avg

Total rainfall (mm) 1558.8 1246.7 2376.3 1666.4 1476.8 2010.6 1337.1 1129.1 1612.4 2157.3 1197.6 1422 1741.9 1414.2 1086.1 1867.9 1541 1803.5 1654.9 1590.7 1806.9 2335.8 1594.4 1419.9 2272.7 1680.3 1690.2 1515.1 1707.1 1301.5 1944.5 1291.6 1615.8 1761.9 1533 1282.5 1624 1523.1 2227.7 2160.3 1486.3 1650.39

Source: IMD, Pune

Rainfall during rainy period (mm)(June–Oct) 1255.2 1047.1 1966.9 1554.3 1285.6 1804.9 1078.3 886.4 1294 1818.4 1050.3 1101.7 1159.7 990.7 773.2 1685.6 1379.7 1431.5 1327.8 1277.4 1511.6 1779.4 1264.4 1150.8 1943.1 1338.4 1272.6 1326.8 1414 890.3 1617.2 1047.5 1254.4 1399 1318.6 1127.7 1335.9 1337.1 1912.5 1876.7 1033.6 1349.2

% 80.5 84.0 82.8 93.3 87.1 89.8 80.6 78.5 80.3 84.3 87.7 77.5 66.6 70.1 71.2 90.2 89.5 79.4 80.2 80.3 83.7 76.2 79.3 81.0 85.5 79.7 75.3 87.6 82.8 68.4 83.2 81.1 77.6 79.4 86.0 87.9 82.3 87.8 85.9 86.9 69.5 81.4

Rainfall during dry period (mm)(Nov–May) 303.6 199.6 409.4 112.1 191.2 205.7 258.8 242.7 318.4 338.9 147.3 320.3 582.2 423.5 312.9 182.3 161.3 372 327.1 313.3 295.3 556.4 330 269.1 329.6 341.9 417.6 188.3 293.1 411.2 327.3 244.1 361.4 362.9 214.4 154.8 288.1 186 315.2 283.6 452.7 301.1

% 19.5 16.0 17.2 6.7 12.9 10.2 19.4 21.5 19.7 15.7 12.3 22.5 33.4 29.9 28.8 9.8 10.5 20.6 19.8 19.7 16.3 23.8 20.7 19.0 14.5 20.3 24.7 12.4 17.2 31.6 16.8 18.9 22.4 20.6 14.0 12.1 17.7 12.2 14.1 13.1 30.5 18.5

42

2  Study Area

India 2011). Agricultural production varies year to year, depending upon the success and failure of the monsoon.

2.5  Principal Crop • Rice The staple crop of the study area is rice (Plate 2.1), and the principal harvest (Table 2.6) is called “aman”. This is sown in the month of June, July and August and is reaped in November, December and January in the most highly cultivated parts. Seeds are first sown in nurseries and afterward transplanted into the moist field specially prepared for it, but the greater portion is sown through broadcast. Yield rates observed for aus, aman and boro are 1876, 2566 and 1921 kg/hec, respectively (District Statistical Handbook 2007). The “aus” or “autumn” rice is sown on dry land in the month of April, May and June and is reaped in August and September. “Boro” or summer rice is planted in November and is cut in March and April that requires irrigation (Moitra Maiti 2010). Local information said that the yield rate is

Plate 2.1  Paddy field of the study area Table 2.6  Harvest period of staple crop in study area Name of crop Aman Aus Boro

Sown June, July and august April, may and June November to December

Source: District Statistical Handbook 2006

Reaped Nov, December and January Aug and September March and April

2.5  Principal Crop

43

increased from 2602 to 3611 (kg/hec) over the years 2007 to 2017 due to the boring of submersible (Statistical Abstract 2008, 2018). • Other Cereal and Pulses Practically Peas, Birhi (Vigna mungo), mung the common lentil called masur, arahar (Cajanus cajan) and khesari (Lathyrus sativus) are the principal varieties of pulses grown in the study area. • Oil Seeds Mustard and rape, linseed, sesamum or til, and sargonja are the chief varieties of oil seeds. Different kinds of mustard and rape are grown in this district, viz. kajli and madhubani or rai. • Rotation of Crop Cultivators don’t follow any regular system of rotation of crops. In the case of double cropped land after the aus or autumn rice being harvested, a second crop of pulses or oil seed is cultivated in the cold weather.

2.6  Land Use and Land Cover Land use-land cover (LULC) classification of the study area was done from satellite imagery IRS P6 (Oct 2007) and Google Imagery from Google Earth, 2014. Multi-­ date data was checked on the ground to identify and delineate the boundaries of the cropland in the Kharif and Rabi seasons. Then agricultural land is further classified as a double cropped area, single cropped area, cultivated waste land and fallow land. It is observed that double cropped land area is mainly located near the river bank (Plate 2.2) where sufficient water is available for irrigation through wells and channels. Cultivated lands are divided into three classes  – high land, low land and “Diara” or river land. High land around the village site which is more or less sandy and is either above ordinary flood level or dries up in time to enables it to be sown with Rabi crops is called “Kala”. It is subdivided into “Bastu” land and “Dhosa”, i.e., land which during the rain is sown aus paddy and in the cold weather bears a second crop of pulses or oil seeds (O’Malley 1911). “Jala” is a low-lying land that separates the village sites from one another. This land is mostly clayey and is under water after rain. Rice is the stapple crop, but in rare cases, “Til” grows. “Diara” lands formed by river silt deposited on the beds and sides of river are the most sought after by the ryots, as the land is replenished every year after food by a deposit of silt partical; no manure is required. This land is most suitable for growing paddy,oil seeds and various vegetables. Maximum likelihood classifier is used with some known training locations in the watershed (Shee and Maiti 2012). Eleven LULC classes (Fig. 2.5) are identified as barren land (8.3%), industrial built-up land (0.08%) (Plate 2.3), agricultural land (32.7%) in which monocrop land is 21.3%, forest land 28.8%, orchard 2.9%, eucalyptus plantation 3.5% (Plate 2.4), water body 0.5%, road and network 2.2% and settlement 1.8%.

44

2  Study Area

Plate 2.2  Double crop land at river bank

Plate 2.3  Industrial built up land

• Vegetation The study area covers 23.7% of degraded and 5.15% of dense Sal forest (Plate 2.5). From a decade the Sal and other large trees are cutting without being replaced by young trees. Upper part of the study area is undulating and picturesque with large tracts, covers with extensive jungles. The soil is arid and scarcely anywhere deep; many tracts are unproductive and almost uninhabited staple. The forest plays an

2.6 Land Use and Land Cover

45

Plate 2.4  Eucalyptus plantations 35.00

Land cover in %

30.00 25.00 20.00 15.00 10.00 5.00 0.00

Fig. 2.5  LULC of the study area

important role on rural economy in the form of wood, fire wood, honey, lac, charcoal, wax, tunnin, etc. Apart from this mango, jackfruit, banana, Sal, seguin, Samuel, Palash, Jarul, eucalyptus, etc. are the example of the major trees. The casts which subsist by collecting and trading in jungle products are the Manjhis, Bhumijs, Santals, Kurmis and Lodhas (O’Malley 1911). There was a wide pasture ground (common pool resource); that was acquired for proposed project.

46

2  Study Area

Plate 2.5  Sal forest at Godapiasal

2.7  Soils In the alluvial tract a clay soil is known as “entel loam” as “doash” and “sandy loam” as “bele doash”. In the laterite tract, the soil (Plate 2.6) is mostly loam and sandy loam having the same name as in the alluvial portion, but its colour is reddish brown and inferior in fertility to the corresponding types of soil in the alluvial tract. The soil is mainly composed of sand (48%) and silt (28%). Water-holding capacity is poor, and as a consequence, rain water is easily drained, and land becomes dry immediately after rain. Textural composition is analysed from soil sample, collected during field survey of the study area and subsequent analysis by sieving. It is observed that high amount of sand exists in the river bed (first order). Cultivated land is composed of high coarse sand with a considerable amount of silt, where as waste land consists of granules and coarse sand. The soil in this part is poor, composed of lateritic and coarse sands, cemented together in a more or less coherent mass. Sometimes, it remains loose and gravelly, passing by various gradations into sandy clay with a few ferruginous nodules. For development, soil is an important factor. pH of the soil ranges between 4.4 and 6.2, while the electric conductivity is observed in the range of 80–732 μmhos/ cm. Other minerals like nitrogen (53.8–129.1 kg/ha), phosphorus (16.8–164.7 kg/ ha), potassium (132.5–539.2 kg/ha) and chlorides (50.5–278.9 mg/kg) are in sufficient category (JSW EIA 2007). A wide range of soil samples are collected for chemical test; it is found that nutrients are present in less to medium amount at upper catchment, and with addition of nutrients, the soil can give better productivity.

2.7 Soils

47

Plate 2.6  Lateritic exposure

2.8  Ecology In the study area, more than 50% of species is phanerophytes followed by therophytes (34%), hemicryptophytes (8%) and hydrophytes (4%). Botanical Survey of India and Forest department did not find any record of the presence of any critically threatened species (JSW EIA 2007).

2.9  Demographic Aspects The area consists of 111 mouzas among which 25 mouzas are uninhabited. As per census 2011, the study area includes 32,689 persons. The males and females constitute to about 50% and 49% of the population, respectively, in the study area. In this area, about 24.06% of the population belongs to SC and 16.27% to ST. As a whole, 40.31% of the population belongs to socially weaker sections, which is an important indicator for social status.

2.10  The Growth Rate of Population The decadal growth rate (Fig. 2.6) of the study area is registered during 1961–1971, 1971–1981, 1981–1991, 1991–2001 and 2001–2011 as 13.9%, 18.7%, 20.4%, 21.4% and 12.7%, respectively. The decadal growth rate is successively reduced

48

2  Study Area

25.00 20.00

in %

15.00 10.00 5.00 0.00

1961-71

1971-81

1981-91 Year

1991-01

2001-11

Fig. 2.6  Decadal population growth in study area between 1961 and 2011

Population Density in Study area 300

Person/ km²

250 200 150 100 50 0

1961

1971

1981

1991

2001

2011

Year Fig. 2.7  Showing the population density (P/km2) of Sundra catchment

during 2001–2011 due to “Maoist” attack, and a remarkable negative growth rate is recorded at Gakulpur, Tung Ni, Jorakusumi, Ashnabani, Dhansol, Pachakua, Amla Bani, Asta Kola, Pukhur Kona, Putigerya and Nutandihi mouza. A regional disparity is observed in population density. The density of population (Fig. 2.7) is varied between 104 and 274 person/km2 since 1961 to 2011. It implies

49

2.10  The Growth Rate of Population

that population pressure on natural resources is increasing at an alarming rate and may create many socio-economic problems in the study area.

2.11  Literacy Structure The economic and social backwardness of the study area is mainly responsible for low level of literacy rate, and that is why they are still now depending on natural resources such as collecting forest products and livestock farming without considering the negative effect of excess and unscientific exploitation for their livelihood. The rate of literacy is increased dramatically from 18.7% in 1961 to 67.1% in 2011 (Fig. 2.8). Female literacy rate also improved from 6.6% in 1961 to 59.9% in 2011. This is a good indicator for socio-economic development.

2.12  Work Participation and Income A majority population of the study area depends on land and forest, and most of these people are poor. It is seen (Table  2.7) that the total workers engaged in cultivation and agricultural labour were around 87% of all total main workers in

90 80 70

in %

60 50 40 30 20 10 0

1961

1971

1981 1991 2001 Year Female Male

Fig. 2.8  Showing the rate of literacy in the study area since 1961–2011

2011

1971 2039 1690 40 12 60 12 38 27 230 0 3828 7976

1981 2711 1466 66 0 0 0 0 0 694 170 4321 9428

1991 3848 1496 83 15 47 22 182 125 339 241 5413 11,811

2001 3398 1786 116 121 1705 0 0 0 157 1357 6860 15,500

2011 2476 2331 94 116 1761 0 0 0 0 3316 7334 17,428

Female 1961 1971 593 61 569 719 6 43 20 3 8 25 0 0 2 3 9 0 36 6 0 0 5242 6471 6485 7331 1981 476 960 34 0 0 0 0 0 17 360 7061 8908

1991 713 982 2 4 41 3 14 1 39 2264 7429 11,492

2001 377 1001 20 78 209 0 0 0 185 2888 9971 14,729

2011 266 990 28 31 359 0 0 0 0 3578 11,603 16,855

Total 1961 2924 1469 56 104 45 22 59 65 285 0 8179 13,208 1971 2100 2409 83 15 85 12 41 27 236 0 10,299 15,307

1981 3187 2426 100 0 0 0 0 0 711 530 11,382 18,336

1991 4561 2478 85 19 88 25 196 126 378 2505 12,842 23,303

2001 3775 2787 136 199 1914 0 0 0 342 4245 16,831 30,229

2011 2742 3321 122 147 2120 0 0 0 0 6894 18,937 34,283

Source: Census of India Note: I = cultivator; II = agricultural labor, III = livestock, forestry, plantation, etc. IV = household industry, V = manufacturing and other than household industry, VI = construction, VII = trade and commerce, VIII = transport, IX = other services, X = marginal workers, XI = non-workers

Male 1961 I 2331 II 900 III 50 IV 84 V 37 VI 22 VII 57 VIII 56 IX 249 X 0 XI 2937 Total 6723

Table 2.7  Showing the work force in a different category of the study area (1961–2011)

50 2  Study Area

2.12 Work Participation and Income

51

1961 and increased to 93% in 1991, but after land acquisition for the proposed JSW industrial project, it drops to 72% in 2011. When it comes to male workers, 85% of the main male workers were engaged in agriculture-related activities in 1961; the figure declined to 72% in 2011. However, female workers decline significantly to 73% in 2011 from 93% in 1961. After land acquisition in 2011, it is observed that a certain percentage of workers adjusted to non-farming sector. During this season, local people were engaged more in non-farming activities like construction, shop keeping, constructor, etc. under JSW project area. As a consequence, other service category is increased from 10% in 1991 to 26% in 2011. As per 2011 census records, the occupational structure indicates that the non-­ workers are the predominant population of the study area. So the problem of unemployment is the dominant economic barrier for development to the people of the study area. Rain-fed and single cropped agriculture is the main source of income that constitutes 50–60% of the total income. Other than agriculture, the major sources of employment and income for the people are livestock farming and collecting forest products like firewood, timber, mushroom, medicated leaf, wild animals, etc. (Plate 2.7). In household industry, leaf binding is the important commodities manufactured in this area. A slow pace of socioeconomic development and lack of infrastructure have largely accounted for majority of the inhabitants to depend on natural resources. But after the announcement of the proposed project in 2008, the local

Plate 2.7  Livelihoods of the study area 

52

2  Study Area

Plate 2.8  NH 60 passes through the study area 

people hoped for a mass employment that would lead to better livelihood, but frustration arose, when JSW authority put the project on hold.

2.13  Road and Traffic Study The whole study area is connected through 90.3  k.m. (85%) unmetal road and 16.4 km metal roads (15%) as in 2011. NH 60 (Plate 2.8) runs through the middle of the study area from north to south direction. A traffic study is conducted during 2011 to 2013 and observed that mainly motorbikes and cycles were modes of transport.

2.14  Proposed JSW Steel Plant JSW Steel signed a pact with West Bengal Government on January 11, 2007, to set up a 10 million-tonne steel plant at Salboni (The Hindu Business Line 2007). This would be the largest single investment in Bengal at this time. The plant would be established on a 4225.4-acre plot. This land was acquired from 700 peasants. The first phase of the steel project was going to invest an amount of INR: 150000 million for a 3-million-tonne steel plant including a 600 MW captive power plant (CPP), an independent 1000 MW power plant, development of coal mines and a berth in the Haldia port. In the next two phases, JSW authority planned to invest an amount of

2.14  Proposed JSW Steel Plant

53

INR 100,000 million to enhance steel production to 10 million tonnes. It will provide direct and indirect employment of 10,000 people. JSW promised for a comprehensive rehabilitation package (Chap. 3).

2.15  Major Findings • The study area is composed of lower Jurassic of the upper Gondwana system covered with quaternary sediments. • The area seems to consist of dissected topography ranging from 15 to 104 m with a mean elevation of 49.27 m. The high lands are commonly found as waste land and rocky outcrops with some lateritic patches. • High relief is the result of intensive dissection and also is responsible for intensive soil erosion. Both of these two lead ultimately to water scarcity and low productivity. • Upper part of the study area consists of maximum slope ranges from 7 to 8°, while the lower catchment ranges from 3 to 4°. • In the study area, most of the area is served by first-order streams. This first-order fingertip streams are mostly undefined and flow through shallow channels mostly filled with silt. • Average maximum monthly temperature ranges from 25.6 °C to 37.2 °C, whereas minimum monthly averages from 13.3 °C to 26.1 °C. Temperature sometimes falls below 5 °C and may also rise to 47 °C. Annual rainfall recorded since 1969 varies from 1086 mm to 2376 mm with an average of 1650.39 mm. • On average, 264.02 mm, 360.51 mm, 333.65 mm, 282.17 mm and 108.91 mm of rainfall are received in the months of June, July, August, September and October amounting to 81.75% of annual average. This seasonal concentration is mainly responsible for water scarcity as moisture retention capacity of the soil and surface is very low, and so no water is available in this prolonged dry period. • Concentrated rain in storms of few days’ duration is the main character of rain. This rain may amount to 630 mm (29.2%) concentrated within 8 days. Almost 16% of annual rain is received within a single storm of 8–10 days that may rise up to 30% of the annual total. • Continuous period for 283 days in the year of 1972 receives no rainfall, and this dry period ranges from 232 to 283 in a year. • The staple crop of the study area is rice. Yield rate, observed for aus, aman and boro is 1876, 2566 and 1921 kg/hec, respectively. • The study area covers barren land (8.3%), industrial built-up land (0.08%) and agricultural land (32.7%) in which monocrop land covers 21.3% of the area, forest land 28.85%, orchard 2.93%, eucalyptus plantation 3.57%, water body 0.51%, road and network 2.28% and settlement 1.8%. • pH of the soil ranges between 4.4 and 6.2, while the electric conductivity is observed in the range of 80–732 μmhos/cm. • The total population of the study area was 12,623 in 1961 which increased by nearly three times within 40 years and became 32,689 in 2011. The SC (24.2%) and ST (15.4%) account more than 39.73% of the total population.

54

2  Study Area

• The literacy rate is 67.08% in 2011 that was 18.71% in 1961. • Total workers engaged in cultivation and agricultural labour were around 87% of all total main workers in 1961, which increased to 93% in 1991, but after land acquisition for the proposed JSW industrial project, it fell substantially to 72% in 2011. • JSW Steel signed a pact with West Bengal Government on January 11, 2007, to set up a 10 million-tonne steel plant. The plant will be established on a 4225.4-­ acre plot.

2.16  Conclusion Study area stretches interfluve between river Tamal and Parang is characterized with undulating and sloppy land with high drainage and soil erosion potentiality. Water scarcity, infertile soil and sloppy land together hinder the development of this region, and so economically and socially marginalized people are mainly living here. In this backdrop, this region witnessed the proposal for setting up the largest integrated steel plant of India by JSW Group of industries in the year 2007. The present work deals with collecting and analysing basement data along with possible impacts from this industrialization in such an underdeveloped region.

References Arnett, R.R., 1971. Slope form and geomorphological process an Australian example. Institute of British Geographers. Vol-3, pp. 81–92. Ascoli, F.D., 1910. The rivers of the delta. J. Asiat. Soc. Bengal, NS, 6, pp.543–556. Bagchi, K., 1944. The Ganges delta: Calcutta. Calcutta Univ. Press, pp. 157. Ball, F., 1877. Geology of the Rajmahal Hills. Geol. Survey India Mem, Vol-13, pp. 1–94. Barbour, G.  B., 1935. Correlation by fluviatile terraces; physiographic history of the Yangtze. Geol. Survey China Mem., A(14), pp. 112. Bloom, A.L., 1998. Geomorphology: a systematic analysis of late Cenozoic landforms. Prentice Hall, pp. 498. Carter, C.S. and Chorley, R.J., 1961. Early slope development in an expanding stream system. Geological Magazine, 98(02), pp.117–130. Census of India., 2011. India General Population Tables, Controller of Publication, Delhi. Cluff, C.B., 1974. Engineering aspects of water harvesting research at the University of Arizona. Proc. Water Harv. Symp. Phoenix, Arizona, pp. 27–29. Colebrooke, M. R. H., 1803. On the course of the Ganges through Bengal. Asiatic Researches, Vol-7, pp. 1–32. Dazzi, R. Gatto, G. Moozi, G. and Zambon, G., 1991. Ground Elevation of Venice and its evolution, In Singh, B. and Saxena , N.C., (Ed) Land Subsidence. Oxford & IBH Pub. Co. Pvt. Ltd, New Delhi, pp. 271–282. Deb, S.C., 1956. Paleoclimatology and Geophysics of Ganga Delta. Geographical Review of India, Vol 28, pp. 11–18. DeTerra, H., 1943. The Pleistocene of Burma. Am. Philos. Soc. Trans., Vol-33, pp. 271–339.

References

55

Directory of District., 2008. Sub division, Panchayat Samiti/Block and Gram Panchayats in West Bengal. West Bengal, National Informatics Centre, India. 2008-03-19. Retrieved 2008-12-06. District Census 2011. Census.co.in. 2011. Retrieved 2011-09-30. District Statistical Handbook., 2006. Bureau of applied Economics and statistics, Paschim Medinipur. Govt. of W.B, pp. 1–91. District Statistical Handbook., 2007. Bureau of applied Economics and statistics, Paschim Medinipur. Govt. of W.B, pp. 1–91. Horton, R.E., 1945. Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Geological society of America bulletin, 56(3), pp.275–370. Howard, A.D., 1997. Badland morphology and evolution: Interpretation using a simulation model. Earth Surface Processes and Landforms, 22(3), pp.211–227. Hunter, W.W., 1876. Statistical Account of Medinipur, Trubner & Co., London; reprint (1997) West Bengal District Gazetteers. Govt. of W.B, Calcutta, pp. 1–295. JSW, EIA., 2007. Rapid environmental impact assessment for the proposed 3.0 mtpa integrated steel plant at Godapiasal, Paschim Medinipur district, West Bengal. Vitma Labs Ltd, 142, IDA, Hyderbad. Kirkby, M. J., 1971. Hillslope process-response models based on the continuity equation. Institute of British Geographers Special Publication (3), pp. 15–30. Ministry of Panchayati Raj., 2020. A Note on the Backward Regions Grant Fund Programme. National Institute of Rural Development, https://www.panchayat.gov.in/documents/20126/0/ PR+8th+Issue+English-­4.pdf/76948cd5-­ed2e-­3240-­9d06-­af078d8027c9?t=1609135293883 Retrieved September 27, 2020. Moitra Maiti, M., 2010. Cognition of the interworking of process associated to water scarcity and feasibility of water harvesting- An action research on a representative drainage basin on Silaboti Kansabati interfluve, West Bengal. Indian council of Social Science Research, F. noRP/02/121/2009, pp. 1–180. Morgan, J.P. and McIntire, W.G., 1959. Quaternary geology of the Bengal basin, East Pakistan and India. Geological Society of America Bulletin, 70(3), pp.319–342. Morisawa, M., 1985. Rivers, Forms and Processes, Longman, pp. 209. O’Malley, L.S.S., 1911. Bengal District Gazetteers: Medinipur. Bengal Secretariat Book Depot. Pasquill, F., 1961. The estimation of the dispersion of windborne material. Meteorol. Mag, 90(1063), pp.33–49. Russell, R.J., 1940. Quaternary history of Louisiana. Geological Society of America Bulletin, 51(8), pp.1199–1233. Saxena, N.C.  Kumar, B.  Tiwary, S.  Loveson, V.J. and Samanta, S., 1995. Impacts of underground Coal Mining at Jhanjra Project in Ranijang Coalfield- A forecast. Minetech, Vol-15(1), pp. 44–51. Schumm, S.A., 1956. Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Geological society of America bulletin, 67(5), pp.597–646. Shee, S.  P. and Maiti, R.  K., 2012. Assessing the necessity of watershed management at Sundra Basin, Paschim Medinipur, West Bengal. Journal of Indian Geomorphology, Vol-1, pp. 107–113. Statistical Abstract,. 2008. Bureau of applied economics & statistics. Govt. of W.B, pp. 1–772. Statistical Abstract,. 2018. Bureau of applied economics & statistics. Govt. of W.B, pp. 1–760. The Hindu Business Line., 2007. JSW Steel inks pact with West Bengal Govt. Retrieved 2007-01-11. The Telegraph., 2006. Jindal seal on steel project -PM brings second dose of industry hope. Retrieved 2008-12-23. Vredenburg, E., 1908. The geological history of the alluvial plain of Bengal: Hemchandra Memorial Series. Part I, pp.24–59. West, W.D., 1949. Geological map of India: India Geol. Survey Records, Vol-81, pp.1–222. Willcocks, W., 1930. Lectures on the ancient system of irrigation in Bengal and its application to modern problems. University of Calcutta.

Chapter 3

Industrial Project

Abstract  JSW Bengal Steel plant was  going to set up an integrated steel plant (Phase I) along with 300 MW (mega watt) Captive Power Plant (CPP) and a cement factory at Godapiasal of Salboni, Paschim Medinipur, West Bengal. This chapter is focused on the nature, size and location as per master plan of the JSW Bengal Steel project. The study is also included on what would be the expected benefit of the study area through compensation package, community service, transportation, employment opportunities, education and medical facility, Modern Township, community park, etc. Keyword  Godapiasal · Salboni · Paschim Medinipur · JSW Bengal Steel plant · mtpa · Captive Power Plant (CPP) · Compensation package · Master plan · Employment opportunities · Modern Township

3.1  Introduction Steel is a cornerstone and key driver for the world’s economy (Alagh et al. 1971; Bade 1983; Farla and Blok 2001). More than two million people worldwide are involved in this industry directly; additionally, 2 million contractors and 4 million people are supported indirectly (Ahluwalia 2011; Barad 2010; World Steel Association 2014). Chinese discovered the iron in and during the Han dynasty in 202 BC until 220 AD. With the change of time and technology, people were able to find even stronger and harder materials than iron, that is, steel (Alexander 1958; Alonso et al. 1964; Breyer 1990; Firoz 2007). India’s steel industry is expanding quickly in the world due to its rapid growth (Bhaktavatsalam and Choudhury 1995; Mineral Processing Pvt. Ltd. 2011). The private sector holds the key to growth in the steel industry. Technological changes and modernization are taking place in both the public and private sector integrated steel plants in India (Gielen and van Dril 1999; Gielen and Moriguchi 2001; Rohini 2004; Radhakrishna 2007; Worker 1998). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. P. Shee, R. Maiti, Land Acquisition, Industrialization and Livelihoods, https://doi.org/10.1007/978-3-030-90244-5_3

57

58

3  Industrial Project

The per capita consumption of steel in India is low with global respect (Jindal Steel and Power Ltd. 2014; Mehrotra and Gandhi 2012). In 2019, India produced approximately 111 million tonnes (MT) of crude steel with an average growth rate of 1.8% (corresponding period last year) and became the world’s second largest producer of crude steel. Our country is also the largest producer of approx. 36.7 mtpa sponge iron with an average growth rate of 7.7% in 2019. In domestic crude steel production is expanded from 109.8  mtpa in 2014–2015 to 142.2  mtpa in 2018–2019 (Ministry of Steel 2020–2021). A multi-pronged strategy is formulated at the national level to achieve the goal. Rural as well as urban development in sustainable context is a complex process that requires a balance between human capital, economic progress and natural resources. In this development, demography, economy, administrative and cultural relation plays an important role (Daniëls and Moll 1998; Ghiurca et al. 2012). In India half of the total population are still now employed in agriculture-related activities, and they contribute 15.4% of the GDP, while industry contributes 25%; 22% are employed of the total workforce for the financial year of 2017 (Government of India, Planning Commission 2012–2017; Indian Economy 2013). Up to now, this source is the largest employment source and a significant piece of the overall socio-­ economic development in India (Chandy and Kharas 2014; GINI index World Bank 2009–2012; Government of India National Steel Policy 2005; Kumar 2013; Ministry of Steel 2005–2006 to 2012–2013). The demand for steel is increasing rapidly beyond expectations in the last decade through rapid infrastructure development and a quantum increase in rural steel consumption (Boventer 1961; Bhat 1972, 1978; Zervas et al. 1996). In terms of supply and demand over the world, the steel industry is enjoying its sixth consecutive year of growth. Asian countries like China, Japan, India and South Korea have contributed more than 50% of global production (Krishna Iron Steel and Power Pvt. Ltd. 2010; World Steel Association 2014). The consumption of steel is taken to be an indicator of economic development (Indian Economy 2013). Planning is necessary in terms of both long- and short-term strategies to improve the productivity of a country and the level of per capita consumption (Bisio 1993; Bhushan Power and Steel Ltd. 2013; Crompton 2000). The strategy may include setting up new steel plant facilities or expansion of existing steel plants. In this backdrop, M/S JSW Bengal Steel Ltd. of Jindal Group started to establish a steel plant in West Bengal.

3.2  Identification of Project and Project Proponent M/S JSW Bengal Steel Ltd. of Jindal group, part of O.P. Jindal, is one of the leading steel producers in India with a significant presence in sector like stainless steel, carbon steel, large diameter pipes, non-ferrous metals and mining. The JSW group also operates power generation and distribution and maritime infrastructure (Jindal Steel and Power Ltd. 2014; Sudalaimuthu and Raj 2009). JSW Steel Ltd. operates a steel plant at Vijayanagara district and Bellary in Karnataka state. The group also owns and operates southern Iron & Steel Company Ltd. which is the only integrated

3.3 Nature, Size and Location of the Project

59

steel plant in Tamil Nadu, and also cold rolling, galvanizing and colour coating plants at Tarapur and Vasind in Maharashtra (JSW EIA 2007). West Bengal is going to a big league of steel producing states like neighbouring Jharkhand and Orissa, under the auspicious of Sajjal Jindal, vice chairman of JSW group. The JSW Steel Ltd. is now India’s third largest steel producer in the private sector and is not only involved in steel manufacturing but is also involved in other core sectors of the economy as power generation, infrastructure building and cement manufacturing (Jindal Steel and Power Ltd. 2014). The proposed steel project of West Bengal would entail an investment of INR 7500 million in three phases to achieve a milestone of 10.0 million tonnes of steel by 2020. “We would produce a 3.0 million tonnes of steel, a 300 MW Captive Power Plant (CPP) and a cement factory from the Bengal unit, first stage capacity will be on stream by 2011-12, when it goes on stream, followed by 6.0 million tonnes by 2015” said Sajjal Jindal. He also added “The first stage is always very time consuming and frustrates the people. So we are starting the project palletizing plant, coke oven plant and power plant and building the water pipeline and township. There are the things needed to create basic infrastructure at an investment of around INR 98000 to 100000 million. We have acquired land and will build a township, create power plant and water system, railway connectivity road networks etc.” Unfortunately, West Bengal does not have any iron ore mines, so the proposed company has tied up with various mining companies in neighbouring Jharkhand and Orissa for its iron ore supply. But for its coal supply, the Jindal group has formed a joint venture with West Bengal Mineral Development Corporation (WBMDC). “Although with them we are going to explore two coal blocks at Asansol in Burdwan district, which have very high potential of coking coal deposits. We are in tandem with some of the best mining technology providers in the world to mine the deep coal reserved” said Jindal. “The company has a plan to develop a seaport in West Bengal, near Digha (Purba Medinipur district), that would cater to both its Salboni and Jharkhand project. But the site is not safe due to cyclonic distribution and high silting; hence, it is abandoned. Now a site in Chittania of Orissa at the mouth of Subarnarekha River and only 250 km from Salboni is identified to develop a port. It would be utilized both to import coal and iron to the steel plants and export finished steel from the respective units” (The Economic Times 2014). The JSW Bengal project faced no protest over land acquisition after they offered farmer jobs and shares in its unit JSW Bengal Steel Ltd. “We are against forcible land acquisition from farmers. We started work with the support of the villagers” said Jindal (The Economic Times 2014).

3.3  Nature, Size and Location of the Project The proposed project would have metallurgical operations involving iron and steel manufacturing, using the blast furnace/basic oxygen furnace. Iron ore, coke, coal, limestone, dolomite, quartzite and manganese ore are the main raw materials required for the project. About 4225 acres (1709.79  ha) of land will be acquired

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3  Industrial Project

Fig. 3.1  Provisional master plan of proposed JSW Bengal Steel project (Source: BLRO, Salboni)

under the colonial Land Acquisition Act, 1894 (JSW EIA 2007). This is detailed in Chap. 4. The proposed project site is located at Salboni block in Paschim Medinipur, West Bengal. The project site is about 15 km west of Medinipur town. The site is by the side of the national highway (NH-60). Godapiasal and Salboni are two nearby railway station of Medinipur-Adra SE (South Eastern) Railway division. Few patches of forest are located surrounding the site. No other ecologically sensitive area, i.e. national park, wildlife sanctuary, biosphere reserve forest, wetlands, etc., is present within 10 km radius of the proposed plant. No critically polluted area as per CEPI (Comprehensive Environmental Pollution Index) is present within a 10 km area of the proposed site. A provisional master plan layout of JSW Bengal Steel plant Ltd. is shown in Fig. 3.1.

3.4  Brief Description of the Project The proposed steel plant will start with a capacity of 3.0 mtpa, and it will expand to a capacity of 10.00 mtpa phase wise in the future. A brief description of the proposed JSW Bengal integrated steel at Salboni block is projected at Table 3.1.

3.4 Brief Description of the Project

61

Table 3.1  Brief description of the JSW Bengal Steel project plant at Salboni Sl no Particulars

Details

1

Name of project Project area (Mouzas)

M/S JSW Bengal Steel Ltd.

3

Latitude

22°32′34″ to 22°35′39”N

4

Longitude

5

Elevation above sea level Due date for phase I production Present land use break-up

87°15′23″ to 87°19′21″E 60 m

2

6

7.

Arabari, Ashnasuli, Banskopna, Barju, Bhalukchati, Chakbhani, Chatibanth, Dubrajpur, Gaighata, Ghagrasol, Hatimari, Jambedya, Khairisol, Kharkasuli, Kulpheni, Masru, Naranchak, Netai Pur, Nutandihi, Nutonbankati, Ramraidi, Shalika

2012

SL No

Name of mouza

Total area of mouza in acres

Project area

Vested area in project in acres

Private land in the project in acres

1

DUBRAJPUR

637.10

637.10

609.40

27.7

2

MASRU

253.64

139.44

139.44

0.0

3

NUTANDIHI

176.11

70.00

59.97

10.0

4

NITAIPUR

298.29

249.70

249.70

0.0

5

BANSHKOPNA

709.66

609.96

548.01

62.0

6

CHATIBANTH

228.48

208.06

206.20

1.9

7

RAMRAIDI

277.94

156.32

140.44

15.9

8

ASNASULI

261.43

127.99

15.81

112.2

9

NUTANBANKATI

26.95

26.95

26.95

0.0

10

CHAKBHANI

80.88

80.88

80.88

0.0

11

NARANCHAK

99.47

99.47

99.47

0.0

12

JAMBEDYA

275.59

116.94

116.16

0.8

13

BHALUKCHATI

425.85

425.86

425.86

0.0

14

KULPHENI

333.02

136.82

95.72

41.1

15

GAIGHATA

211.97

21.08

21.08

0.0

16

HATIMARI

151.78

11.13

5.09

6.0

17

SHALIKA

252.52

69.48

68.86

0.6

18

KHARKASULI

390.34

315.64

309.46

6.2

19

ARABARI

478.62

433.62

406.46

27.2

20

KHAIRISOL

248.05

200.45

157.92

42.5

21

BARJU

244.31

63.83

57.76

6.1

22

GHAGRASOL

284.01

24.72

24.72

0.0

4225.43

3865.36

360.20

Total

A total of 360.20 acres of private land has already been purchased, and government land has been forwarded by DM to government of W.B.

(continued)

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3  Industrial Project

Table 3.1 (continued) Sl no Particulars 8

9 10

11 12

13

14 15 16 17 18

Nature of product annual production

Details SL No 1. 2. 3. 4. 5.

Products

Quantity (Mtpa)

Pellets Hot Metal Liquid steel Direct Reduced Iron (DRI) Slab for sale

3.00 3.05 3.10 0.35 3.00

Cost of the project Land requirement

INR: 100000 million

Water requirement Manpower requirement

The total water requirement for steel plant for phase I will be about 3800 m3/h. including water requirement of 950 m3/h. for CPP from Rupnarayan river

Power requirement and source Nearest NH Nearest railway Nearest town Nearest air port Water body

300 MW from CPP and 25 MW during construction period from Gaighata sub-station

Sl no

Plant facilities

1 2 3 4 5

Area required for 3.0 Mtpa steel plant and CPP Area required for future expansion Area for raw materials storage Green belt development Township, air strip etc. Total

Sl no 1 2 3 4 5 6

Man power category Manager Executive Skilled or technical persons Semi-skilled persons Un-skilled persons Administrative staff total

Area (acre) 1750 565 250 1360 300 4225

(%) 41 13 6 32 7 100

No of persons 150 300 600 1000 800 150 3000

NH-60, 1.2 km east Godapiasal, Salboni, 2.1 km, south Medinipur, 14 km, south Kolkata, 130 km, east Sundra, the tributary of river Tamal, catchment area 85 km2, length 20 km

Source: Shee and Maiti 2019; Dept of BLRO

3.5  Expected Benefit in the Study Area The company is committed to contributing funds and providing services for upliftment of the local community. Once the activity is started, commissioned the socio-­ economic status, employment opportunities, communications and other infrastructure facilities will be improved. The West Bengal Government linked a landmark agreement with the Jindal group to set up the steel plant with an expectation to generate 10,000 indirect secondary and tertiary employments. The people residing in the nearby areas will be benefited directly and indirectly as well. It is anticipated that the proposed steel plant will provide benefits for the locals in two phases, i.e. during the construction phase and during the operation phase of the steel plant, after implementation of project work.

3.5 Expected Benefit in the Study Area

63

3.5.1  Compensation Package for Land Acquisition Under the provisions of the 1894 Land Acquisition Bill, compensation is to be based on the market value of the land at the time of acquisition. On the basis of this principle, the state Government approved a set of market-based rates for different categories of land (Table  3.2). The official definition of “Dhani Caharam” is a monocropped land; “Dhani Soem” and “Dhani Doem” indicate the double-cropped land, and “Dahi Puratan Patit” indicates arable land. Some magnitude of the discrepancy between official documents and household survey reports of compensations offered is striking. It is observed that all types of landowners reported compensations offered are larger (INR 0.3 million/acres for all types of land) than what the Government records. Perhaps this is an additional adjustment over the announced rates made to compensate any construction, trees, water body, etc. falling within the proposed project site. However, from the source of BLRO, JSW Bengal Steel Ltd. will provide the following compensation package for the farmers for losing their land: • The compensation amount would be credited within 1  month from date of acquisition. • It is recommended that a certain amount be put in a fixed deposit at the nearby nationalized bank. This plan ensures an assured monthly payment at the rate of about 7–8% lifetime interest. • After starting the production, the company will issue the share equivalent to the cost of land as a free gift to each land-lost family. • An employment opportunity will be given to one person from each land-lost family as per their educational qualification.

3.5.2  Construction Phase • Employment The major benefit will come during the construction of the main unit and CPP (Captive Power Plant). It is estimated to create a demand of about 6000 per day labour force that will last to a span of over 36 months from start of project execution activities at the site.

Table 3.2  Summary of land rates according to official land acquisition records Land use DHANI CAHARAM DHANI SOME DHANI DOME DHANI AWAN Source: BLRO, 2008

Land rate (INR) 225500.00 225500.00 225500.00 225500.00

Land use KALA AWAN KALA DOEM DAHI PURATAN PATIT

Land rate (INR) 225500.00 225500.00 203000.00

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3  Industrial Project

In addition, the local population would also have employment opportunities in related service activities like supply of construction materials, small contracts/sub-­ contracts, canteen and other infrastructure ancillary facilities, etc. Consequently, this will contribute to the economic empowerment of the area. • Community Service JSW Bengal Steel Ltd. will work in every aspect of development of necessary infrastructures like water supply, sewerage, medical facility, etc. to cater to the needs of project personnel and their families which will be also be beneficial to the locals residing in the area. • Transportation A huge amount of raw materials includes earth work, concrete, steel equipment and other materials which will be needed during the construction phase of the proposed project. Transport of construction materials to the project site will result in increased traffic in the area. JSW Bengal Steel Ltd. proposed to develop a four-lane road with flyover connection from NH-60 to the plant site. As an implication, a local market will be developed along the roadside that will make huge employment opportunities to the local people. As for present conditions, a local market already started to develop at the main entry point (Plate 3.1) of the project site, and more than 50 families will be directly benefited.

