Abordagem bayesiana, método tradicional e modelos mistos para experimentos multiambientes na cultura da soja

The objective of this work was to compare the Bayesian approach and the frequentist methods to estimate means and genetic parameters in soybean multienvironment trials. Fifty-one soybean lines and four controls were evaluated in a randomized complete block design, in six environments, with three replicates, and soybean grain yield was determined. The half-normal prior and uniform distributions were used in combination with parameters obtained from data of 18 genotypes collected in previous and related experiments. The genotypic values of the genotypes of high- and low-grain yield, clustered by the Bayesian approach, differed from the means obtained by the frequentist inference. Soybean assessed through the Bayesian approach showed genetic parameter values of the mixed model (REML/Blup) close to those of the following variables: mean heritability (h2mg), accuracy of genotype selection (Acgen), coefficient of genetic variation (CVgi%), and coefficient of environmental variation (CVe%). Therefore, the mixed model methodology and the Bayesian approach lead to similar results for genetic parameters in multienvironment trials.O objetivo deste trabalho foi comparar a abordagem bayesiana e os métodos frequentistas para estimar as médias e os parâmetros genéticos em experimentos multiambientes de soja. Cinquenta e uma linhagens de soja e quatro testemunhas foram avaliadas em delineamento de blocos ao acaso, em seis ambientes, com três repetições, e a produtividade de grãos foi determinada. As distribuições “half-normal” a priori e uniformes foram utilizadas em combinação com parâmetros obtidos de dados de 18 genótipos coletados em experimentos anteriores e relacionados. Os valores genotípicos de genótipos com alta e baixa produção de grãos, agrupados pela abordagem bayesiana, diferiram das médias obtidas pela inferência frequentista. A soja avaliada pela abordagem bayesiana apresentou valores de parâmetros genéticos de modelos mistos (REML/Blup) próximos daqueles das seguintes variáveis: herdabilidade média (h2mg), acurácia da seleção dos genótipos (Acgen), coeficiente de variação genético (CVgi%) e coeficiente de variação ambiental (CVe%). Portanto, em experimentos multiambientes, a metodologia de modelos mistos e a abordagem bayesiana produzem resultados similares de parâmetros genéticos.

Desempenho de genótipos de batata-doce submetidos ao efeito da calagem em Rio Largo-Alagoas

O objetivo desse trabalho foi avaliar o desempenho de genótipos de batata-doce submetidos ao efeito da calagem em Rio Largo-AL. O delineamento experimental utilizado foi o de blocos casualizados no esquema fatorial (7 x 2),  com sete genótipos de batata-doce e dois tipos de correção do solo, em três repetições. As variáveis avaliadas foram: Número de Raízes Comerciais (NRC); Número Total de Raízes (NTR); Comprimento de Raízes Comerciais (CRC); Diâmetro de Raízes Comerciais (DRC); Rendimento de Raízes Comerciais (RRC); Rendimento Total de Raízes (RTR) e Peso Médio de Raízes Comerciais (PMRC). Dentre os genótipos de batata-doce avaliados no experimento, o Clone-06 apresentou o melhor desempenho, superando todos os genótipos avaliados, inclusive as testemunhas, Rainha de Penedo e Sergipana, tanto em produtividade, com rendimento médio de 15,79 t.ha-1de raízes comerciais, quanto em qualidade, apresentando dimensões de raízes comerciais dentro dos padrões de comercialização como Extra A. O desempenho dos genótipos, bem como a grande maioria das variáveis de batata-doce não foram influenciadas pela correção do solo

Identification of superior genotypes and soybean traits by multivariate analysis and selection index

ABSTRACT The selection of superior genotypes of soybean is a complex process, thus exploratory multivariate techniques can be applied to select genotypes analyzing the agronomic traits altogether, increasing the chance of success of a breeding program. Thus, the objective of this study was to select soybean genotypes carrying the RR gene with good agronomical performance through of multivariate analysis and selection index and identify those traits that influence, also verifying the agreement of multivariate techniques and selection index in the selection process. The experiment was conducted in an increased block experimental being evaluated 227 genotypes of F5 generation, which 85 of those were detected to be glyphosate-resistant by PCR. The following traits were evaluated: number of days to maturity, plant height at maturity, lodging, agronomic value, number of branches per plant, number of pods per plant, hundred seeds weight and grain yield. The principal components analysis resulted in the selection of sixteen genotypes with higher grain yield. The traits related to the production of components exerted great influence on grain yield. The clustering by K-means and Ward's methods were similar because they clustered the specific genotypes for the selected traits in the principal components analysis in the same group. There was an agreement on the results of the multivariate analysis in the selection index of Mulamba and Mock in relation to the selected genotypes. The methodologies applied are efficient for selecting genotypes

Correlations and path analysis in soybean progenies with resistance source to cyst nematode (race 3)

Path analysis is an important study that slices the correlation coefficients between two variables to evaluate whether the relationship between them is of cause and effect. This study aimed to estimate the phenotypic and genotypic correlations between agronomic traits and perform a path analysis in order to identify variables for indirect selection aiming at a higher grain yield. Fourteen soybean F6 lines from the soybean breeding program of FCAV–UNESP, Jaboticabal, São Paulo, Brazil, were analyzed. The experimental design was a randomized block design with three replications. The agronomic traits plant height at maturity (PHM), first pod height (FPH), lodging (Ld), agronomic value (AV), number of pods per plant (NP), number of seeds per plant (NS), and grain yield (GY) were evaluated. Overall, the genotypic correlations were higher than their corresponding phenotypic correlations. The genotypic correlations between grain yield and the traits agronomic value, number of pods per plant, and number of seeds per plant were positive, significant, and of high magnitude. Path analysis showed that the trait number of seeds per plant had the highest direct effect on grain yield, while the trait number of pods per plant had the highest indirect effect through the number of seeds per plant on grain yiel

Identification of superior genotypes and soybean traits by multivariate analysis and selection index

<div><p>ABSTRACT The selection of superior genotypes of soybean is a complex process, thus exploratory multivariate techniques can be applied to select genotypes analyzing the agronomic traits altogether, increasing the chance of success of a breeding program. Thus, the objective of this study was to select soybean genotypes carrying the RR gene with good agronomical performance through of multivariate analysis and selection index and identify those traits that influence, also verifying the agreement of multivariate techniques and selection index in the selection process. The experiment was conducted in an increased block experimental being evaluated 227 genotypes of F5 generation, which 85 of those were detected to be glyphosate-resistant by PCR. The following traits were evaluated: number of days to maturity, plant height at maturity, lodging, agronomic value, number of branches per plant, number of pods per plant, hundred seeds weight and grain yield. The principal components analysis resulted in the selection of sixteen genotypes with higher grain yield. The traits related to the production of components exerted great influence on grain yield. The clustering by K-means and Ward's methods were similar because they clustered the specific genotypes for the selected traits in the principal components analysis in the same group. There was an agreement on the results of the multivariate analysis in the selection index of Mulamba and Mock in relation to the selected genotypes. The methodologies applied are efficient for selecting genotypes.</p></div