CroptimizR

CroptimizR: A Package to estimate parameters of Crop Models

The purpose of CroptimizR is to provide functions for estimating crop model parameters from observations of their simulated variables, a process often referred to as calibration. For that, it offers a generic framework for linking crop models with up-to-date and ad-hoc algorithms, as well as a choice of goodness-of-fit criteria and additional features adapted to the problem of crop model calibration. It facilitates the comparison of different types of methods on different models. It is used in this context in the AgMIP Calibration project on a dozen of crop models.

The Get started page describes the main concepts and features of the package and details how to connect its own crop model to CroptimizR.

The list of functions accessible to the users is provided in the Reference tab.

The package is still under active development, feel free to fill an issue or request a feature here at any time.

If you want to be notified when a new release of this package is made, you can tick the Releases box in the “Watch / Unwatch => Custom” menu at the top right of this page.

Installation

Before installing the package, it is recommended to update all already installed R packages. This can be done using the command update.packages() or clicking on the Update button of the Packages panel of Rstudio.

The latest released version of the package can be installed from GitHub using:

devtools::install_github("SticsRPacks/CroptimizR@*release")

Or using the lightweight remotes package:

# install.packages("remotes")
remotes::install_github("SticsRPacks/CroptimizR@*release")

Examples

A simple introductory example of model calibration using the Nelder-Mead simplex method on the STICS model is given in this vignette.
A more complex one with simultaneous estimation of specific and varietal plant parameters is given here.
An example of application of the AgMIP phase III protocol, designed to calibrate phenology of crop models, as described in detail in Wallach et al (2022), is given here.
An example using the ApsimX model is detailed here.
An example using the DREAM-zs Bayesian algorithm is detailed here.

See here for a detailed description of the input and output arguments of the estim_param function (or type ? estim_param in an R console after having installed and loaded the CroptimizR package).

Getting help

If you have any question or suggestion or if you want to report a bug, please do it via the GitHub issues.

Thanks for that, this would greatly help us to improve this package.

Citation

If you have used this package for a study that led to a publication or report, please cite us. To get the suggested citation, run citation("CroptimizR").

Code of conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

SticsRPacks / CroptimizR