Research Article

Exploring the Effect of Phenotypic Variability on Genetic gain in Groundnut (Arachis hypogaea L.) Yield under Semi Drought Conditions

Shiguftah Khalid1, Muhammad Jahanzaib2*, Haris Khurshid2, Rabia Khalid3, Sundas Waqar3, Faiza Siddique3, Fazal Yazdan Saleem Marwat3 and Zahid Akram1

1Department of Plant Breeding and Genetics, PMAS Arid University Rawalpindi, Pakistan and National Agricultural Research Centre Islamabad, Pakistan; 2Oilseeds Research Program NARC, Islamabad, Pakistan; 3National Agricultural Research Centre Islamabad, Pakistan.

Received | March 10, 2022; Accepted |August 31, 2024; Published | September 26, 2024

*Correspondence | Muhammad Jahanzaib, National Agricultural Research Centre Islamabad, Pakistan; Email: m.jahanzaib548@gmail.com

Citation | Khalid, S., M. Jahanzaib, H. Khurshid, R. Khalid, S. Waqar, F. Siddique, F.Y.S. Marwat and Z. Akram. 2024. Exploring the effect of phenotypic variability on genetic gain in groundnut (Arachis hypogaea L.) yield under semi drought conditions. Pakistan Journal of Agricultural Research, 37(3): 300-313.

DOI | https://dx.doi.org/10.17582/journal.pjar/2024/37.3.300.313

Keywords | Groundnut, Arachis hypogaea L., Correlation, Path analysis, Cluster analysis, Character association, Pod yield

Copyright: 2024 by the authors. Licensee ResearchersLinks Ltd, England, UK.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Introduction

Groundnut (Arachis hypogaea L.) is the important oilseed crop and ranked sixth. Higher and denser energy levels of protein, fats, and many other healthy nutrients for lesser consumption Singh, A., et al. (2021)., increase the importance of peanuts in terms of reducing weight and heart diseases because primary fats in peanuts are monounsaturated (Nunes et al., 2023). A coenzyme Q10 in peanuts is known for protecting the heart during oxygen deficiency (Marappan, G. 2020). Moreover, certain unsaturated fats, vitamins, minerals and bioactive components in peanuts are known to have effects of cancer prevention (Gonzalez and Salvado 2006), such as phytosterols (Woyengo et al., 2009) and resveratrol (Nabavi et al., 2014) were studied in this regard. Awareness of these important benefits of peanut crops can reduce the consumption of undesirable supplements. There is a need to improve and increase the peanut production with best breading strategies keeping in mind the nutrients, oil quality and crop yield (Arya et al., 2016).

Groundnut is signified for different production conditions, such as intercropping and rotation with cereals. Still, due to drought, the low availability of soil nutrients to the crop caused a decrease in its production. Results have shown a reduction in yield contributing parameters like a pod and seed number, pod weight and seed weight, pod filling rate, and harvest index in water stress. However, the principal component analysis revealed a high pod filling rate, good harvest index and good seed yield in genotypes like 55-437, ICGG-4750 and ICG-12697. These parameters can be taken to select genotypes in water deficit conditions (Karim et al., 2020). This in turn reduces overall production and yield of the groundnut unlike other major crops. The groundnut production is highly and variably reduced by the major limiting factors instead of other agro-climatic conditions (Jahanzaib et al., 2019). Globally groundnut production increased marginally in the last decade (2000-2010). About 70% of groundnut is grown under arid and semi-arid areas, where groundnut crop frequently faces drought stresses for different durations and intensities (Nadeem et al., 2024) together with high irradiance and temperature (Kambiranda et al., 2011). Almost 56- 85% yield losses are observed in drought stress, intensity and duration (Ravi et al., 2011), thereby causing a considerable decrease in crop performance respecting plant persistence, economic yield and quality (Hall 2001).

The number of pods per plant and hundred seed weight were indicated as chief traits under path analysis for realizing the progress in yield. Omar and Aisha (2021) noted that kernel weight had negative effect on pod yield through the indirect effect of no. of pods per plant, seeds per pod and shelling percentage. Moreover, economically important traits such as, kernel yield, no. of pods per plant, hundred kernel weights, and dry haulm yield were observed positively and significantly associated with pod yield (Rao et al., 2014). The dry pod yield per hectare revealed a high direct effect towards yield, and a negative relationship was established with days taken to flowering, pod per plant, shoots length, shelling percentage, and specific leaf area Thakur et al. (2013). Moreover, kernel yield per hectare, mature pods per plant, number of branches and plant height were observed with high heritability and genetic coefficient of variation values (Zaman et al., 2011). Kumar et al. (2012), Alam et al. (2014), Kumar et al. (2014) evaluated many characters for pod yield. High magnitude of genotypic correlation coefficients was observed than the corresponding phenotypic correlation coefficients. Mohapatra, and Khan (2020) investigated the relationship between yield-related traits and physiological traits by studying four F3 crosses of three groundnut genotypes. They found a positive and highly significant association of kernel yield per plant and shelling percentage with dry pod yield per plant which are important characters to consider in the selection point of view. Whereas, path analysis in this study facilitates assessing yield-related parameters e.g kernel yield per plant with a high value of positive and direct effect, which can be used to select genotypes with high yield in the next generation. Luz et al. (2011) identified a substantial immediate impact of the number of pegs in the lower plant third on the number of mature pods through a path analysis study. These findings showed the importance of the use of the number of pegs in the lower plant third in the selection of peanut populations for several mature pods. The relationship between yield and its related traits has been exploited by selecting and analyzing traits between yield and its related traits in groundnut genotypes. Correlation, path analysis and multivariate analysis revealed that plant height, hundred seed weight, number of pods per plant and number of secondary branches contribute directly to grain yield. Diversity among the genotypes has been shown through cluster analysis (Nelson et al., 2020). Hence, an effective selection program can be designed to understand the associations among yield components (John et al., 2015).

