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

Abstract

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.

Editorial: Agile data-oriented research tools to support smallholder farm system transformation

“…Milner et al, Marinus et al, and Caulfield et al each compiled secondary data from previous implementations of the Rural Household Multi Indicator Survey (RHoMIS) (Hammond et al, 2017;van Wijk et al, 2020). Gotor et al combined data from RHoMIS and the tricot approach (van Etten et al, 2019;Brown et al, 2022), while Teeken et al combined data from RHoMIS and an application of the 1000 minds tool (Hansen and Ombler, 2008;Balogun et al, 2022). Another three articles collected novel data using the RHoMIS tool and presented analyses based upon that data (Alary et al; MacLaren et al; Périnelle et al).…”

Rank‐based data synthesis of heterogeneous trials to identify the effects of climatic factors on the reaction of Musa genotypes to black leaf streak disease

“…van Etten, de Sousa et al (2019) analyzed farmer-participatory crop experiments in which field data were collected by farmers as rankings, following the "tricot" approach (van Etten, Beza et al, 2019), and then combined with environmental data. Brown et al (2022) demonstrate that data synthesis of tricot trial data of common bean (Phaseolus vulgaris L.) genotypes in Central America provides new insights to climate adaptation by predicting the performance of varieties beyond the locations in which they were tested.…”

“…Brown et al. (2022) demonstrate that data synthesis of tricot trial data of common bean ( Phaseolus vulgaris L.) genotypes in Central America provides new insights to climate adaptation by predicting the performance of varieties beyond the locations in which they were tested.…”