Digital Agricultural Ecosystem E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Digital Agricultural Ecosystem
The book comprehensively explores the dynamic synergy between modern technology and agriculture, showcasing how advancements such as artificial intelligence, data analytics, and smart farming practices are reshaping the landscape to ensure food security in the era of climate change, as well as bridging the gap between cutting-edge research and practical implementation.
Agriculture has historically been the foundation of human civilization and benefits communities all around the world. Agriculture has a creative, adaptable, and innovative history, and as the digital age draws closer, agriculture is once again poised for change. Each of the 20 chapters explores the connection between agricultural and technological advancements, and are divided into four key areas.
Part 1 covers knowledge sharing in the digital agricultural ecosystem. In the context of modern agriculture, the chapters underscore the importance of information flow. Through comprehensive reviews of literature and assessments of farmer participation on social media platforms, these chapters illustrate the value of information sharing for sustainable agriculture.
Part 2 explores the adoption and impact of digital technologies in agriculture. The use of cutting-edge digital technologies in agriculture is examined thoroughly in this section. The chapters included here outline how precision, artificial intelligence, and blockchain technology have the potential to transform methods of agriculture and improve food systems.
Part 3 addresses smart farming and sustainable agriculture. This section focuses on sustainability and offers details on eco-friendly production methods, the significance of smart farming in many nations, including India and the UK, and cost-effective fertilizer sprayer technologies.
Part 4 examines the modeling and analysis of agricultural systems. This last section explores how mathematical modeling and data analytics are used in agricultural systems, with insights on everything from the study of credit access constraints in rural regions to water resource management in irrigation systems.
Audience
The diverse readership includes farmers, agronomists, agricultural researchers, policymakers, environmentalists, information technologists, and students from academic and professional fields who are eager to learn more about how digital innovation and sustainable agriculture can be used to address global issues such as climate change, food security, and smart farming.
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Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright
Dedication
Preface
Part 1: KNOWLEDGE SHARING IN THE DIGITAL AGRICULTURAL ECOSYSTEM
1 Digital Agricultural Ecosystem: An Introduction
1.1 Introduction
1.2 Digital Agricultural Ecosystem
1.3 Definition
1.4 Entities
1.5 Role of Researchers in Digital Agricultural Ecosystem
1.6 Elements
Conclusion
References
2 Smart and Sustainable Agriculture: Systematic Literature Review and Bibliometric Analysis
2.1 Introduction
2.2 Systematic Literature Review
2.3 Bibliometric Analysis
2.4 Related Study
2.5 Conclusion
References
3 Agriculturist Engagement and Knowledge Sharing in Digital Ecosystem: Insights from Social Media
3.1 Introduction
3.2 State of Literature
3.3 Methodology
3.4 Findings
3.5 Discussion
3.6 Limitations and Future Scope
3.7 Conclusions
References
Part 2: ADOPTION AND IMPACT OF DIGITAL TECHNOLOGIES IN AGRICULTURE
4 Electronic National Agriculture Market (e-NAM) so Far…! A Gestation Period Analysis
4.1 Introduction
4.2 The Importance of Agriculture Marketing
4.3 APMC Allahabad (Prayagraj) as a Case Organization
4.4 Objectives of the Study
4.5 Study Area: APMC Allahabad
4.6 Methodology
4.7 Auction and Transaction Process
4.8 Process Review
4.9 General Assessment of Causes
4.10 Discussion
4.11 Development during the COVID Period
4.12 Conclusion
References
Appendix 1
Appendix 2
5 Development of Ecologically Safe Production: Digital Trends in the Agri-Food Sector
5.1 Introduction
5.2 Legislative Support for the Functioning of Ecologically Safe Production
5.3 Market Analysis of Environmentally Sound Goods
5.4 Strategic Directions for Ensuring the Growth of Ecologically Safe Production in the Agrі-Food Complex
5.5 Digital Optimization of Ecologically Safe Production
5.6 Conclusions
References
6 Adoption and Impact of Blockchain Technology on the Silk Industry’s Supply Chain
6.1 Introduction
6.2 Mulberry—The Fodder
6.3 Embryogenesis of the Silkworm
6.4 Silk Rearing—An Art by Itself
6.5 Blockchain Technology
6.6 BCT and the Supply Chain
6.7 The Proposed Model: VL-SS-23
6.8 Conclusion
References
7 Transforming Indian Agriculture: Unleashing the Potential of Digital Agriculture Using Efficiency Analysis
7.1 Introduction—The Role of Agriculture as the Foundation of All Industries
7.2 Analysis of the Agriculture Sector in India
7.3 Methodology
7.4 Discussion
7.5 Implications
7.6 Limitations and Future Directions
7.7 Conclusion
References
8 Digital Agriculture: Transforming Farming Practices and Food Systems for a Sustainable Future
8.1 Introduction
8.2 Need for Digital Agriculture and Food Security
8.3 Role of Digital Agriculture in Economic Transformation
8.4 Digital Value Chain and Food Systems
8.5 Innovation in Agriculture
8.6 Benefits and Limitations of Digital Agriculture
8.7 Digital Agriculture in India
8.8 Future of Digital Agriculture
Conclusion
References
9 Exploring the Impact of Artificial Intelligence on Agriculture - A Study on Farmers’ Level of Awareness
9.1 Introduction
9.2 Review of Literature
9.3 Research Design
9.4 Analysis
9.5 Discussion
9.6 Implications
9.7 Limitations and Scope for Future Research
9.8 Conclusion
References
10 Precision Technologies and Digital Solutions: Catalyzing Agricultural Transformation in Soil Health Management
10.1 Introduction
10.2 Importance of Soil Health Management
10.3 Soil Health Monitoring and Assessment
10.4 Precision Irrigation Management
10.5 AI-Based Models and Irrigation Scheduling
10.6 Conclusions
References
Part 3: SMART FARMING AND SUSTAINABLE AGRICULTURE
11 Blockchain Technology—Adoption, Opportunities, and Challenges for a Sustainable Agricultural Ecosystem
11.1 Introduction
11.2 Blockchain in the Agriculture Ecosystem
11.3 Cases of Blockchain in Agriculture
11.4 Challenges and Future Implications
References
12 Fostering Agriculture Ecosystem for Sustainability
12.1 Introduction
12.2 Agriculture Ecosystem and Agriculture Value Chain
