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Assessment of Variability, Diversity and Performance of Indigenous Pigeonpea Germplasm for Yield and Nutritional TraitsAssessment of Variability, Diversity and Performance of Indigenous Pigeonpea Germplasm for Yield and Nutritional Traits

N. Sandhyakishore1,*, P. Jagan Mohan Rao2, Ch. Ramulu1, N. Sivaraj3, B. Sudhakar Reddy4, B. Edukondalu1, M. Madhu1, G. Padmaja1, D. Veeranna1
  • 0000-0001-8906-6660, 0000-0001-6436-7731, 0000-0002-4793-4025, 0000-0003-2899-5970, 0000-0001-6487-1006, 0000-0002-1528-9690, 0009-0006-3988-4917, 0000-0003-3253-7542, 0000- 0002 0259-7192
1Regional Agricultural Research Station, Warangal, Professor Jayashankar Telangana Agricultural University, Warangal Urban-506 007, Telangana, India.
2Seed Research Technology Center, Professor Jayashankar Telangana Agricultural University, Rajendranagr, Hyderabad-500 030, Telangana, India.
3ICAR-National Bureau of Plant Genetic Resources, Regional Station, Rajendranagar, Hyderabad-500 030, Telangana, India.
4ICAR-Indian Agricultural Research Institute, New Delhi-110 012, India.

  • Submitted10-03-2025|

  • Accepted30-06-2025|

  • First Online 08-08-2025|

  • doi 10.18805/LR-5490

ABSTRACT

Background: Pigeon pea [Cajanus cajan (L.) Millsp.] is a perennial legume vital to subsistence agriculture in semi-arid tropics, providing food, fodder and soil enrichment. Genetic improvement of pigeon pea hinges on understanding the genetic variability among its genotypes.

Methods: Fifty-six pigeon pea accessions were collected from various districts in Telangana, India. Forty-five accessions were characterized for yield, quality and nutritional traits in a randomized block design with two replications during the Late-kharif season of 2022. Data analysis included variance, heritability, genetic advance, correlation, path coefficient analysis, cluster analysis and principal component analysis (PCA).

Result: Significant genetic variation was found among the accessions for all traits. The phenotypic coefficient of variation (PCV) was higher than the genotypic coefficient of variation (GCV), indicating substantial environmental influence. High heritability estimates and genetic advance were observed for traits such as single plant yield, pods per plant and iron content, suggesting strong selection potential. Correlation analysis revealed that traits like plant height, number of branches per plant and 100-seed weight had significant positive associations with yield. Path coefficient analysis identified pods per plant and 100-seed weight as having the highest direct positive effects on yield. Cluster analysis divided the accessions into four clusters, with cluster III showing superior performance in yield-related traits. PCA indicated that four principal components explained 69.16% of the total variability, with PC1 dominated by yield traits.

INTRODUCTION

Pigeon pea [Cajanus cajan (L) Millsp.]-known as Adhaki in Sanskrit, Arhar in Hindi and Tur in Bengali-is a perennial member of the Fabaceae family. Other common names for pigeon pea include red gram, congo pea, gungo pea, and no-eye pea (Wu, 2009). It ranks as the sixth most significant grain legume crop cultivated in the semi-arid tropics of Asia, Africa and the Caribbean, thriving under various cropping systems (Mula and Saxena, 2010). Beyond its role as a food source, pigeon pea serves multiple purposes: It is used in traditional medicine, animal feed, fodder, fuel wood, hedges, windbreaks, roof thatches and green manure. Additionally, pigeon pea helps prevent soil erosion, particularly on sloping land, enriches soil organic matter and supplies nitrogen through symbiotic rhizobia, underscoring its importance in subsistence agriculture.
       
The cultivation of pigeon pea dates back at least 3,000 years, with India recognized as its center of origin. Historical references to this crop appear in ancient Sanskrit and Buddhist literature from 400 BC to 300 AD. The spread of pigeon pea reached East Africa and later the Americas, likely via the slave trade. Presently, pigeon pea is grown extensively in tropical and semi-tropical regions worldwide, with India being the largest producer, accounting for 63% of global production. The crop is cultivated on approximately 5.4 million hectares globally, with India alone accounting for 3.9 million hectares or 72% of the total area dedicated to pigeon pea cultivation (FAO, 2018).
       
