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Assessment of Genetic Variability and Trait Relationships among Traditional Rice Landraces Grown under Organic Farming

V
Velukuru Sahithi Sree1
0009-0001-2554-8553
A
A. Mohammed Ashraf1,*
0000-0001-6230-6185
P
P. Balasubramanian1
0000-0002-1943-7013
J
Jegadeeswaran Mokkaraj2
0000-0001-9066-9477
N
Nagamaniammai Govindarajan3
0000-0003-1433-8419
1Department of Agronomy, SRM College of Agricultural Sciences, SRM Institute of Science and Technology, Baburayanpettai, Chengalpattu-603 201, Tamil Nadu, India.
2Department of Genetics and Plant Breeding, SRM College of Agricultural Sciences, SRM Institute of Science and Technology, Baburayanpettai, Chengalpattu-603 201, Tamil Nadu, India.
3Department of Food Technology, School of Bioengineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu-603 203, Tamil Nadu, India.

ABSTRACT

Background: The preservation and utilization of traditional rice landraces are essential for sustainable agriculture, particularly under organic farming systems where genetic stability, adaptability and resilience are crucial. Understanding the extent of genetic variability and trait relationships is important for effective selection and crop improvement.

Methods: The present study evaluated genetic variability, heritability and trait relationships among traditional rice landraces cultivated under organic farming conditions. Multivariate and statistical analyses such as principal component analysis (PCA), genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV) and correlation analysis were employed to assess the diversity and association among different growth and yield traits.

Result: The analysis revealed that six principal components contributed to 100% of the total variation, with PC1 and PC2 accounting for 44.11% and 23.84%, respectively, mainly representing vegetative and yield-related traits. Cluster analysis indicated significant genetic diversity among the landraces, with Cluster 2 (Karunkuravai and Kaivari Samba) exhibiting higher 1000 seed weight (27.525 g) and superior yield attributes. Traits such as leaf length and number of leaves showed high heritability (>80%) along with high genetic advance, indicating the predominance of additive gene action and the effectiveness of direct selection. Significant positive correlations were observed between plant height and leaf length (r = 0.79*) and between productive tillers and 1000 seed weight (r = 0.72), suggesting strong associations between vegetative growth and yield potential. The study highlights the importance in identifying superior genotypes suitable for organic farming and provides a strong basis for future breeding programs.

INTRODUCTION

More than 3.5 billion people worldwide depend on rice (Oryza sativa L.) as a staple food, accounting for nearly 20% of the global dietary energy supply. Global rice consumption exceeds 520 million metric tonnes annually, with Asia accounting for nearly 90% of total consumption. Traditional rice landraces have remained essential for preserving genetic diversity, resistance to environmental challenges and cultural heritage, even while contemporary high yielding cultivars have made a substantial contribution to productivity advances (Shukla et al., 2023 and Ashraf et al., 2024a). Low-input or organic farming systems are commonly used to cultivate these landraces because they are adaptable to local Agro-ecological circumstances and allow for the full expression of their distinctive genetic features (Pandey et al., 2022; Bisht et al., 2021 and Ashraf et al., 2024b). The preservation and use of historic rice types have received attention in recent years because of their potential benefits to sustainable agriculture, particularly when grown organically. Because organic farming prioritizes ecological balance and the use of minimum synthetic inputs, it is crucial to find and support cultivars that are highly adaptive and genetically stable in these systems (Chandran et al., 2023 and Ashraf and Lokanadan, 2022a). Nevertheless, a thorough grasp of trait variability, heritability and the connections between yield and physical traits is necessary for the genetic development of these landraces.
       
The genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV), two measures of genetic variability, shed light on the level of variability and the proportion of environmental compared to genetically controlled traits (Akinwale et al., 2022 and Ashraf and Lokanadan, 2022b). In breeding strategies, traits with high genetic progress and high GCV and heritability make effective selection objectives (Jahan et al., 2023 and Ashraf et al., 2017a). Agronomically important features under organic systems can be efficiently clustered and prioritized using PCA. Correlation research between features also sheds light on possible trade-offs and synergies between grain quality, yield parameters and vegetative development. It is important to understand the relationships to develop breeding plans that complement production objectives and the agronomic circumstances of organic farming (Ogawa et al., 2023 and Ashraf et al., 2017b). The goal of the current study was to evaluate the genetic variability and trait relationships among traditional rice landraces grown under organic farming systems, given the growing significance of sustainable farming techniques and genetic resource conservation. This study is to find superior genotypes and important selection indices that can aid in the creation of robust, high yielding rice cultivars appropriate for organic farming through the use of PCA, GCV, PCV and correlation analyses.

