Environmental variables in the G x E interaction in soybean in the semiarid

José Ricardo Tavares de Albuquerque, Hamurábi Anizio Lins, Manoel Galdino dos Santos, Márcio Alexandre Moreira de Freitas, Lindomar Maria Silveira, Glauber Henrique de Sousa Nunes, Aurélio Paes Barros Júnior, Paulo Fernado de Melo Jorge Vieira

Resumo


The objective of the present work was to evaluate the influence of environmental variables on the interaction between genotypes and environments and to identify adapted and stable genotypes for grain seed yield. Twenty-one cultivars were evaluated in randomized blocks with four replications in the years 2016, 2017, and 2018 in the northeastern semi-arid region of Brazil, for seed yield and oil content. Factor regression methodologies and principal component analysis were used with predictions of the sum of the genotypic effects and the interaction to quantify the role of five environmental covariates in the genotype x environment interaction; the Harmonic Mean Method of Relative Performance of Genotypic Values was used for identifying adapted and stable genotypes. The covariance biplot model is useful for relating important environmental factors and indicating their relative effect on seed yield and oil content. Rainfall, relative humidity and maximum temperature contribute positively to increasing oil content while minimum temperature and solar radiation reduce it. Within the limits of the work, the maximum temperature positively influences grain production while the minimum reduces it. The most stable genotypes and those adapted for grain seed yield and oil content are BMX OPUS IPRO, P 98Y70 RR, BRS 333 RR, BRS 9280 RR, M 8644 IPRO, M 8372 IPRO, and ST 920 RR.

             

Palavras-chave


Glycine max L. Mixed models. Multivariate analysis. Oilseed. REML/BLUP.

Texto completo:

PDF (English)

Referências


ALLARD, R. W.; BRADSHAW, A. D. Implications of genotype-environment interactions in applied plant breeding. Crop Science, v. 4, n. 5, p. 503-508, 1964.

ALVARES, C. A. et al. Köppen's climate classification map for Brazil. Meteorologische Zeitschrift, v. 22, p. 711-728, 2013.

BASTIAANSE, H. et al. A comprehensive genomic scan reveals gene dosage balance impacts on quantitative traits in Populus trees. Proceedings of National Academy Science of United

States America, v. 16, n. 27, p. 13690-13699, 2019.

BERNARDO, R. Breeding for Quantitative Traits in Plants. 2nd ed. Stemma Press, Woodbury, MN. ISBN 978-0-9720724-1-0, 2010.

BORNHOFEN, E. et al. Statistical methods to study adaptability and stability of wheat genotypes. Bragantia, v. 76, n. 1, p. 1-10, 2017.

BRANCOURT-HULMEL, M.; LECOMTE, C. Effect of environmental varieties of genotype x environment interaction of winter wheat: A comparation of biadditive factorial regression to AMMI. Crop Science, Madison, v. 43, n. 2, p. 608-617, 2003.

BULEGON, L. G. et al. Componentes de produção e produtividade da cultura da soja submetida à inoculação de bradyrhizobium e azospirillum. Terra latinoamericana, v. 34, n. 2, p. 169-176, 2016.

CÂMARA, A. R.; MORAES, R. N. O.; SIMON, G. A. Adaptabilidade e estabilidade de genótipos de soja nos estados de Goiás e Minas gerais. Global Science Technology, v. 11, n. 02, p. 23-36, 2018.

CARNEIRO, A. K. et al. Stability analysis of pure lines and a multiline of soybean in different locations. Crop Breeding and Applied Biotechnology, v. 19, n. 4, p. 395-401, 2019.

CARVALHO, L. C. B. Interpretação da interação genótipos x ambientes em feijão-caupi usando modelos multivariados, mistos e covariáveis ambientais. Tese - Doutorado em Genética e Melhoramento de Plantas - Escola Superior de Agricultura “Luiz de Queiroz”, Piracicaba, 2015.

CARVALHO, M. M. Influência de sistemas de semeadura na população de pragas e nas características morfofisiológicas em cultivares de soja. Dissertação - Mestrado em Agronomia (Proteção de Plantas) - Universidade Estadual Paulista “Júlio de Mesquita Filho” Faculdade De Ciências Agronômicas Campus De Botucatu, Botucatu, 2014.

CRUZ, C. D.; CASTOLDI, F. L. Decomposição da interação genótipos ambientes em partes simples e complexa. Revista Ceres, v. 38, n. 219, p. 422-430, 1991.

GAUCH, H. G. Statistical analysis of yield trials by AMMI and GGE. Crop Science, Madison, v. 46, n. 03, p. 1488-1500, 2006.

GOMES, R. V.; COUTINHO, J. L. B. Recomendações de adubação para o Estado de Pernambuco: 2ª aproximação. 3. ed. Revisada. Recife: Instituto Agronômico de Pernambuco - IPA, 2008. 212 p.

HEIL, C. Rapid, multi-component analysis of soybeans by FT-NIR Spectroscopy. Madison: Thermo Fisher Scientific, 2010. 3 p. Disponível: Acesso em: 28 dez. 2019.

LEMOS, L. B.; FARINELLI, R.; CAVARIANI, C.; ZAPPAROLI, R. L. Desempenho agronômico e produtivo de cultivares de soja em diferentes safras. Científica, v. 39, n. 1/2, p. 44-51, 2011.

LIU, Y. et al. Spatial Adaptabilities of Spring Maize to Variation of Climatic Conditions. Crop Science, v. 53, n. 4, p. 1693-1703, 2013.

MACKAY, T. F. C. Q&A: Genetic analysis of quantitative traits. Journal of Biology, v. 8, n. 23, p. 1-5, 2010.

