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马青荣,王秀萍.河南南阳小麦条锈病春季发病预测模型研究[J].中国农学通报,2018,34(31):5-9..Wheat Stripe Rust: Spring Prevalence Prediction Model in Nanyang, Henan[J].Chinese Agricultural Science Bulletin,2018,34(31):5-9
河南南阳小麦条锈病春季发病预测模型研究
Wheat Stripe Rust: Spring Prevalence Prediction Model in Nanyang, Henan
投稿时间:2017-08-30  修订日期:2018-09-29
DOI:10.11924/j.issn.1000-6850.casb17080127
中文关键词: 小麦条锈病,发病三时段,动态预测,发病速率
英文关键词: wheat  strip rust,three  stages of  disease prevalence, dynamic  prediction, prevalence  rate.
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基金项目: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)。
作者单位E-mail
马青荣 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 zzmqr@163.com 
王秀萍 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 wangxiuping03@126.com 
中文摘要:
      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。
英文摘要:
      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.
作者简介:
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