12.3 Growth Drivers for Sustainable Agriculture
12.4 Role of the Government and Policy Interventions
12.5 Technology Initiatives of Corporates and Start-Ups
12.6 Agritech Investment
12.7 Global Outlook
12.8 Conclusion
References
13 Design of Smart Digital Crop Harvester Monitoring Cluster
13.1 Introduction
13.2 Literature Survey
13.3 Methodology
13.4 Results and Discussion
13.5 Conclusion
References
14 Exploring the Prospects and Challenges of Digital Agriculture for Food Security—A Case Study of the “Hands Free Hectare” Digital Farm in the UK
14.1 Introduction
14.2 Conclusion
References
15 Smart Farming—A Case Study from India
15.1 Introduction
15.2 Technology in Farming
15.3 Discussion
15.4 Conclusion
References
16 Frugal Innovation in Developing a Fertilizer Sprayer—A Case of an Ingenious Design in Maharashtra
16.1 Introduction
16.2 Fertilizers and Their Usage
16.3 Role of Technology in Agriculture
16.4 Research Gap and Objective
16.5 Research Design
16.6 Jugadu Kamlesh—The Inventor-Farmer Turned Agripreneur and His Fertilizer Sprayer
16.7 The Design Journey
16.8 The Shark Tank: India Experience
16.9 Design Thinking
16.10 The Path Ahead
16.11 Conclusion
Conflict of Interest
Acknowledgments
References
17 For Sustainable Farming in India: A Data Analytics Perspective
17.1 Introduction
17.2 Conclusion
References
Part 4: MODELING AND ANALYSIS OF AGRICULTURAL SYSTEMS
18 Modeling Barriers to Access Credit from Institutional Sources in Rural Areas Using the ISM Approach
18.1 Introduction
18.2 Literature Review
18.3 Data and Research Methodology
18.4 Results and Discussion
18.5 Implications of the Research
18.6 Conclusions
References
19 Modeling the Water Consumption Process with the Linear Model and a Local Interpolation Cubic Spline
19.1 Background
19.2 Establishment of the Patterns of Formation of Volumes of Water Resources in Areas of Their Usage
19.3 Forecasting Water Use Based on Mathematical Models of Water Management of Distributed Irrigation Systems
Conclusion
References
20 The Role of Electric Vehicles in the Agriculture Industry Using IoT: Turning Electricity into Food
20.1 Introduction
20.2 Department of Energy
20.3 Electric Vehicles and Robots in the Agricultural Sector
20.4 Blockchain-Based IoT Systems
Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Cluster of seed papers generated with the keyword “smart agriculture...
Table 2.2 Types of cluster, cluster color, and the related significant words....
Chapter 3
Table 3.1 Sample of the dataset.
Chapter 4
Table 4.1 Months’ performance in terms of percentage sales through the e-NAM....
Table 1 Arrival and sales of commodities at Agmarknet and APMC Allahabad.
Table 2 Price ranges of commodities at Agmarknet and APMC Allahabad.
Chapter 5
Table 5.1 Evolution of the market of ecologically safe products and its digita...
Table 5.2 Unmanned systems as an aid in the field of agriculture.
Chapter 6
Table 6.1 Mulberry varieties.
Table 6.2 Recommended NPK dose for cultivation.
Table 6.3 Grainages in India [74, 87].
Table 6.4 Various traits of the silkworm and its characteristics.
Chapter 7
Table 7.1 Imports and exports of agricultural commodities.
Table 7.2 Ranking of the top 17 states based on available agricultural land.
Table 7.3 Results and ranking.
Chapter 9
Table 9.1 Respondents’ demographic profile.
Table 9.2 Regression statistics for usage of mobile applications and farmer’s ...
Table 9.3 ANOVA test for the usage of mobile applications and farmers’ awarene...
Table 9.4 Regression statistics for the usage of social media platforms and fa...
Table 9.5 ANOVA test for the usage of social media platforms and farmers’ awar...
Table 9.6 Regression statistics for the number of acres used for AI adoption a...
Table 9.7 ANOVA test for the number of acres used for AI adoption and the incr...
Chapter 10
Table 10.1 Sensors used in IoT-based irrigation systems
Chapter 11
Table 11.1 Startups renovating the agricultural sector through blockchain.
Chapter 12
Table 12.1 Agro and food processing SEZs in India.
Table 12.2 Summary of technology initiatives of corporates and start-ups.
Table 12.3 Venture capital investments in Indian agtech companies by descripti...
Table 12.4 Venture capital investments in Indian agtech companies by category,...
Appendix 1: Three-year export statement of products by the Agricultural and Pr...
Appendix 2: Government initiatives and policy intervention.
Chapter 13
Table 13.1 Temperature sensor results.
Table 13.2 Battery voltage sensor results.
Table 13.3 Fuel level sensor results.
Table 13.4 Pressure sensor results.
Table 13.5 Engine running hours results.
Table 13.6 RPM values and indicator results.
Chapter 18
Table 18.1 Barriers to institutional credit.
Table 18.2 Descriptive statistics of the barriers.
Table 18.3 Correlation among barriers.
Table 18.4 The structural self-interaction matrix.
Table 18.5 The initial reachability matrix.
Table 18.6 The final reachability matrix.
Table 18.7 First iteration for level partitioning.
Table 18.8 Second iteration for level partitioning.
Table 18.9 Third iteration for level partitioning.
Table 18.10 Fourth iteration for level partitioning.
Table 18.11 Fifth iteration for level partitioning.
Chapter 19
Table 19.1 Data on the use of water resources in the Republic of Uzbekistan by...
Table 19.2 Consumption of water resources by sectors of the country’s economy ...
Table 19.3 Data on the consumption of water resources in the republic by secto...
Table 19.4 The results of calculating the effect of the relationship between t...
Table 19.5 Properties of the exponential distribution.
Table 19.6 Using of water resources by sectors of the economy of the Republic ...
Table 19.7 Properties of the Poisson distribution.
Table 19.8 Uniform distribution properties.
Table 19.9 Water consumption.
Table 19.10 Total water use forecast.
List of Illustrations
Chapter 1
Figure 1.1 Digital agricultural ecosystem
Figure 1.2 Efficiency, sustainability, and profitability
Chapter 2
Figure 2.1 Agricultural revolution phases.
Figure 2.2 Application of PRISMA for literature review.
Figure 2.3 Tree map generated with the keyword “smart agriculture” from Carrot...
Figure 2.4 Sunburst chart generated with the keyword “smart agriculture” from ...
Figure 2.5 List of seed papers generated with the keyword “smart agriculture” ...
Figure 2.6 Cluster of seed papers generated with the keyword “smart agricultur...
Figure 2.7 Binary counting 409/845.
Figure 2.8 Article and patent citation count from 2001 to 2022.
Figure 2.9 Article published and reference count from 2001 to 2022.
