Food Security in the Economy of the Future: Transition from Digital Agriculture to Agriculture 4.0 Based on Deep Learning 303123510X, 9783031235108

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Food Security in the Economy of the Future Transition from Digital Agriculture to Agriculture 4.0 Based on Deep Learning Edited by Elena G. Popkova · Bruno S. Sergi

Food Security in the Economy of the Future

Elena G. Popkova · Bruno S. Sergi Editors

Food Security in the Economy of the Future Transition from Digital Agriculture to Agriculture 4.0 Based on Deep Learning

Editors Elena G. Popkova People’s Friendship University of Russia (RUDN University) Moscow, Russia

Bruno S. Sergi University of Messina Messina, Italy Harvard University Cambridge, USA

ISBN 978-3-031-23510-8 ISBN 978-3-031-23511-5 (eBook) https://doi.org/10.1007/978-3-031-23511-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 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. Cover illustration: © Alex Linch shutterstock.com This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1

Transition to Agriculture 4.0 Under the Influence of the Fourth Industrial Revolution (Introduction) Elena G. Popkova and Bruno S. Sergi

1

Part I Digital Agriculture and New Opportunities for Providing Food Security in the Context of the Fourth Industrial Revolution 2

3

4

Strategic Directions for Smart Agriculture Based on Deep Learning for Future Risk Management of Food Security Elena G. Popkova, Tatiana N. Litvinova, Olga M. Zemskova, Mariya F. Dubkova, and Anna A. Karpova Food and Water Security of the Middle East (the Case of Egypt) Denis A. Mirgorod, Gennadii V. Kosov, Elena A. Soloveva, Alihan M. Israilov, and Alexander A. Pohilko Best Practices and the Digital Model of Agricultural Development in Developed and Developing Countries Elena V. Sofiina, Irina V. Milchik, Igor V. Denisov, and Nadezhda K. Savelyeva

9

19

27

v

vi

5

6

CONTENTS

Monitoring the Compliance of Today’s Agriculture with Food Security Needs for Sustainable Development Elena A. Bratukhina, Berik T. Beisengaliyev, Anastasia A. Sozinova, and Ksenia V. Borzenko Green Finance: Analysis of Prospects of the Russian Market Olga G. Kantor, Yuliya R. Rudneva, Dmitriy Yu. Dunov, Shakhlo T. Ergasheva, and Boris M. Leybert

37

45

Part II Prospects for Food Security of the Future Economy in the Transition to Agriculture 4.0 Based on Deep Learning 7

8

9

10

Food Security in the Digital Economy: Traditional Agriculture vs. Smart Agriculture Based on Artificial Intelligence Aleksei V. Bogoviz, Vladimir S. Osipov, Tatiana M. Vorozheykina, Veronika V. Yankovskaya, and Igor Yu. Sklyarov

59

Transition from Digital Agriculture to Agriculture 4.0 as the Most Promising Scenario for Ensuring Future Food Security Mikhail S. Kyzyurov, Ayapbergen A. Taubayev, Larissa P. Steblyakova, and Larisa V. Shabaltina

75

A New Level of Food Security as a Result of the Transition of Food-Importing Countries to Agriculture 4.0 Based on Deep Learning Anastasia A. Sozinova, Aigul S. Daribekova, Irina P. Lapteva, and Maria V. Makarova

85

Risks of Agricultural Economy and Climate Risk Management for Enterprises of Agriculture 4.0 Based on Deep Learning Tatiana N. Litvinova

93

CONTENTS

11

Prospects for Using Investment by Agricultural Cooperatives of Kyrgyzstan in the Regional Economy of Central Asia Kalil D. Dzhumabayev, Alymkul K. Dzhumabayev, Shukurali A. Jamalov, Elmira K. Kydykbaeva, and Taalaigul Azamat kyzy

vii

101

Part III Applied Recommendations for Shaping Agriculture 4.0 Based on Deep Learning to Ensure the Food Security of the Economy of the Future 12

13

14

15

16

Advanced Digital Technology in Agriculture and Its Contribution to Food Security Elena V. Karanina, Elena A. Vechkinzova, Yuliya A. Kopytina, and Nurlybek T. Malelov Roadmap for the Transition from Digital Agriculture to Agriculture 4.0 Based on Deep Learning in the Economy of the Future by 2030 Nazgul S. Daribekova, Marina A. Sanovich, Nadezhda K. Savelyeva, Tatiana A. Dugina, and Anastasia I. Smetanina

115

123

Automation of Agriculture Based on Deep Learning: Modeling and Management to Improve Quality and Efficiency Natalia V. Przhedetskaya, Eleonora V. Nagovitsyna, Victoria Yu. Przhedetskaya, and Ksenia V. Borzenko

131

Responsible Production and Consumption in Agriculture 4.0 Based on Deep Learning for Sustainable Development Yerlan B. Zhailauov, Natalia V. Przhedetskaya, and Vasiliy I. Bespyatykh

139

Agriculture 4.0: Perspectives on Food Security in the Agricultural Economy of the Future Elena G. Popkova and Bruno S. Sergi

147

Index

149

List of Contributors

Berik T. Beisengaliyev Kazakh University of Economics, Finance and International Trade, Astana, Kazakhstan Vasiliy I. Bespyatykh Vyatka State University, Kirov, Russia Aleksei V. Bogoviz Moscow, Russia Ksenia V. Borzenko Rostov State University of Economics, Rostov-onDon, Russia Elena A. Bratukhina Vyatka State University, Kirov, Russia Aigul S. Daribekova Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan Nazgul S. Daribekova Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan Igor V. Denisov Plekhanov Russian University of Economics, Moscow, Russia Mariya F. Dubkova Volgograd State Agricultural University, Volgograd, Russia Tatiana A. Dugina Volgograd State Agricultural University, Volgograd, Russia

ix

x

LIST OF CONTRIBUTORS

Dmitriy Yu. Dunov Ufa State Petroleum Technological University, Ufa, Russia Alymkul K. Dzhumabayev Zh. Alyshbayev Institute of Economics, National Academy of Sciences of the Kyrgyz Republic, Bishkek, Kyrgyzstan Kalil D. Dzhumabayev Zh. Alyshbayev Institute of Economics, National Academy of Sciences of the Kyrgyz Republic, Bishkek, Kyrgyzstan Shakhlo T. Ergasheva Tashkent Tashkent, Uzbekistan

State

University

of

Economics,

Alihan M. Israilov Pyatigorsk State University, Pyatigorsk, Russia Shukurali A. Jamalov Zh. Alyshbayev Institute of Economics, National Academy of Sciences of the Kyrgyz Republic, Bishkek, Kyrgyzstan Olga G. Kantor Ufa State Petroleum Technological University, Ufa, Russia Elena V. Karanina Vyatka State University, Kirov, Russia Anna A. Karpova Volgograd State Agricultural University, Volgograd, Russia Yuliya A. Kopytina Vyatka State University, Kirov, Russia Gennadii V. Kosov Pyatigorsk State University, Pyatigorsk, Russia Elmira K. Kydykbaeva K. Tynystanov Issyk-Kul State University, Karakol, Kyrgyzstan Taalaigul Azamat kyzy Zh. Alyshbayev Institute of Economics, National Academy of Sciences of the Kyrgyz Republic, Bishkek, Kyrgyzstan Mikhail S. Kyzyurov Vyatka State University, Kirov, Russia Irina P. Lapteva Vyatka State University, Kirov, Russia Boris M. Leybert Ufa State Petroleum Technological University, Ufa, Russia Tatiana N. Litvinova Volgograd Volgograd, Russia

State

Agricultural

University,

LIST OF CONTRIBUTORS

xi

Nurlybek T. Malelov Esil University, Astana, Kazakhstan Maria V. Makarova Vyatka State University, Kirov, Russia Irina V. Milchik Vyatka State Agrotechnological University, Kirov, Russia Denis A. Mirgorod Pyatigorsk State University, Pyatigorsk, Russia Eleonora V. Nagovitsyna Vyatka State University, Kirov, Russia Vladimir S. Osipov MGIMO University, Moscow, Russia Alexander A. Pohilko Pyatigorsk State University, Pyatigorsk, Russia Elena G. Popkova Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia Natalia V. Przhedetskaya Rostov Rostov-on-Don, Russia

State

University

of

Economics,

Victoria Yu. Przhedetskaya Federal State Budgetary Institution National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Rostov-on-Don, Russia Yuliya R. Rudneva Ufa State Petroleum Technological University, Ufa, Russia Marina A. Sanovich Vyatka State University, Kirov, Russia Nadezhda K. Savelyeva Vyatka State University, Kirov, Russia Bruno S. Sergi Harvard University, Cambridge, USA; University of Messina, Messina, Italy Igor Yu. Sklyarov Stavropol State Agrarian University, Stavropol, Russia Anastasia I. Smetanina Institute of Scientific Communications (ISCGroup LLC), Volgograd, Russia Elena V. Sofiina Federal Research Center of Agrarian Economy and Social Development of Rural Areas—All—Russian Research Institute of Agricultural Economics, Moscow, Russia; Vyatka State Agrotechnological University, Kirov, Russia Elena A. Soloveva Pyatigorsk State University, Pyatigorsk, Russia Anastasia A. Sozinova Vyatka State University, Kirov, Russia

xii

LIST OF CONTRIBUTORS

Larisa V. Shabaltina Plekhanov Russian University of Economics, Moscow, Russia Larissa P. Steblyakova State University of Management, Moscow, Russia Ayapbergen A. Taubayev Esil University, Astana, Kazakhstan Elena A. Vechkinzova State University of Management, Moscow, Russia Tatiana M. Vorozheykina Russian State Agrarian University—Moscow Timiryazev Agricultural Academy, Moscow, Russia Veronika V. Yankovskaya Plekhanov Russian University of Economics, Moscow, Russia Olga M. Zemskova Volgograd State Agricultural University, Volgograd, Russia Yerlan B. Zhailauov “Rational Solution” LLP, Karaganda, Kazakhstan

List of Figures

Fig. 4.1 Fig. 6.1

Fig. 6.2

Fig. 7.1

Fig. 7.2

Fig. 7.3

Fig. 7.4

Prospects for digital agriculture (Source Calculated and compiled by the authors Current expenditures on environmental protection by type of economic activity in actual prices, billion rubles (Source Compiled by the authors based on [12]) Current (operating) costs of environmental protection in the Russian Federation in actual prices, bln. rub (Source Compiled by the authors based on [13]) Food security, the share of agriculture in GDP, and food imports in agricultural economies in 2020 (Source Calculated and compiled by the authors) Regression curves of the dependence of the food security manifestations on the share of agriculture in GDP in agricultural economies in 2020 (Source Calculated and compiled by the authors) Food security, the share of agriculture in GDP, and digital competitiveness in digital economies in 2020 (Source Calculated and compiled by the authors) Regression curves of dependence of the manifestations of food security on the share of agriculture in GDP and of the food security manifestations on digital competitiveness in digital economies in 2020 (Source Calculated and compiled by the authors)

33

52

53

66

67

67

69

xiii

xiv

LIST OF FIGURES

Fig. 7.5

Fig. 8.1

Fig. 8.2

Fig. 10.1

Fig. 12.1

Fig. 12.2

Fig. 13.1

Fig. 15.1

Model of provision of food security in the digital economy with the help of the development of smart agriculture based on AI and deep learning (Source Compiled by the authors) Share of organizations using Industry 4.0 and digital technologies in each scenario, % (Source Calculated and compiled by the authors) The benefits of alternative scenarios of technological development of the agricultural economy for food security, % (Source Calculated and compiled by the authors) Potential for increasing food security through climate risk management in digital agriculture (Source Calculated and compiled by the authors) Advanced digital technologies and a regression curve of the contribution of digital competitiveness to food security (Source Calculated and constructed by the authors based on IMD materials [16]) Use of advanced digital technologies in agriculture in Russia by technology in 2021, % (Source Constructed by the authors, based on materials from HSE University [18]) A vision of the transition from digital agriculture to agriculture 4.0 based on deep learning by 2030 (Source Developed and compiled by the authors) Results achieved by 2021 in food security and responsible production and consumption, scores 1–100 (Source Compiled by the authors based on the UNDP materials [20])

71

80

81

96

118

119

127

141

List of Tables

Table 2.1

Table 4.1 Table Table Table Table

4.2 5.1 5.2 6.1

Table 6.2 Table 7.1 Table 8.1

Table 8.2 Table 9.1 Table 10.1 Table 10.2 Table 11.1

Strategic directions for the development of smart agriculture based on deep learning for the risk management of food security in the economy of the future Digital agriculture and influencing factors in developed and developing countries in 2021, points 1–100 Results of regression analysis Empirical basis of the research Results of regression analysis Assessment of investment opportunities for institutional investors in Russia Information about “green” bonds of Russian issuers Food security and the influencing factors of traditional and smart agriculture in 2020 Share of organizations using digital economy and Industry 4.0 technologies and manifestations of food security in 2021 Specification of regression models Food imports, food security, and deep learning statistics in the sample countries 2017–2021 Statistics on food security and climate risk management for digital agriculture in 2021 Comparative analysis of risk management of agricultural enterprises in digital agriculture and agriculture 4.0 Expected volume of agricultural production (r = 1)

12 30 31 40 41 47 49 64

78 79 88 95 97 105

xv

xvi

LIST OF TABLES

Table 11.2 Table 12.1 Table 13.1

Table 13.2

Table 14.1 Table 14.2 Table 15.1 Table 15.2

Expected volume of agricultural production (r = 2) Advanced technology and food security in the top 10 digital economies of the world in 2021, score 0–100 Comparative analysis of the achievement of the priorities of the agricultural economy in digital agriculture and agriculture 4.0 Roadmap for the transition from digital agriculture to agriculture 4.0 based on deep learning in the economy of the future by 2030 Dynamics of the development of the agricultural economy in Russia in 2012–2020 Results of regression analysis Regression dependence of SDG 2 on SDG 12 The benefits of responsible production and consumption in agriculture 4.0 for sustainable development

107 117

125

128 133 134 142 143

CHAPTER 1

Transition to Agriculture 4.0 Under the Influence of the Fourth Industrial Revolution (Introduction) Elena G. Popkova

and Bruno S. Sergi

Food security is one of the primary concerns of humankind because it is at the very bottom of Maslow’s hierarchy of needs and, therefore, determines the sustainability of the entire hierarchy as an embodiment of the socio-economic system. The emergence of the digital economy has provided significant progress in food security through the transition to digital agriculture. Digital agriculture refers to an approach to farming based on digital technology—computer technology and the Internet.

E. G. Popkova (B) People’s Friendship University of Russia (RUDN University), Moscow, Russia e-mail: [email protected] B. S. Sergi University of Messina, Messina, Italy e-mail: [email protected] Harvard University, Cambridge, USA

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_1

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E. G. POPKOVA AND B. S. SERGI

Computerized agricultural machinery has simplified and improved the comfort of agricultural labor, providing a significant increase in productivity and production capacity. Internet-based e-commerce has optimized value chains in the agro-industrial sector. State Internet monitoring of the activities of agricultural enterprises has increased the accountability of their activities and the effectiveness of the agricultural policy. As digital agriculture took shape in the late twentieth and early twentyfirst centuries, it successfully addressed key food security issues of the time. Countries have had the opportunity to implement ambitious food security doctrines and cover the entire population with nutrition security monitoring programs. The then-existing demand for food was temporarily covered. However, the situation has changed dramatically as new global challenges to food security have emerged. Climate change is one of the most significant challenges. Agriculture is totally dependent on natural conditions. Therefore, sudden climate changes threaten food security. The considered challenge is compounded by the fact that even countries that specialize in agriculture and are major food exporters experience increased climatic risks to their agricultural economies. These risks destabilize global food markets, where fast-growing demand is increasingly outstripping supply. Digital agriculture has left the dependence of the agricultural economy on natural conditions virtually unchanged; it cannot provide the climate resilience that is so necessary nowadays. Climate resilience can be (and already is) provided by climate-smart innovations for agriculture, actively used worldwide. Deep learning, the advanced technology of Industry 4.0, allows smart farms to accurately predict and adapt to climate change, learning and improving with each cycle of expanded reproduction in agriculture 4.0. Another challenge relates to the increasing demand for food and stricter requirements for food security. The COVID-19 pandemic created a strong incentive to impose stricter food sanitation measures. Agriculture 4.0, which makes value chains in the agro-industrial complex more transparent and open to state and public control, can provide these measures. Agriculture 4.0 also makes it possible to robotize smart farms, helping to multiply their productivity and production capacity while preserving subsistence agriculture. Another challenge comes from social progress, which, as it would seem, should not create challenges but rather respond to them. The knowledge economy contributes not only to the increase in the level of skills in all

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sectors of the economy but also to the growth of their demands on the organization of jobs (the conditions and nature of work). With digital agriculture, there is still a considerable share of fairly heavy manual labor. Agriculture 4.0 allows automating the most labor-intensive processes in agricultural enterprises and creating comfortable, knowledge-intensive, and high-performance jobs for agricultural personnel. Challenges also include the financial support of the agricultural economy. Investors expect a great return on investment, which digital agriculture cannot provide. In turn, the government is interested in accelerating the rate of economic growth, which is also not the case with digital agriculture. In contrast, in Agriculture 4.0, innovative projects in the agricultural economy become investment-attractive and make it a new vector of high-tech economic growth. The challenges also include the need to develop rural areas. Under digital agriculture, they have accumulated a noticeable lag in the level and pace of socio-economic development. However, available experience shows that the emergence of agriculture 4.0 can slow the rate of urbanization and transform rural areas into competitive economic systems that are favorable for living, tourism, business, investment, labor migration, and human development. Another challenge is the need for import substitution of food worldwide. The COVID-19 pandemic and crisis have shown that global economic ties are not reliable enough to count on for such critical issues as food security. Each country must cover its own basic food needs. However, this is impossible with digital agriculture because there is a clear distinction between favorable and unfavorable countries for farming. Agriculture 4.0, which creates environmentally autonomous smart farms, can overcome this distinction. The considered challenges do not exist in isolation—they complement each other and have a significant systemic impact on the current agricultural economy. The accumulated and described international experience shows that the capabilities of digital agriculture are insufficient to provide a comprehensive and effective response to the full range of challenges to food security. This answer requires a revolutionary shift from digital agriculture to agriculture 4.0, made possible by the Fourth Industrial Revolution. Agriculture 4.0 refers to an approach to farming based on the advanced technologies of Industry 4.0. Although the set of these technologies is quite large, deep learning stands out among them as one of the most

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E. G. POPKOVA AND B. S. SERGI

promising technologies for agriculture, greatly increasing its potential for food security. Deep learning is a technology that integrates a set of advanced technological solutions and involves the accumulation of experience (through the Internet of Things [IoT] and big data) and, with this experience, the rethinking (using artificial intelligence [A]) of agriculture 4.0 with each new cycle of expanded reproduction. Deep learning provides agriculture 4.0 with unprecedented flexibility and adaptability, allowing it to address and systemically respond to all challenges to food security. This book aims to explore the essence and shape the scientific and methodological support for the transition from digital agriculture to agriculture 4.0 based on deep learning under the influence of the Fourth Industrial Revolution. Moreover, the book aims to identify prospects and develop applied recommendations for ensuring food security in the economy of the future based on this transition. These aims are consistently accomplished in four parts of the book. The first part focuses on digital agriculture and new possibilities for food security in the context of the Fourth Industrial Revolution. The second part reveals the importance of green finance for agriculture 4.0 and its contribution to food security based on the best practices of Central Asia. The third part focuses on the food security prospects of the economy of the future in the transition to agriculture 4.0 based on deep learning. The fourth part offers applied recommendations for shaping agriculture 4.0 based on deep learning to ensure the food security of the economy of the future. The academic contribution of this book to the literature is the clear distinction between digital agriculture and agriculture 4.0, which are often equated in available publications, and the line between the two is blurred. By isolating them, the book clarifies the categorical apparatus of the agricultural economy. The novelty of this book lies in the fact that it reveals the conceptual and the applied issues of agriculture 4.0, highlighting the practical experience and prospects of its implementation with the support of deep learning. The practical significance of the book lies in the fact that it highlights and discusses the best practices of agriculture 4.0 in Central Asia. The primary target audience for this book is scholars engaged in the agricultural economy. In this book, they will find a fundamental understanding of agriculture 4.0 and scientific advice for unlocking its potential

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for food security in the economy of the future. Due to its multidisciplinarity, the book will also be of interest and use to representatives of various fields of knowledge, in particular, the agricultural economy, digital economy, bioeconomy, the economy of (agricultural) entrepreneurship, and rural economy. An additional target audience for the book is expert practitioners. In the book, the representatives of agricultural enterprises will find applied recommendations for implementing the transition from agriculture to agriculture 4.0. The subjects of public administration will find the author’s recommendations for improving the state agricultural policy to ensure food security.

PART I

Digital Agriculture and New Opportunities for Providing Food Security in the Context of the Fourth Industrial Revolution

CHAPTER 2

Strategic Directions for Smart Agriculture Based on Deep Learning for Future Risk Management of Food Security Elena G. Popkova , Tatiana N. Litvinova , Olga M. Zemskova , Mariya F. Dubkova , and Anna A. Karpova

Introduction The uniqueness of the Fourth Industrial Revolution lies in the fact that it brought disruptive (disruptive) innovations in agriculture for the first time (compared to the previous three industrial revolutions). For centuries, agricultural traditions have been passed down from generation to generation; the observance of these traditions has been a guarantee of food

E. G. Popkova (B) Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia e-mail: [email protected] T. N. Litvinova · O. M. Zemskova · M. F. Dubkova · A. A. Karpova Volgograd State Agricultural University, Volgograd, Russia O. M. Zemskova e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_2

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security. Things have changed dramatically in the twenty-first century. The anthropogenic impact on the environment has reached such a scale that it has led to noticeable climate change. Simultaneously, there was a critical increase in the world’s population, which greatly increased the demand for food [1, 2]. Nowadays, food security is determined not by traditions but by innovation. The uncertainty of conditions for farming (due to their constant changes) and increased innovation activity of agricultural enterprises contributed to the formation of a risky approach to food security. The advantage of the risk-based approach is that it allows a timely response to emerging risks of the agricultural economy and their management, contributing to the successful implementation of SDG 2. However, the scientific and practical problem lies in the imperfection of the risk-based approach to food security. In the current organization, this approach determines the catch-up development of the agricultural economy, the essence of which is to manage risks as they occur. Tactical risk management helps ensure current food security. In turn, strategic risk management is required to guarantee food security in the future. In this regard, the development of scientific and methodological support for the transition to the advanced development of the agricultural economy, which requires improving the risk-based approach to food security, is relevant. This is the purpose of this research.

Literature Review Smart technology is at the core of a risk-based approach to food security. Smart agriculture managed by artificial intelligence (AI) is much less exposed to current risks through effective risk management [3–5]. Nevertheless, it is vulnerable to future risks because the capabilities of AI are limited to tactical risk management and short-term forecasting, considering only certain factors in the development of the agricultural economy [6–11].

M. F. Dubkova e-mail: [email protected] A. A. Karpova e-mail: [email protected]

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This raises a research question (RQ): “What are the perspective of strategic food security?” The uncertainty of this perspective and the uncertainty of how to overcome the limitations of smart agriculture in food security risk management is a research gap filled in this paper. Deep learning is the technology of the future, allowing us to simultaneously consider the totality of factors and produce systemic risk management. With this in mind, this paper hypothesizes (H0 ) that the development of smart agriculture based on deep learning will open up new opportunities for risk management of food security of the economy of the future—will allow long-term forecasting and anticipatory risk management of agriculture. To find an answer to the set RQ and test the hypothesis H0 , the authors define the risks of the future agricultural economy. In accordance with these risks, the strategic directions of the development of smart agriculture are proposed, and the opportunities and benefits of anticipatory risk management of food security based on deep learning in agriculture are substantiated.

Materials and Method To determine the risks of the agricultural economy of the future, we consider the profile of food security of Ireland—the leader of The Economist [12] rating for 2021. The authors select those indicators for which the values do not reach 100%. The indicators are systematized; their values in countries with a moderate (using Russia as an example) and low (using Burundi that completes the rating as an example) level of food security are also considered. This allows the authors to identify strategic directions for the development of smart agriculture. Each area offers the author’s recommendations for anticipatory food security risk management based on deep learning in agriculture.

Results A comprehensive review of Ireland’s food security profile in 2021 reveals that the country has achieved complete food security in terms of price, quality, and safety. Risks are primarily associated with subsistence agriculture. They are systematized in Table 2.1, which also evaluates these risks in Russia and Burundi and proposes strategic directions for risk management.

Temperature rise Drought Flooding Agricultural water risk—quantity Agricultural water risk—quality

Risks of natural disasters

Risks of water supply

Indicator of UNDP [13] for measuring risk

Food security risks

89.2 50.0 25.2 0.0 25.0

75.0

Russia (average, 23rd place, 74.8 points)

83.7 50.0 55.3 100.0

Ireland (high, 1st place, 84 points)

0.0

37.3 25.0 39.7 50.0

Optimization of precision farming and development of smart hydroponics

Improving the climate resilience of agriculture

Strategic directions for the development Burundi (low, 113th of smart agriculture place, 34.7 points) based on deep learning for risk management

Degree of risk in countries with different levels of food security, %

Table 2.1 Strategic directions for the development of smart agriculture based on deep learning for the risk management of food security in the economy of the future

12 E. G. POPKOVA ET AL.

Land

Marine biodiversity

Food import dependency Agricultural import tariffs Demographic stress

Land risks

Biodiversity risks

Risks of import dependence

100.0 75.9 91.4

72.2 77.2

12.7

84.1

Russia (average, 23rd place, 74.8 points)

51.6

39.3

75.7

Ireland (high, 1st place, 84 points)

9.6

49.1

74.5

31.7

62.6

Increase in productivity and production capacity

Development of smart vertical farms Smart technology applications for biodiversity conservation Import substitution in agriculture

Strategic directions for the development Burundi (low, 113th of smart agriculture place, 34.7 points) based on deep learning for risk management

Degree of risk in countries with different levels of food security, %

Source Developed and compiled by the authors based on the materials of The Economist [12]

Risks of food shortages

Indicator of UNDP [13] for measuring risk

Food security risks

2 STRATEGIC DIRECTIONS FOR SMART AGRICULTURE …

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Based on the data from Table 2.1, the following strategic directions for the development of smart agriculture based on deep learning for the risk management of food security of the economy of the future were identified: . Improving the climate resilience of agriculture. Disaster risks are high, even for the world leader in food security. For example, drought control was only 50% successful in Ireland and Russia and 25% successful in Burundi (high risk). Deep learning will enable high-fidelity prediction of climate change and provide intelligent risk management support; . Optimization of precision farming and development of smart hydroponics. The water supply risks are very high in Russia and Burundi, although they are low in Ireland. Deep learning is appropriate for economical and individualized irrigation of each plant; . Development of smart vertical farms. Land risk management was 84.1% successful in Russia, 75.7% successful in Ireland, and 62.6% successful in Burundi. Deep learning is recommended for the transition from horizontal to vertical farms, virtually independent of the factors and risks associated with the ground; . Applying smart technology to biodiversity conservation. Biodiversity risk management was 39.3% successful in Ireland, 12.7% successful in Russia, and 31.7% successful in Burundi. Deep learning is recommended for automated monitoring and breakthroughs in biodiversity conservation; . Import substitution in agriculture. In Ireland, dependence on imports was high (51.6%). Dependence on imported food prices was also high (72.2%), which was typical for both Russia (75.9%) and Burundi (49.1%). Deep learning is proposed to be used for flexible import substitution in the agricultural economy; . Increasing the productivity and production capacity. Food shortage risk management is 77.2% successful in Ireland, 91.4% in Russia, and 9.6% in Burundi. With deep learning, it is advisable to increase the pace of automation in smart agriculture to manage these risks.

