Introduction
Seabuckthorn, belonging to the genus Hippophae, is a resilient, berry-bearing shrub or tree (Fig. 1) within the Elaeagnaceae family, native to Asia and Europe and introduced to North and South America (Singh, Reference Singh and Sharma2022). The genus comprises seven recognized species: Hippophae rhamnoides, Hippophae salicifolia, Hippophae tibetana, Hippophae neurocarpa, Hippophae goniocarpa, Hippophae gyantsensis and Hippophae litangensis, along with eight subspecies of H. rhamnoides documented globally (Swenson and Bartish, Reference Swenson and Bartish2003). Hippophae species are known for their versatile uses, including as fuel, fodder, fencing, medicinal products, cosmetics and food industry. Furthermore, they are valuable for ecological restoration and play a key role in the rehabilitation of degraded mountain ecosystems through nitrogen fixation and improving soil stability. Consequently, Hippophae species contribute significantly to environmental conservation and economic development in the fragile high mountain farming systems (Thakur et al., Reference Thakur, Sharma and Moorti1988; Pratap, Reference Pratap, Banskota and Karki1994).
Habitat, plant, and plant parts of Seabuckthorn (Hippophae salicifolia D. Don), A: Plant, B: Fruiting, C: Leaves, D: Fruit, E: Seed.
Seabuckthorn exhibits remarkable adaptability and survival capabilities under extreme climatic conditions, tolerating temperatures ranging from −43°C to +55°C, and thriving at altitudes between 60 and 5500 m. Its extensive root system provides exceptional soil binding properties and a high nitrogen fixation rate of 60–180 kg/ha/annum. The fruit of H. salicifolia is particularly notable for its elevated vitamin C content, which surpasses that of many other fruits. In China alone, more than 200 products derived from Seabuckthorn have been developed. Importantly, it serves as a critical agroforestry species, reportedly increasing the yields of crops, such as potatoes, poplars and willows (Jodha et al., Reference Jodha, Banskota and Pratap1992; Khosla et al., Reference Khosla, Seghal, Sharma, Bhatt and Singh1994).
In the context of livestock feeding, hippophae plays an integral role, particularly in harsh, high altitude environments of cold arid region in the Western Himalayas where available grazing resources may be insufficient to meet the nutritional needs of animals, especially during critical periods of the year. Based on these insights, we assessed the proximate composition of H. salicifolia by evaluating key parameters, including fresh leaf weight, dry leaf weight, dry matter content (DM), crude fibre, crude protein, total ash content, nitrogen-free extract and nitrogen. These parameters are essential for determining the fundamental nutrient quality of fodder. Dry matter content indicates the nutrient concentration after moisture removal, which serves as a crucial metric in animal nutrition. Crude fibre, comprising insoluble carbohydrates, such as cellulose and hemicellulose, plays a vital role in digestive health by promoting rumen function. Crude protein is essential for various physiological functions, such as growth, reproduction and lactation, whereas total ash provides an estimate of the mineral content necessary for various biological processes. It is a measure of minerals in the feed that support growth and metabolic processes in animals. Nitrogen-free extract constitutes the soluble carbohydrates, sugars and starches, which are used by animals to generate heat, energy and fats.
Hippophae salicifolia is naturally found in the high-altitude regions of Kinnaur, Lahaul-Spiti and Chamba districts in Himachal Pradesh, as well as the Uttarkashi District in Uttarakhand, characterized by rugged terrains, perpetual snow cover and extreme climatic conditions. In light of the species’ importance, breeding programs aimed at genetic improvements require careful selection of the most promising populations and individuals. Understanding the distribution of genetic diversity is essential for effective resource management within conservation programs (Barrett and Kohn, Reference Barrett, Kohn, Falk and Holsinger1991).
