Rapid detection of stressed agricultural environments in Africa under climatic change 2000–2050 using agricultural resource indices and a hotspot mapping approach

Abstract

Agricultural environments in Africa are most vulnerable to climate change because of extensive reliance on rainfed crop and livestock production, high intra-and inter-seasonal climate variability, recurrent extreme weather and climate events. The dependency of Africa's population on generating income mostly from the agricultural resource base combined with persistent poverty and food insecurity increases exposure and vulnerability to climate variability and climate change. In this paper an approach is outlined for the rapid identification of agricultural areas under stress due to potential mismatches between natural resource availability under changing climatic conditions and needs for a growing and largely agricultural population. In this approach spatial data on climate, soils, topography, and water resources for agriculture were converted into thematic indices to quantify spatial variations in the resource base under baseline 1960–1990 climatic conditions and climate change projections for 2050. These indices were combined to produce integrated agricultural resource availability indices, which were then combined with population density using a per-capita Agricultural Resource Availability Index, which was subjected to a hotspot-cold spot analysis. The hotspot-cold spot analysis allows to make a rapid synthesis of changes in the agricultural resource base under various climate change projections and population pressure. The results of the analysis indicate that in all African countries, the per-capita Agricultural Resource Availability Index tends to decline under climate change projections combined with population growth. Countries that currently already are hotspots (Burkina Faso, Burundi, Nigeria, Rwanda) remain hotspots entirely, whereas countries with significant ‘cold spot’ areas (Tanzania, Zimbabwe, and to a lesser extent Ethiopia) see these areas further turn into hotspots due to combined effect of population growth and climate change scenario used. The impact of climatic change is expected to be greater where rural populations are dense, poorer and largely dependent on income generation from agriculture. The analysis can serve as an exploratory medium-term and broad-spectrum forecasting tool for identifying areas that either already are or could eventually become stressed in their food security due to the combination of climate change and pressure on the resource base from population increase. Introducing capabilities in medium-term warning at the national level could be facilitated greatly by starting or strengthening the process of compiling relevant databases that allow a better synthesis within countries of the likely evolution of the agricultural resource base under climate change. Many data, even if imperfect, already exist for this purpose and could serve as very useful additions for national-level climate change adaptation responses, which offers principles, practices and options for ‘cooling off’ hotspots.