作物模型WOFOST在许昌烟叶生产上的适用性研究OA

To address the issue of relying on experience and lacking quantitative simulation tools in the production management of flue-cured tobacco in central Henan,in order to clarify the applicability of WOFOST model in Xuchang flue-cured tobacco production area,the parameter sensitivity analysis was carried out by OAT(one-at-a-time)method,and the localization calibration and independent verification of the model were completed by'trial and error method'.The study was based on the field observation and meteorological data of Jian'an District and Xiangcheng County of Xuchang,Henan Province from 2021 to 2022,and the model's simulation accuracy for leaf dry weight(WLV),above-ground biomass(AGP),and leaf area index(LAI)was evaluated using the coefficient of determination(R2),consistency index(d),and normalized root mean square error(NRMSE).The main conclusions are as follows.(1)In the calibration of leaf dry weight(WLV),the simulation values from the WOFOST model showed a significant linear relationship with the observed values,indicating high accuracy in simulating leaf dry weight.The calibration accuracy for above-ground biomass(AGP)was also good.However,the calibration of the leaf area index(LAI)was less accurate,with NRMSE ranging from 20%to 30%.(2)The model was validated using data from Xuchang in 2021,Xuchang and Xiangcheng in 2022,and the simulation results were good for the most important biomass,leaf dry weight.Overall,the WOFOST model showed high accuracy in validation.The R2 and consistency index d of 90%(8/9)validation data were higher than 0.8,nearly 80%(7/9)were higher than 0.9,and NRMSE was between 10%and 20%.The correlation and consistency between simulated and measured values were good.(3)After calibration and verification,the WOFOST model could effectively simulate the growth process of tobacco in Xuchang,Henan,providing a foundation for quantitative and digital management of tobacco production.Subsequently,remote sensing data can be coupled to carry out research on crop growth monitoring and yield forecasting.