Dissimilar characteristics in individual location treatment effects can be modelled as a random effect in a community of many and different individual observations. This study demonstrates the excellent performance of higher levels and very recent extensions of the Generalized Linear Mixed Models (GLMM); Hierarchical Generalized Linear Models (HGLM) in the global quest to developing Statistical Models with highest model accuracy. The analyses is based on raw data available at the regional Monitoring and Evaluation office of the Linking Farmers to Markets (FtM) project in Tamale - Ghana. Physical support (Fixed effect) variables measured include; crop type, Financial Credit, Training, Study tour, Demonstrative Practical’s, Networking Events, Post-harvest Equipment, Number of farmers in the FBO and Plot size cultivated. Dependent variable measured is Total Crop Yield whereas the regions and the particular communities were treated as random variables. Results showed that the HGLM 2 had the ability of specifying different suitable fixed effects model from a known distribution, a random effects model allowed to follow conjugates of arbitrary distributions from the GLM family and a dispersion model. We conclude that the HGLM 2 performs far better, gives a more fitting models and improves the quality of the crop yield models significantly.
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