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Dynamic Predictions of Crop Yield and Irrigation in Sub-Saharan Africa Due to Climate Change

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 rain-fed 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 Sub-Saharan 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.

Abstract

Research Region

Model design

Climate Change Results

Irrigation Analysis ReferencesCaylor, K. K., T. M. Scanlon, et al. (2009). ''Ecohydrological optimization of pattern and processes in water-limited ecosystems: A trade-off-based hypothesis.'' Water Resources. Res. 45(8): W08407.Dinar, A., R. Hassan, R. Mendelsohn, and J. Benhin. (2008). Climate Change and Agriculture in Africa: Impact Assessment and Adaptation Strategies. London, EarthScan.Eagleson, P. S. (2002). Ecohydrology : Darwinian expression of forest form and function. Cambridge, New York Cambridge University Press.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: 376-388Scanlon, 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

Acknowledgements

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.

Contact: Tierney Foster-Wittig, tf29@duke.edu

Climate Change Analysis

Tierney Foster-Wittig, Duke University

Prec

ipita

tion

(mm

)

α

λ

Five Climate Change Scenarios:Change in Depth of Rainfall (α). Change in Intertimes of Rainfall (λ)

Water Stress

Sub-Saharan Africa: Kenya to Botswana

Total Irrigated Water

Yield Seasonality

Soil Moisture

Table: IPCC Predicted Regional Climate Change

Model based on Montaldo et al (2005), Williams and Albertson (2005), and AquaCrop (2011)

Climate Change Combined with Irrigation Results

Yield Seasonality

Yield Seasonality

Table: Climate Change MAP for two Cropland Sites

Table: Current Climate at Cropland Sites

Yield