Mathematical Modeling in Agriculture

Inbunden, Engelska, 2024

AvSabyasachi Pramanik,Niranjanamurthy M.,Ankur Gupta,Ahmed J. Obaid

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The main goal of the book is to explore the idea behind data modeling in smart agriculture using information and communication technologies and tools to make agricultural practices more functional, fruitful and profitable. The research in the book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers’ choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models were utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies. Farm management information systems (FMIS) have constantly advanced in complexity as they have incorporated new technology, the most recent of which is the internet. However, few FMIS have fully tapped into the internet’s possibilities, and the newly developing idea of precision agriculture receives little or no support in the FMIS that are now being sold. FMIS for precision agriculture must meet a few more criteria beyond those of regular FMIS, which increases the technological complexity of these systems’ deployment in a number of ways. In order to construct an FMIS that meet these extra needs, the authors here evaluated various cutting-edge web-based methods. The goal was to determine the requirements that precision agriculture placed on FMIS.

Produktinformation

Utgivningsdatum2024-11-01
Vikt857 g
FormatInbunden
SpråkEngelska
Antal sidor464
FörlagJohn Wiley & Sons Inc
ISBN9781394233694

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Sabyasachi Pramanik, PhD, is an associate professor in the Department of Computer Science and Engineering, Haldia Institute of Technology, India. He has many publications in technical conferences and journals, as well as online book chapter contributions. He is also a reviewer for and on numerous editorial boards for technical journals. He has authored one book and edited nine books, including books for Scrivener Publishing. Niranjanamurthy M., PhD, is an assistant professor in the Department of Artificial Intelligence and Machine Learning, BMS Institute of Technology and Management, Yelahanka, Bengalore, India. He has over ten years of teaching experience and two years of industry experience as a software engineer. He has published five books and is working on numerous books for Scrivener Publishing. He has published 54 research papers in various scientific refereed journals and filed ten patents, with two granted so far. He is a reviewer for more than 20 journals and has received numerous awards. Ankur Gupta, MTech, is an assistant professor in the Department of Computer Science and Engineering at Vaish College of Engineering, Rohtak, India. He has many publications in scientific journals and conferences and online book chapter contributions. Ahmed J. Obaid, PhD, is an assistant professor in the Department of Computer Science, Faculty of Computer Science and Mathematics, University of Kufa, Iraq. He has over 14 years of teaching experience and is a board member on numerous scientific journals. He has published over 75 journal research articles, five book chapters, 15 conference papers, 10 conference proceedings, and has edited eight books.

