Dynamic Research Evaluation for Management (DREAM)

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 equilibrium model. With DREAM you can define a range of technology investment, development, and adoption scenarios and save them in an integrated database. Scenarios are described using market, R and D, and adoption information for any number of separate "regions." Some factors, such as taxes, subsidies, growth rates, and price elasticities, can be specified as constant or as changing over the analysis period. Each region in which production takes place may have its own pattern of technology adoption. After specifying the initial conditions for each region, you can simulate the likely effects of technology development and adoption on prices; on quantities produced, consumed, and traded; and on the flow of economic benefits to producers, consumers, and government. DREAM handles simple to relatively complex evaluation problems using a standardized interface. A number of market assumptions are possible: small open economy, closed economy, vertically integrated farm and post-harvest sectors in a single economy, or multiple trading regions. The software also accommodates technology-driven shifts in supply or demand, and users may specify constant or variable shift effects over time in farmers fields. Importantly, DREAM's multiple region specification can simulate various technology "spillover" scenarios wherein a technology may be adopted in more than one region. Changes in the pattern of technology spillovers can significantly alter the size and distribution of R and D benefits. DREAM has been applied to the evaluation of individual projects in a national context as well as to entire commodity sectors at a subcontinental or continental scale. And while it was designed primarily to evaluate options for R and D that is yet to be undertaken (ex ante assessments), DREAM has also been successfully applied to analyzing the effect of past research (ex post assessments).</br

Marketsheds

This tool allows users to explore the mosaic of marketsheds that blanket sub-Saharan Africa. A market is defined as the nearest city or ‘market’ with a population greater than 20,000, 50,000, 100,000, 250,000, or 500,000. The market shed is the total area surrounding each market for which that market has the lowest cost in terms of travel time. After launching the tool, click on one of the buttons above the map to print, share, change the basemap, or create a new map by selecting a different ‘layer’ or market size. To view details about individual marketsheds, click on the map attributes.Non-PREPT

Dynamic Research Evaluation for Management (DREAM) 3.1

IFPRI1; HarvestChoiceEPT

Spatial Data for Development Domain Analysis in East and Central Africa

GIS dataset for constructing three-dimensional Development Domain for ASARECA's operation area in 12 East and Central Africa countries. Data layers of market accessibility, agricultural potential, and population density of 2010 at 5 arc-minute resolution were compiled from HarvestChoice.IFPRI1; HarvestChoiceEPTD; PIMCGIAR Research Program on Policies, Institutions, and Markets (PIM

HCID: Global grid cell identification system at multiple spatial resolutions

HCID is a global grid identification system offering users to refer the location and boundary of a grid cell, available at multiple spatial resolutions, using a single integer number. Instead of using the coordinates (latitude and longitude) of two corners of the grid cell bounding box (i.e., upper-left and lower-right), we assign each grid cell with a sequential integer number, or a grid cell ID, unique to each spatial resolution. This system was developed by HarvestChoice (http://harvestchoice.org) and is being widely used to facilitate analysis of spatial data layers, including the visualization, domain analysis, spatial aggregation/dis-aggregation, and general exchange of spatially-explicit data across disciplines - without needing to use a GIS software and spatial analysis skills. For the five arc-minute resolution of grids, we call the ID system as "CELL5M", whereas ones for 30 arc-second, 30-minute and 1 degree are called CELL30S, CELL30M and CELL1D, respectively. Assigning 0 starting at the upper-left corner (longitude: -180.0, latitude: 90.0) with a geographic projection, for example, CELL5M ranges up to 9,331,199 at the lower-right corner (longitude: 180.0, latitude: -90.0). The grid cell ID at a specific location can be easily computed mathematically, and this can be also easily converted to different resolutions.HarvestChoice; IFPRI2EPTDCGIAR Research Program on Policies, Institutions, and Markets (PIM

Harmonized Male/Female and Urban/Rural Subnational Expenditure, Poverty, and Inequality Indicators at 2011 PPP 1.90/dayand1.90/day and 3.10/day for Africa South of the Sahara

Subnational poverty headcount ratios were derived from 66 nationally representative household surveys and population census information conducted in various years around 2008 for 26 countries. Our poverty calculations are based on the comparison between the household per-capita consumption expenditure (a synthetic indicator expressing the money-metric welfare utility level) and the $1.90 and$3.10/day poverty lines expressed in international equivalent purchasing power parity (PPP) dollars in 2011. Poverty headcount with standard deviation, gap, severity, and Gini index are also provided

