| Dynamic prediction method for winter wheat yield based on WOFOST model |
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| DOI：10.7606/j.issn.1000-7601.2022.06.26 |
| Key Words: winter wheat yield dynamic prediction forecast accuracy WOFOST model |
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| Abstract: |
| To determine the dynamic prediction method of winter wheat yield based on WOFOST winter wheat model and the application effect of yield prediction, the measured data of winter wheat growth period, leaf area index, soil moisture and winter wheat biomass data from agricultural meteorological stations in China were utilized to complete localization and regionalization of the parameters WOFOST-winter wheat model. According to the meteorological data of the whole growth period of winter wheat driving model, which was composed of the daily meteorological data of about 1200 meteorological observation stations and the average climate value of 30 years, total aboveground production and dry weight of organs of winter wheat were obtained. The unit yield of winter wheat at the station and county levels was directly predicted by dry weight of organs. The average unit yield of winter wheat at the provincial level and the national region was predicted according to the variation range of the simulated value between two years. Based on the comparison between the predicted yield and the measured data, the prediction results of unit yield in different spatial scales were tested. The results showed that: (1) The average accuracy of winter wheat yield estimation of 295 agrometeorological stations and times was 81.8%, and the average accuracy of average unit yield estimation of 220 counties and times was 84.3% during 2014-2019. The result was acceptable in business work. (2) The average accuracy of winter wheat yield estimation in 12 main producing provinces was 88.2%～96.4%, and that of nationwide was 93.9%～95.9% from 2003 to 2019. The overall forecast accuracy was high. (3) Through the average accuracy of forecasted per unit yield winter wheat based on WOFOST model was slightly lower than the results in the statistical method, it had more advantages in timeliness and dynamics of forecast and met the needs of agrometeorological operational service. The accuracy of dynamic yield estimation of winter wheat per unit yield at different spatial scales based on WOFOST model showed that WOFOST was feasible in business application. Using crop model for yield prediction not only improved the spatio\|temporal refinement ability at site scale, but also achieved the purpose of agricultural decision\|making and macro\|control by expanding to large\|scale regional application. |
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