Working with Dynamic Crop Models
Evaluation, Analysis, Parameterization, and Applications
Edited by- Daniel Wallach, Institut National de la Recherche Agronomique INRA, ARCHE, Castanet Tolosan, France
- David Makowski, Institut National de la Recherche Agronomique INRA, UMR INRA/INA, Thiverval-Grignon, France
- James Jones, University of Florida, Agricultural and Biological Engineering Department, Gainesville, Florida, U.S.A.
- Daniel Wallach, Institut National de la Recherche Agronomique INRA, ARCHE, Castanet Tolosan, France
Many different mathematical and statistical methods are essential in crop modeling. They are necessary in the development, analysis and application of crop models. Up to now, however, there has been no single source where crop modelers could learn about these methods. Furthermore, these methods are often described in other contexts and their application to crop modeling is not always straightforward.This book aims at making a large range of relevant mathematical and statistical methods accessible to crop modelers. Each methodology chapter starts from basic principles and simple applications and builds gradually to state-of-the-art methods. Crop models are used as examples, and practical advice on applying the methods to crop models is given.Working with Dynamic Crop Models is an essential learning and reference resource for students and researchers who want to understand and apply rigorous methods to crop models. This book will also be of value for other fields which use dynamic models of complex systems.Topics covered include:* Parameter estimation- including Bayesian methods* Model evaluation- including prediction quality and decision quality* Sensitivity analysis- including global analysis and interactions* Data assimilation- the Kalman filter and extensions* Management optimization- including stochastic optimization* Models for multiple fields- emphasizing how to obtain input values* Crop models and crop breeding - recent advances in using cropmodels
Audience
Researchers and graduate students in agronomy, agricultural and biological engineering, agricultural economics and agricultural statistics. Teachers of advanced courses in modeling of biological systems.
Hardbound, 462 Pages
Published: May 2006
Imprint: Elsevier
ISBN: 978-0-444-52135-4
Contents
- 1 The two forms of crop models D. Wallach2 Evaluating crop models D. Wallach 3 Uncertainty and sensitivity analysis for crop models H. Monod, C. Naud, D. Makowski4 Parameter estimation for crop models D. Makowski, J. Hillier, D. Wallach, B. Andrieu, M.-H. Jeuffroy 5 Data assimilation with crop models D. Makowski, M. Guérif, J. W. Jones, W. Graham6 Representing and optimizing management decisions with crop modelsJ. E. Bergez, F. Garcia, D. Wallach 7 Using crop models for multiple fieldsD. Leenhardt, D. Wallach, P. Le Moigne, M. Guérif, A. Bruand, M. A. CasteradSECTION II APPLICATIONS 8 Introduction to section II9 Fundamental concepts of crop models illustrated by a comparative approach N. Brisson, J. Wery, K. Boote 10 Crop models with genotype parameters M-H Jeuffroy, A. Barbottin, J. W. Jones, J. Lecoeur11 Model assisted genetic improvement of cropsC.D. Messina, K.J. Boote, C. Löffler, J.W. Jones, and C.E. Vallejos12 Parameterization and evaluation of a corn crop modelD. Wallach. 13 Evaluation of a model for kiwifruitF. Lescourret and D. Wallach14 Sensitivity and uncertainty analysis of a static denitrification modelB. Gabrielle 15 Sensitivity analysis of PASTIS, a model of nitrogen transport and transformation in the soil P. Garnier 16 Sensitivity analysis of GENESYS, a model for studying the effects of cropping system on gene flowN. Colbach and N. Molinari17 Data assimilation and parameter estimation for precision agriculture with the crop model STICSM. Guérif, V. Houlès, D. Makowski and C. Lauvernet18 Application of extended and ensemble Kalman filters to soil carbon estimation J. Jones and W. Graham19 Analyzing and improving corn irrigation strategies with MODERATO, a combination of a corn crop model and a decision modelJ. E. Bergez, J. M. Deumier and B. Lacroix20 Managing wheat for ethanol production. A multiple criteria approachC. Loyce, J. P. Rellier and J. M. MeynardAppendix. Statistical notions
