Introduction

Mango (Mangifera indica L.) is one of the world’s most important tropical fruit crops, cultivated widely across South Asia, Africa, and America. Its appeal lies in its rich nutritional profile, unique flavor, and substantial economic value as a leading traded fruit. Mango accounts for nearly 85% of all internationally traded tropical fruits1. India dominates global production, cultivating over 2.4 million hectares and producing 22.4 million metric tonnes annually. Yet, national productivity remains lower than that of several other mango-producing countries, reflecting systemic inefficiencies2. In 2023–2024, India exported 32,104 MT of fresh mangoes worth ~ US$60.1 million to major markets including the UAE, UK, USA, and Gulf countries3. Despite this success, productivity continues to be constrained by low-density orchards, inadequate canopy and orchard management, the use of heterogeneous rootstocks of uncertain pedigree, irregular bearing, prolonged juvenility, and large canopy size relative to yield.

Rootstocks are fundamental to the success of grafted perennial fruit crops, as they strongly influence scion vigor, canopy size, yield efficiency, stress tolerance, and overall physiology4,5,6,7,8. In many fruit trees, rootstock-mediated dwarfing has revolutionized orchard systems by enabling high-density and ultra-high-density planting, thereby facilitating mechanization and maximizing productivity9. The physiological basis of dwarfing has been linked to reduced hydraulic conductivity caused by smaller xylem vessels10, altered nutrient uptake and partitioning11, and modifications in hormone transport12. In apple, for instance, dwarfing rootstocks not only reduced tree size but also shortened the juvenile phase, enhanced flowering13, and increased harvest index13. Similar evidence in pear and litchi underscores the profound role of rootstock–scion interactions across perennial fruit crops14,15.

In mango, the polyembryonic genotype ‘Olour’ is widely used as a rootstock due to its dwarfing influence on cultivars such as ‘Amrapali’ and ‘Mallika’ and its yield-enhancing effect on hybrids like ‘Pusa Arunima’16. Olour also imparts salinity tolerance up to 4.23 dS m−117 and improves nutrient uptake and flowering in alternate-bearing cultivars18. These attributes establish Olour as a valuable genetic resource for rootstock improvement. However, its narrow genetic base and the limited characterization of its progenies restrict wider utilization. Since Olour is polyembryonic, most seedlings are nucellar and genetically uniform, but occasional zygotic seedlings from open pollination introduce novel allelic combinations. These zygotic progenies represent an untapped reservoir of genetic diversity that may harbor traits for improved vigor control, stress tolerance, and adaptability across diverse agro-climatic conditions. Systematic evaluation of such progenies could accelerate the identification of stable dwarfing and stress-resilient rootstocks, which are urgently needed to modernize mango orchards.

Globally, orchard intensification relies on dwarfing rootstocks that support ultra-high-density planting (UHDP) systems with up to 3,000–4,000 trees per hectare, leading to dramatic yield improvement19. The absence of reliable dwarfing rootstocks in mango has become a major bottleneck to achieving similar gains. In other fruit crops, interspecific hybridization of rootstocks has combined dwarfing ability with adaptability and disease resistance20,21. Efforts in mango are beginning to adopt molecular tools, such as marker-assisted selection (MAS) and quantitative trait loci (QTL) mapping, to accelerate breeding22,23. However, little is known about the genetic, physiological, or biochemical basis of dwarfing in mango, and no reliable molecular or biochemical markers have yet been identified for this trait.

Characterizing Olour-derived progenies at multiple biological levels morphological, physiological, and biochemical represents an essential first step toward bridging this gap. Germplasm characterization underpins breeding program designed by assessing heritable traits that determine productivity and adaptability24. In perennial crops, traits such as plant height, internodal length, leaf morphology, photosynthetic efficiency, antioxidant enzyme activity, and carbohydrate partitioning are closely associated with vigor and stress response. Additional indicators, including bark-to-wood ratio, specific leaf weight, stomatal density, phenolic content, and antioxidant enzyme profiles have been proposed as markers of dwarfness and climate resilience25,26. Integrating these traits into a comprehensive framework enables robust screening and selection of candidate rootstocks.

Recent work also highlights the potential of innovative grafting strategies to influence tree vigor and stress physiology. Jain et al.27 demonstrated that rootstock–interstock–scion combinations significantly affected morpho-physiological, biochemical, and anatomical traits in mango. For instance, the Olour/Mallika/Olour combination enhanced photosynthesis, stomatal density, and carbohydrate metabolism while reducing phenolic and proline accumulation, whereas Olour/Amrapali/Olour exhibited traits associated with dwarfing. These findings underscore the importance of grafting in modulating vigor, yet interstock approaches cannot substitute for the development of genetically stable rootstocks. Such stability can only be achieved through systematic evaluation of the inherent variation within polyembryonic progenies like those derived from Olour.

In this context, the present study investigates genetic variability among 14 zygotic half-sib progenies of Olour derived from open pollination with nearby wild relatives and exotic cultivars. We hypothesized that these progenies would exhibit substantial diversity in growth, photosynthetic performance, antioxidant activity, and carbohydrate metabolism, thereby providing candidates for dwarfing and stress-tolerant rootstocks. To test this, we assessed 40 morphological, physiological, and biochemical parameters and applied multivariate statistical analyses to classify progenies and identify key trait correlations. The outcomes of this study provide new insights into the variability within Olour progenies, identify promising candidates for rootstock improvement, and establish a foundation for future anatomical, transcriptomic, and proteomic research aimed at elucidating the mechanisms of dwarfing in mango.