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Conclusion Thus, the risks of the agricultural economy of the future have been identified and systematized: disaster risks, water supply risks, land risks, biodiversity risks, import dependency risks, and food shortage risks. The authors also reviewed and assessed these risks in countries with different levels of food security—in all countries the risks were moderate or high. The authors proposed the following strategic directions for the development of smart agriculture based on deep learning for risk management: . Improving the climate resilience of agriculture; . Optimization of precision farming and development of smart hydroponics; . Development of smart vertical farms; . Smart technology applications for biodiversity conservation; . Import substitution in agriculture; . Increasing the productivity and production capacity. The authors developed recommendations for the implementation of these areas using deep learning. The contribution of the article to the literature lies in the identification of a strategic food security perspective and opportunities to overcome the limitations of smart agriculture in food security risk management. As substantiated in the research, these opportunities and prospects are related to the expansion of the spectrum of using deep learning in agriculture, which proves the research hypothesis. The theoretical significance of this research consists in the development of scientific and methodological support for the transition to the advanced development of the agricultural economy. For this purpose, the risk-based approach to food security is improved.

References 1. Karpova, A. A., Zemskova, O. M., & Litvinova, T. N. (2015). The influence of demographic transition as a consequence of qualitative transformations in world economy on economic growth. Actual Problems of Economics, 167 (5), 365–370. 2. Popkova, E. G., Tyurina, Y. G., Sozinova, A. A., Bychkova, L. V., Zemskova, O. M., Serebryakova, M. F., & Lazareva, N. V. (2017). Clustering as a

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growth point of modern Russian business. In E. G. Popkova, V. E. Sukhova, A. F. Rogachev, Y. G. Tyurina, O. A. Boris, & V. N. Parakhina (Eds.), Integration and clustering for sustainable economic growth (pp. 55–63). Springer. https://doi.org/10.1007/978-3-319-45462-7_7 Adamova, M. A., Kardanova, M. L., Yakusheva, A. V., Dyakonova, M. A., & Mankieva, A. V. (2021). Artificial intelligence in politics global leadership and the risks of competitive struggle. In E. G. Popkova & V. N. Ostrovskaya (Eds.), Meta-scientific study of artificial intelligence (pp. 409–417). Information Age Publishing. Hilaire, J., Tindale, S., Jones, G., Pingarron-Cardenas, G., Baˇcnik, K., Ojo, M., & Frewer, L. J. (2022). Risk perception associated with an emerging agri-food risk in Europe: Plant viruses in agriculture. Agriculture and Food Security, 11(1), 21. https://doi.org/10.1186/s40066-022-00366-5 Potashnik, Y. S., Garina, E. P., Kozlova, E. P., Kuznetsova, S. N., & Garin, A. P. (2021). Impact on risk factors of industrial enterprises. In E. G. Popkova & V. N. Ostrovskaya (Eds.), Meta-scientific study of artificial intelligence (pp. 617–623). Information Age Publishing. Adnan, K. M. M., Ying, L., Ayoub, Z., Sarker, S. A., Menhas, R., Chen, F., & Yu, M. (2020). Risk management strategies to cope catastrophic risks in agriculture: The case of contract farming, diversification and precautionary savings. Agriculture, 10(8), 351. https://doi.org/10.3390/agriculture1008 0351 González, C. M. (2020). Automating the risk out of farming: By introducing both flexibility and stability, automation can reduce waste and overcome labor shortages in the agriculture industry. Mechanical Engineering, 142(8), 32–37. https://doi.org/10.1115/1.2020-AUG1 Iglesias, A. (2022). On the risk of climate change on agriculture and water resources. Integrated Environmental Assessment and Management, 18(3), 595–596. https://doi.org/10.1002/ieam.4606 Khatri-Chhetri, A., Regmi, P. P., Chanana, N., & Aggarwal, P. K. (2020). Potential of climate-smart agriculture in reducing women farmers’ drudgery in high climatic risk areas. Climatic Change, 158(1), 29–42. https://doi. org/10.1007/s10584-018-2350-8 Rossi, P., Mangiavacchi, P. L., Monarca, D., & Cecchini, M. (2022). Smart machinery and devices for reducing risks from human-machine interference in agriculture: A review. In M. Biocca, E. Cavallo, M. Cecchini, S. Failla, & E. Romano (Eds.), Safety, health and welfare in agriculture and agro-food systems (pp. 195–204). Springer. https://doi.org/10.1007/9783-030-98092-4_21 Tong, Q., Swallow, B., Zhang, L., & Zhang, J. (2019). The roles of risk aversion and climate-smart agriculture in climate risk management:

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Evidence from rice production in the Jianghan Plain, China. Climate Risk Management, 26, 100199. https://doi.org/10.1016/j.crm.2019.100199 12. The Economist. (2022). Global Food Security Index 2021. https://impact. economist.com/sustainability/project/food-security-index/Index. Accessed 10 May 2022. 13. UNDP. (2022). Sustainable development report 2021: The decade of action for the Sustainable Development Goals. https://dashboards.sdgindex.org/. Accessed 10 May 2022.

CHAPTER 3

Food and Water Security of the Middle East (the Case of Egypt) Denis A. Mirgorod , Gennadii V. Kosov , Elena A. Soloveva , Alihan M. Israilov , and Alexander A. Pohilko

Introduction One of the main components of sustainable development is access to food and water resources. Currently, there are not many States that can be completely self-sufficient in terms of their food and water security. A significant number of countries and regions do not have sufficient access to these resources. In this context, one of the most problematic regions of the world is the Middle East, which, taking into account the demographic situation in the short and medium term, may face a crisis in providing the population with water and food. At the same time, recently the threat of shortage of water and food resources has been most clearly observed in Egypt, which has a specific

D. A. Mirgorod (B) · G. V. Kosov · E. A. Soloveva · A. M. Israilov · A. A. Pohilko Pyatigorsk State University, Pyatigorsk, Russia

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_3

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geographical location and a constant population growth. In addition to the internal factors, the problem of Egypt is also aggravated by the external background, which is associated with Ethiopia’s desire to put the Renaissance Dam into operation as quickly as possible and the current high level of conflict in the world. Thus, in the foreseeable future, there is a threat of an unprecedented crisis for Egypt, which may affect not only the country, but also the entire region, as well as have global implications. Accordingly, the problem of Egypt’s food and water security has important scientific and applied significance within the framework of global, regional, and national sustainable development issues. Moreover, the mentioned threat is also connected with the specifics of the Middle East. The high conflict potential of the region will increase significantly if Egypt, which has been relatively stable until now, is not able to ensure its food security.

Methodology The subject of this study deals with the general subject-area of sustainable global development, which required the involvement of an extensive theoretical base on this issue. In this regard, the authors of the work used the works of such scientists as R. Baumgartner [1], R. Kates [2], A. Steer [3], C. Stevens [4], K. Tomislav [5], J. Zhao [6], and some others. In addition, the authors were guided by theoretical developments in the field of food and water security as one of the main components of the problem area of sustainable development—M. Falkenmark [7], A. Gerlak [8], M. Hameed [9], C. Scott [10], M. Taka [11], and M. Zeitoun [12]. It should be emphasized that the study used theoretical developments on the noted problem both in the country itself and in the Middle East for the purpose of situational analysis of the state of food and water security in Egypt. The situation with the access of the region’s population to water and food resources was determined by the works of M. Behnassi [13], R. Hanna [14], J. Saghir [15], and O. Bozorg-Haddad [16]. The authors also used situational analysis of the state of food and water security within the framework of sustainable development issues. Additionally, a predictive method was used. The combination of these methods allowed us to form an understanding of the state and prospects of food and water security in Egypt.

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Results We believe that it is necessary to begin consideration of the problem of Egypt’s access to food and water resources with an introductory question, since food production, among other things, depends on it. First of all, it should be noted that geographically Egypt’s life is historically based on its main waterway—the Nile, which affects all aspects of the socio-economic and, consequently, political life of the country. Currently, the country’s population is more than one hundred million people [17], which, according to experts, is close to a critical indicator in the distribution of existing water resources [18]. The water crisis in Egypt is also aggravated by an external factor. In Ethiopia, located upstream of the Nile, the construction of the Renaissance dam continues. At the same time, accelerated filling of the dam reservoir can reduce the current Egyptian quota of Nile water consumption (55.5 billion cubic meters) [19]. Thus, there is no doubt that the problem of providing water resources is one of the biggest problems, if not the biggest, that Egypt is facing today in order to achieve the goals of water security and agricultural development. Due to the escalation of the Nile water crisis in recent years with the countries of the basin, whose water supplies 97% of Egyptian water needs, the water crisis is one of the most controversial topics in the scientific and political community [20]. Critically analyzing the current strategies related to water security in Egypt, we will find that they are almost limited to the desire to ensure and increase the volume of water resources. At the same time, many of them neglect issues related to ensuring the safety and quality of water for use and consumption, the possibility of citizens’ access to it, and the stability of its supply. In this regard, the scientific literature on economic development states that when the question of “security” is raised, whether it is food or water security, development efforts should be distributed in parallel and simultaneously in four directions: the availability of the resource in quantities sufficient for the needs of consumers; support in enhancing opportunities in gaining access to it by individuals; stability of water supply; security and quality of supplies. Therefore, it should be pointed out that the possession of water resources is not an end in itself and does not mean the achievement of food and water security. In this regard, it should be pointed out that, for example, the Democratic Republic of the Congo has about 52% of the total reserves of surface

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water resources on the African continent, and the share of water resources per capita is about 17 thousand cubic meters per year, which makes it the richest country in Africa in terms of water resources [21]. Despite this, Congo is one of the African countries that suffer most from the problems of poverty and malnutrition. According to the latest World Bank data, about 66% of the population of Congo live below the poverty line, and at least 40% of them suffer from problems related to water quality and access to it [22]. Thus, water security is not achieved when its adequate quantity is available, but rather depends mainly on the effectiveness of management systems and the use of water resources. Further, the problem of food production should be considered. As in all other developing countries, the agricultural sector remains the largest consumer of water resources in Egypt. On average, the agricultural sector consumes about 70% of all water resources in developing countries due to the method of agricultural production and the efficiency of irrigation water use [23]. The share of the agricultural sector in Egypt in the use of water resources is currently increasing to about 86%, leaving 2.5% for industry and about 11.5% for other uses [24]. At the same time, the efficiency of using water resources is extremely low, which leads to large losses. It should be added that the agricultural sector is still the main activity of the Egyptian rural population, which accounts for 57% of the total population of Egypt and represents about 30% of the total workforce [25]. Studying the experience of emerging economies, we find that the dominant model of structural transformation in these countries considers the agricultural sector as the main engine of economic growth. Taking into account the specifics of the agricultural sector and its vital role in development, it can be argued that this sector should be the focus of strategies aimed at sustainable development with its economic, social, and environmental aspects in Egypt; therefore, the government should pay more attention to the role of the agricultural sector in development [26]. Especially in providing job opportunities, raising incomes, achieving food security and reducing poverty, as well as its interaction with other sectors that form the basis of the economy and development. The situation on world markets can also have a great effect on the food security situation in Egypt. Currently, there is a steady increase in prices for fertilizers, grain, oil, etc. Consequently, Egypt is facing negative external factors that affect its sustainable development in the context of access to food.

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Conclusions Summing up, we point out that the most important component of global sustainable development is access to water and food resources. Many countries and regions of the world are not self-sufficient in this matter. Because of this, they are in a constant risk zone and may face an acute shortage of these resources in the foreseeable future. Consequently, the entire world community may face a systemic crisis that will call into question the very concept of global sustainable development. One of the most problematic regions of the world in terms of ensuring food and water security is the Middle East. This is due to a whole range of factors of a geographical, demographic, socio-political, and economic nature. Currently, a real threat to the availability of water and food resources is being formed in Egypt. Uncontrolled population growth, inefficient water use, as well as many other factors create all conditions for the emergence of a systemic crisis in this Arab country, which will have, among other things, global implications.

References 1. Baumgartner, R. J. (2011). Critical perspectives of sustainable development research and practice. Journal of Cleaner Production, 19(8), 783–786. https://doi.org/10.1016/j.jclepro.2011.01.005 2. Kates, R. W., & Parris, T. M. (2003). Long-term trends and a sustainability transition. Proceedings of the National Academy of Sciences, 100(14), 8062– 8067. https://doi.org/10.1073/pnas.1231331100 3. Steer, A., & Wade-Gery, W. (1993). Sustainable development: Theory and practice for a sustainable future. Sustainable Development, 1(3), 23–35. https://doi.org/10.1002/sd.3460010306 4. Stevens, C. (2018). Scales of integration for sustainable development governance. International Journal of Sustainable Development and World Ecology, 25(1), 1–8. https://doi.org/10.1080/13504509.2017.1282893 5. Tomislav, K. (2018). The concept of sustainable development: From its beginning to the contemporary issues. Zagreb International Review of Economics and Business, 21(1), 67–94. https://doi.org/10.2478/zireb2018-0005 6. Zhao, J. (1991). The theoretical analysis of sustainable development. Ecological Economics, 1, 12–15. https://doi.org/10.2307/2808061 7. Falkenmark, M., & Rockström, J. (2006). The new blue and green water paradigm: Breaking new ground for water resources planning and management. Journal of Water Resources Planning and Management, 132(3),

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129–132. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:3(129) 8. Gerlak, A. K., House-Peters, L., Varady, R. G., Albrecht, T., Zúñiga-Terán, A., de Grenade, R. R., & Scott, C. A. (2018). Water security: A review of place-based research. Environmental Science and Policy, 82, 79–89. https:// doi.org/10.1016/j.envsci.2018.01.009 9. Hameed, M., Moradkhani, H., Ahmadalipour, A., Moftakhari, H., Abbaszadeh, P., & Alipour, A. (2019). A review of the 21st century challenges in the food-energy-water security in the Middle East. Water, 11(4), 682. https://doi.org/10.3390/w11040682 10. Scott, C. A., Albrecht, T. R., De Grenade, R., Zuniga-Teran, A., Varady, R. G., & Thapa, B. (2018). Water security and the pursuit of food, energy, and earth systems resilience. Water International, 43(8), 1055–1074. https:// doi.org/10.1080/02508060.2018.1534564 11. Taka, M., Ahopelto, L., Fallon, A., Heino, M., Kallio, M., Kinnunen, P., & Varis, O. (2021). The potential of water security in leveraging Agenda 2030. One Earth, 4(2), 258–268. https://doi.org/10.1016/j.oneear.2021.01.007 12. Zeitoun, M. (2011). The global web of national water security. Global Policy, 2(3), 286–296. https://doi.org/10.1111/j.1758-5899.2011.00097.x 13. Behnassi, M., Baig, M. B., El Haiba, M., & Reed, M. R. (Eds.). (2021). Emerging challenges to food production and security in Asia, Middle East, and Africa: Climate risks and resource scarcity. Springer Nature. https:// doi.org/10.1007/978-3-030-72987-5 14. Hanna, R. L. (2020). Drivers and challenges for transnational land–water– food investments by the Middle East and North Africa region. Wiley Interdisciplinary Reviews: Water, 7 (2), 14–15. https://doi.org/10.1002/ wat2.1415 15. Saghir, J. (2019). Climate change and conflicts in the Middle East and North Africa. IFI. 16. Bozorg-Haddad, O., Zolghadr-Asli, B., Sarzaeim, P., Aboutalebi, M., Chu, X., & Loáiciga, H. A. (2020). Evaluation of water shortage crisis in the Middle East and possible remedies. Journal of Water Supply: Research and Technology-AQUA, 69(1), 85–98. https://doi.org/10.2166/aqua.201 9.049 17. Central Agency for Public Mobilization and Statistics. (2022). https://www. capmas.gov.eg/HomePage.aspx. Accessed 10 March 2022. 18. Ouda, S., Zohry, A. E. H., & Noreldin, T. (2020). Deficit irrigation: A remedy for water scarcity. Springer Nature. https://doi.org/10.1007/9783-030-35586-9 19. Roussi, A. (2019). Gigantic Nile dam prompts clash between Egypt and Ethiopia. Nature, 574(7777), 159–161. https://doi.org/10.1038/d41 586-019-02987-6

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20. Tutwiler, R. N. (2021). Sustainable water resource management in Egypt. Routledge handbook on contemporary Egypt (pp. 335–347). Routledge. 21. Chitonge, H., Mokoena, A., & Kongo, M. (2020). Water and sanitation inequality in Africa: Challenges for SDG 6. In Africa and the sustainable development goals (pp. 207–218). Springer. https://doi.org/10.1007/9783-030-14857-7_20 22. Livingston, J. (2021). Water scarcity and health in urban Africa. Dædalus, 150(4), 85–102. https://doi.org/10.1162/daed_a_01874 23. McNeill, K., Macdonald, K., Singh, A., & Binns, A. D. (2017). Food and water security: Analysis of integrated modeling platforms. Agricultural Water Management, 194, 100–112. https://doi.org/10.1016/j. agwat.2017.09.001 24. Hamza, W., & Mason, S. (2004). Water availability and food security challenges in Egypt. In Proceedings international forum on food security under water scarcity in the Middle East: problems and solutions. Como. 25. Keulertz, M., & Mohtar, R. (2022). The water-energy-food nexus in Libya, UAE, Egypt and Iraq. Istituto Affari Internazionali (IAI). 26. Russia-Ukraine crisis poses a serious threat to Egypt—The world’s largest wheat importer—2022. The Conversation. Academic rigour, journalistic flair. https://theconversation.com/russia-ukraine-crisis-poses-aserious-threat-to-egypt-the-worlds-largest-wheat-importer-179242. Accessed 20 March 2022.

CHAPTER 4

Best Practices and the Digital Model of Agricultural Development in Developed and Developing Countries Elena V. Sofiina , Irina V. Milchik , Igor V. Denisov , and Nadezhda K. Savelyeva

Introduction Digital agriculture makes a significant contribution to food security. Advanced technologies make agricultural production more resilient to the adverse effects of natural factors (e.g., climate-smart technologies), increase productivity (e.g., AI-controlled precision farming), and reduce cyclicality and achieve year-round growth and yield (e.g., IoT-based smart greenhouses) [5, 9].

E. V. Sofiina Federal Research Center of Agrarian Economy and Social Development of Rural Areas—All—Russian Research Institute of Agricultural Economics, Moscow, Russia e-mail: [email protected] E. V. Sofiina · I. V. Milchik Vyatka State Agrotechnological University, Kirov, Russia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_4

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The problem is that technology alone is not enough to develop digital agriculture. A far-reaching and ubiquitous advanced telecommunications infrastructure is essential to the smooth and high-performance use of advanced technology. In many countries, infrastructure is most developed in large cities, while there is a deficit of it in rural areas. Highly skilled human resources are required to implement, establish, and use advanced technologies in a highly efficient way. In general, the supply of digital workforce is quite high in the labor markets of many countries worldwide. Nevertheless, there may be a direct shortage of agricultural digital personnel, while the use of advanced technology in agricultural production is very different from industry. Given the recognized differences in the availability of investment resources, the level of development of the knowledge society, the effectiveness of public administration institutions, etc., between developed and developing countries, this paper hypothesizes that digitalization factors have different effects on the countries of these categories. The paper aims to explore best practices and identify features of digital models of agricultural development in developed and developing countries.

Literature Review This research is based on the theory of digital agriculture. The existing literature describes the role of various digital factors in the development of the agricultural economy, including the following: . Advanced technologies of agricultural economy and human resources of digital agriculture using them, determining the innovation activity of agricultural enterprises [1, 3, 10, 11];

e-mail: [email protected] I. V. Milchik · N. K. Savelyeva (B) Vyatka State University, Kirov, Russia e-mail: [email protected] I. V. Denisov Plekhanov Russian University of Economics, Moscow, Russia e-mail: [email protected]

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. E-government, in its broad interpretation, covering the provision of electronic services to agricultural enterprises and automated statepublic control of value chains in the agricultural economy [6, 7]; . Rural telecommunications infrastructure and means of collecting, storing, exchanging, processing, and analyzing digital data for agriculture [2, 8]. Although there is no separate statistical record of the selected factors for the agricultural economy, they are calculated for the digital economy as a whole and are components of the AI Readiness Index: these are the indicators “Technology Sector,” “Government,” and “Data and Infrastructure” [4]. Despite the recognition of the importance and extensive coverage of the described factors in the literature, the practical experience of the development of digital agriculture and the specifics of the impact of these factors in developed and developing countries have been studied fragmentarily and haphazardly. The uncertainty of digital models of agricultural development in developed and developing countries reduces the effectiveness of managing these factors and prevents the unlocking of the potential for growth in food security. This paper fills the identified gap by examining best practices and identifying the specifics of the impact of digital factors on agricultural development in developed and developing countries.

Materials and Method To test the hypothesis, the authors apply regression analysis to determine the impact of the selected digitalization factors (based on Oxford Insights statistics [4]) on the results achieved in the implementation of SDG 2 (“Goal 2 Score” calculated by UNDP [12]). To reflect the most successful experience of using digital technologies directly in agriculture, the authors formed a sample of the top 10 developed and top 10 developing countries demonstrating the highest “Goal 2 Score” in 2021 [12] (i.e., advanced agricultural economies with the highest level of food security). The research is based on data for 2021 (Table 4.1).

Results Based on the statistics from Table 4.1, the authors obtained the following results of the regression analysis, which was conducted on a full sample

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Table 4.1 Digital agriculture and influencing factors in developed and developing countries in 2021, points 1–100 Category of countries

Developed countries

Developing countries

Country

South Korea Singapore Ireland Japan Croatia Austria France Hungary Germany Slovak Republic China Paraguay Mauritius Chile Qatar Bahrain Vietnam Kuwait Peru Russia

Goal 2 Score

Government

Technology Sector

y

x1

x2

Data and Infrastructure x3

82.4633

85.27

58.49

85.89

76.1053 75.8949 75.1963 74.9936 73.7671 73.7286 72.6474 72.4446 72.0120

94.88 74.70 81.90 48.70 63.09 82.10 68.09 78.04 67.59

66.69 61.11 59.31 36.48 58.54 60.61 41.65 67.68 41.39

85.80 82.59 87.32 71.71 82.59 86.53 69.40 86.07 75.89

81.1000 73.7635 73.7320 71.9148 71.8287 71.4840 71.0326 70.0239 69.9764 57.5145

83.79 35.32 68.52 69.99 79.56 51.46 70.81 46.53 38.24 67.44

61.33 22.45 33.82 42.14 43.02 31.54 32.78 34.37 29.52 46.46

78.15 54.27 55.80 69.13 78.96 77.62 51.87 71.99 53.90 71.90

Source Compiled by the authors based on the materials of Oxford Insights [4] and UNDP [12]

of 20 developed and developing countries to provide the most reliable results (Table 4.2). The results of the regression analysis did not reveal a reliable dependence of the SDG 2 results on the three factor variables at once. Nevertheless, reliable regression models were obtained for each factor variable separately. The following numerical models of agricultural development were obtained by regression analysis: y = 65.9526 + 0.1051*x1

(4.1)

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Table 4.2 Results of regression analysis Group of indicators

Regression statistics

Variance analysis (regression)

Coefficients

Standard error

t-statistic

P-value

Indicators

Government

Technology Sector

Data and Infrastructure

x1

x2

x3

0.3708

0.3202

4.6539 20 62.1641 62.1641 2.8702 0.1075 67.0655 0.1295

4.7474 20 46.3427 46.3427 2.0562 0.1687 63.2928 0.1325

3.7002 0.0764

6.9082 0.0924

18.1249 1.6942

9.1620 1.4340

0.0000 0.1075

0.0000 1.4340

Correlation 0.3510 (R2 ) Standard error 4.6923 Observations 20 SS 55.6931 MS 55.6931 F 2.5294 Significance of F 0.1292 Constant 65.9526 Dependent 0.1051 variable Constant 4.6034 Dependent 0.0661 variable Constant 14.3269 Dependent 1.5904 variable Constant 0.0000 Dependent 0.1292 variable

Source Calculated and compiled by the authors

Model (1) shows that the development of e-government by one point increases the results in the implementation of SDG 2 by 0.1051 points. The correlation of the indicators (R2) is 35.10%. The model is robust at a significance level of 0.13 (significance F = 0.1292). y = 67.0655 + 0.1295*x2

(4.2)

Model (2) shows that with the development of the technological sector by one point, the results in the implementation of SDG 2 increase by 0.1295 points. The correlation of the indicators (R2) is 37.08%. The model is robust at a significance level of 0.11 (significance F = 0.1075). y = 63.2928 + 0.1325*x3

(4.3)

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Model (3) indicates that a one-point development of data and infrastructure improves the results in SDG 2 by 0.1325 points. The correlation of the indicators (R2) is 32.02%. The model is robust at a significance level of 0.17 (significance F = 0.1687). To clarify the features of the selected categories of countries, the obtained regression models were supplemented by the following results of the correlation analysis: . In developed countries: the implementation of SDG 2 showed the strongest correlation with e-government (38.22%) and data and infrastructure (36.44%); it showed a less pronounced but significant relationship with the technology sector (24.40%); . In developing countries: the implementation of SDG 2 showed a moderate relationship with e-government (16.78%) and the technology sector (14.88%); it showed almost zero relationship with data and infrastructure (1.26%). Based on the given models of multiple linear regression, the authors revealed the prospects for the digital development of agriculture (Fig. 4.1) with the most favorable influence and highly effective management of the studied digital factors of the agricultural economy in three alternative scenarios. As shown in Fig. 4.1, the first scenario assumes the development of e-government, whose maximum progress (up to 100 points, + 47.49% compared to 2021) provides an increase in the results in SDG 2 by 4.63% (to 76.47 points). The second scenario assumes the development of the technological sector, whose maximum progress (up to 100 points, + 115.20% compared to 2021) ensures an increase in the results of SDG 2 by 9.48% (to 80.01 points). The third scenario assumes the development of data and infrastructure, the maximum progress of which (up to 100 points, + 35.37% compared to 2021) provides an increase in the results of SDG 2 by 4.74% (to 76.54 points). There is no doubt that, in practice, the Decade of Action will see the systemic development of e-government, the technology sector, data, and infrastructure. The lack of a general model allows making a highly accurate prediction of the effects of this development; even individual models

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Scenario 1: E-government

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Scenario 2: Technological sector

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Scenario 3: Data and infrastructure

120.00 47.49 100.00 100.00 80.00 67.80 60.00 40.00 20.00 0.00

140.00 120.00 50.00 120.00 100.00 40.00 45.00 100.00 35.00 120.00 100.0035.37 40.00 100.00 80.01 76.47 76.54 115.20 30.00 100.00 35.00 73.08 73.08 73.08 80.00 80.00 73.87 30.00 25.00 80.00 25.00 60.00 60.00 20.00 46.47 60.00 20.00 15.00 15.00 40.00 40.00 40.00 10.00 10.00 5.00 20.00 20.00 20.00 5.00 0.00 0.00 0.00 0.00 0.00 4.63

9.48

Current value in 2021, points 1-100

Future value, points 1-100

4.74

Prospective increase in value, %

Fig. 4.1 Prospects for digital agriculture (Source Calculated and compiled by the authors

clearly indicate that it will provide a significant contribution to SDG 2. Synergistic effects can be expected to occur. The scale of this effect cannot be accurately predicted based on the data available and needs additional research.