Plate 3.1  Local market at entry point of JSW Bengal steel plant (2012)

3.5 Expected Benefit in the Study Area

65

3.5.3  Operation Phase • Employment During the operation phase, they proposed about 3000 people will be employed directly in different posts (Table 3.1). Considering a household size of five persons, there is a likelihood of increases to about 15,000 persons will be getting benefitted. Preference will be given to skilled land losers, one person per family basis. A large mass of the population will be added to the study area that will make a better scope for indirect employment. • Education The company will  operate a nursery school at Masru as a part of their social development and a vocational training centre for improving the communication skill for local persons. JSW Bengal Steel Ltd. also proposed to develop a school in their housing, in view of the increased family population due to the proposed employment. These initiatives will be beneficial to locals residing in the study area. JSW Bengal Steel Ltd. would supply uniforms, school shoes, raincoats and umbrellas free of cost to the children of local primary and secondary schools. The company would also provide equipment like computers, UPS, printers and furniture with the help of Azim Premji Foundation (APF) for computer-based learning and would refurbish school buildings, classrooms, libraries, playgrounds, etc. in the vicinity of the proposed project site on a need basis. • Supporting Sporting Activities School sports equipment would be provided for children. The company would also sponsor school-level sports meets and sponsor students to attend state-level competitions, etc. • Vocation Training Program The authority organized a vocational training course for 6 months for land-losers who have completed HS (higher secondary, class XII)-level educations but are unemployed. Already  158 persons from 700 land-acquired families attended the course. After completion, 12 persons were already selected for second phase or final training at Karnataka JSW project site. The funding and liaison for this program will sponsore by JSW foundation with collaboration of Shramsadhana Vocational Training Centre (SVTC), Vasind. • Skill Enhancement Program A training school also would organize training for boys and girls on cycle/ scooter/motorcycle repairing, sewing and knitting. Women and girls would be given training on housekeeping activities, and later, they could be absorbed by the company. Special focus would be given to handicapped people in training them with tailor-made skills so as to make them employable.

66

3  Industrial Project

• Transportation Major raw materials like iron ore including dolomite, limestone, coal quartzite and manganese ore will be transported by rail to the plant site. However, there will also be an increase in vehicular traffic due to passenger/material transport. It may be expected to have nearly 200 trucks, about 100 cars and about 1000 two or three wheelers per day (JSW EIA 2007). As a consequence, one should expect to start new and frequent transport services in this area and construction of the new metal road (Plate 3.2) that will ultimately uplift the whole area. The company would construct different connecting roads in between the plant and nearby villages which will be affected by the erection of the boundary wall of JSW Bengal. On an urgent basis, temporary roads would be constructed connecting the affected villages; later on, these roads would be made in metal. • Medical Facility With the help of Salboni Rural Hospital staff, local nurses and JSW foundation, a mobile health unit project was launched for the local people, surrounding the project area. The team members conducted a routine health check-up and undertake tests to diagnose cataract or influenza, assess cardiac condition of patients and test for tuberculosis and gynaecological diseases. Health unit members worked well for several patients who droped by the nearby medical centre.

Plate 3.2  New metal road along the JSW project boundary under construction (2018)

3.5 Expected Benefit in the Study Area

67

• Supply of Drinking Water Drinking water unavailability is a perennial problem in this region. JSW Bengal Steel Ltd. will take the responsibility to supply drinking water through water tankers especially in summer month for a temporary period until the steel plant becomes functional. With the help of Salboni Gram Panchayat, under the aegis of UNDP (United Nation Development Program), a water budgeting system was followed to conserve and make efficient use of water; however, the water problem still persists due to the aridness of the locality.

3.5.4  Long-Term Goals The key to success of a company depends on due respect from the local people. The long-term goals are to: • Improve rainwater harvesting facility with the help of IIT Kharagpur faculty members that would supply water to its plant and also to the local villages. • Install a hospital (under construction) (Plate 3.3) in line with Jindal Sanjeevani Hospital, Bellary, Karnataka, to provide modern medical facilities such as ICU (intensive care unit), burns unit, computerized ECG and pathology lab, ultrasound, sonography, radiography and fluoroscopy.

Plate 3.3  Hospital under JSW plant under construction (2012)

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• A modern township will be set up here and would follow the model of Jindal Vijayanagar Steel Township, which has won the Prime Minister’s award for Design and Urban Planning. • The company will develop a community park working hand in hand with the forest department to preserve the flora and fauna of Salboni forest area. • Starting up an art academy to nurture, promote and support the local and tribal art and culture.

3.6  Major Findings • M/S JSW Bengal Steel Ltd. is going to install a proposed 3.0 mtpa integrated steel plant (phase I) and subsequently convert to 10.0  mtpa in the future at Salboni block of Paschim Medinipur. A 300  MW Captive Power Plant and a cement factory will also be installed at initial stage. • No ecological sensitive area, i.e. national park, wildlife sanctuary, biosphere reserve forest, wetlands, etc., is present within 10  km radius of the proposed plant. • Cost estimated for phase I is INR 75040 million. • A township, water system, railway connectivity, road networks, education and hospital facility will be developed here. • Primarily direct and indirect employment of more than 15,000 persons will be created. • 91.5% of the project area falls in “vested” land, and only 9.5% is “rarity” land. The purchase rate is at an average cost of INR 0.3 million/acres including in terms of an employment opportunity to one person from each land-loser family in JSW Bengal Steel plant or in the associated companies of JSW. • At the initial stage, the cost of land would be given directly to the farmers through cheque. The company promised to give the shares equivalent to the cost of land as a gift to each family, once production has started. • In addition to the opportunity of getting employment as construction labourers (5000/day), the local population will be benefitted for employment opportunities in related service activities like petty commercial establishments, small contracts/sub-contracts and supply construction materials for buildings and ancillary infrastructure, etc. • The contractors will provide firewood/kerosene/LPG to the workers to prevent damage to trees. • Dropout students would be given opportunity for training on cycle/scooter/ motorcycle repairing or sewing. Women and girls would be given training on housekeeping activities and then be employed at JSW Bengal plant. • JSW would supply water to its plant and also to local villagers through rainwater harvesting.

References

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3.7  Conclusion The economy of the study area is initially based on subsistence type of agriculture, livestock farming and social forestry. In 2011, 72% of total working population depends on agriculture that was 93% in 1991. The physical parameters like topography has a little undulation and soil moisture does not retain long time that makes the region dry in non-monsoon season. This resource scarcity is responsible for under-­developed agriculture. In 2008, the region was heading towards being a pioneer industrial region of the state after the announcement from JSW authority to set up a 10 mtpa integrated steel plant which will bring opportunities in socio-economic development through infrastructural facilities, industrial positions, resource utilization, increasing urbanization, labour potential and many other aspects of development. JSW authority already started free mobile medical check-up throughout 22 project-affected mouzas; developed free nursery cum play school at Mashru; distributed free-of-cost drinking water supply to surrounding mouzas; developed road and network, green belt and housing complex; and started vocational training courses for land-acquired families, a requirement of night guard securities, local observers, etc. But complexity is observed due to unanticipated delay of project work. Local people are now absorbing only the adverse impact of industrialization. After land acquisition, they lost their land, profession, income and also hope. Most of the money they got as compensation was spent for unproductive purposes. On the other hand, adverse impacts of land use conversion and landscape change on hydrological setup is  already started. A management plan is necessary to reduce the adverse impacts along with support from welfare organizations from both government and non-government agencies.

References Ahluwalia, M.S., 2011. Prospects and policy challenges in the twelfth plan. Economic and Political Weekly, 46(21), pp.88–105. Alagh, Y.K., Subrahmanian, K.K. and Kashyap, S.P., 1971. Regional industrial diversification in India. Economic and Political Weekly, pp.795–802. Alexander, J.W., 1958. Location of manufacturing: methods of measurement. Annals of the Association of American Geographers, 48(1), pp.20–26. Alonso, J.L., Cortés, J.L., Lesmes, F., Boucaud, P. and Rivas, E., 1964. Location Theory, in. In Regional development and planning. Bade, F.J., 1983. Locational behaviour and the mobility of firms in West Germany. Urban Studies, 20(3), pp.279–297. Barad, M.M., 2010. A study of Liquidity management of Indian Steel Industry (Doctoral dissertation, Saurashtra University). Bhaktavatsalam, A.K. and Choudhury, R., 1995. Specific energy consumption in the steel industry. Energy, 20(12), pp.1247–1250. Bhat, L.S., 1972. Regional Planning in India. Calcutta: Statistical Pub. Society. Bhat, L.S., 1978. Indicators of Regional Development. Asian Economic and Social Review, Vol. III, pp. 2–10.

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Bhushan Power & Steel Ltd., 2013. EIA study for proposed integrated steel plant of 3.0 MTPA & 300 CPP at village Pokta, District East Singhbhum (Jharkhand). Retrieved on 2014-09-12, http://environmentclearance.nic.in/onlineSearch.aspx Bisio, G., 1993. Exergy method for efficient energy resource use in the steel industry. Energy, 18(9), pp.971–985. Boventer, E., 1961. The relationship between transportation costs and location rent in transportation problems. Journal of Regional Science, 3(2), pp.27–40. Breyer, S.G., 1990. Regulation and deregulation in the United States: airlines, telecommunications and antitrust. Pinter Publishers. Chandy, L. and Kharas, H., 2014. What do new price data mean for the goal of ending extreme poverty. Brookings, May, 5. Crompton, P., 2000. Future trends in Japanese steel consumption. Resources Policy, 26(2), pp.103–114. Daniëls, B.W. and Moll, H.C., 1998. The base metal industry: Technological descriptions of processes and production routes; status quo and prospects. Center for Energy and Environmental Studies. Farla, J.C. and Blok, K., 2001. The quality of energy intensity indicators for international comparison in the iron and steel industry. Energy Policy, 29(7), pp.523–543. Firoz, A.S., 2007. Indian Steel: Critical Details. Evolving Structure and Strategic Options, Steel Business Briefings, UK. Ghiurca, A., Lamasanu, A. and Mihai, F.C., 2012. Rural-urban relations in the context of sustainable development. Case Study: Cuejdiu Valley Basin, Neamt County. Case Study: Cuejdiu Valley Basin, Neamt County, pp.327–330. Gielen, D. and van Dril, T., 1999, March. CO2 reduction strategies in the basic metals industry: a systems approach. In EPD Congress Proceedings, San Diego, USA, February. Gielen, D.J. and Moriguchi, Y., 2001. Environmental Strategy design for the Japanese Iron and Steel industry, a global perspective. Working document, NIES, Tsukuba GINI index World Bank., (2009–2012) Report on Employment and Unemployment Survey (2012–13). Bureau of Labour Statistics, Indian Government. Retrieved 2014-07-25. Government of India., 2005. Ministry of Steel, National steel Policy, www.steel.nic.in. Retrieved 2014-12-11. Government of India, Ministry of Steel, Annual Reports 2005–06. Government of India, Ministry of Steel, Annual Reports 2007–08. Government of India, Ministry of Steel, Annual Reports 2009–10. Government of India, Ministry of Steel, Annual Reports 2010–11. Government of India, Ministry of Steel, Annual Reports 2011–12. Government of India, Ministry of Steel, Annual Reports 2012–13. Government of India, Ministry of Steel, Annual Reports 2020–21 Government of India, Planning Commission, 12th five years plan (2012–17) Vol I and II, Retrieved 2014-12-05. SAGE Publications India Pvt Ltd, New Delhi 110 044. Jindal Steel and Power Ltd., 2014. EIA report on Greenfield Integrated Steel Plant (6MTPA) at Asnabani, Block: Potka, Jamshedpur District: East Singhbhum (Jharkhand). Retrieved on 2014-11-19 http://environmentclearance.nic.in/Search.aspx, JSW, EIA., 2007. Rapid environmental impact assessment for the proposed 3.0 mtpa integrated steel plant at Godapiasal, Paschim Medinipur district, West Bengal. Vitma Labs Ltd, 142, IDA, Hyderbad. Krishna Iron Steel & Power Pvt. Ltd., 2010. EIA report on Integrated Steel Plant at Village: Kesda, Tehsil: Simga, District: Balodabazar, Chhattisgarh. Retrieved on 2014-11-22. http://environmentclearance.nic.in/Search.aspx Kumar, N.  S., 2013. Unemployment rate increases in India. The Times of India. Retrieved on 2014-09-23. Mehrotra, S. and Gandhi, A., 2012. India’s Human Development in the 2000s: Towards Social Inclusion. Economic and Political Weekly, 47(14), pp.59–64.

References

71

Mineral Processing Pvt. Ltd., (2011). Environmental Impact Assessment Report, Retrieved 2014-11-06, http://environmentclearance.nic.in/Search.aspx. Radhakrishna, B.P., 2007. Broom in India’s iron and steel industry. Current Science, Vol(92) 9, pp. 121–129. Rohini, S., 2004. Steel industry: a performance analysis. Economic and Political Weekly, pp.1613–1620. Shee, S.P. and Maiti, R., 2019. Land acquisition, livelihood and income: the case of JSW Bengal Steel Plant at Salboni Block, Paschim Medinipur, West Bengal, India. Environment, Development and Sustainability, 21(6), pp.2997–3014. Sudalaimuthu, S. and Raj, S.A., 2009. Logistics Management for International Business: Text and Cases. PHI Learning Pvt. Ltd. The Economic Times, 2014. JSW steel-plant project in WB on schedule, https://economictimes.indiatimes.com/industry/indl-­goods/svs/steel/jsw-­steel-­plant-­project-­in-­wb-­on-­schedule/articleshow/3399751.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst Worker, C., 1998. Coal vs natural gas at blast furnaces. Retrieved on 2014-12-08, World Steel Association., 2014. Crude steel production. http://www.worldsteel.org/statistics/ statistics-­archive/yearbook-­archive.html Zervas, T., McMullan, J.T. and Williams, B.C., 1996. Developments in iron and steel making. International journal of energy research, 20(1), pp.69–91.

Websites http://jpcindiansteel.nic.in http://planningcommission.nic.in/ http://www.jsw.in http://www.worldsteel.org Indian Economy (2013) Government of India www.cci.gov.in/images/media/.../Indicussteel_20090420151842.pdf www.indiastat.com www.pib.nic.in https://steel.gov.in/make-­india

Chapter 4

Land Acquisition and Landuse Change – A Mouza Level Study

Abstract  It was expected that the JSW Bengal Steel project would bring several ancillaries that would make a new phase of industrial development and a large number of employment opportunities would be build up for local villagers in the non-­ agricultural sector. This chapter is clearly drawn up the detailed mouza-wise land acquisition scenario and LULC changes and prepares a land acquisition summary of the affected households. Land acquisition as narrated by some affected persons is also included here. Keywords  Non-agricultural sector · Mouza · Land acquisition · LULC · Affected households

4.1  Introduction Land is the greatest gift of God; extend of land is fixed by the nature, which can neither be increased nor decreased by human efforts (Birdar 2011). Economic and social developments are ensured through capital intensive projects, which have taken place according to their location and advantage. But the availability of such places near big cities is not possible which means that most of the planned projects are set up in rural and semi-rural areas, where agriculture is the main occupation and land is the main source of income (Panda and Behera 2008). In India, land is becoming scarce with population growth; per capita land availability will go down to less than 0.2 hectares by 2050 (Visaria and Visaria 1996). Land acquisition improves the utilization of land and increases land value that triggers the emergence of new livelihood (Sarkar 2007), but it may bring negative impact when it evicts the people from their traditional livelihood and surrounding (Kusiluka et al. 2011). It’s not always true that land loss means loss of livelihood, disruption of economic activities and persistent land-related disputes. Inadequate and late compensation is the negative outcome (Kombe 2010). Farming does not make peasants rich, but it supports food security and reduces livelihood vulnerability (Ding 2004). Land acquisition makes a serious impact on the socio-economic condition depending on the land to be acquired © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. P. Shee, R. Maiti, Land Acquisition, Industrialization and Livelihoods, https://doi.org/10.1007/978-3-030-90244-5_4

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(Fahimuddin 2011; Raghu et al. 2009). Ding (2004) discussed how to achieve a balance between farmland preservation and urban spatial expansion because such inequality contributes to raising tensions and distress between peasants and the Government. Kusiluka et al. (2011) examined the negative impacts of land acquisition programs on the environment and the livelihood of local communities in Tanzania. Choudhary (2000) studied the state Government policy and strategy of displacement and resettlement of Maldharis of the Gir Forest in Gujrat. Acharya (2002) conducted a study on the impacts of land acquisition on local villagers of Hazira, near Surat of Gujrat. People who are depending on land and forest as livelihoods, after the acquisition, have lost both their income and livelihoods. Desai et al. (2007) studied on the impacts of land acquisition on the families displaced by Indira Sagar Pariyojna in Madhya Pradesh. After acquisition, the standard of living of these families has started to fall by more than half as compared to that before the acquisition. Guha (2007) observed the socio-economic impacts on local villagers of Khargapur after land acquisition for Tata Metallic Ltd. in Paschim Medinipur of West Bengal. This study clearly revealed the food insecurity of peasant families due to land acquisition. The compensation amount is not sufficient to solve problems. Sarkar (2007) discussed the recent land acquisition and the eviction experience in West Bengal, and it is a long-term problem because the Government did not make any provision for resettlement and rehabilitation of displaced people except compensation. Due to lack of education, training and finance, farmers are the least benefitted from industrialization. Sharma (2008) described the impact of land acquisition on Pelpa villagers of Jharjjar district in Haryana. After land acquisition, the local inhabitants who depend on agriculture were jobless and turned to landless labourers. Smaller and Mann (2009) discussed the issues of displacement due to construction of dams and power projects at Singrauli region on the border of Uttar Pradesh and Madhya Pradesh, India. In West Bengal, for the steel plant at Durgapur, 6,633.44 hectares of land was acquired; as a consequence, 11,300 persons, of which 3.39 percent are tribal, were displaced (Fernandes and Thukral 1989). Sardana (2012) observed that land acquisition resulted in loss of economic security, social status, empowerment, home and kinship. The study also revealed many cases in India that long after the acquisition no industry is constructed there and the peasants are absorbing only the adverse impacts without getting any benefit of employment and income. In fact, the long gap between promises and its fulfilment is the main reason of distress and dissatisfaction among the peasants against land acquisition. In India, there are no policies still developed to tackle this situation. In 2011, the new state Government (TMC, Trinamool Congress) of West Bengal came to power and enacted a law on June 14, 2011, the “Singur Land Rehabilitation and Development Act, 2011”. With this law, the Government has succeeded to reacquire about 1000 acres of land from TATA, and the intention is to return 400 acres of farmland to “unwilling” farmers, but the whole matter has fallen in long legal challenge between the present Government and TATA (Guha 2012). Fahimuddin (2011) studied on the various problems encountered by the farmers in industrial and housing projects in Ghaziabad, Lucknow District of Uttar Pradesh. The land-losers used the compensation money for unproductive purposes like social functions (Singh 2012). Farmers also expressed that

4.2 Data and Methods

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compensation was insufficient and did not replace their previous income. Guha (2012) discussed on land acquisition, rehabilitation and resettlement Bill 2011. He focused on some suggestions to reform the regulatory framework that governs agricultural land and its uses. Ghatak et al. (2013) described the reason behind the refusal to accept the compensation money by many landholders at Singur of West Bengal. A similar study was done by Banerjee et al. (2007) on Nadigram, West Bengal, India. Chan (2003) analysed the problems arising in the different sectors due to land acquisition in China. Ghatak et al. (2012) made an argument for compensation of displaced farmers in terms of what economic theory suggests concerning compensation. Displacement and dispossession of people from their land is now becoming a world issue (Jana et al. 1997) due to the establishment of industrial projects, urbanization and construction of dams, bridges, roads, etc. An estimation is shown that nearly 10 million people are entered into imposed displacement every year globally (Cernea 1995). Roy and Barman (1951) studied in the social process of industrialization in Rourkela. Fernandes and Thukral (1989) studied in a number of cases of population displacement in India. Cernea (1996) enumerated on different impoverishment processes initiated by development-related population displacement. It is expected that the proposed project would bring a number of ancillaries that would make a new phase of industrial development, and a large amount of employment opportunities would be build up for local villagers in the non-agricultural sector. Land acquisition was initiated in 2007 and in 2009 after the completion of 90% land acquisition where 700 households have lost their private land and CPR with the hope of quick employment that would lead their livelihood better. In this chapter, an attempt is made to analyse both positive and negative aspects of land acquisition and policy implementation.

4.2  Data and Methods This study is done on direct intensive observation and interviews based on structured and open-ended questionnaires. The information was collected by non-­random opportunity and snowball sampling method through a field survey that covered 538 peasant families whose lands were acquired. The investigation of the first phase was conducted in 2009–2010 during land acquisition, and repeated investigation on the same household was held in 2012–2014 after acquisition. In the first phase, a questionnaire was prepared on concerning demographic details of households, its land holding, occupation, income and the impact of land acquisition. Mouza maps were collected from DLRO at Medinipur town and registered using ERDAS 8.5 software with the help of ground-truth verification. Plot-wise land use along with ownership records were also collected from BLRO at Salboni input in ARC GIS tool to make a comparison of LULC changes. A sample list of few affected families and provisional master plan of the proposed project was collected from the MKDA office. Apart from these, information regarding land acquisition was also collected from

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the published and unpublished census report as well as block-level survey reports and from social and print media, etc.

4.3  Stages of Land Acquisition From print media (Anadabazar Patrika, August 6, 2011), JSW office, BLRO Salboni, it’s realized that the Government of West Bengal handed over to JSW Bengal Steel Ltd. an area of 3035 acres of “vested” land and 816.3 acres of fodder farmland, which was under the Department of Animal Husbandry. This farmland was distributed in Nutanbankati mouza, Chakbandi mouza, Naranchak mouza and Bhalukchati mouza (Plate 4.1). West Bengal Industrial Nigam also acquired 189.62 acres of land for the proposed project through suitable compensation from “Patta” (has no legal land ownership records) landholders. The company also purchased 294.64 acres of land directly from the market without any clearance from the Land Reform Department. Due to some litigation, this land was undertaken by the new TMC Government (W.B) and handed over to the company in the month of July 2012 (Anandabazar Patrika, July 11, 2012). The company left 108 acres of land for setting up new linked roads surrounding the project boundary (Plate 4.2), playground and green belt (Plate 4.3). Finally, the company selected 4225.41 acres of land for the proposed plant (Plate 4.4). A total of 1222

Plate 4.1  Paddy and industrial land

4.3 Stages of Land Acquisition

Plate 4.2  Road surrounding the project boundary

Plate 4.3  Proposed green belt

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Plate 4.4  Under construction of the proposed plant

households are residing in the 22 project-affected mouzas, out of which 700 peasant families are directly affected in the sense that private land (“Raity” and “Patta”) they owned is acquired.

4.4  Land Acquisition and Land Use Changes The plot-wise details of land use and land acquisition of all affected 22 mouzas are summarized and presented in Table 4.1. It reveals that 22 mouzas consist (Fig. 4.1) of 6340.22 acres of land. Paddy is a staple food, but no systematic irrigation facilities are still now developed. The villagers residing near the riverside (Sundra) cultivate a variety of vegetables on the land adjoining their homestead, owing to very good supply of groundwater tapped through traditional dug wells. As per plot-wise land use record, in 2007, the affected mouzas were covered with 2727.3 acres (43.02 %) of forest land, 1265.32 acres (19.96%) of waste land mainly composed of lateritic exposure and barren land, 816.3 acres (12.88%) of fodder farmland under department of animal husbandry, 26.2 acres (0.4%) of water body and 126.8 acres (2.0%) of bamboo garden, temple, local village market, unmetal road, etc. As per latest census 2011, the total population of the affected mouzas is 5511 of which 2825 (51.2%) are males and 2686 (48.7%) are females. There are 1620 persons (29.3%) in SC category and 2171 persons (39.3%) belong to the ST.  The

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4.4 Land Acquisition and Land Use Changes Table 4.1  Land acquisition and land use change Land use Forest land Waste land Crop land Fodder farm Water body Others Total

Before Land acquisition (2007) in acres 2727.30 1265.32 1378.08 816.3

Land acquired in acres 2362.35 707.51 311.08 816.3

After acquisition (2009) in acres 364.95 557.81 1067.00 0

26.18

3.66

22.52

126.84 6340.02

24.53 4225.43

102.31 2114.59

Source: Shee and Maiti (2019)

Fig. 4.1  Mouza-wise land acquisition and land use. (Source: BLRO, Salboni)

average population density of the project site is 246 persons per km2. The maximum population density is recorded in Hatimari mouza (954 persons/ km2) followed by Gaighata mouza (885 persons/km2). The average household size is 4.6 persons. The major sources of income are agriculture, collecting forest product and livestock farming. There were 816.3 acres of fodder farmland under the Department of Animal Husbandry, W. B Govt., but now the owner is JSW authority. This land was used for grazing and collecting products like firewood, Sal leaf, mushroom, honey, medicinal plant, etc. which were sold in the nearby village market; they earned

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Plate 4.5  HH survey

30–40% of the total income, but now those activities are stopped. Another major economic activity is social forestry. Details on the source of income are discussed in Chap. 6. The proposed industrial project of 4225.43 acres (66.6%) of land from 22 mouzas was acquired in 2009. As a consequence, a huge land use conversion is observed. The area lost 311.1 acres (27.4%) of crop land, 2362.3 acres (86.6%) of forest land, 707.5 acres (55.9%) of waste land and 816.3 acres (100%) of fodder farmland.

4.5  Mouza-Wise Land Acquisition At the first step, we carried out a door-to-door household survey for project-affected mouzas (Plate 4.5) with structured questionnaire covering 84% of the households. In this survey, an attempt was made to collect data on demographic details of the households, their land holdings and the types and amount of land that was acquired. It is observed that 360.2 acres of “private” land is acquired from primarily 700 peasant families. The details of mouza-wise land acquisition and changes on land use are discussed in the following section.

4.5 Mouza-Wise Land Acquisition

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4.5.1  Dubrajpur Mouza The entire whole land under Dubrajpur mouza (Fig.  4.2) was  acquired. The acquired land was covered with 85.7 acres (13.5%) of crop land, 206.5 acres (32.4%) of waste land and 341.2 acres (53.6%) of forest land. The mouza was consisted of 609.40 acres (95.7%) of “vested” land. Plot-wise mouza record reveals that 10.1 acres of crop land (11.7%) was acquired from 30 patta landholders. No settlement or homestead land was acquired in this mouza. The mouza has no record of settlement since 1961. The affected peasants are residing in neighbouring mouzas at Mashru (22 families), Ghagrasol (4 families) and Barju (4 families). Compensation amounts are accepted by all landholders within due time without any objection. All affected 30 families have lost 100% of crop land in this mouza. The sample case study of two peasant families (Tables 4.2 and 4.3) is presented as the token example of affected households.

4.5.2  Masru Mouza Masru mouza (Fig. 4.3) occupied an area of 253.6 acres. One of the tributary of Sundra, it divides the mouza into two parts. The northern part is still now covered with high-productivity crop land of 64.5 acres (25.4%) and 19.1 acres (7.5%) of

Fig. 4.2  Land acquisition and land use change of Dubrajpur mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

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Table 4.2  A short summary of KALACHAND HEMBRAM (Land acquired from Dubrajpur Mouza but living at Mashru mouza) Name- KALACHAND HEMBRAM , Mashru Age- 40+ Educational Qualification –Nil HH Size- 4 members. Total owned land- 0.6 acres in 2007. Land in Dubrajpur-0.4 acres The compensation amount was deposited to each share holder’s bank account through cheque. The total compensation amount was distributed within the family member as per the percentage of their share value. .

2007

Land in Mouza Mashru Dubrajpur Total

Land Acquired By JSW

in acres 0.2 0.4 0.6

0.4 acres from Dubrajpur mouza

Patta Cropland acquisition Amount of Compensation

Share Holders

Name KALACHAND HEMBRAM

0.4 acres 100% INR- 0.12 million Amount in INR 1,20000.00

Cropping pattern

“Kharip” season Cropping land (2009) in acres Cropping land (2007) in acres 0.1 0.5 “Rabi” Season Cropping land (2009) in acres Cropping land (2007) in acres Nil 0.2

Table 4.3  A short summary of Gorachand Saren (Land acquired from Dubrajpur Mouza but living at Mashru mouza) NameGORACHAND SAREN, Mashru Age- 40+ Educational Qualification –Nil HH Size- 4 members. Total owned land- 1.31 acres in 2007. Land in Dubrajpur-0.9 acres Gorachand is the only owner of his land. The land was in Dubrajpur mouza, accrued completely. The rest amount is in Mashru. He fixed 50% of the compensation amount in the bank and enjoying certain interest from it. He has no double-cropping land.

2007

Land in Mouza Mashru Dubrajpur Total

Land Acquired By JSW

in acres 0.41 0.9 1.31

0.9 acres from Dubrajpur mouza

Patta Cropland acquisition Amount of Compensation

0.9 acres 100% INR- 0.27 million

Name GORACHAND SAREN

Amount in INR 270,000.00

Share Holders

Cropping pattern

“Kharip” season Cropping land (2009) in acres Cropping land (2007) in acres 0.21 1.11 “Rabi” Season Cropping land (2009) in acres Cropping land (2007) in acres 0 0

waste land, but before acquisition (2007), the southern part was covered with 162.1 acres (53.6%) of forest land. As per latest census (2011), 229 people are residing in 49 households. Local economy depends on agricultural activities, collecting forest product and livestock farming. Forest land was utilized for eucalyptus plantation. After 5 years, re-plantation was done, and an amount between INR 15,000–20,000 was handed over to the families, those who were involved directly for social forestry, but this activity was  stopped  after

4.5 Mouza-Wise Land Acquisition

83

Fig. 4.3  Land acquisition and land use change of Mashru mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

acquisition. In 2009, an area of 139.44 acres (55.0%) of land was acquired including a large amount of forest land (86%). No private, homestead or crop land was acquired from this mouza. But from the household survey database, it is observed that 38 peasant families are residing here. They lost their land in different mouzas also as Dubrajpur Mouza (22 HH), Kharisol Mouza (7 HH) and Arabari Mouza (9 HH).

4.5.3  Natundihi Mouza Natundihi mouza is located at the left bank of river Sundra (Fig. 4.4) and consisted of 176.11 acres of land. Crop lands are still now fertile with high productivity that shared an area of 42.2 acres (24.0%), supplying feed to 149 local villagers and accommodating an average household size of 4.5. The mouza was  also covered with 38.1 acres (21.6%) of waste land. At the southern part of the mouza, there was a healthy forest cover of 94.9 acres (53.9%) in 2007. For the proposed industrial project, a total of 69.97 acres (39.7%) of land was acquired including 57.34 acres (69.6%) of forest land and 3.7 acres (1.0%) of waste land. An amount of 8.93 acres (21.1%) of crop land was acquired from 14 peasant families. From household survey (Table 4.8), it is observed that 75% of crop land (Plate 4.6) are acquired from 1, 6, 6, and 1 households, respectively.

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Fig. 4.4  Land acquisition and land use change of Natundihi mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

Table 4.4  A short summary of Nakul Murmu Name- Nakul MURMU, Natundihi Mouza Age- 60+ Educational Qualification –Read up to Class II HH Size-7 members. Total owned land- 5.3 acres in 2007. Total compensation amount was divided equally within two brothers and one sister. They utilised maximum amount in unproductive purposed with hope a quick employment within the JSW project site.

2007

Land in Mouza Natundihi

in acres 3.42

Total

3.42

Land Acquired By JSW

3.42 acres from Natundihi mouza 3.0 acres 0.42 86.2% INR- 1.02 million

Raity Patta Cropland acquisition Amount of Compensation Name NANI MURMU ASHALATA MURMU NAKUL MURMU

Share Holders

Cropping pattern

Amount in INR 342,000.00 342,000.00 342,000.00

“Kharip” season Cropping land (2009) in acres Cropping land (2007) in acres 0.8 4.8 “Rabi” Season Cropping land (2009) in acres Cropping land (2007) in acres 0 0

4.5 Mouza-Wise Land Acquisition

85

Plate 4.6  Acqired agricultural land

These families already received the compensation amount without any objection. One person from each land-loser family was selected for 6 months of vocational training course at Sundra for phase-I.  The sample case study of Nakul Murmu (Table  4.4) from Natundihi Mouza is presented as an example of the affected households.

4.5.4  Nitaipur Mouza As per plot-wise land use record in 2007, Nitaipur mouza (Fig. 4.5) occupied an area of 298.3 acres of land. The mouza was predominated by 284.0 acres (95.2%) of waste land. This land was previously used for eucalyptus re-plantation, and after 5 years, a certain amount was credited within the local peasant families, who were involved in social forestry. As per census 2011, there are 13 households with a total population of 69. In 2009, for the JSW Bengal Steel project, 249.70 acres (83.7%) of land was acquired including 245.6 acres (85%) of waste land and 4.1 acres (100%) of crop land from all peasant families. It is observed that Gopal Singh, Arun Mahata and Turi Hembram have lost 100% of their crop lands. Compensation amount was credited to their bank account within due time without any objection. No homestead land was acquired. The sample case study of Adhir Singh (Table 4.5) is presented as an example of the affected households.

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Fig. 4.5  Land acquisition and land use change of Nitaipur mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

Table 4.5  A short summary of Adhir Singh Name- ADHIR SINGH, Age- 45 Educational Qualification –Nil HH Size-6 members. Total owned land- 1.22 acres in 2007. The land is not suitable for cultivation. He has deposited half of the compensation money for a fixed deposit. Due to acquiring of fodder land, he has lost income from livestock farming. Now he is waiting for a job at the project site.

2007

Land in Mouza Nitaipur

in acres 0.75

Total

0.75

Land Acquired By JSW

0.75 acres from Nitaipur mouza

Raity Patta Cropland acquisition Amount of Compensation Name ADHIR SINGH

Share Holders

0.75 100% INR- 0.22 million Amount in INR 225,000.00

Cropping pattern

“Kharip” season Cropping land (2009) in acres Cropping land (2007) in acres 0.0 1.1 “Rabi” Season Cropping land (2009) in acres Cropping land (2007) in acres 0 0

4.5 Mouza-Wise Land Acquisition

87

4.5.5  Banskopna Mouza Banskopna mouza consists of 709.66 acres of land. As per census 2011, this mouza is habituated with 328 persons, residing in 64 households. In 2007, the mouza was covered with 79.9 acres (11.3%) of crop land, 547.0 acres (77.1%) of forest land and 76.5 acres (10.8%) of waste land (Fig. 4.6). Major economy depends on agriculture, collecting forest product and livestock farming. Before land acquisition, average incomes from this sector is 32%, 26% and 34%  respectively. Average monthly income per household is ranged between INR 5000 to 6000. In 2009, JSW authority acquired 610.0 acres (86%) of land including 44.4 acres (52%) of crop land, 512.9 acres (94%) of forest land and 50.0 acres (65%) of waste land; this acquired land would be converted into the proposed built-up land in the future as per JSW’s master plan. It is observed through an intensive HH survey that 62.0 acres (10.2%) of private land was acquired from 59 peasant families. From HH survey database, < 25%, 25–50%, 50–75% and >75% of crop land was acquired form 9, 13, 18 and 19 households, respectively. A peasant, namely, Khepu Sing, has lost 100% of crop land. All families whose land was acquired received the compensation amount with a hope that within a few years they would be appointed as permanent employees. But as a consequence, they have lost both their land and livelihoods. Family income is dropped by more than 50%. The sample case study of

Fig. 4.6  Land acquisition and land use change of Banskopna mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

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Table 4.6  A short summary of Pradip Singh Name-PRADIP SINGH, Banskopna Mouza Age- 62+ Educational Qualification –Read up to class I HH Size-5 members. Total owned land- 1.42 acres in 2007. Pradip has lost 80% of wellirrigated cropland. Now rest of the cropland is not suitable for cultivation because, after the construction of the project boundary wall, surface flow direction has changed. Family income is reduced by 60%.

2007

Land in Mouza Banskopna

in acres 1.11

Total

1.11

Land Acquired By JSW

0.9 acres from Nitaipur mouza 0.9 0.2 88.2% INR- 0.33 million

Raity Patta Cropland acquisition Amount of Compensation Name PRADIP SINGH

Share Holders

Amount in INR 270,000.00

Cropping pattern

“Kharip” season Cropping land (2009) in acres Cropping land (2007) in acres 0.12 1.02 “Rabi” Season Cropping land (2009) in acres Cropping land (2007) in acres 0 0.6

Pradip Singh (Table  4.6) from Banskopna Mouza is presented as an example of affected households.

4.5.6  Chantibandh Mouza Chantibandh mouza (Fig. 4.7) is located at the NE corner of Banskopna mouza, and it occupied a total area of 228.5 acres. As per census 2011, 61 persons are residing here in 13 households. In 2007, the mouza was covered with 207 acres (90.6%) of forest land and 12.9 acres (9.7%) of crop land. Local villagers depended on land and forest. They collected forest products like firewood, sal leaf, fruits, mushroom, medicine, honeybees, etc. for consumption and selling. The forest land also was utilized as common pool resources (CPR) for grazing and plantation. After acquisition of the forest land, income from this sector is dropped by 40–45%. In 2009, for the proposed project, 208.10 acres (91.1%) of land was  acquired including 204.8 acres (99%) of forest land and 5.8 acres (44.9%) of crop land. This crop land was acquired from 11 peasant families. Mangal Hembram from a poor peasant family lost >80% of crop land with the hope of quick employment. Hari Hembram, Munsi Hembram and Mangal Hembram also  lost crop land, ranging between 50 to 75%; Ghata Hembram and Avilas Murmu lost their  crop land by 25–50%. Compensation amount was credited in due time. After acquisition, family income is reduced by >50%.

4.5 Mouza-Wise Land Acquisition

89

Fig. 4.7  Land acquisition and land use change of Chantibandh mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

4.5.7  Ramraidy Mouza Ramraidy mouza (Fig. 4.8) occupied an area of 277.9 acres of land. As per plot-­ wise mouza record in 2007, major land cover was 84.4 acres (30.4%) of crop land, 153.0 acres (55.0%) of forest land and 39.2 acres (14.1%) of waste land. For the proposed industrial project, 156.34 acres (56.2%) of total land was acquired. Due to land acquisition, the mouza lost 140.3 acres (92.0%) of forest land, 20.1 acres (24.1%) of crop land and 12.2 acres (30.6%) of waste land in 2009. These crop land was  acquired from 40 peasant families. From household survey database, it is observed that 75% of crop land per household was acquired from 11 households. 25–50% and 50–75% of crop land was acquired from 15 and 6 HH, respectively. After clearance of forest cover, local inhabitants lost their opportunities of collecting forest product and livestock farming. As a consequence, family income of the affected peasants is reduced by more than 50%.

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4  Land Acquisition and Landuse Change – A Mouza Level Study

Fig. 4.8  Land acquisition and land use change of Ramraidy mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

4.5.8  Asnasuli Mouza Asnasuli mouza occupied an area of 261.4 acres (Fig.  4.9). As per plot-wise mouza record in 2007, the mouza was covered with 116.9 acres (56.8%) of crop land followed by 56.1 acres (31.8%) of forest land and 81.7 acres (10.6%) of waste land. The average monthly family income was between INR 6000 to 7000, which was earned from agricultural sector and from livestock farming sector. As per census record of 2011, the mouza is crowded with 746 people, residing in 164 households. In 2009, JSW Steel Plant Authority acquired an area of 128.01 acres accounting for 49% of the mouza including 79.6 acres (68%) of crop land, 27.5 acres (49%) of forest land and 20.2 acres (25%) of waste land. The affected families are 106. From the database, it is observed that 75% of crop land was acquired from 23, 35 and 33 households, respectively, and 6 peasant families, namely, Suren Sing, Pannalal Mahata, Probodh Mahata, Gorachand Mahata, Khadi Mahata and Bhabataran Mahata, lost 100% of crop land and become agricultural land-less. After acquisition of crop land, an acute food shortage is observed in these affected families. They are already facing problem in cropping, livestock farming and collecting of forest product. Monthly family income is reduced by 50%. For example, an interview of affected HH is presented below.

4.5 Mouza-Wise Land Acquisition

91

Fig. 4.9  Land acquisition and land use change of Asnasuli mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

Haripada Mahata is a farmer who possessed 0.92 acre of land at Asnasuli mouza before acquisition. He is the head of a joint family which consisted of nine members. He has three sons and one daughter. Two sons are married and lived together. The land possessed by this family was a double crop in nature; the crop grew in the land could feed the family throughout the year. The family used to collect forest products which then sold in the nearby markets. They had also livestock farming as a supplementary source of income. But due to land acquisition, the family lost 0.76 acre of “Raity” crop land, and after clearance of forest they sold all the livestock. Haripada deposited the compensation money of 2.28 lakh in bank. His two older sons began to work as daily constructional labour at JSW plant, and his youngest son who is a dropout from class X (10+) was selected for vocational training course. All these activities are now ceased due to the closure of work. Now the family is facing an acute food shortage. The wife of the elder son has started to sell vegetables in the market, while the wife of the other son stayed back at home to do the domestic work. This new kind of division of labour could not last long, and quarrels between the two daughters-in-law began, and the older son left his father’s family and formed his own nuclear family. The second son also followed his elder brother and formed his own nuclear family within a few years. Haripada now lives with his wife and the unmarried youngest son in the ancestral household.

