Preface xiii 1 Analyzing the Impact of Food Safety Regulations on Agricultural Supply Chains: A Mathematical Modeling Perspective 1 Nimit Kumar, Shwetha M.S., Govind Shay Sharma, Nitin Ubale, Nuzhat Fatima Rizvi and Dharmesh Dhabliya 1.1 Introduction 2 1.2 Resources and Techniques 4 1.3 Results and Analysis 6 1.3.1 Knowledge, Application, and Obstacles to Food Modeling 6 1.3.2 Obstacles to Our Company’s Use of Mathematical Modeling 7 1.4 Conclusion 12 References 13 2 Modeling the Effects of Land Degradation on Agricultural Productivity: Implications for Legal and Policy Interventions 17 Amit Verma, Istita Auddy, Murli Manohar Gour, Dhwani Bartwal, Sukhvinder Singh Dari and Ankur Gupta 2.1 Introduction 18 2.2 Materials and Procedures 20 2.2.1 Content of Minerals 23 2.3 Results and Analysis 24 2.4 Conclusion 28 References 29 3 Mathematical Modeling of Carbon Sequestration in Agricultural Soils: Implications for Climate Change Mitigation Policies 33 Kailash Malode, Brijpal Singh Rajawat, Amar Shankar S., Ravindra Kumar, Deepti Khubalkar and Sabyasachi Pramanik 3.1 Introduction 34 3.2 Resources and Techniques 35 3.2.1 Reference Trial 36 3.2.2 Interviews with Agriculturists in London Suburb and Liverpool 38 3.2.2.1 Overall Explanation of the Sampled Region and Organized Interviews 38 3.2.3 Online Tools for Calculating CF 38 3.3 Results 40 3.3.1 Agricultural Data as Model I/P 40 3.3.1.1 Case Study 40 3.3.1.2 From Discussions with Farmers 41 3.3.2 Farms’ Estimated GHG Emissions 43 3.3.3 Effects of Mitigating Measures 44 3.4. Discussion 44 3.4.1 Evaluating the Possible Effects of Mitigating Measures 46 3.5 Conclusions 47 References 48 4 Optimizing Livestock Feed Formulation for Sustainable Agriculture: A Mathematical Modeling Approach 51 Rutul Patel, Upasana, Ashutosh Pattanaik, Deepak Kumar, Ahmar Afaq and Soma Bag 4.1 Introduction 52 4.2 Managing Swine Herds Using Modeling 53 4.2.1 System of a Sow Herd 53 4.2.2 Major Statistical Techniques Used in Modeling Cattle Herds 55 4.2.2.1 Literature Review on Herd Modeling for Cattle 55 4.2.2.2 Models for Simulation 56 4.2.2.3 Models for Optimization 56 4.2.2.4 The Integration of Simulation and Optimization 57 4.3 Models of a Sow Herd 58 4.3.1 Chosen Models 58 4.3.2 Input Criteria 59 4.3.2.1 Parameters Used as Inputs in Optimization Models 59 4.3.2.2 Parameters Used as Inputs in Simulation Techniques 60 4.3.3 Results from the Models 61 4.3.4 The Models’ Validation 62 4.3.5 Opportunities for Implementation and Integration 63 4.3.6 Management of Risk 64 4.3.7 Additional Submissions and Literature Review 64 4.4 Discussion 65 4.5 Conclusions 68 References 69 5 Modeling the Economic Impact of Agricultural Regulations: A Case Study on Environmental Compliance Costs 81 Vikesh Rami, Sunil Kumar, Gautham Krishna, Abhinav, Sukhvinder Singh Dari and Dharmesh Dhabliya 5.1 Introduction 82 5.2 Mechanisms Study Time and Location 83 5.3 Sampling 85 5.4 Analysis, Both Physical and Chemical 85 5.5 Module for Water Quality 87 5.6 Particulate Phosphorus and Suspended Solids 87 5.7 Calculation of PP 88 5.8 Model Caliphy 89 5.9 Scientifications Described by the Model 94 5.10 Simulation of Sediment Trap 96 5.11 Pumping Profile Modifications Simulation 98 5.12 Conclusion 98 References 99 6 Quantifying the Economic Benefits of Precision Agriculture Technologies: A Mathematical Modeling Study 103 Deepak