Conclusion Thus, the hypothesis is proved, and the features of digital models of agricultural development among the categories of countries are identified. In developed countries, the key digital drivers of agricultural development are e-government and rural telecommunications infrastructure; in developing countries, it is advanced agricultural technology, human resources of digital agriculture that use this technology, and e-government. It also reveals less influence of digital factors on agriculture in developed countries compared to developing countries. The contribution of this research to the literature lies in the clarification of the differences in the models of digital agricultural development among the categories of countries and the justification of the need to study them separately. The practical significance of the results and conclusions is that the disclosed nature and strength of the influence of digital factors on agriculture can improve the efficiency of managing these factors. This

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research strengthens the scientific and methodological support for food security and the practical implementation of SDG 2.

References 1. Chernyshov, N. I., Sysoev, O. E., & Kiselev, E. P. (2021). Gantry technology in agriculture greening. In O. G. Shakirova, O. V. Bashkov, & A. A. Khusainov (Eds.), Current problems and ways of industry development: Equipment and technologies (pp. 303–309). : Springer. https://doi.org/10. 1007/978-3-030-69421-0_32 2. Hatanaka, M., Konefal, J., Strube, J., & Glenna, L. (2022). Data-driven sustainability: Metrics, digital technologies, and governance in food and agriculture. Rural Sociology, 87 (1), 206–230. https://doi.org/10.1111/ruso. 12415 3. Osipov, V. S., Vorozheykina, T. M., Bogoviz, A. V., Lobova, S. V., & Yankovskaya, V. V. (2022). Innovation in agriculture at the junction of technological waves: Moving from digital to smart agriculture. In E. G. Popkova, & B. S. Sergi (Eds.), Smart innovation in agriculture (pp. 21–27). Springer. https://doi.org/10.1007/978-981-16-7633-8_3 4. Oxford Insights. (2022). Government AI readiness index 2021. Retrieved from https://www.oxfordinsights.com/government-ai-readiness-index2021 (Accessed 27 April 2022) 5. Popkova, E. G. (2022). Case study of smart innovation in agriculture on the example of a vertical farm. In E. G. Popkova, & B. S. Sergi (Eds.), Smart innovation in agriculture (pp. 303–309). Springer. https://doi.org/ 10.1007/978-981-16-7633-8_34 6. Qin, T., Wang, L., Zhou, Y., Guo, L., Jiang, G., & Zhang, L. (2022). Digital technology-and-services-driven sustainable transformation of agriculture: Cases of China and the EU. Agriculture, 12(2), 297. https://doi.org/ 10.3390/agriculture12020297 7. Sazanova, S. L., & Ryazanova, G. N. (2019). Problems and opportunities of development of the agricultural industry of Russia from the point of view of the Marxist theory. In E. G. Popkova & M. L. Alpidovskaya (Eds.), Marx and modernity: A political and economic analysis of social systems management (pp. 599–608). Information Age Publishing. 8. Scuderi, A., La Via, G., Timpanaro, G., & Sturiale, L. (2022). The digital applications of “Agriculture 4.0”: Strategic opportunity for the development of the Italian Citrus Chain. Agriculture, 12(3), 400. https://doi.org/10. 3390/agriculture12030400 9. Sridhar, A., Balakrishnan, A., Jacob, M. M., Sillanpää, M., & Dayanandan, N. (2022). Global impact of COVID-19 on agriculture: Role of sustainable agriculture and digital farming. Environmental Science and Pollution

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Research. https://doi.org/10.1007/s11356-022-19358-w 10. Tchernyshev, N. I., Kiselyov, E. P., & Sysoev, O. E. (2019). Bridge agriculture as the basis of preserving soiled bioorganisms. IOP Conference Series: Earth and Environmental Science, 272(3), 032021. https://doi.org/10. 1088/1755-1315/272/3/032021 11. Turyanskiy, A. W., Dorofeev, A. F., Dobrunova, A. I., & Kasaeva, T. V. (2021). Forecast for the development of human capital in the agricultural sector at the regional level. In E. G. Popkova & V. N. Ostrovskaya (Eds.), Meta-scientific study of artificial intelligence (pp. 75–84). Information Age Publishing. 12. UNDP. (2021). Sustainable development report 2021. Retrieved from https://s3.amazonaws.com/sustainabledevelopment.report/2021/2021sustainable-development-report.pdf (Accessed 27 April 2022)

CHAPTER 5

Monitoring the Compliance of Today’s Agriculture with Food Security Needs for Sustainable Development Elena A. Bratukhina , Berik T. Beisengaliyev , Anastasia A. Sozinova , and Ksenia V. Borzenko

Introduction Sustainable Development Goal 2 (SDG 2) embodied the growing imbalance in world food markets and the global initiative to rebalance them. The current food demands are high in terms of quantity, price availability, quality, safety for human health, and naturalness and goodness—this is

E. A. Bratukhina (B) · A. A. Sozinova Vyatka State University, Kirov, Russia e-mail: [email protected] A. A. Sozinova e-mail: [email protected] B. T. Beisengaliyev Kazakh University of Economics, Finance and International Trade, Astana, Kazakhstan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_5

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reflected in the relevant indicators of the Economist Group [12]. The demand for food shows an upward trend: quantitatively, through an increase in the world’s population, and qualitatively, through an increase in the demand for food. Against this backdrop, concerns about whether supply can keep up with the growth rate in demand are reasonably growing. The very wording of SDG 2 indicates that some of the (poorest) countries are experiencing hunger and need humanitarian food aid. Developed and dynamic countries with high levels of food security, many of which export food, clearly stand out against this background. In this regard, the research on cause-and-effect relationships of food security is relevant. The research question (RQ) of this paper is whether the conditions for farming determine food security. The economic sense of the RQ is how to manage the agricultural economy to ensure food security. The answer to the RQ is needed to find a universal solution for the practical implementation of SDG 2 that is suitable for all countries of the world. This research aims to monitor the compliance of today’s agriculture with the needs of food security for sustainable development.

Literature Review The existing literature does not form an unambiguous answer to the RQ posed. The uncertainty of food security causality is a research gap. Godrich et al. [3], Setsoafia et al. [9], Sumarwati [10], and Syafiq et al. [11] point out that it is necessary to develop an agricultural economy to ensure food security. Differences in agricultural land area among the countries and unfavorable climate change are recognized as barriers to food security. This view assumes that the elimination of hunger in the poorest countries requires an increase in food exports by the world’s major agricultural economies. This means that the poorest countries are chronically dependent on food imports, which, with the disruptions in global supply chains that characterize the global economic crises, will experience worsening hunger.

K. V. Borzenko Rostov State University of Economics, Rostov-on-Don, Russia e-mail: [email protected]

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In this connection, the alternative point of view is of the greatest scientific and practical interest. This point of view is described in the works of Cansino-Loeza et al. [1], Getaneh et al. [2], Hashmiu et al. [4], Matavel et al. [5], Okolie and Ogundeji [6], Popkova [7], Popkova, et al. [8], and Zongo et al. [15]. It suggests that a favorable environment for the development of agriculture does not mean its high productivity and does not guarantee food security. Based on this (second) point of view, this paper proposes hypothesis (H) that the capacity of today’s agriculture does not meet the needs of food security. The economic sense of this viewpoint and the hypothesis (H) is that, when viewed in reverse, we can see that unfavorable farming conditions do not impede food security. The value of testing the hypothesis is that, if confirmed, it would mean that the poorest countries have the opportunity to ensure their food security and achieve food import substitution, even if the conditions for agriculture in their territory are unfavorable.

Materials and Method To test the hypothesis (H), this paper monitors the compliance of today’s agriculture with the need for food security for sustainable development using the method of regression analysis. The selected method is used to model the econometric dependence of the global food security index in 2021 (by the Economist Group estimates [12]) on agricultural land World Bank [13] and on agriculture, forestry, and fishing, value added in 2020 World Bank [14]. The empirical basis of the research is presented in Table 5.1. The hypothesis (H) is considered proven if there is no positive relationship in the regression model.

Results Monitoring the compliance of today’s agriculture with the needs of food security for sustainable development is based on the data from Table 5.1. The results of their regression analysis are shown in Table 5.2. Based on the results from Table 5.2, the following multiple linear regression model was obtained: y = 112.9197 − 0.4970x1 − 1.3966x2

(5.1)

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Table 5.1 Empirical basis of the research Country

Saudi Arabia Uruguay Kazakhstan South Africa Nigeria UK Denmark Ireland India China

Agricultural land, % of land area

Agriculture, forestry, and fishing, value added, % of GDP

Global Food Security Index, score 0–100

x1

x2

y

80.8 80.1 80.0 79.4 75.9 71.7 65.8 65.6 60.4 56.1

2.6 7.5 5.4 2.5 24.1 0.6 1.3 0.9 18.3 7.7

68.1 68.0 69.2 57.8 41.3 81.0 76.5 84.0 57.2 71.3

Source Compiled by the authors based on the Economist Group [12] and World Bank[13, 14]

Model (1) shows that there is no positive contribution of agricultural land and agriculture in the structure of GDP to food security. The reliability of the obtained model is evidenced by the high R-square: 0.8275, as well as the importance of F: 0.0021 (the model is valid at the 0.01 significance level). This proves the proposed hypothesis (H) and confirms that favorable conditions for the development of agriculture do not mean high productivity and do not guarantee food security.

Conclusion Thus, the research answered the RQ and provided evidence that farming conditions do not determine food security. The experience of the world leaders in terms of food security in 2021 showed that they should not specialize in agriculture to maintain their leadership positions. For example, in the UK, the share of agriculture in the structure of GDP is only 0.6%, and 0.9% in Ireland. The experience of the countries with the largest areas of agricultural land was also representative and demonstrates that neither a large area of agricultural land nor a developed agricultural economy allows them to achieve a high level of food security for reasons unrelated to the initial (natural) conditions for farming. For example, in Nigeria, the share of

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Table 5.2 Results of regression analysis Regression statistics Multiple R R-square Normalized R-square Standard error Observations

0.9097 0.8275 0.7782 5.9574 10

Variance analysis

Regression Balance Total

Constant x1 x2

df

SS

MS

F

Significance of F

2 7 9

1191.5937 248.4303 1440.0240

595.7968 35.4900

16.7877

0.0021

Coefficients

Standard error

t-statistics

P-value

Bottom 95%

Top 95%

112.9197 −0.4970 −1.3966

16.1950 0.2203 0.2506

6.9725 −2.2559 −5.5727

0.0002 0.0587 0.0008

74.6246 −1.0180 −1.9892

151.2149 0.0240 −0.8040

Source Calculated and compiled by the authors

agricultural land is 75.9% of the country’s land area, and the share of agriculture in GDP is one of the highest in the world at 24.1%, but food security is critically low—41.3 points. The contribution of the research to the literature lies in the clarification of the causal relationships of food security. The theoretical significance of the findings is that food security is a complex process that goes beyond natural and climatic conditions. This opened up a new angle for the study of agriculture without a clear reference to land resources. The practical significance of the research results is related to the fact that they emphasized the paramount importance of the efficiency of natural resources over their availability and demonstrated the possibility of achieving climate resilience in agriculture. This strengthens the scientific argumentation of the scientific position that in the context of Industry 4.0, the technological order in agriculture determines food security. It is appropriate to devote further research in this book to an in-depth study of this provision.

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References 1. Cansino-Loeza, B., Munguía-López, A. D. C., & Ponce-Ortega, J. M. (2022). A water-energy-food security nexus framework based on optimal resource allocation. Environmental Science and Policy, 133, 1–16. https:// doi.org/10.1016/j.envsci.2022.03.006 2. Getaneh, Y., Alemu, A., Ganewo, Z., & Haile, A. (2022). Food security status and determinants in North-Eastern rift valley of Ethiopia. Journal of Agriculture and Food Research, 8, 100290. https://doi.org/10.1016/j.jafr. 2022.100290 3. Godrich, S. L., Lo, J., Kent, K., Macau, F., & Devine, A. (2022). A mixedmethods study to determine the impact of COVID-19 on food security, food access and supply in regional Australia for consumers and food supply stakeholders. Nutrition Journal, 21(1), 17. https://doi.org/10.1186/s12 937-022-00770-4 4. Hashmiu, I., Agbenyega, O., & Dawoe, E. (2022). Cash crops and food security: Evidence from smallholder cocoa and cashew farmers in Ghana. Agriculture and Food Security, 11(1), 12. https://doi.org/10.1186/s40 066-022-00355-8 5. Matavel, C., Hoffmann, H., Rybak, C., Steinke, J., Sieber, S., & Müller, K. (2022). Understanding the drivers of food security among agriculture-based households in Gurué District. Central Mozambique. Agriculture and Food Security, 11(1), 7. https://doi.org/10.1186/s40066-021-00344-3 6. Okolie, C. C., & Ogundeji, A. A. (2022). Effect of COVID-19 on agricultural production and food security: A scientometric analysis. Humanities and Social Sciences Communications, 9(1), 64. https://doi.org/10.1057/ s41599-022-01080-0 7. Popkova, E. G. (2022). Case study of smart innovation in agriculture on the example of a vertical farm. In E. G. Popkova, & B. S. Sergi (Eds.), Smart innovation in agriculture (pp. 303–309). Springer. https://doi.org/ 10.1007/978-981-16-7633-8_34 8. Popkova, E. G., Sozinova, A. A., & Sofiina, E. V. (2022). Model of agriculture 4.0 based on deep learning: Empirical experience, current problems and applied solutions. In E. G. Popkova, & B. S. Sergi (Eds.), Smart innovation in agriculture (pp. 333–346). Springer. https://doi.org/10.1007/978-98116-7633-8_37 9. Setsoafia, E. D., Ma, W., & Renwick, A. (2022). Effects of sustainable agricultural practices on farm income and food security in Northern Ghana. Agricultural and Food Economics, 10(1), 9. https://doi.org/10.1186/s40 100-022-00216-9 10. Sumarwati, S. (2022). Traditional ecological knowledge on the slope of Mount Lawu, Indonesia: All about non-rice food security. Journal of Ethnic Foods, 9(1), 9. https://doi.org/10.1186/s42779-022-00120-z

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11. Syafiq, A., Fikawati, S., & Gemily, S. C. (2022). Household food security during the COVID-19 pandemic in urban and semi-urban areas in Indonesia. Journal of Health, Population and Nutrition, 41(1), 4. https:// doi.org/10.1186/s41043-022-00285-y 12. The Economist Group. (2021). Global food security index 2021. Retrieved from https://impact.economist.com/sustainability/project/foodsecurity-index/Index. Accessed 5 April 2022. 13. World Bank. (2000–2018). Agricultural land (% of land area). Retrieved from https://data.worldbank.org/indicator/AG.LND.AGRI.ZS. Accessed 5 April 2022. 14. World Bank. (2000–2020). Agriculture, forestry, and fishing, value added (% of GDP). Retrieved from https://data.worldbank.org/indicator/NV.AGR. TOTL.ZS?name_desc=false&view=chart (Accessed 5 April 2022) 15. Zongo, B., Barbier, B., Diarra, A., Zorom, M., Atewamba, C., Combary, O. S., & Dogot, T. (2022). Economic analysis and food security contribution of supplemental irrigation and farm ponds: Evidence from Northern Burkina Faso. Agriculture and Food Security, 11(1), 4. https://doi.org/10.1186/ s40066-021-00347-0

CHAPTER 6

Green Finance: Analysis of Prospects of the Russian Market Olga G. Kantor , Yuliya R. Rudneva , Dmitriy Yu. Dunov , Shakhlo T. Ergasheva , and Boris M. Leybert

Introduction Ensuring food security in the context of the Fourth Industrial Revolution dictates the need for further modernization of the agricultural economy and the transition to agriculture 4.0. A barrier to this is the lack

O. G. Kantor (B) · Y. R. Rudneva · D. Yu. Dunov · B. M. Leybert Ufa State Petroleum Technological University, Ufa, Russia e-mail: [email protected] Y. R. Rudneva e-mail: [email protected] S. T. Ergasheva Tashkent State University of Economics, Tashkent, Uzbekistan e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_6

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of funding, which is successfully overcome through the development of green finance [1, 2]. In the initial stage of the National Project “Ecology,” the federal project “Implementation of the best available technologies,” which involved incentives for green finance, provided for the government to compensate the issuer of green bonds for a portion of the coupon profit paid. Its value depended on whether the funds raised were directed to purchasing domestic or foreign equipment [3]. At the end of 2020, this project was completed before schedule. However, potential issuers are still waiting for a decision on government incentives for green finance. First, the introduction of compensation for coupon profit would be very significant for issuers. Under the federal project “Implementation of the best available technologies,” this compensation ranged from 70 to 90%. The reintroduction of such subsidies would allow issuers to reduce the cost of borrowing in the market, and investors—to provide increased returns. This tool allows implementing a mechanism for cofinancing environmental projects, which is more effective than direct subsidies because it motivates all participants in the process [4]. Additionally, the possibility of reimbursing the costs of project verification (i.e., the procedure of checking the funded project for compliance [confirmation of the fact that the project is green]) is also discussed. Second, potential investors are also extremely interested in providing tax benefits on the coupon profit received on green bonds. It should be recognized that the introduction of both incentive instruments would result in a double discount on coupon profit. However, it should be considered logical that the government will not charge tax on the coupon payment if most of it is financed from the budget. Third, in addition to direct and indirect financial incentives, it would be advisable to use effective organizational tools. The market of so-called institutional investors is gradually developing in Russia. They accumulate considerable sums under their management, which they invest, among other things, in securities. Some participants in this market have very impressive amounts of reserves. Commercial banks are among the largest investors in the securities market (they account for more than 60%). The remaining organizations have significantly smaller but also quite significant amounts for investment, especially compared with the current volumes of the green finance market (Table 6.1). The total amount of funds available to the reviewed institutional investors increased by 3.4 trillion rubles (13.4%) in the first nine months

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Table 6.1 Assessment of investment opportunities for institutional investors in Russia Institutional investor

Amount, billion rubles As of December 31, 2020

Pension savings of non-state pension funds, total, including: – compulsory pension insurance – private pension provision Mutual funds Equity investment funds Total (without commercial banks) Commercial banks (investments in securities) Total (with commercial banks)

As of September 30, 2021

4466.3

4575.0

2973.3

3031.5

1493.0 5040.1 1.3 9507.7

1543.5 6901.0 1.4 11,477.4

15,691.8

17,100.6

25,199.5

28,578.0

Source Compiled by the authors based on [5]

of 2021. The most significant increase (36.9%) is observed in the assets of mutual funds. This dynamic demonstrates the growing interest of private investors in the stock market and the possibility of attracting them to finance, including green projects.

Methodology In May 2020, the Bank of Russia’s Regulation “On the standards of securities issuance” No. 706-p regulated [6] the procedure and features of green and social bonds. In the early days of green finance in Russia, the standards of international organizations were used to determine the environmental significance of investment projects: the International Capital Market Association (ICMA) and the Climate Bonds Initiative (CBI). In September 2021, the Decree of the Government of the Russian Federation approved the national methodology for green finance, which had been under development since March 2020 and was designed to form the framework of the national system of green finance.

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Another important event in the direction of green finance was the opening of the Sustainability Sector at the Moscow Exchange in August 2019. Requirements for issuers are spelled out in the Listing Rules of the exchange. This market is organized to facilitate the financing of environmental and socially significant projects and activities aimed at implementing Russia’s national projects. The exchange platform allowed concentrating the supply and demand of financial resources in the field of sustainable development (Table 6.2). A total of 329 billion rubles were raised over six years. Of these, only 117 billion rubles of bonds are traded in the Sustainability Sector of the Moscow Stock Exchange. It should be noted that green bonds are traded not only in the Sustainability Sector. It did not include bonds worth 101.1 billion rubles. JSC “Russian Railways” was also the first and so far the only Russian issuer to enter the international green stock market. Returning to the assessment of institutional investors’ ability to finance the green economy, it should be noted that, as of September 30, 2021, their investments in financial assets (excluding bank loans) amounted to 28.6 trillion rubles. This means that the volume of circulating green bonds is just over 1% of their investment. If the issuer can attract the attention of this segment of investors with the help of state monetary and organizational measures, it is possible to ensure significant growth in the green financing sector. From 2016 to 2019, the volume of green bonds was 3–5 billion rubles per year (Table 6.1). In 2019, the amounts increased almost eightfold and approached the mark of 40 billion rubles. This can be explained by the following factors: . Launch of the National Project “Ecology,” which included a package of incentive measures; . Organization of the Sustainability Sector at the Moscow Stock Exchange; . Entry on the Irish Stock Exchange of green bonds of JSC “Russian Railways.” In the following years, despite the COVID-19 pandemic, the market continued to grow with a significant slowdown.

Volume of issuance, million rublesa

February 12, 2020

February 12, 2020

September 27, 2016 November 9, 2017 September 27, 2019

13.52

16.02

6.21

6.71

6.71

February 12, 2020

9.52

RA Expert

RA Expert

RA Expert

RAEX-Europe

RAEX-Europe

RAEX-Europe

December 28, 2020 RA Expert

10

Verifier

December 17, 2019 RA Expert

Date of placement/start of bidding

11.50

Rate of current/last coupon (%)

Information about “green” bonds of Russian issuers

Sustainability sector of the Moscow Exchange Financial and 500 industrial corporation “Garant-Invest” Financial and 500 industrial corporation “Garant-Invest” LLC “Specialized 4700 Financial Society RuSol 1” LLC “Specialized 900 Financial Society RuSol 1” LLC “Specialized 100 Financial Society RuSol 1” LLC “Transport 1241 concession company” LLC “Transport 3533 concession company” LLC “Transport 1374 concession company”

Issuer

Table 6.2

(continued)

Transport

Transport

Transport

Finance

Finance

Finance

Finance

Finance

Scope of work

6 GREEN FINANCE: ANALYSIS OF PROSPECTS …

49

6.71 7.38 7.50 8.70 8.80 9.75

2013

70,000

10,000

10,000

25,000

2000

Total for the 117,000 sustainability sector Moscow Exchange, outside the sustainability sector LLC “RSB KHMAO” 1100 10.00 RZD CAPITAL PLC, 100,000 8.79 JSC “Russian Railways”

7.49

3752

LLC “Transport concession company” LLC “Transport concession company” Government of Moscow JSC “Atomic Energy Complex” JSC “Sinara – Transport Machines” PJSC “Sberbank of Russia” PJSC “KAMAZ”

Rate of current/last coupon (%)

Volume of issuance, million rublesa

(continued)

Issuer

Table 6.2 Verifier

RA Expert

ACRA

ACRA

RA Expert

RA Expert

RA Expert

December 19, 2018 RAEX-Europe September 30, RA Expert 2020

November 12, 2021 November 24, 2021

July 28, 2021

June 25, 2021

September 27, 2016 May 27, 2021

December 12, 2018 RA Expert

Date of placement/start of bidding

Municipal solid waste Transport

Engineering

Finance

Engineering

Power industry

Government

Transport

Transport

Scope of work

50 O. G. KANTOR ET AL.

March 12, 2020

March 23, 2021

November 15, 2019 December 9, 2020

0.84

3.125

8 5.75

May 23, 2019

2.2

ACRA

RAEX-Europe

CBI

Sustainalytics

Sustainalytics

Finance

Finance

Transport

Transport

Transport

Note a Unless otherwise specified; b when calculating the total amount of issues, issues in foreign currency are translated into rubles at the exchange rate of the Central Bank of Russia on the date of placement of bonds Source Compiled by the authors based on [7–10]

Foreign trading floors RZD CAPITAL PLC, e500 million JSC “Russian Railways” RZD CAPITAL PLC, F - 250 million JSC “Russian Railways” RZD CAPITAL PLC, F - 450 million JSC “Russian Railways” Outstanding issues of shares PJC Commercial bank 250 “Center-Invest” PJC Commercial bank 300 “Center-Invest” Total 329363b

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400

300

200

100

0 2018

2019

2020

Other Transportation and storage Supply of electricity, gas, and steam Mineral extraction Water supply; wastewater disposal, organization of waste collection and disposal, activities to eliminate pollution Manufacturing industries

Fig. 6.1 Current expenditures on environmental protection by type of economic activity in actual prices, billion rubles (Source Compiled by the authors based on [12])

Results According to Table 6.1, 62% of all bonds fall on the transport industry (and 94% of them are bonds of JSC “Russian Railways”), 21% of all green bonds are issued by the Moscow government, 10% relate to the sector of “finance,” 4%—to the sector of “engineering”—and 3%—to the power industry. The current structure of green bonds to date compensates, in a sense, for the low share attributable to the transportation industry in total environmental spending (Fig. 6.1), which accounted for 2.3–2.8% of total current environmental spending in 2018–2020. However, the directions of use of raised financing practically support the main activity of the main issuer (JSC “Russian Railways”): the funds from the last bond issue of 100 billion rubles in 2020 were used to refinance (in fact, reimburse) the costs of purchasing electric locomotives [11] in 2017–2020.

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Thus, we have to admit that green financing in the Russian Federation has been transformed from support for projects aimed at reducing the negative impact on the environment to support industries/enterprises originally belonging to areas of activity that are more friendly to the environment, including the development of electric transport. The growing responsibility of Russian enterprises in the context of sustainable development as a prerequisite for a green economy is evidenced by the stable growth rates of current environmental protection costs of Russian enterprises, which averaged 6.4% per year over the period 2012–2020 (Fig. 6.2). The main investors in environmental measures in Russia are the enterprises. Thus, according to the Federal State Statistics Service of the Russian Federation (Rosstat), in 2020, of 196.0 billion rubles of investment in fixed capital aimed at environmental protection and rational use of natural resources in Russia, the share of organizations accounted

Fig. 6.2 Current (operating) costs of environmental protection in the Russian Federation in actual prices, bln. rub (Source Compiled by the authors based on [13])

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for 83.4%, and the share of the federal budget and budgets of subjects of the Russian Federation and local budgets—8.9 and 7.4%. Additionally, companies independently initiate projects aimed at the secondary involvement of associated resources, for example, associated petroleum gas as an additional source of energy. Projects aimed at introducing and implementing the principles of the circular economy into the practice of economic systems are also actively financed [14, 15]. It should be noted that the leaders in the level of investment in environmental protection are enterprises related to the types of economic activity “manufacturing,” “water supply; water disposal, organization of waste collection and disposal, activities to eliminate pollution,” and “mineral extraction,” which in 2018–2020 accounted for about 86% of all current costs in Russia. The above data indicate positive trends in the environmental responsibility of business. However, the sectoral affiliation of companies making real investments in sustainable development in Russia is poorly reflected in the structure of green financial instruments (Table 6.2). In this regard, the full development of the green finance market in the Russian Federation requires effective incentives for the involvement of companies in green projects, an important component of which can be specific measures of government policy. In addition to those listed above, such measures may include support, assistance, and funding for projects that have a minimal impact on the environment, forming and improving green infrastructure.

