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