Spatial statistical analysis and selection of genotypes in plant breeding
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Data
2005-02
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Resumo
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.
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Palavras-chave
Augmented design, Geostatistics, Mixed model, Autocorrelation, Information recovery, Correlated data, Delineamento aumentado, Geoestatística, Modelo misto, Dados correlacionados, Recuperação de informação, Autocorrelação
Citação
DUARTE, João Batista; VENCOVSKY, Roland. Spatial statistical analysis and selection of genotypes in plant breeding. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 40, n. 2, p. 107-114, Feb. 2005.