MATERIALS AND METHODS

This study utilized traditional rice (Oryza sativa L.) landraces cultivated under certified organic conditions. A diverse set of genotypes was selected to represent traditional rice populations grown without synthetic inputs. The experiment was conducted during the Late Samba season (September-February) of 2025-26 at the SRM College of Agricultural Sciences organic farm, Baburayanpettai, Chengalpattu district, Tamil Nadu, India. The site is located at 12.38°N latitude and 79.73°E longitude, with an elevation of 50 m above mean sea level, falling under the North Eastern agro-climatic zone of Tamil Nadu (Gomez and Gomez, 1984).
       
The experiment was laid out in a randomized block design (RBD) with three replications (Kumar and Yadav, 2021). Seven traditional rice landraces were studied: V1: Rathasali, V2: Arbutham Kuravai, V3: Karunkuravai, V4: Kaivari Samba, V5: Karuppu Kavuni, V6: Chithirakar and V7: Kullakar. Seeds were treated with Bacillus subtilis liquid bioinoculant culture (10 g kg-1 seed), along with azospirillum and phosphobacteria carrier-based biofertilizer formulations (30 g kg-1 each) (Kumar et al., 2020). The treated seeds were soaked for 24 hours and incubated for 12 hours before sowing in a nursery enriched with farmyard manure (5 t/ha), neem cake (5 t/ha) and Trichoderma viride (40 kg/ha) (Bhattacharyya et al., 2019).
       
Dhaincha was grown as green manure at 25 kg/ha and incorporated after 45 days (Choudhary and Sharma, 2022), along with neem cake at 250 kg/ha. Seedlings were transplanted at 20 × 10 cm spacing (Ray et al., 2020) after root dip treatment with azospirillum and phosphobacteria. Nutrient needs were met using vermicompost (1000 kg/ha) at key growth stages and panchagavya (3%) was applied at 30 and 45 DAT. Alternate wetting and drying irrigation, manual weeding and biocontrol measures using neem extract, Beauveria bassiana and Bacillus subtilis were followed. Data were recorded on vegetative and reproductive traits from five tagged plants per plot, following standard descriptors (Zhao et al., 2021).
 
Statistical analyses
 
All statistical analyses, including genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), principal component analysis (PCA) and cluster analysis were performed and pearson’s correlation coefficient analysis were performed using RStudio version 2024.12.1+563.

RESULTS AND DISCUSSION

Genotype and phenotype coefficient of variation
 
High variability and heritability were observed for traits such as leaf length (GCV = 465.00%, PCV = 510.10%) and number of leaves (GCV = 100.90%, PCV = 116.20%), indicating the predominance of additive gene action. This suggests strong potential for selection in breeding programs (Akinwale et al., 2022). In contrast, traits like panicle weight (GCV = 11.60%, heritability = 20%) showed greater environmental influence, aligning with findings of Jahan et al., (2023), who reported low heritability for panicle traits under variable conditions. Thousand seed weight (TSW) exhibited high heritability (90%) along with moderate genetic advance (GAM = 34.90%), indicating its suitability as a reliable selection trait (Table 1). Similar results were reported by Babu et al., (2023), confirming TSW as an effective parameter for improving grain yield. High genotypic and phenotypic coefficients of variation indicate the presence of genetic variability among traits. Traits exhibiting high heritability along with high genetic advance are considered to be governed by additive gene action and are therefore useful for effective selection in breeding programs (Kumar et al., 2026 and Prakash et al., 2023).

Table 1: Genetic variability parameters including range (minimum and maximum), mean, genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), broad sense heritability (H2), genetic advance (GA) and genetic advance as per cent of mean (GAM) for growth and yield traits in rice landraces under organic farming conditions.


 
Correlation studies
 
Correlation analysis revealed significant relationships between vegetative and yield traits. A strong positive and significant correlation was observed between plant height and leaf length (r = 0.79*), suggesting that taller plants tend to have longer leaves. Number of tillers (NT) showed positive associations with leaf width (r = 0.71) and leaf length (r = 0.70), indicating that better vegetative growth contributes to increased tillering, although these relationships were non-significant.
       
