Working with Dynamic Crop Models

Working with Dynamic Crop Models

Methods, Tools and Examples for Agriculture and Environment

3rd Edition - September 25, 2018

Write a review

  • Authors: Daniel Wallach, David Makowski, James Jones, Francois Brun
  • Hardcover ISBN: 9780128117569
  • eBook ISBN: 9780128117576

Purchase options

Purchase options
Available
DRM-free (PDF, Mobi, EPub)
Sales tax will be calculated at check-out

Institutional Subscription

Free Global Shipping
No minimum order

Description

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

Readership

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

Table of Contents

  • Section A Background
    1. Basics of Agricultural System Models
    2. The R Programming Language and Software
    3. Simulation with Dynamic System Models
    4. Statistical Notions Useful for Modeling
    5. Regression Analysis, Frequentist

    Section B Basic methods
    6. Uncertainty and Sensitivity Analysis
    7. Calibration of System Models

    8. Parameter Estimation With Bayesian Methods
    9. Model Evaluation
    10. Putting It All Together in a Case Study

    Section C Advanced Methods
    11. Metamodeling
    12. Multimodel Ensembles
    13. Gene-Based Crop Models
    14. Data Assimilation for Dynamic Models
    15. Models as an Aid to Sampling

    Appendix 1:   The Models Included in the ZeBook R Package: Description, R Code, and Examples of Results

    Appendix 2:  An Overview of the R Package ZeBook

Product details

  • No. of pages: 613
  • Language: English
  • Copyright: © Academic Press 2018
  • Published: September 25, 2018
  • Imprint: Academic Press
  • Hardcover ISBN: 9780128117569
  • eBook ISBN: 9780128117576

About the Authors

Daniel Wallach

Daniel Wallach focuses on the application of statistical methods of dynamic systems, specifically on agronomy models. He has published in Agriculture, Ecosystems and Environment; Journal of Agricultural, Biological and Environmental Statistics and European Journal of Agronomy.

Affiliations and Expertise

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

David Makowski

David Makowski is an expert with the European Food Safety authority and the French Agency for Food, Environmental and Occupational Health and Safety and has authored 50 refereed articles and 10 book chapters on statistics, agricultural modeling and risk analysis.

Affiliations and Expertise

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

James Jones

James Jones has authored more than 250 refereed scientific journal articles, developed and teached a graduate course based mostly on this book. He is a Fellow of the American Society of Agricultural and Biological Engineers, Fellow of the American Society of Agronomy, Fellow of the Soil Science Society of America and serves on several international science advisory committees related to agriculture and climate.

Affiliations and Expertise

Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA

Francois Brun

Francois Brun specializes in agricultural modeling systems using the R language, and has published in Journal of Experimental Botany.

Affiliations and Expertise

ACTA-INRA Toulouse, Castanet Tolosan, France

Ratings and Reviews

Write a review

Latest reviews

(Total rating for all reviews)

  • OscarCastillo Tue Jun 04 2019

    Excellent Book

    The authors have a good methodology to go through crop modeling concepts and clear examples in R.