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This second edition of Working with Dynamic Crop Models is meant for self-learning by researchers or for use in graduate level courses devoted to methods for working with dynamic models in crop, agricultural, and related sciences.
Each chapter focuses on a particular topic and includes an introduction, a detailed explanation of the available methods, applications of the methods to one or two simple models that are followed throughout the book, real-life examples of the methods from literature, and finally a section detailing implementation of the methods using the R programming language.
The consistent use of R makes this book immediately and directly applicable to scientists seeking to develop models quickly and effectively, and the selected examples ensure broad appeal to scientists in various disciplines.
- 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
Researchers and students in agronomy, agricultural engineering, agricultural economics and agricultural statistics, and advanced biological systems modeling
Section 1: Basics
Chapter 1. Basics of Agricultural System Models
2 System Models
3 Developing Dynamic System Models
4 Other Forms of System Models
5 Examples of Dynamic Agricultural System Models
Chapter 2. Statistical Notions Useful for Modeling
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
Chapter 3. The R Programming Language and Software
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
10 Statistics and Probability
11 Advanced Data Processing
12 Additional Packages (libraries)
13 Running an External Model from R
14 Reducing Computing Time
Chapter 4. Simulation with Dynamic System Models
2 Simulating Continuous Time Models (differential equation form)
3 Simulation of System Models in Difference Equation Form
Section 2: Methods
Chapter 5. Uncertainty and Sensitivity Analysis
2 A Simple Example using Uncertainty and Sensitivity Analysis
3 Uncertainty Analysis
4 Sensitivity Analysis
6 R code Used in this Chapter
Chapter 6. Parameter Estimation with Classical Methods (Model Calibration)
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
Models for Exercises
Chapter 7. Parameter Estimation with Bayesian Methods
2 Ingredients for Implementing a Bayesian Estimation Method
3 Computation of Posterior Mode
4 Algorithms for Estimating Posterior Probability Distribution
5 Concluding Remarks
Chapter 8. Data Assimilation for Dynamic Models
2 Model Specification
3 Filter and Smoother for Gaussian Dynamic Linear Models
4 Filter and Smoother for Non-Linear Models
5 Concluding Remarks
Chapter 9. Model Evaluation
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
7 R Functions
Chapter 10. Putting It All Together in a Case Study
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
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
Appendix 2. An Overview of the R Package ZeBook
3 Functions and Demos in the Zebook Package
4 How to use the ZeBook Package
5 List of Packages Needed
- No. of pages:
- © Academic Press 2014
- 9th December 2013
- Academic Press
- Hardcover ISBN:
- eBook ISBN:
Daniel Wallach focuses on the application of statistical methods of dynamic systems, specifically on agronomy models. He has published in Agriculture, Ecosystems and Environment; Journal of Agricultural, Biological and Environmental Statistics and European Journal of Agronomy.
Institut National de la Recherche Agronomique INRA, UMR INRA/INP, Toulouse, France
David Makowski is an expert with the European Food Safety authority and the French Agency for Food, Environmental and Occupational Health and Safety and has authored 50 refereed articles and 10 book chapters on statistics, agricultural modeling and risk analysis.
Institut National de la Recherche Agronomique INRA, UMR INRA/INA, Thiverval-Grignon, France
James Jones has authored more than 250 refereed scientific journal articles, developed and teached a graduate course based mostly on this book. He is a Fellow of the American Society of Agricultural and Biological Engineers, Fellow of the American Society of Agronomy, Fellow of the Soil Science Society of America and serves on several international science advisory committees related to agriculture and climate.
Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA
Francois Brun specializes in agricultural modeling systems using the R language, and has published in Journal of Experimental Botany.
ACTA-INRA Toulouse, Castanet Tolosan, France
"This edition adds chapters on the basics of dynamic system models, statistics, and simulation; examples of how the methods can be applied to real-world problems; advanced methods for parameter estimation, model evaluation, and data assimilation; a new chapter on how the topics fit together in a complete modeling project; and information on how to use the R language and platform." --ProtoView.com, April 2014
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