The determination of variability in type ii and iii experiments of improved yields

Abstract

The goal is to determine the variability of yields in Type II and III agricultural experiments involving improved yields in Murangiri, Kenya, while determining an appropriate model that explains this variability. In this study, we investigated the effects of different treatments ranging from organic, inorganic and mixture of the two on the maize yields in Murangiri, Tharaka Nithi constituency in Kenya. We applied three statistical models to the data obtained from Type II and Type III experiments namely;FIXED EFFECTS MODEL, GENERALIZED LINEAR MODEL (GLM) and MIXED EFFECTS MODEL. We focused on interpretations and computation of model parameters and also investigated which model best fits the two datasets from the two experiments. Our study found that the treatments in general had the effects on the Maize yields in the two experiments as shown by all models fitted since the p-values of both mixed and fixed effect model are less than level of significance 0.05 while for GLM by using the deviance we showed that the fitted model with treatments were significant on both cases. On the best model, we used the model comparisons Akaike’s Information Criterion (AIC) to determine model that best fit the two datasets from the Type II and Type III experiments respectively. The study found that the mixed model was the best among the three models considered under this study as it was having the smallest values of AIC 169.071 and 280.01 for Type II and Type III experiments as indicated in tables 5 and 9 respectively. Despite the mixed model showing the smallest AIC value among the three models, the differences among these values were not very significant, implying that all three models could be used to explain the variability of yields in Type II and III agricultural experiments.