Working with Dynamic Crop Models

2nd Edition

Methods, Tools and Examples for Agriculture and Environment

Authors: Daniel Wallach David Makowski James Jones Francois Brun
Hardcover ISBN: 9780123970084
eBook ISBN: 9780444594464
Imprint: Academic Press
Published Date: 9th December 2013
Page Count: 504
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Description

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.

Key Features

New to this edition:

  • 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

Researchers and students in agronomy, agricultural engineering, agricultural economics and agricultural statistics, and advanced biological systems modeling

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

Details

No. of pages:
504
Language:
English
Copyright:
© Academic Press 2014
Published:
Imprint:
Academic Press
Hardcover ISBN:
9780123970084
eBook ISBN:
9780444594464
Paperback ISBN:
9780444638007

About the Author

Daniel Wallach

Affiliations and Expertise

Institut National de la Recherche Agronomique INRA, UMR INRA/INP, Toulouse, France

David Makowski

Affiliations and Expertise

Institut National de la Recherche Agronomique INRA, UMR INRA/INA, Thiverval-Grignon, France

James Jones

Affiliations and Expertise

University of Florida, Agricultural and Biological Engineering Department, Gainesville, Florida, U.S.A.

Francois Brun

Affiliations and Expertise

ACTA-INRA Toulouse, Castanet Tolosan, France

Reviews

"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