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

Kumar, Mukesh; Ramya, V.; Kumar, C.V. Sameer; Raju, T.; Kumar, N. M. Sunil; Seshu, G.; Sathish, G.; Bhadru, D.; Ramana, M. Venkata

doi:10.31742/ijgpb.81.1.7

Cited by 10 publications

(10 citation statements)

References 22 publications

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“…Gauch (2013) suggested that the most accurate model for AMMI can be predicted by using the first two IPCAs. Several researchers used the first two IPCAs for the GGE biplot analysis because they explained a greater percentage of the G × E interaction for sorghum (Al-Naggar et al, 2018), barley (Vaezi et al, 2017), pigeonpea (Rao et al, 2020;Kumar et al, 2021), wheat (Verma et al, 2015), and maize (Solomon et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

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Harnessing Sorghum Landraces to Breed High-Yielding, Grain Mold-Tolerant Cultivars With High Protein for Drought-Prone Environments

et al. 2021

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Intermittent drought and an incidence of grain mold disease are the two major constraints affecting sorghum production and productivity. The study aimed at developing drought-tolerant sorghum varieties possessing a high protein content and tolerance to grain mold with stable performance using additive main effects and multiplicative interaction (AMMI) and genotype and genotype × environment interaction (GGE) biplot methods. Systematic hybridization among the 11 superior landraces resulted in subsequent pedigree-based breeding and selection from 2010 to 2015 evolved 19 promising varieties of grains such as white, yellow, and brown pericarp grains. These grain varieties were evaluated for their adaptability and stability for yield in 13 rainfed environments and for possessing tolerance to grain mold in three hot spot environments. A variety of yellow pericarp sorghum PYPS 2 (3,698 kg/ha; 14.52% protein; 10.70 mg/100 g Fe) possessing tolerance to grain mold was identified as a stable variety by using both AMMI and GGE analyses. Four mega-environments were identified for grain yield and fodder yield. Sorghum varieties PYPS 2, PYPS 4, PYPS 8, and PYPS 11 were highly stable in E2 with a low grain mold incidence. Besides meeting the nutritional demand of smallholder farmers under dryland conditions, these varieties are suitable for enhancing sorghum productivity under the present climate change scenario.

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Section: Discussionmentioning

confidence: 99%

“…The vector view of GGE biplot in the genotype-focused scaling measured their dissimilarity in discriminating the genotypes (Kumar et al, 2021). For grain yield, sorghum varieties PYPS1, PYPS 6, PYPS 7, PYPS 9, PYPS 10, PYPS 17, PYPS 18, and PYPS 19 showed the same group position.…”

Section: Gge Biplot Of Genotype View For Grain Yieldmentioning

confidence: 99%

Harnessing Sorghum Landraces to Breed High-Yielding, Grain Mold-Tolerant Cultivars With High Protein for Drought-Prone Environments

et al. 2021

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“…To encounter this requirement, various crop improvement programs have been initiated all over the world (Anonymous, 2019). In any crop improvement program, the performance of promising genotypes has been tested over different locations each year to identify the genotypes having both high yield qualities and wider adaptability over different environmental conditions (Kumar et al, 2021, Reddy et al, 2022. In Multilocation trials (MLT), most frequently, it is noticed that the genotypes respond differently to the diverse environmental conditions; this differential response of genotypes over diverse environments is known as Genotype environment interaction (GEI) (Pham et al, 1988, Farias et al, 2016, Pagi et al, 2017.…”

Section: Introductionmentioning

confidence: 99%

“…An important characteristic of this GGE biplot is that it removes the environment main effect and retains and combines genotype main effect and GEI. Thus, the biplot generated from the MLT data contains only G and GEI (Kang, 2002, Kumar et al, 2021, Sharma et al, 2016. GGE biplot and AMMI analyses has been carried out in understanding GEI in many crop species including pigeon pea (Rao et al, 2022, Reddy et al, 2022, Kumar et al, 2021, Sharma et al, 2016; wheat (Verma et al, 2021, Singh et al, 2019, Jeberson et al, 2017; ground nut (Khan et al, 2021); maize (Bozovic et al, 2020, Oliveira et al, 2010; rice (Chandra Mohan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis for Assessment of Yield Performance and Adaptability of Selected Mango (Mangifera indica L.) Genotypes under Multi Location Trials in India

