Data collection design for calibration of crop models using practical identifiability analysis

The collection of high-quality calibration data is essential for the estimation of parameter values and reliability of crop models. However, few tools are available to quantify the minimum number of observations needed for parameter estimation. We therefore here applied practical identifiability analysis, based on global sensitivity analysis, to design measurement campaigns on farmers’ fields. We applied the method for parameterization of the AquaCrop model for mid-early potatoes in Belgium. We generated several virtual observational datasets, considering multiple weather and soil conditions, and measurement frequencies and variables. This analysis resulted in experimental designs where measurement campaigns should be conducted over at least two growing seasons and in different soil types, using soil moisture sensors combined with field observations every two weeks. This method showed to be a useful planning tool for the collection of sufficient data for the calibration of process-based crop models.