Genotype x environments interactions for grain yield and its components in urdbean (Vigna mungo L Wilczek) under rainfed conditions of Jammu region

Kumar, Sanjeev; Sharma, Jag Paul; Kumar, Anil; Choudhary, HK; Singh, Aparajita; Sandghu, Rubby; Gupta, Rucku; Singh, Manmohan; Singh, Anjani Kumar

doi:10.31742/ijgpb.80.2.10

Cited by 3 publications

(3 citation statements)

References 8 publications

(9 reference statements)

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“…3). Similar results where the first two PCs clarified most the variance were reported in soybean (Kumar et al 2014), groundnut (Lal et al 2019), urdbean (Kumar et al 2020a) and mungbean (Kumar et al 2020b).…”

Section: Relationship Among the Test Environmentssupporting

confidence: 83%

“…The AMMI and GGE biplot analyses have been used extensively to identify stable cultivars with high yield and yield attributing characters in a wide variety of crops. In the last couple of years, their application was reported in evaluation of genotypes of rice (Dwivedi et al 2020), wheat (Bavandpori et al 2018;Lozada and Carter 2020;Manu et al 2020), maize (Choudhary et al 2019), barley (Kumar et al 2018;Yadav et al 2020), rapeseed (Sara et al 2019), groundnut (Kumar et al 2019;Ajay et al 2020), pigeonpea (Pagi et al 2017;Singh et al 2018;Gaur et al 2020), chickpea (Kanouni 2018), urdbean (Kumar et al 2020a;Kumar et al 2020b), and ashwagandha .…”

Section: Introductionmentioning

confidence: 99%

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Identification of pigeonpea genotypes with wider adaptability to rainfed environments through AMMI and GGE biplot analyses

Kumar¹,

Ramya²,

Kumar³

et al. 2021

Indian J. Genet

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Pigeonpea [Cajanus cajan (L.) Millspaugh] is an important pulse crop grown under Indian rainfed agriculture. Twenty eight pigeonpea genotypes were tested for stability and adaptability across ten rainfed locations in the States of Telangana and Karnataka, India using AMMI (additive main effects and multiplicative interaction) model and GGE (genotype and genotype by environment) biplot method. The grain yields were significantly affected by environment (56.8%) followed by genotype × environment interaction (27.6%) and genotype (18.6%) variances. Two mega environments were identified with several winning genotypes viz., ICPH 2740 (G15), TS 3R (G10), PRG 176 (G8) and ICPL 96058 (G22). E2 (Gulbarga, Karnataka), E3 (Bidar, Karnataka) and E6 (Vikarabad, Telangana) were the most discriminating environments. Genotypes, ICPH 2740, PRG 176 and TS 3R were the best cultivars in all the environments whereas PRG 158 (G9), ICPL 87119 (G12), ICPL 20098 (G19) and ICPL 96058 (G22) were suitable across a wide range of environments. Genotypes, ICPH 2740 and PRG 176 can be recommended on a large scale to the farmers with small holdings to enhance pigeonpea productivity and improve the food security

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Section: Relationship Among the Test Environmentssupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Identification of pigeonpea genotypes with wider adaptability to rainfed environments through AMMI and GGE biplot analyses

Kumar¹,

Ramya²,

Kumar³

et al. 2021

Indian J. Genet

View full text Add to dashboard Cite

show abstract

“…Therefore, GGE biplot model is considered as efficient method for identifying best genotypes and test environments. GGE biplot has been used for MET data in rice (Dwivedi et al 2020), durum wheat (Heidari et al 2016), sorghum (Rakshit et al 2012), baby corn (Choudhary et al 2019) among cereals and groundnut (Ajay 1et al 2021), pigeonpea (Kumar et al 2021) and urdbean (Kumar et al 2020) among pulses.…”

Section: Introductionmentioning

confidence: 99%

GGE biplot analysis for identification of ideal cultivars and testing locations of pearl millet (Pennisetum glaucum L.R. Br.) for peninsular India

Reddy¹,

Satyavathi²,

Khandelwal³

et al. 2022

IJGPB

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Pearl millet is a climate resilient crop grown by resource poor farmers. Identification of the ideal genotypes for broad and specific adaptation and ideal locations for testing in peninsular India (classified as B-zone) will address the needs of farmers and researchers working in this region. In the present study, performance of eight hybrids and four varieties over seven locations across three rainy seasons (2017-2019) was investigated using GGE biplot analysis. Location attributed higher proportion (59.3–89.9%) of the variation for eight traits, while genotype and genotype×environment interaction accounted for 57 to 65% of total variability for grain and dry fodder yields. The hybrids 86M86, KSB and NBH5061 are identified as ideal genotypes for cultivation across B-zone. Majority of the testing locations were highly correlated with Vijayapura, which is most discriminative and representative location. ‘Which-won where’ study partitioned the testing locations into two mega-environments: first with four locations with 86M01 as winning genotype and second encompassed three locations with KSB as the winning genotype. The Vijayapura, Ananthapuram and Dhule locations were identified for initial testing of genotypes. Hybrid advantage over varieties for grain and fodder yields was clearly observed from the study.

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Identification of stable genotypes for grain yield and its components in mungbean (Vigna radiata)

2022

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The present study was conducted at the experimental farm of Advanced Centre for Rainfed Agriculture (ACRA)Dhiansar of SKUAST- Jammu during rainy (kharif) season 2016, 2017 and 2018 for identification of most stablemungbean (Vigna radiata L.) genotypes in diverse environments (E1, E2 and E3). Genotype Pusa Vishal exhibitedregression coefficient equal to unity (b=0) with non-significant deviation from regression coefficient and hence showed wider adaptability under poor or good environments. Genotype EC520016 showed earliness in maturity with wider adaptability. Environmental indices indicated that environment E2 and E3 were most favourable for yield and majority of yield attributing traits whereas E3 alone was important for seed yield per plant. Based on AMMI models, G9, G1, G3, G4 and G8 exhibited higher IPCA scores coupled with high population mean in E3. On the basis of AMMI2, G12 and G22 exhibited high IPCA1 in E3 and G6, G17 and G9 exhibited high IPCA2 in the same environment. G3, G18 and G19 were present very close to centre point and were least affected by G×E interactions and their performance was not affected by harsh environments and G4, G18 and G22 were present away from the point of centre and were more affected by G × E interactions and not stable in performance. Hence, the selected genotypes may be utilized in Vigna radiata (L.) improvement programme at targeted environments with true type of breeding lines.

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Genotype x environments interactions for grain yield and its components in urdbean (Vigna mungo L Wilczek) under rainfed conditions of Jammu region

Cited by 3 publications

References 8 publications

Identification of pigeonpea genotypes with wider adaptability to rainfed environments through AMMI and GGE biplot analyses

Identification of pigeonpea genotypes with wider adaptability to rainfed environments through AMMI and GGE biplot analyses

GGE biplot analysis for identification of ideal cultivars and testing locations of pearl millet (Pennisetum glaucum L.R. Br.) for peninsular India

Identification of stable genotypes for grain yield and its components in mungbean (Vigna radiata)

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