Legume Research

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Principal Component Analysis and Path Coefficient Analysis for Groundnut Yield and Seed Quality Attributes (Arachis hypogaea L.)

B. Sukrutha1, C. Kiran Kumar Reddy2, K.V. Naga Madhuri2, Ch. Bhargava Rami Reddy2, A.R. Nirmal Kumar1, L.N. Vemireddy1, Srividhya Akkareddy2,*
1Sri Venkateswara Agricultural College, Acharya NG Ranga Agricultural University, Tirupati-517 502, Andhra Pradesh, India.
2Institute of Frontier Technology, Regional Agricultural Research Station, Acharya NG Ranga Agricultural University, Tirupati-517 502, Andhra Pradesh, India.
  • Submitted14-11-2022|

  • Accepted18-01-2023|

  • First Online 28-03-2023|

  • doi 10.18805/LR-5075

Background: A complex quantitative characteristic yield is heavily impacted by the environment. The productivity of groundnut can be increased less effectively through direct selection for grain yield. The current study aimed to study the variation among diverse groundnut genotypes.

Methods: Phenotypic data was collected on seven quantitative and six qualitative characters for 24 genotypes under study carried out in randomised block design (RBD). GRAPES software has been used for analysis.

Result: Analysis of variance revealed significant differences among the genotypes for all the characters indicating the prevalence of ample genetic variability within the genotypes. Significant positive associations were observed for primary branches, secondary branches, 100-pod weight, shelling per cent, protein and zinc content. Path analysis revealed that plant height, primary branches per plant, hundred pod weight, shelling percent, protein content and zinc content are the most important characters which could be used as selection criteria for effective improvement of pod yield. Using GRAPES software, Fourteen Principal components are extracted based on mean values of which the first five PCs showed 73.24% variation with eigen values more than 1. Biplot constructed by Principal component analysis revealed Hundred pod weight and hundred kernel weight as important traits for study.
Groundnut is the major oilseed crop of India and is known by several names based on the location such as Pindar in U.S, Monkey nut in UK, Manila nut in Philippines. It contains high oil (45-55 %) and protein (25-30%) content. Globally, it is cultivated in an area of 29.92 Mha with annual production of 55.30 million tonnes and 1851 kg/ha of productivity. India ranks second among different countries in groundnut production with an area of 6.01 million ha, 10.24 million tons of production and 1703 kg/ha of productivity. (Ministry of Agriculture and Farmers Welfare, Govt. of India, 2020-21)​. In Andhra Pradesh, it is cultivated in an area of 0.87 Mha with production of 0.77 Mt and average productivity of 891 kg/ha (AICRP-Annual Report, 2020-2021)​. The nature of the relationship between yield and its constituent parts facilitates the selection of numerous characters simultaneously involved in yield improvement. Yield has a complex personality that is shaped by a lot of interrelated characteristics. These characters’ interdependence will have an impact on kernel yield either directly or indirectly. Path coefficient analysis is utilised for the separation of direct impacts from indirect effects and gives the relationship of the characters.
The present study was carried out in randomised block design( RBD) with 24 groundnut varieties from which 22 varieties viz.,TPT-1, TPT-2, TPT-3, TPT-4,Narayani, Kalahasti, Prasuna, Abhaya, Greeshma, Nithya Haritha, Bheema, Rohini, Dharani, ICGV-00350, Pragathi (TCGS-894), TCGS1073, Kadiri-6, Kadiri-7, Kadiri-8, Kadiri-9, Kadiri Harithandra and Kadiri Amaravathi were released from ANGRAU and other variety TAG-24 released from BARC, Baba Atomic Research Station and JL-24 released from Oilseed Research Station, Jalgoan to investigate direct and indirect effects by path analysis and variation by principal Component analysis. The experimental field work was carried out in Acharya N.G. Ranga Agricultural University (ANGRAU), Regional Agricultural Research Station (RARS), Tirupati in kharif season during the year 2019 employing these 24 genotypes.

