Agricultural Supply Chains and Industry 4.0: Technological Advance for Sustainability 3030727696, 9783030727697

Citation preview

Agricultural Supply Chains and Industry 4.0 Technological Advance for Sustainability Stella Despoudi Konstantina Spanaki Oscar Rodriguez Espindola Efpraxia D. Zamani

Agricultural Supply Chains and Industry 4.0

Stella Despoudi •  Konstantina Spanaki Oscar Rodriguez-Espindola Efpraxia D. Zamani

Agricultural Supply Chains and Industry 4.0 Technological Advance for Sustainability

Stella Despoudi Aston Business School Aston University Birmingham, UK Department of Business Administration University of Western Macedonia Grevena, Greece Oscar Rodriguez-Espindola Aston University Birmingham, UK

Konstantina Spanaki Business School Loughborough University Loughborough, UK Efpraxia D. Zamani Information School The University of Sheffield Sheffield, UK

ISBN 978-3-030-72769-7    ISBN 978-3-030-72770-3 (eBook) https://doi.org/10.1007/978-3-030-72770-3 © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This book provides an in-depth analysis of the current topics in relation to Agricultural 4.0 Supply Chains. Practical insights to the topic are provided through the discussion of relevant industrial examples were agriculture 4.0 technologies have been applied. The current agricultural supply chains are facing many different issues and their future sustainability is questioned. Some of these issues are related to food loss, food safety, food insecurity and accessibility, increased demand for food, decreasing natural resources, raw materials scarcity, and global food crisis. It is important to understand these issues and the role that technology plays in solving these issues. Agriculture 4.0 is a relatively new concept although producers have been using other kinds of technologies for ages. The concept of agriculture 4.0 came from Industry 4.0. Thus, in this book the industry 4.0 and then the agriculture 4.0 concepts are discussed. There is a wide range of industry 4.0 applications which are also adopted as part of agriculture 4.0. In the ASC context the most well-known technological applications are about smart farming applications, smart devices and platforms, IoT, temperature control applications, blockchain applications, tracking and tracing technologies, autonomous land farming robots, autonomous aerial farming robots and smart monitors. Different ASC entities adopt different technologies that are transforming their operations. Changes are evident in all the different ASC entities operations. Sustainability and circular economy are hot topics in ASCs and companies

v

vi 

PREFACE

cannot ignore them anymore. These can be enabled through adoption of relevant agricultural 4.0 technologies and the results could lead to sustainable performance. Agricultural 4.0 technological applications can bring many advantages to ASC companies such as real-time data analysis and decrease of operational costs, increase in revenue and production flexibility, improvement in sustainability and enables circular economy, enhanced reliability and uptime, self-optimisation and quality of service, and improved infrastructure. However, agricultural 4.0 technologies are not panacea. At the moment there are several challenges which are mainly faced by early adopters or SMEs. Some of these challenges are sector heterogeneity, farm size, validation and collaboration, safety and security investment costs, and design and compatibility. Research about agriculture 4.0 is still in this infancy and there are numerous research opportunities some of which are discussed in the end of this book. I would like to thank the co-authors of this book for sharing their ideas from the diverse disciples in which they are working and for making this book possible. Birmingham, UK

Stella Despoudi

Contents

1 Introduction to This Book 1 2 Definition, Current Issues and Areas for Improvement in Agricultural Supply Chains 3 2.1 Definition of Agricultural Supply Chain (ASC) 3 2.2 The Need for Value-Addition in ASC 4 2.3 Key Issues in Agricultural Supply Chains 5 2.4 Summary10 References10 3 From Industry 4.0 to Agriculture 4.013 3.1 The Evolution of Industry 4.013 3.1.1 Definition of Industry 4.013 3.1.2 The Evolution to Industry 4.014 3.1.3 Technological Applications of Industry 4.016 3.1.4 Big Data and Analytics17 3.1.5 Autonomous Robots17 3.1.6 Simulation18 3.1.7 Horizontal and Vertical System Integration18 3.1.8 The Industrial Internet of Things (IoT)18 3.1.9 Cyber Security and Cyber Physical Systems19 3.1.10 The Cloud19 3.1.11 Additive Manufacturing20 3.1.12 Augmented Reality20 vii

viii 

Contents

3.2 The Emergence of the Agriculture 4.0 Concept20 3.2.1 Agriculture 4.020 3.2.2 The Evolution of Agricultural 4.0 Technology24 3.3 Summary26 References26 4 Technological Applications of Agricultural 4.0 Supply Chains29 4.1 Smart Farming29 4.2 Smart Devices and Platforms31 4.3 IoT31 4.4 Temperature Control Applications32 4.5 Blockchain Applications32 4.6 Tracking and Tracing Technologies33 4.7 Autonomous Land Farming Robots33 4.8 Autonomous Aerial Farming Robots34 4.9 Smart Monitors34 4.10 Summary34 References35 5 Data Sharing and the Transformation Agricultural 4.0 Supply Chain Operations37 5.1 Data Sharing in Agriculture 4.037 5.2 The Transformation of the Agricultural 4.0 Supply Chain Operations39 5.2.1 Farmers Operations39 5.2.2 Processors Operations40 5.2.3 Distributors Operations41 5.2.4 Retailers Operations42 5.2.5 Consumers44 5.3 Summary44 References44 6 Sustainability in Agricultural 4.0 Supply Chains47 6.1 Sustainability Definition47 6.2 The Triple Bottom Line Concept48

 Contents 

ix

6.3 Sustainability in Agricultural Supply Chains49 6.4 Sustainable Performance50 6.5 Summary51 References51 7 Circular Economy in Agricultural Supply Chains53 7.1 Definition of Circular Economy53 7.2 The Need for Circular Economy in Agricultural Supply Chains55 7.3 Towards Sustainable and Circular Agricultural 4.0 Supply Chains Through Agriculture 4.057 7.3.1 The Relationship Between CSR, Sustainability, CE, and Agriculture 4.057 7.3.2 Circular Economy Practices58 7.3.3 Circular Economy and Agriculture 4.061 7.4 Summary62 References62 8 Opportunities of Agricultural 4.0 Supply Chains65 8.1 Real-Time Data Analysis and Decrease of Operational Costs65 8.2 Increase in Revenue and Production Flexibility66 8.3 Improves Sustainability and Enables Circular Economy67 8.4 Enhances Reliability and Uptime67 8.5 Self-Optimisation and Quality of Service68 8.6 Improves Infrastructure69 8.7 Summary70 References70 9 Challenges of Agricultural 4.0 Supply Chains73 9.1 Sector Heterogeneity73 9.2 Farm Size74 9.3 Validation and Collaboration74 9.4 Safety and Security75 9.5 Investment Costs76 9.6 Design and Compatibility77 9.7 Concluding Remarks77 9.8 Summary78 References78

x 

Contents

10 Conclusion and the Way Forward81 10.1 Summary84 References85 Index99

About the Authors

Stella Despoudi  is Lecturer in Operations and Supply Chain Management at Aston University, UK and Adjunct Lecturer in Supply Chain Management at the University of Western Macedonia, Greece. She has extensive experience in agricultural supply chain management and her research focuses in the areas of food sustainability, circular economy, resilience, and the Industry 4.0. She has published research papers in the area of food supply chain management in internationally recognised operations, supply chain management, and engineering journals such as International Journal of Production Research, Production Planning and Control, Annals of Operations Research, and Journal of Information Management. She has served as Guest Editor for a Special Issue in the International Journal of Production Research (IJPR). She has been the lead investigator in research projects related to Food Supply Chain Management. Some examples are: ‘Sustainability, Resilience, and the Impact of Industry 4.0  in Agricultural Supply Chains’, and ‘Circular Agricultural Supply Chains’. Konstantina  Spanaki is a Lecturer in Information Management at Loughborough University. Her work focuses at the intersection of Information Systems (IS) and Operations Management (OM). Recently, she is actively involved in projects related to Data and Information Management, Technology Management, Data Sharing, Cloud Computing and Disruptive Technologies. Konstantina’s research has been published in Information Technology and People, Information Systems Frontiers, Computers in Industry, the International Journal xi

xii 

ABOUT THE AUTHORS

of Production Research, Production Planning and Control and other IS/OM outlets. She has served as Guest Editor for Special Issues in Production Planning and Control (PPC) journal, the International Journal of Production Research (IJPR) and International Journal of Information Management (IJIM). Konstantina is a member of the International Editorial Review Board of International Journal of Information Management (IJIM), co-ordinating editor for Information Systems Frontiers. Oscar  Rodríguez-Espíndola is a senior lecturer in Operations and Supply Chain Management at Aston University and a member of the Aston CRISIS centre. He has published in the area of humanitarian logistics in a range of high-quality journals. His research is focused on Supply Chain Management, Operational Research, Humanitarian logistics and Project Management. His expertise includes de use of optimisation models, simulation and geographical information systems for the analysis of the supply chain and the development of tools to support logistics decisions. Efpraxia D. Zamani  is a Senior Lecturer of information Systems at the University of Sheffield. She has received her doctorate from the Department of Management Science and Technology of the Athens University of Economics and Business (Greece). Her research interests are found at the intersection of organizational and social aspects of Information Systems, with an emphasis on how Information Technology shapes and is being shaped by work practices. Her work has appeared in the Information Systems Journal, the Journal of Information Technology, Government Information Quarterly, Technological Forecasting and Social Change, Information System Frontiers and the International Journal of Electronic Commerce, and she has presented her research in numerous conferences. She has worked on several EU and nationally funded research projects.

List of Figures

Fig. 5.1 Fig. 7.1 Fig. 7.2

Typical agricultural 4.0 supply chain. (Source: Authors) Comparison between linear economy and circular economy People, process and technology in agricultural supply chains. (Source: Authors)

39 54 58

xiii

List of Tables

Table 3.1 Table 4.1 Table 5.1

The three evolutions of agricultural technology Technological applications of agriculture 4.0 and implications for agricultural operations Descriptions of website features

25 30 43

xv

CHAPTER 1

Introduction to This Book

Abstract  The introduction chapter presents the aim of this book and provides summaries of the chapters’ contents. Keywords  Industry 4.0 • Agriculture 4.0

This book aims to provide an in-depth understanding of the concepts of agriculture 4.0 and agricultural supply chains 4.0, their evolution, their main characteristics as well as applications. Implications on sustainability and circular economy practices of ASC are presented too. The opportunities and challenges of agricultural 4.0 supply chains are discussed too. In the end future research directions are provided. Chapter 1 i.e. the current chapter introduces the reader to the topics are that are covered in this book. Chapter 2 presents the definition of agricultural supply chains, explains the need for value-addition in agricultural supply chains, and discusses the main issues in agricultural supply chains such as food loss, food safety, food insecurity and accessibility, increased demand for food, decreasing natural resources, raw materials scarcity, and global food crisis. Chapter 3 starts with an introduction to industry 4.0 including its evolution across the years and its different technological applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Despoudi et al., Agricultural Supply Chains and Industry 4.0, https://doi.org/10.1007/978-3-030-72770-3_1

1

2 

S. DESPOUDI ET AL.

Then the emergence of the agriculture 4.0 concept is discussed along with its evolution. Chapter 4 explains the different technological applications of agricultural 4.0 supply chains which are related to smart farming applications, smart devices and platforms, IoT, temperature control applications, blockchain applications, tracking and tracing technologies, autonomous land farming robots, autonomous aerial farming robots and smart monitors. Chapter 5 focuses of data sharing issues and the transformation that agriculture 4.0 brought to supply chains and operations. This transformation relates to changes in farmers operations, processors operations, distributors operations, retailers’ operations and consumers. Chapter 6 discusses the aspect of sustainability in agricultural 4.0 supply chains. It starts by defining sustainability, the triple bottom line concept, and sustainability in agricultural supply chains. The chapter concludes with the concept of sustainability performance and its importance in relation to agriculture 4.0. Chapter 7 is about circular economy in agricultural supply chains. First the definition of circular economy is presented and then the need for circular economy in agricultural supply chains is outlined. This is followed by a discussion of the link among corporate social responsibility, circular economy, agriculture 4.0 and sustainability and this leads to the explanation of the circular economy practices. The chapter concludes with a discussion of the relationship between circular economy and agriculture 4.0. Chapter 8 presents the opportunities of agricultural 4.0 supply chains which include the real-time data analysis and decrease of operational costs, increase in revenue and production flexibility, improvement in sustainability and enablers circular economy, enhanced reliability and uptime, self-­ optimisation and quality of service, and improved infrastructure. Chapter 9 discusses the challenges of agricultural 4.0 supply chains which are identified as the following: sector heterogeneity, farm size, validation and collaboration, safety and security investment costs, and design and compatibility. Chapter 10 is the last chapter of this book. The chapter starts with an overview of the key aspects of the book and then future research avenues are provided.

CHAPTER 2

Definition, Current Issues and Areas for Improvement in Agricultural Supply Chains

Abstract  This chapter starts with an introduction of the agricultural supply chains and then the need for value addition is explained. The chapter aims to introduce the reader to the key issues that ASCs are facing in relation to food loss, food safety, food insecurity and accessibility, increased demand for food, decreasing natural resources, raw materials scarcity, and global food crisis. By discussing these issues areas of improvement emerge which highlight the need for technological solutions such as agriculture 4.0. Keywords  Agricultural issues • Value-addition • Food insecurity • Food loss • Food waste • Natural resource scarcity • Food crisis

2.1   Definition of Agricultural Supply Chain (ASC) The term Agricultural Supply Chain (ASC) describes the activities from production to distribution that bring agricultural or horticultural products from the farm to the table (Aramyan and Van Gogh 2014). ASC’s are formed by organizations responsible for production (producers), distribution, processing, and marketing of agricultural products to the final consumers. There are two different types of ASCs. The first one is the SC of fresh agricultural products, and the second one is the SC for © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Despoudi et al., Agricultural Supply Chains and Industry 4.0, https://doi.org/10.1007/978-3-030-72770-3_2

3

4 

S. DESPOUDI ET AL.

non-perishable agricultural products (Defra 2006). Fresh agricultural products include highly perishable crops (e.g. fresh fruits and vegetables) whose shelf-life can be measured in days, while non-perishable agricultural products are those that can be stored for longer periods of time (e.g. grains, potatoes, and nuts). ASCs have some special characteristics which differentiate them from the other FSC classifications (Foresight 2011b). Some of those characteristics are the following: limited shelf-life, price variability, importance of quality and dependence on weather conditions (FAO 2002). The aforementioned characteristics increase the complexity of ASCs and make it more difficult to manage them than other FSCs. Producing and managing fresh agricultural products is more complex because of their limited shelf-life and the infrastructure needed to maintain them.

2.2   The Need for Value-Addition in ASC Agricultural Supply Chains (ASCs) are an essential part for the survival of society. Agriculture dates back thousands of years and it has evolved considerably through time. It involves several steps to produce commodities able to add value for consumers which are arranged in a “farm-to-fork” sequence. Agricultural supply chains involve “activities such as the processing of raw agricultural commodities, checking consumer safety standards, packing or transport activities add value to food before it is sold. The food chain links all the market players involved in the production, processing and distribution of food to consumers” (European_Commission 2015). Agricultural supply chains have different stages and the historical focus on efficiency makes it essential to identify opportunities to prioritise value adding activities. Humphrey and Memedovic (2006) highlight four links for value addition in agricultural supply chains: • Inputs—It involves companies producing the materials required for farming and it has been significantly focused on seeds, agrochemicals, machinery and equipment; • Production—This stage is related to the agricultural production and it involves farming activities necessary to transform the inputs into consumable commodities; • Processing—This step involves activities ranging from handling, preserving and transferring fresh product for consumption, to the transformation of agricultural outputs into processed products;

2  DEFINITION, CURRENT ISSUES AND AREAS FOR IMPROVEMENT… 

5

• Delivery—This part is focused on the point of sale of products. Agricultural products can be sold through open markers, wholesalers, retail (e.g. in the supermarket) or to become part of the catering industry (e.g. restaurants).

2.3   Key Issues in Agricultural Supply Chains Although ASC stakeholders make efforts to improve and increase the value adding activities there are different extrinsic and intrinsic factors that affect value-adding activities to happen but also show that value addition is not sufficient to make the chain efficient and effective. The increase in demand stemming from population growth, the percentage of undernourished people because of the high level of poverty, constraints in raw materials, and global crisis represent of the most demanding external pressures. At the same time, agricultural supply chains are complex because of the nature of the products and processes. Post-harvest loss, perishability, food safety, food accessibility, increased demand for food, decreasing natural resources, raw materials scarcity, global food crises, and food quality represent significant challenges. The world population has been predicted to reach 9 billion by 2050 and this will require a 70% increase in food production (FAO 2009). Due to that food insecurity concerns are arising as producing sufficient food, distributing it appropriately and avoiding wastage are key (Foresight 2011a). Food security refers to the situation when people have consistent and everyday physical, social and economic access to safe and nutritious food of their preferences (FAO 2011). The COVID-19 pandemic indicated that the food security issue is not affecting only developing countries but also developed ones such as European countries. During the first wave of the pandemic in particular due to the closed borders and the logistics disruptions there were many food shortages. For example, many people even in the UK did not have access to the food of their preference as it was out-of-stock and it was unclear when it will be available again. Therefore, food security is an aspect in ASCs that needs to be improved globally. The reduction of the natural resources available and the expected future scarcity of them highlights the need that resources need to be preserved and alternative ways to produce current food products need to be identified (Despoudi 2019). ASC companies are working with producers to find ways to invest back to the land from which they are sourcing their raw materials by replanting crops, and trees. Also, food manufacturers are

6 

S. DESPOUDI ET AL.

making efforts to find new ways to produce food products in ways that the natural resource usage is reduced, or even alternative raw materials are identified. Although there is progress on that there is still a long way from achieving improvements in natural resources availability. The human activities globally caused significant damage to the environment and this has huge implications on the biodiversity of the species with many plants and animals becoming extinct. This also affected the natural land availability as due to its heavy usage from growing food and the increased use of chemicals in production is limited; this highlight the need of finding new ways to grow crops with different innovative methods that reduce the negative environmental implications (Vidal 2012). Another issue in ASCs is food losses or food waste (Despoudi 2016). Food loss refers to reductions in edible food mass throughout the part of the supply chain that specifically leads to edible food for human consumption (FAO 2011). Food is lost or wasted throughout the supply chain from initial agricultural production down to final household consumption. It has been estimated that between 25% and 50% of the food produced is lost or wasted along the supply chain and does not reach consumers, depending on its position in the supply chain (FAO 2010). In the ASC the majority of food is lost from the producers to the retailers point (Gustavsson et al. 2011). Smallholder producers despite producing more than 70% world’s food, they represent more than half of the world’s hungriest people (Gidney 2012). Most of the research about food loss is focused either at retailers’ or at consumers’ point in the ASC (Despoudi 2020a; WRAP 2011). There is limited research about food loss from the producers’ perspective (Despoudi 2016; Despoudi et al. 2018). Although there is much discussed on food loss within the supply chain management literature, there is limited information on how to reduce and prevent it from happening in the upstream ASC (Parfitt et al. 2010). There is a need for developing a sustainable and fair ASC (Driscoll 2012). Reducing food losses can increase grain supply, food availability and food security without wasting other resources such as land, labour, water and inputs. Therefore, new ways need to be found in order to enable food losses reduction in the upstream supply chain which will enable the chain to become more sustainable. At the consumers stage there are also food losses due to consumers throwing away food or not recycling food packaging. In response to that companies need to invest in sustainable food packaging. Consumers want products with recyclable packaging, however in many cases it is not clear

2  DEFINITION, CURRENT ISSUES AND AREAS FOR IMPROVEMENT… 

7

which packaging is recyclable and how and where it should be recycled. Recycling guidelines need to be simplified and new smart ways to enable recycling need to be identified. Sustainable food packaging is the packaging that enables food waste reduction by providing longer shelf-life, preserves the nutrients of the food products, enhances the food safety credentials, and prevents from possible contamination as well as from food-borne diseases (Green Alliance 2020). Sustainable packing needs to be environmentally sustainable too by addressing the crucial issue of environmentally persistent plastic waste accumulation and the saving of oil and food material resources. However, this might lead to an increase in individual product unit costs and prices, especially during the early phases of product introduction, which may directly affect consumer behaviour and market acceptance (Han et al. 2018). Companies are also making efforts to educate consumers on how to reduce their food loss levels individually through cooking recipes or better shopping plans. Climatic change and future scarcity of natural resources put limits to growth in agriculture and food production, which means that a 70% increase in food production to feed nine billion people is impossible to be achieved (Hodges et al. 2010). Climatic change also has and will continue to have in the future severe negative consequences to the ASC (Askew 2019). Weather changes in the form of extreme weather events, the rise of global temperature, and the increase of greenhouse gas emissions are the main causes of climate change that will impact significantly the ASC. Plant and animals’ diseases are expected to grow and this will impact significantly food production and planetary limits. Rockström et  al. (2009) introduced a set of nine boundaries to quantify the safe limits that the earth can safely live in and outside of which the earth cannot function normally. These boundaries include; climate change, biodiversity loss, biogeochemical, ocean acidification, land use, freshwater availability, stratosphere ozone depletion, atmospheric aerosols and chemical pollution. We have already crossed beyond the safe zone for three boundaries, namely climate change, biodiversity loss and biogeochemical flows; while the others are under immense pressure from ongoing depleting ecological practices (Matthews et al. 2016). New ways to live, preserve and grow crops need to be identified. According to Bennett’s Law increasing wealth pushes people in consumption of higher calories food such as fats, protein, and sugar (Godfray et al. 2010). Those dietary changes affect significantly the ASC as high caloric diets require more natural resources to be spent. Efforts are

8 

S. DESPOUDI ET AL.

made by governments to reduce companies’ unethical behaviour to people’s livelihoods through tax penalties. Consumers have also started to have increasing concerns about the healthiness aspects of the food products that they consume. ASC companies have been criticised for the unhealthy materials and packaging as well with recent research indicating the existence of microplastics in food with plastic packaging. ASC companies are now faced with the challenge to preserve food shelf-life but also reduce plastic food packaging and at the same time have products made out of healthy materials. The governance of the global ASC at both national and international levels is another challenge that the ASC is facing (FAO 2017). The globalisation of the markets led to changes in power imbalance in the ASC and this creates governance issues in the sector. More precisely, producers are the less powerful in the ASC, while large-scale retailers dominating the sector (EU 2017). This creates further issues in the ASC as the pressure coming from the large companies is putting pressure on reducing food prices. ASC in order to survive they may be using unethical practices such as food adulteration and food authenticity issues. There are increasing cases of food fraud cases globally and these lead to consumer mistrust issues. Food authenticity is about the food product matching its description, when they do not match, it is considered as food fraud or else called food crime (Food Standards Agency 2019). ASC to ensure that food fraud is minimised they need to increase ASC visibility and transparency as much as possible. Global crises such as the covid-19 pandemic showed that the current ASCs are very vulnerable and at the same time essential in times of crisis. During this pandemic ASCs were stressed as consumers where panic buying, producers and workers in the sector where in quarantine or working in shifts and international borders were closed. Supermarkets online deliveries are becoming more and more important as safety measures and quarantine periods are in place. However, most of the supermarkets are not able to respond the online consumer orders. ASC SMEs are also facing the technology accessibility issues since all food shopping has moved online. Even in local markets where small-scale producers were able to sell their products there were restrictions on the number of producers allowed per market. This caused significant issues for SMEs and small producers who are unable to sell their produce under the crisis circumstances and it is questionnaire how they are going to survive. Large supermarkets and retailers see their profits increasing substantially as they have access to

2  DEFINITION, CURRENT ISSUES AND AREAS FOR IMPROVEMENT… 

9

technological solutions. Even when current technological solutions are in place the increased demand for online services requires significantly improved technological solutions to be able to cope with the increased customers’ requests. Many people even in developed countries reported that during the pandemic they are facing food insecurity issues due to food not being available or even not being able to buy online as they may not have access to the web. Hence, investing in technology seems to be one solution, but financing of this technology is the key to achieve that. There have been many changes in the ASCs in order to respond to the aforementioned challenges and pressures to increase its sustainability (Otles et al. 2014; Despoudi 2020b). Thus, the world’s food insecurity issue is becoming a major concern. The rising population, the fewer natural resources available, the changing consumer needs and wants, the insufficient acceleration of technology, the high levels of food waste rise, and the unpredictability of future crises are major concerns about world’s food insecurity. Routroy and Behera (2017) suggested that there are three areas of opportunity for the improvement of agricultural supply chains: • Traceability—Enhancing visibility throughout the supply chain will improve reliability of the products, provide more certainty about the origin and quality of the products, and increase accountability to develop consumer trust. • Logistics—The high percentage of cost invested in logistics and the problems created by poor logistics alternatives in the transportation of products make logistics an area with significant potential to reduce the waste and losses of product. • Information technology—Flexibility and responsiveness are advantages that can come from the introduction of technology to develop solutions that can improve the production, management and delivery of agricultural products. Agriculture 4.0 is a new area that is based on these aspects through the use of Industry 4.0 technological applications to improve ASCs. Therefore, on the issues that the ASC is facing it is suggested the investments in agricultural 4.0 technologies could ensure ASC sustainability and competitiveness.

