Research topic
Vulnerability of livestock to climate change: A case study of heat stress risk in South Africa
The global population is projected to increase by more than 9 billion in the year 2050, this will be accompanied by increased demand for food production (Godfray et al., 2010, Thornton et al., 2009). As a result mankind will demand more food from less land (Schneider et al., 2011) due to human settlement expansion, land deterioration and urbanisation. Developing countries are expected to experience rapid urbanisation, income increases (Steinfeld et al., 2006a, Delgado, 2003), therefore driving increased consumption of animal protein and dairy foods (Kearney, 2010) also known as the ‘livestock revolution’(Bosire et al., 2017).
Livestock is a significant global asset, and is an important source of protein, a risk buffer (i.e. animals sustain the farm when crop fails), contributor to crop production (i.e. traction), generator of income, and also plays an economic role. Production to meet the growing demands and sustain food security depends on success of livestock systems practised. But this could be prevented by change in global climate that will cause shifts in local climate, causing an impact on local and global agriculture. Meanwhile, progress in the science and observation of climate change is continuously providing literature on understanding the Earth system and its response to human activities and natural influences (Moss et al., 2010). Addressing the main vulnerabilities to animal production and obtaining a practical perspective on adaptation and mitigation strategies assists in further modelling strategies to these responses. Although, above all, understanding the perspective of a farmer on climatic changes will assist in modification of current global adaptation strategies and policies to suitable approaches in various production systems.
Objectives
- Input historical and future climate projection parameters into livestock (dairy and/beef) discomfort index to identify heat stress vulnerable areas in South Africa.
- Collect information on the degree of awareness concerning climate change and assess vulnerable communities’ likelihood to adapt and mitigate climate change impacts.
- Identify main vulnerabilities and appropriate adaptation practices.
Brief literature
With the projected increases in global population and livestock products demand. The livestock sector will continue contributing to climate change through Greenhouse gas emissions primary cause of global warming. Climate change affects agricultural production due to presence of extreme weather, temperature changes, rainfall fluctuations and seasonal pattern shifts. As a result, this will be a factor in livestock production, whereby different livestock production systems will be vulnerable at different levels. Indirect effects such as variability in feed and water availability will have livestock productivity in question. Heat stress being a direct effect leading to decreased feed intake will hinder proper growth, reproductive efficiency, milk production and increase animals’ susceptibility to diseases, while vectors have also been projected to increase.
Methodology
Historical (1980-2016) daily temperature and humidity data was obtained from 97 ARC weather stations. Projected future scenario data under RCP8.5 (2020-2080) was obtained from CSIR, under 34 stations.
”RCPs are radiative forcing scenarios; The four RCPs: 2.6, 4.5, 6.0, and 8.5 Watts/m2 are numbered according to the change in radiative forcing by 2100 and provide climate models with gridded trajectories of land use and land cover. RCPs 2.6, 4.5 and 6.0 Watts/m2 are climate policy scenarios and the RCP 8.5 scenario corresponds to a future where CO2 and CH4 emissions rise due to continuous use of fossil fuels”
seasonal THI was calculated from daily THI using the equation below. THI index has been developed as a weather safety index to monitor and reduce losses related to heat stress. The index has been used to compute and relate animal heat stress with decline in productivity of milk at various temperature ranges, additionally to determine dry matter intake and other production parameters.Seasonal (Spring and Summer) average THI for historical and future data for all station was determined in MS Excel. The LWSI was used to classify THI values to the extent of cattle vulnerability to heat stress on different South African regions.
THI = 1.8T + 32 – (0.55-0.55RH) (1.8T – 26), Where; T is temperature measured in ̊C, and RH is Relative Humidity in decimal percentage.
Mapping of South African seasonal average THI values was done on arc-GIS Software http://www.arcgis.com/index.html using the Inverse Distance Weighting (IDW) interpolation method. Based on mapped heat stress vulnerability results, cattle farmers on selected regions gave their perception on climate change. These regions were selected based on heat stress vulnerability extent. Surveys conformed to structured questionnaires or interactive group discussion with local cattle farmers. A mixture of participatory methods including open and in-depth key informant interviews allowing farmers to participate by giving their understanding based on experiences and knowledge.