Photo Credit: Neil Palmer / CIAT

Series of Country Agricultural Snapshots authored by Carlo Azzarri, Cleophelia Roberts, and 'Queenie' Yue Gong. Blog post co-written with Cindy Cox.

Ever wanted to know what a typical (or A-typical) agricultural household looks like in Uganda vs Malawi? Does family size and composition affect farm production and entrepreneurship more or less in Ethiopia compared to Ghana? What about the spatial distribution of household characteristics in Tanzania? HarvestChoice recently launched a series of working papers to illustrate the basic characteristics of farming households across a panel of five sub-Saharan African countries (Ethiopia, Ghana, Malawi, Tanzania, and Uganda). These non-technical reports include country-level information that allow for cross-country comparisons on a range of important dimensions related to farming and livestock.

The “Country Agricultural Snapshot” answers relevant starting point questions that all researchers, practitioners, and policy makers consider when approaching the interconnection between agricultural activities and a variety of socio-economic and biophysical dimensions. The snapshots are intentionally accessible publications that help readers more clearly define the problems and questions that drive their research, and that act as an encyclopedia of at least partial answers and solutions. They rely on the simple idea that univariate distribution and bivariate cross-tabulation of the most important farmer attributes go a long way towards understanding constraints and opportunities countries face in the agricultural sector. Additionally, a cross-country comparison of a suite of indicators across five countries in sub-Saharan Africa could quickly generate important research questions that the assembled data (at the household level) could tackle.

We begin each snapshot with the simple question: how many farming households are in the country and what is their spatial distribution? Then we fill in the attributes of households, such as family size, education, headship, wealth, poverty, sources of income, land and livestock holdings, use of agricultural inputs, cropping systems, yield, crop value per hectare, and child malnutrition. Now, a meaningful cross-tabulation between characteristics can shed light on the relationship between, for example, poultry, goats, and welfare in Malawi; or the education of the household head and land size in Ethiopia. We can also look at the increasing share of non-agricultural income by decile in Uganda (see figure below).

As we learn in childhood, our understanding of the world starts with seemingly open-ended questions (why is the sky blue?) and much of what we learn is through rigorous and quantitative exploration. In the case of these country snapshots, an important research question might be triggered by simple statistics. For example, in Tanzania, depending on the season, around 72-73 percent of total cropland area is farmed with no inputs, compared to the low area under no input use in Malawi (only 12% during the dimba and 25% during the rainy season). Is the differential input use between countries due to the so-called “Starter Pack” program and the massive amount of fertilizer investment that the Government of Malawi has promoted during the last 15 years? And why, despite the widespread use of fertilizers, are child nutritional conditions in Malawi still alarming with a stunting (low height for age) rate of 44%, one of the highest in the world (see figure below)?

Child Stunting Rates, Malawi

Each Country Agricultural Snapshot is available and downloadable from this website. Look under The accompanying micro-level dataset of processed variables and the syntax code for their construction will also be available soon. This way, users can easily replicate our output (graphs, tables, and maps) and perform their own analysis thanks to the availability of harmonized data and consistent variables. HarvestChoice cannot guarantee full compatibility of the variables across country, as survey instruments for data collection can substantially differ, depending on the country and data provider. But we can, when necessary, direct users to the original data sources for downloading raw datasets.

And, if you've always wanted to know everything about agriculture and there’s something you cannot find in the snapshot, do not be afraid to ask. The answer might just be in front of you hiding in a pile of numbers and waiting for your research.


HarvestChoice, 2014. "Everything You Always Wanted to Know About Agriculture (But Were Afraid to Ask)." International Food Policy Research Institute, Washington, DC., and University of Minnesota, St. Paul, MN. Available online at

Oct 6, 2014