Finlay–Wilkinson random regression for yield and yield stability prediction in cereals
Abstract
Year-to-year climate variability poses a challenge for agriculture by increasing crop yield variability; therefore, there is a need to identify genotypes that can withstand these fluctuations. With the right selection criteria, genotypes with yield stability across variable environmental conditions can be selected. Methods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasetscommon in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability. Our objective was to examine how the number of environments and the variance among those environments affect stability predictions. We also integrate FWRR as a genomic prediction tool for characterizing yield stability, comparing it to the traditional genomic prediction models as a reference. We used three datasets: one highly unbalanced dataset for oats (Avena sativa L.) and two completely balanced datasets with different numbers of environments for barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). We fit standard Finlay-Wilkinson (FW) and FWRR models to estimate grain yield and stability under various scenarios. We found that the estimated stability values obtained were similar using balanced datasets for FW or FWRR. FWRR also achieved moderate predictive ability for stability using unbalanced datasets under 10-fold cross-validation (CV1) with new genotypes. In terms of environmental representation, selecting the right set of environments for inclusion in the model was more important than adding more environments. Our results suggest the possibility
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BibTeX
@article{sandro2026finlaywilkinson,
title = {{Finlay–Wilkinson random regression for yield and yield stability prediction in cereals}},
author = {Pablo Sandro and Justin Blancon and Jeffrey Neyhart and Julie Dawson and Lucia Gutiérrez},
journal = {Plant Genome},
year = {2026},
month = sep,
doi = {10.1002/tpg2.70269},
url = {https://hal.inrae.fr/hal-05802148v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Finlay–Wilkinson random regression for yield and yield stability prediction in cereals
AU - Pablo Sandro
AU - Justin Blancon
AU - Jeffrey Neyhart
AU - Julie Dawson
AU - Lucia Gutiérrez
PY - 2026
DA - 2026/09/09
JO - Plant Genome
DO - 10.1002/tpg2.70269
UR - https://hal.inrae.fr/hal-05802148v1
AB - Year-to-year climate variability poses a challenge for agriculture by increasing crop yield variability; therefore, there is a need to identify genotypes that can withstand these fluctuations. With the right selection criteria, genotypes with yield stability across variable environmental conditions can be selected. Methods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasetscommon in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability. Our objective was to examine how the number of environments and the variance among those environments affect stability predictions. We also integrate FWRR as a genomic prediction tool for characterizing yield stability, comparing it to the traditional genomic prediction models as a reference. We used three datasets: one highly unbalanced dataset for oats (Avena sativa L.) and two completely balanced datasets with different numbers of environments for barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). We fit standard Finlay-Wilkinson (FW) and FWRR models to estimate grain yield and stability under various scenarios. We found that the estimated stability values obtained were similar using balanced datasets for FW or FWRR. FWRR also achieved moderate predictive ability for stability using unbalanced datasets under 10-fold cross-validation (CV1) with new genotypes. In terms of environmental representation, selecting the right set of environments for inclusion in the model was more important than adding more environments. Our results suggest the possibility
SN - 1940-3372
SN - 1940-3372
ER -