Genetic Variability for Yield, Physiological and Quality Traits in Novel Super-Early Pigeonpea (Cajanus cajan (L.) Millsp.)

Shruthi, H.B.

doi:10.18782/2582-2845.7941

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…Low G × E interaction and moderate-tohigh heritability for most of the traits studied suggest a better selection response. The heritability estimates of agronomic traits stay parallel with several studies (Kumara et al, 2013;Rangare et al, 2013;Obala et al, 2018;Shruthi et al, 2019;Sharma et al, 2020), while the estimates for protein content were variable across studies. The attributable reason may be due to the variable number of genotypes and the environment under evaluation (Obala et al, 2018).…”

Section: Discussionsupporting

confidence: 65%

Grain Nutrients Variability in Pigeonpea Genebank Collection and Its Potential for Promoting Nutritional Security in Dryland Ecologies

et al. 2022

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Pigeonpea, a climate-resilient legume, is nutritionally rich and of great value in Asia, Africa, and Caribbean regions to alleviate malnutrition. Assessing the grain nutrient variability in genebank collections can identify potential sources for biofortification. This study aimed to assess the genetic variability for grain nutrients in a set of 600 pigeonpea germplasms conserved at the RS Paroda Genebank, ICRISAT, India. The field trials conducted during the 2019 and 2020 rainy seasons in augmented design with four checks revealed significant differences among genotypes for all the agronomic traits and grain nutrients studied. The germplasm had a wider variation for agronomic traits like days to 50% flowering (67–166 days), days to maturity (112–213 days), 100-seed weight (1.69–22.17 g), and grain yield per plant (16.54–57.93 g). A good variability was observed for grain nutrients, namely, protein (23.35–29.50%), P (0.36–0.50%), K (1.43–1.63%), Ca (1,042.36–2,099.76 mg/kg), Mg (1,311.01–1,865.65 mg/kg), Fe (29.23–40.98 mg/kg), Zn (24.14–35.68 mg/kg), Mn (8.56–14.01 mg/kg), and Cu (7.72–14.20 mg/kg). The germplasm from the Asian region varied widely for grain nutrients, and the ones from African region had high nutrient density. The significant genotype × environment interaction for most of the grain nutrients (except for P, K, and Ca) indicated the sensitivity of nutrient accumulation to the environment. Days to 50% flowering and days to maturity had significant negative correlation with most of the grain nutrients, while grain yield per plant had significant positive correlation with protein and magnesium, which can benefit simultaneous improvement of agronomic traits with grain nutrients. Clustering of germplasms based on Ward.D2 clustering algorithm revealed the co-clustering of germplasm from different regions. The identified top 10 nutrient-specific and 15 multi-nutrient dense landraces can serve as promising sources for the development of biofortified lines in a superior agronomic background with a broad genetic base to fit the drylands. Furthermore, the large phenotypic data generated in this study can serve as a raw material for conducting SNP/haplotype-based GWAS to identify genetic variants that can accelerate genetic gains in grain nutrient improvement.

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Section: Discussionsupporting

confidence: 65%

Grain Nutrients Variability in Pigeonpea Genebank Collection and Its Potential for Promoting Nutritional Security in Dryland Ecologies

et al. 2022

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“…Hence, ideal cross combination should be conducted to get improvement for target traits effectively. Our results were in agreement with the results of Muniswamy et al (2014), Chaudhary et al (2016) and Shruthi et al (2020). Several researchers' viz., Mohan et al (2021), Remzeena et al (2021 and Basavaraja et al (2021) in other legume crops also gave emphasis on need of high genetic diversity to create the high genetic variation and their results were in conformity with our findings.…”

Section: Cluster Means For Different Characterssupporting

confidence: 94%

Genetic Divergence for Seed Yield Enhancing and Quality Traits in Pigeonpea [Cajanus cajan (L.) Millsp.]

Kaur

Singh

Sethi

et al. 2022

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Background: Pigeonpea has remarkable contribution in flourishing the Indian agricultural economy but over the decades, its productivity has remained stagnant. Presence of genetic diversity in breeding population is the key to develop high yielding varieties. Therefore the current study was aimed to explore genetic divergence among pigeonpea genotypes. Methods: A set of 178 pigeonpea germplasm lines were evaluated in randomized block design to assess the genetic divergence and clustering pattern of pigeonpea genotypes. Observations on various yield component traits were recorded. For the estimation of the Fe and Zn content ICP-OES (Perkin Elmer USA) was used. Mahalanobis D2 analysis was carried out to assess the genetic divergence. Result: Wide range of genetic diversity was revealed among lines for eleven traits studied. Mahalanobis D2 analysis grouped all the genotypes into eleven non-overlapping clusters. The inter cluster D2 values indicated that cluster IV and XI (21046.45) were the most diverse and cluster V and cluster VII (60.67) were the less diverse. Out of the eleven characters studied days to maturity, plant height and yield per plot contributed 93.32 per cent of the total divergence. Based on mean performances, cluster XI and cluster II were found to be beneficial for early flowering and early maturity genotypes, cluster V and cluster VII for number of pods per plant and yield per plot, respectively. The highest mean value for grain iron and zinc content was recorded in cluster III rendering simultaneous improvement for these traits. Therefore, trait-wise selection of diverse parents from different clusters would be desirable and beneficial.

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“…This indicates the genetic control on these traits, so selection of these traits will be effective. Similar results were earlier reported in pigeon pea by Sharma et al (2021) for single plant yield, Shruthi et al (2019) for plant height and Parre et al (2022) for number of primary branches per plant.…”

Section: Coefficients Of Variationsupporting

confidence: 90%

Genetic Variability, Correlation and Principal Component Analysis for Yield Related Traits in Pigeonpea [Cajanus cajan (L.) Millsp.]

Akshaya¹,

Geetha²,

Nirmalakumari³

et al. 2023

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Background: Pigeon pea is an economical source of protein cultivated in arid and semi arid tropical areas of the world. Genetic improvement mainly depends upon the amount of genetic variability available in the population. It is necessary for a breeder to understand the extent of genotypic and phenotypic variation among the germplasm to design an effective breeding programme. Methods: Eighty pigeon pea genotypes were evaluated for their variability with regard to yield and yield contributing traits. The experiment was carried out at Regional Research Station, Paiyur during 2021-2022 following randomized block design with two replications. Result: The traits viz., plant height, number of primary branches per plant, number of pods per plant and single plant yield can be improved following simple selection method as they exhibited high variability in combination with high heritability and genetic advance. Principal Component Analysis (PCA) revealed three significant principal components (PCs), which accounted for 81.24% of phenotypic variation among the studied genotypes. The traits days to 50% flowering, days to maturity, number of primary branches per plant, number of pods per plant and hundred seed weight had significantly positive association and positive direct effect with yield. Hence, these traits can be used as good selection criteria for yield improvement.

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Genetic Variability for Yield, Physiological and Quality Traits in Novel Super-Early Pigeonpea (Cajanus cajan (L.) Millsp.)

Cited by 5 publications

References 8 publications

Grain Nutrients Variability in Pigeonpea Genebank Collection and Its Potential for Promoting Nutritional Security in Dryland Ecologies

Grain Nutrients Variability in Pigeonpea Genebank Collection and Its Potential for Promoting Nutritional Security in Dryland Ecologies

Genetic Divergence for Seed Yield Enhancing and Quality Traits in Pigeonpea [Cajanus cajan (L.) Millsp.]

Genetic Variability, Correlation and Principal Component Analysis for Yield Related Traits in Pigeonpea [Cajanus cajan (L.) Millsp.]

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