| Author(s) | Daniel Wallach, David Makowski, James W. Jones, Francois Brun |
 | Year | 2019 |
 | Pages | 588 |
 | Language | English
|
 | Format | PDF |
 | Size | 13.5 MB
|
 | Publisher | Academic Press |
 | ISBN | 0128117567, 978-0128117569, B07HRGVDY4
|
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 metamodels 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 sampling.
The book
emphasizes both understanding and practical implementation of the
methods that are covered. Each chapter simply and clearly explains 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 be
adapted relatively easily to new problems.
- 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
