中国农业气象 ›› 2026, Vol. 47 ›› Issue (6): 867-874.doi: 10.3969/j.issn.1000-6362.2026.06.005

• 农业生态环境栏目 • 上一篇    下一篇

高标准农田保障中气象为农服务的能力体现与不足

艾艳,张莉,王裕灿,范保松   

  1. 河南省气象探测数据中心,郑州450003
  • 收稿日期:2025-04-05 出版日期:2026-06-20 发布日期:2026-06-18
  • 作者简介:艾艳,E-mail:aiyan2@sina.com
  • 基金资助:
    2025年河南省气象软科学项目(HNQXRXK202510);中国气象局河南省农业气象保障与应用技术重点实验室应用技术研究基金项目(KF202546)

Review of Capability and Shortcomings of Meteorological Services for Agriculture in the High−standard Farmland Construction

AI Yan, ZHANG Li, WANG Yu-can, FAN Bao-song   

  1. Henan Province Meteorological Detection Data Center, Zhengzhou 450003, China
  • Received:2025-04-05 Online:2026-06-20 Published:2026-06-18

摘要: 基于文献与政策整理、业务数据汇总分析以及河南、内蒙古等典型区域案例研究,梳理气象为农服务的现状及其在高标准农田领域的应用进展,分析高标准农田气象服务的主要功能,包括高精度监测、智慧决策支持和灾害主动干预等核心功能。研究表明:以气象数据为底座构建的“监测−分析−决策−干预”服务链条,正推动高标准农田从“经验管理”向“按需精准管理”转变,实现了气象、土壤、作物与农事信息的一体化融合。该体系显著提升了关键环境要素的感知与响应能力,支撑节水增效、绿色防控与作业优化;通过模型与业务规程的结合,将气象预报成果转化为可执行指令,强化了农业经营决策与风险防控;同时,灾害干预实现前移,为稳产增产提供了制度化保障。但现有气象站网布局不足、要素观测不全、数据标准不一及运维保障薄弱仍制约着服务效能。未来应以机理认知牵引智能预报迭代,强化多源观测与模型耦合,完善标准与平台协同建设,构建可持续的气象为农服务体系,为高标准农田的长期稳产和粮食安全提供支撑。

关键词: 高标准农田, 气象数据, 智慧农业, 农业生产, 气象为农服务

Abstract:

This study assessed the current status and application progress of agricultural meteorological services in high−standard farmland (HSF) through a systematic review of literature and policies, comprehensive analysis of operational and in−situ station data, and case studies in typical regions including Henan and Inner Mongolia. Authors identified three core capabilities: high−precision monitoring, intelligent decision support and proactive disaster intervention. A closed−loop "monitoring–analysis–decision–intervention" service chain was found to transform HSF management from experience−based to data−driven precision agriculture, achieving seamless fusion of meteorological, soil, crop and operational data. This system enhanced environmental responsiveness, enabling water conservation, green pest control and optimized field operations. By translating model forecasts into actionable workflows, it strengthened risk prevention and advanced disaster intervention upstream, supporting stable and increased yields. Key constraints included insufficient station coverage, incomplete element observations, non−uniform data standards and weak maintenance. The results propose a sustainable service framework driven by mechanistic intelligent forecasting and multi−source model−data fusion. These findings provide actionable insights for optimizing HSF management and ensuring food security.

Key words: High?standard farmland, Meteorological data, Smart agriculture, Agricultural production, Meteorological services for agriculture