Environmental stratification of rice by genotype x environment interaction analysis using five methods
DOI:
https://doi.org/10.15517/am.v31i1.35187Keywords:
environmental factors, adaptation, Oryza sativa, multivariate analysisAbstract
Introduction. Venezuela has more than two million hectares suitable for rice production, of which about the 25% are exploited, under the production system with flood irrigation with various agronomic management technologies and environments that could affect the phenotypic expression of the genetic materials. Objective. The objective of this work was to quantify the magnitude of the environmental genotype interaction (IGA) to stratify the irrigated rice environments through five analytical methods and determine their association. Materials and methods. Eight rice genotypes were evaluated in twelve environments resulting from the combination of localities and planting seasons in the regions of the Central and Western Plains of Venezuela, during the years 2012-2013. The stratification of environments was made based on five methods: traditional Lin (TL), Euclidean distance (ED), simple percentage of IGA (% SP), Pearson correlation (rxy), and factor analysis (AF). The randomized complete block statistical design was used with three repetitions in 20 m2 plots. Results. ANOVA detected significant IGA, explaining 35% of the total variation. Favorable environments for rice represented 33%. The methods used TL, ED, % SP, rxy, and AF grouped the twelve irrigated rice environments into 10, 5, 8, 10, and 3 groups respectively; they were not efficient in identifying different environments when different planting seasons were used in the same locality. Conclusions. The factor analysis method was more efficient in identifying homogeneous environments, complemented with % SP and TL methods that presented moderate association. The Bancos of San Pedro and Asoportuguesa environments were more informative and indicated for the evaluation of rice genotypes. The opposite occurred with the Araure, Algodonal, and Torunos localities.
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Acevedo M., R. Álvarez, R. Silva, O. Torres y E. Reyes. 2019. Interacción genotipo ambiente en arroz para identificar mega-ambientes y ambientes ideales mediante el modelo de Regresión por sitios (SREG) y biplot GGE. Bioagro 31(1):35-44.
Acevedo, M., E. Reyes, W. Castrillo, O. Torres, C. Marín, R. Álvarez, O. Moreno, y E. Torres. 2010. Estabilidad fenotípica de arroz de riego en Venezuela utilizando los modelos Lin-Binns y AMMI. Agro. Trop. 60(2):131-138.
Benacchio, S., y W. Avilán. 1991. Zonificación agroecológica del cultivo del arroz en Venezuela. Fondo de Investigaciones Agropecuarias (FONAIAP), Maracay, VEN.
Bernardo, R. 2002. Breeding for quantitative traits in plants. 2nd ed. Stemma Press, Woodbury, MN, USA.
Buzinaro, R. 2014. Interação de genótipos de milho vs locais, anos e épocas de semeadura. Diss. MSc., Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, BRA.
Cruz, C.D. 2006. Programa GENES: biometria. Universidade Federal de Viçosa, Viçosa, BRA.
Cruz, C., e P. Carneiro. 2006. Modelos biométricos aplicados ao melhoramento genético. Universidade Federal de Viçosa, Viçosa, BRA.
Cruz, C., e F. Castoldi. 1991. Decomposição da interação genótipos x ambientes em partes simples e complexa. Rev. Ceres 38:422-430.
Eberhart, S., and W. Russell. 1996. Stability parameters for comparing varieties. Crop. Sci. 6:36-40. doi:10.2135/cropsci1966.0011183X000600010011x
FEDEAGRO (Confederación de Asociaciones de Productores Agropecuarios). 2017. Contiúa la recesión agrícola y la indiferencia por la producción de alimentos. Los resultados de la agricultura vegetal en el año 2017. FEDEAGRO, VEN. https://fedeagro.org/resultados-de-la-agricultura-vegetal-del-2017/ (consultado 4 jun. 2018).
Felipe, C., J. Duarte, e L.F. Camarano. 2010. Estratificação ambiental para avaliação e recomendação de variedades de milho no estado de Goiás. Pesq. Agropec. Trop. 40:186-199. doi:10.5216/pat.v40i2.6158
Finney, D. 1980. Statistics for biologists. Chapman and Hall, London, GBR.
Garbuglio, D., A. Gerage, M. de-Araujo, N. Fonseca, e P. Shioga. 2007. Análise de fatores e regressão bissegmentada em estudos de estratificação ambiental e adaptabilidade em milho. Pesq. Agropec. Bras. 42:183-191. doi:10.1590/S0100-204X2007000200006
INIA (Instituto de Investigaciones Agropecuarias). s.f. Datos climáticos 2012-2013. INIA, VEN. http://www.agrometeorologia.inia.gob.ve/index.php/datos-climaticos (consultado 12 jun. 2018).
Lin, C. 1982. Grouping genotypes by a cluster method directly related to genotype-environment interaction mean square. Theor. Appl. Genet. 62:277-280. doi:10.1007/BF00276251
Murakami, D., and C. Cruz. 2004. Proposal of methodologies for environment stratification and analysis of genotype adaptability. Crop. Breed. Appl. Biot. 4:7-11. doi:10.12702/1984-7033.v04n01a02
Peluzio, J., G. Gerominni, J. da-Silva, F. Afférri, e J. Vendruscolo. 2012. Estratificação e dissimilaridade de ambientes para avaliação de cultivares de soja no estado de Tocantins. Biosci. J. 28:332-337.
Pereira, H., A. da-Costa, C. Melo., L.M.J. Del-Peloso, L., Farias, e A. Wendland. 2010. Interação entre genótipos de feijoeiro e ambientes no Estado de Pernambuco: estabilidade, estratificação ambiental e decomposição da interação. Ciências Agrárias 34:2603-2614. doi:10.5433/1679-0359
Pereira, H., L.C. Melo, L. de-Faria, M. Del-Peloso, e A. Wendland. 2013. Estratificação ambiental na avaliação de genótipos de feijoeiro-comum tipo Carioca em Goiás e no Distrito Federal. Pesq. Agropec. Bras. 45:554-562. doi:10.1590/S0100-204X2010000600004
Pimentel-Gomes, F P. 2000. Curso de estatística experimental. Nobel, São Paulo, BRA.
Ramalho, M., J. dos-Santos, A. Abreu, e J. Nunes. 2012. Aplicação da genética quantitativa no melhoramento de plantas autógamas. Universidade Federal de Lavras, BRA.
Ribeiro, J.Z., e M.I. de-Almeida. 2011. Estratificação ambiental pela análise da interação genótipo ambiente em milho. Pesq. Agropec. Bras. 46:875-883. doi:10.1590/S0100-204X2011000800013
SAS Institute Inc. 2002. The SAS system for window. V. 8. SAS Institute Inc. Cary, NC, USA.
Rohlf, F.J., and R. Sokal. 1981. Comparing numerical taxonomic studies. Systematic Zool. 30:459-490. doi:10.1093/sysbio/30.4.459
Tavares, T., S. Sousa, F. Salgados, G. Santos, M. Lopes, e R. Fidelis. 2017. Adaptabilidade e estabilidade da produção de grão em feijão comum (Phaseolus vulgaris). Rev. Ciênc. Agr. 40:411-418. doi:10.19084/RCA16058
Yan, W. 2002. Singular-value partition for biplot analysis of multi-environment trial data. Agron. J. 94:990-996. doi:10.2134/agronj2002.9900
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