Correlation and path coefficient analysis in line × tester derived hybrids of rice (Oryza sativa L.)

Introduction

Rice stands as one of the world's most vital cereal crops, serving as a key food and protein source for approximately half of the global population. Its significance extends beyond nutrition to have substantial economic and food security implications, especially in many Asian countries. Globally, rice is cultivated across 162.76 million hectares, yielding a total of 495.87 million tonnes with an average productivity of 4.55 tonnes per hectare. Despite the considerable advancements in rice production and productivity brought about by the Green Revolution, further improvements have been constrained by a yield plateau, influenced by various biotic and abiotic factors. Ensuring future food security poses a significant challenge, especially for rice breeders in India, where rice is crucial for both food and nutritional security. To overcome the current yield limits, there is a need for rice varieties with higher productivity and adaptable agronomic traits. Hybridization presents a highly effective and practical approach for generating the desired variability which relies on the capacity to identify and select superior genotypes through a careful selection process. Selection criteria are usually based on yield or its components and can be executed either directly or indirectly (Margam et al. 2024) [30] . When traits are correlated, selecting for one trait can influence other related traits as well. Grain yield is affected by various component traits and understanding the association between predictor factors and responder variables and their direct and indirect impacts on yield is crucial for effective yield improvement (Meena et al. 2020) [19] . While correlation measures the degree of association between two traits, it does not explain the causal relationships. Path coefficient analysis, as introduced by Dewey and Lu (1959) [3] , offers insights into the direct and indirect effects of traits on yield. This method, when combined with correlation analysis, helps to clarify the cause-and-effect relationships between different traits (Jayasudha and Sharma, 2010) [10] . Such comprehensive analysis supports breeders for estimating the degree and direction of association thereby employing suitable methods of selection. Therefore, the objective of the current study is to investigate the character associations and path analysis among traits in line x tester hybrids for yield improvement.

Material and Methods

Forty hybrids were obtained from the crosses involving four lines with ten testers through Line x Tester fashion, the method developed by Kempthorne (1957) [12] . Forty hybrids were raised along with parents and 2 checks (KPH 473, US 312) (Table . 1) in a randomized block design replicated thrice at College farm, college of Agriculture, PJTAU, Rajendranagar, Hyderabad, during Rabi 2022. Twenty-five days old seedlings of parents, hybrids and checks were transplanted in the main field with a spacing of 20 x 15 cm. Recommended package of practices with need based plant protection measures were taken up to raise good crop as previously reported in kumar et al. (2023) [16] . The association study was made to assess the relationship among Grain yield/plant (g) with twelve traits viz., Days to fifty percent flowering (DFF), Plant Height (cm), Panicle length (PL) (cm), No. of Productive Tillers per plant (PT), Spikelet fertility (SF%), 1000 grain weight (TGW gm), Kernel length (KL mm), Kernal breadth (KB mm), Kernal L/B ratio (KLB), Hulling (HULL%), Milling (MILL%) and Head rice recovery (HRR%).

Statistical analysis

Correlation was worked out using the formulae suggested by Weber and Moorthy (1952) [28] . Partitioning of the correlation coefficients into direct and indirect effects was carried out using the procedure suggested by Wright (1921) [29] and elaborated by Dewey and Lu (1959) [3] . The characterization of path coefficients was carried out as suggested by Lenka and Mishra (1973) [17] .

Results and Discussion

Correlation coefficient analysis

In order to identify the key traits useful for crop improvement through appropriate breeding programmes, selection based on correlation analysis is essential as it provides a clear understanding of the strength and direction of association between grain yield and its component traits (Table 2; Figure 1). The correlation analysis revealed several significant associations among the studied traits. Days to fifty per cent flowering showed a strong positive and highly significant correlation with plant height (r = 0.593**), and positive significant associations with hulling percentage (r = 0.308*) and milling percentage (r = 0.286*), indicating that later flowering genotypes tended to be taller and associated with better grain quality traits. Plant height exhibited a highly significant positive correlation with panicle length (r = 0.556**) and grain yield per plant (r = 0.490**), while it showed a significant negative association with productive tillers (r = -0.366**). It also recorded positive significant correlations with kernel breadth (r = 0.294*), hulling percentage (r = 0.281*), and head rice recovery (r = 0.282*). Similar positive associations between plant height, panicle length, and grain yield have been reported by Saketh et al. (2023) [31] , Devi et al. (2022) [4] , Deepthi et al. (2022) [2] , Faysal et al. (2022) [6] , Heera et al.

(2023) [9] , and Thuy et al. (2023) [23] . Panicle length showed significant positive correlations with kernel length (r = 0.265*) and grain yield per plant (r = 0.551**). Productive tillers did not exhibit any significant positive association with grain yield and showed a negative association with plant height, supporting earlier findings by Kiran et al.

