Integrating multivariate and machine learning approaches to decipher drought tolerance in okra (Abelmoschus esculentus L.)
- 1Plant Breeding and Genetics Division, Nuclear Institute for Agriculture and Biology (NIAB), Faisalabad, Pakistan
- 2Nuclear Institute for Agriculture and Biology College, Pakistan Institute of Engineering and Applied Sciences (NIAB-C, PIEAS), Faisalabad, Pakistan
Abstract
Background: Drought stress severely limits crop establishment and productivity, driving early-stage screening approaches to identify resilient genotypes under changing climatic conditions. This study evaluated drought tolerance in 15 okra (Abelmoschus esculentus L.) genotypes at the seedling stage using polyethylene glycol (PEG) induced osmotic stress. Germination, morpho-physiological, and biochemical traits were assessed to quantify genotypic responses and identify key drought-adaptive mechanisms.
Results: Significant genotypic variation was observed for most traits, indicating substantial diversity in drought response. Drought-tolerant genotypes exhibited higher germination percentage, faster germination rate, reduced mean germination time, enhanced root growth, and improved chlorophyll stability and antioxidant activity under PEG stress. Genotypes were successfully categorized using multivariate analyses based on integrated trait performance. Although different genotypes ranked highest within individual environments, the multi-trait genotype–ideotype distance index (MGIDI) consistently selected NBO-31, NBO-10, and NBO-4 under both well-watered and drought-stressed conditions, demonstrating their superior stability and broad adaptation across contrasting moisture regimes. Complementary Random Forest analysis identified root morphological, antioxidant, and photosynthetic traits as major contributors to drought tolerance.
Conclusions: Collectively, these results elucidate the extent and trait basis of drought tolerance among okra genotypes and provide a robust early-stage screening framework for selecting drought-resilient parental material in breeding programs.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12870-026-09647-8.
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BibTeX
@article{akram2026integrating,
title = {{Integrating multivariate and machine learning approaches to decipher drought tolerance in okra (Abelmoschus esculentus L.)}},
author = {Saba Akram and Muhammad Kashif Riaz Khan and Abdul Rehman Khan},
journal = {BMC Plant Biology},
year = {2026},
month = aug,
doi = {10.1186/s12870-026-09647-8},
url = {https://doi.org/10.1186/s12870-026-09647-8},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Integrating multivariate and machine learning approaches to decipher drought tolerance in okra (Abelmoschus esculentus L.)
AU - Saba Akram
AU - Muhammad Kashif Riaz Khan
AU - Abdul Rehman Khan
PY - 2026
DA - 2026/08/07
JO - BMC Plant Biology
DO - 10.1186/s12870-026-09647-8
UR - https://doi.org/10.1186/s12870-026-09647-8
AB - Background: Drought stress severely limits crop establishment and productivity, driving early-stage screening approaches to identify resilient genotypes under changing climatic conditions. This study evaluated drought tolerance in 15 okra (Abelmoschus esculentus L.) genotypes at the seedling stage using polyethylene glycol (PEG) induced osmotic stress. Germination, morpho-physiological, and biochemical traits were assessed to quantify genotypic responses and identify key drought-adaptive mechanisms. Results: Significant genotypic variation was observed for most traits, indicating substantial diversity in drought response. Drought-tolerant genotypes exhibited higher germination percentage, faster germination rate, reduced mean germination time, enhanced root growth, and improved chlorophyll stability and antioxidant activity under PEG stress. Genotypes were successfully categorized using multivariate analyses based on integrated trait performance. Although different genotypes ranked highest within individual environments, the multi-trait genotype–ideotype distance index (MGIDI) consistently selected NBO-31, NBO-10, and NBO-4 under both well-watered and drought-stressed conditions, demonstrating their superior stability and broad adaptation across contrasting moisture regimes. Complementary Random Forest analysis identified root morphological, antioxidant, and photosynthetic traits as major contributors to drought tolerance. Conclusions: Collectively, these results elucidate the extent and trait basis of drought tolerance among okra genotypes and provide a robust early-stage screening framework for selecting drought-resilient parental material in breeding programs. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12870-026-09647-8.
SN - 1471-2229
ER -