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Research Article Open access CC BY 4.0

Correlation and Stability Analysis in Bread Wheat (Triticum aestivum L.) under Different Sowing Environments

Chandra Pratap Singh, Shahil Kumar, Mithilesh Kumar Singh, Ankita Singh, Raj Kumar Mandal, Hem Chand Chaudhary, Prashant Vikram

International Journal of Environment and Climate Change · pp. 72–79 · Published 14 Aug 2026

10.9734/ijecc/2026/v16i95627

Abstract

A field investigation involving 40 bread wheat genotypes was conducted across three sowing environments: normal (E1, 15 November 2023), late (E2, 15 December 2023), and very late (E3, 15 January 2024). The experiment used a Randomised Complete Block Design (RCBD) with three replications to characterise genetic variability, trait correlations, and genotype stability. Pooled ANOVA, genetic parameters (GCV, PCV, h², and GAM), genotypic and phenotypic correlation coefficients, and Eberhart–Russell stability analysis were used to evaluate 13 traits across environments. Highly significant (P < 0.01) differences were detected among environments, genotypes, and their interactions for all traits. Seed yield showed a GCV of 4.03%, a PCV of 5.06%, broad-sense heritability (h²) of 39.49%, and a GAM of 6.60%, indicating relatively limited scope for improvement through direct phenotypic selection. Seed yield showed strong positive genotypic correlations with biological yield (rᵍ = 0.748), grains per spike (rᵍ = 0.756), and 1000-seed weight (rᵍ = 0.628). Stability analysis using the Eberhart–Russell model identified Raj 3077, Raj 4027, and GW 387 as stable, high-yielding genotypes across environments. The relatively narrow difference between the PCV and GCV estimates indicated limited environmental influence on the expression of several traits. These stable genotypes, with favourable biofortification traits (Fe, Zn, and protein), may provide useful breeding material for developing climate-resilient, nutrient-rich wheat varieties suited to variable sowing conditions in India.

Triticum aestivum genotype × environment interaction stability analysis genetic parameters biofortification sowing environments Eberhart–Russell model.

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