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


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

Table of Contents


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



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



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



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



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



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

1 Introduction

2 An Overview of Model Calibration

3 The Statistics of Parameter


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© 2014
Academic Press
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"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.", April 2014