Maize Genomics and Genetics 2025, Vol.16, No.3, 139-148 http://cropscipublisher.com/index.php/mgg 146 Eertink J., Heymans M., Zwezerijnen G., Zijlstra J., De Vet H., and Boellaard R., 2022, External validation: a simulation study to compare cross-validation versus holdout or external testing to assess the performance of clinical prediction models using PET data from DLBCL patients, EJNMMI Research, 12: 58. https://doi.org/10.1186/s13550-022-00931-w Fernandes I., Vieira C., Dias K., and Fernandes S., 2024, Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials, Theoretical and Applied Genetics, 137: 189. https://doi.org/10.1007/s00122-024-04687-w Fooladi M., Golmohammadi M., Safavi H., and Singh V., 2021, Fusion-based framework for meteorological drought modeling using remotely sensed datasets under climate change scenarios: resilience, vulnerability, and frequency analysis, Journal of Environmental Management, 297: 113283. https://doi.org/10.1016/j.jenvman.2021.113283 Gharakhanlou N., and Perez L., 2024, From data to harvest: leveraging ensemble machine learning for enhanced crop yield predictions across Canada amidst climate change, The Science of the Total Environment, 951: 175764. https://doi.org/10.1016/j.scitotenv.2024.175764 Hao Z., Li X., Xie C., Weng J., Li M., Zhang D., Liang X., Liu L., Liu S., and Zhang S., 2011, Identification of functional genetic variations underlying drought tolerance in maize using SNP markers, Journal of Integrative Plant Biology, 53(8): 641-652. https://doi.org/10.1111/j.1744-7909.2011.01051.x He K., Yu T., Gao S., Chen S., Li L., Zhang X., Huang C., Xu Y., Wang J., Prasanna B., Hearne S., Li X., and Li H., 2025, Leveraging automated machine learning for environmental data‐driven genetic analysis and genomic prediction in maize hybrids, Advanced Science, 12(17): 2412423. https://doi.org/10.1002/advs.202412423 Ho S., Phua K., Wong L., and Goh W., 2020, Extensions of the external validation for checking learned model interpretability and generalizability, Patterns, 1(8): 100129. https://doi.org/10.1016/j.patter.2020.100129 Hu T., Zhang X., Bohrer G., Liu Y., Zhou Y., Martin J., Li Y., and Zhao K., 2023, Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield, Agricultural and Forest Meteorology, 109458: 447-469. https://doi.org/10.1016/j.agrformet.2023.109458 Jighly A., Hayden M., and Daetwyler H., 2021, Integrating genomic selection with a genotype plus genotype x environment (GGE) model improves prediction accuracy and computational efficiency, Plant, Cell & Environment, .336: 109458. https://doi.org/10.1111/pce.14145 Li C., Sun B., Li Y., Liu C., Wu X., Zhang D., Shi Y., Song Y., Buckler E., Zhang Z., Wang T., and Li Y., 2016, Numerous genetic loci identified for drought tolerance in the maize nested association mapping populations, BMC Genomics, 17: 894. https://doi.org/10.1186/s12864-016-3170-8 Li R., Wang Y., Li D., Guo Y., Zhou Z., Zhang M., Zhang Y., Würschum T., and Liu W., 2024, Meta-Quantitative trait loci analysis and candidate gene mining for drought tolerance-associated traits in maize (Zeamays L.), International Journal of Molecular Sciences, 25(8): 4295. https://doi.org/10.3390/ijms25084295 Ma L., Niu W., Li G., Du Y., Sun J., and Siddique K., 2024, Crop Yield prediction based on bacterial biomarkers and machine learning, Journal of Soil Science and Plant Nutrition, 24: 2798-2814 https://doi.org/10.1007/s42729-024-01705-0 Malphedwar L., Adsul A., Nagare S., Nimse Y., Nimble S., and Pakhle S., 2024, Crop yield prediction using machine learning, International Journal of Advanced Research in Science, Communication and Technology, 4(2): 395-398. https://doi.org/10.48175/ijarsct-22172 Manigben K., Beyene Y., Chaikam V., Tongoona P., Danquah E., Ifie B., Aleri I., Chavangi A., Prasanna B., and Gowda M, 2024, Testcross performance and combining ability of intermediate maturing drought tolerant maize inbred lines in Sub-Saharan Africa, Frontiers in Plant Science, 15: 1471041. https://doi.org/10.3389/fpls.2024.1471041 Manjunath M., and Palayyan B., 2023, An efficient crop yield prediction framework using hybrid machine learning model, Revue d'Intelligence Artificielle, 370428: 1057-1067. https://doi.org/10.18280/ria.370428 Marino R., Ponnaiah M., Krajewski P., Frova C., Gianfranceschi L., Pè M., and Sari-Gorla M., 2009, Addressing drought tolerance in maize by transcriptional profiling and mapping, Molecular Genetics and Genomics, 281: 163-179. https://doi.org/10.1007/s00438-008-0401-y Masuka B., Atlin G., Olsen M., Magorokosho C., Labuschagne M., Crossa J., Bänziger M., Pixley K., Vivek B., Biljon A., Macrobert J., Alvarado G., Prasanna B., Makumbi D., Tarekegne A., Das B., Zaman-Allah M., and Cairns J., 2017, Gains in maize genetic improvement in Eastern and Southern Africa: I. CIMMYT hybrid breeding pipeline, Crop Science, 57: 168-179. https://doi.org/10.2135/CROPSCI2016.05.0343 Nossam S., Katakam R., Pulastya G., and Venugopalan M., 2024, Enhanced crop yield prediction using machine learning techniques, 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 10724901: 1-6. https://doi.org/10.1109/ICCCNT61001.2024.10724901 Nurcahyo A., Heryadi Y., Lukas, Suparta W., and Sonata I., 2023, Interpretable machine learning for multi-class crop yield prediction, 2023 3rd International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), 10428914: 194-200. https://doi.org/10.1109/ICICyTA60173.2023.10428914
RkJQdWJsaXNoZXIy MjQ4ODYzNA==