Yield loss due to water stress can be controlled by selecting and developing such varieties that are better adapted to water-limited conditions (Ravi et al., 2011). Knowledge of physiological and molecular genetics makes understanding about the stress response and helps to develop stress-tolerant varieties. Aminifar et al. (2013) studied the physiology and features of the plant that were correlated with drought tolerance. He observed that water use efficiency (WUE) deals with soil water use efficiency for biomass production, and it was considered an important drought avoidance trait. Mahmood et al. (2019) proposed a genetic modification technique, such as CRISPR/Cas9, for characterization of stress-related genes and epigenomic techniques to understand the stress biology thus helping in developing drought stress tolerant high yielding crop varieties. Different plant breeding methods such as Genomic modification techniques, Molecular markers, Gene expression and Proteomic approaches for constructing high yielding groundnut crops should also be employed. The demand for food security needs genetically improved varieties and cultivars and genetically variability being used as a best option in the past for higher production (Jahanzaib et al., 2020). The main objectives of peanut breeding programs include improvements in seed yield, oil production per unit area, resistance to major diseases and pests, resistance to drought, and plant architecture.

The present study was designed to determine the correlation and path analysis keeping in view the importance of the genetic association among the traits indicating the yield. In peanut breeding program pod yield is an important character under consideration (Woyann et al., 2019) and the selection for desirable genotypes depends on variation present in the population (Andrade, 2019), (Vinithashri et al., 2019), which can be measured employing range, standard error and variance to identify the desired traits (Dinesh et al., 2018). The yield of the groundnut is contributed by polygenic components, which are influenced by environmental challenges and fluctuations (Santos et al., 2014). Therefore, it is important to estimate the breeding value of genotype by separating the phenotypic variation into genetic and environmental component variance and the extent of correlation between yield supporting traits. There is a chance that there may be no specific gene for yield and selection based on the only yield is not effective (Kamdar, et al., 2020). Here correlations provide a picture to understand component characters contributing yield. However, a path analysis study has little correlation difference; it divides correlation coefficients into direct and indirect effects on final yield (Dewey and Lu, 1959; Venkataravana et al., 2020).

Materials and Methods

Plant Material

The experiment was conducted in the Department of Plant Breeding and Genetics, Pir Mehar Ali Shah, Arid Agriculture University, Rawalpindi during Kharif, 2020 with RCBD Design 15 genotypes i.e. BARI 2011, Chakori, 02CG002, BARD-479, Golden, N0334, Banki, abCG005, BARI-2000, BARI-89,11CG005, Chico, 10CG003, Majalaya super and Majalaya with fourreplication

 

Table 1: Genotypes Used in the Experiment.

Sr.No	Genotypes	Sr.No	Genotypes
1	BARI 2011	9	BARI-2000
2	Chakori	10	BARI-89
3	02CG002	11	11CG005
4	BARD-479	12	Chico
5	Golden	13	10CG003
6	N0334	14	Majalaya super
7	Banki	15	Majalaya
8	abCG005

 

Experimental Conditions

Plant × plant and row × row distance was maintained at 12cm and 45cm respectively. The experiment was conducted under rainfed conditions with all suitable agronomic practices. Observations were recorded for 15 morphological characters namely leaf area, leaflet area, number of pegs per plant, number of primary branches, number of secondary branches, plant height, pod weight per plant, shelling percentage, hundred seed weight, number of pods per plant, pod length, number of mature pods per plant, percentage of pegs converted into pods, percentage of pegs converted into mature pods, percentage of mature pods.

The land was brought to fine, and a suitable seed bed was prepared with one deep plowing one month before sowing. The previous crop residues and weeds were removed and the land was leveled.

Under study genotypes of groundnut was obtained from Barani Agriculture Research Institute Chakwal. To control seed borne diseases, the seeds were treated with fungicide Benlate and Vitavax (2 gm /kg of seed).

 

Data Collection

The data were recorded for the following parameters.

S.N.	Parameters	Abbreviation
1	Leaf Area	LA
2	Leaf Area of Leaflets	LLA
3	Number of Primary Branches	PB
4	Number of Secondary Branches,	SB
5	Plant Height (cm)	PH
6	Number of Pegs per Plant	Pegs -1
7	Pod Weight per Plant	PWP
8	Shelling Percentage	SP Shelling percent = Kernel weight (g) * 100 /pod weight (g)
9	Hundred Seed Weight	HSW
10	Number of Pods per Plant	Pods -1
11	Pod Length	PL
12	Number of Mature Pods per Plant	MPP-1
13	Percentage of pegs converted into pods	PPCP
14	Percentage of pegs converted into mature pods	PPCMP
15	Percentage of mature pods	PMP

 

Statistical Analysis

The means calculated from the recorded data for different selected parameters were analyzed statistically and compared at a 5% level of least significant difference following Steel et al. (1997) to determine the significance of the traits studied. Genetic parameters, correlation coefficients were calculated according to the method suggested by Singh and Chaudhary (1979). The significance of the genotypic correlation has tested the help of standard errors as suggested by Kwon and Torrie (1964) and Reeve and Rao (1981). Path analysis measures the direct effect of one variable upon another and enables the splitting of correlation coefficients into components of direct and indirect effects (Dewey and Lu, 1959). Cluster analysis and principal component analysis have been computed to determine relationships and variability among the accessions (Gan et al., 2007, Nelson et al., 2020).

Results and Discussion

Mean Performance

The mean values and range of pod yield and other traits of 15 peanut genotypes during the period of study are given in Table 2. In the present study, mean values of LA among peanut genotypes ranged from 21.52 to 47.83 (cm2), with a mean value of 31.96 (cm2). The maximum value for LA was observed in Majalaya. According to Kiniry et al. (2004) leaf area is an important parameter concerning proper light interception and radiation use efficiency. The no. of PB and SB of given genotypes ranged from 6 to 10 and 4 to 5 respectively, with mean values of 8 and 5 (Table 2). The mean value of genotypes BARD-479 and Majalaya had 10, whereas genotypes N0.334 and 10CG003 showed the minimum number of PB (6). The PH of 15 genotypes ranged from 11.75 to 17.00 cm, with a mean value of 14.16 cm (Table 2). The genotype 02CG002 had 17.00 cm of PH. Whereas, in Chico minimum of 11.75 cm, PH was recorded. The Pegs-1 of 15 genotypes was ranged from 34.25 to 54.5, with a mean value of 44.27 (Table 2). The genotype abCG005 had a maximum no. of Pegs-1 54.5, whereas the minimum no. of pegs of 34.25 per plant was recorded in Chakori.

