Abstract
Agriculture sector plays a significant role in the economy of Pakistan. Amongst the major crops, wheat is essential food of
Pakistani people’s. Wheat production is a function of a number of factors. The identificatsion of important variables that affects
the production of wheat is of prime interest of researcher to meet the basic need of increasing population. With multiple
predictors, statistical modeling becomes complicated mainly due to the collinearity within the predictors. Regularized
regression approaches do variable selection and shrinkage at same time. In this study, a rigorous comparison of the predictive
performance of seven regularized regression approaches was performed via simulation while considering different levels of
multicollinearity and sparsity. The results showed that Smoothly Clipped Absolute Deviation (SCAD) and Minimax Concave
Penalty (MCP) performed better under low and high variation when the true model is sparse for all sample sizes based on mean
squared error prediction (MSEP). Moreover, wheat production forecast model for Punjab province was estimated; while, using
regularized regression methods and the significant predictors were identified. Among six predictors, area under crop, average
retail price of fertilizer and average maximum temperature are important parameters that affect production of wheat. It is
recommended that the farmers of the Punjab must be care full about these factors while sowing the wheat and government
should provide facilities to farmers regarding these factors.
Nadia Idrees, Shahid Kamal. (2021) MODELLING OF WHEAT PRODUCTION IN PUNJAB THROUGH THE REGULARIZED REGRESSION APPROACH WHILE ADDRESSING MULTICOLLINEARITY, Pakistan Journal of Agricultural Sciences, Volume 58, Issue 1.
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