Location-specific information is required to support decision making in crop variety management, especially under increasingly challenging climate conditions. Data synthesis can aggregate data from individual trials to produce information that supports decision making in plant breeding programs, extension services, and of farmers. Data from on-farm trials using the novel approach of triadic comparison of technologies (tricot) are increasingly available, from which more insights could be gained using a data synthesis approach. The objective of our study was to present the applicability of a rank-based data synthesis approach to several datasets from tricot trials to generate location-specific information supporting decision making in crop variety management. Our study focuses on tricot data from 14 trials of common bean (Phaseolus vulgaris L.) performed between 2015 and 2018 across four countries in Central America (Costa Rica, El Salvador, Honduras, and Nicaragua). The combined data of 17 common bean genotypes were rank aggregated and analyzed with the Plackett–Luce model. Model-based recursive partitioning was used to assess the influence of spatially explicit environmental covariates on the performance of common bean genotypes. Location-specific performance was predicted for the three main growing seasons in Central America. We demonstrate how the rank-based data synthesis methodology allows integrating tricot trial data from heterogenous sources to provide location-specific information to support decision making in crop variety management. Maps of genotype performance can support decision making in crop variety evaluation such as variety recommendations to farmers and variety release processes.

中文翻译：

---

四个中美洲国家普通豆农场试验的基于等级的数据综合

需要特定位置的信息来支持作物品种管理的决策，尤其是在日益具有挑战性的气候条件下。数据合成可以汇总来自各个试验的数据，以生成支持植物育种计划、推广服务和农民决策的信息。使用三元技术比较（tricot）新方法的农场试验数据越来越多，使用数据合成方法可以从中获得更多见解。我们研究的目的是展示基于排名的数据合成方法对来自经编试验的多个数据集的适用性，以生成支持作物品种管理决策的特定位置信息。我们的研究侧重于 14 次普通豆试验的经编数据（菜豆L.) 于 2015 年至 2018 年间在中美洲的四个国家（哥斯达黎加、萨尔瓦多、洪都拉斯和尼加拉瓜）进行表演。将 17 种常见豆类基因型的组合数据进行排名聚合，并使用 Plackett–Luce 模型进行分析。基于模型的递归划分用于评估空间显式环境协变量对普通豆类基因型性能的影响。预测了中美洲三个主要生长季节的特定地点表现。我们展示了基于排名的数据合成方法如何允许整合来自异质源的经编试验数据以提供特定于位置的信息以支持作物品种管理中的决策制定。