* State-of-the-art methods explained simply and illustrated specifically for crop models
* Parameter estimation - applying statistical methods to the complex case of crop models, including Bayesian methods
* Includes model evaluation, understanding and estimating prediction error
* Offers a unique data assimilation by using the Kalman filter and beyond
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.
Researchers and graduate students in agronomy, agricultural and biological engineering, agricultural economics and agricultural statistics. Teachers of advanced courses in modeling of biological systems.