Nutritionally, pigeon pea is a rich source of protein, fiber, minerals, and vitamins. It contains 20-26% protein, 1-2% fat, 53-65% carbohydrate, and 3.8-8.1% ash Ajayi et al., (2010); Saxena and Kumar, (2010). The seeds are particularly rich in sulfur-containing amino acids, methionine and cystine, and provide essential minerals such as phosphorus, magnesium, iron, calcium, sulfur and potassium, while being low in sodium (Kunyanga et al., 2013). For example, 100 g of dry pigeon peas provide 117% of the daily recommended value for copper, 65% for iron, 78% for manganese, 52% for phosphorus, 15% for selenium, 13% for calcium and 25% for zinc.
       
Understanding the nature and extent of genetic variation in pigeon pea genotypes is crucial for plant breeding programs. The processes of collecting, conserving, and characterizing genotypes form the foundation of any crop improvement endeavor, which depends on the genetic diversity present within the gene pool. This genetic variability enables breeders to develop new and improved cultivars with desirable traits. Historically, the natural genetic variability within crop species has been harnessed to meet subsistence food requirements. Today, this variability is being leveraged to address the needs of growing populations and to produce surplus food.
               
Despite its numerous benefits, pigeon pea is often considered an “orphan crop” in many countries, receiving less attention and investment compared to major staple crops. Nonetheless, recognizing its importance and harnessing its genetic potential can significantly contribute to sustainable agriculture and food security.

MATERIALS AND METHODS

Plant material and collection
 
The experimental materials comprised pigeon pea and its wild relatives’ germplasm collected from various parts of Jayashankar Bhupalpally, Warangal, Mulug and Mahabubabad districts in Telangana during January 2020. The collection was conducted by the ICAR-National Bureau of Plant Genetic Resources Regional Station, Rajendranagar, Hyderabad, in collaboration with the Regional Agricultural Research Station, PJTSAU, Warangal. A total of 56 accessions of pigeon pea were collected during the survey, showcasing a significant level of diversity in the germplasm.
 
Germplasm handling and evaluation
 
The collected germplasm samples were homogenized and subjected to characterization, evaluation and multiplication processes at the Regional Agricultural Research Station in Warangal (RARS, Warangal). These steps were undertaken to better understand and utilize the genetic resources of pigeon pea and its wild relatives.
 
Experimental design and data collection
 
Forty-five accessions were selected based on their performance. Data for various yield, quality and nutritional characters were recorded, including days to 50% flowering, days to maturity, plant height, number of branches per plant, pods per plant, 100-seed weight (g), copper (Cu, mg/kg), zinc (Zn, mg/kg), iron (Fe, mg/kg), manganese (Mn, mg/kg), protein content (%) and single plant yield (g). Data were collected for each replication by selecting 5 random plants in each row, and mean values were estimated. The experiment was conducted during the Late-kharif season of 2022 using a randomized block design with two replications. The replication-wise mean values of each genotype for the twelve different characters were used for statistical analysis.
 
Statistical analysis
 
Analysis of variance for each character was calculated as per the standard procedure suggested by Panse and Sukhatme (1967). Data management and all statistical analyses were performed using the R software packages.

RESULTS AND DISCUSSION

The analysis of variation revealed highly significant differences among the accessions for all the characters studied, including days to 50% flowering, days to maturity, plant height, number of branches per plant, pods per plant, 100-seed weight (g), Cu (mg/kg), Zn (mg/kg), Fe (mg/kg), Mn (mg/kg), protein content (%) and single plant yield (g), indicating the existence of considerable genetic variation in the experimental material. Examination of the variance components revealed that the phenotypic coefficient of variation (PCV) was higher than the genotypic coefficient of variation (GCV) for all characters studied, indicating the role of environmental variance in the total variance (Table 1).

Table 1: Genetic parameters of twelve characters.


