Precision-weighted mixed models identify high-yielding and temporally stable cowpea germplasm across three growing seasons
- 1Division of Genetics and Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K)
- 2Division of Plant Pathology, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K)
- 3Plant Molecular Biology and Biotechnology, Council of Scientific and Industrial Research–Indian Institute of Integrative Medicine (CSIR–IIIM)
Abstract
Introduction: Cowpea is an important multipurpose legume that contributes to food and nutritional security, particularly in regions affected by environmental stress. Identifying high-yielding and stable germplasm is essential for improving breeding efficiency and developing resilient cultivars. However, distinguishing persistent genetic merit from seasonal environmental noise remains challenging when trial precision varies among years.
Methods: Evaluated 150 International Institute of Tropical Agriculture cowpea germplasm lines and four repeated checks over three growing seasons (2020–2022) at a temperate site in Kashmir, India, using augmented block trials. Restricted maximum likelihood and best linear unbiased prediction models accounted for genotype × year interaction and year-specific residual variances.
Results: Seed yield per plant showed high genetic variability (GCV 59.75%), Cullis heritability of 0.991, and prediction accuracy of 0.992. The genotype × year-to-genotype variance ratio was 0.017, while across-year genetic correlations for yield were 0.980–0.998, indicating strong preservation of genotype rankings. Residual variance was markedly larger in 2022, and modeling heterogeneous residuals improved AIC by 50.29. IITA-122 had the highest predicted yield (156.94 g plant ⁻¹ ), 100.60% above the best check, whereas IITA-84 had the best productivity–stability profile (WAASBY 93.85; WAASB 1.61). Thirteen genotypes met an integrated high-confidence selection rule.
Discussion: The selected lines demonstrated persistent genetic merit and temporal stability despite differences in trial precision among years. They warrant replicated, multi-location validation because the present study demonstrates temporal stability at a single location rather than broad adaptation.
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BibTeX
@article{jan2026precisionweighted,
title = {{Precision-weighted mixed models identify high-yielding and temporally stable cowpea germplasm across three growing seasons}},
author = {Sami Jan and M. Ashraf Bhat and Akshay Kumar and Nakeeb-un- Nisa and M. Altaf Wani and Farooq Ahmad Bhat and Phalisteen Sultan and Zafir Ahmad Naik},
journal = {Frontiers in Plant Science},
year = {2026},
month = oct,
doi = {10.3389/fpls.2026.1926051},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1926051/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Precision-weighted mixed models identify high-yielding and temporally stable cowpea germplasm across three growing seasons
AU - Sami Jan
AU - M. Ashraf Bhat
AU - Akshay Kumar
AU - Nakeeb-un- Nisa
AU - M. Altaf Wani
AU - Farooq Ahmad Bhat
AU - Phalisteen Sultan
AU - Zafir Ahmad Naik
PY - 2026
DA - 2026/10/07
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1926051
UR - https://www.frontiersin.org/articles/10.3389/fpls.2026.1926051/full
AB - Introduction: Cowpea is an important multipurpose legume that contributes to food and nutritional security, particularly in regions affected by environmental stress. Identifying high-yielding and stable germplasm is essential for improving breeding efficiency and developing resilient cultivars. However, distinguishing persistent genetic merit from seasonal environmental noise remains challenging when trial precision varies among years. Methods: Evaluated 150 International Institute of Tropical Agriculture cowpea germplasm lines and four repeated checks over three growing seasons (2020–2022) at a temperate site in Kashmir, India, using augmented block trials. Restricted maximum likelihood and best linear unbiased prediction models accounted for genotype × year interaction and year-specific residual variances. Results: Seed yield per plant showed high genetic variability (GCV 59.75%), Cullis heritability of 0.991, and prediction accuracy of 0.992. The genotype × year-to-genotype variance ratio was 0.017, while across-year genetic correlations for yield were 0.980–0.998, indicating strong preservation of genotype rankings. Residual variance was markedly larger in 2022, and modeling heterogeneous residuals improved AIC by 50.29. IITA-122 had the highest predicted yield (156.94 g plant ⁻¹ ), 100.60% above the best check, whereas IITA-84 had the best productivity–stability profile (WAASBY 93.85; WAASB 1.61). Thirteen genotypes met an integrated high-confidence selection rule. Discussion: The selected lines demonstrated persistent genetic merit and temporal stability despite differences in trial precision among years. They warrant replicated, multi-location validation because the present study demonstrates temporal stability at a single location rather than broad adaptation.
SN - 1664-462X
ER -