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).

Crop Response Tool

This is a selection of management input scenarios and simulated crop responses for maize, rice, sorghum, wheat, groundnut, soybean, bean, cassava, potato, and yams.

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.

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 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.

[South Africa] Census of Commercial Agriculture, 2007: Financial and Production Statistics

This publication updates Statistical Release P1101, Census of commercial agriculture 2007, and in many respects can be compared with Report 11-02-01, Census of commercial agriculture 2002.

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.

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