Multi-trait multi-environment models for selecting high-performance and stable eucalyptus clones.
2023
FERREIRA, F. M. | CHAVES, S. F. da S. | PEIXOTO, M. A. | ALVES, R. S. | COELHO, I. F. | RESENDE, M. D. V. de | SANTOS, G. A. dos | BHERING, L. L. | FILIPE MANOEL FERREIRA, UNIVERSIDADE FEDERAL DE VIÇOSA; SAULO FABRÍCIO DA SILVA CHAVES, UNIVERSIDADE FEDERAL DE VIÇOSA; MARCO ANTÔNIO PEIXOTO, UNIVERSIDADE FEDERAL DE VIÇOSA; RODRIGO SILVA ALVES, INSTITUTO NACIONAL DE CIÊNCIA E TECNOLOGIA DO CAFÉ; IGOR FERREIRA COELHO, UNIVERSIDADE FEDERAL DE VIÇOSA; MARCOS DEON VILELA DE RESENDE, CNPCa; GLEISON AUGUSTO DOS SANTOS, UNIVERSIDADE FEDERAL DE VIÇOSA; LEONARDO LOPES BHERING, UNIVERSIDADE FEDERAL DE VIÇOSA.
Multi-trait multi-environment (MTME) models were fitted to eucalyptus breeding trials data to assess residual variance structure, genetic stability and adaptability. To do so, 215 eucalyptus clones were evaluated in a randomized complete block design with 30 replicates and one plant per plot in four environments. At 36 months of age, tree diameter at breast height (DBH) and pilodyn penetration (PP) were measured. Two MTME models were fitted, for which residuals were considered homoscedastic and heteroscedastic, with the best MTME model selected using Bayesian information criterion. The harmonic mean of the relative performance of the genotypic values (HMRPGV) was used to determine stability and adaptability. Of the two models, the heteroscedastic MTME model had better fit and provided greater accuracy. In addition, genotype-by-environment interaction was complex, and there was low genetic correlation between DBH and PP. Rank correlation between the clones selected by the MTME models was high for DBH but low for PP. The HMRPGV facilitated clone selection through simultaneous evaluation of stability, adaptability, and productivity. Thus, our results suggest that heteroscedastic MTME model / HMRPGV can be efficiently applied in the genetic evaluation and selection of eucalyptus clones.
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Эту запись предоставил Empresa Brasileira de Pesquisa Agropecuária