The effects of climate change on food production in Ghana: evidence from Maki (2012) cointegration and frequency domain causality models

Abstract

Climate change has been a major issue in policy discourse over the past four decades due to its impact on the agriculture sector. This study investigates the effects of climate change variables on food production in Ghana from 1970 to 2019. The results reveal that temperature is inimical to overall food production, maize and roots and tubers production while precipitation is good for cereal and maize production. Furthermore, Carbon dioxide (CO₂) emissions impact maize production negatively. The frequency-domain causality test reveals that CO₂ Granger causes food production in the long-run. The results suggest that high temperature is harmful to over all food production and some selected crops in Ghana. From a policy perspective, efforts to develop temperature-resilient crops, especially maize, roots, and tubers are necessary to improve food production in Ghana. In addition, there is the need to improve adaptation strategies to reduce the adverse effects of climate change on the food production.

Keywords:

• Climate change
• food security
• food production
• Maki cointegration
• frequency domain causality
• Ghana

PUBLIC INTEREST STATEMENT

Climate change has been a major issue in policy discourse over the past four decades. This is as a result of its detrimental effect on food security, humanity and the environment. Climate change poses threat to most economies and hence policies to be implemented aimed at reducing its impact are very crucial. In addition, investigating the effect of climate change on food production is a worthwhile task as it is linked to the sustainable development goals (SDGs) such as the zero hunger and no poverty. To this end, this study investigates the effect of climate change on food production in the Ghanaian context. Indeed, the study has shown that high temperature and carbon dioxide emissions are inimical to food production in Ghana, and therefore there is the need to design and implement policies to curb this negative effect of climate change.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. This term was initially used to describe warmer-water currents that occasionally flow along the coast of Ecuador and Peru, affecting the local fishery. However, it is currently synonymous with the basin-wide warming of the global Pacific Ocean east of the dateline. The fluctuation of the global-scale tropical and subtropical surface pressure trends (Southern Oscillation) coupled with the atmosphere-ocean phenomenon over approximately two to seven years is known as El Niño Southern Oscillation. Mostly, it is measured by surface pressure anomaly difference between the sea surface temperatures in the Central and Eastern Equatorial Pacific or Tahiti and Darwin (Sarginson et al., 2012).

2. Given the six variables used to capture the dependent variable, Equation (2)(2) $lnF{P}_t={\beta }_0+{\beta }_{1}lnTE{M}_t+{\beta }_{2}PR{E}_t+{\beta }_{3}lnC{O}_2+{\beta }_{4}lnRGD{P}_t+{\beta }_{5}lnPO{P}_t+{\beta }_{6}lnIN{F}_t+{\epsilon }_t$(2) is estimated for each of the dependent variables (cereal, maize, rice, roots and tubers and production).

3. We perform the Maki cointegration test using cereal, maize, rice, roots and tubers, and, vegetable production as the dependent variable in the four models. Except for vegetable production, all the variables record at least one cointegration equation. Maize and rice production record cointegration at level and level shift with trend while cereal and roots and tubers are cointegrated at level shift only. For brevity, the table for these tests is not reported. Also, since no cointegration was established for vegetable production, we exclude it from the DOLS and the FMOLS estimations. However, we include it in the causality model.

4. We do not include the results for the individual crops in this table. They are reported in Table 6.

Additional information

Funding

The authors did not receive any funding for this research.

Notes on contributors

Bright Tetteh

Bright Tetteh is currently a master’s student in Economics, specializing in Economic Science at the School of Economics, University of Cape Town (UCT), South Africa under the Mastercard Foundation Scholarship Program (MCFSP). He obtained his bachelor’s degree in Economics from the Kwame Nkrumah University of Science and Technology, Kumasi Ghana. His research interests include macroeconomics, energy economics, climate change, and econometric time series modelling.

Samuel Tawiah Baidoo

Samuel Tawiah Baidoo is currently a PhD candidate at the Department of Economics, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi-Ghana and a lecturer at the Department of Accounting and Finance, School of Business, Christian Service University College, Kumasi-Ghana. His research interests include microeconomics, monetary economics, international economics, public economics and economic policy analysis.

Paul Owusu Takyi

Paul Owusu Takyi is currently a lecturer at the Department of Economics, KNUST, Ghana. He obtained his Ph.D. and Master of Arts in Economics from the National Graduate Institute for Policy Studies (GRIPS), Tokyo, Japan. His current teaching and research interests are in the areas of Public Economics, Macroeconomics, Development Finance (Financial Inclusion), and Monetary Economics.