The Pods-1 of under-study genotypes ranged from 17.75 to 42 (Table 2). Maximum no. of Pods-1 was recorded in Golden (42.00) and N0334 (40.5), whereas Chico showed a minimum no. of Pods-1 with 17.75. The PPCP of 15 genotypes was seen to be range from 52.83 to 83.2%, with a mean value of 74.55% (Table 2). The recorded PPCP in genotype BARI-89 was 83.20, whereas in Chico of minimum 52.83 PPCP. The MPP-1 and PPCMP of under study genotypes ranged from 11.25 to 28.25 and 30.58 to 69.85, respectively with a mean value of 19.98 and 48.25% (Table 2). Maximum no. of MPP-1 was recorded in Golden with 28.25 whereas 10CG003 had the minimum number of MPP-1 (11.25). The number of mature pods determines the actual pod yield. According to the percentage of pegs conversion in mature pods maximum percentage was seen in BARI-89 with 69.85%, whereas, Majalaya had the minimum PPCMP of 30.58%. The PWP for 15 given genotypes ranged from 11g to 33.25g, with a mean value of 26.33 (Table 2). The genotype which showed maximum PWP was N0334 with 33.25 g. The minimumo. recorded PWP was 11.00g in genotype Chico.

 

The SP per plant of under-study genotypes ranged from 61.38 to 71.10% with the mean value of 65.94 (Table 2). The maximum recorded SP was in genotype Chico with 71.10%, whereas genotype Golden showed a minimum percentage of 61.38%. The APL per plant ranged from 1.92 to 3.24 with a mean value of 2.8 (Table 2). The genotype which showed the maximum APL per plant was 10CG003 with 3.24 cm, whereas the minimum pod length was recorded in genotype Chico with 1.92cm. HSW per plant of studied genotypes ranged from 30.73g to 53.08g with an average value of 40.89g (Table 2). The maximum HSW was calculated in genotype Banki with 53.08g, whereas genotype Majalaya showed a minimum HSW with 30.73g. Higher HSW in genotypes like Banki (58.17g), Chakori (52.1g) and BARI-2000 (52.1g) was recorded. Analysis of variance indicated highly significant differences among all genotypes except PH and PPCP for pod yield and yield-related traits. In contrast, non-significant differences were observed for replications except PWP and HSW (Table 2). This showed that the influence of the environment was less on traits.

Trait Association and Correlation Coefficient

The results revealed by the correlation coefficient study between 15 genotypes are given in Table 3. A highly significant difference is present between all the genotypes for under study attributes with higher genotypic correlation coefficients than phenotypic correlation coefficient, which indicate less environmental influences. Pod yield showed positive significant genotypic correlation with PH (r=0.80**), number of Pegs-1 (r=0.53*), number of Pods-1 (r=0.73**), PPCP (r=0.89**), MPP-1 (r=0.74**), APL (r=0.60*), HSW (0.74**). Similarly, a positive and highly significant genotypic correlation was observed between the number of Pods-1 and number of Pegs-1 (r=0.83**), number of Pods-1 and MPP-1 (r=0.66**). The negative significant genotypic correlation coefficient of pod yield with LA (r=-0.73**) and LLA (r=-0.91**) was also observed. Negatively recorded significant genotypic correlation coefficient between the SP and APL was r =-0.64*. The phenotypic correlation coefficient results showed that number of Pods-1 (r=0.57*) and MPP-1 (r=0.56*) had a significant and positive correlation with pod yield. At the same time, the phenotypic correlation of pod yield with all other characters was non-significant.

Direct and Indirect effect of Traits on Yield

The estimates of direct and indirect effects of LA, LLA, PB, SB, PH, PPCP, MPP-1, PPCMP, PMP, PWP, SP APL and HSW upon pod yield are given

 

 

 

in Table 4. According to path analysis study, it was shown that the number of Pods-1 (3.99), PPCP (0.63), PPCMP (0.31), PMP (1.19), SP (0.63), APL (1.07) and HSW (1.53) had important direct and positive contribution towards pod yield. Whereas, LA (-6.28), PB (-0.68), SB (-0.45), PH (-0.29), number of Pegs-1 (-0.59), and MPP-1 (-4.10) also appeared to influence the yield negatively and directly (Table 4).

Variation among yield and yield related traits in peanut genotypes

Principal component analysis (PCA) biplot has been demonstrated in (Figure 1) to check the variability among genotypes for selected traits. Biplot indicates overall variability of 57.34% among genotypes. It has been observed that APL, SB and SP have shown more significant variability for peanut genotypes overall. The most important yield contributing trait, PPCP, also had greater variability in genotypes Chakori, 02CG002, BARI-2000, and Golden and also closely associated with Pod-1 and PWP.

Genotypes Majalaya, 10CG003 and Majalaya. Sare showing variability for Leaf area (LA) and Primary branches (PB), while genotypes BARI 2011, BARI 89, and Chico have shown variation for shelling percentage with large vector. Genotypes including Chakori, 02CG002, BARI-2000 and Golden depicted considerable variation in Plant Height and pod-1 while greater variation for Average pod length showed a larger vector. Percentage of pegs converted into pods and pod weight per plant have shown considerably less variation than APL, PH and Pod-1 for these genotypes. Genotypes N0334, Banki, 11CG005 and abCG005 had a greater variation for

 

secondary branches (SB), whereas, PMP and MPP-1 have shown considerably greater variation for these genotypes, whereas PPCMP, Peg-1 and HSW have shown less variation for these genotypes. It was observed that, HSW, Pegs-1 and PPCMP had less variation in genotypes, but in genotypes Banki, 11CG005 and abCG005 a considerable magnitude of variability has been observed for these traits.

