Fostering bioscience innovation: lessons from BIO-EARN

This paper synthesises the findings of, and distils lessons from a study which has sought to illuminate the process of bioscience innovation in three East African countries: Kenya, Tanzania and Uganda. More specifically, we attempt to trace specific efforts to foster biotechnology innovations in those countries and to determine in what ways and to what extent the innovation system in place impinges on the final outcome of those innovations. The paper concludes with a set of policy recommendations that may enhance bioscience innovations in East Africa

Combined household and GIS analysis of farmer strategies: an application to feeding practices on smallholder Kenyan dairy farms

Traditional studies of agricultural technology adoption have long been constrained by a limited ability to include spatially-differentiated data. Typically, crude proxies or location dummy variables are used to approximate spatial effects. GIS tools, however, now allow spatially explicit data to be included in household econometric models of technology adoption. This paper describes a study that combined GIS and survey variables to examine the cattle feeding strategies on farms in highland Kenya. Data from a large geo-referenced household survey were combined with GIS-derived variables to comprehensively evaluate the spatial, agro-ecological, market and farm resource factors that determine variability of feeding strategies on smallholder dairy farms. Roads, urban populations, milk collection and processing facilities were digitised, and integrated with spatial coverages of agro-ecology. These were then combined, using econometric methods, to quantify the main spatial and local determinants of the probability of adoption of: a) stall feeding or zero-grazing, and b) planted fodder in the form of Napier grass. The results show the influence not only of agro-ecology, but also of market infrastructure and support services on the adoption of improved feeding strategies. A comparison of predicted uptake using GIS and household variables shows that after first calibrating GIS-derived variables through a household survey, broad but reliable predictions of technology uptake in other areas may be possible