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Climate change and South Africa’s commercial farms: an assessment of impacts on specialised horticulture, crop, livestock and mixed farming systems

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Abstract

South Africa, a main food exporter in SADC, is characterised by a dual agricultural economy consisting of a well-developed commercial sector and smallholder, often subsistence, farming. Using the Ricardian cross-sectional framework, we examine the impact of climate change on a nationwide sample of crop, horticulture, livestock and mixed commercial farming systems. We find that a simultaneous decrease in precipitation and an increase in temperature will reduce productivity; and that an increase in temperature alone negatively affects farm output more than a decrease in precipitation. One of the most robust findings is the difference in the extent to which different commercial production systems will be impacted. That is, the results indicate that the strongest impact will be amongst specialised commercial crop farming system. In contrast, mixed farming systems appear to be the least vulnerable. This finding is consistent with studies on small-holder farms in sub-Saharan Africa. Hence, it appears that despite the likely benefits derived from economies of scale, commercial farms are, somewhat, equally vulnerable to climate change. Further, a province-wise assessment revealed that areas that already face disadvantageous climatic conditions will become even less productive. Overall, the findings suggest that practicing mixed farming methods will strengthen the resilience of commercial farms to climate change and that access to extensions—insurance and irrigation—is likely to reduce the risks.

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Notes

  1. According to the World Bank, in Eastern Africa, e.g. in Tanzania, agriculture contributed 33 % towards GDP, in Kenya we find 29 % and in Ethiopia agriculture formed 45 % of GDP in 2013. In Western Africa, we also find a large contribution of agriculture, for instance, 37 % of Benin’s GDP was from agriculture and 23 % in Burkina Faso and Cameroon; in Ghana, we observe a 22 % contribution of agriculture to GDP; 20 % in Guinea; 21 % in Nigeria; and 42 % in Mali. Also in Central Africa, we find a large contribution of agriculture to GDP, e.g. in Central African Republic, agriculture contributes 58 %;, in Chad the contribution of agriculture is 52 %, while in Congo we find 22 %. This is also evident in Southern Africa, although the contribution is relatively low in comparison with other parts of Africa, e.g. 2 % in South Africa in 2013; in Zambia agriculture contributes 10 %; in Zimbabwe we observe 12 %; 6 % in Lesotho; 29 % in Mozambique; 33 % in Malawi; and 6 % in Namibia. See http://data.worldbank.org/data-catalog/country-profiles for more details.

  2. For example, Christiaensen et al. (2006) and Diao et al. (2010) found that when GDP growth is led by agriculture growth, then this has a greater effect of reducing poverty than when the GDP growth rate is driven by non-agriculture growth.

  3. It is worth noting that although the, somewhat, general consensus alludes to the importance of agriculture productivity to economic growth, this view is not shared by all (for this discussion, see Dethier and Effenberger 2012). For example, Gollin (2010) in a comprehensive review of agriculture productivity and economic growth notes that the correlation between agricultural productivity and economic growth is well documented, however, because of econometric identification, less is clear about the causal relationship between the two. Gollin (2010) further notes that despite this, agriculture remains important for economic growth in many developing countries.

  4. For example, Kabubo-Mariara and Karanja (2007), Seo et al. (2009), Molua (2009), Mendelsohn et al. (2010), Di Falco et al. (2012), Bezabih and Di Falco (2012), Bryan et al. (2013), Teklewold et al. (2013), Wossen et al. (2014), Tibesigwa et al. (2015a), and Wossen and Berger (2015).

  5. It is worth noting that according to FAO (2014), family farms make up 90 % of the globe farms. Further, there is a high variation in family farm size, with some large family farms operating as commercial enterprises, while the small family farms are subsistence (i.e. non-commercial) holdings used as a survival strategy to supplement household food and income.

  6. According to FAO (2014), small farms have in general less than 2 hectares and account for 80 % of global farms. While these small farms occupy 12 % of the world’s farmland, the large farms with more than 50 hectares cover approximately 66 % of the world’s farmland. Also worth noting is the fact that, in developing country, many farms operate on a small-scale basis and relatively few are large scale (Dethier and Effenberger 2012).

  7. Because of the advantages commercial farms have over non-commercial farms, irrespective of size, there is a current drive to commercialise the African agriculture on the premise that this is likely to reduce poverty more than promoting subsistence farming. For this discussion, see WB (2008) and Collier and Dercon (2014).

  8. We use the multinomial probit model (MPM) because unlike the multinomial logit model (MLM), the MPM does not impose the strong assumption of independence of irrelevant alternative (see Wooldridge, 2001).

  9. It is worth noting that the log-linear model is recommended when the outcome variable is highly skewed and strictly positive (see Guajarati 1995; Chatzopoulos and Lippert 2015).

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Acknowledgments

The current study is part of a larger study funded and commissioned by the Fiscal and Financial Commission (FFC) and executed jointly by Environmental Policy and Research Unit (EPRU) and Anchor Environmental Consultant. In addition, we gratefully acknowledge helpful comments from numerous participants at the 5th annual Environment for Development (EfD) meeting.

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  1. Environmental-Economics Policy Research Unit, School of Economics, University of Cape Town, Private Bag, Rondebosch, Cape Town, 7700, South Africa

    Byela Tibesigwa, Martine Visser & Jane Turpie

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Tibesigwa, B., Visser, M. & Turpie, J. Climate change and South Africa’s commercial farms: an assessment of impacts on specialised horticulture, crop, livestock and mixed farming systems. Environ Dev Sustain 19, 607–636 (2017). https://doi.org/10.1007/s10668-015-9755-6

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