Panicle length exhibited positive associations with number of tillers (r = 0.73) and leaf width (r = 0.74), emphasizing the role of vegetative growth in panicle development. Thousand seed weight (TSW) showed a significant positive correlation with number of productive tillers (r = 0.72), indicating that genotypes with more productive tillers tend to produce heavier grains (Fig 1). TSW also exhibited weak but positive correlations with plant height, leaf length, leaf width, number of leaves and tillers, suggesting that improved vegetative growth contributes to grain weight. These findings are consistent with Zhang et al., (2022) and Ogawa et al., (2023), who reported similar relationships. However, negative associations between TSW and panicle traits suggest possible trade-offs, as observed by Dong et al., (2022), where increased panicle size reduced grain filling efficiency. Number of productive tillers per plant and thousand grain weight showed positive and significant correlation with grain yield per plant, along with strong direct effects, indicating their importance as key selection criteria for yield improvement (Gunasekaran et al., 2017).

Fig 1: Pearson correlation matrix among the vegetative and yield traits.


 
Principal component analysis (PCA)
 
Principal Component Analysis (PCA), along with cluster analysis, revealed significant genetic variability among the genotypes. Six principal components explained 100% of the total variation. PC1 accounted for the largest share (44.11%) and was associated with plant height, leaf length, leaf width, panicle length and number of tillers, representing overall vegetative vigor (Fig 2) (Zhao et al., 2021 and Aditya et al., 2022).

Fig 2: Principal component analysis (PCA) P1 TO P6.


       
PC2 contributed 23.84% and was dominated by yield-related traits such as productive tillers and TSW, confirming their importance in yield determination (Kumar et al., 2023). PC3 (13.10%) was associated with productive tillers and panicle weight but showed a negative relationship with leaf number, indicating trade-offs between vegetative growth and reproduction (Sharma et al., 2022 and Mahendran et al., 2024) (Fig 3).

Fig 3: PCA biplot showing vegetative and yield trait composition and genotype distribution in traditional rice landraces under organic farming.


       
PC4 (10.01%) emphasized panicle length and TSW, highlighting the importance of reproductive traits, while PC5 (6.22%) was linked to leaf width and plant weight, reflecting biomass accumulation. PC6 (2.72%) contributed minimal variation but showed minor associations with panicle length and productive tillers, along with a negative association with TSW.
 
Mean performances
 
Cluster-wise analysis showed variation in trait performance among genotypes. Cluster 1 (Rathasali and Arbutham Kuravai) exhibited moderate vegetative growth with lower TSW (20.56 g). Cluster 2 (Karunkuravai and Kaivari Samba) recorded the highest plant height (123.85 cm), leaf length (93.64 cm) and maximum TSW (27.52 g), indicating superior yield potential.
       
Cluster 3 (Karuppu Kavuni) showed the highest number of leaves (30.5) and a relatively lower TSW (20.82 g), suggesting good vegetative growth and satisfactory panicle filling.
       
Cluster 4 (Chitharakar and Kullakar) exhibited maximum vegetative growth (leaf length = 113.05 cm, plant height = 123.6 cm) but fewer productive tillers, indicating inefficient conversion of biomass into yield (Table 2).

Table 2: Mean performances of quantitative traits across four clusters.


 
Intra and inter cluster
 
Inter-cluster distance analysis revealed genetic divergence among clusters. The smallest distance was observed between Clusters 1 and 3 (3.023), indicating close similarity, whereas the largest distance was between Clusters 3 and 4 (5.97), suggesting maximum genetic divergence.
       
Clusters 2 and 4 also showed considerable divergence (4.491), while Clusters 1 and 2 showed moderate similarity (3.418) (Table 3). These results highlight the potential for selecting genetically diverse parents for hybridization. Hundred-seed weight showed positive correlation with hydration and swelling capacity, but a negative association with seed density. Hydration capacity was positively correlated with hydration index, swelling capacity and swelling index, indicating strong inter-relationships among cooking quality traits (Srivastava et al., 2023 and Ashraf and Lokanadan, 2020).

