Mingliang Zhou

Shaoxing Yuanchu Ecological Agriculture Development Co., Ltd, Shaoxing 312000, Zhejiang, China
Author    Correspondence author
Computational Molecular Biology, 2026, Vol. 16, No. 3   
Received: 05 May, 2026    Accepted: 07 Jun., 2026    Published: 22 Jun., 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.

Sorghum (Sorghum bicolor L.) is one of the most important cereal crops in semi-arid and drought-prone regions due to its remarkable tolerance to heat and water limitation. However, sorghum productivity remains highly dependent on climatic conditions, particularly temperature and rainfall variability. This review synthesizes current knowledge on the biological and physiological mechanisms underlying sorghum yield formation and examines how temperature, rainfall, heat stress, drought stress, and their interactions influence grain number, grain weight, and overall yield stability. The review further evaluates major approaches used in sorghum yield prediction, including empirical statistical models, process-based crop simulation models, remote sensing technologies, and machine learning methods. Case studies from semi-arid regions demonstrate that reproductive-stage heat stress, post-flowering drought, and irregular rainfall distribution are among the most critical factors limiting yield. Future climate change is expected to intensify these challenges, highlighting the need for climate-resilient cultivars, adaptive agronomic management, and integrated decision-support systems. The review concludes that combining biological understanding with advanced modeling techniques can substantially improve yield prediction accuracy and support sustainable sorghum production under changing climatic conditions.

Sorghum; Temperature; Rainfall; Yield prediction; Climate change