SPATIAL MODELING OF FIELD VARIABILITY IN IMPROVING THE POTENCY OF VARIETAL CONTRAST

Abstract

*In this paper, large wheat varietal experiment was comparatively studied and analyzed through classical ANOVA and latest spatial modeling approach. Spatial modeling technique captures the trend of field variability which consequently results in an unbiased varietal contrast and considerable improvement in precision of underlying experiment. An experiment based on the layout of alpha lattice design with 24 wheat varieties replicated three times was conducted for the purpose of varietal comparison. Post blocking technique was used to re-analyze the experiment using RCBD which was actually conducted using the layout of alpha design. Variogram used to capture the spatial dependence between neighboring wheat field plots which suggests serial correlation among adjacent plots. Run test was also carried out to know the pattern of variation in underlying experiment. Linear mixed spatial model was used as novel statistical method for modeling all possible sources of variation present in field trial thus get significant results using spatial modeling approach in reduction of Standard Error of Difference (SED) as compared to traditional ANOVA. Three main sources of variations were tried to capture during spatial modeling. Among five different proposed spatial and non spatial models, the best model was the row-column spatial model with a first-order spatial auto-regressive correlated error process which detains two way variability of the experiment.