Eficiência da análise estatística espacial na classificação de famílias do feijoeiro - estudo via simulação

• Ano de defesa: 2011
• Autor(a) principal: Campos, Josmar Furtado de
• Orientador(a): Carneiro, Antônio Policarpo Souza
• Banca de defesa: Santos, Gérson Rodrigues dos , Finger, Fernando Luiz
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Viçosa
• Programa de Pós-Graduação: Mestrado em Estatística Aplicada e Biometria
• Departamento: Estatística Aplicada e Biometria
• País: BR
• Palavras-chave em Português: Modelo não-linear; Dependência espacial; Semivariograma; Acurácia seletiva; Eficiência de seleção; Modelo exponencial; Geoestatística
• Palavras-chave em Inglês: Nonlinear model; Spatial dependence; Semivariogram; Accuracy selective; Efficiency of selection; Exponential model; Geostatistics
• Área do conhecimento CNPq: CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETAL
• Link de acesso: http://locus.ufv.br/handle/123456789/4039

The aim of this study was to evaluate the efficiency of spatial analysis, which considers spatially dependent errors, for classification of common bean families in relation to traditional analysis in randomized blocks and lattice that assuming independent errors. Were considered different degrees of spatial dependence and experimental precision. Were taken as reference to simulate the results of seven experiments carried out in simple square lattice for genetic evaluation of yield (g/plot) of families and bean cultivars of winter crops and water used in 2007 and 2008. From the results presented in the simulation, it was possible to assess the quality of their experiments based on different analysis (Block, lattice and Spatial) and simulated average of 100 families in different scenarios for Spatial Dependence (DE) and Accuracy Selective (AS). In the process of simulation, the average yield (645 g/plot) and the residual variance (7744.00), was defined based on the analysis results of the tests in blocks of bean breeding program at UFV. To make up the four simulated scenarios were considered magnitude of spatial dependence (null, low, medium and high), corresponding to ranges of 0, 25, 50 and 100% of the maximum distance between plots. Were also simulated three classes of selective accuracy (0.95, 0.80 and 0.60), corresponding to the experimental precision very high, high and average, respectively. The actual classification of families was used to evaluate the efficiency of analysis methods tested by Spearman correlation applied to orders and genotypic classification of Selection Efficiency between classifications based on tested methodologies and the actual classification for the selection of 10, 20 and 30% of the best families. To compare the efficiency of adjustment of the models tested, was used the Akaike information criterion (AIC), based on likelihood. Spatial analysis has provided estimates of residual variance very close to the simulated residual variance and higher selective accuracy estimated in all scenarios, indicating greater experimental accuracy. With the reduction in the accuracy and selective increase in spatial dependence, there was greater influence of analysis on the classification of families, and the spatial analysis showed the best results, providing more efficient selection of bean families than traditional analysis of randomized blocks and lattice, mainly for the selection of fewer families. The results for selective accuracy estimated on the basis of F statistics were very close to those obtained with the Spearman correlation between estimated and simulated averages for families, indicating that the accuracy should be used selectively as a measure of experimental precision tests of genetic evaluation.