Materials and methods

Experimental site and plants

The present study was conducted using fourteen zygotic half-sib mango open-pollinated progenies (Table 1) of 5-year-old age showing different tree growth habits planted at the nursery of Main Orchard, Division of Fruits and Horticultural Technology, ICAR-Indian Agricultural Research Institute, New Delhi (28°38ˈ47.1"N, 77°09ˈ23.8“E) situated at 228.6 m above mean sea level during 2022–2023. The experimental site is in India’s agro-climatic zone of Trans-Gangetic plains and has a semi-arid sub-tropical climate, which features a cold winter and a hot to dry summer. The warmest months in this region are May and June, with maximum temperatures ranging from 41 to 44 °C; while the coldest months are December and January, with minimum temperatures varying between 3 and 7°C.

Fourteen genotypes were selected from a pool of 20 Olour zygotic half-sib progenies maintained in the orchard after hybrid identification using 25 SSR markers (Author’s unpublished data; Supplementary Fig. S1a, S1b, Table S1). These genotypes, as half-sibmates were obtained as a result of open-pollination between Olour mother parent and wild species/varieties (M. camptosperma, M. griffithii, M. odorata, M. sylvatica, M. zeylanica, rootstock Hybrid 13-1, and exotic variety Tommy Atkins) in the proximity (2 m x 2 m), and the seeds were sown from the fertilized fruits. The trees received uniform cultural practices. Physiological observations were collected from February to March, while morphological and biochemical observations were taken from February to July.

Table 1 List of 14 olour zygotic half-sib Mango progenies and their tree growth habit.
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Experimental design and replication

The experiment was arranged as a Randomized Complete Block Design (RCBD). For morphological observations, five replications were maintained for each progeny, using leaf tissue as the source material. Physiological observations were also recorded from five replications per progeny using fully expanded leaves. For biochemical observations, three replications were taken for each progeny, with both bark and leaf tissues used to estimate total phenol and starch content. All data are reported as replication means, and the respective sample sizes (n) are specified in the table and figure captions.

Morphological parameters

Studies on genetic variability in Olour open-pollinated half-sib progenies were conducted using the descriptors of Bioversity International28 and PPVFRA Mango Descriptors29. Morphological traits were recorded on five replicated plants per progeny.

Plant height (initial and final) was measured from the soil surface to the shoot apex using a measuring scale and expressed in meters (m), with the relative increase calculated as a percentage change. Stem girth was recorded 50 cm above the soil surface using a digital Vernier caliper and expressed in centimeters (cm). Internodal length was measured on current-season shoot segments with a ruler and expressed in centimeters (cm). The number of primary branches per seedling and the total number of leaves per plant were counted manually.

Leaf morphological characters, including length (LL), width (LW), and area (LA), were measured on fully expanded leaves using WINFOLIA Pro 2020a30 leaf image analysis software. Specific leaf weight (SLW) was calculated as the ratio of leaf dry weight (g) to corresponding leaf area (cm2) and expressed in g cm−2, following the procedure of Pearce et al.31.

For bark and wood biomass determination, branches of uniform length were sampled at a minimum height of 50 cm above the ground. Fresh weights of leaf, bark, and wood were recorded immediately using an electronic balance. Samples were then oven-dried at 70 °C until a constant weight was achieved to obtain the dry biomass (g). Bark: wood ratio (BWR) was derived as the ratio of bark dry weight to wood dry weight (unitless).

Physiological parameters

Leaf gas exchange

Net photosynthetic rate (A; µmol CO2 m−2 s−1), stomatal conductance (gs; mol H2O m−2 s−1), transpiration rate (E; m.mol H2O m−2), and internal CO2 concentration (Ci; µmol CO2 m−2 s−1) were measured with a portable infrared gas analyzer (IRGA, LICOR, Lincoln, NE, USA). Observations were taken between 10:00 and 12:00 h under a natural photo period (11 h), with a mean ambient temperature of 32.7 °C and a relative humidity of 72%.

Stomatal density

Stomatal distribution was observed on the abaxial leaf surface using the impression method (Sampson32. Transparent nail varnish imprints were prepared, mounted on glass slides, and stomata were counted under a compound microscope (Olympus CX33, 10× magnification). Counts per 0.25 mm2 field were converted to stomata per mm2.

Leaf photosynthetic pigments

The total chlorophyll content of the mango leaves was estimated using dimethyl sulfoxide (Hiscox and Israetstam33. The pigment concentration in leaves was expressed in (Chl) mg g−1 FW (Arnon34. The formulae are given below;

$$Chl\, {`a`} ({\rm mg g}^{-1} {\rm FW})=\:\frac{\left(12.7\:\times\:\text{O}\text{D}663\right)-\:\left(2.69\:\times\:\text{O}\text{D}645\right)\:\text{V}}{1000\:\times\:\text{w}},$$
$$Chl\, {`b`} ({\rm mg g}^{-1} {\rm FW})=\:\frac{\left(22.9\:\times\:\text{O}\text{D}645\right)-\:\left(4.68\:\times\:\text{O}\text{D}663\right)\:\text{V}}{1000\:\times\:\text{w}},$$
$${\text{Total Chl}} ({\rm mg g}^{-1} {\rm FW})=\:\frac{\left(20.7\:\times\:\text{O}\text{D}645\right)\:+\:\left(8.02\:\times\:\text{O}\text{D}663\right)\:\text{V}}{1000\:\times\:\text{w}},$$

where OD663- absorbance at 663 nm, OD 645 -absorbance at 645 nm, V- volume of sample used in ml, and w- weight of the sample in milligrams.

The total carotenoids in the leaf tissue were determined by measuring the absorbance on a UV-Vis spectrophotometer at a wavelength of 480 nm (Sadler et al.35) and substituting the value into the equation according to Lichtenthaler and Welburn36.

$${\text{Total carotenoids}} ({\rm mg g}^{-1} {\rm FW})\:\frac{(\text{O}\text{D}480\:+\:(0.114\:\times\:\text{O}\text{D}663)\:-\:(0.638\:-\:\text{O}\text{D}645)\:\times\:\text{V}}{10000\:\times\:\text{w}}$$

where OD480 -absorbance at 480 nm, OD663 - absorbance at 663 nm and OD645 - absorbance at 645 nm.