Chapter 3
Figure 3.1 Year-wise frequency of video posting.
Figure 3.2 The top 20 most viewed videos.
Figure 3.3 Top channels with the most video uploads.
Figure 3.4 Topic-wise word correlation.
Figure 3.5 Year-wise frequency of comments.
Figure 3.6 Sentiments trending over time.
Figure 3.7 In-depth distribution of sentiments over the years.
Chapter 4
Figure 4.1 The general organisational structure of APMCs
Chapter 5
Figure 5.1 Classification of branches of ecologically safe production. Built b...
Figure 5.2 The place of Ukraine in world organic production land, 2021. Built ...
Figure 5.3 The place of Ukraine in European organic production, 2021. Built by...
Figure 5.4 Dynamics of the development of organic production in Ukraine. Built...
Figure 5.5 Export volumes of organic products. Built by the authors based on s...
Figure 5.6 World export volumes—2021, thousand tons. Built by the authors base...
Figure 5.7 Export volumes to EU countries, 2021–2022. Built by the authors bas...
Figure 5.8 Structure of revenues from the sale of organic products for export ...
Figure 5.9 Strategic directions for the development of ecologically safe produ...
Chapter 6
Figure 6.1 (a) The cellule [17]. (b) The ring for the moth to lay eggs [86].
Figure 6.2 (a) Egg box carrier. (b) The counted eggs are placed inside the egg...
Figure 6.3 The sealed egg packets [86].
Figure 6.4 Biological method of moth emergence from the cocoon [86].
Figure 6.5 (a) Blue-colored pinhead egg. (b) Moth emergence [85].
Figure 6.6 Worm fed with fresh mulberry in the rearing tray [76].
Figure 6.7 (a) The mounting instrument Chandrika. (b) Mountages [77].
Figure 6.8 Ramanagara marketplace in Karnataka [83].
Figure 6.9 Grasserie-infected worm [12].
Figure 6.10 Flacherie-infected worm [12].
Figure 6.11 Muscardine-infected worms [12].
Figure 6.12 Pebrine-infected worms [12].
Figure 6.13 Kenchu-infected worms [12].
Figure 6.14 (a) An Uzi fly-infected worm. (b) Uzi trap from the Sericulture De...
Figure 6.15 Dermestid beetles.
Figure 6.16 The blockchain framework.
Figure 6.17 User process of information of BCT [64].
Figure 6.18 The proposed VL-SS-23 model.
Chapter 7
Figure 7.1 Conventional techniques applied to Indian agriculture.
Figure 7.2 The output-oriented DEA model.
Figure 7.3 Data management tools.
Figure 7.4 Technologies used by farmers.
Figure 7.5 Data acquisition sensing.
Figure 7.6 Analysis of imports and exports of agriculture.
Chapter 8
Figure 8.1 Farmer with his crop (www.freepik.com).
Figure 8.2 Transport of food grains (www.freepik.com).
Figure 8.3 Digital food value chain (authors’ own).
Figure 8.4 ITC’s e-Choupal (www.freepik.com).
Figure 8.5 Smart farming (www.freepik.com).
Figure 8.6 Innovation in agriculture (www.freepik.com).
Figure 8.7 Indian food chain: creating value for marketing success (www.freepi...
Figure 8.8 Digital techniques in India (authors’ own).
Chapter 9
Figure 9.1 Application of AI technology in agricultural activities (source: de...
Figure 9.2 Farmers’ awareness of different AI technologies (source: compiled b...
Figure 9.3 Farmers’ satisfaction level in the usage of AI technologies (source...
Chapter 10
Figure 10.1 Soil ecosystem services considered during soil health management....
Figure 10.2 Pyramids of the benefits and challenges with AI- and IoT-based pre...
Chapter 11
Figure 11.1 Features of blockchain.
Figure 11.2 Agriculture exports from India
Figure 11.3 Agri-cluster of India
Figure 11.4 Stakeholders of the agricultural supply chain.
Figure 11.5 IBM applications of the blockchain technology in the agri-sector....
Chapter 12
Figure 12.1 Growth in agriculture and its allied sectors.
Figure 12.2 Agriculture value chain (farm to fork).
Figure 12.3 Strategies for implementing the NMSA mission document.
Chapter 13
Figure 13.1 Cluster model with dimensions.
Figure 13.2 System block diagram.
Figure 13.3 Voltage divider circuit for the fuel sensor.
Figure 13.4 Voltage divider circuit for temperature sensor.
Figure 13.5 Voltage divider circuit for the battery sensor.
Figure 13.6 Voltage divider circuit for the pressure sensor.
Figure 13.7 RPM sensor transistor circuit.
Figure 13.8 Power supply circuit.
Figure 13.9 Reverse polarity protection circuit.
Figure 13.10 Flowchart showing the algorithmic working of the ATMEGA328 microc...
Figure 13.11 Typical TouchGFX screen UI along with its major components [7].
Figure 13.12 STM32CubeIDE programming [11].
Figure 13.13 HAL (hardware abstraction layer) functions in use for GPIOs [11]....
Figure 13.14 Sample code showing the software-generated code blocks vs. the us...
Figure 13.15 STM32CubeMX with the pinout view of STM32F429 [12, 13].
Figure 13.16 Flowchart showing the working of the STM32 microcontroller.
Figure 13.17 (a) Final product—smart digital crop harvester monitoring cluster...
Chapter 14
Figure 14.1 The mechanics of smart farming [5]
Figure 14.2 Oz the robot assists a farmer [7]
Figure 14.3 IOT-based open source
Figure 14.4 The hands-free farm team
Chapter 15
Figure 15.1 Foodgrain production in India during the two seasons for the time ...
Figure 15.2 Technology in agriculture (WoS search for keywords by the authors)...
Figure 15.3 Agriculture technology (WoS search for keywords by the authors)....
Figure 15.4 Text research survey on smart farming using the VOSviewer software...
Figure 15.5 Density mapping of a text research survey on smart farming using t...
Figure 15.6 Network research chart of bibliographic research study on smart fa...
Figure 15.7 Dimensions of smart farming.
Figure 15.8 Pre- and post-laser land leveling process on an uneven agriculture...
Figure 15.9 India: agriculture, forestry, and fisheries value addition to GDP....
Figure 15.10 India: yield per agriculture laborer during 2001–2002 till 2020–2...
Figure 15.11 India: cereal yield per kilogram of fertilizer use during 2001–20...
Figure 15.12 India: agriculture irrigated land to permanent cropland during 20...
Figure 15.13 India: cereal yield to irrigated land during 2001–2002 till 2020–...
