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.
By
Daniel Wallach, Institut National de la Recherche Agronomique INRA, ARCHE, Castanet Tolosan, France
Description
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 crop
models
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.
