Towards improved calibration of crop models – Where are we now and where should we go?

• Published in: European journal of agronomy 2018-03, Vol.94, p.25-35
• Main Authors: Seidel, S.J., Palosuo, T., Thorburn, P., Wallach, D.
• Format: Article
• Language: English
• Subjects: Crop modelling; Environmental Sciences; Humanities and Social Sciences; Life Sciences; Model calibration; Parameter estimation; Simulation modelling; Survey; Uncertainty
• ISSN: 1161-0301; 1873-7331
• DOI: 10.1016/j.eja.2018.01.006

•We analyze 211 responses to a web-based online survey on crop model calibration practices.•Questions concerned data, parameters, method, software, uncertainty and evaluation.•Most calibration studies involve fewer than 10 parameters and multiple stages.•Least-squares, Bayesian, GLUE and ad hoc approaches to calibration were all represented.•An outline of guideline topics for crop model calibration is proposed. Crop simulation models are increasingly used in agricultural decision making. Calibration is a demanding and critical step in developing and applying a model. Despite its importance little attention has been paid to documenting and analysing current calibration practices. This study reports the results from 211 responses to a web-based survey of calibration practices. The survey questions covered multiple choices that are required when doing a calibration exercise. Concerning data, most respondents used field data, but regional data and a combination of field and regional data were also used. Almost all respondents used multiple data types, the most common being phenology and yield data. The median number of estimated parameters was 6, and often this number was only slightly smaller than the number of environments that provided the data. Most respondents fit the data in multiple stages, starting in most cases with phenology data. Many respondents searched for parameter values that minimized a sum of squared errors, but substantial groups used an ad hoc measure of goodness-of-fit, the GLUE method, a weighted least squares method or a Bayesian approach. Nearly half the respondents simply used trial-and-error to search for the best-fit parameters. The other respondents were split more or less equally between those who used existing software and those who wrote new software. Slightly less than half the respondents obtained information on parameter uncertainty. Model evaluation was based on goodness-of-fit or data splitting or cross validation. The median time devoted to crop model calibration was 25 days. Based on these results, a list of topics that should be covered in guidelines for calibration is suggested.