Preface xiii1 Analyzing the Impact of Food Safety Regulations on Agricultural Supply Chains: A Mathematical Modeling Perspective 1Nimit Kumar, Shwetha M.S., Govind Shay Sharma, Nitin Ubale, Nuzhat Fatima Rizvi and Dharmesh Dhabliya1.1 Introduction 21.2 Resources and Techniques 41.3 Results and Analysis 61.3.1 Knowledge, Application, and Obstacles to Food Modeling 61.3.2 Obstacles to Our Company’s Use of Mathematical Modeling 71.4 Conclusion 12References 132 Modeling the Effects of Land Degradation on Agricultural Productivity: Implications for Legal and Policy Interventions 17Amit Verma, Istita Auddy, Murli Manohar Gour, Dhwani Bartwal, Sukhvinder Singh Dari and Ankur Gupta2.1 Introduction 182.2 Materials and Procedures 202.2.1 Content of Minerals 232.3 Results and Analysis 242.4 Conclusion 28References 293 Mathematical Modeling of Carbon Sequestration in Agricultural Soils: Implications for Climate Change Mitigation Policies 33Kailash Malode, Brijpal Singh Rajawat, Amar Shankar S., Ravindra Kumar, Deepti Khubalkar and Sabyasachi Pramanik3.1 Introduction 343.2 Resources and Techniques 353.2.1 Reference Trial 363.2.2 Interviews with Agriculturists in London Suburb and Liverpool 383.2.2.1 Overall Explanation of the Sampled Region and Organized Interviews 383.2.3 Online Tools for Calculating CF 383.3 Results 403.3.1 Agricultural Data as Model I/P 403.3.1.1 Case Study 403.3.1.2 From Discussions with Farmers 413.3.2 Farms’ Estimated GHG Emissions 433.3.3 Effects of Mitigating Measures 443.4. Discussion 443.4.1 Evaluating the Possible Effects of Mitigating Measures 463.5 Conclusions 47References 484 Optimizing Livestock Feed Formulation for Sustainable Agriculture: A Mathematical Modeling Approach 51Rutul Patel, Upasana, Ashutosh Pattanaik, Deepak Kumar, Ahmar Afaq and Soma Bag4.1 Introduction 524.2 Managing Swine Herds Using Modeling 534.2.1 System of a Sow Herd 534.2.2 Major Statistical Techniques Used in Modeling Cattle Herds 554.2.2.1 Literature Review on Herd Modeling for Cattle 554.2.2.2 Models for Simulation 564.2.2.3 Models for Optimization 564.2.2.4 The Integration of Simulation and Optimization 574.3 Models of a Sow Herd 584.3.1 Chosen Models 584.3.2 Input Criteria 594.3.2.1 Parameters Used as Inputs in Optimization Models 594.3.2.2 Parameters Used as Inputs in Simulation Techniques 604.3.3 Results from the Models 614.3.4 The Models’ Validation 624.3.5 Opportunities for Implementation and Integration 634.3.6 Management of Risk 644.3.7 Additional Submissions and Literature Review 644.4 Discussion 654.5 Conclusions 68References 695 Modeling the Economic Impact of Agricultural Regulations: A Case Study on Environmental Compliance Costs 81Vikesh Rami, Sunil Kumar, Gautham Krishna, Abhinav, Sukhvinder Singh Dari and Dharmesh Dhabliya5.1 Introduction 825.2 Mechanisms Study Time and Location 835.3 Sampling 855.4 Analysis, Both Physical and Chemical 855.5 Module for Water Quality 875.6 Particulate Phosphorus and Suspended Solids 875.7 Calculation of PP 885.8 Model Caliphy 895.9 Scientifications Described by the Model 945.10 Simulation of Sediment Trap 965.11 Pumping Profile Modifications Simulation 985.12 Conclusion 98References 996 Quantifying the Economic Benefits of Precision Agriculture Technologies: A Mathematical Modeling Study 103Deepak Kumar, Apexaben Rathod, Sachchida Nand Singh, Meena Y. R., Rushil Chandra and Ankur Gupta6.1 Introduction 1046.2 Method and Materials 1076.3 Conclusion and Results 1106.4 Conclusions 112References 1137 Optimizing Resource Allocation in Agribusinesses: A Mathematical Modeling Approach Considering Legal Factors 115Vishvendra Singh, Navghan Mahida, Anand Janardan Madane, Sudhakar Reddy, Parth Sharma and Sabyasachi PramanikIntroduction 116Methods 119A Framework for the Transmission and Commandof Brucellosis: A Case Study Overview 120Brucellosis Nominal Transmission Modeling 120Modeling Disease Costs and Control Capabilities 124Creating a Cost Model and Confronting the Challenge of Control Design 125Analysis, Design, and Parameterization Techniques 127Overview of the Control and Surveillance Design 128Network Model Identification and Validation for Zoonoses 129Results 130Indicative Model 131Control Strategy Modeling 135Optimized Approaches 137Parameterization 143Discussion 143Wide-Ranging Perspectives on High-Performance Control 144Talking About Parameterzing Models 147Conclusion 148References 1508 Modeling the Dynamics of Agricultural Cooperatives and Legal Implications for Farmer Organizations 153Shiv Shankar Shankar, Prashantkumar Zala, Ashutosh Awasthi, Ezhilarasan G., Sukhvinder Singh Dari and Soma Bag8.1 Introduction 1548.2 Resources and Techniques 1558.3 Conclusion 160References 1609 Optimizing Agroforestry Systems for Sustainable Agriculture: A Mathematical Modeling Approach 163Beemkumar Nagappan, Aakriti Chauhan, Chandni Mori, Praveen Kumar Singh, Shilpa Sharma and Sabyasachi Pramanik9.1 