First experiences with a novel farmer citizen science approach: crowdsourcing participatory variety selection through on-farm triadic comparisons of technologies (TRICOT)

Rapid climatic and socio-economic changes challenge current agricultural R&D capacity. The necessary quantum leap in knowledge generation should build on the innovation capacity of farmers themselves. A novel citizen science methodology, triadic comparisons of technologies or tricot, was implemented in pilot studies in India, East Africa, and Central America. The methodology involves distributing a pool of agricultural technologies in different combinations of three to individual farmers who observe these technologies under farm conditions and compare their performance. Since the combinations of three technologies overlap, statistical methods can piece together the overall performance ranking of the complete pool of technologies. The tricot approach affords wide scaling, as the distribution of trial packages and instruction sessions is relatively easy to execute, farmers do not need to be organized in collaborative groups, and feedback is easy to collect, even by phone. The tricot approach provides interpretable, meaningful results and was widely accepted by farmers. The methodology underwent improvement in data input formats. A number of methodological issues remain: integrating environmental analysis, capturing gender-specific differences, stimulating farmers' motivation, and supporting implementation with an integrated digital platform. Future studies should apply the tricot approach to a wider range of technologies, quantify its potential contribution to climate adaptation, and embed the approach in appropriate institutions and business models, empowering participants and democratizing science

Household-specific targeting of agricultural advice via mobile phones: Feasibility of a minimum data approach for smallholder context

In recent years, agricultural extension services in developing countries have increasingly introduced modern information and communication technologies (ICT) to deliver advice. But to realize efficiency gains, digital applications may need to address heterogeneous information needs by targeting agricultural advisory contents in a household-specific way. We explore the feasibility of an automated advisory service that collects household data from farmers, for example through the keypads of conventional mobile phones, and uses this data to prioritize agricultural advisory messages accordingly. To reduce attrition, such a system must avoid lengthy inquiry. Therefore, our objective was to identify a viable trade-off between low data requirements and useful household-specific prioritizations of advisory messages. At three sites in Ethiopia, Kenya, and Tanzania in-dependently, we collected experimental preference rankings from smallholder farmers for receiving information about different agricultural and livelihood practices. At each site, we identified socio-economic household variables that improved model-based predictions of individual farmers’information preferences. We used the models to predict household-specific rankings of information options based on 2–4 variables, requiring the farmer to answer between 5 and 10 questions through an ICT interface. These predicted rankings could inform household-specific prioritizations of advisory messages in a digital agro-advisory application. Household-specific “top 3” options suggested by the models were better-fit to farmers’preferences than a random selection of 3 options by 48–68%, on average. The analysis shows that relatively limited data inputs from farmers, in a simple format, can be used to increase the client-orientation of ICT-mediated agricultural extension. This suggests that household-specific prioritization of agricultural advisory messages through digital two-way communication is feasible. In future digital agricultural advisory applications, collecting little data from farmers at each interaction may feed into learning algorithms that continuously improve the targeting of advice

Crop variety management for climate adaptation supported by citizen science

Crop adaptation to climate change requires accelerated crop variety introduction accompanied by recommendations to help farmers match the best variety with their field contexts. Existing approaches to generate these recommendations lack scalability and predictivity in marginal production environments. We tested if crowdsourced citizen science can address this challenge, producing empirical data across geographic space that, in aggregate, can characterize varietal climatic responses. We present the results of 12,409 farmer-managed experimental plots of common bean (Phaseolus vulgaris L.) in Nicaragua, durum wheat (Triticum durum Desf.) in Ethiopia, and bread wheat (Triticum aestivum L.) in India. Farmers collaborated as citizen scientists, each ranking the performance of three varieties randomly assigned from a larger set. We show that the approach can register known specific effects of climate variation on varietal performance. The prediction of variety performance from seasonal climatic variables was generalizable across growing seasons. We show that these analyses can improve variety recommendations in four aspects: reduction of climate bias, incorporation of seasonal climate forecasts, risk analysis, and geographic extrapolation. Variety recommendations derived from the citizen science trials led to important differences with previous recommendations

What's law got to do with it Part 2: Legal strategies for healthier nutrition and obesity prevention

This article is the second in a two-part review of law's possible role in a regulatory approach to healthier nutrition and obesity prevention in Australia. As discussed in Part 1, law can intervene in support of obesity prevention at a variety of levels: by engaging with the health care system, by targeting individual behaviours, and by seeking to influence the broader, socio-economic and environmental factors that influence patterns of behaviour across the population. Part 1 argued that the most important opportunities for law lie in seeking to enhance the effectiveness of a population health approach

Replication data for: "Crop variety management for climate adaptation supported by citizen science"

Farmers were invited to collaborate as citizen science volunteers in the crowdsourcing approach "tricot" (triadic comparison of technologies). We organized tricot trials, testing 10 common bean varieties (Phaseolus vulgaris) in Nicaragua during five growing seasons, 62 durum wheat (Triticum durum) modern and farmer varieties in Ethiopia during three seasons and 21 bread wheat (Triticum aestivum) varieties in India during four seasons. Sets of three varieties (four in the case of Ethiopia) were allocated randomly to blocks, corresponding to one block. Farmers observed and reported the performance of the assigned varieties evaluating which one presented the best and worst performance. This dataset contain the trial data related to yield (Nicaragua and India), overall performance and overall performance of tested varieties against the local variety used by farmers (Nicaragua). We linked farmers' observations via their geographic coordinates and planting dates to obtain environmental variables derived from public databases with global coverage. The lists of the bean, durum and bread wheat varieties tested are also provided