Panel threshold effect of climate variability on agricultural output in Eastern African countries

Abstract

Recent scientific literature shows that in many developing countries, variability in rainfall and temperature in growing season has detrimental effects on agricultural output, especially when the variability is high. It is yet unclear to what extent or threshold these variations impair the agricultural productivity in some parts of Africa. In this study, we answer this research question using a dynamic panel threshold model on a panel dataset of East African countries for the period 1961–2020. We incorporate climate variables disaggregated into growing and non-growing seasons. The empirical results indicate that growing rainfall variability has significant effects on agricultural output. More specifically, we found a significant negative effect from rainfall variability in spring and summer, when precipitation variability exceeds thresholds of −0.533 mL and −0.902 mL respectively. However, these effects are indistinguishable from zero in fall season. Regarding a growing-season temperature variability, we found no significant effects across seasons. Policy implications are discussed.

Impact statement

The findings suggest that African countries should speed up renovating/investing in small scale technologies to alleviate the impact of the within growing season precipitation variability. To mitigate the effect caused by the growing seasonal variability in precipitation, technologies such as flexible planting, rainwater harvesting, smart water-management systems that use drop-bydrop, sprinkler irrigation processes to improve agricultural output. Furthermore, new policies should be implemented by governments to encourage innovation in technology.

Keywords:

• Adaptation policy
• climate change
• Eastern Africa
• dynamic panel threshold

Reviewing Editor:

• Lanouar Charfeddine, Qatar University, Qatar

Subjects:

• Agriculture and Food
• Environment & Economics
• Development Economics
• Ecological Economics

JEL CODE:

• Q10
• Q18
• Q54

Disclosure statement

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

Notes

1 Ricardian approach estimates the impact of climate variables in the agriculture sector (Mendelsohn, 2014). Ricardian regression specification typically includes historical average precipitation, historical average temperature, and growing and non-growing seasons (Abraha-Kahsay & Hansen, 2016).

2 The quadratic term in Ricardian approach is used to define the net revenue climate response function in order to reflect its nonlinear structure, which shows how the marginal effect will vary as one goes away from the mean (Mendelsohn et al., 1994). The net revenue function has a U-shaped form when the quadratic component is positive (Huong et al., 2019), meaning increasing temperature during dry season significantly reduces the households’ net revenue.

3 Wang et al. (2009) revealed that at least one of the climate variables is significant in every season except for autumn temperature and summer precipitation. The fact that several of the squared coefficients of climate variables are significant suggests that the impacts of climate change on the crop net revenue are nonlinear. However, their findings revealed also that it is challenging to understand the coefficients themselves due to the quadratic structure of the climatic variables. Hence, determining a threshold is crucial.

4 Livestock capital input refers to FAO’s head count of cattle, sheep, and goats (Abraha-Kahsay & Hansen 2016; Barrios et al. 2008). In addition to include the most significant animal types, the proxy also overcomes the weighting problem raised by Barrios et al. (2008) by leaving out less significant animal groupings.