10 

S. DESPOUDI ET AL.

2.4   Summary This chapter started with the definition of agricultural supply chains and then the need for value addition in this sector. It introduced the reader to the key issues surrounding agricultural supply chains which are related to food loss, food safety, food insecurity and accessibility, increased demand for food, decreasing natural resources, raw materials scarcity, and global food crisis. This discussion led the reader to conclusion that there is a need for technological solutions in this sector which will come through agriculture 4.0 technological applications.

References Aramyan, L.  H., & Van Gogh, J.  B. (2014). Reducing postharvest food losses in developing economies by using a network of excellence as an intervention tool. Presented in the IFAMA 2014 and CCA food and agribusiness world forum ‘people feed the world’ in Cape Town, South Africa, June. http://www.ifama. org/files/conf/papers/988.pdf Askew, A. (2019). The climate crisis is already hitting food production: An urgent system-wide response is needed. https://www.foodnavigator.com/ Article/2019/06/06/The-climate-crisis-is-already-hitting-food-productionAn-urgent-system-wide-response-is-needed Defra. (2006). Food industry sustainability strategy. http://www.defra.gov.uk/ publications/files/pb11649-­fiss2006-­060411.pdf Despoudi, S. (2016). An investigation of the collaboration—Postharvest food loss relationship and the effect of the environmental turbulence factors. Loughborough University. https://dspace.lboro.ac.uk/dspace-­jspui/bitstream/2134/ 21785/1/Thesis-­2016-­Despoudi.pdf. Despoudi, S. (2019). 8—Optimized food supply chains to reduce food losses. In Saving food production, supply chain, food waste and food consumption (pp. 227–248). San Diego: Elsevier. Despoudi, S. (2020a). Challenges in reducing food losses at producers’ level: The case of Greek agricultural supply chain producers. Industrial Marketing Management, 93, 520–532. https://doi.org/10.1016/j.indmarman.2020. 09.022. Despoudi, S. (2020b). Green food supply chain. In C. Galanakis (Ed.), Food industry and the environment. https://doi.org/10.1016/B978-­0-­12-­816449-­5.00002-­3. Despoudi, S., Papaioannou, G., Saridakis, G., & Dani, S. (2018). Does collaboration pay in agricultural supply chain? An empirical approach. International Journal of Production Research, 56, 4396–4417.

2  DEFINITION, CURRENT ISSUES AND AREAS FOR IMPROVEMENT… 

11

Driscoll, M. (2012). How can we build a sustainable farming system for all?http:// www.theguardian.com/sustainable-­b usiness/blog/farming-­s ystem-­ principles-­based-­sustainable European_Commission. (2015). You are part of the food chain. Key facts and figures on the food supply chain in the European Union. European_Commission. (2017). Digital transformation monitor. Industry 4.0 in agriculture: Focus on IoT aspects [Online]. Available: https://ec.europa.eu/ growth/tools-­d atabases/dem/monitor/sites/default/files/DTM_ Agriculture%204.0%20IoT%20v1.pdf FAO. (2002). World agriculture: Towards 2015/2030 summary report. ftp://ftp. fao.org/docrep/fao/004/y3557e/y3557e.pdf FAO. (2009). Climate change implications for fisheries and aquaculture overview of current scientific knowledge. http://www.fao.org/fileadmin/user_upload/ newsroom/docs/FTP530.pdf FAO. (2010). FAO—World Bank workshop on reducing post-harvest losses in grain supply chain in Africa. http://www.fao.org/fileadmin/user_upload/ags/publications/FAO_WB_ph_web.pdf FAO. (2011). Global food losses and waste. http://www.fao.org/fileadmin/user_ upload/ags/publications/GFL_web.pdf FAO. (2017). The future of food and agriculture: Trends and challenges. http:// www.fao.org/3/i6583e/i6583e.pdf Food Standards Agency. (2019). Food crime, understanding food crime and how to report it. https://www.food.gov.uk/safety-hygiene/food-crime Foresight. 2011a. The future of Food and Farming: Final Project Report. London: The Government Office for Science. Foresight. (2011b). Foresight project on global food and farming futures. In Synthesis report C7: Reducing waste. London: The Government Office for Science. Gidney, M. (2012). Why a fair supply chain is key to achieving a sustainable food system. http://www.theguardian.com/sustainable-­business/fairtrade-­partner-­ zone/fair-­supply-­chain-­sustainable-­food-­system Godfray, H. C., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Nisbett, N., Pretty, J., Robinson, S., Toulmin, C., & Whiteley, R. (2010). The future of global food system. Philosophical Transactions of the Royal Society B, 365, 2769–2777. Green Alliance. (2020). Plastic promises. https://www.green-alliance.org.uk/ plastic_promises.php Gustavsson J., Cederberg C., Sonesson U., Otterdijk R., & Meyberg A. (2011). Global food losses and waste. http://www.fao.org/fileadmin/user_upload/ags/ publications/GFL_web.pdf

12 

S. DESPOUDI ET AL.

Han, J. W., Ruiz-Garcia, L., Qian, J. P., & Yang, X. T. (2018). Food packaging: A comprehensive review and future trends, comprehensive reviews in food science and food safety. Institute of Food Technologists (Vol. 17), https://doi. org/10.1111/1541-4337.12343. Hodges, R., Buzby, J. C., & Benett, B. (2010). Postharvest losses and waste in developed and less developed countries: Opportunities to improve resource use. Journal of Agricultural Science. Cambridge University. Humphrey, J., & Memedovic, O. (2006). Global value chains in the agrifood sector. https://www.unido.org/sites/default/files/2009-05/Global_value_chains_ in_the_agrifood_sector_0.pdf Matthews, L. et al. (2016). Building bridges: Toward alternative theory of sustainable supply chain management. Journal of Supply Chain Management, 52(1), 72–72. https://doi.org/10.1111/j.1745-493X.2010.03201.x. Otles, S., Despoudi, S., Bucatariu, C., & Kartal, C. (2014). Food waste production and sustainability in the food industry. In C. Galanakis (Ed.), Food waste recovery: Processing technologies and techniques. Elsevier. Parfitt, J., Bartherl, M., & Macnaughton S. (2010). Food waste within food supply chains: Quantification and Potential for Change to 2050. Philosophical Transactions of the Royal Society B, 365, 3065–3081. Rockström, J. et al. (2009). Planetary boundaries: Exploring the safe operating space for humanity recommended citation. Available at: http://pdxscholar.library.pdx. edu/iss_pub; http://www.ecologyandsociety.org/vol14/iss2/art32/ Routroy, S., & Behera, A. (2017). Agriculture supply chain: A systematic review of literature and implications for future research. Journal of Agribusiness in Developing and Emerging Economies, 7, 275–302. Vidal, J. (2012). The future of food. Guardian. http://www.theguardian.com/ global-­development/2012/jan/22/future-­of-­food-­john-­vidal WRAP. (2011). Reducing food waste through retail supply chain collaboration. http://www.wrap.org.uk/sites/files/wrap/WRAP_IGD_supply_chain_ report.pdf

CHAPTER 3

From Industry 4.0 to Agriculture 4.0

Abstract  This chapters starts through the explanation of the industry 4.0 concepts, its evolution, and its different technological applications. As part of Industry 4.0 a wide range of technological applications are discussed which are: big data analytics, autonomous robots, simulation, horizontal and vertical system integration, IoT, the cloud, additive manufacturing, augmented reality. Then the concept of agriculture 4.0 and its emergence are introduced, and this is followed by the evolution of agricultural technologies. Keywords  Industry 4.0 • Agriculture 4.0 • Evolution • Blockchain • Big data analytics • Autonomous robots • Simulation • Horizontal and vertical system integration • IoT • The cloud • Additive manufacturing • Augmented reality

3.1   The Evolution of Industry 4.0 3.1.1  Definition of Industry 4.0 Industry 4.0 has become known as a buzzword for the shift towards the automation and digitization of manufacturing practices (Oesterreich and Teuteber 2016). However, to precisely define a concept that is still in its relative development has proved challenging, with recent studies finding more than 100 different definitions (Moeuf et al. 2017). Rejikumar et al. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Despoudi et al., Agricultural Supply Chains and Industry 4.0, https://doi.org/10.1007/978-3-030-72770-3_3

13

14 

S. DESPOUDI ET AL.

(2019) have attempted to draw together the different identified attributes of Industry 4.0, concluding that Industry 4.0 is both ‘the means for autonomous operations of process from manufacturing to service sectors’, and is ‘a bundle of technologies, which companies can adopt for business excellence’. Whilst this is slightly more refined than defining Industry 4.0 as a co-operation project between the private sector, academia and government (Kang et al. 2016) that revolves around ‘networks of manufacturing resources (manufacturing machinery, robots, conveyor and warehousing systems and production facilities) that are autonomous, capable of controlling themselves in response to different situations, selfconfiguring, knowledge-based, sensor equipped and spatially dispersed that also incorporate the relevant planning and management systems’ (Kagermann et al. 2013), it still only offers limited clarity. This idea that Industry 4.0 is an integration of cyber-physical technologies into business processes (including through the Internet of Things) was also championed by Oberg and Graham (2016), but still does not offer a precise definition. Instead, it offers a broad concept that works well as a label, but less well when required to provide a more specific insight. Despite Kagermann’s wide-ranging synopsis, there is still debate between both researchers and practitioners over which elements comprise Industry 4.0, how they link and interact with each other, and where Industry 4.0 is even applicable (Hermann et al. 2016). At the more extreme end of the debate surrounding Industry 4.0, the argument has also been made by Drath and Horch (2014) that the concept is nothing new; rather, it is simply a combination of existing technologies and concepts packaged up and given a catchy name. For the benefit of this study, the definition of Industry 4.0 to be used is that put forward by Oberg and Graham (2016): Industry 4.0 is an integration of cyber-physical systems (CPS) in different processes with the use of the Internet of Things (IoT) and services in business processes. 3.1.2  The Evolution to Industry 4.0 Industry 4.0 has produced the evolution of manufacturing and industry practices through the use of emergent technologies and promoting connectivity. The digitalisation of industry is achieved through the convergence of the physical and the virtual realities (Hahn 2020). The trend created by Industry 4.0 has been extending to different human activities and it has been suggested as a valuable tool to support sustainability. The emerging phenomenon of Industry 4.0 can be tentatively compared with the three industrial revolutions that occurred in the last centuries. As a

3  FROM INDUSTRY 4.0 TO AGRICULTURE 4.0 

15

result, one could argue that the disruptive changes in manufacturing came as a succeeding result of the evolving operational transformation (Pereira and Romero 2017; Schmidtet al. 2015; Ben-Daya et al. 2017). The path towards Industry 4.0 can be presented through multiple studies in the area, but the precise context, definition and applications are still in progress (Pereira and Romero 2017; Shrouf et al. 2014; Liao et al. 2017). The Industrial Revolution has evolved in four phases (from Industry 1.0 to Industry 4.0) and progressed in parallel with the innovations and developments in the manufacturing process. Those advances are described as: • Industry 1.0 (end of eighteenth century to early nineteenth century): this period involved the use of steam power. • Industry 2.0 (late nineteenth century): this period saw the move from the stem power to the electrical power, where the latter was used to advance production towards a massive production model. • Industry 3.0 (mid-twentieth century): this period saw the enhancement of the massive-production paradigms through the use of ICTs and microelectronics, which were introduced in the production line. • Industry 4.0 (today): the previous periods paved the way for the latest evolution through the deployment of cyber-physical systems, the popularisation of the concept of the Internet-of-Things (IoT), the Internet-of-Services (IoS), cloud technologies, Artificial Intelligence (AI) and Big Data for the industrial context. As an expected and inevitable change, the manufacturing sector was proactively prepared for the transformational potential of Industry 4.0 (the most suitable manufacturing model was defined early in advance, and the operational processes and targets were pre-planned), along with the associated challenges (Pereira and Romero 2017). From a technological viewpoint, Industry 4.0 can be seen as the ‘increased digitisation and automation in addition to an increased communication enabled by the creation of a digital value chain’ (Oesterreich and Teuteberg 2016). Comparatively, the OECD definition describes Industry 4.0 as ‘entail[ing] a confluence of technologies ranging from a variety of digital technologies (e.g. 3D printing, Internet of Things, advanced robotics) to new materials (e.g. bio or nanobased) to new processes (e.g. data-driven production, artificial intelligence, synthetic biology) and these technologies will be available in the near future.’ Along these lines, the main features introduced by Industry 4.0 may be divided across three major dimensions of integration, following, namely: (1) horizontal integration through value networks, (2) vertical integration and networked manufacturing systems and (3) end-to-end digital

16 

S. DESPOUDI ET AL.

integration of engineering across the entire value chain. In turn, the primary enablers for the adoption of Industry 4.0 paradigm are the Internetof-Things and Services (IoT and IoS), Big Data and Cloud Computing (Pereira and Romero 2017). Technology has always had a place in the agricultural processes, and we can trace it back to the Agricultural Age (ancient years). Today, the growing interest around digital technologies and Big Data, witnessed across industries, has not gone unnoticed by the primary sector and agriculture. This has led to the increased use of technology during the Information Age and the Big Data Evolution. Indeed, Big Data is an important trend that can find a place on the farm, as well, and has moved farming into what can be described as ‘smart farming’. 3.1.3  Technological Applications of Industry 4.0 The definition of Industry 4.0 has generated debate between academics and practitioners without producing a clear answer. Despite this, it is clear that Industry 4.0 is a structure which relies upon the integration of vertical and flat processes, the digitisation of administration and systems, and the introduction of inventive models for business (Rejikumar et al. 2019). Further, Rejikumar et al. attempted to draw together the different attributes that they could identify when studying different applications of Industry 4.0 and were able to conclude that it is both ‘the means for autonomous operations of process from manufacturing to service sectors’, and is ‘a bundle of technologies, which companies can adopt for business excellence’. Within this work, they were able to pinpoint eight dimensions that firms must have awareness and control of when implementing an Industry 4.0 strategy: product, customer, operation, technology, strategy, governance, culture and people. These dimensions further complement the work of Gilchrist (2016), which had identified six core principles for the successful implementation of Industry 4.0: interoperability, virtualisation, decentralisation, real-time capability, service orientation, and modularity. The work of both Gilchrist and Rejikumar et al. demonstrates an understanding from practitioners about how best to create an environment for Industry 4.0, as well the clear parameters that must be applied. Whilst there is a general acceptance of what the concept’s purpose is, and which functions and dimensions need to be aligned to facilitate it, identifying the specific components is still a problematic area (even within the most recent academic literature). Thus, whilst Industry 4.0 is an applicable title to a broad range of technologies, Rossini et al. (2019) identified that, of sixteen specific technologies that they could identify within their

3  FROM INDUSTRY 4.0 TO AGRICULTURE 4.0 

17

research, the three most commonly associated are ‘Big Data’, ‘cloud computing’ and ‘augmented reality’. However, technologies such as ‘real-time scanning by smartphone/tablet’, ‘integrated engineering systems’ and ‘digital automation without sensors’ were recognised as Industry 4.0 technologies on a much less frequent scale within the studied literature. This imbalance highlights the problematic nature with clearly defining Industry 4.0; the technologies are all interdependent on one another as part of an Industry 4.0 system of manufacturing but are often neglected. Based on the above, in a network of such highly inter-dependent technologies it is, therefore, more useful to identify those that are critical in underpinning the others. As per Vaidya et al., the nine pillars of Industry 4.0 are: • Big Data and Analytics • Autonomous robots • Simulation • Horizontal and Vertical System Integration • The Industrial Internet of Things (IoT) • Cyber Security and Cyber Physical Systems (CPS) • The Cloud • Additive Manufacturing • Augmented Reality 3.1.4  Big Data and Analytics As per Forrester (2019), Big Data consists of four dimensions: volume of data, variety of data, velocity of generation of new data and analysis, and value of data. For production equipment and systems, Industry 4.0 will be central in the support of decision making as the previously collected data will be used to identify threats during earlier production cycles, as well as forecasting them further into the future. This forecasting will enable organisations to anticipate and eliminate wastage and inefficiencies that previously would have been unavoidable. 3.1.5  Autonomous Robots Robots are rapidly becoming more autonomous, flexible and co-­operative; Academics and practitioners who have studied Industry 4.0 believe it is a certainty that robots will soon be working in co-operation both with each

18 

S. DESPOUDI ET AL.

other and with human operatives, learning from them and, consequently, becoming exponentially more productive and efficient. These autonomous robots can complete any given task precisely and intelligently within the designated time frame, whilst also focusing on safety, flexibility, versatility and collaboration (Bahrin et al. 2016). 3.1.6  Simulation Plant operations will use simulations more extensively in order to leverage real-time data and mirror the physical world within a virtual setting. This virtual setting can incorporate machines, products and humans, enabling organisations to reduce machine set-up times and to improve quality, whilst also minimising production failures. Simulations can also improve decision-making within the production process, making it faster, more accurate, and more responsive. 3.1.7  Horizontal and Vertical System Integration Within any industrial organisation, the two major mechanisms are integration and self-optimisation. In the context of Industry 4.0, there are three core dimensions of integration: horizontal integration across the entire value creation network, vertical integration and networked manufacturing systems, and end-to-end engineering across the entire product life cycle. 3.1.8  The Industrial Internet of Things (IoT) The Internet of Things (IoT) refers to the worldwide network of interconnected and uniform addressed objects that communicate via standard protocols (Hozdic 2015). The three key features of IoT are context, omnipresence and optimisation; context refers to the possibility for advanced object interaction with an existing environment and immediate response if anything changes, whilst omnipresence provides information on key metrics, such as location, physical conditions, and atmospheric conditions. Optimisation is, arguably the most important of the three key features- it highlights the fact that today’s objects are connected in a way that goes beyond the traditional device/human interface. As per

3  FROM INDUSTRY 4.0 TO AGRICULTURE 4.0 

19

Schumacher et  al., the value chain should be intelligent, agile and networked by integrating physical objects, human factors, intelligent machines, smart sensors, production processes and production lines together across the boundaries of an organisation. 3.1.9  Cyber Security and Cyber Physical Systems With the increase in connectivity and the use of standard communications protocols that come with an Industry 4.0 implementation, there is a vital need to protect critical industrial systems and manufacturing lines from cyber-security threats. Cyber Physical Systems (CPS) have been defined as the systems in which natural and human-made systems are tightly integrated with computation, communication, and control processes (Bagheri et al. 2015), with decentralisation and autonomous behaviour their main characteristics. This close integration, when combined with autonomous behaviour, means that CPS will be continuously evolving within an organisation in response to continually changing data generated by real-time monitoring and interpretation of the manufacturing process. Further, a key feature of CPS, in conjunction with the IoT and Big Data, will be the use of sensors to both identify failures and, subsequently, prepare the machine in question for fault repairs. Beyond this, and of critical value to an organisation, CPS will find the optimum utilisation of each work station, including cycle time (Kolberg et al. 2016). This information is vital for the reduction of wastage and machine set-up times within any production process. 3.1.10  The Cloud Cloud-based IT platforms serve as the technical backbone for the connection and communication of core elements of Industry 4.0 (Lanherr et al. 2016). For organisations which are pursuing an Industry 4.0 implementation, the sharing of data between sites, divisions, regions etc. on its own is not sufficient; it is imperative that this data transfer can occur almost instantaneously. The concept of ‘digital production’ is centred around the idea of multiple devices being connected to the same cloud, thus allowing them to share data with one another, with the potential to extend this to also incorporate machines from the shop floor, as well as any other devices within the manufacturing facility.

20 

S. DESPOUDI ET AL.

3.1.11  Additive Manufacturing In an Industry 4.0 context, additive manufacturing methods will be widely used to produce small batches of customised products that offer construction advantages, such as complex, lightweight designs. High- performance, decentralised additive manufacturing systems will reduce transport distances and stock inventory. In the face of ever-­ changing customer requirements, increased individualisation of particular products, and a shorter time to market, are two key challenges faced by organisations. Therefore, additive manufacturing systems, such as fused deposition, and selective laser melting, should reduce both production costs and lead times. With the increased digitisation that occurs as a result of Industry 4.0, additive manufacturing techniques should remain simple and easy for operatives to understand, despite the increase in process complexity brought about by the more complicated structures required to support the increased individualisation of products (Brettel et al. 2014). 3.1.12  Augmented Reality Augmented reality-based systems support a variety of services within the manufacturing process, from inputs through to maintenance. For example, they can be used to aid a picker in selecting the most appropriate component, or to provide a maintenance engineer with real-time instruction when repairing a machine. The benefit of augmented reality is that the engineer will be given support when looking directly at the part they are repairing, ensuring a quicker completion time, less wastage and a reduced chance of failure.