Numerous studies have documented variations in H. salicifolia across various locations, including the study by Singh et al. (Reference Singh, Bemal and Sudesh2004), Sharma et al. (Reference Sharma, Dwivedi, Awasthi and Verma2014), Yadav et al. (Reference Yadav, Sharma, Rao, Yadav and Radhakrishna2014) and Sumit et al. (Reference Nangla, Sankhyan and Rana2022a). However, there is a notable absence of research focusing on evaluating the fodder quality parameters of populations in the Lahaul valley and Yamunotri valley. This is the first study conducted on Hippophae salicifolia D. Don for fodder estimation in the Western Himalayas, focusing on two key valleys. The study will be useful during fodder scarcity in high altitude regions in Himachal Pradesh and Uttarakhand and also in adjacent states. The best strains will be identified and used in improvement programs, which will prove beneficial for livestock. Therefore, the present study was conducted to investigate the variation in fodder quality characteristics among different populations of H. salicifolia D. Don, filling a crucial research gap in this area.
Materials and methods
The present study was conducted in the Lahaul valley of Himachal Pradesh and the Yamunotri valley of Uttarkashi District, Uttarakhand, India (Fig. 2). Research was conducted from 2021 to 2023 at the field sites and in the laboratories at the Department of Tree Improvement and Genetic Resources, Dr. Y. S. Parmar University of Horticulture and Forestry, Nauni, Solan, Himachal Pradesh.
Study sites from Himachal Pradesh and Uttarakhand region indicated by red point.
A comprehensive survey was conducted to identify suitable genotypes and populations. Five natural populations were selected from each valley, with five female genotypes chosen during the fruit set phase (August–September). Mature leaves were harvested from these genotypes in August 2022 and September 2023 and the samples were transported to the laboratory for further analysis. Each female genotype was evaluated with three replicates to ensure reliability. The altitudes of the selected populations ranged from 2156 m to 3163 m above the mean sea level (amsl), as given in Table 1. The environmental conditions during leaf collection (e.g., temperature, rainfall, soil fertility) are presented in Table S1.
Selected experimental sites showing population, code number, latitude, longitude and altitude of Seabuckthorn populations in Lahaul valley and Yamunotri valley area under study
Measurements were taken for key fodder quality characteristics, including 100-leaves fresh weight, 100-leaves dry weight, dry matter content, crude fibre, crude protein, total ash, nitrogen-free extract and nitrogen content (Figure S1). These analyses were conducted to evaluate the nutritional quality of Hippophae salicifolia and to provide a comprehensive assessment of its fodder potential.
i. Leaf fresh weight (g)
A composite sample of 100 leaves was taken from each selected main branch separately of each plant and fresh weight was recorded in an electronic top-pan balance.
ii. Leaf dry weight (g)
100 fresh leaves were dried at 80°C in an oven until constant weight was attained and dry weight was taken on an electric top pan balance.
iii. Leaf dry matter content (%)
The difference in 100-leaf weight was calculated based on the loss of moisture content before and after drying. The dry matter was expressed as:
\begin{equation*}\text{Dry matter content} \left( \% \right) = \frac{\text{weight of dried sample}}{\text{weight of fresh sample}} \times 100\end{equation*}iv. Crude fibre (%)
The crude fibre content was determined using an acid-alkali digestion method in accordance with protocols established by the Association of Official Analytical Chemists (AOAC, 1995) and Sankaram (Reference Sankaram1966). A 1.5 g fat-free sample, obtained after an ether extraction, was placed in a 500 mL beaker, to which 150 mL of 1.25% sulphuric acid (H₂SO₄) was added. The mixture was then refluxed on a hot plate for 30 min.
After refluxing, the beaker was removed and the mixture was filtered through a muslin cloth using a vacuum pump. The residue was washed with hot water until it was free from acid, and then transferred back to the beaker. Subsequently, 150 mL of 1.25% sodium hydroxide (NaOH) solution was added, and the mixture was refluxed for 30 min. Subsequently, the content was filtered and washed with hot water to remove any alkali.
