Effects of Climate Change on Maize Production in Ghana - A Comparative Study of Parametric and Non-Parametric Regression Models

Albert Buabeng, Lewis Brew, Benjamin Odoi, Francis Obiri-Yeboah

Abstract


The devastating effects of climate change on the production of agricultural commodities has become a source of worry for many developing countries and therefore demands due attention. For these reasons, this paper sought to formulate models for analysing the effect of climate change on maize production in Ghana as there has been an alarming fluctuation in productivity across the country. Agroclimatic data such as wind speed, temperature, humidity, carbon dioxide and precipitate were obtained. First, a Multiple Regression Analysis (MRA) was performed using all the variables that resulted in high multicollinearity levels. Factor Analysis (FA) was employed to transform the dataset into a set of uncorrelated features to remedy the multicollinearity problem and perform a reliable analysis. Thus, the resulting features were used in developing two models based on parametric MRA and non-parametric Multivariate Adaptive Regression Splines (MARS). The results from the analysis indicate that the MARS model based on extracted features achieved a higher prediction accuracy of 76.59% when compared with the MRA’s model (73.73%). Moreover, the MARS model produced the least Mean Absolute Percentage Error (MAPE) of 8.32% when compared to MRA’s 12.12% during validation.

Keywords


Maize Productivity; Climate Change; Factor Analysis; Parametric; Non-parametric; Regression

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References


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