As indicated in the biplot, genotypes in cluster I are showing closer association of leaf area, leflet area and primary braches. Moreover, results have also shown that genotypes falling in cluster II have shown a maximum number of Pods-1, which is the most economically important character. Whereas genotypes falling in cluster III were also important regarding producing maximum number of pegs and hundred seed weight. The maximum number of pegs must be converted into mature pods from the total number of pegs produced; genotypes falling in cluster III have shown this property.

Regarding the association between parameters, there are also three clusters (Figure 2). In cluster I, there are three parameters LA, LLA, PB and there are four parameters in cluster II including SB, SP, PPCMP and PMP, while seven parameters such as Peg-1, Pods -1, MPP-1, PWP, PH, HSW, PPCP and ALP in cluster III have shown closer relation. The results are shown bycorrelation analyses also have shown the closer association of the above-mentioned arameters.

Genotypes were holding a significant variation for considered traits suggesting the importance of these candidate traits for the selection of genotypes regarding yield. Results provide the information of the amount of association of characters in correlation and splitting the correlation value into indirect and direct effects of characters to yield is useful for selection of traits in a breeding program.

The recorded percentage of pegs converted into pods in genotype BARI-89 was 83.20. Similarly, in Chico minimum of 52.83 percent pegs were converted into pods. Yadava et al. (1981) reported that an increased number of pods are not always the trait in favor of increased yield. An increase in number may decrease the seed size. Viability of pegs and time is also very important in relation to pods converted into proper pod. Sometime pegs cannot reach the soil till the proper time; this phenomenon is not in favors of high yield related to pegs. Similar findings were exhibited by Luz et al. (2011).

Number of mature pods determines the actual pod yield. According to percentage of pegs conversion in mature pods maximum percentage was seen in BARI-89 with 69.85%, whereas, Majalaya had a minimum percentage of pegs converted into mature pods of 30.58%. Attributes of mature pods are hard ls, filled and not shrunken. These features also favor quality and safety from fungal attack. Many previous researchers, Kumar (2004) and Mahalaksmi et al. (2005) emphasized mature pods for the improvement of groundnut yield. It is evident that a number of pegs present at the lower portion of the groundnut plant penetrate well in the soil and can easily reach to the soil surface and the possibility of the number of pegs conversion into mature pods is increased (Godoy et al., 1999 and Santose et al., 2000; Jahanzaib et al., 2020).

The maximum recorded shelling percentage was in genotype Chico with 71.10%, whereas genotype Golden showed a minimum shelling percentage with 61.38%. Effects of shelling percentage and mature kernels and shelling percentage through 100 seed weight exhibit positive results towards yield. Higher 100 seed weight in genotypes like Banki (58.17g), Chakori (52.1g) and BARI-2000 (52.1g) was recorded. Thus higher 100 seed weight can be attributed to high vigor of seeds of different peanut genotypes. These results are following the findings of Alam et al. (2014); Jeyaramraja et al. (2014).

(Arachis hypogaea L.), pod yield is a complex trait and depends upon the interaction between number of components traits. In this way, a correlation study helps understand each component’s contribution to yield.

Association among the yield and yield related traits

Several important yield contributing characters were associated with each other and with yield, showing significant and highly significant genotypic correlation such as the number of pegs per plant (pegs-1), percentage of pegs converted into pods (PPCP), mature pods per plant (MPP-1) and average pod length (APL) showed positive and significant correlation with yield. These results could be proved helpful in the plan of improvement of character components of yield by taking these characters simultaneously in the next breeding program (Shukla et al., 2014; Kumara et al., 2015; Jain et al., 2016; Ramakrishnan et al., 2017; Trivikrama et al., 2017; Rajarathinam et al., 2017; Venkataravana et al., 2020).

Information of extent of relationship between various characters is important for plant breeding researchers, to accomplish the goal of increased production by enhancing the yield potential of crop. In case of groundnut (Arachis hypogaea L.) pod yield in is a complex trait and depends upon the interaction between numbers of components traits. In this way correlation study helps in understanding the contribution of each component on yield.

LA and LLA were negative and highly significantly correlated with pod yield, indicating that increased vegetative growth may cause a decrease in pod yield. PB and SB had non-significant and negative genotypic correlation; similar results were reported by (Kumar et al., 2014). Similarly, the results of other characters like PH, number of Pegs-1 and MPP-1 were positive and significant among themselves and with pod yield found in conformity with (Sadeghi et al., 2012; Zaman et al., 2011; Luz et al., 2011). MPP-1, number of Pods-1, HSW, PH were significantly and positively associated with pod yield which directed that these components are important for improving pod yield in groundnut crops. Similar results have been reported by Ofori et al. (1996) regarding no. of mature pods per plant and total pods per plant respectively. Due to the pleiotropic effect and tight linkage between different characters, correlation arises. Thus, an association of yield-related characters is considered important in selecting preferred genotypes (Hampannavar et al., 2018).

Direct and indirect effect of yield related traits

The correlation only provides the amount of relationship of characters, whereas at the same time, path analysis measures the direct effect of one variable upon another and enables the splitting of correlation coefficients into components of direct and indirect effects (Dewey et al., 1959).

The Pegs-1, Pods-1, HSW, APL, PPCMP and MPP-1 are important traits under path analysis for taking in the improvement of yield. Hampannavar et al. (2018); Kumar et al. (2014) also observed that MPP-1 effect on pod yield greatly but indirectly through kernel yield indicating the significance of these traits in the enhancement of seed yield.

LA, number of Pegs-1, PPCP and MPP-1 had a positive direct effect greater than the genotypic correlation coefficient. Similarly No. of Pods-1 and PPCMP have shown negative direct and greater effect than correlation coefficient which indicated that correlation is due to direct effect. While, PB, SB and SP had lesser negative direct effect and PH, APL and HSW demonstrated positive direct effect lesser than correlation coefficient which indicated that correlation is due to indirect effects (Shoba et al. (2012), (Thirumala Rao. (2016); Rajarathinam et al. (2017); Trivikrama Reddy et al., 2017).

