Agro-morphological characterization and genetic variability analysis in rice (Oryza sativa L.)

Introduction

Rice (Oryza sativa L.,) 2n = 2x = 24 is a monocotyledonous, semi-aquatic, self-pollinated annual grass belonging to the family Poaceae and subfamily Bambusoideae. It stands as a global staple, frequently feeding more than one-third of humanity and providing nearly 27% of global dietary energy and 20% of protein intake. Cultivated across a vast spectrum of geographic environments, its nutritional profile includes essential carbohydrates, vitamins (thiamine, riboflavin, niacin), and minerals (Verma et al., 2006) [4] . As a result, global rice cultivation spans approximately 173.20 million hectares, yielding an output of 545.73 million tonnes (Anonymous, 2026a) [2] . India acts as a major global hub for production, within which the state of Chhattisgarh traditionally celebrated as the "Rice Bowl of India" ranks fifth nationally, producing 10.36 million tonnes across 3.8 million hectares (Anonymous, 2026b) [3] . With steady population expansion and diminishing arable land resources, global rice production must expand by an estimated 40% by 2030 to secure future food security (Khush, 2005) [18] . Surpassing current yield plateaus requires the continuous development of highyielding, elite cultivars. Evaluation and characterization of existing landraces of rice are very important in order to achieve the varietal improvement. Because grain yield is a complex, polygenic trait determined by the interaction of multiple independent components, raw phenotypic selection based on yield alone is often inefficient. Systematic crop improvement relies heavily on assessing the nature and magnitude of genetic variability present in available germplasm lines (Adhikari et al., 2018) [1] . Quantifying genetic parameters like the Genotypic Coefficient of Variation (GCV) and Phenotypic Coefficient of Variation (PCV) helps distinguish heritable variation from environmental noise. Furthermore, estimating broad-sense heritability alongside genetic advance offers a more accurate prediction of genetic gain under selection, confirming whether target characteristics are governed by predictable additive gene action.

Materials and Methods

The experimental material includes 55 rice accessions including 5 check varieties (IR 64, Swarna, Jaya, Dagad Desi and MTU 1010) were used. These fifty rice germplasm accessions were taken from DBT Network Project. The accessions of rice were transplanted in the field of Research Cum Instructional Farm, Department of Genetics and Plant Breeding, IGKV, Raipur during kharif 2025. Seeds were sown directly on July 7 th , 2025, and the seedlings were transplanted to the field with a complete randomized block design with two replications. All necessary agronomical practices were followed to raise the crop. Days to 50% flowering, plant height (cm), panicle length (cm) number of effective tillers per plant, number of filled grains per panicle, spikelet fertility (%), 100 grain weight (g), harvest index (%) and grain yield per plant (g) were recorded.

