Comparative analysis of genetic variability, path analysis and correlation coefficient in rice (Oryza sativa L.) cultivars under Prayagraj agro-climatic conditions

Introduction

Although rice (Oryza sativa L.) is a staple meal for more than half of the world's population, its yield and quality are essential to ensuring global food security. Grain shape and palatability are two of the most important aspects affecting rice quality. Grain shape, which comprises properties such as grain length, width, and thickness, significantly affects both the production potential and market value of rice Wang et al., (2012) [20] & Jahani et al.,(2013) [12] Niu et al., (2020) [17] . For customer acceptance and marketability, palatability which includes flavor, texture, and overall eating quality is equally crucial. In Bangladesh, rice is the most significant grain crop and a staple diet item. On 75% of the total cultivated land, or 11.58 million hectares, we produce roughly 30 million tons of rice annually (BBS, 2018). After Boro, Aman rice accounts for the second-largest portion of all rice output. Bangladeshi agriculture is distinguished by its extensive crop production using a cropping strategy based on rice. Our nation's average rice output is about 4.57 t ha⁻¹, which is disappointingly lower than Australia's maximum yield of 9.65 t ha⁻¹ and lower than the global average of 7.48 t ha⁻¹ (FAO, 2019). Throughout the two Green Revolutions, rice production increased along with advancements in tolerance and architecture Khush et al. (2001) [14] . Both conventional breeding methods and contemporary molecular breeding techniques have shown that plant architecture and stress tolerance are closely related, complementing one another during the rice reproductive process and simultaneously influencing rice output Guo et al., (2020) [10] . The photosynthetic efficiency, biomass, and stress tolerance of plants can all be enhanced by optimal plant architecture Ma et al., (2020).

The initial NPT rice architecture was for irrigated and suitable rainfed tropical lowlands. Correspondingly, it is crucial to regionalize the criteria according to local cultivation and climate conditions Feng et al., (2013) [8] . Numerous nations continued to release NPT rice varietals. A few examples are as follows: Fatmawati Abdullah et al., (2008) [1] . IPB3S in Indonesia; and Pusa 1266 in India Marathi et al., (2012) [16] . The NPT rice variety Shennong265, which was first introduced in China in 1997, has a yield potential of 11-11.8 t ha⁻¹. Farmers are widely using this type as well as others like Shennong 174 and Shennong 606 Chen et al., (2001) [3] . Increasing the number of grains is the simplest method to boost the grain yield in cereals. The main determinants of grain number are branch differentiation and panicle architecture, which are closely related to vascular development and phytohormone pathways Duan et al., (2019) [6] & Deveshwar et al., (2020) [4] . According to many research, the broad-sense heritability of grain number ranges from about 70 to 90% Tuhina et al., (2015) [19] & Roy et al., (2020) [18] . Demonstrating that the primary determinant of grain number is genetic. Thus, one effective way for breeders to boost rice production would be to analyze the genetic mechanism controlling the number of grains per panicle.

Material and Method 2.1 Experimental Site

The present experiment was conduct in Kharif season 2025-26 at Agricultural Research Farm of Prof. Rajendra Singh (Rajju Bhaiya) University, Naini, Prayagraj, Uttar Pradesh. The of Mirzapur Road, approximately I5 km from Prayagraj city. It lies at a latitude of 25935'85"alongitude of 8189'3"and a clevation of 98 meters above sea level. Area is located south of Prayagraj, on the right bank of the Ganga and Yamuna River, and East.

Experimental Material

Prayagraj district falls within the subtropical zone of southcastem Uttar Pradesh, characterized by intensely hot summers and moderately cold winters. The maximum temperature in this region varies between 25 °C to 35 °C and rarely drops below 5°C to 6°C.The relative humidity fluctuates between 13.9% to 99.7%, while the area receives an average annual rainfall of approximately 1042 mm.

Recording of Quantitative Traits

To assess growth, physiological performance, and yield features, the following quantitative traits were noted from randomly chosen plants in each genotype:

1. Days to 50% flowering after transplanting: This refers to the number of days taken for about half of the plants in a population to start flowering, indicating the crop's earliness.

Plant height at 30, 60, and 90 DAS:

The height of the plant measured at different growth stages (30, 60, and 90 days after sowing) to understand growth patterns over time.

Days to maturity:

The total duration from sowing to the stage when the crop is fully mature and ready for harvest.

Number of tillers per plant:

The total number of shoots produced by a single plant, which directly influences yield potential.

The length of the flowering head, which is an important trait affecting grain number and yield.

Flag leaf area (cm²):

The size of the uppermost leaf, which plays a key role in photosynthesis and grain filling.

