Genetic study of yield variation in medium to early duration irrigated rice (Oryza sativa L.) genotypes

Introduction

Rice (Oryza sativa L.) is one of the most important cereal crops in the world, belonging to the genus Oryza and family Poaceae. The genus Oryza comprises two cultivated and 22 wild species, of which Oryza sativa and Oryza glaberrima are grown commercially. Rice is primarily a self-pollinated crop with a remarkable adaptation to diverse ecological conditions, ranging from irrigated lowlands to upland rainfed systems. Globally, rice is considered the staple food for more than half of the world's population, serving as the cheapest and most accessible source of dietary carbohydrates. In many rice-growing countries, it contributes over 70% of daily caloric intake, underscoring its critical role in ensuring food and nutritional security. In India, rice contributes significantly to both dietary energy and agricultural economy, providing nearly 43% of the caloric requirement of the human diet and generating about 20-25% of agricultural income due to its large-scale and intensive cultivation. Medium to early duration rice varieties are particularly important as they mature within 115-120 days, fitting well into multiple cropping systems and ensuring timely harvest with reduced risk of exposure to biotic and abiotic stresses. During 2023-24, global rice production was estimated at 521.52 million metric tonnes, with India ranking first in production. The country recorded an area of 49 million hectares under rice cultivation, producing nearly 147 million metric tonnes, which marked an increase of 16.17 million tonnes compared to the previous year (Anonymous, 2023-24) [2] . At the regional level, Chhattisgarh, known as the "Rice Bowl of India," plays a crucial role in national rice production. In 2023-24, the state reported about 3.91 million hectares under rice cultivation with a total production of 14.7 million tonnes (Anonymous, 2023-24) [2] . Given its dominance in the agrarian economy and daily diet, rice remains not only a vital staple food but also a strategic crop for addressing challenges of food security, livelihood improvement, and sustainable agricultural growth in India and beyond.

Materials and methods

The present investigation was conducted during Kharif 2024 at the Research Farm of Barrister Thakur Chhedilal College of Agriculture and Research Station, Bilaspur, under Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh. The experiment comprised fourteen rice genotypes along with four standard checks, namely MTU-1010, US-312, CHANDRAHASINI, and Gontra Bidhan-3. Seedlings of all genotypes were first raised in a nursery bed and subsequently transplanted in the main field. Each genotype was grown in a plot size of 1.20 m × 9.50 m = 11.40 m² per entry per replication. The crop was managed with the recommended package of practices, including fertilizer application and intercultural operations, to ensure uniform growth. Observations were recorded on twelve quantitative traits using five competitive plants selected from each replication, and the averaged data were subjected to statistical analysis. The variability among genotypes was assessed through analysis of variance following the procedure of Singh and Chaudhary (1985) [25] . Phenotypic and genotypic coefficients of variation (PCV and GCV) were computed as per Falconer (1964) [8] . Heritability (broad sense) was estimated using the method of Allard (1960) [1] , and from these estimates, genetic advance (GA) was calculated following Burton and Devane (1953) [5] . Genetic advance as a percentage of mean was further classified into categories as suggested by Johnson et al. (1955) [12] . Correlation coefficients were estimated according to the formula proposed by Miller et al. (1958) [15] , while path coefficient analysis was carried out based on the method suggested by Wright (1921) [28] and later elaborated by Dewey and Lu (1959) [7] .

Results and discussion

Analysis of variance

The Analysis of variance of fourteen genotypes of rice with respect to twelve quantitative characters is presented in the Table 1. The analysis of variance for twelve characters revealed significant differences among the genotypes for all the traits studied. ANOVA was done based on mean values of twelve quantitative traits in fourteen rice genotypes. The ANOVA analysis (Table 1) indicated that the material used in the study possesses inherent variability among themselves. The range of variability indicated that biological yield (q/ha), number of fertile spikelet's per panicle, grain yield (q/ha), plant height (cm), days to 50% flowering and days to maturity had highly significant variability, indicating that parents should be of diverse origin and that these characteristics should be used for further selection.

Genetic parameter of variation

In this study, the PCV values were greater than GCV values with minimal differences for all the characters studied, indicating less effect on the expression of all studied traits. The high to moderate PCV and GCV (Table 2) were recorded for number of effective tillers per plant, biological yield (q/ha) and 1000 grains weight (g) respectively indicating that these traits were under the major influence of genetic control and less variable due to environmental factors. In the present experimental set, high heritability coupled with high genetic advance as percentage of mean was recorded for number of fertile spikelet's per panicle followed by biological yield (q/ha), number of effective tillers per plant and grain yield (q/ha). The above results indicated that these characters had additive gene effect indicating effectiveness of selection for the improvement of these traits in medium to early duration irrigated rice genotypes. Similar findings were revealed by Saran et al. (2023) [20] , Faysal et al. (2022) [9] , Jasmine et al, (2022) [11] , Sheera et al. (2021) [22] , Gupta et al. (2020) [10] , Sravan et al. (2012) [26] .

