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用途、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。在过去四十年中，CGIAR 和其他研究团队提出了许多参与式方法，试图通过具有更高外部效度的实验试验来捕捉农民在代表性生长环境中的优先事项/偏好和作物品种田间表现。然而，没有一个项目能够克服可扩展性、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 （tricot）。Tricot 是一种由众包公民科学支持的去中心化实验方法。在本文中，我们通过我们自己的研究结果回顾了 tricot 方法的发展、验证和演变，并回顾了 tricot 方法已成功应用的文献。初步结果表明，经编聚合的农民主导的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。成本低于目前的参与式方法。将经编方法扩展到大型农场测试网络，成功地记录了作物品种性能在代表性生长环境中的特定气候影响。Tricot 最近在植物育种网络中与决策相关的应用已经 （i） 认识到社会经济异质性，推进了植物育种系，以及 （ii） 确定了消费者的偏好和市场需求，从而产生了替代育种设计的优先事项。 我们回顾了从经编应用中吸取的经验教训，这些经验使大规模的扩大工作成为可能，这应该会导致在作物改良方面做出更有力的决策，并在小农农业中增加对改良品种的使用。