Types of Research
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This research considers how public good characteristics of different types of research and development (R&D) and the motivations of different providers of R&D funding affect the relative advantages of alternative funding sources. We summarize the public good characteristics of R&D for agriculture in general and for commodity and subsistence crops in particular, as well as R&D for health in general and for neglected diseases in particular, with a focus on Sub-Saharan Africa and South Asia. Finally, we present rationales for which funders are predicted to fund which R&D types based on these funder and R&D characteristics. We then compile available statistics on funding for agricultural and health R&D from private, public and philanthropic sources, and compare trends in funding from these sources against expectations. We find private agricultural R&D spending focuses on commodity crops (as expected). However contrary to expectations we find public and philanthropic spending also goes largely towards these same crops rather than staples not targeted by private funds. For health R&D private funders similarly concentrate on diseases with higher potential financial returns. However unlike in agricultural R&D, in health R&D we observe some specialization across funders – especially for neglected diseases R&D - consistent with funders’ expected relative advantages.
There is a wide gap between realized and potential yields for many crops in Sub-Saharan Africa (SSA). Experts identify poor soil quality as a primary constraint to increased agricultural productivity. Therefore, increasing agricultural productivity by improving soil quality is seen as a viable strategy to enhance food security. Yet adoption rates of programs focused on improving soil quality have generally been lower than expected. We explore a seldom considered factor that may limit farmers’ demand for improved soil quality, namely, whether farmers’ self-assessments of their soil quality match soil scientists’ assessments. In this paper, using Tanzania National Panel Survey (TZNPS) data, part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA), we compare farmers’ own assessments of soil quality with scientific measurements of soil quality from the Harmonized World Soil Database (HWSD). We find a considerable “mismatch” and most notably, that 11.5 percent of survey households that reported having “good” soil quality are measured by scientific standards to have severely constrained nutrient availability. Mismatches between scientific measurements and farmer assessments of soil quality may highlight a potential barrier for programs seeking to encourage farmers to adopt soil quality improvement activities.
Household survey data are a key source of information for policy-makers at all levels. In developing countries, household data are commonly used to target interventions and evaluate progress towards development goals. The World Bank’s Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) are a particularly rich source of nationally-representative panel data for six Sub-Saharan African countries: Ethiopia, Malawi, Niger, Nigeria, Tanzania, and Uganda. To help understand how these data are used, EPAR reviewed the existing literature referencing the LSMS-ISA and identified 415 publications, working papers, reports, and presentations with primary research based on LSMS-ISA data. We find that use of the LSMS-ISA has been increasing each year since the first survey waves were made available in 2009, with several universities, multilateral organizations, government offices, and research groups across the globe using the data to answer questions on agricultural productivity, farm management, poverty and welfare, nutrition, and several other topics.
This report reviews approaches to results measurement used by multilateral and bilateral donor organizations and highlights trends and gaps in how donors measure and report on their performance. Our review consists of assessing donor organizations in terms of their institutional design and levels of evaluation for results measurement, their organizational processes for measuring types of results including coordination and alignment with recipients, outputs and implementation, outcomes and impacts, and costs and effectiveness, and their processes for reporting and using results information. We collect evidence on 12 bilateral organizations and 10 multilateral organizations. The evidence review includes multi-country reviews of aid effectiveness, peer reviews by other donor organizations, donor evaluation plans and frameworks, and donor results and reporting documents. The report is based on an accompanying spreadsheet that contains the coded information from the 22 donor organizations. We find that donors report several types of results, but that there are challenges to measuring certain results at the aggregate donor level, due to challenges with funding and coordination for results measurement at the project, country, portfolio, and donor levels. Approaches to results measurement vary across donor organizations. We identify some trends and differences among groups of donors, notably between bilateral and multilateral donors, but overall there are no clear delineations in how donors approach results measurement.
Common estimates of agricultural productivity rely upon crude measures of crop yield, typically defined as the weight harvested of a crop divided by the area harvested. But this common yield measure poorly reflects performance among farm systems combining multiple crops in one area (e.g., intercropping), and also ignores the possibility that farmers might lose crop area between planting and harvest (e.g., partial crop failure). Drawing on detailed plot-level data from Tanzania’s National Panel Survey, our research contrasts measures of smallholder productivity using production per hectare harvested and production per hectare planted.
An initial analysis (Research Brief - Rice Productivity Measurement) looking at rice production finds that yield by area planted differs significantly from yield by area harvested, particularly for smaller farms and female-headed households. OLS regression further reveals different demographic and management-related drivers of variability in yield gains – and thus different implications for policy and development interventions – depending on the yield measurement used. Findings suggest a need to better specify “yield” to more effectively guide agricultural development efforts.
This brief reviews the evidence of realized yield gains by smallholder farmers attributable to the use of high-quality seed and/or improved seed varieties. Our analysis suggests that in most cases, use of improved varieties and/or quality seed is associated with modest yield increases. In the sample of 395 trials reviewed, positive yield changes accompanied the use of improved variety or quality seed, on average, in 10 out of 12 crops, with rice and cassava as the two exceptions.
Aid results information is often not comparable, since monitoring and evaluation frameworks, information gathering processes, and definitions of “results” differ across donors and governments. This report reviews approaches to results monitoring and evaluation used by governments in developing countries, and highlights trends and gaps in national monitoring and evaluation (M&E) systems. We collect evidence on 42 separate government M&E systems in 23 developing countries, including 17 general national M&E systems and 25 sector-specific national M&E systems, with 14 focused on HIV/AIDS, 8 on health, and 3 on agriculture. The evidence review includes external case studies and evaluations of M&E systems, government M&E assessments, M&E plans, strategic plans with an M&E component, and multi-country reviews of M&E, accountability, and aid effectiveness. We evaluate harmonization of government and development partner M&E systems, coordination and institutionalization of government M&E, challenges in data collection and monitoring, and analysis and use of results information. We also report on key characteristics of M&E systems in different sectors.
A farmer’s decision of how much land to dedicate to each crop reflects their farming options at the extensive and intensive margins. The extensive margin represents the total amount of agricultural land area that a farmer has available in a given year (referred to interchangeably as ‘farm size’ or ‘agricultural land’). A farmer increases land use on the extensive margin by planting on new agricultural land. The intensive margin represents area planted of crops as a proportion of total farm size. A farmer increases the intensive margin by increasing output within a fixed area. This analysis examines cropping patterns for households in Tanzania between 2008 and 2010 using data from the Tanzania National Panel Survey (TZNPS). This brief describes changes in farm size, total area planted, and area planted of select annual crops to highlight the dynamic nature of farmer’s cropping choices for a sample population of 2,246 agricultural households that reported having any agricultural land in 2008 or 2010. Throughout the brief, we present summary statistics at the national level and compare them with household-level data to show how results vary depending on how the sub-population is defined and how average measures can mask household level changes. We analyze these questions in the context of smallholders (defined as households with total agricultural land area as less than two hectares) and farming systems.