Guifang Li^1,2

1 Jiande Qingrun Modern Agriculture Development Co.,Ltd., Jiande, 311600, Zhejiang, China
2 Zhejiang Agronomist College, Hangzhou, 310021, Zhejiang, China
Author    Correspondence author
Computational Molecular Biology, 2026, Vol. 16, No. 2   
Received: 25 Jan., 2026    Accepted: 28 Feb., 2026    Published: 13 Mar., 2026

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Rice yield formation is jointly influenced by temperature and water conditions; these two factors not only determine the progression of rice growth and development but also directly impact photosynthesis, dry matter accumulation, and grain-filling efficiency. With the intensification of global climate change and the increasingly prominent issue of agricultural water scarcity, the development of rice yield formation models-based on the management of temperature and water-holds significant importance for enhancing rice production efficiency and ensuring food security. This paper systematically reviews the physiological and ecological foundations of rice yield formation, with a particular focus on analyzing the mechanisms by which temperature, water, and their interactions influence rice growth and yield components. Furthermore, it compares and summarizes empirical statistical models, process-based mechanistic models, and AI-driven predictive models, while exploring the application of model parameterization, calibration, and validation methods in yield forecasting. Additionally, by incorporating typical management strategies-such as alternate wetting and drying (AWD) irrigation-the paper analyzes rice yield simulation results under various hydrothermal conditions and evaluates their practical value in agricultural applications. The findings indicate that rational temperature regulation and water management can significantly enhance water-use efficiency and yield stability, and that the fusion of multi-source data coupled with intelligent modeling will constitute a key direction for future research on rice yield modeling. This paper serves as a theoretical reference and provides technical support for precision agriculture, the optimal allocation of water resources, and the management of stable rice production within the context of climate change.

Rice yield model; Temperature regulation; Water management; Crop simulation; Precision agriculture