Assessment of genetic diversity of kenaf genotypes (Hibiscus cannabinus L.)

Introduction

Kenaf (Hibiscus cannabinus L.) is an annual, fast-growing, and eco-friendly bast fibre crop belonging to the Malvaceae family. It is one of the most economically important fibre crops after jute and has been traditionally cultivated in Africa and Asia for centuries (FAO, 2013) [5] . Though its exact origin remains unclear, historical records suggest that kenaf has been (Ahmed et al., 2013) [1] . Its biomass serves as a renewable energy source and soil amendment. Moreover, kenaf is a phytoremediator capable of absorbing heavy metals from contaminated soils and sequestering large amounts of atmospheric CO₂-up to 10 tons per acre per season (Webber et al., 2002) [20] . In countries like Bangladesh, the Bangladesh Jute Research Institute (BJRI) has developed improved kenaf varieties such as HC-2 and HC-95. These are cultivated across nearly 30,000 hectares, significantly contributing to fibre production and rural employment (Miah et al., 2020) [12] . BJRI also maintains over 6,000 germplasm accessions of jute, kenaf, and related species, highlighting the genetic diversity available for breeding. Despite its potential, genetic improvement in kenaf remains limited. A narrow genetic base restricts breeding progress and stress tolerance. Thus, evaluating existing germplasm for variability is essential. Understanding genetic parameters like genotypic and phenotypic coefficient of variation (GCV and PCV), heritability, and genetic advance helps breeders identify traits under strong genetic control (Burton & Devane, 1953) [3] . Additionally, correlation and path coefficient analysis aid in determining direct and indirect effects of traits on fibre yield. Cluster analysis, based on Mahalanobis D² statistics, allows classification of genotypes into distinct groups based on trait performance. Genotypes from divergent clusters are suitable parents for hybridization, maximizing heterosis and genetic gain (Rao, 1952) [15] . Kenaf's rising industrial relevance, adaptability, and environmental benefits highlight the need for genetic enhancement. Evaluating its genetic diversity provides a foundation for developing improved, high-yielding varieties suitable for various ecological regions and industrial demands.

Materials and Methods

The present study entitled "Assessment of Genetic Diversity in Kenaf (Hibiscus cannabinus L.) Genotypes" was carried out during the Kharif season of 2021 at the Cotton Improvement Project, Department of Agricultural Botany, Mahatma Phule Krishi Vidyapeeth (MPKV), Rahuri, Maharashtra, India. The experiment was designed to assess genetic variability, correlations, and divergence among diverse kenaf genotypes using established biometric tools.

Experimental Site

The experiment was conducted at Rahuri, located at latitude 19°47′ N and longitude 74°50′ E, with an elevation of 495 meters above sea level. The climate is semi-arid with moderately dry weather and hot summers. The soil at the site is deep black cotton soil (Vertisol), well-drained and medium in fertility, suitable for kharif fibre crops. The average annual rainfall ranges between 450-500 mm, typically received from June to September, which supported the crop growth during the experiment.

Experimental Material

The study included 52 kenaf genotypes, comprising 50 experimental entries and 2 check varieties (HC-583 + and AMC-108 +), sourced from the germplasm collection maintained by the Cotton Improvement Project, MPKV, Rahuri. These genotypes represented wide agro-morphological diversity with potential differences in flowering behavior, plant vigour, biomass production, fibre recovery, and tolerance to abiotic stresses. The genotypes, identified by accession numbers (e.g., KIN-232, KIN-256, KIM-02, KIM-35), were chosen based on preliminary evaluation for variability and represent both indigenous and exotic lines.

Experimental Design and Layout

The experiment was laid out in a Randomized Block Design (RBD) with three replications to minimize the effect of environmental variability and improve the precision of the results. Each genotype was planted in a single-row plot of 4.5 m × 0.3 m with a row-to-row spacing of 30 cm and plant-to-plant spacing of 5-7 cm. Sowing was done manually on 12th June 2021 using the dibbling method after the receipt of adequate monsoon rainfall. The field was well-prepared by one ploughing followed by two cross-harrowings and planking to achieve a fine seedbed. Proper land leveling was done to ensure uniform water distribution.

Fertilizer Application and Crop Management

A basal dose of 60:30:30 kg NPK/ha was applied at sowing. The nitrogen component was split-50% applied at sowing and the remaining 50% top-dressed at 30 days after sowing. All recommended cultural operations including timely weeding, thinning, earthing up, and plant protection measures were carried out to maintain a healthy crop stand. No major pest or disease outbreaks were observed during the cropping season. Irrigation was not required due to sufficient and welldistributed rainfall throughout the growth period. Crop harvesting was done manually at physiological maturity when lower leaves started yellowing and fibre content was considered optimal.