MALOSETTI, M.; BUSTOS-KORTS, D.; BOER, M. P.; VAN EEUWIJK, F. A. Predicting responses in multiple environments: Issues in relation to genotype environment interactions. Crop science, v. 56, n. 5, p. 2210-2222, 2016.

MARCHIORI, R. et al. Adaptability and stability of transgenic soybean lines and cultivars in the Brazilian macroregion 3 assessed by using parametric and nonparametric methods. African Journal of Biotechnology, v. 14, n. 49, p. 3248-3256, 2015.

MATEI, G. et al. Agronomic performance of modern soybean cultivars in multi-environment trials. Pesquisa agropecuária brasileira, v. 52, n. 7, p. 500-511, 2017.

MILIOLI, A. S. et al. Yield stability and relationships among stability parameters in soybean genotypes across years. Chilean Journal of Agricultural Research, v. 78, n. 2, p. 299-309, 2018.

MONTVERDE, E. et al. Integrating molecular markers and environmental covariates to interpret genotype by environment interaction in rice (Oryza sativa L.) grown in subtropical areas. G3: Genes Genomes Genetics, v. 9, n. 5, p. 1519-1531, 2019.

NUNES, G. H. N. et al. Influência de variáveis ambientais sobre a interação genótipos x ambientes em meloeiro. Revista Brasileira de Fruticultura, v. 33, n. 4, p. 1194-1199, 2011.

ODA, M. C. et al. Estabilidade e adaptabilidade de produção de grãos de soja por meio de metodologias tradicionais e redes neurais artificiais. Scientia Agraria Paranaensis, v. 18, n. 2, p. 117-124, 2019.

OLIVEIRA, A. B. et al. Environmental and genotypic factors associated with genotype by environment interactions in soybean. Crop Breeding and Applied Biotechnology, v. 6, n. 1, p.79-86, 2006.

OLIVEIRA, A. B.; DUARTE. J. B.; CHAVES, L. J.; COUTO, M. A. Environmental and genotypic factors associated with genotype by environment interactions in soybean. Crop Breeding and Applied Biotechnology, v. 6, n. 1, p. 79-86, 2006.

OLIVOTO, T. et al. Mean performance and stability in multi-environment trials II: selection based on multiple traits. Agronomy Journal, v. 111, n. 6, p. 2961-2969, 2019.

PEREIRA, W. A. et al. Performance of transgenic and conventional soybean plants subjected to bioassay for detection of glyphosate tolerant seeds. Crop Breeding and Applied Biotechnology, v. 18, n. 1, p. 39-46, 2018.

R CORE TEAM (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Disponível em: . Acesso em: 20 jan. 2020.

RAMASAMY, P. et al. Contribution of weather variables to G x E interaction in finger millet genotypes. International Sorghum and Millets Newsletter, v. 37, n. 1, p. 79-81, 1996.

RAMBURAN, S. A.; ZHOU, M. A.; LABUSCHAGNE, M. Interpretation of genotype x environment interactions of sugarcane: Identifying significant environmental factors. Field

Crops Research, v. 124, n. 3, p. 392-399, 2011.

RESENDE, M. D. V. de. Matemática e estatística na análise de experimentos e no melhoramento genético. Embrapa Florestas, Colombo. 2007. 435 p.

RESENDE, M. D. V. Software Selegen-REML/BLUP: a useful tool for plant breeding. Crop Breeding and Applied Biotechnology, v. 16, n. 4, p. 330-339, 2016.

RESENDE, M. D. V.; DUARTE, J. B. Precisão e controle de qualidade em experimentos de avaliação de cultivares. Pesquisa Agropecuária Tropical, v. 37, n. 3, p. 182-194, 2007.

RODRIGUES, A. R.; ABREU, M. L.; OLIVEIRA, E. S. Cultivo da soja em sistemas de semeadura em linhas cruzadas e convencional. Energia na Agricultura, v. 32, n. 1, p. 17-23, 2017.

SAEED, M.; FRANCIS, C. A. Association of weather variables with genotype x environment interactions in grain sorghum. Crop Science, Madison, v. 24, n. 1, p. 13-16, 1984.

SILVA, K. B. et al. Adaptability and phenotypic stability of soybean cultivars for grain yield and oil content. Genetics and Molecular Research, v. 15, n. 2, p. 1-11, 2016.

SILVEIRA, D. A. et al. Adaptability and stability of grain yield in soybean. Australian Journal of Crop Science, v. 12, n. 05, p. 717-725, 2018.

TOLORUNSE, K. D.; GANA, A. S.; BALA, A.; SANGODELE, E. A. Yield stability studies of soybean (Glycine max (L.) Merrill) under rhizobia inoculation in the savanna region of Nigeria. Plant Breeding, v. 137, n. 3, p. 262-270, 2018.

VAN EEUWIJK, F. A.; DENIS, J. B.; KANG, M. S. Incorporating additional information on genotypes and environments in models for twoway genotype by environment tables. In ‘Genotype-by-environment interaction’. (Eds MS Kang, HG Gauch, I Goldringer) pp. 15–50. (CRC Press: Boca Raton, FL), 1996.

VENCOVSKY, R.; BARRIGA, P. Genética biométrica no fitomelhoramento. Ribeirão Preto: Sociedade Brasileira de Genética, 1992. 486 p.

VOLTAS, J.; LÓPES-CÓRCOLES, H.; BORRÁS, G. Use of biplot analysis and factotial regression for the investigation of superior genotypes in multi-environment trails. European Journal of Agronomy, v. 22, n. 3, p. 309-324, 2005.




Revista Ciência Agronômica ISSN 1806-6690 (online) 0045-6888 (impresso), Site: www.ccarevista.ufc.br, e-mail: ccarev@ufc.br - Fone: (85) 3366.9702 - Expediente: 2ª a 6ª feira - de 7 às 17h.