Types of Research
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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.
Self-Help Groups (SHGs) in Sub-Saharan Africa can be defined as mutual assistance organizations through which individuals undertake collective action in order to improve their own lives. “Collective action” implies that individuals share their time, labor, money, or other assets with the group. In a recent EPAR data analysis, we use three nationally-representative survey tools to examine various indicators related to the coverage and prevalence of Self-Help Group usage across six Sub-Saharan African countries. EPAR has developed Stata .do files for the construction of a set of self-help group indicators using data from the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA), Financial Inclusion Index (FII), and FinScope.
We compiled a set of summary statistics for the final indicators using data from the following survey instruments:
- Ethiopia Socioeconomic Survey (ESS), Wave 3 (2015-16)
- Kenya FinScope, Wave 4 (2015)
- Kenya FII, Wave 4 (2016)
- Nigeria FII, Wave 4 (2016)
- Rwanda FII, Wave 4 (2016)
- Tanzania National Panel Survey (TNPS), Wave 4 (2014-15)
- Tanzania FinScope, Wave 4 (2017)
- Tanzania FII, Wave 4 (2016)
- Uganda FinScope, Wave 3 (2013)
- Uganda FII, Wave 4 (2016)
The raw survey data files are available for download free of charge from the World Bank LSMS-ISA website, the Financial Sector Deepening Trust website, and the Financial Inclusion Insights website. The .do files process the data and create final data sets at the household (LSMS-ISA) and individual (FII, FinScope) levels with labeled variables, which can be used to estimate summary statistics for the indicators.
All the instruments include nationally-representative samples. All estimates from the LSMS-ISA are household-level cluster-weighted means, while all estimates from FII and FinScope are calculated as individual-level weighted means. The proportions in the Indicators Spreadsheet are therefore estimates of the true proportion of individuals/households in the national population during the year of the survey. EPAR also created a Tableau visualization of these summary statistics, which can be found here.
We have also prepared a document outlining the construction decisions for each indicator across survey instruments and countries. We attempted to follow the same construction approach across instruments, and note any situations where differences in the instruments made this impossible.
The spreadsheet includes estimates of the following indicators created in our code files:
- Proportion of individuals who have access to a mobile phone
- Proportion of individuals who have official identification
- Proportion of individuals who are female
- Proportion of individuals who use mobile money
- Proportion of individuals who have a bank account
- Proportion of individuals who live in a rural area
- Individual Poverty Status
- Two Lowest PPI Quintiles
- Middle PPI Quintile
- Two Highest PPI Quintiles
Coverage & Prevalence
- Proportion of individuals who have interacted with a SHG
- Proportion of individuals who have used an SHG for financial services
- Proportion of individuals who depend most on SHGs for financial advice
- Proportion of individuals who have received financial advice from a SHG
- Proportion of households that have interacted with a SHG
- Proportion of households in communities with at least one SHG
- Proportion of households in communities with access to multiple farmer cooperative groups
- Proportion of households who have used an SHG for financial services
In addition, we produced estimates for 29 indicators related to characteristics of SHG use including indicators related to frequency of SHG use, characteristics of SHG groups, and individual/household trust of SHGs.
The FAO defines a farming system as “a population of individual farm systems that have broadly similar resource bases, enterprise patterns, household livelihoods and constraints, and for which similar development strategies and interventions would be appropriate. Depending on the scale of the analysis, a farming system can encompass a few dozen or many millions of households.” We use the farming systems as defined by the Food and Agriculture Organization (FAO) for Sub-Saharan Africa. The FAO identifies eight main farming systems in Tanzania 1) maize mixed, 2) root crop, 3) coastal artisanal fishing, 4) highland perennial, 5) agro-pastoral millet/sorghum, 6) tree crop, 7) highland temperate mixed, and 8) pastoral. This analysis uses data from the Tanzanian National Panel Survey (TZNPS) LSMS – ISA to provide a comparison of farming systems throughout Tanzania. The TZNPS is a nationally-representative panel survey that includes households from seven of the eight FAO farming systems with only the smallest farming system, pastoral, lacking any representation.
After cereals, root and tuber crops - including sweetpotato and yam (in addition to cassava and aroids), are the second most cultivated crops in tropical countries. This literature review examines the environmental constraints to, and impacts of, sweetpotato and yam production systems in Sub-Saharan Africa (SSA) and South Asia (SA). The review highlights crop-environment interactions at three stages of the sweetpotato/yam value chain: pre-production (e.g., land clearing), production (e.g., soil, water, and input use), and post-production (e.g., waste disposal, crop storage and transport). We find that sweetpotato and yam face similar environmental stressors. In particular, because sweetpotato and yam are vegetatively propagated, the most significant (and avoidable) environmental constraints to crop yields include disease and pest infection transmitted through the use of contaminated planting materials. Published estimates suggest yield gains in the range of 30–60% can be obtained through using healthy planting material. Moreover, reducing pest damage in the field can greatly increase the storage life of root and tuber crops after harvest – currently losses from rot and desiccation can claim up to 100% of stored sweetpotato and yam on smallholder farms.
