Working with Dynamic Crop Models
Methods, Tools and Examples for Agriculture and Environment
Description
Key Features
- 50% new content – 100% reviewed and updated
- Clearly explains practical application of the methods presented, including R language examples
- Presents real-life examples of core crop modeling methods, and ones that are translatable to dynamic system models in other fields
Readership
Table of Contents
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
Product details
- No. of pages: 504
- Language: English
- Copyright: © Academic Press 2013
- Published: November 25, 2013
- Imprint: Academic Press
- Hardcover ISBN: 9780123970084
- eBook ISBN: 9780444594464