Chapter 16
Figure 16.1 Screenshot from his first YouTube post
Figure 16.2 The working model—first version (side view)
Figure 16.3 Iterations of the equipment during the design stage.
Chapter 17
Figure 17.1 Various threats to the farming industry (source: authors).
Figure 17.2 Data analytics in the farming industry (source: authors).
Figure 17.3 Capacity building toward the implementation of data analytics in t...
Figure 17.4 Mapping data analytics with SDGs (source: authors).
Chapter 18
Figure 18.1 Diagraph of the contextual relationships.
Figure 18.2 MICMAC analysis of the barriers to institutional credit.
Chapter 19
Figure 19.1 Pair linear regression.
Figure 19.2 Functions
f
(
t
),
R
(
t
), and
h
(
t
) for exponential distribution.
Figure 19.3 Diagram of water consumption in the sectors of the economy of the ...
Figure 19.4 Poisson distribution for different values of the
λ
parameter....
Figure 19.5 Diagram of water consumption coefficient
K
of crop types in relati...
Figure 19.6 (a, b, c) Functions for uniform distribution.
Figure 19.7 Water intake of housing and utility services of the Republic of Uz...
Figure 19.8 Solar radiation power. Water intake of the housing and utility ser...
Figure 19.9 Water intake of the industry of the Republic of Uzbekistan dependi...
Figure 19.10 Water intake of agricultural water supply of the Republic of Uzbe...
Figure 19.11 Representation of the analysis based on this model.
Figure 19.12 Water distribution in 2021, 2024, 2027, and 2030.
Figure 19.13 A water consumption model.
Figure 19.14 The predictive model.
Chapter 20
Figure 20.1 Evidence of smart farming technology (Dankan Gowda
et al.
, 2021) [...
Figure 20.2 Evidence of electric vehicle driving efficiency in agriculture (Ro...
Guide
Cover Page
Table of Contents
Title Page
Copyright Page
Dedication
Preface
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Digital Agricultural Ecosystem
Revolutionary Advancements in Agriculture
Edited by
Kuldeep Singh
School of Management, Gati Shakti Vishwavidyalaya, Vadodara, India
and
Prasanna Kolar
School of Humanities and Social Sciences, Jain (Deemed-to-be University), Bengaluru, India
This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-24293-1
Cover image: Pixabay.ComCover design by Russell Richardson
Dedication
We humbly dedicate this edited book, ‘Digital Agricultural Ecosystem,’ to the cherished memories of those whose invaluable influence and unwavering support have shaped our lives and inspired the pursuit of knowledge in the field of agriculture. We pay homage to Shri Harishchandra Master Ji, the grandfather of Dr. Kuldeep Singh, whose profound wisdom as a science teacher in Jhunjhunu, Rajasthan, continues to resonate in our hearts. After his honorable retirement, he imparted invaluable insights about agriculture, its challenges, and its significance at the grassroots level. In loving memory of Late Fauji Ravinder Singh, the father of Dr. Kuldeep Singh, who devoted his entire career to serving in the Indian Army. His dedication, discipline, and selflessness have been a guiding light, inspiring us to strive for excellence in our endeavors. With boundless love and gratitude, we acknowledge Mrs. Suresh Devi, the mother of Dr. Kuldeep Singh, whose genuine affection for the soil and profound knowledge of agricultural practices has been a wellspring of motivation. Her insights into the realities of rural agriculture in Northern India have been a driving force behind our efforts. To the parents of Dr. Prasanna Kolar, Mr. Ashok Kolar, and Mother Shakuntala Kolar, we extend our heartfelt appreciation. Their unyielding support and encouragement have nurtured the seeds of curiosity and dedication, enabling us to explore the depths of Agricultural Economics. In remembrance of those who have come before us and in appreciation of those who walk alongside us, this book stands as a testament to the enduring legacy of knowledge and the collective pursuit of a sustainable and thriving agricultural ecosystem.
With utmost gratitude and reverence,Dr. Kuldeep Singh and Dr. Prasanna KolarJanuary 2024
Preface
Agriculture has historically been the foundation of human civilization. Its provision of sustenance and resources benefits communities all around the world. Agriculture has been practiced from the dawn of time, when our ancestors worked the land and had a close relationship with nature. Agriculture has a creative, adaptable, and innovative history, and as the digital age draws closer, agriculture is once again poised for change.
This book demonstrates the combined efforts of all the authors and co-authors involved, and will stand as a vital resource for academics and professionals who work in the sectors of agricultural and digital technologies.
The twenty chapters herein explore the connection between agricultural and technological advancements, which suggests a diversified environment. Each chapter delves into diverse tracks on four key areas.
Part 1 covers knowledge sharing in the digital agricultural ecosystem. In the context of modern agriculture, the materials here underline the importance of information flow. Through comprehensive reviews of literature and assessments of farmer participation on social media platforms, these chapters illustrate the value of information sharing for sustainable agriculture.
Part 2 explores the adoption and impact of digital technologies in agriculture. The use of cutting-edge digital technologies in agriculture is examined thoroughly in this section. The chapters included here outline how precision, artificial intelligence, and blockchain technology have the potential to transform methods of agriculture and improve food systems.
Part 3 addresses smart farming and sustainable agriculture. This section focuses on sustainability and offers details on eco-friendly production methods, the significance of smart farming in many nations, including India and the UK, and cost-effective fertilizer sprayer technologies.
Part 4 examines modelling and analysis of Agricultural systems. The last section explores how mathematical modelling and data analytics are used in agricultural systems, with insights on everything from the study of credit access constraints in rural regions to water resource management in irrigation systems.
The editors have greatly valued the contributions of each author. The information collected in this volume is largely inspired by their academic work and research experience. We would also like to express our gratitude to the co-authors whose dedication and hard work have enriched this publication. Finally, we have greatly appreciated the dedicated support and valuable assistance rendered by Martin Scrivener and the Scrivener Publishing team during the publication of this book.
