This review focussed on 8 food crops - rice, wheat, maize, sorghum, millet, cassava, yam, plantain and sugarcane
In many developing countries, agriculture is the cornerstone of their economy, the basis of economic growth and the main source of livelihood. But agriculture in the developing world is often cited as being one of the sectors most vulnerable to climate change. In Africa, for example, the majority of available fresh water is used for agriculture; farming techniques are relatively simple; and much of the continent is already hot and dry. Any changes in precipitation and temperature patterns will thus have major impacts on the viability and yields in crop production. To exacerbate the situation, recent studies warn of an unprecedented confluence of pressures on agriculture – with population growth and development driving up global demand for food and competition for land, water and energy intensifying as the impacts of climate change starts to take effect. In this context, any strategy to enhance agricultural productivity in Africa and South Asia needs to ensure that natural resources are managed sustainably and adapted to climate change.
This review focussed on 8 food crops, namely rice, wheat, maize, sorghum, millet, cassava, yam, plantain and sugarcane, which collectively account for over 80% of total agricultural production in Africa and South Asia. A protocol was produced detailing the methodology; search strategy and search terms; study inclusion criteria; database sources; and approaches for data synthesis and presentation. After completing the searches of published and grey literature, 1144 sources were identified. These were ultimately filtered down to 53 based on title and abstract screening (representing 257 observations).
For each crop and region, data were extracted on the projected impacts of climate change on crop productivity (principally yield) expressed as a yield “variation” (that is projected yield for the given future scenario as a percentage of current, or baseline, yield). The review was constrained to studies using bio-physical models for impact assessment rather than statistical sensitivity analyses. Following an initial scoping, a narrative synthesis with quantitative evidence was proposed. Various metaanalyses were subsequently undertaken, although the results need to be interpreted with caution given the wide range of ‘effect modifiers’. These include, for example, the use of different general circulation models (GCM), downscaling approaches, emissions scenarios, crop varieties, husbandry techniques, agro-ecological conditions and reported scale of enquiry (local to regional). The reported yield variations thus inevitably include both the potential impacts of climate change as well as the effect of many other factors implicit in the studies.
Cranfield University, Cranfield, Bedfordshire, UK, 77 pp.