Year Published
- 2008 (0)
- 2009 (0)
- 2010 (0)
- 2011 (3) Apply 2011 filter
- 2012 (7) Apply 2012 filter
- 2013 (4) Apply 2013 filter
- 2014 (1) Apply 2014 filter
- 2015 (2) Apply 2015 filter
- (-) Remove 2016 filter 2016
- 2017 (5) Apply 2017 filter
- 2018 (0)
- 2019 (0)
- (-) Remove 2020 filter 2020
- 2021 (0)
Research Topics
Populations
- Countries/Governments (0)
- Rural Populations (1) Apply Rural Populations filter
- Smallholder Farmers (1) Apply Smallholder Farmers filter
- Women (0)
Types of Research
- Data Analysis (1) Apply Data Analysis filter
- Literature Review (2) Apply Literature Review filter
- Portfolio Review (0)
- Research Brief (0)
Geography
- East Africa Region and Selected Countries (1) Apply East Africa Region and Selected Countries filter
- Global (0)
- South Asia Region and Selected Countries (0)
- Southern Africa Region and Selected Countries (0)
- Sub-Saharan Africa (2) Apply Sub-Saharan Africa filter
- West Africa Region and Selected Countries (0)
Dataset
- ASTI (1) Apply ASTI filter
- FAOSTAT (1) Apply FAOSTAT filter
- Farmer First (0)
- (-) Remove LSMS & LSMS-ISA filter LSMS & LSMS-ISA
- Other Datasets (1) Apply Other Datasets filter
Current search
- (-) Remove 2020 filter 2020
- (-) Remove Agricultural Inputs & Farm Management filter Agricultural Inputs & Farm Management
- (-) Remove LSMS & LSMS-ISA filter LSMS & LSMS-ISA
- (-) Remove 2016 filter 2016
Recent research has used typologies to classify rural households into categories such as “subsistence” versus “commercialized” as a means of targeting agricultural development interventions and tracking agricultural transformation. Following an approach proposed by Alliance for a Green Revolution in Africa, we examine patterns in two agricultural transformation hallmarks – commercialization of farm output, and diversification into non-farm income – among rural households in Ethiopia, Nigeria, and Tanzania from 2008-2015. We classify households into five smallholder farm categories based on commercialization and non-farm income levels (Subsistence, Pre-commercial, Transitioning, Specialized Commercial, and Diversified Commercial farms), as well as two non-smallholder categories (Largeholder farms and Non-farm households). We then summarize the share of households in each of these categories, examine geographic and demographic factors associated with different categories, and explore households’ movement across categories over time. We find a large amount of “churn” across categories, with most households moving to a different (more or less commercialized, more or less diversified) category across survey years. We also find many non-farm households become smallholder farmers – and vice versa – over time. Finally, we show that in many cases increases in farm household commercialization or diversification rates actually reflect decreased total farm production, or decreased total income (i.e., declines in the denominators of the agricultural transformation metrics), suggesting a potential loss of rural household welfare even in the presence of “positive” trends in transformation indicators. Findings underscore challenges with using common macro-level indicators to target development efforts and track progress at the household level in rural agrarian communities.
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.
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.