K.¹,

Chaudhary²,

Kumar³

2022

IJBSM

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A study was conducted in April 2020 based on the data collected from the All-India Coordinated Research Project on Sub-Tropical Fruits (AICRP-STF) and Central Institute for Subtropical Horticulture (CISH), Lucknow, India. The objective was to identify the high yielding and stable genotypes of mango using Additive main effects and multiplicative interaction (AMMI) and Genotype plus Genotype×Environment interaction (GGE) biplot analyses. Data on sixteen genotypes of mango tested across four locations, viz., Rewa, Sabour, Sangareddy, and Vengurla, over nine years was considered for the study. Combined analysis of variance showed highly significant differences (p<0.01) for genotype, environmental main effects and genotype×environment interaction (GEI) effects. The significant GEI contributes about 42.81% of the total sum of squares. AMMI analysis partitioned the GEI into fifteen interaction principal component axes and a residual term. The first two interaction principal component axes (IPCA1, IPCA2) collectively accounted for 40.80% of GEI sum of squares. AMMI analysis recommended Zardalu, Mankurad and GGE biplot analysis recommended Totapari, Mankurad as superior mango genotypes for cultivation in all the test locations. GGE biplot analysis classified the test locations into two mega environments. The first mega environment includes Rewa and Sangareddy with Neelum as the best suitable genotype; the second includes Vengurla and Sabour with Suvarnarekha as the best suitable genotype. The present study also concluded that GGE biplot analysis was the best analytical tool for identifying location-specific genotypes of mango, and AMMI analysis was the best for identifying superior genotypes having high yield with stability across all the test locations.

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“…Reddy et al (2011) evaluated ten pigeonpea genotypes over three years under rainfed conditions using the Eberhart and Russell method to select those with good stability for grain yield, number of pods, and seed size. In another study, Kumar et al (2021) recommended two out of 28 genotypes evaluated under rainfed conditions in ten locations using AMMI and GGE biplot analyses.…”

Section: Introductionmentioning

confidence: 99%

Research Article Adaptability and stability parameters for grain production in pigeonpea lines in the Brazilian semiarid region

Costa¹,

Santos²

2022

Genet. Mol. Res.

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Pigeonpea is a multipurpose species and can be used for grain, forage production, and as a soil improver. In Brazil, the scarcity of productive cultivars adapted to various growing conditions contribute to the underutilization of this legume crop. The genotype × environment (G×E) interaction is one of the main challenges to the development and recommendation of cultivars. Estimates of adaptability and stability parameters make it possible to predict the behavior and effects of the G×E interaction to reduce possible inconsistencies in cultivar recommendation. From this perspective, we assessed the adaptability and stability parameters for grain yield of 21 pigeonpea lines developed in the breeding program of this pulse at Embrapa Semiárido, Petrolina, PE, Brazil, and the variety "guandu Petrolina" (control), in order to recommend new cultivars for general use. The genotypes were evaluated in eight environments, with experiments conducted in five irrigated and three rainfed environments using a randomized complete block design with three replications. Grain yield was corrected by covariance based on the average plant stand. The Eberhart and Russel, AMMI, and GGE Biplot methods were used in this study. The coefficient of variation for the experiments was 12.41%, with data transformed to square root. The effects of genotypes, the environments, and the genotype-byenvironment interaction were highly significant (p < 0.01). The mean ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 21 (4): gmr19066A.E. da S. Costa et al. 2 grain yield of the genotypes was 1,516 kg ha -1 . Lines 87, 100, and 158 simultaneously showed wide adaptability and good predictability according to the three methods, with mean yields of 1,530, 1,701, and 1,812 kg ha -1 , respectively, and reaching yields of up to 2,725, 2,928, and 2,955 kg ha -1 in some environments. These lines are indicated for recommendation as new pigeonpea cultivars for the semi-arid region of Brazil.

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

Cited by 10 publications

References 22 publications

Harnessing Sorghum Landraces to Breed High-Yielding, Grain Mold-Tolerant Cultivars With High Protein for Drought-Prone Environments

Harnessing Sorghum Landraces to Breed High-Yielding, Grain Mold-Tolerant Cultivars With High Protein for Drought-Prone Environments

Stability Analysis for Assessment of Yield Performance and Adaptability of Selected Mango (Mangifera indica L.) Genotypes under Multi Location Trials in India

Research Article Adaptability and stability parameters for grain production in pigeonpea lines in the Brazilian semiarid region

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