Correlation coefficient analysis

The correlation study (Table 3) revealed that Grain yield (q/ha) had strong positive association with plant height (r g = 0.996** r p = 0.321**), panicle length (r g = 0.996** r p = 0.687**), number of effective tillers per plant( r g = 0.972** r p = 0.680**), number of fertile spikelet's per panicle (r g = 0.930** r p = 0.840**),1000 grains weight (r g = 0.932** r p = 0.643**), biological yield(r g = 0.935** r p = 0.854**), paddy grain L/B ratio(r g = 0.202 r p = 0.129), paddy grain length(r g = 0.448** r p = 0.364), paddy grain width (r g = 0.504** r p = 0.36**) and days to 50% flowering (r g = 0.372* r p = 0.266) Similar findings were revealed by Singh et al. (2023) [23] for plant height, number of effective tillers per plant and number of fertile spikelet's per plant ; Katkani et al. (2019) [13] for panicle length, 1000 grains weight and number of fertile spikelet's per panicle, paddy grain L/B ratio; Bhujel et al.( 2018) [3] , for days to maturity, biological yield; Rathore et al. (2016) [19] ; Bitew et al. (2018) [4] for days to 50% flowering, days to maturity and 1000 grains weight; Khare et al. (2014) [14] for days to 50% flowering, panicle length, number of fertile spikelet's per panicle; Singh et al. (2014) [24] for days to 50% flowering, number of fertile spikelet's per panicle, paddy grain length, paddy grain width.

Path coefficient analysis

Path coefficient analysis (Table 4 andTable 5) of the present material for twelve characters revealed that highest positive direct effect on grain yield at genotypic level was exhibited by number of fertile spikelet's per panicle followed by days to maturity, biological yield, paddy L/B ratio and plant height. Similar findings were revealed by Sudeepthi et al. (2020) [27] for test weight; Devi et al. (2019) [6] for panicle length, test weight, plant height and spikelet fertility(%); Katkani et al. (2019) [13] for days to 50% flowering, panicle length, test weight and total number of grains per panicle; Naseem et al. (2014) [16] for days to maturity and total number of grains per panicle; Nayak et al. (2016) [17] for panicle length, plant height and days to 50% flowering; Patil et al.(2016) [18] for days to 50% flowering, test weight, filled grains per panicle; Seyoum et al.(2012) [21] for total number of grains per panicle, days to 50% flowering, panicle length, spikelet fertility and test weight. to 50% flowering, DM = Days to maturity, PH = Plant height (cm), PL= Panicle length (cm), NETPP =Number of effective tillers per plant, NFSPP=Number of fertile spikelet's per panicle, TW=1000 grains weight(g), GY = Grain yield (q/ha),BY= Biological yield (q/ha), L/B Ratio= Paddy grain length and breadth ratio, Ple=Paddy grain length(mm), PW=Paddy grain width(mm) to 50% flowering, DM = Days to maturity, PH = Plant height (cm), PL= Panicle length (cm), NETPP =Number of effective tillers per plant, NFSPP=Number of fertile spikelet's per panicle, TW=1000 grains weight(g), GY = Grain yield (q/ha),BY= Biological yield (q/ha), L/B Ratio= Paddy grain length and breadth ratio, Ple=Paddy grain length(mm), PW=Paddy grain width(mm)

Conclusion

The genotypes evaluated in the present study exhibited significant variation among themselves. High heritability coupled with high genetic advance as a percentage of mean was recorded for the number of fertile spikelet's per panicle, followed by biological yield (q/ha), number of effective tillers per plant, and grain yield (q/ha), indicating the predominance of additive gene action for these traits. Path coefficient analysis further revealed that the number of fertile spikelet's per panicle and days to maturity were the most influential traits contributing to grain yield, highlighting their importance in selection programmes aimed at improving yield in medium to early duration irrigated rice genotypes. Among the tested entries, five genotypes-HRI-216, RNC-099, HRI-217, RRX-3341, and YNPK-7258-demonstrated superior performance for grain yield over the standard checks MTU-1010, US-312, CHANDRAHASINI, and Gontra Bidhan-3. These promising genotypes may therefore be recommended for advancement to further multi-location and advanced yield trials.

Acknowledgement

The author expresses sincere gratitude to Indira Gandhi Krishi Vishwavidyalaya, Raipur, and BTC College of Agriculture and Research Station, Bilaspur, for providing the necessary facilities and support to conduct this research. Heartfelt thanks are extended to Dr. D.J. Sharma, Associate Professor, for his invaluable guidance, encouragement, and constructive suggestions throughout the course of this study. The author also acknowledges the constant support and insightful advice of Dr. Roshan Parihar, Dr. Awaneet Kumar, Dr. Dinesh Pandey, Dr. N.K. Chaure, whose expertise greatly contributed to the completion of this research. The cooperation of technical staff and fellow scholars of the Department of Genetics and Plant Breeding is also deeply appreciated.

Conflict of Study

The authors declare that there is no conflict of interest regarding the publication of this research work.

Future Scope

1. The identified superior genotypes can be further validated through multi-location and multi-season trials to assess their stability and adaptability. 2. Genotypes showing desirable traits may be utilized in hybridization and breeding programs to develop highyielding and stress-tolerant varieties. 3. Integration of molecular marker-assisted selection with traditional breeding can accelerate the development of improved cultivars. 4. Further studies on genotype × environment interactions will help in identifying location-specific high-performing rice genotypes. 5. Advanced genomic tools can be applied to explore the genetic basis of yield and yield-attributing traits for more precise selection.

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