Francois Brun – författare

Mathematical models are being used more and more widely to study complex dynamic systems (global weather, ecological systems, hydrological systems, nuclear reactors etc. including the specific subject of this book, crop-soil systems). The models are important aids in understanding, predicting and managing these systems. Such models are complex and imperfect. One fundamental research direction is to seek a better understanding of how these systems function, and to propose mathematical expressions embodying that understanding. However, this is not sufficient. It is also essential to have tools (often mathematical and statistical methods) to aid in developing, improving and using the models built from those equations. The book is specifically concerned with the application of methods to crop models, but much of the material is also applicable to dynamic system models in other fields. The goal of this book is to fill that gap.* State-of-the-art methods explained simply and illustrated specifically for crop models* Parameter estimation – applying statistical methods to the complex case of crop models, including Bayesian methods * Includes model evaluation, understanding and estimating prediction error* Offers a unique data assimilation by using the Kalman filter and beyond

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

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

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

This book has been designed as a methodological guide and shows the interests and limitations of different statistical methods to analyze data from experimental networks and to perform meta-analyses. It is intended for engineers, students and researchers involved in data analysis in agronomy and environmental science.