Triticeae Genomics and Genetics, 2024, Vol.15, No.5, 277-286 http://cropscipublisher.com/index.php/tgg 285 Biswas D., Saha S., and Dey A., 2021, CRISPR-Cas genome-editing tool in plant abiotic stress-tolerance, Plant Gene, 26: 100286. https://doi.org/10.1016/j.plgene.2021.100286 Bjornson M., Balcke G., Xiao Y., de Souza A.J., Wang J.Z., Zhabinskaya D., Tagkopoulos I., Tissier A., and Dehesh K., 2017, Integrated omics analyses of retrograde signaling mutant delineate interrelated stress-response strata, The Plant Journal, 91(1): 70-84. https://doi.org/10.1111/tpj.13547 Cooper M., and Messina C., 2022, Breeding crops for drought-affected environments and improved climate resilience, The Plant Cell, 35: 162-186. https://doi.org/10.1093/plcell/koac321 Das G., and Rao G., 2015, Molecular marker-assisted gene stacking for biotic and abiotic stress resistance genes in an elite rice cultivar, Frontiers in Plant Science, 6: 689. https://doi.org/10.3389/fpls.2015.00698 Golebiowska-Paluch G., and Dyda M., 2023, The genome regions associated with abiotic and biotic stress tolerance, as well as other important breeding traits in triticale, Plants, 12: e30619. https://doi.org/10.3390/plants12030619 Hossain A., Skalický M., Brestič M., Maitra S., Alam M., Syed M.A., and Islam T., 2021, Consequences and mitigation strategies of abiotic stresses in wheat (Triticum aestivumL.) under the changing climate, Agronomy, 11: 241. https://doi.org/10.3390/AGRONOMY11020241. Hura T., Tyrka M., Hura K., Ostrowska A., and Dziurka K., 2017, QTLs for cell wall-bound phenolics in relation to the photosynthetic apparatus activity and leaf water status under drought stress at different growth stages of triticale, Molecular Genetics and Genomics, 292: 415-433. https://doi.org/10.1007/s00438-016-1276-y Jamil I.N., Remali J., Azizan K.A., Muhammad N.N., Arita M., Goh H., and Aizat W., 2020, Systematic multi-omics integration (MOI) approach in plant systems biology, Frontiers in Plant Science, 11: 944. https://doi.org/10.3389/fpls.2020.00944 Jiang L.R., 2024, Expanding genetic horizons: the role of magic populations in enhancing plant breeding efficiency, Molecular Plant Breeding, 15(3): 100-111. https://doi.org/10.5376/mpb.2024.15.0012 Jim F., 2024, Breeding 4.0: the breeding revolution of genetic information integration and editing, Molecular Plant Breeding, 15(1): 15-26. Kapazoglou A., Gerakari M., Lazaridi E., Kleftogianni K., Sarri E., Tani E., and Bebeli P., 2023, Crop wild relatives: a valuable source of tolerance to various abiotic stresses, Plants, 12: 20328. https://doi.org/10.3390/plants12020328 Kumar M., Prusty M.R., Pandey M., Singh P., Bohra A., Guo B., and Varshney R., 2023, Application of CRISPR/Cas9-mediated gene editing for abiotic stress management in crop plants, Frontiers in Plant Science, 14: 1157678. https://doi.org/10.3389/fpls.2023.1157678 Li J., Xie T., Chen Y., Zhang Y., Wang C., Jiang Z., Yang W., Zhou G., Guo L., and Zhang J., 2022, High-throughput UAV-based phenotyping provides insights into the dynamic process and genetic basis of rapeseed waterlogging response in the field, Journal of Experimental Botany, 73(15): 5264-5278. https://doi.org/10.1093/jxb/erac242 Losert D., Maurer H.P., Weissmann S., and Wü rschum T., 2016, Hybrid breeding for biomass yield in winter triticale: I. Hybrid performance, trait correlations and heterosis, Plant Breeding, 135: 560-566. https://doi.org/10.1111/pbr.12402 Ludovisi R., Tauro F., Salvati R., Khoury S., Scarascia Mugnozza G., and Harfouche A., 2017, UAV-based thermal imaging for high-throughput field phenotyping of black poplar response to drought, Frontiers in Plant Science, 8: 1681. https://doi.org/10.3389/fpls.2017.01681 Makumbi D., Assanga S., Diallo A., Magorokosho C., Asea G., Worku M., and Bänziger M., 2018, Genetic analysis of tropical midaltitude-adapted maize populations under stress and nonstress conditions, Crop Science, 58: 1492-1507. https://doi.org/10.2135/cropsci2017.09.0531 Mathew I., Shimelis H., Shayanowako A., Laing M., and Chaplot V., 2019, Genome-wide association study of drought tolerance and biomass allocation in wheat, PLoS ONE, 14(12): e0225383. https://doi.org/10.1371/journal.pone.0225383 Medina C., Kaur H., Ray I., and Yu L.X., 2021, Strategies to increase prediction accuracy in genomic selection of complex traits in alfalfa (Medicago sativa L.), Cells, 10(12): 3372. https://doi.org/10.3390/cells10123372 Menkir A., Crossa J., Meseka S., Bossey B., Muhyideen O., Riberio P.F., and Olaoye G., 2020, Stacking tolerance to drought and resistance to a parasitic weed in tropical hybrid maize for enhancing resilience to stress combinations, Frontiers in Plant Science, 11: 166. https://doi.org/10.3389/fpls.2020.00166 Nakayasu E., Nicora C., Sims A., Burnum-Johnson K., Kim Y.M., and Metz T., 2016, MPLEx: A robust and universal protocol for single-sample integrative proteomic, metabolomic, and lipidomic analyses, MSystems, 1(3): e00043-16. https://doi.org/10.1128/mSystems.00043-16 Ndoye M.S., Burridge J., Bhosale R., Grondin A., and Laplaze L., 2022, Root traits for low input agroecosystems in Africa: lessons from three case studies, Plant, Cell and Environment, 45: 284-300. https://doi.org/10.1111/pce.14256
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