The effect of climate change and adaptation policy on agricultural production in Eastern Africa

Abstract

We estimate the production function for agricultural output in Eastern Africa incorporating climate variables disaggregated into growing and non-growing seasons. We find a substantial negative effect of within growing season variance of precipitation. We simulate predicted climate change for the region and find a resulting output reduction of between 1.2% and 4.5%. We also find substantial potential for mitigating the effects of within growing season precipitation variability through conventional technologies such as flexible planting and rainwater harvesting that substantially exceeds the potential loss from predicted climate change.

Effect of Climatic and Non-Climatic Factors on Cassava Yields in Togo: Agricultural Policy Implications

“…In line with previous crop yield response studies in Africa (e.g., [36,37]), we assume a Cobb-Douglas functional form for the base model, and rewrite Equation (1) as follows: …”

“…), yam and cassava (RPYamCass ( [36]; a similar definition is used for the lean season, but using months in the lean season); the within-lean-season rainfall variability (LSavprec_Var); the main-season mean temperature (MSavtemp); and the lean-season mean temperature (LSavtemp). In line with previous crop yield response studies in Africa (e.g., [36,37]), we assume a Cobb-Douglas functional form for the base model, and rewrite Equation (1) as follows: …”

“…From Equation (1), the yield of cassava (YCass) is assumed to be a function of the area of cassava harvested (ACass); the total rural population (Rul pop-a proxy for labour availability); the real producer price ratios between maize and cassava (RPMaiCass (RP Mai t /RP Cass t , RP Mai t : real producer price o f maize at time t, RP Cass t : real producer price o f cassava at time t), yam and cassava (RPYamCass (RP Yam t /RP Cass t , RPYam t : real producer price o f yam at time t)), and common beans and cassava (RPBeaCass (RP Bea t /RP Cass t , RP bea t : real producer price o f common beans at time t)); the nominal exchange rate (Exr); the total volume of rainfall for the main season (MSavprec); the total volume of rainfall for the lean season (LSavprec), the within-main-season rainfall variability (MSavprec_Var (measured as the standard deviation of the monthly (months in the main season) means expressed as a percentage of their respective seasonal means [36]; a similar definition is used for the lean season, but using months in the lean season); the within-lean-season rainfall variability (LSavprec_Var); the main-season mean temperature (MSavtemp); and the lean-season mean temperature (LSavtemp).…”

“…In analyzing the yield response of crops, several approaches have so far been documented in the literature, including simple Ordinary Least Squares (OLS) estimation of multiple regression (e.g., [36]), Nerlovian adjustment cum adaptive expectation model [38], and cointegration analysis (e.g., [39,40]). In this study, emphasis is placed on cointegration analysis.…”

Mobility and Sustainability: A Computational Model of African Pastoralists

“…For example, global agricultural production, even with contemporary technological advancement, entails a substantial amount of crop yield variability, which is largely due to climate variations (Kahsay & Hansen, 2016;Ray, Gerber, MacDonald, & West, 2015;Urban, Roberts, Schlenker, & Lobell, 2012;Wheeler & von Braun, 2013). The recent surge in demand for biofuel has made such production variability even more pronounced, as several studies on inter-annual variations in corn stover supply for the US suggest (Golecha & Gan, 2016;Thompson, Meyer, & Westhoff, 2009).…”