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4  Land Acquisition and Landuse Change – A Mouza Level Study

4.5.9  Natunbankati Mouza This mouza was under the Department of Animal Husbandry, Govt. of West Bengal, and the entire mouza land was handed over to JSW Bengal Steel plant authority for the proposed project. No inhabitant was there. As per plot-wise mouza record, no private land was there. The mouza consisted of 26.95 acres of land and was covered with 8.7 acres (32.5%) of crop land and 18.2 acres (67.7%) of fodder farmland (Fig. 4.10). No forest cover and homestead land was observed here. The acquired land will be converted to proposed built-up land as per JSW master plan. After acquisition of this common pool resources, local inhabitants who were engaged in livestock farming previously are now facing a huge economic loss.

4.5.10  Chakbhani Mouza As of plot-wise mouza record 2007, the mouza was under the Department of Animal Husbandry, Govt. of W.B. The mouza consisted of 80.88 acres of land including 5.6 acres (7.0%) of crop land and 75.3 acres (93.0%) of fodder farmland. Entire mouza (Fig. 4.11) was handed over to JSW Bengal Steel plant authority for the proposed

Fig. 4.10  Land acquisition and land use change of Natunbankati mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

4.5 Mouza-Wise Land Acquisition

93

Fig. 4.11  Land acquisition and land use change of Chakbhani mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

project. There was no record of inhabitant since 1961. This land was used as common pool resources. Local people used it for livestock farming.

4.5.11  Naranchak Mouza Naranchak mouza (Fig. 4.12) consisted of 99.47 acres of land under the Department of Animal Husbandry of W.B.  Govt. This land basically was used for livestock farming. In 2009, the entire mouza was acquired including 94.6 acres (95.0%) of fodder land and 4.1 acres (4.2%) of crop land. No inhabitant was recorded here since 1961. This fodder land was the source of income for local villagers, and they lost income from livestock farming after acquisition.

4.5.12  Jambediya Mouza Jambediya mouza (Fig. 4.13) consisted of 275.59 acres of land, and it is located 2 km away from NH 60. An unmetal road divides the mouza into two parts. The northern part of the mouza is now covered with fertile crop land with an area of 112.5 acres (41.0%); this land was the major source of income for the existing 177

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4  Land Acquisition and Landuse Change – A Mouza Level Study

Fig. 4.12  Land acquisition and land use change of Naranchak mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

Fig. 4.13  Land acquisition and land use change of Jambedya mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

4.5 Mouza-Wise Land Acquisition

95

Fig. 4.14  Land acquisition and land use change of Bhalukchati mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

households. Before acquisition (2007), the southern part of the mouza was covered with 132.9 acres (48.4%) of forest land. This land was an additional source of income through livestock farming and collecting forest product for local inhabitants. In 2009, for the proposed project, 113.76 acres (42.4%) of this mouza was acquired including 116.9 acres (88.0%) of forest land and 3.2 acres (2.8%) of crop land. Only five household families lost their land here. Shakti Pada Chalak lost 72% of crop land. All the affected families received the compensation amount through cheque within due time. After clearance of the forest, the local inhabitants are suffering from a shortage of firewood and grazing land.

4.5.13  Bhalukchati Mouza No inhabitant is recorded in this mouza since 1961. As per plot-wise record in 2007, this mouza (Fig. 4.14) was covered with only 425.9 acres of fodder farmland, and for the proposed project, Dept. of Animal Husbandry (W.B.) handed over the entire mouza to JSW Bengal Steel authority. Now this fodder farmland is converted to waste land. Surrounding people depended on it for livestock farming, but after acquisition of land, they have lost their livelihood opportunities.

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4  Land Acquisition and Landuse Change – A Mouza Level Study

4.5.14  Kulpheni Mouza Kulpheni mouza (Fig. 4.15) consisted of 333.02 acres of land and is located at NE corner of the JSW Bengal Steel project boundary. SE railway passes N to S through it. The eastern part of the mouza was covered with 139.0 acres (42.0%) of fertile crop land supplying feed to 101 households. The western part was predominated by 81.3 acres (24.6%) of forest land in 2007. For the proposed industrial project, 136.82 acres (41.1%) of land from this mouza was acquired in 2009 including 79.6 acres of forest land (98%), 38.6 acres (42.0 %) of waste land and 18.5 acres (13.0%) of crop land. Private land of 41.1 acres was acquired from 78 peasant families. It is observed that more than 50% of crop land was  acquired from 45 peasant families in this mouza. After acquisition, the mouza has consisted mainly of 120.5 acres of crop land and 47.8 acres of waste land. No homestead land or settlement land was acquired from this mouza. All land-loser families received the cheque within due time without any objection with hope that they would be selected shortly for permanent employment. Now they are realizing that they have lost both their land and income.

Fig. 4.15  Land acquisition and land use change of Kulpheni mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

4.5 Mouza-Wise Land Acquisition

97

4.5.15  Gaighata Mouza Gaighata mouza (Fig. 4.16) consisted of 211.97 acres of land. Both NE railway and NH 60 pass through it. There was  fertile crop land of 128.8 acres (61.4%) between two tracks supplying feed to 32 peasant families, constituting a total population of 159. The western side of railway track is predominated by 24.1 acres (11.5%) of forest land. In 2009, for the proposed industrial project, 21.08 acres (9.9%) of land of this mouza was acquired including 16.28 acres (87%) of forest land and 4.8 acres (3.7%) of crop land. The local inhabitants who depend on collecting and livestock farming are suffering from a serious crisis of firewood and fodder for cattle. During household survey, it was observed that crop land was  acquired from 12 peasant families. Namely, Suklal Mandi and Mangal Murmu lost almost 100% of crop land. Apart from this, 50–75% of crop land was acquired from three families. A compensation amount of INR 0.3 million per acres was received by all land-acquired families within due time without any objection. No homestead or settlement land was acquired.

4.5.16  Hatimari Mouza Hatimari mouza occupied (Fig. 4.17) 151.78 acres of land and lies between NE railway and NH 60. This mouza consisted of fertile crop land supplying feed to 586 people in 128 households. As per plot-wise record in 2007, the mouza was covered

Fig. 4.16  Land acquisition and land use change of Gaighata mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

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4  Land Acquisition and Landuse Change – A Mouza Level Study

Fig. 4.17  Land acquisition and land use change of Hatimari mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

with 131.2 acres (85.6%) of crop land and 6.2 acres (4.0%) of waste land. There was no forest land. In 2009, for the proposed project, 11.09 acres (7.3%) of land was acquired including 10.5 acres (14.1%) of crop land and 0.74 acres (12.1%) of waste land. Crop land was acquired from 17 peasant families. During household survey, four peasants, namely, Sambhunath Rajbhar, Manu Mandi, Manglu Hembram and Sombari Maiti, lost >75% of crop land. All of the affected families received the compensation amount of INR 0.3 million per acres within due time. No homestead land acquired. But after few months of land acquisition, different ancillary companies started to purchase land here, and land rate reached an apex up to INR 1.5 million per acres during 2009–2014.

4.5.17  Shalika Mouza Shalika mouza (Fig. 4.18) consisted of an area of 252.52 acres of land. SE railway passes through it. As per plot-wise record in 2007, the mouza was covered with 75.1 acres (29.8 %) of crop land, 93.7 acres (37.2%) of forest land and 55.9 acres (22.2%) of waste land. In 2009, for the proposed project, 69.46 acres (27.5%) of land

4.5 Mouza-Wise Land Acquisition

99

Fig. 4.18  Land acquisition and land use change of Shalika mouza. (Source: BLRO, Salboni, JSW Master plan, HH Survey)

was  acquired including 28.4 acres (30.0%) of forest land, 37.8 acres (68.0%) of waste land and 3.26 acres (4.34%) of crop land. Crop land was acquired from nine peasant families in which six peasant families are residing nearby Metal mouza. One local inhabitant, Ravi Pan, lost 100% of crop land. More than 75% of crop land was acquired from two peasants, and 50–75%, 25–50% and 80%

6

20–80%

8

80% of land area from six mouzas, namely, Nitaipur, Banskopna, Chatibanth, Kharkasuli, Arabari and Khairisol, and 90%) is altered from Masru, Natundihi, Chatibandh, Jambediya, Gaighata and Kharkasuli mouza, and an amount of 30–80% of forest land is acquired from Dubrajpur, Banskopna, Ramridy, Asnasuli, Kulpheni, Shalika, Arabari and Khairisol; as a consequence, local livelihoods are severely affected. • More than 75% of crop land is acquired from 150 households in which 61 households have become agriculturally landless. • No homestead or settlement land is acquired for the proposed project. Not a single family became landless. • Without any objection, all land-losers have collected the cheques within due time.

“Vested” land acquired in acres 609.4 139.44 59.97 249.7 548.01 206.2 140.44 15.81 26.95 80.88 99.47 116.16 425.86 95.72 21.08 5.09 68.86 309.46 406.46 157.92 57.76 24.72 3865.3

Source: JSW, EIA (2007); BLRO Salboni and HH survey

Land acquisition Total area of mouza in Mouza land acres acquired % Name of mouzas DUBRAJPUR 637.1 100 MASRU 253.64 55 NUTANDIHI 176.11 39.7 NITAIPUR 298.29 83.7 BANSHKOPNA 709.66 86 CHATIBANTH 228.48 91.1 RAMRAIDI 277.94 56.2 ASNASULI 261.43 49 NUTANBANKATI 26.95 100 CHAKBHANI 80.88 100 NARANCHAK 99.47 100 JAMBEDYA 275.59 42.4 BHALUKCHATI 425.85 100 KULPHENI 333.02 41.1 GAIGHATA 211.97 9.9 HATIMARI 151.78 7.3 SHALIKA 252.52 27.5 KHARKASULI 390.34 80.9 ARABARI 478.62 90.6 KHAIRISOL 248.05 80.8 BARJU 244.31 26.1 GHAGRASOL 284.01 8.7 Total 6346.0 “Private” land acquired in acres 27.7 0 10 0 62 1.9 15.9 112.2 0 0 0 0.8 0 41.1 0 6 0.6 6.2 27.2 42.5 6.1 0 360.2

Table 4.8  Land acquisition scenario of JSW Bengal Steel plant, Salboni

Crop land acquired in % 100 0 21 100 52.3 44.9 24.1 68.2 0 0 0 2.8 0 13.2 3.7 14.1 4.34 73.8 47.1 49.2 0.60 0

25– 75% 100% surveyed 0 0 0 0 0 0 1 0 14 5 3 16 19 1 60 1 0 11 11 0 40 39 6 112 0 0 0 0 0 0 0 0 0 1 0 5 0 0 0 16 0 78 3 2 14 4 0 17 3 1 10 24 13 52 2 0 21 9 0 38 2 0 18 10 4 32 150 30 538

106 4  Land Acquisition and Landuse Change – A Mouza Level Study

4.7  Major Findings

107

4.8  Conclusion The maximum amount of private land was acquired from Asnasuli mouza (76%) followed by Banskopna (69.8 acres) and Kharkasuli mouza (50.2 acres). Analysis also reveals that the maximum amount of forest land (>90%) was acquired from Masru, Natundihi, Chatibandh, Jambediya, Gaighata and Kharkasuli mouza. From land use data analysis, the entire (100%) land area of five mouzas, namely, Dubrajpur, Nutanbankati, Chakbhani, Naranchak and Bhalukchati, was acquired, as well as more than 80% of land area was acquired from six mouzas, namely, Nitaipur, Banskopna, Chatibanth, Kharkasuli, Arabari and Khairisol. This silent land use conversion may have a bright prospect of development of the state through industrialization, but it has much adverse consequence upon the local peasantry in the long term. One of the major consequences of industrialization is water scarcity in the study area due to massive forest clearance. Land acquisition significantly reduces the incomes of affected families, despite their efforts to increase incomes from non-­ farming sectors. Agricultural workers are more affected than non-agricultural workers, leading to food insecurity of the landowners and loss of employment to landless agricultural labours. More than 75% of crop land was acquired from 150 households in which 61 households become agriculturally landless. As a consequence, fragmentation of families (Fig. 4.23) resulted as the impact of land acquisition along with a series of interrelated impacts like school dropout, increased workload for Landuse vulnerability Insecurity of food

Female member will do extra domestic jobs

Land purchase/ construction

Land acquisition

Compensation money will be utilised for daughter marriage

Work load will be increased especially for

Increase of dropout children

Intra-family conflict

Fig. 4.23  Schematic diagram showing the accompanying events of family fragmentation triggered by land acquisition. (After Guha 2007)

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4  Land Acquisition and Landuse Change – A Mouza Level Study

female, early marriage and intra-family conflict. Land acquisition without proper rehabilitation thus led to increasing food insecurity and loss of economic and social wellbeing of the affected families.

References Acharya, A., 2002. Land acquisition, loss for employment and women’s participation in income generation: A case study of coastal belt of South Gujrat. Man and Development, 24(3), pp. 79–86. Anandabazar Patrika., 2011. Proposed project will be started after completion of land acquisition. Kolkata-01, Retrieved 06-08-2011. Anandabazar Patrika., 2012. Proposed project will be started soon: Jindal. Kolkata-01, Retrieved 11-07-2012. Banerjee, A.V.  Gertler, P. and Ghatak, M., 2007. Beyond Nandigram: Industrialization in West Bengal. Economic and Political Weekly, 42(17). Birdar, S.G., 2011. Land acquisition rehabilitation and resettlement paradigm shift and emerging scenario. Southern Economist, 50(7), pp. 19–26. Cernea, M., 1995. Social integration and population displacement. International Social Science Journal, 143(1), pp. 91–112. Cernea, M.M., 1996. Eight main risks: Impoverishment and social justice in resettlement. World Bank Environment Department, Washington, DC. Chan, N., 2003. Land acquisition compensation in China–problems and answers. International Real Estate Review, 6(1), pp. 136–152. Choudhary, K., 2000. Development dilemma: Resettlement of Gir maldharis. Economic and Political Weekly, pp. 2662–2668. Desai, K., Jain, V., Pandey, R., Srikant, P. and Trivedi, U., 2007. Rehabilitation of the Indira Sagar Pariyojana Displaced. Economic and Political Weekly, pp.27–36. Ding, C., 2004. Effects of Land Acquisition on China’s Economic Future. Land Lines, 16(1), pp.59–65. Fahimuddin., 2011. Land acquisition and payment: Some ground realities in Uttar Pradesh. Man and Development, 23(2), pp. 43–58. Fernandes, W. and Thukral, E.G., 1989. Development, displacement, and rehabilitation: Issues for a national debate. Indian Social Institute. Ghatak, M., Mitra, S., Mookherjee, D. and Nath, A., 2012. Land acquisition and compensation in Singur: what really happened?. Institute for Economic Development, Boston University. Ghatak, M., Mitra, S., Mookherjee, D. and Nath, A., 2013. Land acquisition and compensation in Singur: what really happened?. Economic and Political Weekly, 48(21), pp. 32–43. Guha, A., 2007. Peasant resistance in West Bengal a decade before Singur and Nandigram. Economic and Political weekly, pp.3706-3711. Guha, A., 2012. Social Impact Assessment in the Draft Land Acquisition and Rehabilitation and Resettlement Bill, 2011: A Critical Overview. In Anthropological Impact Assessment of Development Initiatives (2012)., Eds A.K. Danda, K.K. Basa, K.K. Misra. Jhargarm: Indian Nation Confederation and Academy of Anthropologists. Jana, L., Barman, T. and Guha, A., 1997. Dispossession of the tribal people from their agricultural land: a case study of Koras in Medinipur. Man and Life, 23(1–2), pp.85–89. JSW, EIA., 2007. Rapid environmental impact assessment for the proposed 3.0 mtpa integrated steel plant at Godapiasal, Paschim Medinipur district, West Bengal. Vitma Labs Ltd, 142, IDA, Hyderabad. Kombe, W., 2010. Land Conflicts in Dar es Salaam: who gains? Who loses?. Cities and Fragile States Working Paper, 82.

References

109

Kusiluka, M.M., Kongela, S., Kusiluka, M.A., Karimuribo, E.D. and Kusiluka, L.J., 2011. The negative impact of land acquisition on indigenous communities’ livelihood and environment in Tanzania. Habitat International, 35(1), pp.66–73. Panda, D.K. and Behera, N.K., 2008. Development and displacement: An Orissa experience. Journal of Rural Development, 27(1), pp. 91–109. Raghu, R. T. L. and Kakani, R. K., 2009. Framework for evaluation of land acquisition in India sustainable development perspective. India Infrastructure report, Jamshedpur, XLRI (08–12), pp. 90–96. Roy and Barman, B.K. 1951. Social process in industrialisation of Rourkela. Census of India, New Delhi. Sardana, M. M. K., 2012. Land acquisition issues. Institute for studies in industrial development. http://isidev.nic.in. Sarkar, A., 2007. Development and displacement: land acquisition in West Bengal. Economic and Political Weekly, pp.1435–1442. Sharma, S., 2008. Locational disadvantage. Mainstream weekly, 46(19). www.mainstreamweely. net/article666.html Shee, S.P. and Maiti, R., 2019. Land acquisition, livelihood and income: the case of JSW Bengal Steel Plant at Salboni Block, Paschim Medinipur, West Bengal, India. Environment, Development and Sustainability, 21(6), pp. 2997–3014. Singh, R., 2012. Inefficiency and abuse of compulsory land acquisition: an enquiry into the way forward. Econom Polit Week, 47(19), pp.46–53. Smaller, C. and Mann, H.L., 2009. A Thirst for Distant Lands: Foreign investment in agricultural land and water. International Institute for Sustainable Development= Institut international du développement durable. Visaria, L. and Visaria, P.M., 1996. Prospective Population Growth and Policy Options for India, 1991–2101. Gujarat Institute of Development Research.

Chapter 5

Hydrological Impact of Landuse Conversion

Abstract  The study area is under Sundra catchment that is connected through a systematic interaction of land, water, soil, land use and livelihood. Any significant changes in one part leads to considerable downstream impacts. Changes in the land use land cover (LULC) under the industrial project caused more runoff, high intensity of soil erosion and water scarcity in all the downstream mouzas. This chapter is focussed on creating an expected water budget due to land use conversion of the study area. Model watershed management plan is also prepared using recent GIS techniques that recommend constructing farm ponds at suitable location to reduce the intensity of flood at the lower ridge and utilize water in dry season. Keywords  Sundra catchment · Runoff · Land use conversion · Watershed management · Water budget · Farm pond

5.1  Introduction Economic activities and social relations are articulated, reproduced and changed depending on the availability of water (De Souza 2010; Heathcote 2009; Mosse 1997). The availability of water is guided by site-specific and unique interworking topographic, edaphic and land use factors within the watershed boundary. Land cover change within a watershed is also recognized as an important factor to affect runoff (Chang 2007), and it is possible that the transformation of land could have a greater influence on runoff than the climate change (Vörösmarty et al. 2000). But development may encroach upon the rural landscape such as agriculture, forestry, pasture land, etc.; this phenomenon is known as urbanization, and it leads to an increase of impervious areas (Paul and Meyer 2001), which decrease the amount of water that infiltrates into the soil (Dunne and Leopold 1978; Harbor 1994; Klein 1979); as a consequence of less resistance to flow, it leads to increased runoff velocity (Beighley et al. 2003; Neller 1988). This land use alteration not only increases the chances of more frequent flooding and associate monetary losses but also impacts stream habitat adversely and can cause serious environmental damages (Booth 1990; Graf 1975; Hammer 1972). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. P. Shee, R. Maiti, Land Acquisition, Industrialization and Livelihoods, https://doi.org/10.1007/978-3-030-90244-5_5

111

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5  Hydrological Impact of Landuse Conversion

In the present study, an attempt is made to understand the surface water budget through estimating runoff, infiltration and evapotranspiration with the help of popular hydrological models to assess the possible impact of land use conversion for industrialization and the need for integrated management. For planning and development, estimation of water resources is made with response from catchment characteristics (Kumar et  al. 2007). The possibility of improving surface water availability through rational intervention is examined by analysing system behaviour of present and that may arise in the future due to possible alteration in land use and other elements in the process of projected industrialization. Hydrological modelling is a powerful technique for understanding the interaction between climate, topography and hydrological elements of a watershed. The hydrological characteristic of a watershed depends on a variety of factors, including regional climate and land use. Changes in LULC can alter the volume and timing of runoff throughout the watershed (Franczyk and Chang 2009; Koudstaal et al. 1992; Miller et al. 2002). With the development of Geographic Information System (GIS) and remote sensing techniques, the hydrological catchment models have become more physical based, and attempts are made to enumerate various interactive hydrological processes considering spatial heterogeneity. Several watershed-scale hydrologic models are available such as HSPF (Bicknell et  al. 2001), SWAT (Neitsch et  al. 2011), inverse distance weighting (Teegavarapu et  al. 2005), MIKE-SHE (Graham and Buts 2005), etc.

5.2  Data and Methods The rate of infiltration at study area was calculated by field measurement and expressed graphically with rate as the ordinate and time as the abscissa (Fig 5.7). Time consumed for percolation of each litter of water through 1 foot × 1 foot area was recorded continuously till a constant rate is attained following Goudie (1990). The initial and terminal rate was used as the Horton’s (1939) model input (Equation 5.1). SWAT analysis tool was used to sub-watershed delineation (Fig. 5.11) using channel configuration, and digital elevation model was prepared from SRTM (2009). The Soil Conservation Service (SCS) of the US Department of Agriculture has developed the Curve Number (CN) method (SCS, CN 1972) used in this study to evaluate the potential changes in the hydrology of Sundra catchment owing to projected land use-land cover (LULC) alteration. This model is originally developed for predicting runoff volumes from agricultural fields and small watershed (Moitra et al. 2009; Schwab et al. 1993; Shee and Maiti 2012). The advantage of this model lies in its ability to detect changes in hydrological response in connection to land use, soil types and drainage condition. An IRS P6 image of 2007 was enhanced and registered using ERDAS 8.5 software with the help of ground-truth verification and classified into different land use types to detect the different LULC

5.3 Relief

113

before land acquisition. The blue print of the proposed industrial site, Google imagery, Toposheet map within the basin was used as information of the possible land use alteration. Using the Surfer tool, a cross profile (Fig 5.2) and 3D slope (Fig. 5.3) map of the study area is prepared. The district-based soil map was converted to digital format using on-screen digitizing method using ARC GIS 9.1.3 software. Soils are classified into hydrological soil groups (HSG) (A, B, C and D) based on physical characteristics following the SCS-USDA (1972) method. The samples were collected from all land use classes – farmland, settlement area, forest, etc., representative of different heights and distance from the river (Fig. 5.6). Soil samples were tested in the laboratory for assessing textural composition (Table 5.2) and are linked with the rate of infiltration measured in the field. Daily weather data from 1989 to 2015 was used as data input to Penman (1948, 1963) equation for estimating evapotranspiration (Equation 5.7). After necessary calculations, a month-wise supply-demand budget is prepared (Table 5.6). Mass curves are drawn with the cumulative values of present and future demand and supply using MS-Excel. It needs to be mentioned that the rainfall data used in this work is collected from Medinipur, which is 20  km away from the study area and may be slightly different from the rainfall actually received. The interpolation of point information to an areal one may also yield some deviations due to representative estimates of average precipitation. Actual runoff data for the Sundra catchment is not available due to lack of gauge station.

5.3  Relief To understand the inferred relief pattern of the area, a digital elevation model (Fig. 5.1) is prepared with the help of GIS techniques. The area seems to consist of dissected topography ranging from 15 to 104 m, where relative relief is high above 90 m and mean elevation of 49.2 m. The source region represents a steeper gradient and major undulation, where the gradient gradually declines at the lower part. The course of the catchment is rocky (Plate 5.1) and so is turbulent enough during peak flow. Some lateritic patches (Plate 5.2) are also found in this region. Steep slopes along the river banks are affected during monsoon. A greater percentage of area at the upper catchment is characterized with higher relief, where along the channel at the lower catchment, it extends with lower relief ranging from 15 to 38 m with average relative relief of 20 m (Fig. 5.2). Higher relief is also responsible for soil erosion, and as a consequence, it leads to water scarcity and low productivity. No such variation is found in the study area.

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Fig. 5.1  DEM of the Sundra catchment. (Source: SRTM DEM, 2010)

Plate 5.1  Rocky out crop along the channel

Plate 5.2  Lateritic patches

Fig. 5.2  Long profile of the Sundra catchment

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5  Hydrological Impact of Landuse Conversion

5.4  Slope The surface and sub-surface water direction are guided by the orientation and direction of the slope (Arnett 1971; Carter and Chorley 1961; Strahler 1950). In the study area, SW part of the upper catchment (Fig. 5.3) consists of a maximum slope ranging between 7 and 8 degrees, with slope ranging between 3 and 4 degrees at the lower catchment.

Fig. 5.3  Distribution of Slope within the study area

The steepness is the main factor for water scarcity that guides both surface and sub-surface water flow (Cluff 1974; Moitra Maiti 2008, 2010; Pandey 2002). An increase in mean and maximum slope angle is linked to an increase in the intensity of basal erosion (Kirkby 1971; Schumm 1956). Drainage density and slope angles are correlated positively, and as a consequence, it makes quick erosion (Howard 1997; Tucker et al. 2001). A proper understanding of slope aspect is necessary to arrest both surface and sub-surface water in the purpose of constructing a farm pond; check dam, contour bounding and other engineering structures are necessary to reduce the water scarcity and for better management of the region (Bloom 1998; Horton 1945; Morisawa 1985).

5.6 Drainage

117

5.5  Hydrology The area with underlying hard rock is characterized by very high saturation within the alluvium (O’Malley 1911). The shallow ground water exists in an unconfined zone where deeper water holds in confined state. The existence of granular materials, which are saturated during and immediately after rainfall with low retain capacity, makes the situation to collect yield water collect for cultivation. Annual rainfall provides about 120.38 million m3 of water, with an average annual runoff of 17.09 million m3. The depth of weathering even goes up to 120 m below ground level, and it is important from the point of ground water storage as it forms a good repository for ground water. At the lower part of the basin and along the channel with gentle gradient, water availability is high, and those are the most productive land in the region.

5.6  Drainage Sundra river (Fig.  5.4) consists of maximum fingertip non-perennial first-order streams and is the tributary of Tamal that ultimately pours into the river Silaboti, which is the principal tributary of the Rupnarayan (Hunter 1876).

Fig. 5.4  Sundra river is the tributary of river Tamal. (Source: SRTM, DEM 2010)

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The study area is under rain-fed agriculture, and water is available during the rainy season only. Channels flow according to natural slope from NW to SE direction. Some natural depressions (Plate 5.3) help store surface water for use in irrigation and domestic purposes. Proper watershed management may be an effective process to reduce the water scarcity (Schumm 1956). Using GIS techniques, a drainage map is prepared from SWAT tool to identify all the smaller channels that are not prominent on the surface as drainage lines. Numbers of cross profiles are drawn by ground survey (Plate 5.4) to make the long profile. At the upper section, no such definite channel is observed as it has a tendency of sheet wash (Morisawa 1985). The length of the Sundra River is 19.5 km and covers an area of 84.2 km2. The relative relief at the source is about 50 m, and means elevation is 69.25 m. Both long and cross profiles are observed steep slopes that make speedy drainage. GIS technique was applied to understand the nature of relief that would be helpful to arrest in situ moisture for better management options.

Plate 5.3  Natural depression on study area

5.7 SCS CN, USDA 1972 Method

119

Plate 5.4  Cross-profile survey on river Sundra

5.7  SCS CN, USDA 1972 Method SCS CN (1972) is applied for rural areas which is a well-accepted tool in hydrology, which uses land condition factor called “the curve number”. It includes several important properties (Fig. 5.5) as soil permeability, land use and antecedent moisture condition (AMC) of a watershed (Al-Jabari et al. 2007; Jackson et al. 1977). This model is originally developed for predicting runoff volumes from agricultural fields and small watershed (Moitra et al. 2009; Schwab et al. 1993).

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Fig. 5.5  Showing the relationship between HSG, LULC and AMC in CN estimation

5.7.1  Hydrological Soil Group (HSG) As defined by SCS USDA 1972, soil may be classified into four hydrological groups A, B, C and D depending on infiltration and grain size. Soil properties of the following four hydrological soil groups play an important role in estimation runoff and are also basically used to determine hydrological soil cover complexes. • Hydrological soil group A (Low runoff potential): In this group of soil, when it is thoroughly wetted, it indicates high infiltration rates. The soil consists chiefly of well to excessive drained with sands or gravels texture. High rate of water transmission at >10 cm/ hr. • Hydrological soil group B (Moderately low runoff potential): In this group of soil, when it is thoroughly wetted, it indicates moderate infiltration rates. The soil consists chiefly of moderately well to well and moderately deep to deep drained soils with moderately coarse textures. Rate of transmission is between 5.0 and 10.0 cm/hr. • Hydrological soil group C (Moderately high runoff potential): Soils letting a slow infiltration rate when thoroughly wetted. The soil also consists chiefly of moderately well to deep drained soils with moderately fine to moderately coarse textures. Rate of infiltration ranges between 2.5 cm and 5.0 cm/hr.

5.7 SCS CN, USDA 1972 Method

121

• Hydrological soil group D (High runoff potential): In this group of soil, when it is thoroughly wetted, it indicates a very slow infiltration rate. The soil consists chiefly of clay soils with fine texture and has a tendency of high swelling potential with a permanent high water table. The rate of water transmission is < 2.5 cm/hr. 5.7.1.1  Soil and Infiltration Test Eighty-six soil samples were collected randomly from the study area, and Unified Soil Classification System (USCS) was adopted to classify the soil sample. Sieve analysis and moisture content experiments were carried out to classify the soil samples (Fig. 5.6). The rate of infiltration was measured in the field followed by Goudie (1990) method. For monitoring infiltration rate in the field, several attempts were made on different sites with varied topographic and soil attributes. One experimental arrangement is set up here (Table 5.1; Fig. 5.7; Plate 5.5) as an example for such experiment. The constant head method is followed. Duration of time required for infiltration of 0.5 L of water through 1 sq ft area was recorded for calculation of infiltration rate.

Fig. 5.6  Showing the location of soil sample along with sieve analysis results. (Source: Field survey 2012)

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Table 5.1  Record book for measuring infiltration by constant head method at Banskopna village Water added No. (ml) 1 500 2 500 3 500 4 500 5 500 6 500 7 500 8 500 9 500 10 500 11 500 12 500 13 500 14 500 15 500

Time in sec for passing 0.5L water through 1 sq ft area 64 120 270 300 320 360 420 455 480 500 522 540 578 600 620

Infiltration capacity (cm/h) 29.8 16.1 7.1 6.4 6.0 5.3 4.6 4.2 4.0 3.8 3.8 3.5 3.3 3.2 3.1

Water added No. (ml) 16 500 17 500 18 500 19 500 20 500 21 500 22 500 23 500 24 500 25 500 26 500 27 500 28 500 29 500

Time in sec for passing 0.5L water through 1 sq ft area 646 650 653 660 680 720 740 720 900 920 940 960 980 985

Infiltration capacity (cm/h) 3.0 2.9 2.9 2.9 2.8 2.6 2.6 2.6 2.1 2.1 2.0 2.0 1.9 1.9

Source: Field observation

Infiltra on Rate (Field Measurement Following Goudie, 1990) 50 45 40 Rate (cm/h)

35 30 25 20 15 10 5 0

1

3

5

7

9

11

13 15 17 19 No of Recording

Fig. 5.7  Infiltration curve through field measurement (Banskopna)

21

23

25

27

29

5.7 SCS CN, USDA 1972 Method

123

Plate 5.5  Field measurement of infiltration rate at Banskopna

The cumulative infiltration volume is estimated by the integral of equation (5.1) after (Horton 1939). The initial and terminal rate of infiltration is used as inputs in Horton’s equation.

f p  fc   fo  fc 

e kt

(5.1)

fp = the infiltration capacity (depth/time) at some time t k = a constant indicating the rate of decrease in f capacity fc = a final and equilibrium capacity fo = the initial infiltration capacity On the basis of average result of infiltration rate and soil sample analysis (Table 5.2), a HSG map (Fig. 5.8) of the study area is prepared.

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Table 5.2  Showing the location of soil sample along with sieve analysis result and rate of infiltration Sl no Lat

Long

Texture

Rate of infiltration Sl in cm/h no Lat

1

22.58444 87.29403 Coarse

12.4

2

22.58953 87.28164 Coarse

11.3

3

22.58964 87.27414 Fine silty clay

2.1

4

22.592

4.3

5 6 7 8

9

87.26408 Fine silty clay 22.59442 87.25975 Fine silty clay 22.59581 87.25711 Coarse 22.60094 87.25667 Fine silty clay 22.60642 87.25558 Fine silty clay 22.61169 87.254

Fine silty clay 10 22.61092 87.24964 Fine silty clay 11 22.61114 87.24014 Coarse

12 22.60736 87.23128 Fine silty clay 13 22.60908 87.225 Sandy clay loom 14 22.60961 87.22203 Coarse 15 22.61828 87.24225 Fine silty clay 16 22.58719 87.30142 Fine silty clay 17 22.59489 87.30231 Coarse 18 22.59522 87.29736 Sandy clay loom 19 22.60002 87.28803 Sandy clay loom 20 22.60002 87.28803 Moderate coarse texture

5.3 10.8 4.8 2.1

1.2 3.2 9.6

4.3 3.5 14.7 5.2 3.2 19.2 4.5 6.4 5.6

Long

Texture

25 22.54481 87.32919 Sandy clay loom 26 22.55547 18.32975 Moderate coarse texture 27 22.58444 87.29403 Moderate coarse texture 28 22.57131 87.31787 Fine silty clay 29 22.56642 87.31915 Sandy clay loom 30 22.56 87.32442 Sandy clay loom 31 22.55569 87.32381 Sandy clay loom 32 22.55742 87.33406 Moderate coarse texture 33 22.56592 87.35086 Fine silty clay 34 22.56394 87.35838 Fine silty clay 35 22.58953 87.28164 Moderate coarse texture 36 22.56961 87.36572 Sandy clay loom 37 22.56703 87.36242 Coarse

Rate of infiltration in cm/h 4.5 7.5

6.8

1.5 4.7 3.6 3.1 6.3

2.1 2.3 7.3

4.5 10.2

38 22.56975 87.36806 Sandy 5.3 clay loom 39 22.57269 87.37781 Fine silty 3.2 clay 40 22.57661 88.36894 Coarse 9.8 41 22.58147 87.30383 Fine silty clay 42 22.58342 87.29842 Fine silty clay 43 22.58122 87.27758 Sandy clay loom 44 22.57933 87.2735 Fine silty clay

2.1 2.2 2.9 2.4

(continued)

5.7 SCS CN, USDA 1972 Method

125

Table 5.2 (continued) 21 22.60928 87.27723 Fine silty clay 22 22.61297 87.27311 Sandy clay loom 23 22.61347 87.26453 Sandy clay loom 24 22.62047 87.28642 Fine silty clay 49 22.62047 87.28642 Moderate coarse texture 50 22.60306 87.29278 Coarse 51 22.59814 87.29156 Sandy clay loom 52 22.59136 87.28611 Fine silty clay 53 22.59456 87.31011 Fine silty clay 54 22.59769 87.31933 Fine silty clay 55 22.59769 87.31933 Moderate coarse texture 56 22.60418 87.32278 Fine silty clay

1.2

45 22.59414 87.2645

3.4

46 22.59367 87.26678

5.6

47 22.59319 87.25747

1.3

48 22.60094 87.25667

6.4

68 22.58772 87.23978

12.5

69 22.59214 88.24833 Sandy clay loom 70 22.59714 87.23339 Fine silty clay 71 22.57575 87.25492 Fine silty clay 72 22.57464 87.26061 Coarse

4.5 2.1 2.5 2.3 7.8

1.9

5.5

3.2 5.3

3.5

6.9 3.2 3.1 17.2 2.1 2.2

7.4

4.3

16.4

5.5

80 22.55067 88.27725 Coarse

11.9

3.2

81 22.558

7.3

18.4

59 22.60394 87.3075

15.3

60 22.56361 87.33097 Sandy clay loom 61 22.56239 87.33436 Sandy clay loom 62 22.561 87.34586 Sandy clay loom

73 22.56658 87.25694 Fine silty clay 74 22.56997 87.26731 Fine silty clay

2.4

75 22.57314 87.25989 Moderate coarse texture 76 22.56319 87.26364 Fine silty clay 77 22.56097 87.2685 Fine silty clay 78 22.55972 87.27122 Fine silty clay 79 22.55328 87.27307 Coarse

57 22.60019 87.31383 Fine silty clay 58 22.60394 87.3075 Coarse Coarse

Fine silty clay Moderate coarse texture Fine silty clay Moderate coarse texture Sandy clay loom

1.6

63 22.55911 87.35136 Coarse

11.2

64 22.55911 87.35136 Moderate coarse texture

9.3

87.28114 Moderate coarse texture 82 22.55875 87.28353 Fine silty clay 83 22.55467 87.29381 Coarse

2.4 2.3 2.3

2.1 19.2

(continued)

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Table 5.2 (continued) 65 22.55911 87.35136 Moderate coarse texture 66 87.34856 22.54778 Moderate coarse texture 67 87.34856 22.54778 Moderate coarse texture

8.7

84 22.59592 87.33167 Sandy 5.4 clay loom

8.5

85 22.55794 87.30028 Coarse

17.3

9.4

86 22.56033 87.29807 Coarse

14.3

Source: Author’s own calculation based on field analysis

Fig. 5.8  HSG distribution of Sundra catchment. (Source: Department of agriculture; soil survey and landuse planning, field survey, 2012)

5.7.2  Land Use-Land Cover (LULC) Classification Land use changes can result in the change in flood frequency (Brath et  al. 2006; Crooks and Davies 2001), severity (De Roo et al. 2001), base flow (Wang et al. 2006) and annual mean discharge (Costa et al. 2003; Mustafa et al. 2012). It can also be changed in retention capacity, infiltration and ground water recharge potentiality. The dominant LULC type of Sundra catchment is dense and degraded forest land that accounts for about 28.85% of the entire area at present, but before land acquisition in 2007 (Fig. 5.9), it accounted for 38.9%. The land use conversion due to the proposed industrialization brought a considerable reduction (Table 5.3) in forest land by 10.1%. It also registered a decrease of agricultural land by 2.51%, eucalyptus plantation by 2.79% and fodder farm land by 3.93%. After clearance of ground cover

5.7 SCS CN, USDA 1972 Method

127

within the project area, cultivated waste/open land category increased by 14.6%. But after implementation of project plant (Fig. 5.10), built-up area (areas for residence, traffic, industry and thermal power plant) would be expected to increase by 12.9%. This land use conversion may set a serious effect on the hydrological system.

Fig. 5.9  LULC map of the Sundra catchment before land acquisition (2007). (Source: Shee and Maiti, 2019; Google earth 2007 and IRS P6 2007 (OCT))

Fig. 5.10  Expected LULC map (Source: Google earth 2014 and JSW master plan)

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Table 5.3  LULC conversion of the Sundra catchment Land use (in acres) Barren land Built up land Agricultural land Cultivated waste/open land Fodder farm land Forest land Orchard Eucalyptus plantation Water body Road and network Settlement area Total

2007 895.74 0 7324.3 791.82 816.3 8097.33 795.39 1321.37 155.37 195.06 393.02 20785.7

% 4.31 0.00 35.24 3.81 3.93 38.96 3.83 6.36 0.75 0.94 1.89 100

2014 1725.23 15.86 6801.03 3821.74 0 5996.09 608.35 742.11 105.37 474.06 495.88 20785.7

% 8.3 0.08 32.72 18.39 0 28.85 2.93 3.57 0.51 2.28 2.39 100

Change in % 3.99 0.08 −2.52 14.58 −3.93 −10.11 −0.90 −2.79 −0.24 1.34 0.50

Source: Satellite image, ground survey and Google Earth imagery

5.7.3  Antecedent Moisture Condition The antecedent moisture condition (AMC) value is a combined effect of infiltration on both the volume and rate of runoff according to the infiltration curve that indicates the present moisture level in the soil at a given time. Soil Conservation Service (SCS) has recognized its significance and developed a guideline on how to adjust the CN (Curve number) according to AMC based on the total rainfall in the 5-day period preceding a storm (Al-Jabari et al. 2007). SCS developed three antecedent moisture conditions and labelled them as I, II and III. These AMCs correspond to the following moisture conditions: • AMC-I Soil is dry but not to wilting point; satisfactory cultivation has taken place. • AMC-II Average condition. • AMC-III Saturated soil; last 5 days received heavy rainfall and low temperature. The weighted CN of the study area is calculated on normal condition (AMC II), the CN for the other two condition; the dry condition (AMC I) and the wet condition (AMC III) are obtained using Equations (5.2) and (5.3) (Chow et al. 1988). CN  I  

4.2  CN  II 

CN  III  



10  0.058  CN  II  23  CN  II 



10  0.13  CN  II 

 (5.2)  (5.3)

5.8 Determination of Sub-watershed-Wise Weighted CN

129

5.8  Determination of Sub-watershed-Wise Weighted CN The curve number values are derived on the basis of soil-vegetation-land (SVL) complex and antecedent moisture condition (AMC). The soils are grouped into four types according to their hydrological character. The land use with respect to their hydrology, i.e. draining conditions is analysed with due importance. Erosion and evapotranspiration, deforestation, industrialization, urbanization and other land use activities can significantly alter the seasonal and annual distribution of stream flow (Dunne and Leopold 1978; Kumar et al. 1991). Human intervention as well as natural changes in land use can be identified by comparing the satellite imageries in different times. In the present study, present land use is estimated by analysing the available imagery. The expected changes in land use are estimated by incorporating JSW master plan as data input. The soil series are classified into hydrological soil groups (A, B, C and D) based on the physical soil characteristics following the USDA 1972 method. The CN of a given soil-vegetation-land complex in a specific antecedent moisture condition (AMC) takes on values from 0 to 100. For detailed estimation of hydrological parameters and clear understanding of the possible impacts of basin hydrology due to LULC conversion after land acquisition, Sundra watershed (approx. 84.2  km2) is divided into fourteen (14) sub-­ watersheds (Fig.  5.11) by using SWAT tool based on channel configuration and digital elevation model, prepared from SRTM data and SOI Topographical map. The entire sub-watershed land use is divided according to their HSG, and then CN values are assigned from CN table to estimate weighted CN followed by SCS CN USDA 1972 technique. Weighted CN (Equation 5.4) is calculated based on the following steps: • Step-I The land use classification is made and distributed to HSG. • Step-II The curve number for each land use category under certain soil group is selected to estimate the weighted curve number with the following formula: Weighted CN 

 Ai  CN i  Ai

(5.4)

where CN is the weighted curve number and Ai is the area for each curve number. The weighted curve number of the study area is estimated sub-watershed-wise for the normal condition (AMC II) of the Sundra catchment.

