Agronomic aspects of soybean and predicted genetic relationships
Abstract
The objective of the study is to understand the performance of cultivars, highlight the genetic contribution to the phenotypic manifestation and predict the ranking of genotypes. This study was developed in the agricultural years 2023/2024, in the experimental area of the farm school of the Universidade Regional do Noroeste do Estado do Rio Grande do Sul. The experimental design used was strips with randomized blocks, consisting of 10 cultivars and five blocks. Sowing was carried out on November 8, 2023, with a target population of 14 seeds per linear meter. FPS 2063 IPRO cultivar presented the highest grain yield. Plant height, height of the productive zone, vegetable grain weight with two grains, vegetable weight with three grains and plant grain weight stands out as the components with the greatest genetic influence.
Downloads
References
Agudamu, T. S., Yoshihira, T., & Shiraiwa, T. (2016). Branch development responses to planting density and yield stability in soybean cultivars. Plant Production Science, 19(3), 331-339. https://doi.org/10.1080/1343943X.2016.1157443
Bergamini, L. H. T., & Streit, S. M. F. (2023). Technological advances in agribusiness, taking transgenic soy as an example. Mato Grosso Journal of Management, Innovation and Communication, 2(1), 92-100. http://104.207.146.252:3000/index.php/REMAGIC/article/view/271/250
CONAB – Companhia Nacional de Abastecimento. (2024). Monitoring the Brazilian Grain Harvest (v. 11, harvest 2023/24, 9th survey). Brasília, DF: CONAB. https://www.conab.gov.br/info-agro/safras/graos/boletim-da-safra-de-graos
Cruz, C. D., Carneiro, P. C. S., & Regazzi, A. J. (2014). Biometric Models Applied to Genetic Improvement. (5th ed.). Viçosa, MG: Editoria UFV.
Di Pietro, O. J. H., & Moreira, A. O. (2020). Family Farming: a model for realizing socio-environmental and economic rights. Argumenta Journal, 33, 205-553. https://www.proquest.com/scholarly-journals/agricultura-familiar-um-modelo-para-efetivação-de/docview/2541397983/se-2?accountid=146858
Duval, M. E., Galantini, J. A., Capurro, J. E., & Martinez, J. M. (2016). Winter cover crops in soybean monoculture: Effects on soil organic carbon and its fractions. Soil and Tillage Research, 161, 95-105. https://doi.org/10.1016/j.still.2016.04.006
Dubreuil, V., Fante, K. P., Planchon, O., & Sant'Anna Neto, J. L. (2018). The types of annual climates in Brazil: an application of the Köppen classification from 1961 to 2015. Confins, Revue franco-brésilienne de géographie, 37. https://doi.org/10.4000/confins.15738
EMBRAPA – Empresa Brasileira de Pesquisa Agropecuária. (2020). Brazil in global food production. Brasília, DF: EMBRAPA.
Foleto, E. E., Ottonelli, A. K. F., Carvalho, I. R., Silva, J. A. G., Conceição, G. M., Bandeira, W. J. A., Bruinsma, G. M. W.,& Sangiovo, J. P.(2024). Multivariate approach applied to phenotypic traits as a function of the selection of soybean cultivars. Agronomy Science and Biotechnology, 10, 1-16. https://doi.org/10.33158/ASB.r205.v10.2024
Giordani, W., Goncalves, L. S. A., Moraes, L. A. C., Ferreira, L. C., Neumaier, N., Farias, J. R. B., Nepomuceno, A. L., Oliveira, C. N., & Mertz-Henning, L. M. (2019). Identification of agronomical and morphological traits contributing to drought stress tolerance in soybean. Australian Journal of Crop Science, 13(1), 35-44. https://search.informit.org/doi/10.3316/informit.337951904526560
Henderson, C. R. (1963). Selection index and expected genetic advance. In: Hanson, H. F., & Robinson, W. D. (Eds.). Statistical Genetics and Plant Breeding. p. 141-163. Washington, DC: National Academy of Sciences, National Research Council.
