MPB_2024v15n1

Molecular Plant Breeding 2024, Vol.15, No.1, 15-26 http://genbreedpublisher.com/index.php/mpb 23 References Anwar A., and Kim J., 2020, Transgenic breeding approaches for improving abiotic stress tolerance: recent progress and future perspectives, International Journal of Molecular Sciences, 21(8): 2695. https://doi.org/10.3390/ijms21082695 PMid:32295026 PMCid:PMC7216248 Bortesi L., and Fischer R., 2015, The CRISPR/Cas9 system for plant genome editing and beyond, Biotechnology Advances, 33(1): 41-52. https://doi.org/10.1016/j.biotechadv.2014.12.006 PMid:25536441 Buntaran H., Bernal-Vasquez A., Gordillo A., Sahr M., Wimmer V., and Piepho H., 2022, Assessing the response to genomic selection by simulation, Theoretical and Applied Genetics, 135(8): 2891-2905. https://doi.org/10.1007/s00122-022-04157-1 PMid:35831462 PMCid:PMC9325815 Cushman J., and Bohnert H., 2000, Genomic approaches to plant stress tolerance, Current Opinion in Plant Biology, 3(2): 17-24. https://doi.org/10.1016/S1369-5266(99)00052-7 PMid:10712956 de Faria Silva L., Alkimim E.R., Barreiro P.R.R.M., Leichtweis B.G., Silva A.C.A., da Silva R.A., Sousa T.V., Nascimento M., and Caixeta E.T., 2022, Genome-wide association study of plant architecture and diseases resistance in Coffea canephora, Euphytica, 218(7): 92. https://doi.org/10.1007/s10681-022-03042-8 Ding Y., Li H., Chen L., and Xie K., 2016, Recent advances in genome editing using CRISPR/Cas9, Frontiers in Plant Science, 7: 703. https://doi.org/10.3389/fpls.2016.00703 Donald C., 1968, The breeding of crop ideotypes, Euphytica, 17: 385-403. https://doi.org/10.1007/BF00056241 Feldmann M., Piepho H., and Knapp S., 2022, Average semivariance directly yields accurate estimates of the genomic variance in complex trait analyses, G3: Genes Genomes Genetics, 12(6): jkac080. https://doi.org/10.1093/g3journal/jkac080 PMid:35442424 PMCid:PMC9157152 Gabur I., Simioniuc D., Snowdon R., and Cristea D., 2022, Machine learning applied to the search for nonlinear features in breeding populations, Frontiers in Artificial Intelligence, 5: 876578. https://doi.org/10.3389/frai.2022.876578 PMid:35669178 PMCid:PMC9164111 Galán-Ávila A., Gramazio P., Ron M., Prohens J., and Herraiz F.J., 2021, A novel and rapid method for agrobacterium-mediated production of stably transformed Cannabis sativa L. plants, Industrial Crops and Products, 170: 113691. https://doi.org/10.1016/j.indcrop.2021.113691 Galeano E., Vasconcelos T.S., Vidal M., Mejia-Guerra M. K., and Carrer H., 2015, Large-scale transcriptional profiling of lignified tissues in Tectona grandis, BMC Plant Biology, 15(1): 1-21. https://doi.org/10.1186/s12870-015-0599-x PMid:26369560 PMCid:PMC4570228 Gao L., Turner M.K., Chao S., Kolmer J., andAnderson J.A., 2016, Genome wide association study of seedling and adult plant leaf rust resistance in elite spring wheat breeding lines, PLoS One, 11(2): e0148671. https://doi.org/10.1371/journal.pone.0148671 PMid:26849364 PMCid:PMC4744023 Goldman I., 1999, Teaching recurrent selection in the classroom with wisconsin fast plants, Horttechnology, 9: 579-584. https://doi.org/10.21273/HORTTECH.9.4.579 Harmer S., and Kay S., 2000, Microarrays: determining the balance of cellular transcription, Plant Cell, 12: 613-615. https://doi.org/10.1105/tpc.12.5.613 PMid:10810138 PMCid:PMC526005 Hilli H., 2022, An overview on phenomics applications in different agriculture disciplines, International Journal of Plant & Soil Science, 34(22): 631-637. https://doi.org/10.9734/ijpss/2022/v34i2231417 Jang G., Kim D., Kim H., and Chung Y., 2023, Short communication: spatial dependence analysis as a tool to detect the hidden heterogeneity in a kenaf field, Agronomy, 13(2): 428. https://doi.org/10.3390/agronomy13020428 Kawall K., 2021, Genome-edited Camelina sativa with a unique fatty acid content and its potential impact on ecosystems, Environmental Sciences Europe, 33: 1-12. https://doi.org/10.1186/s12302-021-00482-2 Khan M., Wang S., Wang J., Ahmar S., Saeed S., Khan S., Xu X., Chen H., Bhat J., and Feng X., 2022, Applications of artificial intelligence in climate-resilient smart-crop breeding, International Journal of Molecular Sciences, 23(19): 11156. https://doi.org/10.3390/ijms231911156 PMid:36232455 PMCid:PMC9570104

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