欢迎访问《中国农学通报》，
中国农学通报

中国农学通报 ›› 2025, Vol. 41 ›› Issue (12): 88-93.doi: 10.11924/j.issn.1000-6850.casb2024-0627

• 资源·环境·生态·土壤 • 上一篇    下一篇

内蒙古突泉县春玉米大斑病气象风险预报技术研究

张玲1(), 杨玉辉2(), 周怡诺1(), 杨东旭1   

  1. 1 内蒙古突泉县气象局，内蒙古突泉 137500
    2 内蒙古扎赉特旗气象局，内蒙古扎赉特旗 137600
  • 收稿日期:2024-10-12 修回日期:2025-03-25 出版日期:2025-04-25 发布日期:2025-04-24
  • 通讯作者:
    周怡诺，女，2000年出生，内蒙古科右前旗人，助理工程师，本科，研究方向：气象服务与应用气象。通信地址：137500 内蒙古突泉县气象局，E-mail：；
    杨玉辉，男，1983年出生，辽宁康平人，高级工程师，硕士研究生，研究方向：气象服务与应用气象、综合观测。通信地址：137600 内蒙古兴安盟扎赉特旗 扎赉特旗气象局，E-mail：。
  • 作者简介:

    张玲，女，1973年出生，内蒙古突泉人，高级工程师，本科，研究方向：农业气象。通信地址：137500 内蒙古兴安盟突泉县突泉镇兴安居委会内蒙古突泉县气象局，E-mail：。

  • 基金资助:
    内蒙古自治区气象局科技创新项目“玉米大斑病气象风险预报技术研究”(nmqxkjcx202413)

Research on Meteorological Risk Forecasting Technology for Spring Corn Spot Disease in Tuquan County, Inner Mongolia

ZHANG Ling1(), YANG Yuhui2(), ZHOU Yinuo1(), YANG Dongxu1   

  1. 1 Meteorological Bureau of Tuquan County, Tuquan, Inner Mongolia 137500
    2 Meteorological Bureau of Zhalaite Banner, Zhalaite, Inner Mongolia 137600
  • Received:2024-10-12 Revised:2025-03-25 Published:2025-04-25 Online:2025-04-24

RichHTML

1

PDF (PC)

1

摘要：

为明确并掌握突泉县春玉米大斑病发生发展的气象条件规律以及大斑病发生发展气象条件风险等级预报技术，本研究利用突泉县近10 a（2014—2023年）的空气温度、相对湿度、降水量、2 min风速等气象条件与突泉县近10 a（2014—2023年）春玉米大斑病发生的时间、程度、影响面积等农业生产资料进行耦合分析，建立玉米大斑病发生预测模型。分析结果表明，2014—2023年5—9月突泉县各乡镇的4个气象要素整体变化趋于一致；自2019年起，发生面积比例呈现加重趋势，基本维持在10%左右。利用这4个气象要素因子建立玉米大斑病发生发展的气象指标，就能够依据常年气象要素统计值与下一年气象要素预报结论，通过预测模型对春玉米大斑病发生面积进行预测，从而为农业生产提供科学防范依据。

关键词: 玉米, 大斑病, 气象风险, 规律, 预报技术

Abstract:

In order to clarify and grasp the meteorological conditions and risk level forecasting technology for the occurrence and development of spring maize leaf spot disease in Tuquan County, a coupled analysis was conducted using the meteorological conditions of air temperature, relative temperature, precipitation, and 2-minute wind speed in Tuquan County over the past 10 years (2014-2023) and agricultural production data such as the time, degree, and affected area of spring maize leaf spot disease occurrence in Tuquan County over the past 10 years (2014-2023) to establish a prediction model for maize leaf spot disease occurrence. The analysis showed that the overall changes in four meteorological elements in each township of Tuquan County from May to September 2014-2023 tended to be consistent; Since 2019, there had been an increase in the proportion of the area, which had remained at around 10%. Utilizing four meteorological factors to establish meteorological indicators for the occurrence and development of maize leaf spot disease, it is possible to predict the occurrence area of spring maize leaf spot disease through a predictive model based on the statistical values of perennial meteorological factors and the forecast conclusions of meteorological factors for the next year, providing a scientific prevention basis for agricultural production.

Key words: corn, leaf spot disease, meteorological risk, pattern, forecasting technology