Biochemical parameters

Total phenols and starch contents

For the estimation of total phenolic content, the Folin-Ciocalteau reagent method given by Singleton et al.37 was used. 5 g of sample was taken and then thoroughly homogenized, followed by washing with 20 ml of 80% methanol for 2–3 times, and the supernatant was collected. Subsequently, 0.2 mL of 1 N FCR and 3.3 mL of deionized water were mixed, and a 0.5 mL aliquot was taken from it. After 2 min, 1 mL of 20% Na2CO3 [Analytical Reagent (AR) grade (Merck, India)] was added to it, followed by the appearance of a blue color in the solution. The absorbance value was noted at 765 nm in a visible spectrophotometer (UV SHIMAZDU 1900i), and total phenol content was measured in (TP) mg Gallic acid equivalent g−1 FW using the formula,

$$\:\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{p}\text{h}\text{e}\text{n}\text{o}\text{l}\:\text{c}\text{o}\text{n}\text{t}\text{e}\text{n}\text{t}\:(\text{m}\text{g}\:\text{o}\text{f}\:\text{G}\text{A}\text{E}\:\text{g}-1\:\text{F}\text{W})\:=\frac{\text{x}\:\text{p}\text{p}\text{m}\:\text{x}\:\text{T}\text{V}\:\times\:100}{\text{V}\:\times\:\text{w}\:\times\:1000},$$

where x-concentration of the sample calculated from the standard curve in ppm, TV-total volume of the extract (ml), V-Assay volume (ml), w-weight of the tissue (g).

Starch estimation in leaf and bark was done by Anthrone reagent method given by Hedge and Hofreiter38. 100 mg of sample was added with 5 ml of hot 70% ethanol (v/v), followed by centrifugation at 12,000 rpm for 15 min. After retaining the residue, it was rewashed with the same concentration of hot ethanol, followed by centrifugation, with the addition of 2% perchloric acid (HClO4) and 5 mL of water. One more centrifugation is advised for re-extraction using HClO4 alone. After pooling up the supernatant and diluting to 100 ml volume, a suitable quantity of aliquot was taken and treated with 5 ml of anthrone reagent and heated for 10 min. The absorbance value after rapid cooling was noted at 620 nm, and the AR grade glucose concentrations were used to plot a standard curve. The value was then multiplied by a factor of 0.9 g corresponding to a starch yield g−1 glucose.

Antioxidant enzyme bioassays and osmolyte content in leaf tissue

Preparation of enzyme extract and bioassays

About 10 ml of chilled phosphate extraction buffer (pH 7.5; 100 mM), which contained EDTA (0.5 mM), was used to analyse the activities of catalase (EC:1.11.1.6), peroxidase (EC: 1.11.1.7), and polyphenol oxidase (EC:1.10.3.1). For antioxidant enzyme assays, fresh mango leaves were collected in an ice box, followed by excising one gram of the cleaned sample well-grounded in a pre-chilled mortar and pestle with 10 ml of chilled phosphate extraction buffer (pH 7.5; 100 mM). Peroxidase was estimated using the method described by Castillo et al.39 and expressed as µmol guaiacol oxidized min−1 mg−1 protein—catalase enzyme activity based on the absorbance of hydrogen peroxide (H2O2) at 240 nm in UV-range. A decrease in the absorbance was noted over a period as suggested by Aebi40and expressed as µmol H2O2 decomposed min−1 mg−1 protein. The polyphenol oxidase activity assay was done by the method given by Kruger et al.41 and described as ΔA410 min−1 g−1 FW. Total soluble protein content was determined by Bradford’s protein assay technique42 and expressed as mg g−1 FW, while the leaf proline content was estimated by the method given by Bates et al.43 and described as µg g−1 FW.

Statistical analysis

Data from morphological, physiological, and biochemical observations were subjected to statistical analysis using a randomized complete block design (RCBD) structure with the respective replication schemes (five for morphological, physiological, and three for biochemical traits). Replication means were used for all statistical tests.

Trait-wise analysis of variance (ANOVA) was performed using OPSTAT44 and WASP45 to assess the significance of progeny effects. Post-hoc pairwise comparisons were conducted with Tukey’s Honestly Significant Difference (HSD) test at a 5% probability level (α = 0.05). Results are reported as mean ± standard deviation, with significant groupings indicated by letter notations in tables and figures.

Correlation analysis among traits was carried out in RStudio46. Pearson’s correlation coefficients were calculated for normally distributed variables, while Spearman’s rank correlation was applied for non-normal datasets. To account for multiple testing, p-values were adjusted using the Benjamini–Hochberg false discovery rate47 procedure. Significant associations (adjusted p ≤ 0.05) were visualized as a correlation heatmap.

Principal component analysis (PCA) was performed using the prcomp (PCA) function in R, with data centered and scaled to unit variance. Eigenvalues, proportion of variance explained, and variable loadings for the first four principal components (PCs) were extracted. A scree plot of eigenvalues and cumulative variance was generated (Supplementary Fig. S1).

Agglomerative hierarchical clustering (AHC) was conducted on the same standardized dataset using Ward’s minimum variance method48with Euclidean distances. Cluster validity was assessed by silhouette widths, and cluster membership of progenies was presented in a dendrogram (Fig. 5). Phylogenetic analysis of progenies based on trait similarity was carried out using XLSTAT software49 to complement multivariate grouping.

Results

Morphological characterization

Plant growth parameters

Plant height and stem girth

Wide variability was observed among the Olour half-sib progenies in vertical and radial growth (Table 2). Final plant height ranged from 1.44 m in O14 to 4.62 m in O3, representing more than a threefold difference across the progeny set. The lowest relative increase in height was observed in O9 (3.36%), followed by O15 (4.37%) and O19 (4.57%). In contrast, O1 recorded the highest increase (9.89%). Similarly, stem girth varied from 9.00 cm in O17 to 17.40 cm in O20. Progeny O15 showed the maximum percentage increase in stem girth (8.42%), while O14 exhibited the lowest (1.37%). These results suggest that dwarfing progenies (O14, O17, O9) restricted both vertical growth and stem thickening, whereas vigorous genotypes, such as O3, O4, and O20, expressed strong height and girth expansion.