Table 3: Intra-and inter-cluster distances among four clusters of rice landraces; diagonal values represent intra-cluster distances, while off-diagonal values indicate inter-cluster distances.


 
Cluster analysis
 
Cluster analysis further confirmed genetic variability among genotypes. Cluster 2 (Karunkuravai and Kaivari Samba) showed superior performance with high TSW and productive tillers, consistent with high-yielding traits (Islam et al., 2023). Cluster 4 (Chitharakar and Kullakar) exhibited high vegetative growth but low productive tillers, indicating inefficient resource allocation, as also reported by Ramesh et al., (2020) (Table 3).
       
The maximum inter-cluster distance between Clusters 3 and 4 suggests high heterotic potential. This supports the findings of Singh et al., (2021), who emphasized the importance of crossing genetically distant genotypes to enhance hybrid vigor. Cluster analysis grouped the genotypes into distinct clusters, indicating the presence of considerable genetic diversity. Genotypes within different clusters exhibited superior traits, suggesting that selection of parents from divergent clusters would be effective for breeding programmes (Islam et al., 2020 and Roshan et al., 2025).
 
Biplot analysis
 
The PCA biplot (PC1 and PC2) explained 67.9% of total variation and grouped genotypes based on trait associations. Quadrant I included Arbutham Kuravai and Kullakar, associated with plant height, TSW and leaf length, indicating strong yield potential.
       
Quadrant II contained Karuppu Kavuni, linked to number of leaves and productive tillers, suggesting strong vegetative growth. Quadrant III included Rathasali and Karunkuravai, associated with plant and panicle weight, indicating biomass-oriented traits. Quadrant IV included Kaivari Samba, associated with tillers, leaf width and panicle length, indicating efficient yield traits (Fig 4).

Fig 4: Hierarchial clustering dendrogram showing 8 clusters of traditional rice landraces based on vegetative and yield traits.


       
These findings align with Mamun et al., (2022) and Sukrutha et al., (2023), who identified similar yield-related traits as key contributors to variability.
 
Ward’s hierarchical cluster analysis
 
Ward’s clustering grouped genotypes into distinct clusters, confirming genetic diversity. Cluster I (Rathasali and Arbutham Kuravai) showed similarity in vegetative traits, while Cluster II (Karunkuravai and Kaivari Samba) grouped based on panicle traits and biomass.
       
Cluster III (Karuppu Kavuni) stood as a unique genotype, indicating distinct genetic potential. Cluster IV (Chitharakar and Kullakar) grouped based on plant height and leaf traits. The clustering pattern highlights the importance of morphological traits in genetic classification (Kumar et al., 2024).

CONCLUSION

The study revealed significant genetic variability and strong trait relationships among traditional rice landraces under organic farming. Traits such as leaf length, number of leaves and TSW showed high heritability and genetic advance, indicating the dominance of additive gene action and suitability for direct selection.
       
Correlation analysis highlighted strong relationships between vegetative and yield traits, particularly between productive tillers and grain weight, emphasizing their role in yield determination. PCA and cluster analysis effectively classified genotypes and revealed wide genetic divergence.
       
Cluster 2 (Karunkuravai and Kaivari Samba) emerged as the most promising group due to superior performance in yield traits. The high inter-cluster distance between Clusters 3 and 4 suggests strong potential for heterotic hybrid development.
       
Overall, the findings provide valuable insights for selecting superior genotypes and traits for breeding programs aimed at improving rice yield while maintaining vegetative vigor under organic farming conditions.

ACKNOWLEDGEMENT

The authors express their sincere gratitude to the SRM Institute of Science and Technology Baburayanpettai, Chengalpattu district, Tamil Nadu, India and its authorities for providing the necessary facilities, infrastructure and technical support to carry out the research on “Assessment of Genetic Variability and Trait Relationships among Traditional Rice Landraces grown under Organic Farming.”
 
Disclaimers
 
The opinions and findings presented in this article are the writers’ own and may not necessarily reflect those of the organizations with which they are affiliated. The writers disclaim any liability for any direct or indirect losses resulting from the use of this content, however they are accountable for the accuracy and completeness of the information presented.
 
Informed consent
 
As no animal subjects are involved in the study, no ethical statement is required.

CONFLICT OF INTEREST

The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.

