Photo Credit: Neil Palmer / CIAT

One of the things we are working on at HarvestChoice is the diffusion of agricultural technologies. There is a deep and longstanding literature on the subject going back to 1957 with Griliches’s seminal paper. Assuming the choices are rational, there are certainly economic dimensions to the questions of how, when, and why farmers choose to adopt a particular new technology.

Hybrid and other improved varieties of maize have been around in some form since the 1930s. They were widespread in the United States by the early 1960’s. Although these technological advancements helped increase the productivity of American farms, they are still not widely accepted in many parts of sub-Saharan Africa. Using the analyses of people like Griliches and many others, we can understand how technology diffuses in areas with (near) perfect markets as in the United States. It is more difficult, however, to understand why farmers in sub-Sahara Africa aren’t taking full advantage of new technologies to enhance the productivity of their farms.

A number of papers, including Griliches (1957) and Grant (1975), include maps that show how technology moves across areas. Some of us at HarvestChoice recently began to construct a similar map for Tanzania using agricultural census data. And because we have such high quality data from 2007, we are able to map the use of technology at a much higher resolution than what had been done in the previous literature. To produce the map, we calculated the percent (%) of farmers in a particular ward (third level administrative district) who are using improved maize. We then merged the data with a GIS shapefile of the wards in Tanzania, provided by the International Livestock Research Institute (ILRI), one of our CGIAR partners, to produce the map below.

Greener areas indicate a low percentage of farmers using improved seeds, while redder areas indicate a high percentage of farmers using improved seeds. White areas are areas for which we have no data. Looking at the geography gives us a rough idea of how to understand the spatial dimension of technological use.

While this map is insightful, it provides us only a rough outline of technological adoption. What this map does not capture are some key criteria that could help determine the use of improved seeds: altitude, rainfall, or population density. It would be difficult to capture all of these in one map, but it could be that geography is determining all of the above. In any case, it’s definitely a first step towards understanding the geography of technological diffusion In Tanzania and possibly guides our attention on where to focus for improving the adoption of improved crop varieties.

As is usually the case with the scientific process, generating this map led to many more questions. By better visualizing the diffusion of technology throughout Tanzania, we hope that this map will spark more conversation about what might be influencing farmer decisions in sub-Saharan Africa.


HarvestChoice, 2014. "Mapping the State of Technology in Tanzania." International Food Policy Research Institute, Washington, DC., and University of Minnesota, St. Paul, MN. Available online at

Oct 6, 2014