Integrated Technology of Yield Dynamic Prediction of Winter Wheat in Shandong Province

Using winter wheat yield and growth data of 17 prefecture-level city, daily meteorological data from 1980 to 2011, and daily 20 cm depth soil moisture data of 14 representative meteorological stations from 1992 to 2011, methods for dynamic prediction of winter wheat yield are established in 4 regions of Shandong Province, considering historical meteorological influence index for bumper or poor harvest of crop yield, key meteorological factors influence index, the climatic suitability influence index and the WOFOST crop growth model, respectively. A newly developed statistical method, cluster analysis of statistical test (CAST), which divides planting areas of winter wheat in Shandong Province into four regions. These four methods are used to predict yield of winter wheat in regions of Shandong Province from 2004-2011. An integrated prediction method is established in which the weight coefficients of each method is determined based on the prediction accuracy, and the prediction method with accuracy lower than 90.0% in each period is removed.The comparison result shows the prediction accuracy in each region and period of four single yield prediction method is very unstable and has a large fluctuation range. Forecast results of the historical meteorological influence index for bumper or poor harvest of crop yield are relatively good in region of C1 and C3. The accuracy of key meteorological factor influence index in region C1 and C2 is relatively consistent, while not quite stable in region C3. The prediction accuracy of the climatic suitability influence index generally is more than 80%. And the prediction accuracy of WOFOST in four regions all reaches 90.0%, except for certain instability and fluctuation. Through integrating these methods, the accuracy in each region and each period is significantly improved, which is generally above 95.0%, and the prediction result is stable. Therefore, the integrated prediction method could overcome shortcomings of the single forecast method, and it is more suitable for application.