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    Full Research Article

    Assessment of Genetic Variability and Trait Relationships among Traditional Rice Landraces Grown under Organic Farming

    V
    Velukuru Sahithi Sree1
    0009-0001-2554-8553
    A
    A. Mohammed Ashraf1,*
    0000-0001-6230-6185
    P
    P. Balasubramanian1
    0000-0002-1943-7013
    J
    Jegadeeswaran Mokkaraj2
    0000-0001-9066-9477
    N
    Nagamaniammai Govindarajan3
    0000-0003-1433-8419
    1Department of Agronomy, SRM College of Agricultural Sciences, SRM Institute of Science and Technology, Baburayanpettai, Chengalpattu-603 201, Tamil Nadu, India.
    2Department of Genetics and Plant Breeding, SRM College of Agricultural Sciences, SRM Institute of Science and Technology, Baburayanpettai, Chengalpattu-603 201, Tamil Nadu, India.
    3Department of Food Technology, School of Bioengineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu-603 203, Tamil Nadu, India.

    ABSTRACT

    Background: The preservation and utilization of traditional rice landraces are essential for sustainable agriculture, particularly under organic farming systems where genetic stability, adaptability and resilience are crucial. Understanding the extent of genetic variability and trait relationships is important for effective selection and crop improvement.

    Methods: The present study evaluated genetic variability, heritability and trait relationships among traditional rice landraces cultivated under organic farming conditions. Multivariate and statistical analyses such as principal component analysis (PCA), genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV) and correlation analysis were employed to assess the diversity and association among different growth and yield traits.

    Result: The analysis revealed that six principal components contributed to 100% of the total variation, with PC1 and PC2 accounting for 44.11% and 23.84%, respectively, mainly representing vegetative and yield-related traits. Cluster analysis indicated significant genetic diversity among the landraces, with Cluster 2 (Karunkuravai and Kaivari Samba) exhibiting higher 1000 seed weight (27.525 g) and superior yield attributes. Traits such as leaf length and number of leaves showed high heritability (>80%) along with high genetic advance, indicating the predominance of additive gene action and the effectiveness of direct selection. Significant positive correlations were observed between plant height and leaf length (r = 0.79*) and between productive tillers and 1000 seed weight (r = 0.72), suggesting strong associations between vegetative growth and yield potential. The study highlights the importance in identifying superior genotypes suitable for organic farming and provides a strong basis for future breeding programs.

    INTRODUCTION

    More than 3.5 billion people worldwide depend on rice (Oryza sativa L.) as a staple food, accounting for nearly 20% of the global dietary energy supply. Global rice consumption exceeds 520 million metric tonnes annually, with Asia accounting for nearly 90% of total consumption. Traditional rice landraces have remained essential for preserving genetic diversity, resistance to environmental challenges and cultural heritage, even while contemporary high yielding cultivars have made a substantial contribution to productivity advances (Shukla et al., 2023 and Ashraf et al., 2024a). Low-input or organic farming systems are commonly used to cultivate these landraces because they are adaptable to local Agro-ecological circumstances and allow for the full expression of their distinctive genetic features (Pandey et al., 2022; Bisht et al., 2021 and Ashraf et al., 2024b). The preservation and use of historic rice types have received attention in recent years because of their potential benefits to sustainable agriculture, particularly when grown organically. Because organic farming prioritizes ecological balance and the use of minimum synthetic inputs, it is crucial to find and support cultivars that are highly adaptive and genetically stable in these systems (Chandran et al., 2023 and Ashraf and Lokanadan, 2022a). Nevertheless, a thorough grasp of trait variability, heritability and the connections between yield and physical traits is necessary for the genetic development of these landraces.
           
    The genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV), two measures of genetic variability, shed light on the level of variability and the proportion of environmental compared to genetically controlled traits (Akinwale et al., 2022 and Ashraf and Lokanadan, 2022b). In breeding strategies, traits with high genetic progress and high GCV and heritability make effective selection objectives (Jahan et al., 2023 and Ashraf et al., 2017a). Agronomically important features under organic systems can be efficiently clustered and prioritized using PCA. Correlation research between features also sheds light on possible trade-offs and synergies between grain quality, yield parameters and vegetative development. It is important to understand the relationships to develop breeding plans that complement production objectives and the agronomic circumstances of organic farming (Ogawa et al., 2023 and Ashraf et al., 2017b). The goal of the current study was to evaluate the genetic variability and trait relationships among traditional rice landraces grown under organic farming systems, given the growing significance of sustainable farming techniques and genetic resource conservation. This study is to find superior genotypes and important selection indices that can aid in the creation of robust, high yielding rice cultivars appropriate for organic farming through the use of PCA, GCV, PCV and correlation analyses.

