The Resource Working with dynamic crop models : methods, tools and examples for agriculture and environment, [edited by] Daniel Wallach, David Makowski, James W. Jones, Francois Brun, (electronic book)
Working with dynamic crop models : methods, tools and examples for agriculture and environment, [edited by] Daniel Wallach, David Makowski, James W. Jones, Francois Brun, (electronic book)
Resource Information
The item Working with dynamic crop models : methods, tools and examples for agriculture and environment, [edited by] Daniel Wallach, David Makowski, James W. Jones, Francois Brun, (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item Working with dynamic crop models : methods, tools and examples for agriculture and environment, [edited by] Daniel Wallach, David Makowski, James W. Jones, Francois Brun, (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Liverpool.
This item is available to borrow from 1 library branch.
 Summary
 This second edition of Working with Dynamic Crop Models is meant for selflearning 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, reallife examples of the methods from literature, and finally a section detailing implementation of the methods using the R programmi
 Language
 eng
 Edition
 Second edition.
 Extent
 1 online resource (xvi, 487 pages)
 Note
 6 Read from and Write to File System
 Contents

 Front Cover; Working with Dynamic Crop Models; Copyright Page; Contents; Preface; 1 Basics; 1 Basics of Agricultural System Models; 1 Introduction; 2 System Models; 2.1 Systems Approach; 2.2 System Environment and Boundary; 2.3 System Model and Simulation; 2.3.1 System Model; 2.3.2 Simulation; 2.3.3 General Form of a Dynamic System Model; 2.4 State Variables U(t); 2.5 Explanatory Variables and Parameters; 3 Developing Dynamic System Models; 3.1 Methods; 3.2 Example Development of a System Model; 4 Other Forms of System Models; 4.1 Random Elements in Dynamic Equations
 4.2 A Dynamic System Model as a Response Model4.2.1 Random Elements in System Response Equations; 5 Examples of Dynamic Agricultural System Models; 5.1 Simple Maize Crop Model; 5.2 Dynamic Soil Water Model and Drought Index; 5.2.1 The ARID Soil Water Model; 5.2.2 Combining Soil and Crop Models; 5.2.3 Extending the Soil Water Model for NonHomogenous Soils; 5.3 Population Dynamics Models; 5.3.1 Homogenous Population with Limited Food Supply; 5.3.2 Population Dynamics Model with Age Classes; 5.3.3 PredatorPrey Population Dynamics Model; 5.3.4 Modeling Spatial Variations in Population Dynamics
 ExercisesEasy; Moderate; Difficult; References; 2 Statistical Notions Useful for Modeling; 1 Introduction; 1.1 In This Chapter; 2 Random Variable; 3 The Probability Distribution of a Random Variable; 3.1 Cumulative Distribution and Density Functions; 3.2 Expectation, Variance, and Quantiles of a Random Variable; 3.3 Best Predictor of Y Using a Constant; 3.4 Particular Distributions; 4 Several Random Variables; 4.1 Joint Distribution; 4.2 Marginal Distribution; 4.3 Conditional Distribution and Independence; 4.4 Covariance and Correlation
 4.5 Expectation and Variance for Multiple Random Variables4.6 Best Predictor of Y Using a Function of X; 4.7 The Multivariate Normal Distribution; 5 Samples, Estimators, and Estimates; 5.1 Simple Random Samples; 5.2 Sampling in Agronomy; 5.3 Estimators and Estimates; 5.4 Effective Sample Size; 6 Regression Models; 7 Bayesian Statistics; 7.1 The Difference Between Bayesian and Frequentist Statistics; 7.2 Basic Ideas of Bayesian Statistics; 7.3 Bayesian Parameter Estimation in Modeling; 7.4 Frequentist or Bayesian?; Exercises; References; 3 The R Programming Language and Software
 1 Introduction1.1 What Is R?; 1.2 Why R?; 1.3 What's in This Chapter?; 2 Getting Started; 2.1 How to Install the R Software; 2.2 The R Interface; 2.3 Notation for R Code; 2.4 Using R as a Simple Calculator; 2.5 Using a Script Editor; 2.6 The Notion of an R Program; 2.7 Debugging an R Program; 2.8 Need Help?; 3 Objects in R; 3.1 Creating Objects; 3.2 Types of Objects; 4 Vectors (numerical, logical, character); 4.1 Creation of a Vector; 4.2 Subscripting a Vector; 4.3 Operations on Vectors; 4.4 Combining Vectors; 5 Other Data Structures; 5.1 Matrices; 5.2 Data Frames; 5.3 Lists
 Isbn
 9780444594464
 Label
 Working with dynamic crop models : methods, tools and examples for agriculture and environment
 Title
 Working with dynamic crop models
 Title remainder
 methods, tools and examples for agriculture and environment
 Statement of responsibility
 [edited by] Daniel Wallach, David Makowski, James W. Jones, Francois Brun
 Language
 eng
 Summary
 This second edition of Working with Dynamic Crop Models is meant for selflearning 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, reallife examples of the methods from literature, and finally a section detailing implementation of the methods using the R programmi
 Cataloging source
 EBLCP
 Dewey number

