Climate Change Impacts and Adaptation in Rainfed Farming Systems: A Modeling Framework for Scaling-Out Climate Smart Agriculture in Sub-Saharan Africa
Affiliation(s)
1 Department of Geology, University of Botswana, Gaborone, Botswana. 2 Centre for Coordination of Agricultural Research & Development in Southern Africa (CCARDESA), Gaborone, Botswana.
1 Department of Geology, University of Botswana, Gaborone, Botswana. 2 Centre for Coordination of Agricultural Research & Development in Southern Africa (CCARDESA), Gaborone, Botswana.
ABSTRACT
Improving agricultural water productivity, under rainfed or irrigated conditions, holds significant scope for addressing climate change vulnerability. It also offers adaptation capacity needs as well as water and food security in the southern African region. In this study, evidence for climate change impacts and adaptation strategies in rainfed agricultural systems is explored through modeling predictions of crop yield, soil moisture and excess water for potential harvesting. The study specifically presents the results of climate change impacts under rainfed conditions for maize, sorghum and sunflower using soil-water-crop model simulations, integrated based on daily inputs of rainfall and evapotranspiration disaggregated from GCM scenarios. The research targets a vast farming region dominated by heavy clay soils where rainfed agriculture is a dominant practice. The potential for improving soil water productivity and improved water harvesting have been explored as ways of climate change mitigation and adaptation measures. This can be utilized to explore and design appropriate conservation agriculture and adaptation practices in similar agro-ecological environments, and create opportunities for outscaling for much wider areas. The results of this study can suggest the need for possible policy refinements towards reducing vulnerability and adaptation to climate change in rainfed farming systems.
Improving agricultural water productivity, under rainfed or irrigated conditions, holds significant scope for addressing climate change vulnerability. It also offers adaptation capacity needs as well as water and food security in the southern African region. In this study, evidence for climate change impacts and adaptation strategies in rainfed agricultural systems is explored through modeling predictions of crop yield, soil moisture and excess water for potential harvesting. The study specifically presents the results of climate change impacts under rainfed conditions for maize, sorghum and sunflower using soil-water-crop model simulations, integrated based on daily inputs of rainfall and evapotranspiration disaggregated from GCM scenarios. The research targets a vast farming region dominated by heavy clay soils where rainfed agriculture is a dominant practice. The potential for improving soil water productivity and improved water harvesting have been explored as ways of climate change mitigation and adaptation measures. This can be utilized to explore and design appropriate conservation agriculture and adaptation practices in similar agro-ecological environments, and create opportunities for outscaling for much wider areas. The results of this study can suggest the need for possible policy refinements towards reducing vulnerability and adaptation to climate change in rainfed farming systems.
KEYWORDS
Climate Change, Adaptation, Rainfed Farming Systems, A Modeling, Climate Smart Agriculture, Southern Africa
Climate Change, Adaptation, Rainfed Farming Systems, A Modeling, Climate Smart Agriculture, Southern Africa
Cite this paper
Alemaw, B. and Simalenga, T. (2015) Climate Change Impacts and Adaptation in Rainfed Farming Systems: A Modeling Framework for Scaling-Out Climate Smart Agriculture in Sub-Saharan Africa. American Journal of Climate Change, 4, 313-329. doi: 10.4236/ajcc.2015.44025.
Alemaw, B. and Simalenga, T. (2015) Climate Change Impacts and Adaptation in Rainfed Farming Systems: A Modeling Framework for Scaling-Out Climate Smart Agriculture in Sub-Saharan Africa. American Journal of Climate Change, 4, 313-329. doi: 10.4236/ajcc.2015.44025.
References
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http://dx.doi.org/10.1126/science.1152339 [17] Fischer, G., Shah, M., Tubiello, F.N. and Van Velhuizen, H. (2005) Socio-Economic and Climate Change Impacts on Agriculture: An Integrated Assessment, 1990-2080. Philosophical Transactions of the Royal Society B, 360, 2067-2083.
http://dx.doi.org/10.1098/rstb.2005.1744 [18] Challinor, A., Wheeler, T., Garforth, C., Crauford, P. and Kassam, A. (2007) Assessing the Vulnerability of Food Crop Systems in Africa to Climate Change. Climatic Change, 83, 381-399.
http://dx.doi.org/10.1007/s10584-007-9249-0 [19] World Bank (2009) Agriculture and Rural Sector South Asia: Shared Views on Development and Climate Change. World Bank, Washington DC, 97-107. [20] Connolly-Boutin, L. and Smit, B. (2015) Climate Change, Food Security, and Livelihoods in Sub-Saharan Africa. Regional Environmental Change.
http://dx.doi.org/10.1007/s10113-015-0761-x [21] Makondo, C.C., Chola, K. and Moonga, B. (2014) Climate Change Adaptation and Vulnerability: A Case of Rain Dependent Small-Holder Farmers in Selected Districts in Zambia. American Journal of Climate Change, 3, 388-403.
http://dx.doi.org/10.4236/ajcc.2014.34034 [22] Nakicenovic, N. and Swart, R. (2000) Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, 599 p. [23] Jones, J.W. (1986) Decision Support System for Agrotechnology Transfer. Agrotechnology Transfer, 2, 1-5. [24] Williams, J.R. (1995) The EPIC Model. In: Singh, V.P., Ed., Computer Models of Watershed Hydrology, Water Resources Publications, Highlands Ranch, 909-1000. [25] Jones, C.A. and Kiniry, J.R. (1986) CERES-Maize: A Simulation Model of Maize Growth and Development. Texas A&M University Press, College Station, 194 p. [26] McCown, R.L., Hammer, G.L., Hargreaves, J.N.G., Holzworth, D. and Huth, N.I. (1995) APSIM—An Agricultural Production System Simulation Model for Operational Research. Mathematics and Computers in Simulation, 39, 225-231.
http://dx.doi.org/10.1016/0378-4754(95)00063-2 [27] FAO (1992) Cropwat—A Computer Program for Irrigation Planning and Management. FAO Irrigation and Drainage Publications, No. 46, Rome. [28] Alemaw, B.F., Chaoka, T.R. and Totolo, O. (2006) Investigation of Sustainability of Rain-Fed Agriculture through Soil Moisture Modeling in the Pandamatenga Plains of Botswana. Physics and Chemistry of the Earth, 31, 960-996.
http://dx.doi.org/10.1016/j.pce.2006.08.009 [29] Fant, C. (2009) CliCrop: A One-Dimensional Model to Calculate Water Stress on Crops. University of Colorado at Boulder, UMI Dissertations Publishing, Ann Arbor. [30] SCS (1985) National Engineering Handbook, Section 4: Hydrology, Soil Conservation Service. USDA, Washington DC. [31] Crespo, O., Hachigonta, S. and Tadross, M. (2011) Sensitivity of southern African Maize Yields to the Definition of Sowing Dekad in a Changing Climate. Climate Change, 106, 267-283.
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