Application and relative importance of spatial analysis for replicated multi-environment yield trials in sugarcane (Saccharum Spp.)

Abstract

Data obtained from multi-environment yield trials of sugarcane are usually analyzed using traditional models that detect only those variations existed between plots or test genotypes. However, blocking does not control spatial heterogeneity existed within and between trials. This investigation was conducted to evaluate the application and relative importance of spatial analysis for sugarcane multi-environment trials conducted under different designs. Yield data obtained from 20 yield trials were analyzed using classical linear and autoregressive models on the basis of mixed model. Results of the within trial analysis indicated both Akaike information criterion (AIC) and relative efficiency selected the classical linear model as best model in most of the cases. The anisotropic autoregressive model was the best model in modeling cane and sugar yields data of plant cane crop trials which received poor management practices and should be applied under these circumstances. When we analyze the combined multi-environment trials data, the heteroscedastic anisotropic autoregressive model showed better adjustments for cane and sugar yield data modeled across locations, while heteroscedastic linear model was the best model for cane and sugar yields data modeled over crop years. Thus, the heteroscedastic isotropic autoregressive model is recommended to be applied in sugarcane METs data analysis. Moreover, our investigation insures the applicability of the spatial analysis for data obtained from yield trials conducted under simple lattice design.