These results were in agreement with those of Durga Rani et al. (1987) for the number of pods, shelling percentage and 100- seed weight and Ofori (1996) for the number of pods per plant, Vasnathi (1998) for 100-pod weight and Arjunan et al. (1999) for leaf area.

Conclusion and Recommendations

The mean values for genotypes N0334, abCG005 and BARI-2000 had maximum pod yield per plant among all the genotypes. In correlation studies yield related important parameters e.g No. of Pegs-1, No. of pods-1, MPP-1 demonstrated very highly significant and positive correlation with pod yield. The results of this correlation study can provide information for future breeding plans. No. of Pegs-1, PH, PPCP and HSW were directly associated with yield. Path coefficient analysis results can be proved helpful in following careful restricted simultaneous selection model in case of higher direct effect and case of a lesser direct effect than correlation coefficient then it indicated that correlation is due to indirect effects. In Biplot analysis 02CG002, BARI-2000 and Golden have shown variation for Pod-1 and PWP and N0334, Banki, 11CG005 and abCG005 abCG005 had a considerably greater variation for PMP and MPP-1 whereas, PPCMP, Peg-1 and HSW have also shown variation but in less amount. This analysis has indicated the genotypes showing variation for different traits. Genotypes clustered in groups have shown their closer association regarding parameters selected. Parameters clustered in groups have also demonstrated this relationship in correlation analysis which can be proved helpful in selection in further evaluation.

Novelty Statement

The mean values for genotypes N0334, abCG005 and BARI-2000 had maximum pod yield per plant among all the genotypes. In correlation studies yield related important parameters e.g No. of Pegs-1, No. of pods-1, MPP-1 demonstrated very highly significant and positive correlation with pod yield.

Author’s Contribution

Shiguftah Khalid: Execute research plan.

Muhammad Jahanzaib: Wrote manuscript and logistics.

Haris Khurshid: Statistical analysis.

Rabia Khalid, Sundas Waqar and Faiza Siddique: Collected data.

Fazal Yazdan Saleem Marwat: Agronomic management.

Zahid Akram: Plan and supervise research.

Conflict of interest

The authors have declared no conflict of interest.

References

Alam, M.S., A.R.M.S. Rahman and A.B.M.A. Khair. 1985. Genetic variability and character association in groundnut (Arachis hypogaea L.). Bot. J. Agric., 10(4):9-16.

Arya, S.S., A.R. Salve and S. Chauhan. 2016. Peanuts as functional food: a review. J. food Sci. technol., 53(1), 31-41. https://doi.org/10.1007/s13197-015-2007-9

Alam, A. M., Somta, P., & Srinives, P. (2014). Generation mean and path analyses of reaction to mungbean yellow mosaic virus (MYMV) and yield-related traits in mungbean (Vigna radiata (L.) Wilczek). SABRAO Journal of Breeding & Genetics, 46(1).

Aminifar, J., Mohsenabadi, G., & Biglouei, M. H. (2013). Effect of zinc and potassium priming and salinity stress on germination and early seedling growth of corn (Zea mays L. VAR SC 704). International Journal of Agriculture, 3(3), 580.

Andrade, L. R. B. D., Sousa, M. B. E., Oliveira, E. J., Resende, M. D. V. D., & Azevedo, C. F. (2019). Cassava yield traits predicted by genomic selection methods. PLoS One, 14(11), e0224920.

Arjunan, P., Silsbee, M. R., & Roy, D. M. (1999). Sulfoaluminate-belite cement from low-calcium fly ash and sulfur-rich and other industrial by-products. Cement and Concrete Research, 29(8), 1305-1311.

Baker, R.J. 1986. Selection indices in plant breeding. Boca Raton, F1. CRC Press, Inc. 218.

Balakisan, P. 1979. Studies on the genetic parameters in the F2 generation of eight crosses of groundnut (Arachis hypogaea L.). M.Sc. Ag. Thesis, Andhra Pradesh Agricultural University, Hyderabad.

Bhagat, N.R., T. Ahmad, H.B. Lalwani and G. Natraj. 1986. Variation in character asociation and path analysis in improved groundnut varieties. Indian J. Agric.Sci., 56:300-302.

Coffelt, T.A and R.O. Hammons. 1974. Correlation and heritability study of nine characters in parental intra – specific cross population of Arahis hypogaea L. Oleagineux 29:23-27.

Deshmukh, S.N., M.S. Basu and P.S. Reddy. 1986. Genetic variability, character association and path coefficient of quantitative traits in Virginia bunch varieties of groundnut. Indian J. Agric. Sci., 56:816-821.

Dewey, D.R. and K. Lu. 1959. A correlation and path-coefficient analysis of components of crested wheatgrass seed production. Agron. J., 51(9): 515-518. https://doi.org/10.2134/agronj1959.00021962005100090002x

Dinesh, H. B., Lohithaswa, H. C., Viswanatha, K. P., Singh, P., Manjunatha, L., Ambika, D. S., & Kumar, M. K. (2018). Genetic analysis and marker assisted backcrossing for transfer of mosaic virus resistance in cowpea [Vigna unguiculata (L.) Walp.]. Legume Research-An International Journal, 41(5), 663-668.

Durgarani, C.M.V., D. Subramanyam, N.S.R. Reddy and G.V.H. Rao. 1987. Correlation and path analysis in groundnut (Arachis hypogaea L.). The Andhra Agric. J., 34:321-324.

Gan, G., C. Ma, and J. Wu. 2007). Data clustering: Theory, algorithms, and applications (pp. 466). Siam. https://doi.org/10.1137/1.9780898718348

Godoy, E., Yáñez, G., & Vera, E. (1999). Inversion of an Oligocene volcano-tectonic basin and uplifting of its superimposed Miocene magmatic arc in the Chilean Central Andes: first seismic and gravity evidences. Tectonophysics, 306(2), 217-236.

