Spatial and neighbourhood analysis in plant breeding. / AnÃlise Espacial e de VizinhanÃa no Melhoramento GenÃtico de Plantas.

AUTOR(ES)
DATA DE PUBLICAÇÃO

2003

RESUMO

The aim of this work was to look for alternatives to improve the efficiency of progeny evaluation experiments with common bean and maize. The focus was on analytical techniques that could enhance the experimental precision. Grain yield data were submitted to different types of analyse, ignoring or taking into account the local control of the design. For each analysis, four types of models were used: normal Gauss-Markov (GMN), moving averages on raw data (MM), moving averages on residual data (Papadakis - PPD) and spatial analysis modeling residual covariances (AE). We also investigated the consequences of considering the treatment effects as being fixed or random on modeling for genotype ranking and selection. The comparison of the different types of analysis was based on their efficiencies. Residual variances ( ), progeny variances ( ), relative semi-amplitudes (SA) of the confidence intervals of and Akaike information criteria (AIC) were calculated. Results showed that ignoring the experimental design in the model can rarely be effective, with the aid of spatial information. MM and PPD models in many cases improved the original model justified by design, although AE did not. In addition, there were differences in the progeny ranking when representing them by random effects (compared to the usual recovery of interblock information with fixed effects to treatments). AE, if ineffective, does not change variance components estimates. This property guarantees that the combination of random effects of treatments and AE do not violate the assumptions (some of these justified by the design). This is specially usefull with large experiments (with a huge number of progenies).

ASSUNTO(S)

agronomia mixed model modelo misto anÃlise espacial neighbourhood analysis spatial analysis anÃlise de vizinhanÃa

ACESSO AO ARTIGO

Documentos Relacionados