Journal of Agricultural Big Data >
Sensitivity Analysis and Adaptability Evaluation of RiceSM Model
Received date: 2022-07-26
Online published: 2023-08-15
Crop model can quantitatively describe crop growth and development processes and their relationships with environmental factors, and have important applications in agricultural production management decisions and other areas. Model parameter debugging is an important step before crop growth simulation models are applied, and often requires a lot of time and effort for debugging. Sensitivity analysis can screen out sensitive parameters with high efficiency, and is an important part of model localization, which is of great significance for model application. Sensitivity analysis was conducted on the crop parameters of the RiceSM model based on the Morris method and EFAST method to screen out the sensitive parameters of maturity, leaf area index, total biomass and yield among the output variables, and to compare and analyze the similarities and differences between the results of the two methods. The results showed that basic development factor from transplanting to jointing stage K3, basic development factor from seeding to transplanting stage K2 and dry matter distribution coefficients of leaf from transplanting to jointing stage CLV1 were the most sensitive parameters affecting the main output results of RiceSM model, and the results of the sensitive parameters obtained by the two methods were generally consistent, but the importance of each sensitive parameter differed slightly. The validation results showed that the normalized root mean square error (NRMSE) of simulated and measured values of early and late rice leaf area index ranged from 21.63% to 47%, and the NRMSEs of simulated and measured values of stem, leaf, spike, aboveground biomass and yield of early and late rice ranged from 4.77% to 39.51%, 5.46% to 6.64%, 3.78% to 4. 15% and 2.78% to 3.52% and between 9.29% and 12.12% respectively. The model was able to better simulate the dynamics of bio-mass, leaf area index and yield formation in early and late rice. The results of the study provide a reference for the localization of the model.
Key words： rice; RiceSM model; sensitivity analysis
XueYing WANG , XianGuan CHEN , ShunJie TANG , LiPing FENG . Sensitivity Analysis and Adaptability Evaluation of RiceSM Model[J]. Journal of Agricultural Big Data, 2023 , 5(2) : 97 -108 . DOI: 10.19788/j.issn.2096-6369.230215
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