Dr. Kuldeep SinghDr. Prasanna KolarJanuary 2024
Part 1KNOWLEDGE SHARING IN THE DIGITAL AGRICULTURAL ECOSYSTEM
1Digital Agricultural Ecosystem: An Introduction
Kuldeep Singh1*, Prasanna Kolar2 and Rebecca Abraham3
1School of Management, Gati Shakti Vishwavidyalaya, Vadodara, India
2School of Humanities and Social Sciences, Jain (Deemed-to-be University), Bengaluru, India
3Huizenga College of Business and Entrepreneurship, Nova Southeastern University, Fort Lauderdale, Florida, USA
Abstract
The primary goal of this chapter is to offer a comprehensive examination of digital agriculture from a critical perspective with a specific emphasis on forming an ecosystem that highlights the linkages between agriculture and technology. This chapter examines various definitions of digital agriculture and explores the theoretical foundation that supports this concept and emphasizes the essential elements required for establishing this ecosystem. The present chapter also discusses how technology has affected the development of agriculture, with a focus on the potential benefits of digital agriculture for productivity, sustainability, and profitability. Such an objective should be a top priority for government stakeholders and decision-makers due to the possible policy consequences. The research also emphasizes the necessity for the adoption of clear ethical and regulatory rules in order to secure the long-term viability of digital technologies in agriculture for the benefit of all stakeholders.
Keywords: Digital, agriculture, ecosystem, sustainability
1.1 Introduction
The agriculture sector is an important pillar of the global economy and also contributes significantly to food security, economic growth, and the livelihoods of millions of people around the world. On one side, traditional agricultural methods face several challenges, such as rising costs, lower profitability, and higher demand for sustainable and environmentally friendly agro-practices. On the other side, digital agriculture has emerged as a capable solution to the abovementioned challenges with the potential to transform agroindustry through the integration of modern technology such as blockchain, artificial intelligence, big data, and the Internet of Things (IoT).
One of the most widely used definitions of digital agriculture is based on the integration of technology and data into farm practices to increase efficiency, sustainability, and profitability [1–3]. Basically, this definition encompasses various technologies such as precision agriculture, remote sensing, and decision support systems. However, this definition is still open to interpretation and lacks specificity in terms of the types of technologies and practices that fall under the digital agriculture umbrella.
If we look at the recent research on digital agriculture, success depends on several factors like access to technology, adoption of innovative tools by farmers, and the ability to integrate digital tools into existing agricultural systems. Despite the growing interest and investment in digital agriculture, there are several issues and controversies surrounding its use. One of the primary concerns is the digital divide where farmers in low-income countries may lack access to the necessary technology and infrastructure. Additionally, there are other concerns such as data privacy, the potential for technology to exacerbate existing inequalities, and the need for clear regulations and standards to ensure the ethical use of digital tools in agriculture.
Xie et al.[4] mentioned in their study that the role of technology in rural agricultural development is critical. Digital agriculture has the potential to transform farm practices and improve the livelihoods of farmers through increased yields, cost reduction, and better market access. While it is pertinent to recognize the unequal distribution of benefits in digital agriculture, there is a potential risk of exacerbating the existing digital divide due to insufficient emphasis on prioritizing technology access, particularly in low-income countries.
This article aims to provide a critical review of the available literature on the digital agriculture ecosystem with a focus on defining the relationship between agriculture and technology. The study further aims to investigate diverse interpretations and past advancements in digital agriculture. Moreover, it intends to scrutinize the fundamental components and constituents that constitute a digital agriculture ecosystem.
1.2 Digital Agricultural Ecosystem
The history of digital agriculture can be traced back to the 1970s when the first computerized decision support systems were developed to optimize agricultural practices [5]. Since then, digital agriculture has evolved significantly mainly with the integration of various technologies and the emergence of new business models and practices [6].
Upon examining the latest technological advancements in agriculture, it is evident that the digital agriculture ecosystem is a multifaceted framework that encompasses a multitude of technological constituents and participants who collaborate with each other to enhance agricultural processes. Such digital agriculture ecosystem encompasses the capacity to utilize technology and data to augment the effectiveness, sustainability, and profitability of agricultural operations. However, there is still a lack of clarity around what digital agriculture entails and how it should be defined. Therefore, by taking into consideration most of the entities and elements, we construct an ecosystem that enables us to better understand the association between technology and agriculture.
Based on the available literature on digital agriculture, it is evident that the digital agricultural ecosystem comprises a variety of entities and elements that interact with each other to optimize agricultural practices (see Figure 1.1). The entities section includes farmers, technology providers, researchers, policymakers, and customers. The technology providers mainly include firms that provide hardware, software, and data services for agriculture, while researchers develop new technologies and practices to improve agricultural practices. The regulatory framework that governs the adoption and implementation of digital technologies in agriculture is significantly impacted by policymakers, while customers influence the demand for sustainably produced food.
Figure 1.1 Digital agricultural ecosystem
(source: authors’ own).
On the other side, the different elements that form the digital agricultural ecosystem include hardware, software, data, and human capital. Hardware includes sensors, drones, and other devices that collect data from the field. Software includes tools for data analysis, modeling, and visualization. Data include various types of information such as weather data, soil data, and market data. Human capital includes the skills and knowledge required to develop, deploy, and use digital agriculture technologies effectively.
The complete network of the digital agriculture ecosystem functions as a collaborative network of interdependent entities and constituents working toward shared objectives (see Figure 1.1). These objectives include improvement in the efficiency, sustainability, and profitability of farm practices; reduced environmental impact; and enhanced food security. The digital agriculture ecosystem can propose novel business prospects and value chains that are advantageous to farmers, technology providers, and consumers.
1.3 Definition
According to a FAO report [7], “digital technologies have the potential to enhance agricultural productivity and sustainability, particularly in developing countries. It highlights the role of mobile phones, drones, satellite imagery, and other digital tools in improving access to information, markets, and financial services for smallholder farmers. It also emphasizes the need to address challenges related to digital literacy, infrastructure, and policy frameworks to ensure equitable and inclusive adoption of digital agriculture.” However, the digitalization of agriculture also faces challenges such as cybersecurity, data protection, labor replacement, and digital divide. Despite these challenges, FAO is committed to bridge multidisciplinary digital divides to ensure that everyone benefits from the emergent digital society. According to the United Nations Global Compact, “digital agriculture is the use of advanced technologies integrated into a system to improve food production for farmers and stakeholders [8]. Unlike traditional methods, digital agriculture systems gather data frequently and accurately, often with external sources such as weather information [8].” As per this statement, digital agriculture integrates new and advanced technologies to enable farmers to make informed decisions based on frequent and accurate data leading to improved food production through the use of robotics and advanced machinery.
Mark Shepherd [9] mentioned how digital agriculture can offer social advantages that satisfy the needs and requirements of different stakeholders such as farmers, processors, regulators, and consumers. Based on the utilization of digital technologies, agricultural production can be boosted while minimizing environmental harm. The result is a more efficient transportation and logistics system, improved work conditions for workers, and timely delivery of products that align with consumer needs. Digital agriculture can also address consumer demands for responsible and sustainable production as well as provide evidence of socio-ethical factors and product origin. As per Hackfort [10], digital agriculture refers to digitization that involves the transformation of analog information into digital data, and digitalization is the social process of accepting computer technologies.