3.2   The Emergence of the Agriculture 4.0 Concept 3.2.1  Agriculture 4.0 Agriculture 4.0 introduces a radical change employing both digital technologies and digital practices. The former covers a broad range of technologies such as Internet of Things, Blockchain, Big Data, 3D printing, Artificial Intelligence and Robotics, among others. Digital practices include open innovation, cooperation and mobility (European Commission 2017). Agriculture 4.0 intends to improve production processes through

3  FROM INDUSTRY 4.0 TO AGRICULTURE 4.0 

21

digitalisation and is associated to many concepts such as Smart Farming, Precision Agriculture, Decision Agriculture, Digital Agriculture, and Numerical Agriculture (Klerkx et al. 2019). The purpose is to do more with less. Essentially, the introduction of technology can allow farmers and producers to manage raw materials and resources in a way that can maximise their use and reduce waste. This is significantly beneficial to reduce the use of key resources such as water, to allow soil conservation and to limit carbon emissions (Lezoche et  al. 2020). Hence, this trend can be significantly valuable to support the Sustainable Development Goals from the United Nations and boost the transition towards more sustainable cities and communities. Agriculture 4.0 alters the value chain, which also affects the stakeholders involved. For instance, connectivity providers, software application providers or data management specialists become an essential part of supply chains. Therefore, the transition has to be carefully managed to account for the needs stemming from the new processes and tools. Successful implementation of Agriculture 4.0 requires tackling needs such as the introduction of technological standards to make sure the equipment is compatible at the present and for the future, the support for farmers and producers during the transition to deal with financial constraints and investing in capabilities required for implementation such as training, and the investment on communication networks in rural areas to leverage the potential of Agriculture 4.0 (European_Commission 2017). These challenges are certainly significant, especially for developing economies with constrained resources which often rely on agricultural production. The evolution of Industry 4.0 in the farming sector, referred henceforth as Agriculture 4.0, has manifested as new Big Data initiatives and applications in various areas that are now becoming increasingly economically viable. The proliferation of data and associated applications have evolved thanks to the lower (comparatively to previous years) costs of sensor-based solutions, data storage and processing in cloud infrastructures, to the development and expansion of mobile networks and the transmission of data sets from agricultural fields around the world. Data can improve not only the practices and operations of single farms but also those of large farm groups that utilise the information, and the relevant technologies and equipment, thus benefitting from a wide range of services. As a result, the data evolution and the transformation of the agrarian operating models should not only focus on value creation for the

22 

S. DESPOUDI ET AL.

consumers, but also the individual farmer and the society at large through the use of smart services. More than a decade ago, The Guardian (2008) highlighted that the world’s natural resources started becoming extinct, and that the food systems were in crisis, as humans were using 30% more resources than it was sustainable. This is still the case. A quarter of all farmland is highly degraded, and the trend must be reversed if the world’s growing population is to be fed (Guardian 2011). Farming, agriculture and food systems are facing huge challenges during the last decades according to a recent review by the UN Food and Agriculture Organization (UN 2009). The world will need to produce 70% more food in 2050 than it did in 2006 in order to feed the Earth’s growing population (Eurostat 2015). However, this seems like a significant issue: 40% of the world’s harvest is lost to insects, diseases and weeds and by 2030 water needs will exceed current supplies by 40% (UN 2009). What this means is that, for example in the EU, who occupies 40% of the total utilised agricultural area, the challenge will be to increase crop yields using smart, precision farming solutions rather than focusing on farming more land. On this basis, a rough estimation by NESTA (2015) points to a potential 17.9% annual increase in profit for a mixed-use (wheat with grazing livestock) average size farm in England. As a result, adopting a different approach to farming is not a matter of choice anymore but a matter of urgency since the existing systems are not viable nor sustainable. New ways, methods and techniques should be employed to address the increasing demands of the world food production and to improve environmental efficiency. Applying data-intensive, smart practices and Internet-of-Things (IoT) technologies in the areas of agriculture and farming can provide a lens for ensuring the transparency of the farming practices and sustainability of the Agri-Food Supply Chains and production processes. The focus of Agriculture 4.0 on digitalised ways of farming can generate renewed interest in transforming the traditional operating and process models to digital data-intensive ones, focusing on analytics and decision-making practices. However, there is still an open discussion, and multiple challenges surrounding Agriculture 4.0, as small and medium farms, raise a number of concerns with regards to data sharing practices and access control policies rightly when data are made available and shared among multiple parties. The need for technology in the farming field stems from the strong motivation to feed the world population, and which evolved through the years

3  FROM INDUSTRY 4.0 TO AGRICULTURE 4.0 

23

to facilitate modern practices and processes to meet the ever-growing needs (Corallo et al. 2018). The numerous applications of technology in farming seek to enhance agrarian operations through sophisticated information and communication developments. Aspects of the agricultural industry, such as crop cultivation management and control, quality management, transport of food products and food preservation, may all be enhanced by taking into account their domain-specific requirements and translating them into the respective functional design, development and applications by ICT experts (Barmpounakis et al. 2015). The terms of ‘Digital Farming’, ‘Smart Farming‘, ‘Farming 4.0’ and ‘Agriculture 4.0‘often refer to similar concepts and contexts of use and therefore are used interchangeably (Kagermann et  al. 2013). However, most typically ‘Agriculture 4.0′ is understood as the “evolution in agriculture and agricultural engineering from Precision Farming to connected, knowledge-based farm production systems” (CEMA 2017). Extending, therefore, the concept of Industry 4.0 to the agricultural sector, we note that “Digital Farming is structurally similar to the concept of Industry 4.0” (Yahya 2018). Nevertheless, the critical distinguishing parameters of the agricultural production processes are quite different from the more typical industrial processes because agriculture is heavily determined by natural and biological factors, and therefore the ‘physical’ artefact itself should be taken under careful consideration. While Agriculture 4.0 draws heavily from Precision Farming technology, it also takes recourse to intelligent networks and data management tools, with the aim to use all available information and expertise and enable the automation of sustainable processes (Wolfert et al. 2017). Within this agricultural context, there is a strong emphasis on the use of ICTs in the cyber-physical farm management cycle and new technologies, such as the Internet of Things and Cloud Computing. The Agricultural 4.0 Technologies are expected to leverage the latest development and introduce data sharing, artificial intelligence (AI) and machine learning techniques in the farming sector. This radical transformation of the traditional agricultural landscape, which has led to Agriculture 4.0, further encompasses advances in Big Data and the Internet of Things. Therefore, the concept of agriculture 4.0 refers to the various technological applications that are used in the farming operations such as applications using big data, IOT, cloud, cyber-physical systems, and augmented reality.

24 

S. DESPOUDI ET AL.

3.2.2  The Evolution of Agricultural 4.0 Technology Agricultural 4.0 Technology (AgriTech) was not always defined and understood the same way as today. The current definition is strongly associated with progression from farming to smart farming and evolved over three periods (Miranda et al. 2019): • Agricultural Evolution: The Agricultural Evolution refers mostly to preindustrial agriculture. It began in the ancient years and spanned all the way to the 1920’s. Its main characteristics reflect the labour intensity and the essential subsistence farming in the form of small-­ scale farms, whereby the focus was on feeding each family). • Industrial Evolution: As a result of the technological advancements stemming from the Industrial Evolution, a model of industrial and massive agriculture started forming, following a highly industrialised pattern. The industrialisation of agriculture happened essentially through the use of tractors, harvesters, chemical fertilisers and seeds, all of which helped in turn towards the development of the large-­ scale commercial farm model. • Information and Data Evolution: The industrial model of farming started becoming unsustainable (Rigby et al. 2001), and thus new practices began being introduced, leveraging data-intensive methods for addressing multiple agricultural problems. As a result, the Information and Data Evolution gave way to the Smart and Precision Agriculture, which is characterised by the exploitation of multi-­ source data, the use of sensors on farm equipment and plants, the use of satellite images and weather tracking monitoring of water and fertiliser use (precision farming). The model of data-intensive agriculture can be used by both large- and small-scale farms, and it can transform the way they operate by creating value for the farmer, the consumer, as well as the society. Table 3.1. summarises the three evolutions of Agricultural Technology and briefly presents their characteristics, their scope and the major technological advances that mobilised them. The data evolution and cutting-edge, disruptive technologies have shifted the paradigm of conventional and modern agriculture and farming to smart and intelligent approaches. Following the advent of data-driven analytical technologies and high-performance computing, the AI context

3  FROM INDUSTRY 4.0 TO AGRICULTURE 4.0 

25

Table 3.1  The three evolutions of agricultural technology

Agricultural evolution

Industrial evolution

Information and data evolution

Period

Characteristics

Preindustrial agriculture (ancient years to appx. 1920) Industrial and massive agriculture (1920 to appx. 2010) Smart and precision agriculture (2010 onwards)

Labour intensity

Scope

Technological advances

Essentially subsistence farming (small farms) Industrialisation Large commercial farms.

Manual processes, conventional farming tools

Data intensity

Exploiting multi-source data, sensors on farm equipment and plants, satellite images and weather tracking monitoring of water and fertiliser use (precision farming)

Smart farms (larger or smaller)

Tractors, harvesters, chemical fertilisers and seeds

Source: Authors

was reshaped and re-emerged anew, creating numerous opportunities for smart and data-intensive solutions within the AgriTech domain. Hence, AgriTech could be defined in today’s Agricultural context as the use of data-driven smart technologies and analytical methods for enhancing the farming practices and the associated operations management and decision-­ making in order to achieve the economic efficiency and environmental sustainability of the Agricultural field (Tsolakis et al. 2019). Therefore, the new AgriTech concept is developed drawing from a cross-section of disciplines, involving a variety of smart and data-intensive approaches, disruptive technologies and AI applications—spanning from smart devices, sensors, and Big Data to drone technology and robotics. Smart monitoring, irrigation, images and temperature from the field, as well as the soil or livestock conditions, to name only a few, can provide a pool of data for tailored recommendations to the farmer and any interested parties (Karim et al. 2017). Data analytics, machine learning, robotics, or any other AI technique applied in the farm through automated practices can provide recommendations, early warning, or even efficiency monitoring and enhance the farming operations. This suggests a number of opportunities for agriculture to become viable again.

26 

S. DESPOUDI ET AL.

3.3   Summary This chapter defined the concepts of industry 4.0 and agriculture 4.0. The evolution of both concepts was discussed as well as how they emerged. The different technological applications of industry 4.0 are discussed too which are: big data analytics, autonomous robots, simulation, horizontal and vertical system integration, IoT, the cloud, additive manufacturing, augmented reality. The next chapter will be focussing on the technological applications as part of industry 4.0 specifically for agricultural supply chains.

References Bagheri, B., Yang, S., Kao, H., & Lee, J., (2015). Cyber-physical systems architecture for self-aware machines in industry 4.0 environment. IFAC Conference, 38(3), 1622–1627. Bahrin, M., Othman, M., Nor, N., & Azli, M., (2016). Industry 4.0: A review on industrial automation and Robotic. Jurnal Teknologi (Sciences and Engineering), 1, 137–143. Barmpounakis, S., Kaloxylos, A., Groumas, A., et  al. (2015). Management and control applications in agriculture domain via a future internet business-to-­ business platform. Information Processing in Agriculture. https://doi. org/10.1016/j.inpa.2015.04.002. Ben-Daya M, Hassini E, Bahroun Z (2017) Internet of things and supply chain management: a literature review. Int. J. Prod. Res. Brettel, M., Friedrichsen, N., Keller, M., & Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective. International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 8(1), 37–44. Corallo, A., Latino, M. E., & Menegoli, M. (2018). From industry 4.0 to agriculture 4.0: A framework to manage product data in Agri-food supply chain for voluntary traceability. International Journal of Nutrition and Food Engineering, 12(5), 146–150. CEMA—European Agricultural Machinery. (2017). Farming 4.0: The future of agriculture? Retrieved 10 September, 2019. http://www.cema-agri.org/ page/farming-40-futureagriculture. Drath, R., & Horch, A. (2014). Industry 4.0: Hit or hype?. IEEE Industrial Electronics Magazine, 8(2), 56–58. European_Commission. (2017). Digital transformation monitor. Industry 4.0 in agriculture: Focus on IoT aspects [Online]. Available: https://ec.europa.eu/ growth/tools-­d atabases/dem/monitor/sites/default/files/DTM_ Agriculture%204.0%20IoT%20v1.pdf

3  FROM INDUSTRY 4.0 TO AGRICULTURE 4.0 

27

Eurostat. (2015). Farm structure statistics. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Farm_structure_statistics&oldid=233234 Forrester, V. (2019). School management information systems: Challenges to educational decision-making in the big data era. International Journal on Integrating Technology in Education, 8(1), 1–11. Gilchrist, A. (2016). Industry 4.0: The industrial internet of things. New York, NY: Apress. Guardian. (2008). World is facing a natural resources crisis worse than financial crunch. https://www.theguardian.com/environment/2008/oct/29/ climatechange-­endangeredhabitats Guardian. (2011). UN: Farmers must produce 70% more food by 2050 to feed population. https://www.theguardian.com/environment/2011/nov/28/ un-­farmers-­produce-­food-­population Hahn, G.  J. (2020). Industry 4.0: A supply chain innovation perspective. International Journal of Production Research, 58, 1425–1441. Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for industrie 4.0 scenarios—paper presented at the 49th Hawaii international conference on system sciences. Koloa, Hawaii: HICSS. Hozdic, E. (2015). Smart factory for industry 4.0: A review. International Journal of Modern Manufacturing Technologies, 7(1), 28–35. Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for implementing the strategic initiative Industrie 4.0: Securing the future of German manufacturing industry; Final report of the Industrie 4.0 Working Group, Berlin: Acatech. Kang, H. et al. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing-Green Technology, 3(1), 111–128. Karim, F., Karim, F., & Frihida A. (2017). Monitoring system using web of things in precision agriculture. Procedia Computer Science, 110, 402–409. Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS—Wageningen Journal of Life Sciences, 90–91, 100315. Kolberg, D., Knobloch, J., & Zuehlke, D. (2016). Towards a lean automation interface for workstations. International Journal of Production Research, 55, 2845–2856. Lanherr, M., Schneider, U., & Bauerhansl, T. (2016). The application centre industrie 4.0–industry-driven manufacturing, research and development. 49th CIRP conference on manufacturing systems. Procedia CIRP, 51, 26–31. Lezoche, M., Hernandez, J.  E., Alemany Díaz, M.  D. M.  E., Panetto, H., & Kacprzyk, J. (2020). Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Computers in Industry, 117, 103187. Liao, Y., Deschamps, F., Loures, E. de FR, Ramos, LFP. (2017). Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. Int. J. Prod. Res.

28 

S. DESPOUDI ET AL.

Miranda, J., Ponce, P., Molina, A., & Wright, P. (2019). Sensing, smart and sustainable technologies for Agri-food 4.0. Computers in Industry, 108, 21–36. https://doi.org/10.1016/j.compind.2019.02.002. Moeuf, A. et al. (2017). The industrial management of SMEs in the era of industry 4.0. International Journal of Production Research, 92(1), 1–19. NESTA. (2015). Precision agriculture: Almost 20% increase in income possible from smart farming. https://www.nesta.org.uk/blog/precision-agriculture-almost20-increase-in-income-possible-from-smart-farming/ Oberg, C., & Graham, G. (2016). How smart cities will change supply chain management: A technical viewpoint. Production Planning & Control, 27(6), 529–538. Oesterreich, T., & Teuteber, F. (2016). Understanding the implications of digitisation and automation in the context of Industry 4.0; A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 83, 121–139. Pereira A. C., Romero F. (2017). A review of the meanings and the implications of the Industry 4.0 concept. Procedia Manuf. https://doi.org/10.1016/j. promfg.2017.09.032 Rejikumar, G. et al. (2019). Industry 4.0: Key findings and analysis for the literature arena. Benchmarking: An International Journal, 26(8), 2514–2542. Rigby, D., Woodhouse, P., Young, T., & Burton, M. (2001). Constructing a farm level indicator of sustainable agricultural practice. Ecological Economics, 39(3), 463–478. https://doi.org/10.1016/S0921-8009(01)00245-2. Rossini, M., Costa, F., Tortorella, G., & Portioli-Staudacher, A. (2019). The interrelation between industry 4.0 and lean production: And empirical study on lean manufacturers. The Interantional Journal of Advanced Manufacturing Technology, 102(1), 3963–3976. Schmidt R, Möhring M, Härting R-C, et al (2015) Industry 4.0-potentials for creating smart products: empirical research results. Int Conf Bus Inf Syst. https://doi.org/10.1007/978-3-540-79396-0 Shrouf F., Ordieres, J., Miragliotta, G. (2014). Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. In: IEEE International Conference on Industrial Engineering and Engineering Management. Tsolakis, N., Bechtsis, D., & Bochtis, D. (2019). Agros: A robot operating system based emulation tool for agricultural robotics. Agronomy, 9. https://doi. org/10.3390/agronomy9070403. UN Food and Agriculture Organization. (2009). 2050: A third more mouths to feed. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.  J. (2017). Big data in smart farming—A review. Agricultural Systems, 53, 69–80. Yahya, N. (2018). Agricultural 4.0: Its implementation toward future sustainability. In Green energy and technology. Singapore: Springer. https://www.springerprofessional.de/en/agricultural-4-0-its-implementation-toward-futuresustainability/15370556

CHAPTER 4

Technological Applications of Agricultural 4.0 Supply Chains

Abstract  This chapter discusses the different technological applications of Agricultural Supply Chains 4.0. By doing so a wide range to technological applications are explained which are: smart farming applications, smart devices and platforms, IoT, temperature control applications, blockchain applications, tracking and tracing technologies, autonomous land farming robots, autonomous aerial farming robots and smart monitors. Keywords  Technological applications • Agriculture 4.0 • Smart farming • IoT • Blockchain • Autonomous farming • Tracking and tracing • Smart monitors In A4SCs a wide rang‑e of disruptive technologies are employed such as smart devices, sensors, AI and big data to drone technology and robotics in order to benefit and improve agricultural operations. Table 4.1 provides as summary of the agriculture 4.0 applications and their implications for A4SC operations as per Spanaki et al. (2021a).

4.1   Smart Farming Smart farming utilises modern technology to increase the quantity and quality of agricultural products (Schuttelaar 2017). This can be achieved by having access to GPS, soil scanning, data management, and Internet of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Despoudi et al., Agricultural Supply Chains and Industry 4.0, https://doi.org/10.1007/978-3-030-72770-3_4

29

30 

S. DESPOUDI ET AL.

Table 4.1  Technological applications of agriculture 4.0 and implications for agricultural operations Agriculture 4.0 applications

Implications for agricultural operations

Smart devices and platforms for farming analytics

Remote monitoring of the yield, crops, machinery and livestock. Obtaining stats on the livestock feeding, living conditions and produce. Real-time data collection from the farming field for condition variables. Measuring the conditions of the soil for acidity, nutrients, moisture, humidity and temperature. Collecting and comparing information about weather forecasts to predict weather patterns for the short-term future. Obtaining detailed maps of the geographical conditions, topographical aspects and resources of the area. Improve labour by handling essential agricultural tasks such as harvesting crops at a higher volume and faster pace. More acreage to be working for more extended periods. Remotely managing irrigation systems with a significant impact on the world’s water supply. Rendering maps processing spatial data, and applying analytical methods to geographic datasets, including the use of geographic information systems (GIS) Surveillance of the farming land and simultaneously generating crop data, Calculating precise statistical predictions for the crops and livestock. Assisting on mapping of the fields, monitoring crop canopy remotely and checking for anomalies.

IoT sensors in fields

Autonomous farming robots

Drones for surveillance tasks over the farming land

Things technologies. In this way, farmers can precisely measure variations in a field and adapt their strategies accordingly which can greatly increase the effectiveness of pesticides and fertilizers or even lead to no usage of chemicals. Smart farming can also be applied to animals by adjusting their nutrition and thus preventing disease and enhancing herd health. Some examples of Big Data applications in Smart farming processes are (Wolfert et al. 2017): (a) smart sensing and monitoring e.g. robotics, sensors, and GPS; (b) smart analysis and planning e.g. crop health, yield modelling, and energy management; (c) smart control e.g. precision farming, and climate control; and (d) Big Data in the cloud e.g. weather data, satellite data, market information, and social media. For example,

4  TECHNOLOGICAL APPLICATIONS OF AGRICULTURAL 4.0 SUPPLY CHAINS 

31

4.2   Smart Devices and Platforms The most popular agriculture 4.0 technologies that are used so far are the smart devices and platforms for farming analytics which are usually referred as precision or intelligent farming. These are widely used by A4SC entities for remote monitoring of the crops, and the yield as well as machinery remote checking and livestock management. Data are collected, analysed and informed decisions for the whole farm management are made based on these. For example, data are water consumption, nutrients, moisture, and soil temperature data are collected from a farm on a daily basis and based on that the optimal watering and fertiliser schedule is produced. CropMetrics technology provides irrigation optimisation solutions to farmers using a cloud software in order to improve topography or soil variability and maximize efficiency and yield performance (CropMetrics 2019). In this way, climate conditions are mapped, and farmers can choose the appropriate crops and plan better for increased yields. GPS is frequently used to track the farming land and locate the precise position of the field such as GPS trackers in tractors. In terms of the livestock monitoring statistical data can be collected and analysed to develop an optimal feeding schedule for the animals.

4.3   IoT IoT sensors in fields are used for weather pattern prediction as well as for assessing plant and/ or animal healthiness (Spanaki et al. 2021b). Weather forecasting is one of the most frequent applications of IoT sensors which can provide weather prediction on a daily, weekly, or monthly basis. IoT sensors are also used to measure and assess plant healthiness such as acidity, nutrients, moisture, humidity and soil temperature (Kaloxylos et  al. 2012; Barmpounakis et al. 2015). By understanding the plant’s needs the appropriate nutrients can be provided in this way the crops can be of high quality, the yield can be longer, and wastage can be avoided. There are also several applications of IoT sensors on animals in order to assess their health and wellbeing. For example, sensors can be used for checking the behaviour and the living conditions of cows (Yazdanbakhsh et al. 2017). Smart Elements (2019) also provides smart farming sensors which are located across fields to collect environmental data and transfer them to the cloud for climate monitoring purposes.

32 

S. DESPOUDI ET AL.

4.4   Temperature Control Applications The perishable nature of the food products requires special transportation conditions i.e. cold chain logistics. Temperature data loggers can be used to monitor environmental features and temperature settings during the transportation of the food products. This enables the reduction of temperature control issues which cause quality deterioration, and in many cases food waste. For example, Omega provides temperature and humidity loggers for egg transportation that cover temperatures from 0 to 60 °C (32 to 140 °F) and 0 to 95% RH. These loggers are egg-shaped, they can be included in the shipping packages, and they keep over 32,000 readings in memory (Omega 2019). This helps companies to monitor the food products temperature during the transportation and avoid product contaminations in case of temperature changes in trucks.

4.5   Blockchain Applications Blockchain applications are also used in A4SC to improve traceability, prevent incidents of food fraud, and increase food safety. Automated payments and self-executed smart contracts could be used as agricultural insurance, and this will also enable food product traceability (Tomu 2019). Consumers request for food products to be traceable and blockchain technology can provide that. Food frauds related to illegal harvesting and shipping frauds can be traced using blockchain applications such as procurement tracking, agriculture financing, crop condition managing, and insurance schemes. For example, procurement tracking can be used in order to ensure smooth and guaranteed delivery and payment of the delivered and ordered goods through agents. This can reduce the number of intermediaries involved and thus decreased costs and increase product’s time to the market. Blockchain applications can track and trace food product information since its harvest and this can ensure product safety and quality. In terms of the agricultural finance, credit history and contractual agreements can be tracked to protect small-scale farmers and other entities that may have liquidity contracts or may be disadvantaged. Insurance contracts as part of blockchain applications can be used as social protection mechanisms for farmers who are affected by severe weather conditions. The insurance claims can be validated and processed faster due to increased transparency.

4  TECHNOLOGICAL APPLICATIONS OF AGRICULTURAL 4.0 SUPPLY CHAINS 

33

4.6   Tracking and Tracing Technologies Tracking and tracing technologies such as RFID are used during the transportation of the food products. RFID is a technology that automatically identifies items and records metadata with the use of radio frequency signals. The standard RFID system consists generally of three main components: (a) the electronic carrying device, also known as RFID tag or transponder, (b) the RFID reader that enables tag interrogation, and (c) the software application system, which controls the RFID equipment and the generated RFID data (Finkenzeller 2010). The RFID tag is a microchip which is connected to an antenna. The antenna on the reader provides the RFID tag with energy and receives data. RFID is used in the food supply chain to ensure food traceability and food safety as it is used to capture data in critical control points, while the food products are transported and stored across the food supply chain (Hong et al. 2011). EAgile developed RFID-enabled packaging technology that can be used on food cartons, beverage bottles and other containers to monitor food quality, and ensure traceability and food safety at all stages of the supply chain (Spinner 2014). This special packaging technology will give the ability to customers to access product specific safety information at the point of purchase.