The washed residue was then transferred to a pre-weighed crucible and dried in a hot-air oven at 100°C until a constant weight was achieved. The dry residue was ignited in a muffle furnace at temperatures between 550°C and 600°C for 3 hours. After cooling, the crucible was weighed again, and the weight loss due to ignition represented the crude fibre content. The percentage of crude fibre was calculated using an appropriate formula.
\begin{equation*}\text{Crude fibre}\left( \% \right) = \frac{\text{weight of Crude fibre}}{\text{original weight of sample}} \times 100\end{equation*}v. Crude protein (%)
Crude protein content was estimated using the MicroKjeldahl method (Sankaram, Reference Sankaram1966). For crude protein estimation, nitrogen content in leaves was estimated as described later and obtained by multiplying the nitrogen by 6.25.
vi. Total ash (%)
The total ash content was determined following methodologies established by the Association of Official Analytical Chemists (AOAC, 1995) and Sankaram (Reference Sankaram1966). A 2 g sample of oven-dried powdered material was placed in a crucible and ignited using an electric heater until no visible smoke remained. The crucible was then transferred to a muffle furnace where the temperature was raised to 600°C and held for 2 hours to ensure complete ash formation. After the heating period, the crucible was cooled in a desiccator to prevent moisture absorption. The weight of the crucible containing the ash was recorded and the total ash content was calculated as a percentage using the appropriate formula.
\begin{equation*}\text{Total ash}\left( \% \right) = \frac{\text{weight of ash}}{\text{original weight of sample}} \times 100\end{equation*}vii. Nitrogen-free extract (%)
Nitrogen-free extract was determined by subtracting the sum of crude protein, crude fibre, ether extract and total ash content from one hundred as outlined by AOAC (1995) and Sankaram (Reference Sankaram1966).
Mineral nutrient composition
viii. Nitrogen (%)
The nitrogen content was quantified using the MicroKjeldahl method (AOAC, 1995). A 1 g powdered sample was placed in a clean digestion flask, to which 25 mL of concentrated sulphuric acid (H₂SO₄) and 3 g of a digestion mixture (20 parts K₂SO₄ and 1 part CuSO₄) were added. The mixture was digested on an electric heater for 2–2.5 hours, with intermittent shaking until a light blue colour indicated completion.
After cooling, the digested material was transferred to a 100 mL volumetric flask and diluted to the mark with distilled water. A 10 mL aliquot of this solution was placed in a MicroKjeldahl assembly. A sufficient amount of 40% sodium hydroxide (NaOH) solution (20 mL) was added to alkalinize the contents and the apparatus was sealed.
Subsequently, 20 mL of N/100 sulphuric acid (H₂SO₄) with methyl red indicator was placed in a conical flask positioned under the condenser. Distillation was performed for 10–15 minutes, after which the conical flask was removed and rinsed with distilled water. The total volume of the distillate was titrated against standard N/100 NaOH until the endpoint was reached, as indicated by the colour change from pink to yellow. The volume of NaOH used in the titration was recorded to calculate the nitrogen content based on the amount of sulphuric acid absorbed by the ammonia.
\begin{equation*}1 \text{ml N}/100\ {\text{H}_2}\text{S}{\text{O}_4} = 0.00014 \ \text{g nitrogen}\end{equation*}
\begin{equation*}\text{Nitrogen}\,\left( \% \right) = \frac{{{V_1} \times {V_2} \times 0.00014 \times D}}{{W \times A}} \times 100\end{equation*}where V1 = volume of N/100 H2SO4 taken; V2 = volume of N/100 NaOH taken for titration; D = dilution factor (volume made in volumetric flask); W = weight (g) of powdered sample taken; A = aliquot taken.
Data analysis
Analysis of variance (ANOVA) for fodder quality characteristics was performed following the procedures outlined by Panse and Sukhatme (Reference Panse and Sukhatme1967) and Chandel (Reference Chandel1984). A randomized block design (RBD) was employed, comprising 10 treatments, 5 genotypes, and 3 replications. The correlation coefficient was calculated using the formula provided by Panse and Sukhatme (Reference Panse and Sukhatme1967).
\begin{equation*}{r_{xy}} = \frac{{cov\left( {xy} \right)}}{{v\left( x \right) \times v\left( y \right)}}\end{equation*}A biplot of principal components (PC1 and PC2) was generated using PAST software (version 4.0; Hammer et al., Reference Hammer, Harper and Ryan2001). Additionally, data on the assessed characteristics were analysed using agglomerative hierarchical clustering, which was also conducted using PAST software version 4.0.