Conclusion An objective trend of the development of society is the development of environmental protection activities, which requires substantial upfront capital investment and has a long payback period. Appropriate green funding policies will address this gap. Simultaneously, the role of the government should implement two areas: reform of financial instruments to ensure affordable ways to attract funding and the management of taxation and allocation policies. In particular, the introduction of green bonds is of great importance to ensure a positive impact on the environment, as well as for issuers and investors. For the former, they provide an opportunity to attract cheaper financing. The second can receive a yield higher than bank deposits and relatively low-risk investments [16]. Studies show that the implementation of green projects increases the market capitalization of the organization in the long term. The positive

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effect is amplified in the presence of state support, strict environmental regulation, and a high level of financial development [17]. The practical significance of these results is that they open up new opportunities for ensuring food security in Russia through the transition to agriculture 4.0 with the support of green finance.

References 1. Dragneva, R. (2022). Chapter 8: Russia’s agri-food trade within the Eurasian Economic Union. In S. K. Wegren, & F. Nilssen (Eds.), Russia’s role in the contemporary international agri-food trade system (pp. 225–251). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-77451-6_9 2. Götz, L., Heigermoser, M., & Jaghdani, T. J. (2022). Chapter 4: Russia’s food security and impact on agri-food trade. In S. K. & Wegren, F. Nilssen (Eds.), Russia’s role in the contemporary international agri-food trade system (pp. 115–137). Palgrave Macmillan. https://doi.org/10.1007/978-3-03077451-6_5 3. Government of the Russian Federation. (2019). Resolution “On approval of the rules for granting subsidies from the federal budget to Russian organizations for reimbursement of coupon profit on bonds issued under investment projects to implement the best available technologies” (April 30, 2019 No. 541, as amended July 13, 2021). Moscow, Russia. Retrieved from http://www.consultant.ru/document/cons_doc_ LAW_324112. Accessed 12 February 2022. 4. Boyko, A., & Grinkevich, D. (2021, September 16). The government is divided on incentives for green finance. Vedomosti. Retrieved from https:// www.vedomosti.ru/economics/articles/2021/09/16/887048-zelenogo-fin ansirovaniya. Accessed 12 February 2022. 5. Central Bank of the Russian Federation. (n.d.). Pension funds and collective investments. Retrieved from https://cbr.ru/RSCI/statistics/. Accessed 12 February 2022. 6. Central Bank of the Russian Federation. (2019). Regulation of the Bank of Russia “On the standards of securities issuance” (December 19, 2019 No. 706-P, as amended October 1, 2021). Moscow, Russia. Retrieved from http://www.consultant.ru/document/cons_doc_ LAW_344933. Accessed 12 February 2022. 7. Expert Analytical Platform “Infrastructure and Finance for Sustainable Development ‘Infragreen’,” National Rating Agency, & Eurasian Integration Research Center. (2020). Green finance in Russia: Annual report 2020. Retrieved from https://www.ra-national.ru/sites/default/files/analitic_art icle/INFRAGREEN_Green_Finance_Russia_29122020.pdf. Accessed 12 February 2022.

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8. FINAM Holdings. (n.d.). Bond market. Retrieved from https://bonds.fin am.ru/. Accessed 12 February 2022. 9. Moscow Exchange. (n.d.). Sustainability Sector. Retrieved from https:// www.moex.com/s3019. Accessed 12 February 2022. 10. Russian Railways JSC. (n.d.). Sustainable financing. Retrieved from https:// company.rzd.ru/ru/9972/page/103290?id=18195#main-header. Accessed 12 February 2022. 11. RAExpert. (2021, September 30). Conclusion on the compliance of perpetual bonds of JSC “Russian Railways” with the principles of green bonds. Retrieved from https://www.raexpert.ru/releases/2021/sep30g/ (Accessed 12 February 2022) 12. Komin, M. O., & Kazantsev K. I. (2021). Green investments: Environmental protection costs in Russian regions according to CEPA Classification. Rosstat. Retrieved from https://data-in.ru/data-catalog/datasets/171/. Accessed 12 February 2022. 13. Federal State Statistics Service of the Russian Federation. (2021). Key indicators of environmental protection: Statistical bulletin. Rosstat. Retrieved from https://rosstat.gov.ru/storage/mediabank/oxr_bul_2021.pdf. Accessed 12 February 2022. 14. Leybert, T. B., & Khalikova, E. A. (2020). Economic evaluation of the effectiveness of design solutions for the installation of a compressor station for the preparation and transportation of associated petroleum gas. SOCAR Proceedings, 1, 70–78. https://doi.org/10.5510/OGP20200100425 15. Vanchukhina, L. I., Leybert, T. B., Khalikova, E. A., Rudneva, Y. R., & Kantor, O. G. (2020). Methodological foundations of measuring the effectiveness of implementation of the circular economy in the economic systems’ practice. In E. G. Popkova, & A. V. Bogoviz (Eds.), Circular economy in developed and developing countries: Perspective, methods and examples (pp. 67–85). Emerald Publishing Limited. https://doi.org/10.1108/9781-78973-981-720201011 16. Wang, Y., & Zhi, Q. (2016). The role of green finance in environmental protection: Two aspects of market mechanism and policies. Energy Procedia, 104, 311–316. https://doi.org/10.1016/j.egypro.2016.12.053 17. Hu, J., Li, J., Li, X., Liu, Y., Wang, W., & Zheng, L. (2021). Will green finance contribute to a green recovery? Evidence from green financial pilot zone in China. Frontiers in Public Health, 9, 794195. https://doi.org/10. 3389/fpubh.2021.794195

PART II

Prospects for Food Security of the Future Economy in the Transition to Agriculture 4.0 Based on Deep Learning

CHAPTER 7

Food Security in the Digital Economy: Traditional Agriculture vs. Smart Agriculture Based on Artificial Intelligence Aleksei V. Bogoviz , Vladimir S. Osipov , Tatiana M. Vorozheykina , Veronika V. Yankovskaya, and Igor Yu. Sklyarov

Introduction Food security is a part of economic security and thus determined the independence, stability, and sustainability of economic systems. In the first decade of the twenty-first century, in the conditions of the post-industrial economy, there formed an idea on the preferable structure of GDP which should be dominated by the service sphere. Afterward, the 2008 financial crisis demonstrated the increased cyclicity (susceptibility to crises) of

A. V. Bogoviz (B) Moscow, Russia e-mail: [email protected] V. S. Osipov MGIMO University, Moscow, Russia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_7

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the model of the post-industrial economy. The formation of Industry 4.0 attracted the attention of the world community to the industry as the basis of economic systems’ stability. The transition to the innovative and digital economy set before economic systems a new strategic priority—specialization in high technologies and hi-tech products. The new model acknowledges the importance of agriculture and opens wide opportunities for its improvement based on the leading technologies. However, the problem is as follows: which agricultural practices are preferable for the provision of food security in the digital economy: traditional agriculture or smart agriculture based on AI? At the level of agricultural companies, digitalization is a commercial, investment project, which is assessed from the positions of duration and probability of return, possible profitability, and risk. Digitalization allows for multiple increases of efficiency, reduction of agricultural risks caused by the influence of the natural and climate factors (bad harvest, harvest damage), and reduction of social risks (e.g., the loss of valuable personnel, unexpected dismissal of employees, deadtime due to temporary incapacity for work and employees’ vacations), thus ensuring the growth of profit. Simultaneously, the level of capital gain in agriculture of a lot of countries (e.g., Russia) is lower than in other spheres on the whole. Due to this, large-scale investments in smart agricultural technologies will not be returned through additional profit. Also, agricultural companies often face a deficit of their resources and inaccessibility to external venture investments and credit resources, which does not allow them to perform digitalization. It should also be noted that the use of smart technologies

T. M. Vorozheykina Russian State Agrarian University—Moscow Timiryazev Agricultural Academy, Moscow, Russia e-mail: [email protected] V. V. Yankovskaya Plekhanov Russian University of Economics, Moscow, Russia e-mail: [email protected] I. Yu. Sklyarov Stavropol State Agrarian University, Stavropol, Russia e-mail: [email protected]

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leads to new risk—cyber-risk (e.g., risks of failures of Internet connection, theft or loss of digital data and information, or failure of equipment and technologies), which also needs management. At the level of an economic system, the main attention is paid to the non-commercial consequences of agriculture digitalization, connected to the provision of food security. On the one hand, the implementation of smart technologies allows for multiple increases of efficiency and provision of the stability of food production volume, thus dealing with its deficit. On the other hand, digitalization increases the cost of agricultural production, due to which food products might become unaffordable for wide groups of the population, which will lead to hunger with the simultaneous crisis of overproduction of food and/or losses of agricultural companies. That is why digitalization of agriculture should not be treated as a simple and widely accessible tool of the provision of food security, but it should be a last resort if there are other alternative measures of the provision of food security—an increase in the volume of agricultural production and increase in food import. The hypothesis of this research is as follows: digitalization is a perspective tool of the provision of food security in countries where traditional agriculture is inaccessible, or where the increase in the food production volume cannot be achieved. However, in countries with favorable conditions and specialization in agriculture, it is more preferable to develop traditional agriculture for the provision of food security, since digitalization is less effective due to large capital intensity and complex requirements to infrastructure (i.e., inaccessibility). The purpose of this research is to compare the contribution to the provision of food security made by traditional agriculture and smart agriculture and to develop the applied recommendations in the sphere of the provision of food security in the digital economy based on smart agriculture based on AI and deep learning. The originality of this research consists in the critical analysis of digitalization and consideration of traditional agriculture as an important alternative to smart agriculture. The novelty of this research lies in the following: for the first time, traditional agriculture and smart agriculture are compared from the positions of non-profit effectiveness through the lens of their contribution to the provision of food security rather than the positions of commercial profit. The uniqueness of this research is explained by the fact that the perspectives of development of smart agriculture are considered in a wider manner, to conform to the interests of the provision of food security

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(not in an isolated manner, as a goal in itself). Due to this, we study—in a systemic manner—smart agriculture, which does not limit production and covers food distribution. This work has the following structure. This introduction is followed by the literature review and then by the description of the materials and research methodology. It is followed by the results which include the following: . Traditional agriculture in the modern agricultural economies and the perspectives of ensuring their food security; . Smart agriculture in digital economies and the necessary scale of its development for the provision of food security; . The model of the food security provision in the digital economy with the help of the development of smart agriculture based on AI and deep learning. Conclusions sum up the research.

Literature Review The theoretical basis of this research consists of the existing scientific works on the following topics. First, on the topic of the provision of food security: [1–5]. Second, on the topic of traditional agriculture development for the provision of food security based on the experience of agricultural economies: [6–10]. Third, on the topic of the transition to smart agriculture in the digital economy and the consequences for food security: [11–18]. Thus, the issue of the food security provision has been studied in detail in the existing scientific literature. However, the gap analysis has shown that traditional agriculture and smart agriculture, based on AI, are still considered to be alternatives. Though the technological differences between them have been thoroughly studied, the specifics of their contribution to the provision of food security have not been determined. To fill this gap, we conduct the following research.

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Materials and Method The methodology of this research is based on regression analysis. It is used to conduct a detailed factor analysis of food security. The research objects are as follows: . Countries with traditional agriculture, which are the leaders in the ranking by World Bank [19] by the share of agriculture in the structure of GDP, i.e., specialize in agriculture, and are not presented in the Digital Competitiveness Ranking by IMD [20], i.e., have not formed the digital economy yet; . Countries with smart agriculture, which are presented in the Digital Competitiveness Ranking by IMD [20], i.e., have formed the digital economy, and are characterized by a small share of agriculture in the structure of GDP (with a peripheral position in the ranking by World Bank [19]), i.e., do not specialize in agriculture. In countries with traditional agriculture, we consider a general level of food security according to The Economist Intelligence Unit [21]. Then, we determine the dependence of detailed indicators of food security— affordability, availability, and quality and safety of food—on the share of agriculture in GDP, according to World Bank [19]. The working hypothesis is deemed confirmed if there is a positive regression dependence in one or both cases. Similarly, for countries with smart agriculture, we consider a general level of food security according to The Economist Intelligence Unit [21]. Then, we determine the dependence of detailed indicators of food security—affordability, availability, and quality and safety of food—on the share of agriculture in GDP, according to World Bank [19], and on the Digital Competitiveness Ranking, according to IMD [20]. The hypothesis is considered to be proved if the regression dependence of the indicators of food security on digitalization is positive or higher than the dependence on the share of agriculture, which could even be negative. The statistics of food security and the influencing factors of traditional and smart agriculture in 2020 are shown in Table 7.1.

7.3 7.1

Malaysia China

Countries with smart agriculture

57.4 42.6 38.2 37.3 34.1 33.9 28.7 28.4 26.9 25.5 8.0

Sierra Leone Chad Niger Mali Kenya Ethiopia Tanzania Sudan Benin Malawi Thailand

Countries with traditional agriculture

Agriculture, forestry, and fishing, value-added, % of GDP

Country

Factors

82.390 84.292

– – – – – – – – – – 68.434

Digital Competitiveness Index, points 0–100

73.8 71.0

39.0 36.9 46.9 54.4 50.7 49.2 47.6 45.7 51.0 42.5 65.1

Food security index, points 0–100

Results

81.7 74.8

40.8 40.3 50.2 45.9 56.7 49.7 45.1 47.1 48.6 39.4 77.1

Affordability, points 0–100

67.7 66.9

40.3 34.9 53.6 60.1 48.0 52.6 50.4 44.4 54.9 48.6 58.7

Availability, point s 0–100

Food security and the influencing factors of traditional and smart agriculture in 2020

Technological mode

Table 7.1

70.6 72.6

30.6 33.5 37.4 59.9 43.2 39.0 45.9 46.0 46.4 33.1 52.6

Quality and safety, points 0–100

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6.9 6.7 6.4 5.8 4.4 4.4 3.4

54.029 56.126 59.793 86.026 72.623 57.346 70.406

63.3 69.4 69.8 78.8 67.3 70.1 69.7

Source Compiled based on IMD [20], The Economist Intelligence Unit [21], World Bank [19]

Peru Colombia Turkey New Zealand Kazakhstan Brazil Russia

69.1 73.7 74.7 84.6 77.5 77.0 79.8

59.0 65.6 64.8 75.5 57.7 58.8 60.1

60.4 69.3 71.1 73.5 68.3 84.0 70.9

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Results Traditional Agriculture in the Modern Agricultural Economies and the Perspectives of Provision of Their Food Security Traditional agriculture in the modern agricultural economies and the perspectives of provision of their food security, according to the data from Table 7.1, are described in Figs. 7.1, 7.2 and 7.3. As shown in Fig. 7.1, the share of agriculture in GDP is rather large in agricultural economies (countries with traditional agriculture)—35.3% on average in 2020. The values of the food security index are relatively low—46.39% on average. Food import is performed actively; its share in the structure of merchandise imports is 21.3% on average. As shown in Fig. 7.2, in agricultural economies with traditional agriculture, an increase in the share of agriculture in the structure of GDP by 1% leads to a decrease in food availability by 0.3516 points (moderate correlation 20.55%) and a decrease in food quality and safety by 0.358 70

Countries with traditional agriculture

60 57.4 50 40

39 33

30

54.4 42.6 36.9 24

46.9 38.2

37.3

50.7

34.1

49.2

28.7 17

51 48

45.7

42.5

33.9

25

20

47.6

14

28.4

26.9

25.5

18

15

12 7

10 0 Sierra Leone

Chad

Niger

Mali

Kenya Ethiopia Tanzania Sudan

Benin Malawi

Share of agriculture in GDP, % Food security index, points 0-100 Share of import in the structure of commodity import, %

Fig. 7.1 Food security, the share of agriculture in GDP, and food imports in agricultural economies in 2020 (Source Calculated and compiled by the authors)

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Quality and safety of food, points 0-100

Availability, points 0-100

70 60 50 40 30 20

y = -0.3516x + 61.191 R² = 0.2055

10 0 0

20 40 60 80 Share of agriculture in GDP, %

67

70 60 50 40 30 20

y = -0.358x + 54.137 R² = 0.1533

10 0 0

50 100 Share of agriculture in GDP, %

Fig. 7.2 Regression curves of the dependence of the food security manifestations on the share of agriculture in GDP in agricultural economies in 2020 (Source Calculated and compiled by the authors)

Countries with “smart” agriculture 100 90 80 70 60 50 40 30 20 10 0

82.4 82.4

84.3 84.3

68.4 68.4

8

7.3

86.0

56.1 59.859.8 54.0 54.0 56.1

7.1

6.9

6.7

Share of agriculture in GDP, %

6.4

5.8

86.0 72.6

72.6 57.3 57.3

4.4

4.4

70.470.4

3.4

Food security index, points 0-100

Digital competitiveness index, points 0-100

Fig. 7.3 Food security, the share of agriculture in GDP, and digital competitiveness in digital economies in 2020 (Source Calculated and compiled by the authors)

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points (moderate correlation 15.33%). There is no statistically significant correlation with food affordability. The obtained results show that an increase in the volume of agricultural production with the traditional technological mode does not stimulate but even reduces food security. This could be explained by the fact that the growth of the volume of agricultural production leads to a complication of the control of quality and safety of agricultural products. The increase and use of production capacities in agriculture with the traditional technological mode do not lead to the growth of the production volume due to the risk of crop failure. Thus, the deficit of food is preserved, as well as the larger power of sellers in the market and high prices for agricultural products. Thus, in agricultural economies with traditional agriculture, the provision of food security is impossible in principle. To determine the perspectives of solving this problem with the help of a transition to smart agriculture, let us use the experience of digital economies. Smart Agriculture in Digital Economies and the Necessary Scale of Its Development for the Provision of Food Security Smart agriculture in digital economies and the necessary scale of its development for the provision of food security according to the data from Table 7.1 are described in Fig. 7.3 and 7.4. As shown in Fig. 7.3, in digital economies (countries with smart agriculture), the share of agriculture in GDP is relatively small—6.04% in 2020 on average. The values of the food security index are rather high—69.83% on average. The average digital competitiveness of these countries’ economies is 69.15%. As shown in Fig. 7.4, in digital economies that do not specialize in agriculture, an increase in the share of agriculture in the structure of GDP by 1% leads to a decrease in quality and safety of food by 3.0745 points (moderate correlation 31.34%). There is no statistically significant correlation with food affordability and food availability in the share of agriculture in GDP. The obtained results show that an increase in the volume of agricultural production in the digital technological economy partially raises food security but does not ensure its full-scale (systemic) growth. This is because

y = -3.0745x + 87.9 R² = 0.3134

0 5 10 Share of agriculture in GDP, %

90 80 70 60 50 40 30 20 10 0

y = 0.2606x + 58.978 R² = 0.5267

0 50 100 Digital competitiveness index, points 0-100

Availability, points 0-100

90 80 70 60 50 40 30 20 10 0

FOOD SECURITY IN THE DIGITAL ECONOMY: …

Affordability, points 0-100

Food quality and safety, points 0-100

7

80 70 60 50 40 30 20 10 0

69

y = 0.2805x + 44.088 R² = 0.3605

0 50 100 Digital competitiveness index, points 0-100

Fig. 7.4 Regression curves of dependence of the manifestations of food security on the share of agriculture in GDP and of the food security manifestations on digital competitiveness in digital economies in 2020 (Source Calculated and compiled by the authors)

in the course of growth of the agricultural production volume its quantitative affordability grows and its deficit is overcome, but the price and food quality and safety remain unchanged. Therefore, food security in digital economies cannot be fully ensured through an increase in the share of agriculture in the structure of GDP. That is why we shall consider an alternative variant—the development of smart agriculture through increased digitalization of the economy. In digital economies with smart agriculture, an increase in digital competitiveness by one point leads to an increase in food affordability by 0.2606 points (high correlation 52.67%) and an increase in food availability by 0.2805 points (moderate correlation 36.05%). No statistically significant correlation was found with food quality and safety to the digital competitiveness index. The obtained results show that an increase in digital competitiveness allows for a systemic increase and provision of food security in all its manifestations. The optimization with the help of the simplex method (Microsoft Excel) allowed determining that the values of all parameters of food security of the leader of the global ranking by The Economist Intelligence Unit [21] for 2020 (Singapore) are simultaneously achieved (or exceeded) in digital economies in case of an increase in the level of their digital competitiveness by 210.93% (i.e., by 3.11 times), up to 215 points.

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In this case, food affordability grows by 49.36%, up to 115.01 points (Singapore in 2020—95.40 points), food availability grows by 64.45% up to 104.40 points (Singapore in 2020—83 points), while quality and safety of food grow by 14.68%, up to 79.51 points (Singapore in 2020— 79.40 points). Thus, due to the transition to the fourth technological mode, digital economies can fully provide their food security. However, a moderate or partial digitalization of agriculture is not enough for this—it should be based on the leading smart technologies. The Model of Provision of Food Security in the Digital Economy with the Help of the Development of Smart Agriculture That Is Based on AI and Deep Learning For the provision of food security in the digital economy with the help of the development of smart agriculture that is based on AI and deep learning, we have developed and offer for practical application the following model (Fig. 7.5). As shown in Fig. 7.5, the management in the offered model is conducted at the level of the sphere of agriculture (agro-industrial complex) rather than at the level of a company. Due to this, AI is reoriented from private commercial interests to public non-commercial interests that are related to the provision of food security. The management is cyclical. One cycle covers the following: . Growing crops with the help of robots, manipulators, drones, and other smart technologies with the automatic sorting and marking; . Gathering and storing with the automatic determination of the optimal conditions of storing of agricultural products; . Processing and transformation of the agricultural resources into finished agricultural products with the automatic selection of the optimal packaging; . Packaging for sales with the automatic pricing; . Sales via e-commerce. That is, AI conducts the monitoring of food security during the whole chain of the creation of food products’ added value. The Internet of Things is used for the automatic management of production. Ubiquitous

7

Consideration of the previous cycles of production requirements to quality and security, and the forecast for the next cycle Feedback collection

71

Decision making for the following cycle of production: Volume of food produciton; -Establishing food prices Deep learning

Subject of management: artificial intelligence monitoring of food security Internet of Things

robots, manipulator automatic sorting and marking s, drones

Growing agricultures

FOOD SECURITY IN THE DIGITAL ECONOMY: …

Analytics and determination of the optimal storing conditions

Harvesting and storing

Ubiquitous computing information on affordability, availability, and quality and safety of food

Selection of optimal packaging

Transformation

blockchain

pricing

Packaging for sales

Online trade

sales

Consumers of food products

Fig. 7.5 Model of provision of food security in the digital economy with the help of the development of smart agriculture based on AI and deep learning (Source Compiled by the authors)

computing is used for collecting information from the consumers of food products on affordability, availability, quality, and safety. The control of the added value chain is performed by AI with the help of blockchain (distributed register). Using deep learning, AI takes into account and analyzes the experience of the current cycle of production and the requirements to quality and safety and compiles a forecast for the next cycle. AI makes a decision for the next cycle of production regarding the volume of food production and food prices.

Conclusion Thus, the hypothesis has been disproved. Based on the precise quantitative data and using a reliable methodology of regression analysis, it has been proved that in the traditional technological mode agriculture cannot—in principle—ensure food security. It should be noted that even

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an increase in food production volume does not ensure the increase in availability of food and overcoming of food deficit. Food import further reduces food security. Digitalization is an effective tool of food security provision. In the digital technological mode, the food deficit could be overcome through the increase in agricultural production volume. However, to achieve a systemic effect and the full-scale provision of food security, it is necessary to raise the economy’s digital competitiveness. The performed calculations have demonstrated that the achievement of the maximum (like with Singapore in 2020) level of food security requires the increase in digital competitiveness of the considered digital economies by 3.11 times, up to 215 points. This means digitalization of agriculture based on breakthrough technologies: robots, blockchain, ecommerce, ubiquitous computing, and the Internet of Things. To achieve a systemic effect, we have developed a model of food security provision in the digital economy with the help of the development of smart agriculture that is based on AI and deep learning. The model’s advantage is the use of deep learning. Due to this, the management of food security is conducted not in a linear but a cyclical fashion, with the accumulation and reconsideration of experience and the constant improvement of the technologies of production, sorting, marking, storing, packaging, and optimization of pricing and sales channels. The developed model would be interesting not only for digital but also for agricultural economies since it would allow ensuring their food security under the condition of their rapid digitalization. The contribution of the performed research is as follows: smart technologies allow ensuring food security even in countries with conditions that are unfavorable for agriculture, while the refusal from digitalization does not allow ensuring food security in countries that are based on traditional agriculture. This conclusion shows the absence of alternatives for digitalization and development of smart agriculture to provide global food security.

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References 1. Asitik, A. J., & Abu, B. M. (2020). Women empowerment in agriculture and food security in Savannah Accelerated Development Authority zone of Ghana. African Journal of Economic and Management Studies, 11(2), 253– 270. https://doi.org/10.1108/AJEMS-03-2019-0102 2. Fan, S., Si, W., & Zhang, Y. (2020). How to prevent a global food and nutrition security crisis under COVID-19? China Agricultural Economic Review, 12(3), 471–480. https://doi.org/10.1108/CAER-04-2020-0065 3. Gilmour, B. W. (2019). Achieving food security in China: The challenges ahead. China Agricultural Economic Review, 11(2), 443–446. https://doi. org/10.1108/CAER-05-2019-257 4. Mahrous, W. (2019). Climate change and food security in EAC region: A panel data analysis. Review of Economics and Political Science, 4(4), 270–284. https://doi.org/10.1108/REPS-12-2018-0039 5. Ujunwa, A., Okoyeuzu, C., & Kalu, E. U. (2019). Armed conflict and food security in West Africa: Socioeconomic perspective. International Journal of Social Economics, 46(2), 182–198. https://doi.org/10.1108/IJSE-112017-0538 6. Bilal, A. R., & Baig, M. M. A. (2019). Transformation of agriculture risk management: The new horizon of regulatory compliance in farm credits. Agricultural Finance Review, 79(1), 136–155. https://doi.org/10.1108/ AFR-05-2018-0038 7. Mamatzakis, E., & Staikouras, C. (2020). Common agriculture policy in the EU, direct payments, solvency and income. Agricultural Finance Review, 80(4), 529–547. https://doi.org/10.1108/AFR-04-2019-0047 8. Matzembacher, D. E., & Meira, F. B. (2019). Sustainability as business strategy in community-supported agriculture: Social, environmental and economic benefits for producers and consumers. British Food Journal, 121(2), 616–632. https://doi.org/10.1108/BFJ-03-2018-0207 9. Singh, P., & Agrawal, G. (2019). Efficacy of weather index insurance for mitigation of weather risks in agriculture: An integrative review. International Journal of Ethics and Systems, 35(4), 584–616. https://doi.org/10. 1108/IJOES-09-2018-0132 10. Yan, J., Chen, C., & Hu, B. (2019). Farm size and production efficiency in Chinese agriculture: Output and profit. China Agricultural Economic Review, 11(1), 20–38. https://doi.org/10.1108/CAER-05-2018-0082 11. Inshakova, A. O., & Bogoviz, A. V. (Ed.). (2020). Alternative methods of judging economic conflicts in the national positive and soft law. A volume in the series. In E. G. Popkova (Ed.), Advances in Research on Russian Business and Management. Information Age Publishing.