Branching and leaf production

Branching intensity and leaf production also distinguished progenies (Table 2). O3 produced the maximum number of branches (22) and leaves (502), followed by O4 (20 branches; 613 leaves) and O20 (19 branches; 523 leaves). In contrast, O17 developed only two branches and 15 leaves, while O11 and O14 produced < 60 leaves with fewer than six branches. Such sparse branching and low leaf numbers in O11, O14, and O17 are characteristic of restricted canopy development, whereas O3, O4, and O20 represent highly vigorous types with dense foliage.

Internodal length

Internodal length, a critical indicator of vegetative vigour, ranged from 3.78 cm in O17 to 9.50 cm in O3 (Table 2). Longer internodes were consistently associated with vigorous progenies (O3, O4), while shorter internodes characterized dwarfing types (O14, O17, O11). Intermediate values were recorded in progenies such as O1, O7, O8, and O10, reflecting moderate vigour.

Overall, progenies O3, O4, and O20 emerged as vigorous types, combining tall stature, thicker stems, longer internodes, and prolific branching. In contrast, O9, O11, O14, and O17 consistently displayed compact growth, reduced branching, and short internodes, highlighting their potential as dwarfing rootstock candidates. Progenies O7, O8, O10, O15, and O16 exhibited intermediate vigour, balancing growth and canopy development.

Table 2 Tree growth parameters of 14 Olour zygotic half-sib progenies and mother genotype.
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Bark-wood biomass allocation

Progenies also differed in partitioning between bark and wood tissues (Table 3). O15 and O9 showed the highest bark-to-wood dry weight ratios (0.682 and 0.782, respectively), traits often associated with restricted vigour and potential dwarfing. Conversely, vigorous progenies such as O3 and O20 maintained much lower ratios (< 0.47), consistent with enhanced secondary growth and structural robustness.

Taken together, progenies O1, O3, and O8 exhibited a combined large leaf area, high SLW, and a favourable bark–wood balance, marking them as vigorous types. In contrast, O9, O11, and O14 expressed smaller leaves, lower biomass, and disproportionately high bark–wood ratios, highlighting them as promising dwarfing rootstock candidates.

Leaf traits

Substantial variation was observed among the Olour half-sib progenies in leaf size and architecture (Fig. 1; Table 3). Leaf area ranged from only 38.84 cm2 in O14 to more than 100 cm2 in O1 and O10, with O1 also recording the greatest leaf length (25.20 cm) and width (7.32 cm). By contrast, dwarfing progenies such as O14, O11, and O9 consistently produced smaller leaves (< 65 cm2), indicating reduced assimilatory surface.

The leaf length-to-width ratio (L: W) was highest in O14 (4.52) and the mother genotype Olour (5.20), reflecting narrower laminae, while vigorous progenies O3, O4, and O8 displayed broader leaves with lower L: W ratios. Specific leaf weight (SLW) ranged from 0.003 g cm−2 in O14 to 0.032 g cm−2 in O3. Vigorous progenies (O3, O16, O8) exhibited higher SLW, suggesting thicker, metabolically active leaves, whereas dwarfing progenies (O9, O11, O14) showed the lowest values.

Leaf biomass followed a similar trend. O19 exhibited the highest fresh weight (3.28 g), while O11 and O9 had < 1.5 g. Leaf dry weight varied significantly among the Olour-derived progenies, ranging from 0.15 g in O15 to 1.92 g in O3. The highest leaf dry weight was recorded in O3 (1.92 g) and O8 (1.89 g), followed by O19 (0.88 g) and O4 (0.80 g), whereas markedly lower values were observed in O15 (0.15 g) and O11 (0.35 g). The parent genotype Olour exhibited an intermediate dry weight (0.80 g). Petiole length varied significantly, from 1.64 cm in O4 to 4.92 cm in O8, with longer petioles supporting broader leaf display in spreading types.

Fig. 1
figure 1

Leaf images of progenies (a) O1 (b) O14 (c) Olour (mother).

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Fig. 2
figure 2

Stomatal density in Olour zygotic half-sib progenies under 10× magnification. (a) Progeny O10 showing high stomatal density; (b) progeny O14 with sparse stomata. Scale bar = 100 μm. The images illustrate the anatomical differences that underpin measured variation in stomatal density and associated physiological performance.

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Table 3 Leaf dimensions, specific leaf weight, and bark wood characteristics of 14 Olour zygotic accessions compared to the nucellar Olour control.
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Physiological traits

Photosynthetic pigments

Chlorophyll and carotenoid concentrations varied markedly among progenies (Table 4). Total chlorophyll ranged from 1.36 mg g−1 FW in O17 to 4.04 mg g−1 FW in O7, with the latter also recording the highest chlorophyll a (3.07 mg g−1 FW) and chlorophyll b (0.924 mg g−1 FW). In contrast, O17, O9, and the mother genotype Olour exhibited the lowest pigment concentrations (< 1.6 mg g−1 FW). The chlorophyll a: b ratio was highest in O10 (4.29), while lower values were observed in O11, O14, and O17 (< 2.5), indicating differences in light-harvesting efficiency. Total carotenoids ranged between 0.092 mg g−1 FW (O17) and 0.372 mg g−1 FW (O9), suggesting differential investment in photoprotection across progenies.