Introduction 1649.2 Relationships Between Structure and Activity (SAR) and the Level of Toxicological Involvement 1699.3 Threshold Approaches 1749.4 Reciprocal Analysis 1789.5 Chemical-Specific Adjustments 183Conclusion 184References 18510 Simulating the Effects of Climate-Smart Agriculture Practices on Farm Resilience: A Mathematical Modeling Approach 189Kiran K. S., Meenakshi Dheer, Mukesh Laichattiwar, Devendra Pal Singh, Vaidehi Pareek and Soma Bag10.1 Introduction 19010.2 Definitions, Concepts, and Methods for the Analytical Framework 19110.3 Results 19410.4 Consequences for Political Implementations 20310.5 Advanced Research 20410.6 Conclusions 206References 20711 Modeling the Dynamics of Agrochemical Regulations and Impacts on Agricultural Productivity 211Hannah Jessie Rani, Akanchha Singh, Aishwary Awasthi, Ashwani Rawat, Nuvita Kalra and Ankur Gupta11.1 Introduction 21211.2 Resources and Techniques 21311.3 Results 21611.4 Discussion 21711.5 Conclusion 219References 22012 Optimizing Energy Consumption in Greenhouse Production: A Mathematical Modeling Approach 223Beemkumar Nagappan, Arun Gupta, Sachin Gupta, Diksha Nautiyal, Aarti Kalnawat and Dharmesh Dhabliya12.1 Introduction 22412.2 Literature Review 22712.3 The Creation of Mathematical Models a Range of Models 22912.4 Formulation of a Model 23112.5 Modeling of Groundwater Quality 24212.6 Conclusion 244References 24413 Analyzing the Economic and Legal Impacts of Intellectual Property Rights on Plant Breeding Innovations: A Mathematical Modeling Study 249Gopalakrishna K., Bhirgu Raj Maurya, Rajeev Kumar, Sushila Arya, Himanshi Bhatia and Ankur Gupta13.1 Introduction 25013.2 Competition Postulates 25113.3 Transparent Competition 25113.3.1 Effect of Competitiveness-Density 25213.3.2 Changes to the Population’s Size Structure 25213.4 Concurrence Inter-Specific 25313.4.1 Adding Damage 25413.4.2 Neighborhood Function 25613.4.3 Innovative Design and Analysis 25613.5 Dynamic Plant Growth and Competition Models 25613.5.1 Dynamic Population 25813.6 Aspects Impacting the Result of Competitiveness 25913.7 Crop-Weed Competition Models Applied in Practical Situations 26013.8 Conclusion 261References 26214 Simulating the Effects of Land Use Regulations on Agricultural Land Values: A Mathematical Modeling Study 265Ashwani Rawat, Ramachandran T., Yogesh Chandra Gupta, Manoj Kumar Mishra, Gabriela Michael and Sabyasachi Pramanik14.1 Introduction 26614.2 Models of Component Agricultural Systems 26714.3 Present-Day Farming System Frameworks in Relation to Certain Application Situations 28414.4 Discussion 286References 29015 Simulating the Effects of Agricultural Land Fragmentation on Farm Effciency: A Mathematical Modeling Analysis 295Diksha Nautiyal, Manjunath H. R., Praveen Kumar Singh, Umesh Kumar Tripathi, Saurabh Raj and Soma Bag15.1 Introduction 29615.2 Conceptual Foundation 29715.3 Resources and Techniques Household Polls 29915.4 Results 30615.5 Discussion 31315.6 Conclusions 316References 31716 Simulating the Effects of Land Use Policies on Agricultural Productivity: A Mathematical Modeling Perspective 321Vinaya Kumar Yadav, Sushila Arya, Asha Rajiv R., Devendra Pal Singh, Siddharth Ranka and Dharmesh Dhabliya16.1 Introduction 32216.2 Upcoming Applications of NextGen Farming Frameworks 32616.3 Envisioned Consumers of the Application Chain Beneficiaries 33116.4 Conclusion and Research Plan 340References 34117 Quantifying the Economic Benefits of Agricultural Extension Services: A Mathematical Modeling Analysis 345Rajeev Kumar, Satendra Kumar, Pradeepa P., Akanchha Singh, Karun Sanjaya and Ankur Gupta17.1 Introduction 34617.2 Creating New Models for the Future: A Demand-Driven, Prospective Strategy 34717.3 Potential Improvements to Model Elements 35517.4 Conclusions 367References 36818 Modeling the Impact of Agricultural Investment Incentives on Rural Development: Legal and Economic Perspectives 373Dal Chandra, Manoj Kumar Mishra, Ankit Pant, Ahmadi Begum, Sukhvinder Singh Dari and Dharmesh Dhabliya18.1 Introduction 37418.2 Approach 37618.3 Conversation 38418.4 Conclusion 390References 39119 Optimizing Harvest Scheduling in Agriculture: A Mathematical Modeling Approach Considering Legal Restrictions 397Heejeebu Shanmukha Viswanath, Umesh Kumar Tripathi, Minnu Sasi, Kishore Kumar Pedapenki, Prashant Dhage and Ankur Gupta19.1 Initialization 39819.2 Structure of the System 40619.3 Irrigation Community Event 40919.4 Assessment and Authentication 41219.5 Conclusions 416References 41820 Quantifying the Economic Benefits of Agricultural Data Sharing: A Mathematical Modeling Perspective 421Aruno Raj Singh, Vinaya Kumar Yadav, Laishram Zurika, Dasarathy A. K., Abhishekh Benedict and Dharmesh Dhabliya20.1 Introduction 42220.2 Model for Data Mining Process 42320.3 Techniques for Machine Learning 42420.4 Website Tools 42920.5 Case Study: Grading of Mushrooms 43120.6 Conclusion 432References 433Index 437