Coriander (Coriandrum sativum L.) seed yield and its attributing characteristics are analysed based on variability, heritability, correlation coefficient and path analysis

Introduction

Coriander (Coriandrum sativum L.), an annual spice herb, is grown as a summer or winter annual crop depending on the climate. Coriander is commonly known as dhania or cilantro (particularly in America) that belongs to the family Umbelliferae (Apiaceae). On the basis of pollinating nature, coriander (Coriandrum sativum L.) is cross pollinated. Coriander (Coriandrum sativum L.) is diploid with 2n = 22. The name of coriander took from the Greek world "Koris" meaning "bed bug" and the unripe fruits of coriander smelled like bed bug (Sandhu et al., 2018) [14] . It is indigenous to the Mediterranean and parts of Western Asia (Awas et al., 2015) [6] . The plant can tolerate light frost but hot temperatures will cause bolting. Coriander grows best in well-draining soil and also can be grown in loam, clay and sandy loam soil as long as there is sufficient drainage. In the full sunny positioned plants will grow best. Coriander is directly propagated from seeds. The most critical and initial phases in every crop development effort are the collection, management and evaluation of germplasm. The quantity of genetic variety contained in the experimental material is a fundamental prerequisite for a breeding effort to be effective. To overcome genetic yield obstacles, it is vital to examine different features and how they relate to one another. While correlation studies are useful in identifying the constituent parts of complicated variables like yield. However, correlation coefficient does not give a precise estimate of the direct and indirect effects' contribution to the yield. Path coefficient analysis is a crucial technique in this situation for separating the direct and indirect effects of the independent variable on the dependent variable from the correlation coefficient.

Instructional Farm during the rabi season of 2019-20. Three replications of the experiment were set up using a randomized block design (RBD). The Seed Spices Research Station, SDAU, Jagudhan (Gujarat), provided the experimental materials. The genotypes were planted at a spacing of 45 cm × 10 cm. From each row in each replication, five competitive plants were chosen at random. Phenotypic data were recorded for twelve characters, including days to flowering, days to maturity, plant height, number of branches per plant, number of seeds per umbellate, number of umbellates per umbel, number of umbels per plant, test weight, harvest index, biological yield per plant, volatile oil, and seed yield per plant. The method recommended by Panse & Sukathme (1978) was used to analyse the mean data for various parameters. The genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) were determined as recommended by Burton & Devene (1952) [7] . The formula proposed by Allard (1960) [3] was used to calculate heritability. Al-Jibouri et al.'s (1958) [2] proposed the variance and covariance components, which were used to calculate the coefficients of correlation between two features on both the genotypic and phenotypic level.

Results and Discussion

There was enough variation among the forty coriander genotypes, as shown by the analysis of variance for all the examined features, which exhibited a difference that was extremely significant between genotypes (Table 1). For all of the traits examined, PCV values were found to be greater than GCV values, showing that these qualities were influenced by environmental influences. The attributes days to flowering, days to maturity and plant height showed tight affinity between PCV and GCV, indicating that the environment had a limited impact on how these traits were expressed. Number of seeds per umbellate had showed moderate to high PCV and GCV (Table 2). However, for the features of number of branches per plant, number of umbels per plant, harvest index and volatile oil were shown moderate PCV and GCV. Verma et al. (2018) [15] observed similar results for the number of branches per plant, Natwaria et al. (2020) [12] for the number of umbels per plant and Jyothi et al. (2017) [8] for the harvest index. Number of umbellates per umbel, test weight, biological yield per plant and seed yield per plant all had low to moderate PCV and GCV values. Similar findings were made for test weight by Jyothi et al. (2017) [8] and for the number of umbellates per umbel by Natwaria et al. (2020) [12] . The presence of additive gene action in volatile oil was demonstrated by the high genetic advance as a percentage of mean along with high heritability (Table 2), comparable results were reported by Acharya et al. (2020) [1] . According to Jyothi et al. (2017) [8] and Verma et al. (2018) [15] , moderate genetic advance as a percentage of mean along with high heritability were detected for plant height, indicating the involvement of additive or fixable gene effects. Low genetic advance as a percentage of mean combined with high heritability was detected for days to flowering, indicating that the non-additive gene action and selection would not be effective, similar findings were discovered by Nagappa et al. (2018) [11] . The number of branches per plant and number of seeds per umbellate both showed high genetic advance as a percentage of mean along with moderate heritability, indicating additive or fixable gene effects. Moderate heritability along with moderate genetic advance as a percentage of mean was detected for number of umbellates per umbel, number of umbels per plant, test weight and harvest index, indicating that the additive or fixable gene effect was more or less influenced by environmental factors and the attribute could be improved by selection, similar findings were dicovered for test weight by Jyothi et al. (2017) [8] & Natwaria et al. (2020) [12] and for harvest index by Acharya et al. (2020) [1] . Similar result found by Anilkumar et al. (2018) [4] , who discovered low genetic advance as a percentage of mean along with moderate heritability for days to maturity, indicating narrow chances of improvement of the genotypes based on simple selection. For every character under study, higher genotypic correlation values were found than their corresponding phenotypic correlations. This indicated that there was a strong genetic correlation between the two variables, but that the impact of environmental conditions on phenotypic expression limited its potential. It also demonstrated the significance of these features for increasing seed yield per plant. The results of a correlation study revealed that the number of branches per plant, harvest index and biological yield per plant were significant and positively correlated with the seeds yield of per plant at both the phenotypic and genotypic levels (Table 3); similar findings for harvest index were discovered by Sandhu et al. (2018) [14] & Anilkumar et al. (2019) [5] . The plant height, number of seeds per umbellate, number of umbellates per umbel, number of umbels per plant and test weight had genotypically positive and significant correlation with seeds yield per plant. Anilkumar et al. (2019) [5] found similar results for plant height, number of umbels per plant, number of umbellates per umbel, number of seeds per umbellate and test weight. Therefore, in future breeding programs for development of genotype for better seed yield per plant, above traits should be kept in consideration at the time of selection. The results of a path analysis study showed that umbels per plant had the highest positive and direct effect on seeds yield per plant, followed by test weight, plant height, harvest index, number of seeds per umbellate and biological yield per plant (Table 4). Similar findings were made for plant height by Mengesha et al. (2013) [10] , for number of umbels per plant Meena et al. (2014) [9] & Natwaria et al. (2020) [12] and for test weight & harvest index Sandhu et al. (2018) [14] . Whereas, positive and negligible direct effects on seed yield per plant were seen in biological yield per plant. Whereas the number of umbellates per umbel, days to maturity and volatile oil had the most negative and direct effects on seed yield per plant. While the number of branches per plant & days to flowering had a negative and insignificant direct impact on seed yield, Natwaria et al. (2020) [12] found similar result for the number of branches per plant. Therefore, in a selection program to increase coriander's seed yield, the number of umbels per plant, test weight, plant height, harvest index, number of seeds per umbellate and biological yield per plant should all be given the appropriate weight.

