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The AquaCrop model is a powerful tool for crop monitoring, providing a daily estimation of soil-crop-atmosphere dynamics. The model requires a substantial number of input variables and parameters, highlighting the need for identifying those that significantly influence model outputs. Sensitivity analysis is a vital method for this purpose. A key objective of this study is to examine the performance of the AquaCrop model in simulating wheat yield and irrigation water requirement in drylands under two scenarios: first running the model employing a minimal amount of in situ data, and second using all available in situ data. A second focus is to analyze the sensitivity to all crop and soil related input variables and parameters. To do this, a pilot-scale study was undertaken, focusing on a commercial farm in the Al-Jouf province of Saudi Arabia. The farm comprised 200 center-pivot fields of mainly wheat crops. In situ data was collected to calibrate the model for two consecutive growing seasons (2019-2020 and 2020-2021). Using the variance-based Sobol technique, the sensitivity of the AquaCrop model outputs, particularly wheat yield and irrigation water requirement, to crop and soil related input variables and parameters was examined, as were the influential and non-influential inputs on these outputs. Results showed that the second scenario (all data) outperformed the first (minimal data), demonstrating more accurate wheat yield predictions with rRMSE values of 17% and 21% for the 2019-2020 and 2020-2021 growing seasons, respectively. Regarding irrigation water requirement estimations, the second scenario also exhibited lower rRMSE values of 20% and 19% for the same growing seasons. Results also demonstrated that the sensitivity indices of variables and parameters varied with model outputs and growing seasons. By synthesizing inputs sensitivities under different conditions, the influential input variables and parameters were distinguished. Overall, six variables and parameters held significant influence on the analyzed model outputs based on their total-order sensitivity indices. These included duration from sowing to senescence (senescence), duration from sowing to harvesting (maturity), duration from sowing to yield formation (HIstart), base temperature below which growth does not progress (Tbase), minimum air temperature below which pollination failure begins (Tminᵤp), and shape factor describing reduction in biomass production (fshabeb). It was revealed that most variables and parameters were non-influential, which might allow them to be fixed within their ranges to optimize model calibration. The research represents the performance assessment and sensitivity analysis of the AquaCrop model over a desert farming system and offers guidelines for model calibration by delivering information on influential and non-influential input variables and parameters.
Almalki et al. (Fri,) studied this question.