González, C. A., and Salas-Salvadó, J. 2006. The potential of nuts in the prevention of cancer. British J. Nutri., 96(S2), S87-S94.

Hall, A. 2001. Considerations of crop responses to environment in plant breeding. Crop Responses to Environment. Baco Ratan, Florida, USA: CRC press LLC:197-208.

Hampannavar, M.R., H. Khan, B.V. Temburne, P.Janila, and A. Amaregouda. 2018. Genetic variability, correlation and path analysis studies for yield and yield attributes in groundnut (Arachis hypogaea L.). J. Pharmacogn. Phytochem., 7(1), 870-874.

Iezzoni, A.F., M.P. and Pritts, 1991. Applications of principal component analysis to horticultural research. Hort Sci., 26(4), 334–338. https://doi.org/10.21273/HORTSCI.26.4.334

Isleib, T.G., J. Wynne, J.C. Wynne and S.N. Nigam. 1994. Groundnut breeding. The Groundnut Crop, Springer :552-623. https://doi.org/10.1007/978-94-011-0733-4_14

Jahanzaib, M., N. Nawaz, M. Arshad, H. Khurshid, M. Hussain and S.A. Khan. 2020. Genetic; variability, traits association and path coefficient analysis in advanced lines of groundnut (Arachis hypogaea L.). J. Innovative Sci., 7(1): 88-97. https://doi.org/10.17582/journal.jis/2021/7.1.88.97

Jahanzaib, M., N. Nawaz, H. Khurshid, S.A. Jan, M. Arshad and I. Hassan, 2019. Estimating genotype × environment interaction for groundnut seed yield across different ecological zones. Intl. J. Agric. Biol., 22: 139‒145.

Jain, S., Singh, P.B. and P.P Sharma. 2016.Correlation and path analysis in groundnut (Arachis hypogaea L.). Int. J. Curr. Res., 8(08): 35811-35813.

Jayalakshmi V., C.R. Reddy, P.V. Reddy and G.L.K. Reddy. 2000. Character association among morphological attributes in parental genotypes and groundnut hybrids. Legume Res., 23:102-105.

Jeyaramraja, P.R. and F. Woldesenbet. 2014. Characterization of yield components in certain groundnut (Arachis hypogaea L.) varieties of Ethiopia. J. Exp. Bio. Agric. Sci., 2(6):2320–8694.

John, C. C., Carabin, H., Montano, S. M., Bangirana, P., Zunt, J. R., & Peterson, P. K. (2015). Global research priorities for infections that affect the nervous system. Nature, 527(7578), S178-S186.

Kambiranda, D. M., Vasanthaiah, H. K., Katam, R., Ananga, A., Basha, S. M., and Naik, K. 2011. Impact of drought stress on peanut (Arachis hypogaea L.) productivity and food safety. Plants Environ., 1, 249-272.

Kamdar, J.H., M.D. Jasani, S.K. Bera, and J.J. Georrge. 2020. Effect of selection response for yield related traits in early and later generations of groundnut (Arachis hypogaea L.). Crop Breeding and Appl., 20(2). https://doi.org/10.1590/1984-70332020v20n2a31

Kanwal, N., F. Ali, Q. Ali, H.A. Sadaqat. 2019. Phenotypic tendency of achene yield and oil contents in sunflower hybrids. Acta Agriculturae Scandinavica, Section BSoil & Plant Sci., 2019; 69(8):690-705. https://doi.org/10.1080/09064710.2019.1641546

Karim, T.D.A., A.T.T.A. Sanoussi, I.M. Maârouhi, H.A.M.I.D.O.U. Falalou and B.A.K.A.S.S.O. Yacoubou. 2020. Agro-morphological response of some groundnut genotypes (Arachis hypogaea L.) in water deficit conditions. Afr. J. Agric. Res., 16(5), 622-631. https://doi.org/10.5897/AJAR2020.14775

Kiniry, J. R., Bean, B., Xie, Y., & Chen, P. Y. (2004). Maize yield potential: critical processes and simulation modeling in a high-yielding environment. Agricultural Systems, 82(1), 45-56.

Kumar, D. R., M.R. Sekhar, K.R. Reddy and S. Ismail. 2012. Character association and path analysis in groundnut (Arachis hypogaea L.). Int. J. Appl. Biol. Pharm. Technol., 3(1):0976-4550.

Kumar, A., Dixit, S., Ram, T., Yadaw, R. B., Mishra, K. K., & Mandal, N. P. (2014). Breeding high-yielding drought-tolerant rice: genetic variations and conventional and molecular approaches. Journal of experimental botany, 65(21), 6265-6278.

Kumar, H. 2004. Estimation of Genetic Parameters and Association Analysis of Yield and Yield Attributing Traits of sorghum (Sorghum bicolor L. moench). Seed, 900, 100.

Kumara, A. D. N. T., Chandrashekharaiah, M., Kandakoor, S. B., & Chakravarthy, A. K. (2015). Status and management of three major insect pests of coconut in the tropics and subtropics. New horizons in insect science: Towards sustainable pest management, 359-381.

Kuriakose, K.P. and C.A. Joseph. 1986. Variability and correlation studies in groundnut. Agric. Res.J. Kerala. 24:101-110.

Kwon, S. H., & Torrie, J. H. (1964). Heritability of and interrelationships among traits of two soybean populations 1. Crop science, 4(2), 196-198.

Lakshmidevamma, T.N., M. Byregowda and P. Mahadevu. 2004. Character association and path analysis in groundnut (Arachis hypogaea L.). Mysore J. of Agric. Sci., 38:221-226.