Gas exchange parameters

Marked contrasts were observed in leaf photosynthetic performance (Table 4). Net photosynthetic rate (A) was maximum in O3 (9.74 µmol CO2 m−2 s−1), followed by O8 (8.50 µmol CO2 m−2 s−1), whereas O7 exhibited the lowest (1.17 µmol CO2 m−2 s−1). Intermediate rates were recorded in progenies such as O19 (4.63 µmol CO2 m−2 s−1) and O20 (6.39 µmol CO2 m−2 s−1). Stomatal conductance (gs) was highest in O19 (0.102 mol H2O m−2s−1), whereas the mother Olour rootstock recorded a near-zero value (0.003 mol H2O m−2s−1), consistent with its reduced gas exchange. Transpiration rate (E) paralleled gs, with O19 (4.66 m.mol H2O m−2) and O8 (3.99 m.mol H2O m−2) surpassing other progenies, while O14 and Olour showed negligible values (< 0.30 m.mol H2O m−2).

Internal CO2 concentration (Ci) also differed significantly, ranging from 208.79 µmol CO2 m−2 s−1 in O15 to 387.34 µmol CO2 m−2 s−1 in O14. Progenies with lower Ci (O3, O15) generally exhibited higher photosynthetic efficiency, whereas those with higher Ci (O14, O1) displayed restricted assimilation despite open stomata.

Stomatal traits

Stomatal density varied more than threefold across the progenies. These differences are clearly illustrated in Fig. 2, where progeny O10 (panel a) had the highest density (1186 mm−2), followed by O14 (panel b) displayed sparse stomata, reflecting its minimum density (326 mm−2). Vigorous progenies (O1, O3, O19) maintained intermediate densities (650–900 mm−2), while O10 and O19 demonstrated a combination of high stomatal density and superior gas exchange, suggesting a favourable balance between conductance and assimilation.

Overall, progeny O3 combined a high photosynthetic rate with moderate pigment content and stomatal density, identifying it as a vigorous type. O8 and O19 also exhibited superior gas exchange, with O19 displaying the highest stomatal conductance and transpiration rates. In contrast, O7, O14, and O17 were characterized by reduced photosynthetic activity, low pigment concentrations, and extreme stomatal traits, indicating that they are less vigorous but potentially more adaptive under stress.

Table 4 Photosynthetic pigments (Chlorophyll ‘a’, chlorophyll ‘b’, chlorophyll a: b, total carotenoids) and photosynthetic parameters (Net photosynthetic rate, stomatal conductance, transpiration rate, internal CO2 concentration and stomatal density) of 14 Olour zygotic half-sib progenies compared to the nucellar Olour (mother genotype).
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Biochemical traits

Total phenolics

Substantial differences were observed among Olour half-sib progenies for total phenolic content in both leaf (TPL) and bark tissues (TPB) (Fig. 3). Across all progenies, bark consistently accumulated higher phenolics than leaves. The highest phenolic levels were recorded in the bark of O17 (8,203 mg GAE 100 g−1 FW), followed by O14 (7,633 mg GAE 100 g−1 FW) and O16 (7,422 mg GAE 100 g−1 FW). In contrast, O9 and O1 displayed the lowest phenolic accumulation in both leaf and bark (< 2,500 mg GAE 100 g−1 FW). Leaf phenolic content also showed variation, with O17 and O16 exhibiting the highest values (> 2,500 mg GAE 100 g−1 FW), while O9, O11, and O14 had the lowest (< 1,000 mg GAE 100 g−1 FW). These results suggest that progenies O14 and O17, which combine elevated bark phenolics with reduced growth vigour, may rely on higher phenolic accumulation as a biochemical marker of dwarfing and stress tolerance.

Fig. 3
figure 3

Total phenols content in leaf and bark tissues in the Olour zygotic half-sib progenies.

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Total starch content

Marked variability was also observed in carbohydrate allocation between leaf (TSL) and bark (TSB) tissues (Fig. 4). Bark starch content was consistently greater than leaf starch content across all progenies. The highest bark starch reserves were found in O3 (30.64 mg g−1 FW), O19 (28.36 mg g−1 FW), and O20 (20.75 mg g−1 FW), exceeding even the mother genotype Olour (12.5 mg g−1 FW). Leaf starch content followed a similar trend, with O19 recording the highest value (16.43 mg g−1 FW), followed by O3 (12.21 mg g−1 FW) and O1 (11.92 mg g−1 FW). On the other hand, dwarfing progenies O9, O11, and O15 had the lowest starch accumulation in both leaves and bark (< 5 mg g−1 FW).

Overall, progenies O3 and O19 demonstrated superior starch storage capacity, supporting their vigorous growth and biomass accumulation, whereas O14 and O17 stood out for high phenolic accumulation, which coincided with restricted growth and possible dwarfing potential. These findings indicate that total phenolics and starch content serve as complementary biochemical markers for distinguishing vigorous and dwarfing rootstock candidates.

Fig. 4
figure 4

Total starch content in leaf and bark tissues in the Olour zygotic half-sib progenies.

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Leaf antioxidant enzymes and osmolyte content

Peroxidase activity (POX)

Peroxidase activity varied significantly among the progenies (Table 5). The highest activity was recorded in O15 (0.282 µmol guaiacol oxidized min−1 mg−1 TSP), followed by O11 (0.205 µmol guaiacol oxidized min−1 mg−1 TSP) and O10 (0.194 µmol guaiacol oxidized min−1 mg−1 TSP). In contrast, O9 (0.018 µmol guaiacol oxidized min−1 mg−1 TSP) and O20 (0.022 µmol guaiacol oxidized min−1 mg−1 TSP) displayed the lowest activities, alongside the mother genotype Olour (0.046 µmol guaiacol oxidized min−1 mg−1 TSP). Elevated POX activity in progenies such as O15 and O11 may have contributed to enhanced ROS scavenging and stress resilience, whereas consistently low values in O9 and O20 suggest limited antioxidative capacity.

Catalase activity (CAT)

Catalase activity was generally lower than POX activity but still showed significant variation (Table 5). O10 and O19 exhibited the highest activities (0.008 µmol H2O2 decomposed min−1 mg−1 TSP), while O4, O8, O14, O15, and Olour recorded minimal levels (0.001–0.002 µmol H2O2 decomposed min−1 mg−1 TSP). The contrast between high CAT activity in O19 and O10 and near-zero activity in several others indicates the differential ability of progenies to detoxify hydrogen peroxide.

