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In many countries in Sub-Saharan Africa and South Asia smallholder farmers are among the most vulnerable to climatic changes, and the observed shocks and stresses associated with these changes impact agricultural systems in many ways. This research brief offers findings on observed or measured changes in precipitation, temperature or both, on five biophysical pathways and systems including variable or changing growing seasons, extreme events, biotic stressors, plant species density, richness and range, impacts to streamflow, and impacts on crop yield. These findings are the result of a review of relevant documents cited in Kilroy (2015), references included in the IPCC draft Special Report on Food Security, and targeted searches from 2015 - present for South Asia and Sub-Saharan Africa.
Studies of improved seed adoption in developing countries almost always draw from household surveys and are premised on the assumption that farmers are able to self-report their use of improved seed varieties. However, recent studies suggest that farmers’ reports of the seed varieties planted, or even whether seed is local or improved, are sometimes inconsistent with the results of DNA fingerprinting of farmers' crops. We use household survey data from Tanzania to test the alignment between farmer-reported and DNA-identified maize seed types planted in fields. In the sample, 70% of maize seed observations are correctly reported as local or improved, while 16% are type I errors (falsely reported as improved) and 14% are type II errors (falsely reported as local). Type I errors are more likely to have been sourced from other farmers, rather than formal channels. An analysis of input use, including seed, fertilizer, and labor allocations, reveals that farmers tend to treat improved maize differently, depending on whether they correctly perceive it as improved. This suggests that errors in farmers' seed type awareness may translate into suboptimal management practices. In econometric analysis, the measured yield benefit of improved seed use is smaller in magnitude with a DNA-derived categorization, as compared with farmer reports. The greatest yield benefit is with correctly identified improved seed. This indicates that investments in farmers' access to information, seed labeling, and seed system oversight are needed to complement investments in seed variety development.
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.
Mobile technology is associated with a variety of positive development and social outcomes, and as a result reaching the “final frontier” of uncovered populations is an important policy issue. We use proprietary 2012 data on mobile coverage from Collins Bartholomew to estimate the proportion of the population living in areas without mobile coverage globally and in selected regions and countries, and use spatial analysis to identify where these populations are concentrated. We then compare our coverage estimates to data from previous years and estimates from the most recent literature to provide a picture of recent trends in coverage expansion, considering separately the trends for coverage of urban and rural populations. We find that mobile coverage expansion rates are slowing, as easier to reach urban populations in developing countries are now almost entirely covered and the remaining uncovered populations are more dispersed in rural areas and therefore more difficult and costly to reach. This analysis of mobile coverage trends was the focus of an initial report on mobile coverage estimates. In a follow-up paper prepared for presentation at the 2016 APPAM International Conference, we investigate the assumption that levels of mobile network coverage are related to the degree of market liberalization at the country level.
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.
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.
This report reviews and summarizes the existing evidence on the impact of access to financial services/products on measures of production, income and wealth, consumption and food security, and resilience for smallholder farmers and other rural customers and their households in Sub-Saharan Africa. This study covers four main types of financial products/services: 1) credit; 2) savings; 3) insurance; 4) transactional products. We also review the very limited evidence on the effectiveness of bundling these products/services together and of combining them with other offerings such as trainings or support for access to markets, and of providing them via digital channels. We note when financial products/services have been specifically designed to serve the needs of rural customers or smallholder farmers, since the needs of these groups are often very different from those of other stakeholders.
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 report provides a summary of findings from six Financial Inclusion Insights (FII) data analysis reports conducted by various agencies for the Bill & Melinda Gates Foundation (BMGF). These reports investigate barriers to financial inclusion and use of digital financial services (DFS) in Bangladesh, India, Kenya, Nigeria, Pakistan, Tanzania, and Uganda. We compile comparable gender-specific statistics, summarize the authors’ findings to determine commonalities and differences across countries, and highlight gender-specific conclusions and recommendations provided in the studies.