Assessment of genetic variability and diversity in rice (Oryza sativa L.)

Introduction

Rice (Oryza sativa L.; 2n = 24) belongs to the family Poaceae and is one of the most important cereal crops in the world. It serves as the staple food for nearly half of the global population and contributes about 20% of the world's dietary energy. Among the genus Oryza, only two species, Oryza sativa L. and Oryza glaberrima L., are cultivated, while the remaining wild species constitute valuable genetic resources for crop improvement. Rice is widely adapted to diverse agro-climatic conditions and plays a vital role in ensuring global food and nutritional security (Kanwar et al., 2017) [8] . Rice is cultivated in more than 100 countries, with Asia accounting for nearly 90% of global production. India is one of the leading rice-producing countries, cultivating rice over about 51 million hectares with a production of approximately 151 million tonnes during 2025. Chhattisgarh, popularly known as the "Rice Bowl of India," occupies about 38.99 lakh hectares under rice cultivation with a production of nearly 10.37 million tonnes, highlighting its significant contribution to the country's rice economy (Anonymous, 2025) [3] . The success of any crop improvement programme depends on the availability and efficient utilization of genetic variability. Morphological characterization provides the basis for identifying diverse genotypes, while estimates of genetic variability, heritability and genetic advance help breeders assess the effectiveness of selection for yield improvement. Since grain yield is a complex quantitative trait influenced by several interrelated characters, understanding the association among these traits is essential for developing superior genotypes (Gupta et al., 2022) [7] .

Grain yield is a complex quantitative trait controlled by the interaction of several component characters and is greatly influenced by environmental factors. Therefore, the study of character association and genetic diversity plays a crucial role in understanding the relationship among yieldcontributing traits and identifying superior and genetically diverse parents for breeding programmes. Among the various statistical approaches, Mahalanobis D² analysis has been widely used to assess genetic divergence, classify genotypes into distinct clusters and facilitate the selection of diverse parental lines for the development of high-yielding rice cultivars (Adhikari et al., 2018) [1] .

Materials and Methods

The experimental material consisted of fifty rice germplasm accessions and five check varieties, namely IR 64, Swarna, Jaya, Dagad Desi and MTU 1010, obtained from the DBT Network Project. The experiment was conducted during kharif 2025 at the Research cum Instructional Farm, Department of Genetics and Plant Breeding, Indira Gandhi Krishi Vishwavidyalaya (IGKV), Raipur. The trial was laid out in a Randomized Complete Block Design (RCBD) with two replications. Seeds were sown on 7 July 2025, and the seedlings were subsequently transplanted to the experimental field. All agronomical practices were adopted to raise crop in good condition.

Results and Discussion

Genetic variability is the foundation of any successful crop improvement programme, as it provides the basis for effective selection and the development of superior cultivars. The magnitude of variability present in a breeding population determines the scope for genetic improvement through selection. In the present study, analysis of variance revealed highly significant differences among the 55 rice genotypes for all the characters studied, indicating the presence of substantial genetic variability that can be effectively utilized in rice breeding programmes. Similar findings were reported by Saha et al. (2019) [11] , who observed considerable variability among rice genotypes for important agronomic traits. Likewise, Rajesh et al. (2016) [10] also reported significant variation for key yield and yield-contributing characters, including days to 50% flowering, plant height, panicle length, number of effective tillers per plant, number of filled grains per panicle and grain yield per plant, which corroborates the results of the present investigation. Agro morphological characterization: Morphological traits are largely controlled by genetic factors and are therefore less influenced by environmental conditions. Substantial variation was observed for traits including early plant vigour, basal leaf sheath colour, leaf blade colour, leaf pubescence, ligule shape, stigma colour, panicle exsertion, panicle type, flag leaf angle, awn presence, leaf senescence, hull colour, kernel colour, sterile lemma colour, apiculus colour, and threshability. However, no variation was detected for ligule shape, coleoptile colour, and auricle colour. These findings are consistent with the observations reported by Dhruw et al. (2025) [6] . Distinct rice accessions were identified based on qualitative agro-morphological traits. Long partial awning was observed in IC463241. Purple hull colour was recorded in IC466026, whereas tawny hull colour was observed in IC203514 and IC512795. Light brown kernel colour was found in IC463006, while black kernel colour was recorded in IC514106. Purple sterile lemma colour was exhibited by IC514106 and IC388828, whereas black sterile lemma colour was observed in IC203514. Purple apiculus colour (purple apex) was recorded in IC323571 and IC464632.