4.7   Autonomous Land Farming Robots Autonomous farming robots are also used in A4SC in order to increase labour productivity, collect farmland data, and remote management of farm (Robinson et al. 2019). For example, robots are used for harvesting crops such as melons, and weed recognition in grass lands. These robotic applications can also collect farm data and decrease the time to the market as crops healthiness and readiness for harvesting can be tracked in real time. Robotic applications usually utilise machine learning technologies and in this way appropriate patterns for future crop management can be available early on (Spanaki et  al. 2021b). Robots are coming to replace farmers in tractors as they are able to perform all the farming activities such as spray, plant, and weed without any human intervention.

34 

S. DESPOUDI ET AL.

4.8   Autonomous Aerial Farming Robots Robotic aerial farming technologies i.e. drones are also part of the A4SC for surveillance of the farming land, crop data generation, analyse spatial data, mapping fields, aerial spraying, aerial planting (EC 2018). Satellite imagery is quite costly, and drones can provide accurate, frequent, and cost-effective assessment of the crops to monitor crop development and assess effective and ineffective practices. At the same time drones can produce 3D maps that can be used for assessing nitrogen and develop appropriate crop planting patterns. Aerial crop spraying can be done through drone as they can adjust their flight paths using the surrounding topography and perform fast and precise crop spraying. Aerial planting with drones is still in infancy with the goal of providing accurate and fast planting without any human intervention which can drastically reduce labour costs.

4.9   Smart Monitors Smart monitoring, irrigation, images and temperature from the field, as well as the soil or livestock conditions, to name a few, can provide a pool of data for tailored recommendations to the farmer and any interested parties (Karim et  al. 2017; Tsolakis et  al. 2019). Data analytics, machine learning, robotics, or any other AI technique applied in the farm through automated practices could provide recommendations, warnings, or even efficiency monitoring and enhance the farming operations, suggesting opportunities for Agriculture to be viable again (Corallo et al. 2018).

4.10   Summary This chapter discussed the different technological applications of Agricultural Supply Chains 4.0. Different technological applications of agriculture 4.0 were explained which are: smart farming applications, smart devices and platforms, IoT, temperature control applications, blockchain applications, tracking and tracing technologies, autonomous land farming robots, autonomous aerial farming robots and smart monitors.

4  TECHNOLOGICAL APPLICATIONS OF AGRICULTURAL 4.0 SUPPLY CHAINS 

35

References Barmpounakis, S., Kaloxylos, A., Groumas, A., et  al. (2015). Management and control applications in agriculture domain via a future internet business-to-­ business platform. Information Processing in Agriculture. https://doi. org/10.1016/j.inpa.2015.04.002. Corallo, A., Latino, M.  E., & Menegoli, M. (2018). From industry 4.0 to agriculture 4.0: A framework to manage product data in Agri-food supply chain for voluntary traceability. International Journal of Nutrition and Food Engineering, 12(5), 146–150. CropMetrics. (2019). Irrigation solutions. https://cropmetrics.com/ the-solutions/ EC. (2018). Dronies in agriculture. https://ec.europa.eu/growth/tools-­ databases/dem/monitor/sites/default/files/Drones_vf.pdf Finkenzeller, K. (2010). RFID Handbook: Fundamentals and Applications in Contactless Smart Cards, Radio Frequency Identification and Near-Field Communication. New York: Wiley. Hong, H., Dang, J., Tsai,Y., Liu, C., Lee, W., Wang, M., Chen, P. (2011). An RFID application in the food supply chain: A case study of convenience stores in Taiwan. Journal of Food Engineering, 106(2), 119–126. Kaloxylos, A., Eigenmann, R., Teye, F., et al. (2012). Farm management systems and the future internet era. Computers and Electronics in Agriculture. https:// doi.org/10.1016/j.compag.2012.09.002. Karim, F., Karim, F., & Frihida A. (2017). Monitoring system using web of things in precision agriculture. Procedia Computer Science, 110, 402–409. Omega. (2019). Temperature monitoring during transportation, storage and processing of perishable products. https://www.omega.co.uk/technicallearning/ temperature-monitoring-during-transportation.html Robinson, A., Mulvany, L., & Stringer, D. (2019). Robots take the wheel as autonomous farm machines hit fields. https://www.bloomberg.com/news/ ar ticles/2019-­0 5-­1 5/robots-­t ake-­t he-­w heel-­a s-­a utonomous-­f arm­machines-­hit-­the-­field Schuttelaar. (2017). BLOG: Smart farming is key for the future of agriculture. https://www.schuttelaar-partners.com/news/2017/smart-farming-iskey-for-the-future-of-agriculture Smart Elements. (2019). Smarter agriculture. https://smartelements.io/ Spanaki, K., Sivarajah, U., Fakhimi, M., Despoud, S., & Irani, Z. (2021a). Disruptive technologies in agricultural operations: a systematic review of AI-driven AgriTech research. Annals of Operations Research. https://doi. org/10.1007/s10479-020-03922-z. Spanaki, K., Karafili, E., Sivarajah, U., Despoud, S., & Irani, Z. (2021b). Artificial intelligence and food security: Swarm intelligence of AgriTech drones for smart AgriFood operations. Production Planning and Control, 1–19. https://doi. org/10.1080/09537287.2021.1882688.

36 

S. DESPOUDI ET AL.

Spinner, J. (2014). Intelligent design: eAgile launches RFID smart packaging. https://www.foodnavigator.com/Article/2014/08/12/Food-packagingtechnology-uses-RFID-to-increase-safety Tomu. (2019). Blockchain for agriculture. https://medium.com/swlh/ blockchain-­for-­agriculture-­5b0a0baa0aa3 Tsolakis, N., Bechtsis, D., & Bochtis, D. (2019). Agros: A robot operating system based emulation tool for agricultural robotics. Agronomy, 9. https://doi. org/10.3390/agronomy9070403. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.  J. (2017). Big data in smart farming—A review. Agricultural Systems, 53, 69–80. Yazdanbakhsh, O., Zhou, Y., & Dick, S. (2017) An intelligent system for livestock disease surveillance. Information Sciences, 378, 26–47.

CHAPTER 5

Data Sharing and the Transformation Agricultural 4.0 Supply Chain Operations

Abstract  This chapter discusses the data sharing issues in ASCs and how the agriculture 4.0 applications have transformed the operations of the ASC entities. The latter are discussed in relation to all the different ASC entities which are farmers, processors, distributors, retailers, and consumers. Keywords  Data sharing • Agriculture 4.0 transformation • Producers • Processors • Distributors • Retailers • Consumers

5.1   Data Sharing in Agriculture 4.0 Agriculture 4.0 entails a significant transformation in the operating models of the agricultural sector and associated shifts in the roles of the involved actors and the power relations among them within the current food supply chain networks (Trkman and McCormack 2009). The interplay of various actors outside the immediate boundaries of the individual farm suggests the entry of multiple new ones besides of the traditional farming stakeholders (e.g., Agri-Tech vendors, venture capitalists, start-ups etc.) and subsequently the emergence of conflicting interests among them (Braziotis et al. 2013). With regards to the data sharing context, several governmental institutions publish open data, under the condition that the privacy of individuals is guaranteed (Spanaki et al. 2019). Indeed, there is a change © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Despoudi et al., Agricultural Supply Chains and Industry 4.0, https://doi.org/10.1007/978-3-030-72770-3_5

37

38 

S. DESPOUDI ET AL.

of this perspective in light of the data protection regulation for EU and other legal frameworks around the world (Karafili et al. 2019). As smart machines and sensors crop up in farms and farm data grow in quantity and scope, farming processes will become increasingly data-driven and data-­ enabled (Pham and Stack 2018). Agricultural technologies (Agri-Tech) are increasingly being introduced in the farming field (Lehmann et  al. 2012) as developments in Big Data, the Internet of Things and cloud and distributed platforms trigger the Smart Farming evolution. While Precision Agriculture is just taking in-field variability into account, Agriculture 4.0 goes beyond that, by supporting management tasks not only on location but also on data, enhanced by context and situation awareness, triggered by real-time events, such as weather, diseases, humanitarian crises, which require sudden and agile actions (Kaloxylos et al. 2012). The context of Agriculture 4.0 emphasises on the use of data and technology in the cyber-physical farm management cycle, with a strong focus on data-intensive, informed decisions for the agricultural practices in the ASC (Nukala et al. 2016). Agrarian data, but also linked data, metadata, information and knowledge could be vast and can include anything associated with them. Examples could include yield monitoring data (crop yield by time and distance, distance and bushels per load, number of loads and fields), spatial coordinates (mapping fields), fertilisation management data, data from mapping weeds, variable spraying data, topographic data, salinity data, field assessment data, pertinent data, images, geospatial data etc. (Kamilaris et al. 2017). The volume of this list is enormous and continuously expanding, as technological developments arise. The volume of the data could potentially hinder progress and complicate processes if the necessary capabilities and requirements are not acknowledged and met. Processing such volumes for Agriculture 4.0 requires advanced intellectual and technical resources to capture, store, distribute, manage and analyse the data and the use of smart farming leveraging ASC data may unfold in a continuum of three directions (Wolfert et al. 2017): 1. Farmers and other stakeholders of the agricultural sector become participants in closed, proprietary systems of a highly integrated Agri-Food supply chain. 2. Farmers and other stakeholders become parts of a collaborative supply chain network, sharing data, information and experience with their peers via online platforms.

5  DATA SHARING AND THE TRANSFORMATION AGRICULTURAL 4.0 SUPPLY… 

39

3. Farmers and other stakeholders become knowledge/information sharing and decision-making entities of a supply chain network of open, collaborative systems, who are flexible in choosing business partners both for the technology and for the food production side. In all the three directions, the development of data platforms and infrastructures as well as the regulatory and legal frameworks regarding data use are essential aspects for the individual farmers and the farming sector as a whole. One challenge is to focus on data sharing and control for developing sustainable operating models for smart farming and the ASC overall, especially for small and medium farms.

5.2   The Transformation of the Agricultural 4.0 Supply Chain Operations In order to understand further the data sharing aspects in Agriculture 4.0, the different entities that operate in ASCs and their interaction with Agriculture 4.0 technologies needs to be clarified. Based on the above the Agricultural 4.0 Supply Chain (A4SC) involves collaboration and sharing of technological infrastructure across the different supply chain entities i.e. producers, processors, distributors, retailers, and consumers. Figure  5.1 below shows a typical A4SC indicating that the various technological applications of agriculture 4.0 are used across the A4SC. 5.2.1  Farmers Operations These technological applications will transform the business operations of each ASC entity in unique ways. For example, at the farmers operations who are not very technology savvy, adoption of technological applications

big data, IOT, cloud, cyber-physical systems, augmented reality

farmers

processors

distributors

retailers

Fig. 5.1  Typical agricultural 4.0 supply chain. (Source: Authors)

consumers

40 

S. DESPOUDI ET AL.

could start through the use of basic technological equipment that could increase their yields, improve quality of crops, and increase speed. For example, GPS applications could be used in tractors to enable efficient and effective management of the fields. Precision agriculture technologies could be used to manage natural resources efficiency such as water, but also to increase crop quality by understanding the plants or animals needs in real time. Technologies to harvest the produce could also be used to understand which crops are ready to be harvested and also harvest them faster and safer. Drones and other kinds of robots could be used to supervise fields in areas which are far away or not easily accessible. Robotic applications could also assess plant healthiness, spray the required ingredients and this will reduce food waste. This may also help to improve the security of the crops as theft incidents are common in unsupervised areas. Meteorological applications that enable weather prediction in real-time and with detailed weather forecasts in particular areas could prevent food wastages and facilitate farming activities that can be possibly performed under specific weather conditions. The interconnection of the various technological applications at the farmers stage could also bring farmers closer to their sellers and develop a more transparent ASC.  The sellers could order online and check their orders in real-time. All relevant documentation related to the safety and quality requirements could be made available in the commonly shared platforms. The COVID-19 pandemic showed that farmers operations are impossible to be stopped if we want to survive in this planet. Even when local lockdowns are in place farmers through a wide range of technological applications they need to ensure that the crops are planted, growing and they are ready to be collected. 5.2.2  Processors Operations Processors are also benefiting from Agriculture 4.0 applications through technological applications that can detect the condition, shape, and size of the produce. These enablers faster sorting out of the different agricultural producers and ensures that the right products are placed on different baskets and the overall through output time of their operations is increasing. This has further implications on the capacity of their operations which can be significantly modernised. Investments in 3D printing equipment can also enable the transformation and increase their competitiveness by offering customised solutions to their customers. Processors are also investing in smart packaging as consumers ask for it. Some examples of smart

5  DATA SHARING AND THE TRANSFORMATION AGRICULTURAL 4.0 SUPPLY… 

41

packaging are the RFID labels and smart tags, to protect against counterfeit goods, and enable better monitoring of their distribution (EUFIC 2018). Different kinds of technological innovations in packaging are employed some of which are: • Active packaging: shelf life extension, oxygen scavenger, antimicrobial; • Intelligent packaging: interaction with the environment, self-­ cleaning, self-healing, indication of deterioration; • Monitoring product conditions: time temperature indicator (TTI), freshness indicator, leakage indicator, gas detector; • Nanosensor: indication of food quality, growth of microorganisms; • Nanocoatings: Information on product: RFID, nano-barcode, product authenticity. Processors need to ensure that blockchain applications for ASC products track and tracing are embedded in the packaging. Robotic applications in their packing operations can ensure that the ASC products are packed faster, effectively, and that their shelf-life is extended. For example, there are specific gases that are used in combination with smart packaging to ensure the extension of the shelf-life of salads. By scanning the barcode on the product’s packing relevant information about the ingredients and the producer of the product should appear. Smart packaging also ensures the safety of the product, which is something that concerns highly food processors. It could detect the development of any microorganisms or the inclusion of any non-suitable materials by mistake. However, the new packaging materials may be costly and non-compatible with existing equipment. The COVID-19 crisis indicated that there is a need for food production to be increased to ensure enough safety stock is in place in case there further future lockdowns that will stop processors operations. ASC processors need to ensure that they are using the latest technologies in their packaging as consumers track and tracing concerns are increasing. 5.2.3  Distributors Operations Distributions operations are also transformed through agriculture 4.0 applications. Globalisation increased the need for companies to become greener as there is a global need for products to be transported around the world. Transportation companies are investing in transport optimisation solutions in order to identify the greener route. Through the use of IoT

42 

S. DESPOUDI ET AL.

applications, smart devices and platforms environmental footprint reduction in logistics can be achieved by using ‘scale’ in the transportation of goods (e.g. using larger containers or even larger ships) and creating more efficient ways of transporting and storing of goods. As transportation increases there is a need for using bigger means of transport i.e. containers, vessels or creating ‘hubs’ in large ports/central locations as intermediaries to facilitate the logistics function. Smart platforms and connected devices enable the sharing of deliveries with suppliers or other partners (i.e. optimizing trucks load capacity) and improving transportation modes efficiency (using alternative fuelling, ensuring vehicles’ eco-operation). With efficient and effective information sharing the delivery of the ASC products can be optimized and the products do not need to be stored locally. This is a huge benefit for retailers that are trying to find ways to minimize stock holding but at the same time increase product range and product availability in short time. During crisis such as COVID-19 distributors operations were transformed significantly as consumers ask for touch free deliveries and worldwide demand for delivery services increased exponentially. This is a unique opportunity for distributors to gain market share through the use of the latest technologies. 5.2.4  Retailers Operations Retailers are investing in a wide range of agriculture 4.0 applications. They are using business analytics to gather consumer insight from a wide range of platforms such as social media. This helps them to manage their product range better but also to increase ASC product offering and at the same time offer a customised experience. This is very important for managing the perishable nature of the ASC products, as by gaining consumer insights about their food preferences the relevant stock could be detained in store. Loyalty cards are an add-on to that which can further improve customer experience through personalised offers and coupons. Retailers can promote relevant products to their customers and ensure that customers a loyal. Consumers ask for more product information and they want to know the details of where each ASC product was produced. Therefore, retailers need to have access to the latest IoT technologies and smart devices in their stores and online ready for the customers to access this information. All ASC should be barcoded with smart labels. The in-store experience can be increased through the use of a wide range of technologies such as picture-taking of products or automatic check-out in the end.

5  DATA SHARING AND THE TRANSFORMATION AGRICULTURAL 4.0 SUPPLY… 

43

The COVID-19 crisis indicated that retailers online ordering systems need to be improved as they were found to be incapable of handing such an increased demand. The simultaneous access of many users as well as the online shopping experience needs to be improved. Retailers are staring to invest in autonomous order processing technologies which includes robots preparing consumers orders without any human interaction. This in combination with a touch-free delivery or drone deliveries will increase consumers food safety concerns. The ease of the online shopping interface could significantly improve consumers purchase frequency. Retailers could improve this by using interactive videos of how the products can be used, cooked, stored or even encourage customers to provide feedback on their purchased products. Research by Kong (2011) investigated retail websites and concluded 20 website features which the main examined retailers obtained. This study chooses 10 out of the 20 website features to research, which are a keyword, category, additional suggestion, highlighted information, description, product attribute, FAQs, third-party payment, live chat with online customer agent and online enquiry. Only ten elements are selected for the reason that these ten variables are presented and available on every grocery website, which is simple to find and use (see Table 5.1).

Table 5.1  Descriptions of website features Description of retail website features 1. Keyword 2. Category 3. Suggestion 4. Information 5. Description 6. Attribute 7. FAQs 8. Payment 9. Agent 10. Enquiry

Enables shoppers to search for products with keywords Enables shoppers to browse products in detailed categories Suggests alternative or complementary products Highlight products on special offers Provides detailed description of products (name, country) Enables consumers to select a list of products by attributes Provides FAQs (frequently asked questions) with quick answers Directs customers to third-party payment websites Enables consumers to chat online with customer service agents Provides online forums to discuss questions

44 

S. DESPOUDI ET AL.

5.2.5  Consumers Consumers needs and wants as well as lifestyles are changing. The global market requires more and more products to be available all around the word. These products need to be safety packed and shipped and consumers want to know where their products are at any time. As consumers are becoming busier they are looking for ASC products that can save them time and be consumed while doing other kind of activities. Smart applications educate the consumer about the origin of the products and the healthy ingredients. Therefore, IoT applications and smart shopping and fitness applications help the consumer to fulfil his needs and wants. Innovative packaging solutions make the life of the consumer easy as the shelf-life of the ASC products is increased and access to production information is available. 3D future printing applications are already in the market and these can create customized and innovative food products with the right ASC ingredients. Consumers ask for easily recycled products and many companies are responding to this by providing recycling guidelines in their products labels which can be usually accessed on the web.

5.3   Summary This chapter discussed the data sharing issues in ASCs and how the agriculture 4.0 applications have transformed the operations of the ASC entities. The changes that agriculture 4.0 brough to all the different ASC entities operations were discussed which are: farmers, processors, distributors, retailers, and consumers.

References Braziotis, C., Bourlakis, M., Rogers, H., & Tannock, J. (2013). Supply chains and supply networks: Distinctions and overlaps. Supply Chain Management, 18, 644–652. EUFIC. (2018). Fighting food waste by innovative food packaging. https://www. e u f i c . o rg / e n / f o o d - p r o d u c t i o n / a r t i c l e / f i g h t i n g - f o o d - w a s t e - b y innovative-food-packaging Kaloxylos, A., Eigenmann, R., Teye, F., et al. (2012). Farm management systems and the future internet era. Computers and Electronics in Agriculture. https:// doi.org/10.1016/j.compag.2012.09.002.

5  DATA SHARING AND THE TRANSFORMATION AGRICULTURAL 4.0 SUPPLY… 

45

Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. Karafili, E., Spanaki, K., & Lupu, E. C. (2019). Access control and quality attributes of open data: Applications and techniques. In W. Abramowicz & A. Paschke (Eds.), Business information systems workshops. BIS 2018 (Lecture notes in business information processing, Vol. 339). Cham: Springer. https:// doi.org/10.1007/978-3-030-04849-5_52. Kong, X. Y. (2011). Exploring Online Customer Experience: Website Features, Customer Activities, and Repurchase Intentions. Thesis (PhD), p. 100, University of Warwick. Lehmann, R. J., Reiche, R., & Schiefer, G. (2012). Future internet and the agri-­ food sector: State-of-the-art in literature and research. Computers and Electronics in Agriculture, 89, 158–174. Nukala, R., Panduru, K., Shields, A., Riordan, D., Doody, P., & Walsh, J. (2016). Internet of things: A review from “Farm to Fork.” In 27th Irish Signals and Systems Conference (ISSC), Londonderry, 2016, pp. 1–6. https://doi. org/10.1109/ISSC.2016.7528456 Pham, X., & Stack, M. (2018). How data analytics is transforming agriculture. Business Horizons. https://doi.org/10.1016/j.bushor.2017.09.011. Spanaki, D., Karafili, E., & Despoudi, S. (2019). Data sharing in agriculture 4.0: Concepts and challenges. EurOMA conference 2019—Finland. Trkman, P., & McCormack, K. (2009). Supply chain risk in turbulent environments—A conceptual model for managing supply chain network risk. International Journal of Production Economics. https://doi.org/10.1016/j. ijpe.2009.03.002. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.  J. (2017). Big data in smart farming—A review. Agricultural Systems, 53, 69–80.

CHAPTER 6

Sustainability in Agricultural 4.0 Supply Chains

Abstract  This chapter discusses the sustainability aspect of agricultural 4.0 supply chains. It starts with the definitions of sustainability, triple bottom line, and ASC sustainability. The chapter concludes with a discussion of the concept of sustainability performance and an explanation of its importance in relation to agriculture 4.0. The efficient and effective use of agriculture 4.0 applications could lead to increased sustainability and increase sustainability performance therefore this aspect is important to be understood. Keywords  Sustainability • Triple bottom line • Agricultural sustainability • Sustainability performance

6.1   Sustainability Definition Cost reduction and efficiency have always been embedded as core goals pursued in ASC management. However, that has led to very high rates of resource depletion combined with an increased level of pollution globally. Global societies eventually came to the realisation that maintaining that level of exploitation of resources would prove to be impossible in the near future. The “Report of the World Commission on Environment and Development: Our Common Future” (i.e. the Brundtland Report) published in 1987 by the United Nations reflects the global concern about the impact of human activities. The report underscores the importance to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Despoudi et al., Agricultural Supply Chains and Industry 4.0, https://doi.org/10.1007/978-3-030-72770-3_6

47

48 

S. DESPOUDI ET AL.

“make development sustainable to ensure that it meets the needs of the present without compromising the ability of future generations to meet their own needs”. That is one of the most accepted definitions of sustainability. The reason is because that is a powerful statement, as it suggests that current human activities need to account not only for the present, but also for the future impact of the way resources are being currently used. That represents a change in the general mind-set from exclusively looking at financial benefits of processes to considering the impact associated with those processes, which involve different stakeholders. The shift towards sustainability has been fuelled by the proposal of the Sustainable Development Goals (SDGs) from the United Nations. The SDGs are a collection of 17 goals calling to collective action to ensure short-term and long-term peace and prosperity (UN 2015). These goals integrate prosperity and productivity with people and planet with specific goals focussing on aiming for zero hunger, good health and wellbeing, industry innovation, and responsible consumption and production.

