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中国农学通报 ›› 2018, Vol. 34 ›› Issue (31): 5-9.doi: 10.11924/j.issn.1000-6850.casb17080127

所属专题: 小麦

• 农学 农业基础科学 • 上一篇    下一篇

河南南阳小麦条锈病春季发病预测模型研究

马青荣,王秀萍   

  1. 中国气象局河南省农业气象保障与应用技术重点实验室/河南省气象科学研究所,中国气象局河南省农业气象保障与应用技术重点实验室/河南省气象科学研究所
  • 收稿日期:2017-08-30 修回日期:2018-09-29 接受日期:2017-12-19 出版日期:2018-11-01 发布日期:2018-11-01
  • 通讯作者: 马青荣
  • 基金资助:
    公益性行业(气象)科研专项“小麦条锈病的气象成因及气象指标研究”(YHY201406036)。

Wheat Stripe Rust: Spring Prevalence Prediction Model in Nanyang, Henan

  • Received:2017-08-30 Revised:2018-09-29 Accepted:2017-12-19 Online:2018-11-01 Published:2018-11-01

摘要: 为突破文献中小麦春季条锈病预报基本上建立在冬春季旬月气象因子基础上的研究预测,改善预报模型的预报准确率和时效性,本研究利用2008—2015年河南南阳小麦条锈病周报资料的动态发病面积及动态气象因子,通过划分发病时段和计算发病速率2种方法进行动态预测研究,结果表明:建立的三个发病时段预报模型拟合预测率分别为90%(发病流行始期)、95%(发病流行发展期)、94%(发病流行盛期);发病速率的动态预测模型拟合预测准确率分别为 r1(发生面积速率)70%、r2(发生面积+防治面积的速率)80%、r3(发生面积+防治面积/2的速率)90%。本研究的预测模型提升了小麦条锈病发病预报准确率和时效性。

关键词: 土地利用, 土地利用, 地形位梯度, 分布指数, 陕西省

Abstract: The paper aims to overcome previous wheat strip rust prediction during winter and spring which were mainly based on fixed ten-day or monthly climate factors, and improve the accuracy and timeliness of extended prediction. Figures of the prevalence area used in this study were derived from the weekly wheat stripe rust reports in Nanyang from 2008 to 2015. Two methods were adopted to establish the dynamic prediction model based on the prevalence stages and the prevalence rate, respectively. The prediction accuracy of the prediction model based on three prevalence stages was 90% for the beginning stage of the prevalence, 95% for the developing stage of the prevalence, and 94% for the peak period of the prevalence. The prediction accuracy of the prediction model based on prevalence rate was 70%, 80% and 90% for the prevalence area rate (r1), prevalence area plus controlled area rate (r2) and prevalence area plus half controlled area rate (r3), respectively. The prediction model of this research improved the accuracy and timeliness for wheat strip rust prediction.