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Fig. 5.11  Fourteen (14) sub-watersheds of Sundra catchment. (Source: Based on SWAT analysis tool, 2009)

5.8.1  Sub-watershed A Sub-watershed A is located at the upper catchment (Fig. 5.12); it occupies an area of 9.9 km2, of which about 2.9 km2 is the agricultural land. LULC (2014) is dominated by forest land (51.7%) and crop land (29.1%). Its soil has high infiltration rates and is mainly composed of sands or gravels. This area is covered with HSG A and B by 66% and 33%, respectively. Entire land use is divided according to HSG (2014), and then CN values are assigned from CN table to compute the weighted CN. The weighted CN of sub-watershed A is 62.3. No major land use conversion is observed in this sub-watershed.

5.8 Determination of Sub-watershed-Wise Weighted CN

131

Fig. 5.12  LULC classification according to HSG in sub-watershed A. (Source: Based on Google earth 2014; Department of Agriculture soil classification map)

5.8.2  Sub-watershed B Sub-watershed B (Fig. 5.13) is covered with an area of 3.5 km2 and mainly consists of degraded forest (60.4%) followed by monocrop land (16.1%) and barren land (7.7%). High percentage of HSG A (56.3%) and HSG B (41.1%) indicates that the soils have high transmission rates of more than 8 cm/hr. Weighted CN of sub-­ watershed B is 61.05.

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Fig. 5.13  LULC classification according to HSG in sub-watershed B. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)

5.8.3  Sub-watershed C Sub-watershed C (Fig. 5.14) is mainly composed of degraded forest (39.4%), followed by dense forest (22.9 %), monocrop land (16.6 %) and double cropping land (7.1%); in the present condition (2014), the entire sub-watershed occupies an area of 5.5 km2. Soil has high to moderate water transmission rate, ranging between 5 and 10 cm/hr. The percentage of HSG in category of A, B and C is observed, respectively, to be 69.2%, 24.6% and 6.2%. The weighted CN is calculated to be as 52.9 at the sub-watershed.

5.8 Determination of Sub-watershed-Wise Weighted CN

133

Fig. 5.14  LULC classification according to HSG in sub-watershed C. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)

5.8.4  Sub-watershed D Sub-watershed D (Fig. 5.15) is located in between sub-watershed B and C and covers an area of 1.5 km2 of which 41.8% is monocrop land and 26.0% is well-irrigated land suitable for “Rabi” crop. Eucalyptus plantation covers 15.6% of the area. Soil has low to very slow infiltration rate ranging between 2 and 5 cm/hr. This sub-­ watershed is covered of HSG B by 37.6% and HSG C by 58.1%. The weighted CN is estimated 74.2.

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Fig. 5.15  LULC classification according to HSG in sub-watershed D. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)

5.8.5  Sub-watershed E This sub-watershed (Fig. 5.16) is mainly composed of HSG A by 41.3% and HSG B by 57.2%. It indicates that soil is not suitable for agriculture due to high transmission rate. It occupies an area of 3.9 km2 including 28.9% of degraded forest, 23.0% of monocrop land, 8.7% settlement area and 18.9% eucalyptus plantation. The weighted CN is estimated as 62.7.

5.8 Determination of Sub-watershed-Wise Weighted CN

135

Fig. 5.16  LULC classification according to HSG in sub-watershed E. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)

5.8.6  Sub-watershed F This sub-watershed is located in the central position of the entire basin (Fig. 5.17). It covers an area of 5.9 km2 including 31.3% of monocrop land followed by 17.3% of degraded forest, 14.8% of eucalyptus plantation, 12.3% of double cropping land and 11.1% of area with lateritic exposures. Majority of HSG is B (41.1%). HSG C covers an area of 36.2%. The estimated weighted CN is 68.7.

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5  Hydrological Impact of Landuse Conversion

Fig. 5.17  LULC classification according to HSG in sub-watershed F. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)

5.8.7  Sub-watershed G Sub-watershed G (Fig. 5.18) encompasses an area of 6.3 km2, representing an undulating topography of interfluve region. The concerned region is mainly composed of HSG A (31.0%), HSG B (21.6%) and HSG C (42.3%). Major LULC is observed as monocrop land (29.1%), double cropped land (20.9%), degraded forest (15.2%) and eucalyptus plantation (8.1%). The weighted CN is 70.4.

5.8 Determination of Sub-watershed-Wise Weighted CN

137

Fig. 5.18  LULC classification according to HSG in sub-watershed G. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)

5.8.8  Sub-watershed H The concerned sub-watershed (Fig. 5.19) shows a major part of the lands of fertile land that includes an area of 33.4% of well-irrigated crop land, 16.9% of monocrop land and 32.8% of degraded forest. The soil textures normally observed here are clay loam, silt clay loam, sandy clay, silt clay and clay. This soil has a very low rate of water transmission that varies between 0.5 and 2 cm/hr. This soil shows a high swelling potential with a permanent high water table. As per HSG classification, 67.8% of the area falls in category C and the remaining 32.2% in category D. This small sub-catchment covers an area of 4.5 km2, and weighted CN is 80.2.

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Fig. 5.19  LULC classification according to HSG in sub-watershed H. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)

5.8.9  Sub-watershed I This small sub-watershed (Fig. 5.20) covers an area of 1.5 km2 and is located at the lowermost section of the catchment. Due to availability of surface and sub-surface water throughout the season, it concentrates in the cultivation of “Rabi” crop as rice, while rapeseed, beans and potato are the major optional crop in winter. The sub-­ watershed mainly includes monocrop land (33.1%), double cropped land (57.3%) and orchard (4.0%). The soil is mainly composed of clay (48%) and silt (28%). The weighted CN is 82.

5.8 Determination of Sub-watershed-Wise Weighted CN

139

Fig. 5.20  LULC classification according to HSG in sub-watershed I. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)

5.8.10  Sub-watershed J Sub-watershed J (Fig. 5.21) occupies an area of 6.2 km2 and is mainly composed of fine to moderately coarse texture. Water transmission rate ranges between 2 and 5 cm/hr. HSG distribution in the sub-catchment shows that a greater percentage area of HSG is characterized by group C (68.7%) and group D (31.3%). The LULC includes double cropped land (25.7%), degraded forest (23.7%), monocrop land (22.3%) and barren land (15.2%). As per SCS CN USDA 1972 method, weighted CN is calculated as 81.2.

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5  Hydrological Impact of Landuse Conversion

Fig. 5.21  LULC classification according to HSG in sub-watershed J. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)

5.8.11  Sub-watershed K Sub-watershed K (Fig. 5.22) occupies an area of 6.1 km2 including 33.4 % of monocrop land, 17.3% of degraded forest and 14.8% of double cropped land; 15.0% of the area however is not suitable for cultivation due to lack of water supply. A lateritic exposure is observed by 8.2%. Soil has moderate to high rate of water transmission ranging between 5 and 9 cm/hr. Weighted CN is 82.4.

5.8 Determination of Sub-watershed-Wise Weighted CN

141

Fig. 5.22  LULC classification according to HSG in sub-watershed K. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)

5.8.12  Sub-watershed L Sub-watershed L (Fig. 5.23) represents a little undulation, but much variation is not found. The area is served by fingertip first-order streams which are mostly undefined and flow through channels that are mostly filled with silt. Water flows only after rain. Concerned sub-watershed occupies an area of 12.4  km2 including degraded forest (43.1%), monocrop land (17.9%), double cropped land (10.4%) and dense forest (15.0%). Soil has moderately low to moderately high runoff potential with water transmission rate between 4 and 8 cm/hr. The calculated weighted CN is 70.5.

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5  Hydrological Impact of Landuse Conversion

Fig. 5.23  LULC classification according to HSG in sub-watershed L. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)

5.8.13  Sub-watershed M Soil texture of sub-watershed M (Fig.  5.24) is mainly composed of sand, sandy loam and silt loamy with high infiltration rate and low runoff potentiality. This sub-­ watershed falls under the proposed JSW industrial project area, and a huge land use alteration is observed after land acquisition (2009) in this 9.2 km2 sub-watershed. The proposed industrialization brought a considerable reduction in degraded forest (38.4%), monocrop land (6.1%) and double cropped land (4.2%). As a consequence, after the ground clearance, existing land use is categorized as open land/cultivated waste land. The weighted CN in present condition is 68.0. But after industrialization due to increase of built-up land and impervious area by more than 80%, the expected weighted CN would be as 87.4. The weighted CN was 59.3 before land acquisition (2007).

5.8 Determination of Sub-watershed-Wise Weighted CN

143

Fig. 5.24  LULC classification according to HSG in sub-watershed M. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)

5.8.14  Sub-watershed N Similar type of land use conversion is observed at another project-affected sub-­ watershed N (Fig. 5.25); it occupies an area of 7.7 km2. Due to ground clearance for the proposed JSW industrial project, in 2009, agricultural land (11.7%), degraded forest land (34.9%) and eucalyptus plantation (11.2%) were abolished from this sub-watershed, but alternatively, a built-up area would be expected to increase by 81.9% after completion of the proposed industry. The weighted CN is expected to change from 64.3 in present conditions to 86.5 in the future after implementation of total project work. The weighted CN was 62.1 before land acquisition (2007).

144

5  Hydrological Impact of Landuse Conversion

Fig. 5.25  LULC classification according to HSG in sub-watershed N. (Source: Based on SWAT tool; Google earth 2014; Department of agriculture, field survey)

5.8.15  Change in Curve Number Pre- and post-developmental drainage situation in terms of weighted curve numbers is plotted sub-watershed-wise in Figs.  5.26 and 5.27, respectively. It is observed from the above analysis that due to huge land use alteration for the proposed JSW industrial project, sub-watershed M and N would be affected severely. As a consequence, average weighted CN of the catchment is expected to change from 67.6 to 74.1 in expected condition, while it was 63.2 before land use conversion (2007); it may set a serious alteration in basin hydrology.

5.9 Calculation of Discharge (in Million m3)

145

Fig. 5.26  Sub-watershed-wise weighted CN based on existing (2014) LULC and soil condition (AMC II). (Source: Based on SCS CN USDA, 1972 method)

Fig. 5.27  Sub-watershed-wise weighted CN after implementation of JSW industrial work (AMC II). (Source: Based on SCS CN USDA, 1972 method)

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5  Hydrological Impact of Landuse Conversion

5.9  Calculation of Discharge (in Million m3) To calculate the surface runoff depth, the hydrological equation (5.5) is applied. The equation depends on the daily rainfall data in mm (P) and watershed storage (S) which is calculated from adjusted CN (Moitra Maiti and Maiti 2009; Schwab et al. 1993).

 P  0.2S  Q  P  0.8S 

2



 Equation applicable to small watershed  (5.5)

where:



S

25400  254 CN

 inmm 

(5.6)

Q = actual direct runoff (in mm) P = total rainfall (in mm) CN = SCS runoff curve number Initial abstraction (Ia)  It consists mainly of interception, infiltration and surface storage, all of which occur before runoff begins. An exact determination of initial abstraction is very difficult, and for practical purposes, it can be related to S. The Soil Conservation Service (1969) found that initial abstraction is to be roughly equal to 0.2S (Maitra Maiti and Maiti 2009). It can also be estimated by relating to the antecedent soil moisture index, as done by Allen et al. (1998); Hamon (1963); and Neitsch et al. (2011), among others. Potential Maximum Retention (S)  It includes Ia, and the infiltration occurring after runoff begins. This infiltration is controlled by the rate of infiltration at the soil surface or by the rate of transmission in the soil profile by the water storage capacity of the profile, whichever is the limiting factor (Hand Book of Hydrology, 1972). The maximum limit of “S” depends on soil character, soil cover complex and the intensity of rainfall. During a successive period of storm, the magnitude of “S” will be reduced. The “S” factor is also related with AMC determined by the total rainfall in the 5 day’s period preceding a storm. Based on SCS CN 1972 method, volume of the runoff is equal to the surface runoff (depth) multiplied by the watershed area. The result of runoff volume in the study area is estimated from a given daily rainfall data and antecedent moisture condition using an appropriate curve number. Seasonal variation of runoff (in Mm3) before acquisition (2007), after acquisition (2014) and on expected condition after full construction following JSW master plan will be estimated (Table 5.4).

5.9 Calculation of Discharge (in Million m3)

147

Table 5.4  Sub-watershed-wise runoff (Mm3) estimation in 2007, 2014 and expected condition Sub-­ watershed A B C D E F G H I J K L M N Total

Runoff estimation (Mm3) in pre-monsoon 2007 2014 Expected 0.09 0.09 0.10 0.03 0.03 0.04 0.02 0.02 0.04 0.03 0.03 0.04 0.03 0.03 0.04 0.07 0.08 0.11 0.09 0.11 0.21 0.17 0.17 0.17 0.06 0.06 0.09 0.24 0.25 0.27 0.26 0.26 0.28 0.18 0.22 0.34 0.03 0.07 0.64 0.05 0.09 0.61 1.35 1.51 2.98

Runoff estimation (Mm3) in monsoon 2007 2014 Expected 0.84 0.84 0.86 0.27 0.27 0.27 0.21 0.24 0.35 0.25 0.28 0.33 0.32 0.32 0.32 0.71 0.74 0.82 0.91 0.94 0.97 1.17 1.17 1.17 0.46 0.46 0.46 1.76 1.80 1.88 1.79 1.83 1.91 1.77 1.78 1.79 0.26 0.58 3.59 0.32 0.74 3.29 11.03 12.01 18.02

Runoff estimation (Mm3) in post-monsoon 2007 2014 Expected 0.07 0.07 0.07 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.07 0.07 0.08 0.06 0.08 0.10 0.12 0.12 0.12 0.05 0.05 0.05 0.16 0.18 0.24 0.18 0.18 0.18 0.16 0.16 0.16 0.03 0.05 0.28 0.04 0.06 0.15 1.03 1.11 1.54

Source: Author’s own calculation

5.9.1  Pre-monsoon Based on sub-watershed-wise weighted CN and hourly rainfall data of Sundra catchment, it is observed that during pre-monsoon project-affected sub-watershed M and N, together, they produce almost double 0.16 Mm3 of runoff after land acquisition, but it was 0.08 Mm3 in 2007. An estimation also observed that after implementation (expected) of proposed project work, the total runoff amount will be voluminous by 1.47 Mm3 (Table 5.4). As a consequence, the basin will produce an average of 1.51 Mm3 and 2.98 Mm3 runoff, respectively, in 2014 (Fig. 5.28) and in expected condition (Fig. 5.29).

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Fig. 5.28  Sub-watershed-wise runoff variation (Mm3) in pre-monsoon. (Source: Based on SCS CN USDA, 1972 method)

Fig. 5.29  Sub-watershed-wise runoff variation (Mm3) in pre-monsoon. (Source: Based on SCS CN USDA, 1972 method)

5.9 Calculation of Discharge (in Million m3)

149

5.9.2  Monsoon During monsoon, the basin area produces an average of 12.01 Mm3 of runoff after LULC alteration (Fig. 5.30), but it would be expected to reach nearly 18.02 Mm3 after implementation of the proposed project (Fig.  5.31). Only the affected sub-­ watershed (M and N) contributes 0.74 Mm3 more runoff, and it will be expected to contribute 5.57  Mm3 of additional runoff. After implementation of the proposed project, total runoff volume is expected to high by 6.01  Mm3 (Table  5.4) during monsoon; it may cause water scarcity in the ambient region of affected mouzas in dry season; also, greater intensity flood will be observed at the lower reach of the parent stream, the Silabati.

Fig. 5.30  Showing sub watershed wise runoff variation (Mm3) in monsoon after land acquisition in 2014. (Source: Based on SCS CN USDA, 1972 method)

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5  Hydrological Impact of Landuse Conversion

Fig. 5.31  Sub-watershed-wise runoff variation (Mm3) in monsoon in expected condition based on JSW master plan. (Source: Based on SCS CN USDA, 1972 method)

An initial impact of land use conversation after land acquisition (2009) during monsoon was observed at field. On July 5, 2013, at project site, 106 mm rainfall was received within 48 hrs. Due to the clearance of forest land, the runoff volume was increased by 0.15 Mm3, and this voluminous runoff destroyed the boundary wall of JSW industry at Arabari (Plate 5.6), Barju (Plate 5.7), Asnasuli (Plate 5.8) and Banskopna (Plate 5.9).

Plate 5.6  JSW project boundary collapsed at Arabari

5.9 Calculation of Discharge (in Million m3)

Plate 5.7  JSW project boundary collapsed at Barju

Plate 5.8  JSW project boundary collapsed at Asnasuli

151

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5  Hydrological Impact of Landuse Conversion

Plate 5.9  JSW project boundary collapsed at Banskopna

This again disrupted the local transport networks (Plate 5.10), and agricultural land was filled with sand and gravels (Plate 5.11) due to overflow of runoff near the boundary wall. Due to disruption of the drainage system, water logging caused considerable damage to the local houses (Plate 5.12).

5.9 Calculation of Discharge (in Million m3)

Plate 5.10  Disrupted local transport network near Banskopna

Plate 5.11  Damaged agricultural field at Banskopna

153

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5  Hydrological Impact of Landuse Conversion

Plate 5.12  Damaged local houses at Natundihi

5.9.3  Post-monsoon An opposite picture is observed during dry season. Soil moisture does not retain for a long time at upper catchment due to high gradient and coarse texture of soil, and as a consequence, water scarcity is observed in domestic and agriculture sector (Plate 5.13). Quick and swift drainage due to ground surface clearing restricts retention of moisture that ultimately leads to water scarcity in prolonged dry season.

5.9 Calculation of Discharge (in Million m3)

155

Plate 5.13  Water scarcity at an agriculture field

Proposed industrialization may increase the built-up area by 12.9% of total catchment area at the cost of barren, agricultural and forest land. Land use conversion may set a serious alteration in basin hydrological system. Annual runoff may become voluminous by 7.91  Mm3 (Table  5.5). Maximum runoff volume will be increased in the month of July by 1.88 Mm3 (Fig. 5.32). In spite of several limitations of validation of this calculation, it may be concluded that average rainfall is quite sufficient to support agriculture and related activities in the present condition. But proper planning is required to retain this additional runoff within the basin. A monthly water budget (Table 5.6) is constructed for such planning.

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Table 5.5  The average rainfall and runoff volume in the study area Avg rainfall (mm) Month January 11.9 February 15.3 March 44.6 April 58.2 May 143.3 June 279.7 July 359.0 August 296.1 September 250.6 October 107.3 November 19.15 December 4.7 Total 1590.5

Runoff volume (106 × m3) in 2014 0.01 0.04 0.21 0.19 1.09 3.66 3.48 2.49 2.39 0.95 0.12 0 14.63

Runoff volume (106 × m3) in expected condition 0.04 0.08 0.38 0.36 2.15 5.11 5.36 3.94 3.62 1.31 0.17 0.01 22.54

Change in runoff volume (106 × m3) 0.03 0.04 0.17 0.17 1.06 1.45 1.88 1.45 1.23 0.36 0.05 0.01 7.91

Source: Author’s own calculation

6.00

volume in Mm3

5.00 4.00 3.00 2.00 1.00 0.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014

Expected

Fig. 5.32  Expected change in runoff volume (Mm3)

5.10 Evapotranspiration

157

5.10  Evapotranspiration In the present work, anticipated water scarcity in the future as a result of land use conversion is estimated in an attempt for proactive management at the initial construction stage of the proposed industrial complex in Sundra basin. Crop water requirement is estimated with a consideration of double cropping through introduction of irrigation in the near future. Expected ET during Oct–Feb at present temperature level is calculated and is considered as requirement of water for irrigation. Jensen et al.’s (1990) equation and Penman (1948 and 1963) used the 24-hour crop evapotranspiration (ETc in mm per day) using daily rainfall data from Medinipur (Abash; 1989–2015) for this purpose. Crop coefficient (Kc) is used as an input to compute the actual crop water requirement (ETc). The equation is given as:

 ET0 

  6.43 1.0  0.53v2  Rn  G      



 e

S

 ed 

 Penman, 1963

 (5.7)

where: λET0 = reference ET expressed as latent heat flux density for a well-watered grass, MJ m−2day−1 Δ = saturation vapour pressure curve slope in kPa/0C γ = psychrometric constant in kPa/0C Rn = net radiation in MJ m−2day−1 G = heat flux density to the soil in MJ m−2day−1 v2 = average wind speed (m/s) at a height of 2 m es = mean air temperature saturated vapour pressure in kPa ed = mean dew-point temperature saturated vapour pressure in kPa (es × mean relative humidity) The following equation and constants were summarized from (Penman 1963) and (Jensen et al. 1990). Values for Δ can be obtained from Equation 5.7.   0.20  0.00738T  0.8072   0.000116 7



(5.8)

where T= mean air temperature in 0C. The psychrometric constant γ in kPa/0C is calculated using Equation 5.3.

  0.00163 P

 (5.9)

where:

P  101.3  0.01055  EL 

(5.10)

  2.501  0.002361T (5.11)

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5  Hydrological Impact of Landuse Conversion

where P= estimated atmospheric pressure in kPa. MJ EL = elevation in m kg λ= latent heat of vaporization of water in The net radiation can be calculated from Equation 5.7.





0.5 Rn  1    Rs   Ta4 0.34  0.139  ed   0.1  0.9 n N (5.12)  

where:

Rn = solar radiation received by the surface of earth in MJ m−2day−1 α = radiation reflection or albedo with values near 0.25 for green crops σ = Stefan-Boltzmann constant (4.903 × 10−9MJm−2day−1ok−4) Ta = absolute air temperature in ok (oC + 273) n = ratio of actual to possible hours of sunshine N If solar radiation is not measured, it can be obtained from Equation 5.13.



Rs  0.35  0.61 n



N

R

so

(5.13)

where Rso is the mean solar radiation for cloudless skies in MJ m−2day−1. The saturation vapour pressures at mean air temperature (es) and at mean dew-­ point temperature (ed) are calculated from Equations (5.14) and (5.15):

8 es  3.38639  0.00738T1  0.8072   0.00019 1.8T1  48  0.001316    (5.14)

where: T1 = the mean air temperature in oC ed  3.38639  0.00738Td  0.8072   0.00019 1.8Td  48  0.001316    (5.15) 8

where: Td = the mean dew point temperature Equation 5.16 is based on the August-Roche-Magnus approximation used to calculate the dew point Td in the given relative humidity (RH) and the actual temperature T of air as: Td 

b  T ,RH 

a    T ,RH 

(5.16)

5.11 Water Budget

159

where:



  T ,RH  

aT  ln  RH / 100  bT (5.17)

where temperature is in °C and “ln” means the natural logarithm. The constant are: a = 17.271 b = 237.7 oC Since Equation 5.7 estimates reference evapotranspiration as latent heat flux density for a well-watered grass, actual evapotranspiration for other crops is estimated with crop coefficients from Jensen et al. (1990) and Makurira et al. (2007). Actual evapotranspiration (in mm/day) is calculated using the reference evapotranspiration following Jensen et al. (1990):



ETc  K c 

 ETo

 (5.18)

where: ETc = the estimated ET for a crop in mm/day Kc = the crop coefficient for a specific crop and location.

5.11  Water Budget Crop coefficient (Kc) is defined as the ratio of the crop evapotranspiration to the reference evapotranspiration to compute the actual crop water requirement (ETc) (Allen et al. 1998; Biswas 2004; Makurira et al. 2007; Van der Zaag 2005). The trends of crop evapotranspiration (ETc) for “Rabi” season is illustrated in Table 5.6. Only the monsoon season receives sufficient rainfall, while the rest of the seasons require supplemental irrigation. The irrigation water requirement is considered to be equivalent to amount of evapotranspiration. For full-scale irrigation (Table  5.7), 2.27 × 106m3 of additional water would be required for 1111 acres to grow rice, the preferred “Rabi” crop. The farmers reported that whenever they have excess water from the canal, they try to save it for future use (Plate 5.14). Generally, farmers do not require water to irrigate immediately after a good spell of rainfall as their irrigation schedule is adjusted by the rainfall pattern. During rainfall events, farmers capture excess runoff by drain and store it in the pond although all the excess runoff could not be stored. A good amount of surplus runoff is available throughout the year, but all the runoff is not available at one time. If farm ponds are constructed at suitable locations (Fig. 5.33), excess runoff may be effectively stored for future use. Seven farm pond locations are proposed based on surface configuration, contributing area and runoff flow direction.

0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

0.02

0.13 0.57 0.86 1.02 0.87 0.51 0.44 0.37 0.26 0.28 0.24

0.28

Source: Author’s own calculation

FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Total

Month JAN

0.15 0.59 0.88 1.04 0.89 0.53 0.46 0.39 0.28 0.3 0.26

0.3

Household Agricultural demand demand Total Water demand in 2014 (in Mm3)

68.5 104.3 127.3 135.9 113.6 99.7 97.8 86.3 78.6 60.8 52.8

99.4 169.2 212.8 226.7 175.6 122.4 110.8 94.3 66.7 91.3 130.9

ETo ETc mm/month mm/ month/m2 56.3 111.74

C 53.0

D 41.2

E 37.6

F 109.5

M 407.5

0.01 0.01 0.02 0.08

0.03 0.04 0.06 0.22

0.11 0.15 0.22 0.82

0.01 0.02 0.02 0.09

0.03 0.03 0.05 0.19

0.01 0.02 0.03 0.10

0.18 0.16

0.04 0.02 0.02 0.02 0.05 0.04 0.02 0.02 0.02 0.04 Monsoon season no water shortage

Amount of water requirement (Mm3) for “Rabi” crop

A 94.5

Sub-watershed-wise area (acres) for double cropping

Table 5.6  Water budget 2019 for proposed crop water requirement (“Rabi” crop)

0.1 0.14 0.2 0.76

0.17 0.15

N 368.2

Supply (Mm3) 0.01 0.02 0.11 0.10 0.63 2.18 2.02 1.44 1.38 0.55 0.06 0.00 8.50

Available runoff (Mm3)

160 5  Hydrological Impact of Landuse Conversion

5.11 Water Budget

161

Plate 5.14  Water storage for future use

Fig. 5.33  Location of proposed farm ponds at upper and middle catchment of Sundra watershed

A supply and demand analysis for each proposed farm pond is made to estimate the size of storage. Mass curves (Figs. 5.34, 5.35, 5.36, 5.37, 5.38, 5.39 and 5.40) are drawn to estimate the amount of maximum deficit of a year is required to be stored in the concerned farm ponds to meet yearlong demand.

162

5  Hydrological Impact of Landuse Conversion

FARM POND 1 1.20 1.00

Supply

Expected Irrigated land 94.5 acre

IN Mm³

0.80 Demand

0.60 0.40

Expected Reservoir Volume 0.23 Mm³

0.20 0.00

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Fig. 5.34  Mass curve of proposed farm pond at sub-watershed A

FARM POND 2 0.50 0.45 0.40

Supply Expected Irrigated land 53.03 acre Demand

INMm³

0.35 0.30 0.25 0.20 0.15

Expected Reservoir Volume 0.14 Mm3

0.10 0.05 0.00

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Fig. 5.35  Mass curve of proposed farm pond at sub-watershed C

5.11 Water Budget

163

FARM POND 3 0.45 0.40 0.35

Expected Irrigated land 41.20 acre

Supply

IN Mm³

0.30 Demand

0.25 0.20 0.15

Expected Reservoir Volume 0.11 Mm3

0.10 0.05 0.00 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Fig. 5.36  Mass curve of proposed farm pond at sub-watershed D

FARM POND 4 0.45 0.40 0.35

Expected Irrigated land 37.67 acre

Supply

IN Mm³

0.30 Demand

0.25 0.20 0.15

Expected Reservoir Volume 0.11 Mm3

0.10 0.05 0.00

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Fig. 5.37  Mass curve of proposed farm pond at sub-watershed E

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5  Hydrological Impact of Landuse Conversion

FARM POND 5 1.20 1.00

Supply Expected Irrigated land 109.57 acre

IN Mm³

0.80

Demand

0.60 0.40

Expected Reservoir Volume 0.28 Mm3

0.20 0.00

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Fig. 5.38  Mass curve of proposed farm pond at sub-watershed F

FARM POND 6 5.00 4.50 4.00

Expected Irrigated land 407.56 acre

Supply

IN M m3

3.50 3.00

Demand

2.50 2.00 1.50

Expected Reservoir Volume 0.98 Mm3

1.00 0.50 0.00

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Fig. 5.39  Mass curve of proposed farm pond at sub-watershed M

5.11 Water Budget

165

FARM POND 7 4.50 4.00 3.50

Supply Expected Irrigated land 368.23 acre

IN Mm³

3.00 Demand

2.50 2.00 1.50

Expected Reservoir Volume 0.82 Mm3

1.00 0.50 0.00

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Fig. 5.40  Mass curve of proposed farm pond at sub-watershed N

Estimation shows (Table 5.7) that an amount of approximately 2.7 Mm3 of runoff accounted to store at suitable parts of the upper catchment through construction of cascading check dams would be needed to meet the demand in the “Rabi” season.

Table 5.7  Reservoir volume (Mm3) of proposed farm ponds Sub-­ watershed 1 2 3 4 5 6 7 Total

No of farm ponds 1 1 1 1 1 1 1 7

Expected irrigated land (acres) 94.5 53.03 41.2 37.67 109.57 407.56 368.23 1111.7

Source: Author’s own calculation

Water requirement (Mm3) for “Rabi” crop 0.19 0.1 0.09 0.08 0.22 0.82 0.76 2.26

Reservoir volume (Mm3) 0.23 0.14 0.11 0.11 0.28 0.98 0.82 2.67

Contribution of runoff to the pond Mm3 0.62 0.46 0.4 0.4 1.14 2.57 2.2 7.79

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5  Hydrological Impact of Landuse Conversion

The number of farmers that would be benefited from a given farm pond depends on the topography of the area and the population of the village. An estimated 500 families would become the beneficiaries, and expectedly their crop income will be raised by 20–25% if the proposed farm ponds are constructed at suitable locations and maintained properly.

5.12  Major Findings • LULC conversion for the proposed industrialization brought a considerable reduction in forest land by 10.1%, agricultural land by 2.51%, eucalyptus plantation by 2.79% and fodder farm land by 3.93%. After clearance of ground cover within the project area, cultivated waste/open land category increased by 14.6%. Built-up land and metal road area increased by 0.08% and 1.34%, respectively. • The entire 84.2 km2 Sundra watershed is divided into 14 sub-watersheds. • Composite curve number as a result of land use and soil complex of Sundra catchment is 67.6, but after implementation of the proposed plant, it would be 74.1. • The maximum volume of runoff (4.51  Mm3) is expected from sub-watershed “M” after proposed land use change. • Runoff volume is expected to rise in the future by 1.47  Mm3, 6.02  Mm3 and 0.43  Mm3, respectively, in pre-monsoon, monsoon and post-monsoon after implementation of the proposed project. Maximum runoff volume will be increased on the month of July by 1.88 Mm3. • Annual runoff calculated on the basis of average rainfall is 14.59 Mm3, and due to land use alteration, it may reach to 22.52 Mm3. • The infiltration rate over the basin is estimated to be 1.26–11.78 cm/hr following (Horton 1945). • Monthly rate of evapotranspiration ranges from 1.71mm/sq m/day to 4.39 mm/ sq m/day. • Maximum ET loss in the month of April amounts to 4.25 mm/sq m/day, and in May, it becomes 4.39 mm/sq m/day. • The annual domestic demand is estimated to be 0.2 x106 m3 in existing condition, while the agriculture demand becomes 5.83 x 106 m3 for 2882 acres of land. A scheme of arresting 2.7 x 106 m3 of runoff in at least “7” cascading farm ponds at upper and middle catchment would provide irrigation to 1111 acres of land. A total of 500 cultivator families would be benefited.

References

167

5.13  Conclusion LULC conversion may have serious impacts on regional hydrology. To understand possible impacts, the entire basin has been sub divided into 14 sub-watersheds. Curved number (CN) method of USDA is followed to estimate the impacts. In the context of reduction of vegetation coverage, agriculture and farm lands, the CN is likely to be higher (~74.0) in expected condition compared to the earlier one (67.6). As a consequence, it is expected that  runoff will be voluminous by 1.47  Mm3, 6.02 Mm3 and 0.43 Mm3, respectively, in pre-monsoon, monsoon and post-­monsoon after implementation of the proposed project. At present, the water demand is relatively low, and it is fulfilled from natural sources. But due to low retention capacity of soil moisture, “Rabi” crop is not of popular choice at upper catchment. With the help of local materials and manpower, rainwater harvesting (RWH) through 7 (seven) cascading farm ponds at upper and middle catchment seems to be a beneficial method for managing water scarcity in the study area. It may provide irrigation to 1111 acres of land, extending benefits to a total of 500 cultivator families. Farm ponds may be constructed and maintained with community participation through watershed development scheme under NREGA (National Rural Employment Guarantee Act), DPAP and DAP scheme as a pilot project.

References Al-Jabari, S.J., Sharkh, M.A. and Mimi, Z., 2007. Estimation of runoff for agricultural watershed using SCS curve number and GIS. Birzeit University. Allen, R.G., Pereira, L.S., Raes, D. and Smith, M., 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9), p.D05109. Arnett, R.R., 1971. Slope form and geomorphological process an Australian example. Institute of British Geographers. Vol-3, pp. 81–92. Beighley, R.E., Melack, J.M. and Dunne, T., 2003. Impacts of California's climatic regimes and coastal land use change on streamflow characteristics. Journal of American Water Resources Association, 39(6), pp. 1419–1433. Bicknell, B.R., Imhoff, J.C., Kittle Jr, J.L., Jobes, T.H., Donigian Jr, A.S. and Johanson, R., 2001. Hydrological simulation program-Fortran: HSPF version 12 user’s manual. AQUA TERRA Consultants, Mountain View, California. Biswas, A.K., 2004. From Mar del Plata to Kyoto: an analysis of global water policy dialogue. Global Environmental Change, 14, pp.81–88. Bloom, A.L., 1998. Geomorphology: a systematic analysis of late Cenozoic landforms. Prentice Hall, pp. 498. Booth, D.B., 1990. Stream-channel incision following drainage-basin urbanization. JAWRA Journal of the American Water Resources Association, 26(3), pp.407–417. Brath, A., Montanari, A. and Moretti, G., 2006. Assessing the effect on flood frequency of land use change via hydrological simulation (with uncertainty). Journal of Hydrology, 324(1), pp.141–153. Carter, C.S. and Chorley, R.J., 1961. Early slope development in an expanding stream system. Geological Magazine, 98(02), pp.117–130.

168

5  Hydrological Impact of Landuse Conversion

Chang, H., 2007. Comparative streamflow characteristics in urbanizing basins in the Portland Metropolitan Area, Oregon, USA. Hydrological Processes, 21(2), pp.211–222. Chow, V.T., Maidment, D.R. and Mays, L.W., 1988. Applied hydrology. Cluff, C.B., 1974. Engineering aspects of water harvesting research at the University of Arizona. Proc. Water Harv. Symp. Phoenix, Arizona, pp. 27–29. Costa, M.H., Botta, A. and Cardille, J.A., 2003. Effects of large-scale changes in land cover on the discharge of the Tocantins River, Southeastern Amazonia. Journal of Hydrology, 283(1), pp.206–217. Crooks, S. and Davies, H., 2001. Assessment of land use change in the Thames catchment and its effect on the flood regime of the river. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(7), pp.583–591. De Roo, A., Odijk, M., Schmuck, G., Koster, E. and Lucieer, A., 2001. Assessing the effects of land use changes on floods in the Meuse and Oder catchment. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(7), pp.593–599. De Souza, A., 2010. Water and Development: Forging Green Communities for Watersheds. Orient Blackswan. Dunne, T. and Leopold, L., 1978. Water in environmental planning WH Freeman. San Francisco, CA, 50. Franczyk, J. and Chang, H., 2009. The effects of climate change and urbanization on the runoff of the Rock Creek basin in the Portland metropolitan area, Oregon, USA. Hydrological Processes, 23(6), pp.805–815. Goudie, A., 1990. Geomorphological Techniques. 2nd Ed, Unwin Hyman, pp. 482. Graf, W.L., 1975. The impact of suburbanization on fluvial geomorphology. Water Resources Research, 11(5), pp.690–692. Graham, D.N. and Butts, M.B., 2005. Flexible, integrated watershed modelling with MIKE SHE. Watershed models, 849336090, pp.245–272. Hammer, T.R., 1972. Stream channel enlargement due to urbanization. Water Resources Research, 8(6), pp.1530–1540. Hamon, W.R., 1963. Computation of direct runoff amounts from storm rainfall. publisher not identified. Harbor, J.M., 1994. A practical method for estimating the impact of land-use change on surface runoff, groundwater recharge and wetland hydrology. Journal of the American Planning Association, 60(1), pp.95–108. Heathcote, I. W., 2009. Integrated watershed management principles and practice. 2nd Ed, John Wiley and Sons Inc. USA, pp. 1–439. Horton, R.E., 1939. Analysis of runoff-plat experiments with varying infiltration-capacity. Eos, Transactions American Geophysical Union, 20(4), pp.693–711. Horton, R.E., 1945. Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Geological society of America bulletin, 56(3), pp.275–370. Howard, A.D., 1997. Badland morphology and evolution: Interpretation using a simulation model. Earth Surface Processes and Landforms, 22(3), pp.211–227. Jackson, T.J., Ragan, R.M. and Fitch, W.N., 1977. Test of Landsat-based urban hydrologic modeling. Jensen, M.E., Burman, R.D. and Allen, R.G., 1990. Evapotranspiration and irrigation water requirements. ASCE. Klein, R.D., 1979. Ubranization and stream quality impairment. Water Resources Bulletin, 15(4), pp.948–963. Koudstaal, R., Rijsberman, F.R. and Savenije, H., 1992. Water and sustainable development. Natural Resources Forum, Vol. 16, No. 4, pp. 277–290. Kirkby, M. J., 1971. Hillslope process-response models based on the continuity equation. Institute of British Geographers Special Publication (3), pp. 15–30.

References

169

Kumar, P., Tiwart, K.N. and Pal, D.K., 1991. Establishing SCS runoff curve number from IRS digital database. Journal of the Indian Society of Remote Sensing, 19(4), pp.245–252. Kumar, R., Chatterjee, C., Singh, R.D., Lohani, A.K. and Kumar, S., 2007. Runoff estimation for an ungauged catchment using geomorphological instantaneous unit hydrograph (GIUH) models. Hydrological Processes, 21(14), pp.1829–1840. Makurira, H., Mul, M.L., Vyagusa, N.F., Uhlenbrook, S. and Savenije, H.H.G., 2007. Evaluation of community-driven smallholder irrigation in dryland South Pare Mountains, Tanzania: A case study of Manoo micro dam. Physics and Chemistry of the Earth, Parts A/B/C, 32(15), pp.1090–1097. Miller, S.  N., Kepner, W.  G., Mehffey, M.  H., Harnandez, M., Miller, R.  C., Goodrich, D.  C., Devonald, K. N., Heggem, D. T., and Miller, W. P., 2002. Integrating landscape assessment and hydrological modelling for land cover change analysis. Journal of American Water Resources Association, 38(4), pp. 195–129. Moitra Maiti, M., 2008. Crisis of water and its impact on the social environment of Darjiling Town. Unpubished Ph.D Thesis (Science), University of Calcutta, pp. 1–58. Moitra Maiti, M. Maiti R. K., 2009. Sub watersheds for Rational Management of Water Crisis of Darjilling Town, W.B. Geographical Review of India, 71(2), pp. 117–129. Moitra Maiti, M., 2010. Cognition of the interworking of process associated to water scarcity and feasibility of water harvesting- An action research on a representative drainage basin on Silaboti Kansabati interfluve, West Bengal. Indian council of Social Science Research, F. noRP/02/121/2009, pp. 1–180. Morisawa, M., 1985. Rivers, Forms and Processes, Longman, pp. 209. Mosse, D., 1997. The Ideology and Polities of Community Participation: Tank Irrigation Development in Colonial and Contemporary Tamil Nadu. In Grillo, R.D. and Stirrat, R.L. (ed) Discourses of Development. Anthropological Perspective, Oxford & New York, pp. 255–291. Mustafa, Y.M., Amin, M.S.M., Lee, T.S. and Shariff, A.R.M., 2012. Evaluation of land development impact on a tropical watershed hydrology using remote sensing and GIS. Journal of spatial hydrology, 5(2). Neitsch, S.L., Arnold, J.G., Kiniry, J.R. and Williams, J.R., 2011. Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute. Neller, R.J., 1988. A comparison of channel erosion in small urban and rural catchments, Armidale, New South Wales. Earth Surface Processes and Landforms, 13(1), pp.1–7. O’Malley, L.S.S., 1911. Bengal District Gazetteers: Medinipur. Bengal Secretariat Book Depot. Pandey, R., 2002. Development of Roof Top Water Harvesting System in Darjeeling. The Administrator, vol-45 Dec, pp. 97–105. Paul, M.J and Meyer, J.L., 2001. Streams in the urban landscape. Annual Review of Ecological Evolution and Systematics, 32, pp. 333–365. Penman, H. L., 1963. Vegetation and Hydrology, Tech. Communication No. 53, Commonwealth Bureau of Soils, Harpenden, England, pp. 125. Penman, H.L., 1948, April. Natural evaporation from open water, bare soil and grass. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences (Vol. 193, No. 1032, pp. 120–145). The Royal Society. Schumm, S.A., 1956. Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Geological society of America bulletin, 67(5), pp.597–646. Schwab, G.O., Fangmeier, D.D., Elliot, W.J. and Frevert, R.K., 1993. Soil and water conservation engineering. John Wiley & Sons, Inc. Strahler, A.N., 1950. Equilibrium theory of erosional slopes approached by frequency distribution analysis. Part I. American Journal of Science, 248, pp.673–696. Teegavarapu, R.S. and Chandramouli, V., 2005. Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. Journal of Hydrology, 312(1), pp.191–206. Tucker, G.E., Catani, F., Rinaldo, A. and Bras, R.L., 2001. Statistical analysis of drainage density from digital terrain data. Geomorphology, 36(3), pp.187–202.