 631.5
 631.5/8/015118
 631.58015118
 Illustrations
 illustrations
 Index
 index present
 LC call number
 SB112.5
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 http://library.link/vocab/relatedWorkOrContributorDate

 1942
 1972
 1944
 http://library.link/vocab/relatedWorkOrContributorName

 Wallach, Daniel
 Makowski, David
 Jones, James Wigington
 Brun, François
 http://library.link/vocab/subjectName

 Crop yields
 Crops
 Agricultural mathematics
 Crop yields
 Crop yields
 Crop yields
 Crops
 Crops
 TECHNOLOGY & ENGINEERING / Agriculture / General
 Label
 Working with dynamic crop models : methods, tools and examples for agriculture and environment, [edited by] Daniel Wallach, David Makowski, James W. Jones, Francois Brun, (electronic book)
 Note
 6 Read from and Write to File System
 Bibliography note
 Includes bibliographical references and index
 Contents

 Front Cover; Working with Dynamic Crop Models; Copyright Page; Contents; Preface; 1 Basics; 1 Basics of Agricultural System Models; 1 Introduction; 2 System Models; 2.1 Systems Approach; 2.2 System Environment and Boundary; 2.3 System Model and Simulation; 2.3.1 System Model; 2.3.2 Simulation; 2.3.3 General Form of a Dynamic System Model; 2.4 State Variables U(t); 2.5 Explanatory Variables and Parameters; 3 Developing Dynamic System Models; 3.1 Methods; 3.2 Example Development of a System Model; 4 Other Forms of System Models; 4.1 Random Elements in Dynamic Equations
 4.2 A Dynamic System Model as a Response Model4.2.1 Random Elements in System Response Equations; 5 Examples of Dynamic Agricultural System Models; 5.1 Simple Maize Crop Model; 5.2 Dynamic Soil Water Model and Drought Index; 5.2.1 The ARID Soil Water Model; 5.2.2 Combining Soil and Crop Models; 5.2.3 Extending the Soil Water Model for NonHomogenous Soils; 5.3 Population Dynamics Models; 5.3.1 Homogenous Population with Limited Food Supply; 5.3.2 Population Dynamics Model with Age Classes; 5.3.3 PredatorPrey Population Dynamics Model; 5.3.4 Modeling Spatial Variations in Population Dynamics
 ExercisesEasy; Moderate; Difficult; References; 2 Statistical Notions Useful for Modeling; 1 Introduction; 1.1 In This Chapter; 2 Random Variable; 3 The Probability Distribution of a Random Variable; 3.1 Cumulative Distribution and Density Functions; 3.2 Expectation, Variance, and Quantiles of a Random Variable; 3.3 Best Predictor of Y Using a Constant; 3.4 Particular Distributions; 4 Several Random Variables; 4.1 Joint Distribution; 4.2 Marginal Distribution; 4.3 Conditional Distribution and Independence; 4.4 Covariance and Correlation
 4.5 Expectation and Variance for Multiple Random Variables4.6 Best Predictor of Y Using a Function of X; 4.7 The Multivariate Normal Distribution; 5 Samples, Estimators, and Estimates; 5.1 Simple Random Samples; 5.2 Sampling in Agronomy; 5.3 Estimators and Estimates; 5.4 Effective Sample Size; 6 Regression Models; 7 Bayesian Statistics; 7.1 The Difference Between Bayesian and Frequentist Statistics; 7.2 Basic Ideas of Bayesian Statistics; 7.3 Bayesian Parameter Estimation in Modeling; 7.4 Frequentist or Bayesian?; Exercises; References; 3 The R Programming Language and Software
 1 Introduction1.1 What Is R?; 1.2 Why R?; 1.3 What's in This Chapter?; 2 Getting Started; 2.1 How to Install the R Software; 2.2 The R Interface; 2.3 Notation for R Code; 2.4 Using R as a Simple Calculator; 2.5 Using a Script Editor; 2.6 The Notion of an R Program; 2.7 Debugging an R Program; 2.8 Need Help?; 3 Objects in R; 3.1 Creating Objects; 3.2 Types of Objects; 4 Vectors (numerical, logical, character); 4.1 Creation of a Vector; 4.2 Subscripting a Vector; 4.3 Operations on Vectors; 4.4 Combining Vectors; 5 Other Data Structures; 5.1 Matrices; 5.2 Data Frames; 5.3 Lists
 Control code
 SCIDI865335096
 Dimensions
 unknown
 Edition
 Second edition.
 Extent
 1 online resource (xvi, 487 pages)
 Form of item
 online
 Isbn
 9780444594464
 Other physical details
 illustrations
 Specific material designation
 remote
 Label
 Working with dynamic crop models : methods, tools and examples for agriculture and environment, [edited by] Daniel Wallach, David Makowski, James W. Jones, Francois Brun, (electronic book)
 Note
 6 Read from and Write to File System
 Bibliography note
 Includes bibliographical references and index
 Contents

