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

David Brown*, Sytze de Bruin, Kauê de Sousa, Amílcar Aguilar, Mirna Barrios, Néstor Chaves, Marvin Gómez, Juan Carlos Hernández, Lewis Machida, Brandon Madriz, Pablo Mejía, Leida Mercado, Mainor Pavón, Juan Carlos Rosas, Jonathan Steinke, José Gabriel Suchini, Verónica Zelaya, Jacob van Etten

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2246-2266
JournalCrop Science
Issue number6
Early online date22 Jul 2022
Publication statusPublished - Nov 2022


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