Matching crop varieties to their target use context and user preferences is a challenge faced by many plant breeding programs serving smallholder agriculture. Numerous participatory approaches proposed by CGIAR and other research teams over the last four decades have attempted to capture farmers’ priorities/preferences and crop variety field performance in representative growing environments through experimental trials with higher external validity. Yet none have overcome the challenges of scalability, data validity and reliability, and difficulties in capturing socio-economic and environmental heterogeneity. Building on the strengths of these attempts, we developed a new data-generation approach, called triadic comparison of technology options (tricot). Tricot is a decentralized experimental approach supported by crowdsourced citizen science. In this article, we review the development, validation, and evolution of the tricot approach, through our own research results and reviewing the literature in which tricot approaches have been successfully applied. The first results indicated that tricot-aggregated farmer-led assessments contained information with adequate validity and that reliability could be achieved with a large sample. Costs were lower than current participatory approaches. Scaling the tricot approach into a large on-farm testing network successfully registered specific climatic effects of crop variety performance in representative growing environments. Tricot’s recent application in plant breeding networks in relation to decision-making has (i) advanced plant breeding lines recognizing socio-economic heterogeneity, and (ii) identified consumers’ preferences and market demands, generating alternative breeding design priorities. We review lessons learned from tricot applications that have enabled a large scaling effort, which should lead to stronger decision-making in crop improvement and increased use of improved varieties in smallholder agriculture.

将作物品种与其目标使用环境和用户偏好相匹配是许多服务于小农农业的植物育种计划面临的挑战。CGIAR和其他研究团队在过去四十年提出的许多参与性方法试图通过具有更高外部有效性的实验来捕捉农民的优先事项/偏好以及代表性种植环境中作物品种的田间表现。然而，没有一个能够克服可扩展性、数据有效性和可靠性的挑战，以及捕捉社会经济和环境异质性的困难。基于这些尝试的优势，我们开发了一种新的数据生成方法，称为技术选项的三元比较（经编）。特里科经编是一种由众包公民科学支持的分散实验方法。在本文中，我们通过我们自己的研究成果并回顾了经编方法已成功应用的文献，回顾了经编方法的发展、验证和演变。第一个结果表明，由农民主导的经编综合评估包含具有足够有效性的信息，并且可以通过大样本实现可靠性。成本低于当前的参与式方法。将经编方法扩展到大型农场测试网络，成功记录了代表性生长环境中作物品种表现的特定气候影响。特里科最近在与决策相关的植物育种网络中的应用已经（i）先进的植物育种品系认识到社会经济异质性，以及（ii）确定了消费者的偏好和市场需求，产生了替代育种设计优先事项。我们回顾了从经编应用中吸取的经验教训，这些应用促成了大规模的努力，这应该会导致在作物改良方面做出更强有力的决策，并增加小农农业中改良品种的使用。