 
Variability and heritability
 
The genotypic coefficient of variation for various characters ranged from 5.154% to 46.681%, while the phenotypic coefficient of variation ranged from 5.228% to 46.681%. High genotypic and phenotypic coefficients of variation were observed for single plant yield (g), Fe (mg/kg), and number of pods per plant. Moderate GCV and PCV were observed for protein content (%), number of branches per plant, 100-seed weight (g), and Cu (mg/kg). Low GCV and PCV values were observed for days to 50% flowering, plant height, Zn (mg/kg), Mn (mg/kg) and days to maturity. Heritability estimates are crucial for determining the heritable portion due to genetic variation. High heritability estimates, indicating that the characters are least influenced by environmental factors and can be transmitted to subsequent generations, were observed for almost all the characters studied. Moderate heritability estimates were observed for the number of branches per plant. The success of genetic advance under selection depends on the magnitude of genetic variability in the base population and the heritability of the character under consideration. Genetic advance is usually expressed as a percentage of the mean. High genetic advance as a percentage of the mean was observed for Fe (mg/kg), single plant yield (g), and pods per plant. Moderate genetic advance as a percentage of the mean was observed for plant height, number of branches per plant, 100-seed weight (g), Cu (mg/kg), Zn (mg/kg) and Mn (mg/kg). Similar findings  reported by Edukondalu et al., (2023); Vanniarajan et al., (2023); Anuj et al., (2018); Pushpavalli et al., (2018); Mallesh et al., (2017); Ram et al., (2016); Shunyu et al., (2013); Sharma et al., (2012) and Hamid et al., (2011).
       
According to Johnson et al., (1955), heritability along with genetic advance is mostly useful and reliable in predicting the resultant effects of selection. Selection can only be achieved when high heritability is accompanied by high genetic advance (Burton, 1952). In the present study, high estimates of heritability coupled with high genetic advance as a percentage of the mean were observed for single plant yield (g), pods per plant and Fe (mg/kg), indicating better scope for these traits for direct selection.
 
Correlation of attributing characters with grain yield
 
Genotypic and phenotypic correlations between single plant yield and other traits were depicted in (Fig 1). Single plant yield had positive and highly significant associations with plant height (rg=0.246*, rp=0.225*), number of branches per plant (rg=0.540**, rp=0.377**), number of pods per plant (rg=0.748**, rp=0.720**) and 100-seed weight (g) (rg=0.231*, rp=0.218*) at both genotypic and phenotypic levels, Similar results reported by Vanniarajan et al., (2023) and Sharma et al., (2023). Conversely, significant negative relationship was observed with Mn content (mg/kg) (rg=-0.279**, rp=-0.259*) and Cu (mg/kg) (rg=-0.228*, rp=-0.181*) at both genotypic and phenotypic levels. Days to maturity (rg=, rp=0.011) and Fe (mg/kg) (rg=, rp=0.005) had slightly positive but non-significant associations at both genotypic and phenotypic levels with single plant yield.

Fig 1: Correlation matrix.


       
In contrast, Days to 50% flowering (rg=-0.006, rp=-0.002), Zn (mg/kg) (rg=-0.077, rp=-0.073) and protein content (%) (rg=-0.009, rp=-0.009) exhibited negative and non-significant associations with single plant yield at both genotypic and phenotypic levels. These results align with findings reported by Edukondalu et al., (2023), Vanniarajan et al., (2023); Sharma et al., (2023); Ramasamy et al., (2021); Kandarkar et al., (2020); Narayanan et al., (2018); Anuj et al., (2018); Pandey et al., (2016); Hemavathy et al., (2019); Chaudhary et al., (2023).
 
Direct and indirect effects of attributes on grain yield
 
Path coefficient analysis, which splits the correlation coefficient into direct and indirect effects, was performed to gain a clear picture of the interrelationships among various component traits with yield (Table 2). In the present study, the number of pods per plant (genotypic path coefficient, 1.0918; phenotypic path coefficient, 0.8192) exhibited the maximum positive direct effect on single plant yield, followed by 100-seed weight (g) (0.2924, 0.2658), Zn content (mg/kg) (0.2053, 0.1189), protein content (%) (0.2018, 0.1319), and days to 50% flowering (0.1813, 0.1092).  In contrast, the number of branches per plant (-0.3307, -0.0296), Cu content (mg/kg) (-0.3545, -0.2465), and days to maturity (-0.062, -0.0179) had negative direct effects on single plant yield.

