This paper was a transcript of a power point presentation and was presented inthe Crawford Fund 2017 annual conference in the "Transformational Change Based on Innovation Platforms".  The paper discuss about the barriers to adoption digital agriculture and how to overcoming the human barrier of the adoption of these technologies.

People, places, and production contributing the least to climate change will suffer the most. This calls for adaptation as a key climate change response. But adaptation is surrounded by problems. Finance is uncertain and fragmented, mainstreaming into development is complicated, and technical solutions often overshadow existing social relations and institutions.

Agricultural innovation systems are complex, multi-layered, and can be difficult to define and analyse. In this paper, we provide examples of ‘systems analysis’: describing the context, what was done, and how the outcomes informed broader research and development activities. The five cases describe analyses of: i) agricultural systems in North-West Vietnam; ii) household food security in Central Vietnam; iii) agricultural innovation systems in Central Africa; iv) wheat commodity systems in Sub-Saharan Africa, and v) the national agricultural research system in Papua New Guinea.

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 report provides summary findings and conclusions from a set of five case studies examining the scaling up of pro-poor agricultural innovations through commercial pathways in developing countries.

Sustainable intensification of agriculture will have to build on various innovations, but synergies between different types of technologies are not yet sufficiently understood. We use representative data from small farms in Kenya and propensity score matching to compare effects of input-intensive technologies and natural resource management practices on household income. When adopted in combination, positive income effects tend to be larger than when individual technologies are adopted alone.