Rank‐based data synthesis of common bean on‐farm trials across four Central American countries

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. Core Ideas We aggregate data from trials established by different organizations across different seasons and locations. We generate location‐specific insights on genotype performance and environmental interaction. We characterize uncertainty of model predictions using Shannon's entropy and area of applicability assessment.