Unveiling the genetic diversity in curry leaf (Murraya koenigii L. Spreng) genetic resources for nutritional traits

Curry leaf (Murraya koenigii L. Spreng), a widely used spice and medicinal crop, rich in essential oils, bioactive compounds, and nutrients. The present study aimed to evaluate 80 curry leaf germplasm ex situ, to explore genetic variability in mineral nutrient content and their interrelationships. The observed ranges of these nutrients on a dry weight basis were, calcium (0.30–5.97%), phosphorus (0.11–0.77%), potassium (0.09–3.52%), magnesium (0.10–0.78%), sulphur (0.06–0.41%), iron (51.7–439.16 ppm), zinc (4.55–42.60 ppm), boron (26.02–143.38 ppm), copper (2.26–47.41 ppm), and manganese (4.12–92.47 ppm). Correlation analysis revealed a positive association of calcium with manganese, copper, and magnesium. Manganese and copper also exhibited a positive correlation with iron, while, zinc was positively correlated with phosphorus and magnesium. Both phenotypic and genotypic coefficients of variation were high for all traits studied. Furthermore, high heritability, coupled with substantial genetic advance, was observed, indicating the potential for improving these traits through breeding interventions. Principal component analysis demonstrated considerable population divergence in mineral nutrient composition, with calcium, magnesium, phosphorus, sulphur, and potassium contributing most to the overall variability. Hierarchical cluster analysis further revealed a significant regional pattern in mineral nutrient concentrations. The highest levels of calcium and iron were found in accessions collected from Karnataka, followed by those from Odisha and Himachal Pradesh.

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Publication Details

Journal
Journal of Horticultural Sciences
Published
2025-06-30
DOI
https://doi.org/10.24154/jhs.v20i1.3793
Primary Topic
Plant chemical constituents analysis
Type
article
Field-Weighted Citation Impact
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article

Unveiling the genetic diversity in curry leaf (Murraya koenigii L. Spreng) genetic resources for nutritional traits

Journal of Horticultural Sciences
Plant chemical constituents analysis
article

Unveiling the genetic diversity in curry leaf (Murraya koenigii L. Spreng) genetic resources for nutritional traits

article en

Abstract

Curry leaf (Murraya koenigii L. Spreng), a widely used spice and medicinal crop, rich in essential oils, bioactive compounds, and nutrients. The present study aimed to evaluate 80 curry leaf germplasm ex situ, to explore genetic variability in mineral nutrient content and their interrelationships. The observed ranges of these nutrients on a dry weight basis were, calcium (0.30–5.97%), phosphorus (0.11–0.77%), potassium (0.09–3.52%), magnesium (0.10–0.78%), sulphur (0.06–0.41%), iron (51.7–439.16 ppm), zinc (4.55–42.60 ppm), boron (26.02–143.38 ppm), copper (2.26–47.41 ppm), and manganese (4.12–92.47 ppm). Correlation analysis revealed a positive association of calcium with manganese, copper, and magnesium. Manganese and copper also exhibited a positive correlation with iron, while, zinc was positively correlated with phosphorus and magnesium. Both phenotypic and genotypic coefficients of variation were high for all traits studied. Furthermore, high heritability, coupled with substantial genetic advance, was observed, indicating the potential for improving these traits through breeding interventions. Principal component analysis demonstrated considerable population divergence in mineral nutrient composition, with calcium, magnesium, phosphorus, sulphur, and potassium contributing most to the overall variability. Hierarchical cluster analysis further revealed a significant regional pattern in mineral nutrient concentrations. The highest levels of calcium and iron were found in accessions collected from Karnataka, followed by those from Odisha and Himachal Pradesh.

Journal of Horticultural SciencesVol. 20(1)
Indian Institute of Horticultural Research (IN), Indian Veterinary Research Institute (IN)
Indian Council of Agricultural Research
Zero hunger
Openalex Percentile: Top 31%
Plant chemical constituents analysis
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