Sawant et al.[11] mentioned that data science and machine learning are crucial for agricultural data analysis and decision processes in digital farm, noting that data mining, analytics, and data science have significantly benefited digital agriculture. Studies conducted on various agricultural elements have derived models and optimized resource usage and facilitated data-driven analysis for forecasts, resource optimization, and understanding of agricultural processes.
1.4 Entities
1.4.1 Role of Farmers in Digital Agricultural Ecosystem
Digital agriculture has brought significant changes in the way farmers collect and analyze data to improve agricultural production and reduce waste. For digital agriculture to be successful, the active participation of farmers is crucial. Farmers play an essential role in the adoption and implementation of digital technologies, and their feedback and expertise are vital to enhance the effectiveness and efficiency of digital tools and practices.
Farmers play a vital role in the usage of digital tools and techniques as they are often the first entity to try new technology. In this way, farmers can identify any issues or areas for improvement for further enhancement and upgradation [12].
However, farmers also play a proactive role in the construction of digital tools and procedures. By having a close interaction with technology developers, farmers can make sure that digital solutions are easy to use and meet the needs of the agricultural industry.
At the usage level, farmers are responsible for the implementation of digital technologies and practices. It is needed to understand the technology and to modify farmers’ current agricultural methods to allow them to use the tools provided by technology developers effectively.
Farmers play a significant role in the collection and analyses of data in the digital agriculture ecosystem. With the use of digital technology, farmers can collect and use vast amounts of data about their farms in order to optimize their farm practices and make data-driven decisions.
However, to use data efficiently in the context of digital agriculture, farmers need specialized knowledge and skills to gather and analyze data. In a rural context, farmers may consider the specific needs of their farm operation, too.
Farmers play a vital role in the development and implementation of sustainable and resilient agricultural practices. The integration of digital technology has the potential to significantly enhance productivity and efficiency in agricultural operations. It is incumbent upon farmers to acknowledge their responsibility in adopting practices that are environmentally sustainable and socially responsible, thereby avoiding any adverse effects on the environment and ensuring the well-being of communities.
1.4.2 Role of Technology Providers in Digital Agricultural Ecosystem
The integration of digital technologies with age-old agricultural practices has given birth to a remarkable era in agriculture, commonly known as digital agriculture. This paradigm shift equips farmers with a diverse range of tools and resources to unlock the full potential of their land, optimizing agricultural productivity, bolstering efficiency, and minimizing the ecological footprint. Within this evolving landscape, technology providers assume a paramount role, leading the charge in developing, designing, and implementing cutting-edge digital solutions tailored to empower farmers and refine their age-old methods [13].
At the heart of the digital agriculture ecosystem lies the crucial responsibility of technology providers: to create and offer a host of digital tools and technologies that seamlessly integrate with farmers’ practices, enabling them to unleash the full potential of their agricultural endeavors. Precision agriculture, smart irrigation systems, enhanced crop management systems, and sophisticated data analytics tools are a few of the most noteworthy examples of these disruptive technologies. These revolutionary breakthroughs can assist farmers in making informed decisions that result in improved productivity, efficiency, and sustainable agricultural practices.
Farmers can develop complex maps that allow them to change their agricultural practices with remarkable precision and perfectly address the unique requirements of each crop, by carefully gathering important information on soil conditions, crop growth patterns, and a variety of environmental elements. With the exact application of fertilizers, herbicides, and water supplies made possible by this method, waste is decreased, while yields increase to levels that were previously unattainable.
Empowered by this wealth of information, farmers can effortlessly optimize their irrigation practices, ensuring that water resources are utilized with the utmost efficiency and efficacy. The implementation of such smart irrigation systems not only contributes to water conservation efforts but also assists farmers in reducing costs while maximizing agricultural yields. Additionally, these solutions prove instrumental in reducing costs while simultaneously bolstering agricultural yields.
Data analytics tools are also instrumental in the digital agriculture ecosystem. Technologies empower farmers to collect and analyze data from various sources, including soil sensors, weather stations, and crop sensors. With this, farmers can build sophisticated models that forecast crop yields, identify potential issues, and receive tailored recommendations on enhancing their agricultural practices.
1.5 Role of Researchers in Digital Agricultural Ecosystem
The role of researchers in the digital agricultural ecosystem is to generate new knowledge and insights that can be applied to improve agricultural practices and productivity. This is related to the development of new digital technologies and the examination and refinement of existing technologies. On the other hand, it is also related to conducting research on the social, economic, and environmental impacts of digital agriculture. Basically, researchers help to ensure that digital technologies are deployed in a responsible and effective way that benefits all the stakeholders related to agriculture.
The researchers’ responsibilities include the design and testing of innovative tools such as sensors, drones, machine learning algorithms, and other devices that enable the collection and analysis of data crops, soils, weather patterns, and environmental factors. The utilization of such insights results in improved yields and enhanced profitability for farmers.
Also, researchers have a crucial responsibility to test and refine present digital technologies to ensure their effectiveness across diverse agricultural contexts. In addition to their involvement in the development of digital technologies, researchers also undertake research on the social, economic, and environmental impacts of digital agriculture.
For researchers to excel in their roles, they must possess a profound comprehension of the needs and priorities of farmers and other stakeholders within the digital agricultural ecosystem. Such engagement in terms of dialogue with stakeholders allows researchers to ensure that their research will be utilized to inform decision processes and practical implementation in the agricultural domain.
1.5.1 Role of Policymakers in the Digital Agricultural Ecosystem
Policymakers’ unwavering commitment encompasses the formulation of robust regulations that not only foster the widespread adoption of groundbreaking technologies but also guarantee the utmost safety, reliability, and accessibility for farmers.
Policymakers play a crucial role in advancing the evolution of digital infrastructure, encompassing the development and enhancement of essential policies such as financial inclusion, availability of resources, price determination, and community welfare.
In terms of association, policymakers collaborate closely with farmers, academics, and technology suppliers, coalescing their expertise to design inclusive policies that prioritize digital data privacy and security within the agricultural realm. Through stakeholders’ active involvement, policymakers contribute to the establishment of a robust legal framework that safeguards sensitive agricultural data, fostering a conducive environment for innovation and exponential growth in the realm of digital agriculture.
One effective approach involves providing financial support for the development of training programs and instructional materials that equip farmers with the proficiency to effectively navigate the intricacies of digital technologies. Moreover, policymakers may implement a range of financial incentives, including tax breaks and subsidies, as effective motivators to encourage the widespread adoption of digital tools and solutions within agricultural operations [12].