Difficult threshability was observed in the check varieties Swarna and IR 64. These distinct qualitative traits may serve as useful descriptors for the identification and characterization of rice germplasm. [9] and Akshay et al. (2022) [2] . Broad-sense heritability ranged from 54.25% for spikelet fertility to 98.68% for panicle length. High heritability was recorded for panicle length, days to 50% flowering, plant height, 100-seed weight, number of filled grains per panicle, grain yield per plant, harvest index, and number of effective tillers per plant, indicating the predominance of genetic effects on these traits.

Genetic variability analysis

Genetic advance as percent of mean (GAM) ranged from 4.33% to 37.50%. The highest GAM was recorded for plant height (37.50%), followed by grain yield per plant (34.21%), harvest index (31.38%), 100-seed weight (26.15%), and number of filled grains per panicle (22.07%). High heritability coupled with high GAM for plant height, grain yield per plant, 100-seed weight, and number of filled grains per panicle indicated the predominance of additive gene action, suggesting that these traits can be effectively improved through direct selection. These findings are in agreement with Chacko et al. (2023) [5] . Analysis of variance revealed highly significant differences (at 1% level of significance) among the 55 rice genotypes for all the studied traits, indicating the presence of considerable genetic variability. Similar findings were reported by Saha et al. (2019) [11] . Likewise, Rajesh et al. (2016) [10] also observed significant variation in grain yield and its associated traits among rice genotypes. Range and Mean of each character: Analysis of variance revealed highly significant differences ((at 1% level of significance) among the 55 rice genotypes for all the yield and yield-attributing traits, indicating the presence of substantial genetic variability in the experimental material. The mean performance of the evaluated traits showed considerable variation, with the highest mean value recorded for the number of filled grains per panicle (138.39), followed by plant height cm), days to 50% flowering (94.26 days) and spikelet fertility (91.05%). The genotypic coefficient of variation (GCV) ranged from 2.86% for spikelet fertility to 19.25% for grain yield per plant, whereas the phenotypic coefficient of variation (PCV) varied from 3.88% for spikelet fertility to 22.49% for harvest index. The consistently higher PCV values than the corresponding GCV values indicated the influence of environmental factors on trait expression. However, the narrow differences between GCV and PCV for most of the characters suggested that environmental effects were relatively low and that the observed variability was predominantly under genetic control, indicating good prospects for effective selection and genetic improvement in rice.

Genetic divergence analysis

Mahalanobis D² analysis followed by Tocher's clustering grouped the 55 rice genotypes into ten distinct clusters, revealing substantial genetic diversity among the studied genotypes. The lower intra-cluster distances compared with inter-cluster distances indicated greater homogeneity within clusters and significant divergence between clusters. Cluster I comprised the maximum number of genotypes (23), followed by clusters II (12), IV (6), III (4), V (3), VI and VII (2 each), whereas clusters VIII, IX, and X each contained a single genotype, suggesting their distinct genetic constitution. Character contribution analysis revealed that panicle length contributed the highest proportion to total genetic divergence (51.733%), followed by days to 50% flowering (18.353%), plant height (9.774%), 100-seed weight (5.086%), grain yield per plant (4.826%), number of filled grains per panicle (4.497%), harvest index (2.974%), number of effective tillers per plant (1.612%), and spikelet fertility (1.145%). Higher inter-cluster distances indicated substantial genetic divergence among the rice genotypes. The maximum intercluster distance was observed between clusters III and V (1481.43), whereas the minimum was recorded between clusters II and X (184.22). Cluster VII exhibited the highest intra-cluster distance (178.33), while clusters VIII, IX and X showed zero intra-cluster distance as each contained a single genotype. These results suggest that hybridization between highly divergent clusters could be useful for developing superior recombinants.

Panicle length contributed the maximum to genetic divergence (51.733%), followed by days to 50% flowering (18.353%) and plant height (9.774%), indicating that these traits were the major sources of variability among the studied rice genotypes. Similar findings were reported by Bose et al. (2005) [4] and Zaman et al. (2005) [15] , who also identified these traits as major contributors to genetic divergence in rice.

Conclusion

Analysis of variance confirmed significant genetic variability among the 55 rice germplasm accessions for all the traits studied. Higher PCV than GCV indicated a comparatively low environmental influence on trait expression. High heritability coupled with high genetic advance for plant height, grain yield per plant, harvest index, 100-seed weight, and number of filled grains per panicle suggested the predominance of additive gene action. Grain yield per plant was positively associated with harvest index, filled grains per panicle, effective tillers, spikelet fertility, panicle length, and 100-seed weight, while path analysis identified harvest index as the major positive direct contributor to grain yield. Cluster analysis revealed the highest intra-cluster distance in Cluster VII and the maximum inter-cluster distance between Cluster III and Cluster V, indicating substantial genetic divergence and suggesting that hybridization between genotypes from these clusters could be effective for developing superior rice genotypes.

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