Polyphenol oxidase activity (PPO)

PPO activity ranged nearly twelve-fold among the progenies as indicated in Table 5. The maximum was observed in O11 (0.024 ΔA410 g−1 min−1), followed by O16 and O17 (0.020 ΔA410 g−1 min−1 each), while the lowest activity was found in O4 (0.002 ΔA410 g−1 min−1) and Olour (0.003 ΔA410 g−1 min−1). High PPO activity in O11, O16, and O17 may be linked to increased phenolic metabolism, potentially enhancing disease resistance and stress adaptation.

Table 5 Leaf antioxidant enzymatic activities in 14 Olour zygotic half-sib progenies compared to the nucellar Olour (mother genotype).
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Leaf proline content

Proline concentration varied from 0.074 µg g−1 FW in O14 to 0.617 µg g−1 FW in O19 (Table 5). Besides O19, high proline accumulation was also noted in O9 (0.358 µg g−1 FW) and O17 (0.207 µg g−1 FW), while most other progenies maintained intermediate levels. Elevated proline in O19 underscores its role in osmotic adjustment and stress tolerance.

Total soluble proteins

TSP content ranged from 1.14 mg g−1 FW in O4 to 4.46 mg g−1 FW in O10 (Table 5). Progenies O9, O10, and O17 showed the highest protein levels (> 4.0 mg g−1 FW), while Olour and O15 had the lowest (< 1.6 mg g−1 FW). Elevated protein levels in O10 and O19 align with their strong antioxidative enzyme activities, reinforcing their stress-responsive status.

Overall, progenies O11 and O15 stood out for high peroxidase and PPO activities, while O10 and O19 combined strong catalase activity with elevated soluble protein and proline content. These traits highlight O10 and O19 as vigorous yet stress-resilient candidates, whereas O11 and O15 may have contributed more through biochemical defense pathways. Conversely, progenies O9, O14, and O20 expressed weak antioxidative profiles, with low POX and PPO activities, suggesting limited biochemical resilience.

Based on the integration of morphological, physiological, and biochemical traits, the 14 Olour half-sib progenies were classified into four growth categories: vigorous, intermediate, dwarfing, and stress-resilient (Fig. 5a). Vigorous progenies such as O3, O4, and O20 combined tall stature, thick stems, longer internodes, and high photosynthetic performance, whereas O9, O11, O14, and O17 consistently displayed compact growth, reduced branching, and low assimilatory surface, marking them as dwarfing candidates. Progenies O7, O8, O10, O15, and O16 occupied an intermediate position, balancing growth and canopy development, while O10, O15, and O19 exhibited traits indicative of stress resilience. The distribution of progenies across categories is summarized in Fig. 5b, showing that vigorous types were most abundant, followed by dwarfing and stress-resilient genotypes.

Fig. 5
figure 5

Classification of Olour half-sib progenies by growth type. (a) Progenies classified into four growth categories-Vigorous (green), intermediate (orange), dwarfing (red), and stress-resilient (blue) based on morphological, physiological, and biochemical traits. Each point represents an individual progeny, positioned according to its growth type. (b) Distribution of progenies across categories, showing that vigorous types were most frequent (n = 5), followed by dwarfing (n = 4), intermediate (n = 3), and stress resilient (n = 3). Together, the figure summarizes the classification and relative abundance of progenies within each group.

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Multivariate analysis

Principal component analysis (PCA) was performed on the mean values of 40 traits, including 22 morphological, 10 physiological, and 8 biochemical parameters (Fig. 6). Four principal components with eigenvalues greater than one were retained (Supplementary Fig. S2), collectively explaining 40.17% of the total variance among progenies. The first two components accounted for the largest share, with PC1 contributing 26.90% and PC2 explaining 13.26% of the variance.

In PC1, chlorophyll-related variables (Chl a, Chl b, and total chlorophyll) and morphological traits such as number of leaves, number of branches, initial stem girth, and final stem girth exhibited strong positive loadings. In contrast, bark fresh weight and polyphenol oxidase (PPO) activity showed strong negative loadings. PC2 displayed strong positive loadings for chlorophyll variables and several morphological traits, but negative loadings for net photosynthetic rate and leaf size parameters (length, width, and area). Notably, progenies O4 and O3 emerged as distinct outliers for vegetative vigour traits. At the same time, O17 was separated by its high bark-to-wood ratio, elevated stress-related enzyme activity, and greater leaf phenolic content, occupying a unique position in the PCA biplot.

Agglomerative hierarchical clustering (AHC) further classified the 14 progenies into two major groups based on the integrated dataset (Fig. 7; Table 6). Group I encompassed progenies O1, O9, O10, O15, and O19, with distance-to-centroid values ranging from 0.212 to 1.412. Within this group, O1 and O9 formed one subgroup, while O15 and O19 clustered together in another. Group II comprised 10 progenies (O3, O4, O7, O8, O11, O14, O16, O17, O20, and Olour), with greater spread in distance-to-centroid values (0.250–1.420). Four subgroups were identified: (i) O11 with the mother rootstock Olour, (ii) O16 with O17, (iii) O3 with O4, and (iv) O7 with O8.

Together, PCA and clustering analyses highlight the separation of vigorous progenies (O3, O4, O20) from dwarfing types (O14, O17, O11), while stress-resilient progenies (O10, O15, O19) formed a distinct subgroup, underscoring the potential of multivariate approaches in discriminating growth types and identifying candidate rootstocks.

Fig. 6
figure 6

The principal component analysis results of the observed traits in Olour zygotic half-sib progenies.