6.2   The Triple Bottom Line Concept Economic activities certainly involve the internal stakeholders in the company, but the appearance of sustainability in the spotlight fostered the discussion about the wider impact of human activities. What impact and to whom? Elkington (1994) argue that beyond the profit generated for company owners, it is important to consider the impact to the environment and the wider society. Therefore, the latter author proposed one of the most widely known concepts in the area of sustainability: the triple bottom line. The triple bottom line is based on three pillars: the economic, social and environmental. The economic dimension has been a vital part of the survival of any company as it is linked to the financial side of the operation including elements such as production cost, revenue, and effectiveness of investment, among others. On the other hand, rather than looking at the side of the company, the social dimension takes the perspective of the society and the impact of human activities on people. That includes looking into aspects such as impact on labour, local organisations/companies, lifestyle, among others. Additionally, the environmental aspect of sustainability is taking the perspective of the planet. It looks into the way resources are being exploited, the impact on the ecosystem, pollution, among others. The Brundtland Report highlighted the need to foster a renewed emphasis on economic growth with considerations of social and

6  SUSTAINABILITY IN AGRICULTURAL 4.0 SUPPLY CHAINS 

49

environmental sustainability. Hence, the concept of the triple bottom line intertwines the three dimensions to provide a holistic view of the impact of any human activity. There are claims that these dimensions can often be in conflict with each other. For instance, using local suppliers in ASC rather than the cheapest suppliers can be beneficial for social sustainability but counterproductive from the perspective of economic sustainability. However, there is research showing the potential of interventions that can be beneficial for the three dimensions. Using the same example, although using local suppliers rather than the cheapest suppliers can increase costs in the short term, it also has the potential to create partnerships with suppliers and mitigate the impact of disruptions in transportation and fulfilment. The ASC that heavily relies in natural resources is even more crucial to become more sustainable. Therefore, it is important to identify ways to measure the impact on the social, economic and environmental dimensions in ASCs.

6.3   Sustainability in Agricultural Supply Chains Sustainability in the ASC is not a new concept, but it is increasingly receiving attention because of the strong social and environmental components surrounding their activities. It is only natural that developments in this Industry have to consider the ethical and societal implications of their implementation (Food_Ethics_Council 2004). The importance of those social implications in combination with the level of depletion of natural resources are good motivators to consider sustainability in the agricultural industry. However, perhaps one of the most important drivers is associated to food security. Food security will be achieved “when all people, at all times, have physical and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life” (FAO 2016). This is a complex definition which includes four dimensions: food availability, food access, utilisation and stability (FAO 2006). Unfortunately, FAO (2019) reported that nearly 11% of the global population is undernourished, which translates into 820 million people in the world. Those numbers underscore the urgency of improving agricultural systems at the same time as social and environmental considerations are taken into account. The importance of considering the environmental dimension in the agricultural industry is portrayed by Meadows et al. (1972). The study included population, food production, industrialization, pollution, and

50 

S. DESPOUDI ET AL.

consumption of non-renewable natural resources as main variables to explore the interaction between human activities and the environment. The simulation assumed an increasing trend of resource usage and the results highlighted the possibility of exceeding the capacity of the Earth to cope with human activities. Nearly 50 years later, the picture is not very encouraging. According to Global_Footprint_Network (2016), the ecological footprint of all countries surpassed the biocapacity of the Earth since 1970, and it shows an increasing trend. Those aspects have informed the actions from the SDGs and led the development of regulation embedding sustainability in Agricultural policy. For instance, Article 39 of the Treaty on the Functioning of the European Union (TFEU) balances increasing productivity, stabilising markets, assuring fair living standards for producers, promoting reasonable prices for consumers and assuring the availability of resources. This mind set allows to recognise the interaction between the different dimensions and the impact they have on each other (UK_Parliament 2011). The goals presented are certainly ambitious and are aligned to the definition of sustainable agriculture. A key aspect of this concept mention the notion that long-term viability is equally important as short-term benefits. Brodt et al. (2011) argue the importance of considering a systems approach to have a holistic view integrating production and distribution inclusive of social, economic and environmental aspects. The current impact to the environment, the limitations to ensure nourishment for everybody and the constraints for increasing productivity are barriers that need to be tackled simultaneously. Therefore, there has been a growth in innovation to cope with those challenges.

6.4   Sustainable Performance The success and survival of any ASC company is a result of its performance on different metrics stemming from its vision, goals and the characteristics of the market. Knowledge about the latter is key to set the right metrics and targets for any organisation. Failing to recognise the priorities or requirements of the market can lead to focus on less important factors. For example, whereas a grower focused in cost would choose exclusively non-­ organic fertilizers, another grower aiming for long-term viability and quality would invest more money on a mixed application of organic and inorganic fertilizers. Traditional measures in Operations Management include cost, time, flexibility, reliability and quality (Brandon-Jones et al. 2013). However, performance measurement nowadays involves more

6  SUSTAINABILITY IN AGRICULTURAL 4.0 SUPPLY CHAINS 

51

metrics. The rise of the triple bottom line implies renewed goals for organisations considering priorities beyond financial and efficiency metrics looking at the social and environmental impact of human activities. Sustainable performance ensures the inclusion of social, environmental and economic metrics in the output of any process. This concept has rapidly evolved globally because of the increase in customer awareness. Consumers are still price sensitive, but new aspects are becoming order winners and order qualifiers. For instance, reputation, impact on the environment, level of nutrients, production processes, among others. Hence, sustainable performance is closely linked to Social Corporate Responsibility (CSR) and compliance with environmental regulations. This has been reflected in different industries, including the agricultural industry. As part of the sustainable performance the digital performance of companies is becoming more and more important. Companies are making use of a wide range of technological applications which are brough as part of Industry 4.0 and the digital presence as well engagement with the relevant target groups could impact significantly business success. However, companies even if they adopt a wide range of technological applications they need to use them effectively as many of them are facing issues of having too much data from their potential consumers, but they have no idea how to use them.

6.5   Summary This chapter discussed the sustainability aspect of agricultural 4.0 supply chains. It started with the definitions of sustainability, triple bottom line, and ASC sustainability. The chapter concluded with a discussion of the concept of sustainability performance and an explanation of its importance in relation to agriculture 4.0.

References Brandon-Jones, A., Slack, N., & Johnson, R. (2013). Operations Management. 7th edn, Pearson Prentice Hall, Harlow, England. Brodt, S., Six, J., Feenstra, G., Ingels, C., & Campbell, D. (2011). Sustainable agriculture. Nature Education Knowledge, 3(10), 1. Elkington J. (1994). Towards the sustainable corporation. California Management Review, 90–100 (Winter).

52 

S. DESPOUDI ET AL.

FAO. (2006). Postharvest management of fruit and vegetables in the Asia-Pacific region. http://www.apo-tokyo.org/00e-books/AG-18_PostHarvest/AG-18_ PostHarvest.pdf FAO. (2016). The state of food and agriculture. http://www.fao.org/3/w1358e/ w1358e.pdf FAO. (2019). World hunger is still not going down after three years and obesity is still growing – UN report. http://www.fao.org/news/story/en/item/ 1200484/icode/ Meadows, D. H., Meadows, D. L., Randers, J., & Behrens, W.W. (1972). The Limits to growth: a report for the club of Rome’s project on the predicament of mankind. New York: Universe Books. UN. (2015). Sustainable development goals. https://sdgs.un.org/goals

CHAPTER 7

Circular Economy in Agricultural Supply Chains

Abstract  This chapter starts with the definition of circular economy then it discusses the need for circular economy in ASCs. Then the relationship between corporate social responsibility, circular economy, agriculture 4.0 and sustainability are discussed. In relation to that the principles of circular economy are explained. The chapter concludes with a discussion about circular economy and agriculture 4.0. Keywords  Circular economy • Agriculture 4.0 • Circular economy practices • Corporate social responsibility • Sustainability

7.1   Definition of Circular Economy Circular Economy (CE) is defined as a system which reinstates ‘end of life’ theory with sustainability, recycling and reuse at macro, micro and meso levels to attain value, prosperity, environment protection and ensuring future growth and expansion. In the past, supply chains were designed in a linear fashion, however this was not a sustainable option as materials were wasted after consumption and their life ended there. Unused products typically ended in trash after shelf life (Michelinia et  al. 2017). However today’s community are looking for circular supply chains which help in developing a system to unite the beginning to the end. This links the restoration of ecosystems, impacts society, generates value, and enhances human health (Abdul et al. 2017). It is a concept of everlasting © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Despoudi et al., Agricultural Supply Chains and Industry 4.0, https://doi.org/10.1007/978-3-030-72770-3_7

53

54 

S. DESPOUDI ET AL.

loop where the resources lose very minimal value and the idea of wastage is eliminated as much as possible. There is a general assumption that this concept mainly aims at waste reduction. However, it also provokes ideas of reusing, recycling and re-innovating. Circular economy, as a relatively new concept, emphasizes the sustainable use of resources and energy in the economic activities, thereby minimizing its impact on the environment. Figure 7.1 shows the difference between traditional linear economy and circular economy. It can be seen that, compared with the traditional “take-­ make-­use-disposal” model of linear economy, the resources in the circular economy model can be reused reasonably, which not only provides economic value, but also contributes to environmental value and social value in the process of circulation. Therefore, applying the concept of circular economy to supply chain management will also contribute greatly to the sustainable development as well as sustainable performance of s supply chain (Batista et al. 2018). The application of circular economy in ASCs provides companies with a new supply chain sustainable development plan and it involves all aspects of the supply chain, including the selection of raw materials, product design, transportation, warehousing, recycling and disposal, etc. (Geissdoerfer et al. 2018). This also means that stakeholders in a circular ASC need to be considered from the original raw material supplier to the end customer (Ying and Li-jun 2009). Relevant practices need to be embedded for all the ASC entities.

Fig. 7.1  Comparison between linear economy and circular economy

7  CIRCULAR ECONOMY IN AGRICULTURAL SUPPLY CHAINS 

55

7.2   The Need for Circular Economy in Agricultural Supply Chains Recently, companies have moved on from historic consumption and production systems to grasping new business models which encourages layout for recycling and restoration of materials. The main concern of the food supply chain is to establish systems which are flourishing and sustainable. These systems could lead to the creation of new business models (Farooque et al. 2019). These models will be based on concepts that encourage the use of raw materials which are available domestically and do not have a negative impact on the surrounding. This will benefit the ecological system and the wastage can be returned without any treatment. This is not just concerned with reduction of waste dumping but also create a relative method which is self-sustaining, and helps the residue to be reused (Bocken et al. 2014). It turns waste into opportunity as control on by-product disposal becomes tighter and also benefits the company as it creates a unique revenue source for products that were meant to be discarded (Farooque et al. 2019). By recycling raw materials, companies realise the utmost benefit for which they purchased. The overall cost of refurbishing and reusing is less. By intertwining the linear supply chain with the starting point, companies get to save more money in purchase thereby by decreasing the overall cost which provides a competitive edge over other companies. Some of its other benefits include reduction in carbon emission, creation of new jobs, social aid, resource efficiency, improved product lifespan, tax shift, environment protection, climate change mitigation, reduced stress on ecological systems, efficient use of fossil fuels and other non-renewable energy (Wijkman and Skånberg 2017). Consumers are more aware and informed about the commodity they purchase and are becoming concerned about the surroundings in which they are prepared and disposed (Abdul et al. 2017). Their expectations have increased such that they want to return the products they do not like or mistakenly bought. Sometimes, they want to return old products for new ones, so they do not have the burden of disposing or recycling it (Farooque et al. 2019). So, companies looking to make profit in the future must look for ways to minimise the reverse logistics cost and improve customer satisfaction. Therefore, adopting the circular supply chain model is important.

56 

S. DESPOUDI ET AL.

The agricultural industry is essential for the survival of society and as such it consumes a considerable amount of resources. Food production and supply requires more freshwater resources than any other industry and over 25% energy consumption globally (UN 2020). Despite the level of use of these resources, the challenges for food security lead to an excess of 2 billion people without continuous access to “safe, nutritious and sufficient food” (FAO 2019). At the same time, expansion is not much of an option. Beyond the limitations in water and electricity, the potential for cultivated land expansion is restricted as cultivated areas represent more than one fifth of the land use in several areas except on the sub-Saharan and Northern Africa regions (FAO 2011). Delving into the issues from the European standpoint, EMF (2013) argues that there are three problems in food systems: the system is wasteful, it is responsible for significant environmental externalities, and it fails to produce healthy outcomes. Hence, the problematic moves from the availability of resources to the efficiency and effectiveness of the system. Think for instance of the production of potatoes. Households around the world consume potatoes on daily basis. Nevertheless, between 53% and 55% of the initial potato production is lost, with half of those losses because potatoes do not reach quality standards (Willersinn et al. 2015). At the same time, potato production requires the application of fungicides, which can have an effect on the environment (Zubrod et al. 2019). Indeed, looking at the numbers in different crops, it seems there is a significant potential to enhance the effectiveness of agricultural systems. CE is a paradigm that allows us to re-think production processes to improve quality, reduce waste, and decrease collateral impact on the environment of the agricultural industry. Using the ReSOLVE framework as basis, EMF (2013) argue that circular economy in the agricultural industry could improve from: • Introducing more efficient agricultural practices leveraging technology • Using regenerative farming practices to preserve natural capital and optimise long-term yields • Embedding closed loops of nutrients and other materials in agricultural practices through the extraction or recovery of them from waste • Promoting the restoration and preservation of natural ecosystems • Implementing urban and peri-urban farming • Developing digital supply chains

7  CIRCULAR ECONOMY IN AGRICULTURAL SUPPLY CHAINS 

57

These represent valuable alternatives to implement the circular economy paradigm to improve food systems. Process improvement is at the heart of many of these solutions, but they also require close interaction with technological advances.

7.3   Towards Sustainable and Circular Agricultural 4.0 Supply Chains Through Agriculture 4.0 7.3.1  The Relationship Between CSR, Sustainability, CE, and Agriculture 4.0 There is a prominent need to introduce sustainable processes in the agricultural systems to tackle the challenges faced by society because of the excessive exploitation of resources and the percentage of people undernourished. The implementation of sustainable solutions, however, has to consider the interaction of three dimensions: people, processes and technology as shown in Fig. 7.2. From the people standpoint, knowledge and awareness to alter current practices and corporate social responsibility (CSR) are important aspects to consider (Yogesh et al. 2019). The introduction of innovation requires ensuring workers meant to perform the tasks are well-trained, convinced and supported to avoid rejection and a smooth transition. CSR plays a major role in that. Research has shown that creating a good working environment can lead to more flexible and incentivised employees. In the agricultural industry, it means supporting farmers and workers to enhance working conditions, remuneration and facilitating guidance and training for the use of innovation. Process improvement is at the hearth of circular economy, thus that paradigm is useful to redesign, re-think and reconfigure processes (Despoudi and Dora 2020). Processes are essential part of any human activity and different examples can be easily found around us. Transformation processes allow us to turn inputs into outputs through a series of steps leading the interaction between people and resources. Therefore, one of the main trends to shift current practice has been the improvement of processes. Circular economy has been one of the most meaningful changes in paradigm aiming to achieve sustainable processes. The concept has its origins in China in the 16th national congress of the communist party of China, which states the need for a “circular (or recycling) economy” and many advances have been reported in the country.

58 

S. DESPOUDI ET AL.

Fig. 7.2  People, process and technology in agricultural supply chains. (Source: Authors)

CE

CSR Sustainability

Agriculture 4.0

Circular economy has been defined as “the activities of decrement, recycling and resource recovery in production, circulation and consumption” (People’s_Republic_of_China 2008). This concept is currently under the spotlight globally because of the increased attention to the environmental impact of economic activities. Additionally, circular economy can effectively increase productivity up to 3% per year and a growth of nearly 7 points of GDP in Europe (EMF 2013). The aims of this approach are to avoid a “linear” economy, to reduce waste and to define value creation. A traditional approach to achieving those aims are the use of loops to reduce resource consumption and maximise efficiency through the implementation of the 3 Rs: reduce, reuse and recycle. 7.3.2  Circular Economy Practices 7.3.2.1 Reduce The reduce practices refer to the reduction of the use of non-renewable resources as input in economic activities. The reduction principle’s primary objective is to maximise the efficiency of production and consumption. This is done by producing products with greater value that will have a less impact on the economy, using smaller number of resources, and

7  CIRCULAR ECONOMY IN AGRICULTURAL SUPPLY CHAINS 

59

avoiding products which can damage the ecosystem (Figge et al. 2014). Eco-efficiency can be achieved by encouraging more frugal ways like basic packaging, smaller and weightless raw materials, promoting innovation of environment friendly technologies etc. (Ghisellini et al. 2016). Some of the examples which can be used for reducing food waste are: following FIFO (First in, First out) method to avoid expiry, increasing awareness, planning meals ahead, monitoring and storing food in air tight container to increase the lifetime, freezing the food based on its type and donating the rest (Harirs 2014). Reducing the consumption of scarce resources is related to changing the input materials, modifying the design, improving the process, and enhancing the monitoring and management at the production and consumption stages (Goyal et al. 2018; Geng and Doberstein 2008). Introducing drip irrigation to use less water in agricultural processes would be an example of reducing the use of input materials. 7.3.2.2 Reuse The Reuse practices involve re-introducing used products or product packaging in different processes. For instance, a useful practice can be the reuse of water. That can be using water from the shower in toilets (EMF 2013). The purpose is to ensure a product is used many times as possible and in various ways to prevent it from becoming wasted too early (Ghisellini et al. 2016). Hence, this approach allows to extend the life-cycle of products by reintroducing end-of-life items into the supply chain, to save raw materials, and to increase the efficient use of labour and energy during the design, manufacture and use of products or components (Goyal et  al. 2018). The reuse principle refers to those practices were the products have the capacity to not be thrown out as waste and are utilized again for the same purpose for which it was designed. Reuse can only be successful if the products are designed in such a way that they are long-­lasting for many phases of utilisation (Prendeville and Sherry 2014). This process can be encouraged by providing subsidies and when customers start realising the importance of reusing the products (Ghisellini et al. 2016). When compared to traditional methods, reusing is very eco-­friendly as it needs less raw materials, power, and employment. Added advantage to reuse is that it prevents carbon and other degrading emissions (Castellani et al. 2015). In the food sector this concept is illustrated by returning the wasted food to alternate markets, feeding animals, and donating it to the people who are in need of it.

60 

S. DESPOUDI ET AL.

7.3.2.3 Recycle The recycling principle refers to the process by which the used products are not thrown out as waste and are recovered into functional or usable substances. Recycling is the alternative when products or components cannot be reused or reduced. It implies the transformation of waste items into useful one. In the example above, wastewater can be filtered or recycled for surface cleaning or landscape irrigation. Recycling allows to reduce the depletion of non-renewable resources. It can be embedded into product design to improve processes and reduce resource consumption. Some illustrations of recycling in the food sector are: using by-products and left overs, incinerating to produce fuel, rendering, composting, converting scrap into bio gas and reprocessing those packaging material which comes along with the food products. The implementation of these practices and combinations of them at different stages of the operation allows organisations to decrease its impact on the environment and become more sustainable through the transition to a grow-make-use-restore approach. Different authors have proposed frameworks to guide the implementation of circular economy extending the 3Rs. One of the most renowned is the ReSOLVE framework for circular economy. The steps are as follows (EMF 2013): • Regenerate—Reclaim, retain and regenerate the health of ecosystems, shift to the use of renewable resources, and return recovered resources to the biosphere • Share—Reduce product loop speed and maximise shared use, promoting reuse and extending useful life span. • Optimise—Increase performance of products, reduce waste from production to end-of-life, and leveraging technology. • Loop—Use remanufacturing, recycling, anaerobic digestion, and extraction bio-chemicals from organic waste to produce closed loops. • Virtualise—Deliver utility using virtual means. • Exchange—Replace materials, employ innovative technology, and use new products and services. There are different examples of their use in practice, but the implementation of circular economy as a paradigm in the agricultural industry has shown remarkable advances.

7  CIRCULAR ECONOMY IN AGRICULTURAL SUPPLY CHAINS 

61

7.3.3  Circular Economy and Agriculture 4.0 Wang defined Agriculture 4.0 as tool which makes use of developing technology and improvement in machines to cope up with changes worldwide which will make production efficient, improve quality and lead to easier maintenance in ASCs. By implementing Agriculture 4.0 applications, ASC companies can improve their operational competence, enhance the flow of data, increase efficiency, and reduce waste. Some of the main concerns which CE principles are facing while being implemented are related to disparities and data inconsistencies (Rajput and Singh 2019). The lack of interactive platforms and designs to support circularity are the main reasons which generate the need to involve advanced technologies (Rosa et al. 2020). The primary obstacle is that the existing materials need to be discarded as they are not developed with the system of reusing or recycling owning to the lack of technology, most countries are lacking the ability to provide remanufactured product that have good quality (Kirchherr et al. 2017). The issue of transparency can lead to inadequate data on the product (Kirchherr et al. 2017). This can affect the activities of a company as they will be clueless about the type of raw materials used in the final product. This becomes even more problematic when there are many number of dealers who have obtained the product from unknown sub-dealers (Pheifer 2017). The lack of adequate expertise on how to use the technology and the kind of skills to be adopted are issues which have to be addressed (Amsterdam 2013). Some technologies which are linear in nature are very well established in the economy which makes it even harder to introduce new circular systems (Amsterdam 2013). However, with the assistance of Industry 4.0, companies can employ CE with the help of three main drivers such as the knowledge of location, knowledge of condition and knowledge of availability (Formisani 2020). By monitoring the location of assets or data with the help of feedback loops, companies can gain advantage as they can optimise the routes, assess storage, and ensure proper maintenance (Rosa et al. 2020). By having appropriate information about the conditions of the assets, the companies can improve their uptime. Finally, by realizing the ability of a specific asset, companies can ensure optimum utilisation of resources which can aid circularity as well (Garcia-Muiña et al. 2018). All of the

62 

S. DESPOUDI ET AL.

above where farfetched initially, but with the introduction of IoT and cloud computing these drivers can be accomplished with ease (Rosa et al. 2020). Therefore, agriculture 4.0 technologies seem to enable CE implementation and this in turn leads to sustainable performance and increased competitiveness.

7.4   Summary This chapter started with the definition of circular economy then it discussed the need for circular economy in ASCs. After this the relationship between corporate social responsibility, circular economy, agriculture 4.0 and sustainability was explained. In relation to that the principles of circular economy were outlined. The chapter concluded with a discussion about circular economy and agriculture 4.0.

