Commodity Dashboard

HarvestChoice Commodity Dashboard provides a one-page view of national time-series and sub-national statistics for 21 commodity groups.

Crop Reporter

Based on the country-level crop production statistics retrieved from FAOSTAT in 2006, this tool instantly shows users the custom ranking across countries and regions in terms of their reported harvest area, production, production value, and yield of major crops.

Yield Target and Poverty Reduction Model for South Asia

Yield Target and Poverty Reduction Model for South Asia

Comparative static model used to estimate long-term aggregate potential yield gains and aggregate potential poverty reduction effects from narrowing yield gaps for selected commodities in selected regions. The model allows for differentiated yield gap closure and technology adoption scenarios in 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).

Yield Target and Poverty Reduction Model for Sub-Saharan Africa

This comparative static model estimates potential yield increases and poverty reduction effects based on user-selected yield closure gap assumptions. A poverty-productivity elasticity (extracted from the relevant literature) and crop-specific adjustments are used to link productivity gains to a lowering of poverty prevalence rates and poverty headcounts compounded over a 20 year period.

Region Dashboard

HarvestChoice region dashboard provides a one-page view of national time-series and sub-national statistics for 52 countries in sub-Saharan Africa.

Agricultural Productivity in China

Shifting Patterns of Agricultural Production and Productivity in the United States

The Changing Landscape of Global Agriculture

Generating Plausible Crop Distribution and Performance Maps for Sub-Saharan Africa Using a Spatially Disaggregated Data Fusion and Optimization Approach

Agricultural production statistics reported at country or sub-national geopolitical scales are used in a wide range of economic analyses, and spatially explicit (geo-referenced) production data are increasingly needed to support improved approaches to the planning and implementation of agricultural development. However, it is extremely challenging to compile and maintain collections of sub-national crop production data, particularly for poorer regions of the world.

Generating Global Crop Distribution Maps: From Census to Grid

In order to evaluate food security, technology potential and the environmental impacts of production in a strategic and regional context, it is critical to have reliable information on the spatial distribution and coincidence of people, agricultural production, and environmental services.

Syndicate content