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12. Lobova, S. V., & Bogoviz, A. V. (2019). Embracing artificial intelligence and digital personnel to create high-performance jobs in the cyber economy. Contributions to Economics, 2(1), 169–174. 13. Sagarna Garcia, J. M., & Pereira Jerez, D. (2019). Agro-food projects: analysis of procedures within digital revolution. International Journal of Managing Projects in Business, 13(3), 648–664. https://doi.org/10.1108/ IJMPB-02-2019-0039. 14. Sergi, B. S., Popkova, E. G., Bogoviz, A. V., & Litvinova, T. N. (2019a). Understanding Industry 4.0: AI, the internet of things, and the future of work. Emerald Publishing Limited. 15. Sergi, B. S., Popkova, E. G., Bogoviz, A. V., & Ragulina, J. V. (2019b). Costs and profits of technological growth in Russia. In B. S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia (pp. 41– 54). Emerald Publishing Limited. 16. Sergi, B. S., Popkova, E. G., Bogoviz, A. V., & Ragulina, J. V. (2019c). Entrepreneurship and economic growth: The experience of developed and developing countries. In B. S. Sergi, & C. C. Scanlon (Eds.), Entrepreneurship and development in the 21st century (pp. 3–32). Bingley, UK: Emerald Publishing Limited. 17. Shi, L., Shi, G., & Qiu, H. (2019). General review of intelligent agriculture development in China. China Agricultural Economic Review, 11(1), 39–51. https://doi.org/10.1108/CAER-05-2017-0093 18. Trivelli, L., Apicella, A., Chiarello, F., Rana, R., Fantoni, G., & Tarabella, A. (2019). From precision agriculture to Industry 4.0: Unveiling technological connections in the agrifood sector. British Food Journal, 121(8), 1730–1743. https://doi.org/10.1108/BFJ-11-2018-0747 19. World Bank. (2020). Agriculture, forestry, and fishing, value added (% of GDP). Retrieved from https://data.worldbank.org/indicator/NV.AGR. TOTL.ZS?most_recent_value_desc=true. Accessed 26 September 2020. 20. IMD. (2020). World digital competitiveness ranking 2019. Retrieved from https://www.imd.org/wcc/world-competitiveness-center-rankings/worlddigital-competitiveness-rankings-2019/. Accessed 23 September 2020. 21. The Economist Intelligence Unit. (2020). Global Food Security Index 2020. Retrieved from https://foodsecurityindex.eiu.com/Index. Accessed 26 September 2020.

CHAPTER 8

Transition from Digital Agriculture to Agriculture 4.0 as the Most Promising Scenario for Ensuring Future Food Security Mikhail S. Kyzyurov , Ayapbergen A. Taubayev , Larissa P. Steblyakova , and Larisa V. Shabaltina

Introduction The Fourth Industrial Revolution opened up new opportunities for the technological modernization of agriculture. However, for practical implementation, modernization must be justified—have a solid scientific rationale for the tangible benefits for food security of the economy of the future. The transition to agriculture 4.0 is a process of innovative development of the agricultural economy. Therefore, it inevitably entails high

M. S. Kyzyurov Vyatka State University, Kirov, Russia A. A. Taubayev Esil University, Astana, Kazakhstan L. P. Steblyakova (B) State University of Management, Moscow, Russia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_8

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costs of financial resources, the need for a profound transformation of organizational and managerial approaches of agricultural enterprises, and social risks (e.g., the risk of reducing employees during automation). In the COVID-19 pandemic and general downward wave of development of the world economic system, the agricultural economy faces high barriers to technological modernization associated with a reduced flow of investment, increased threats of reorganization (threat of losing competitive advantages, threat of losses), and public expectations and state requirements regarding the preservation of jobs. The alternative to agriculture 4.0 is digital agriculture, whose costs, risks, and threats are significantly lower. The problem is that the existing literature reveals only individual characteristics of the considered alternatives of agricultural development, which does not allow us to compare them with each other. A systemic interpretation and a clear comparison of available alternatives to agricultural development from the perspective of efficiency acquires relevance in terms of determining the prospects for food security of the economy of the future. The paper aims to identify the most promising scenario for ensuring the food security of the economy of the future.

Literature Review This research is based on scientific provisions of the concept of food security. The existing works by Byankin et al. [1], Cavazza et al. [2], Franchi et al. [3], Hackfort [4], Ivanov et al. [5], and Srivetbodee and Igel [6] consider digital agriculture as agriculture that relies on basic digital technologies, the main one being fixed broadband Internet. Erdo˘gan [7], Latino et al. [8], Majumdar et al. [9], Popkova et al. [10], and Spanaki et al. [11] define agriculture 4.0 as agriculture based on the high technologies of Industry 4.0: cloud technology, the Internet of Things (IoT), big data, and artificial intelligence (AI). In this regard, the transition from digital agriculture to agriculture 4.0 involves the introduction of more advanced technologies and a significant increase in the level of automation of production and distribution

L. V. Shabaltina Plekhanov Russian University of Economics, Moscow, Russia

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processes in the agricultural economy. Nevertheless, the scientific rationale for this transition in terms of efficiency is not formed in the existing literature. Without the scientific rationale, it is difficult to ensure the food security of the future economy. This research fills this gap by comparing digital agriculture and agriculture 4.0 in terms of efficiency.

Materials and Methods This paper uses scenario analysis to compare digital agriculture and agriculture 4.0. Using the chosen method, the authors evaluate the consequences of different scenarios of the development of the agricultural economy for the food security of the future economy (in the context of its manifestations). The research sample includes ten countries with a high level of the development of Industry 4.0 technology, the statistics for which are presented in the HSE University materials [12] for 2021. Table 8.1 presents the initial data for the research.

Results Models of Contribution of Industry 4.0 and Digital Technologies to Food Security To determine the contribution of Industry 4.0 and digital technologies to food security, a regression analysis of the data from Table 8.1 was conducted, which resulted in the following system of equations: ⎧ y = 79.80 + 0.14 ∗ x3 + 0.11 ∗ x4 + 0.08 ∗ x5 ; ⎪ ⎪ ⎨ 1 y2 = 56.18 + 0.03 ∗ x3 + 0.30 ∗ x4 + 0.10 ∗ x5 ; ⎪ y = 83.39 + 0.04 ∗ x1 + 0.33 ∗ x4 + 0.01 ∗ x5; ⎪ ⎩ 3 y4 = 54.00 + 0.18 ∗ x3 + 0.24 ∗ x4 + 0.06 ∗ x5 .

(8.1)

All regression models in the system of Eqs. (8.1) were reliable, as evidenced by their specification (Table 8.2). As shown in Table 8.2, we obtained rather high values of correlation coefficients of resulting variables with factor variables in all models: 78.91% for y1 , 38.71% for y2 , 53.77% for y3 , and 49.65% for y4 . Tstatistics and p-values also confirm the reliability of the obtained models. This allows us to use the system of Eqs. (8.1) in a scenario analysis of technological development of the agricultural economy.

9 n/d n/d 23 n/d 23 40 10 44

20

26 53 33 67 51 59 75 27 29

70

13

9 25 17 24 22 7 19 10 9

x3

9

5 4 7 11 22 8 12 6 6 94

73 95 95 100 n/d 95 99 96 98

x2

x1

x4

Fixed broadband Internet access for organizations x5

Artificial intelligence

Internet of Things

Cloud services

Big data analysis

Digital technology, %

Technology of industry 4.0, %

91

86.9 91.1 90.1 93.1 92.9 89.3 89 90.3 88.3

y1

Affordability

62.7

64.9 72.7 69.3 61.4 75.1 71.5 76.9 67 69.1

y2

Availability

92.3

85.8 89.6 87.8 93.5 94 86.2 86.4 92.1 81.4

y3

Quality and safety

Manifestations of food security, points 1–100

Source Compiled by the authors based on the materials of HSE University [12] and the Economist Group [13]

Russia UK Germany Denmark Ireland Italy Finland France Czech Republic Sweden

Country

67.3

59.9 69 66 56.9 74.1 51.8 65.1 67.5 70.9

y4

Natural resources and resilence

Table 8.1 Share of organizations using digital economy and Industry 4.0 technologies and manifestations of food security in 2021

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Table 8.2 Specification of regression models Group of indicators

Regression statistics

t-statistic

P-value

Indicators

Correlation (R2) Standard error Observations Constant x1 x3 x4 x5 Constant x1 x3 x4 x5

Affordability

Availability

Quality and safety

y1

y2

y3

Natural resources and resilence y4

0.7891

0.3871

0.5377

0.4965

1.4606

5.8058

4.2167

7.1915

10.0000 3.4069 – 0.0801 0.1637 0.0262 25.0052 – 2.0211 1.0779 0.4131

10.0000 68.5301 – 0.3183 0.6509 0.1040 5.0603 – 0.2500 0.2859 −0.2708

10.0000 7.9484 0.6220 – 0.1303 −0.3998 0.0002 0.5568 – 0.9006 0.7032

10.0000 4.4990 – 0.6870 −0.4945 −1.0322 0.0041 – 0.5178 0.6386 0.3418

Source Compiled by the authors

Scenarios of Technological Development of the Agricultural Economy According to the system of Eqs. (8.1), the key technologies supporting food security are two technologies of Industry 4.0—Big Data analysis and artificial intelligence, as well as a key digital technology—fixed broadband access to the Internet for organizations. To comprehensively reflect the prospects of food security of the economy of the future, we consider three possible scenarios of technological development of the agricultural economy (Fig. 8.1): 1. Scenario of the development of digital agriculture, assuming 100% fixed broadband access to the Internet for organizations (with other factor variables unchanged); 2. The moderate development scenario for Industry 4.0, which assumes a 20% increase in the share of organizations using each technology of Industry 4.0 (compared to the level of 2021);

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100

Fixed broadband Internet access for organizations

98 49

94

94

33.8

100

16.9

100

15.5

Internet of Things

100

9 18

Artificial intelligence

Scenario of the development of digital agriculture

Cloud services

Scenario of moderate development of agriculture 4.0

31

100

Big data analysis

Scenario of fullscale development of agriculture 4.0

Fig. 8.1 Share of organizations using Industry 4.0 and digital technologies in each scenario, % ( Source Calculated and compiled by the authors)

3. Scenario of full-scale development of agriculture 4.0, which assumes an increase in the share of organizations using each technology of Industry 4.0 to 100%. According to Fig. 8.1, the scenario of full-scale development of agriculture 4.0 involves the greatest changes. Therefore, it is associated with the maximum costs and risks. The increase in manifestations of food security under alternative scenarios of technological development of the agricultural economy is shown in Fig. 8.2. Figure 8.2 shows that the scenario of the development of digital agriculture makes almost no contribution to the food security of the future economy. Therefore, despite the lowest costs and risks, it is not feasible to implement this scenario. The scenario of moderate development of agriculture 4.0 demonstrates notable benefits for food security and can be realized in the medium term. The scenario of full-scale development of agriculture 4.0 provides a considerable increase in food security but its implementation is only possible in the long term.

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Fig. 8.2 The benefits of alternative scenarios of technological development of the agricultural economy for food security, % ( Source Calculated and compiled by the authors)

Conclusion Thus, the transition from digital agriculture to agriculture 4.0 is the most promising scenario for food security in the economy of the future. Although associated with the greatest risks of change and costs (the growth of activity using cloud services by two times, the Internet of Things by almost six times, big data analysis by almost six times, and artificial intelligence by 11 times), only the transition from digital agriculture to agriculture 4.0 can guarantee food security in the long term. The benefits of transitioning to agriculture 4.0 are associated with a 23.92% increase in food availability, a 43.32% decrease in prices, a 35.61% increase in food quality and safety, and a 57.17% increase in environmental friendliness and sustainability of agriculture. The contribution of the research to the literature lies in the scientific rationale for the transition from digital agriculture to agriculture 4.0 in terms of efficiency. The practical significance of the author’s conclusions is that they constitute a useful policy tool for improving the food security of the economy of the future.

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References 1. Byankin, A. S., Babkin, A. V., Baykov, E. A., Burdakova, G. I., & Usanov, G. I. (2021). Strategies for the development of complex organizational and economic systems in the conditions of digitalization. In O. G. Shakirova, O. V. Bashkov, & A. A. Khusainov (Eds.), Current problems and ways of industry development: Equipment and technologies (pp. 381–388). Springer. https://doi.org/10.1007/978-3-030-69421-0_41 2. Cavazza, F., Galioto, F., Raggi, M., & Viaggi, D. (2020). Digital irrigated agriculture: Towards a framework for comprehensive analysis of decision processes under uncertainty. Future Internet, 12(11), 181. https://doi.org/ 10.3390/fi12110181 3. Franchi, N., Fettweis, G. P., & Herlitzius, T. (2021). The significance of the Tactile Internet and 5G for digital agriculture. At-Automatisierungstechnik, 69(4), 281–286. https://doi.org/10.1515/auto-2020-0130 4. Hackfort, S. (2021). Patterns of inequalities in digital agriculture: A systematic literature review. Sustainability, 13(22), 12345. https://doi.org/10. 3390/su132212345 5. Ivanov, A. A., Ivanov, A. A., & Ivashchenko, Y. S. (2022). Digital technologies of the self: Instrumental rationality or creative integrity? In D. Bylieva & A. Nordmann (Eds.), Technology, innovation and creativity in digital society (pp. 139–147). Springer. https://doi.org/10.1007/978-3030-89708-6_13 6. Srivetbodee, S., & Igel, B. (2021). Digital technology adoption in agriculture: Success factors, obstacles and impact on corporate social responsibility performance in Thailand’s smart farming projects. Thammasat Review, 24(2), 149–170. https://doi.org/10.14456/tureview.2021.22 7. Erdo˘gan, M. (2022). Assessing farmers’ perception to Agriculture 4.0 technologies: A new interval-valued spherical fuzzy sets based approach. International Journal of Intelligent Systems, 37 (2), 1751–1801. https:// doi.org/10.1002/int.22756 8. Latino, M. E., Menegoli, M., Lazoi, M., & Corallo, A. (2022). Voluntary traceability in food supply chain: A framework leading its implementation in Agriculture 4.0. Technological Forecasting and Social Change, 178, 121564. https://doi.org/10.1016/j.techfore.2022.121564 9. Majumdar, P., Mitra, S., & Bhattacharya, D. (2021). IoT for promoting Agriculture 4.0: A Review from the perspective of weather monitoring, yield prediction, security of WSN protocols, and hardware cost analysis. Journal of Biosystems Engineering, 46, 440–461. https://doi.org/10.1007/s42853021-00118-6 10. Popkova, E. G., Sozinova, A. A., & Sofiina, E. V. (2022). Model of Agriculture 4.0 based on deep learning: Empirical experience, current problems

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and applied solutions. In E. G. Popkova, & B. S. Sergi (Eds.), Smart innovation in agriculture (pp. 333–346). Springer. https://doi.org/10.1007/ 978-981-16-7633-8_37 11. Spanaki, K., Karafili, E., & Despoudi, S. (2021). AI applications of data sharing in Agriculture 4.0: A framework for role-based data access control. International Journal of Information Management, 59, 102350. https:// doi.org/10.1016/j.ijinfomgt.2021.102350 12. Gokhberg, L. M., Kuzminov, Ya. I., Parshin, M. V., Shapoval, I. N., & Yatselenko, N. S. (2022). Digital economy: 2022: A brief statistical collection. HSE University. https://publications.hse.ru/books/553808522 (Accessed 27 April 2022) 13. The Economist Group. (2021). Global Food Security Index 2021. Retrieved from https://impact.economist.com/sustainability/project/foodsecurity-index/Index (Accessed 27 April 2022)

CHAPTER 9

A New Level of Food Security as a Result of the Transition of Food-Importing Countries to Agriculture 4.0 Based on Deep Learning Anastasia A. Sozinova , Aigul S. Daribekova , Irina P. Lapteva , and Maria V. Makarova

Introduction Dependence on food imports is a considerable problem, which is relevant for many countries, including countries that are major agricultural economies and occupy leading positions in world food markets. The main obstacle to solving this problem is an unfavorable and changing climate.

A. A. Sozinova (B) · I. P. Lapteva · M. V. Makarova Vyatka State University, Kirov, Russia e-mail: [email protected] I. P. Lapteva e-mail: [email protected] M. V. Makarova e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_9

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Significant progress in solving the problem in question has been made in recent decades by reducing the dependence of agriculture on natural and climatic factors. For example, precision farming allows for economic and uniform distribution of scarce fertilizers and water among the grown plants, while the closed (greenhouse) soil protects the plants from bad weather. Lowtech agricultural innovations improve plant establishment, reduce plant mortality, and increase crop yields. However, it only satisfies basic food needs—to overcome hunger [1, 2]. In countries with a supply of essential food products (e.g., bread, meat, sunflower oil, milk), it is not enough to increase agricultural productivity because these countries will remain dependent on exports of agricultural products considered unsuitable for growing in their territory (e.g., exotic fruits). For example, the world markets for cocoa beans, coconuts, and bananas are still characterized by low concentration and a monopolistic (from oligopolistic competition to natural monopoly) structure. In this regard, the search for promising solutions that will bridge the gap between import substitution in the food sector and food security (to ensure their systemic achievement) is relevant. This paper hypothesizes that such a promising solution is high-tech innovations of agriculture 4.0, in particular deep learning. The paper aims to investigate the contribution to food security and food import substitution achieved by food-importing countries from the transition to agriculture 4.0 based on deep learning.

Literature Review The concept of agriculture 4.0 is quite highly developed and widely represented in the existing literature. Chernyshov et al. [3], Karbekova et al. [4], and Tchernyshev et al. [5] note the advantages of the transition to agriculture 4.0 based on high-tech innovations for food-importing countries with its moderate deficit. However, these authors discuss it only in the aspect of productivity growth in the production of plants traditionally grown by these countries. This allows them to increase their exports but does not reduce their dependence on scarce and deemed unsuitable for growing agricultural products on their territory.

A. S. Daribekova Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan

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Mishra et al. [6], Oruma et al. [7], and Scuderi et al. [8] also point to the benefits of agriculture 4.0 for food-importing countries with acute shortages, but these benefits are temporary. Automation allows bringing the cultivation technology into line with current natural and climatic conditions. Nevertheless, as soon as these conditions change, the technology becomes inefficient, and the problem of food shortages is exacerbated. Thus, despite the high degree of elaboration of the concept of agriculture 4.0, the prospects for systemic import substitution in agriculture and strategic food security in food-importing countries remain unclear. The uncertainty of the considered perspectives is a research gap filled in this paper by identifying the potential of deep learning in agriculture 4.0.

Materials and Method Deep learning is a high-tech industry 4.0 innovation based on big data and artificial intelligence. The deep learning mechanism involves the accumulation of experience of previous experiments and information about the factors affecting them (big data) with constant intellectual analysis and rethinking of this experience by artificial intelligence [9–11]. Therefore, the “Use of big data and (AI) analytics” indicator calculated by IMD [12] most accurately characterizes the use of deep learning. To account for the progress made in agriculture 4.0, the authors consider data for 2017 and 2021 and estimate a five-year trend (increase). To test the hypothesis and determine the contribution of deep learning to food security, the authors apply regression analysis to determine the relationship between the increase in the global food security index (as estimated by The Economist [13]) and the use of big data and analytics. The research is conducted on the example of countries with the highest share of food imports in the structure of merchandise imports in 2020– 2021 according to the World Bank [14], for which there are no data gaps for the other indicators studied in this paper (Table 9.1). The regression dependence of food imports on the use of big data and analytics is additionally considered. According to Table 9.1, the share of food imports in the sample countries2020–2021 averaged 13.1% in . In 2021, food security was estimated to average 69.9 points and increased by 1.07% over 2017 (69.38 points). The use of deep learning was estimated to be 38.4 in 2021, with an increase of 1.82% over 2017 (38.6 place).

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Table 9.1 Food imports, food security, and deep learning statistics in the sample countries 2017–2021 Country

Saudi Arabia Portugal Peru Philippines Greece Denmark Indonesia Bulgaria Russia New Zealand

Food imports (FI), % of merchandise imports

Global food security index, score 0–100

2020–2021

2017

2021

Growth (FS), %

15 14 14 13 13 13 13 13 12 11

69.3 76.2 65.4 55.7 75.9 78.4 57.1 67.7 69.8 78.3

68.1 75.2 64.6 60.0 73.3 76.5 59.2 70.5 74.8 76.8

−1.73 −1.31 −1.22 7.72 −3.43 −2.42 3.68 4.14 7.16 −1.92

Use of big data and analytics, place 1–64

2017

2021

Growth (dl)*, %

18 40 55 35 53 15 27 63 46 34

28 58 48 37 45 13 32 59 31 33

−35.71 −31.03 14.58 −5.41 17.78 15.38 −15.63 6.78 48.39 3.03

Note * Because the lower the value of “Use of big data and analytics” (measured in places in the rankings), the better, the increase is estimated in 2017 compared to 2021 Source Compiled and calculated by the authors based on the materials of IMD [12], The Economist [13], and the World Bank [14]

Results The following two regression models were obtained to test the hypothesis based on the data from Table 9.1. . Model 1: FS = 0.99 + 0.04*dl. It means that a 1% increase in the use of deep learning increases leads to a 0.04% increase in food security. Food security at 25.11% (correlation) is due to the use of deep learning. Nevertheless, the statistical significance of the estimated coefficients was not high enough to recognize the model as highly reliable. Thus, the correlation coefficient was 25.08%, and the significance of F was 0.4846. In this regard, the established dependencies of indicators show only the general nature of their relationship. Nevertheless, they do not allow achieving high accuracy of forecasting based on Model 1;

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. Model 2: FI = 13.15–0.03*dl. It means that for a 1% increase in deep learning use, food imports decrease by 0.03%. Food imports (correlation) at 58.13% are due to the use of deep learning. The statistical significance of the estimated coefficients was high enough to recognize the model highly reliable. Thus, the correlation coefficient was 58.13%, and the significance of F was 0.0780 (the model is reliable at a significance level of 0.1). In this regard, the established dependencies allow achieving a high accuracy of forecasting based on Model 2. According to the obtained models, the method of least squares found that total import substitution (FI = 0) can be achieved with an increase in the use of deep learning up to 514.47% (six times). This will also provide a 22.59% increase in food security (from 69.9 points in 2021 to 85.69 points). This will provide a new level of food security for food-importing countries. Nevertheless, it must be recognized that, unlike the effect for import substitution, the effect for food security is not guaranteed. Therefore, the development of deep learning in agriculture cannot be considered a sufficient measure to ensure food security in a strategic perspective. Additional measures related to the use of other digital technologies, including agricultural robots, blockchain, and other technologies, are required. Import substitution strategies can be based primarily on the development of deep learning in agriculture as a self-sufficient measure. For a correct interpretation of the obtained quantitative results, let us supplement them with the author’s qualitative interpretation. Deep learning provides two benefits for agriculture at once. The first advantage is a continuous adaptation to natural and climatic conditions. This overcomes the lag of the agricultural economy from climate change and achieves their uniform transformation. This fundamentally distinguishes deep learning from low-tech agricultural innovations that allow only a one-time adaptation to current climate conditions, but when they change, agricultural productivity declines again. This advantage allows us to ensure food security in the long term. The second advantage is the constant improvement of cultivation technology. This allows selecting a technology that will make it possible to grow exotic plants with productivity not less than in the countries (territories) where they are traditionally grown. This advantage allows achieving a comprehensive import substitution of food.

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Conclusion Thus, the hypothesis is confirmed. It is substantiated that the combined benefits of deep learning in agriculture 4.0 allow achieving systemically deficit-free basic agricultural products and food import substitution (i.e., moving to a new level of food security). The contribution of this research to the literature lies in proving the preference for high-tech innovations, such as deep learning (over low-tech innovations such as precision farming and open ground), in agriculture. The practical significance of the results and conclusions based on them is that they open the possibility for a fundamental change in the approach to the modernization of the agricultural economy. The transition to agriculture 4.0 achieves strategic rather than a one-time adaptation of crop production to climate change. The significant potential of deep learning uncovered in this work can be useful in practice for the development of agriculture 4.0 in food-exporting countries for systemic agricultural import substitution and strategic food security. The limitation of the findings is the limited relationship of food security and deep learning in agriculture. While the research formed a sound scientific and methodological basis for the strategic management of food import substitution based on deep learning in agriculture, the issue of the prospects of food security needs further in-depth elaboration. Future studies are recommended to assess the contribution of other digital technologies (e.g., robots and blockchain) to food security, as well as to form a systemic view of the prospects for food security in agriculture 4.0.

References 1. Dudukalov, E. V., Rodionova, N. D., Sivakova, Y. E., Vyugova, E., Cheryomushkina, I. V., & Popkova, E. G. (2016). Global innovational networks: Sense and role in development of global economy. Contemporary Economics, 10(4), 299–310. https://doi.org/10.5709/ce.1897-9254.217 2. Kornilov, A. M. (2021). Prospects and pitfalls of innovation development. In E. G. Popkova & V. N. Ostrovskaya (Eds.), Meta-scientific study of artificial intelligence (pp. 649–660). Information Age Publishing. 3. Chernyshov, N. I., Sysoev, O. E., & Kiselev, E. P. (2021). Gantry technology in agriculture greening. In O. G. Shakirova, O. V. Bashkov, & A. A. Khusainov (Eds.), Current problems and ways of industry development:

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Equipment and technologies (pp. 303–309). Springer. https://doi.org/10. 1007/978-3-030-69421-0_32 Karbekova, A. B., Osmonalieva, D. A., Sadyraliev, Z., & Urmatayim, A. K. (2019). The main directions of digital modernization of the agro-industrial complex of a modern region. In E. G. Popkova (Ed.), Ubiquitous computing and the internet of things: Prerequisites for the development of ICT (pp. 949– 955). Springer. https://doi.org/10.1007/978-3-030-13397-9_98 Tchernyshev, N. I., Kiselyov, E. P., & Sysoev, O. E. (2019). Bridge agriculture as the basis of preserving soiled bioorganisms. IOP Conference Series: Earth and Environmental Science, 272, 032021. https://doi.org/10.1088/ 1755-1315/272/3/032021 Mishra, A. K., Kumar, A., Joshi, P. K., & Dsouza, A. (2022). Monopsonists, disruptive innovation and food security: The case of high-value commodity. Applied Economic Perspectives and Policy, 44(1), 460–476. https://doi.org/ 10.1002/aepp.13122 Oruma, S. O., Misra, S., & Fernandez-Sanz, L. (2021). Agriculture 4.0: An implementation framework for food security attainment in Nigeria’s postCOVID-19 era. IEEE Access, 9, 83592–83627. https://doi.org/10.1109/ ACCESS.2021.3086453 Scuderi, A., La Via, G., Timpanaro, G., & Sturiale, L. (2022). The digital applications of “Agriculture 4.0”: Strategic opportunity for the development of the Italian citrus chain. Agriculture, 12(3), 400. https://doi.org/10. 3390/agriculture12030400 Pacillo, G., Bao-Nam, N.-V., Burra, D. D., Trinh, H. T., Le, T.-D., Truong, M.-T., Nguyen, S. D., Tran, D. T., & Läderach, P. (2022). Disruptive innovations for well-functioning food systems: The data-driven “food and nutrition security under climate evolution” framework. Frontiers in Sustainable Food Systems, 5, 726779. https://doi.org/10.3389/fsufs.2021. 726779 Popkova, E. G., Sozinova, A. A., & Sofiina, E. V. (2022). Model of Agriculture 4.0 based on deep learning: Empirical experience, current problems and applied solutions. In E. G. Popkova, & B. S. Sergi (Eds.), Smart innovation in agriculture (pp. 333–346). Springer. https://doi.org/10.1007/ 978-981-16-7633-8_37 Saleem, M. H., Potgieter, J., & Arif, K. M. (2021). Automation in agriculture by machine and deep learning techniques: A review of recent developments. Precision Agriculture, 22, 2053–2091. https://doi.org/10. 1007/s11119-021-09806-x IMD. (2022). World Digital Competitiveness Ranking, 2021. https://www. imd.org/centers/world-competitiveness-center/rankings/world-digital-com petitiveness/ (Accessed 3 May 2022)

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13. The Economist. (2022). Explore the year-on-year trends for the Global Food Security Index. https://impact.economist.com/sustainability/project/foodsecurity-index/Index (Accessed 3 May 2022) 14. World Bank. (2022). Food imports (% of merchandise imports). https:// data.worldbank.org/indicator/TM.VAL.FOOD.ZS.UN?most_recent_value_ desc=true (Accessed 3 May 2022)

CHAPTER 10

Risks of Agricultural Economy and Climate Risk Management for Enterprises of Agriculture 4.0 Based on Deep Learning Tatiana N. Litvinova

Introduction Like any area of farming, the agricultural economy is subject to many risks, including financial (lack of funding for agriculture, the outflow of investment from the agricultural economy), social (lack of qualified personnel or increased costs for human resources), logistics (loss or spoilage of food due to long transportation or improper storage conditions), etc. However, the uniqueness of agriculture, its fundamental difference from other sectors of the economy is that it is strongly influenced by climate risks [1]. The essence of climatic risks of agriculture (in crop production) is that an unfavorable climate and its sudden change lead to the death of plants, crop failure, and deterioration in the quality of agricultural products.