References Abdul, M. H., Genovese, A., Acquaye, A. A., Koh, S. C. L. & Yamoah, F. r. (2017). Comparing linear and circular supply chains: A case study from the construction industry. International Journal of Production Economics, 183, 433–457. Amsterdam, I. (2013). Unleashing the Power of the Circular Economy. The circle economy. https://www.eesc.europa.eu/en/news-media/eescinfo/092019/ articles/72586 Batista, L. et al. (2018). Circular supply chains in emerging economies–a comparative study of packaging recovery ecosystems in China and Brazil. International Journal of Production Research, 7543, 5. https://doi.org/10.1080/ 00207543.2018.1558295 Bocken, N., Short, S., Rana, P., & Evans, S. (2014). A literature and practice review to develop sustainable business model archetypes. Available from: J. Clean. Prod., 65, 42e56. Castellani, V., Sala, S., & Mirabella, N. (2015). Beyond the throwaway society: a life cycle-based assessment of the environmental benefit of reuse. Integr. Environ. Assess. Manag., 11(3), 373e382. Despoudi, S., & Dora, M. (2020). Circular food supply chains. Institute of Food Science and Technology, 34, 48–51. EMF. (2013). Growth within: A circular economy vision for a competitive Europe. Going for Growth: A practical route to a Circular Economy. FAO. (2011). Global food losses and waste. http://www.fao.org/fileadmin/user_ upload/ags/publications/GFL_web.pdf

7  CIRCULAR ECONOMY IN AGRICULTURAL SUPPLY CHAINS 

63

FAO. (2019). The state of food security and nutrition in the world. Farooque, M., Zhang, A., Thürer, M., Qu, T., & Huisingh, D. (2019). Circular supply chain management: A definition and structured literature review. Journal on Cleaner Production, 228, 882–900. Fernando E. Garcia-Muiña, R. G.-S., Anna Maria Ferrari & Settembre-Blundo, D. (2018). The paradigms of industry 4.0 and circular economy as enabling drivers for the competitiveness of businesses and territories: The case of an Italian ceramic tiles manufacturing company, Social sciences, 7(12), 255.https:// doi.org/10.3390/socsci7120255. Figge, F., Young, W., & Barkemeyer, R. (2014). Sufficiency or efficiency to achieve lower resource consumption and emissions? The role of the rebound effect. Journal of Cleaner Production, 69, 216–224. Formisani, L. (2020). Smart industry for circular economy. [online] Available from: https://www.eni.com/en-IT/circular-economy/smart-industry.html Garcia-Muiña, F. E., González-Sánchez, R., Ferrari, A. M., & Settembre-Blundo, D. (2018) The paradigms of industry 4.0 and circular economy as enabling drivers for the competitiveness of businesses and territories: The case of an Italian ceramic tiles manufacturing company. Social Sciences, 7(12), 255. https://doi.org/10.3390/socsci7120255. Geissdoerfer, M., Morioka, S. N., de Carvalho M. M., & Evans S. 2018. Business models and supply chains for the circular economy. J. Clean. Prod., 190, pp. 712–721. Geng, Y., & Doberstein, B. (2008). Developing the circular economy in China: Challenges and opportunities for achieving ‘leapfrog development’. International Journal of Sustainable Development & World Ecology, 15, 231–239. Ghisellini, P., Cialani, C., & Ulgiati, S. (2016). A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems. Journal of Cleaner Production, 114, 11–32. Goyal, S., Esposito, M. & Kapoor, A. (2018). Circular economy business models in developing economies: Lessons from India on reduce, recycle, and reuse paradigms. Thunderbird International Business Review, 60, 729–740. Harirs, S. (2014). Food waste: Reduce, reuse, recycle, re-think. A paper presented at the International Conference of Engineering, Information Technology, and Science, 2014 (ICEITS 2014), held at Infrastructure University Kuala Lumpur, Unipark Suria, Jalan Ikram-Uniten, 43000 Kajang, Selangor Darul Ehsan, Malaysia 11–32. Kirchherr, J., Reike, D., & Hekkert, M. (2017). Conceptualizing the circular economy: An analysis of 114 definitions. Resources, Conservation and Recycling, 127(April), 221–232. Michelini, G., Moraes, R. N., Cunha, R. N., Costa, J. M. H., Ometto, A. R. (2017). From linear to circular economy: PSS conducting the transition. Procedia CIRP, 64, 2–6.

64 

S. DESPOUDI ET AL.

People’s_Republic_of_China. (2008). Circular economy promotion law of the people’s republic of China. China. Pheifer, A. G. (2017). Barriers & enablers to circular business models. https:// www.circulairondernemen.nl/uploads/4f4995c266e00bee8fdb8fb3 4fbc5c15.pdf Prendeville, S. & Sherry, J. (2014). Circular Economy. Is it Enough? Ecodesign Centre. https://www.researchgate.net/publication/301779162_ Circular_Economy_Is_it_Enough Rosa, P., Sassanelli, C., Urbinati, A., Chiaroni, D., & Terzi, S. (2020). Assessing relations between Circular Economy and Industry 4.0: A systematic literature review. International Journal of Production Research 2020, 58(6), 1662–1687. UN. (2020). Water, food and energy [Online]. Available: https://www.unwater. org/water-facts/water-food-and-energy/ [Accessed 24th June 2020]. Wijkman, A., & Skånberg, K. (2017). The Circular Economy and Benefits for Society Jobs and Climate Clear Winners in an Economy Based on Renewable Energy and Resource Efficiency. A study report at the request of the Club of Rome with support from the MAVA Foundation. https://circulareconomy.europa.eu/ platform/sites/default/files/the-circular-economy-czech-republic-andpoland.pdf Willersinn, C., Mack, G., Mouron, P., Keiser, A., & Siegrist, M. (2015). Quantity and quality of food losses along the Swiss potato supply chain: Stepwise investigation and the influence of quality standards on losses. Waste Manag, 46, 120–32. Yogesh, K. S., Sachin, K. M., Pravin, P. P., & Liu, S. (2019). When challenges impede the process: For circular economy-driven sustainability practices in food supply chain. Management Decision, 57, 995–1017. Zubrod, J. P., Bundschuh, M., Arts, G., Brühl, C. A., Imfeld, G., Knäbel, A., Payraudeau, S., Rasmussen, J. J., Rohr, J., Scharmüller, A., Smalling, K., Stehle, S., Schulz, R., & Schäfer, R. B. (2019). Fungicides: An Overlooked Pesticide Class? Environmental Science & Technology, 53, 3347–3365. https://digitalcommons.unl.edu/cseconfwork/147

CHAPTER 8

Opportunities of Agricultural 4.0 Supply Chains

Abstract  In this chapter the opportunities of agricultural 4.0 supply chains are presented. These include real-time data analysis and decrease of operational costs, increase in revenue and production flexibility, improvement in sustainability and enables circular economy, enhanced reliability and uptime, self-optimisation and quality of service, and improved infrastructure. Keywords  Agriculture 4.0 opportunities • Real-time data analysis • Operational costs • Revenue • Production flexibility • Sustainability • Circular economy • Reliability and uptime • Self-optimisation • Quality of service • Infrastructure

8.1   Real-Time Data Analysis and Decrease of Operational Costs ASC organisations around the world already started to adopt agriculture 4.0 concepts in their operations. However, although agriculture 4.0 is a very new concept and there is scarce information regarding how it affects the general performance of a business, it is expected to have a major effect on global economies. Research studies so far found that organisations which used elements of agriculture 4.0 were able to transform their operational business model, to reduce costs and achieve better customer © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Despoudi et al., Agricultural Supply Chains and Industry 4.0, https://doi.org/10.1007/978-3-030-72770-3_8

65

66 

S. DESPOUDI ET AL.

experience. The common denominator in all researched companies was the use of CPS and the ability to perform real-time data analysis (Hill 2015). Reduction of number of errors and defects and increased product quality are also advantages of Smart factories. It is argued that the top 100 European manufacturers would decrease the operational costs by 160 billion, if they are able to reduce the product defects to zero levels (Davies 2017). Furthermore, the use of Industry 4.0 in predictive maintenance has improved a lot efficiency but also increased plant safety as the rate of accidents caused by human workers was reduced.

8.2   Increase in Revenue and Production Flexibility Organisations also understand that the economic benefits from investing in agriculture 4.0 will not be realised immediately but in the course of time. More specifically they expect an average of 15% increase in revenue and approximately 12% cost reduction during the next five years. Also, there is an anticipation of increase in high-skilled and well-­paid jobs due to the expertise agriculture 4.0 requires. Production flexibility and adaptability will increase product customisation and product variety which will result in improved customer satisfaction and potential new market segments for organisations (European Parliament 2016). A recent analysis of the outlook of German manufacturing sector indicated that agriculture 4.0 will bring benefits in four main areas. Depending on the various sectors productivity will be boosted between €90 billion and €150 billion as well as consumer demand for a wide variety of increasingly customised products will drive additional revenue growth by almost 1% of Germany’s GDP. Furthermore, the application of Industry 4.0 will result in investments of 1% to 1.5% from manufacturers which will have positive effects in the general employment levels of Germany. Business executives understand the benefits that agriculture 4.0 and its concepts will bring, and they admit that it will become a competitive differentiator in the next years. Enhancements to customer service and satisfaction are identified as the main business benefits in addition to quality improvements in products and services and the development of new products and services (Harvard Business Review Analytic Services 2019).

8  OPPORTUNITIES OF AGRICULTURAL 4.0 SUPPLY CHAINS 

67

8.3   Improves Sustainability and Enables Circular Economy Agriculture 4.0 also will indirectly improve the sustainability of organisations and of the society. Waste reduction across the supply chain and resource efficiency across all processes will result to improved compliance with the strict environmental standards and improvements in the work environment. Communities will not be against the creation of environmentally friendly manufacturing facilities as they will be aware of their contribution to the creation of much desired circular economies. Agriculture 4.0 provides efficient practices which can reduce consumption and maintain low levels of energy input as it can offer clear and scalable manufacturing expertise (Kalmykova et  al. 2018). Along with this, it can be programmed in a such a way that it identifies products which have the potential to be reused and indirectly extend the good’s life (Kalmykova et  al. 2018). The overall process of circular economy is benefited with the help of technologies like IoT as it aids in recuperation and repair process at different stages of the product lifecycle. Similarly, with the help of agriculture 4.0 components, parts and equipment are made to be reusable which in turn improves the efficiency of the resources and promotes the implementation of recycling in the supply chain of the company (Garcia-Muiña et al. 2018). For example, IoT can be used for developing a food waste tracking system which provides information about how the food waste can be measured, categorised, and transferred to the cloud (S.jagtap and Rahimifard 2019). This data can be used for making key decisions on methods to reduce waste in the future (S.jagtap and Rahimifard 2019).

8.4   Enhances Reliability and Uptime The applications of agriculture 4.0 have the ability to not only enable human to machine communication but also machine to machine communication (Roblek et  al. 2016). This enables it to predict when an equipment or a machinery is likely to fail. So, this helps the company to change the parts when there is an actual damage taken place instead of changing it on a regular basis even if the part function properly (Tan and Wang 2010). With the help of IoT, those systems and software which could not be connected through ordinary means can be

68 

S. DESPOUDI ET AL.

connected easily. This will increase the interaction between the different devices which results in reliability. For instance, agriculture 4.0 has simulation software which can be used for collecting real time data and model a manufacturing system in the food sector (Hasnan and Yusof 2018). This will enable testing to be done virtually before any change is made physically. Some software programs like ARENA simulation and Tecnomatix plant simulation are few examples (Hasnan and Yusof 2018). This software can be applied to food processing which can simulate the overall process and provide data to the management that will be reliable, reduce down time and avoid manufacturing failures (Hasnan and Yusof 2018).

8.5   Self-Optimisation and Quality of Service Even during the time of hardship or emergency the applications of agriculture 4.0 have the capacity to hold on to the communication of other devices (Tague 2013). This will further increase the command and control as data can still be transferred. Since it has a diverse network, data can be retrieved and monitored without any influence from effects of other environment (Tague 2013). Since circular economy is designed for self-­restoration, the introduction of Industry 4.0 helps in interconnections that will enable it to transform the current business model in such a way that it will produce value and innovation that can in turn benefit the circular economy principles (Fernando E.  Garcia-Muiña and Settembre-­ Blundo 2018). The applications of agriculture 4.0 certifies that the systems have the capacity to change its performance according to the dynamic environment. With the option of high variability, it can become accustomed to operational adjustments as well (Brettela et al. 2016). This system is has a continuous learning process which helps the company to be highly robust and handles unpredicted behaviours autonomously (Brettela et al. 2016). This not only helps supply chains be more vibrant but also aids circular economy to adapt to the different behaviours across different territories. The leap to fully mechanized systems will enable the food manufacturing plants to meet consumers needs instantly while boosting efficiency simultaneously (Colwill et al. 2016; Sharp 2019). The above process was accomplished by constant stream of data which are linked to all the systems of the plant that can understand and adjust to change (Sharp 2019). One of the pillars of circular economy and

8  OPPORTUNITIES OF AGRICULTURAL 4.0 SUPPLY CHAINS 

69

sustainability is to maintain similar levels of quality to certify health and safety. Industry 4.0 ensures that importance is given to various data forms for managing resources so that the service is improved across the entire network. This enables transferring large amount of data in a reliable and efficient manner. By increasing the credibility of data transfer the overall service quality rises (Hashem and Khan 2017). For example, the use of cameras or other visioning software can monitor and detect deviations in the process (Hasnan and Yusof 2018). This saves cost for the company because deviations are detected within seconds and also allows company to maintain food safety levels (Hasnan and Yusof 2018).

8.6   Improves Infrastructure The basis of circular economy does not limit itself to the upfront design and asset utilisation but also depends on other infrastructure like operations, protection, and clearance (Toyne 2019). Equipment with distinct features cannot be used optimally if they are not integrated with each other. Consequently, Industry 4.0 can be used for implementing these machineries and know-hows which will result in an agile supply chain (Govindan and Hasanagic 2018). Additionally, cross sector collaboration can be promoted, products can be made according to the needs of individual customers and machinery with similar functions can be reused which will result in reduction of materials (Kalmykova et  al. 2018; Despoudi et al. 2020). Different devices will require different power levels and sophistications. If there are wireless sensors involved in tracking goods, then there will be a need for multi-applicative and multi interaction-­ model (E. Beigne et al. 2015). IoT has an upper hand as it has an effective applicative flexibility which can be used for adapting mixed signals and ensure control (E. Beigne et al. 2015). Therefore, this proves that it can cope up to changes efficiently. So when new device or technology is introduced the system incorporates it with ease and increases efficiency. With the help of digitation, the speed and reaction time can be increased significantly which increases competence and saves resources, which are crucial for implementing circular economy principles (Fernando E. Garcia-Muiña and Settembre-Blundo 2018). With the introduction of “Smart Factories” systems are able to communicate with each other, inspect and understand problems with least human interference (Hasnan

70 

S. DESPOUDI ET AL.

and Yusof 2018). It can complete tasks autonomously with high level of flexibility. Some of technologies are enterprise resource planning (ERP), Food quality assurance and Manufacturing execution system (MES) (Hasnan and Yusof 2018).

8.7   Summary This chapter discussed the opportunities of agricultural 4.0 supply chains including real-time data analysis and decrease of operational costs, increase in revenue and production flexibility, improvement in sustainability and enables circular economy, enhanced reliability and uptime, self-­optimisation and quality of service, and improved infrastructure.

References Beigne, E., Christmann, J., Valentian, A., Billoint, O., Amat, E., & Morche, D. (2015). UTBB FDSOI technology flexibility for ultra low power internet-of-things applications. 2015 45th European Solid State Device Research Conference (ESSDERC), Graz, Austria, 2015, 164–167. https://doi. org/10.1109/ESSDERC.2015.7324739. CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS 2015. Colwill, J., Despoudi, S., & Bhamra, R. (2016). A review of resilience within the UK food manufacturing sector. In Y. M. Goh & K. Case (Eds.), Advances in transdisciplinary engineering (Vol. 3). https://doi.org/10.3233/978­1-­61499-­668-­2-­451. Davies, R. (2017). Review of socio-technical considerations to ensure successful implementation of industry 4.0. Procedia Manufacturing, 11, 1288–1295. Despoudi, S., Papaioannou, G., & Dani, S. (2020). Producers responding to environmental turbulence in the Greek agricultural supply chain: Does buyer type matter? Production Planning and Control. https://doi.org/10.108 0/09537287.2020.1796138. European Parliament. (2016). Industry 4.0. https://www.europarl.europa.eu/ RegData/etudes/STUD/2016/570007/IPOL_STU(2016)570007_EN.pdf Fernando E. Garcia-Muiña, R. G.-S., Anna Maria Ferrari & Settembre-Blundo, D. (2018). The Paradigms of Industry 4.0 and Circular Economy as Enabling Drivers for the Competitiveness of Businesses and Territories: The Case of an Italian Ceramic Tiles Manufacturing Company. Govindan, K., & Hasanagic, M. (2018). A systematic review on drivers, barriers, and practices towards circular economy: A supply chain perspective. International Journal of Production Research. Taylor & Francis, 56(1–2), 278–311.

8  OPPORTUNITIES OF AGRICULTURAL 4.0 SUPPLY CHAINS 

71

Harvard Business Review Analytic Services. (2019). Accelerating the internet of things timeline. https://hbr.org/sponsored/2019/05/accelerating-theinternet-of-things-timeline. Hashem, I. A. T., & Khan, I. (2017). The role of big data analytics in internet of things. Computer Networks: The International Journal of Computer and Telecommunications Networking, December 2017. https://doi.org/10.1016/ j.comnet.2017.06.013. Hasnan, N. Z. N., & Yusof, Y. M. (2018). Short review: Application areas of industry 4.0 technologies in food processing sector. Conference: 2018 IEEE Student Conference on Research and Development (SCOReD) Project: Industry 4.0 Technologies. Hill, J. E. (2015). The circular economy: From waste to resource stewardship, part I. Waste and Resource Management, 168(1), 3–13. Kalmykova, Y., Sadagopan, M., & Rosado, L. (2018). Circular economy – From review of theories and practices to development of implementation tools. Resources, Conservation and Recycling, 135, 190–201. Lu Tan & Neng Wang, (2010). Future internet: The Internet of Things, 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), Chengdu, China, 2010, pp. V5-376-V5-380, https://doi. org/10.1109/ICACTE.2010.5579543 Malte Brettel, P. D. M., Fischer, F.G., Bendig, D., Weber, A. R., & Wolff, B. (2016). Enablers for self-optimizing production systems in the context of Industrie 4.0. 48th. Roblek, V., Meško, M., & Krapež, A. (2016). A Complex View of Industry 4.0. SAGE Open April-June 2016, 1–11, https://doi.org/10.1177/ 2158244016653987. Sharp, B. (2019). How Industry 4.0 Affects Food Safety and Quality Management. [online], https://foodsafetytech.com/column/how-industry-4-0-affectsfood-safety-and-quality-management/ Athreya, A. P., & Tague, P. (2013). Network Self-Organization in the Internet of Things. IEEE International Workshop of Internet-of-Things Networking and Control, https://doi.org/10.1109/SAHCN.2013.6644956. Toyne, P. (2019). How can the circular economy make infrastructure more sustainable? [online] https://www.eco-business.com/opinion/how-canthecircular-economy-make-infrastructure-moresustainable/#:~:text=The%20 sustainable%20element%20of%20infrastructure,basis%20of%20a%20circul a r % 2 0 e c o n o m y. & t e x t = S o % 2 C % 2 0 w h e n % 2 0 d e s i g n i n g % 2 0 a n d % 2 0 building,lifecycle%20needs%20to%20be%20considered.

CHAPTER 9

Challenges of Agricultural 4.0 Supply Chains

Abstract  This chapter presents the challenges of agricultural 4.0 supply chains which are identified as being sector heterogeneity, farm size, validation and collaboration, safety and security, investment costs, and design and compatibility. Keywords  Agriculture 4.0 challenges • Sector heterogeneity • Farm size • Validation and collaboration • Safety and security • Investment costs • Design and compatibility Even though this concept has its own benefits, it faces a series of objections and questions before it can be implemented successfully. Industry 4.0 is a relatively a new concept which is likely to drive the future, but with lack of help from industry, government and economy it proves difficult to accomplish it (Zhou and Taigang Liu 2015). This faces additional challenges if the form of modelling, digitisation, automation, computation, and execution (Zhou and Taigang Liu 2015; Rajput and Singh 2019).

9.1   Sector Heterogeneity Besides the myriad of opportunities, data-driven agricultural practices, smart farming and the applications of IoT in agriculture introduce considerable challenges, both for the farm but for the technical infrastructure the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Despoudi et al., Agricultural Supply Chains and Industry 4.0, https://doi.org/10.1007/978-3-030-72770-3_9

73

74 

S. DESPOUDI ET AL.

farming sector encompasses [45]. Within Agriculture 4.0, the food systems comprise multiple actors, who are extremely heterogeneous in nature. They may range from very large actors, such as supermarkets, suppliers (of seeds, machinery etc.), commodity traders and many others, to very small ones, such as artisan cheesemakers, microbreweries, roadside fruit and vegetable sellers etc. [45]. Notwithstanding, there is not a ‘solution that fits all’ approach that can potentially account for players and cases because there is a diverse range of technological, business model and regulatory needs across these heterogeneous farming contexts.

9.2   Farm Size While focusing on the economics of digital farming, there are multiple constraints regarding farm size and capital investment costs [33, 45]. There is a greater tendency among capital-intensive farms for the uptake of IoT technology, and couple of previous investments in new equipment and machinery with smart devices (e.g., smart tractors and farming equipment). Nonetheless, the challenge for small-scale farms comes when they have to share their data, and this involves fears of data misuse or losing their competitive advantage to larger farms. As a result, the vendors of such smart devices try to make their IoT offerings sufficiently attractive. Persuading small farmers to make the leap forward for their farms could be mostly facilitated through trial-mode funded programmes or through the limited number of financing options that are currently available. Yet, the concerns regarding data sharing still remain, even when the financing of smart farming goes through funded programmes. This is because small farms have to access a supply chain network of smart farms and share their details and information with the multiple AgriTech applications available over the network.

9.3   Validation and Collaboration As the applications are interconnected in the real and the cyber world, it involves close interaction as changes in one world will result in a series of changes in the other. So, precautions are required to be taken when its interacting with humans because a small change can lead to physical damages (Ying Tan and Steve 2009). With high levels of autonomy and authentication, standardisation has to be improved from time to time in order to certify that it can handle unique obligations (Armando et  al.

9  CHALLENGES OF AGRICULTURAL 4.0 SUPPLY CHAINS 

75

2016; Ying Tan and Steve 2009). Similarly, lack of collaboration will affect the implementation of circular economy principles as well. This is mainly because of the lack of trust which makes it even more difficult to know when and whom to collaborate with (Mishra et  al. 2019; Papaioannou et al.,). Even though integration improves efficiency, if the system fails to match the demand of the product then it might result in over or under production. This can affect the company in many ways because food products are perishable, fragile and has a specific date within which it has to be consumed. Therefore, this forces food companies to install advanced applications throughout. Furthermore, the new technologies will change completely the requirements for employees across all different levels of the supply chain. Employees will need to adapt to these changes by adopting new skills and qualifications as the business models will become more agile and data based. Education systems will need to evolve, and companies will have to invest heavily in employee training programs. This shortage in skills and qualifications of the existing manpower seem as one of the major barriers.

9.4   Safety and Security Interlinking devices and technologies increase security issues. Since internet can be accessed by everyone, there is a need to safeguard, monitor and inspect the data which is uploaded on a regular basis (Mahmoud Elkhodr 2016). Cyber-security is another topic which organisations are concerned for. As everything related with Industry 4.0 is interconnected with the use of Internet and as the technology is dependent on physical devices for collection and transmission of data, one vulnerability in the device level can compromise the entire network of connected things. Furthermore, manufacturing companies do not have the experience of how to handle these types of cyber-attacks. Finally, the current digital infrastructure on the country or organisational level is not able to cope with the requirements for Industry 4.0 concepts and technologies. There are a series of decisions to be made regarding the authentication of information, number of people permitted and type of security measures to implemented (Mahmoud Elkhodr 2016). Issues regarding authorisation will tend to arise if the devices do not have the same level of technology or skill. At the same time, the integrity of the data must be considered because the information should not be altered when it is being transferred from one device to another (Mahmoud Elkhodr 2016). While

76 

S. DESPOUDI ET AL.

implementing circular economy there will be interactions with humans and machines where the company will be bound to face several challenges ensuring safety for humans in the workplace (Rajput and Singh 2019). Systems which are safe but not secure will be vulnerable to cyber-attacks which can affect the weakest link in the entire supply chain of the company (Stanley 2019). For instance, if a small supplier has very few protection measures for their plant then they will act as the weakest link. When they are threatened, the companies which are integrated with it are also equally affected by it (Stanley 2019).

