Determining factors for the selection of soybean cultivars and the cause and effect relationships with grain yield
Resumo
The objective of the present study was to apply the trail analysis model to extract the cause and effect action on soybean grain yield as a function of agronomic attributes. The present study was developed in the agricultural years of 2023 and 2024. The experimental design used was strips with randomized blocks, consisting of 10 cultivars and five replications. Through the means comparison test, the highest grain yields were observed in the cultivars C 2531 E, BMX Vênus CE, B 5595 CE and NEO581 CE. It was observed that in addition to the higher grain yield, the cultivar C 2531 CE also presented a higher grain weight per plant, despite having the lowest final plant height and productive zone height among the cultivars. As for BMX Vênus CE, it was observed that despite its medium height, it presented a shorter internode length on the main stem, which optimized the number of total nodes on the main stem, in addition to presenting a high grain weight per plant. Cultivar B 5595 CE can be highlighted for its greater final plant height, as well as greater height of the productive zone, promoting a greater number of total nodes on the main stem. Another highlight of this cultivar is the high number of plants per final linear meter, indicating its adaptability in the field. The cultivar NEO581 E, despite having one of the smallest heights among the cultivars, presented one of the highest grain yields, which can be attributed to the stability of the cultivar in the field, as it showed intermediate performance for all agronomic traits.
Downloads
Referências
Almeida, L. G. B., Oliveira, R., Matos, D. J. C., Souza, J. E. B., & Ribeiro, V. A. (2018). Phenotypic variation of agronomic traits in soybean genotypes. Ipê Agronomic Journal, 2 (2), 58 – 67.
Artuzo, F. D., Foguesatto, C. R., Souza, Â. R. L. D., & Silva, L. X. D. (2018). Costs management in maize and soybean production. Revista Brasileira de Gestão de Negócios, 20, 273-294. https://doi.org/10.7819/rbgn.v20i2.3192
Bagateli, J. R., Franco, J. J., Meneghello, G. E., & Villela, F. A. (2020). Seed vigor and population density: effects on plant morphology and soybean productivity. Brazilian Journal of Development, 6 (6), 38686-38718. DOI:10.34117/bjdv6n6-422
Blalock Jr, H. M. (1963). Correlated independent variables: The problem of multicollinearity. Social Forces, 42(2), 233-237. https://doi.org/10.2307/2575696
Carmo, E. L., Braz, G. B. P., Simon, G. A., Silva, A. G., & Rocha, A. G. C. (2018). Soybean performance at different sowing times and plant distribution. Journal of Agroveterinary Sciences, 17 (1), 61-69. https://doi.org/10.5965/223811711712018061
CONAB – Companhia Nacional de Abastecimento. (2024). Monitoring the Brazilian grain harvest. Brasília, DF: CONAB.
Cruz, S. C. S., Sena Junior, D. G., Santos, D. M. A., Lunezzo, L. O., & Machado, C. G. (2016). Soybean cropping under different seeding densities and spatial arrangements. Neotropical Agriculture Journal, 3 (1), 1-6.https://doi.org/10.32404/rean.v3i1.431
Dan, H. A., Dan, L. G. M., Barroso, A. L. L., Procópio, S. O., Oliveira, J. R. S., Silva, A. G., Lima, M. D. B., & Feldkircher, C. (2010). Residual activity of herbicides used in soybean agriculture on grain sorghum crop succession. Planta Daninha, 28, 1087–1095. https://doi.org/10.1590/S0100-83582010000500016
EMBRAPA – Empresa Brasileira de Pesquisa Agropecuária. (2018). Brazilian system of soil classification. (5th ed.). Brasília, DF: Embrapa.
Ferrari, E., Paz, A., & Silva, A. C. (2015). Water deficit on the soybean metabolism in early sowing. Nativa, 3 (1), 67-77. https://doi.org/10.31413/nativa.v3i1.1855
Follmann, D. N., Cargnelutti Filho, A., Souza, V. Q., Nardino, M., Carvalho, I. R., Demari, G. H., Ferrari, M., Pelegrin, A. J., & Szareski, V. J. (2017). Linear relationships between off-season soybean traits. Journal of Agricultural Sciences, 40 (1), 213-221.
Franz, E., Tironi, S. P., Luz, G. L., Cezarotto, L. A., Zago, D. V., Munaretto, D., & Dalcanton, F. (2020). Competitive ability of canola cultivars in competition with turnip. Brazilian Journal of Development, 6 (10), 82507-82523. https://doi.org/10.34117/bjdv6n10-617
Godoy, C. V., Seixas, C. D. S., Soares, R. M., Marcelino-Guimarães, F. C., Meyer, M. C., & Costamilan, L. M. (2016). Asian soybean rust in Brazil: past, present, and future. Pesquisa Agropecuária Brasileira, 51 (5), 407-421. https://doi.org/ 10.1590/S0100-204X2016000500002
Grácio, M. C. C., & Oliveira, E. F. T. (2015). Proximity indicators in author co-citation analysis: a comparative study between Pearson's Correlation coefficient and Salton's Cosine. Information & Society: Studies, 25 (2), 105-116.
Hajiboland, R., Panda, C. K., Lastochkina, O., Gavassi, M. A., Habermann, G., & Pereira, J. F. (2023). Aluminum toxicity in plants: Present and future. Journal of Plant Growth Regulation, 42(7), 3967-3999. https://doi.org/10.1007/s00344-022-10866-0
Herrera, GC., Poletine, JP., Brondani, ST., Barelli, M. A. A., & Silva, VP. (2020). Adaptability and stability of soybean lines in southern Brazil through mixed modeling. Journal of Agronomic Sciences, 9, 185-202.
