Computational Molecular Biology 2026, Vol.16, No.3, 205-217 http://bioscipublisher.com/index.php/cmb 2 15 Several gaps remain. First, many studies still rely on seasonal climate summaries that are too coarse to represent the biological reality of stage-specific stress. Second, genotype differences are often acknowledged but insufficiently parameterized in operational models. Third, interactions among heat, drought, soil constraints, and excess rainfall remain under-modeled in many sorghum systems. Fourth, strong local case studies exist, but transferability across regions is still limited. Finally, predictive accuracy is improving faster than interpretability in some data-driven studies, which risks producing models that are useful technically but harder to trust agronomically. Future work should move toward integrated sorghum modeling systems that connect phenology, plant physiology, remote sensing, and climate analytics in the same framework. More attention is needed on stress timing around flowering and grain filling, on genotype-specific calibration of water-use and heat-response traits, and on decision tools that translate model output into locally actionable advice. For both researchers and practitioners, the most productive perspective may be to treat sorghum neither as a miracle crop nor as a victim crop, but as a biologically understandable crop whose yield can be better stabilized when climate signals are interpreted through the lens of development, physiology, and carefully chosen models. Acknowledgments I am deeply grateful to Professor R. Cai for his multiple reviews of this paper and for his constructive revision suggestions. References Adotey R.E., Patrignani A., Bergkamp B., Kluitenberg G., Prasad P.V.V., and Jagadish S.V.K., 2021, Water-deficit stress alters intra-panicle grain number in sorghum, Crop Science, 61(4): 2680-2695. https://doi.org/10.1002/csc2.20532 Ali K.H., and Kothari K., 2026, Assessing future climate change impacts and adaptation strategies for sorghum yield in North Wollo, Ethiopia, Theoretical and Applied Climatology, 157(1): 36. https://doi.org/10.1007/s00704-025-05986-y Al-Salman Y., Cano F.J., Mace E., Jordan D., Groszmann M., and Ghannoum O., 2024, High water use efficiency due to maintenance of photosynthetic capacity in sorghum under water stress, Journal of Experimental Botany, 75(21): 6778-6795. https://doi.org/10.1093/jxb/erae418 Baye W., Xie Q., and Xie P., 2022, Genetic architecture of grain yield-related traits in sorghum and maize, International Journal of Molecular Sciences, 23(5): 2405. https://doi.org/10.3390/ijms23052405 Carcedo A.J.P., Mayor L., Demarco P., Morris G.P., Lingenfelser J., Messina C.D., and Ciampitti I.A., 2022, Environment characterization in sorghum (Sorghum bicolor L.) by modeling water-deficit and heat patterns in the Great Plains region, United States, Frontiers in Plant Science, 13: 768610. https://doi.org/10.3389/fpls.2022.768610 Chadalavada K., Gummadi S., Kundeti R.K., Kadiyala D.M., Deevi K.C., Dakhore K.K., Diana R.K.B., and Thiruppathi S.K., 2022, Simulating potential impacts of future climate change on post-rainy season sorghum in India using CERES-Sorghum model, Sustainability, 14(1): 334. https://doi.org/10.3390/su14010334 Deng L.Q., Li Y.Y., Liu X.F., Zhang Z.M., Mu J.J., Jia S.J., Yan Y.Q., and Zhang W.P., 2025, Sorghum yield prediction using UAV multispectral imaging and stacking ensemble learning in arid regions, Frontiers in Plant Science, 16: 1636015. https://doi.org/10.3389/fpls.2025.1636015 Diancoumba M., KholováJ., Adam M., Famanta M., Clerget B., Traore P.C.S., Weltzien E., Vacksmann M., McLean G., Hammer G.L., van Oosterom E.J., and Vadez V., 2024, APSIM-based modeling approach to understand sorghum production environments in Mali, Agronomy for Sustainable Development, 44(3): 25. https://doi.org/10.1007/s13593-023-00909-5 Fazel F., Ansari H., and Aguilar J., 2023, Determination of the most efficient forage sorghum irrigation scheduling strategies in the U.S. Central High Plains using the AquaCrop model and field experiments, Agronomy, 13(10): 2446. https://doi.org/10.3390/agronomy13102446 Ferraz M.A.J., Barboza T.O.C., Piza M.R., Von Pinho R.G., and dos Santos A.F., 2024, Sorghum grain yield estimation based on multispectral images and neural network in tropical environments, Smart Agricultural Technology, 9: 100661. https://doi.org/10.1016/j.atech.2024.100661 Fontanet-Manzaneque J.B., Hernández D.M., Giordano A., and Caño-Delgado A.I., 2025, Sorghum as a monocot model for drought research, Frontiers in Plant Science, 16: 1665967. https://doi.org/10.3389/fpls.2025.1665967
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