Five plants were randomly selected for each genotype from each replication. Observations on seven quantitative parameters viz., plant height (cm), number of primary branches/plant, number of secondary branches/plant, pod yield/plant (g), hundred pod weight (g), hundred kernel weight (g) and shelling percentage were recorded for all the genotypes. Averages of 5 plants were calculated and mean values of three replications were taken for statistical analysis. Statistical analysis such as Path Coefficient analysis and Principal component analysis was carried out using GRAPES software developed by Kerala Agricultural University, Kerala India.
Based on the data recorded on the genotypes in the present investigation, the results of the analysis of variance showed that all of the characters’ differed significantly, demonstrating the prevalence of ample genetic diversity among the genotypes (Table 1).

Table 1: Analysis of variance for yield and seed quality traits in groundnut.


 
Character association study
 
The genotypic and phenotypic correlations were estimated to determine direct and indirect effects of yield and yield contributing characters and presented in Table 2 and Fig 1.

Table 2: Correlation coefficients studied for yield and seed quality characters.



Fig 1: Correlation coefficients for yield and seed quality characters in groundnut.



Pod yield per plant showed positive and significant correlation with primary branch number (0.591), hundred pod weight (0.654), hundred kernel weight (0.694) whereas positive correlation with secondary branch number (0.11) and quality parameters viz., protein content (0.402), sucrose content (0.122), total free aminoacids (0.052), iron (0.315) and zinc content (0.143) which suggests that increase or improvement in these characters lead to improvement in pod yield/ plant (Table 2, Fig 1). Similar kind of significant positive correlation of pod yield/plant with hundred pod weight and protein content was observed by Kumar et al., (2019), Bhargavi et al., (2016) and Shoba et al. (2012). Among the quality traits, as protein content showed a negative correlation with oil content (-0.396).
 
Path coefficient analysis
 
The results of path coefficient analysis of yield and yield contributing characters are presented in Table 3.

Table 3: Direct (diagonal bold) and Indirect effects of component characters on character of interest.



The study of the interactions and relative contributions of many traits to crop development is greatly aided by genetic association. Estimates of correlation coefficients did not reflect the direct and indirect impacts of various features on the yield; they only showed the relationship between yield and yield components. This is so because the attributes that are associated do not exist alone; rather, they are connected to other elements. Dewey and Lu (1959) path coefficient analysis suggests useful assessments of the direct and indirect causes of association and illustrates the relative value of each element contributing to the final yield. The cause-and-effect link between yield as a whole and yield component qualities was looked at using path coefficient analysis in order to obtain the developmental relations.

Plant height had positive direct effect on pod yield per plant (0.246) while the correlation of plant height with pod yield was positive and significant (0.314). The correlation between plant height and pod yield was positive and significant mainly due to positive indirect effect contribution through hundred kernel weight (0.513), shelling percent (0.713), protein content (0.096), seed micronutrient content i.e., Zinc content (0.078). The positive direct effect of plant height on pod yield had been reported by Jain et al., (2016), Raut et al. (2010) and John et al., (2019).

Hundred pod weight exhibited a positive direct effect on pod yield per plant (5.36) while the correlation with pod yield per plant was also positive and significant (0.74). Shelling percentage exhibited a positive direct effect on pod yield per plant (4.22) while the correlation with pod yield per plant was also positive and significant (0.819). Similar findings are seen with Korat et al., (2010), Zaman et al., (2011), Shoba et al. (2012) and Reddy​ et al. (2017a and 2017b). Hundred kernel weight had direct negative phenotypic effect (-1.825) on pod yield per plant. whereas the correlation was negative significant (-0.282). Hundred kernel weight exerted negative direct effect (-1.825) on pod yield per plant as observed earlier by Patel and Shelke (1992).

