Working with dynamic crop models : methods, tools and examples for agriculture and environment / Daniel Wallach...[y otros]

Contributor(s):

Wallach, Daniel [autor]

Material type:

TextSan Diego, California, US : Elsevier : Academic Press, 2014Edition: 2a ediciónDescription: xvi, 487 páginas : ilustraciones, gráficas, tablasISBN:

978-0-12-397008-4

Subject(s):

DDC classification:

631.558 W67 2014 20

LOC classification:

SB112.5 .W67 2014

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

Scope and content: 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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Libro	Ingenieria Agroindustrial		General		631.558 W67 2014 (Browse shelf(Opens below))				Ej. 1	Available (Préstamo interno)			1102013171

Incluye bibliografías

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

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.

There are no comments on this title.

to post a comment.