An association mapping panel consisting of 380 genotypes of chickpea was evaluated for three different years, including 2014–2015, 2015–2016 and 2016–2017, for yield-contributing parameters, including the seed number and seed weight. The AMMI analysis presented mainly concentrated on the seed weight and seed number, which are the two most important yield-contributing traits. The genotypes contributed 93.08% of the total variance, while the interaction effect was comparatively low, with 4.1% for the two traits. AMMI biplot analysis identified IG5986, IG5982, ILC6025 and ICCV14307 as desirable genotypes for the seed weight and IG5893, ILC6891 and IG5856 for the seed number. Identifying stable genotypes would help in strategic planning for yield improvement through component trait breeding.

Context: Micronutrient enrichment of pearl millet (Pennisetum glaucum (L.) R.Br.), an important food source in arid and semi-arid Asia and Africa, can be achieved by using stable genotypes with high iron and zinc content in breeding programs. Aims: We aimed to identify stable expression of high grain iron and zinc content in pearl millet lines across environments. Methods: In total, 29 genotypes comprising 25 recombinant inbred lines (RILs), two parental lines and two checks were grown and examined from 2014 to 2016 in diverse environments. Best performing genotypes were identified through genotype + genotype × environment interaction (GGE) biplot and additive main-effects and multiplicative interaction (AMMI) model analysis. Key results: Analysis of variance showed highly significant (P < 0.01) variations. The GGE biplot accounted for 87.26% (principal component 1, PC1) and 9.64% (PC2) of variation for iron, and 87.04% (PC1) and 6.35% (PC2) for zinc. On the basis of Gollob’s F validation test, three interaction PCs were significant for both traits. After 1000 validations, the real root-mean-square predictive difference was computed for model diagnosis. The GGE biplot indicated two winning RILs (G4, G11) across environments, whereas AMMI model analysis determined 10 RILs for iron (G12, G23, G24, G7, G15, G13, G25, G11, G4, G22) for seven for zinc (G14, G15, G4, G7, G11, G4, G26) as best performers. The most stable RILs across environments were G12 for iron and G14 for zinc. Conclusions: High iron and zinc lines with consistent performance across environments were identified and can be used in the development of biofortified hybrids. Implications: The findings suggest that AMMI and GGE, as powerful and straightforward techniques, may be useful in selecting better performing genotypes.