Andrew Farrow

SUMMARY: The success of scaling out depends on a clear understanding of the factors that affect adoption of grain legumes and account for the dynamism of those factors across heterogeneous contexts of sub-Saharan Africa. We reviewed literature on adoption of grain legumes and other technologies in sub-Saharan Africa and other developing countries. Our review enabled us to define broad factors affecting different components of the scaling out programme of N2Africa and the scales at which those factors were important. We identified three strategies for managing those factors in the N2Africa scaling out programme: (i) testing different technologies and practices; (ii) evaluating the performance of different technologies in different contexts; and (iii) monitoring factors that are difficult to predict. We incorporated the review lessons in a design to appropriately target and evaluate technologies in multiple contexts across scales from that of the farm to whole countries. Our implementation of this design has only been partially successful because of competing reasons for selecting activity sites. Nevertheless, we observe that grain legume species have been successfully targeted for multiple biophysical environments across sub-Saharan Africa, and to social and economic contexts within countries. Rhizobium inoculant and legume specific fertiliser blends have also been targeted to specific contexts, although not in all countries. Relatively fewer input and output marketing models have been tested due to public–private partnerships, which are a key mechanism for dissemination in the N2Africa project.

The Sub-Saharan African smallholder agricultural sector faces multiple and usually complex challenges, which can potentially be overcome by collective action. Smallholder farmers and other value chain stakeholders can tackle temporal, structural and contextual challenges by joining multi-level innovation networks to benefit collectively from shared information, knowledge, improved capacities and economies of scale in a process of innovation. Ambidexterity is a capability of innovation networks to balance exploration and exploitation dynamics in an innovation process, and is applicable at multiple levels: individuals, leaders, champions, teams and clusters. In the paradigm of open innovation, these levels become intertwined in hybrid social structures of innovation netchains. The objective of this paper is to describe the roles and identify the stakeholders that play those roles in an innovation process. We present case studies on farmer groups who participate in collective action and we compare multi-stakeholder platforms with other configurations of actors that tackle challenges in potato netchains in three Sub-Saharan African countries. We track and analyse innovation trajectories for six cases adapting netchain analysis techniques linking roles with the challenges faced at particular stages of each innovation trajectory. We find three management designs for fostering exploration and exploitation: (1) exploratory or exploitative management designs for small innovation networks; (2) exploitative management designs for larger networks; and (3) ambidextrous management designs for multi-stakeholder networks. Traditional roles played by managers are identified to manage exploration and exploitation in an ambidextrous way, but also evidence of roles of civil society actors facilitating collective action for the emergence of multi-stakeholder cooperatives. Since ambidexterity is about dynamism, we identified three types of mobility to be fostered when tackling challenges in an innovation process: (1) mobility-dynamism of the innovation process over time; (2) structural-knowledge mobility in innovation networks; and (3) boundary mobility.

SUMMARY: This paper seeks to establish the concept that the analysis of high temporal resolution meteorological data adds value to the investigation of the effect of climatic variability on the prevalence and severity of agricultural pests and diseases. Specifically we attempt to improve disease potential maps of root rots in common beans, based on a combination of inherent susceptibility and the risk of exposure to critical weather events. We achieve this using simulated datasets of daily rainfall to assess the probability of heavy rainfall events at particular times during the cropping season. We then validate these simulated events with observations from meteorological stations in East Africa. We also assess the utility of remotely sensed daily rainfall estimates in near real time for the purposes of updating the risks of these events over large areas and for providing warnings of potential disease outbreaks. We find that simulated rainfall data provide the means to assess risk over large areas, but there are too few datasets of observed rainfall to definitively validate the probabilities of heavy rainfall events generated using rainfall simulations such as those generated by MarkSim. We also find that selected satellite rainfall estimates are unable to predict observed rainfall events with any power, but data from a sufficiently dense network of rain gauges are not available in the region. Despite these problems we show that remotely sensed rainfall estimates may provide a more realistic assessment of rainfall over large areas where rainfall observations are not available, and alternative satellite estimates should be explored.