170

5  Hydrological Impact of Landuse Conversion

Shee, S. P. and Maiti, R. K., 2012. Assessing the necessity of watershed management at Sundra Basin, Paschim Medinipur, West Bengal. Journal of Indian Geomorphology, Vol-1, pp. 107–113. United States Development of Agriculture (USDA), Soil Conservation Service (SCS)., 1972. Hydrology. In National Engineering Hand book, Section 4, GPO, Washington, DC. Van der Zaag, P., 2005. Integrated Water Resources Management: Relevant concept or irrelevant buzzword? A capacity building and research agenda for Southern Africa. Physics and Chemistry of the Earth, Parts A/B/C, 30(11), pp.867–871. Vörösmarty, C.J., Green, P., Salisbury, J. and Lammers, R.B., 2000. Global water resources: vulnerability from climate change and population growth. science, 289(5477), pp.284–288. Wang, G., Zhang, Y., Liu, G. and Chen, L., 2006. Impact of land-use change on hydrological processes in the Maying River basin, China. Science in China Series D: Earth Sciences, 49(10), pp.1098–1110.

Chapter 6

Land Acquisition and Livelihood

Abstract  The compensation amount they own, was spent more in unproductive head; on the other hand the proposed JSW plant work is stopped; as a consequence a critical economic condition has been  started  here. This chapter is focused on income and livelihood dynamics of the affected household and also tries to make a comparison with non-affected household in the study area. The livelihood asset index method (after Yan et al., 2010) is used to make a relationship between livelihood diversification index. Keywords  Compensation · Unproductive · Livelihood dynamic · Livelihood asset index · Livelihood diversification index

6.1  Introduction Farm size becomes too small due to land acquisition that directly affected the local livelihoods (Chambers and Conway 1992; Hastenrath and Greischar 1997) in the development site. Livelihood in that situation is no longer based on land resources but from a variety of sources and activities. Most of the livelihood strategies then turned complex, diverse and versatile (Chambers 1989; Khanduri et  al. 2002). Livelihood diversification is an important livelihood strategy for people in developing countries (Yan et al. 2010) by which rural households construct a diverse portfolio of activities and social support capabilities (Khatun and Roy 2012) to reduce the livelihood vulnerabilities, ensure food security and reduce the threat of famine (Block and Webb 2001; Ellis 1998, 2005; Glavovic and Boonzaier 2007; Shackleton et al. 2007) in post-acquisition situation. The livelihood strategies are the sum of all different activities that people do in the context of employment and income and are based on the access to and combination of five forms of capital assets, i.e. human, natural, financial, social and physical (Bebbington 1999; Nair et al. 2007). Education, training and other human quantities help man to diversify sources of income and help to set off-farm employment (Elbers and Lanjouw 2001; Illukpitiya and Yanagida 2010). Natural assets like quantity and quality of land and forests and access to these are important factors of livelihood in developing countries in general © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. P. Shee, R. Maiti, Land Acquisition, Industrialization and Livelihoods, https://doi.org/10.1007/978-3-030-90244-5_6

171

172

6  Land Acquisition and Livelihood

and in the study area in particular where most of the workers are involved in primary economic activities (Cramb et  al. 2004; Kangalawe and Liwenga 2005; Ouyang et al. 2010; Yan et al. 2006; Zhang et al. 2008). Land acquisition reduces the opportunity of collecting common pool resources (CPR). Access to arable land becomes the limiting factor in changing conditions (Holden et al. 2004; Pender 2004). Despite having more financial assets from compensation money, land loser families may not be able to improve their livelihood as they do not know how to mobilize their capital to generate income. They usually spent the money for unproductive purpose of collecting basic needs like food and clothes and for social function (Hiremath 2007; Zhang et al. 2008). The quality and quantity of physical assets like physical equipment and live storage are likely to decline as the availability of grazing land is reduced by land acquisition. The study area is a typically ecologically sensitive area. However, livelihoods of local residents from project sites mainly rely on land and forest. The rapid growth of population, land acquisition and deforestation severely affected them, so they are trying to seek livelihood strategies other than cultivation and collection of non-­ timber forest product (NTFP), pasturing, etc. The present study is focused on livelihood diversification as a way to overcome risks and uncertainties. According to livelihood portfolio, a household’s wealth is the combination of physical, social, human and economic assets (Bebbington 1999; De Sherbinin et al. 2008). The fact is that households are intimately related to those assets, and by extension the impact of household on the environment is mediated by the mobilization of those four forms of capital (Heubach et al. 2011; Pretty et al. 2003). The ability of the household to accumulate and utilize these forms of capital is further mediated by a number of factors including institutional factors, cultural factors and economic factors (Kumar et al. 2006; Saha and Sundriyal 2012). Delay of the project implementation will make great changes in the project site which may create enormous stress for the land and other natural resources (Koczberski and Curry 2005; Shiyani and Pandya 1998). With the conflict between land shortage and labour surplus, local farmers started to be aware of the importance of non-farm activities. Most of the households attempt to cope with distress situation by reducing household incomes, cropping pattern and searching for jobs in other places (Sharma 2010). Diversification of economic activities of a household can occur for two purposes: first, to increase household income by increasing the number of workers engaged in different economic activities in the household and second, to minimize the risk of livelihood failure by letting each member to participate in more than one economic activity (Saha and Bahal 2010). To reduce the risk of livelihood failure, diversification is the way to spread income across more than one source.

6.2  Data and Method Stratified random sampling survey was applied for non-project area, covering 1604 household and over 538 household information from project affected area through non-random opportunity and snowball sampling method. The investigation in the

6.2 Data and Method

173

first phase was conducted in 2009–2010 during land acquisition, and the repeated investigation on the same household was held in 2012–2014 after acquisition and in 2016–2017 after suspension of work on both the project and non-project affected mouzas of the study area. During the second and third phases of investigation, questionnaire survey was made on 1066 households over non-project areas and 538 households covering 22 project affected mouzas. Questionnaires were designed focusing on livelihood assets and livelihood activities of farmers. The main information was collected as follows: (1) Human assets, including family size, gender, age, number of children going to school, number of illiterates, number of bidders, amount of labour, labours that involved digging herb, labours involved in secondary and tertiary industry, family members who are sick, treatment cost, etc. (2) Natural assets, including area of arable land per capita, yield of arable land, production reduction caused by natural disasters, etc. (3) Physical assets, including number of livestock and number of livestock for sale (4) Social assets and financial assets; family members employed in government sectors, low-interest rate or interest-free loans, alliance, any type of subsistence allowances provided by governments (5) Livelihood activities, family members engaged in; (6) Constrain in livelihood diversification (7) Future livelihood strategies Livelihood activities of sample families are taken into account for calculating livelihood diversification index after Yan et al. (2010). Each livelihood activity is assigned by number 1; for example, if a family engages in farm labour and livestock farming, this family’s livelihood diversification index is assigned to 2. Sample family’s livelihood diversification index of each mouza is made average and taken as this mouza’s livelihood diversification index. Diversification is measured using various indicators like the number of income sources and their shares, Simpson index, ogive index, entropy index, modified entropy index, composite entropy index, etc. Simpson index is used in this study because of its computational simplicity, robustness and wider applicability. The formula is given below. N



SID  1  Pi 2 i

(6.1)

where N is the total number of income sources and Pi represents income proportion of the i-th income source. SID ranges from 0 to 1. Accordingly households with most diversified incomes will have the largest SID and those with the less diversified incomes are referred to the smallest SID. Different livelihood asset index is then correlated with livelihood diversification index using Pearson correlation coefficient (significance level of 0.01). If the absolute value of correlation is between 0.7 and 1.0, then the two variables are considered highly related. If the value ranges between 0.4 and 0.7, the two variables are moderately related. When it ranges between 0 and 0.4, the two variables are less related.

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6  Land Acquisition and Livelihood

6.3  L  and Ownership Pattern at the Project Site in 2007 and 2012 We conducted a sample survey on a total of 538 households, spread over 22 mouzas of the study area. These 22 mouzas are affected directly by land acquisition. The observation reveals that this land acquisition brings negative consequences in their family budget due to delay in the proposed project. The statistics reveals (Table 6.1) that prior to acquisition (2007), the majority of the 182 HH (>30%) and 143 HH (>25%) owned 1 to 2 acres and 0.5−1 acre of land, respectively. Fewer HH (10%) in this affected mouzas occupied more than 3 acres of land.

Table 6.1  Land ownership pattern of affected households at the project site in 2007 (before acquisition)

Affected mouza Dubrajpur Masru Nutandihi Nitaipur Banshkopna Chantibandh Ramraydi Ashna Shuli Nutanbankati Chak Bhagi Naran Chak Jamdedya Bhalukchati urf Birbanchati Kulpheni Gaighata Hatmari Shalika Kharka Suli Arabari Khairisol Barju Ghagrasol Total Average (%)

Surveyed HH 30 14 13 59 11 40 106 0 0 0 5 0 78 12 17 9 39 21 38 18 28 538

Source: Household Survey (2007)

Land ownership pattern 5 acres 2

2 12 1 5 17

1 9

1

4

1 9

2 5

1 4

31 7 5

9 3 3

4

1

15 7 17 7 3 182 34.0

4 4 2 5

2 1

67 12.3

33 6.1

1

2

1 4 10 1.9

10 1.9

6.3  Land Ownership Pattern at the Project Site in 2007 and 2012

175

After acquisition (2012) a considerable change in land holding pattern (Table 6.2) is observed. The number of HH belonging to the category of 1–2 acres is reduced to 85 HH (16.0%) from earlier, before acquisition which was 183 HH (34.0%). 175 HH (33%) are recorded to belong in 10%). The affected families initially deposited their compensation money (>30%) in the bank, but after a few years, it comes to nil. A considerable share of their compensation money was spent on social programmes like marriage (>8%), payment against previous loans (>5%), etc. Only a meager percentage (25%) and domestic consumption (>10%) in hopes that they would be appointed quickly. But due to the delay and presently the suspension of the project work, it makes the situation reverse. Less job opportunities at project affected mouzas made HAI to decline from 0.38 to 0.26. After clearance of forest covers, the average NAI declined from 0.38 to 0.24. The average PAI also declined from 0.32 to 0.25 after clearance of fodder farm land. No strong economic support is observed here. Off-­farm employment is an alternative livelihood option at the project site to reduce the vulnerability, but after closure of the proposed project work, livelihoods are still dependent on natural resources.

References Barrett, C.B., Clark, M.B., Clay, D.C. and Reardon, T., 2005. Heterogeneous constraints, incentives and income diversification strategies in rural Africa. Quarterly journal of international agriculture, 44(1), pp.37–60. Barrett, C.B., Reardon, T. and Webb, P., 2001. Nonfarm income diversification and household livelihood strategies in rural Africa: concepts, dynamics, and policy implications. Food policy, 26(4), pp.315–331. Bebbington, A., 1999. Capitals and capabilities: a framework for analyzing peasant viability, rural livelihoods and poverty. World development, 27(12), pp.2021–2044. Block, S. and Webb, P., 2001. The dynamics of livelihood diversification in post-famine Ethiopia. Food policy, 26(4), pp.333–350. Bradstock, A., 2006. Land reform and livelihoods in South Africa's Northern Cape province. Land Use Policy, 23(3), pp.247–259.

196

6  Land Acquisition and Livelihood

Carney, D., 2003. Sustainable livelihoods approaches: progress and possibilities for change. London: Department for International Development. Chambers, R. and Conway, G., 1992. Sustainable rural livelihoods: practical concepts for the 21st century. Institute of Development Studies (UK). Chambers, R., 1989. Editorial introduction: vulnerability, coping and policy. IDS bulletin, 20(2), pp.1–7 Cramb, R.A., Purcell, T.C.S.H. and Ho, T.C.S., 2004. Participatory assessment of rural livelihoods in the Central Highlands of Vietnam. Agricultural systems, 81(3), pp.255–272. De Janvry, A. and Sadoulet, E., 2001. Income strategies among rural households in Mexico: The role of off-farm activities. World development, 29(3), pp.467–480. De Sherbinin, A., VanWey, L.K., McSweeney, K., Aggarwal, R., Barbieri, A., Henry, S., Hunter, L.M., Twine, W. and Walker, R., 2008. Rural household demographics, livelihoods and the environment. Global Environmental Change, 18(1), pp.38–53. Elbers, C. and Lanjouw, P., 2001. Intersectoral transfer, growth, and inequality in rural Ecuador. World Development, 29(3), pp.481–496. Ellis, F., 1998. Household strategies and rural livelihood diversification. The journal of development studies, 35(1), pp.1–38. Ellis, F., 2005. Small farms, livelihood diversification, and rural-urban transitions: Strategic issues in Sub-Saharan Africa. The future of small farms, 135. Fafchamps, M. and Quisumbing, A.R., 2003. Social roles, human capital, and the intrahousehold division of labor: evidence from Pakistan. Oxford Economic Papers, 55(1), pp.36–80. Glavovic, B.C. and Boonzaier, S., 2007. Confronting coastal poverty: Building sustainable coastal livelihoods in South Africa. Ocean and Coastal Management, 50(1), pp.1–23. Hastenrath, S. and Greischar, L., 1997. Glacier recession on Kilimanjaro, East Africa, 1912-89. Journal of Glaciology, 43(145), pp.455–459. Heubach, K., Wittig, R., Nuppenau, E.A. and Hahn, K., 2011. The economic importance of non-­ timber forest products (NTFPs) for livelihood maintenance of rural west African communities: A case study from northern Benin. Ecological Economics, 70(11), pp.1991–2001. Hiremath, B.N., 2007, December. The changing faces of rural livelihoods in India. In National Civil Society Conference on What it takes to Eradicate Poverty, New Delhi (pp. 4–6). Holden, S., Shiferaw, B. and Pender, J., 2004. Non-farm income, household welfare, and sustainable land management in a less-favoured area in the Ethiopian highlands. Food Policy, 29(4), pp.369–392. Illukpitiya, P. and Yanagida, J.F., 2010. Farming vs forests: Trade-off between agriculture and the extraction of non-timber forest products. Ecological Economics, 69(10), pp.1952–1963. Jodha, N.S. and Mascarenhas, A.C., 1983. Adjustment to climatic variability in self provisioning societies: Some evidence from India and Tanzania. Economics Program Progress Report-­ International Crops Research Institute for the Semi-Arid Tropics. Kangalawe, R.Y. and Liwenga, E.T., 2005. Livelihoods in the wetlands of Kilombero Valley in Tanzania: Opportunities and challenges to integrated water resource management. Physics and Chemistry of the Earth, Parts A/B/C, 30(11), pp.968–975. Khanduri, V.P., Sharma, C.M., Ghildiyal, S.K. and Puspwan, K.S., 2002. Forest composition in relation to socio-economic status of people at three high altitudinal villages of a part of Garhwal Himalayas. Indian forester, 128(12), pp.1335–1345. Khatun, D. and Roy, B.C., 2012. Rural livelihood diversification in west Bengal: determinants and constraints. Agricultural Economics Research Review, 25(1), pp.115–124. Koczberski, G. and Curry, G.N., 2005. Making a living: Land pressures and changing livelihood strategies among oil palm settlers in Papua New Guinea. Agricultural Systems, 85(3), pp.324–339. Kumar, P., Singh, N.P. and Mathur, V.C., 2006. Sustainable agriculture and rural livelihoods: A synthesis. Agricultural Economics Research Review, 19(conf), pp.1–22.

References

197

Lay, J., Mahmoud, T.O. and M’Mukaria, G.M., 2008. Few opportunities, much desperation: The dichotomy of non-agricultural activities and inequality in Western Kenya. World Development, 36(12), pp.2713–2732. McCusker, B. and Carr, E.R., 2006. The co-production of livelihoods and land use change: Case studies from South Africa and Ghana. Geoforum, 37(5), pp.790–804. Nair, K.N., Paul, A. and Menon, V., 2007. Livelihood risks and coping strategies: a case study in the agrarian village of Cherumad, Kerala. Ouyang, W., Skidmore, A.K., Hao, F. and Wang, T., 2010. Soil erosion dynamics response to landscape pattern. Science of the Total Environment, 408(6), pp.1358–1366. Pender, J., 2004. Development pathways for hillsides and highlands: some lessons from Central America and East Africa. Food policy, 29(4), pp.339–367. Pretty, J.N., Morison, J.I. and Hine, R.E., 2003. Reducing food poverty by increasing agricultural sustainability in developing countries. Agriculture, ecosystems and environment, 95(1), pp.217–234. Rahut, D.B. and Micevska Scharf, M., 2012. Livelihood diversification strategies in the Himalayas. Australian Journal of Agricultural and Resource Economics, 56(4), pp.558–582. Saha, B. and Bahal, R., 2010. Livelihood Diversification Pursued by Farmers in West Bengal. Indian Research Journal of Extension Education, 10(2), pp.1–9. Saha, D. and Sundriyal, R.C., 2012. Utilization of non-timber forest products in humid tropics: Implications for management and livelihood. Forest Policy and Economics, 14(1), pp.28–40. Schwarze, S., Zeller, M., Berdegué, J.A., Abruzzese Aguirre, R.A., Abanikanda, J., Hamzat, R.A., Akinola, A., Famaye, A.O., Olaiya, A.O., Sanusi, R.A. and Hammed, L.A., 2009. Income diversification of rural households in Central Sulawesi, Indonesia (No. FAO 338.1898 I59). Georg-August-University Göttingen, Göttingen (Alemania).. Shackleton, C.M., Shackleton, S.E., Buiten, E. and Bird, N., 2007. The importance of dry woodlands and forests in rural livelihoods and poverty alleviation in South Africa. Forest policy and economics, 9(5), pp.558–577. Sharma, R., 2010. Diversification in rural livelihood strategies: a macro-level evidence from Jammu and Kashmir. Sharp, K., 2003. Measuring destitution: Integrating qualitative and quantitative approaches in the analysis of survey data. Shiyani, R.L. and Pandya, H.R., 1998. Diversification of agriculture in Gujarat: A spatio-temporal analysis. Indian journal of agricultural economics, 53(4), p.627. Soini, E., 2005. Land use change patterns and livelihood dynamics on the slopes of Mt. Kilimanjaro, Tanzania. Agricultural Systems, 85(3), pp.306–323. Yan, J., Wu, Y., Zhang, Y. and Zhou, S., 2010. Livelihood diversification of farmers and nomads of eastern transect in Tibetan Plateau. Journal of Geographical Sciences, 20(5), pp.757–770. Yan, J., Zhang, Y., Liu, L., Bai, W., Zhu, H., Shi, Y. and Zheng, D., 2006. Residents’ perspectives and responses to environmental degradation in the upper Dadu River, eastern Tibetan Plateau. Journal of Geographical Sciences, 16(3), pp.293–305. Zhang, L., Zhang, Y., Yan, J. and Wu, Y., 2008. Livelihood diversification and cropland use pattern in agro-pastoral mountainous region of eastern Tibetan Plateau. Journal of Geographical Sciences, 18(4), pp.499–509.

Chapter 7

Uncertainity and Suffering

Abstract  The peasants protest the land acquisition in Singur and Nandigram but not at Salboni because of the assumption was that the people would be benefitted here if the industrialization occurs on monocrop land. The JSW authority announced on 30th November 2014 to put the proposed project on hold due to lack of raw materials. The local people were totally confused and frustrated with the company’s attitude; they began to protest against this decision and try to draw the attention of the national and international media, the leading economists and intellectuals of the country and human rights activists. Finally, suspension was announced on 16th August 2015. A detailed household survey was done to understand the community perception on the different aspects of industrialization. This chapter tries to make a brief summary of “A Journey of JSW Bengal Steel Plant 2007–2015”. A list of land acquisition as narrated by some affected households is included here to point out what they expected and now what they won. Keywords  Singur · Nandigram · Salboni · Frustrated · Protest · Media · Suspension · Community perception · Affected household

7.1  Introduction Paschim Medinipur is primarily dependent on agriculture but some medium-size industries play an increasingly significant role in the economy (Brush 1952; Martin and Rogers 1995; Rocha 2004; Van de Ven 1993). Still now agriculture accounts for the largest share of labour force (Feldman and Florida 1994). In West Bengal industries are mostly localized in the Kolkata region, the mineral-rich western highlands and Haldia port region (Banerjee et  al. 2002; Sen and Das 2000; West Bengal Human Development Report 2004). West Bengal is India’s sixth largest economy and recorded a gross state domestic product (GSDP) of INR 135.13 trillion in 2020–2021. In between 2015–2016 and 2020–2021 the average annual GSDP rate is about 12.6% (Industrial Infrastructure 2012). Since May 2011, West Bengal received an investment proposal from the planning commission of INR 1.12 trillion

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. P. Shee, R. Maiti, Land Acquisition, Industrialization and Livelihoods, https://doi.org/10.1007/978-3-030-90244-5_7

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and with potential to create 314,000 jobs. The year 2012 was particularly a rugged year for the industry; statistics reveals only INR 3.12 billion was implemented, which was a fall of 85% from the previous year and 97% over 2010 (Business Standard 2013; Industrial infrastructure 2012). The major problem is the unavailability of land in West Bengal (Banerjee 2006). In it’s draft the industrial policy stated that the government is against any kind of forcible land acquisition, where the government is expected to acquire land for industries in the state through initiatives. No forcible land acquisition is reported against JSW Bengal Steel plant. Without any litigation the company acquired the 4225.43 acres of land. After laying down the cornerstone in 2008, no major project work is started. This chapter is focused on the delay of the proposed project work, leading to uncertainty and disappointment of local villagers.

7.2  Data and Method A third phase household survey was conducted in 2016–2017 on 538 project affected households. The investigation is focused on the impacts of livelihood status due to the delay in the project work. In this study both primary and secondary data were used. A semi-structured questionnaire was developed based on the information acquired during land acquisition. The questionnaire consisted of open and closed questions and involved rating and ranking procedure. For the analysis households were put into three different livelihood conditions in 2017 after 2 years of suspension. Earlier different investigation was conducted immediately after suspension in 2014–2015 and the condition they had before land acquisition in 2007. This continious close observation was done through group networking from 2009 to 2017. In each mouza we settled a group of educated local college and university students; they were interconnected through social media under the supervision of local leader Mr. Arun Mahata at Jambediya. Different variables that were recorded during field observation (2009–2017) related with livelihood status and household characteristics are included in the analysis. Secondary information was obtained from different print media, local panchayat, B.D.O (Block Development Officer) at Salboni, JSW Office, etc.

7.3  A Journey of JSW Bengal Steel Plant 2007–2015 JSW Bengal Steel, the largest project in the state, which is supposed to change the lives of people in the region, is stuck for years due to global recession and lack of raw materials (Plate 7.1) (The Telegraph 30th November 2014). Now they are going to start a cement factory on the acquired land for the proposed steel plant.

7.3  A Journey of JSW Bengal Steel Plant 2007–2015

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Plate 7.1  Mining given to JSW. (Source: The Telegraph, 30th November 2014)

In 2007, JSW signed a development agreement to invest INR 35,0000 million for a 10 million tonne steel plant and 1680 MW power plant. The plant also included a cement unit to utilize the slag generated from the blast furnace of the steel plant (Business Standard 6th March 2013). According to plan the plant will be started (Plate 7.2) with 3.0 million tonne steel plant and 300  MW captive power plant (CPP) at the first phase (JSW, EIA 2007).

Plate 7.2  JSW Bengal Steel plant, Salboni block of Paschim Medinipur (2011)

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In 2008 on 2 November the cornerstone was laid down and immediately after that only 36 km long boundary work was started, but the other major construction was started to delay due to fiscal deficit in the world market in 2009. From print media it came to be known that, immediately after the change in state government in 2011, the new govt. publicly expressed dissatisfaction on the delay of the proposed project at Salboni. Immediately after that around 1000–1200 local people, who would be employed permanently in the upcoming plants, were appointed on temporary basis for the construction of the residential complex, a 30-acre mini

Plate 7.3  “Ankur” residential complex of JSW Bengal Steel plant under construction (2012)

township called “Ankur” (Plate 7.3). After the closure of the work in 2014, they become jobless. West Bengal Industrial Development Corporation pointed out that 189 acres of private land was acquired for the project, without any clearance by the company from the land reforms department (Plate 7.4). It was one of the reasons for delaying the project work. The TMC Government resolved this by vesting the acquired land with itself and then leasing it to the company, thus paving the way for signing the

7.3  A Journey of JSW Bengal Steel Plant 2007–2015 Plate 7.4 Lease agreement with Government of W.B of 189 acres ceiling exceed private land for JSW Bengal steel project. (SourceAnadabazar Patrika, 5th May, 2012)

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lease agreement and completion of land acquisition for the proposed JSW Bengal Steel project in July 2012 (Ananda Bazar Patrika 5th May 2012).

Plate 7.5  Cancellation of coal block allotment in Bengal. Source- The Telegraph, 21st Sep, 2012

7.3  A Journey of JSW Bengal Steel Plant 2007–2015

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On 21 September 2012 (Plate 7.5), the Bengal Steel project fell through again when the Supreme Court scrapped allocation of three Bengal coal mines last year along with 200 more in the country (The Telegraph 21st September 2012).

Plate 7.6  After coal block, iron ore fix makes Salboni project on hold (2013) (Source: The Telegraph, 28th April 2013)

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Plate 7.7  Official announcement by JSW chairperson come managing director to put the proposed JSW Bengal Steel project on hold (Anadabazar patrika 1st December, 2014)

After coal block allocation, now the subject of iron ore became a focal point (Plate 7.6) (The Telegraph 29th April 2013). After a series of deliberation on iron ore mining by the apex court, the availability of raw materials in Bengal became uncertain and Jindal put the steel project (Plate 7.7) on hold in 2014. As a consequence of putting the proposed industrial project on hold, related socio-economic activities become closed. The existing condition of the social economic development initiatives in the project area is shown in Table 7.1.

Development initiatives (2009–2014) Employed 6 doctors and 24 local nurses. Visiting hours from 08:00 am to 6:00 pm to cover at least 3 villages in a day; diagnosis and medicine free of cost 3 submersible (Plate 7.8) wells installed at Arabari, Barju and Ghagrasol Still working, 3 villages and surrounding 2 villages; 52 HH benefitted

Existing status (2014–2018) After 2014 this facility stopped

Construction of Link road between Godapiasal and project site (Plate 7.9) Only 30% of works completed road started in 2012 and stopped in 2014 and after suspension no maintenance

Drinking water facility

Scheduled project work Mobile health unit

Table 7.1  A scheduled socio-economic initiation of JSW Bengal Steel plant at Salboni

(continued)

Plate 7.9  Link road between Godapiasal and JSW (2013)

Plate 7.8  At Arabari

Remarks Local poor people are suffering

7.3  A Journey of JSW Bengal Steel Plant 2007–2015 207

Scheduled project work

Development initiatives (2009–2014) Other link road between Godapiasal to Mashru

Table 7.1 (continued) Existing status (2014–2018) 70% of works completed (Plates 7.10 and 7.11)

Plate 7.11  Same link road at Barju (2015)

Plate 7.10  Link road under construction at Barju (2013)

Remarks

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Supporting education

Scheduled project work Setting up of new shop and market (Plate 7.12)

Plate 7.14  JSW nursery school (2014)

Setting up of nursery school at Mashru (Plate 7.14)

Plate 7.12  Setting up new market (2012)

Development initiatives (2009–2014)

Stopped (Plate 7.15)

Existing status (2014–2018) Closed (Plate 7.13)

Plate 7.15  JSW nursery school (2016) (continued)

Plate 7.13  Closed market complex (2018) Children are admitted to nearby school

Remarks Loss of money

7.3  A Journey of JSW Bengal Steel Plant 2007–2015 209

Plate 7.17  Mukulda-r canteen at Jambedya (2013)

Food arrangement for at least 20–30 persons per day (Plate 7.17)

Development initiatives (2009–2014) After skill mapping 437 persons from land loser families were called for 6 months of preliminary training. Among them 7 persons were sent to Karnataka for 2nd phase training. Total employment was nearly 100 persons

Source: Author’s observation and print media

Canteen

Scheduled project work Skill mapping and vocational training course and employment

Table 7.1 (continued)

Stopped (Plate 7.18)

Existing status (2014–2018) 100 people become jobless, training personnel sent back to home (Plate 7.16)

Plate 7.18  Mukulda-r canteen at Jambedya (2015)

Loss of livelihood and employment

Plate 7.16  Ramu Sing from Mashru (2009)

Remarks Loss of employment opportunities

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7.4  Uncertainty and Disappointment After a long-term delay in implementing the JSW Bengal project, on 1st December 2014 our chief minister Mamata Banerjee said on a public meeting at Salboni that the company cannot sit idle with such a large land parcel. She also asked the company to make monthly compensation of INR 5000 to every land loser family but JSW Steel is silent on this issue in its official response (The Hindu 16th July 2014). On 30th November 2014, Jindal announced to put on hold the proposed project due to lack of raw materials (Anadabazar patrika 2014). This make the locals disappointed who are eagerly waiting to find job opportunities in connection with the proposed industrialization. Anil Mahato, local resident, said “The local contractor paid INR 150–200 a day for construction work at Ankur, the only work we ever got under JSW Steel. But that too is going to be stopped”. A short summary of some interviews (Tables 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.8, 7.9, 7.10, 7.11, 7.12, 7.13, 7.14 and 7.15) are presented in the following section to cite Table 7.2  A short summary of interview with Sukal Tudu (Khairisol mouza) 2007 Land character Crop land Settlement

Name: Sukal Tudu, Khairisol Age: 37 Educational qualification : Primary HH size : 7 members. Total owned land: 2.3 acres in 2007 During 2010–2014 Daily labour under JSW contractor at Ankur

Source: Household Survey

In acres 2.2 0.1

Land acquired by 2.0 acres of crop land JSW Crop land acquisi90.0% tion Amount of comINR 6.0 lakh pensation (equally distributed among 2 brothers) Crop land amount (in acres) 2007 2014 2.2 0.2 Change in profession 2007 2014 Farming, collecting, Daily labour, 100 livestock farming days’ work, out working Change in monthly income 2007 2014 INR 5500 INR 4000 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problems faced (%) 2017 Reduced family in91 come Collecting 60 Livestock farming 56

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Table 7.3  A short summary of interview with Rani Mahato (Ashnasuli mouza) 2007 Land character Crop land Settlement

Name: Rani Mahato, Ashnasuli. Age: 66+ Educational Qualification : Nil HH Size: 5 members. Total owned land: 2 acres in 2007 During 2010–2014 Daily labour under JSW contractor at Ankur.

Source: Household Survey

Land acquired by JSW Crop land acquisition Amount of compensation

In acres 1.8 0.2

1.4 acre of crop land 78.0%

INR 4.2 lakh (equally distributed among 3 sons) Crop land amount (in acres) 2007 2014 1.8 0.4 Change in profession 2007 2014 Farming, collecting, Daily labour, 100 livestock farming days’ work, out working Change in monthly income 2007 2014 INR 4800 INR 3300 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problems faced (%) 2017 Reduced family income 91 Collecting 60 Livestock farming 67

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7.4  Uncertainty and Disappointment Table 7.4  A short summary of interview with Hari Hemram (Arabari mouza) 2007 Land character In acres Crop land 2.85 Settlement 0.21

Name: Hari Hemram , Arabari. Age:46 Educational qualification:Nil HH size : 7 members. Total owned land: 3.06 acres in 2007 During 2010–2014 Daily labour under JSW contractor at Ankur

Source: Household Survey

Land acquired by JSW Crop land acquisition Amount of compensation

1.265 acre of land 44.3% INR 3.8 lakh (equally distributed among 4 brothers

Crop land amount (in acres) 2007 2014 2.85 1.585 Change in profession 2007 2014 Farming, collecting, Daily labour, 100 livestock farming days’ work, out working Change in monthly income 2007 2014 INR 5700 INR 3800 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problems faced (%) 2017 Reduced family income 60 Collecting 45 Livestock farming 57

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Table 7.5  A short summary of interview with Biswanath Hemram (Chatibandh mouza) 2007 Land character Crop land Settlement

Name: Biswanath Hemram, Chatibandh. Age: 40 Educational qualification: Primary HH size: 12 members. Total owned land- 2.34 acres in 2007 During 2010–2014 Daily labour under JSW contractor at Ankur

Source: Household Survey

Land acquired by JSW Crop land acquisition Amount of compensation

In acres

2.22 0.12

1.0 acre of crop land 36.0% INR 2.33 lakh (equally distributed among 7 brothers)

Crop land amount (in acres) 2007 2014 2.22 1.22 Change in profession 2007 2014 Farming, collecting, Farming, daily lalivestock farming, bour, out working daily labour Change in monthly income 2007 2014 INR 6500 INR 5000 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problems faced (%) 2017 Reduced family income 75 Collecting 80 Livestock farming 69

7.4  Uncertainty and Disappointment

215

Table 7.6  A short summary of interview with Satis Patra (Ashnasuli mouza) 2007 Land character Crop land Double crop land Settlement

Name: Satis Patra, Ashnasuli Age: 65 Educational qualification: Primary HH size: 6 members. Total owned land: 3.16 acres in 2007 During 2010–2014 Daily labour under JSW contractor at Ankur

Source: Household Survey

Land acquired by JSW Crop land acquisition Amount of compensation

In acres 2.2 0.8 0.16 1.45 acre of crop land 49.0%

INR 4.35 lakh ( equally distributed among 2 sons) Crop land amount (in acres) 2007 2014 3.0 1.55 Change in profession 2007 2014 Farming, pension Farming, pension, daily labour Change in monthly income 2007 2014 INR 12000 INR 8500 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problem faced (%) 2017 Reduced family income 61

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Table 7.7  A short summary of interview with Laxmikanta Chalok (Kulpheni mouza) 2007 Land character Crop land Settlement

Name: Laxmikanta Chalok, Kulpheni Age: 45 Educational qualification: Primary HH size: 4 members Total owned land: 1.34 acre in 2007 During 2010–2013 Daily labour under JSW contractor at Ankur

Source: Household Survey

In acres 1.2 0.14

Land acquired by 0.45 acre of crop JSW land Crop land acquisi38.0% tion Amount of comINR 1.35 lakh pensation Crop land amount (in acres) 2007 2014 1.2 0.75 Change in profession 2007 2014 Farming, livestock Farming, daily lafarming bour Change in monthly income 2007 2014 INR 4500 INR 4000 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problems faced (%) 2016 Reduced family income 50 Collecting 51 Livestock farming 80

217

7.4  Uncertainty and Disappointment Table 7.8  A short summary of interview with Sushila Chalok (Kulpheni mouza) 2007 Land character Crop land Settlement

Name: Sushila Chalok, Kulpheni Age: 41 Educational qualification :Nil HH size: 5 members Total owned land: 1.5 acre in 2007 During 2010–2013 Daily labour under JSW contractor at Ankur

Source: Household Survey

In acres 1.4 0.1

Land acquired by 0.63 acre of crop JSW land Crop land acquisi45.0% tion Amount of comINR 1.89 lakh pensation Crop land amount (in acres) 2007 2014 1.4 0.77 Change in profession 2007 2014 Farming, collecting, Farming, collectlivestock farming ing, daily labour Change in monthly income 2007 2014 INR 5300 INR 4000 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problems faced (%) 2016 Reduced family income 40 Collecting 25 Livestock farming 60

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Table 7.9  A short summary of interview with Kalipada Mahato (Ramrydi mouza) 2007 Land character Crop land Settlement

Name: Kalipada Mahato, Ramrydi Age: 56 Educational qualification: Primary HH size: 13 members Total owned land: 2.83 acres in 2007 During 2010–2013 Daily labour under JSW contractor at Ankur

Source: Household Survey

Land acquired by JSW Crop land acquisition Amount of compensation

In acres 2.63 0.2 1.83 acre of crop land 70.0%

INR 5.49 lakh ( equally distributed among 4 brothers) Crop land amount (in acres) 2007 2014 2.63 0.80 Change in profession 2007 2014 Farming, collecting, Farming, livestock livestock farming, farming, daily ladaily labour bour, out working Change in monthly income 2007 2014 INR 8500 INR 6000 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problem faced (%) 2016 Reduced family income 70 Collecting 53 Livestock farming 40

219

7.4  Uncertainty and Disappointment Table 7.10  A short summary of interview with Charan Baskey (Ramrydi mouza) 2007 Land character Crop land Settlement

Name- Charan Baskey, Ramrydi Age: 45 Educational qualification: Primary HH size: 6 members Total owned land: 1.85 acres in 2007 During 2010–2014 Daily labour under JSW contractor at Ankur

Source: Household Survey

Land acquired by JSW Crop land acquisition Amount of compensation

In acres 1.65 0.2 1.05 acre of crop land 64.0%

INR 3.15 lakh ( equally distributed among 2 brothers) Crop land amount (in acres) 2007 2014 1.65 0.6 Change in profession 2007 2014 Farming, daily labour Daily labour Change in monthly income 2007 2014 INR 3600 INR 2000 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problem faced (%) 2016 Reduced family income 64 Collecting 67 Livestock farming 43

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Table 7.11  A short summary of interview with Turi Hemram (Kharkasuli mouza) 2007 Land character Crop land Settlement

Name: Turi Hemram, Kharkasuli Age: 62 Educational qualification: Nil HH size: 4 members Total owned land: 1.6 acre in 2007 During 2010–2013 Daily labour under JSW contractor at Ankur

Source: Household Survey

In acres 1.4 0.2

Land acquired by 1.4 acre of crop land JSW Crop land acquisi100.0% tion Amount of compenINR 1.8 lakh sation Crop land amount (in acres) 2007 2014 1.4 0.0 Change in profession 2007 2014 Farming, leaf bindDaily labour, Leaf ing, livestock farmbinding , livestock ing farming, out working Change in monthly income 2007 2014 INR 3600 INR 2000 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problems faced (%) 2017 Reduced family income 80 Collecting 80 Livestock farming 83

221

7.4  Uncertainty and Disappointment Table 7.12  A short summary of interview with Banshidhar Sing (Banskopna mouza) 2007 Land character Crop land Settlement

Name: Banshidhar Sing, Banskopna Age: 68 Educational Qualification: Nil HH size: 14 members Total owned land: 3.1 acres in 2007 During 2010–2013 Daily labour under JSW contractor at Ankur

Source: Household Survey

In acres 2.7 0.4

Land acquired by 2.4 acres of crop land JSW Crop land acquisi89.0% tion Amount of comINR 7.2 Lakh ( pensation (in equally distributed Lakh) among 6 brothers) Crop land amount (in acres) 2007 2014 2.7 0.3 Change in profession 2007 2014 Farming, collecting, Daily labor, collectlivestock farming, ing, livestock Daily labour farming, out working Change in monthly income 2007 2014 INR 7600 INR 4600 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problems faced (%) 2017 Reduced family income 86 Collecting 75 Livestock farming 80

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Table 7.13  A short summary of interview with Ramu Sing (Mashru mouza) 2007 Land character Crop land Settlement

Name: Ramu Sing, Mashru Age: 24 Education: B.A Father’s name: Baidyanath Sing HH size: 7 members Total owned land: 1.97 acre in 2007 (1.17 acre at Dubrajpur mouza) Training period during 2010–2014 First phase: Refresher course on Hindi, English and mathematics; duration 6 months Second phase: Training period at Karnataka JSW plant since 2012–2014. 2014–2018- Jobless; now at home.