The ratio of grain yield to total biological yield, indicating how efficiently the plant converts biomass into economic yield.

The amount of grain produced by an individual plant, an important measure for selection in breeding. 14. Grain yield (q/ha): The total grain production per hectare, representing the overall productivity of the crop under field conditions.

Statistical Analysis

The experimental data were compiled by taking mean of 5 selected plants for each treatment (plot) in each replication. The data will be subjected to various statistical analyses. 1. Analysis of variance for Randomised Block Design as per given by (Panse and Sukhatme, 1967).

Estimation of coefficient of variability (Burton and de

Vane, 1953) 3. Estimation of Heritability by (Allard et al., 1960) and genetic advance by (Robinson et al., 1949). 4. Estimation of correlation coefficients as per method given by (Searle, 1961) [29] . 5. Estimation of Path-coefficient analysis (Dewey and Lu, 1959).

Results and Discussion

Analysis of variance

The majority of the observed features were statistically significant, according to genotypic mean square values from ANOVA, suggesting that genetic heterogeneity exists among rice genotypes. At the 1% probability level, traits with high genotypic mean square values and the symbol "" were significant, indicating that genotype differences were highly dependable and not the result of chance. (2013) [5] , Ketan and Sarkar (2014) [13] , Abebe et al. (2017) [2] , Gupta et al. (2021) [11] , and Parihar et al. (2023) [22] all reported significant genetic variability for various rice yield and qualityrelated parameters. [24] and Parimala and Devi (2019) [23] , Yugandhar et al., (2018) [25] .

Overall, traits such as tillers per plant, spikelets per panicle, fertile spikelets, test weight, and grain yield are key targets for effective selection and genetic improvement. A very strong positive genotypic correlation existed between NSP and NFS (0.99**), indicating close genetic association between spikelet formation and grain filling. NSP also showed significant negative correlation with HI (-0.59*).Biological yield (BYP) showed highly significant positive association with GYP (0.90**) and significant positive correlation with GY (0.58*), confirming that higher biomass production contributes directly toward grain yield. Grain yield per plant (GYP) exhibited strong positive correlation with grain yield (GY, 0.67**), suggesting that selection for GYP can effectively improve grain productivity in rice breeding programs. Similar results were reported by Roy et al. (1957) [28] .

Phenotypic correlation

The phenotypic correlation analysis revealed significant associations among the studied traits, indicating their interdependence and potential contribution to yield improvement. Grain yield (GY) showed a significant and positive correlation with biological yield per plant (GYP) (0.41*) and biomass yield per plant (BYP) (0.36), suggesting that these traits are major contributors to overall productivity. Similarly, flag leaf area (FLA) exhibited a positive and significant correlation with grain yield (0.41**), indicating the importance of photosynthetic surface in yield enhancement. Days to flowering (DF) showed a significant positive association with number of seeds per plant (NSP) (0.44*) and number of fertile spikes (NFS) (0.37), but had a negative correlation with plant height (PH) (-0.34*) and panicle length (PL) (-0.60**). This suggests that early flowering genotypes may favor better vegetative growth and reproductive efficiency. Number of tillers (NT) demonstrated a strong positive correlation with panicle branching (PB) (0.42*) but showed negative associations with FLA (-0.47) and GY (-0.47*), indicating a possible trade-off between tillering and yield efficiency. A very high positive correlation was observed between NSP and NFS (0.98**), suggesting that these traits are closely related and can be improved simultaneously through selection. However, both NSP and NFS exhibited significant negative correlations with harvest index (HI) and GYP, indicating the need for balanced selection strategies. Thousand grain weight (TGW) showed moderate positive correlations with GY (0.25) and GYP (0.28), indicating its supportive role in yield determination. Kishore et al and Sandeep et al. (2018) [26] Overall, the study suggests that traits such as GYP, BYP, and FLA are key contributors to grain yield, and selection based on these traits could effectively enhance crop productivity. However, negative correlations among certain traits highlight the complexity of selection and the need for a balanced breeding approach.

