Selection of recombinant soybean lines with high grain productivity performance for low altitude regions
DOI:
https://doi.org/10.18406/2316-1817v18nunico20262094Keywords:
Glycine max; Heritability; Mixed models; multi-trait indices.Abstract
Superior soybean genotype selection for breeding programs involves several processes, which can be considered complex because important traits are mostly quantitative and highly influenced by the environment. The objective of this work was to evaluate the performance of recombinant soybean lines through genetic parameters and the performance of qualitative and quantitative agronomic traits, with emphasis on grain yield in low altitude regions. A total of 126 recombinant F10 soybean lines were evaluated in an augmented block design with interspersed checks. The traits assessed were plant height (PH), number of pods per plant (NLP), insertion of the first pod (IFL), grain mass per plant (GMP), and grain yield (GY). Mixed models (REML/BLUP) were used to estimate the genetic parameters, and based on the BLUP predicted values, the MGIDI index was applied to select lines closest to the desired ideotype. High heritability values (>0.90) were observed for all characteristics assessed and experimental accuracy was also high. The MGIDI index selected the lines RIL18_IRC001, RIL103_IRC035, RIL73_IRC038, RIL124_IRC039, RIL71_IRC012, RIL115_IRC013, RIL113_IRC017, and RIL114_IRC038, which showed the greatest proximity to the intended agronomic ideotype. The line RIL13_IRC038 was selected simultaneously for reduction of IFL and increase in PH, NLP, GMP, and GY, thus investment in this line is recommended. The use of mixed models and multi-trait indices maximizes the potential for selecting superior genotypes for future cultivars adapted to low-altitude environments.
References
ALVARES, C. A.; STAPE, J. L.; SENTELHAS, P. C.; GONÇALVES, J. L. de M.; SPAROVEK, G. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, v. 22, n. 6, p. 711-728, 2014.
BATTISTI, R.; SENTELHAS, P. C.; PILAU, F. G. Assessment of soybean yield with altered water-related genetic improvement traits under climate change in Southern Brazil. European Journal of Agronomy, v. 83, p. 1-14, 2017. DOI: https://doi.org/10.1016/j.eja.2016.11.004.
CRUZ, C.D.; CARNEIRO, P.C.S.; REGAZZI, A. Modelos biométricos aplicados ao melhoramento genético. Volume 2, 3. Viçosa - MG: Ed. UFV, 2014. 668p.
CRUZ, D. D. M.; HEINEMANN, A. B.; TORGA, P. P.; ALVES, E. A.; RESENDE, R. T. Genotype x environment interaction in a national network of common bean trials across the three cropping seasons in Brazil. Euphytica, v. 222, n. 76, p. 1-21, 2026. DOI: https://doi.org/10.1007/s10681-026-03725-6.
DA COSTA, R. B.; RESENDE, M. D.; ARAÚJO, A. J. de. Seleção combinada univariada e multivariada aplicada ao melhoramento genético da seringueira. Pesquisa Agropecuária Brasileira. v.35, n.2, p.381-388, 2000.
GUARIENTI, V. F.; PAVÃO, L. F. S.; MALDANER, I. C.; FOLLMANN, D. N.; PES, L. Z.; FARIAS, M. S.; RUBERT, K.; SANTOS, E. D. Desempenho agronômico, teores de proteína e óleo de cultivares de soja em ambiente de terras baixas com ocorrência de La Niña. Observatório de La Economía Latinoamericana, v. 22, n. 2, e3338-e3338, 2024. DOI: https://doi.org/10.55905/oelv22n2-161
KNEBEL, E. L. G.; CARVALHO, I. R.; LORO, M. V.; DEMARI, G. H.; OURIQUE, R. S.; ROZA, J. P. D. Strategic positioning of soybean cultivars in the state of Rio Grande do Sul. Scientia Agraria Paranaensis, v.20, n.4, p. 378-388, 2021. DOI: https://doi.org/10.18188/sap.v20i4.29136
LENHARDT, G. M.; CARVALHO, I. R.; LORO, M. V.; PORT, E. D.; LAUTENCHLEGER, F.; PRADEBON, L. C. Agronomic performance in different segregating soybean progenies. Agropecuária Catarinense, v. 36, n.1, p. 73-80, 2023.