Results
Variation in fodder quality traits among 50 genotypes selected from 10 locations
Highly significant variation was observed across 50 genotypes for all fodder quality parameters (Tables S2 and S4). Leaf fresh weight ranged between 5 and 25 g, leaf dry weight ranged between 2.55 and 9.24 g, dry matter content ranged between 25.86 and 84.24%, crude fibre ranged between 10.71 and 18.72%, crude protein ranged between 17.65 and 24.69%, total ash ranged between 3.72 and 8.10%, nitrogen-free extract ranged between 47.63 and 61.50% and nitrogen ranged between 2.83 and 3.95%. The highest leaf fresh weight was observed in BAP5 (25 g; p < 0.0001), leaf dry weight in SP1 (9.24 g; p < 0.0001), leaf dry matter content in GP4 (84.24%; p < 0.0001), crude fibre in JP4 (18.72; p < 0.0001) statistically at par with JP1, JP3, JP5, KHP4, BAP1, BAP3, BAP4, KP1, KP2 (18.21%, 18.17%, 18.04%, 17.87%, 17.75%, 18.43%, 17.70%, 18.32%, 18.23%; p < 0.0001), crude protein in SHP4 (24.69%; p < 0.0001) statistically at par with SHP3, SHP5 (24.25%, 24.27%; p < 0.0001), total ash in KP2 (8.10; p < 0.0001) statistically at par with KP1, KP4, GP4 (7.71%, 7.73%, 7.66%; p < 0.0001), nitrogen free extract in SP4 (61.50%; p < 0.0001) statistically at par with SP2 (60.86%; p < 0.0001) and nitrogen in SHP4 (3.95; p < 0.0001) statistically at par with SHP3, SHP5(3.88%, 3.88%; p < 0.0001).
Variation in fodder quality traits across 10 locations
Highly significant variation was observed across 10 sites for all fodder quality parameters (Table 2; Table S3). 100-leaf fresh weight ranged between 7.50 and 16.40 g, 100-leaf dry weight ranged between 3.42 and 6.82 g, dry matter content ranged between 29.85 and 61.54%, crude fibre ranged between 11.75 and 18.14%, crude protein ranged between 18.63 and 23.91%, total ash ranged between 4.55 and 7.42%, nitrogen free extract ranged between 49.74 and 60.17 % and nitrogen ranged between 2.98 and 3.82%. Highest 100-leaf fresh weight was observed in Badya (16.40 g; p < 0.001) statistically at par with Kardang, Sissu, Kharsali, Beef, Tinu (15.70 g, 15.40 g, 12.80 g, 12.80 g, 12.60 g; p < 0.001), 100-leaf dry weight in Kardang (6.82 g; p < 0.0001) statistically at par with Sissu (6.81 g; p < 0.0001), leaf dry matter content in Sansha (61.54%; p < 0.0001) statistically at par with Gondla (55.59%; p < 0.0001), crude fibre in Jankichatti (18.14; p < 0.0001) statistically at par with Badya, Kardang (17.28%, 17.21%; p < 0.0001), crude protein in Sansha (23.91%; p < 0.0001), total ash in Kardang (7.42; p < 0.0001) statistically at par with Gondla (7.05%; p < 0.0001), nitrogen free extract in Sissu (60.17%; p < 0.0001) and nitrogen in Sansha (2.98; p < 0.0001).
Variation among populations in 100 leaves fresh weight (g), 100 leaves dry weight (g), dry matter content (%), crude fibre (%), crude protein (%), total ash (%), nitrogen free extract (%), nitrogen (%) among selected H. salicifolia D. Don populations
Results are represented as the mean value of 5 genotypes (quintuplicate) among populations. LFW: leaf fresh weight, LDW: leaf dry weight, LDMC: leaf dry matter content, CF: crude fibre, CP: crude protein, TA: total ash, NFE: nitrogen free extract, P: protein (nitrogen).
