ArticleBMC plant biology2026
Investigating the spatial adaptation for iron and zinc content using AMMI and GGE biplot model in lentil (Lens culinaris L.).
Article in BMC plant biology, 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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Abstract
backgroundLentil is a globally important pulse crop and a major dietary source of iron (Fe) and zinc (Zn). However, genotype × environment interaction (G × E) often leads to inconsistent micronutrient expression across environments, limiting genetic gain and the development of stable, nutrient-dense cultivars.
methodsTwenty-five lentil genotypes, including three check varieties, were evaluated across seven environments using a randomised block design with three replications. Grain Fe and Zn concentrations were quantified using ICP-MS. Analysis of variance, AMMI, GGE biplot, and ASV (AMMI stability value) analyses were employed to assess G × E interaction, genotype stability, and adaptability.
resultsThe AMMI model revealed that IPCA1 and IPCA2 were highly significant (P < 0.001), together explaining 85.7% of the variation for Fe and 88.2% for Zn. The AMMI model exhibits G7 and G13 to be stable genotypes for iron, while G23 shows general stability for zinc content. Based on ASV, G8, G13, and G12 were the most stable for Fe, whereas G23, G13, and G1 were the most stable for Zn. GGE biplot analysis showed that PC1 and PC2 accounted for 84.41% of the total variation in Fe and 82.54% in Zn. According to the GGE biplot, Genotype G13 and G3 exhibited superior performance for Fe, while G7, G24, and G21 were identified as high-performing for Zn. Three distinct mega-environments were identified, with each exhibiting different winning genotypes for iron, and three distinct mega-environments were identified with different winning genotypes for zinc.
conclusionSignificant G × E interaction influences Fe and Zn accumulation in lentil. The identification of stable, high-micronutrient genotypes and distinct mega-environments underscores the importance of multi-environment testing. AMMI and GGE biplot analyses proved effective for dissecting G × E interaction and guiding the selection of nutritionally superior and stable lentil genotypes.
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