T. N. Litvinova (B) Volgograd State Agricultural University, Volgograd, Russia e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_10

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Digital technology has significantly reduced the climate risks of agriculture. Nevertheless, based on the works of Howland and Francois Le Coq [2], Iglesias [3], and Jones and Leibowicz [4], this paper hypothesizes that, despite digitalization, agricultural climate risks continue to be high and that risk management prospects are linked to the development of agriculture 4.0 based on deep learning. The paper aims to investigate the climate risks of the agricultural economy and justify the benefits of risk management of enterprises in agriculture 4.0 based on deep learning.

Literature Review The basics of digital agriculture are revealed in the works of KhatriChhetri et al. [5], Shena et al. [6], Tong et al. [7], and Zougmoré et al. [8]. These works note the contribution of digital agriculture to reducing the climate risks of the agricultural economy, provided through the individualization of care for each plant. Digital technology allows for an even distribution of light, fertile soil, water, fertilizer, etc. among the growing plants, significantly reducing plant mortality and increasing the farm’s yield. Nevertheless, the degree of climate resilience of crop production provided by digital agriculture remains unknown, which is a gap in the literature. Agriculture 4.0 opens up fundamentally new opportunities for agriculture, including increased accountability and controllability of production and distribution processes in the agricultural economy, even greater flexibility of agricultural enterprises, and the implementation of advanced technology in these enterprises [9–11]. Simultaneously, the features of climate risk management in crop production in agriculture 4.0 are insufficiently developed, which is another gap in the literature. This paper fills the identified gaps by modeling the risks of the agricultural economy in digital agriculture and identifying perspectives on risk management of enterprises of agriculture 4.0 based on deep learning.

Materials and Methods To test the hypothesis, the authors chose the method of regression analysis, which simulates the dependence of food security indicators (as a result of risk management in the agricultural economy)—affordability, adequacy, quality, and safety of food—on the management of climatic

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Table 10.1 Statistics on food security and climate risk management for digital agriculture in 2021 Level of food security

High (over 70 points)

Average (40–70 points)

Low (below 40 points)

Country

Ireland Canada Japan New Zealand Russia Kazakhstan Mexico Brazil Indonesia South Africa Malawi Sudan Mozambique Yemen Burundi

Food affordability (Afford)

Food availability (Avail)

Affordability

Availability

92.9 87.6 90.0 90.9 86.9 83.0 73.8 68.7 74.9 63.1 23.6 31.8 42.9 39.3 24.0

75.1 77.7 75.7 63.2 64.9 58.5 60.9 46.4 63.7 49.4 40.9 31.6 30.4 27.6 33.7

Food Climate risk quality management in and safety agriculture (pca) (Q and S) Quality Political and Safety commitment to adaptation 94.0 94.5 83.4 82.0 85.8 81.0 81.0 90.0 48.5 72.1 37.1 52.4 33.8 37.4 45.7

95.4 49.2 93.8 90.8 90.8 69.2 70.8 7.7 3.9 59.2 66.8 40.6 24.6 17.7 36.3

Source Compiled by the authors based on the materials of The Economist [12]

risks in agriculture in 2021. Since digital agriculture currently prevails, the indicator values for 2021 shown in Table 10.1 refer to it.

Results To test the hypothesis, the following econometric model of the dependence of food security indicators on climate risk management in digital agriculture was obtained using statistics from Table 10.1: ⎧ ⎨ Afford = 42.55 + 0.41pca; (10.1) Avail = 35.74 + 0.32pca; ⎩ QandS = 46.79 + 0.39pca. Model (1) shows that the success of climate risk management in digital agriculture determines affordability (correlation 50.65%, t-statistic:

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2.11), sufficiency (correlation 58.68%, t-statistic: 2.61), and food quality and safety (correlation 54.39%, t-statistic: 2.34). The equations in the model (1) are valid at significance levels of 0.1, 0.05, and 0.05, respectively. Using the model (1), the authors determine the maximum possible potential increase in the values of food security indicators through the management of climate risks in digital agriculture (Fig. 10.1). As shown in Fig. 10.1, climate risk management in digital agriculture makes significant progress but falls far short of fully addressing food security issues, increasing food affordability to only 83.58 points, food sufficiency to 68.01 points, and food quality and safety to 85.58 points. In contrast, the benefits of agriculture 4.0 are demonstrated in Table 10.2. As shown in Table 10.2, agriculture 4.0 relies on deep learning to automate production and management. The information base for risk management is big data of the company and other companies on the market. Agriculture 4.0 involves a rational approach to risk management based on artificial intelligence (AI). Agriculture 4.0 provides the highest possible accuracy of prediction and assessment of agricultural climate risks, systemic risk management, and risk prevention (anticipatory management to prevent the occurrence of risk events). Thanks to deep learning, a cyclic algorithm of risk management is implemented, allowing one to increasingly reduce risks with each new cycle of agricultural reproduction.

Fig. 10.1 Potential for increasing food security through climate risk management in digital agriculture (Source Calculated and compiled by the authors)

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Table 10.2 Comparative analysis of risk management of agricultural enterprises in digital agriculture and agriculture 4.0 Comparison criterion

Digital agriculture

Agriculture 4.0

Risk management technology

Digital farm control (automation of the execution of the manager’s commands) Accumulated experience in farming Intuitive (relying on the manager’s intuition) Low

Deep learning on the farm (decision automation)

Incomplete, fragmented risk management Risk management when risk events occur

Complete, systemic risk management Risk prevention (proactive management to prevent the occurrence of risk events) Cyclic

Information base of risk management Approach to risk management Accuracy of forecasting and risk assessment Completeness of risk management coverage Risk management procedure Risk management algorithm

Linear

Generated big data database Rational (relying on artificial intelligence) High

Source Compiled by the authors

Conclusion Thus, the research hypothesis is confirmed, and the limitations of digital agriculture, in which the management of climatic risks of the agricultural economy improves but not fully ensures food security, are identified. Prospects for risk management are related to the development of agriculture 4.0 based on deep learning, the benefits of which are systemic, preventive, more flexible, rational, and effective management of climate risks in the agricultural economy. The contribution of the research to the literature lies in the identification of the features of climate risk management in crop production in agriculture 4.0. This highlighted a new and understudied area of agriculture 4.0 related to food security risks. The practical significance of the obtained results is associated with the fact that they provided a reliable scientific and methodological basis for improving climate risk management of the agricultural economy based on agriculture 4.0, which is based on advanced technology of deep learning.

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References 1. Potashnik, Y. S., Garina, E. P., Kozlova, E. P., Kuznetsova, S. N., & Garin, A. P. (2021). Impact on risk factors of industrial enterprises. In E. G. Popkova & V. N. Ostrovskaya (Eds.), Meta-scientific study of artificial intelligence (pp. 617–623). Information Age Publishing. 2. Howland, F., & Francois Le Coq, J. (2022). Disaster risk management, or adaptation to climate change? The elaboration of climate policies related to agriculture in Colombia. Geoforum, 131, 163–172. https://doi.org/10. 1016/j.geoforum.2022.02.012 3. Iglesias, A. (2022). On the risk of climate change on agriculture and water resources. Integrated Environmental Assessment and Management, 18(3), 595–596. https://doi.org/10.1002/ieam.4606 4. Jones, E. C., & Leibowicz, B. D. (2022). Climate risk management in agriculture using alternative electricity and water resources: A stochastic programming framework. Environment Systems and Decisions, 42(1), 117– 135. https://doi.org/10.1007/s10669-021-09838-8 5. Khatri-Chhetri, A., Regmi, P. P., Chanana, N., & Aggarwal, P. K. (2020). Potential of climate-smart agriculture in reducing women farmers’ drudgery in high climatic risk areas. Climatic Change, 158(1), 29–42. https://doi. org/10.1007/s10584-018-2350-8 6. Shena, S., Basist, A., & Howard, A. (2010). Structure of a digital agriculture system and agricultural risks due to climate changes. Agriculture and Agricultural Science Procedia, 1, 42–51. https://doi.org/10.1016/j.aaspro. 2010.09.006 7. Tong, Q., Swallow, B., Zhang, L., & Zhang, J. (2019). The roles of risk aversion and climate-smart agriculture in climate risk management: Evidence from rice production in the Jianghan Plain, China. Climate Risk Management, 26, 100199. https://doi.org/10.1016/j.crm.2019.100199 8. Zougmoré, R. B., Partey, S. T., Ouédraogo, M., Torquebiau, E., & Campbell, B. M. (2018). Facing climate variability in sub-saharan africa: Analysis of climate-smart agriculture opportunities to manage climate-related risks. Cahiers Agricultures, 27 (3), 34001. https://doi.org/10.1051/cagri/201 8019 9. Inshakova, A. O., Sozinova, A. A., & Litvinova, T. N. (2021). Corporate fight against the COVID-19 risks based on technologies of industry 4.0 as a new direction of social responsibility. Risks, 9(12), 212. https://doi.org/ 10.3390/risks9120212 10. Litvinova, T. N. (2020). Managing the development of infrastructural provision of AIC 4.0 on the basis of artificial intelligence: Case study in the agricultural machinery market. In E. G. Popkova, & B. S. Sergi (Eds.), Digital economy: Complexity and variety vs. rationality (pp. 317–323). Springer. https://doi.org/10.1007/978-3-030-29586-8_37

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11. Popkova, E. G. (2022). Vertical farms based on hydroponics, deep learning, and AI as smart innovation in agriculture. In E. G. Popkova, & B. S. Sergi (Eds.), Smart innovation in agriculture (pp. 257–262). Springer. https:// doi.org/10.1007/978-981-16-7633-8_28 12. The Economist. (2022). Global Food Security Index 2021. https://impact. economist.com/sustainability/project/food-security-index/Index (Accessed 7 May 2022)

CHAPTER 11

Prospects for Using Investment by Agricultural Cooperatives of Kyrgyzstan in the Regional Economy of Central Asia Kalil D. Dzhumabayev, Alymkul K. Dzhumabayev, Shukurali A. Jamalov, Elmira K. Kydykbaeva, and Taalaigul Azamat kyzy

Introduction Under market conditions of the development of the regional economies of Central Asia, the issues of investment, food security, reducing inflation, unemployment, and migration have significantly increased. In our opinion, under these conditions, it is necessary to exclude the possibility of unilateral state support for small business entities. These changes

K. D. Dzhumabayev (B) · A. K. Dzhumabayev · S. A. Jamalov · T. A. kyzy Zh. Alyshbayev Institute of Economics, National Academy of Sciences of the Kyrgyz Republic, Bishkek, Kyrgyzstan e-mail: [email protected] E. K. Kydykbaeva K. Tynystanov Issyk-Kul State University, Karakol, Kyrgyzstan

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_11

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require new developments on topical problems of the theory and methodology of consolidation of small-scale peasant (farm) enterprises and the formation of market-type agricultural cooperatives, which cause the need to apply tools and methods of public administration adapted to digital technology. A significant increase in the price of raw materials, fuel, and lubricants, the lack of development of digital technology, transport logistics, trade and logistics centers, and the acute lack of investment resources led to a decline in the rate of the development of the gross domestic product. Thus, in 1991, the rate of the development of GDP was 92.1%; it declined to 91.4% by 2020. The share of commodity production of agricultural products in the GDP fell sharply from 35.3% in 1991 to 13.5% in 2020 [1]. In 2020, the average milk yield from one cow in state and collective farms was 2253 kg, while the same indicator in peasant (farm) enterprises was 1964 kg (289 kg less). The average annual laying rate of laying hens on peasant (farm) enterprises compared to state and collective farms and agricultural cooperatives decreased to 52 eggs; the average annual wool from a sheep (in physical weight) decreased by 0.2 kg [2]. These indicators show that the currently existing 349.1 thousand peasant (farm) enterprises are practically unable to comply with elementary technologies of livestock breeding and crop cultivation [1]. As a result, the productivity of livestock and fields remains low, and the level of poverty and migration outflow of the rural population increases. The paper aims to substantiate the effectiveness of the use of investment by agricultural cooperatives and its advantage over small-scale peasant (farm) enterprises under conditions of regional integration.

Materials and Methods Problems and prospects of investment and effective use of land resources in agricultural cooperatives in conditions of regional integration are studied in the works of A. S. Arkhipova [3]; N. N. Bondina et al. [4]; I. A. Bursa and E. I. Artemova [5]; K. Marx [6]; I. A. Minakov et al. [7]; O. A. Moiseeva [8]; A. O. Oruzbaev et al. [9]; A. A. Pavlichenko [10]; V. M. Rozhnov [11]; A. A. Romanov [12]; I. Yu. Sigidov [13]; A. N. Tkachev [14]; M. Turgumbayev [15]; I. G. Ushachev [16]; Fam Ngok Van [17]. The contributions of international organizations also considered the following questions:

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. “Investment fuels global emerging-market growth” [18]; . “OECD’s producer support estimate and related indicators of agricultural support. Concepts, calculations, interpretation, and use” [19]; . “World Investment Report” [20]. Despite a certain degree of research and elaboration on this problem, as well as a considerable number of publications and recommendations on related topics, the issue of effective use of agricultural cooperatives’ investment in conditions of regional integration is insufficiently investigated, especially in countries with economies in transition such as Kyrgyzstan. As a result, economic entities in agriculture still remain low-profit and unprofitable. In our opinion, the solution to this problem is government support aimed at developing and expanding agricultural cooperatives and providing them with full investment in favorable conditions, as well as the targeted use of land of the State Fund of Agricultural Land. Thus, according to paragraph 2 of the Decree of the Cabinet of Ministers of the Kyrgyz Republic “On the development of agricultural cooperatives, seed and breeding farms” (December 17, 2021 No. 309), the Ministry of Agriculture of the Kyrgyz Republic must take into perpetual use the lands referred to in paragraph 1 of this decree in a prescribed manner [21]. In our opinion, this document provides a real opportunity for the effective use of lands of the State Fund of Agricultural Land by agricultural cooperatives for the production and sale of agricultural products. For this, we consider it necessary to strengthen the country’s role in implementing ESG initiatives in matters of investment and food security through the organization of specialized agricultural cooperatives of enterprises and regions based on the digital economy. We researched and calculated the profitability of investments received from the lands of the State Fund of Agricultural Land for the development and expansion of agricultural cooperatives. For this purpose, we applied the methods of analytical and multifactor mathematical forecasting models. The research objects are agricultural cooperatives in regions with different amounts of land resources and cattle. We considered two options: (1) the amount of investment received and (2) the amount of land in the State Fund of Agricultural Land received by agricultural cooperatives. In the first option of the development of the agricultural cooperatives, the number of cattle remained unchanged, but the number of crops

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increased due to the leased area. Under the development option r = 1, the need for cooperative agricultural investment is five million Kyrgyzstani soms. Using these investments, the sowing area will be expanded from 650 to 900 hectares; the number of contained milking cows and steers (heifers) for meat will remain unchanged—100 dairy cows and 80 steers and heifers. In the second option of the development of the agricultural cooperative, the number of cattle is 200 dairy cows and 150 steers and heifers. Under the development option r = 2, the need for investments is 10 million Kyrgyzstani soms. Sowing areas will be expanded from 850 to 1100 hectares; the number of milking cows and steers (heifers) for meat will remain unchanged—200 milking cows and 150 steers and heifers.

Results Table 11.1 presents the results of analytical and multifactor mathematical models for forecasting the production activity of the agricultural cooperative in the region with 100 milking cows and 80 steers and heifers, which received an investment of five million Kyrgyzstani soms (KGS) for ten years at 6% per annum. Table 11.2 presents the results of analytical and multifactor mathematical models for forecasting of production activity of the agricultural cooperative with 200 dairy cows and 150 steers and heifers, which received an investment of ten million KGS for ten years at 6% per annum. As shown in Tables 11.1 and 11.2, the effective use of investment by agricultural cooperatives in conditions of regional integration provides an increase in agricultural production and profitability of economic entities of the agricultural sector of the economy. The results of analytical and multifactor mathematical models are presented in Table 11.1. As a result of attracting investments in the amount of five million KGS, the net profit of the agricultural cooperative of the first option (r = 1) amounted to 161,625.2 thousand KGS. The results of analytical and multifactor mathematical models are presented in Table 11.1. As a result of attracting investments in the amount of ten million KGS, the net profit of the agricultural cooperative of the second option (r = 2) amounted to 128,556.3 thousand KGS.

50,750.0 126,950.0 115,200.0 194,400.0 1,152,000.0 252,000.0 36,500.0

–

50,750.0 126,950.0 1,799,885.0 194,400.0 1,152,000.0 252,000.0 36,500.0 200,000.0 350,000.0

1. Wheat 2. Barley 3. Perennial grasses 3.1 Hay 3.2 Haylage 3.3 Green feeds 4. Corn 4.1 Silage 4.2 Grain 5. Vegetable farming 5.1 Onion 5.2 Potato

3

2

1

Internal needs (kg)

Produced agricultural products (kg)

Name of crops

Table 11.1 Expected volume of agricultural production (r = 1)

200,000.0 350,000.0

1,684,685.0

– –

4

Sales volume (kg)

12.0 10.0

0 8.0

5.0 0 0

14.0 12.0

5

Price (KGS/kg)

2400.0 3500.0

8423.4

6

Farm incomes (thousand KGS)

(continued)

7

Farm expenditure (thousand KGS)

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3,750,000.0 3,000,000.0

316,800.0 24,000.0

4

Sales volume (kg)

30.0 15.0

20.0 130.0

5

Price (KGS/kg)

181,279.4 161,625.2

112,500.0 45,000.0

6336.0 3120.0

14,323.4

6

Farm incomes (thousand KGS)

Source Compiled by the authors based on Agricultural Cooperative “Niva” (Sokuluk District, Chui Region, Kyrgyz Republic) [2]

Total Profit of the agricultural cooperative

7.1 Grape 7.2 Apple Payment for a loan

6.1 Milk 6.2 Meat 7. Horticulture

3

Internal needs (kg)

100 dairy cows and 80 steers and heifers 360,000.0 43,200.0 24,000.0 150 ha. of grapes, 100 ha. of apples 3,750,000.0 3,000,000.0 Received: five million KGS for ten years at 6% per annum

2

1

Income/expense 6. Livestock

Produced agricultural products (kg)

Name of crops

Table 11.1 (continued)

19,654.2

4500.0 2000.0 800.0

5550.0 1520.0

5284.2

7

Farm expenditure (thousand KGS)

106 K. D. DZHUMABAYEV ET AL.

98,775.0 249,375.0 225,000.0 378,000.0 2,250,000.0 495,000.0 71,175.0

–

98,775.0 249,375.0 1,867,418.0 378,000.0 2,250,000.0 495,000.0 71,175.0 405,000.0 630,000.0

1. Wheat 2. Barley 3. Perennial grasses 3.1 Hay 3.2 Haylage 3.3 Green feeds 4. Corn 4.1 Silage 4.2 Grain 5. Vegetable farming 5.1 Onion 5.2 Potato

3

2

1

Internal needs (kg)

Produced agricultural products (kg)

Name of crops

Table 11.2 Expected volume of agricultural production (r = 2)

405,000.0 630,000.0

1,642,418.0

– –

4

Sales volume (kg)

12.0 10.0

0 8.0

5.0 0 0

14.0 12.0

5

Price (KGS/kg)

4860.0 6300.0

8212.0

6

Farm incomes (thousand KGS)

(continued)

7

Farm expenditure (thousand KGS)

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3,000,000.0 Received: ten million RGS for ten years at 6% per annum

81,000.0

3

Internal needs (kg)

2,500,000.0 3,000,000.0

639,000.0 45,000.0

4

Sales volume (kg)

30.0 15.0

20.0 130.0

5

Price (KGS/kg)

158,002.0 128,556.3

75,000.0 45,000.0

12,780.0 5850.0

19,372.0

6

Farm incomes (thousand KGS)

Source Compiled by the authors based on Agricultural Cooperative “Niva” (Sokuluk District, Chui Region, Kyrgyz Republic) [2]

Total Profit of the agricultural cooperative

7.1 Grape 7.2 Apple Payment for a loan

6.1 Milk 6.2 Meat 7. Horticulture

200 dairy cows and 150 steers and heifers 720,000.0 45,000.0 100 ha. of grapes, 100 ha. of apples 2,500,000.0

2

1

Income/expense 6. Livestock

Produced agricultural products (kg)

Name of crops

Table 11.2 (continued)

29,445.7

2900.0 2000.0 1600.0

11,200.0 3000.0

8745.7

7

Farm expenditure (thousand KGS)

108 K. D. DZHUMABAYEV ET AL.

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Conclusion Thus, the study of the problem of effective use of investment of agricultural cooperatives in the regional economy of Central Asian countries proved the need to attract investment for the development and expansion of agricultural cooperatives that received investments from the State Fund of Agricultural Land and export-oriented agricultural products in conditions of integration as one of the promising developments of the agricultural economy [22]. The targeted use of investment resources and lands of the State Fund of Agricultural Land stimulates the development of agricultural cooperatives, reduces poverty, slows down rural migration outflow, and provides a new quality and export advantage of agricultural products that ensure the economic growth of the agricultural economy. The problem of effective use of investment by agricultural cooperatives in market conditions is extremely relevant in implementing ESG initiatives in the practice of enterprises and regions of Central Asia to ensure the financial and food security of the country based on the digital economy. Its solution can determine the further quality of development of the agricultural sector of the economy and the prospects for developing the countries with economies in transition. Future research papers on the effective use of investment by agricultural cooperatives in conditions of regional integration should be directed to the prospects of agricultural development.

References 1. National Statistical Committee of the Kyrgyz Republic. (1993–2021). Kyrgyzstan in figures. http://www.stat.kg/ru/publications/sbornik-kyrgyz stan-v-cifrah/. Accessed 18 April 2022. 2. National Statistical Committee of the Kyrgyz Republic. (2021). Agriculture of the Kyrgyz Republic. http://www.stat.kg/ru/publications/sbornikselskoe-hozyajstvo-kyrgyzskoj-respubliki/. Accessed 18 April 2022. 3. Arkhipova, A. S. (2012). Mathematical simulation in management of investment attraction of Agro-Industrial Complex. Scientific Journal of KubSAU, 76(02), 1027–1037. http://ej.kubagro.ru/2012/02/pdf/10.pdf. Accessed 13 April 2022. 4. Bondina, N. N., Bondin, I. A., Shirokova, E. V., & Pavlova, I. V. (2022). Land is the main element of the means of agricultural production. Moscow Economic Journal, 7 (2), 304–317. https://doi.org/10.55186/2413046X_ 2022_7_2_132

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5. Bursa, I. A., & Artemova, E. I. (2013). Efficiency of investment processes in the dairy food subcomplex. KubSAU. 6. Marx, K. (2011). Capital: A critique of political economy (Vol. I; Chapter 11: About cooperation (Original work published 1887). https://www.marxists. org/russkij/marx/1867/capital_vol1/24.htm. Accessed 15 April 2022. 7. Minakov, I. A., Sabetova, L. A., & Kulikov, N. I. (2004). Economics of agricultural enterprise. Koloss. 8. Moiseeva, O. A. (2021). Problems and prospects of development of cooperation in agriculture of border geostrategic territories. International Agricultural Journal, 6(384), 95–99. https://doi.org/10.24412/2587-67402021-6-95-99 9. Oruzbaev, A. O., Dzhailov, Dzh. S., & Ertazin, K. (2000). The deepening of agricultural reform and the problems of agribusiness development. 10. Pavlichenko, A. A. (2019). State support of small forms of economics in the agrarian sector of economy of the Amur region. Moscow Economic Journal, 2, 304–317. https://doi.org/10.24411/2413-046X-2019-10047 11. Rozhnov, V. M. (2015). State support for investment activities in agriculture under WTO conditions (Thesis of Dissertation of Doctor of Economics). VNIOPTUSKh. 12. Romanov, A. A. (2014). Analysis of the effectiveness of investment activities in agriculture. Economics, Management, Management Systems, 12(2), 302– 308. 13. Sigidov, I. Y. (2003). Investment as a factor in increasing the efficiency of production in agrarian formations. Scientific Journal of KubSAU, 7 , 3–9. 14. Tkachev, A. N. (2004). Methodology of investment management of the regional agro-industrial complex. KubSAU. 15. Turgumbaev, M. (2021). Role of investments in the development of the agro-industrial complex in the Kyrgyz Republic. Science, New Technologies and Innovations in Kyrgyzstan, 7 , 136–139. http://science-journal.kg/ media/Papers/nntiik/2021/7/HHT-7_2021g_136-139.pdf. Accessed 18 April 2022. 16. Ushacev, I. G. (Ed.). (2015). Agricultural policy of Russia in the context of international and regional integration: Proceedings of the International Scientific and Practical Conference (Part I). VNIIESKh. 17. Fam Ngok Van. (2020). Logistics processes in Vietnam’s agro-industrial complex: Problems and solutions. Journal of Economics, Entrepreneurship and Law, 10(5), 1565–1576. https://doi.org/10.18334/epp.10.5.110144 18. Oxford Analitika. (2011, December 7). Investment fuels global emergingmarket growth. https://dailybrief.oxan.com/Analysis/DB172454/Invest ment-fuels-global-emerging-market-growth. Accessed 21 April 2022. 19. OECD. (2016). OECD’s producer support estimate and related indicators of agricultural support. Concepts, calculations, interpretation and use (The PSE

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Manual). https://www.oecd.org/agriculture/topics/agricultural-policymonitoring-and-evaluation/documents/producer-support-estimates-man ual.pdf. Accessed 21 April 2022. 20. United Nations. (2012). World investment report: Towards a view generation of investment policies. UIVCTAD. https://unctad.org/system/files/officialdocument/wir2012_embargoed_en.pdf. Accessed 21 April 2022. 21. Cabinet of Ministers of the Kyrgyz Republic. (2021). On the development of agricultural cooperatives, seed and breeding farms (December 17, 2021 No. 309). http://cbd.minjust.gov.kg/act/view/ru-ru/158793. Accessed 18 April 2022. 22. Eurasian Economic Union. (2017). Treaty on the Customs Code of the Eurasian Economic Union (adopted April 11, 2017). http://www.eurasi ancommission.org/en/act/tam_sotr/dep_tamoj_zak/SiteAssets/Customs% 20Code%20of%20the%20EAEU.pdf. Accessed 16 April 2022.

