explanations and descriptions of the latest methods for working with dynamic
systems models, including real-world examples and computer code
- An expanded introductory section presents the
basics of dynamic system modeling, with numerous examples from multiple
fields, plus chapters on numerical simulation, statistics for modelers
and the R language.
- Covers in detail the basic methods; uncertainty and
sensitivity analysis, model calibration (both frequentist and Bayesian),
model evaluation and data assimilation.
- Every method chapter has numerous examples of
applications, based on real problems as well as detailed instructions
for applying the methods to new problems using R.
- Each chapter has multiple exercises, for
self-testing or for classroom use.
- An R package with much of the code from the book
can be freely downloaded from the CRAN package repository.
Working With Dynamic Crop Models: Methods,
Tools and Examples for Agriculture and Environment 3e is a complete guide to working with
dynamic system models, with emphasis on models in agronomy and environmental
science. The introductory section presents the foundational information for
the book including the basics of system models, simulation, the R programming
language and the statistical notions necessary for working with system
models. The most important methods of working with dynamic system models,
namely uncertainty and sensitivity analysis, model calibration (frequentist
and Bayesian), model evaluation and data assimilation are all treated in
detail, in individual chapters.
New chapters cover the use of multi-model
ensembles, the creation of meta-models that emulate the more complex dynamic
system models, the combination of genetic and environmental information in
gene-based crop models and the use of dynamic system models to aid in
The book emphasizes both understanding and
practical implementation of the methods that are covered. Each chapter
explains simply and clearly the underlying principles and assumptions of each
method that is presented, with numerous examples and illustrations. R code
for applying the methods is given throughout. This code is designed so that
it can relatively easily be adapted to new problems.
AUDIENCE: Researchers and advanced students in agronomy, agricultural and biological
engineering, agricultural economics and agricultural statistics