Luz, L.N., R.C. Santos and P.A.l.M. Filho. 2011. Correlations and path analysis of peanut traits associated with the peg. Crop Breeding and Appl. Biotechnol., 11:88-93. https://doi.org/10.1590/S1984-70332011000100013

Mahmood, T., S. Khalid, M. Abdullah, Z. Ahmed, M.K.N. Shah, A. Ghafoor and X. Du. 2020. Insights into Drought Stress Signaling in Plants and the Molecular Genetic Basis of Cotton Drought Tolerance. Cells. 9(1), 105. https://doi.org/10.3390/cells9010105

Mahmood, T., Khalid, S., Abdullah, M., Ahmed, Z., Shah, M. K. N., Ghafoor, A., & Du, X. (2019). Insights into drought stress signaling in plants and the molecular genetic basis of cotton drought tolerance. Cells, 9(1), 105.

Manoharan, V., S. Kalaimani and R.R. Sethupathi. 1993. Path analysis in the F2 population of inter-subspecific cross in groundnut. Madras Agric. J., 80:472-474.

Mohapatra, N., and H. Khan. 2020. Character association and path analysis in F3 segregating generations for yield and its component traits in groundnut (Arachis hypogaea L.). J. Pharmacogn. Phytochem., 9(4), 794-799. https://doi.org/10.20546/ijcmas.2020.907.266

Mubai, N., J. Sibiya, J. Mwololo, C. Musvosvi, H. Charlie, W. Munthali and P. Okori. 2020. Phenotypic correlation, path coefficient and multivariate analysis for yield and yield-associated traits in groundnut accessions. Cogent Food & Agric., 6(1), 1823591. https://doi.org/10.1080/23311932.2020.1823591

Mahalakshmi, P., Manivannan, N., & Muralidharan, V. (2005). Variability and correlation studies in groundnut (Arachis hypogaea L.). Legume Research-An International Journal, 28(3), 194-197.

Marappan, G. 2020. Coenzyme Q10: Regulators of Mitochondria and beyond. In Apolipoproteins, Triglycerides and Cholesterol. London, UK: IntechOpen. https://doi.org/10.5772/intechopen.89496

Nabavi, S., Rafraf, M., Somi, M. H., Homayouni-Rad, A., & Asghari-Jafarabadi, M. (2014). Effects of probiotic yogurt consumption on metabolic factors in individuals with nonalcoholic fatty liver disease. Journal of dairy science, 97(12), 7386-7393.

Nadeem, F., A. RehmanUllah, A., Farooq, M., and K.H. Siddique. (2024). 7 Managing Drought in Semi-Arid Regions through Improved Varieties and Choice of Species. In Managing Soil Drought (pp. 212-234). CRC Press. https://doi.org/10.1201/b23132-7

Nagabhushanam, G.V.S., D. Subramanyam and N.S.R. Reddy. 1982. Studies on variability, heritability and genetic advance in groundnut (Arachis hypogaea L.). The Andhra Agric. J., 29:264-267.

Nelson, C. A., Bhutta, Z. A., Harris, N. B., Danese, A., & Samara, M. (2020). Adversity in childhood is linked to mental and physical health throughout life. bmj, 371.

Nigam, S.N., S.L.D. Vedi, T.S.N. Sigamani and R.W. Gibbons. 1984. Character association among vegetative and reproductive traits in advanced generations of inter sub-specific and inter-specific crosses in peanut. Peanut Sci., 11:95-98. https://doi.org/10.3146/i0095-3679-11-2-12

Nunes, Y. C., G. de Oliveira Santos, N.M. Machado, A.M. Otoboni, L.F. Laurindo, A. Bishayee and S.M. Barbalho. 2023. Peanut (Arachis hypogaea L.) seeds and by-products in metabolic syndrome and cardiovascular disorders: A systematic review of clinical studies. Phytomedicine, 155170. https://doi.org/10.1016/j.phymed.2023.155170

Ofori, I. 1996. Correlation and path coefficient analysis of components of seed yield in Bambara groundnut (Vigna subterranean). Euphytica (91):103-107. https://doi.org/10.1007/BF00035280

Omar, G. and A.M. Aisha. 2021. Character Association And Path Coefficient Analysis For Yield Components And Pod Yield Of Groundnut [Arachis hypogaea (L.)] In Sudan Savanna Of Nigeria. Fudma J. Agric. Agricultural Technol., 7(1), 80-86.

Qazi, M.H., A.S. Mohmahd, M.A. Rana, P. Khaliq and J.P. Moss. 1992. Current status and future prospects of biotechnology research in Pakistan. Biotec. Crop Imp. Asia :53-59.

Rajarathinam, P., M., Narayana, M. Alagirisamy and I.S. Moshin., 2017. Variability, correlation and path coefficient analysis in groundnut (Arachis hypogaea L.). S. Vignesh and A. Philip Arokiadoss (ed.) Statistical Approaches on Multidisciplinary Research, Volume I, Surragh Publishers, India, 2017.

Ramakrishnan, P., N. Manivannan, A. Mothilal, and L. Mahalingam. 2017. Correlation studies in back cross derived population for foliar disease resistance in groundnut (Arachis hypgaea L.). Int. J. Curr. Microbiol. Appl. Sci., 6(5): 266-272. https://doi.org/10.20546/ijcmas.2017.605.031

Rao, V.T., V. Venkanna, D. Bhadru and D. Bharathi. 2014. Studies on Variability, Character Association and Path Analysis on Groundnut (Arachis hypogaea L.). Int. J. Pure Appl. Biosci., 2 (2): 194-197.

Ravi, K., Vadez, V., Isobe, S., Mir, R. R., Guo, Y., Nigam, S. N., and Varshney, R. K. (2011). Identification of several small main-effect QTLs and a large number of epistatic QTLs for drought tolerance related traits in groundnut (Arachis hypogaea L.). Theoretical and Applied Genetics, 122, 1119-1132.

Reeve, Y.U. and J.S. Rao. 1981. Path analysis of yield components in black gram. Indian J. Agric. Sci., 51: 378-381.