CELL5M: A geospatial data and analytics platform of harmonized multi-disciplinary data layers for Africa South of the Sahara

Spatially-explicit data is increasingly becoming available across disciplines, yet they are often limited to a specific domain. In order to use such datasets in a coherent analysis, such as to decide where to target specific types of agricultural investment, there should be an effort to make such datasets harmonized and interoperable. For Africa South of the Sahara (SSA) region, the HarvestChoice CELL5M Database was developed in this spirit of moving multidisciplinary data into one harmonized, geospatial database. The database includes over 750 biophysical and socio-economic indicators, many of which can be easily expanded to global scale. The CELL5M database provides a platform for cross-cutting spatial analyses and fine-grain visualization of the mix of farming systems and populations across SSA. It was created as the central core to support a decision-making platform that would enable development practitioners and researchers to explore multi-faceted spatial relationships at the nexus of poverty, health and nutrition, farming systems, innovation, and environment. The database is a matrix populated by over 350,000 grid cells covering SSA at five arc-minute spatial resolution. Users of the database, including those conduct researches on agricultural policy, research, and development issues, can also easily overlay their own indicators. Numerical aggregation of the gridded data by specific geographical domains, either at subnational level or across country borders for more regional analysis, is also readily possible without needing to use any specific GIS software. See the HCID database ( http://dx.doi.org/10.7910/DVN/MZLXVQ ) for the geometry of each grid cell. The database also provides standard-compliant data API that currently powers several web-based data visualization and analytics tools.IFPRI2; HarvestChoiceEPTDCGIAR Research Program on Policies, Institutions, and Markets (PIM

Data Africa

Data Africa is an open data visualization platform designed to provide information on key themes for research and development, such as agriculture, climate, poverty and child health across 13 countries in Africa South of the Sahara at the sub-national level. Users of the tool can explore and visualize location-specific data from various sources including HarvestChoice’s data products such as CELL5M ( http://dx.doi.org/10.7910/DVN/G4TBLF ) and Spatial Production Allocation Model ( http://dx.doi.org/10.7910/DVN/DHXBJX ), as well as the reanalysis of secondary datasets from University of East Anglia Climatic Research Unit ( http://www.cru.uea.ac.uk ), World Bank PovcalNet ( http://iresearch.worldbank.org/PovcalNet ), and USAID DHS Program ( http://dhsprogram.com ). The main goal of the online tool is to present the themes to a wide, even non-technical audience through easily accessible visual narratives. Data Africa is a HarvestChoice website. Development of Data Africa was undertaken as part of the HarvestChoice project and the CGIAR Research Program on Policies, Institutions, and Markets (PIM), led by the International Food Policy Research Institute (IFPRI), in collaboration with Datawheel and Barefoot Education for Afrika Trust (BEAT). Funding support for this platform was provided by the Bill and Melinda Gates Foundation and USAID Bureau for Food Security. The visualizations in Data Africa are powered by D3plus ( http://d3plus.org ), an open-source visualization engine that was created by Datawheel. Contents in this website have not gone through IFPRI’s standard peer-review procedure. The opinions expressed here belong to the authors, and do not necessarily reflect those of PIM, IFPRI, or CGIAR.Non-PRIFPRI2; HarvestChoiceEPTD; PIMCGIAR Research Program on Policies, Institutions, and Markets (PIM

Rapid yield gap assessment: African Development Bank's priority commodities

Yield gap of nine priority commodities of the African Development Bank was assessed and aggregated at two levels across the Africa continent: 1) agro-ecological zones and 2) agro-ecological zones by country. In this rapid assessment, the yield gap was defined as the percentage difference between the actual yield estimated from spatially-disaggregated crop production statistics database and the potential yield retrieved from the FAO's Global Agro-Ecological Zones database, both at the 10-km pixel level.HarvestChoice; IFPRI1EPTDCGIAR Research Program on Policies, Institutions, and Markets (PIM

Five-year yield growth rates of major crops in the CGIAR CRP II priority countries

Yield growth rate (%) of major crop commodities in CGIAR CRP II priority countries were computed using the FAOSTAT-retrieved national crop production statistics data for five most recent years (2009-2013 in most countries; where available, 2014 data was also included).IFPRI1; HarvestChoiceEPTD; PIMCGIAR Research Program on Policies, Institutions, and Markets (PIM