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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:
- Ethiopia Socioeconomic Survey (ESS), Wave 3 (2015-16)
- Kenya:
- Kenya FinScope, Wave 4 (2015)
- Kenya FII, Wave 4 (2016)
- Nigeria
- Nigeria FII, Wave 4 (2016)
- Rwanda:
- Rwanda FII, Wave 4 (2016)
- Tanzania:
- Tanzania National Panel Survey (TNPS), Wave 4 (2014-15)
- Tanzania FinScope, Wave 4 (2017)
- Tanzania FII, Wave 4 (2016)
- Uganda:
- 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:
Sub-Populations
- 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
Characteristics
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.
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.
This brief present our analysis of sorghum and millet cultivation 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). In the 2007-2008 long and short rainy seasons, 13% of Tanzanian farming households cultivated sorghum and 6% cultivated millet, making these crops some of the least frequently cultivated priority crops in Tanzania. As a result, detailed analysis and determining statistical significance was limited by the low number of observations, particularly of millet. While sorghum and millet are often grouped together, our results suggest that in Tanzania there were differences among the households that cultivated these distinct crops. A separate appendix includes additional detail on our analyses.
This brief explores how two datasets – The Tanzania National Panel Survey (TZNPS) and the TNS-Research International Farmer Focus (FF) – predict the determinants of inorganic fertilizer use among smallholder farmers in Tanzania by using regression analysis. The (TZNPS) was implemented by the Tanzania National Bureau of Statistics, with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) team and includes extensive information on crop productivity and input use. The FF survey was funded by the Bill and Melinda Gates Foundation and implemented by TNS Research International and focuses on the on the behaviors and attitudes of smallholder farmers in Tanzania. The two datasets produce relatively comparable results for the primary predictors of inorganic fertilizer use: agricultural extension and whether or not a household grows cash crops. However, other factors influencing input use produce results that vary in magnitude and direction of the effect across the two datasets. Distinct survey instrument designs make it difficult to test the robustness of the models on input use other than inorganic fertilizer. This brief uses data inorganic fertilizer use, rather than adoption per se. The TZNPS did not ask households how recently they began using a certain product and although the FF survey asked respondents how many new inputs were tried in the past four planting seasons, they did not ask specifically about inorganic fertilizer.
This report provides a general overview of the wheat market in Bangladesh. The first section describes trends in wheat production and consumption over the past twenty years and summarizes recent trade policy related to wheat. The second section presents the findings of a literature review of the wheat value chain in Bangladesh, beginning with seed selection and ending with sales. Finally, wheat consumption in Bangladesh is discussed in more depth, including nutritional information about wheat, substitute grain markets, and projected consumption in 2030. We find that wheat production in Bangladesh has been volatile and continues to reflect significant yield gaps. While wheat consumption has increased, rice is the most important crop and food grain. Increased demand by private traders for higher quality wheat for processing has fueled rising import levels, and the the gap between domestic supply and demand is projected to grow to over 4 million tons by 2030.
Over the past 20 years, global wheat production and consumption have increased significantly. Production has increased 28%, or about 1.3% annually, and consumption has increased about 24%, or 1.1% annually. A small number of countries consistently account for over 90% of the export market, but the import market is more diversified and involves many more countries. Wheat is primarily used for food, seed, and industry; only 20% of wheat production is used for animal feed. This brief provides a global overview of the wheat value chain, but with specific attention to three focus countries: Ethiopia, India (specifically the Bihar region), and Bangladesh. While these three countries currently have a limited impact in the global wheat market, projections of wheat production and demand suggest that over the next 20 years demand in Bangladesh and Ethiopia will increasingly exceed supply, while India will become a net importer by 2030.