LORO, M. V.; CARVALHO, I. R.; SILVA, J. A. G.; ALBAN, A. A.; CHALLIOL, M. A.; LAUTENCHLEGER, F. Strategic positioning of white oat cultivars in the organic system. Scientia Agraria Paranaensis, v. 20, n.4, p. 336-345, 2022. DOI: https://doi.org/10.18188/sap.v20i4.28638.
MATEI, G.; WOYANN, L.G.; MILIOLI, A.S.; OLIVEIRA, I.B.; ZDZIARSKI, D.; ZANELLA, R.; COELHO, A.S.G.; FINATTO, T.; BENIN, G. Genomic selection in soybean: accuracy and time gain in relation to phenotypic selection. Molecular Breeding, v. 38, e 117, 2018. DOI: https://doi.org/10.1007/s11032-018-0872-4.
NASA. National Aeronautics and Space Administration. NASA Prediction of Worldwide Energy Resources. 2025. Available at: https://power.larc.nasa.gov/. Accessed on: Jan 15, 2025.
OLIVEIRA, L. P.; RESEN, O.; ALMEIDA, A. B.; PAULA, I. S. M.; CABRAL, J. C. O. Composição físico-química dos grãos de soja em função da altura na planta. Científica, v. 51, p. 1-10, 2023. DOI: https://doi.org/10.5016/1984-5529.2023.v51.1455
OLIVOTO, T.; LÚCIO, A. D. metan: an R package for multi-environment trial analysis. Methods in Ecology and Evolution, v. 11, p. 783-789, 2020. DOI: http://doi.org/10.1111/2041-210X.13384.
OLIVOTO, T.; NARDINO, M. MGIDI: Toward an effective multivariate selection in biological experiments. Bioinformatics, v. 37, n. 10, p. 1383-1389, 2021.
PARAGINSKI, J. A.; MORAES, M. P.; SOUZA, R. R. D.; MELLO, A. D. C.; TOEBE, M. Correlation between productive components and grain yield of soybean cultivars sown in the northwest region of Rio Grande do Sul. Revista Ceres, v. 71, e71017, 2024. DOI: https://doi.org/10.1590/0034-737X2024710017
PORT, E. D.; CARVALHO, I. R.; PRADEBON, L. C.; LORO, M. V.; COLET, C. D. F.; SILVA, J. A. G; SAUSEN, N. H. Early selection of resilient progenies to seed yield in soybean populations. Ciência Rural, v. 54, n. 1, e20230287, 2024. DOI: https://doi.org/10.1590/0103-8478cr20230287
PRADEBON, L. C.; CARVALHO, I. R.; LORO, M. V.; PORT, E. D.; BONFADA, B.; SFALCIN, I. C.; SANTOS, I. J.; BRUINSMA, G. M.; SILVA, J. A. G.; ALBAN, M. A.; CHALLIOL, M. A. Soybean adaptability and stability analyzes to the organic system through AMMI, GGE Biplot and mixed models methodologies. Ciência Rural, v. 53, n. 9, e20220262, 2023. DOI: https://doi.org/10.1590/0103-8478cr20220262.
PRADEBON, L. C.; CARVALHO, I. R.; SILVA, J. A. G.; LORO, M. V.; DALLA ROZA, J. P. White oat progenies based on the agronomic ideotype due to response to environmental stimulus. Ciência Rural, v. 55, n. 3, e20230195, 2025. DOI: http://doi.org/10.1590/0103-8478cr20230195.
R CORE TEAM. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing, 2025. Available at: https://www.R-project.org. Accessed on: Jan 27, 2025.
RIBEIRO, A. B. M.; BRUZI, A. T.; ZUFFO, A. M.; ZAMBIAZZI, E. V.; SOARES, I. O.; VILELA, N. J. D.; PEREIRA, J. L. A. R.; MOREIRA, S. G. Productive performance of soybean cultivars grown in different plant densities. Ciência Rural, v. 47, n.7, e20160928, 2017. DOI: https://doi.org/10.1590/1678-4499.201700928
ROCHA, R. S.; SILVA, J. A. L. D.; NEVES, J. A.; SEDIYAMA, T.; TEIXEIRA, R. D. C. Desempenho agronômico de variedades e linhagens de soja em condições de baixa latitude em Teresina-PI. Revista Ciência Agronômica, v.43, n. 1, p.154-162, 2012. DOI: https://doi.org/10.1590/S1806-66902012000100019
SANTOS, H. G.; JACOMINE, P. K. T.; ANJOS, L. H. C.; OLIVEIRA, V. A.; LUMBRERAS, J. F.; COELHO, M. R.; ALMEIDA, J. A.; ARAUJO FILHO, F. C. de; OLIVEIRA, J. B. de; CUNHA, F. F. Sistema Brasileiro de Classificação de Solos. 5. ed. Brasília: EMBRAPA, 2018. 356 p.
