Types of Research
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The purpose of this analysis is to provide a measure of marketable surplus of maize in Tanzania. We proxy marketable surplus with national-level estimates of total maize sold, presumably the surplus for maize producing and consuming households. We also provide national level estimates of total maize produced and estimate “average prices” for Tanzania which allows this quantity to be expressed as an estimate of the value of marketable surplus. The analysis uses the Tanzanian National Panel Survey (TNPS) LSMS – ISA which is a nationally representative panel survey, for the years 2008/2009 and 2010/2011. A spreadsheet provides our estimates for different subsets of the sample and using different approaches to data cleaning and weighting. The total number of households for Tanzania was estimated with linear extrapolation based on the Tanzanian National Bureau of Statistics for the years 2002 and 2012. The weighted proportions of maize-producing and maize-selling households were multiplied to the national estimate of total households. This estimate of total Tanzanian maize-selling and maize-producing households was then multiplied by the average amount sold and by the average amount produced respectively to obtain national level estimates of total maize sold and total maize produced in 2009 and 2011.
How development organizations, NGOs, and governments can best allocate scarce resources to those in need has long been debated. As opposed to universal allocation of resources, a more targeted approach attempts to minimize program costs while maximizing benefits among those with the greatest need or market opportunity. Many international development organizations strategically target clients based on geographic location (e.g., community, region, country) or socio-economic indicators, such as the World Bank’s “$1 a day” poverty line. Drawing on literature from several sectors, this brief presents additional methods of beneficiary targeting that international development organizations might consider. We find that beneficiary targeting/segmentation has the potential to make organizational and program efforts more equitable and efficient. With limited resources, smaller organizations have tended to use single robust indicators or simple heuristics, whereas agribusinesses and private sector firms have used more data-intensive marketing tools to position their products. Technological innovation and better access to data have made targeting more prevalent and potentially more affordable in agricultural development. However, creating valid and reliable target segments remains the most significant challenge.
Contract farming (CF) is an arrangement between farmers and a processing or marketing firm for the production and supply of agricultural products, often at predetermined prices. This literature review builds on EPAR's review of smallholder contract farming in Sub-Saharan Africa (SSA) and South Asia (EPAR Technical Report #60) by specifically examining the evidence on impacts and potential benefits of contract farming for women in SSA. Key takeaways suggest women’s direct participation in contract farming is limited, with limited access to land and control over the allocation of labor and cash resources key constraints hindering women’s ability to benefit from CF. Further, we find that the impact of contract farming on women is often mediated by their relative bargaining power within the household.
This report presents data on selected agricultural commodities for the fourth quarter of 2009 (October through December 2009) and the months of January and February 2010, where available. More specifically, this report provides a summary of recent changes and trends in prices, demand, supply, and market conditions for key agricultural commodities. We find that agricultural commodity prices declined significantly in 2009 from peak 2008 levels. At the end of 2009, however, commodity prices began to rebound, which contributed to concerns over a possible return to high prices. In January and February, gains in the value of the U.S. Dollar helped keep agricultural commodity prices subdued.