Furthermore, in order to drive transformative change, policymakers forge partnerships with esteemed international organizations such as the United Nations. It helps to harness collective wisdom to foster the utilization of digital technology in agriculture. Collaboratively, policymakers direct stakeholders regarding resources and allocate funds toward cutting-edge research and development initiatives. Such worldwide cooperation aims to spark a significant revolution in the industry, boosting food security and advancement in the socioeconomic growth of countries attempting to overcome agricultural issues.
1.5.2 Role of Customers in the Digital Agricultural Ecosystem
Digital agriculture has opened up new avenues for customer engagement and connectivity with agriculture. Customers encompass individuals and organizations alike who procure agricultural products or services for personal or commercial purposes. Customers as an entity play an integral role in the complex web structure that forms the digital agriculture ecosystem. Their significance lies in their capacity to shape the demand for products and services, thereby exerting influence over the intricate web of the supply chain and the overall functionality of the agriculture sector.
This digital framework facilitates customers’ active involvement in various aspects, encompassing investment in agricultural operations, provision of capital for production, acquisition of agricultural produce, and even sharing in the ensuing profits.
Customers further contribute to the agricultural landscape by providing invaluable feedback to farmers and other stakeholders. Leveraging the capabilities offered by digital technologies, farmers can seamlessly collect and meticulously analyze customer feedback. This overall feedback loop provides farmers with an advantageous position in planning their production and pricing strategies, boosting their overall performance and enhancing overall customer satisfaction.
Additionally, digital technologies have acted as a catalyst for direct-to-consumer (DTC) marketing channels, eliminating the need for middlemen and enabling farmers to connect directly with their customers. The prevalence of online platforms and social media also acts as powerful enablers, which allow farmers to sell their produce at competitive rates and also assure that they get a fair and equitable share of the earnings. This innovative approach has found huge success in the digital age as consumers gravitate toward locally sourced, high-quality food products [14].
Customers can influence public policy by promoting environmentally and socially responsible farming practices. Customers can effect change by strongly supporting policy changes that put an emphasis on sustainability and ethical behavior. They can do this by applying significant pressure to lawmakers to enact rules and incentives that would accelerate the general adoption of sustainable agriculture practices. Customers and legislators working together in harmony have a revolutionary effect that affects the entire agriculture industry and ushers in a new era of progress.
1.6 Elements
1.6.1 Hardware
Hardware encompasses a diverse array of devices, including sensors, drones, robots, GPS receivers, and cameras. These instrumental tools are the main players in terms of efficiency and optimization in agricultural production and in gathering, transferring, and interpreting data.
The hardware’s vital role within the digital agricultural ecosystem can be demonstrated by considering its extensive participation in the three key areas of data collection, transmission, and analysis. One instance of how hardware is essential to data collection is the usage of sensors. These cutting-edge instruments are adept at gathering crucial data on nutrient concentrations, temperature swings, and soil moisture levels. Whether they are discretely affixed to plants or deeply submerged in the soil, these sensors operate in real time, flawlessly delivering a continuous stream of valuable information. Drones, which are essential hardware elements for data collection, support this endeavor. Drones use their flying capabilities to acquire high-resolution photographs of crops, enabling thorough crop growth monitoring, disease or pest identification, and evaluation of overall crop health. Additionally, the installation of GPS receivers makes it possible to get exact information about the movement and placement of equipment, providing opportunities for the field operations’ optimization.
The hardware plays a crucial part in enabling the data’s flawless transmission to the cloud for in-depth analysis after it has been carefully collected. The landscape of digital agriculture attests to the necessity of technology in the field of data transfer. Wireless sensors become crucial partners, transferring the cautiously gathered data across cellular or internet networks, guiding it toward the vast world of the cloud. Additionally, by utilizing their wireless capabilities, drones and robots allow the quick transmission of data to the cloud, significantly increasing the effectiveness of the entire system. In the meantime, GPS receivers accurately communicate location data, making it possible to optimize field operations and hence improve agricultural practices broadly.
The importance of hardware in maximizing the potential of massive datasets has been directly observed in the field of data analysis. High-performance computers and servers diligently analyze vast data, including satellite images and meteorological information, as necessary. In order to help people make well-informed decisions, these computing giants carefully unearth invaluable data regarding crop health and growth trends. GPUs, which serve as accelerators and improve the system’s capabilities, greatly speed up the analysis process and ensure quicker, more accurate findings. Hardware accelerators like field-programmable gate arrays (FPGAs), which speed up the processing of certain algorithms like those used in machine learning models, also play a significant role. By overcoming traditional computational limitations, these accelerators shift the paradigm and increase the speed and accuracy of data processing.
Hardware has an impact on visualization, enabling farmers to understand and use the revelations brought about by data analysis. In this area, displays and monitors take the front stage, giving farmers the tools they need to visualize data in a useful and natural way. This promotes agricultural practices based on a deep grasp of the underlying facts by enabling quick comprehension and well-informed decision-making. Additionally, cutting-edge innovations like virtual reality (VR) and augmented reality (AR) are powerful tools that give farmers access to immersive experiences that enable the visualization and interactive exploration of data in unique and revolutionary ways.
1.6.2 Software
A rise in agricultural productivity has been required as a result of the expanding world population and a remarkable increase in food consumption. The use of contemporary technology must be integrated in order to accomplish this ambitious objective. Software emerges as one of the key elements in the field of digital agriculture, providing the ability to gather, analyze, and interpret data, thus allowing informed decision-making and boosting production effectiveness.
Software technologies shape the landscape of digital agriculture by allowing farmers to collect, store, and evaluate massive volumes of data from many sources. Information regarding weather patterns, soil quality, crop health, and animals are just a few of the numerous rich data sources that software applications can access. By filtering and analyzing the collected data, these cutting-edge technologies offer priceless insights into trends and patterns that serve as indicators that guide farmers in the direction of prudent decisions. Using datadriven insights, farmers may increase crop yields, minimize losses, and manage resources effectively.
The importance of software applications is demonstrated by precision agriculture, which enables the careful monitoring and management of crop health, soil moisture, and other elements that affect crop growth. Real-time information about crop growth and environmental conditions is provided by software applications, which act as channels for data collection from sensors and other sources. The farmers are advised by this invaluable data to optimize irrigation, fertilization, and other inputs, reducing waste and increasing production.
