Taking Maize Agronomy to Scale in Africa (TAMASA) is a 4-year project (November 2014-October 2018) seeking to improve productivity and profitability for small-scale maize farmers in Ethiopia, Tanzania and Nigeria.

The overall purpose of TAMASA is to use innovative approaches to transform agronomy that:

  • Use available geospatial and other data and analytics to map maize areas, soil constraints, and actual and yields at different scale
  • Work with service providers (i.e. input suppliers, government and private research and extension services, agro-dealers, and others) to identify and co-develop systems and applications that transform this data and information to useable products that support their businesses or programs to reach clients more effectively
  • Build capacity in national programs to support and sustain these approaches

News & Updates feed

  • 7th AgMIP Global Workshop - Mobile Phone Based Advisories for Small-holder Farmers

    The strategic application of ICT (information and communication technology) to the agricultural industry, the largest economic sector in most African countries, offers the best opportunity for economic growth and poverty alleviation on the continent World Bank 2015 “The little we know” Mobile services can: disseminate information provide financial services (MPESA in East ...

  • Spatially-Explicit Farmgate Price Data in Africa: Why We Should Care and What to Do About It

    The use of inorganic fertilizer and other purchased inputs remains very low amongst African smallholder farmers. A central hypothesis about why this is the case, is that fertilizer use is simply not profitable, given the effective price of fertilizer at the farm gate, as well as the farm gate price ...

  • Looking for a needle in a haystack: The trials and tribulations of spatial sampling frames

    Collecting and using large amounts of geo-referenced data, all part of so-called ‘big data’, is all the rage these days.  In TAMASA we have a spatial sampling frame for collecting household and farmer yield data, which on a map looks very neat and tidy, and eminently ‘doable’.  For example, in ...

  • Why is farmer yield so hard to predict spatially?

    There is increasing interest, and use, for spatial and temporal estimates of on-farm or farmer yield. These data are most obviously needed for food security assessments and national or regional planning (i.e. greatest need and greatest opportunity for investment). Sustainable intensification and closing the yield gap are very much in ...