| This article has been：Browse 352Times Download 83Times |
scan it! |
|
|
| DOI：10.13522/j.cnki.ggps.2025361 |
|
| Monitoring and early warning of heat-drought stress at the flowering stage of summer maize and its impact on kernel number per ear |
|
LI Junling, LI Mengxia, CHU Ronghao, XUE Changying,
MA Zhihong, ZHANG Xihe, LI Shuyan
|
|
1. CMA·Henan Agrometeorological Support and Applied Technique Key Laboratory, Zhengzhou 450003, China;
2. Henan Institute of Meteorological Sciences, Zhengzhou 450003, China
|
| Abstract: |
| 【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 HTI outperformed the TSI in 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 RMSE of the modelled kernel number per ear by 6.04% and increased the absolute value of R by 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. |
| Key words: summer maize; combined heat and drought stress; kernel number per ear; multi-source data fusion; flowering stage; risk early warning |
|
|
