夏玉米花期高温干旱复合胁迫监测预警与穗粒数响应机制研究OA

[Objective]Flowering stage is a critical stage for summer maize,during which concurrent heat and drought stress could severely undermine its yield formation.We developed a reliable method for monitoring and assessing the risk of combined heat and drought stress for summer maize during this growth stage.[Method]The study area is Henan Province.Multi-source data,including ground-based agrometeorological observations and CLDAS(China Land Data Assimilation System)grid data,were integrated to construct a comprehensive Heat-Drought Stress Index(HTI)by coupling the Temperature Stress Index(TSI)with soil moisture correction coefficients.Based on the 2025 intelligent grid forecast products of the China Meteorological Administration,an early warning method for heat-drought stress was developed.Field survey data from 28 counties in five regions across the province were used for post-disaster hindcast analysis,and the feasibility of the monitoring indicators for heat-drought stress at the flowering stage,as well as their applicability for early warning,was systematically evaluated.[Result]The Support Vector Machine(SVM)model is optimal for correcting the CLDAS_2mT and CLDAS_20cmRSM data,with the correlation coefficient(R)between observed and grid values reaching 0.999 8-1.0,and the Root Mean Square Error(RMSE)reduced by more than 60%compared with the raw data.The HTIoutperformed the TSIin characterizing heat-drought stress,showing a highly significant negative correlation with kernel number per ear(r=-0.520 2,P＜0.001).Compared with the TSI,the HTI reduced the RMSEof the modelled kernel number per ear by 6.04%and increased the absolute value of Rby 5.5%.The model identified 2017-2019,2022 and 2024 as years with severe heat-drought stress,with the stress intensity in 2025 exceeding previous levels.Risky zones with high stress were mainly in the eastern,southern and central parts of the province.Most regions exhibited a significant synergistic effect of heat and drought,and the kernel numbers per ear in high-risk zones were significantly lower than those in low-risk zones.[Conclusion]The 1-km resolution CLDAS data corrected by the SVM model meet the requirements for regional-scale assessment of heat-drought stress.The HTI can effectively quantify stress intensity and demonstrates that kernel numbers per ear is the key yield-limiting factor.In 2025,high-risky areas were mainly in the eastern,centra and southern parts of the province.The early warning method developed using TSI,soil moisture correction coefficients,and HTI provides a robust support for enhancing stress resilience and ensuring a stable summer maize yield.