Source: Household Survey

Land acquired by JSW Crop land acquisition Amount of compensation

In acres 1.97 0.2 1.17 acre of crop land 67.0%

INR 3.96 lakh (equally distributed among 2 brothers) Crop land amount (in acres) 2007 2014 1.97 0.80 Change in profession 2007 2014 Farming, livestock Farming, daily lafarming bour Change in monthly income 2007 2014 INR 6500 INR 5000 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problems faced (%) 2017 Reduced family income 67 Collecting 73 Livestock farming 79

7.4  Uncertainty and Disappointment

223

Table 7.14  A short summary of interview with Nishit Mahato (Nitaipur mouza) 2007 Land character Crop Land Settlement

Name: Nishit Mahato, Nitaipur Age: 18 Education: 12 Father’s name: Arun Mahato HH size: 4 members Total owned land: 1.1 acres in 2007 Training period during 2010–2014 First phase: Refresher course on Hindi, English and mathematics; duration 6 months Second phase: Training period at Karnataka JSW plant since 2012–2014 2014–2018: Jobless; now at home

Source: Household Survey

In acres 1.0 0.1

Land acquired by 0.36 acre of crop JSW land Crop land acquisi36.0% tion Amount of compenINR 1.08 lakh sation Crop land amount (in acres) 2007 2014 1.0 0.64 Change in profession 2007 2014 Farming, collecting, Farming, daily lalivestock farming bour Change in monthly income 2007 2014 INR 4200 INR 3000 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problems faced (%) 2017 Reduce family income 73 Collecting 80 Livestock farming 63

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7  Uncertainity and Suffering

Table 7.15  A short summary of interview with Samir Mahato (Ashnasuli mouza) 2007 Land character Crop land Double crop land Settlement

Name: Samir Mahato, Asnasuli Age: 22 Education: 12+ Father’s name: Bimal Mahato HH size: 5 members Total owned land: 2.05 acres in 2007 Appointed: JSW Supervisor 2014–2018: At home

In acres 1.25 0.5 0.3

Land acquired by 1.25 acre of crop JSW land Crop land acquisi75.0% tion Amount of comINR 3.75 lakh pensation Crop land amount (in acres) 2007 2014 1.75 0.5 Change in profession 2007 2014 Farming, daily laFarming, collecting, livestock farming bor, out working Change in monthly income 2007 2014 INR 4500 INR 2700 Expectation from JSW 2007 2014 A permanent job Loss of livelihood, crop land and income Problems faced (%) 2017 Reduced family income 91 Collecting 60 Livestock farming 60

Source: Household Survey

some token examples among the villagers who are suffering from and become disappointed by the delay of the industrial project after so many years of land acquisition. Local villagers gave up their farm land with huge expectation from JSW Bengal Steel’s rehabilitation package that included cash compensation for both Raity and Patta land holder in addition to employment opportunity for at least one person per family and free shares equivalent to the price of the land they have surrendered. “The JSW equity share will be kept in a trust under the jurisdiction of the district magistrate. But the district magistrate has advised us to visit the JSW Steel office in Kolkata”, says representative of the land owners (The Telegraph, 17th August 2015). Samir Mahato (Table 7.15), 24 years old, was selected for 2nd phase training in an attempt of capacity building and to qualify for a job in the industry in future that would help his family to put a strong economic support. He becomes disheartened in 2014 when the training was stopped.

7.4  Uncertainty and Disappointment

225

After the announcement by the JSW chairman to put the proposed project (Plate 7.19) on hold, the existing JSW employees and local villagers including land loser families came down to road (Plate 7.20) holding placards and demanding immediate initiatives to start the project work.

Plate 7.19  JSW Bengal Steel plant on hold, Salboni block of Paschim Medinipur (2015)

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Plate 7.20  Uncertainty and disappointment by land loser (2014)

After a long movement in 2015, the JSW Company announced for a cement making unit through investment of INR 615 crore. For the cement a fraction of precious land parcel will be utilized. It requires 135 acres accounting to 3.5% of the acquired land parcel. Even when the plant will come into production, the company will be able to employ only 200 people.

7.5  C  ommunity Perception on Different Aspects of Industrialization (10-Point Scale) A community perception survey was conducted in 2016–2017 & 2018 to assess the sufferings and perception of the possible effects of the delay or closure of the project work among the household living in 22 project affected mouzas with scheduled questionnaire. The survey focused on different aspects of industrialization such as living standard, major income sources, labour input orientation, medical facility and problems faced on daily life such as collecting fodder and fuel, food insecurities, family income, livestock farming and overall assessment of the proposed project at a 10-point scale. Majority of households (>90%) are dissatisfied with the current living standard at the project site because they have lost their land, livelihood, income, accessibility, etc. by land acquisition. After clearance of the ground, the affected households are facing problems in collecting and livestock farming. Table 7.16 reveals that Masru,

3.11

3.6 4.4 4.7 2.3 1.5 2.9 1.4 3.1 2.5

30 25 25 5 50 10 10 30 30 22 48 57 66 85 33 77 36 88

39

34 23 47 36 89 69 52 46 12

8

5 49 31 46 37 28 87

75 84 83 61 73 70 58

2.2 3.2 3.5 2.5 2.2 2.8 2.5

25 Uninhabited

No. of HH surveyed Uninhabited 30 20 3 60 15 35 100 Uninhabited

Source: Household Survey

Name of mouzas Dubrajpur Masru Nutandihi Nitaipur Banshkopna Chantibandh Ramraydi Ashna Shuli Nutanbankati Chak Bhagi Naran Chak Jambedya Bhalukchati urf Birbanchati Kulpheni Gaighata Hatmari Shalika Kharka Suli Arabari Khairisol Barju Ghagrasol

Problems faced (%) Collecting fodder Food and fuel insecurities

10-point scale Overall assessment

25 25 41 49 74 35 69 34 59

16

49 40 47 49 52 39 52

Family income

34 45 37 27 68 26 78 22 14

51

51 22 41 71 72 32 67

Livestock farming

Table 7.16  Community perception of project affected people on different aspects of industrialization (2018)

0 0 0 0 0 0 0 0 0

0

0 0 0 0 0 0 0

Satisfied

90 92 90 92 93 94 98 91 97

91

97 96 95 94 97 90 94

Dissatisfied

Evaluation of living standards (%) of respondents

7.5  Community Perception on Different Aspects of Industrialization (10-Point Scale) 227

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7  Uncertainity and Suffering

Nutandihi, Nitaipur, Chantibandh, Ramraydi, Kharka Suli and Khairisol mouzas are facing difficulties in collecting fodder and fuel, and Banshkopna, Chantibandh, Khairisol mouza are facing maximum difficulties in livestock farming. As a consequence family income reduces in majority of cases. In addition, Kharka Suli, Khairisol Arabari, Hatimari, Banshkopna, Nutandihi and Ashna Shuli mouzas are suffering from severe shortage of food production. As a consequence ultimately a huge economic loss is estimated. Since 2014 the local people have lost at least 1000 x 365 workforces in a calendar year. A minimum 350 acres agricultural land becomes productionless since 2009. Each household has lost income nearly INR 20–25 thousand annually hard cash from CPR since 2012.

7.6  Major Findings • The inauguration of Bengal project of JSW was started on 2 November 2008 and delayed for various reasons. On 30 November 2014 the authority put on hold its proposed 3.0 Mt plant in Bengal due to lack of long-term supplies of iron ore and de-allocation of coal block for the proposed project, and after that the social activity as medical unit facility, education facility, drinking water facility and vocational training institution are also stopped. • The issue of land acquisition was resolved in 2012 after the new government in West Bengal came into power. By this time 1000x365x5 workforces were already lost, and also at least 350 acres of agricultural land becomes productionless since 2009. • Nearly INR 10–15 thousand per household annually economic loss is noted due to closure of both direct and indirect collection of forest product that is estimated. • A greater number of larger households of land loser families are fragmented into smaller nuclear ones to cope with the situation of livelihood loss and reduced income. • The recent survey observed that more than 90% of local villagers are dissatisfied with the present situation and hope for a quick response from the government. • Khairisol and Kharka Suli mouzas are facing maximum difficulties in terms of collecting of forest product, communication, cropping and livestock farming. • Nearly 40–50 night guard, 24 nurses, 6 doctors and supervisors who are engaged in this activity become jobless. • 10–12 training personnel and 7 persons selected for 2nd phase of training at Karnataka were sent back to their home. • During construction, the affected families who are temporarily engaged in construction activity became jobless. • >90% of the households are dissatisfied with the current living standard at the project site. • Land losers are still demanding immediate initiatives to start the proposed project work.

References

229

7.7  Conclusion The peasants protest the land acquisition in Singur and Nandigram but not here at Salboni because of the assumption that people would benefit if industrialization occurs on monocrop land. Without any challenges from local people, the company acquired 4226 acres including 360.2 acres of private land from 700 peasant families. After land acquisition, problems have been started. In 2011, land allegation of 189 acres of private land was exposed for the first time by print media; after an immediate solution of the problem in 2012, the company fell through into coal block (2012) and iron ore (2013) litigation that ultimately leads to the delay of the proposed project work. In the meantime the JSW authority announced on 30 November 2014 to put the proposed project on hold due to a lack of raw materials. The local people are totally confused and frustrated with the company’s attitude; they began to protest against this decision and try to draw the attention of the national and international media, the leading economists and intellectuals of the country and human rights activists. Nearly 350 acres of agricultural land remains unproductive since 2009, after land acquisition. The local people have lost their casual work under various constructional teams. A considerable number of security, training personnel and other service providers have lost their job due to the closure of the project. The issues on productivity, employment, income and livelihood opportunities arise at the project affected areas after land acquisition. But if we take a larger look into the impacts of land acquisition for this private industry, a sharp regional disparity is observed between project and non-project areas, which is addressed in the next chapter.

References Anadabazar patrika,. 2014, December 1. Official announcement by JSW chairperson come managing director put the proposed JSW Bengal steel project hold. Retrieved on 2nd December, 2014. Anandabazar Patrika,. 2012, May 5. Lease agreement with Government of W.B. Retrieved on 5th May, 2012. Banerjee, A., Bardhan, P., Basu, K., Chaudhuri, M.D., Ghatak, M., Guha, A.S., Majumdar, M., Mookherjee, D. and Ray, D., 2002. Strategy for economic reform in West Bengal. Economic and Political Weekly, pp.4203–4218. Banerjee, P., 2006. Land acquisition and peasant resistance at singur. Economic and Political Weekly, pp.4718–4720. Brush, J.E., 1952. The iron and steel industry in India. Geographical Review, 42(1), pp.37–55. Business Standard., 2013, March 6. Kolkata, Retrieved on 6th March, 2013. Feldman, M.P. and Florida, R., 1994. The geographic sources of innovation: technological infrastructure and product innovation in the United States. Annals of the Association of American Geographers, 84(2), pp.210–229. Industrial Infrastructure., 2012.West Bengal Industrial Development Corporation, Retrieved 15th May, 2021. JSW, EIA., 2007. Rapid environmental impact assessment for the proposed 3.0 mtpa integrated steel plant at Godapiasal, Paschim Medinipur district, West Bengal. Vimta Labs Ltd, 142, IDA, Hyderabad.

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Martin, P. and Rogers, C.A., 1995. Industrial location and public infrastructure. Journal of International Economics, 39(3), pp.335–351. Rocha, H.O., 2004. Entrepreneurship and development: The role of clusters. Small business economics, 23(5), pp.363–400. Sen, R.N. and Das, S., 2000. An ergonomics study on compatibility of controls of overhead cranes in a heavy engineering factory in West Bengal. Applied ergonomics, 31(2), pp.179–184. The Hindu., 2014, July 16. Working with government to resolve issues: JSW Steel. Kolkata, Retrieved on 16th July. The Telegraph., 2012, September 21. JSW moves court over coal block. Retrieved on 21st September, 2012. The Telegraph., 2013, April 29. Salboni on hold till iron ore fix. Retrieved on 29st April, 2013. The Telegraph., 2014, November 30. Hope dims for salboni project. Retrieved on 1st December, 2014. The Telegraph., 2015, August 17. East on radar. Kolkata, Retrieved 17th August, 2015. Van de Ven, A.H., 1993. The emergence of an industrial infrastructure for technological innovation. Journal of comparative economics, 17(2), pp.338–365. West Bengal Human Development Report., 2004. Introduction and Human development indices for West Bengal. Development and Planning Department, Govt of West Bengal, May, pp.4–6.

Chapter 8

Level of Development: A Comparative Study Between Project and Non-project Area

Abstract  Development is a multidimensional process and it cannot be estimated fully by one or two indicators. Moreover, when we try to use one or two indicators individually, we cannot get any integrated and easily comprehensive picture of reality. So, it necessitates to build a composite index of development based upon optimal combination of different developmental indicators in the analysis for assessing the level of development. The main objective of the study is to estimate the level of development separately for agriculture, infrastructural facilities and socio-economic fields. Outcome of level of development at mouza level will help to identify where a given mouza stands in relation to other mouzas. Keywords  Development indicators · Composite index · Level of development · Mouza level · Agriculture · Infrastructure · Socio-economic

8.1  Introduction Proper developmental planning should improve the quality of life of the people (Inglehart 1997; Ohlan 2013). Development couldn’t be possible without altering the existing physical and social situation of the concerned region (Long et al. 2007); as a consequence it may bring unanticipated adverse impacts (Madon 2000; Szirmai 2015) on its surroundings. Development indicator brings uniformity in regional development (Akama and Kieti 2007; Choudhury 1992) through reducing the adverse impact at every stage of development (Dasgupta 1971; Sarker 1994). There are several studies on different regions to provide various dimensions (Choudhury 1992; Das 1999) of development planning (Arief 1982; Parihar and Srivastava 2003) and these studies revealed wide disparities in the level of development. To reduce these regional disparities and make a uniform development, a continuous study is needed on same ground in different years (Sudalaimuthu and Raj 2009). A continuous study was done in Karnataka (Narain et al., 1997, 2003), Tamil Nadu (Narain et  al. 2000), Orissa (Narain et  al. 1992, 1993), Madhya Pradesh (Narain et al. 2002), Assam (Narain et al. 2004), Andhra Pradesh (Narain et al. 1994a, 2009), Kerala (Narain et al. 1994b, 2005), Uttar Pradesh (Narain et al. 1995), Maharashtra © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. P. Shee, R. Maiti, Land Acquisition, Industrialization and Livelihoods, https://doi.org/10.1007/978-3-030-90244-5_8

231

232

8  Level of Development: A Comparative Study Between Project and Non-project Area

(Narain et al. 1996), Jammu and Kashmir (Narain et al. 1991) and in West Bengal (Narain et al. 2011). The studies are very helpful to point out the backward parts of the district and also point out the parts which are in better development (Raja and Yousaf 2014). It must be helpful to researchers and policymakers, who are trying to provide the various dimensions to the concept of development. Development can’t be fully estimated by a single indicator; it is a multidimensional process (Myrdal 1972; Dreze and Sen 1999; Shee and Maiti 2017). It’s quite hard to get any integrated and easily comprehensive picture of reality when using one or two indicators individually (Bhattacharya and Mahalanobis 1967; Narian et al. 2011). On the basis of various dimensions and different optimal combination of development, a combined indicator known as composite index (C.I) is built up (Demurger 2001; Florek et al. 1951; Narain et al. 2012). Socio-economic composite index is an easily accessible socio-economic indicator which contains information about the degree of satisfaction of human needs (Drewnowski 1972). The main objective of the study is to estimate the level of development separately for agriculture, infrastructural facilities and overall socio-economic fields at mouza level that will help to identify where a given mouza of the entire study area is standing in relation to other mouzas. The analysis would help the policymakers to make a better development opportunity in future.

8.2  Data and Method There are several methods for combining the effects of various indicators such as principal component analysis, multiple factor analysis, aggregation method, monetary index, ratio index and ranking method, etc., but most of these methods have their own limitations (Gostkowski 1972; Johnstone 1976; Land 1975). There are some major limitations and it may arrive from when the assumption is made about the developmental indicators themselves and their weightage in the aggregate index. According to Narain et al. (2011), when we try to use a number of indicators, the result doesn’t provide an integrated and easily comprehensive picture of reality. To overcome this problem, a composite index of development is constructed for this study using Wroclaw taxonomic method (after Ohlan 2013) to obtain a statistical method of determining homogenous units in “n”-dimensional vector space. The taxonomy method was first applied for ranking and comparing between different countries by Hellwig (1967) from Wroclaw School of Economics. This is a wonderful statistical tool introducing the concept of regional disparities which may prove to be very useful in planning (Harbison et al. 1968). Later the method was applied by Ewusi (1976) to find out the disparities in levels of regional development in Ghana. Bhatia and Rai (2004) applied the method in small area and prepared a project report of planning commission of India. In this study we applied the method to find out the regional disparities in agriculture, infrastructural and socio-economic development. A huge amount of primary and secondary information like mouza-­ wise crop land, yield rate, fertilizer consumption, household size, education,

8.3 Measuring the Level of Development

233

livestock information, family income sources, demographic information (total population, marginal worker, no. of main cultivators, literacy rate, agricultural labour, etc.), amenities, loan information, etc. were used as input in this method. Primary data was collected through a continuous household survey from both projects of affected and non-affected mouzas during different time periods in 2009–2010, 2012–2014 and 2016–2017. Census of India, district statistical handbook, local panchayat office and govt. office are sources of secondary information. A brief introduction of Wroclaw taxonomic method used in this study is discussed below.

8.3  Measuring the Level of Development Let [Xij] be the data matrix giving the value of variables of ith mouza (i = 1, 2, . …n) (no. of mouzas) and jth indicator (j = 1, 2, …k) (no. of indicators). For combined analysis [Xij] is transformed to the matrix of standardized indicators [Zij] using the following equation (Eq. 8.1):  Z ij  



Xij  X j

j

(8.1)

where N

Xj 

X i 1

N

ij

 N and  j    Xij  X j  i 1





2

1/ 2

  



From [Zij] the optimal value of each indicator is identified based on the situation of the study area. Let it be denoted by Z0j. It’s very important the optimal value will be either the maximum value or minimum value of the indicator depending upon the direction of the impact of an indicator on the level of development. For example, an increase in female literacy rate would positively affect the development, while increasing marginal workers or population density may adversely affect the development. For obtaining the pattern of development Ci of the ith mouza, the square of the deviation of the individual value of a variate from the best value is calculated (Ohlan 2013; Shee and Maiti 2017). In other words Pij is calculated using Eq. (8.2):

Pij   Z ij  Z oj 

2

(8.2)



For each i and j pattern of development, it is given by



 k  Ci   Pi /  cv j    j 1 

1/ 2

(8.3)

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8  Level of Development: A Comparative Study Between Project and Non-project Area

where (cvj)=coefficient of variation of the jth indicator in Xij. The composite index “measure of development” (Di) is given by Di = Ci / C



(8.4)

where C  C  3 Ci

where N

C

C i 1

N

i

1/ 2

 N  and  Ci     Ci  C    i 1 



The smaller Di will indicate more development and higher value of Di will indicate a low level of development. The value of Di ranges between 0 and 1.

8.4  The Level of Development A composite index of development is estimated on 111 mouzas within the study area. For relative comparison among the different mouzas, a simple rank is given to all mouzas on the basis of the composite indices and it would be sufficient for classificatory purposes. At first the “level of development” is classified into four stages as high level, high middle level, low middle level and low level of development. If the composite indices of the given mouza is less than or equal to (mean-SD) (SD means standard deviation), it is indicated as a high-level development, and if the mouza’s composite indices is greater than or equal to (mean + SD), it is considered as a low-level development. If the composite indices of any mouza is in between (mean) and (mean-SD), it indicates a high middle level development, and if the mouza is in between (mean) and (mean + SD), it is described as a low middle level development (Ohlan 2013; Shee and Maiti 2017). Composite indices of development is obtained by using the following indicators: • • • • • • • • • •

Net sown area (%). Irrigated area (%). Non-irrigated area (%). Fallow land (%). Forest cover (%). Productivity of rice (kg/ac). Area under fruits and vegetable (%). Number of livestock. Agricultural land (in acre). No. of poultry farms.

8.4 The Level of Development

• • • • • • • • • • • • • • • • • • • • • • • • • • • •

235

No. of local vegetable markets/shops. Average crop income. Income from NTFP. Fertilizer consumption (kg/acre). No. of tube wells and pumping sets. Marginal workers. Main cultivators. Agricultural labour. Education facility (0–10-point scale). Drinking water availability. Medical facility (0–10-point scale). Transport, local market facility. Male literacy rate. Female literacy rate. Literacy rate in SC population. Population density (persons/sq. km). Problem faced for NTFP (0–10-point scale). Transport facility (0–10-point scale). Road density (in km2). No. of shops. No. of people working in shop, factory, hotels, etc. Average no. of vehicles (household-wise). Electricity consumption (INR). Income from livestock farming (INR). Income from non-firm sector (INR). Sex ratio. Percentage of SC and ST population. No. of health centre/allopathic or homeopathic dispensaries.

The level of development is estimated for agriculture, infrastructure and socio-­ economic field separately within the study area. The analysis would help to identify where the 22 project affected mouzas are standing in relation to non-project affected mouzas in 2018 and compare them with the data in 2007. Some of the few most interacting indicators are discussed below. • Percentage of Net Sown Area. A relative comparison among the different mouzas of the study area is made between 2007 and 2018 regarding net sown area. An amount of 0.3 Mm3 excess runoff is receiving continuously at lower catchment (Chap. 5) an impact of LULC alteration. Satellite imagery, Google map and data are available from the district irrigation department and the local panchayat; out of 111 mouzas, 10 mouzas as Tyangrasol (36%), Mahishlot (35%), Sitanathpur (25%), Nadarya (20%), Dakshinsol (20%), Srikrishnapur (25%), Krishnapur (15%), Kharpuri (10%), Kalichak (10%) and Asta Kola (10%) mouzas have  improved  in net sown area. At project sites Kharka Suli (75%), Nutandihi (85%), Kulpheni (55%), Gaighata (50%), Ghagrasol (40%) and Nitaipur (35%) mouzas have lost their net sown area (Fig. 8.1).

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8  Level of Development: A Comparative Study Between Project and Non-project Area

Fig. 8.1  Positive and negative changes in net sown area between 2007 and 2018

• Major Work Participation. A majority of the population in the study area are poor and depend on land and forest. It is observed from census database (Table 2.7) that the total workers engaged in cultivation and agricultural labour are around 5280 and 4753, respectively, in 2001 and 2011. The total number of main workers in 2011 is 6684 and it was 7167 in 2001. For male workers 83% of the main male workers were engaged in agricultural-­ related activities in 2001; the figure is 71% in 2011. However, the percentage of female workers declined significantly, from 91% in 2001 to 73% in 2011. According to the census of India, the number of marginal workers are increased from 840 in 2001 to 1117 in 2011. The changing pattern of marginal workers between 2001 and 2011 within the study area is projected in Fig. 8.2. The total population increased by 12.8% during the last census year, but the average net sown area remains the same except for some mouzas like Dakshinsol, Gamaria, Bhurruchati, Saraswatipur, Tung Ni, Jorakusumi, Bhad Kuri, Madhupur, Krishnapur, Chensol, Jhar Bhanga, Nutandihi, Srikrishnapur, Sundarpur, Jaynarayanpur, Maheswaripur, Shyamchandpur and Jagyeswarpur which are facing poor productivity due to poor soil quality and lack of irrigation facility. Within the project affected mouzas like Khairisol, Nutandihi, Arabari, Banshkopna, Chantibandh and Ashna Shuli, a huge percentage of jobless workers during household observation, which is inversely related to development, is also recorded.

8.4 The Level of Development

237

Fig. 8.2  The changes of marginal workers between 2001 and 2011

There is no such change in percentage of cultivators since 1961. Poor soil quality, lack of irrigation facility and large forest cover makes the population more dependent on land and forest  at upper catchment. Other causes of backwardness include larger grain size, very low crop return, lack of soil testing facility, low educational level, no area under commercial crop, very poor crop density, low fertilizer consumption, etc. (Shee and Maiti 2017). After acquisition of land, irrigation facility is started to improve through bore well construction and rainwater harvesting basically out of the site of the project area like Bhangaband, Ahammadpur, Raghunath Chak, Gobardda, Putigerya, Kharpuri, Kadalawa, Brindabanpur, Rajbandh, Jhar Bhanga, Metal, Chensol and Maheswaripur mouzas and experienced an increase in percentage of cultivators by 23% (Fig. 8.3). But on the other site of the project affected mouzas’ area, land acquisition makes the farm size too small, which is not sufficient to maintain the people’s livelihood. LULC conversion also makes changes in infiltration rate that leads to soil moisture holding capacity. As a consequence in Nutandihi, Banshkopna, Chantibandh, Kharka Suli, Nitaipur, Ramraydi, Hatmari and Khairisol mouzas, more than 35% of agricultural workers are now jobless (Shee and Maiti 2017). In the manufacturing and small-scale industry, household dependency is increasing rapidly from 1914 to 2120 according to the last census year.

238

8  Level of Development: A Comparative Study Between Project and Non-project Area

Fig. 8.3  The changes in main cultivators between 2001 and 2011

• Percentage of Irrigated Area. It is observed that 35 mouzas like Ahammadpur, Sitanathpur, Saraswatipur, Gobardda, Pirrakuli, Amla Bani and Ramakata are experiencing an increase in net irrigated land area through bore well construction and rainwater harvesting. GPS handset was used for monitoring the actual location of bore wells (Plate 8.1) and ponds, and information regarding cropping land is used as input in ARC GIS tool to monitor the impacts (Fig. 8.4). Nearly 9324.2 acres of single cropping land is converted for double cropping. But Banshkopna, Ashna Shuli, Chantibandh, Hatmari and Kulpheni mouzas experienced a negative production rate. LULC alteration leads to soil erosion and decreased soil moisture holding capacity, resulting in a decline in net crop production in the surrounding mouzas of the project area.

8.4 The Level of Development

Plate 8.1  Boring for Submersible

Fig. 8.4  The changes in irrigated area between 2007 and 2018

239

240

8  Level of Development: A Comparative Study Between Project and Non-project Area

• Percentage of Crop Income. Land can’t make a farmer rich but gives food security. The majority of the population of the study area is engaged in primary activities. From household survey database, it is observed that better irrigation facility is shown at lower catchment gaining a healthy production rate due to improvement in irrigation system (Plate 8.2). Between 2007 and 2014, Banshkopna, Chantibandh, Kulpheni, Gaighata, Kharka Suli, Ashna Shuli, Arabari and Khairisol mouzas experienced a sudden drop in crop income from 75% to 22% (Fig. 8.5). But few mouzas now experienced some positive changes in crop income (2018) through rainwater harvesting and bore well construction (Fig. 8.6).

Plate 8.2  Crop rotation

Fig. 8.6  The changes in crop income 2007 2014

Fig. 8.5  The variation of crop income (%) at the project site Ghagrasol

Barju

Khairisol

Arabari

Dubrajpur

Kharka Suli

Bhalukcha Dakshin

Shalika

Hatmari

Gaighata

Kulpheni

Bhalukcha urf…

Jamdedya

Naran Chak

Chak Bhagi

Nutanbanka

Ashna Shuli

Ramraydi

Chanbandh

Bansh kopna

Nitaipur

Nutandihi

Masru

income in %

8.4 The Level of Development 241

90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

242

8  Level of Development: A Comparative Study Between Project and Non-project Area

• Education Facility. The study area includes 36 primary institutions (up to class IV) and only 3 higher secondary institutions at Nadarya, Godapiasol and Dhansol mouzas (up to class XII). No higher educational facility is available here (Fig. 8.7). The study area has more than 6500 pupils to read up to 10th class (Secondary) standards; they need to travel more than 20–25 km in a day that reveals the poor education system of the study area. Only one primary institution is there at Natundihi within the project affected mouzas. The JSW authority started a free education facility at Masru (Plate 8.3) but now it is stopped.

Fig. 8.7  The location of educational institution at Sundra catchment in 2014

8.4 The Level of Development

243

Plate 8.3  Free education sponsored by JSW

• Drinking Water Availability. Drinking water shortage is a perennial problem in the study area; it becomes acute during dry season (Plate 8.4). Salboni Gram Panchayat and the UNDP (United Nations Development Programme)  have  undertook a water budgeting system to manage the situation but still the water problem persists in dry season here. Based on the availability of drinking water (Plates 8.5 and 8.6) and fetch distance with the help of public opinion, a drinking water availability map is prepared (Figs. 8.8 and 8.9). From the database it is observed that only 15 mouzas like Saiyadpur, Metal, Kontai, Benachapra, Sundra, Maheswaripur, Kali Nagar, Dudiabandi, Ghosh Khira, Gobardda, Amla Bani, Asta Kola, Betbani Radha Khauki, Pukhur Kona and Banshkona at lower catchment have drinking water available during all season, and from field observation, it is measured that the ground water level varies between 2 to  3.5  m. Drinking water facility is very poor at 25 mouzas, viz. Kalichak, Kumirmara, Bhurruchati, Bagasol, Balarampur, Chihardalan, Jambani, Brindabanpur, Kusmisol, Dubrajpur, etc. and existing wells become dry for more than 4 months (March–June). Ground water levels vary between 2 and 16 m. Women fetch more than 5 km to collect drinking water.

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8  Level of Development: A Comparative Study Between Project and Non-project Area

Plate 8.4  Drying pond at Bagasol in summer

Plate 8.5  Monitoring ground water level during winter season

8.4 The Level of Development

245

Plate 8.6  Monitoring ground water level during dry season

Moderate availability of drinking water is mapped in 63 mouzas including project affected areas. Throughout the study area ground water level varies between 4–9 m, 0.5–1 m and 2–6 m in pre-monsoon, monsoon and post-monsoon, respectively. During dry season (April–June), local people are facing the problem of water scarcity. The JSW authority bored 3 submersibles at Arabari, Barju and Ghagrasol and a reservoir at the project site (Plate 8.7); local panchayat also sanctioned more than 15 bore wells (submersible) in different mouzas. Now the number goes to 66 in 2017, while it was nearly 40 in 2010 (Fig. 8.8).

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8  Level of Development: A Comparative Study Between Project and Non-project Area

Plate 8.7  Reservoir at the JSW project site

Fig. 8.8  Availability of drinking water before land acquisition (2010)

8.4 The Level of Development

247

Fig. 8.9  Availability of drinking water after suspension (2017)

Table 8.1 reveals the availability of drinking water facility between 2007 and 2017. Study shows the provision of ground water facility is added in both project and non-project sites. 52 households become the beneficiaries of the facility. Table 8.1  Showing the drinking water facility in the study area between 2007 and 2017 Drinking water availability Very poor Poor Medium Available

2007 No. of mouzas 25 6 63 15

% of total population 5.2 2.7 66.5 25.6

2017 No. of mouzas 23 6 55 25

% of total population 4.9 2.2 61.8 31.1

Source: Author’s own calculation

• Medical Facility. Very poor medical facility is observed in the study area during the observation. No infrastructural or emergency facilities, physician or specialist doctors or pathologists are there at Kharga Diha, Betbani Radha Khauki and Kontai medical sub-­ centre. Only few  primary treatment facilities are available there. In Metal, Khas Jangal, Kharga Diha, Betbani Radha Khauki, Kontai, Benachapra, Sundra, Saiyadpur, Gobardda and Jagyeswarpur, some private practitioners are available.

248

8  Level of Development: A Comparative Study Between Project and Non-project Area

On the basis of public opinion a medical facility map was prepared (Fig. 8.10) in 2007. It is observed that during that period 53.2% of people from 80 mouzas have no medical facilities, 30% of people in 20 mouzas are getting only primary treatment and only 10% of people in 5 mouzas are getting relatively better medical facilities. A mobile medical unit was started in 2011 by the JSW Foundation with the help of local nurses and it was available at some selected mouzas. For free diagnosis a medical team was there with a group of 6 doctors along with 24 local nurses and the target was to cover at least 3 villages in a day. Prescribed medicine was given to the patient free of cost. As a consequence more than 5000 people from 22 affected mouzas became beneficiaries (Fig. 8.11). During that period in Khas Jangal, Metal, Benachapra and Kharga Diha, some private practices like allopathic and homeopathic dispensaries are started and still now it’s running successfully. From public opinion in 2017 more than 56% of the total population are now under medical facility, 12% of the population are getting better treatment facility in respect to previous observation and 45% is under medium to better treatment facility. District hospital or Salboni Gramin Hospital is the nearest medical centre, which is 10–25 km away by road from the centre of the study area.

Fig. 8.10  The medical facility before land acquistion

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249

Fig. 8.11  The medical facility during construction

• Transport, Local Market Facility. NH 60 and SE Railway pass through N to S direction and divide the study area into two equal parts. There is a link road between Godapiasal and Anandapur. In 2007 apart from this, there was no metal road. The study area is covered with only 16.4 km of metal road and 90.3 km of soil road. Based on public opinion, a road network and transport facility map are prepared. It is observed that in 2007 the transport facility was very poor. Nearly 70% of people from 81 mouzas reported very poor transport system and 20% of the population from 7 mouzas reported medium transport facility (Fig.  8.12). Metal, Khas Jangal, Ashnabani, Chensol, Gobardda, Amla Bani, Raghunath Chak, Benachapra, Sundra, Maheswaripur, Jagyeswarpur and Sitanathpur mouzas are now  getting higher transport facility (Plate 8.8).

250

8  Level of Development: A Comparative Study Between Project and Non-project Area

Plate 8.8  Link road between NH 60 to Ashnabani, 2012

In 2012 a metal road from Gaighata (Farm road) to JSW plant and another from Godapiasol to JSW plant was sanctioned and it touches through Bhalukchati, Kharka Suli, Barju, Arabari, Khairisol, Ghagrasol and Nitaipur mouza. Apart from this another metal road was sanctioned from Sundra to Anandapur via Gobardda (Plate 8.9). In 2016 road density is increased from 0.6 to 1 km per km2. More than 30% of the population avail better transport facility, and 35% are enjoying medium facility. They are hoping that the transport facilities would be better in the coming years. At the project site more than 1500 local people from Masru, Nitaipur, Banshkopna, Chantibandh, Kulpheni, Kharka Suli and Arabari mouzas are enjoying better transport facility than previously (Fig. 8.13).

8.4 The Level of Development

Plate 8.9  Link road between Sundra to Annandapur, 2012

Fig. 8.12  Availability of transport facility before land acquisition

251

252

8  Level of Development: A Comparative Study Between Project and Non-project Area

Fig. 8.13  Availability of transport facility after suspension (2016)

During construction period some new stalls (shops) and local markets (Plate 8.10) were placed along the road side from Farm road (NH 60) to JSW entry point at Jambedya. Power consumption also increased during that period. Local people offered their rooms for rent for outside people that made their income level at the apex, but now those rooms are left vacant.

8.4 The Level of Development

253

Plate 8.10  Local market installation along the NH 60 (2014)

• Total Population. As per census 2001, the study area included 30,229 persons with an average population density of 239 persons per km2 and it increased to 34,283 persons in 2011 with an average population density of 276 persons per km2. As per census 2001, less than 200 population were distributed among 38 mouzas. In 25 mouzas such as Dakshinsol, Metal, Sitarampur, Gakulpur, Lengtisol, Juyalbhanga, Bhangaband, Benagere, Ashnabani, Kharga Diha, Kali Nagar, Amla Bani, Pukhur Kona, Ramakata, Kharpuri, etc., the total population  were ranged between 200 and 500 (Fig. 8.14).

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8  Level of Development: A Comparative Study Between Project and Non-project Area

Fig. 8.14  The population distribution of the study area Table 8.2  The demographic changes of the study area 2001 Population range No population 1500

No. of mouzas 24 36 27 23 1

% of the total population NA 10.57 27.22 55.83 6.38

2011 No. of mouzas 19 38 23 29 2

% of the total population NA 6.43 22.20 59.12 12.26

Source: Census of India

Table 8.2 depicts the demographic changes between 2001 and 2011 in the study area. It is observed that more than 60% of the total population prefer the mouzas which are either located along the river side or near the NH 60 like Dakshinsol, Brindabanpur, Jhar Bhanga, Saiyadpur, Sitarampur, Srikrishnapur and Maheswaripur mouzas due to better transport and irrigation facility. It is also observed that after announcement of the proposed JSW Bengal Steel project, local people are giving preference to the project affected mouzas (Fig.  8.15) such as Arabari, Nitaipur, Ramraydi, Khairisol and Barju in hopes of speedy socio-economic development.

8.4 The Level of Development

255

Fig. 8.15  The population distribution of study area (2011)

• Rate of Literacy. As on census 2001, in the study area the average literacy rate was 55.5% of the total population, with male literacy rate of 65.8% and female literacy rate of 44.7%. This figure is changed in 2011. According to the census in 2011 the existing literacy rate is 67.1% with 73.6% for male and 59.9% for female. This change is a positive indicator of socio-economic development. Table 8.3  The changes of literacy rate at the study area 2001 Literacy rate (%) 70

No. of mouzas 33 46 9

% of the total population 32.56 58.48 6.49

2011 No. of mouzas 15 41 38

% of the total population 10.25 49.30 40.45

Source: Census of India

Table 8.3 reveals the changes in the percentage of literacy rate during 2001 and 2011. In 2001 (Fig. 8.16) only 6.49% of the total population from 9 mouzas was registered as >70% educated; in 2011 (Fig. 8.17) the figure is changed to 40.4% of the total population from 38 mouzas registered in this category. Seven project affected mouzas such as Banshkopna, Ramraydi, Jamdedya, Gaighata, Hatmari, Shalika and Arabari are registered to have more than 70% of literacy rate.

256

8  Level of Development: A Comparative Study Between Project and Non-project Area

Fig. 8.16  The literacy rate in the study area (2001)

Fig. 8.17  The literacy rate in the study area (2011)

8  Level of Development: A Comparative Study Between Project and Non-project Area

257

• Monthly Family Income. Agricultural and livestock farming are the major source of income for local people. They are also involved in different non-farm sectors like small business, small household industry (leaf binding, honey collection, poultry farm, etc.), outworking, contractor, MGNREGA activities, etc. Fig. 8.18 presented the monthly family income among the different mouzas between 2007 and 2018. The average monthly income was INR 4000.0 within the study area in 2007. At lower catchment Kadalawa, Saiyadpur, Rajbandh, Bagpi Chula, Dudiabandi, Ghosh Khira, Gobardda, Amla Bani, Asta Kola, Rajbandh and Kalichak mouzas’ average monthly income is ranging between INR 5000 to 6000 due to better productivity of crop land, but at upper catchment it is ranging between INR 2000 to 4500 due to water shortage and lack of non-agricultural activity. Before land acquisition (2007), the monthly family income at project affected mouzas was ranged between INR 3000 to 6000 with an average of INR 4000. Monthly family incime (in INR) 10000 8000

4000

2018

Khairisol

Dharmma Danga

Hatmari

Kharka Suli

Chak Bhagi

Nitaipur

Ramraydi

Jagyeswarpur

Maheswaripur

Jaynarayanpur

Nutandihi

Pachashamar

Parasia

Baragada

Kontai

2007

Mouzas

Bhalukcha
urf Birbancha


Change (%)

Pirrakuli

Talchhara

Ramakata

Gughu Danga

Pachakua

Dudiabandi

Chensol

Dubrajpur

Jambani

Bhad Kuri

Benagere

Balarampur

Bhangaband

Kalabere

Leng
sol

Metal

Nadarya

Khas Jangal

-4000

Rajbandh

0 -2000

Mahishlot

2000 Tyangrasol

income in Rs

6000

Fig. 8.18  The monthly family income before (2007) and after suspension (2018)

In 2018 it is observed that the monthly family income is increased by more than INR 2000 at Jagyeswarpur, Ghosh Khira, Maheswaripur, Rajbandh, Gobardda, Kharpuri and Kontai mouzas due to improvement in irrigation system. But among the project affected mouzas local people are in severe condition. They have lost their agricultural land; due to the delay in project work employment opportunities started to reduce that expands the gap between expenditure and income and the average monthly income also dropped to INR. 3000. The average monthly income reduced in Arabari, Chantibandh, Ghagrasol, Banshkopna, Ashna Shuli and Kharka Suli mouzas amounting to more than INR 3000.

8.4.1  A  gricultural Composite Index (C.I) of Development and Rank The composite index (C.I) of agricultural development along with ordinal rank and level of development is calculated for 111 mouzas to be compared with the situation between pre-acquisition (2007) and in 2018. From Table 8.4 in 2007, Dudiabandi mouza was ranked in the first place followed by Amla Bani, Ghosh Khira, Kharpuri, Bagpi Chula, Rajbandh, Putigerya, Kharka Suli, Asta Kola and Saiyadpur mouzas.

