Types of Research
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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.
Common aid allocation formulas incorporate measures of income per capita but not measures of poverty, likely based on the assumption that rising average incomes are associated with reduced poverty. If declining poverty is the outcome of interest, however, the case of Nigeria illustrates that such aid allocation formulas could lead to poorly targeted or inefficient aid disbursements. Using data from the World Bank and the Nigerian National Bureau of Statistics, we find that while the relationship between economic growth and poverty in Nigeria varies depending on the time period studied, overall from 1992-2009 Nigeria’s poverty rate has only declined by 6% despite a 70% increase in per capita gross domestic product (GDP). A review of the literature indicates that income inequality, the prominence of the oil sector, unemployment, corruption, and poor education and health in Nigeria may help to explain the pattern of high ongoing poverty rates in the country even in the presence of economic growth. Our analysis is limited by substantial gaps in the availability of quality data on measures of poverty and economic growth in Nigeria, an issue also raised in the literature we reviewed, but our findings support arguments that economic growth should not be assumed to lead to poverty reduction and that the relationship between these outcomes likely depends on contextual factors.
Cereal yield variability is influenced by initial conditions such as suitability of the farming system for cereal cultivation, current production quantities and yields, and zone-specific potential yields limited by water availability. However, exogenous factors such as national policies, climate, and international market conditions also impact farm-level yields directly or provide incentives or disincentives for farmers to intensify production. We conduct a selective literature review of policy-related drivers of maize yields in Ethiopia, Kenya, Malawi, Rwanda, Tanzania, and Uganda and pair the findings with FAOSTAT data on yield and productivity. This report presents our cumulative findings along with contextual evidence of the hypothesized drivers behind maize yield trends over the past 20 years for the focus countries.
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.
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.
Cassava (Manihot esculenta Crantz) is a widely-grown staple food in the tropical and subtropical regions of Africa, Asia, and Latin America. In this brief we examine the environmental constraints to, and impacts of, smallholder cassava production systems in Sub-Saharan Africa (SSA) and South Asia (SA), noting where the analysis applies to only one of these regions. We highlight crop-environment interactions at three stages of the cassava value chain: pre-production (e.g., land clearing), production (e.g., soil, water, and input use), and post-production (e.g., crop storage). At each stage we emphasize environmental constraints on production (poor soil quality, water scarcity, crop pests, etc.) and also environmental impacts of crop production (e.g., soil erosion, water depletion and pesticide contamination). We then highlight good practices for overcoming environmental constraints and minimizing environmental impacts in smallholder cassava production systems. Evidence on environmental issues in smallholder cassava production is relatively thin, and unevenly distributed across regions. The literature on cassava in South Asian smallholder systems is limited, reflecting a crop of secondary importance (though it is widely found elsewhere in Asia such as South East Asia), in comparison to cassava in much of SSA. The majority of the research summarized in this brief is from SSA. The last row of Table 1 summarizes good practices currently identified in the literature. However, the appropriate strategy in a given situation will vary widely based on contextual factors, such as local environmental conditions, market access, cultural preferences, production practices and the policy environment.
This overview introduces a series of EPAR briefs in the Agriculture-Environment Series that examine crop-environment interactions for a range of crops in smallholder food production systems in Sub-Saharan Africa (SSA) and South Asia (SA). The briefs cover the following important food crops in those regions; rice (#208), maize (#218), sorghum/millets (#213), sweet potato/yam (#225), and cassava (#228).
Drawing on the academic literature and the field expertise of crop scientists, these briefs highlight crop-environment interactions at three stages of the crop value chain: pre-production (e.g., land clearing and tilling), production (such as water, nutrient and other input use), and post-production (e.g., waste disposal and crop storage). At each stage we emphasize environmental constraints on crop yields (including poor soils, water scarcity, crop pests) and impacts of crop production on the environment (such as soil erosion, water depletion and pest resistance). We then highlight best practices from the literature and from expert experience for minimizing negative environmental impacts in smallholder crop production systems.
This overview (along with the accompanying detailed crop briefs) seeks to provide a framework for stimulating across-crop discussions and informed debates on the full range of crop-environment interactions in agricultural development initiatives.
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.
This report provides a general overview of the markets for yams in Nigeria. The first section describes trends in yam production and consumption and international trade since 1990. The second section summarizes the varieties grown in Nigeria and their uses, followed by a discussion of the importance of yams as a source of nutrition and household income. The final section provides details about the production and marketing systems for yams in Nigeria, including environmental and gender considerations. Nigeria is the world’s largest yam producer in terms of quantity. Yam production and consumption have increased over the past twenty years, though more recently, production has been somewhat in decline and yields have been stagnant. The Nigerian government has played a more active role in improving agricultural production and export of root and tuber crops including yams in recent years, but so far with limited success. Yam producers and traders report diverse constraints to their full participation in the market, including high cost of inputs, planting materials and labor, lack of credit, limited access to proper, secure storage facilities, and high transportation costs.