MAPA – Ministério da Agricultura, Pecuária e Abastecimento. (2024). Cultivar Web. Brasília, DF: MAPA. Accessed May 15, 2024. Available at: https://sistemas.agricultura.gov.br/snpc/cultivarweb/cultivares_registradas.php/
NASA POWER. (2024). Prediction of Worldwide Energy Resource Applied Science Program. Accessed June 3, 2024. Available at: https://power.larc.nasa.gov/docs/
Neumaier, N., Farias, J. R. B., Nepomuceno, A. L., Mertz-Henning, L. M., Foloni, J. S. S., Moraes, L. A. C., & Goncalves, S. L. (2020). Ecofisiologia da Soja. In: Seixas, C. D. S.; Neumaier, N.; Balbinot Junior, A. A.; Krzyzanowski, F. C.; Leite, R. M. V. B. C. (Eds.). Tecnologias de produção de soja. Sistemas de Produção, 17. p. 33–54. Londrina, PR: Embrapa Soja.
Pípolo, A. E., & Mandarino, J. M. G. (2016). Soy protein content and quality for industry. Newsletter of the Brazilian Soil Science Society, 42(2), 30-32. https://www.alice.cnptia.embrapa.br/alice/bitstream/doc/1056554/1/Pagesfromvol42n22.p.3032.pdf
Port, E. D., Carvalho, I. R., Pradebon, L. C., Loro, M. V., Colet, C. D. F., Silva, J. A. G. D., & Sausen, N. H. (2024). Early selection of resilient progenies to seed yield in soybean populations. Rural Science, 54, e20230287. https://doi.org/10.1590/0103-8478cr20230287
Prauchner, E. D., Lima, L. R., Heusner, L. B., Carvalho, I. R., Bruinsma, G. M. W.,Bandeira, W. J. ., Sangiovo, J. P., Silva, J. A. G.,& Conceição, G. M. (2024). Agronomic performance of soybean and its relation with the production environment. Agronomy Science and Biotechnology, 10, 1-13. https://doi.org/10.33158/ASB.r211.v10.2024
R Core Team. (2023). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
Resende, M. D. V., Silva, F. F. E., & Azevedo, C. F. (2014). Mathematical, biometric and computational statistics: Mixed, Multivariate, Categorical and Generalized Models (REML/BLUP), Bayesian Inference, Random Regression, Genomic Selection, QTL-GWAS, Spatial and Temporal Statistics, Competition, Survival. (1st ed.). Visconde do Rio Branco, MG: Supreme.
Silva, F., Borém, A., Sediyama, T., & Câmara, G. (2022). Soja; do plantio à colheita. (2nd ed.). São Paulo, SP: Ofitexto Livraria Técnica.
Schünemann, L. L., Jung, J. S., Carvalho, I. R., Schneider, J. M., Bandeira, W, J. A., Sangiovo, J. P., Bruinsma, G. M. W., Silva, J. A. G., & Conceição, G. M. (2024). Determining factors for the selection of soybean cultivars and the cause and effect relationships with grain yield. Agronomy Science and Biotechnology, 10, 1-18. https://doi.org/10.33158/ASB.r207.v10.2024
Zuse, G. H., Carvalho, I. R., Fillipin, G. H., Conceição, G. M., Silva, J. A.G., Pradebon, L. C., Bruinsma, G. M. W., Porazzi, F. U., & Pattenon, A. (2024). Grain yield predicton model using gronomic aspects and vegetative indices of soybean. Agronomy Science and Biotechnology, 10, 1-11. https://doi.org/10.33158/ASB.r208.v10.2024
Copyright (c) 2024 Agronomy Science and Biotechnology
This work is licensed under a Creative Commons Attribution 4.0 International License.
doi.org/10.33158/ASB