Results and Discussions Agro-morphological characterisation

Each variety can be distinguished on the basis of its unique, and reliable, morphological characters. Such characters may be controlled by one gene (monogenic) or many genes (polygenic). In an attempt to differentiate among varieties, it would be preferable to use stable morphological characters as recognizers. Each variety needs to be composed from characteristic diagnostic characters in order to differ from the other varieties. These characters need to be heritable and reliably detected over several generations in order to be considered as valid morphological markers. Distinct rice accessions were identified based on agro-morphological traits, basal leaf sheath colour (IC466426), purple stigma colour (IC214441), horizontal flag leaf angle (IC390288), long fully awning (IC124860), purple hull colour (IC379591), black hull colour (IC459308), red kernal colour (IC459308), black kernal colour (IC618941), purple apiculus colour (IC459258X), threshability difficult (Swarna and Jaya). The most variable traits among the germplasm were leaf blade colour, basal leaf sheath colour, panicle type, flag leaf angle, leaf senescence, awning hull colour, kernel colour and threshability. In contrast, traits like ligule shape and aroma did not show any variation among the 55 germplasm samples. Similar findings have been reported by Lavanya et al. (2021) [15] , Sao et al. (2025) [19] and Kumar et al. (2026) [14] . Frequency distribution of agro-morphological traits, flag leaf angle (10), awning (11) and leaf senescence (12), aroma (13), hull colour (14), kernal colour (15), sterile lemma colour (16), apiculus colour (17) and threshability (18). The analysis of variance (ANOVA) (table 2.) performed indicated highly significant differences (P≤0.01) between the mean values of all the investigated traits among the rice genotypes. The significantly different mean sum of squares indicates the presence of a high variation among the studied characters, which signifies a greater variation among the experimental material. Genetic diversity is one of the major factors in the development of improved varieties through breeding activities. Therefore, the availability of enough genetic variation is the key factor for a successful crop improvement program. Similar results have been observed by Koli et al. (2026) [17] , Kumar et al. (2026) [14] , and Ali et al. (2026) [5] for the study of rice genotypes for yield and yield contributing characters, such as flowering time, plant height, effective tillers, panicle length, filled grains per panicle, and grain yield. The estimated PCV and GCV values for yield and yieldrelated traits, giving an evaluation of the amount of variability available for crop improvement and selection presented in table 3. The PCV values for all the traits in the present study were higher than the corresponding GCV values, suggesting the effect of environmental factors. However, a strong correlation was observed between PCV and GCV revealing that the traits were mostly governed by the hereditary control. The GCV varied from 5.37 for the spikelet fertility to 25.32 for harvest index. The highest genotypic coefficient of variation (GCV) was recorded for harvest index (25.32%). Traits that showed moderate GCV included the grain yield per plant (19.21%), the grain yield per plant (19.21), number of effective tillers per plant (18.81%), plant height (14.72), number of filled grains per plant (11.61), hundred seed weight (11.60) and days to 50% flowering (10.03). Traits with low GCV (less than 10%) are panicle length (7.57) and spikelet fertility percentage (5.37). In the present study the phenotypic coefficient of variation (PCV) ranges from 6.33% in spikelet fertility percentage to 25.61% for harvest index. A high degree of PCV was observed for harvest index (25.61%), followed by the grains yield per plant (19.21%) and number of effective tiller (18.81%). Moderate percentage of PCV were recorded for plant height (14.72%), number of filled grains per panicle (11.61%), hundred seed weight (11.30) and days to 50% flowering (10.23%). A low degree of phenotypic variation was noted in days to 50% flowering (9.05), brown rice length (8.85%), paddy length (7.70%), and spikelet fertility (5.92%). Lower percentage of PCV were showed by panicle length (7.57%) and spikelet fertility percentage (5.37%). The presence of highly significant genotypic coefficient of variation in specific phenotypes suggests the existence of large diversity at the population level. Since the PCV tends to be greater than GCV, this difference may be attributed to effects of environment. However, the difference between GCV and PCV was found to be relatively small, because of minimizing environmental effects on most of the measured traits. Corresponding results to this study have been documented by Pathak et al. (2024) [11] , Sai Venkat et al. (2024) [12] , Srividhya et al. (2025) [10] , and Modarresi et al. (2026) [13] .

Genetic variability analysis

Heritability (Broad Sense)

The percentage of phenotypic variation that may be ascribed to genetic factors or gene action is known as heritability. Higher heritability indicates more possibility for successful selection, making it a crucial indicator for choosing traits in a breeding program. Heritability is a statistical metric that measures the proportion of phenotypic variation (observed traits) in a population that is attributable to genetic variation among individuals as defined by Lush, 1936. Three levels are typically used to classify heritability estimates: low (less than 30%), moderate (between 30% and 60%), and high (more than 60%). As shown in table 4.3, heritability for yield and yield-related traits in the current study were computed broadly and expressed as a percentage. [8] , Debsharma et al. (2022) [9] and Srividhya et al. (2025) [10] .

Genetic advance as a percent of mean

Genetic Advance as a Percent of Mean (GAM) is the predicted genetic gain or improvement in the mean genotypic value of a selected population over its base population expressed as a relative percentage of the grand mean of the selected population. It allows plant breeders to compare the efficiency of selection across many traits easily as it places the absolute genetic progress on a common scale regardless of the measurement units of a particular trait.

Genetic advance as a percent of mean is an important statistical parameter commonly used in plant breeding to predict the expected gain in a trait by selection. It is expressed as a percent of a population mean. The Johnson, Robinson and Comstock (1955) [7] [6] and Ali et al.

Conclusion

 Agromorphological traits display a substantial degree of hereditary control, making them reliable indicators for screening germplasm accessions during the early phases of crop selection.  Distinct rice accessions were identified based on agromorphological traits, basal leaf sheath colour (IC466426), purple stigma colour (IC214441), horizontal flag leaf angle (IC390288), long fully awning (IC124860), purple hull colour (IC379591), black hull colour (IC459308), red kernal colour (IC459308), black kernal colour (IC618941), purple apiculus colour (IC459258X), threshability difficult (Swarna and Jaya).  The presence of significant treatment mean squares in the ANOVA confirms that the current germplasm collection harbors vast genetic variability, opening immense avenues for future rice improvement initiatives.  Traits such as harvest index, grain yield per plant, and number of effective tillers should be highly prioritized during selection because they combine high GCV values with high broad-sense heritability estimates.  The simultaneous presentation of highly elevated heritability alongside maximum genetic advance as a percent of mean for grain yield per plant, number of effective tillers, and plant height implies that selection for these characters will be highly effective due to the control of additive gene action.

References

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