    MATERIALS AND METHODS

    This study utilized traditional rice (Oryza sativa L.) landraces cultivated under certified organic conditions. A diverse set of genotypes was selected to represent traditional rice populations grown without synthetic inputs. The experiment was conducted during the Late Samba season (September-February) of 2025-26 at the SRM College of Agricultural Sciences organic farm, Baburayanpettai, Chengalpattu district, Tamil Nadu, India. The site is located at 12.38°N latitude and 79.73°E longitude, with an elevation of 50 m above mean sea level, falling under the North Eastern agro-climatic zone of Tamil Nadu (Gomez and Gomez, 1984).
           
    The experiment was laid out in a randomized block design (RBD) with three replications (Kumar and Yadav, 2021). Seven traditional rice landraces were studied: V1: Rathasali, V2: Arbutham Kuravai, V3: Karunkuravai, V4: Kaivari Samba, V5: Karuppu Kavuni, V6: Chithirakar and V7: Kullakar. Seeds were treated with Bacillus subtilis liquid bioinoculant culture (10 g kg-1 seed), along with azospirillum and phosphobacteria carrier-based biofertilizer formulations (30 g kg-1 each) (Kumar et al., 2020). The treated seeds were soaked for 24 hours and incubated for 12 hours before sowing in a nursery enriched with farmyard manure (5 t/ha), neem cake (5 t/ha) and Trichoderma viride (40 kg/ha) (Bhattacharyya et al., 2019).
           
    Dhaincha     was grown as green manure at 25 kg/ha and incorporated after 45 days (Choudhary and Sharma, 2022), along with neem cake at 250 kg/ha. Seedlings were transplanted at 20 × 10 cm spacing (Ray et al., 2020) after root dip treatment with azospirillum and phosphobacteria. Nutrient needs were met using vermicompost (1000 kg/ha) at key growth stages and panchagavya (3%) was applied at 30 and 45 DAT. Alternate wetting and drying irrigation, manual weeding and biocontrol measures using neem extract, Beauveria bassiana and Bacillus subtilis were followed. Data were recorded on vegetative and reproductive traits from five tagged plants per plot, following standard descriptors (Zhao et al., 2021).
     
    Statistical analyses
     
    All statistical analyses, including genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), principal component analysis (PCA) and cluster analysis were performed and pearson’s correlation coefficient analysis were performed using RStudio version 2024.12.1+563.

    RESULTS AND DISCUSSION

    Genotype and phenotype coefficient of variation
     
    High variability and heritability were observed for traits such as leaf length (GCV = 465.00%, PCV = 510.10%) and number of leaves (GCV = 100.90%, PCV = 116.20%), indicating the predominance of additive gene action. This suggests strong potential for selection in breeding programs (Akinwale et al., 2022). In contrast, traits like panicle weight (GCV = 11.60%, heritability = 20%) showed greater environmental influence, aligning with findings of Jahan et al., (2023), who reported low heritability for panicle traits under variable conditions. Thousand seed weight (TSW) exhibited high heritability (90%) along with moderate genetic advance (GAM = 34.90%), indicating its suitability as a reliable selection trait (Table 1). Similar results were reported by Babu et al., (2023), confirming TSW as an effective parameter for improving grain yield. High genotypic and phenotypic coefficients of variation indicate the presence of genetic variability among traits. Traits exhibiting high heritability along with high genetic advance are considered to be governed by additive gene action and are therefore useful for effective selection in breeding programs (Kumar et al., 2026 and Prakash et al., 2023).

    Table 1: Genetic variability parameters including range (minimum and maximum), mean, genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), broad sense heritability (H2), genetic advance (GA) and genetic advance as per cent of mean (GAM) for growth and yield traits in rice landraces under organic farming conditions.


     
    Correlation studies
     
    Correlation analysis revealed significant relationships between vegetative and yield traits. A strong positive and significant correlation was observed between plant height and leaf length (r = 0.79*), suggesting that taller plants tend to have longer leaves. Number of tillers (NT) showed positive associations with leaf width (r = 0.71) and leaf length (r = 0.70), indicating that better vegetative growth contributes to increased tillering, although these relationships were non-significant.
           