Karl Pearson’s correlation coefficients
The correlation analysis of fodder quality traits in selected populations of Seabuckthorn revealed significant correlation among various traits given in Table S5. 100-Leaf fresh weight (LFW) showed a highly significant (p < 0.01) positive correlation with 100-leaves dry weight (LDW) (0.613). Leaf dry matter content (LDMC) showed a highly significant (p < 0.01) positive correlation with crude protein (CP) (0.458) and significant (p < 0.05) positive correlation with 100-leaf dry weight (0.361) and significant (p < 0.01) negative correlation with Crude Fibre (CF) (−0.367) and 100-leaf fresh weight (LFW) (−0.448). Crude fibre (CF) showed a significant (p < 0.01) negative correlation with nitrogen-free extract (NFE) (−0.694). Crude protein showed a significant (p < 0.05) positive correlation with total ash (TA) (0.250) and a non-significant correlation with 100-leaf fresh weight, 100-leaf dry weight and Crude fibre. Total ash (TA) showed significant (p < 0.01) positive correlation with both 100-leaf dry weight (LDW) (0.463) and 100-leaf dry matter content (LDMC) (0.527). Nitrogen-free extract showed a significant (p < 0.01) negative correlation with crude fibre (CF) (−0.694) and crude protein CP (−0.589) and non-significant correlation with leaf fresh weight (LFW), 100-leaf dry weight (LDW) and leaf dry matter content (LDMC).
Principal component analysis
Principal Component Analysis (PCA) was conducted to identify major sources of variation among the fodder quality traits studied and to reduce data dimensionality. The results are summarized in Table S6. A total of four principal components with eigenvalues greater than 1 were extracted, accounting for 99.48% of the total variance. The first two components alone contributed 92.80% of the variance. PC1 accounted for 85.272% of the total variance, exhibited strong positive loadings for leaf dry matter content (LDMC) (0.98495) and crude protein (CP) (0.0608). PC2 explained 7.5372% of the variance, exhibited strong positive loadings for nitrogen-free extract (NFE) (0.61386) and 100 leaf fresh weight (LFW) (0.57428). PC3 accounted for 5.3551% of the variance and exhibited strong positive loadings for 100 leaf fresh weight (LFW) (0.71185) and leaf dry matter content (LDMC) (0.70701), PC4, contributing 1.3205% to the variance, exhibited strong negative loadings for crude protein (−0.7465) and positive loadings for crude fibre (0.62041), suggesting that protein and fibre levels may influence aspects of fodder quality not captured by the first three components.
The scatter biplot (Fig. 3) clearly separated genotypes with superior nutritive profiles in the positive plane of PC1 and PC2. Genotypes SP1, SP3, SP5, GP2, GP3, GP4, GP5, KP3, TP1, TP3, TP4 and TP5 demonstrated promising fodder quality traits, making them superior among the studied genotypes.
Principal component analysis of 50 Hippophae salicifolia D. Don accessions across first two principal components for 7 fodder quality parameters using scatter plot of given scores. S, Sissu: G, Gondla: SH, Sansha: K, Kardang: T, Tinu: R, Ranachatti: BA, Badya: KH, Kharsali: B, Beef: J, Jankichatti: P, Plant. LFW, leaf fresh weight(g): LDW, Leaf dry weight(g): LDMC, Leaf dry matter content(%): CF, Crude fibre(%): CP, Crude protein(%): TA, Total ash (%): NFE, Nitrogen free extract(%).
Cluster analysis
Cluster analysis of fodder quality parameters in H. salicifolia D. Don genotypes, using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA), revealed the formation of two main clusters. Main cluster I consisted of six genotypes, grouped with Lahaul valley populations, and represented highest crude protein (SHP3, SHP4 and SHP5) and leaf dry matter content (GP4). Main cluster II consisting of 44 genotypes. The main clusters were further divided into sub-clusters with main cluster 1 divided into Ia and Ib sub-cluster and main cluster II divided into IIa and IIb sub-cluster. Ia sub-cluster contained minor cluster Iaa with four genotypes: GP3, SHP3, SHP4 and SHP5, and the minor cluster Iab with one genotype SHP2 (genotype with second highest dry matter content). Ib sub-cluster included one genotype GP4 (highest dry matter content). Similarly, Minor cluster IIaa contained 40 genotypes with highest total ash content value (KP2) and IIab represented the highest leaf fresh weight (BAP5). The sub-cluster IIb consisted of Lahaul valley populations grouping and contained minor cluster IIba and IIbb. IIba contained two genotypes (SP3 and GP2) represent third highest leaf dry weight, and IIbb represented the highest leaf dry weight and the third most highest nitrogen free extract value with genotype SP1 (Fig. 4).
