Intro
Preface
Contents
Part I: Modelling for Precision Agriculture
Introduction
1 Rationale
2 Crop Modelling
3 Precision Agriculture
4 Use of Models in Precision Agriculture
4.1 Regression Models
4.2 Simple Dynamic Models
4.3 Crop Growth Models
4.4 Digital Twins
4.5 Machine Learning
5 Chapters of the Book
6 Current State of Modelling for Precision Agriculture and Work Needed
6.1 Data
6.2 Models
6.3 Actuation
7 Conclusion
References

Process-Based Modelling of Soil-Crop Interactions for Site-Specific Decision Support in Crop Management
1 Introduction
2 General Character of Process-Based Agro-ecosystem Models
3 Modelling Spatial Variation
4 Site Sensitivity of Agro-ecosystem Models
5 Examples of Model Use for Precision Agriculture
5.1 Identification of Site-Specific Management Using Long-Term Simulations
5.2 Inverse Modelling to Derive Unknown Properties
5.3 Operational Use for Site-Specific Management Operations
6 Conclusions
References
Models in Crop Protection

1 Introduction to Plant Disease Models and Modelling Approaches
1.1 What Is a Model?
1.2 Types of Models and Modelling Approaches
1.2.1 Qualitative vs. Quantitative Models
1.2.2 Deterministic vs. Stochastic Models
1.2.3 Empirical vs. Fundamental Models
1.2.4 Simulation vs. Predictive Models
1.3 A Brief History of Plant Disease Modelling
2 Development of Process-Based Models
2.1 Definition of the Intended Use of the Model
2.2 Conceptualization of the System
2.3 Development of the Mathematical Framework
2.4 Model Evaluation
3 Models for IPM

3.1 Decision-Making in IPM
3.2 Strategic Disease Management
3.2.1 A First Example: Epidemics of Grape Downy Mildew
3.2.2 A Second Example: Effect of Biocontrol on Epidemic Development
3.3 Tactical Disease Management
3.3.1 A First Example: Prediction of Secondary Infections of Grape Downy Mildew
3.3.2 A Second Example: Prediction of Stem Rust Infections on Wheat
3.4 Multi-modelling for Decision-Making
3.4.1 Modelling the Host Plant
3.4.2 Modelling the Effects of Fungicides
3.4.3 Combining Models
4 Conclusions and Perspectives
References

Development and Adoption of Model-Based Practices in Precision Agriculture
1 Introduction
2 Design, Development and Delivery of MBPs
2.1 Design
2.2 Development
2.3 Delivery
3 Social Factors Affecting Adoption of MBPs
3.1 Demographic Factors
3.2 Societal Factors
3.3 Farm Size
4 Behavioural Approaches to the Adoption of Models for Precision Farming
4.1 Risk Attitudes
4.2 Behavioural Factors Beyond Risk Attitudes
5 An Empirical Approach to Research on Adoption of Models
6 Discussion
References
Part II: State of the Art