These proceedings include all the papers presented during the AISA workshop either as oral papers or as posters. It also includes the edited text resulting from the Living Keynote process, an innovation in itself.
The AISA workshop was held on 29-31 May 2013 in Nairobi, Kenya, as part of an international week devoted to Agricultural innovation in Africa. The AISA workshop focused on active social learning among participants, developed a collective "living keynote" about the following issues:
This paper examines the role of innovation brokers in stimulating innovation system interaction and innovation capacity building, and illustrates this by taking the case of Dutch agriculture as an example. Subsequently, it reflects upon the potential role of innovation brokers in developing countries’ agriculture. It concludes that innovation brokerage roles are likely to become relevant in emerging economies and that public or donor investment in innovation brokerage may be needed to overcome inherent tensions regarding the neutrality and funding of such players in the innovation system.
This paper contributes to the ongoing discussion in the scientific literature on the advantages and disadvantages of privatization of extension and advisory services and the shift from thinking in terms of the traditional Agricultural Knowledge System towards a broader Agricultural Innovation System.
According to the authors of this paper, actual methods of scaling are rather empirical and based on the premise of ‘find out what works in one place and do more of the same, in another place’. These methods thus would not sufficiently take into account complex realities beyond the concepts of innovation transfer, dissemination, diffusion and adoption. As a consequence, scaling initiatives often do not produce the desired effect.
This study analyse how agricultural extension can be made more effective in terms of increasing farmers’ adoption of pro-nutrition technologies, such as biofortified crops. In a randomised controlled trial with farmers in Kenya, the authors implemented several extension treatments and evaluated their effects on the adoption of beans biofortified with iron and zinc. Difference-in-difference estimates show that intensive agricultural training can increase technology adoption considerably.
The latest comprehensive research agenda in the Journal of Agricultural Education and Extension was published in 2012 (Faure, Desjeux, and Gasselin 2012), and since then there have been quite some developments in terms of biophysical, ecological, climatological, social, political and economic trends that impact farming and the transformation of agriculture and food systems at large as well as new potentially disruptive technologies.
Background
Weather risk is a serious issue in the African small farm sector that will further increase due to climate change. Farmers typically react by using low amounts of agricultural inputs. Low input use can help to minimize financial loss in bad years, but is also associated with low average yield and income. Increasing small farm productivity and income is an important prerequisite for rural poverty reduction and food security. Crop insurance could incentivize farmers to increase their input use, but indemnity-based crop insurance programs are plagued by market failures.
Classical innovation adoption models implicitly assume homogenous information flow across farmers, which is often not realistic. As a result, selection bias in adoption parameters may occur. We focus on tissue culture (TC) banana technology that was introduced in Kenya more than 10 years ago. Up till now, adoption rates have remained relatively low.
Most micro-level studies on the impact of agricultural technologies build on cross-section data, which can lead to unreliable impact estimates. Here, we use panel data covering two time periods to estimate the impact of tissue culture (TC) banana technology in the Kenyan small farm sector. TC banana is an interesting case, because previous impact studies showed mixed results. We combine propensity score matching with a difference-in-difference estimator to control for selection bias and account for temporal impact variability.