Transcript View ePoster - 2015 AGU Fall Meeting

Dynamic Predictions of Crop Yield and Irrigation
in Sub-Saharan Africa Due to Climate Change
Tierney Foster-Wittig, Duke University
Abstract
Model design
The highest damages from climate change are predicted to be in the agricultural sector in sub-Saharan
Africa. Agriculture is predicted to be especially vulnerable in this region because of its current state of
high temperature and low precipitation. It is usually rain-fed or relies on relatively basic technologies
which therefore limit its ability to sustain in increased poor climatic conditions (Kurukulasuriya et al,
2010). The goal of this research is to quantify the vulnerability of this ecosystem by projecting future
changes in agriculture due to IPCC predicted climate change impacts on precipitation and temperature.
This research will provide a better understanding of the relationship between precipitation and rainfed agriculture in savannas. In order to quantify the effects of climate change on agriculture, the impacts
of climate change are modeled through the use of a land surface vegetation dynamics model previously
developed combined with a crop model (Raes, 2010; Williams and Albertson, 2005). In this project, it will
be used to model yield for point cropland locations within sub-Saharan Africa between Kenya and
Botswana with a range of annual rainfall. With this model, future projections are developed for what can
be anticipated for the crop yield based on two precipitation climate change scenarios; (1) decreased depth
and (2) decreased frequency as well as temperature change scenarios; (3) only temperature increased, (4)
temperature increased and decreased precipitation depth, and (5) temperature increased and decreased
precipitation frequency. Therefore, this will allow conclusions to be drawn about how mean precipitation
and a changing climate effect food security in sub-Saharan Africa.
As an additional analysis, irrigation is added to the model as it is thought to be the solution to protect
food security by maximizing on the potential of food production. In water-limited areas such as SubSaharan Africa, it is important to consider water efficient irrigation techniques such as demand-based
micro-irrigation where less water is lost to evaporative demand. Demand-based irrigation is based on two
main parameters; a trigger level, to initiate the irrigation, and a target level to calculate the amount of
irrigation (Vico et al, 2011). In order to understand the impact of these two parameters on amount of
irrigated water and yield, irrigation is added to the model with variations of these two parameters
considered. This analysis will provide the information needed to understand whether irrigation is a
feasible and sustainable solution to the loss of food production due to climate change.
Model based on Montaldo et al (2005), Williams and Albertson (2005), and AquaCrop (2011)
Climate Change Combined with Irrigation Results
Soil Moisture
Water Stress
Yield
Research Region
Climate Change Results
Yield Seasonality
Yield Seasonality
Irrigation Analysis
Yield Seasonality
Sub-Saharan Africa: Kenya to Botswana
Table: IPCC Predicted Regional Climate Change
Climate Change Analysis
Precipitation (mm)
Five Climate Change Scenarios:
Change in Depth of Rainfall (α).
Change in Intertimes of Rainfall (λ)
Total Irrigated Water
www.PosterPresentations.com
Caylor, K. K., T. M. Scanlon, et al. (2009). ''Ecohydrological optimization of pattern and processes in water-limited ecosystems: A trade-off-based
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Dinar, A., R. Hassan, R. Mendelsohn, and J. Benhin. (2008). Climate Change and Agriculture in Africa: Impact Assessment and Adaptation
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IPCC (2007) Climate Change 2007: The Physical Science Basis. Cambridge University Press, Cambridge, 1056 pp.
Kurukulasuriya,P. and Mendelsohn, R.(2008).''How Will Climate Change Shift Agro-Ecological Zones and Impact African Agriculture?''. The World
Bank, Sustainable Rural and Urban Development Team, Development Research Group.
Montaldo, N., Rondena, Roberta, Albertson, John D., and Mancini, Marco (2005). ''Parsimonious Modeling of Vegetation Dynamics for
Ecohydrological Studies of Water-Limited Ecosystems.'' Water Resources Research 41(W10416).
Raes, D., Steduto, P., Hsiao, T., and Fereres, E. (2011). Chapter 3: Calculation Procedure. . AquaCrop Reference Manual Version 3.1 Plus.
Scanlon, T. M., Albertson, John D., Caylor, Kelly K., and Williams, Chris A. (2002). ''Determining Land Surface Fractional Cover from NDVI and
Rainfall Time Series for a Savanna Ecosystem.'' Remote Sensing of Environment 82: 376-388
Scanlon, T. M., and Albertson, John D. (2003). "Inferred Controls on Tree/Grass Composition in a Savanna Ecosystem: Combining 16-year
Normalized Difference Vegetation Index Data with a Dynamic Soil Moisture Model." Water Resources Research 39(8): 12-11 - 12-13.
Vico, G. and A. Porporato (2011). "From rainfed agriculture to stress-avoidance irrigation: I. A generalized irrigation scheme with stochastic soil
moisture." Advances in Water Resources 34(2): 263-271.
Williams, C., and Albertson, John (2005). ''Contrasting Short- and Song-Timescale Effects of Vegetation Dynamics on Water and Carbon Fluxes in
Water-Limited Ecosystems.'' Water Resources Research 41: 1-13
Table: Current Climate at Cropland Sites
Acknowledgements
α
The material is based upon work supported by the National Aeronautics and Space Administration(NASA)under grant 08-SC-NASA-1094. The
authors would like to thank Eric Wood and Kaiyu Guan at Princeton University for their collaboration on the 'Optimal Dynamic Predictions of
Semi-Arid Land Cover Change and Implication for Ecosystem Goods and Services' project as well as my advisor Dr. John Albertson.
λ
Table: Climate Change MAP for two Cropland Sites
RESEARCH POSTER PRESENTATION DESIGN © 2011
References
Contact: Tierney Foster-Wittig, [email protected]