 Front Cover; Working with Dynamic Crop Models; Copyright Page; Contents; Preface; 1 Basics; 1 Basics of Agricultural System Models; 1 Introduction; 2 System Models; 2.1 Systems Approach; 2.2 System Environment and Boundary; 2.3 System Model and Simulation; 2.3.1 System Model; 2.3.2 Simulation; 2.3.3 General Form of a Dynamic System Model; 2.4 State Variables U(t); 2.5 Explanatory Variables and Parameters; 3 Developing Dynamic System Models; 3.1 Methods; 3.2 Example Development of a System Model; 4 Other Forms of System Models; 4.1 Random Elements in Dynamic Equations
 4.2 A Dynamic System Model as a Response Model4.2.1 Random Elements in System Response Equations; 5 Examples of Dynamic Agricultural System Models; 5.1 Simple Maize Crop Model; 5.2 Dynamic Soil Water Model and Drought Index; 5.2.1 The ARID Soil Water Model; 5.2.2 Combining Soil and Crop Models; 5.2.3 Extending the Soil Water Model for NonHomogenous Soils; 5.3 Population Dynamics Models; 5.3.1 Homogenous Population with Limited Food Supply; 5.3.2 Population Dynamics Model with Age Classes; 5.3.3 PredatorPrey Population Dynamics Model; 5.3.4 Modeling Spatial Variations in Population Dynamics
 ExercisesEasy; Moderate; Difficult; References; 2 Statistical Notions Useful for Modeling; 1 Introduction; 1.1 In This Chapter; 2 Random Variable; 3 The Probability Distribution of a Random Variable; 3.1 Cumulative Distribution and Density Functions; 3.2 Expectation, Variance, and Quantiles of a Random Variable; 3.3 Best Predictor of Y Using a Constant; 3.4 Particular Distributions; 4 Several Random Variables; 4.1 Joint Distribution; 4.2 Marginal Distribution; 4.3 Conditional Distribution and Independence; 4.4 Covariance and Correlation
 4.5 Expectation and Variance for Multiple Random Variables4.6 Best Predictor of Y Using a Function of X; 4.7 The Multivariate Normal Distribution; 5 Samples, Estimators, and Estimates; 5.1 Simple Random Samples; 5.2 Sampling in Agronomy; 5.3 Estimators and Estimates; 5.4 Effective Sample Size; 6 Regression Models; 7 Bayesian Statistics; 7.1 The Difference Between Bayesian and Frequentist Statistics; 7.2 Basic Ideas of Bayesian Statistics; 7.3 Bayesian Parameter Estimation in Modeling; 7.4 Frequentist or Bayesian?; Exercises; References; 3 The R Programming Language and Software
 1 Introduction1.1 What Is R?; 1.2 Why R?; 1.3 What's in This Chapter?; 2 Getting Started; 2.1 How to Install the R Software; 2.2 The R Interface; 2.3 Notation for R Code; 2.4 Using R as a Simple Calculator; 2.5 Using a Script Editor; 2.6 The Notion of an R Program; 2.7 Debugging an R Program; 2.8 Need Help?; 3 Objects in R; 3.1 Creating Objects; 3.2 Types of Objects; 4 Vectors (numerical, logical, character); 4.1 Creation of a Vector; 4.2 Subscripting a Vector; 4.3 Operations on Vectors; 4.4 Combining Vectors; 5 Other Data Structures; 5.1 Matrices; 5.2 Data Frames; 5.3 Lists
 Control code
 SCIDI865335096
 Dimensions
 unknown
 Edition
 Second edition.
 Extent
 1 online resource (xvi, 487 pages)
 Form of item
 online
 Isbn
 9780444594464
 Other physical details
 illustrations
 Specific material designation
 remote
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