Taking Maize Agronomy to Scale in Africa (TAMASA) was a six-year project funded by the Bill and Melinda Gates Foundation, seeking to improve productivity and profitability for small-scale maize farmers in Ethiopia, Tanzania and Nigeria. The project run from November 2014 through September 2020.
Within each country, TAMASA’s activities were structured within a nested impact geography, consisting of four stratification levels:
- Maize area: defined as where maize is grown using the AfSIS crop mask, SPAM and national production statistics
- Maize-based systems: defined as areas within the maize area where >50% of the crop in more than half the seasons is maize
- Area of Interest (AOI): defined using intensification criteria of areas within maize-based systems where population density >25 km2 and that are <4 hours travel time to an urban market.
- Focal areas: defined as areas within the AOI where TAMASA and its primary partners will conduct research and deliver outputs and impact.
Project Structure – Workstream 1 – 7
Workstreams 2 through 5 were focused on the development of decision-support tools.
Workstream 1 addressed core data gaps. There were several components of this work:
- Collect georeferenced data on plot-level soil characteristics, agronomic management, and maize yields, supplemented by farm-level data on resources, management decisions and production outcomes. Much of these data were ollected on panel observations, i.e. repeated visits to the same plots in successive seasons over the duration of the project.
- Analyze the data collected, along with complementary data sources generated by other projects (e.g. LSMS-ISA data) to characterize management, input use and yields over time and space.
- To collect data within a centrally managed repository which is accessible by project researchers as well as national partners.
- To evaluate alternative data collection methods (e.g. mobile-camera-based yield estimates over crop cuts; UAV-based yield estimates), and promote such methods if/where they are cost-effective.
Workstream 2 established a spatially-explicit framework for ex ante analysis of alternative targeting of productive investment, e.g. where new fertilizer blends may be more profitable than existing blends.
Workstream 3 established working versions of a Nutrient Expert tool which will provide site-specific nutrient management advice to farmers. This work is done in collaboration with IPNI.
Workstream 4 established a tool for the selection of maize varieties most appropriate for particular geographic locations and specified trait requirements.
Workstream 5 consisted of developing data to support the identification of opportunities for new fertilizer blends. This work is currently being done in Nigeria in collaboration with OCP.
Workstream 6 focused on evaluating the actual and potential adoption and impact of the tools developed in Workstreams 2-5. Data collected under Workstream 1 (i.e. the agronomic panel survey) used to evaluate adoption via experimental methods. Within Workstream 6, there was also space for evaluating complementary agronomic and other interventions.
Workstream 7 consisted of project management activities. Within this Workstream were included monitoring, evaluation and learning (ME&L) activities, communication, and collaborative management of training and research conducted by doctoral students who were sponsored by the project and who conducted their coursework at Wageningen University (Netherlands), Leuven University (Belgium) and Reading University (UK).