Abstract

Conventional yield stability analyses are focused on yield stability itself by using single linear regression method and/or additive main effect and multiplicative interaction (AMMI) analysis. It is likely that yield stability for a genotype is associated with many factors such as fertilizer level, soil types, weather conditions, and/or yield components. Detection of factors highly associated with yield stability, therefore, will help breeders develop cultivars adapted to diverse environments or to specific environments. In this study, we conducted correlation analysis based on both environments and genotypes for a data set with 22 spring wheat genotypes, which were evaluated in 18 environments (combinations of years and locations) in South Dakota from 2009 to 2011. In addition, a multiple linear regression method was used to detect the associations of three agronomic traits with yield stability. The results showed that yield had diverse correlations each of three traits among different environments, indicating the importance of these three traits varied among environments. Our results also showed that plant height played a consistent important role on spring wheat yield production while the other two traits played less frequent role on yield production based on multiple linear regression analyses.

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Apr 28th, 4:00 PM

DETECTING FACTORS ASSOCIATED WITH SPRINGWHEAT YIELD STABILITY IN SOUTH DAKOTA ENVIRONMENTS

Conventional yield stability analyses are focused on yield stability itself by using single linear regression method and/or additive main effect and multiplicative interaction (AMMI) analysis. It is likely that yield stability for a genotype is associated with many factors such as fertilizer level, soil types, weather conditions, and/or yield components. Detection of factors highly associated with yield stability, therefore, will help breeders develop cultivars adapted to diverse environments or to specific environments. In this study, we conducted correlation analysis based on both environments and genotypes for a data set with 22 spring wheat genotypes, which were evaluated in 18 environments (combinations of years and locations) in South Dakota from 2009 to 2011. In addition, a multiple linear regression method was used to detect the associations of three agronomic traits with yield stability. The results showed that yield had diverse correlations each of three traits among different environments, indicating the importance of these three traits varied among environments. Our results also showed that plant height played a consistent important role on spring wheat yield production while the other two traits played less frequent role on yield production based on multiple linear regression analyses.