9.5   Investment Costs Modernisation of existing infrastructure and operations is also identified as one of the biggest obstacles. Especially in complex corporations, it is very difficult to identify from where to begin in order to gain the benefits of Industry 4.0 and its concepts. Although, projects related with Industry 4.0 are backed from senior executives, the lack of previous industry experiences and expertise are becoming the major roadblocks. In order to systematize the foundation of this concept, large amounts of investment are required. This may demotivate companies because as it will add on to the pressure of profitability and responsibility. Sometimes companies will not have access to the financial resources as this does not certify an instant return on investment. Even though this process promises to be cost effective in the long run, many companies are put off by their huge initial investment. Markets readiness for the circular products are yet to be realised so companies are discouraged form these massive investments. These costs are not limited to the initial step up, but also comes with other maintenance cost. There is a constant need for upkeep of the machinery and software along with high salaries which has to be paid for the employees who operate them. Sometimes, food companies spend a lot on solutions which do not cater to the current needs thereby resulting in waste money. With high cost needed for both circular economy and Industry 4.0, the initial investment cost is seen as a huge barrier (Rajput and Singh 2019).

9  CHALLENGES OF AGRICULTURAL 4.0 SUPPLY CHAINS 

77

9.6   Design and Compatibility A common specification guideline needs to be set to enable communication and interface across the industry (Armando et al., 2016). But this is a primary issue as many companies do not have the basic infrastructure needed to implement this. So, there are requirements to find new ways which can keep components running without making significant changes to the overall structure (Armando et al., 2016; Rajput and Singh 2019). Also, in order to implement circular economy several compatibility issues arise in the form of conflict of interest, business models, inadequate services, network support and poor foundation (Bianchini et al. 2014). Applications of industry often faces questions regarding integration of various devices as most of them are unique with its own set of properties and functions. So, when a device with an outdated technology has to be combined with another which has the latest technology, the compatibility will be challenging and complicated. Hence, a protocol has to be established for different means through which data is transferred (Rajput and Singh 2019).

9.7   Concluding Remarks Regardless of the above disadvantages and any others which will probably appear in the future, Industry 4.0 technologies will affect critically the way industries and organisations are operating. Those who will show the required flexibility and adaptation, will be ahead towards achieving the much-desired long-term competitive advantage. As a result, to be successful and beneficial for all, Agriculture 4.0, and to an extent Industry 4.0, applications have to explore the associated appropriate business models to identify the requisite privacy settings and control over data and confidentiality levels, that will allow farms and other agri-­food actors to monetise the data they are producing rigorously. The farms should also acquire the necessary IT skills that will allow them to, first, understand how they share their data, and then how to use agriculture 4.0 applications in an optimum way. Ultimately, there are important issues around agriculture 4.0 applications and the technological underpinnings of Agriculture 4.0 (Stojanovic et al. 2017), which span a number of areas. The challenges comprise the lack of interoperability (common data protocols, standards etc.), the lack of connectivity (especially for disperse areas, with poor network coverage),

78 

S. DESPOUDI ET AL.

the data processing power (which impacts disproportionately small and medium scale farmers), and, most importantly, the lack of transparent data governance, since regulations and legal frameworks are only slowly catching up with the current technological realities. Despite the above issues and a wide range of new ones, which will inevitably emerge from practice, the new generation of farmers, large companies in the domain of food production, innovative start-ups, hi-tech professionals and tech-enthusiasts show a keen interest in the agri-food domain. At the same time, Agriculture 4.0 and the evolution of digital farming have started having a strong presence in developing countries, too. They are gradually making Data Science, sensor-based applications, and innovative technologies core components of the agri-food sector and the associated processes.

9.8   Summary This chapter presented the challenges of agricultural 4.0 supply chains which were identified as being sector heterogeneity, farm size, validation and collaboration, safety and security, investment costs, and design and compatibility.

References Armando, P., Colombo, W., & Karnouskos, S. T. (2016, September). Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Computers in Industry, 81, 11–25. Bianchini, et al. (2014). overcoming the main barriers of circular economy implementation through a new visualization tool for circular business models. Sustainability 2019, 11(23), 6614. https://doi.org/10.3390/su11236614. Elkhodr, M., Shahrestani, S., & Cheung, H. (2016). THE internet of things: New interoperability, management and security challenges. International Journal of Network Security & Its Applications (IJNSA), 8(2), https://arxiv.org/ftp/ arxiv/papers/1604/1604.04824.pdf Mishra, J. L., Chiwenga, D., & Ali, K. (2019). Collaboration as an Enabler for Circular Economy: A Case Study of a Developing Country. Article in Management Decision. March 2019. Rajput, S., & Singh, S. P. (2019). Connecting circular economy and industry 4.0. International Journal of Information Management, 49, 98–113. Stanley, N. (2019). Safety and security in Industry 4.0 - are you ready? [online] https://www.infosecurity-magazine.com/opinions/safety-industry-4-1-1/

9  CHALLENGES OF AGRICULTURAL 4.0 SUPPLY CHAINS 

79

Stojanovic, V., Falconer, R. E., Isaacs, J., et al. (2017). Streaming and 3D mapping of AGRI-data on mobile devices. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2017.03.019. Ying Tan, M. C. V., Steve Goddard (2009). Spatio-temporal event model for cyber-physical systems. 29th IEEE International Conference on Distributed Computing Systems Workshops. Zhou, K., & Taigang Liu, L. Z. (2015). Industry 4.0: Towards future industrial opportunities and challenges. 12th International Conference on Fuzzy Systems and Knowledge Discovery.

CHAPTER 10

Conclusion and the Way Forward

Abstract  This chapter presents an overview of the key aspects and key conclusions from all the topics discussed in this book. It concludes with future research avenues. Keywords  Agriculture 4.0

The emerging era of agriculture 4.0, where the use of disruptive technologies, such as AI and the associated practices, are providing new ways to increase the yield, optimise the processes and enhance the sustainability of the food production, can support and address the food security goals for the future. Agriculture 4.0 comprises of applications, such as mobile harvesting with unmanned vehicles, the use of Geographical Positioning Systems (GPS) for supporting harvest during predefined times and dates, intelligent supply chains, mobile processing of crops (quality checks, cleaning, packaging etc.) and the aerial surveillance of livestock for improved welfare, and these are only a few of the most notable and still emerging applications within the ASC. The broader research on agriculture 4.0 shows that the scope of Big Data and AI in the farming sector goes beyond primary food production. Smart Intelligent Farming has already a substantial influence on the entirety of production systems and can complement the food supply chain, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Despoudi et al., Agricultural Supply Chains and Industry 4.0, https://doi.org/10.1007/978-3-030-72770-3_10

81

82 

S. DESPOUDI ET AL.

overhauling transportation and logistics as well as food-waste processes. Smart farming is, therefore, reshaping the associated operating models as highlighted, following on the footsteps of most digital transformations patterns we have been witnessing to date. The new operating models are shaped to follow a data-intensive approach and provide predictive insights for farming operations, driving real-time operational decisions, redesigning and delivering AgriFood-focused game-changing production processes. New approaches to farming are deemed as necessary for the achievement of the Sustainable Development Goals (SDGs) and confront word’s food insecurity. Smart Farming, in light of the recent advances of AI in agriculture 4.0, provides a new operational model for the farming sector towards the sustainability of the existing food systems. The increasing demands of the world food production and environmental efficiency ask for the most viable solution for solving the current problem of feeding the world population, ecologically and sustainably. Further research is needed in this area to identify and test the new business operating models that agriculture 4.0 brings. The context of Agriculture 4.0 draws on the use of Precision Farming technology, yet also takes recourse to intelligent networks and data management tools. Data management applications can extract all available information, so as the expertise can leverage the automation of sustainable processes in agriculture. Within this agricultural context, there is a strong emphasis on the use of information and communication technology in the cyber-physical farm management cycle and new technologies such as the Internet of Things and Cloud. The agriculture 4.0 applications are expected to leverage the latest development and introduce data sharing, artificial intelligence and machine learning techniques in the farming sector. This radical transformation of the traditional agricultural landscape, called agriculture 4.0 applications, is encompassed by the evolution of “Big Data” and Internet of Things. The scope of “Big Data” applications in Agriculture 4.0 (Smart Farming) goes beyond primary food production. Smart Farming context has substantial influence on the entire production systems and ties with the food supply chain, transportation and logistics and food-waste and therefore is reshaping the whole sector following the same way as most digital transformations have gone through. Data are used to provide predictive insights in farming operations, drive real-time operational decisions, and redesign business processes for game-­ changing business models.

10  CONCLUSION AND THE WAY FORWARD 

83

Agriculture 4.0 entails associated shifts in the roles of actors involved and power relations among them in current ASC networks. The interplay of various actors out of the immediate boundaries of the individual farm involves multiple new entrants except for the traditional farming stakeholders (e.g., Agri-Tech vendors, venture capitalists, start-ups etc.) and subsequently the conflicting interests among them. Regarding the data sharing context, several governmental institutions publish open data, under the condition that the privacy of individuals is guaranteed. Indeed there is a change of this perspective in light of the data protection regulation for EU and other legal frameworks around the world. However, data sharing concerns are arising. In this aspect further research is needed to ascertain what kind of data sharing limitations a company should have and how data sharing ethical issues could be avoided. As smart machines and sensors crop up on farms and farm’s data grow in quantity and scope, farming processes will become increasingly data-­ driven and data-enabled. Agricultural technologies (Agri-Tech) are introduced in the farming field more than ever before, as rapid developments in “Big Data”, Internet of Things, Cloud, and distributed platforms are triggering the Smart Farming evolution. While Precision Agriculture is just taking in-field variability into account, agriculture 4.0 goes beyond that by supporting management tasks. Management tasks are informed not only on location but also on data, but also they are enhanced by context- and situation awareness. Real-time events can trigger situation awareness as weather, diseases, humanitarian crises which require sudden and agile actions. The COVID-19 pandemic showed that ASC globally are highly vulnerable. Although there is already some kind of adoption of agriculture 4.0 technologies these are not sufficient enable ASC resilience. Future research should explore how agricultural 4.0 supply chains could be transformed to be able to respond to uncertain situations, climatic changes, pandemics and other humanitarian crises. Future research needs to examine the role of agricultural 4.0 technologies in increasing ASC resilience to pandemics and crisis. Access to these technologies especially by agricultural SMEs is another topic that needs to be further researched. New ways of working remotely need to be identified through the use of agriculture 4.0 technologies in order ASC to be able to work and operate machinery from anywhere. Sustainability and circular economy are the future competitive advantages of ASC business. However, the way that these interact with agriculture 4.0 is not yet ascertained. Future research should focus on identifying

84 

S. DESPOUDI ET AL.

new business models the enable agricultural 4.0 technologies to increase their potential by responding to environmental, social and economic issues. Consumers trends in relation to their needs of increased ASC transparency, healthy diets, vegetarian diets and climatrian diets will need to be addresses through agriculture 4.0 technological applications. Further research is needed to understand the changing consumer behaviour and their acceptance of the technologies that will transform the ASC. Agriculture 4.0 technologies seem to enable circular economy practices implementation. However, the latter relationship needs to be further researched. In particular, the relationship between circular economy and agriculture 4.0 needs to be further researched under different ASC contexts.

10.1   Summary This chapter presented an overview of the key aspects and key conclusions from all the topics discussed in this book. It concluded with future research avenues.

References

Abdul, M. H., Genovese, A., Acquaye, A. A., Koh, S. C. L. & Yamoah, F. r. (2017). Comparing linear and circular supply chains: A case study from the construction industry. International Journal of Production Economics, 183, 433–457. Almada-Lobo, F. (2016). The industry 4.0 revolution and the future of manufacturing execution systems (MES). Journal of Innovation Management, 3(4), 16–21. https://doi.org/10.24840/2183-0606_003.004_0003. Amsterdam, I. (2013). Unleashing the Power of the Circular Economy. The circle economy. https://www.eesc.europa.eu/en/news-media/eescinfo/092019/ articles/72586 Aramyan, L.  H., & Van Gogh, J.  B. (2014). Reducing postharvest food losses in developing economies by using a network of excellence as an intervention tool. Presented in the IFAMA 2014 and CCA food and agribusiness world forum ‘people feed the world’ in Cape Town, South Africa, June. http://www.ifama. org/files/conf/papers/988.pdf Armando, P., Colombo, W., & Karnouskos, S. T. (2016, September). Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Computers in Industry, 81, 11–25. Askew, A. (2019). The climate crisis is already hitting food production: An urgent system-wide response is needed. https://www.foodnavigator.com/ Article/2019/06/06/The-climate-crisis-is-already-hitting-food-productionAn-urgent-system-wide-response-is-needed

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Despoudi et al., Agricultural Supply Chains and Industry 4.0, https://doi.org/10.1007/978-3-030-72770-3

85

86 

References

Bagheri, B., Yang, S., Kao, H., & Lee, J., (2015). Cyber-physical systems architecture for self-aware machines in industry 4.0 environment. IFAC Conference, 38(3), 1622–1627. Bahrin, M., Othman, M., Nor, N., & Azli, M., (2016). Industry 4.0: A review on industrial automation and Robotic. Jurnal Teknologi (Sciences and Engineering), 1, 137–143. Barmpounakis, S., Kaloxylos, A., Groumas, A., et  al. (2015). Management and control applications in agriculture domain via a future internet business-to-­ business platform. Information Processing in Agriculture. https://doi. org/10.1016/j.inpa.2015.04.002. Batista, L. et al. (2018). Circular supply chains in emerging economies–a comparative study of packaging recovery ecosystems in China and Brazil. International Journal of Production Research, 7543, 5. https://doi.org/10.1080/ 00207543.2018.1558295 Beigne, E., Christmann, J., Valentian, A., Billoint, O., Amat, E., & Morche, D. (2015). UTBB FDSOI technology flexibility for ultra low power internetof-things applications. 2015 45th European Solid State Device Research Conference (ESSDERC), Graz, Austria, 2015, 164–167. https://doi. org/10.1109/ESSDERC.2015.7324739. Bianchini, et al. (2014). overcoming the main barriers of circular economy implementation through a new visualization tool for circular business models. Sustainability 2019, 11(23), 6614. https://doi.org/10.3390/su11236614. Bocken, N., Short, S., Rana, P., & Evans, S. (2014). A literature and practice review to develop sustainable business model archetypes. Available from: J. Clean. Prod., 65, 42e56. Braziotis, C., Bourlakis, M., Rogers, H., & Tannock, J. (2013). Supply chains and supply networks: Distinctions and overlaps. Supply Chain Management, 18, 644–652. Brettel, M., Friedrichsen, N., Keller, M., & Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective. International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 8(1), 37–44. Brodt, S., Six, J., Feenstra, G., Ingels, C., & Campbell, D. (2011). Sustainable agriculture. Nature Education Knowledge, 3(10), 1. Castellani, V., Sala, S., & Mirabella, N. (2015). Beyond the throwaway society: a life cycle-based assessment of the environmental benefit of reuse. Integr. Environ. Assess. Manag., 11(3), 373e382. Colwill, J., Despoudi, S., & Bhamra, R. (2016). A review of resilience within the UK food manufacturing sector. In Y. M. Goh & K. Case (Eds.), Advances in transdisciplinary engineering (Vol. 3). https://doi.org/10.3233/9781-61499-668-2-451.

 References 

87

Corallo, A., Latino, M. E., & Menegoli, M. (2018). From industry 4.0 to agriculture 4.0: A framework to manage product data in Agri-food supply chain for voluntary traceability. International Journal of Nutrition and Food Engineering, 12(5), 146–150. CEMA—European Agricultural Machinery. (2017). Farming 4.0: The future of agriculture? Retrieved 10 September, 2019. http://www.cema-agri.org/ page/farming-40-futureagriculture. CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS 2015. Cropmetrics. (2019). Irrigation solutions. https://cropmetrics.com/thesolutions/ Davies, R. (2017). Review of socio-technical considerations to ensure successful implementation of industry 4.0. Procedia Manufacturing, 11, 1288–1295. Defra. (2006). Food industry sustainability strategy. http://www.defra.gov.uk/ publications/files/pb11649-fiss2006-060411.pdf Despoudi, S. (2016). An investigation of the collaboration—Postharvest food loss relationship and the effect of the environmental turbulence factors. Loughborough University. https://dspace.lboro.ac.uk/dspace-jspui/bitstream/2134/21785/ 1/Thesis-2016-Despoudi.pdf. Despoudi, S. (2019). 8—Optimized food supply chains to reduce food losses. In Saving food production, supply chain, food waste and food consumption (pp. 227–248). San Diego: Elsevier. Despoudi, S., & Dora, M. (2020). Circular food supply chains. Institute of Food Science and Technology, 34, 48–51. Despoudi, S. (2020a). Challenges in reducing food losses at producers’ level: The case of Greek agricultural supply chain producers. Industrial Marketing Management, 93, 520–532. https://doi.org/10.1016/j.indmarman.2020. 09.022. Despoudi, S. (2020b). Green food supply chain. In C. Galanakis (Ed.), Food industry and the environment. https://doi.org/10.1016/B978-0-12-816449-5.00002-3. Despoudi, S., & Dora, M. (2020). Circular food supply chains. Institute of Food Science and Technology, 34, 48–51. Despoudi, S., Papaioannou, G., Saridakis, G., & Dani, S. (2018). Does collaboration pay in agricultural supply chain? An empirical approach. International Journal of Production Research, 56, 4396–4417. Despoudi, S., Papaioannou, G., & Dani, S. (2020). Producers responding to environmental turbulence in the Greek agricultural supply chain: Does buyer type matter? Production Planning and Control. https://doi.org/10.1080/0953728 7.2020.1796138. Drath, R., & Horch, A. (2014). Industry 4.0: Hit or hype?. IEEE Industrial Electronics Magazine, 8(2), 56–58. Driscoll, M. (2012). How can we build a sustainable farming system for all? http://www.theguardian.com/sustainable-business/blog/farming-systemprinciples-based-sustainable

88 

References

EC. (2018). Dronies in agriculture. https://ec.europa.eu/growth/tools-databases/dem/monitor/sites/default/files/Drones_vf.pdf Elkington J. (1994). Towards the sustainable corporation. California Management Review, 90–100 (Winter). Elkhodr, M., Shahrestani, S., & Cheung, H. (2016). THE internet of things: New interoperability, management and security challenges. International Journal of Network Security & Its Applications (IJNSA), 8(2), https://arxiv.org/ftp/ arxiv/papers/1604/1604.04824.pdf EMF. (2013). Growth within: A circular economy vision for a competitive Europe. Going for Growth: A practical route to a Circular Economy. EUFIC. (2018). Fighting food waste by innovative food packaging. https://www. eufic.org/en/food-production/article/fighting-food-waste-by-innovativefood-packaging European_Commission. (2015). You are part of the food chain. Key facts and figures on the food supply chain in the European Union. European_Commission. (2017). Digital transformation monitor. Industry 4.0 in agriculture: Focus on IoT aspects [Online]. Available: https://ec.europa.eu/ growth/tools-databases/dem/monitor/sites/default/files/DTM_ Agriculture%204.0%20IoT%20v1.pdf European Parliament. (2016). Industry 4.0. https://www.europarl.europa.eu/ RegData/etudes/STUD/2016/570007/IPOL_STU(2016)570007_EN.pdf Eurostat. (2015). Farm structure statistics. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Farm_structure_statistics&oldid=233234 FAO. (2002). World agriculture: Towards 2015/2030 summary report. ftp://ftp. fao.org/docrep/fao/004/y3557e/y3557e.pdf FAO. (2006). Postharvest management of fruit and vegetables in the Asia-Pacific region. http://www.apo-tokyo.org/00e-books/AG-18_PostHarvest/AG-18_ PostHarvest.pdf FAO. (2009). Climate change implications for fisheries and aquaculture overview of current scientific knowledge. http://www.fao.org/fileadmin/user_upload/ newsroom/docs/FTP530.pdf FAO. (2010). FAO—World Bank workshop on reducing post-harvest losses in grain supply chain in Africa. http://www.fao.org/fileadmin/user_upload/ags/publications/FAO_WB_ph_web.pdf FAO. (2011). Global food losses and waste. http://www.fao.org/fileadmin/user_ upload/ags/publications/GFL_web.pdf FAO. (2017). The future of food and agriculture: Trends and challenges. http:// www.fao.org/3/i6583e/i6583e.pdf FAO. (2019). World hunger is still not going down after three years and obesity is still growing – UN report. http://www.fao.org/news/story/en/item/1200484/ icode/ FAO. (2019). The state of food security and nutrition in the world.

 References 

89

Farooque, M., Zhang, A., Thürer, M., Qu, T., & Huisingh, D. (2019). Circular supply chain management: A definition and structured literature review. Journal on Cleaner Production, 228, 882–900. Fernando E. Garcia-Muiña, R. G.-S., Anna Maria Ferrari & Settembre-Blundo, D. (2018). The paradigms of industry 4.0 and circular economy as enabling drivers for the competitiveness of businesses and territories: The case of an Italian ceramic tiles manufacturing company, Social sciences, 7(12), 255. https://doi.org/10.3390/socsci7120255. Fernando E. Garcia-Muiña, R. G.-S., Anna Maria Ferrari & Settembre-Blundo, D. (2018). The Paradigms of Industry 4.0 and Circular Economy as Enabling Drivers for the Competitiveness of Businesses and Territories: The Case of an Italian Ceramic Tiles Manufacturing Company. Figge, F., Young, W., & Barkemeyer, R. (2014). Sufficiency or efficiency to achieve lower resource consumption and emissions? The role of the rebound effect. Journal of Cleaner Production, 69, 216–224. Finkenzeller, K. (2010). RFID Handbook: Fundamentals and Applications in Contactless Smart Cards, Radio Frequency Identification and Near-Field Communication. New York: Wiley. Food Standards Agency. (2019). Food crime, understanding food crime and how to report it. https://www.food.gov.uk/safety-hygiene/food-crime Formisani, L. (2020). Smart industry for circular economy. [online] Available from: https://www.eni.com/en-IT/circular-economy/smart-industry.html Forrester, V. (2019). School management information systems: Challenges to educational decision-making in the big data era. International Journal on Integrating Technology in Education, 8(1), 1–11. Foresight. (2011). Foresight project on global food and farming futures. In Synthesis report C7: Reducing waste. London: The Government Office for Science. Foresight. 2011a. The future of Food and Farming: Final Project Report. London: The Government Office for Science. Foresight. (2011b). Foresight project on global food and farming futures. In Synthesis report C7: Reducing waste. London: The Government Office for Science. Garcia-Muiña, F. E., González-Sánchez, R., Ferrari, A. M., & Settembre-Blundo, D. (2018) The paradigms of industry 4.0 and circular economy as enabling drivers for the competitiveness of businesses and territories: The case of an Italian ceramic tiles manufacturing company. Social Sciences, 7(12), 255. https://doi.org/10.3390/socsci7120255. Geissdoerfer, M., Morioka, S. N., de Carvalho M. M., & Evans S. 2018. Business models and supply chains for the circular economy. J. Clean. Prod., 190, pp. 712–721.

