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中国农学通报 ›› 2026, Vol. 42 ›› Issue (17): 121-130.doi: 10.11924/j.issn.1000-6850.casb2026-0427

• 资源环境生态土壤气象 • 上一篇    下一篇

基于AgERA5与CMIP6的甘肃天水冬小麦主产区产量气象响应及近未来情景预估

杨雯博(), 夏权, 薛杨, 马佳宁   

  1. 兰州资源环境职业技术大学, 兰州 730021
  • 收稿日期:2026-05-25 修回日期:2026-07-06 出版日期:2026-09-09 发布日期:2026-09-09
  • 作者简介:

    杨雯博,女,1991年出生,甘肃兰州人,讲师,硕士研究生,主要从事气象教学工作。通信地址:730021 甘肃省兰州市城关区窦家山36号 兰州资源环境职业技术大学,Tel:0931-8799685,E-mail:

  • 基金资助:
    2024年度甘肃省自然科学基金项目“气候环境因子对黄芪生理生态参数的影响及其协同机制研究”(24JRRA742); 2026年度兰州资源环境职业技术大学校级科研创新基金项目“气候变化背景下陇中马铃薯气候适宜度精细化区划与未来情景预估”(X2026A-06)

Meteorological Response of Winter Wheat Yield and Near-future Scenario Projection in Main Production Areas of Tianshui, Gansu Based on AgERA5 and CMIP6

YANG Wenbo(), XIA Quan, XUE Yang, MA Jianing   

  1. Lanzhou Resources & Environment Voc-Tech University, Lanzhou 730021
  • Received:2026-05-25 Revised:2026-07-06 Published:2026-09-09 Online:2026-09-09

摘要:

为识别甘肃东南部冬小麦产量对气象因子的响应特征,并评估近未来县域单产变化趋势,本文以天水市的秦州区、麦积区、秦安县、甘谷县和武山县为研究区,基于2015—2024年冬小麦单产数据、AgERA5农业气象再分析资料和CMIP6未来气候数据,构建涵盖不同生育阶段的气象指标体系,综合采用相关分析、偏相关、灰色关联、多元线性回归、随机森林及KNN等方法开展分析。结果表明:研究区冬小麦单产对热量条件较敏感,全生育期日平均气温最大值和灌浆成熟期日平均气温最大值与单产呈稳定负相关;返青拔节期的太阳辐射、极端低温及灌浆成熟期的降水等关键气象指标也对小麦产量具有较高影响。随机森林模型在历史数据拟合效果优于多元线性回归,KNN残差模型在2024年独立验证中表现较好。2025—2035年预测结果显示,研究区域内五县区的冬小麦单产总体呈波动变化,未表现出持续上升趋势,且县域之间的单产变化存在一定空间差异。研究表明,高温热胁迫是限制区域冬小麦稳产的重要气象风险因素,返青拔节期和灌浆成熟期是气候影响小麦产量的关键阶段。

关键词: 冬小麦, 气象因子, AgERA5, 随机森林, KNN, CMIP6

Abstract:

To identify the response characteristics of winter wheat yield in southeastern Gansu Province to meteorological factors and assess the trend of county-level yield changes in the near future, this study selected Qinzhou District, Maiji District, Qin'an County, Gangu County, and Wushan County in Tianshui City as the research areas. Based on the winter wheat yield data from 2015 to 2024, AgERA5 agrometeorological reanalysis data, and CMIP6 future climate data, a meteorological index system for different growth stages was constructed. Correlation analysis, partial correlation, grey correlation, multiple linear regression, random forest, and KNN methods were comprehensively adopted for analysis. The results show that the winter wheat yield in the study area is highly sensitive to heat conditions. The maximum average temperature during the entire growth period and the filling and maturation period has a stable negative correlation with the yield. Indicators such as solar radiation during the reviving-jointing period, extreme low temperature, and precipitation during the filling and maturation period also have significant impacts. The historical fitting effect of the random forest was better than that of multiple linear regressions, and the KNN residual model performed well in the independent validation in 2024. The prediction results from 2025 to 2035 indicate that the winter wheat yield in the five counties and districts shows fluctuating changes and does not show a continuous upward trend, and there are certain spatial differences among the counties. The study suggests that high-temperature heat stress is an important meteorological risk limiting the stable production of winter wheat in the region, and the reviving-jointing period and the filling and maturation period are the key stages of climate affecting wheat yield.

Key words: winter wheat, meteorological factors, AgERA5, random forest, KNN, CMIP6

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