Kumar, Apexaben Rathod, Sachchida Nand Singh, Meena Y. R., Rushil Chandra and Ankur Gupta 6.1 Introduction 104 6.2 Method and Materials 107 6.3 Conclusion and Results 110 6.4 Conclusions 112 References 113 7 Optimizing Resource Allocation in Agribusinesses: A Mathematical Modeling Approach Considering Legal Factors 115 Vishvendra Singh, Navghan Mahida, Anand Janardan Madane, Sudhakar Reddy, Parth Sharma and Sabyasachi Pramanik Introduction 116 Methods 119 A Framework for the Transmission and Command of Brucellosis: A Case Study Overview 120 Brucellosis Nominal Transmission Modeling 120 Modeling Disease Costs and Control Capabilities 124 Creating a Cost Model and Confronting the Challenge of Control Design 125 Analysis, Design, and Parameterization Techniques 127 Overview of the Control and Surveillance Design 128 Network Model Identification and Validation for Zoonoses 129 Results 130 Indicative Model 131 Control Strategy Modeling 135 Optimized Approaches 137 Parameterization 143 Discussion 143 Wide-Ranging Perspectives on High-Performance Control 144 Talking About Parameterzing Models 147 Conclusion 148 References 150 8 Modeling the Dynamics of Agricultural Cooperatives and Legal Implications for Farmer Organizations 153 Shiv Shankar Shankar, Prashantkumar Zala, Ashutosh Awasthi, Ezhilarasan G., Sukhvinder Singh Dari and Soma Bag 8.1 Introduction 154 8.2 Resources and Techniques 155 8.3 Conclusion 160 References 160 9 Optimizing Agroforestry Systems for Sustainable Agriculture: A Mathematical Modeling Approach 163 Beemkumar Nagappan, Aakriti Chauhan, Chandni Mori, Praveen Kumar Singh, Shilpa Sharma and Sabyasachi Pramanik 9.1 Introduction 164 9.2 Relationships Between Structure and Activity (SAR) and the Level of Toxicological Involvement 169 9.3 Threshold Approaches 174 9.4 Reciprocal Analysis 178 9.5 Chemical-Specific Adjustments 183 Conclusion 184 References 185 10 Simulating the Effects of Climate-Smart Agriculture Practices on Farm Resilience: A Mathematical Modeling Approach 189 Kiran K. S., Meenakshi Dheer, Mukesh Laichattiwar, Devendra Pal Singh, Vaidehi Pareek and Soma Bag 10.1 Introduction 190 10.2 Definitions, Concepts, and Methods for the Analytical Framework 191 10.3 Results 194 10.4 Consequences for Political Implementations 203 10.5 Advanced Research 204 10.6 Conclusions 206 References 207 11 Modeling the Dynamics of Agrochemical Regulations and Impacts on Agricultural Productivity 211 Hannah Jessie Rani, Akanchha Singh, Aishwary Awasthi, Ashwani Rawat, Nuvita Kalra and Ankur Gupta 11.1 Introduction 212 11.2 Resources and Techniques 213 11.3 Results 216 11.4 Discussion 217 11.5 Conclusion 219 References 220 12 Optimizing Energy Consumption in Greenhouse Production: A Mathematical Modeling Approach 223 Beemkumar Nagappan, Arun Gupta, Sachin Gupta, Diksha Nautiyal, Aarti Kalnawat and Dharmesh Dhabliya 12.1 Introduction 224 12.2 Literature Review 227 12.3 The Creation of Mathematical Models a Range of Models 229 12.4 Formulation of a Model 231 12.5 Modeling of Groundwater Quality 242 12.6 Conclusion 244 References 244 13 Analyzing the Economic and Legal Impacts of Intellectual Property Rights on Plant Breeding Innovations: A Mathematical Modeling Study 249 Gopalakrishna K., Bhirgu Raj Maurya, Rajeev Kumar, Sushila Arya, Himanshi Bhatia and Ankur Gupta 13.1 Introduction 250 13.2 Competition Postulates 