258

8  Level of Development: A Comparative Study Between Project and Non-project Area

Table 8.4  Agricultural composite index (C.I) of development and rank of the mouzas between 2007 and 2018 2007

SL no. Mouza 1 Rajbandh 2

Putigerya

3

Dudiabandi

4

Gobardda

5

Kontai

6

Asta Kola

7

Maheswaripur

8

Ghosh Khira

9

Ahammadpur

10

Amla Bani

11

Kharpuri

12

Bagpi Chula

13

Talchhara

14

Madhupur

15

Sarasbedya

16

Baragada

17

Saiyadpur

18

Tyangrasol

2018

Position gained (+) or lost (−) with Area respect of to 2007 mouza Level of Level of C.I in sq. C.I Rank development C.I Rank development ranking km 1.67 0.3 7 High 0.28 1 High (+6) development development 1.12 0.28 4 High 0.37 2 High (+2) development development 0.28 0.14 1 High 0.44 3 High (−2) development development 0.92 0.37 13 High middle 0.45 4 High (+9) development development 0.34 0.39 16 High middle 0.45 5 High (+11) development development 1.94 0.33 9 High 0.45 6 High (+3) development development 0.49 0.38 14 High middle 0.47 7 High (+7) development development 1.03 0.27 3 High 0.48 8 High (−5) development development High (+10) 0.44 0.4 19 High middle 0.5 9 development development 0.54 10 High (−8) 1.06 0.26 2 High development development 1.35 0.28 5 High 0.55 11 High (−6) development development 0.55 12 High (−6) 0.18 0.29 6 High development development 0.63 0.35 11 High 0.56 13 High middle (−2) development development 0.86 0.39 17 High middle 0.56 14 High middle (+3) development development 0.37 0.46 36 High middle 0.58 15 High middle (+21) development development 1.97 0.44 22 High middle 0.58 16 High middle (+6) development development 0.55 0.35 10 High 0.58 17 High middle (−7) development development 0.45 0.51 64 Low middle 0.58 18 High middle (+46) development development (continued)

8.4 The Level of Development

259

Table 8.4 (continued) 2007

SL no. Mouza 19 Kadalawa 20

Sitanathpur

21 22

Raghunath Chak Dhansol

23

Gakulpur

24

Mahishlot

25

Saraswatipur

26

Nadarya

27

Brindabanpur

28

Dakshinsol

29

Lengtisol

30

Sundra

31

Srikrishnapur

32

Arabari

33

Benagere

34

Karamsol

35

Bagbasa

36

Palaibani

37

Banshkona

2018

Position gained (+) or lost (−) with Area respect of to 2007 mouza Level of Level of C.I in sq. C.I Rank development C.I Rank development ranking km 2.29 0.36 12 High 0.59 19 High middle (−7) development development 0.25 0.5 62 Low middle 0.59 20 High middle (+42) development development 1.21 0.41 20 High middle 0.6 21 High middle (−1) development development 1.56 0.47 46 Low middle 0.6 22 High middle (+24) development development 1.12 0.47 45 Low middle 0.6 23 High middle (+22) development development 3.55 0.6 83 Low 0.6 24 High middle (+59) development development 0.9 0.47 43 Low middle 0.6 25 High middle (+18) development development 1.26 0.48 51 Low middle 0.61 26 High middle (+25) development development High middle (−9) 1.15 0.4 18 High middle 0.61 27 development development 1.74 0.55 75 Low middle 0.62 28 High middle (+47) development development 0.26 0.47 44 Low middle 0.62 29 High middle (+15) development development 1.52 0.48 52 Low middle 0.62 30 High middle (+22) development development 1.19 0.49 56 Low middle 0.62 31 High middle (+25) development development 1.12 0.45 29 High middle 0.62 32 High middle (−3) development development 0.72 0.47 42 Low middle 0.63 33 High middle (+9) development development 1.89 0.46 37 High middle 0.63 34 High middle (+3) development development 0.18 0.46 32 High middle 0.65 35 High middle (−3) development development 0.99 0.45 24 High middle 0.65 36 High middle (−12) development development 7.5 0.45 25 High middle 0.66 37 High middle (−12) development development (continued)

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8  Level of Development: A Comparative Study Between Project and Non-project Area

Table 8.4 (continued) 2007

SL no. Mouza 38 Kharga Diha 39

Jaynarayanpur

40

Pirrakuli

41

Shal Dahara

42 43

Betbani Radha Khauki Ashnabani

44

Krishnapur

45

Dubrajpur

46

Benachapra

47

Pukhur Kona

48

Banshkopna

49

Kali Nagar

50

Masru

51

Bhad Kuri

52

Chantibandh

53

Ashna Shuli

54

Arabari

55

Sitarampur

56

Kalabere

2018

Position gained (+) or lost (−) with Area respect of to 2007 mouza Level of Level of C.I in sq. C.I Rank development C.I Rank development ranking km 0.74 0.48 47 Low middle 0.66 38 High middle (+9) development development 2.33 0.46 35 High middle 0.66 39 High middle (−4) development development 2.67 0.47 41 Low middle 0.66 40 High middle (+1) development development 0.98 0.51 65 Low middle 0.66 41 High middle (+24) development development 1.35 0.5 61 Low middle 0.67 42 High middle (+19) development development 1 0.5 63 Low middle 0.67 43 High middle (+20) development development 2.59 0.52 70 Low middle 0.68 44 High middle (+26) development development 2.59 NA NA NA 0.68 45 High middle NA development High middle (+11) 0.26 0.49 57 Low middle 0.69 46 development development 0.7 0.49 55 Low middle 0.69 47 Low middle (+8) development development 3.18 0.45 23 High middle 0.72 48 Low middle (−25) development development 2.87 0.52 68 Low middle 0.72 49 Low middle (+19) development development 0.05 0.45 31 High middle 0.72 50 Low middle (−19) development development 1.02 0.55 74 Low middle 0.73 51 Low middle (+23) development development 1.68 0.45 26 High middle 0.73 52 Low middle (−26) development development 0.41 0.43 21 High middle 0.73 53 Low middle (−32) development development 0.4 0.45 27 High middle 0.73 54 Low middle (−27) development development 0.09 0.56 76 Low middle 0.73 55 Low middle (+21) development development 0.47 NA NA NA 0.75 56 Low middle NA development (continued)

8.4 The Level of Development

261

Table 8.4 (continued) 2007

2018

Position gained (+) or lost (−) with Area respect of to 2007 mouza Level of Level of C.I in sq. SL C.I Rank development C.I Rank development ranking km no. Mouza 57 Kulpheni 0.08 0.38 15 High middle 0.75 57 Low middle (−42) development development 58 Biridanga 0.17 0.49 53 Low middle 0.76 58 Low middle (−5) development development 59 Jagyeswarpur 1.26 0.46 38 High middle 0.76 59 Low middle (−21) development development 60 Ramakata 0.73 0.51 66 Low middle 0.76 60 Low middle (+6) development development 61 Sundarpur 0.13 NA NA NA 0.76 61 Low middle NA development 62 Khas Jangal 0.74 0.45 28 High middle 0.77 62 Low middle (−34) development development 63 Bhangaband 0.93 0.57 78 Low middle 0.77 63 Low middle (+15) development development 64 Nitaipur 1 0.46 33 High middle 0.77 64 Low middle (−31) development development Low middle (−6) 65 Hatmari 0.48 0.49 59 Low middle 0.78 65 development development 66 Barju 0.6 0.45 30 High middle 0.78 66 Low middle (−36) development development 67 Juyalbhanga 0.58 0.52 69 Low middle 0.78 67 Low middle (+2) development development 68 Tung Ni 3.03 0.63 84 Low 0.78 68 Low middle (+16) development development 69 Durgadaspur 0.61 0.48 50 Low middle 0.79 69 Low middle (−19) development development 70 Katalkuli 1.09 0.52 67 Low middle 0.79 70 Low middle (−3) development development 71 Pachashamar 2.01 NA NA NA 0.8 71 Low middle NA development 72 Shyamchandpur 1.98 0.57 79 Low middle 0.8 72 Low middle (+7) development development 73 Kharka Suli 1.12 0.31 8 High 0.8 73 Low middle (−65) development development 74 Jamdedya 1.13 0.5 60 Low middle 0.8 74 Low middle (−14) development development 75 Metal 0.38 0.55 73 Low middle 0.8 75 Low middle (−2) development development (continued)

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8  Level of Development: A Comparative Study Between Project and Non-project Area

Table 8.4 (continued) 2007

SL no. Mouza 76 Parasia 77

Chandan Kath

78

Shalika

79

Kalichak

80

Godapiasol

81

Gaighata

82

Ramraydi

83

Bhalukmari

84

Chensol

85

Nutandihi

86

Ghagrasol

87

Danmari

88

Salgeria

89

Jhar Bhanga

90

Khairisol

91

Pachakua

92

Jorakusumi

93 94 95 96

Gamaria Kumirmara Bhurruchati Bagasol

2018

Position gained (+) or lost (−) with Area respect of to 2007 mouza Level of Level of C.I in sq. C.I Rank development C.I Rank development ranking km 2.35 0.46 34 High middle 0.81 76 Low middle (−42) development development 1.16 0.59 82 Low 0.82 77 Low (+5) development development 0.24 0.49 58 Low middle 0.82 78 Low (−30) development development 1.71 NA NA NA 0.82 79 Low NA development 0.47 0.67 85 Low 0.83 80 Low (+5) development development 0.8 0.47 40 Low middle 0.83 81 Low (−41) development development 0.26 0.48 48 Low middle 0.83 82 Low (−34) development development 2.05 0.53 72 Low middle 0.84 83 Low (−11) development development Low (−13) 0.7 0.53 71 Low middle 0.86 84 development development 1.58 0.46 39 High middle 0.86 85 Low (−46) development development 0.2 0.48 49 Low middle 0.87 86 Low (−37) development development 0.83 0.59 81 Low 0.87 87 Low (−6) development development 0.6 0.93 87 Low 0.89 88 Low (−1) development development 2.59 0.72 86 Low 0.9 89 Low (−3) development development 1.71 0.49 54 Low middle 0.91 90 Low (−36) development development 0.91 0.58 80 Low 0.92 91 Low (−11) development development 1.49 0.57 77 Low middle 0.94 92 Low (−15) development development 1.38 NA NA NA NA NA NA NA 0.85 NA NA NA NA NA NA NA 1.01 NA NA NA NA NA NA NA 1.68 NA NA NA NA NA NA NA (continued)

8.4 The Level of Development

263

Table 8.4 (continued) 2007

SL no. 97 98 99 100 101 102 103 104 105 106 107 108

Mouza Balarampur Chihardalan Jambani Brindabanpur Kusmisol Shushnibera Gughu Danga Beldangri Nutanbankati Chak Bhagi Naran Chak Bhalukchati urf Birbanchati 109 Bhalukchati Dakshin 110 Dubrajpur 111 Dharmma Danga

2018

Position gained (+) or lost (−) with Area respect of to 2007 mouza Level of Level of C.I in sq. C.I Rank development C.I Rank development ranking km 1.12 NA NA NA NA NA NA NA 0.71 NA NA NA NA NA NA NA 0.54 NA NA NA NA NA NA NA 1.07 NA NA NA NA NA NA NA 1.3 NA NA NA NA NA NA NA 0.91 NA NA NA NA NA NA NA 0.34 NA NA NA NA NA NA NA 0.33 NA NA NA NA NA NA NA 0.35 NA NA NA NA NA NA NA 0.39 NA NA NA NA NA NA NA 0.37 NA NA NA NA NA NA NA 0.82 NA NA NA NA NA NA NA

0.08

NA

NA

NA

NA

NA

NA

NA

0.33 0.41

NA NA

NA NA

NA NA

NA NA

NA NA

NA NA

NA NA

Source: Author’s own calculation

Since 2014 irrigation facility was started to improve and local people are trying to invest more attention in this sector because there are no alternative livelihood opportunities. In 2018, mouza Rajbandh is ranked first in agricultural development followed by Putigerya, Dudiabandi, Kontai, Gobardda, Asta Kola, Maheswaripur, Ghosh Khira, Ahammadpur and Amla Bani mouzas. These lower catchment mouzas are highly depending on agricultural activity due to the availability of runoff and good quality of soil. On an average 65.2% of the total area is a net sown area. More than 45% of the area improved their irrigation facility. On an average, 60.2% of the total population are engaged in agricultural activity now. A dramatic change in mouzas’ ordinal rank is observed among the projected affected mouzas. Most of the project affected mouzas retreated from their previous position. In 2007, Kharka Suli, Kulpheni, Asnasuli, Banshkopna and Chantibandh mouzas were ranked 8th, 15th, 21st, 23rd and 26th, respectively, but in 2018 these mouzas are standing on 73rd, 57th, 53rd, 48th and 52nd position, respectively. A cursory look at the C.I column of agricultural development (0.28–0.94) indicates the greatest regional disparity is in relatively advanced stage with comparison of lower and upper catchments.

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8  Level of Development: A Comparative Study Between Project and Non-project Area

8.4.1.1  R  elative Share of Population Under Different Levels of Agricultural Development Mouzas are lying in different levels on the basis of the measure of development in agricultural sector as presented in Tables 8.5 and 8.6.

Table 8.5  Number of mouzas with percentage of population and area under different levels of agricultural development in 2018 Number of Level of development mouzas Name of mouzas Agricultural level of development High 12 Rajbandh, Putigerya, Dudiabandi, Gobardda, Kontai, Asta Kola, Maheswaripur, Ghosh Khira, Ahammadpur, Amla Bani, Kharpuri, Bagpi Chula High middle 34 Talchhara, Madhupur, Sarasbedya, Baragada, Saiyadpur, Tyangrasol, Kadalawa, Sitanathpur, Raghunath Chak, Dhansol, Gakulpur, Mahishlot, Saraswatipur, Nadarya, Brindabanpur, Dakshinsol, Lengtisol, Sundra, Srikrishnapur, Arabari, Benagere, Karamsol, Bagbasa, Palaibani, Banshkona, Kharga Diha, Jaynarayanpur, Pirrakuli, Shal Dahara, Betbani Radha Khauki, Ashnabani, Krishnapur, Dubrajpur, Benachapra Low middle 30 Pukhur Kona, Banshkopna, Kali Nagar, Masru, Bhad Kuri, Chantibandh, Ashna Shuli, Arabari, Sitarampur, Kalabere, Kulpheni, Biridanga, Jagyeswarpur, Ramakata, Sundarpur, Khas Jangal, Bhangaband, Nitaipur, Hatmari, Barju, Juyalbhanga, Tung Ni, Durgadaspur, Katalkuli, Pachashamar, Shyamchandpur, Kharka Suli, Jamdedya, Metal, Parasia Low 16 Chandan Kath, Shalika, Kalichak, Godapiasol, Gaighata, Ramraydi, Bhalukmari, Chensol, Nutandihi, Ghagrasol, Danmari, Salgeria, Jhar Bhanga, Khairisol, Pachakua, Jorakusumi Source: Author’s own calculation

% of the total population

% of the total area

13.9

18.4

27.2

36.7

34.1

26.4

18.3

15.5

8.4 The Level of Development

265

Table 8.6  Number of mouzas with percentage of population and area under different levels of agricultural development in 2007 Number of Stages of development mouzas Name of mouzas Agricultural level of development High 12 Rajbandh, Putigerya, Dudiabandi, Asta Kola, Ghosh Khira, Amla Bani, Kharpuri, Bagpi Chula, Talchhara, Saiyadpur, Kadalawa, Kharka Suli High middle 27 Gobardda, Kontai, Maheswaripur, Ahammadpur, Madhupur, Sarasbedya, Baragada, Raghunath Chak, Brindabanpur, Arabari, Karamsol, Bagbasa, Palaibani, Banshkona, Jaynarayanpur, Banshkopna, Masru, Chantibandh, Ashna Shuli, Arabari, Kulpheni, Jagyeswarpur, Khas Jangal, Nitaipur, Barju, Parasia, Nutandihi Low middle 40 Tyangrasol, Sitanathpur, Dhansol, Gakulpur, Saraswatipur, Nadarya, Dakshinsol, Lengtisol, Sundra, Srikrishnapur, Benagere, Kharga Diha, Pirrakuli, Shal Dahara, Betbani Radha Khauki, Ashnabani. Krishnapur, Benachapra, Pukhur Kona, Kali Nagar, Bhad Kuri, Sitarampur, Biridanga, Ramakata, Bhangaband, Hatmari, Juyalbhanga, Durgadaspur, Katalkuli, Shyamchandpur, Jamdedya, Metal, Shalika, Gaighata, Ramraydi, Bhalukmari, Chensol, Ghagrasol, Khairisol, Jorakusumi Low 8 Mahishlot, Tung Ni, Chandan Kath, Godapiasol, Danmari, Salgeria, Jhar Bhanga, Pachakua

% of the total population

% of the total area

13.9

10.3

29.7

32.3

42.8

36.5

13.4

10.2

Source: Author’s own calculation

It is observed that in 2018 (Fig. 8.19), 12 mouzas are located at lower catchment and covering 18.4% of the total area are in high level of development category, while in 2007 (Fig. 8.20) 11.9% of the total population were covering 10.3% of the area. The net sown area of Gobardda, Kontai, Maheswaripur and Ahammadpur mouzas increased by more than 20%. The high middle level development category consists of 34 mouzas that cover 36.7% of the total area and include 27.2% of the total population. In 2007, there was 29.7% of the total population in 27 mouzas, covering 32.3% of the total area. In 2018, Chandan Kath, Shalika, Kalichak, Godapiasol, Gaighata, Ramraydi, Nutandihi, Ghagrasol, Danmari, Salgeria, Khairisol, Pachakua and Jorakusumi project sites surrounding the mouzas are in low-level development and 18.3% of the total population are continuously struggling for food security. Project affected mouzas are lying in different levels of development as shown in Table 8.7. In 2007, in high middle level category there were 9 mouzas but in 2018 there are no mouzas in this category. In 2018, in low-level development category, 6 mouzas are there while previously it was nil. A proper developmental planning is required for improving the level of development in agriculture sector.

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8  Level of Development: A Comparative Study Between Project and Non-project Area

Fig. 8.19  Level of development in agricultural sector after suspension (2018)

Fig. 8.20  Level of development in agricultural sector before land acquisition (2007)

8.4 The Level of Development

267

Table 8.7  Number of project affected mouzas with percentage of population and area under different levels of agricultural development in 2018 and 2007 Stages of development (2018) High High middle Low middle

Number of mouzas Name of mouzas

% of the total population

% of the total area

NIL NIL 11

NIL NIL 11.5

NIL NIL 12.2

Low

6

4.7

4.8

(2007) High High middle

1 9

– 7.7

1.3 10.1

Low middle

7

8.5

5.7

Low

NIL

NIL

NIL

NIL NIL Banshkopna, Masru, Chantibandh, Ashna Shuli, Arabari, Kulpheni, Nitaipur, Hatmari, Barju, Kharka Suli, Jamdedya Shalika, Gaighata, Ramraydi, Nutandihi, Ghagrasol, Khairisol Kharka Suli Banshkopna, Masru, Chantibandh, Ashna Shuli, Arabari, Kulpheni, Nitaipur, Barju, Nutandihi Hatmari, Jamdedya, Shalika, Gaighata, Ramraydi, Ghagrasol, Khairisol NIL

Source: Author’s own calculation

8.4.2  Infrastructural Development To know the reaction of local people after the suspension of the proposed project, a 0–10-point scale based on a social survey was conducted in 2016–2017. The survey focused on education facility (based on distance from home, no. of educational institution in a mouza), medical facility (based on the number of health subcentre in a mouza and existing amenities), drinking water facility (based on the availability and fetch from home), banking (based on loan facility), power supply (based on monthly power consumption) and transport facility (based on the frequency of public and private transport). The survey was also focused on road and network development (road density) and local market (number of shops and amount of monthly turnover) during the construction phase. Infrastructural facilities are very important to make a planning for development in different sectors of economy. 8.4.2.1  Infrastructural Composite Index (C.I) of Development and Rank On the basis of the above indicators, the composite index of infrastructural development along with ordinal rank and level of development is calculated. A brief perusal of Table 8.8 shows that in 2007 Chandan Kath (1st), Jamdedya (2nd), Gaighata (3rd), Nadarya (4th), Amla Bani (5th), Metal (6th), Parasia (7th), Betbani Radha Khauki (8th), Krishnapur (9th) and Sitanathpur (10th) mouzas were found to be occupying the first ten positions in the study area. These mouzas consisted of an average population density of 437 people per km2, average literacy rate of 60% and average monthly

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Table 8.8  Infrastructural composite index (C.I) of development and rank of the mouzas in 2007 and 2018

SL no. 1

Mouza Kulpheni

2

Jamdedya

3

Hatmari

4

Chandan Kath

5

Pachashamar

6

Gaighata

7

Nadarya

8

Ramraydi

9

Biridanga

10

Godapiasol

11

Kali Nagar

12

Banshkopna

13

Kharka Suli

14

Maheswaripur

15

Benachapra

16

Kadalawa

17

Amla Bani

18

Madhupur

19

Gobardda

20

Sitarampur

2007

2018

Level of C.I Rank development 0.23 21 High middle development 0.15 2 High development 0.28 45 High middle development 0.14 1 Low development 0.4 80 Low development 0.16 3 High development 0.17 4 High development 0.28 44 High middle development 0.31 59 Low middle development 0.25 28 High middle development 0.27 38 High middle development 0.3 53 High middle development 0.23 19 High middle development 0.28 42 High middle development 0.29 46 High middle development 0.21 15 High middle development 0.18 5 High development 0.28 39 High middle development 0.23 22 High middle development 0.23 20 High middle development

Level of C.I Rank development 0.15 1 High development 0.19 2 High development 0.2 3 High development 0.21 4 Low development 0.22 5 High development 0.23 6 High development 0.24 7 High development 0.27 8 High development 0.29 9 High development 0.29 10 High development 0.32 11 High development 0.33 12 High middle development 0.33 13 High middle development 0.34 14 High middle development 0.35 15 High middle development 0.35 16 High middle development 0.35 17 High middle development 0.36 18 High middle development 0.36 19 High middle development 0.37 20 High middle development

Position gained (+) or lost (−) with respect to 2007 C.I ranking (+20) No change (+42) (−3) (+75) (−3) (−3) (+36) (+50) (+18) (+27) (+41) (+6) (+28) (+31) (−1) (−12) (+21) (+3) No change (continued)

269

8.4 The Level of Development Table 8.8 (continued) 2007

SL no. 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Level of C.I Rank development 0.22 17 High middle development Salgeria 0.35 67 Low middle development Bagpi Chula 0.25 26 High middle development Danmari 0.25 29 High middle development Kontai 0.39 79 Low middle development Khairisol 0.21 13 High middle development Ghagrasol 0.25 24 High middle development Bagbasa 0.37 72 Low middle development Sitanathpur 0.21 10 High middle development Krishnapur 0.21 9 High development Banshkona 0.32 61 Low middle development Khas Jangal 0.42 83 Low development Juyalbhanga 0.3 56 High middle development Saiyadpur 0.27 37 High middle development Jhar Bhanga 0.29 47 High middle development Putigerya 0.31 57 Low middle development Raghunath Chak 0.21 11 High middle development Barju 0.28 43 Low development Chensol 0.24 23 High middle development Talchhara 0.43 85 Low development Mouza Benagere

2018

Level of C.I Rank development 0.37 21 High middle development 0.37 22 Low development 0.37 23 High middle development 0.38 24 High middle development 0.38 25 High middle development 0.38 26 High middle development 0.38 27 High middle development 0.38 28 High middle development 0.39 29 High middle development 0.39 30 High middle development 0.39 31 High middle development 0.39 32 High middle development 0.39 33 High middle development 0.4 34 High middle development 0.4 35 High middle development 0.4 36 High middle development 0.4 37 High middle development 0.4 38 High middle development 0.4 39 High middle development 0.41 40 High middle development

Position gained (+) or lost (−) with respect to 2007 C.I ranking (−4) (+45) (+3) (+5) (+54) (−13) (−3) (+44) (−19) (−21) (+30) (+51) (+23) (+3) (+12) (+21) (−26) (+5) (−16) (+45) (continued)

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Table 8.8 (continued)

SL no. 41

Mouza Arabari

42

Palaibani

43

Kharpuri

44

Sundra

45

Rajbandh

46

Mahishlot

47

Gakulpur

48

Parasia

49

Baragada

50 51

Betbani Radha Khauki Pirrakuli

52

Asta Kola

53

Ashna Shuli

54

Shal Dahara

55

Karamsol

56

Masru

57

Shyamchandpur

58

Bhangaband

59

Bhalukmari

60

Pachakua

61

Metal

2007

2018

Level of C.I Rank development 0.35 66 Low middle development 0.29 52 High middle development 0.26 32 High middle development 0.28 40 High middle development 0.25 25 High middle development 0.26 31 High middle development 0.21 14 High middle development 0.21 7 High development 0.26 34 High middle development 0.21 8 High middle development 0.29 49 High middle development 0.25 27 High middle development 0.32 62 Low middle development 0.35 68 Low middle development 0.37 74 Low middle development 0.27 35 High middle development 0.33 64 Low middle development 0.3 54 High middle development 0.43 84 Low development 0.34 65 Low middle development 0.2 6 High development

Level of C.I Rank development 0.41 41 High middle development 0.41 42 High middle development 0.41 43 High middle development 0.41 44 High middle development 0.41 45 High middle development 0.42 46 High middle development 0.42 47 High middle development 0.42 48 High middle development 0.42 49 High middle development 0.42 50 High middle development 0.42 51 High middle development 0.43 52 Low middle development 0.43 53 Low middle development 0.43 54 Low middle development 0.45 55 Low middle development 0.45 56 Low middle development 0.45 57 Low middle development 0.46 58 Low middle development 0.46 59 Low middle development 0.48 60 Low middle development 0.48 61 Low middle development

Position gained (+) or lost (−) with respect to 2007 C.I ranking (+25) (+10) (−11) (−4) (−20) (−15) (−33) (−41) (−15) (−42) (−2) (−25) (+9) (+14) (+19) (−21) (+7) (−4) (+25) (+5) (−55) (continued)

271

8.4 The Level of Development Table 8.8 (continued)

SL no. 62

Mouza Kalabere

63

Chantibandh

64

Tung Ni

65

Lengtisol

66

Sundarpur

67

Shalika

68

Sarasbedya

69

Arabari

70

Saraswatipur

71

Ahammadpur

72

Nutandihi

73

Dakshinsol

74

Brindabanpur

75

Bhad Kuri

76

Jorakusumi

77

Kharga Diha

78

Ghosh Khira

79

Jaynarayanpur

80

Jagyeswarpur

81

Pukhur Kona

82

Katalkuli

2007

2018

Level of C.I Rank development 0.37 75 Low middle development 0.28 41 High middle development 0.26 33 High middle development 0.29 51 High middle development 0.36 69 Low middle development 0.25 30 High middle development 0.31 58 Low middle development 0.32 60 Low middle development 0.22 16 High middle development 0.46 87 Low development 0.27 36 High middle development 0.23 18 High middle development 0.32 63 Low middle development 0.3 55 High middle development 0.29 48 High middle development 0.38 76 Low middle development 0.36 71 Low middle development 0.37 73 Low middle development 0.29 50 High middle development 0.21 12 High middle development 0.39 78 Low middle development

Level of C.I Rank development 0.48 62 Low middle development 0.48 63 Low middle development 0.48 64 Low middle development 0.48 65 Low middle development 0.48 66 Low middle development 0.49 67 Low middle development 0.49 68 Low middle development 0.5 69 Low middle development 0.5 70 Low middle development 0.5 71 Low middle development 0.5 72 Low middle development 0.5 73 Low middle development 0.51 74 Low middle development 0.54 75 Low middle development 0.54 76 Low middle development 0.54 77 Low middle development 0.55 78 Low development 0.56 79 Low development 0.56 80 Low development 0.56 81 Low development 0.58 82 Low development

Position gained (+) or lost (−) with respect to 2007 C.I ranking (+13) (−22) (−31) (−14) (+3) (−37) (−10) (−9) (−54) (+16) (−36) (−55) (−11) (−20) (−28) (−1) (−7) (−6) (−30) (−69) (−4) (continued)

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8  Level of Development: A Comparative Study Between Project and Non-project Area

Table 8.8 (continued)

SL no. 83

Mouza Dhansol

84

Ramakata

85

Ashnabani

86

Kalichak

87

Srikrishnapur

88

Dudiabandi

89

Tyangrasol

90

Durgadaspur

91

Nitaipur

92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108

2007

2018

Level of C.I Rank development 0.38 77 Low middle development 0.36 70 Low middle development 0.42 82 Low development 0.43 86 Low development 0.41 81 Low development 0.64 90 Low development 0.7 91 Low development 0.52 89 Low development 0.48 88 Low development NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Level of C.I Rank development 0.58 83 Low development 0.59 84 Low development 0.59 85 Low development 0.6 86 Low development 0.61 87 Low development 0.64 88 Low development 0.69 89 Low development 0.75 90 Low development 0.75 91 Low development NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Gamaria Kumirmara Bhurruchati Bagasol Balarampur Chihardalan Jambani Brindabanpur Kusmisol Shushnibera Dubrajpur Gughu Danga Beldangri Nutanbankati Chak Bhagi Naran Chak Bhalukchati urf Birbanchati 109 Bhalukchati NA Dakshin 110 Dubrajpur NA 111 Dharmma Danga NA

Position gained (+) or lost (−) with respect to 2007 C.I ranking (−6) (−14) (−3) No Change (−6) (+2) (+2) (−1) (−3)

NA

NA

NA

NA

NA

NA

NA NA

NA NA

NA NA

NA NA

NA NA

NA NA

Source: Author’s own calculation

8.4 The Level of Development

273

family income of INR. 6000–7000. After land acquisition medical, transport, drinking water, power supply and banking facilities are  started to improve at the surroundings of the project affected mouzas. In 2018 mouza Kulpheni stands in the first position, followed by Jamdedya (2nd), Hatmari (3rd), Chandan Kath (4th), Pachashamar (5th), Gaighata (6th), Nadarya (7th), Ramraydi (8th), Biridanga (9th) and Godapiasol (10th) mouza. The C.I of infrastructural development varies from 0.15 to 0.75 that depicts that a greatest regional disparity is observed within the study area. 8.4.2.2  R  elative Share of Population Under Different Levels of Infrastructural Development Tables 8.9 and 8.10 present the number of mouzas lying in different levels of development in infrastructural category in 2018 and what was in 2007, before acquisition. Table 8.9  Number of mouzas with percentage of population and area under different levels of infrastructural development in 2018 Number of Level of development mouzas Name of mouzas Infrastructural development High 10 Kulpheni, Jamdedya, Hatmari, Pachashamar, Gaighata, Nadarya, Ramraydi, Biridanga, Godapiasol, Kali Nagar High middle 39 Banshkopna, Kharka Suli, Maheswaripur, Benachapra, Kadalawa, Amla Bani, Madhupur, Gobardda, Sitarampur, Benagere, Bagpi Chula, Danmari, Kontai, Khairisol, Ghagrasol, Bagbasa, Sitanathpur, Krishnapur, Banshkona, Khas Jangal, Juyalbhanga, Saiyadpur, Jhar Bhanga, Putigerya, Raghunath Chak, Barju, Chensol, Talchhara, Arabari, Palaibani, Kharpuri, Sundra, Rajbandh, Mahishlot, Gakulpur, Parasia, Baragada, Betbani Radha Khauki, Pirrakuli Low middle 26 Asta Kola, Ashna Shuli, Shal Dahara, Karamsol, Masru, Shyamchandpur, Bhangaband, Bhalukmari, Pachakua, Metal, Kalabere, Chantibandh, Tung Ni, Lengtisol, Sundarpur, Shalika, Sarasbedya, Arabari, Saraswatipur, Ahammadpur, Nutandihi, Dakshinsol, Brindabanpur, Bhad Kuri, Jorakusumi, Kharga Diha Low 14 Chandan Kath, Salgeria, Ghosh Khira, Jaynarayanpur, Jagyeswarpur, Pukhur Kona, Katalkuli, Dhansol, Ramakata, Ashnabani, Kalichak, Srikrishnapur, Dudiabandi, Tyangrasol, Durgadaspur, Nitaipur Source: Author’s own calculation

% of the total population

% of the total area

16.5

12.2

53.1

41.8

20.5

22.3

9.9

12.1

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8  Level of Development: A Comparative Study Between Project and Non-project Area

Table 8.10  Number of mouzas with percentage of population and area under different levels of infrastructural development in 2007 Number of Stages of development mouzas Name of mouzas Infrastructural development High 8 Jamdedya, Gaighata, Nadarya, Amla Bani, Krishnapur, Parasia, Betbani Radha Khauki, Metal High middle 46 Kulpheni, Hatmari, Ramraydi, Godapiasol, Kali Nagar, Banshkopna, Kharka Suli, Maheswaripur, Benachapra, Kadalawa, Madhupur, Gobardda, Sitarampur, Benagere, Bagpi Chula, Danmari, Khairisol, Ghagrasol, Sitanathpur, Juyalbhanga, Saiyadpur, Jhar Bhanga, Raghunath Chak, Chensol, Palaibani, Kharpuri, Sundra, Rajbandh, Mahishlot, Gakulpur, Baragada, Pirrakuli, Asta Kola, Masru, Bhangaband, Chantibandh, Tung Ni, Lengtisol, Shalika, Saraswatipur, Nutandihi, Dakshinsol Bhad Kuri, Jorakusumi, Jagyeswarpur, Pukhur Kona Low middle 23 Biridanga, Salgeria, Kontai, Bagbasa, Banshkona, Putigerya, Arabari, Ashna Shuli, Shal Dahara, Karamsol, Shyamchandpur, Pachakua, Kalabere, Sundarpur, Sarasbedya, Arabari, Brindabanpur, Kharga Diha, Ghosh Khira, Jaynarayanpur, Katalkuli, Dhansol, Ramakata Low 14 Chandan Kath, Pachashamar, Khas Jangal, Barju, Talchhara, Bhalukmari, Ahammadpur, Ashnabani, Kalichak, Srikrishnapur, Dudiabandi, Tyangrasol, Durgadaspur, Nitaipur

% of the total population

% of the total area

9.8

10.1

53.9

44.1

26.8

20.1

9.4

14.3

Source: Author’s own calculation

The availability of infrastructural facilities plays a vital role in enhancing the level of development of different sectors of the economy. From analysis it is observed that 10 mouzas are covering 12.2% of the total area and 16.5% of the total population are in high development category in 2018 (Fig. 8.21), while in 2007 this category consisted of 10.1% of the total area and 9.8% of the total population (Fig. 8.22).

8.4 The Level of Development

275

Fig. 8.21  The level of infrastructural development after suspension (2018)

Fig. 8.22  The level of infrastructural development before land acquisition

During construction period 2012–2014, the infrastructural facilities started to improve at the surroundings of the project site. The JSW authority focused on improvement in electrical facility, power supply, water, roads, bridges, education and health facilities. Within the project site, five mouzas, namely, Kulpheni, Jamdedya, Hatmari, Gaighata and Ramraydi, are now experiencing a high level of infrastructural development (Table 8.11).

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8  Level of Development: A Comparative Study Between Project and Non-project Area

Table 8.11  Number of project affected mouzas with percentage of area and population under different levels of infrastructure development in 2018 and 2007

Stages of Number of development mouzas Name of mouzas After land acquisition (2018) High 5 Kulpheni, Jamdedya, Hatmari, Gaighata, Ramraydi High middle 6 Banshkopna, Kharka Suli, Khairisol, Ghagrasol, Barju, Arabari Low middle 6 Ashna Shuli, Masru, Chantibandh, Shalika, Arabari, Nutandihi Low 3 Chandan Kath, Salgeria, Nitaipur Before land acquisition (2007) High 2 Jambedya, Gaighata High middle 11 Kulpheni, Hatmari, Ramraydi, Banshkopna, Kharka Suli, Khairisol, Ghagrasol, Masru, Chantibandh, Shalika, Nutandihi Low middle 3 Chandan Kath, Barju, Nitaipur Low 4 Salgeria, Arabari, Ashna Shuli, Arabari

% of the % of the total total area population 9.2

6.1

3.9

8.6

3.1

6.1

1.2

2.3

4.9 8.2

2.3 14.1

1.6 2.8

1.9 3.9

Source: Author’s own calculation

Those mouzas cover 9.2% of the total population. Prior to acquisition in 2007 only two project affected mouzas were found in the high-level development category and these mouzas covered 4.9% of the total population and only 2.3% of the area. Kulpheni, Hatmari, Ramraydi, Banshkopna, Kharka Suli, Khairisol, Ghagrasol, Masru, Chantibandh, Shalika and Nutandihi mouzas are already enjoying some facilities like good power supply, communication, local markets, etc. due to them being nearer to NH 60. A subpower station is already there at Gaighata.