Path Coefficient Analysis 3.4.1 Genotypic path analysis

The genotypic path analysis reveals that grain yield (GYP) is influenced not only by direct effects but also substantially through indirect pathways among traits Among direct effects, NSP (19.9424) showed the highest positive direct contribution, making it the most decisive trait for yield improvement. In contrast, NFS (-20.2020) had a very strong negative direct effect. Moderate positive direct effects were observed for FLA (0.7718), TGW (0.4020), and PL (0.3111), while PH (-0.9321), NT (-0.4276), HI (-1.9791), and BYP (-2.1600) exhibited negative direct influences. DF contributed indirectly in a positive manner via NSP (9.8386) and HI (0.5402), but showed negative indirect effects through NFS (-8.6289) and SF (-1.5070). DM had a strong positive indirect effect through NSP (6.3142), while negative indirect effcts were observed via NFS (-4.2921) and SF (-2.2439). PH, despite its negative direct effect, showed small positive indirect contributions via FLA (0.0590) and TGW (0.0857), but negative effects via NFS (-0.8339) and BYP (-0.3035). NT exhibited a large negative indirect effect through NSP (-9.0483) but a strong positive effect via NFS (8.2460) and BYP (1.4127). PB showed negative indirect effects via NSP (-10.0795) and SF (-1.5298), but positive contributions through NFS (12.2735) and FLA (0.6361). PL had a strong negative indirect effect through NSP (-12.5478) but this was compensated by a high positive effect via NFS (12.4630) and SF (0.6208). FLA contributed positively via NSP (8.3140) but negatively via NFS (-8.6965). NSP itself showed negative indirect effects through NFS (-19.9529) but positive via FLA (0.3217) and BYP (0.5668). NFS had a strong positive indirect effect through NSP (19.6965) but negative through BYP (-0.6149). TGW showed positive indirect effects via NFS (9.3978) and PL (0.1534), but negative via NSP (-9.1737). BYP contributed positively through NFS (5.7515) and FLA (0.4243), but negatively via NSP (-5.2335). HI showed positive indirect effects via NFS (11.7973) and DM (0.4349), but negative via NSP (-11.7124). GYP interactions also reflected both positive (NFS 8.6527) and negative (NSP -7.8444) indirect pathways. Similar results were also reported by Gawai et al. (2006) [9] for tillers per plant and harvest index and plant height and Ekka et al. (2011) [7] for panicle length. Overall, the data clearly demonstrate that NSP is the key positive contributor through direct effect, while many traits influence yield indirectly through strong interactions with NSP and NFS. The very low residual value (0.0133) confirms that these traits collectively explain most of the variability in grain yield.

Phenotypic path analysis

The phenotypic path analysis demonstrates that grain yield (GYP) is influenced by both direct and indirect effects of various traits, though the magnitude of effects is comparatively lower than genotypic estimates. Considering direct effects (diagonal values), GYP (0.8305) itself showed the highest positive direct contribution. Among yield components, DM (0.2938), TGW (0.4622), and FLA (0.0257) exhibited positive direct effects, indicating their importance in improving yield at the phenotypic level. In contrast, several traits showed negative direct effects, including PH (-0.4829), NT (-0.339), PB (-0.1667), NFS (-1.1753), NSP (-1.2094), SF (-0.111), and HI (-0.4423), suggesting that their direct selection may not be beneficial for yield improvement. DF had a negative direct effect (-0.029) but contributed positively through DM (0.1759) and PH (0.1654), while showing negative influence via NSP (-0.5284) and TGW (-0.1609). DM showed positive indirect effects via PH (0.192) and NT (0.0827), but negative via NSP (-0.3493) and TGW (-0.1771). PH, despite a negative direct effect, contributed positively through TGW (0.0848) and PB (0.0354). NT had negative indirect effects through NFS (-0.3608) but positive via NSP (0.416) and BYP (0.2278). PB showed positive indirect contributions via NSP (0.2303) but negative via NFS (-0.2599). PL exhibited strong positive indirect effect via NSP (0.6233), while a negative effect was seen through NFS (-0.5868). FLA contributed positively through NFS (0.3726) but negatively via NSP (-0.3834). NSP and NFS showed strong mutual indirect effects, with NSP → NFS (1.1479) and NFS → NSP (-1.1812), indicating a complex interaction. TGW had positive indirect contribution via NSP (0.5329) but negative via NFS (-0.5085). BYP exhibited a strong positive indirect effect on yield through GYP (0.7176) but negative via TGW (-0.126). HI showed positive indirect effects via NSP (0.5327) but negative via NFS (-0.472). Prasad et al. (2017) [21] and Welalaw et al. Estimation of the genotypic correlation coefficient for various quantitative paddy attributes. Overall, the results indicate that traits such as TGW and DM are important positive contributors, while NSP and NFS show complex and often negative direct effects but strong indirect interactions. The relatively higher residual effect (0.2844) suggests that environmental factors also play a considerable role in influencing grain yield at the phenotypic level.

Acknowledgement

The Rajendra Singh (Rajju Bhaiya) University Prayagraj, UP, India and Sciences are gratefully acknowledged by the authors for providing all necessary facilities.

Author contribution

Contributed to the conception and design of the analysis paper. Contributed to the data collection. Data and analysis tools were used to write the analyzed paper. Also evaluated the paper and then suggested publishing it in this journal. and resource

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