90 

References

Geng, Y., & Doberstein, B. (2008). Developing the circular economy in China: Challenges and opportunities for achieving ‘leapfrog development’. International Journal of Sustainable Development & World Ecology, 15, 231–239. Gidney, M. (2012). Why a fair supply chain is key to achieving a sustainable food system. http://www.theguardian.com/sustainable-business/fairtrade-partnerzone/fair-supply-chain-sustainable-food-system Ghisellini, P., Cialani, C., & Ulgiati, S. (2016). A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems. Journal of Cleaner Production, 114, 11–32. Gilchrist, A. (2016). Industry 4.0: The industrial internet of things. New York, NY: Apress. Godfray, H. C., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Nisbett, N., Pretty, J., Robinson, S., Toulmin, C., & Whiteley, R. (2010). The future of global food system. Philosophical Transactions of the Royal Society B, 365, 2769–2777. Govindan, K., & Hasanagic, M. (2018). A systematic review on drivers, barriers, and practices towards circular economy: A supply chain perspective. International Journal of Production Research. Taylor & Francis, 56(1–2), 278–311. Goyal, S., Esposito, M. & Kapoor, A. (2018). Circular economy business models in developing economies: Lessons from India on reduce, recycle, and reuse paradigms. Thunderbird International Business Review, 60, 729–740. Green Alliance. (2020). Plastic promises. https://www.green-alliance.org.uk/ plastic_promises.php Guardian. (2008). World is facing a natural resources crisis worse than financial crunch. https://www.theguardian.com/environment/2008/oct/29/climatechangeendangeredhabitats Guardian. (2011). UN: Farmers must produce 70% more food by 2050 to feed population. https://www.theguardian.com/environment/2011/nov/28/ un-farmers-produce-food-population Gustavsson J., Cederberg C., Sonesson U., Otterdijk R., & Meyberg A. (2011). Global food losses and waste. http://www.fao.org/fileadmin/user_upload/ags/ publications/GFL_web.pdf Hahn, G.  J. (2020). Industry 4.0: A supply chain innovation perspective. International Journal of Production Research, 58, 1425–1441. Han, J. W., Ruiz-Garcia, L., Qian, J. P., & Yang, X. T. (2018). Food packaging: A comprehensive review and future trends, comprehensive reviews in food science and food safety. Institute of Food Technologists (Vol. 17), https://doi. org/10.1111/1541-4337.12343. Harirs, S. (2014). Food waste: Reduce, reuse, recycle, re-think. A paper presented at the International Conference of Engineering, Information Technology, and Science, 2014 (ICEITS 2014), held at Infrastructure University Kuala Lumpur, Unipark Suria, Jalan Ikram-Uniten, 43000 Kajang, Selangor Darul Ehsan, Malaysia 11–32.

 References 

91

Harvard Business Review Analytic Services. (2019). Accelerating the internet of things timeline. https://hbr.org/sponsored/2019/05/accelerating-the-internetof-things-timeline. Hashem, I. A. T., & Khan, I. (2017). The role of big data analytics in internet of things. Computer Networks: The International Journal of Computer and Telecommunications Networking, December 2017. https://doi.org/10.1016/ j.comnet.2017.06.013. Hasnan, N. Z. N., & Yusof, Y. M. (2018). Short review: Application areas of industry 4.0 technologies in food processing sector. Conference: 2018 IEEE Student Conference on Research and Development (SCOReD) Project: Industry 4.0 Technologies. Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for industrie 4.0 scenarios—paper presented at the 49th Hawaii international conference on system sciences. Koloa, Hawaii: HICSS. Hill Research, J. E. (2015, February). The circular economy: From waste to resource stewardship, part I. Waste and Resource Management, 168(1), 3–13. Hodges, R., Buzby, J. C., & Benett, B. (2010). Postharvest losses and waste in developed and less developed countries: Opportunities to improve resource use. Journal of Agricultural Science. Cambridge University. Hong, H., Dang, J., Tsai,Y., Liu, C., Lee, W., Wang, M., Chen, P. (2011). An RFID application in the food supply chain: A case study of convenience stores in Taiwan. Journal of Food Engineering, 106(2), 119–126. Hozdic, E. (2015). Smart factory for industry 4.0: A review. International Journal of Modern Manufacturing Technologies, 7(1), 28–35. Humphrey, J., & Memedovic, O. (2006). Global value chains in the agrifood sector. https://www.unido.org/sites/default/files/2009-05/Global_value_chains_ in_the_agrifood_sector_0.pdf Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for implementing the strategic initiative Industrie 4.0: Securing the future of German manufacturing industry; Final report of the Industrie 4.0 Working Group, Berlin: Acatech. Kaloxylos, A., Eigenmann, R., Teye, F., et al. (2012). Farm management systems and the future internet era. Computers and Electronics in Agriculture. https:// doi.org/10.1016/j.compag.2012.09.002. Kalmykova, Y., Sadagopan, M., & Rosado, L. (2018). Circular economy – From review of theories and practices to development of implementation tools. Resources, Conservation and Recycling, 135, 190–201. Karafili, E., Spanaki, K., & Lupu, E. C. (2019). Access control and quality attributes of open data: Applications and techniques. In W. Abramowicz & A. Paschke (Eds.), Business information systems workshops. BIS 2018 (Lecture notes in business information processing, Vol. 339). Cham: Springer. https:// doi.org/10.1007/978-3-030-04849-5_52.

92 

References

Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. Kang, H. et al. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing-Green Technology, 3(1), 111–128. Karim, F., Karim, F., & Frihida A. (2017). Monitoring system using web of things in precision agriculture. Procedia Computer Science, 110, 402–409. Kirchherr, J., Reike, D., & Hekkert, M. (2017). Conceptualizing the circular economy: An analysis of 114 definitions. Resources, Conservation and Recycling, 127(April), 221–232. Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS—Wageningen Journal of Life Sciences, 90–91, 100315. Kolberg, D., Knobloch, J., & Zuehlke, D. (2016). Towards a lean automation interface for workstations. International Journal of Production Research, 55, 2845–2856. Kong, X. Y. (2011). Exploring Online Customer Experience: Website Features, Customer Activities, and Repurchase Intentions. Thesis (PhD), p. 100, University of Warwick. Lanherr, M., Schneider, U., & Bauerhansl, T. (2016). The application centre industrie 4.0–industry-driven manufacturing, research and development. 49th CIRP conference on manufacturing systems. Procedia CIRP, 51, 26–31. Lehmann, R. J., Reiche, R., & Schiefer, G. (2012). Future internet and the agri-­ food sector: State-of-the-art in literature and research. Computers and Electronics in Agriculture, 89, 158–174. Lezoche, M., Hernandez, J.  E., Alemany Díaz, M.  D. M.  E., Panetto, H., & Kacprzyk, J. (2020). Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Computers in Industry, 117, 103187. Lu Tan & Neng Wang, (2010). Future internet: The Internet of Things, 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), Chengdu, China, 2010, pp. V5-376-V5-380, https://doi. org/10.1109/ICACTE.2010.5579543 Malte Brettel, P. D. M., Fischer, F.G., Bendig, D., Weber, A. R., & Wolff, B. (2016). Enablers for self-optimizing production systems in the context of Industrie 4.0. 48th. Matthews, L. et al. (2016). Building bridges: Toward alternative theory of sustainable supply chain management. Journal of Supply Chain Management, 52(1), 72–72. https://doi.org/10.1111/j.1745-493X.2010.03201.x. Meadows, D. H. (1972). The limits to growth. Earth Island Ltd. Meola, A. (2016). Why IoT, big data & smart farming is the future of agriculture—Business insider Deutschland. In Business insider. https://www.businessinsider.com/smart-farming-iot-agriculture

 References 

93

Michelini, G., Moraes, R. N., Cunha, R. N., Costa, J. M. H., Ometto, A. R. (2017). From linear to circular economy: PSS conducting the transition. Procedia CIRP, 64, 2–6. Miranda, J., Ponce, P., Molina, A., & Wright, P. (2019). Sensing, smart and sustainable technologies for Agri-food 4.0. Computers in Industry, 108, 21–36. https://doi.org/10.1016/j.compind.2019.02.002. Mishra, J. L., Chiwenga, D., & Ali, K. (2019). Collaboration as an Enabler for Circular Economy: A Case Study of a Developing Country. Article in Management Decision. March 2019. Moeuf, A. et al. (2017). The industrial management of SMEs in the era of industry 4.0. International Journal of Production Research, 92(1), 1–19. NESTA. (2015). Precision agriculture: Almost 20% increase in income possible from smart farming. https://www.nesta.org.uk/blog/precision-agriculturealmost-20-increase-in-income-possible-from-smart-farming/ Nukala, R., Panduru, K., Shields, A., Riordan, D., Doody, P., & Walsh, J. (2016). Internet of things: A review from “Farm to Fork.” In 27th Irish Signals and Systems Conference (ISSC), Londonderry, 2016, pp. 1–6. https://doi.org/10.1109/ ISSC.2016.7528456 Oberg, C., & Graham, G. (2016). How smart cities will change supply chain management: A technical viewpoint. Production Planning & Control, 27(6), 529–538. Omega. (2019). Temperature monitoring during transportation, storage and processing of perishable products. https://www.omega.co.uk/technicallearning/temperature-monitoring-during-transportation.html Oesterreich, T., & Teuteber, F. (2016). Understanding the implications of digitisation and automation in the context of Industry 4.0; A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 83, 121–139. Otles, S., Despoudi, S., Bucatariu, C., & Kartal, C. (2014). Food waste production and sustainability in the food industry. In C. Galanakis (Ed.), Food waste recovery: Processing technologies and techniques. Elsevier. Papaioannou, G., Mohammed, A., Despoudi, S., Saridakis, G., & Papadopoulos, T. (2020). The role of adverse economic environment and human capital on collaboration within agri-food supply chains. International Journal of Information Management, 52. https://doi.org/10.1016/j.ijinfomgt.2020.102077. Parfitt, J., Bartherl, M., & Macnaughton S. (2010). Food waste within food supply chains: Quantification and Potential for Change to 2050. Philosophical Transactions of the Royal Society B, 365, 3065–3081. People’s_Republic_of_China. (2008). Circular economy promotion law of the people’s republic of China. China. Pereira, A. C., & Romero, F. (2017). A review of the meanings and the implications of the industry 4.0 concept. Procedia Manufacturing. https://doi. org/10.1016/j.promfg.2017.09.032.

94 

References

Pham, X., & Stack, M. (2018). How data analytics is transforming agriculture. Business Horizons. https://doi.org/10.1016/j.bushor.2017.09.011. Pheifer, A. G. (2017). Barriers & enablers to circular business models. https:// www.circulairondernemen.nl/uploads/4f4995c266e00bee8fdb8fb3 4fbc5c15.pdf Popović, T., Latinović, N., Pešić, A., et al. (2017). Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: A case study. Computers and Electronics in Agriculture, 140, 255–265. https://doi. org/10.1016/j.compag.2017.06.008. Prendeville, S. & Sherry, J. (2014). Circular Economy. Is it Enough? Ecodesign Centre. https://www.researchgate.net/publication/301779162_Circular_ Economy_Is_it_Enough Rajput, S., & Singh, S. P. (2019). Connecting circular economy and industry 4.0. International Journal of Information Management, 49, 98–113. Rejikumar, G. et al. (2019). Industry 4.0: Key findings and analysis for the literature arena. Benchmarking: An International Journal, 26(8), 2514–2542. Rigby, D., Woodhouse, P., Young, T., & Burton, M. (2001). Constructing a farm level indicator of sustainable agricultural practice. Ecological Economics, 39(3), 463–478. https://doi.org/10.1016/S0921-8009(01)00245-2. Roblek, V., Meško, M., & Krapež, A. (2016). A Complex View of Industry 4.0. SAGE Open April-June 2016, 1–11, https://doi.org/10.1177/ 2158244016653987. Robinson, A., Mulvany, L., & Stringer, D. (2019). Robots take the wheel as autonomous farm machines hit fields. https://www.bloomberg.com/news/ articles/2019-05-15/robots-take-the-wheel-as-autonomous-farm-machineshit-the-field Rockström, J. et al. (2009). Planetary boundaries: Exploring the safe operating space for humanity recommended citation. Available at: http://pdxscholar.library.pdx. edu/iss_pub; http://www.ecologyandsociety.org/vol14/iss2/art32/ Rosa, P., Sassanelli, C., Urbinati, A., Chiaroni, D., & Terzi, S. (2020). Assessing relations between Circular Economy and Industry 4.0: A systematic literature review. International Journal of Production Research 2020, 58(6), 1662–1687. Rossini, M., Costa, F., Tortorella, G., & Portioli-Staudacher, A. (2019). The interrelation between industry 4.0 and lean production: And empirical study on lean manufacturers. The Interantional Journal of Advanced Manufacturing Technology, 102(1), 3963–3976. Routroy, S., & Behera, A. (2017). Agriculture supply chain: A systematic review of literature and implications for future research. Journal of Agribusiness in Developing and Emerging Economies, 7, 275–302. Schmidt, R., Möhring, M., Härting, R.-C., et al. (2015). Industry 4.0-potentials for creating smart products: Empirical research results. International Conference on Information Systems. https://doi.org/10.1007/978-3-540-79396-0.

 References 

95

Schuttelaar. (2017). BLOG: Smart farming is key for the future of agriculture. https://www.schuttelaar-partners.com/news/2017/smart-farming-is-key-for-thefuture-of-agriculture Sharp, B. (2019). How Industry 4.0 Affects Food Safety and Quality Management. [online], https://foodsafetytech.com/column/how-industry-4-0-affectsfood-safety-and-quality-management/ Smart Elements. (2019). Smarter agriculture. https://smartelements.io/ Spanaki, D., Karafili, E., & Despoudi, S. (2019). Data sharing in agriculture 4.0: Concepts and challenges. EurOMA conference 2019—Finland. Spanaki, K., Sivarajah, U., Fakhimi, M., Despoud, S., & Irani, Z. (2021a). Disruptive technologies in agricultural operations: a systematic review of AI-driven AgriTech research. Annals of Operations Research. https://doi. org/10.1007/s10479-020-03922-z. Spanaki, K., Karafili, E., Sivarajah, U., Despoud, S., & Irani, Z. (2021b). Artificial intelligence and food security: Swarm intelligence of AgriTech drones for smart AgriFood operations. Production Planning and Control, 1–19. https://doi. org/10.1080/09537287.2021.1882688. Spinner, J. (2014). Intelligent design: eAgile launches RFID smart packaging. https://www.foodnavigator.com/Article/2014/08/12/Food-packagingtechnology-uses-RFID-to-increase-safety Stanley, N. (2019). Safety and security in Industry 4.0 - are you ready? [online] https://www.infosecurity-magazine.com/opinions/safety-industry-4-1-1/ Stojanovic, V., Falconer, R. E., Isaacs, J., et al. (2017). Streaming and 3D mapping of AGRI-data on mobile devices. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2017.03.019. Athreya, A. P., & Tague, P. (2013). Network Self-Organization in the Internet of Things. IEEE International Workshop of Internet-of-Things Networking and Control, https://doi.org/10.1109/SAHCN.2013.6644956. Tomu. (2019). Blockchain for agriculture. https://medium.com/swlh/ blockchain-for-agriculture-5b0a0baa0aa3 Trkman, P., & McCormack, K. (2009). Supply chain risk in turbulent environments—A conceptual model for managing supply chain network risk. International Journal of Production Economics. https://doi.org/10.1016/j. ijpe.2009.03.002. Tsolakis, N., Bechtsis, D., & Bochtis, D. (2019a). Agros: A robot operating system based emulation tool for agricultural robotics. Agronomy, 9. https://doi. org/10.3390/agronomy9070403. Tsolakis, N., Bechtsis, D., & Srai, J. S. (2019b). Intelligent autonomous vehicles in digital supply chains: From conceptualisation, to simulation modelling, to real-world operations. Business Process Management Journal, 25, 414–437. https://doi.org/10.1108/BPMJ-11-2017-0330.

96 

References

Toyne, P. (2019). How can the circular economy make infrastructure more sustainable? [online] https://www.eco-business.com/opinion/how-canthe-circular-economy-make-infrastructure-moresustainable/#:~:text=The%20 sustainable%20element%20of%20infrastructure,basis%20of%20a%20circul a r % 2 0 e c o n o m y. & t e x t = S o % 2 C % 2 0 w h e n % 2 0 d e s i g n i n g % 2 0 a n d % 2 0 building,lifecycle%20needs%20to%20be%20considered. UN. (2015). Sustainable development goals. https://sdgs.un.org/goals UN. (2020). Water, food and energy [Online]. Available: https://www.unwater. org/water-facts/water-food-and-energy/ [Accessed 24th June 2020]. UN Food and Agriculture Organization. (2009). 2050: A third more mouths to feed. Vidal, J. (2012). The future of food. Guardian. http://www.theguardian.com/ global-development/2012/jan/22/future-of-food-john-vidal Vlachos, I. P., Bourlakis, M., & Karalis, V. (2008). Manufacturer—Retailer collaboration in the supply chain: Empirical evidence from the Greek food sector. International Journal of Logistics Research and Applications. https://doi. org/10.1080/13675560701768517. Weltzien, C. (2016). Digital agriculture—Or why agriculture 4.0 still offers only modest returns. Landtechnik, 71, 66–68. Wijkman, A., & Skånberg, K. (2017). The Circular Economy and Benefits for Society Jobs and Climate Clear Winners in an Economy Based on Renewable Energy and Resource Efficiency. A study report at the request of the Club of Rome with support from the MAVA Foundation. https://circulareconomy.europa.eu/ platform/sites/default/files/the-circular-economy-czech-republic-andpoland.pdf Willersinn, C., Mack, G., Mouron, P., Keiser, A., & Siegrist, M. (2015). Quantity and quality of food losses along the Swiss potato supply chain: Stepwise investigation and the influence of quality standards on losses. Waste Manag, 46, 120–32. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.  J. (2017). Big data in smart farming—A review. Agricultural Systems, 53, 69–80. WRAP. (2011). Reducing food waste through retail supply chain collaboration. http://www.wrap.org.uk/sites/files/wrap/WRAP_IGD_supply_chain_ report.pdf Yahya, N. (2018). Agricultural 4.0: Its implementation toward future sustainability. In Green urea. Green energy and technology. Springer, Singapore. https:// doi.org/10.1007/978-981-10-7578-0_5 Yazdanbakhsh, O., Zhou, Y., & Dick, S. (2017) An intelligent system for livestock disease surveillance. Information Sciences, 378, 26–47. Yin, Y., Stecke, K. E., & Li, D. (2018). The evolution of production systems from industry 2.0 through industry 4.0. International Journal of Production Research. https://doi.org/10.1080/00207543.2017.1403664. Ying Tan, M. C. V., Steve Goddard (2009). Spatio-temporal event model for cyber-physical systems. 29th IEEE International Conference on Distributed Computing Systems Workshops.

 References 

97

Yogesh, K. S., Sachin, K. M., Pravin, P. P., & Liu, S. (2019). When challenges impede the process: For circular economy-driven sustainability practices in food supply chain. Management Decision, 57, 995–1017. Zubrod, J. P., Bundschuh, M., Arts, G., Brühl, C. A., Imfeld, G., Knäbel, A., Payraudeau, S., Rasmussen, J. J., Rohr, J., Scharmüller, A., Smalling, K., Stehle, S., Schulz, R., & Schäfer, R. B. (2019). Fungicides: An Overlooked Pesticide Class? Environmental Science & Technology, 53, 3347–3365. https://digitalcommons.unl.edu/cseconfwork/147 Zhou, K., & Taigang Liu, L. Z. (2015). Industry 4.0: Towards future industrial opportunities and challenges. 12th International Conference on Fuzzy Systems and Knowledge Discovery.

Index

A Active packaging, 41 Additive manufacturing, 17, 20 Agricultural Evolution, 24, 25 Agricultural 4.0 Supply Chain (A4SC), 39 Agricultural Supply Chain, 3–4 Agricultural supply chains 4.0, 1 Agriculture 4.0, v, vi, 1, 2, 9, 10, 13–26, 29–31, 34, 37–42, 44, 51, 57–62, 65–68, 74, 77, 78, 81–84 Agri-Food Supply Chains, 22 Analytics, 17 Augmented reality, 17, 20 Autonomous aerial farming robots, 34 Autonomous land farming robots, 33 Autonomous robots, 17–18 B Bennett’s Law, 7 Big Data, 15–17, 19–21, 23, 25, 30, 38, 81–83 Blockchain applications, 32

C Circular Economy, 53–62 Climatic change, 7 The Cloud, 17, 19 Cloud Computing, xi, 16, 23 Collaboration, vi, 2, 18, 39, 69, 74–75, 78 Consumers, 2–4, 6, 8, 22, 32, 39–44, 50, 51, 55, 68, 84 Corporate social responsibility (CSR), 57 COVID-19 pandemic, 5, 40, 83 Cyber Security and Cyber Physical Systems (CPS), 17, 19 D Data sharing, 37–44 Data sharing issues, 2, 44 Decision Agriculture, 21 Dietary changes, 7 Digital Agriculture, 21 Digitalisation, 14, 21 Distributors operations, 41–42

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Despoudi et al., Agricultural Supply Chains and Industry 4.0, https://doi.org/10.1007/978-3-030-72770-3

99

100 

INDEX

E Environmental implications, 6 F Farmers, 38–40 Farming 4.0, 23 Food chain, 4 Food insecurity, v, 1, 5, 9, 10, 82 Food losses or food waste, v, 1, 6, 7, 10 Food manufacturers, 5 Food production, 5, 7, 22, 39, 41, 49, 78, 81, 82 Food safety, v, 1, 5, 7, 10, 32, 33, 43, 69 H Healthy materials, 8 Horizontal and vertical system integration, 17, 18 I Industrial Evolution, 24, 25 The Industrial Internet of Things (IoT), 17–19 Industry 4.0, v, xi, 9, 13–26, 51, 61, 66, 68, 69, 73, 75–77 Information and Data Evolution, 24, 25 Internet-of-Things and Services, 16 IoT, v, 2, 14–19, 22, 26, 30, 31, 34, 41, 42, 44, 62, 67, 69, 73, 74 L Logistics, 9 N Nanocoatings, 41

Nanosensor, 41 Natural resources, v, 1, 5, 7, 9, 10, 22, 40, 49, 50 O Online shopping, 43 P Precision Agriculture, 21, 24, 38, 83 Processors operations, 40–41 Producers, v, 3, 5, 6, 8, 21, 39, 40, 50 R Raw materials, v, 1, 5, 10, 21, 54, 55, 59, 61 Recycle, 60 Recycling, 7, 60 Reduce, 58–60 Retailers, 2, 6, 8, 39, 42, 44 Retailers operations, 42–44 Reuse, 59 S Simulation, 17, 18 Smart devices and platforms, 30, 31 Smart farming, 21, 23, 29–30, 38, 82, 83 Smart monitors, 34 Supermarkets, 8, 74 Supply chain, xi, xii, 6, 9, 33, 37–39, 54, 55, 59, 67, 69, 74–76, 81, 82 Sustainability, v, vi, xi, 1, 2, 9, 14, 22, 25, 47–51, 53, 57–58, 62, 67, 69, 70, 81–83 Sustainable, vi, 6, 21–23, 39, 48–51, 53–55, 57, 60, 62, 82 Sustainable Development Goals, 21, 48, 82 Sustainable performance, 50–51

 INDEX 

T Technology, v, 8, 9, 16, 21–23, 25, 29, 31–33, 38, 39, 56–58, 60, 61, 69, 74, 75, 77, 82 Temperature control applications, 32 Traceability, 9

101

Tracking and tracing technologies, 33 Triple bottom line, 2, 48–49, 51 W Weather changes, 7