The Climate Change Impact on Crop Yield in Sub-Saharan African Countries Production Function Approach

摘要

The issue of climate variability has received only limited attention in the empirical literature. There are no clear results on the link between variability in crop yield to weather variability. The purpose of the first part of this analysis is to fill this gap in the empirical literature of climate change.;Some major studies have shown that climate change have negative impact on crop yield and crop production in general. Understanding the dynamics of climatic variables impact on the mean and variance of crop yield functions is important step towards developing an optimal policy to deal with climate change. The first part of this study examines the effect of climatic variables and crop area on crops yield as Maize and Millet in the context of Sub-Sahara African (SSA) countries.;The production function approaches were used in estimating the first model. The Cobb Douglas and quadratic functional forms were used to estimate the first model. The first model results suggested that the variability of climatic variables have significant impact on Maize and Millet yield functions. The result has indicated further that temperature and precipitation impact on Maize mean is non-linear, meaning that there is always an optimum level of climate that will help in achieving the highest yield. Maize and Millet in SSA respond non-linearly to excessive temperature and precipitation. The generally negative coefficients of the squared precipitation or temperature variables indicate that the relationship between crop yield and climate is inverse U shaped. Many major studies confirmed that, extreme temperature that is higher than 32 degree Celsius is found to be harmful for Maize and other crops yield. This result is consistent across all yield model specifications.;In the second part of this study the Panel Autoregressive Modeling (P-var) has been used to estimate the model. P-var model is traced from the traditional vector autoregression (VAR) introduced by Sims (1980). Panel-var used mostly in dynamic macroeconomics analysis and proved to be more flexible, traces individual heterogeneity and improve asymptotic results (Rymaszewska 2012).;The second part of the study finds that, for the baseline model there is a significant positive effect from temperature and significant negative effect from precipitation to agriculture production index in the short run. The result shows that the use of fertilizers and machinery both have negative significant impact on agriculture production index, whereas, Livestock has positive significant effect on agriculture production index for SSA countries.