MPB_2024v15n5

Molecular Plant Breeding 2024, Vol.15, No.5, 295-307 http://genbreedpublisher.com/index.php/mpb 307 Toda Y., Wakatsuki H., Aoike T., Kajiya-Kanegae H., Yamasaki M., Yoshioka T., Ebana K., Hayashi T., Nakagawa H., Hasegawa T., and Iwata H., 2020, Predicting biomass of rice with intermediate traits: modeling method combining crop growth models and genomic prediction models, PLoS One, 15(6): e0233951. https://doi.org/10.1371/journal.pone.0233951 PMid:32559220 PMCid:PMC7304626 Tong H., and Nikoloski Z., 2020, Machine learning approaches for crop improvement: leveraging phenotypic and genotypic big data, Journal of Plant Physiology, 257: 153354. https://doi.org/10.1016/j.jplph.2020.153354 PMid:33385619 Varshney R., Barmukh R., Roorkiwal M., Qi Y., Kholová J., Tuberosa R., Reynolds M., Tardieu F., and Siddique K., 2021a, Breeding custom-designed crops for improved drought adaptation, Advanced Genetics, 2(3): e202100017. https://doi.org/10.1002/ggn2.202100017 PMid:36620433 PMCid:PMC9744523 Varshney R., Bohra A., Yu J., Graner A., Zhang Q., and Sorrells M., 2021b, Designing future crops: genomics-assisted breeding comes of age, Trends in Plant Science, 26(6): 631-649. https://doi.org/10.1016/j.tplants.2021.03.010 PMid:33893045 Villanueva D., Smale M., Jamora N., Capilit G., and Hamilton R., 2020, The contribution of the International Rice Genebank to varietal improvement and crop productivity in Eastern India, Food Security, 12: 929-943. Wang Y., Wang X., Zhai L., Zafar S., Shen C., Zhu S., Chen K., Wang Y., and Xu J., 2023, A novel Effective Panicle Number per Plant 4 haplotype enhances grain yield by coordinating panicle number and grain number in rice, The Crop Journal, 12(1): 202-212. https://doi.org/10.1016/j.cj.2023.11.003 Wang H., Cimen E., Singh N., and Buckler E., 2020, Deep learning for plant genomics and crop improvement, Current Opinion in Plant Biology, 54: 34-41. https://doi.org/10.1016/j.pbi.2019.12.010 PMid:31986354 Wang H., Pandey S., and Feng L., 2020, Econometric analyses of adoption and household-level impacts of improved rice varieties in the uplands of Yunnan, China, Sustainability, 12(17): 6873. https://doi.org/10.3390/su12176873 Wang X., Jing Z., He C., Liu Q., Jia H., Qi J., and Zhang H., 2021, Breeding rice varieties provides an effective approach to improve productivity and yield sensitivity to climate resources, European Journal of Agronomy, 124: 126239. https://doi.org/10.1016/j.eja.2021.126239 Wei X., Chen M., Zhang Q., Gong J., Liu J., Yong K., Wang Q., Fan J., Chen S., Hua H., Luo Z., Zhao X., Wang X., Li Wei., Cong J., Yu X., Wang Z., Huang R., Chen J., Zhou X., Qiu J., Xu P., Murray J., Wang H., Xu Y., Xu C., Xu G., Yang J., Han B., and Huang X., 2024, Genomic investigation of 18 421 lines reveals the genetic architecture of rice, Science, 385: 6704. https://doi.org/10.1126/science.adm8762 PMid:38963845 Wu W., Dong X., Chen G., Lin Z., Chi W., Tang W., Yu J., Wang S., Jiang X., Liu X., Wu Y., Wang C., Cheng X., Zhang W., Xuan W., Terzaghi W., Ronald P., Wang H., Wang C., and Wan J., 2024, The elite haplotype OsGATA8-H coordinates nitrogen uptake and productive tiller formation in rice, Nature Genetics, 56: 1516-1526. https://doi.org/10.1038/s41588-024-01795-7 PMid:38872029 PMCid:PMC11250373 Xiong L., Uga Y., and Li Y., 2020, Rice functional genomics: theories and practical applications, Molecular Breeding, 40: 72. https://doi.org/10.1007/s11032-020-01150-8 Xu J., Xing Y., Xu Y., and Wan J., 2021, Breeding by design for future rice: genes and genome technologies, Crop Journal, 9: 491-496. https://doi.org/10.1016/j.cj.2021.05.001 Zheng C., Amadeu R., Muñoz P., and Endelman J., 2020, Haplotype reconstruction in connected tetraploid F1 populations, Genetics, 219(2): iyab106. https://doi.org/10.1093/genetics/iyab106 PMid:34849879 PMCid:PMC8633103

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