Table 2: Direct and indirect effects of component characters on grain yield in pigeon pea.


       
The number of pods per plant, 100-seed weight (g), Zn content (mg/kg), and protein content (%) had significant positive direct effects on grain yield. Similar findings were observed by Edukondalu et al., (2023); Kandarkar et al., (2020) and Pandey et al., (2016). Conversely, the number of branches per plant, Cu content (mg/kg), and days to maturity showed low negative direct effects on single plant yield. Such direct effects were also reported by Sharma et al. (2023); Ramasamy et al., (2021); Narayanan et al., (2018); Anuj et al., (2018); Verma et al., (2018); Sharma et al., (2023); Bhadru et al., (2010).
 
Cluster analysis
 
The D2 analysis classified the genotypes into relatively homogeneous groups to minimize within-cluster diversity and maximize between-cluster diversity. The respective genotypes from diverse clusters can be utilized in breeding programs depending on the breeding objectives.
       
A set of 45 indigenous germplasm of pigeon pea was subjected to D2 analysis for twelve characters. Based on D2 values, four clusters were formed (Table 3, Fig 2). This indicated substantial diversity in the available gene pool of pigeon pea. Cluster analysis results revealed that Cluster III was the largest, consisting of 14 accessions, followed by Cluster I with 12 accessions, Cluster II with 11 accessions and Cluster IV with 8 accessions, similar results were reported by Naing et al., (2022). The clustering pattern demonstrated that the pigeon pea germplasm accessions collected from different locations in Telangana were genetically diverse. Hence, the genotypes studied are reliable for hybridization and selection.

Table 3: Grouping of pigeonpea germplasm accessions in various clusters on the basis of D2 values.



Fig 2: Dendrogram depicting genetic relationships among 45 pigeonpea accessions.


       
The mean values for different characters were compared across the clusters and are presented in (Table 4). Results revealed that Cluster I was better for the early days to 50% flowering and days to maturity, whereas Cluster II exhibited the highest values for 100-seed weight (g), Cu (mg/kg) and Zn (mg/kg). Similarly, Cluster III had better genotypes for plant height, number of branches per plant, pods per plant and single plant yield (g), while Cluster IV exhibited the highest values for Fe (mg/kg), Mn (mg/kg), and protein content (%). These findings align with previous studies by Edukondalu et al., (2024); Ranjani et al., (2023); Nyirenda et al., (2020); Ranjani et al., (2021) and Reddy et al., (2015). Similar patterns have been reported in studies Bhatt et al., (2024); Kalyan et al., (2025); Kaur et al., (2023).

Table 4: Cluster means of different characters in pigeon pea germplasm.


 
Principle component analysis
 
The scree plot illustrated the percentage of variance for each principal component, with PC1 showing 23.22% variability and an eigenvalue of 2.786, which then declined gradually (Table 5, Fig 3). PCA on agro-morphological, yield, and nutritional components of pigeon pea indicated that four principal components (PCs) with eigenvalues greater than 1 accounted for 69.16% of the total variability. PC1 contributed 23.22%, PC2 18.58%, PC3 15.11% and PC4 12.24% (Fig 4).

Table 5: Eigen values, % variance and cumulative eigen values of pigeon pea genotypes.



Fig 3: Screen plot.



Fig 4: Biplot diagram of principal components.


       
The rotated component matrix (Table 6, Fig 5) revealed that PC1 was dominated by yield traits like plant height, number of branches per plant, number of pods per plant, and single plant yield (g), while PC2 was associated with days to 50% flowering and days to maturity. PC3 and PC4 were primarily related to mineral and protein content traits such as Cu, Zn, Mn and protein content percentage (Table 7).

Table 6: Rotated component matrix.



Fig 5: Rotated component matrix.



