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Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization, and Applications
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.
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
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.
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
462 pages, Kindle Edition
Published May 10, 2006
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