Publishing Details

Tool Type: 
Model
Publication Year: 
2011
Publisher: 
HarvestChoice/International Food Policy Research Institute (IFPRI)
Years Covered: 
2010 - 2030
Version: 
v1.5
Funding Agency: 
Bill and Melinda Gates Foundation
Primary Contact: 
Melanie Bacou
Lowest Geographic Unit: 
Country

Abstract

Methods

Comparative static model used to estimate annualized and 20-year compounded aggregate potential yield gains and aggregate potential poverty reduction effects from narrowing yield gaps for targeted commodities in targeted regions.

The model allows for differentiated yield gap closure and technology adoption scenarios between focus and non-focus countries and for focus and non-focus crops. The model also accounts for varying "poverty-productivity" elasticities across commodities (a synthetic measure linking productivity gains and poverty reduction).

Data sources

In the more recent versions we use production and harvested area statistics from FAO (2004-06 national averages), SPAM crop statistics for all India provinces and more recent state government statistics for the provinces of Bihar and Orissa.

Actual yield estimates are also FAO 2004-06 averages and we used SPAM estimated yields for all India provinces.

In this analysis yield gaps for each country/commodity pair are defined as the median percent difference between low- and high-input yields sourced from GAEZ and extracted at 5 arc-minute resolution. Note that we did not apply the usual .7 adjustment factor to yield gaps (contrary to previous exercises). The spread of GAEZ reported low- and high-input yield potentials for selected crops and countries are shown in Charts 1 and 2 below. Further adjustment to the resulting yield gaps were necessary to address inconsistencies between SPAM and GAEZ crop yields (Chart 3).

Estimates of the number of South Asia residents living on less than $2/day are from World Bank, WDI, 2008.

Chart 1: GAEZ low- and high-input yield potentials for South Asia

Chart 2: GAEZ reported low- and high-input yield potentials for South Asia

Chart 3: Mean GAEZ low- and high-input yield potentials against mean SPAM yields for selected crops.

Aggregate and commodity-specific productivity-poverty elasticies are derived from the literature, in particular:

  • Delgado, C., et.al (1998). Agricultural Growth Linkages in Sub-Saharan Africa. Washington, DC: International Food Policy Research Institute (IFPRI), Research Report 107. http://www.ifpri.org/publication/agricultural-growth-linkages-sub-saharan-africa
  • Diao, X., P. Hazell, D. Resnick, and J. Thurlow ( 2007). The Role of Agriculture in Development: Implications for Sub-Saharan Africa. Washington, DC: International Food Policy Research Center (IFPRI), Research report 153. http://www.ifpri.org/publication/role-agriculture-development-0
  • Thirtle C, L. Lin L, and J. Piesse (2003). "The Impact of Research-led Agricultural Productivity Growth on Poverty Reduction in Africa, Asia and Latin America," World Development 31(12):1959-1975.

Citation

HarvestChoice, 2011. "Yield Target and Poverty Reduction Model for South Asia." International Food Policy Research Institute, Washington, DC., and University of Minnesota, St. Paul, MN. Available online at http://harvestchoice.org/node/4754.

Additional Attributes

Data Sources/Credits:

This model was used to inform the 2011's Bill and Melinda Gates Foundation Strategy Refresh in the area of agricultural productivity.

On-line data sources include:

Keywords: Afghanistan, Agro-ecological Zones (AEZ), Asia, Bangladesh, Comparative Static Model, Crop Yields, India, Models, Nepal, Pakistan, Poverty, Spatial Allocation, Spatial Production Allocation Model (SPAM), Sri Lanka, Technology Adoption, Yield Gap
Format/Platform: MSExcel 2007, R