(2023) [13] . Spikelet fertility recorded highly significant positive correlations with thousand grain weight (r = 0.497**), kernel breadth (r = 0.368**), hulling percentage (r = 0.370**), milling percentage (r = 0.389**), head rice recovery (r = 0.414**), and grain yield per plant (r = 0.399**), indicating its strong influence on both yield and quality traits. Thousand grain weight showed highly significant positive correlations with kernel length (r = 0.427**) and kernel breadth (r = 0.374**). Kernel length was positively associated with kernel breadth (r = 0.392**) and kernel length-breadth ratio (r = 0.498**), whereas kernel breadth exhibited a highly significant negative correlation with kernel length-breadth ratio (r = -0.597**), indicating their inverse relationship. Among quality traits, hulling percentage showed a very strong positive association with milling percentage (r = 0.963**), while both hulling percentage and milling percentage were positively and significantly correlated with head rice recovery (r = 0.686** and r = 0.700**, respectively), reflecting their close interdependence.

Grain yield per plant exhibited highly significant positive correlations with plant height (r = 0.490**), panicle length (r = 0.551**), spikelet fertility (r = 0.399**), and head rice recovery (r = 0.460**). It also showed significant positive correlations with kernel breadth (r = 0.318*), hulling percentage (r = 0.327*), and milling percentage (r = 0.329*). Similar positive associations between single plant yield and plant height, panicle length, and thousand grain weight have been reported by Kumar et al. (2024) [15,18] , Panika et al. (2022) [22] , Sahithi et al. (2023) [23] , Pathak et

International Journal of Advanced Biochemistry Research https://www.biochemjournal.com al. (2024) [22] , Abdul Fiyaz et al. (2011) [32] , Farheen et al.

(2023) [5] , Kiran et al. (2023) [13] , Kondi et al. (2022) [14] , Surjaye et al. (2022) [26] , and Satish Chandra et al. (2024) [24] . Overall, the results indicate that plant height, panicle length, spikelet fertility, and grain quality parameters exhibited strong and consistent positive associations with grain yield, suggesting their importance as key selection criteria for yield improvement in rice breeding programmes.

Path Coefficient Analysis

Simple correlation alone does not adequately explain the nature of relationships among yield contributing traits. Therefore, phenotypic correlation coefficients were further partitioned into direct and indirect effects using path coefficient analysis. This approach helps in distinguishing the true direct influence of each trait on grain yield from its indirect effects mediated through other characters, thereby providing a clearer understanding of cause-and-effect relationships (Wright, 1921) [29] . The estimates of path coefficient analysis for yield and yield component characters are presented in Table 3 and Figure 2. The results revealed considerable variation in the magnitude and direction of direct effects among the traits studied. Among all the traits, panicle length exhibited the highest positive direct effect (0.5738) on grain yield, indicating its major role in yield determination. This was followed by plant height (0.5092), spikelet fertility (0.4119), and head rice recovery (0.4790), all of which showed strong positive direct effects on grain yield. Similar findings of strong positive direct effects on grain yield were also reported by Hani and Thouseem (2024) [8] , Kumar et al. (2024) [15,18] , Sudeepthi et al. (2020) [25] , and Bhargava et al. (2021) [1] . Moderate positive direct effects were observed for kernel breadth (0.3368), hull content (0.3723), kernel length (0.2414), and days to 50% flowering (0.2283), indicating that these traits contribute positively to grain yield either directly or indirectly through their association with other traits. Among the remaining traits, thousand grain weight (0.1689) also exhibited a positive direct effect, suggesting its supportive role in yield expression, which is in agreement with the findings of Kalaiselvan et al. (2019) [11] and Parimala et al. (2020) [21] . In contrast, productive tillers (-0.3017) and kernel length-breadth ratio (-0.0925) showed negative direct effects on grain yield, indicating limited direct contribution to yield improvement and a greater influence through indirect pathways. Similar negative direct effects for these traits have also been reported by Hani and Thouseem (2024) [8] , while negative effects of productive tillers were earlier observed by Kalaiselvan et al. (2019) [11] and Parimala et al. (2020) [21] . The indirect effects indicated that several traits influenced grain yield through their associations with key yield components such as panicle length, spikelet fertility, and kernel traits, highlighting the importance of trait interrelationships in yield formation. Overall, the study suggests that panicle length, spikelet fertility, plant height, head rice recovery, kernel traits, and days to 50% flowering are the major contributors to grain yield in rice due to their strong positive direct effects. These traits should therefore be given priority in selection programmes aimed at genetic improvement and yield enhancement in rice.

Conclusion

The present study on rice (Oryza sativa L.) revealed that grain yield is influenced by several yield component traits. Correlation analysis showed significant positive associations of grain yield with plant height, panicle length, spikelet fertility, head rice recovery, and kernel quality traits. Path coefficient analysis further confirmed that panicle length, plant height, spikelet fertility, and head rice recovery had strong positive direct effects on grain yield, indicating their major role in yield determination. Other traits contributed either indirectly or showed comparatively lower influence. Overall, panicle length, spikelet fertility, plant height, and head rice recovery were identified as key traits for selection in rice breeding programmes aimed at yield improvement.

Acknowledgement

AcknowledgementThe first author is thankful to Council of Scientific and Industrial Research (CSIR) for providing Senior Research Fellowship (File number 08/1285(12045)/2021-EMR-I. The authors are thankful to PJTAU, Hyderabad; RARS, Warangal; RARS, Jagtial and RS&RRS, Rudrur for providing necessary facilities to carry out the experiment.

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