Here you find all the training materials for tricot crowdsourcing citizen science projects. Tricot, stands for “triadic comparison of technologies”, an approach developed by Bioversity International for the rapid assessment of on-farm evaluation trails in small-scale agriculture1.
To create your project, please visit the ClimMob portal.
In the tricot approach, each participant receives a set of technologies (generally three) randomly allocated as incomplete blocks from a larger set. It maintains spatial balance by assigning roughly equal frequencies of the varieties to each area. The trials required three moments of contact with the participants: (i) explaining the experiment and distributing the trial technologies, (ii) collecting evaluation data, and (iii) returning the results1.
Fig. 1: Example of a set of varieties randomly assigned to a farmer in a tricot project
Participants’ independent observations are compiled and analysed centrally with the PlackettLuce model2. A simple ranking-based feedback format allows even participants with low literacy skills to contribute their evaluation data through various channels, including mobile telephones.
Current evidence has demonstrated that: (i) the tricot approach engage motivated participants as citizen scientists3; (ii) the data generated by citizen scientists is reliable and can produce meaningful results for agricultural research4; (iii) addresses the challenge of variety replacement for climate adaptation in a way that is, at the same time, scalable and demand led5.
Fig. 2: Variety recommendations based on average season predictions from Plackett-Luce models using climatic variables for (A) common bean in Nicaragua (Apante season), (B) durum wheat in Ethiopia (Meher season), and (C) bread wheat in India (Rabi season). Map categories show the top two varieties for each area according to their probability of winning over a base period (2002-2016). Source: van Etten et al (2019).
1. van Etten, J., Beza, E., Calderer, L. & van Duijvendijk, K. et al. First experiences with a novel farmer citizen science approach: Crowdsourcing participatory variety selection through on-farm triadic comparisons of technologies (tricot). Experimental Agriculture 1–22 (2016).
2. Turner, H. L., Etten, J. van, Firth, D. & Kosmidis, I. Modelling rankings in R: the PlackettLuce package. Computational Statistics (2020). doi:10.1007/s00180-020-00959-3
3. Beza, E., Steinke, J., van Etten, J. & Reidsma, P. et al. What are the prospects for citizen science in agriculture? Evidence from three continents on motivation and mobile telephone use of resource-poor farmers. PLOS ONE 12, e0175700 (2017).
4. Steinke, J., van Etten, J. & Zelan, P. M. The accuracy of farmer-generated data in an agricultural citizen science methodology. Agronomy for Sustainable Development 37, 32 (2017).
5. van Etten, J., de Sousa, K., Aguilar, A. & Barrios, M. et al. Crop variety management for climate adaptation supported by citizen science. Proceedings of the National Academy of Sciences 116, 4194–4199 (2019).