Data Collection

Observations were measured for eight quantitative traits across all genotypes. Except for flowering time and plant height (which were measured for a plot basis), all traits were measured using data from five randomly selected competitive plants per plot, and the mean values were used for analysis.

The recorded traits were 1. Days to initiation of flowering-Number of days from

sowing to appearance of the first flower. 2. Days to 50% flowering-Number of days from sowing to the date when 50% of the plants had flowered. 3. Plant height (cm)-Distance from the base to the tip of the plant using a meter scale. 4. Basal diameter (cm)-Stem thickness measured near the soil surface using a Vernier caliper. 5. Fresh green weight/plant (g)-Total fresh biomass including leaves and stem. 6. Dry fibre yield/plant (g)-Weight of extracted fibre after retting, washing, and drying. 7. Stick yield/plant (g)-Weight of non-fibrous stem portion after fibre extraction. 8. Fibre recovery (%)-(Dry fibre yield/Fresh green weight) × 100. Additionally, qualitative traits such as leaf shape, leaf margin, phyllotaxy, growth habit, flower color, and branching pattern were recorded using standard descriptors.

Fibre Extraction and Retting

For fibre yield estimation, harvested plants were bundled and submerged in clean water under anaerobic conditions for retting, a microbial process to separate fibre from the woody stem. After adequate retting (usually 7-10 days), the fibre was extracted manually, washed thoroughly to remove impurities, and sun-dried before weighing.

Statistical Analysis

The data were statistically analyzed using standard biometric methods to estimate variability, heritability, trait associations, and genetic divergence:  Analysis of Variance (ANOVA) was performed as per Panse and Sukhatme (1967) to test the significance of differences among genotypes for all traits.  Genotypic and Phenotypic Coefficients of Variation (GCV and PCV) were calculated using the formulas of Burton and De Vane (1953) [3] to measure the extent of variability.

 Broad-sense heritability (h²) and Genetic Advance (GA)

were estimated using the procedures described by Johnson et al. (1955) [9] . Genetic Advance as Percent of Mean (GAM) was also computed to evaluate the efficiency of selection.  Genotypic and Phenotypic correlation coefficients were calculated to understand the direction and strength of association among traits using the methods of Singh and Chaudhary (1979).  Path coefficient analysis was performed to decompose the correlation coefficients into direct and indirect effects at the genotypic level following Dewey and Lu (1959) [4] .  Genetic divergence was assessed through Mahalanobis' D² statistics (Mahalanobis, 1936) [11] and clustering of genotypes was done using Tocher's method (Rao, 1952) [15] to group genetically similar genotypes.

Intra-and inter-cluster distances were calculated to assess the extent of genetic variability among genotypes. These statistics were essential to identify genetically divergent parents for future hybridization programs. All statistical analyses were performed using OPSTAT software and Microsoft Excel.

Results and Discussion

Quality Parameters

Fifty-two kenaf genotypes were evaluated for nine qualitative morphological traits such as leaf type, leaf colour, leaf margin, phyllotaxy, leaf apices, growth habit, branching pattern, stem colour, and inflorescence type. Most genotypes showed palmately compound leaves, green coloration, and serrated margins. Whorled phyllotaxy, acute leaf apices, erect growth habit, monopodial branching, and corymbous inflorescence were predominant across the genotypes. This qualitative assessment revealed a narrow variability in morphological descriptors, which are generally stable across environments.

Analysis of Variance (ANOVA)

Analysis of variance indicated highly significant differences (p<0.01) among genotypes for six of the eight studied quantitative traits: days to initiation of flowering, days to 50% flowering, plant height, dry fibre yield, stick yield, and fibre recovery. However, basal diameter and green weight showed non-significant variation, indicating limited environmental and genotypic influence on these parameters. These findings indicate substantial genetic variability among genotypes for most traits, which is crucial for effective selection and breeding. Similar results have been reported in earlier studies by Ghosh Dastidar et al. (1993) [7] , Prakash et al. (2003) [13] .

Mean Performance and Range of Variability

The genotypes exhibited considerable variation for all traits (Table 3). Days to initiation of flowering ranged from 69 to 94 days, with genotype KIN-259 flowering earliest. Days to 50% flowering ranged between 77 to 102 days, with KIN-259 again being the earliest. Plant height ranged from 183.7 cm (KIN-255) to 362.7 cm (KIM-24), indicating strong diversity. Basal diameter ranged from 0.78 to 2.44 cm; green weight varied between 0.156 to 0.488 kg/plant. Stick yield and fibre recovery ranged from 73.67 to 254.67 g and 5.75% to 10.69%, respectively. Dry fibre yield showed a wide range from 3.65 to 24.13 g/plant, with KIM-14 being the highest-yielding genotype.

