Global sensitivity and uncertainty analyses of a web based crop simulation model (web InfoCrop wheat) for soil parameters

Background and aims Sensitivity and uncertainty analyses are critical for identifying the most sensitive parameters of the crop simulation models. These analyses will help the users to concentrate more on calibrating those sensitive parameters. The soil parameters are important input parameters as the soils regulate the supply of nutrients and water required for the growth and yield of crops. The aim of this investigation is to perform the sensitivity and uncertainty analyses of the soil parameter inputs of the Web InfoCrop Wheat model, especially under the water deficit- and high temperature stress conditions for different agro-climatic conditions of India. Methods Sensitivities of soil input parameters (15) were analysed by crop simulations under the no stress (potential), water deficit- (limited irrigation), high temperature- (+3 °C above ambient), and their combined stress conditions for a period of 10 years across four sites in the wheat-growing regions of India. The partial rank correlation coefficients (PRCC) were computed, followed by the top-down concordance coefficients. Results Amongst the soil input parameters (15) and outputs (10) considered, the nutrient parameters such as NITRATE, ORG CARB (Organic carbon), AMMONIUM were the most sensitive input parameters under the no stress (potential) and high temperature stress conditions. On the contrary, the parameters related to the soil moisture such as CLAY (Clay %), FLD_CAP (Field capacity), SATU (Saturation) and MOISTURE (initial soil moisture contents) were the most sensitive input parameters under water deficit stress conditions. Conclusions Both the sensitivities and uncertainty of soil parameters under different crop production and environmental conditions influenced the outputs of the Web InfoCrop model significantly. Identification of the most sensitive soil parameters that need to be measured accurately improved the simulation efficiencies of this crop model.