Hierarchical clustering computed based on Euclidean similarity index and Paired group (UPGMA) algorithm showing grouping of the 50 H. salicifolia D. Don accessions based on 7 fodder quality traits.
Discussion
The main finding of the research revealed that a total six out of eight fodder quality traits, found best in Lahaul valley compared to Yamunotri valley. Therefore, the current study highlights the suitability of Lahaul valley for breeding, conservation and agricultural practices, leading to the augmentation of the fodder quality. Leaf dry weight and Total ash content were found to be superior in Kardang population. Dry matter content, crude protein and nitrogen content were found superior in Sansha population and Nitrogen free extract was found to be superior in Sissu population in Lahaul valley. Leaf fresh weight was found superior in Badya population and crude fibre was found superior in Jankichatti population in Yamunotri valley. Genotypes SHP4, SHP5 were found best in crude protein and nitrogen, JP4 in crude fibre, BAP5 in leaf fresh weight, GP4 in leaf dry matter content, SP1 in leaf dry weight, KP2 in total ash and SP4 in nitrogen free extract.
Leaf fresh weight, leaf dry weight, crude fibre and nitrogen free extract were higher than those reported by Sumit et al. (Reference Nangla, Sankhyan and Rana2022a) respectively indicating Seabuckthorn germplasm native to Lahaul and Yamunotri valleys are superior in Western Himalayas in respective characteristics and crude protein (25.61%) was to be found superior in Chitkul population of Sangla valley. This variation is attributed to differences in climatic and edaphic conditions, which influence nutritional composition (Sharma et al., Reference Sharma, Dwivedi, Awasthi and Verma2014). Nitrogen content was also found to be higher in Hippophae salicifolia D Don than reported by Sharma et al. (Reference Sharma, Dwivedi, Awasthi and Verma2014) in H. rhamnoides leaves in western Himalayas. Crude protein content found similar as reported by Singh et al. (Reference Singh, Shukla, Bhattacharyya, Sirohi, Singh, Kumar and Mamta2016) in the Seabuckthorn leaves. Crude fibre, crude protein and total ash content were found consistent with earlier findings by Singh et al. (Reference Singh, Bemal and Sudesh2004). Overall the regions under study possess an optimal nutrient levels conducive to livestock nutrition with superior values in leaf fresh weight, leaf dry weight, crude fibre and nitrogen-free extract.
In comparison with related alpine forages reported from Western Himalayas by Sheikh et al. (Reference Sheikh and Majeed Gand Ajaz2011), the Crude protein (CP) content in H. salicifolia was found to be higher than that of Kiker leaves (Acacia nilotica), Toot leaves (Morus alba), Prangs (Prangs pabularia), Bathwa (Chenopodium alvum), Willow leaves (Salix tetragona), Poplar leaves (Populus tremula), Apple leaves (Malus domestica), Sarsing leaves (Elaeagnus angustifolia), Seabuckthorn (Hippophae rhamnoides), Apricot leaves (Prunus armeniaca), Nayargal (Oxytropis microphylla), Asmania (Ephedra gerardiana), Gyapshan (Poa pratensis), Toma (Caragana vesicaria). Similarly, crude fibre (CF) in H. salicifolia was higher compared to Morus alba, Prangs pabularia, Chenopodium alvum, Salix tetragona, Populus tremula, Elaeagnus angustifolia, Hippophae rhamnoides, Prunus armeniaca. Nitrogen Free Extract (NFE) was also greater in H. salicifolia than in Morus alba, Malus domestica, Hippophae rhamnoides, Prunus armeniaca, Oxytropis microphylla, Ephedra gerardiana, Poa pratensis, Caragana vesicaria, Longma/Shangsho (Lepidium latifolium). Moreover, total ash content in H. salicifolia exceeded that of Acacia nilotica, Salix tetragona, Elaeagnus angustifolia but H. salicifolia found low in Dry matter content. This study confirmed H. salicifolia leaves are a rich source of Crude protein (CP), crude fibre (CF), total ash (TA) and nitrogen free extract (NFE). Based on present fodder evaluation, H. salicifolia considered a valuable forage resource and a good source of nutrients for feeding of range ruminants in alpine region.
