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Sensitivity of simulated crop yield and nitrate leaching of the wheat-maize cropping system in the North China Plain to model parameters
- Jabloun, Mohamed, Li, Xiaoxin, Zhang, Xiying, Tao, Fulu, Hu, Chunsheng, Olesen, Jørgen E.
- Agricultural and forest meteorology 2018 v.263 pp. 25-40
- corn, crop yield, cropping systems, crops, evapotranspiration, fertilizer application, field experimentation, leaching, nitrates, nitrogen, nitrogen fertilizers, screening, simulation models, statistical analysis, weather, winter, China
- Process-based crop simulation models are often over-parameterised and are therefore difficult to calibrate properly. Following this rationale, the Morris screening sensitivity method was carried out on the DAISY model to identify the most influential input parameters operating on selected model outputs, i.e. crop yield, grain nitrogen (N), evapotranspiration and N leaching. The results obtained refer to the winter wheat-summer maize cropping system in the North China Plain. In this study, four different N fertiliser treatments over six years were considered based on a randomised field experiment at Luancheng Experimental Station to elucidate the impact of weather and nitrogen inputs on model sensitivity. A total of 128 parameters were considered for the sensitivity analysis. The ratios [output changes/parameter increments] demonstrated high standard deviations for the most relevant parameters, indicating high parameter non-linearity/interactions. In general, about 34 parameters influenced the outputs of the DAISY model for both crops. The most influential parameters depended on the output considered with sensitivity patterns consistent with the expected dominant processes. Interestingly, some parameters related to the previous crop were found to affect output variables of the following crop, illustrating the importance of considering crop sequences for model calibration. The developed RDAISY toolbox used in this study can serve as a basis for following sensitivity analysis of the DAISY model, thus enabling the selection of the most influential parameters to be considered with model calibration.