An Investigation of Agricultural Farm Dynamics using Bivariate Binary Regression

For two correlated binary responses, bivariate binary logistic regression is a suitable way to identify the related covariates and, their interactions validity can be investigated in terms of logarithm of odds ratio. Model parameters can be estimated in Bayesian approach with available prior information. Present study uses data obtained from a longitudinal survey conducted in 2002–2005 to make a guideline for the agricultural development and food security in Africa. Defining the terms ‘Extensification’ and ‘Intensification’ as two traditions for the farm dynamics, a simple data analysis is done using bivariate binary regression in WinBUGS. Results indicate that some factors, for instance, ‘availability of new crop technology’, ‘import of Maize’ and ‘stopped intercropping’ shows negative association with farm dynamics, discouraging the production of Maize and areal increase of its cultivation. Whereas ‘Change in fertilizer use’, ‘cultivated area increase’ and ‘started selling maize’ are the factors supporting the argument of areal cultivation and Maize production increase. Farm holders’ access to modern crop technologies, in combination with commercial incentives to staple crop production emerges as the most important explanation of dynamism.