Advances in crop modelling for a sustainable agriculture (Burleigh Dodds Series in Agricultural Science, 75)

This collection summarises key advances in crop modelling, with a focus on developing the next generation of crop and whole-farm models to improve decision making and support for farmers. Chapters in Part 1 review advances in modelling individual components of agricultural systems, such as plant responses to environmental conditions, crop growth stage prediction, nutrient and water cycling as well as pest/disease dynamics. Building on topics previously discussed in Part 1, Part 2 addresses the challenges of combining modular sub-systems into whole farm system, landscape and regional models. Chapters cover topics such as integration of rotations and livestock, as well as landscape models such as agroecological zone (AEZ) models. Chapters also review the performance of specific models such as APSIM and DSSAT and the challenges of developing decision support systems (DSS) linked with such models. The final part of the book reviews wider issues in improving model reliability such as data sharing and the supply of real-time data, as well as crop model inter-comparison. With its distinguished editor and range of experienced and expert chapter authors, this collection will be a standard reference for crop modellers and developers of decision support systems to improve the efficiency and sustainability of farming. Improving data management and decision support systems in agriculture Contents Series list Introduction Part 1 General issues Chapter 1 Improving data access for more effective decision making in agriculture 1 Introduction 2 Key issues in current availability of data 3 Use of data for decision making: case studies 4 Current trends 5 Conclusions 6 Where to look for further information 7 References Chapter 2 Improving data standards and integration for more effective decision-making in agriculture 1 Introduction 2 Business process modelling to identify data requirements 3 Data flows for a particular process: the example of variable rate fertilization 4 Linking platforms and software 5 Creating a reference architecture for interoperability, replicability and reuse 6 Key elements in data management 7 Conclusions 8 Where to look for further information 9 References Chapter 3 Improving data identification and tagging for more effective decision making in agriculture 1 Introduction 2 Structuring the data 3 Case study: plant phenotyping 4 Conclusion and future trends 5 Where to look for further information 6 Acknowledgements 7 References Chapter 4 Advances in data security for more effective decision-making in agriculture 1 Introduction 2 Security challenges in PA systems 3 System architecture and legal recourse 4 Security framework considerations for PA systems 5 Modern cyberattack methods 6 Classifying cyberattack source psychology 7 Cybersecurity frameworks for PA 8 Case study: PA system assessment 9 Future trends 10 Conclusion 11 Where to look for further information 12 References 13 Appendix Chapter 5 Advances in artificial intelligence (AI) for more effective decision making in agriculture 1 Introduction 2 Agricultural DSS using AI technologies: an overview 3 Data and image acquisition 4 Core AI technologies 5 Case study 1: AgData DSS tool for western Australian broad acre cropping 6 Case study 2: GeoSense 7 Case study 3: Rice-based DSS 8 Summary and future trends 9 Where to look for information 10 References Chapter 6 Improving data management and decision-making in precision agriculture 1 Introduction 2 Remote sensing technologies 3 Geographic information system (GIS) technologies 4 Sensors and sensor networks 5 Statistical and crop simulation models 6 Identifying variability in crop production systems 7 Summary and future trends 8 Where to look for further information 9 References Part 2 Case studies Chapter 7 Decision support systems (DSS) for better fertiliser management 1 Introduction 2 Direct methods for determining crop nitrogen requirements for decision support 3 Indirect methods for determining crop nitrogen requirements for decision support: simulation models 4 Indirect methods for determining crop nitrogen requirements for decision support: yield forecasts using data-driven approaches 5 Indirect methods for determining crop nitrogen requirements for decision support: yield forecasts based on water supply 6 Decision support in action: case studies 7 Case study 1: nitrogen fertiliser applications using a data-driven approach 8 Case study 2: nitrogen fertiliser decision-making based on soil moisture predictions 9 Comparing the two approaches 10 Conclusion and future trends 11 References Chapter 8 Developing decision-support systems for crop rotations 1 Introduction 2 Key information challenges 3 Ecological theory 4 Agronomic models 5 Encoding farmer decisions 6 Design principles 7 Outlook 8 Where to look for further information 9 References Chapter 9 Decision-support systems for pest monitoring and management 1 Introduction 2 Pest identification 3 Pest monitoring 4 Pest forecasting 5 Integrated pest management (IPM) 6 Case studies 7 Summary and future trends 8 Where to look for further information 9 References Chapter 10 Developing decision support systems for improving data management in agricultural supply chains 1 Introduction 2 Decisions in supporting data management 3 Decision tools 4 Principal case studies 5 Conclusion and future trends 6 References Chapter 11 Developing decision support systems for optimizing livestock diets in farms 1 Introduction 2 Mathematical programming models for livestock production: a review 3 Linear programming (LP) models to minimize feed costs: solutions and sensitivity analysis 4 Goal programming (GP) models: balancing costs and environmental impact 5 Decision support systems and data management for sustainable diets 6 Case study 1: sustainable rations for intensive broiler production 7 Case study 2: reducing emissions in pig production 8 Summary and future trends in research 9 Acknowledgements 10 Where to look for further information 11 References Chapter 12 Developing decision-support systems for pasture and rangeland management 1 Introduction 2 Decision-support systems (DSSs) in pasture and rangeland management 3 Decision-making processes of pasture and rangeland farmers 4 Development of effective decision-support tools 5 Case studies of decision-support system (DSS) development in pasture and rangeland management 6 Conclusion and future trends 7 Where to look for further information 8 References Index "This collection summarises key advances in crop modelling, with a focus on developing the next generation of crop and whole-farm models to improve decision making and support for farmers. Chapters in Part 1 review advances in modelling individual components of agricultural systems, such as plant responses to environmental conditions, crop growth stage prediction, nutrient and water cycling as well as pest/disease dynamics. Building on topics previously discussed in Part 1, Part 2 addresses the challenges of combining modular sub-systems into whole farm system, landscape and regional models. Chapters cover topics such as integration of rotations and livestock, as well as landscape models such as agroecological zone (AEZ) models. Chapters also review the performance of specific models such as APSIM and DSSAT and the challenges of developing decision support systems (DSS) linked with such models. The final part of the book reviews wider issues in improving model reliability such as data sharing and the supply of real-time data, as well as crop model inter-comparison."--Page 4 of cover "This collection provides a comprehensive review of key advances in crop modelling, with a focus on developing the next generation of whole-farm models to improve decision making and support for farmers. Chapters review advances in modelling individual components of agricultural systems such as plant responses to environmental conditions, crop growth stages, nutrient and water cycles as well as pest/disease dynamics. The book also addresses the challenges of combining modular sub-systems into whole farm system models, including integration of rotations, intercropping and livestock. Chapters also review the performance of specific models such as APSIM and DSSAT and the challenges of developing decision support systems (DSS) from such models. The book also considers landscape models such as agroecological zone (AEZ) models and Coordinated Global and Regional Assessments (CGRA). The final part of the book reviews wider issues in improving model reliability such as data sharing and the supply of real-time data as well as crop model inter-comparison"-- Provided by publisher