251 13.3 Transparent Competition 251 13.3.1 Effect of Competitiveness-Density 252 13.3.2 Changes to the Population’s Size Structure 252 13.4 Concurrence Inter-Specific 253 13.4.1 Adding Damage 254 13.4.2 Neighborhood Function 256 13.4.3 Innovative Design and Analysis 256 13.5 Dynamic Plant Growth and Competition Models 256 13.5.1 Dynamic Population 258 13.6 Aspects Impacting the Result of Competitiveness 259 13.7 Crop-Weed Competition Models Applied in Practical Situations 260 13.8 Conclusion 261 References 262 14 Simulating the Effects of Land Use Regulations on Agricultural Land Values: A Mathematical Modeling Study 265 Ashwani Rawat, Ramachandran T., Yogesh Chandra Gupta, Manoj Kumar Mishra, Gabriela Michael and Sabyasachi Pramanik 14.1 Introduction 266 14.2 Models of Component Agricultural Systems 267 14.3 Present-Day Farming System Frameworks in Relation to Certain Application Situations 284 14.4 Discussion 286 References 290 15 Simulating the Effects of Agricultural Land Fragmentation on Farm Effciency: A Mathematical Modeling Analysis 295 Diksha Nautiyal, Manjunath H. R., Praveen Kumar Singh, Umesh Kumar Tripathi, Saurabh Raj and Soma Bag 15.1 Introduction 296 15.2 Conceptual Foundation 297 15.3 Resources and Techniques Household Polls 299 15.4 Results 306 15.5 Discussion 313 15.6 Conclusions 316 References 317 16 Simulating the Effects of Land Use Policies on Agricultural Productivity: A Mathematical Modeling Perspective 321 Vinaya Kumar Yadav, Sushila Arya, Asha Rajiv R., Devendra Pal Singh, Siddharth Ranka and Dharmesh Dhabliya 16.1 Introduction 322 16.2 Upcoming Applications of NextGen Farming Frameworks 326 16.3 Envisioned Consumers of the Application Chain Beneficiaries 331 16.4 Conclusion and Research Plan 340 References 341 17 Quantifying the Economic Benefits of Agricultural Extension Services: A Mathematical Modeling Analysis 345 Rajeev Kumar, Satendra Kumar, Pradeepa P., Akanchha Singh, Karun Sanjaya and Ankur Gupta 17.1 Introduction 346 17.2 Creating New Models for the Future: A Demand-Driven, Prospective Strategy 347 17.3 Potential Improvements to Model Elements 355 17.4 Conclusions 367 References 368 18 Modeling the Impact of Agricultural Investment Incentives on Rural Development: Legal and Economic Perspectives 373 Dal Chandra, Manoj Kumar Mishra, Ankit Pant, Ahmadi Begum, Sukhvinder Singh Dari and Dharmesh Dhabliya 18.1 Introduction 374 18.2 Approach 376 18.3 Conversation 384 18.4 Conclusion 390 References 391 19 Optimizing Harvest Scheduling in Agriculture: A Mathematical Modeling Approach Considering Legal Restrictions 397 Heejeebu Shanmukha Viswanath, Umesh Kumar Tripathi, Minnu Sasi, Kishore Kumar Pedapenki, Prashant Dhage and Ankur Gupta 19.1 Initialization 398 19.2 Structure of the System 406 19.3 Irrigation Community Event 409 19.4 Assessment and Authentication 412 19.5 Conclusions 416 References 418 20 Quantifying the Economic Benefits of Agricultural Data Sharing: A Mathematical Modeling Perspective 421 Aruno Raj Singh, Vinaya Kumar Yadav, Laishram Zurika, Dasarathy A. K., Abhishekh Benedict and Dharmesh Dhabliya 20.1 Introduction 422 20.2 Model for Data Mining Process 423 20.3 Techniques for Machine Learning 424 20.4 Website Tools 429 20.5 Case Study: Grading of Mushrooms 431 20.6 Conclusion 432 References 433 Index 437

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.