Types of Research
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According to AGRA's 2017 Africa Agriculture Status Report, smallholder farmers make up to about 70% of the population in Africa. The report finds that 500 million smallholder farms around the world provide livelihoods for more than 2 billion people and produce about 80% of the food in sub-Saharan Africa and Asia. Many development interventions and policies therefore target smallholder farm households with the goals of increasing their productivity and promoting agricultural transformation. Of particular interest for agricultural transformation is the degree to which smallholder farm households are commercializating their agricultural outputs, and diversifying their income sources away from agriculture. In this project, EPAR uses data from the World Bank's Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) to analyze and compare characteristics of smallholder farm households at different levels of crop commercialization and reliance on farm income, and to evaluate implications of using different criteria for defining "smallholder" households for conclusions on trends in agricultural transformation for those households.
Crop yield is one of the most commonly used partial factor productivity measures. It is used to estimate the ratio of quantity of crop output, generally measured in kilograms or tons, to a sole input, land area. Ongoing EPAR research explores the policy implications of measuring yield by area planted versus area harvested. In this brief, we consider implications for crop yield estimates of other decisions in how to construct yield measures from household survey microdata. Using data from three waves of the Tanzania National Panel Survey (TNPS) and two waves of the Ethiopia Socioeconomic Survey (ESS), both part of the World Bank’s Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), we calculate separate crop yield estimates across survey waves following different decisions on disaggregating yield by gender(s) of the plot decision-maker(s) and for pure-stand and mixed stand (intercropped) plots, on including crop production from multiple growing seasons, and on how to treat outlier observations.
By examining how farmers respond to changes in crop yield, we provide evidence on how farmers are likely to respond to a yield-enhancing intervention that targets a single staple crop such as maize. Two alternate hypotheses we examine are: as yields increase, do farmers maintain output levels but change the output mix to switch into other crops or activities, or do they hold cultivated area constant to increase their total production quantity and therefore their own consumption or marketing of the crop? This exploratory data analysis using three waves of panel data from Tanzania is part of a long-term project examining the pathways between staple crop yield (a proxy for agricultural productivity) and poverty reduction in Sub-Saharan Africa.
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.
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.