PART III

Applied Recommendations for Shaping Agriculture 4.0 Based on Deep Learning to Ensure the Food Security of the Economy of the Future

CHAPTER 12

Advanced Digital Technology in Agriculture and Its Contribution to Food Security Elena V. Karanina , Elena A. Vechkinzova , Yuliya A. Kopytina , and Nurlybek T. Malelov

Introduction Digital technologies are developing and becoming increasingly popular. Nevertheless, the problem lies in the lack of consideration of the peculiarities of the digital agricultural economy in this process. The very concept of Industry 4.0 originally targeted the Fourth Industrial Revolution to

E. V. Karanina (B) · Y. A. Kopytina Vyatka State University, Kirov, Russia e-mail: [email protected] Y. A. Kopytina e-mail: [email protected] E. A. Vechkinzova State University of Management, Moscow, Russia e-mail: [email protected] N. T. Malelov Esil University, Astana, Kazakhstan

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_12

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create and disseminate breakthrough technologies in the industry. In this industry, digital technologies are designed to limit the impact of social factors (“human factors”) on the industry: to overcome productivity growth limits through automation, provide intelligent decision support in the design of mass industrial production to extract “economies of scale,” etc. Digitalization of the agricultural economy has a very different meaning—it is aimed at limiting the impact of natural factors on agriculture: to reduce climate risks, increase the predictability of agricultural production, and ensure an individual approach to each plant and consider its characteristics [1, 2]. Thus, the difference between the introduction of digital technology in the industry compared to agriculture is related to the following: . The peculiarities of the use of human resources: their shift to the digital industry, while in agriculture, digitalization is aimed at creating knowledge-intensive employment, improving working conditions, and unlocking human potential; . Different approaches to managing production processes: mass in the automated industry with individualization of agriculture based on digitalization. The noted specifics of agriculture do not allow its high-tech modernization based on digital technologies developed for industry. Therefore, it is hypothesized that advanced digital technologies in agriculture make a significant contribution to food security, but the potential for growth of this contribution is not disclosed. The paper aims to review advanced digital technologies in agriculture and assess their contribution to food security.

Literature Review The existing literature pays considerable attention to the benefits and prospects of using advanced digital technology in agriculture. The list of these technologies is quite extensive and well-known. It includes the following: . Cloud technology [3, 4];

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. . . .

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Big data [5–8]; Internet of Things [9–11]; Artificial intelligence [12, 13]; Industrial robots/automated lines [14, 15].

Nevertheless, the practical experience of using these technologies is studied insufficiently; their actual contribution to food security is not defined, which is a gap in the literature. The identified gap is filled in this paper through a study of practical experience and based on its analysis of the contribution of advanced digital technologies in agriculture to food security.

Materials and Methods The method of regression analysis was applied to test the research hypothesis. This method is used to determine the influence of the Digital Competitiveness Index (according to IMD [16]) on the Global Food Security Index (according to the Economist Group [17]). For the research, the authors form a sample of the top 10 digital economies of the world for which food security statistics are available. The research is based on data for 2021 (Table 12.1). Table 12.1 Advanced technology and food security in the top 10 digital economies of the world in 2021, score 0–100

Country

USA Sweden Denmark Singapore Switzerland Netherlands Norway United Arab Emirates Finland South Korea

Digital Competitiveness Index x

Global Food Security Index y

100.00 95.189 95.158 95.137 94.939 93.309 91.295 90.517

79.1 77.9 76.5 77.4 80.4 79.9 76.0 71.0

90.134 89.724

80.9 71.6

Source Compiled by the authors

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Since international statistics do not keep separate statistics on the use of digital technology directly in agriculture, the authors carry out an additional case study of the experience of applying advanced digital technology in agriculture in Russia (a large digital agricultural economy) with a high level of technology detail in 2021.

Results To determine the contribution of advanced digital technologies in agriculture to food security, we obtained a regression curve based on the data from Table 12.1. This regression curve is shown in Fig. 12.1, showing statistics on the use of the most common technologies—robots and big data in the sample countries in 2021. The regression curve in Fig. 12.1 shows that a one-point increase in digital competitiveness contributes to a 0.5075-point increase in food security. The correlation of the indicators was 0.2215. There is also a wide variation in the activity of using advanced digital technologies among the countries in the sample in 2021. To clarify our results, let us turn to the case study of the use of advanced digital technologies in agriculture in Russia with technology specifications in 2021 (Fig. 12.2).

Fig. 12.1 Advanced digital technologies and a regression curve of the contribution of digital competitiveness to food security (Source Calculated and constructed by the authors based on IMD materials [16])

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Fig. 12.2 Use of advanced digital technologies in agriculture in Russia by technology in 2021, % (Source Constructed by the authors, based on materials from HSE University [18])

According to Fig. 12.2, in Russia, 17.8% of agricultural enterprises use cloud technology, and 17.2% use big data. In general, the activity of using advanced digital technology in agriculture is high with significant growth potential.

Conclusion Thus, the research hypothesis is confirmed. It is proved that advanced digital technology in agriculture significantly contributes to food security, but the growth potential of this contribution is not disclosed. Advanced digital technologies have not yet been properly adapted to the peculiarities of agriculture and therefore make a limited contribution to its development. The theoretical significance of the results and conclusions lies in the substantiation of the need to consider the peculiarities of the digitalization of agriculture when introducing advanced technologies to it. In future research, it is appropriate to focus on achieving agricultural sensitivity and making the best use of advanced digital technology in agriculture to increase food security.

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Acknowledgements The article was prepared with the support of the grant of the President of the Russian Federation NSh-5187.2022.2 for state support of the leading scientific schools of the Russian Federation within the framework of the research topic “Development and justification of the concept, an integrated model of resilience diagnostics of risks and threats to the security of regional ecosystems and the technology of its application based on a digital twin.”

References 1. Morozova, I. A., & Litvinova, T. N. (2019). The development of the agroindustrial complex in the cyber economy. In V. Filippov, A. Chursin, J. Ragulina, & E. Popkova (Eds.), The cyber economy (pp. 195–201). Springer. https://doi.org/10.1007/978-3-030-31566-5_21 2. Osipov, V. S., Vorozheykina, T. M., Bogoviz, A. V., Lobova, S. V., & Yankovskaya, V. V. (2022). Innovation in agriculture at the junction of technological waves: Moving from digital to smart agriculture. In E. G. Popkova & B. S. Sergi (Eds.), Smart innovation in agriculture (pp. 21–27). Springer. https://doi.org/10.1007/978-981-16-7633-8_3 3. Dozono, K., Amalathas, S., & Saravanan, R. (2022). The impact of cloud computing and artificial intelligence in digital agriculture. In X. S. Yang, S. Sherratt, N. Dey, & A. Joshi (Eds.), Proceedings of sixth international congress on information and communication technology (pp. 557–569). Springer. https://doi.org/10.1007/978-981-16-2377-6_52 4. Vishwanath, Y., Upendra, R. S., & Ahmed, M. R. (2021). A review on advent of IoT, cloud, and machine learning in agriculture. In J. S. Raj (Eds.), International conference on mobile computing and sustainable informatics (pp. 595–603). Springer. https://doi.org/10.1007/978-3-03049795-8_57 5. Anita, M., & Shakila, S. (2021). Climatic analysis for agriculture cultivation in geography using big data analytics. In S. L. Peng, S. Y. Hsieh, S. Gopalakrishnan, & B. Duraisamy (Eds.), Intelligent computing and innovation on data science (pp. 63–72). Springer. https://doi.org/10.1007/978981-16-3153-5_9 6. Cravero, A., & Sepúlveda, S. (2021). Use and adaptations of machine learning in big data—Applications in real cases in agriculture. Electronics, 10(5), 552. https://doi.org/10.3390/electronics10050552 7. Osinga, S. A., Paudel, D., Mouzakitis, S. A., & Athanasiadis, I. N. (2022). Big data in agriculture: Between opportunity and solution. Agricultural Systems, 195, 103298. https://doi.org/10.1016/j.agsy.2021.103298 8. Su, Y., & Wang, X. (2021). Innovation of agricultural economic management in the process of constructing smart agriculture by big data. Sustainable

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Computing: Informatics and Systems, 31, 100579. https://doi.org/10. 1016/j.suscom.2021.100579 de Abreu, C. L., & van Deventer, J. P. (2022). The application of artificial intelligence (AI) and internet of things (IoT) in agriculture: A systematic literature review. In E. Jembere, A. J. Gerber, S. Viriri, & A. Pillay (Eds.), Artificial intelligence research (pp. 32–46). Springer. https://doi.org/10. 1007/978-3-030-95070-5_3 Ouafiq, E. M., Saadane, R., & Chehri, A. (2022). Data management and integration of low power consumption embedded devices IoT for transforming smart agriculture into actionable knowledge. Agriculture, 12(3), 329. https://doi.org/10.3390/agriculture12030329 Phasinam, K., Kassanuk, T., Shinde, P. P., Thakar, C. M., Sharma, D. K., Mohiddin, M. K., & Rahmani, A. W. (2022). Application of IoT and Cloud Computing in Automation of Agriculture Irrigation. Journal of Food Quality, 2022, 8285969. https://doi.org/10.1155/2022/8285969 Popkova, E. G. (2022). Vertical farms based on hydroponics, deep learning, and AI as smart innovation in agriculture. In E. G. Popkova & B. S. Sergi (Eds.), Smart innovation in agriculture (pp. 257–262). Springer. https:// doi.org/10.1007/978-981-16-7633-8_28 Spanaki, K., & Karafili, E., & Despoudi, S. (2021). AI applications of data sharing in agriculture 4.0: A framework for role-based data access control. International Journal of Information Management, 59, 102350. https:// doi.org/10.1016/j.ijinfomgt.2021.102350 Savelyeva, N. K., & Semenova, A. A., Popova, L. V., & Shabaltina, L. V. (2022). Smart technologies in agriculture as the basis of its innovative development: AI, ubiquitous computing, IoT, robotization, and blockchain. In E. G. Popkova & B. S. Sergi (Eds.), Smart innovation in agriculture (pp. 29–35). Springer. https://doi.org/10.1007/978-981-16-7633-8_4 Sparrow, R., & Howard, M. (2021). Robots in agriculture: Prospects, impacts, ethics, and policy. Precision Agriculture, 22(3), 818–833. https:// doi.org/10.1007/s11119-020-09757-9 IMD. (2022). World digital competitiveness ranking—2021. https://www. imd.org/centers/world-competitiveness-center/rankings/world-digital-com petitiveness/. Accessed 5 April 2022. The Economist Group. (2022). Global Food Security Index 2021. https:// impact.economist.com/sustainability/project/food-security-index/Index. Accessed 5 April 2022. Gokhberg, L. M., Kuzminov, Y. I., Parshin, M. V., Shapoval, I. N., & Yatselenko, N. S. (2022). Digital economy: 2022: A brief statistical collection. HSE University. https://publications.hse.ru/books/553808522. Accessed 5 April 2022.

CHAPTER 13

Roadmap for the Transition from Digital Agriculture to Agriculture 4.0 Based on Deep Learning in the Economy of the Future by 2030 Nazgul S. Daribekova , Marina A. Sanovich , Nadezhda K. Savelyeva , Tatiana A. Dugina , and Anastasia I. Smetanina

Introduction The Fourth Industrial Rrevolution dictates the need to modernize the agricultural economy. The agricultural economy is now (as of 2022) dominated by digital agriculture, involving a clear linkage to soil and

N. S. Daribekova Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan M. A. Sanovich · N. K. Savelyeva Vyatka State University, Kirov, Russia e-mail: [email protected] N. K. Savelyeva e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_13

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climate and a reliance on manual labor with its fragmented and small-scale automation [1–3]. There is an urgent need to move to agriculture 4.0, which allows for high-tech, fully automated, and environmentally friendly food production while preserving its naturalness (eco-friendliness) [4, 5]. The benefits of agriculture 4.0 are based on breakthrough technologies, among which an important place belongs to deep learning, which is especially in demand in the agricultural economy because it increases its adaptability to climate change [6, 7]. The problem lies in the lack of understanding of this transition process. The current phase of the Sustainable Development Goals (SDGs) is the “Decade of action,” which will last until 2030. This is when the transition to agriculture 4.0 is expected to occur because this transition is critical for the full implementation of the SDGs. Given the marked high relevance of the scientific study of organizational and managerial aspects of the transition from digital agriculture to agriculture 4.0, the goal of this research is to develop a roadmap for this transition based on deep learning in the economy of the future until 2030.

Literature Review Agriculture develops in accordance with the three main priorities of the agricultural economy. A comparative analysis of their achievements in digital agriculture and agriculture 4.0 is presented in Table 13.1. The first priority is ending hunger and ensuring food security (SDG 2). The challenge in realizing this priority in digital agriculture is limited production capacity, the unpredictability of quality, and lack of security assurance. The benefits of agriculture 4.0 are increased production capacity, specified quality, and a guarantee of safety [8]. The second priority is supporting employment, unlocking the human potential of agricultural personnel, and increasing the contribution of

T. A. Dugina Volgograd State Agricultural University, Volgograd, Russia e-mail: [email protected] A. I. Smetanina (B) Institute of Scientific Communications (ISC-Group LLC), Volgograd, Russia e-mail: [email protected]

Source Developed and compiled by the authors

Profitability and investment attractiveness of agricultural entrepreneurship, development of rural areas (SDG 11)

Supporting employment, unlocking the human potential of agricultural personnel, and increasing the contribution of the agricultural economy to economic growth (SDG 8) Adaptation to climate change (SDG 13) and conservation of ecosystems (SDGs 14–15)

Fighting hunger and ensuring food security (SDG 2)

Priorities for the agricultural economy

Greater dependence on climate, soil depletion, and expansion of horizontal farms in wilderness areas High business and investment risks in agriculture

Limited production capacity, the unpredictability of quality, and no guarantee of safety Hard manual labor in agriculture, low labor productivity

Prevention of losses and increased profits of agricultural enterprises, high-tech development of rural areas

Climate resilience, preservation of ecosystems through vertical farms

Knowledge-intensive and highly productive employment in agriculture, supporting the growth of the agricultural economy

Increased production capacity, specified quality, and guarantee of safety

Achieving priorities Benefits of agriculture 4.0

ROADMAP FOR THE TRANSITION FROM DIGITAL …

Challenges of digital agriculture

Table 13.1 Comparative analysis of the achievement of the priorities of the agricultural economy in digital agriculture and agriculture 4.0 13

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the agricultural economy to economic growth (SDG 8). The problem of implementing this priority in digital agriculture is manual labor in agriculture and its low productivity. The benefits of agriculture 4.0 are knowledge-intensive and high-productivity employment in agriculture, supporting the growth of the agricultural economy [9]. The third priority is the adaptation to climate change (SDG 13) and ecosystem conservation (SDGs 14–15). The challenge of realizing this priority in digital agriculture is the great dependence on climate, soil depletion, and the expansion of horizontal farms in wilderness areas. The benefits of agriculture 4.0 are climate resilience and the preservation of ecosystems through vertical [10]. The fourth priority is the profitability and investment attractiveness of agricultural entrepreneurship and the development of rural areas (SDG 11). The problem of implementing this priority in digital agriculture is the high entrepreneurial and investment risks in agriculture. The benefits of agriculture 4.0 are prevention of losses, increased profits of agricultural enterprises, and high-tech development of rural areas [11]. Thus, the transition from agriculture to agriculture 4.0 is necessary. The literature review showed that the essence and benefits of agriculture 4.0 are disclosed in sufficient detail. Nevertheless, the organization and management of the transition from digital agriculture to agriculture 4.0 are underdeveloped, which is a gap in the literature. This research fills this gap by developing a roadmap for the discussed transition.

Materials and Method This article relies on the planning method for creating the roadmap. To develop the most detailed and prepared for practical use “road map,” the authors create it using the program-targeted approach, which allows identifying key stages and their time limits, working out the functional content of each stage, and offering recommendations for monitoring the success of the completion of each stage and readiness to move to the next stage. For clarity, the roadmap is presented graphically based on the formalization method.

Results The author’s vision of the transition from digital agriculture to agriculture 4.0 based on deep learning by 2030 is illustrated in Fig. 13.1.

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Digital Agriculture (2022)

20222024

ROADMAP FOR THE TRANSITION FROM DIGITAL …

Preparing the infrastructur

20252026

Launch of institutes

20272029

Market activation

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Agriculture 4.0 (2030)

Fig. 13.1 A vision of the transition from digital agriculture to agriculture 4.0 based on deep learning by 2030 (Source Developed and compiled by the authors)

Figure 13.1 shows that the first stage of the considered transition should be the preparation of infrastructure support for agriculture 4.0. This phase has actually already begun in today’s digital economies—its time frame is 2022–2024. The second stage is the launch of agriculture 4.0 institutions based on the formed infrastructure. Its time frame is 2025–2026. The final third stage is associated with the activation of the market and the emergence of agricultural entrepreneurship 4.0. This is the most complex and longest stage—its time frame is defined from 2027 to 2029. As a result, agriculture 4.0 will be formed by 2030. The roadmap for this vision is shown in Table 13.2. As shown in Table 13.2, the tasks of the first stage are the launch and implementation of infrastructure projects by state regulators of the agricultural economy through the instrument of public-private partnership (PPP). At this stage, the task of agricultural enterprises is to inform the government about infrastructure needs and participate in PPP projects. The metrics for determining the extent to which the goal of this phase has been achieved are the relevancy of the infrastructure and the extent to which the needs of the infrastructure have been met. The objectives of the second phase are to create a regulatory and legal framework by state regulators of the agricultural economy and pilot testing of deep learning by agricultural enterprises. The metrics for determining the extent to which the goal of this phase has been achieved are the effectiveness and relevancy of institutions of agriculture 4.0. The tasks of the third stage are the stimulation of digital competition of agricultural enterprises by state regulators of the agricultural economy and the active use of deep learning in agriculture by agricultural enterprises. The metrics for determining the extent to which the goal of this phase is achieved are the competitiveness of agriculture 4.0 and food security.

1. Preparatory

Parameters of stages

Electronic government

The up-to-date infrastructure and the extent to which needs are met

PPPa

State regulators of Agricultural the agricultural enterprises economy

Note a PPP—public-private partnership Source Developed and compiled by the authors

Responsible (the subject of the implementation of tasks) Tools for achieving objectives Metrics for determining the degree of achievement of a goal

Time frame of the 2022–2024 stage Goal of the stage Creation of infrastructural support for agriculture 4.0 Stage objectives Launch and Informing the implementation of state about the infrastructural need for projects infrastructure

Stage parameters

Effectiveness and relevancy of institutions of agriculture 4.0

State regulators Agricultural of the enterprises agricultural economy Modernization of Innovation legislation

Innovation, marketing, and integration Competitiveness of agriculture 4.0, food security

Agricultural enterprises

Active use of deep learning in agriculture

Formation of agriculture 4.0

Formation of institutions of agriculture 4.0 Creation of a Pilot testing of regulatory and deep learning legal framework

Encouraging digital competition of agricultural enterprises State regulators of the agricultural economy Antitrust and fiscal regulation

2027–2029

3. Market

2025–2026

2. Institutional

Table 13.2 Roadmap for the transition from digital agriculture to agriculture 4.0 based on deep learning in the economy of the future by 2030

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Conclusion Thus, the developed roadmap for the transition from digital agriculture to agriculture 4.0 based on deep learning in the economy of the future until 2030 formed the scientific basis for the organization and management of this process. The roadmap will be useful for countries with high digital competitiveness, particularly those included in the IMD’s 2021 ranking (64 developed and developing countries) [12]. Due to the high level of detail, the proposed roadmap is ready for practical use. Its advantage is a demonstration of the distribution of powers and responsibilities of state regulators of the agricultural economy and agricultural entrepreneurship.

References 1. Ingram, J., Maye, D., Bailye, C., Barnes, A., Bear, C., Bell, M., Cutress, D., Davies, L., de Boon, A., Dinnie, L., Gairdner, J., Hafferty, C., Holloway, L., Kindred, D., Kirby, D., Leake, B., Manning, L., Marchant, B., Morse, A., … Wilson, L. (2022). What are the priority research questions for digital agriculture? Land Use Policy, 114, 105962. https://doi.org/10.1016/j.lan dusepol.2021.105962 2. Mushi, G. E., Serugendo, G. D. M., & Burgi, P.-Y. (2022). Digital technology and services for sustainable agriculture in Tanzania: A literature review. Sustainability, 14(4), 2415. https://doi.org/10.3390/su14042415 3. Qin, T., Wang, L., Zhou, Y., Guo, L., Jiang, G., & Zhang, L. (2022). Digital technology-and-services-driven sustainable transformation of agriculture: Cases of China and the EU. Agriculture, 12(2), 297. https://doi.org/ 10.3390/agriculture12020297 4. Majumdar, P., Mitra, S., & Bhattacharya, D. (2021). IoT for promoting Agriculture 4.0: A review from the perspective of weather monitoring, yield prediction, security of WSN protocols, and hardware cost Analysis. Journal of Biosystems Engineering, 46(4), 440–461. https://doi.org/10.1007/s42 853-021-00118-6 5. Singh, G., & Yogi, K. K. (2022). Internet of things-based devices/robots in agriculture 4.0. In P. Karrupusamy, V. E. Balas, & Y. Shi (Eds.), Sustainable communication networks and application (pp. 87–102). Springer. https:// doi.org/10.1007/978-981-16-6605-6_6 6. Bernhardt, H., Bozkurt, M., Brunsch, R., Colangelo, E., Herrmann, A., Horstmann, J., Kraft, M., Marquering, J., Steckel, T., Tapken, H., Weltzien, C., & Westerkamp, C. (2021). Challenges for agriculture through industry 4.0. Agronomy, 11(10), 1935. https://doi.org/10.3390/agronomy1110 1935

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7. Morozova, I. A., & Litvinova, T. N. (2019). The development of the agroindustrial complex in the cyber economy. In V. Filippov, A. Chursin, J. Ragulina, & E. Popkova (Eds.), The cyber economy (pp. 195–201). Springer. https://doi.org/10.1007/978-3-030-31566-5_21 8. Martinho, V. J. P. D. (2022). Systematic review of agriculture and Era 4.0: The most relevant insights. In Trends of the agricultural sector in Era 4.0 (pp. 49–64). Springer. https://doi.org/10.1007/978-3-030-98959-0_2 9. Aiello, G., Catania, P., Vallone, M., & Venticinque, M. (2022). Worker safety in agriculture 4.0: A new approach for mapping operator’s vibration risk through machine learning activity recognition. Computers and Electronics in Agriculture, 193, 106637. https://doi.org/10.1016/j.compag. 2021.106637 10. Rastogi, R., Maheshwari, S., Garg, P., Rastogi, M., & Kumar, P. (2022). Analysis of agriculture production and impacts of climate change in South Asian region: A concern related with Healthcare 4.0 using ML and sensors. In P. Kumar, A. J. Obaid, K. Cengiz, A. Khanna, & V. E. Balas (Eds.), A fusion of artificial intelligence and Internet of Things for emerging cyber systems (pp. 41–65). https://doi.org/10.1007/978-3-030-76653-5_3 11. Eastwood, C. R., Edwards, J. P., & Turner, J. A. (2021). Review: Anticipating alternative trajectories for responsible Agriculture 4.0 innovation in livestock systems. Animal, 15, 100296. https://doi.org/10.1016/j.animal. 2021.100296 12. IMD. (2022). World digital competitiveness ranking, 2021. https://www. imd.org/centers/world-competitiveness-center/rankings/world-digital-com petitiveness/. Accessed 5 April 2022.

CHAPTER 14

Automation of Agriculture Based on Deep Learning: Modeling and Management to Improve Quality and Efficiency Natalia V. Przhedetskaya , Eleonora V. Nagovitsyna , Victoria Yu. Przhedetskaya , and Ksenia V. Borzenko

Introduction The Global Sustainable Development Goal (SDG) 2, “No hunger,” although adopted and coordinated at the supranational level, is in practice implemented to a large extent by the initiatives of every country in the world. In support of the implementation of SDG 2 in Russia, the President of the Russian Federation [1] approved the Food Security Doctrine of the Russian Federation by Decree No. 20 of January 21, 2020. This Doctrine indicates that the priorities of the agricultural

N. V. Przhedetskaya (B) · K. V. Borzenko Rostov State University of Economics, Rostov-on-Don, Russia e-mail: [email protected] E. V. Nagovitsyna Vyatka State University, Kirov, Russia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_14

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economy are improving the food quality and increasing the efficiency of agricultural entrepreneurship. As part of this Doctrine, the Ministry of Agriculture of the Russian Federation [2] launched the departmental project “Digital agriculture,” in which the automation of agriculture is noted as a promising source of food security. Robots, artificial intelligence (AI), and big data are noted as the agricultural automation tools used and planned for further distribution in Russia. The works of Khan et al. [3], Rao et al. [4], Saleem et al. [5], and Wang et al. [6] pay considerable attention to another breakthrough technology—deep learning—that has the potential to contribute to agricultural development. Deep learning is based on AI and big data and is therefore available in Russia. This research aims to model the automation of agriculture and identify the prospects for improving the management of this process based on deep learning to improve the quality and efficiency of the agricultural economy of Russia.

Literature Review The issues of automation of agriculture are disclosed in sufficient detail in the available works of Popkova et al. [7], Przhedetsky et al. [8], Sozinova et al. [9], and Varlamov et al. [10]. The cited sources note the numerous advantages of automation. Nevertheless, they do not indicate how great these advantages are compared with alternative directions of development of the agricultural economy, in particular, with the traditional direction associated with the increase in cultivated areas. This is a research gap. From the point of view of production factors, automation implies an improvement in technology, and planted acreage implies an increase in land. This raises the research question (RQ) of what the ratio of technology to land is in terms of importance to food quality and efficiency. The answer to the posed RQ is important to determine the direction for developing the agricultural economy that makes the greatest contribution to food security.

V. Yu. Przhedetskaya Federal State Budgetary Institution National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Rostov-on-Don, Russia

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Table 14.1 Dynamics of the development of the agricultural economy in Russia in 2012–2020 Year

Investments in fixed capital (in actual prices), billion rubles

Sown area, million hectares

Profitability of sold agricultural products, %

Food security index, points 1–100

2012 2013 2014 2015 2016 2017 2018 2019 2020

276.3 307.1 313.8 304.7 379.8 400.5 431.7 469.7 466.0

55.7 56.1 55.3 55.1 54.7 54.4 53.6 53.3 52.7

12.0 5.8 18.6 21.7 16.4 13.6 15.4 14.0 20.3

65.4 66.5 65.9 65.6 63.5 69.8 72.6 73.4 73.9

Source Compiled by the authors based on the materials of Rosstat [12] and The Economist [11]

To answer the posed RQ, this research compares the contribution of agricultural automation and acreage to food quality and efficiency using Russia as an example. The research hypothesis (H) is that automation contributes much more to agricultural quality and efficiency than crop area and is therefore preferable.

Materials and Methods To test hypothesis (H) and compare the contribution of agricultural automation and sown area to food quality and efficiency, this research conducts a regression analysis of the dependence of quality (food security index calculated by The Economist [11]) and efficiency (profitability of sold agricultural products) on automation (investment in fixed capital) and sown area in Russia in 2012–2020 based on statistics from the Federal State Statistics Service of the Russian Federation (Rosstat) [12] (Table 14.1).

Results As a result of econometric modeling of the contribution that the considered alternative directions for the development of agricultural economy (automation and increase of sown area) have on the quality and efficiency of agriculture, the following results (Table 14.2).