Genetic Variability Parameters

The estimates of GCV and PCV revealed that fibre recovery and dry fibre yield exhibited high variability. The GCV ranged from 6.91% (days to 50% flowering) to 26.83% (fibre recovery), while PCV ranged from 7.00% to 30.15%. For all traits, PCV was higher than GCV, indicating the influence of environmental factors. Heritability (broad sense) was highest for days to 50% flowering (97.50%), followed closely by days to initial flowering (97.40%). Dry fibre yield showed high heritability (70.70%) and high genetic advance (67.46), indicating the predominance of additive gene action and suitability for direct selection. [9] , and Senapati et al. (2006) [18,19] , confirming that fibre yield and associated traits are heritable and responsive to selection.

Correlation Analysis

Correlation analysis revealed that dry fibre yield had significant positive correlations at both genotypic and phenotypic levels with: These results suggest that improvement in any of these traits may result in higher fibre yield. Significant correlations between these traits themselves indicate close physiological relationships that can be exploited during selection.

Path Analysis

Path coefficient analysis revealed that fibre recovery had the highest positive direct effect (1.417) on dry fibre yield, followed by days to 50% flowering (0.361) and plant height (0.273). These traits also showed significant correlations with fibre yield, indicating a true relationship and indicating they are ideal for selection. Some traits like green weight (-0.145) and stick yield (-0.559) had negative direct effects but still showed positive correlation with fibre yield due to indirect contributions via other traits like fibre recovery and plant height. These findings agree with earlier reports by Ghosh Dastidar and Bhaduri (1983) [6] , Dewey and Lu (1959) [4] , and Senapati (2006) [18,19] , who observed strong direct effects of fibre-related traits on yield.

Genetic Diversity and Clustering

The 52 genotypes were grouped into four clusters using Trait contribution to genetic divergence showed that days to initial flowering contributed the most (32.28%), followed by stick yield (21.42%) and dry fibre yield (18.25%), while fibre recovery and green weight contributed the least.

Variability, Heritability, and Genetic Advance

Significant variation was observed among the genotypes for most traits. The widest range of variation was recorded for plant height, followed by dry fibre yield and stick yield, indicating a broad genetic base. The phenotypic coefficient of variation (PCV) was greater than the genotypic coefficient of variation (GCV) for all traits, indicating that environmental factors had a notable influence. High GCV and PCV were observed for dry fibre yield and fibre recovery, while moderate values were recorded for traits such as plant height, green weight, and stick yield. Days to initiation and 50% flowering exhibited low GCV and PCV. Heritability in the broad sense was high for days to initiation of flowering (97.40%), days to 50% flowering (97.50%), fibre recovery (79.20%), and dry fibre yield (70.70%). These traits also showed high genetic advance as a percentage of the mean, indicating the predominance of additive gene action and the potential for effective genetic improvement through direct selection.

Correlation and Path Analysis

Dry fibre yield showed a significant positive correlation with most other traits at both genotypic and phenotypic levels, excluding stick yield, which had a weak or nonsignificant association. This suggests that simultaneous improvement of traits like plant height, basal diameter, and green weight would likely enhance fibre yield. Path coefficient analysis revealed that fibre recovery had the highest positive direct effect on dry fibre yield, followed by days to 50% flowering and plant height. Traits like green weight and stick yield exhibited negative direct effects but still contributed indirectly through their associations with key traits. This analysis supports the selection of traits with both significant correlation and strong direct effects for improving fibre yield.

Genetic Divergence and Clustering

The genetic divergence study grouped the 52 genotypes into four clusters. Cluster I had the maximum number of genotypes (43), while Cluster IV had only one. The highest inter-cluster distance (12.14) was recorded between Clusters I and II, indicating wide genetic diversity that can be exploited for hybridization.

Conclusion

Based on variability, heritability, trait associations, and genetic divergence, traits like flowering time, fibre recovery, and dry fibre yield emerged as promising targets for improvement. Genotypes such as KIM-02, KIN-256, KIN-233, KIM-32, KIM-26, KIM-04, KIM-22, KIM-35, KIN-242 showed superior performance and are recommended for use as parents in future kenaf breeding programs aimed at enhancing fibre yield and overall productivity.

Acknowledgments

Authors express their sincere gratitude to Department of Agricultural Botany, Mahatma Phule Krishi Vidyapeeth, Rahuri, Maharashtra, India for guidance and technical support.

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