Identification of superior parental lines for seed quality and storability through GGE biplot analysis of line × tester data in grain sorghum

Kannababu, N.; Rakshit, S; Madhusudhana, R.; Tonapi, Vilas A.; Das, I. K.; Raghunath, K.

doi:10.5958/0975-6906.2017.00037.2

Cited by 10 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mean sum of squares due to genotypes was highly significant for all the traits studied which indicated the presence of a substantial amount of variation in the genetic material studied. These results agree with the findings of Revilla et al (2006) in maize, in forage sorghum and Kannababu et al (2016) in sweet sorghum and Kannababu et al (2017) in grain sorghum.…”

Section: G X E Interactionsupporting

confidence: 93%

Stability analysis for seed longevity in landraces of sorghum [Sorghum bicolor (L.) Moench]

Kannababu¹,

Murthy²,

Elangovan³

et al. 2020

IJGPB

Self Cite

View full text Add to dashboard Cite

Seed longevity in sorghum is a major determinant in seed production and germplasm preservation. Forty-six local landraces representing nine genetic races of sorghum were evaluated under accelerated aging and natural storage conditions to study the genotype-environment interactions and the stability of landraces for seed longevity. Genotype-environment interactions were highly significant indicating the influence of storage conditions on seed longevity. The stability of landraces was estimated using mean (xi), regression coefficient (bi) and regression deviation (S2di). Environmental indices for the seed longevity traits were high in E1 (fresh seed) followed by E2 (accelerated aged seeds), E3 (stored seeds for 12 months) and E4 (stored seeds for 24 months). Seven sorghum landraces viz., IC-345729 (Bicolor), IC-347571 (Caudatum), IC-347577 (Durra), IC-345244 (Durra), IC-415803 (Durra), IC-415822 (Durra bicolor) and IC-415829 (Guinea bicolor) were stable for seed longevity. These genotypes are therefore, recommended for use in further breeding to improve seed longevity in sorghum, which otherwise is generally poor.

show abstract

Section: G X E Interactionsupporting

confidence: 93%

Stability analysis for seed longevity in landraces of sorghum [Sorghum bicolor (L.) Moench]

Kannababu¹,

Murthy²,

Elangovan³

et al. 2020

IJGPB

Self Cite

View full text Add to dashboard Cite

show abstract

“…3). Distance between two environments was visualised through the length of environmental vectors which environments, genotypes and the GEI (Kannababu et al 2017;Yihunie and Gesesse, 2018). As per the discriminative ability and representativeness, all the testing environments were found apt to be used for Graphical visualization through GGE Biplot analysis has expediently aided in identification of stable and superior soybean genotypes across testing environments of North Western Himalayan hills of India.…”

Section: Environmental Evaluation Based On Gge Biplotsmentioning

confidence: 99%

Stability analysis of soybean [Glycine max (L.) Merrill] genotypes under multi-environments rainfed condition of North Western Himalayan hills

Bhartiya¹,

Aditya²,

Kumari³

et al. 2018

IJGPB

View full text Add to dashboard Cite

In the present study, performance of five promising soybean genotypes over 4 locations during kharif 2013, 2014 and 2015 were investigated using GGE biplot analysis. Location attributed the highest proportion of the variation for all the traits except 100 seed weight ranging from 26.97-86.81% whereas, genotype contributed only 3.01-60.51% and genotype x location interaction contributed 6.01-31.42% of total variation. For 100 seed weight genotype has contributed major proportion of variation (66.26%) than location (31.08%) and genotype x location interaction (2.65%). Superior genotypes for key traits viz., grain yield (VLS 86) and 100 seed weight (Himso 1685) were effectively identified using GGE biplot graphical approach. It may be stated from present study that, VLS 86 was the closest to ideal genotype with stability for high grain yield as well as earliness. ‘Which-won-where’ study partitioned the testing locations into two mega-environments: first with three locations with VLS 86 as the winning genotype; second mega environment encompassed only one location with Himso 1685 as the winning genotype. Existence mega environments was found correlated with the rainfall pattern and clearly suggested that different entries need to be selected and deployed for realising maximum grain yield in hill zone.

show abstract

“…The Genotype by Environment Interaction Biplot (GGE Biplot) approach commonly has been used to investigate a plant's combining ability and line potential (Kannababu et al 2017;Khalil dan Raziuddin 2017;Ahmed et al 2019). This technique is suitable for data originating from the line  tester crosses based on the data structure (Ruswandi et al 2015b;Kahriman et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…The GGE biplot will provide clear explanations of the source of variation, making it easier for breeders to choose the desired genotype (Yan 2001). The GGE biplot analysis on the line × tester has also been applied to other crops such as sorghum (Kannababu et al 2017), sunflower (Ahmed et al 2019), corn (Ruswandi et al 2015b;Momeni et al 2020). Unfortunately, there has been limited study into how GGE-biplot may be used to identify the best parent lines with good combining ability in physiological aspects such as anthocyanin content.…”

Section: Introductionmentioning

confidence: 99%

Assessment of combining ability in purple corn parents under line × tester mating design using GGE biplot

2021

View full text Add to dashboard Cite

Abstract. Mufidah N, Sugiharto AN, Waluyo B. 2021. Assessment of combining ability in purple corn parents under line × tester mating design using GGE biplot. Biodiversitas 22: 4545-4554. One strategy to increase corn quality is the development of new corn cultivars by incorporating the character of nutritional content. The study aims to obtain information on the combining ability of purple corn lines as well as determine the best parental lines for purple corn hybrid development using the GGE biplot approach. This study used 24 hybrids generated from crossings of 8 purple corn lines with 3 testers, which were crossed using a line × tester mating design. Plant performance was assessed using a randomized block design of three replications. The F-test was used to assess and test the experiment variance. The combining ability of purple corn lines was examined using the GGE biplot method. The result showed that the GGE biplot approach successfully identified the best combiner and combinations generated between purple corn lines and non-purple corn lines. Purple corn lines P1Y1 had the best general combining ability for fresh ear yield and total anthocyanin content, meanwhile, P1Y2 had the best general combining ability in most of the agronomic traits, namely ear diameter, shelled ear weight, number of grains row-1, and as well as total anthocyanin content. These 2 lines were also identified as the best parent chosen in the best combination in this study.

show abstract

Identification of superior parental lines for seed quality and storability through GGE biplot analysis of line × tester data in grain sorghum

Cited by 10 publications

References 0 publications

Stability analysis for seed longevity in landraces of sorghum [Sorghum bicolor (L.) Moench]

Stability analysis for seed longevity in landraces of sorghum [Sorghum bicolor (L.) Moench]

Stability analysis of soybean [Glycine max (L.) Merrill] genotypes under multi-environments rainfed condition of North Western Himalayan hills

Assessment of combining ability in purple corn parents under line × tester mating design using GGE biplot

Contact Info

Product

Resources

About