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中国农学通报 ›› 2023, Vol. 39 ›› Issue (29): 74-78.doi: 10.11924/j.issn.1000-6850.casb2022-1008

• 资源·环境·生态·土壤·气象 • 上一篇    下一篇

水稻纹枯病气象等级预报方法与应用

楚岱蔚1(), 张舟娜2, 王琼洁1(), 洪冉3, 薛文璟1   

  1. 1 杭州市余杭区气象局,杭州 311100
    2 杭州市余杭区农业生态与植物保护总站,杭州 311100
    3 杭州市气象局,杭州 310051
  • 收稿日期:2022-11-30 修回日期:2023-01-05 出版日期:2023-10-15 发布日期:2023-10-11
  • 通讯作者: 王琼洁,女,1993年出生,浙江杭州人,助理工程师,学士,研究方向:天气预报服务。通信地址:311100 浙江省杭州市余杭区文一西路1500号,Tel:0571-89150516,E-mail:sucy921@qq.com。
  • 作者简介:

    楚岱蔚,女,1990年出生,浙江杭州人,工程师,硕士,研究方向:农业气象服务。通信地址:311100 浙江省杭州市余杭区文一西路1500号,Tel:0571-89150516,E-mail:

  • 基金资助:
    浙江省气象科技计划青年项目“水稻纹枯病气象等级预报技术研究与示范应用”(2019QN09)

Methodology and Application of Weather Level Forecasting of Rice Sheath Blight

CHU Daiwei1(), ZHANG Zhouna2, WANG Qiongjie1(), HONG Ran3, XUE Wenjing1   

  1. 1 Yuhang Meteorological Bureau of Hangzhou, Hangzhou 311100
    2 Yuhang Agricultural ecology and Plant protect Station of Hangzhou, Hangzhou 311100
    3 Hangzhou Meteorological Bureau, Hangzhou 310051
  • Received:2022-11-30 Revised:2023-01-05 Published-:2023-10-15 Online:2023-10-11

摘要:

研究旨在建立基于天气预报产品的水稻纹枯病预测模型,判断天气趋势对病害发生影响程度,对病害进行早期预警,提高防效。通过对1987—2019年水稻纹枯病田间观测数据的综合分析,筛选出病害监测时段和促病气象指标,基于病害发生机理,提出日促病指数计算方法,构建基于日促病指数的病株率预测模型和分级方案,并结合天气预报产品,在2020年开展业务应用。结果表明:日促病指数与日病株率存在极显著线性相关关系,相关系数为0.797 (P<0.001),Y=-0.032+0.147X (R2=0.634),模型回代检验准确率83.3%;2018—2020年应用模型平均预报准确率82.4%。气象预报等级与田间实际病情较吻合,可用于日常纹枯病气象等级预报业务中。

关键词: 纹枯病, 日促病指数, 日病株率, 预测, 应用

Abstract:

To establish a prediction model for rice sheath blight based on weather forecasting products, so that we can judge the degree of the influence of weather trends on disease occurrence and provide early warning of it, and improve control efficiency. By means of comprehensive analysis of rice sheath blight field observation data from 1987 to 2019, we screened out disease monitoring periods and disease promoting meteorological indicators. Next, we proposed a daily disease promoting index calculation method based on the disease occurrence mechanism. Meanwhile, we constructed a disease plant rate prediction model and grading scheme on account of daily disease promoting index, combined with weather forecasting products for operational application in 2020. The results showed that there was a highly significant linear correlation between the daily disease promotion index and daily disease plant rate which came to 0.797 (P<0.001), Y=-0.032+0.147X (R2=0.634). In addition, the accuracy of model back verification was 83.3%; the average forecast accuracy of the applied model from 2018 to 2020 was 82.4%. In consequence, the meteorological forecast levels are in good agreement with the actual disease in the field and can be used in daily weather level forecasting operations for rice sheath blight.

Key words: rice sheath blight, daily disease promotion index, daily disease plant rate, prediction, application