Sabiel, S.A., M.I. Ismail, E. Abdalla, A.K. Osman. 2014. Genetic Variation of Groundnut (Arachis hypogaea L.) Genotypes in Semi-Arid Zone Sudan. Int. J. Env., 3(3):16-23. https://doi.org/10.3126/ije.v3i3.11060

Sadeghi, A., Karimi, E., Dahaji, P. A., Javid, M. G., Dalvand, Y., & Askari, H. (2012). Plant growth promoting activity of an auxin and siderophore producing isolate of Streptomyces under saline soil conditions. World Journal of Microbiology and Biotechnology, 28, 1503-1509.

Sandhu B.S and Khehra. 1977. Inter-relationships in semi-spreading x bunch and semi - spreading x semi – spreading crosses of groundnut. Indian J. Genet. Plant Breeding 37:22-26.

Santos, M. G., Oliveira, M. T., Figueiredo, K. V., Falcao, H. M., Arruda, E. C., Almeida-Cortez, J., and Antonino, A. C. (2014). Caatinga, the Brazilian dry tropical forest: can it tolerate climate changes?. Theoretical and Experimental Plant Physiology, 26, 83-99.

Santos, R. A., Campagnole-Santos, M. J., & Andrade, S. P. (2000). Angiotensin-(1–7): an update. Regulatory peptides, 91(1-3), 45-62.

Sangha, A. S., M. Singh and K.S. Labana. 1979. Variability and correlation studies in groundnut after hybridization. Madras Agric. J., 66:565-570. https://doi.org/10.29321/MAJ.10.A02991

Singh, A., S.N. Raina, M. Sharma, M. Chaudhary, S. Sharma, and V.R. Rajpal. 2021. Functional uses of peanut (Arachis hypogaea L.) seed storage proteins. Grain and seed proteins functionality, 121, 142-142. https://doi.org/10.5772/intechopen.96871

Singh, N. T., Vig, A. C., Singh, R., & Chaudhary, M. R. (1979). Influence of Different Levels of Irrigation and Nitrogen on Yield and Nutrient Uptake by Wheat 1. Agronomy Journal, 71(3), 401-404.

Shashi Kumara, P., B.G. Sanjeev, and P. Venkata Ravana. 2015. Studies on correlation and path analysis for traits related to water use efficiency and pod yield and its components in groundnut (Arachis hypogaea L.). The Bioscan, 10(4): 2155-2158.

Shoba, D., N. Manivannan and P. Vindhiyavarman. 2012. Correlation and path coefficient analysis in groundnut (Arachis hypogaea L.). Madras Agric. J., 99(1-3): 18-20.

Shukla, A.K. and P. Kumar Rai. 2014. Evaluation of groundnut genotypes for yield and quality traits. Ann. Pl. Soil Res., 16(1):41-44.

Steel, A., Nussberger, S., Romero, M. F., Boron, W. F., Boyd, C. A., & Hediger, M. A. (1997). Stoichiometry and pH dependence of the rabbit proton-dependent oligopeptide transporter PepT1. The Journal of physiology, 498(3), 563-569.

Thakur, V. K., Thakur, M. K., & Gupta, R. K. (2013). Rapid synthesis of graft copolymers from natural cellulose fibers. Carbohydrate polymers, 98(1), 820-828.

Thirumala Rao, V. 2016. Genetic variability, correlation and path coefficient analysis under drought in groundnut (Arachis hypogaea L.). Legume Res., 39(1): 319-322. https://doi.org/10.18805/lr.v39i2.9536

Trivikrama Reddy, A. Reddi Sekhar, M. Vijayabharathi, A. Lakshmi Pathy, T. Lakshmikantha Reddy, G. and V. Jayalakshmi. 2017. Correlation and path analysis of kernel yield and its components in Groundnut (Arachis hypogaea L.). Int. J. Current Microbiol. Appl. Sci., 6(12), 10-16. https://doi.org/10.20546/ijcmas.2017.612.002

Vaddoria, M.A. and V.J. Patel. 1992. Genetic variability in Virginia runner groundnut (Arachis hypogaea L.). J. Maharashtra Agric. Univ., 15:120-132.

Venkataravana, P., H.D.S.K. Pushpa, Priyadarshini and V.D. Sheela, 2020. Genetic variability, correlation and path analysis studies in segregating generation of three crosses for pod yield and its attributing characters in groundnut (Arachis hypogaea L.). J. Pharmacogn. Phytochem., 9(1), 771-776.

Vasanthi, S., & Bhat, R. V. (1998). Mycotoxins in foods-occurrence, health & economic significance & food control measures. Indian Journal of Medical Research, 108, 212.

Vinithashri, G., Manivannan, N., Viswanathan, P. L., & Selvakumar, T. (2019). Genetic variability, heritability and genetic advance of yield and related traits in F3 generation of groundnut (Arachis hypogaea L.). Electronic Journal of Plant Breeding, 10(3), 1292-1297.

Woyengo, T. A., Ramprasath, V. R., and Jones, P. J. H. 2009. Anticancer effects of phytosterols. Eur. J. Clin. Nutr., 63(7), 813-820.

Woyann, L. G., Meira, D., Zdziarski, A. D., Matei, G., Milioli, A. S., Rosa, A. C., ... & Benin, G. (2019). Multiple-trait selection of soybean for biodiesel production in Brazil. Industrial Crops and Products, 140, 111721.

Yadava, R. D. S. (1981). The bubble coalescence model of radiation blistering. Journal of Nuclear Materials, 98(1-2), 47-62.

Yadlapalli, S. 2014. Research article. Genetic variability and character association studies in groundnut (Arachis hypogaea L.). Int. J. Plant Animal and Env. Sci., 4(4):298-300.

Yan, W. and J. Frégeau-Reid. 2008. Breeding line selection based on multiple traits. Crop Sci., 48(2):417-423. https://doi.org/10.2135/cropsci2007.05.0254

Zaman, M.A., M. Tuhina-Khatun, M.Z. Ullah, M. Moniruzzamn, and K.H. Alam. 2011. Genetic variability and path analysis of groundnut (Arachis hypogaea L.). The Agriculturists. 9(1-2), 29-36. https://doi.org/10.3329/agric.v9i1-2.9476