Wallach D., Makowski D., Jones J.W., Brun F. (2014). Working with dynamic crop models: methods, tools and examples for agriculture and environment. 2 ed. Londres (Royaume-Uni) : Academic Press. 487 p.
Titre : | Working with dynamic crop models: methods, tools and examples for agriculture and environment |
Auteurs : | D. Wallach ; D. Makowski ; J.W. Jones ; F. Brun |
Type de document : | Ouvrage |
Mention d'édition : | 2 |
Editeur : | Londres [Royaume-Uni] : Academic Press, 2014 |
ISBN/ISSN/EAN : | 978-0-12-397008-4 |
Format : | 487 p. |
Langues : | Anglais |
Langues du résumé : | Anglais |
Catégories : |
Catégories principales 06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.4 - Production Agricole. Système de ProductionThésaurus IAMM MODELE ; EVALUATION ; MODE DE CULTURE ; SYSTEME DE CULTURE ; CONCEPT ; METHODE ; MODELE DYNAMIQUE ; AGRICULTURE ; SYSTEME DE PRODUCTION ; MODELE DE SIMULATION ; METHODE STATISTIQUE |
Résumé : | 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. |
Note de contenu : |
SECTION 1: BASICS
• Chapter 1. Basics of Agricultural System Models • Chapter 2. Statistical Notions Useful for Modeling • Chapter 3. The R Programming Language and Software • Chapter 4. Simulation with Dynamic System Models SECTION 2: METHODS • Chapter 5. Uncertainty and Sensitivity Analysis • Chapter 6. Parameter Estimation with Classical Methods (Model Calibration) • Chapter 7. Parameter Estimation with Bayesian Methods • Chapter 8. Data Assimilation for Dynamic Models • Chapter 9. Model Evaluation • Chapter 10. Putting It All Together in a Case Study |
Cote : | I03-WAL-2014 |
Exemplaires (1)
Code-barres | Cote | Support | Localisation | Section | Disponibilité |
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27321 | I03-WAL-2014 | Papier | Centre de documentation | Espace Thématique | Disponible |