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Table 6 Agglomerative hierarchical cluster analysis, revealing a total of 5 and 10 progenies in cluster one and cluster 2, with 0.965 and 0.707 cluster variance by K-means clustering.
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Fig. 7
figure 7

The agglomerative hierarchical clustering (AHC) analysis of 14 zygotic half-sib progenies of Olour mango using morphological, physiological, and biochemical traits using XLSTAT software, revealed two primary clusters.

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Correlation analysis

Correlation analysis revealed clear trait modules that link growth, physiology, and biochemistry (Figs. 8 and 9). Plant height and increase in stem girth (ISG) were strongly correlated (r = 0.85–0.99, p ≤ 0.05) and both were associated positively with leaf number (r = 0.90) and branching, confirming the coordinated development of vigour and canopy density. Leaf area correlated with leaf length (r = 0.89) and width (r = 0.69), while specific leaf weight (SLW) was positively related to photosynthetic rate (A; r = 0.74), highlighting the role of thicker leaves in enhancing assimilation. Photosynthesis showed strong positive correlations with chlorophyll a and chlorophyll b (r = 0.99 − 0.91) and carotenoids (r = 0.43), whereas stomatal conductance correlated with transpiration (r = 0.62) and proline content (r = 0.34) but negatively with internal CO2 concentration (Ci; r = − 0.42), suggesting efficient stomatal regulation in vigorous progenies. In contrast, the bark-to-wood ratio (BWR) correlated negatively with height, girth, and photosynthesis (r = − 0.36 to − 0.52), while positively with bark dry weight (r = 0.53), linking higher bark allocation to dwarfing tendencies. Biochemically, leaf phenolics (TPL) were negatively correlated with leaf area (r = − 0.43) and photosynthesis (r = − 0.62), whereas bark phenolics (TPB) associated positively with bark biomass. Starch reserves in both leaf and bark correlated with leaf weight and photosynthesis (r = 0.39–0.59), supporting their role in vigour. In contrast, proline correlated positively with stomatal conductance and transpiration, reinforcing its role in osmotic adjustment and stress tolerance. Together, these associations highlight a growth–canopy–photosynthesis module underpinning vigour, a bark–phenolic module linked to dwarfing, and a stomatal–proline module conferring resilience. A pairwise scatterplot matrix illustrating these relationships in detail is provided as Supplementary Fig. S3.

Fig. 8
figure 8

Correlation matrix showing correlations of important traits in zygotic half-sib Olour progenies.

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Fig. 9
figure 9

Correlation matrix covering the correlation of 40 traits in Olour mango zygotic half-sib progenies.

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Discussion

Characterization of germplasm is essential for assessing trait diversity for breeding applications24. In grafted fruit trees, rootstocks play a pivotal role for regulating scion vigour, stress resilience, and adaptability. In this study, Olour half-sib progenies showed substantial variation across morphological, physiological, and biochemical traits, enabling identification of vigorous and dwarfing candidates.

Growth and canopy variation

Plant height, stem girth, and branching traits exhibited wide variability among progenies, consistent with observations in pear14 and apple50. Biplot analysis confirmed that plant and leaf traits accounted for the most variation. Dwarfing progenies (O9, O14, O17) showed compact growth, short internodes, and a higher bark-to-wood ratio (BWR), whereas vigorous types (O3, O4, O20) displayed taller stature, thicker stems, and extensive branching. ISG correlated positively with leaf area, internodal length, and leaf width, suggesting that radial growth is closely linked to assimilatory surface expansion. Similarly, branch number correlated with leaf number and photosynthetic rate, reinforcing canopy architecture as a determinant of vigour51. These patterns are consistent with genetic diversity reported for leaf traits in mango52,53,54. and the structural rules of leaf arrangement described by Hallé et al.55

Among leaf traits, specific leaf weight (SLW) emerged as a key functional indicator. Progeny O3 exhibited high SLW, consistent with enhanced photosynthate accumulation56, while O9 combined reduced SLW with high BWR and poor height increment, reinforcing its dwarfing tendency. Similar associations have been reported in flying dragon rootstock of Poncirus trifoliata25, Himalayan crab apple (Malus baccata)26, and dwarf litchi selections15.

Leaf traits and photosynthetic physiology

Leaf dimensions varied widely, with vigorous progenies producing larger leaves and dwarfing types maintaining smaller laminae. SLW, a proxy for photosynthetic efficiency56,57, was highest in O3 and O16 and correlated positively with photosynthetic rate (A) and internodal length. Internodal length showed a negative relationship with plant height, echoing reports in apple where shorter internodes accompany compact growth50. Mass-based traits such as SLW [inverse of Specific leaf area (SLA) or Leaf mass area (LMA)] have recently been shown to reliably predict vigour across woody perennials, consistent with the global leaf economics spectrum58,59.

Chlorophyll concentration and pigments showed limited predictive value for A, diverging from earlier mango studies60 but consistent with Rymbai et al.56 and Mustafa et al.61. Gas exchange parameters (gs, E) showed variable associations with A, reflecting adaptive plasticity of mango leaves under fluctuating water regimes62,63. Stomatal density correlated positively with height and girth increments, identifying it as a potential physiological marker of vigour64, consistent with recent evidence linking stomatal traits to scion vigour and stress responses in fruit crops65,66.

Phenolics, starch reserves, and biochemical markers

Phenolic accumulation showed a strong negative association with growth and photosynthetic traits. Progenies with high bark phenolic content (O14, O17) clustered separately from vigorous types, in agreement with observations in apple68, cherry68, and Fig. 669. Phenolics act as antioxidants70 but also restrict growth under stress, where their accumulation is associated with thick leaves, low nitrogen, and enhanced disease resistance71,72,73. The growth-suppressing role of phenolics arises from effects on cell wall permeability and auxin transport, influencing its polarity and quantity74. Recent studies show that enhanced phenolic/flavonoid metabolism alters auxin transport through effects on PIN proteins and transporter expression, reducing polar auxin transport in dwarfing rootstocks75,76,77. Classic studies further established flavonoids as negative regulators of auxin transport78 and reported altered auxin transport in dwarfing apple rootstocks9.

