MAPPR

MAPPR

MAPPR provides easy access to over one hundred layers of spatially-explicit data for sub-Saharan Africa. Use MAPPR to “drill through” and extract information from fine-resolution data layers (each layer holds ~300,000 10km x 10km grid cells covering the sub-Saharan African region).

Domain Reporter

Browse and download HarvestChoice spatial data layers for sub-Saharan Africa. Statistics are shown for all countries and level-1 administrative units.

Commodity Dashboard

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

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.

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

Large gaps exist in our knowledge of the current geographic distribution and spatial patterns of performance of crops, and these gaps are unlikely to be filled.

Crop Production: SPAM

Dragging and dropping the red marker (Droppr) to any location in SSA provides information on:
• Country name
• District name
• Average cropping intensity (SPAM)
• Crop name (SPAM)

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.

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