http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
methods, tools and examples for agriculture and environment
1 online resource (xvi, 597 pages) : illustrations
Working with Dynamic Crop Models: Methods, Tools and Examples for Agriculture and Environment, 3e, is a complete guide to working with dynamic system models, with emphasis on models in agronomy and environmental science. The introductory section presents the foundational information for the book including the basics of system models, simulation, the R programming language, and the statistical notions necessary for working with system models. The most important methods of working with dynamic system models, namely uncertainty and sensitivity analysis, model calibration (frequentist and Bayesian), model evaluation, and data assimilation are all treated in detail, in individual chapters.New chapters cover the use of multi-model ensembles, the creation of metamodels that emulate the more complex dynamic system models, the combination of genetic and environmental information in gene-based crop models, and the use of dynamic system models to aid in sampling.The book emphasizes both understanding and practical implementation of the methods that are covered. Each chapter simply and clearly explains the underlying principles and assumptions of each method that is presented, with numerous examples and illustrations. R code for applying the methods is given throughout. This code is designed so that it can be adapted relatively easily to new problems.
Key Features:- An expanded introductory section presents the basics of dynamic system modeling, with numerous examples from multiple fields, plus chapters on numerical simulation, statistics for modelers, and the R language- Covers in detail the basic methods: uncertainty and sensitivity analysis, model calibration (both frequentist and Bayesian), model evaluation, and data assimilation- Every method chapter has numerous examples of applications based on real problems, as well as detailed instructions for applying the methods to new problems using R- Each chapter has multiple exercises for self-testing or for classroom use- An R package with much of the code from the book can be freely downloaded from the CRAN package repository.
Front Cover; Working with Dynamic Crop Models: Methods, Tools and Examples for Agriculture and Environment; Copyright; Contents; Preface; Acknowledgments; Section A: Background; Chapter 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 Model4. Other Forms of System Models; 4.1. Random Elements in Dynamic Equations; 4.2. A Dynamic System Model as a Response Model; 4.2.1. 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 Classes5.3.3. Predator-Prey Population Dynamics Model; 5.3.4. Modeling Spatial Variations in Population Dynamics; Exercises; References; Chapter 2: The R Programming Language and Software; 1. Introduction; 1.1. 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 Objects4. 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; 6. Read From and Write to File System; 7. Control Structures; 7.1. Loops; 7.2. Conditions; 8. Functions; 8.1. Writing Your Own Functions; 9. Graphics; 10. Statistics and Probability; 10.1. Probability Distributions; 10.2. Controlling the Randomness in Random Numbers; 10.3. Basic Statistical Analyses; 11. Advanced Data Processing.
12. Additional Packages (Libraries)12.1. What Are Packages?; 12.2. Retrieve, Install, and Load a Package; 12.3. The ZeBook Package; 13. Running an External Model From R; 14. Reducing Computing Time; 14.1. Good Programming Practices; 14.2. Measuring the Computation Time for Each Piece of Code; 14.3. Recoding a Portion of the R Code in an Externally Compiled Language; 14.4. Parallelization of R Code; Exercises; References; Chapter 3: Simulation With Dynamic System Models; 1. Introduction; 2. Simulating Continuous Time Models (Differential Equation Form).
Daniel Wallach, David Makowski, James W. Jones, Francois Brun.
2.1. Mathematical Basis for Continuous Simulation.
Includes bibliographical references and index.
Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force. WlAbNL
Amsterdam : Elsevier/Academic Press, [2019]