|Dynamic prediction method for winter wheat yield based on WOFOST model
|中文关键词: 冬小麦 产量 动态预报 准确率 WOFOST模型
|英文关键词:winter wheat yield dynamic prediction forecast accuracy WOFOST model
| 为了确定基于WOFOST作物模型的冬小麦产量动态预报方法及产量预报业务应用效果，利用全国冬小麦主产区内174个农业气象站冬小麦生育期、叶面积指数和土壤湿度等观测资料以及15个农业气象试验站冬小麦生物量观测资料,完成 WOFOST冬小麦模型参数本地化和区域化。利用全国冬小麦主产区约1 200个气象观测站起报日前的逐日气象资料及起报日后30 a平均气候值组成的冬小麦全生育期气象数据驱动模型，模拟得到冬小麦地上总生物量和穗干重,站点和县级尺度的冬小麦单产直接采用穗干重来进行产量预报,省级和全国区域冬小麦平均单产根据模拟值2 a间的变化幅度进行产量预报。根据不同空间尺度的历史年预报冬小麦单产与实际产量数据的对比，进行基于WOFOST模型的冬小麦产量预报方法效果检验。结果表明：（1）2014—2019年期间295个农业气象站次冬小麦产量估测平均准确率为81.8%，220个次县冬小麦单产估测平均准确率为84.3%，预报结果具有可用性；（2）12个主产省（市、区）冬小麦单产2003—2019年平均预报准确率为88.2%～96.4%，全国冬小麦单产预报准确率为93.9%～95.9%，总体预报准确率较高，说明基于WOFOST模型的冬小麦产量动态预报方法具有可行性；（3）基于WOFOST模型与统计方法的冬小麦平均单产估产结果准确率略偏低，但预报的时效性和动态性具有更好的优势，能满足作物产量预报业务需求。基于WOFOST模型的不同空间尺度冬小麦单产动态产量估测的准确率验证，说明WOFOST在作物产量预报业务应用具有可行性；利用作物模型进行基于站点尺度的产量预报能够提高作物产量预报时空精细化能力，也能扩展到大尺度区域应用以达到对农业决策和宏观调控的目的。
| 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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