Blog entries filtered by: Southern Africa, West Africa, Agroecology
Risk: we all know what that means, right? But why is it such a critical part of our HarvestChoice portfolio, what does it mean when you look through the lens of farming in Sub-Saharan Africa?
Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) and HarvestChoice are now working together to apply innovative, “bio-economic” approaches to improve the food security of poor people in sub-Saharan Africa and South Asia. Together, our organizations will merge biological, environmental and economic tools to track global food pests and better target strategic investments to improve global food security.
For crop modeling researchers who are in need of finding soil profiles at regional-scale in Sub-Saharan Africa (SSA), this post gives a spatial dataset that delineates SSA into 588 units and corresponding soil profiles, based on the WISE v1.1 and HC27 soil profile databases.
Mapping the global extent of soil constraints to crop growth plays an important role in developing strategies for agricultural production, environmental protection, and sustainable development at regional and global scales. The most widely used dataset is the Soil Fertility Capability Classification System (FCC) developed by Center for International Earth Science Information Network (CIESIN) and the Tropical Agriculture Program of the Earth Institute at Columbia University.
Many of HarvestChoice spatial datasets are organized and released on 10-km grids. To make spatial analyses easier for researchers (even without having access to GIS platform), we put data layers from multiple themes together in one denormalized big table. This post describes the methodology and presents a prototype.
Growing seasons define the period of time when temperature and moisture conditions are suitable for crop growth.
The spatial team broke new ground first in negotiating access to many previously unavailable national poverty maps (based on national poverty lines) and then, while acknowledging significant conceptual and methodological issues remain, constructing the first sub-national poverty map of the developing world...
As a quick demonstration to estimate crop yield levels at regional-scale with various management assumptions, this post describes how crop systems models can be used to assess yield gap of rainfed maize due to the limited supply of soil nitrogen. This methodology can help researchers to find what is the most critical factor that limits crop yield productivity in a given environment condition and how to address the constraint.
Many of HarvestChoice’s spatial analyses are done using raster datasets at various spatial resolutions (e.g., 1 km, 10 km, or 50 km grids).