A method for evaluating climate change adaptation strategies for small-scale farmers using survey, experimental and modeled data.
Sub-Saharan Africa (SSA) is predicted to experience considerable negative impacts of climate change.
Abstract
Sub-Saharan Africa (SSA) is predicted to experience considerable negative impacts of climate change. The IPCC Fourth Assessment emphasizes that adaptation strategies are essential. Addressing adaptation in the context of small-scale, semi-subsistence agriculture raises special challenges. High data demands including site-specific bio-physical and economic data are an important constraint. This paper applies a new approach to impact assessment, the Tradeoff Analysis model for Multi-Dimensional Impact Assessment (TOA-MD), which simulates technology adoption and associated economic, environmental and social outcomes in a heterogeneous farm population for a regional impact assessment. The methodology uses the kinds of survey, experimental and modeled data that are typically available in countries where semi-subsistence systems are important, combined with future socio-economic scenarios based on new scenario pathway concepts being developed by the climate change and impact assessment modeling communities. Characteristics of current and future agricultural systems, including land use, output, output price, cost of production, and farm and household size are analyzed and compared for both current and projected future climate (2030), with and without adaptation, and for different socio-economic scenarios. The methodology is applied to two study areas in Kenya. These case studies show the potential of this approach to provide a flexible, generic framework that can use available and modeled data to evaluate climate impact and adaptation strategies under a range of socio-economic scenarios.
Citation
Claessens, L.; Antle, J.M.; Stoorvogel, J.J.; Valdivia, R.O.; Thornton, P.K.; Herrero, M. A method for evaluating climate change adaptation strategies for small-scale farmers using survey, experimental and modeled data. Agricultural Systems (2012) 111: 85-95. [DOI: 10.1016/j.agsy.2012.05.003]