CMB_2026v16n2

Computational Molecular Biology 2026, Vol.16, No.2, 85-97 http://bioscipublisher.com/index.php/cmb 97 Li N., Zhao Y., Han J., Yang Q., Liang J., Liu X., Wang Y., and Huang Z., 2024, Impacts of future climate change on rice yield based on crop model simulation: A meta-analysis, Science of the Total Environment, 930: 175038. https://doi.org/10.1016/j.scitotenv.2024.175038 Li S., Fleisher D., Timlin D., Reddy V.R., Wang Z., and McClung A., 2020, Evaluation of different crop models for simulating rice development and yield in the U.S. Mississippi Delta, Agronomy, 10(12): 1905. https://doi.org/10.3390/agronomy10121905 Liu B., Meng S., Yang J., Wu J., Peng Y., Zhang J., and Ye N., 2025, Carbohydrate flow during grain filling: Phytohormonal regulation and genetic control in rice (Oryza sativa), Journal of Integrative Plant Biology, 67(6): 1086-1104. https://doi.org/10.1111/jipb.13904 Liu K., Zhang K., Zhang Y., Cui J., Li Z., Huang J., Li S., Zhang J., Deng S., Zhang Y., Huang J., Ren L., Chu Y., Zhao H., and Chen H., 2024, Optimizing the total spikelets increased grain yield in rice, Agronomy, 14(1): 152. https://doi.org/10.3390/agronomy14010152 Miller J.O., de Barros P.R., Schulenburg A.N., Tully K.L., 2025, Coastal stressors reduce crop yields and alter soil nutrient dynamics in low-elevation farmlands, Discover Agriculture, 3(1): 119. https://doi.org/10.1007/s44279-025-00303-7 Nurulhuda K., Muharam F.M., Shahar N.A.N., Hashim M.F.C., Ismail M.R., Keesman K.J., Zulkafli Z., 2022, ORYZA (v3) rice crop growth modeling for MR269 under nitrogen treatments: Assessment of cross-validation on parameter variability, Computers and Electronics in Agriculture, 195: 106809. https://doi.org/10.1016/j.compag.2022.106809 Proctor J., Rigden A., Chan D., Huybers P., 2022, More accurate specification of water supply shows its importance for global crop production, Nature Food, 3(9): 753-763. https://doi.org/10.1038/s43016-022-00592-x Pereira L.S., Paredes P., Melton F., Johnson L., Wang T., López-Urrea R., Cancela J.J., Allen R.G., 2020, Prediction of crop coefficients from fraction of ground cover and height: background and validation using ground and remote sensing data, Agricultural Water Management, 241: 106197. https://doi.org/10.1016/j.agwat.2020.106197 Rezvi H.U.A., Tahjib-Ul-Arif M., Azim M.A., et al., 2022, Rice and food security: Climate change implications and the future prospects for nutritional security, Food and Energy Security, 12(1): e430. https://doi.org/10.1002/fes3.430 Shrestha S., Giri D., Dhital M., Chaudhary B., Pandey R., Bastakoti B., 2022, Effect of different nitrogen levels on yield and yield attributes of different rice varieties in DDSR condition at Kanchanpur, Nepal, Archives of Agriculture and Environmental Science, 7(3): 310–317. https://doi.org/10.26832/24566632.2022.070302 Saha S., Chant D., Welham J., 2025, A systematic review of the prevalence of schizophrenia, PLoS Medicine, 22(5): e141. https://doi.org/10.1371/journal.pmed.0020141 Sishodia R.P., Ray R.L., Singh S.K., 2020, Applications of remote sensing in precision agriculture: A review, Remote Sensing, 12(19): 3136. https://doi.org/10.3390/rs12193136 Setiya P., Satpathi A., Das B., Nain A.S., Jha P.K., Singh S., 2023, Comparative analysis of statistical and machine learning techniques for rice yield forecasting for Chhattisgarh, India, Sustainability, 15(3): 2786. https://doi.org/10.3390/su15032786 Sheehy J.E., Mitchell P.L., Allen L.H., Ferrer A.B., 2006, Mathematical consequences of using various empirical expressions of crop yield as a function of temperature, Field Crops Research, 98(2): 216-221. https://doi.org/10.1016/j.fcr.2006.02.008 Wickramasinghe W.M.D.M., Devasinghe D.A.U.D., Dissanayake D.M.D., Benaragama D.I.D.S., Egodawatta W.C.P., Suriyagoda L.D.B., 2021, Growth physiology and crop yields of direct-seeded rice under diverse input systems in the dry zone of Sri Lanka, Tropical Agricultural Research, 32(3): 325-337. https://doi.org/10.4038/tar.v32i3.8496 Zhou J., Li J., Zhang Y., Yang Y., Lv Y., Pu Q., Deng X., Tao D., 2025, Introgression among subgroups is an important driving force for genetic improvement and evolution of the Asian cultivated rice (Oryza sativa L.), Frontiers in Plant Science, 16: 1535880. https://doi.org/10.3389/fpls.2025.1535880

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