8.4.3  Socio-economic Development Socio-economic development is a multidimensional process. It requires the satisfaction of economic, social, political and cultural rights, equitable distribution of development benefits and opportunities, dignified living environment, gender equality and empowerment of the poor and marginalized. 8.4.3.1  Socio-economic Composite Index (C.I) of Development and Rank On the basis of the above indicators, the socio-economic development composite index (C.I) is calculated. It is evident from the information presented in Table 8.12 that Asta Kola, Dudiabandi, Gakulpur, Rajbandh, Palaibani, Mahishlot, Putigerya, Kontai, Kharpuri and Sarasbedya mouzas are placed in the top ten, enjoying the status of the socio-economically highly developed category with respect to overall

277

8.4 The Level of Development

Table 8.12  Socio-economic composite index of development (C.I) and rank of the mouzas in 2007 and 2018

SL no. 1

Mouza Asta Kola

2

Dudiabandi

3

Gakulpur

4

Rajbandh

5

Palaibani

6

Mahishlot

7

Putigerya

8

Kontai

9

Salgeria

10

Kharpuri

11

Sarasbedya

12

Gaighata

13

Ramraydi

14

Jamdedya

15

Madhupur

16

Sitarampur

17

Pukhur Kona

18

Kalabere

19

Godapiasol

20

Hatmari

21

Banshkona

2007

2018

Level of C.I Rank development 0.46 8 High development 0.47 10 High development 0.37 3 High development 0.73 60 Low middle development 0.51 12 High development 0.72 57 Low middle development 0.36 1 High development 0.61 31 High middle development 0.66 43 Low development 0.7 51 Low middle development 0.62 32 High middle development 0.64 39 High middle development 0.69 50 Low middle development 0.55 21 High middle development 0.64 38 High middle development 0.52 13 High development 0.59 23 High middle development 0.37 2 High development 0.6 25 High middle development 0.7 54 Low middle development 0.54 17 High middle development

Level of C.I Rank development 0.36 1 High development 0.53 2 High development 0.57 3 High development 0.58 4 High development 0.59 5 High development 0.61 6 High development 0.63 7 High development 0.63 8 High development 0.63 9 Low development 0.65 10 High development 0.65 11 High development 0.65 12 High development 0.65 13 High development 0.65 14 High development 0.66 15 High development 0.67 16 High middle development 0.68 17 High middle development 0.69 18 High middle development 0.69 19 High middle development 0.69 20 High middle development 0.69 21 High middle development

Position gained (+) or lost (−) with respect to 2007 C.I ranking (+7) (+8) No change (+56) (+7) (+51) (−6) (+23) (+34) (+41) (+21) (+27) (+37) (+7) (+23) (−3) (+6) (−16) (+6) (+34) (−4) (continued)

278

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Table 8.12 (continued) 2007

SL no. 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

Level of C.I Rank development 0.65 41 High middle development Kadalawa 0.45 7 High development Saiyadpur 0.78 73 Low middle development Raghunath Chak 0.54 15 High middle development Kharka Suli 0.41 5 High development Amla Bani 0.67 45 High middle development Tyangrasol 0.78 72 Low development Nadarya 0.78 71 Low middle development Krishnapur 0.76 66 Low middle development Khas Jangal 0.76 68 Low middle development Pachashamar 0.39 4 High development Banshkopna 0.46 9 High development Juyalbhanga 0.49 11 High development Pachakua 0.54 16 High middle development Kali Nagar 0.6 26 High middle development Kulpheni 0.52 14 High development Lengtisol 0.59 24 High middle development Chensol 0.63 35 High middle development Kharga Diha 0.55 19 High middle development Benachapra 0.78 70 Low middle development Chandan Kath 0.7 53 Low development Mouza Jhar Bhanga

2018

Level of C.I Rank development 0.69 22 High middle development 0.7 23 High middle development 0.7 24 High middle development 0.7 25 High middle development 0.7 26 High middle development 0.71 27 High middle development 0.71 28 High middle development 0.71 29 High middle development 0.71 30 High middle development 0.71 31 High middle development 0.72 32 High middle development 0.72 33 High middle development 0.72 34 High middle development 0.73 35 High middle development 0.73 36 High middle development 0.74 37 High middle development 0.74 38 High middle development 0.75 39 High middle development 0.76 40 High middle development 0.76 41 High middle development 0.76 42 Low development

Position gained (+) or lost (−) with respect to 2007 C.I ranking (+19) (−16) (+49) (−10) (−21) (+18) (+44) (+42) (+36) (+37) (−28) (−24) (−23) (−19) (−10) (−23) (−14) (−4) (−21) (+29) (+11) (continued)

279

8.4 The Level of Development Table 8.12 (continued)

SL no. 43

Mouza Bagbasa

44

Sitanathpur

45

Sundra

46

Gobardda

47

Jagyeswarpur

48

Bagpi Chula

49

Karamsol

50 51

Betbani Radha Khauki Maheswaripur

52

Ghosh Khira

53

Ghagrasol

54

Dhansol

55

Danmari

56

Baragada

57

Shalika

58

Saraswatipur

59

Biridanga

60

Bhangaband

61

Metal

62

Benagere

63

Katalkuli

2007

2018

Level of C.I Rank development 0.68 46 High middle development 0.54 18 High middle development 0.87 82 Low development 0.6 29 High middle development 0.72 59 Low middle development 0.61 30 High middle development 0.62 33 High middle development 0.63 37 High middle development 0.65 42 High middle development 0.62 34 High middle development 0.58 22 High middle development 0.75 64 Low middle development 0.81 74 Low middle development 0.82 75 Low middle development 0.71 56 Low middle development 0.6 27 High middle development 0.85 77 Low development 0.63 36 High middle development 0.72 58 Low middle development 0.64 40 High middle development 0.73 61 Low middle development

Level of C.I Rank development 0.76 43 High middle development 0.76 44 High middle development 0.77 45 High middle development 0.78 46 Low middle development 0.78 47 Low middle development 0.78 48 Low middle development 0.78 49 Low middle development 0.78 50 Low middle development 0.8 51 Low middle development 0.8 52 Low middle development 0.8 53 Low middle development 0.81 54 Low middle development 0.81 55 Low middle development 0.81 56 Low middle development 0.82 57 Low middle development 0.82 58 Low middle development 0.83 59 Low middle development 0.83 60 Low middle development 0.83 61 Low middle development 0.83 62 Low middle development 0.84 63 Low middle development

Position gained (+) or lost (−) with respect to 2007 C.I ranking (+3) (−26) (+37) (−17) (+12) (−18) (−16) (−13) (−9) (−18) (−31) (+10) (+19) (+19) (−1) (−31) (+18) (−24) (−3) (−22) (−2) (continued)

280

8  Level of Development: A Comparative Study Between Project and Non-project Area

Table 8.12 (continued)

SL no. 64

Mouza Kalichak

65

Jaynarayanpur

66

Masru

67

Nutandihi

68

Chantibandh

69

Khairisol

70

Tung Ni

71

Ashna Shuli

72

Nitaipur

73

Ramakata

74

Shal Dahara

75

Arabari

76

Durgadaspur

77

Bhad Kuri

78

Talchhara

79

Brindabanpur

80

Ahammadpur

81

Jorakusumi

82

Arabari

83

Barju

84

Ashnabani

85

Bhalukmari

2007

2018

Level of C.I Rank development 0.42 6 High development 0.98 91 Low development 0.66 44 High middle development 0.93 87 Low development 0.6 28 High middle development 0.55 20 High middle development 0.71 55 Low middle development 0.73 62 Low middle development 0.88 83 Low development 0.82 76 Low middle development 0.78 69 Low middle development 0.76 67 Low middle development 0.96 90 Low development 0.87 81 Low development 0.87 80 Low development 0.68 47 High middle development 0.95 89 Low development 0.7 52 Low middle development 0.69 48 Low middle development 0.69 49 Low middle development 0.86 78 Low development 0.75 65 Low middle development

Level of C.I Rank development 0.84 64 Low middle development 0.85 65 Low middle development 0.86 66 Low middle development 0.88 67 Low middle development 0.88 68 Low middle development 0.88 69 Low middle development 0.89 70 Low middle development 0.89 71 Low middle development 0.9 72 Low development 0.9 73 Low development 0.9 74 Low development 0.91 75 Low development 0.91 76 Low development 0.92 77 Low development 0.92 78 Low development 0.92 79 Low development 0.93 80 Low development 0.93 81 Low development 0.93 82 Low development 0.93 83 Low development 0.94 84 Low development 0.94 85 Low development

Position gained (+) or lost (−) with respect to 2007 C.I ranking (−58) (+26) (−22) (+20) (−40) (−49) (−15) (−9) (+11) (+3) (−5) (−8) (+14) (+4) (+2) (−32) (+9) (−29) (−34) (−34) (−6) (−20) (continued)

8.4 The Level of Development

281

Table 8.12 (continued)

SL no. 86

Mouza Shyamchandpur

87

Sundarpur

88

Parasia

89

Pirrakuli

90

Dakshinsol

91

Srikrishnapur

92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108

2007

2018

Level of C.I Rank development 0.87 79 Low development 0.9 85 Low development 0.74 63 Low middle development 0.93 86 Low development 0.89 84 Low development 0.95 88 Low development NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Level of C.I Rank development 0.95 86 Low development 0.95 87 Low development 0.95 88 Low development 0.97 89 Low development 0.98 90 Low development 0.99 91 Low development NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Gamaria Kumirmara Bhurruchati Bagasol Balarampur Chihardalan Jambani Brindabanpur Kusmisol Shushnibera Dubrajpur Gughu Danga Beldangri Nutanbankati Chak Bhagi Naran Chak Bhalukchati urf Birbanchati 109 Bhalukchati NA Dakshin 110 Dubrajpur NA 111 Dharmma Danga NA

Position gained (+) or lost (−) with respect to 2007 C.I ranking (−7) (−2) (−25) (−3) (−6) (−3)

NA

NA

NA

NA

NA

NA

NA NA

NA NA

NA NA

NA NA

NA NA

NA NA

Source: Author’s own calculation

socio-economic condition in 2018. These mouzas consist of an average of 71.8% literacy rate and the monthly income is ranging between INR 12000 to 15,000. It is evident from the information presented in the column of Table  8.12 that project affected mouzas like Kharka Suli, Banshkopna, Kulpheni, Khairisol, Jamdedya, Ghagrasol and Chantibandh were at 5th, 9th, 14th, 20th, 21st, 22nd and 28th position before acquisition in 2007, but after acquisition those mouzas are at 26th,

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8  Level of Development: A Comparative Study Between Project and Non-project Area

33rd, 37th, 69th, 14th, 53rd and 68th, respectively, in recent year (2018). It implies that after suspension they become jobless, common pool resources already acquired and family income is continuously decreasing, Covid 19 pandemic makes more complex the situation, a detail investigation will be needed to know it’s prolonged effect on local economy. 8.4.3.2  R  elative Share of Population Under Different Levels of Socio-economic Development Tables 8.13 and 8.14 reveal the percentage of the total population and the percentage of the area cover in different levels of development in overall socio-economic fields in 2018 and in 2007, before acquisition. Table 8.13  Number of mouzas with percentage of population and area under different levels of socio-economic development in 2018 Number of Level of development mouzas Name of mouzas Socio-economic level of development High 14 Asta Kola, Dudiabandi, Gakulpur, Rajbandh, Palaibani, Mahishlot, Putigerya, Kontai, Kharpuri, Sarasbedya, Gaighata, Ramraydi, Jamdedya, Madhupur High middle 29 Sitarampur, Pukhur Kona, Kalabere, Godapiasol, Hatmari, Banshkona, Jhar Bhanga, Kadalawa, Saiyadpur, Raghunath Chak, Kharka Suli, Amla Bani, Tyangrasol, Nadarya, Krishnapur, Khas Jangal, Pachashamar, Banshkopna, Juyalbhanga, Pachakua, Kali Nagar, Kulpheni, Lengtisol, Chensol, Kharga Diha, Benachapra, Bagbasa, Sitanathpur, Sundra Low middle 26 Gobardda, Jagyeswarpur, Bagpi Chula, Karamsol, Betbani Radha Khauki, Maheswaripur, Ghosh Khira, Ghagrasol, Dhansol, Danmari, Baragada, Shalika, Saraswatipur, Biridanga, Bhangaband, Metal, Benagere, Katalkuli, Kalichak, Jaynarayanpur, Masru, Nutandihi, Chantibandh, Khairisol, Tung Ni, Ashna Shuli Low 22 Salgeria, Chandan Kath, Nitaipur, Ramakata, Shal Dahara, Arabari, Durgadaspur, Bhad Kuri, Talchhara, Brindabanpur, Ahammadpur, Jorakusumi, Arabari, Barju, Ashnabani, Bhalukmari, Shyamchandpur, Sundarpur, Parasia, Pirrakuli, Dakshinsol, Srikrishnapur Source: Author’s own calculation

% of the total population

% of the total area

22.1

12.9

42.3

29.3

22.2

26.4

13.6

19.8

8.4 The Level of Development

283

Table 8.14  Number of mouzas with percentage of population and area under different levels of socio-economic development in 2007 Stages of Number of development mouzas Name of mouzas Socio-economic level of development High 14 Asta Kola, Dudiabandi, Gakulpur, Palaibani, Putigerya, Sitarampur, Kalabere, Kadalawa, Kharka Suli, Pachashamar, Banshkopna, Juyalbhanga, Kulpheni, Kalichak High middle 32 Kontai, Sarasbedya, Gaighata, Jamdedya, Madhupur, Pukhur Kona, Godapiasol, Banshkona, Jhar Bhanga, Raghunath Chak, Amla Bani, Pachakua, Kali Nagar, Lengtisol, Chensol, Kharga Diha, Bagbasa, Sitanathpur, Gobardda, Bagpi Chula, Karamsol, Betbani Radha Khauki, Maheswaripur, Ghosh Khira, Ghagrasol, Saraswatipur, Bhangaband, Benagere, Masru, Chantibandh, Khairisol, Brindabanpur Low middle 27 Rajbandh, Mahishlot, Kharpuri, Ramraydi, Hatmari, Saiyadpur, Nadarya, Krishnapur, Khas Jangal, Benachapra, Jagyeswarpur, Dhansol, Danmari, Baragada, Shalika, Metal, Katalkuli, Tung Ni, Ashna Shuli, Ramakata, Shal Dahara, Arabari, Jorakusumi, Arabari, Barju, Bhalukmari, Parasia Low 18 Salgeria, Tyangrasol, Chandan Kath, Sundra, Biridanga, Jaynarayanpur, Nutandihi, Nitaipur, Durgadaspur, Bhad Kuri, Talchhara, Ahammadpur, Ashnabani, Shyamchandpur, Sundarpur, Pirrakuli, Dakshinsol, Srikrishnapur

% of the % of the total total population area 11.8

10.6

46.9

32.6

30.8

28.5

10.3

15.1

Source: Author’s own calculation

With respect to socio-economic development in 2018, only 14 mouzas cover 12.9% of the area, with 22.1% of the population are at development category. Twenty-nine mouzas are in high middle level category, covering 29.3% of the area and 42.3% of the population. Twenty-six mouzas with 26.4% of the area and 22.2% of the total population are found in low middle and low development categories (Fig. 8.23). Special attention should be taken to develop co-operative societies and regulated farm produce markets in this region. In 2007 (Fig. 8.24) 14 mouzas covered 10.6% of the area and 11.8% of the population was in high-level category. At the project affected mouzas only 9.5% of the affected population are lying in high development category in 2018 but the majority of the population are now exists in the low middle and low development category that reveals  the  poor  socio-­ economic development here.

284

8  Level of Development: A Comparative Study Between Project and Non-project Area

Fig. 8.23  The level of socio-economic development after suspension

Fig. 8.24  The level of socio-economic development before land acquisition

8.5 Major Findings

285

The study area is mainly under rural population and economically poor. The mouzas at upper catchment are found to be low developed either in agricultural development or in infrastructural development. The level of education reflects as well as depends on all-round development of manpower and human resources that are very low here.

8.5  Major Findings • A relative comparison in terms of agricultural development, infrastructural development and socio-economic development in the study area between project and non-project sites is conducted using Wroclaw taxonomic method. • Maximum reduction in net sown area is recorded at Dubrajpur (75%), Chantibandh (85%), Chak Bhagi (55%), Ramraydi (50%), Ashna Shuli (40%) and Nitaipur (35%) mouza. • The number of marginal workers increased rapidly by 17% within the study area. It was nearly 23.0% in 2001 and increased to 40.4% in 2011. • Major reduction in crop income is observed at Banshkopna, Chantibandh, Kulpheni, Gaighata, Kharka Suli, Ashna Shuli, Arabari and Khairisol mouzas at the project site. • At project affected mouzas more than 30% of agricultural workers are engaged in construction activities but now become jobless. • The composite index of agricultural development ranges from 0.28 to 0.94 that indicates the regional disparity within the agricultural sectors. • The combined indicator of agricultural development shows that before acquisition no project affected mouza was registered under low category of development. After acquisition 11.5% of the population presently belongs to low development category. • Outside the project area 28 mouzas are found to be in low and low middle development category in 2018 that covers 31.7% of the total area and 27.3% of the population, but before acquisition (2007) 40 mouzas are observed in this category that covered 40.2% of the total area and 46.5% of the total population. • Agricultural development is observed at lower catchment through utilization of excess runoff. The major cause of low development in the project site is the reduction of farm size due to land acquisition. • The project site road density is increased from 0.6 to 1 km per km2 and more than 12% of the population avail better transport facility. • The value of C.I of infrastructural development varies from 0.15 to 0.75 that depicts the greatest regional disparity. • A high level of infrastructural development is observed in project affected mouzas. The development area was expanded from 10.6% (2007) to 27.4% (2014), but after suspension of the project work some facilities dropped and now the area covered 12.2%. • At the project site in 2014 high level of infrastructural category covered 27.4% of the total project affected mouza area and 53.47% of the total project affected population, but it’s cover area dropped to 23.6% in 2018.

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• In infrastructure development outside of the project area, 12.2% of the total area and 16.5% of the total population are in high development category. Before acquisition (2007) this category consists of 10.1% of the total area and 9.8% of the total population. • Infrastructural development is focused along the NH 60 and project affected mouzas. • The average monthly income reduced in Arabari, Chantibandh, Ghagrasol, Banshkopna, Ashna Shuli and Kharka Suli mouzas amounting to more than INR 2500–3000. • The C.I value of socio-economic development varies from 0.36 to 0.99 in 2018 that indicates wide regional disparities present at the study area and require immediate attention for all inclusive development. • In 2018, 14 mouzas cover 12.9% of the total area, including 22.1% of the total population of the study area observed to be in high-level category. • At the project affected mouzas only 9.5% of the affected population is lying in high development category that covers 10.59% of the total project affected area in 2018, but before acquisition (2007) it was 7.9% of the affected population and 22.48% of the total project affected area. • The project affected mouzas were in low development category in 2007, covered by 10.3% of total affected area and including 3.4% of the total population of study area.

8.6  Conclusion Economic planning is necessary from grassroots level that would bring about uniform regional development. The level of development of project affected and non-­project affected mouzas is studied with the help of composite index based on optimum combination of different development indicators. In order to get a clear picture of regional disparities, the level of development is assessed separately for agricultural sector, infrastructural sector and socio-economic sector using Wroclaw taxonomic method developed by Ohlan (2013). Extensive landuse alteration is observed in the project areas after land acquisition. The maximum reduction in net sown area is observed at Kharka Suli (75%), Nutandihi (85%), Kulpheni (55%), Gaighata (50%), Ghagrasol (40%) and Nitaipur (35%). After acquisition farm sizes become small that leads to maximum share of marginal workers at Khairisol, Nutandihi, Arabari, Banshkopna, Chantibandh and Ashna Shuli. Major reduction in crop income is observed at Banshkopna, Ashna Shuli, Kharka Suli and Gaighata. More than 30% of agricultural workers from project area are now engaged in non-­farm activities, trying to reduce the livelihood vulnerability. After suspension of the project, the affected people already try to settle with different non-agricultural activities and even some of them migrate to other state. The composite index of agricultural development varies from 0.28 to 0.94 that indicates a greatest regional disparity existing in agricultural development. Although considerable attempts of developing infrastructural facilities are observed in project sites earlier through improving drinking water facility, medical facility and transport facility, these were closed after the project was postponed. After losing their productive land and profession of primary activities, people from project sites are suffering from steep

References

287

reduction in income and employment. There is a sharp difference in development between project affected and non-affected areas. The average monthly income is reduced in Arabari, Chantibandh, Ghagrasol, Banshkopna, Ashna Shuli and Kharka Suli mouza amounting to more than INR 3000, although a high level of infrastructural development is observed in project affected mouzas. The development area is expanded from 10.6% (2007) to 27.4% in 2018. Before acquisition in 2007 only two project affected mouzas were found in high-level development category. The value of C.I of infrastructural development is ranging between 0.15 and 0.75 that depicts a considerable regional disparity between project and non-­project mouzas. The C.I value of socioeconomic development is between 0.36 and 0.99 in 2018 that indicates wide regional disparities present in the study area and requires immediate attention for all inclusive development. Global Covid 19 pandemic makes more complex the over all situation. A proper developmental planning on project affected mouzas is necessary with the help of local NGOs and government to make alternative livelihood opportunities.

References Akama, J.S. and Kieti, D., 2007. Tourism and socio-economic development in developing countries: A case study of Mombasa Resort in Kenya. Journal of sustainable tourism, 15(6), pp.735–748. Arief, S., 1982. Regional disparities in Malaysia. Social Indicators Research, 11(3), pp.259–267. Bhatia, V.K. and Rai, S.C., 2004. Evaluation of socio-economic development in small areas. New Delhi, India.http://planningcommission.nic.in/reports/sereport/ser/std_smlarea.pdf. Retrieved on 11-06-2014. Bhattacharya, N. and Mahalanobis, B., 1967. Regional disparities in household consumption in India. Journal of the American Statistical Association, 62(317), pp.143–161. Choudhury, U.D.R., 1992. Inter-state and intra-state variations in economic development and standard of living. Economic and Political Weekly, pp.2651–2655. Das, A., 1999. Socio-economic development in India: A regional analysis. Development and Society, 28(2), pp.313–345. Dasgupta, B., 1971. Socio-economic classification of districts: a statistical approach. Economic and Political Weekly, pp.1763–1774. Demurger, S., 2001. Infrastructure development and economic growth: an explanation for regional disparities in China?. Journal of Comparative economics, 29(1), pp.95–117. Drewnowski, J., 1972. Social indicators and welfare measurement: remarks on methodology. The Journal of Development Studies, 8(3), pp.77–90. Dreze, J. and Sen, A., 1999. India: Economic development and social opportunity. OUP Catalogue. Ewusi, K., 1976. Disparities in levels of regional development in Ghana. Social indicators research, 3(1), pp.75–100. Florek, K., Łukaszewicz, J., Perkal, J., Steinhaus, H. and Zubrzycki, S., 1951. Taksonomia wrocławska, ʻPrzegląd Antropologicznyʼ. Vol. XVII. Gostkowski, Z. ed., 1972. Towards a System of Human Resources Indicators for Less Developed Countries. Wrocław: Zakład Narodowy im. Ossolinskich. Harbison, F.H., Maruhnic, I. and Rerwick, J.R., 1968. An evaluation of human resources development indicators, Princeton. Hellwig, Z., 1967. Procedure of evaluating high-level man power data and typology of countries by means of the taxonomic method (Unpublished UNESCO working paper). http://unesdoc. unesco.org/images/0001/000123/012349eo.pdf. Retrieved on 07-07-2014. Inglehart, R., 1997. Modernization and postmodernization: Cultural, economic, and political change in 43 societies (Vol. 19). Princeton, NJ: Princeton University Press. Johnstone, J.N., 1976. Typology formation across socio-economic indicators. Socio-Economic Planning Sciences, 10(4), pp.167–171.

288

8  Level of Development: A Comparative Study Between Project and Non-project Area

Land, K.C., 1975. Theories, Models and Indicators of Social Change. International Social Science Journal. Long, H., Heilig, G.K., Li, X. and Zhang, M., 2007. Socio-economic development and land-use change: Analysis of rural housing land transition in the Transect of the Yangtse River, China. Land Use Policy, 24(1), pp.141–153. Madon, S., 2000. The Internet and socio-economic development: exploring the interaction. Information technology and people, 13(2), pp.85–101. Myrdal, G., 1972. Asian drama; an inquiry into the poverty of nations, Vol 2. http://www.jstor.org/ stable/20634250. Narain, P. Bhatia, V.K. Rai, S.C., 2011. Pattern of regional disparities in Socio-economic development in West Bengal. Journal of the Indian society of agricultural statistics, 65(1), pp. 27–35. Narain, P. Bhatia, V.K. Rai, S.C., 2012. Pattern of regional disparities in Socio-economic development in West Bengal. Journal of the Indian society of agricultural statistics, 65(1), pp. 27–35. Narain, P., Rai, S.C. and Bhatia, V.K., 1997. Regional pattern of socio-economic development in Karnataka. J. Ind. Soc. Agril. Statist, 50, pp.380–391. Narain, P., Rai, S.C. and Sarup, S., 1992. Classification of districts based on socio-economic development in Orissa. Yojana, 36(23), pp.9–12. Narain, P., Rai, S.C. and Sarup, S., 1993. Evaluation of economic development in Orissa. J Ind Soc Agric Stat, 45(2), pp.249–278. Narain, P., Rai, S.C. and Sarup, S., 1994a a. Inter-districts disparities in socio-economic development in Kerala. Journal of Indian Society of Agricultural Statistics, 46, pp.362–377. Narain, P., Rai, S.C. and Sarup, S., 1994b b. Regional dimensions of socio-economic development in Andhra Pradesh. Jour. Ind. Soc. Agril. Stat, 46, pp.156–165. Narain, P., Rai, S.C. and Sarup, S., 1995. Regional disparities in the levels of development in Uttar Pradesh. J. Indian Soc. Agril. Stat, 47(3), pp.288–304. Narain, P., Rai, S.C. and Sarup, S., 1996. Dynamics of socio-economic development in Maharashtra. J. Indian Soc. Agril. Stat, 48, pp.360–372. Narain, P., Sharma, S.D., Rai, S.C. and Bhatia, V.K., 1991. Dimensions of socio-economic development in Jammu & Kashmir. Development, 1981, p.82. Narain, P., Sharma, S.D., Rai, S.C. and Bhatia, V.K., 2000. Regional disparities in socio-economic development in Tamil Nadu. J. Ind. Soc. Agril. Statist, 53, pp.35–46. Narain, P., Sharma, S.D., Rai, S.C. and Bhatia, V.K., 2002. Dimensions of regional disparities in socio-economic development in Madhya Pradesh. J. Ind. Soc. Agril. Statist, 55, pp.88–107. Narain, P., Sharma, S.D., Rai, S.C. and Bhatia, V.K., 2003. Evaluation of economic development at micro level in Karnataka. J. Ind. Soc. Agril. Statist, 56, pp.52–63. Narain, P., Sharma, S.D., Rai, S.C. and Bhatia, V.K., 2004. Estimation of socio-economic development in hilly states. J. Ind. Soc. Agril. Statist, 58, pp.126–135. Narain, P., Sharma, S.D., Rai, S.C. and Bhatia, V.K., 2005. Estimation of socio-economic development of different districts in Kerala. J. Ind. Soc. Agril. Statist, 59, pp.48–55. Narain, P., Sharma, S.D., Rai, S.C. and Bhatia, V.K., 2009. Inter-district variation of socio-­ economic development in Andhra Pradesh. J. Ind. Soc. Agril. Statist, 63(1), pp.35–42. Ohlan, R., 2013. Pattern of regional disparities in socio-economic development in India: District level analysis. Social indicators research, 114(3), pp.841–873. Parihar, H.P.S. and Srivastava, S., 2003. Statistical Measures of Development for an Ideal Village. Indian Journal of Extension Education, 3, pp.100–103. Raja, T.A and Yousaf, T., 2014. Statistical evaluation of socio-economic development with a new composite index of Kashmir valley, International Research Journal of Agricultural Economics and Statistics, 5(2), pp. 317–320. Sarker, P.C., 1994. Regional imbalances in Indian economy over plan periods. Economic and Political Weekly, pp.621–633. Shee, S.P. and Maiti, R., 2017. Agricultural level of development: a comparative study between project and non-project area of JSW Bengal Steel Ltd. at Sundra Basin of Salboni block, Paschim Medinipur, West Bengal, India. City, Territory and Architecture, 4(1), pp.1–16. Sudalaimuthu, S. and RAJ, S.A., 2009. Logistics Management for International Business: Text and Cases. PHI Learning Pvt. Ltd. Szirmai, A., 2015. Socio-economic development. Cambridge University Press.

Chapter 9

Environmental Impact Assessment

Abstract  In the present study, an attempt was made to determine the assimilative capacity of the ambient atmosphere, water quality and noise at the surroundings of the industrial region. The baseline status of the ambient air quality is assessed through scientifically designed ambient air quality monitoring network. The ISCST3 air dispersion model was used to predict the concentration distribution pattern of primary pollutants, namely, SO2 and SPM on local environment. Keywords  Atmosphere · Water quality · Noise · Industrial region · Network · ISCST3 · SO2 · SPM

9.1  Introduction Development, which relieves poverty and improves the standard of living of a nation, may often produce undesirable and unanticipated environmental impact. In developing countries, development cannot proceed without using more resources. The people of the United States (USA) first realized its future impacts when such problems started to deteriorate their quality of life (Boyle 1998; Chadwick et  al. 2005; Hirji and Ortolano 1991). Once it’s realized that environmental impact assessment (EIA) is not only one of the available tools to satisfy this need but also essential to forecast, interpret and communicate information about the potential impact of the proposed project plans, policies, programmes and operational procedure to ensure the best possible development with minimum environmental degradation (Panigrahi and Amirapu 2012), then it becomes an essential requirement and is incorporated into the framework of several international organizations (Ahmad and Wood 2002). As a result over the last three decades there is a remarkable growth of interest in environmental issues, such as Convention on Environmental Impact Assessment (EIA) in Trans-boundary Context, Protocol on Environmental Protection to the Antarctica Treaty (1991), Biodiversity Treaty (1992), United Nations Framework Convention on Climate Change (1992) (Zubair 2001; Wang

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. P. Shee, R. Maiti, Land Acquisition, Industrialization and Livelihoods, https://doi.org/10.1007/978-3-030-90244-5_9

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et al. 2003) in sustainability and the better management of development in harmony with the environment (Elliott and Thomas 2009), but it is still far from perfection and is often misapplied or misused. Before undertaking any development proposal for preparing EIA, greater attention is needed to be paid at an early stage for the initial identification of most likely important impacts. The stages of the EIA usually depend on the requirements of the country or donor organization and may sometimes be also controlled by the purpose of the project. The first step of the EIA is to determine whether an EIA is necessary or not for the proposed project, and if it is required, then the level of requirement is assessed. A Project “Screening” is used to narrow down the application of EIA that is usually determined by the EIA regulation operating in a country at the time of assessment (Clark et al. 1984; Chadwick et al. 2005). “Scoping” is the most crucial method to be paid greater importance in situation, where the resources for EIA are limited, and it is important to identify the most likely impacts from the number of possible impacts which need to be considered for further investigation in detail individually. Many impact identification methods are developed in response to National Environment Protection Act (NEPA) and it would be expanded and refined (Chadwick et  al. 2005; Goodland and Tillman 1996). EIA is spreading rapidly worldwide, in the appraisal of major projects, programmes and policies, leading to a considerable research into the development of methods to aid analysis. Since the 1970s, numerous EIA methodologies are developed to define any changes in physical, chemical, biological, cultural or socio-economic environment systems to predict and assess the impact of the proposed action (Wood 1995; Trivedi and Raj 1997). Industrialization and rapid growth of vehicular traffic may cause an adverse effect on ambient air quality, water quality of both surface and ground water, health of humans and animals (Rama Krishna et al. 2005), quality of vegetations and materials, weather and climate and visibility reduction (Arya 1999). In India, air and water pollution become a serious problem due to industrialization and urbanization (Sivacoumar et  al. 2001), vehicle exhausts, industrial fumes, garbage burning, domestic cooking and heating, etc. (Fenger 1999; Johnson et al. 2011; Molina and Molina 2004; Parrish and Zhu 2009; Shah and Nagpal 1997; Schwela et al. 2006). It is necessary to control the air and water pollutants to provide a better and safe environment for the future generation (Bachmann 2007). The dispersion of different pollutants using air quality models of Gaussian type is used in this assessment for different cities, e.g. Timilsina and Shrestha (2009) for China, India, Indonesia, Republic of Korea, Malaysia, Pakistan, Sri Lanka and Thailand; Zhang et al. (2008) for Hangzhou, China; Tung et al. (2011) for Hanoi, Vietnam; Olivier (2012) for EU nations; Okamoto and Shizawa (1978) for Keihin area; Gurjar et al. (2004), Goyal and Rama Krishna (2002), Goyal et al. (2003), Goyal and Rao 2007), Mohan et al. (2007), Ramchandra (2009), Reddy and Venkataraman (2002) and Singh et  al. (1990) for Delhi; Goyal et al. (1994) for Agra; and Goyal et al. (1995) for applied vehicle air model for traffic in Delhi. Balakrishnan et al. (2011), Guttikunda and Jawhar (2012), Lorber et al. (2000), Manju et al. (2002) and Sax and Isakov (2003) studied mathematical models for air quality monitoring. No such major

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construction work of JSW Bengal Steel plant is started. An EIA study was  conducted on JSW Bengal project to identify the probable impacts of air, water and noise pollution to reduce their adverse impacts to the environment.

9.2  Data and Method In the present study, an attempt is made to determine the assimilative capacity of ambient atmosphere, water quality and noise of the proposed industrial region. Hourly wind direction, wind speed, air pressure and sunshine hour data were collected (2007–2017) from meteorological department at Abash and NC Rana Sky Observation Station of Medinipur College and used as input in the ISCST3 air dispersion model to predict the concentration distribution pattern of primary pollutants, namely, SO2 and SPM, on the local environment. The model is developed by the USEPA. Water samples were also collected from different places and tested at the laboratory for basic chemical analysis. The JSW authority also conducted a study to collect the water samples for chemical analysis and heavy metal and bacteriological parameter analysis following the “Standard Methods for the Examination of Water and Wastewater” procedure to predict the anticipated impact of the proposed JSW project on surface and ground water. Noise level also measured by the authority using integrated sound level meter manufactured by Quest Technology, USA (JSW EIA 2007). Noise level monitoring was carried out during winter season continuously for 24 hours with 1 hour interval starting at 00:30 hour to 00:00 hour the next day. The studied ambient air quality, water sample analysis and noise level were done by the JSW authority in 2006–2007. Due to a lack of air quality monitoring station within the study area and the surroundings, those data were collected from different govt. sources such as MKDA, BDO office and JSW office and used as input in ISCST3 model to measure the ambient air quality of the surroundings. The ISCST3 model is based on a steady-state Gaussian-plume algorithm for assessing air quality impact from area, point and volume sources in terms of hourly or annual period.

9.3  A  mbient Air Quality Monitoring Method (ISCST3 Model) Air quality was estimated following two different approaches. The first approach in terms of ventilation coefficient (VC) is computed through micro-meteorological parameter. The industrial stack that would be involved in the process and operation is considered as the elevated point source. The stack characteristics of the elevated point source such as stack height, stack diameter (m), exit velocity (m/s), exit temperature (K), the location (X,Y) of area sources on the Cartesian grid along with SO2 (sulphur dioxide) and SPM (suspended particulate matter) emission (kg h-1) are considered as the basic input (Table 9.1) in this model.

Stack attach to Captive power plant DG SETS-5 MVA X 2 No’s Dr Plant BOF converter Fume extraction system FOF LHF1 NO Lime and dolo plant Slag cement Incinerator (gas based) Blast furnace stove Blast furnace cast house FE Blast furnace stock house Coal drier-1 Coke oven stack-NOS CD pushing emission-2 NOS Ammonia cracker-1 NOS

Stack Coordinate X Y 0 0 1000 −872 350 −895 2565 −955 2910 −943 2182 −943 1742 −1271 185 −2082 −433 −976 1070 −1100 1471 −1165 1106 −1512 1070 −1382 −1863 −990 −1860 −830 −2471 −1106

Source: JSW office, MKDA, BDO office

Sl no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Stack height (m) 270 30 60 70 45 40 47 40 50 60 40 40 30 90 40 50

Stack diameter (m) 4.5 1 5 2 5 1.5 1 1.5 1.5 5 5 3.5 2 2 2 2 Exit velocity (m/s) 22 18 5 10 17 15 12 16 8 15 17 14 18 7 7 9

Table 9.1  Details of industrial unit of JSW iron and steel plant at Salboni block, Paschim Medinipur Temp (K) 410 480 1320 330 330 330 340 330 470 470 470 470 470 420 470 420

Emission rate (Kg/h) SPM SO2 105.4 1495 4.6 12.1 6.3 496.7 14.5 160.7 28.3 4.2 2.3 13.1 4.8 34.1 113.2 47.1 28.3 2.9 44 7.5 10.3

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The ISCST3 model accepts hourly meteorological data record to estimate the concentration or deposition value for each source receptor. Steady-state Gaussian-­plume equation is used to estimate the hourly concentration of SO2 and SPM at downwind distance X (meters) and crosswind distance Y (meters). The equation is given as:

where:

2  y  QKVD  C exp /0.5    :    26us :y :z  y   (9.1)

Q = pollutant emission rate (g/s) K = scaling coefficient, used to convert the calculated concentrations to the desired units V = vertical term of Gaussian plume D = decay term accounting for the removal process of both pollutant physical and chemicals :y:z = sd (standard deviation) of both lateral and vertical concentration distributions (m) us = mean wind speed (m/s) y = crosswind distance (m) The expression for V in Eq. (9.1) is given by:





  z  he V  exp  0.5  r   z 

  

2

   z  he   exp  0.5  r     z   

2     H   H   exp  0.5  1    exp  0.5  2

  i 1   z    z    2    H3    H4   exp 0.5     exp  0.5      z    z  



  

2

  

2

  

2

   

   

     (9.2)

where:

he  hs  h



H1  zr   2izi  he 





H 2  zr   2izi  he 





H3  zr   2izi  he 





H 4  zr   2izi  he 

(9.3)

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where zr = receptor height above ground (m) he = effective stack height (m) hs = physical stack height (m) Δh = plume rise (m) zi = mixing height (m) The infinite series term in Eq. (9.2) accounts for the effects of the restriction on vertical plume growth at the top of the mixing layer. It should be noted that, if the effective stack height (he) exceeds the mixing height (zi), the plume is assumed to fully penetrate the elevated inversion and the ground-level concentration is set equal to zero. The expression for D (Eq. 9.1) is as follows: for  0

 exp  x  u D  1  s   for  0

where ψ = decay coefficient (s−1) and x = downwind distance (m).

9.3.1  C  oncentration of SO2 and SPM over the Proposed JSW Industrial Project The assimilative capacity of SO2 and SPM in the proposed JSW plant site within the study area is determined using the ISCST3 dispersion model. The seasonal variations of wind speed and mixing height are illustrated in Figs. 9.1 and 9.2 of the year 2016–2017. Mixing Height (in m) 3500 3000 2500 2000 1500 1000

Pre-monsoon

Monsoon

Fig. 9.1  Seasonal variation of mixing height (in m)

Post-monsoon

24.00

23.00

22.00

21.00

20.00

19.00

18.00

17.00

16.00

15.00

14.00

13.00

12.00

11.00

9.00

10.00

8.00

7.00

6.00

5.00

4.00

3.00

2.00

0

1.00

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9.3  Ambient Air Quality Monitoring Method (ISCST3 Model)

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Mixing height (Fig. 9.1) starts to increase with heating of the Earth’s surface due to insolation and reaches maximum at noon and finally starts to decrease when night falls. The lowest value of mixing height is observed to be less than 50 m in post- monsoon and as high as 3200 m in pre-monsoon. The hourly wind speed curve is observed to have similar tendency; it is slightly higher (3.8 m/s) in daytime compared to night-time. A fair estimation of the dispersion of pollutants in the atmosphere is possible based on the frequency distribution of wind direction as well as wind speed. The wind rose diagrams (Fig.  9.2a, 9.2b and 9.2c) illustrated that the study area is mostly affected by calm wind (45%) at night and

Fig. 9.2a  Seasonal variation of wind speed (m/s) in pre-monsoon

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Fig. 9.2b  Seasonal variation of wind speed (m/s) in monsoon

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Fig. 9.2c  Seasonal variation of wind speed (m/s) in post-monsoon

early morning that makes the atmosphere in stable condition. The expected concentration of SO2 over the study region is shown in Fig. 9.3. The figures reveal that the early morning and late evening hours generally experience higher concentration in comparison to those during the daytime. The mixing height and wind speed increases in daytime that implies better dilution of pollutants. It is estimated that at afternoon when the VC is relatively higher, the model predicted concentration value of SO2 indicates potentially safe hours. On the other hand, a low value of VC during early morning and afternoon indicates high pollution potential and this is consistent with the higher values of the model’s predicted concentration. The model prediction values of concentration at the project site would be observed at

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9  Environmental Impact Assessment 24 hrs average concentraton of SO2 35

Concentraon (µg/m³)

30 25 20 15 10 5 0 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Times (hrs)

Pre-monsoon

monsoon

Post-monsoon

Fig. 9.3  Seasonal hourly variation of SO2 concentration (μg/m3)

Fig. 9.4  Concentration of SO2 (μg/m3) in post-monsoon

late night and early morning in the range of 14.09 μg/m3 to 32.28 μg/m3 in postmonsoon (Fig. 9.4), while low predicted concentration is observed ranging from 5.01 μg/m3 to 8.06 μg/m3 in pre-­monsoon (Fig. 9.5) and 3.05 μg/m3 to 9.01 μg/m3 in monsoon (Fig. 9.6), respectively.

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Fig. 9.5  Concentration of SO2 (μg/m3) in pre-monsoon

Fig. 9.6  Concentration of SO2 (μg/m3) in Monsoon

Similar pattern of SPM predicted concentration is observed to be maximum during post-monsoon, and low concentration is observed in monsoon and pre monsoon season. The model predicted concentration of SPM is computed at the project site in post-monsoon and it would range from 652.9 μg/m3 to 257.9 μg/m3 (Fig. 9.7); in pre-monsoon it would range from 160.7 μg/m3 to 58.19 μg/m3 (Fig. 9.8) and in

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Fig. 9.7  Concentration of SPM (μg/m3 ) in post-monsoon

Fig. 9.8  Concentration of SPM (μg/m3) in pre-monsoon

monsoon it would range from 85.6 μg/m3 to 56.1 μg/m3 (Fig. 9.9). High winds and convective atmospheric condition may help dispersion of pollutants, but gustiness triggered by turbulence may cause the level high of SPM in pre-monsoon. The presence of low-level ground-based inversion may cause the concentration level

9.4 Ambient Water Quality Monitoring Method

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Fig. 9.9  Concentration of SPM (μg/m3) in monsoon

high in winter. Heavy rain in monsoon may wash out the pollutants and it also makes the moisture level high; as a consequence it may reduce the concentration level in monsoon. Isopleths drawn for SO2 and SPM for the three seasons are shown in Figs. 9.4– 9.9. The percentage of calm winds may cause the peak concentration of the two pollutants in winter. The peak model predicted values of concentration would be observed as 32.2 μg/m3 and 652.9 μg/m3 for SO2 and SPM, respectively. The isopleths show that Karamsol, Parasia, Salgeria, Ghagrasol, Khairisol, Arabari, Barju, Banskopna, Dubrajpur, Sitanathpur, Bhalukmari, Chantibandh, Ramraydi, Ashnasuli, Nutanbankati, Chak Bhagi, Naranchak, Kulpheni, Jambedya, Gaighata, Hatmari, Dharmma Danga, Chandan Kath and Shalika mouzas would be severely affected due to concentration of pollutants for all three seasons.

9.4  Ambient Water Quality Monitoring Method The JSW authority collected the water samples from both surface and ground surface to examine the physico-chemical properties. The authority analysed the collected samples as per the procedures specified in “Standard Methods for the Examination of Water and Wastewater” published by the American Public Health Association. The analytical technique and the test detectable limits are given in Table 9.2.

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Table 9.2  Analytical techniques for water sampling analysis Parameter Electrical conductivity Total suspended solids Total dissolved solids COD Chloride Sulphates Nitrates Fluoride Alkalinity

Method APHA-2510 D APHA-2540 B APHA-2540 B APHA-5220 B APHA 4500-Cl APHA-4500 SO4 APHA-3120 B APHA -4500 F APHA 2320 B

Source: JSW office, MKDA, BDO office

9.4.1  A  mbient Water Quality Monitoring over the Proposed JSW Industrial Project Tables 9.3 and 9.4 reveal the results of surface and ground water quality of different mouzas at present condition. From the above analysis it is observed that for surface water TDS (total dissolved solid) concentration ranged between 650 and 716 mg/l. Other properties such as pH (7.3–7.6), total hardness (406–471 mg/l), dissolved oxygen (5.2–6.0 mg/l), chlorides (200.8–228.2 mg/l), sulphite (34.8–40.1 mg/l) and alkalinity (240–253mg/l) including arsenic, selenium, phenolic, zinc and mercury are observed within the permissible limits.

Parameter Fluoride as F Sulphate as SO4 Alkalinity Nitrates as NO3 Cyanides as CN Calcium as Ca Magnesium as Mg PH Colour Conductivity Dissolved oxygen BOD (3 days at 27°C) Total dissolved solids Total hardness Chloride as Cl Sodium as Na Potassium as K Iron as Fe Chromium as Cr Cadmium as Cd Coliform organisms Aluminum as Al Anionic detergents as MBAS Oil and grease Lead as Pb Copper as Cu Arsenic as As

Source: JSW office, MKDA, BDO office

SL no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Table 9.3  Surface water quality

Hazen units mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l MPN/100 ml mg/l mg/l mg/l mg/l mg/l mg/l

Units mg/l mg/l mg/l mg/l mg/l mg/l mg/l

IS: 2296 class C limits 1.5 400 $ $ 0.05 $ $ 6.5–8.5 300 $ 4 min 3 1500 $ 600 $ $ 50 0.05 0.01