In contrast, carbohydrate accumulation in bark and leaves was positively associated with vigour, branching, and leaf number. Vigorous progenies (O3, O19) clustered with high starch reserves and superior growth, while dwarfing progenies (O9, O11, O15) accumulated the lowest reserves. In mango, over 80% of non-structural carbohydrates are stored as starch in xylem parenchyma79, serving as reserves for flowering, fruit set, and spring flushes80. Low carbohydrate levels are associated with poor flowering and alternate bearing81, while leaf starch buffers photosynthesis during fluctuations82. In this study, O3 combined the highest starch content with the most extensive canopy, while dwarfing progenies showed reduced reserves, consistent with reports that smaller canopies assimilate and store less carbohydrate83. The weak correlation between bark starch and plant height suggests that allocation patterns, rather than absolute starch reserves, explain vigour differences. Studies in peach and citrus confirm that rootstock-dependent partitioning between stems and roots is more decisive than bulk starch concentration84,85.

Antioxidant enzymes and stress physiology

Plants possess a robust antioxidant defense system, including enzymes such as peroxidase (POX) and catalase (CAT), to scavenge reactive oxygen species (ROS) generated during abiotic stress86. ROS, often referred to as oxygen radicals and their derivatives, not only cause oxidative damage but also act as key signaling molecules in acclimatization responses. Under unfavorable conditions, enzymatic and non-enzymatic antioxidants maintain the balance between ROS generation and detoxification, with H2O2 functioning as a central signaling compound that mediates stress tolerance87. In the present study, rootstocks with dwarfing potential exhibited significant differences in POX activity, with the highest activity observed in O15 and the lowest in O9. Similar observations were reported by Wang et al.88, who suggested that enhanced enzymatic activity reduces oxidative damage. CAT activity, although consistently lower than POX, was greatest in O10 and O19 and lowest in O4 and O14, likely reflecting differences in environmental stress exposure. Earlier studies also showed that salt-treated Olour rootstocks expressed higher SOD, CAT, and POX activities than untreated controls89.

Correlation analysis revealed a significant negative association between plant height and POX/PPO activity, indicating that elevated ROS metabolism and lignification may restrict cell elongation90,91. PPO activity was strongly and positively correlated with total phenol content, highlighting its role in phenolic accumulation and stress defense92. In this study, PPO activity was highest in progeny O11, followed by O16 and O17, suggesting enhanced investment in defense metabolism. By contrast, vigorous progenies (O10, O19) maintained higher CAT activity and soluble protein content, supporting sustained growth despite oxidative stress. The elevated proline levels in O19 further emphasize its role in osmotic adjustment and resilience under stress92,93,94. Taken together, variation in antioxidant enzyme activity illustrates distinct biochemical defense strategies: dwarfing progenies favor oxidative enzyme activity and phenolic metabolism9,95, whereas vigorous ones prioritize catalase-mediated detoxification and protein protection96,97.

Integrating multivariate and correlation insights

The multivariate analysis (biplot) clearly separated vigorous and dwarfing progenies based on growth and biochemical traits. Plant and leaf traits explained most of the observed variation, while biochemical markers such as phenolics and antioxidant enzymes loaded strongly in the opposite direction of growth traits. This separation higlights a dual adaptive strategy among progenies: vigorous types invest in photosynthetic efficiency and carbohydrate storage, while dwarfing types rely on phenolic accumulation and oxidative defense, a pattern consistent with earlier observation in apple rootstocks95,98,99 and citrus rootstocks100,101. Correlation networks further supported this divergence, with bark–wood ratio (BWR) negatively associated with multiple growth and photosynthetic traits, whereas starch reserves and SLW aligned with vigour, in agreement with recent studies linking carbohydrate partitioning and rootstock induced vigour102. These results mirror multivariate analyses in perennial fruit crops that consistently separate rootstocks into vigour-associated and dwarfing-associated groups9,26,85. Recent advances strengthen this evidence: Li et al.98 demonstrated that apple rootstocks under drought stress diverge into distinct physiological and transcriptomic clusters, confirming that multivariate separation is a general feature of rootstock influence, while Li et al.103 provided haplotype-resolved apple genomes that identified auxin-response regulators underlying dwarfing, thereby offering genetic and mechanistic validation of the observed vigour–dwarfing dichotomy.

Implications for rootstock breeding

Together, these findings indicate that vigour in mango rootstocks is underpinned by coordinated morphological, physiological, and biochemical traits, while dwarfing results from a trade-off between growth and defense metabolism as summarized in Venn diagram (Fig. 10). Vigorous progenies such as O3, O8, and O19 emerge as candidates for enhancing biomass and productivity. In contrast, dwarfing types such as O9, O14, and O17 hold promise for size control and high-density planting systems. By integrating correlation analysis and multivariate clustering, this study provides a systems-level framework for trait-based rootstock selection in mango, complementing earlier evidence from apple26, pear14, and citrus25. Future studies combining anatomical, hormonal, and molecular analyses will further refine the mechanistic understanding and accelerate deployment of climate-resilient, productivity-enhancing rootstocks.

Fig. 10
figure 10

Conceptual model of trait strategies and genotypes in Olour mango progenies.

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Conclusion

The comprehensive morpho‑physio‑biochemical characterization of 14 Olour half‑sib progenies revealed clear divergence between vigorous and compact types. Progenies O1, O3, O4, O7, and O8 exhibited traits associated with enhanced vegetative growth, while O9, O14 and O17 showed compact stature and biochemical markers consistent with dwarfing potential. Several progenies (O10, O11, O15, O19) exhibited biochemical profiles indicative of enhanced stress resilience. These results identify prioritized candidates for further validation through grafting trials, root and wood anatomical studies, hydraulic conductance assays, and hormonal/transcriptomic analyses. The present work lays a robust empirical basis for targeted rootstock development, but does not, by itself, establish orchard-level performance or suitability for high-density planting.