Global sensitivity analysis measures the quality of parameter estimation: The case of soil parameters and a crop model

One common limitation of the use of crop models for decision making in precise crop management is the need for accurate values of soil parameters for a whole field. Estimating these parameters from data observed on the crop, using a crop model, is an interesting possibility. Nevertheless, the quality of the estimation depends on the sensitivity of model output variables to the parameters. The goal of this study is to explain the results for the quality of parameter estimation based on global sensitivity analysis (GSA). The case study consists of estimating the soil parameters by using the STICS-wheat crop model and various synthetic observations on wheat crops (LAI, absorbed nitrogen and grain yield). Suitable criteria summarizing the sensitivity indices of the observed variables were created in order to link GSA indices with the quality of parameter estimation. We illustrate this link on 16 different configurations of different soil, climatic and crop conditions. The GSA indices were computed by the Extended FAST method and a function of RMSE was computed with an importance sampling method based on Bayes theory (GLUE). The proposed GSA-based criteria are able to rank the parameters with respect to their quality of estimation and the different configurations (especially climate and observation set) with respect to their ability to estimate the whole parameter set. They may be used as a tool for predicting the performance of different observation datasets with regard to parameter estimation.

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