Evans School Policy Analysis & Research Group (EPAR)

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.

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.  

This brief summarizes the literature on caloric and lipid deficiencies and their contribution to nutritional outcomes, and identifies key studies and pieces of literature related to this topic.

Without availability and access to a variety of foods, populations in the developing world are suffering from deficiencies in iron, zinc, iodine, vitamin A, and other micronutrients in addition to deficiencies in energy and protein. Supplementation and fortification programs have demonstrated effectiveness, but there is an increasing interest in potentially more sustainable solutions via agricultural interventions. The review examines the literature regarding agricultural interventions and pathways to diet diversification and whether desired nutritional outcomes are achieved.  We find a strong sentiment that agricultural interventions can improve dietary diversity, and that dietary diversity can improve nutrition and related health outcomes. The programs with demonstrated ability to improve nutrition outcomes are most often cross-cutting interventions, borrowing from the agriculture, nutrition, and public health traditions. While these multi-platform programs can be costly to evaluate and difficult to implement, the evidence supports their potential to create sustainable quality-of-life improvements in target regions. The pathways by which agricultural interventions achieve impact are not fully clear, however. The greatest knowledge gaps are directly related to the lack of integration between program design and evaluation. Many evaluations are based on small sample sizes, lack control groups or baseline data, are subject to selection bias, or face other challenges to rigorous statistical analysis.