Coefficient of correlation and path coefficient analysis in garlic (Allium sativum L.) mutant lines for bulb yield and its components

Introduction

Garlic (Allium sativum L.) is an annual aromatic herbaceous spice that belongs to the Alliaceae family. After onion, it is the second most widely cultivated spice crop. Garlic dates back to prehistoric times in Central Asia and the Mediterranean region. Before 2000 B.C., garlic cultivation was discovered in Egypt, and nearly 5000 years ago in India. Garlic is produced worldwide. It is consumed as spice, flavouring agent and also has pharmaceutical benefits. "Allium" is the widest and the most diverse representative genus of the Alliaceae. It is spread across in North Africa, North America, Europe and Asia and contains around 700 different species. China is the leading producer of garlic and accounts for 20.7 million tons of production, followed by India with production of 2.9 million. In India, Madhya Pradesh is the leading producer of followed by other states like Rajasthan, Uttar Pradesh, and Gujarat (Anon., 2021a). Whereas in Chhattisgarh it occupies 4,411 ha area with production of around 26,185 MT (Anon., 2021b). In recent years, mutation breeding has gained popularity as a convenient tool for crop development and as a way to complement current germplasm for cultivar improvement in breeding programmes. Furthermore, Mutation are defined as a sudden heritable changes in gene. It can be either spontaneous or induced. It can be induced either in seed or propagating material.

Material and method

The study was conducted in Rabi season 2021-2022 at Research cum Demonstration Farm, College of Agriculture, Indira Gandhi Krishi Vishwavidyalaya, Raipur (C.G.) -492012.

It lies between 21 0 16'N latitude and 81 0 36'E longitude at an altitude of 289.56 meters above mean sea level. Correlation refers to the degree and direction of association between two or more variables. It measures the mutual relationship between various plant characters and determines the component characters on which selection can be based for genetic improvement of dependent character. For all possible combinations of characters at the phenotypic and genotypic levels, the correlation coefficient was calculated following the approach given by Searle (1961) As suggested by Wright (1921) [18] and explored by Dewey and Lu (1959) [7] , the path coefficient analysis allows for further splitting of the genotypic correlation coefficient into direct and indirect effects. The path-coefficient was calculated independently for all the essential characters that consider yield as a dependent variable.

A simultaneous equation was used to calculate the pathcoefficient. These equations represent a basic relationship between correlation coefficient and path coefficient. These equations were solved by presenting them in matrix notation

The solution for the vector 'C' may be obtained by multiplying both sides with inverse of 'B' matrix i.e.

After calculation of values of path coefficient i.e. 'C' vector, we can obtain path values for residual (R). The Residual effect was estimated by using the formula suggested by Singh and Choudhary (1985).

Where, d i = direct effect of ith character r ij = Correlation coefficient of i th character with j th character The direct and indirect effect of different characters on yield was calculated at genotypic level.

Parameters under study

Galton (1889) [10] first suggested the use of correlation index to describe the association for effectiveness of indirect selection process. Association between bulb yield and its components characters were estimated at phenotypic (P) and genotypic (G) levels. The results are depicted in Table 1 and Table 2, ilucidated here under. Total yield per hectare had recorded highly positive significant phenotypic correlation with plant height, neck thickness, average weight of bulb, TSS and high significant positive phenotypic correlation with collar thickness, fourth leaf length, fourth leaf width. At genotypic level, total yield per hectare showed significant highly positive correlation with plant height, collar thickness, fourth leaf length, fourth leaf width, neck thickness, weight of 10 outer cloves, average weight of bulb, TSS and high significant positive genotypic coefficient with number of leaves. Path analysis help in determining yield contributing characters.

It is a standardized partial regression coefficient, which is free from unit and is a useful tool for making interpretation. It has immense value to breeder for finding out the direct influence of various character on yield and also help in explaining the total correlation between dependent and independent traits. The genotypic correlation coefficient for bulb yield and its component were partitioned into direct and indirect effect taking marketable yield and total yield as dependent variable in Table 3.

The data revealed that highest positive direct effect on yield was through weight of 10 outer cloves followed by average weight of bulb, plant height, TSS, number of leaves, number of cloves, neck thickness, fourth leaf width and negative indirect effect on yield was through equatorial diameter, polar diamete, fourth leaf length, collar thickness, collar height. [16] .

Result and discussion

References

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