Spatial statistical analysis and selection of genotypes in plant breeding

Pesquisa Agropecuária Brasileira

João Batista Duarte²  Roland Vencovsky¹ 

[1] ,Universidade Federal de Goiás Escola de Agronomia e Engenharia de Alimentos Goiânia GO ,Brazil

关键词: augmented design;    mixed model;    information recovery;    autocorrelation;    correlated data;    geostatistics;    delineamento aumentado;    modelo misto;    recuperação de informação;    autocorrelação;    dados correlacionados;    geoestatística;   

DOI  :  10.1590/S0100-204X2005000200002

来源: SciELO

【 摘 要 】

The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.

【 授权许可】

CC BY   
 All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License