Oil content had direct positive effect (0.193) on pod yield per plant. Its correlation with pod yield per plant was negative and significant (-0.618). The correlation between oil content and pod yield per plant was negative and significant mainly due to negative indirect effect contribution through plant height (-0.022), number of secondary branches per plant (-0.689), hundred kernel weight (-0.824) and shelling percentage (-2.289). Protein content had direct positive effect on pod yield per plant (0.505) while its correlation with pod yield was positive significant (0.683). The correlation between protein content and pod yield per plant was positive is mainly due to positive indirect effect influence through plant height (0.047), hundred kernel weight (0.043), shelling percent (3.586), sucrose content (0.036) and seed micronutrient Fe (0.039) and Zn content (0.118). Total free aminoacids (-0.216), Total soluble sugars (-0.045) and iron content (-0.202) exerted negative direct effect on pod yield per plant. The lower residual effect (0.0076) indicated that sufficient contribution in pod yield per plant has been explained by the independent variables included in the analysis.

Path coefficient analysis revealed that Hundred pod weight (5.36) exerted the highest positive direct effect on pod yield per plant followed by shelling percentage (4.22), primary branches per plant, hundred pod weight, oil content and protein content. The negative direct effect was showed on pod yield by hundred kernel weight, sucrose content, total soluble sugars and iron content.
 
Principal component analysis (PCA)
 
The PCA based on correlation matrix on the mean values of the groundnut genotypes was performed which provided a reduced a dimension model that could indicate measured differences among the genotypes in the population. The results revealed the importance of first five Principal Components (PCs) in discriminating the groundnut population. Since first five PCs selected as it explains 73.24% of variation and had Eigen values greater than 1. The eigen values and associated cumulative percentage of variation explained by eigen vectors have been presented in Table 4 and Table 5 which shows the scree plot graph (Fig 2) for variation explained by various principal components.

Table 4: Eigen values and proportion of variation for different principal components.



Table 5: Eigen vectors for different Principal components.



Fig 2: Scree plot for variation explained by principal components.



The first principal component gave high positive weight (0.459) to Hundred pod weight and Hundred kernel weight (0.414), similarly second, third, fourth and fifth Principal components gave high positive weights to Shelling percentage (0.458), sucrose content (0.51), secondary branches per plant (0.369) and Iron content (0.443) respectively. From the eigen loadings, the first principal component is strongly correlated with primary branch number, pod yield/plant, hundred pod weight and kernel weight. Out of these, PC1 was most strongly correlated with hundred pod weight and hundred kernel weight.
 
Biplot analysis
 
An attempt has been made to observe the variation explained by seven quantitative and six qualitative characters along one and two principal component vectors i.e., Biplot (Fig 3 and Fig 4).

Fig 3: Genotype by trait Biplot showing distribution of genotypes across first two PCs.



Fig 4: PCA Biplot showing variation among traits.



From Biplot, 14 characters were grouped into five groups. Primary branches per plant, Zinc content, Protein content, Pod yield per plant were grouped in same cluster; hundred kernel weight and hundred pod weight as single group and Secondary branches per plant, total free aminoacids, total soluble sugars and plant height as one group. Those genotypes nearer to each trait can be said as best suited for those traits respectively. The genotypes TAG-24 and Abhaya are best suited for shelling percentage. Genotype Rohini was highly suitable for oil content and Dheeraj for total soluble sugars. Genotype Nithya Haritha was highly suitable for protein content and contributed more to this trait. There is high correlation between hundred kernel weight and hundred pod weight and also between total free Aminoacids and plant height.
Among the yield component traits, significant positive correlations were observed for primary branches/ plant, secondary branches/ plant, hundred pod weight and shelling per cent. Path analysis revealed that plant height, primary branches per plant, hundred pod weight, shelling percent, protein content and zinc content are the most important characters which could be used as selection criteria for effective improvement of pod yield. Biplot constructed by Principal component analysis revealed hundred pod weight and hundred kernel weight as important traits for study. Therefore, it is suggested that preference should be given to these characters in the selection programme to isolate superior lines with genetic potentiality for higher yield in peanut genotypes.
The authors are thankful to Acharya N.G. Ranga Agricultural University for providing the necessary facilities. Also special thanks to the Department of Genetics and Plant Breeding, S.V. Agricultural College, Tirupati.
None

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