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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 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 the LSMS-ISA in Tanzania, Nigeria, and Ethiopia, we show how various yield measurement decisions affect estimates of smallholder yields for a variety of crops. We consider the effect of measuring production by plot area, area planted, and area harvested, of trimming the top 1% and 2% of values, and of considering different groups of farmers according to total area planted.
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 is "Section B" of a report that presents estimates and summary statistics from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). We present our analyses of household characteristics by gender and by administrative zone, considering landholding size, number of crops grown, yields, livestock, input use, and food consumption.
This is "Section E" of a report that presents estimates and summary statistics from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). We present our analyses of livestock and livestock by-product characteristics by gender of household head and by zones, as well as our analyses of livestock disease, vaccines, and theft.
This is "Section D" of a report that presents estimates and summary statistics from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). We present our analyses of basic farm characteristics, land and labor productivity, crop sales, yield measures, intercropping, and pre- and post-harvest losses, including comparisons by gender of household head and by zone.
This is the introductory section of a report that presents estimates and summary statistics from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). We present an overview of report sections, as well as an executive summary of findings on crops and livestock, constraints to productivity, and productivity and nutrition outcomes.
This is "Section C" of a report that presents estimates and summary statistics from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). We present our analyses of the basic characteristics of household heads and other household members, as well as our analyses of education for adults, children, and household heads by gender and zone.
This brief explores agricultural data for Tanzania from the LSMS-ISA and Farmer First household surveys. We first present the differences in the LSMS and Farmer First survey design and in basic descriptives from the two data sources. We then present the results of our initial LSMS data analysis using the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), focusing on the agricultural data, before presenting our analysis of farmer aspirations and of gender differences using the Farmer First data.
This report combines analyses from four previous EPAR briefs on the effects of climate change on maize, rice, wheat, sorghum, and millet production in Sub-Saharan Africa (SSA). In addition, this brief presents new analysis of the projected impact of climate changes in SSA. We include comparisons of the importance of each crop, of their vulnerability to climate change, and of the research and policy resources dedicated to each. Especially with respect to climatic susceptibility, these rankings provide a comparative summary based upon the analysis conducted in the four previous EPAR briefs, statistical analyses of historical yield and climate data, and future climate model predictions. According to the indicators analyzed, our research suggests that maize leads the cereal crops in terms of importance within SSA and in terms of research and policy attention. Our analysis of climate conditions and the crop’s physical requirements suggests that many maize-growing areas are likely to move outside the range of ideal temperature and precipitation conditions for maize production. Rice is the third most important crop in terms of consumption dependency, fourth in terms of production, but second only to maize in terms of research funding and FTEs. Sorghum and millet rank second and third in production importance and second and fifth in consumption importance, but rank below maize and rice in terms of FTE researchers. Their role is complicated by the fact that they are often considered inferior goods; SSA consumers often substitute away from sorghum and millet consumption if they are able to do so. Wheat is the least-produced crop of the five, and the second to last in terms of consumption importance. However, it still ranks above millet in terms of FTE researchers.