SAVICKI, A. D. M.; CARVALHO, I. R.; LORO, M. V.; PRADEBON, L. C.; SCHMIDT, A. L. S.; SFALCIN, I. C.; SCHULZ, A. D.; MACHADO, P. P. N.; ALCHIERI, A. C. A.; SILVA, J. A. G.; ALBAN, A. A.; CHALLIOL, M. A. Positioning of white oat cultivars in different environments for high grain productivity in organic system. Tropical and Subtropical Agroecosystems, v. 26, n. 2, #034, 2023. DOI: http://dx.doi.org/10.56369/tsaes.4405.
SEDIYAMA T.; MATSUO E.; OLIVEIRA, R. C. T.; GLASENAPP, J. S. Características agronômicas de cultivares. In: SEDIYAMA, T. (Ed.) Melhoramento genético da soja. Londrina: Mecenas, 2015. p.73-82.
SEGATTO, T. A.; CARVALHO, I. R.; KEHL, K.; HOFFMANN, J. F.; MEOTTI, M. G. L.; PORT, E. D.; LORO, M. V.; SFALCIN, I. C.; PRADEBON, L. C.; OURIQUE, R. S. Adaptability and stability of wheat genotypes for amino acids in grains. Agropecuária Catarinense, v. 35, p. 82-89, 2022. DOI: https://doi.org/10.52945/rac.v35i3.1502.
SENTELHAS, P. C.; BATTISTI, R.; CÂMARA, G. M. S.; FARIAS, J. R. B.; HAMPF, A. C.; NENDEL, C. The soybean yield gap in Brazil: magnitude, causes and possible solutions for sustainable production. Journal of Agricultural Science, v. 153, n. 8, p. 1394-1411, 2015. DOI: https://doi.org/10.1017/S0021859615000313.
SILVA, C. O.; TOSHIYUKI, H. O.; NOGUEIRA, A. P. O.; ALMEIDA, M. R. C. de; CASTRO, D. G.; MARQUES, F. S.; HAMAWAKI, R. L.; HAMAWAKI, C. D. L; CARDOSO, G. M.; DINIZ, V. R. Genetic parameters and selection indexes in F2 and F2: 3 soybean populations. Agronomy Journal, v. 113, n. 4, p. 2991-3004, 2021. DOI: https://doi.org/10.1002/agj2.20692
SILVEIRA, D. C.; MACHADO, J. M.; MOTTA, E. A. M.; BARBOSA, M. R.; SIMIONI, C.; WEILER, R. L.; MILLS, A.; SAMPAIO, R.; BRUNES, A. P.; DALL’AGNOL, M. Genetic parameters, prediction of gains and intraspecific hybrid selection of Paspalum notatum Flügge for forage using REML/BLUP. Agronomy, v. 12, n. 7, 1654, 2022. DOI: https://doi.org/10.3390/agronomy12071654
SMIDERLE, O. J.; SOUZA, A. D. G.; GIANLUPPI, V.; GIANLUPPI, D.; COSTA, K. N. A.; GOMES, H. D. S. Correlação entre componentes de produção de soja BRS tracajá e diferentes densidades de plantas no cerrado Roraima. Revista Brasileira de Agropecuária Sustentável, v. 9, n. 1, p. 34-40, 2019. DOI: https://doi.org/10.21206/rbas.v9i1.3065
SOUZA, V. Q.; BELLÉ, R.; FERRARI, M.; de PELEGRIN, A. J.; CARON, B. O.; NARDINO, M.; FOLLMANN, D. N.; CARVALHO, I. R. Componentes de rendimento em combinações de fungicidas e inseticidas e análise de trilha em soja. Global Science and Technology, v. 8, n. 1, p. 167-176, 2015. DOI: 10.14688/1984-3801/gst.
