Evidence map›Paper›PMID 42775232›Full record

ArticleFrontiers in plant science2026

Adaptation of faba bean cultivars across Norwegian environments: identification of elite material and definition of optimal breeding and cultivar testing procedures.

Stefano Zanotto, Jon Arne Dieseth, Wendy Waalen, Paolo Annicchiarico, Anne Kjersti Uhlen

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Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Stefano ZanottoDepartment of Plant Science, Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway.
Jon Arne DiesethGraminor AS, Hamar, Norway.
Wendy WaalenDivision of Food Production and Society, Norwegian Institute for Bioeconomy Research (NIBIO), Ås, Norway.
Paolo AnnicchiaricoCouncil for Agricultural Research and Economics (CREA), Research Centre for Animal Production and Aquaculture, Lodi, Italy.
Anne Kjersti UhlenDepartment of Plant Science, Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Faba bean cultivation in northern Europe is challenged by high variability in yield performance due to strong genotype × environment interaction (GEI). Appropriate GEI modeling and interpretation is essential for both cultivar recommendation and breeding for Nordic conditions. Methods: Grain yield data from fifteen spring faba bean cultivars evaluated in eleven Norwegian environments during four growing seasons (2022-2025) were analyzed using the Additive Main Effects and Multiplicative Interaction (AMMI) model. Climatic drivers of GEI were investigated using factorial regression, while genome-wide SNP markers were used to assess the relationship between molecular diversity and adaptive differentiation. Results: Environment, genotype, and GEI significantly influenced grain yield. The AMMI model with three GEI principal components (AMMI3) represented the biologically meaningful GEI signal and explained 83.4% of the GEI variation. The predictive accuracy of this model closely approached that of BLUP and GBLUP. Factorial regression showed that maximum and mean September temperatures explained 38.5% of the GEI variation, indicating that late-season thermal conditions are the principal environmental drivers of cultivar adaptation. We identified three top-yielding genotypes (Ketu, Birgit, and Vire) that can be object of site-specific recommendation. Two sub-regions emerged as possibly distinct breeding targets, namely, a major one, and a smaller one featuring cooler late-cycle temperatures and specific adaptation of very early material. A significant Mantel correlation between genomic and adaptive dissimilarity (r = 0.40, P = 0.029) suggested that differences in environmental adaptation have a measurable genetic basis. Conclusions: This study identified elite cultivars for recommendation and generated crucial information for future crop breeding and variety testing in Norway. Breeding for wide adaptation across climatically diversified environments is justified by the modest size of the smaller sub-region and its expected decreasing importance due to climate change. The relationship between genomic and adaptive dissimilarity, if confirmed for a larger genotype set, could be exploited for a preliminary screening of regional breeding material and novel plant introductions.

Indexed as

AMMIcultivar adaptationfactorial regressiongenotype × environment interactionmulti-environment trialsplant breedingVicia faba L.

Identifiers

PMID42775232
PMCPMC13595569

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