Impact of climate change on food yield in Senegal: FAVAR approach
Source: By:Mamadou Abdoulaye KONTE, Gnalenba ABLOUKA, Paoli BEHANZIN
DOI: https://doi.org/10.30564/jesr.v2i1.447
Abstract:The main objective of this research is to evaluate the impact of climate change on food crop yields in Senegal using the Factor Augmented Vector Auto Regression (FAVAR) approach. The estimation method used is principal components analysis. We identified two major shocks representative of climate change. The first is an increase of temperature (thermal shock) and the second is a decrease in the quantity of precipitation (rainfall shock). The data covers the period 1970-2014 and each of the shocks is carried out over the prior year. The impact of each shock is observed along a time horizon of 10 years. The results show a positive impact of the thermal shock on the yields of rice, maize and millet, with a much greater impact on rice and maize yield. Rising temperatures are, however, detrimental to sorghum. A decline in rainfall has a negative impact on the yields of all cereals, which is in line with expectations.
References:[1]ANSD. Situation économique et sociale du Sénégal en 2012 [R]. (2015) [2]Stads G. J., Sène L. Recherche et innovation agricoles du secteur privé au Sénégal. Tendances récentes relatives aux ressources financières et humaines et aux politiques gouvernementales [R]. Institut international de recherche sur les politiques alimentaires, Rutgers University and McGill University. (2011) [3]ANACIM. Climate risk and food security in Senegal: Analysis of climate impacts on food security and livelihoods [R]. (2013) [4]Anil Kumar Misra. Climate change and challenges of water and food security [J]. International Journal of Sustainable Built Environment Vol. 3, Issue No. 1, June 2014: 153-165 (10.1016/j.ijsbe.2014.04.006) [5]Cline William R. Réchauffement climatique et agriculture [J]. Finances et Développement (2008): 1-5 [6]Stock James H., and Mark W. Watson. Forecasting using principal components from a large number of predictors [J]. Journal of the American statistical association Vol. 97, Issue No. 460 (2002): 1167-1179 [7]Bernanke Ben S., Jean Boivin. Monetary policy in a data-rich environment? [J]. Journal of Monetary Economics, Vol. 50 (2003): 525–546 [8]Geman, S., and Geman, D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images [J]. Journal of Applied Statistics, Vol. 20, Issue No. 5-6 (1993): 25-62. (10.1080/02664769300000058) [9]Bai Jushan, and Serena Ng. Determining the number of factors in approximate factor models [J]. Econometrica, Vol. 70, Issue No. 1 (2002): 191-221 [10]Koop Gary, and Dimitris Korobilis. Bayesian multivariate time series methods for empirical macroeconomics [J]. Foundations and Trends R in Econometrics, Vol. 3, Issue No. 4 (2010): 267-358 (10.1561/0800000013) [11]Bernanke Ben S., Jean Boivin, and Piotr Eliasz. Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach [J]. The Quarterly journal of economics, Vol. 120, Issue No. 1 (2005): 387-422 (10.3386/w10220) [12]Soares, Rita. Assessing monetary policy in the euro area: a factor-augmented VAR approach [J]. Applied Economics, Vol. 45, Issue No. 19 (2013): 2724-2744 (10.1080/00036846.2012.676736) [13]Singh, Bhawan, et al. Influence d’un changement climatique dû à une hausse de gaz à effet de serre sur l’agriculture au Québec [J]. Atmosphere-Ocean, Vol. 34, Issue No. 2 (1996) : 379-399 (10.1080/07055900.1996.9649569)