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Table 14.2 Results of regression analysis Type of indicators

Indicators

Regression statistics

Multiple R R2 Normalized R 2 Standard error Observations F Significance of F Constant Investments in fixed capital (x1 ) Sown area (x2 ) Constant Investments in fixed capital Sown area Constant Investments in fixed capital Sown area Constant Investments in fixed capital Sown area

Variance analysis Coefficients

Standard error

t-statistic

P-value

Profitability of sold agricultural products (y1 )

Food security index (y2 )

0.8120 0.6593 0.5458 3.2303 9.0000 5.8064 0.0395 638.2342 −0.1396

0.8656 0.7493 0.6657 2.2896 9.0000 8.9654 0.0158 156.1195 0.0198

−10.4681 191.3938 0.0492

−1.7416 135.6553 0.0349

3.1881 3.3347 −2.8343

2.2597 1.1509 0.5684

−3.2835 0.0157 0.0298

−0.7707 0.2936 0.5904

0.0167

0.4701

Source Calculated and compiled by the authors

According to Table 14.2, the quality and efficiency of agriculture in Russia are largely determined by automation: the index of food security at 74.93% (R 2 -0.7493), and the profitability of sold agricultural products at 65.93% (R 2 -0.6593). This makes it possible to create the following multiple linear regression models: y1 = 638.2342 − 0.1396 × x1 − 10.4681 × x2 ; y2 = 156.1195 + 0.0198 × x1 − 1.7416 × x2 . According to the obtained models, the reduction of sown area by one million hectares contributes to an increase in the profitability of sold agricultural products by 10.4681% and increases food security index by

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1.7416 points. The increase in investments in fixed capital by one billion rubles provides an increase in food security index by 0.0198 points. Both models are valid at the 0.05 significance level (significance F = 0.0395 for the first model; significance F = 0.0158 for the second model). Based on the obtained regression results (econometric models), it was found that food security in Russia can reach 100% (+36.23% compared to 2020) with the number of crops decreased by 28.78% (to 37.53 million hectares). This will increase the profitability of agriculture by 787.96% (to 175.53%). In this case, it is recommended to increase the volume of investment in fixed capital by 7.30% to 500 billion rubles. It is recommended to invest in automation of agriculture based on deep learning. This will enable the transition from horizontal farms to smart vertical farms, making it possible to increase productivity while reducing the cultivated area and produce products with defined and improved nutritional properties. This will achieve better quality and efficiency. Prospects for improving food security management to improve the quality and efficiency of the agricultural economy of Russia are associated with the introduction of advanced technology of deep learning in the activities of agricultural enterprises. The advantages of deep learning (compared to AI-based automation and big data) are related to the following: . Multitasking and the possibility of polycriteria optimization, allowing for simultaneous improvement of quality and efficiency (considering their contradictory nature) in agriculture; . The possibility of identifying new (previously unknown) factors of quality and efficiency in agriculture and finding solutions to manage these factors; . Comprehensive automation of management and production in agriculture, allowing one to constantly identify and correct errors, achieving the best results for quality and efficiency.

Conclusion In conclusion, hypothesis (H) proved to be correct, and its confirmation answered the RQ posed. Technology is a more important factor in production than land for food quality and efficiency. Consequently,

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automation makes the greatest contribution to food security and is therefore preferable. Further automation of agriculture is advisable based on deep learning because it will achieve more pronounced results in the form of increased food quality and increased efficiency of agricultural entrepreneurship. The contribution of the article to the literature lies in the fact that the resulting econometric model of the development of the agricultural economy revealed and quantified the pattern of growth in food quality and efficiency depending on the level of automation. The considered experience of Russia can be useful for other countries. The practical significance of this research is related to the fact that the proposed recommendations allow for improving the quality and efficiency of agriculture and successfully implementing SDG 2 through the automation of agriculture based on deep learning.

References 1. Presidential Executive Office. (2022). Decree “On approval of the Food Security Doctrine of the Russian Federation” (January 21, 2020, No. 20). http:// www.kremlin.ru/acts/bank/45106. Accessed 9 May 2022. 2. Ministry of Agriculture of the Russian Federation. (2022). Departmental project “Digital agriculture.” https://mcx.gov.ru/upload/iblock/900/900 863fae06c026826a9ee43e124d058.pdf. Accessed 9 May 2022. 3. Khan, S., Tufail, M., Khan, M. T., Khan, Z. A., & Anwar, S. (2021). Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer. Precision Agriculture, 22, 1711– 1727. https://doi.org/10.1007/s11119-021-09808-9 4. Rao, I., Shirgire, P., Sanganwar, S., Vyawhare, K., & Vispute, S. R. (2022). An overview of agriculture data analysis using machine learning techniques and deep learning. In J. I.-Z. Chen, J. M. R. S. Tavares, A. M. Iliyasu, & K L. Du (Eds.), Second international conference on image processing and capsule networks (pp. 343–355). Springer. https://doi.org/10.1007/9783-030-84760-9_30 5. Saleem, M. H., Potgieter, J., & Arif, K. M. (2021). Automation in agriculture by machine and deep learning techniques: A review of recent developments. Precision Agriculture, 22, 2053–2091. https://doi.org/10. 1007/s11119-021-09806-x 6. Wang, C., Liu, B., Liu, L., Hou, J., Liu, P., & Li, X. (2021). A review of deep learning used in the hyperspectral image analysis for agriculture. Artificial Intelligence Review, 54, 5205–5253. https://doi.org/10.1007/ s10462-021-10018-y

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7. Popkova, E. G., Sozinova, A. A., & Sofiina, E. V. (2022). Model of agriculture 4.0 based on deep learning: Empirical experience, current problems and applied solutions. In E. G. Popkova & B. S. Sergi (Eds.), Smart innovation in agriculture (pp. 333–346). Springer. https://doi.org/10.1007/ 978-981-16-7633-8_37 8. Przhedetsky, Y. V., Przhedetskaya, N. V., & Przhedetskaya, V. Y. (2020). Developing countries on the path of implementing the national oncological projects: Market barriers and marketing tools for overcoming them. In E. Popkova, B. Sergi, L. Haabazoka, & J. Ragulina (Eds.), Supporting inclusive growth and sustainable development in Africa (Vol. I, pp. 335–344). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-41979-0_25 9. Sozinova, A. A., Sofiina, E. V., Petrenko, Y. S., & Bencic, S. (2022). International features of using smart technology in agriculture: Overview of innovative trends. In E. G. Popkova & B. S. Sergi (Eds.), Smart innovation in agriculture (pp. 167–173). Springer. https://doi.org/10.1007/ 978-981-16-7633-8_18 10. Varlamov, A. V., Kitsay, Y. A., Przhedetskaya, N. V., & Zabaznova, T. A. (2020). The mechanism of social adaptation of AI for organization of intellectual consumption in the digital economy. In E. Popkova & B. Sergi (Eds.), Artificial intelligence: Anthropogenic nature vs. social origin. ISC conference—Volgograd 2020 (pp. 352–358). Springer. https://doi.org/10. 1007/978-3-030-39319-9_40 11. The Economist. (2022). Global food security index: Rankings and trends 2012–2021. https://impact.economist.com/sustainability/project/food-sec urity-index/Index. Accessed 9 May 2022. 12. Federal State Statistics Service of the Russian Federation (Rosstat). (2022). Agriculture in Russia: Statistical collection. https://rosstat.gov.ru/folder/ 210/document/13226. Accessed 9 May 2022.

CHAPTER 15

Responsible Production and Consumption in Agriculture 4.0 Based on Deep Learning for Sustainable Development Yerlan B. Zhailauov , Natalia V. Przhedetskaya , and Vasiliy I. Bespyatykh

Introduction The systemic implementation of all 17 Sustainable Development Goals (SDGs) is not only preferred but also successfully achieved in current economic practice. A typical example is an industry. In the concept of

Y. B. Zhailauov “Rational Solution” LLP, Karaganda, Kazakhstan e-mail: [email protected] N. V. Przhedetskaya (B) Rostov State University of Economics, Rostov-on-Don, Russia e-mail: [email protected] V. I. Bespyatykh Vyatka State University, Kirov, Russia e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_15

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a sustainable industrial economy, responsible production and consumption in the industry saves resources with a fuller utilization of production capacity and satisfaction of demand (growth of resource efficiency). Moreover, it increases the environmental friendliness of industrial products, waste, and consumption. The advanced technology of Industry 4.0 plays an important role in these processes. The concept of a sustainable agricultural economy focuses on food security, which limits it to a single SDG 2. In this regard, the potential of agriculture to support sustainable development is not fully explored (unknown) and revealed. In the existing literature, Gennari et al. [1], Morales and Elkader [2], Schwindenhammer and Gonglach [3], and Soberón et al. [4] provide theoretical arguments and case studies showing that agriculture can and does contribute to the implementation of various SDGs (not only SDG 2). On this basis, it is hypothesized that agriculture has the potential to systemically support all 17 SDGs, and the development of responsible production and consumption in agriculture 4.0 based on deep learning is necessary to unlock this potential. This paper explores the international experience and justifies the benefits of responsible production and consumption in agriculture 4.0 based on deep learning for sustainable development.

Literature Review Responsible production and consumption in agriculture has been studied and covered in detail in the works of Buele et al. [5], Chernyshov et al. [6], Gorelikov et al. [7], Inshakova et al. [8], Karbekova et al. [9], Regan [10], Shabaltina et al. [11], Tchernyshev et al. [12], and Tzachor et al. [13]. The basics of farming 4.0, in particular in terms of the use of deep learning, are revealed in the works of Latino et al. [14], Lee et al. [15], Lin et al. [16], Martinho [17], Rao et al. [18], and Ren et al. [19]. However, the contribution of agriculture 4.0 to the development of responsible food consumption and production, the realization of the SDGs, and the importance of deep learning to achieve this contribution is unknown, which is a gap in the literature. This research fills this gap by rethinking the essence and clarifying the cause-and-effect relationship of responsible production and consumption in agriculture 4.0 based on deep learning from the perspective of sustainable development.

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Fig. 15.1 Results achieved by 2021 in food security and responsible production and consumption, scores 1–100 (Source Compiled by the authors based on the UNDP materials [20])

Materials and Methodss To determine the contribution of responsible production and consumption to food security, this research applies the method of regression analysis. This method is applied to find the dependence of the results in the implementation of SDG 2 on the achievements in the implementation of SDG 7. The research sample includes countries with different levels of SDG 2 implementation: high (over 70 points: China, Japan, and Vietnam), medium (56–70 points: Saudi Arabia, Cuba, Bolivia, and Russia), and low (55 points and less: Trinidad and Tobago, Brunei Darussalam, and Barbados) (Fig. 15.1). According to Fig. 15.1, by 2021, there were strong results in food security (China’s maximum score is 81.10) and responsible production and consumption (Vietnam’s maximum score is 91.90).

Results Based on the statistics from Fig. 15.1, the authors conducted a regression analysis of the dependence of food security on the responsibility of production and consumption in 2021, the results of which are presented in Table 15.1. According to Table 15.1, a one-point increase in production and consumption responsibility increases food security by 0.3534 points (the

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Table 15.1 Regression dependence of SDG 2 on SDG 12 Regression statistics Multiple R R2 Normalized R Standard error Observations

0.5559 0.3090 0.2226 9.0640 10

Variance analysis df

SS

MS

F

Significance of F

Regression Balance Total

1 8 9

293.8987 657.2473 951.1460

293.8987 82.1559

3.5773

0.0952

Coefficients

Standard error

t-statistics

P-value

Bottom 95% Top 95%

36.3190 0.3534

13.8084 0.1868

2.6302 1.8914

0.0302 0.0952

4.4768 −0.0775

Constant Goal 12 score

68.1613 0.7843

Source Calculated and compiled by the authors

model is robust at the 0.1 significance level). The correlation between SDG 2 and SDG 12 is high (55.59%). To determine how responsible production and consumption in agriculture can be integrated into a system of 17 SDGs, the authors conduct a qualitative study that considers deep learning opportunities (Table 15.2). As shown in Table 15.2, responsible production and consumption in agriculture not only supports SDG 2 and SDG 12 but also develops effective institutions (SDG 16) and community-business partnerships for sustainable development (SDG 17). Deep learning provides intelligent support for purchasing decisions, encouraging responsible consumers to prefer the products of responsible businesses and natural (organic) products. Responsible agriculture 4.0 creates gender-neutral workplaces, conducts corporate training, and unlocks human potential because deep learning offers increasingly new and optimal human resource

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Table 15.2 The benefits of responsible production and consumption in agriculture 4.0 for sustainable development Economic processes of the agricultural economy

The use of agricultural economy personnel in agriculture (labor)

Use of natural resources in agriculture (land) Financing innovation in the agricultural economy (capital) Production and sale of food in the agro-industrial complex (transformation of production factors) Disposal of waste from the agricultural economy

Organization in agriculture 4.0 based on deep learning Responsible consumption (SDGs 2, 12, 16, and 17)

Responsible production (SDGs 2, 12, 16, and 17)

Preference for the products of responsible businesses in intelligent support of purchasing decisions

Creating gender-neutral workplaces, corporate training, and unlocking human potential Regenerative environmental management ESG-funded innovation with a growing return on investment Productivity growth and increase in the production capacity of subsistence agriculture

Preference for organic products

Preference for biodegradable packaging, continuously improving environmentally safe waste management

Benefits for sustainable development

Support of SDGs 4, 5, and 8

Support of SDG 13

Support of SDGs 1, 6, 7, 9, 10, and 11

Support of SDGs 3 and 10

Support of SDGs 14 and15

Source Developed by the authors

management solutions. Deep learning makes regenerative environmental management more accessible, scalable, and effective. There is productivity growth, an increase in the productive capacity of subsistence agriculture, and ESG-funded innovations with increasing returns on investment. Responsible consumers and producers in agriculture 4.0 prefer biodegradable packaging, continuously improving with deep learning eco-friendly waste management.

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Conclusion Summarizing the research results, we can conclude that responsible production and consumption, as well as agriculture 4.0 based on deep learning, provide systemic support for all 17 SDGs in the agricultural economy, which confirms the research hypothesis. Responsible production and consumption in agriculture 4.0 based on deep learning allows for increasing the sustainability of all production and distribution processes: from the involvement of factors of production (shown using labor, land, and capital as an example) to their transformation into finished products and their sale, as well as the disposal of waste. The theoretical significance of these results is that they substantiate the benefits of responsible production and consumption in agriculture 4.0 based on deep learning for sustainable development and reveal the potential of systemic implementation of all 17 SDGs in the agricultural economy. The practical significance of the author’s conclusions is that the recommended development of responsible production and consumption in agriculture 4.0 will allow accelerating the process of sustainable development in the “Decade of Action.” The author’s recommendations are universal and applicable to all countries worldwide.

References 1. Gennari, P., Rosero-Moncayo, J., & Tubiello, F. N. (2019). The FAO contribution to monitoring SDGs for food and agriculture. Nature Plants, 5(12), 1196–1197. https://doi.org/10.1038/s41477-019-0564-z 2. Morales, M. L. V., & Elkader, M. A. A. (2020). Logistics 4.0 technologies in agriculture systems: Potential impacts in the SDG. Proceedings of the 29th International Conference of the International Association for Management of Technology, IAMOT 2020: Towards the Digital World and Industry X.0 (pp. 976–989). Cairo, Egypt. 3. Schwindenhammer, S., & Gonglach, D. (2021). SDG implementation through technology? Governing food-water-technology nexus challenges in urban agriculture. Politics and Governance, 9(1), 176–186. https://doi.org/ 10.17645/pag.v9i1.3590 4. Soberón, M., Sánchez-Chaparro, T., Urquijo, J., & Pereira, D. (2020). Introducing an organizational perspective in SDG implementation in the public sector in Spain: The case of the former ministry of agriculture, fisheries, food and environment. Sustainability, 12(23), 9959. https://doi.org/ 10.3390/su12239959

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5. Buele, I., Zúñiga, D., & Tobar, L. (2021). Principles for responsible investment in agriculture and food systems and their social impact: Application to universitary projects. Academy of Entrepreneurship Journal, 27 (4), 1–13. 6. Chernyshov, N. I., Sysoev, O. E., & Kiselev, E. P. (2021). Gantry technology in agriculture greening. In O. G. Shakirova, O. V. Bashkov, & A. A. Khusainov (Eds.), Current problems and ways of industry development: Equipment and technologies (pp. 303–309). Springer. https://doi.org/10. 1007/978-3-030-69421-0_32 7. Gorelikov, K. A., Komarov, A. V., & Bezsmertnaya, E. R. (2021). A paradigm shift in business management in the context of industry 4.0. In E. G. Popkova & V. N. Ostrovskaya (Eds.), Meta-scientific study of artificial intelligence (pp. 469–476). Information Age Publishing. 8. Inshakova, A. O., Goncharov, A. I., & Agibalova, E. N. (2022). Legal status and delineation of responsibility in the development and use of artificial intelligence systems. In A. O. Inshakova & E. E. Frolova (Eds.), The transformation of social relationships in industry 4.0 (pp. 251–266). Information Age Publishing. 9. Karbekova, A. B., Osmonalieva, D. A., Sadyraliev, Z., & Urmatayim, A. K. (2019). The main directions of digital modernization of the agro-industrial complex of a modern region. In E. Popkova (Ed.), Ubiquitous computing and the internet of things: Prerequisites for the development of ICT (pp. 949– 955). Springer. https://doi.org/10.1007/978-3-030-13397-9_98 10. Regan, Á. (2021). Exploring the readiness of publicly funded researchers to practice responsible research and innovation in digital agriculture. Journal of Responsible Innovation, 8(1), 28–47. https://doi.org/10.1080/23299460. 2021.1904755 11. Shabaltina, L. V., Karbekova, A. B., Milkina, E., & Pushkarev, I. Y. (2021). The social impact of the downturn in business and the new context of sustainable development in the context of the 2020 economic crisis in developing countries. In E. G. Popkova & B. S. Sergi (Eds.), Modern global economic system: Evolutional development vs. revolutionary leap (pp. 74–82). Springer. https://doi.org/10.1007/978-3-030-69415-9_9 12. Tchernyshev, N. I., Kiselyov, E. P., & Sysoev, O. E. (2019). Bridge agriculture as the basis of preserving soiled bioorganisms. IOP Conference Series: Earth and Environmental Science, 272, 032021. https://doi.org/10.1088/ 1755-1315/272/3/032021 13. Tzachor, A., Devare, M., King, B., Avin, S., & Ó hÉigeartaigh, S. (2022). Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nature Machine Intelligence, 4(2), 104–109.https://doi.org/10.1038/s42256-022-00440-4 14. Latino, M. E., Menegoli, M., Lazoi, M., & Corallo, A. (2022). Voluntary traceability in food supply chain: A framework leading its implementation in

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agriculture 4.0. Technological Forecasting and Social Change, 178, 121564. https://doi.org/10.1016/j.techfore.2022.121564 Lee, K., Silva, B. N., & Han, K. (2020). Deep learning entrusted to fog nodes (DLEFN) based smart agriculture. Applied Sciences, 10(4), 1544. https://doi.org/10.3390/app10041544 Lin, C.-H., Wang, W.-C., Liu, C.-Y., Pan, P.-N., & Pan, H.-R. (2019). Research into the e-learning model of agriculture technology companies: Analysis by deep learning. Agronomy, 9(2), 83. https://doi.org/10.3390/ agronomy9020083 Martinho, V. J. P. D. (2022). Systematic review of agriculture and era 4.0: The most relevant insights. In Trends of the agricultural sector in era 4.0 (pp. 49–64). Springer. https://doi.org/10.1007/978-3-030-98959-0_2 Rao, I., Shirgire, P., Sanganwar, S., Vyawhare, K., & Vispute, S. R. (2022). An overview of agriculture data analysis using machine learning techniques and deep learning. In J. IZ. Chen, J. M. R. S. Tavares, A. M. Iliyasu, & KL. Du (Eds.), Second international conference on image processing and capsule networks (pp. 343–355). Springer. https://doi.org/10.1007/9783-030-84760-9_30 Ren, C., Kim, D.-K., & Jeong, D. (2020). A survey of deep learning in agriculture: Techniques and their applications. Journal of Information Processing Systems, 16(5), 1015–1033. https://doi.org/10.3745/JIPS.04.0187 UNDP. (2022). Sustainable Development Report 2021: The decade of action for the Sustainable Development Goals. https://dashboards.sdgindex.org/. Accessed 10 May 2022.

CHAPTER 16

Agriculture 4.0: Perspectives on Food Security in the Agricultural Economy of the Future Elena G. Popkova and Bruno S. Sergi

Agriculture 4.0 offers new and broad prospects for food security in the agricultural economy of the future. As shown by the best practices of Central Asia discussed in this book, agriculture 4.0 is already actively pursued and provides benefits for food security. The Fourth Industrial Revolution changes the technological landscape in the agricultural economy, ensuring climate resilience of agricultural enterprises, improving the quality of food while preserving its naturalness, providing

E. G. Popkova (B) Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia e-mail: [email protected] B. S. Sergi Harvard University, Cambridge, MA, USA e-mail: [email protected] University of Messina, Messina, Italy

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5_16

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knowledge-intensive employment and unlocking human potential in the agricultural economy, turning agriculture 4.0 into a vector of economic growth and an attractive investment project, unprecedentedly accelerating rural development, and supporting import substitution of food. The book demonstrated the significant potential for the development of agriculture 4.0 and the leading role of deep learning in this process. Deep learning is the technology of the future that is being mastered in the present. For agriculture 4.0, deep learning offers opportunities for continuous development and comprehensive response to increasing global challenges in the long term. Simultaneously, the new knowledge gained also raised new research questions. Since agriculture 4.0 involves the conversion of economic processes in the agricultural economy into an electronic format with the creation of cyber-biological systems, the main issues that have arisen are related to cybersecurity. The first issue has to do with how to ensure the smooth operation of smart farms. The point of this question comes down to ways to avoid the paralysis of smart farms during power outages or equipment breakdowns. If agriculture 4.0 is unstable, instead of solving food security problems, it could exacerbate them. The second question is how to protect digital data and information in the agricultural economy of the future. If agriculture 4.0 involves electronic storage of information about each unit of food throughout the value chain, there is a risk of distortion or loss of this information, which should not be tolerated. The third question comes down to how to protect plants—to prevent cyber-biological systems from disrupting the natural growth conditions of plants. This question boils down to how to preserve true naturalness and prevent the prevalence of smart sensors over living plants—to preserve the natural look of agriculture 4.0. The questions raised are complex and urgent, and it is suggested that further scientific research based on this book be devoted to investigating them and finding answers to them.

Index

A Advanced digital technology, 2, 3, 27, 28, 72, 76, 94, 97, 116–119, 124, 132, 135, 140 Agricultural cooperatives, 102–104, 109 Agricultural development, 21, 28–30, 33, 76, 109, 132 Agricultural economy, 2–5, 10, 11, 14, 15, 28, 29, 32, 38, 40, 45, 75–77, 79–81, 89, 90, 93, 94, 97, 109, 115, 116, 118, 123–129, 132, 133, 135, 136, 140, 143, 144, 147, 148 Agricultural innovations, 86, 89, 90 Agriculture, 1–5, 9–15, 28–30, 32, 33, 38–41, 45, 60–63, 66, 68–70, 72, 75–77, 79–81, 86, 87, 89, 90, 93–97, 103, 109, 115–119, 123–129, 132–136, 140, 142–144, 148 Agriculture 4.0, 2–5, 45, 75–77, 80, 81, 86, 87, 90, 94, 96, 97, 124–129, 140, 142–144, 148

Artificial intelligence, 4, 10, 60–62, 70–72, 76, 79, 81, 87, 96, 117, 132 Automation, 14, 76, 87, 97, 116, 124, 132–136 B Best practices, 4, 28, 29, 60, 90, 139, 147 C Case, 3, 19, 63, 118 Central Asia, 4, 101, 109, 147 Climate risk, 93–97, 116 Compliance, 38, 39, 46 Cultivation of plants, 86, 89, 93, 94, 148 D Deep learning, 2, 4, 11–15, 61, 62, 70–72, 86–90, 94, 96, 97, 124, 126–129, 132, 135, 136, 140, 142–144, 148

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Popkova and B. S. Sergi (eds.), Food Security in the Economy of the Future, https://doi.org/10.1007/978-3-031-23511-5

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Developed countries, 30, 32, 33 Developing countries, 22, 28–30, 32, 33, 129 Digital agriculture, 1–4, 27–30, 33, 76, 77, 79–81, 94–97, 123–129, 132 Digital competitiveness, 67–69, 72, 118, 129 Digital economies, 1, 5, 29, 60–63, 68–72, 78, 103, 109, 117, 127 Digital model, 28, 29, 33 E Economic transition, 4, 5, 10, 14, 15, 45, 55, 60, 62, 68, 70, 75, 76, 81, 86, 90, 103, 109, 124, 126–129, 135 Economy of the future, 4, 5, 11, 12, 14, 75, 76, 79, 81, 124, 128, 129 Efficiency, 22, 33, 41, 60, 61, 76, 77, 81, 132–136, 140 Egypt, 19–23 Environmental protection, 52–54 F Farming, 1, 3, 10, 14, 15, 27, 38–40, 86, 90, 93, 140 Food import, 13, 38, 39, 61, 66, 72, 85–90 Food-importing countries, 38, 39, 61, 66, 72, 85–90 Food security, 1–5, 10, 11, 14, 15, 20, 22, 27, 34, 38–41, 45, 59–64, 66–72, 75–77, 79–81, 86–90, 94–97, 101, 103, 109, 116–119, 124, 132, 134–136, 140, 141, 147, 148 G Green bonds, 46, 48, 52, 54

Green finance, 4, 46–48, 54, 55 I Investment, 3, 28, 46–48, 53, 54, 60, 76, 93, 101–104, 109, 126, 133, 135, 143, 148 K Kyrgyzstan, 103 M Management, 10–12, 14, 15, 22, 32, 46, 54, 61, 70, 72, 90, 94–97, 126, 129, 132, 135, 143 Management for enterprises, 94 Middle East, 19, 20, 23 Modeling, 94, 133 Monitoring, 2, 14, 38, 39, 70, 126 P Prospects, 4, 15, 20, 32, 33, 76, 79, 87, 90, 94, 97, 102, 109, 116, 132, 135, 147 Q Quality, 11, 12, 21, 22, 37, 63, 66, 68–71, 81, 93, 94, 96, 109, 124, 125, 132–136, 147 R Regional economy, 19, 20, 23, 103, 109 Responsible production and consumption, 140–144 Risk management, 10–15, 94, 96, 97 Risks, 2, 10, 11, 14, 15, 60, 61, 68, 76, 80, 81, 93, 94, 96, 97, 120, 126, 148 Roadmap, 124, 126–129

INDEX

151

Russia, 11–14, 46–48, 53–55, 60, 65, 88, 95, 118, 119, 131–136 Russian market, 54

Sustainable development, 19, 20, 22, 23, 38, 39, 48, 53, 54, 140, 142–144

S Scenario, 32, 76, 77, 79–81 SDG, 124, 139, 140, 142, 144 SDG 2, 10, 29–34, 37, 38, 124, 131, 136, 140–142 Smart agriculture, 10–12, 14, 15, 60–64, 68–72 Strategic directions, 11, 12, 14, 15

T Technological modernization, 75, 76 Traditional agriculture, 60–63, 66, 68, 72 W Water security, 19–23