Maize has expanded through the 20th and into the 21st century to become the principle staple food crop produced and consumed by smallholder farm households in Sub-Saharan Africa (SSA), and maize production has also expanded in South Asia (SA) farming systems. In this brief we examine the environmental constraints to, and impacts of, smallholder maize production systems in SSA and SA, noting where findings apply to only one of these regions. We highlight crop-environment interactions at three stages of the maize value chain: pre-production (e.g., land clearing), production (e.g., fertilizer, water, and other input use), and post-production (e.g., waste disposal and crop storage). At each stage we emphasize environmental constraints on maize production (such as poor soil quality, water scarcity, or crop pests) and also environmental impacts of maize production (such as soil erosion, water depletion, or chemical contamination). We then highlight best or good practices for overcoming environmental constraints and minimizing environmental impacts in smallholder maize production systems. Evidence on environmental constraints and impacts in smallholder maize production is uneven. Many environmental concerns such as biodiversity loss are commonly demonstrated more broadly for the agroecology or farming systems in which maize is grown, rather than specifically for the maize crop. And more research is available on the environmental impacts of agrochemical-based intensive cereal farming in Asia (where high-input maize is a common component) than on the low-input subsistence-scale maize cultivation more typical of SSA. Decisive constraint and impact estimates are further complicated by the fact that many crop-environment interactions in maize and other crops are a matter of both cause and effect (e.g., poor soils decrease maize yields, while repeated maize harvests degrade soils). Fully understanding maize-environment interactions thus requires recognizing instances where shortterm adaptations to environmental constraints might be exacerbating other medium- or long-term environmental problems. Conclusions on the strength of published findings on crop-environment interactions in maize systems further depend on one’s weighting of economic versus ecological perspectives, physical science versus social science, academic versus grey literature, and quantity versus quality of methods and findings.
In this brief we examine the environmental constraints to, and impacts of, smallholder sorghum and millet production systems in Sub-Saharan Africa (SSA) and South Asia (SA). Millet in this paper primarily refers to pearl millet (Pennisetum glaucum), although a number of other millets of significance to smallholder production and food security are also discussed. Sorghum and millets are known for being more tolerant of major environmental stresses including drought and poor soil quality than other major cereals. But water availability is still among the greatest constraints to increased grain production, and soil fertility also significantly limits yields, especially in cases where cultivation occurs on marginal lands and where crop residues are removed for alternative uses. Ultimately sorghum and millets’ relatively higher tolerance to abiotic stresses is expected to promote an increase in global cropping area for sorghum and millets as an adaptation to climate change. Sorghum and millet exhibit relatively few of the environmental impacts commonly associated with more intensively cultivated crops such as fertilizer runoff, pesticide contamination, or water depletion, since both of these crops are overwhelmingly grown by smallholder farmers with few, if any, chemical or irrigation inputs. Nevertheless, the tendency to grow sorghum and millet on marginal and heavily sloped lands does pose some environmental risks – including soil degradation and erosion – that can be mitigated through the adoption of best practices as described in the brief.
In this brief we analyze patterns of intercropping and differences between intercropped and monocropped plots among smallholder farmers in Tanzania using data 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). Intercropping is a planting strategy in which farmers cultivate at least two crops simultaneously on the same plot of land. In this brief we define intercropped plots as those for which respondents answered “yes” to the question “Was cultivation intercropped?” We define “intercropping households” as those households that intercropped at least one plot at any point during the year in comparison to households that did not intercrop any plots. The analysis reveals few significant, consistent productivity benefits to intercropping as currently practiced. Intercropped plots are not systematically more productive (in terms of value produced) than monocropped plots. The most commonly cited reason for intercropping was to provide a substitute crop in the case of crop failure. This suggests that food and income security are primary concerns for smallholder farmers in Tanzania. A separate appendix includes the details for our analyses.
Local crop diversity and crop cultivation patterns among smallholder farmers have implications for two important elements of the design of agricultural interventions in developing countries. First, crop cultivation patterns may aid in targeting by helping to identify geographic areas where improved seed and other productivity enhancing technologies will be most easily applicable. Second, these patterns may help to identify potential unintended consequences of crop interventions focused on a single crop (e.g. maize). This report analyzes the distribution of crop diversity and crop cultivation patterns, and factors that can lead to changes in these patterns among smallholder farmers in Tanzania with a focus on regional patterns of crop cultivation and changes in these patterns over time, the factors that affect crop diversity and changes in crop diversity, and the level of substitutability between crops grown by smallholder farmers. All analysis is based on the Tanzania National Panel Survey (TNPS) datasets from 2008 and 2010. The paper is structured as follows. Section I provides a description of regional patterns of crop cultivation and crop diversity between the two years of the panel. Section II presents background on the theoretical factors affecting crop choice, and presents our findings on the results of a multivariate analysis on the factors contributing to crop diversity. Finally, Section 3 provides a preliminary analysis of the level of substitutability between cereal crop of importance in Tanzania (maize, rice and sorghum/millet) and also between these cereal crops and non-cereal crops.
Rice is the most important food crop of the developing world and is grown on over 155 million ha worldwide. Food security of the poor, especially in Asia, depends critically on rice availability at an affordable price. In this brief we examine the environmental constraints to, and impacts of, smallholder rice production systems in South Asia (SA) and Sub-Saharan Africa (SSA), noting where the analysis applies to only one of these regions. We highlight crop-environment interactions at three stages of the rice value chain: pre-production (e.g., land clearing), production (e.g., water and other input use), and post-production (e.g., waste disposal). At each stage we emphasize environmental constraints on production (e.g., poor soil quality, water scarcity, crop pests) and also environmental impacts of crop production (e.g., soil erosion, water depletion, pest resistance). We then highlight best or good practices for minimizing negative environmental impacts in smallholder rice production systems. Evidence on environmental issues in smallholder rice production is uneven. Far more research is available for Asian rice production systems, as compared to African rice systems. And with the possible exception of the evidence on water limits to increasing productivity, conclusions on the strength of published findings on crop-environment interactions in rice depends on one’s weighting of economic versus ecological perspectives, physical science versus social science, academic versus grey literature, and quantity versus quality of methods and findings.