Conclusion

The study revealed significant genetic variability among coriander genotypes, indicating good potential for improvement through selection. High heritability with genetic advance for volatile oil content suggested strong additive gene action. Seed yield showed positive association with traits such as branches per plant, plant height, seeds per umbellate, umbels per plant, test weight, harvest index, and biological yield. Path analysis confirmed that these traits had direct positive effects on yield. Therefore, these characters should be prioritized in breeding programs for developing high-yielding coriander genotypes.

Acknowledgement

AcknowledgementWe are very grateful to C P College of Agriculture, Sardarkrushinagar Agronomy Instructional Farm, Seed Spices Research Station, Jagudan for giving resources and facilities for research trial, along with experimental material and biochemical analysis facility.

References

1. Acharya VR, Singh M, Patel RK, Goyal L. Genetic diversity of coriander for yield and its attributing characters. Int J Curr Microbiol Appl Sci. 2020;9(2):2319-7706.
2. Al-Jibouri HA, Miller PA, Robinson HF. Genotypic and environmental variance and covariance in an upland cotton cross of interspecific origin. Agron J. 1958;50:633-637.
3. Allard RW. Principles of plant breeding. New York: John Wiley and Sons Inc.; 1960. p.145.
4. Anilkumar GS, Umesha K, Shivapriya M, Halesh GK, Kumar N. Genetic variability, heritability and genetic advance studies in coriander (Coriandrum sativum L.). Int J Pure Appl Biosci. 2018;6(3):426-430.
5. Anilkumar GS, Umesha K, Shivapriya M, Halesh GK, Maruthiprasad BN, Darshan G. Character association and path analysis for yield traits in coriander (Coriandrum sativum L.). Electron J Plant Breed. 2019;10(1):224-229.
6. Awas G, Mekbib F, Ayana A. Variability, heritability and genetic advance for yield and yield related traits and oil content in Ethiopian coriander (Coriandrum sativum L.) genotypes. Int J Plant Breed Genet. 2015;9(3):116-125.
7. Burton GW, DeVane EH. Estimating heritability in tall fescue (Festuca arundinacea) from replicated clonal material. Agron J. 1952;45:478-481.
8. Jyothi K, Mishra RP, Sujatha M, Joshi V. Genetic variability, heritability and genetic advance for yield and its components in indigenous collection of coriander (Coriandrum sativum L.) germplasm. Int J Pure Appl Biosci. 2017;5(3):301-305.
9. Meena YK, Kale VS, Meena OP. Correlation coefficient and path analysis in coriander. Int J Sci Res Publ. 2014;4(6):2250-3153.
10. Mengesha B, Alemaw G, Tesfaye B. Correlation studies and path coefficient analysis for seed yield and yield components in Ethiopian coriander accessions. Afr Crop Sci J. 2013;21(1):51-59.
11. Nagappa MK, Emmanuel N, Reddy ML, Dorajeerao AVD. Heritability and genetic advance for yield and its attributes in coriander. Int J Curr Microbiol Appl Sci. 2018;7(6):3729-3740.
12. Natwaria MR, Gothwal DK, Kajla SL. Variability parameters, character association and genetic divergence studies in coriander accessions. Int J Chem Stud. 2020;8(3):2130-2133.
13. Panse VG, Sukhatme PV. Statistical methods for agricultural workers. New Delhi: ICAR; 1978. p.381.
14. Sandhu AS, Singh L, Darvhankar MS. Evaluation of genetic diversity and relationships among coriander genotypes using correlation and path analysis. Plant Arch. 2018;18(2):1557-1562.
15. Verma MK, Pandey VP, Singh D, Kumar S, Kumar P. Studies on genetic variability in germplasm of coriander (Coriandrum sativum L.). J Pharmacogn Phytochem. 2018;SPI:2490-2493.