    Panicle length exhibited positive associations with number of tillers (r = 0.73) and leaf width (r = 0.74), emphasizing the role of vegetative growth in panicle development. Thousand seed weight (TSW) showed a significant positive correlation with number of productive tillers (r = 0.72), indicating that genotypes with more productive tillers tend to produce heavier grains (Fig 1). TSW also exhibited weak but positive correlations with plant height, leaf length, leaf width, number of leaves and tillers, suggesting that improved vegetative growth contributes to grain weight. These findings are consistent with Zhang et al., (2022) and Ogawa et al., (2023), who reported similar relationships. However, negative associations between TSW and panicle traits suggest possible trade-offs, as observed by Dong et al., (2022), where increased panicle size reduced grain filling efficiency. Number of productive tillers per plant and thousand grain weight showed positive and significant correlation with grain yield per plant, along with strong direct effects, indicating their importance as key selection criteria for yield improvement (Gunasekaran et al., 2017).

    Fig 1: Pearson correlation matrix among the vegetative and yield traits.


     
    Principal component analysis (PCA)
     
    Principal Component Analysis (PCA), along with cluster analysis, revealed significant genetic variability among the genotypes. Six principal components explained 100% of the total variation. PC1 accounted for the largest share (44.11%) and was associated with plant height, leaf length, leaf width, panicle length and number of tillers, representing overall vegetative vigor (Fig 2) (Zhao et al., 2021 and Aditya et al., 2022).

    Fig 2: Principal component analysis (PCA) P1 TO P6.


           
    PC2 contributed 23.84% and was dominated by yield-related traits such as productive tillers and TSW, confirming their importance in yield determination (Kumar et al., 2023). PC3 (13.10%) was associated with productive tillers and panicle weight but showed a negative relationship with leaf number, indicating trade-offs between vegetative growth and reproduction (Sharma et al., 2022 and Mahendran et al., 2024) (Fig 3).

    Fig 3: PCA biplot showing vegetative and yield trait composition and genotype distribution in traditional rice landraces under organic farming.


           
    PC4 (10.01%) emphasized panicle length and TSW, highlighting the importance of reproductive traits, while PC5 (6.22%) was linked to leaf width and plant weight, reflecting biomass accumulation. PC6 (2.72%) contributed minimal variation but showed minor associations with panicle length and productive tillers, along with a negative association with TSW.
     
    Mean performances
     
    Cluster-wise analysis showed variation in trait performance among genotypes. Cluster 1 (Rathasali and Arbutham Kuravai) exhibited moderate vegetative growth with lower TSW (20.56 g). Cluster 2 (Karunkuravai and Kaivari Samba) recorded the highest plant height (123.85 cm), leaf length (93.64 cm) and maximum TSW (27.52 g), indicating superior yield potential.
           
    Cluster 3 (Karuppu Kavuni) showed the highest number of leaves (30.5) and a relatively lower TSW (20.82 g), suggesting good vegetative growth and satisfactory panicle filling.
           
    Cluster 4 (Chitharakar and Kullakar) exhibited maximum vegetative growth (leaf length = 113.05 cm, plant height = 123.6 cm) but fewer productive tillers, indicating inefficient conversion of biomass into yield (Table 2).

    Table 2: Mean performances of quantitative traits across four clusters.


     
    Intra and inter cluster
     
    Inter-cluster distance analysis revealed genetic divergence among clusters. The smallest distance was observed between Clusters 1 and 3 (3.023), indicating close similarity, whereas the largest distance was between Clusters 3 and 4 (5.97), suggesting maximum genetic divergence.
           
    Clusters 2 and 4 also showed considerable divergence (4.491), while Clusters 1 and 2 showed moderate similarity (3.418) (Table 3). These results highlight the potential for selecting genetically diverse parents for hybridization. Hundred-seed weight showed positive correlation with hydration and swelling capacity, but a negative association with seed density. Hydration capacity was positively correlated with hydration index, swelling capacity and swelling index, indicating strong inter-relationships among cooking quality traits (Srivastava et al., 2023 and Ashraf and Lokanadan, 2020).