Applications of software also play a significant role in the management of the agricultural supply chain. These tools carefully monitor crop quality, storage, and transportation to ensure the prompt and effective delivery of commodities to the market. Farmers can make sure that their harvests arrive in markets in perfect shape by utilizing these applications. Farmers can employ supply chain management software to track their products and keep an eye on prices, giving them the information they need to make wise decisions about when and where to sell their crops.
1.6.3 Data
Data, a catalyst that provides farmers with the information they need to make well-informed choices at every stage of the agricultural cycle, from planting and fertilizing to harvesting and selling their crops, are at the center of the digital agricultural ecosystem.
Digital agriculture’s cornerstone, precision agriculture, is built on a solid database. Digital agriculture is being recognized more and more as an effective tool for improving the productivity, sustainability, and profitability of agricultural firms. Farmers can gather and study enormous volumes of data containing critical elements like crop status, soil quality, weather patterns, and more thanks to this innovative approach. This vast amount of data is obtained by utilizing cutting-edge technologies like sensors, drones, satellites, and other data collection mechanisms and is then rigorously analyzed using cutting-edge algorithms and machine learning approaches. Farmers can optimize their planting tactics, fertilization routines, irrigation techniques, and harvesting strategies, thanks to the insights that come as a result.
Data-driven agriculture offers a number of advantages, including the ability to make precise, focused decisions that save a lot of water, which is important in areas where there is a water shortage. Additionally, the position of data in the digital agricultural location is crucial since it allows for the tracking of crop trajectories from farm to market and gives farmers invaluable insights into consumer trends. Farmers are better equipped to decide on price, distribution, and market timing based on this intelligence, ensuring that their goods are delivered to customers quickly and effectively.
In addition to its immediate implications on supply chain management and precision agriculture, data have immense promise for assuring the sustainability of agricultural output. With the newfound understanding, farmers are better able to safeguard the long-term financial success of their farms while reducing the environmental effect of their agricultural operations.
Several initiatives have evolved to encourage data exchange throughout the sector in order to foster a data-driven strategy in agriculture. The Global Open Data for Agriculture and Nutrition (GODAN) initiative serves as a noteworthy example. It promotes the dissemination of open data to improve food security worldwide. It functions as a vital network. GODAN facilitates the transmission of agricultural data on a worldwide scale by establishing connections between data suppliers, users, and policymakers [15].
The European Union-backed SmartAgriHubs project, which seeks to create a network of digital innovation hubs, is another important aspect [16]. To promote precision agriculture technologies, these hubs act as collaborative platforms that bring together farmers, academics, and technology vendors.
1.6.4 Human Capital
In the digital agricultural ecosystem, human capital—which consists of the skills and knowledge of professionals like software engineers, data scientists, agronomists, and agricultural experts—plays a crucial role. These experts use their expertise to construct userfriendly interfaces, assess data, and offer suggestions for enhanced agricultural practices. They also make vital contributions to the design, development, and maintenance of technology and data systems. They are involved in more than just the beginning stages; they are also involved in the deployment and adoption of these technologies, assuring their efficient use through in-depth instruction and training. Their knowledge is crucial to the continued upkeep and development of technology and data systems, and their involvement actively promotes stakeholder collaboration and aids in the creation of policies that support the sustainability and prosperity of the ecosystem. Given the importance of their position, it is crucial that farmers and agricultural workers receive thorough training on how to use these systems effectively and efficiently. This calls for the presence of trainers and educators who are knowledgeable about both technology and agricultural practices.
Human capital is also necessary for the ongoing maintenance and improvement of technology and information systems. As technology advances and new data are received, adjustments and enhancements are needed to guarantee the continued transmission of accurate and important information.
The growth and viability of the digital agricultural ecosystem depend on the active participation and involvement of farmers and agricultural workers. They provide additional evidence of their significant contributions to the ecosystem’s human capital through their perceptive observations on the usability and effectiveness of technical and data systems.
Additional areas where human capital is essential include relationship-building and maintenance as well as encouraging effective stakeholder participation. Communication and teamwork experts establish a climate that supports the expansion and development of the digital agriculture ecosystem.
Additionally, human capital is essential for the creation and application of laws and rules that support and advance the digital agriculture environment.
1.6.5 Efficiency, Sustainability, and Profitability of Agricultural Operations
Digital agriculture has gained industry adoption and is now a powerful instrument for enhancing the effectiveness, sustainability, and profitability of agricultural operations. Using digital technologies, farmers can gain access to vast amounts of information regarding crucial factors that affect agricultural productivity, such as crops, soils, and weather patterns. The abundance of information available to farmers allows them to make well-informed decisions that maximize their use of resources like water, fertilizers, and pesticides while minimizing waste. This approach is best demonstrated by the use of precision irrigation systems, which allow farmers to provide each plant with the exact amount of water that it needs. By carefully controlling water use, considerable consumption decreases and increased crop yields are achieved, highlighting the potential of digital agriculture to maximize output and optimize resource use (see Figure 1.2).
Additionally, digital agriculture gives farmers advanced monitoring tools that let them keep a close eye on their crops and quickly spot any potential problems at an early stage. Farmers are able to minimize crop loss, decrease the severity of problems, and take rapid corrective action thanks to this early detection capacity.
Digital agriculture has a big influence beyond efficiency gains. This optimization helps to decrease the impact of conventional farming practices on the environment by minimizing runoff and contamination of water sources. The implementation of resource- and energy-efficient and ethical farming practices is also supported by digital agriculture.
Figure 1.2 Efficiency, sustainability, and profitability
(source: authors’ own).
In addition to promoting sustainability, digital agriculture helps agricultural businesses become more profitable. It first offers farmers the chance to make the most of their inputs, reduce costs, enhance yields, and ultimately increase profitability. By adopting digital technologies for close crop monitoring, farmers can immediately identify and address issues, effectively prevent crop loss, and optimize returns on investment. Digital agriculture has also given farmers new ways to enter untapped markets and diversify their revenue streams.
Conclusion
This chapter provides the first-of-its-kind introduction to the digital agriculture ecosystem with references to several key components and stakeholders. The study also emphasizes waste reduction, the promotion of ecological practices, and increasing agricultural output. The roles of significant players, including farmers, technology providers, researchers, policymakers, customers, and human capital, are thoroughly discussed. This study also underlined the importance of cooperation between stakeholders and other elements in order to achieve common objectives. It is acknowledged that hardware and software are crucial components for efficient data collection and processing. This study is not without limitations. The study mostly relies on earlier research, publications, and records from organizations that influence policy choices, which may not exactly reflect the state of technology at the moment. We propose that future studies should draw inspiration from this work and conduct qualitative or quantitative analyses to better understand the elements of the digital agricultural ecosystem.
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