摘要:在多类冬小麦单产统计预报模型中筛选出预报能力强的模型，并对优选出的模型进行加权集成，以此提高产量预报准确率，对保障粮食安全具有重要意义。利用1993-2018年江苏省69个基本气象观测站逐日气象资料和冬小麦产量数据及生育期资料，在5种气象产量分离方法（线性分离、差值百分率、5年滑动平均、3年滑动平均、二次曲线）的基础上，采用准确率、标准差、相关系数、泰勒图等检验法，评估分析了丰歉相似年法、关键气象因子法、气候适宜度法与集成预报法在江苏省冬小麦单产预报中的模拟效果。结果表明：1）对于同一种预报方法，不同的产量分离法对预报精度影响较大，二次曲线分离法要好于其他4种方法；丰歉相似年预报方法中加权法的预报精度高于大概率法。1993-2013年丰歉相似年法、关键气象因子法、气候适宜度法平均准确率分别为89.67%、94.86%和94.96%。2）集成预报法近5年预报准确率在96.33%以上，高于丰歉加权模型、关键气象因子二次曲线分离模型、气候适宜度二次曲线分离模型等单个最优模型，在一定程度上可以弥补单一预报方法预报结果稳定性差的不足。3）起报时间越接近成熟期，预报因子信息越全面，则预报模型准确率越高。研究结果可为江苏省冬小麦采用合理的单产预报模型提供科学依据，同时模型筛选思路也可供外省借鉴。
关键词:冬小麦/
产量预报/
丰歉相似年法/
关键气象因子法/
气候适宜度法/
产量分离方法/
最优模型集成
Abstract:We screened for the highest performance model among several winter wheat yield predicting models. The selected model was weighted and integrated in order to improve the accuracy of prediction, as it plays a key role in ensuring food security. Daily meteorological observations, winter wheat yield data, and growth period observations were obtained from 69 basic meteorological stations in Jiangsu Province from 1993 to 2018. Then, five methods of meteorological yield and trend yield separation (linear separation, percentage difference, 5-or 3-year sliding average, and quadratic curve) were compared. On this base, by using the fitting test and hind-casting test, we evaluated and analyzed the simulation effects of yield prediction methods based on similar years with bumper or poor harvest, key meteorological factors and climate suitability, and integrated the methods for Jiangsu winter wheat yield prediction. The results revealed the following:1) For the same yield prediction method, the yield separation methods had a greater effect on prediction accuracy. The quadratic curve method was the best among the linear separation, percentage difference, 5-or 3-year sliding average and quadratic curve methods. The prediction accuracy of the weighting method was higher than the large probability method in the similar years with bumper or poor harvest prediction method. From 1993 to 2013, the average accuracy of the methods of the similar years with bumper or poor harvest prediction, key meteorological factor, and climate suitability were 89.67%, 94.86%, and 94.96%, respectively. 2) The accuracy of the integrated prediction method was more than 96.33% in the past 5 years, and it was higher than that of the similar years with bumper or poor harvest-weighting model, key factor-quadratic curve model and climate suitability-quadratic curve model. This could probably overcome the less stability of prediction accuracy of a single prediction method. 3) The closer the predicted time to the maturity period and the more comprehensive the prediction factor information, the higher the accuracy of the prediction model. These results provide a scientific basis for selecting an optimized prediction model for winter wheat yield in Jiangsu, and the methodology of model screening can also be used in other provinces.
Key words:Winter wheat/
Yield prediction/
The method of similar years with bumper or poor harvest/
The method of key meteorological factors/
The method of climate suitability/
Separation method of meteorological yield and trend yield/
Integration of optimal model result
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