SZARESKI, V. J.; SOUZA, V. Q. de; CARVALHO, I. R.; NARDINO, M.; FOLLMANN, D. N.; DEMARI, G. H.; FERRARI, M.; OLIVOTO, T. Ambiente de cultivo e seus efeitos aos caracteres morfológicos e bromatológicos da soja. Revista Brasileira de Agropecuária Sustentável, v. 5, n. 2, p. 79-88, 2015. DOI: https://doi.org/10.21206/rbas.v5i2.247
TISOTT, J. V.; CARVALHO, I. R.; PRADEBON, L. C.; LORO, M. V.; MALLMANN, G.; GONZATTO, D. Agronomic performance and selection of ideal soybean genotypes. Agri-Environmental Sciences, v. 9, n. 1, p. 1-12, 2023. DOI: https://doi.org/10.36725/agries.v9i1.8476.
TORRES, F. E.; TEODORO, P. E.; SAGRILO, E.; CECCON, G.; CORREA, A. M. Interação genótipo x ambiente em genótipos de feijão-caupi semiprostrado via modelos mistos. Bragantia, v. 74, n. 3, p. 255-260, 2015. DOI: https://doi.org/10.1590/1678-4499.0099.
TROYJACK, C.; THAÍSA, I.; DUBAL, P.; KOCH, F. Attributes of growth, physiological quality and isoenzymatic expression of common bean seeds produced under the effect of gibberellic acid. Australian Journal of Crop Science, v. 11, n. 9, p. 1116-1122, 2017. DOI: https://doi.org/10.21475/ajcs.17.11.09.pne531.
UMBURANAS, R. C.; KAWAKAMI, J.; AINSWORTH, E. A.; FAVARIN, J. L.; ANDERLE, L. Z.; DOURADO-NETO, D.; REICHARDT, K. Changes in soybean cultivars released over the past 50 years in southern Brazil. Scientific Reports, v. 12, n. 508, p. 1-14, 2022.
UNITED STATES DEPARTMENT OF AGRICULTURE. Brazil soybean area, yield and production. Country Summary – Soybean, [S. l.], [2025]. Available at: https://ipad.fas.usda.gov/countrysummary/Default.aspx?id=BR&crop=Soybean. Accessed on: July 11th, 2025.
VIEIRA, R. A.; NOGUEIRA, A. P. O.; FRITSCHE-NETO, R. Optimizing the selection of quantitative traits in plant breeding using simulation. Frontiers in Plant Science, v. 16, p. 1-19, 2025. DOI: https://doi.org/10.3389/fpls.2025.1495662.
WICKHAM, H. Ggplot2: elegant graphics for data analysis. New York: Springer-Verlag, 2016. Available at: https://ggplot2.tidyverse.org/. Accessed on: July 23th, 2025.
WOYANN, L. G.; ZDZIARSKI, A. D.; MATEI, G. Multiple-trait selection of soybean for biodiesel production in Brazil. Industrial Crops and Products, v. 140, 111721, 2019. doi: https://doi.org/10.1016/j.indcrop.2019.111721.
YANG, Q.; LIN, G.; LV, H.; WANG, C.; YANG, Y.; LIAO, H. Environmental and genetic regulation of plant height in soybean. BMC Plant Biology, v. 21, e 63, 2021. doi: https://doi.org/10.1186/s12870-021-02836-7.
ZDZIARSKI, D.; WOYANN, L. G.; MILIOLI, A. S.; ZANELLA, R.; COELHO, A. S. G.; BENIN, G. Adaptability and stability of soybean cultivars in different environments. Bragantia, v. 77, n. 2, p. 309-318, 2018. DOI: https://doi.org/10.1590/0034-737X202168040010.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Victor Donato Trolle, Ivan Carvalho, Leonardo Cesar Pradebon, Willyan Júnior Adorian Bandeira, Gabriel Mathias Weimer Bruinsma , Jaqueline Piesanti Sangiovo, João Pedro Dalla Roza
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Notice
All of the Revista Agrogeoambiental’s articles and reviews are free to access immediately after its publication.
When submitting a paper to Revista Agrogeoambiental, the authors accept the following copyright policies:
- Authors maintain the copyrights and grant the journal the right of first publication. The paper is licensed under the Creative Commons Attribution License, which allows the sharing of the work as long as it acknowledges the author and the initial publication in this journal.
- Authors are free and encouraged to store their work elsewhere, for instance in institutional repositories or websites such as academia.edu to increase circulation.
- Authors assume their authorship and are responsible for the originality and opinions in their papers.
- After the article acceptance and publishing by Revista Agrogeoambiental, the authors allow the editor to publicize the work in other medias.
- Moral rights are granted to the authors.
