Impact of Climate Change on Cocoa Yield in Ghana Using Vector Autoregressive Model

Abstract

This study examined the impact of four main climate variables (maximum temperature, minimum temperature, precipitation and number of rainy days) on cocoa yield in Ghana. The Vector Autoregression (VAR) model with the use of Granger Causality test, impulse response functions and variance decomposition for the data was used to examine the dynamic impact of climate change on cocoa yields. It was observed that the maximum temperature was the climatic variable which had the highest number of significant cross correlation (lags) on yield followed by the minimum temperature. The VAR(2) model was selected as the adequate model among competing models to describe the association between yield of cocoa and climatic factors. The R² value indicated that 48.5% of the total variation in the yield of cocoa can be explained by the climate variables considered in this study. Granger causality test indicated that the direction of causality is from maximum temperature (maxt), minimum temperature (mint) and precipitation (PRE) to yield since their corresponding F-statistic are significant. However, there was no causation from the number of rainy days to yield, since the F-statistic is statistically insignificant. Maximum temperature (by significant lag), number of rainy days have negative effects on yield, whereas minimum temperature and precipitation affect yield positively. It is therefore recommended that, the government should develop adaptation strategies that would fit into climatic condition and the agriculture sector should develop new seedling of cocoa that will have resistance to higher temperature, low precipitation in order to maintain high production of cocoa beans yield in Ghana.