In 2019, we began working on a project in partnership with the Centre for Agricultural Transformation to accelerate the transformation of Malawian agricultural production away from tobacco to other sustainable agri-food value chains that can provide positive economic and livelihood outcomes. Growing agri-food markets requires farmers to have more affordable and timely access to the right farm inputs, financial services, and expanding fresh and processed commodity markets at home and abroad. The obvious initial questions were which value chains to prioritize, and where in the country does one target interventions to best meet growing urban and export opportunities? But where does one find the requisite market intelligence?

While there are plenty of one-off, glossy, consultancy reports available for specific value chains in Malawi, they left us frustrated. The types of data reported are useful in providing broad contextual information but make it difficult to reach actionable conclusions. By their very nature, these reports provide static snapshots that don’t lend themselves to more user-driven questions.

Not All Farmers Are the Same

Farmers, and the farms they manage, vary in a myriad of ways that have important market development implications. Many Malawian farmers are smallholders, but there are mid-to-larger sized farms as well. They grow different crops, in different locations, with different degrees of input and output market participation, and vary in their resilience to climate and market risks. Identifying and then targeting appropriate technology, innovations and other investments towards the right market segment is key to agricultural transformation.

The Market Segmentation Tool (otherwise known as MST) is a user-driven, digital “market intelligence” tool to better inform value-chain investments. It is designed for all sorts of agri-food market participants in Malawi, notably farmer-based organizations, non-profit agencies, for-profit firms, startups and established enterprises, NGOs, government, and research organizations. This first-in-class tool was designed by the GEMS Informatics team at the University of Minnesota to fill an important agricultural development void. It allows users to better tailor their products and services to the unique needs of the particular on- and post-farm market segments they seek to serve.

Intuitive Interactive Intelligence

Available through a publicly available URL, MST uses sophisticated on-the-fly analytics to allow non-technical users to quickly interrogate large, complex, and disparate data on the environment, income, market accessibility and agricultural production. MST draws on more than 100 relevant variables in a spatially explicit way and presents insights through simple charts, graphs and mapped representations. These customizable figures can be saved via a screenshot and the underlying data are available for download in a .csv format at a district-level.

For example, if a user was interested in exploring the production practices of smallholders, rather than simply looking at farmers who cultivate less than 1 hectare of land, users can create a more nuanced smallholder definition. Within MST agricultural households can be readily categorized based on up to 6 segmentation variables: poverty, cropland assets, livestock assets, output market orientation, input market orientation, and income diversity. For instance, users could characterize a market cohort defined by agricultural households that cultivate between 0.5 and 1 hectare and have an average per capita consumption expenditure between 400 and 800 Kwacha per day.

Once the market cohorts are defined, the user then navigates through the dashboard to explore the historic production, market orientation and income diversification practices of households that meet the criteria defined by our poverty and cropland thresholds. Data are displayed in multi-level geographical maps as well as infographic charts, many of which are interactive, and all of which can be filtered by farm type, gender, and tobacco producers across three World Bank LSMS survey waves (2010-11, 2015-16, and 2019-20).

Spatially explicit market intelligence is further enhanced with grid-based data on market access and agro-ecological information such as time to market, road density, population density, agro-ecological zones, climate variables (including temperature and precipitation) and soil types, which can be accessed through the environment and market access modules.

Bottom Line. Our tool enables users to identify which farmers, for which markets, in which locales, make the most sense for their particular value-chain investments.

MST can be explored at http://mst.agroinformatics.org/ and user help videos are available at our YouTube channel.

View the original post on the University of Minnesota's blog.

This posting was produced as part of the Centre for Agricultural Transformation, an effort led by Land O’Lakes Venture37 and funded with a grant from the Foundation for a Smoke-Free World, Inc. (“FSFW”), a U.S. nonprofit 501(c)(3) private foundation. The contents, selection and presentation of facts, as well as any opinions expressed herein, are the sole responsibility of the authors and under no circumstances should they be regarded as reflecting the positions of FSFW.