Table 7: Interpretation of rotated component matrix for the traits having values >0.5 in each PCs.


 
PC scores of the genotypes
 
Genotypes were selected based on PC scores, which can propose precise selection indices. High PC scores indicate high values for specific traits in those genotypes (Singh and Chaudhary, 1977). PC scores showed positive and negative values (Table 8) across components:

Table 8: PC scores of pigeon pea germplasm in the study programme.


       
In PC1, the positive scores ranged from 0.0950 (IC-0634391) to 3.741 (IC-0634411), while negative values ranged from -0.0255 (IC-0634419) to -2.3433 (IC-063443). In PC2, the positive values ranged from 0.0057 (IC-0634412) to 1.9786 (IC-0634435) and negative values ranged from -0.0182 (IC-0634398) to -1.7855 (IC-0634414). In PC3, the positive values ranged from 0.0131 (IC-0634385) to 2.0743 (IC-0634424) and negative values ranged from -0.0169 (IC-0634395) to -2.3487 (IC-0634392). In PC4, the positive values ranged from 0.0243 (IC-0634426) to 1.9577 (IC-0634411), while negative values ranged from -0.0585 (IC-0634407) to -4.2902 (IC-0634400).
               
Top PC scores of positive values in each PC were selected in four principal components. Collection IC-0634411 performed well across all components. IC-0634401 and IC-0634398 excelled in yield-related traits in PC1 and PC2, while IC-0634403, IC-0634416, and IC-0634435 showed strong performance in protein and mineral content traits in PC3 and PC4. These results align with findings by Edukondalu et al., (2024); Dhanushasree and Hemavathy (2022); Hemavathy et al., (2017) and Rekha et al. (2013). Similar patterns of genotype performance across principal components have been reported in studies Jain et al., (2023); Mohanlal et al., (2023); Gupta et al., (2023) and Kumar et al., (2023), highlighting the effectiveness of PCA in identifying superior genotypes for breeding programs.

CONCLUSION

This study on the assessment of variability, diversity, and performance of indigenous pigeon pea germplasm for yield and nutritional traits reveals significant genetic variation among the accessions. High heritability and genetic advance for traits such as single plant yield, pods per plant, and iron content indicate strong potential for direct selection and genetic improvement. Correlation and path coefficient analyses identified key traits, including plant height, number of branches, number of pods and 100-seed weight, as crucial for yield enhancement.
       
Cluster analysis demonstrated substantial diversity, with the formation of four distinct clusters, highlighting the genetic divergence among the pigeon pea accessions from different locations in Telangana. Principal component analysis further reinforced the importance of yield-related traits, accounting for 69.16% of total variability, with the first principal component dominated by traits such as plant height and number of pods per plant.
               
These findings provide valuable insights for breeding programs aiming to develop high-yielding pigeon pea cultivars with enhanced nutritional quality. The genetic diversity observed in this study emphasizes the potential for using these accessions in hybridization and selection to achieve sustainable agricultural practices and improve food security.

ACKNOWLEDGEMENT

The present study was supported by ICAR-National Bureau of Plant Genetic Resources, Regional Station, Rajendranagar, Hyderabad, in collaboration with the Regional Agricultural Research Station, Professor Jayashankar Telangana Agricultural University (PJTAU), Warangal.
 
Disclaimers
 
The views and conclusions presented in this article are solely those of the authors and do not necessarily reflect the official policies or positions of their affiliated institutions. While the authors have made every effort to ensure the accuracy and completeness of the information provided, they do not assume any responsibility for errors, omissions, or any outcomes resulting from the use of the content.
 
Informed consent
 
This study did not involve human participants. All procedures involving animals were conducted in accordance with institutional guidelines and were approved by the Committee for the Ethical Use of Animals in Research at the University. Proper care and handling techniques were followed throughout the experimental process.

CONFLICT OF INTEREST

The authors declare that there are no conflicts of interest associated with the publication of this article. The design of the study, data collection, analysis, interpretation of results, decision to publish and preparation of the manuscript were conducted independently and were not influenced by any financial or commercial relationships.

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