陕西小麦条锈病发生面积预测模型

为提高陕西省小麦条锈病发生面积的预测准确度，以2010年-2018年陕西省小麦条锈菌冬繁区和越冬区的发生县区数、发生面积、温度和降雨量为数据集，通过Pearson相关性分析筛选病害流行的主要影响因子，利用全子集回归筛选病害流行的因子集。以筛选得到的影响病害流行的5个因子，即累计发生县区数、冬繁区条锈病发生面积、1月平均温度、1月平均降雨量和3月平均降雨量为自变量，采用全子集回归和BP神经网络算法开展小麦条锈病发生面积的预测研究。结果表明，全子集回归和BP神经网络算法对2019年-2020年的小麦条锈病发生面积预测准确度均达90%以上，预测2021年陕西省小麦条锈病发生面积分别为46.11万hm~2和52.85万hm~2。

Prediction model for the occurrence area of wheat stripe rust in Shaanxi

In order to improve the prediction accuracy of the occurrence area of wheat stripe rust in Shaanxi province, the number of counties, the occurrence area, temperature, and rainfall in the winter propagation and overwintering regions of wheat stripe rust from 2010 to 2018 were used as the data set to construct prediction model. Relevant factors were screened through Pearson correlation analysis, and factor sets were screened by full subsets regression. Taking the five screened factors, including the total number of counties with stripe rust, the occurrence area of stripe rust in winter propagation, the monthly mean temperature and mean rainfall in January, and the mean rainfall in March as independent variables, the predicted occurrence area of wheat stripe rust was carried out using full subsets regression and BP neural network. The results showed that the prediction accuracy of full subsets regression and BP neural network on the wheat stripe rust occurrence area in 2019-2020 were both over 90%, and the predicted occurrence area of wheat stripe rust in Shaanxi province in 2021 were 461 100 and 528 500 hm2, respectively.