IFPRI HarvestChoice Dataverse

HarvestChoice generates knowledge products to help guide strategic decisions to improve the well-being of the poor in sub-Saharan Africa through more productive and profitable farming. To this end, HarvestChoice has developed and continues to expand upon a spatially explicit, landscape level evaluation framework. HarvestChoice’s evolving list of knowledge products includes maps, datasets, working papers, country briefs, user-oriented tools, and spatial and economic models designed to target the needs of investors, policymakers, and research analysts who are working to improve the food supply of the world's poor http://harvestchoice.org.

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21 to 25 of 25 Results

Agro-Ecological Zones for Africa South of the Sahara Oct 31, 2015 HarvestChoice; International Food Policy Research Institute (IFPRI), 2015, "Agro-Ecological Zones for Africa South of the Sahara", https://doi.org/10.7910/DVN/M7XIUB, Harvard Dataverse, V3 Agro-Ecological Zones (AEZ) for Africa South of the Sahara (SSA) were developed based on the methodology developed by FAO and IIASA. The dataset includes three classification schemes: 5, 8, and 16 classes, referred to as the AEZ5, AEZ8, and AEZ16, respectively.
Urban Extent of Africa 2010 Oct 16, 2015 HarvestChoice, International Food Policy Research Institute (IFPRI), 2015, "Urban Extent of Africa 2010", https://doi.org/10.7910/DVN/RUNZJD, Harvard Dataverse, V1 Accurate delineation of the urban and rural areas has a broad range of implications on the quality and reliability of agricultural production and socio-economic statistics, design of household survey, establishment of agricultural development strategies and policies, and effective resource allocation. Two most widely-used urban/rural mapping datase...
Global High-Resolution Soil Profile Database for Crop Modeling Applications Oct 15, 2015 International Research Institute for Climate and Society (IRI); Michigan State University (MSU); HarvestChoice, International Food Policy Research Institute (IFPRI), 2015, "Global High-Resolution Soil Profile Database for Crop Modeling Applications", https://doi.org/10.7910/DVN/1PEEY0, Harvard Dataverse, V2 One of the obstacles in applying advanced crop simulation models such as DSSAT at a grid-based platform is the lack of gridded soil input data at various resolutions. Recently, there has been many efforts in scientific communities to develop spatially continuous soil database across the globe. The most representative example is the SoilGrids 1km re...
Agro-ecological Zones of Africa May 12, 2015 - IFPRI Dataverse Sebastian, Kate, 2009, "Agro-ecological Zones of Africa", https://doi.org/10.7910/DVN/HJYYTI, Harvard Dataverse, V2 Agroecological zones (AEZs) are geographical areas exhibiting similar climatic conditions that determine their ability to support rainfed agriculture. At a regional scale, AEZs are influenced by latitude, elevation, and temperature, as well as seasonality, and rainfall amounts and distribution during the growing season. The resulting AEZ classifica...
Dynamic Research Evaluation for Management (DREAM) Feb 24, 2014 - IFPRI Dataverse HarvestChoice, 1995, "Dynamic Research Evaluation for Management (DREAM)", https://doi.org/10.7910/DVN/4KCBHF, Harvard Dataverse, V2 What Is DREAM? DREAM, or Dynamic Research Evaluation for Management, is a menu-driven software package for evaluating the economic impacts of agricultural research and development (R and D). Users can simulate a range of market, technology adoption, research spillover, and trade policy scenarios based on a flexible, multi-market, partial equilibriu...

Agro-Ecological Zones for Africa South of the Sahara

Oct 31, 2015

HarvestChoice; International Food Policy Research Institute (IFPRI), 2015, "Agro-Ecological Zones for Africa South of the Sahara", https://doi.org/10.7910/DVN/M7XIUB, Harvard Dataverse, V3

Agro-Ecological Zones (AEZ) for Africa South of the Sahara (SSA) were developed based on the methodology developed by FAO and IIASA. The dataset includes three classification schemes: 5, 8, and 16 classes, referred to as the AEZ5, AEZ8, and AEZ16, respectively.

Urban Extent of Africa 2010

Oct 16, 2015

HarvestChoice, International Food Policy Research Institute (IFPRI), 2015, "Urban Extent of Africa 2010", https://doi.org/10.7910/DVN/RUNZJD, Harvard Dataverse, V1

Accurate delineation of the urban and rural areas has a broad range of implications on the quality and reliability of agricultural production and socio-economic statistics, design of household survey, establishment of agricultural development strategies and policies, and effective resource allocation. Two most widely-used urban/rural mapping datase...

Global High-Resolution Soil Profile Database for Crop Modeling Applications

Oct 15, 2015

International Research Institute for Climate and Society (IRI); Michigan State University (MSU); HarvestChoice, International Food Policy Research Institute (IFPRI), 2015, "Global High-Resolution Soil Profile Database for Crop Modeling Applications", https://doi.org/10.7910/DVN/1PEEY0, Harvard Dataverse, V2

One of the obstacles in applying advanced crop simulation models such as DSSAT at a grid-based platform is the lack of gridded soil input data at various resolutions. Recently, there has been many efforts in scientific communities to develop spatially continuous soil database across the globe. The most representative example is the SoilGrids 1km re...

Agro-ecological Zones of Africa

May 12, 2015 - IFPRI Dataverse

Sebastian, Kate, 2009, "Agro-ecological Zones of Africa", https://doi.org/10.7910/DVN/HJYYTI, Harvard Dataverse, V2

Agroecological zones (AEZs) are geographical areas exhibiting similar climatic conditions that determine their ability to support rainfed agriculture. At a regional scale, AEZs are influenced by latitude, elevation, and temperature, as well as seasonality, and rainfall amounts and distribution during the growing season. The resulting AEZ classifica...

Dynamic Research Evaluation for Management (DREAM)

Feb 24, 2014 - IFPRI Dataverse

HarvestChoice, 1995, "Dynamic Research Evaluation for Management (DREAM)", https://doi.org/10.7910/DVN/4KCBHF, Harvard Dataverse, V2

What Is DREAM? DREAM, or Dynamic Research Evaluation for Management, is a menu-driven software package for evaluating the economic impacts of agricultural research and development (R and D). Users can simulate a range of market, technology adoption, research spillover, and trade policy scenarios based on a flexible, multi-market, partial equilibriu...

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