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.
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. 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.
Section I Basics 1. Basics of Agricultural System Models 2. Statistical notions useful for modeling 3. The R programming language and software 4. Simulation with dynamic system models Section II Methods 5. Uncertainty and sensitivity analysis 6. Parameter estimation with classical methods 7. Bayesian methods for parameter estimation 8. Data assimilation for dynamic models 9. Model evaluation 10. Putting it all together in a case study Appendices 1. Model descriptions 2. An overview of the R package ZeBook