Working with Dynamic Crop Models

Preface

Section 1: Basics

Chapter 1. Basics of Agricultural System Models

1 Introduction

2 System Models

3 Developing Dynamic System Models

4 Other Forms of System Models

5 Examples of Dynamic Agricultural System Models

Exercises

References

Chapter 2. Statistical Notions Useful for Modeling

1 Introduction

2 Random Variable

3 The Probability Distribution of a Random Variable

4 Several Random Variables

5 Samples, Estimators, and Estimates

6 Regression Models

7 Bayesian Statistics

Exercises

References

Chapter 3. The R Programming Language and Software

1 Introduction

2 Getting Started

3 Objects in R

4 Vectors (numerical, logical, character)

5 Other Data Structures

6 Read from and Write to File System

7 Control Structures

8 Functions

9 Graphics

10 Statistics and Probability

11 Advanced Data Processing

12 Additional Packages (libraries)

13 Running an External Model from R

14 Reducing Computing Time

Exercises

References

Chapter 4. Simulation with Dynamic System Models

1 Introduction

2 Simulating Continuous Time Models (differential equation form)

3 Simulation of System Models in Difference Equation Form

Exercises

References

Section 2: Methods

Chapter 5. Uncertainty and Sensitivity Analysis

1 Introduction

2 A Simple Example using Uncertainty and Sensitivity Analysis

3 Uncertainty Analysis

4 Sensitivity Analysis

5 Recommendations

6 R code Used in this Chapter

Exercises

References

Chapter 6. Parameter Estimation with Classical Methods (Model Calibration)

1 Introduction

2 An Overview of Model Calibration

3 The Statistics of Parameter Estimation

4 Application of Statistical Principles to System Models

5 Algorithms for OLS

6 R Functions for Parameter Estimation

Exercises

Models for Exercises

References

Chapter 7. Parameter Estimation with Bayesian Methods

1 Introduction

2 Ingredients for Implementing a Bayesian Estimation Method

3 Computation of Posterior Mode

4 Algorithms for Estimating Posterior Probability Distribution

5 Concluding Remarks

Exercises

References

Chapter 8. Data Assimilation for Dynamic Models

1 Introduction

2 Model Specification

3 Filter and Smoother for Gaussian Dynamic Linear Models

4 Filter and Smoother for Non-Linear Models

5 Concluding Remarks

Exercises

References

Chapter 9. Model Evaluation

1 Introduction

2 A Model as a Scientific Hypothesis

3 Comparing Simulated and Observed Values

4 From the Sample to the Population

5 The Predictive Quality of a Model

6 Summary

7 R Functions

Exercises

References

Chapter 10. Putting It All Together in a Case Study

1 Introduction

2 Description of the Case Study

3 How Difficult and Time-Consuming is Each Step?

4 R Code Used in This Chapter

Appendix 1. The Models Included in the ZeBook R Package: Description, R Code, and Examples of Results

1 Introduction

2 SeedWeight Model

3 Magarey Model

4 Soil Carbon Model

5 WaterBalance Model

6 Maize Crop Model

7 Verhulst Model

8 Population Age Model

9 Predator-Prey Model

10 Weed Model

11 EPIRICE Model

References

Appendix 2. An Overview of the R Package ZeBook

1 Introduction

2 Installation

3 Functions and Demos in the Zebook Package

4 How to use the ZeBook Package

5 List of Packages Needed

Index