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.
- 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.
Section I 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
Section II Basic methods 6. Uncertainty and sensitivity analysis 7. Parameter estimation, classical (frequentist) approach 8. Parameter estimation, Bayesian approach 9. Model evaluation 10. Putting it all together in a case study
Section III Advanced methods 11. Meta-models 12. Working with ensembles of models 13. Gene based modeling 14. Data assimilation for dynamic models 15. Models as an aid to sampling
Appendices 1. Model descriptions 2. An overview of the R package ZeBook
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.
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.
Jones, James W.
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.
Francois Brun specializes in agricultural modeling systems using the R language, and has published in Journal of Experimental Botany.