    Table 3: Intra-and inter-cluster distances among four clusters of rice landraces; diagonal values represent intra-cluster distances, while off-diagonal values indicate inter-cluster distances.


     
    Cluster analysis
     
    Cluster analysis further confirmed genetic variability among genotypes. Cluster 2 (Karunkuravai and Kaivari Samba) showed superior performance with high TSW and productive tillers, consistent with high-yielding traits (Islam et al., 2023). Cluster 4 (Chitharakar and Kullakar) exhibited high vegetative growth but low productive tillers, indicating inefficient resource allocation, as also reported by Ramesh et al., (2020) (Table 3).
           
    The maximum inter-cluster distance between Clusters 3 and 4 suggests high heterotic potential. This supports the findings of Singh et al., (2021), who emphasized the importance of crossing genetically distant genotypes to enhance hybrid vigor. Cluster analysis grouped the genotypes into distinct clusters, indicating the presence of considerable genetic diversity. Genotypes within different clusters exhibited superior traits, suggesting that selection of parents from divergent clusters would be effective for breeding programmes (Islam et al., 2020 and Roshan et al., 2025).
     
    Biplot analysis
     
    The PCA biplot (PC1 and PC2) explained 67.9% of total variation and grouped genotypes based on trait associations. Quadrant I included Arbutham Kuravai and Kullakar, associated with plant height, TSW and leaf length, indicating strong yield potential.
           
    Quadrant II contained Karuppu Kavuni, linked to number of leaves and productive tillers, suggesting strong vegetative growth. Quadrant III included Rathasali and Karunkuravai, associated with plant and panicle weight, indicating biomass-oriented traits. Quadrant IV included Kaivari Samba, associated with tillers, leaf width and panicle length, indicating efficient yield traits (Fig 4).

    Fig 4: Hierarchial clustering dendrogram showing 8 clusters of traditional rice landraces based on vegetative and yield traits.


           
    These findings align with Mamun et al., (2022) and Sukrutha et al., (2023), who identified similar yield-related traits as key contributors to variability.
     
    Ward’s hierarchical cluster analysis
     
    Ward’s clustering grouped genotypes into distinct clusters, confirming genetic diversity. Cluster I (Rathasali and Arbutham Kuravai) showed similarity in vegetative traits, while Cluster II (Karunkuravai and Kaivari Samba) grouped based on panicle traits and biomass.
           
    Cluster III (Karuppu Kavuni) stood as a unique genotype, indicating distinct genetic potential. Cluster IV (Chitharakar and Kullakar) grouped based on plant height and leaf traits. The clustering pattern highlights the importance of morphological traits in genetic classification (Kumar et al., 2024).

    CONCLUSION

    The study revealed significant genetic variability and strong trait relationships among traditional rice landraces under organic farming. Traits such as leaf length, number of leaves and TSW showed high heritability and genetic advance, indicating the dominance of additive gene action and suitability for direct selection.
           
    Correlation analysis highlighted strong relationships between vegetative and yield traits, particularly between productive tillers and grain weight, emphasizing their role in yield determination. PCA and cluster analysis effectively classified genotypes and revealed wide genetic divergence.
           
    Cluster 2 (Karunkuravai and Kaivari Samba) emerged as the most promising group due to superior performance in yield traits. The high inter-cluster distance between Clusters 3 and 4 suggests strong potential for heterotic hybrid development.
           
    Overall, the findings provide valuable insights for selecting superior genotypes and traits for breeding programs aimed at improving rice yield while maintaining vegetative vigor under organic farming conditions.

    ACKNOWLEDGEMENT

    The authors express their sincere gratitude to the SRM Institute of Science and Technology Baburayanpettai, Chengalpattu district, Tamil Nadu, India and its authorities for providing the necessary facilities, infrastructure and technical support to carry out the research on “Assessment of Genetic Variability and Trait Relationships among Traditional Rice Landraces grown under Organic Farming.”
     
    Disclaimers
     
    The opinions and findings presented in this article are the writers’ own and may not necessarily reflect those of the organizations with which they are affiliated. The writers disclaim any liability for any direct or indirect losses resulting from the use of this content, however they are accountable for the accuracy and completeness of the information presented.
     
    Informed consent
     
    As no animal subjects are involved in the study, no ethical statement is required.

    CONFLICT OF INTEREST

    The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.

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