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. - 50% new content - 100% reviewed and updated - Clearly explains practical application of the methods presented, including R language examples - Presents real-life examples of core crop modeling methods, and ones that are translatable to dynamic system models in other fields

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.

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.

• 50% new content - 100% reviewed and updated
• Clearly explains practical application of the methods presented, including R language examples
• Presents real-life examples of core crop modeling methods, and ones that are translatable to dynamic system models in other fields

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