David Makowski

François Piraux

François Brun

L’analyse de données joue un rôle croissant dans la recherche agronomique, dans l’expertise scientifique et dans les études prospectives. Cet ouvrage, conçu comme un guide méthodologique, présente les intérêts et les limites de différentes méthodes statistiques permettant d’analyser des données agronomiques issues de réseaux expérimentaux et de réaliser des méta-analyses.

Méthodes et applications avec le logiciel R pour les sciences agronomiques et environnementales

De l’analyse des réseaux expérimentaux à la méta-analyse

James W. Jones

Daniel Wallach

Francois Brun

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. New to this edition: 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

Methods, Tools and Examples for Agriculture and Environment

Working with Dynamic Crop Models

David Makowski

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