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RESEARCH ARTICLE

Elucidating genotype × environment interactions for grain iron and zinc content in a subset of pearl millet (Pennisetum glaucum) recombinant inbred lines

Tripti Singhal https://orcid.org/0000-0002-5766-4823 A B , C. Tara Satyavathi https://orcid.org/0000-0001-6501-8736 C * , S. P. Singh https://orcid.org/0000-0002-2476-9530 A * , Mukesh Sankar https://orcid.org/0000-0001-5459-392X A , Mallik M. https://orcid.org/0000-0001-6872-5313 A , Thribhuvan R. A , Sunaina Yadav A and C. Bharadwaj https://orcid.org/0000-0002-1651-7878 A
+ Author Affiliations
- Author Affiliations

A ICAR-Indian Agricultural Research Institute, New Delhi, India. Email: tribhuvanbr1993@gmail.com, sunainay780@gmail.com

B Amity Institute of Biotechnology, Amity University Campus, Sector 125, Noida, India.

C ICAR-Indian Institute of Millets Research, Rajendranagar, Hyderabad, Telangana 500 030, India.


Handling Editor: Jairo Palta

Crop & Pasture Science 75, CP23120 https://doi.org/10.1071/CP23120
Submitted: 10 April 2023  Accepted: 16 February 2024  Published: 14 March 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing

Abstract

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.

Keywords: AMMI, GEI, GGE, iron, multi-environment, pearl millet, stability, zinc.

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