Correlation analyses demonstrated a significant positive correlation between crude protein with leaf dry matter content, highlighting the role of LDMC in improving crude protein levels and total ash indicating that higher protein levels linked to better mineral composition. Crude protein negatively correlated with nitrogen free extract suggesting that higher fibre and protein content may diminish energy efficiency in fodder, emphasizing the need for balancing these components for optimal livestock nutrition. Correlations suggest that enhancing one quality trait may concurrently improve other desired attributes, such as increase in crude protein linked to higher leaf dry matter content and total ash facilitating more effective breeding programs Divakara et al. (Reference Divakara and Das2011). Similar analyses on fodder quality were also reported by Poonam et al. (Reference Poonam, Sharma and Singh2014) and Sumit et al. (Reference Nangla, Sankhyan and Dutt2022b).
The PCA results suggest that observed variation in the studied traits is largely governed by LDMC, CP, NFE and LFW. PC1 explaining the highest variance, was dominantly influenced by nutrient-related parameters. PC2 appeared to represent (NFE, LFW and LDW) energy and biomass-related parameters. The biplot classified genotypes such as SP1, SP3, SP5, GP2, GP3, GP4, GP5, KP3, TP1, TP3, TP4 and TP5 with superior fodder quality traits in the positive plane of PC1 and PC2. This classification may aid in selecting accessions with optimal nutritional traits for livestock feed enhancement. These findings align with those of Udensi and Edu (Reference Udensi and Edu2015), who reported similar results for leaf proximate principles and mineral content in Vigna unguiculata. Similar PCA analyses on Seabuckthorn leaf tea were also reported by Chen et al. (Reference Chen, Jiang, Zhao, Liang, Zhang, Xu, Cao and Li2022).
Cluster analysis represents the grouping of geographic locations, which validates that the genotypes belong to same geographic locations and less gene flow occurs between geographic locations. Genotypes BP1, BP2, JP1 and JP3 were closely related, whereas GP3 and SP1 were the most distantly related. The significant inter-cluster distance between these genotypes suggests hybridization between distant genotypes of H. salicifolia. D Don might enhance hybrid vigour, as similar study reported by Mathew et al. (Reference Mathew, Parimelazhagan, Gomez and Ahmed2007) and Yadav et al. (Reference Yadav, Sharma, Rao, Yadav and Radhakrishna2014) in H. rhamnoides L. and H. salicifolia D Don, respectively.
The significant variability observed in fodder quality traits among mother trees may stem from within-species differences and varying climatic and edaphic conditions. Factors, such as age and micro-environmental effects, could also contribute to this variability Babu et al. (Reference Babu, Iyanar and Kalamani2017). Overall, significant variation across Seabuckthorn populations presents opportunities for selecting suitable genotypes to enhance the nutritional value and facilitate the commercial exploitation of this valuable plant.
In the future, the best germplasm lines such as SHP4, SHP5 best in crude protein and nitrogen, JP4 in crude fibre, BAP5 in leaf fresh weight, GP4 in leaf dry matter content, SP1 in leaf dry weight, KP2 in total ash and SP4 in nitrogen free extract should be evaluated against different locations by multi-location trials for plantation in future years. Further, investigation warrants elucidating the mineral nutrients composition (P, K, Ca, Mg, Fe, Zn, Cu, Mn), and molecular mechanisms underpinning the correlation between various fodder parameters. Further, future research should focus on analysis of fodder quality traits and other important traits such as P, K, Ca, Mg, Fe, Zn, Cu, and Mn in other locations covering more geographical area, in aim to widely use such an important crop that provides dietary nutrients to livestock and ameliorates soil fertility.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S1479262125100282.
Acknowledgements
The authors are very thankful to the Dean, College of Forestry, Dr. Y. S. Parmar University of Horticulture and Forestry, Nauni, Solan, Himachal Pradesh, India for providing facilities to work in the laboratories of the Department of Silviculture and Agroforestry in the College of Forestry for Nitrogen analysis.
Competing interests
The authors declare no competing interests.
