基于MaxEnt模型的河北省小麦赤霉病发生时空特征及风险区预测  

Spatiotemporal Characteristics and the Prediction of Risk Areas for Wheat(Triticum aestivum) Fusarium Head Blight in Hebei Province Based on MaxEnt Model

作　　者：陶晡[1] 齐永志[1] 赵绪生[1] 曹志艳[1] 甄文超[2] TAO Bu;QI Yong-Zhi;ZHAO Xu-Sheng;CAO Zhi-Yan;ZHEN Wen-Chao(College of Plant Protection,Hebei Agricultural University,Baoding 071001,China;College of Agronomy,Hebei Agricultural University/State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Regulation and Control of Crop Growth of Hebei,Baoding 071001,China)

机构地区：[1]河北农业大学植物保护学院,保定071001 [2]河北农业大学农学院/华北作物改良与调控国家重点实验室/河北省作物生长调控重点实验室,保定071001

出　　处：《农业生物技术学报》2021年第10期1869-1880,共12页Journal of Agricultural Biotechnology

基　　金：国家重点研发计划(2017YFD0300906,2018YFD0300502);河北省现代农业产业技术体系(HBCT2018010205)。

摘　　要：赤霉病(Fusarium head blight, FHB)严重威胁小麦(Triticum aestivum)生产,已成为影响小麦产业可持续发展的世界性难题。20世纪90年代中期以来,该病在河北省由零星出现逐渐演变成连片发生,年均发生面积26.7万hm^(2)以上,已成为小麦主要病害之一。本研究运用MaxEnt模型对该病在河北省的发生风险区进行了预测,采用Z-score标准化法对2003~2018年河北省小麦赤霉病发生面积和发生面积比等相关数据进行标准化处理,分析其发生时空特征;基于河北省小麦赤霉病发生分布特点和环境变量数据,运用MaxEnt模型预测小麦赤霉病在河北省的发生风险,并通过接受者操作特征(receiver operating characteristic, ROC)曲线下面积(area under the curve, AUC)评估预测模型的精度。结果表明,河北省小麦赤霉病发生存在中等以上空间相关性;MaxEnt模型预测结果的AUC值为0.816,表明小麦赤霉病预测发生风险与其实际分布拟合度较好。小麦赤霉病发生高风险区、中等风险区面积分别占河北省总面积的14.98%、10.19%,主要集中在冀中、冀南麦区;其中,保定南部、石家庄中部和东部、衡水、邢台中部和东部、邯郸中部和东部等66县为高风险区。通过Jackknife刀切法获取的环境变量重要性分析结果表明,最暖季度平均温度(Bio 10)、最暖月最高温度(Bio 5)、最冷季度平均温度(Bio 11)、最冷月最低温度(Bio 6)对小麦赤霉病发生影响较大。其中,Bio 10相对贡献率最高,达67.9%,重要性占22.2%。河北省小麦赤霉病中高风险区面积占河北省总面积25.17%,主要集中在河北省中南部,且该病发生存在较高风险。该模型的建立可为病害预测预报和有效防控提供依据。Fusarium head blight(FHB), a serious threat to wheat production, has become a worldwide problem affecting the sustainable development of wheat(Triticum aestivum). Since the middle of 1990 s, the disease occurred from spots to large areas and become one of the main diseases of wheat in Hebei province,with an average annual occurrence area of 267 000 hm^(2) or more. In order to provide the basis for scientific prevention and control FHB, MaxEnt model was used to predict the risk area of the disease in Hebei province in this study. Z-score method was used to standardize the occurrence area and area ratio of FHB in Hebei province from 2003 to 2018, and the features in time and space of FHB occurrence were analyzed. Based on the distribution characteristics of FHB in Hebei and the data of environmental variables, MaxEnt model was used to predict the potential risk area of FHB in Hebei. The area under the curve(AUC) of receiver operating characteristic(ROC) was used to evaluate the accuracy of the prediction model. The occurrence and prevalence of FHB in Hebei had a certain periodicity and spatial correlation, and the AUC value of MaxEnt model was 0.816, which indicated that the predicted distribution area of FHB had a good fit with the actual distribution area. The high and middle risk areas of FHB accounted for 14.98% and 10.19% of the total area of the whole province, respectively, mainly concentrated in the middle and south of Hebei. Among them, 66 counties were high risk areas, including the south of Baoding, the middle and east of Shijiazhuang, Hengshui,the middle and East of Xingtai, and the middle and east of Handan. The results of environmental variables analysis showed that the mean temperature of the warmest quarter(bio 10), the max temperature of the warmest month(bio 5), the mean temperature of the coldest quarter(bio 11) and the minimum temperature of the coldest month(bio 6) had a greater impact on the potential distribution of FHB. Among them, the relative contribution rate of bio10 was the important, a

关 键 词：小麦 赤霉病(FHB) 禾谷镰孢菌 MaxEnt模型 风险区 

分 类 号：S431.2[农业科学—农业昆虫与害虫防治]