Köeppen, W. (1948). Climatology. Buenos Aires: Panamericana.
Kopf, J. C., & Brum, A. L. (2019). The exports of the agricultural metallomechanical sector as a factor of development: the case of the functional region of planning 7 of Rio Grande do Sul. Brazilian Journal of Development, 5 (10), 19.287-19.307. https://doi.org/10.34117/bjdv5n10-156
Leite, W. D. S., Pavan, B. E., Matos Filho, C. H. A., Feitosa, F. S., & Oliveira, C. B. (2015). Estimates of genetic parameters and correlations between agronomic traits in soybean genotypes. Nativa, 3 (4), 241-245. http://hdl.handle.net/11449/158365
Lima, L. G., & Santos, F. (2018). In the Semiarid of Alagoas, the resistance germinates on the land: the territorial struggle in defense of native seeds. Nera Journal, 21(41), 192-217.
Marcon, E. C., Romio, S. C., Maccari, V. M., Klein, C., & Lájus, C. R. (2017). Use of different sources of nitrogen in soybean cultivation. Thema Journal, 14 (2), 298-308. https://doi.org/10.15536/thema.14.2017.298-308.427
Meier, C., Meira, D., Marchioro, V. S., Olivoto, T., Klein, L. A., Moro, E. D., Lunkes, A., Rigatti, A., Bello, R. F., Bueno, R. B., & Souza, V. Q. (2019). Agronomic performance and linear correlation between yield traits in the second–harvest soybean. Journal of Agricultural Sciences, 42 (4), 933-941. https://doi.org/10.19084/rca.17995
Mello, E. S., & Brum, A. L. (2020). The soybean production chain and some impacts on the regional development of Rio Grande do Sul. Brazilian Journal of Development, 6 (10), 74734-74750. https://doi.org/10.34117/bjdv6n10-049
Menegon, AHM., Lima, SF., Alves, VCD., Contardi, LM., Cordeiro, MAS., Vendruscolo, EP., Nunes, R. C. B., & Nogueira, A. R. F. (2024). Soybean Population Management Seeking Greater Grain Productivity. Social and Environmental Management Magazine, 18 (2), e04294-e04294. https://doi.org/10.24857/rgsa.v18n2-017
Minosso, R. R., Sostisso, G. L., & Dranski, J. A. L. (2021). Yield and yield components of soybeans cultivated with hydrogel. Rural Scientific Journal, 23 (1), 62-82. https://doi.org/10.30945/rcr-v23i1.3139
NASA - National Aeronautics and Space Administration. (2023). NASA Prediction of Worldwide Energy Resources. Florida, EUA: NASA. Available in: https://power.larc.nasa.gov/. Access in: Abril 20th, 2024.
Nepumoceno, A., Farias, J., & Neumaier, N. (2017). Characteristics of Soya. Londrina, PR: Embrapa Technological Information Agency, EMBRAPA.
Oliveira, F. C., Benett, C. S., Benett, K. S., Silva, L. M., & Vieira, B. C. (2017). Different doses and application times of zinc in soybean crop. Neotropical Agriculture Journal, 4 (5), 28-35. https://doi.org/10.32404/rean.v4i5.2188
Oliveira, Z. B., Knies, A. E., Rodrigues, L. R., Schmidt, D. A., & Kury, A. G. (2021). Soybean productivity as a function of sowing time and supplementary irrigation in the central region of RS. IRRIGA Journal (Brazilian Journal of Irrigation & Drainage), 26 (4), 774-786.
Olivoto, T., Lúcio, A. D. (2020). Metan: An R package for multi‐environment trial analysis. Methods in Ecology and Evolution, 11 (6), 783-789. https://doi.org/10.1111/2041-210X.13384
R Core Team. (2024). A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Available in: <https://www.R-project.org> Accessed in: April 20th, 2024.
Santos, E. R., Spehar, C. R., Capone, A., & Pereira, P. R. (2018). Estimation of genetic variation parameters in F2 soybean progenies and parents with the presence and absence of lipoxygenases. Nucleus (16786602), 15 (1). https://doi.org/10.3738/1982.2278.2169
Semnaninejad, H., Nourmohammadi, G., Rameeh, V., & Cherati, A. (2021). Correlation and path coefficient analyses of phenological traits, yield components and quality traits in wheat. Brazilian Journal of Agricultural and Environmental Engineering, 25(9), 597-603. https://doi.org/10.1590/1807-1929/agriambi.v25n9p597-603
Shimizu, G. D., Marubayashi, R. Y. P., & Goncalves, L. S. A. (2023). AgroR: Experimental Statistics and Graphics for Agricultural Sciences. Londrina, PR. https://agronomiar.github.io/AgroR_package/index.html
Silva, S. I. A., Souza, T., Santos, D., & Souza, R. F. S. (2019). An assessment of production components in landraces of broad beans cultivated in the Agreste of Paraíba. Journal of Agricultural Sciences, 42 (3), 731-742.
Siqueira, C. B., Oliveira, F. S., Peixoto, P. M. C., & Amaral, A. A. D. (2021). Importance and management of spontaneous plants from the perspective of agroecology-review. Nucleus, (16786602), 18 (2).
USDA - United States Department of Agriculture. (2023). Production, supply and distribution. (2023). Available in: https://apps.fas.usda.gov/psdonline/app/index.html#/app/advQuery. Accessed in: May 25th.
Vieira Filho, J. E. R., & Fishlow, A. (2017). Agriculture and industry in Brazil. Brasília, DF: Institute of Applied Economics Research.
Copyright (c) 2024 ASB Journal
This work is licensed under a Creative Commons Attribution 4.0 International License.
