Genetic advance for chip colour in potatoes

Euphytica ◽  
1995 ◽  
Vol 84 (2) ◽  
pp. 133-138 ◽  
Author(s):  
A. da S. Pereira ◽  
G. C. C. Tai ◽  
R. Y. Yada ◽  
R. H. Coffin ◽  
V. Souza-Machado
Keyword(s):  
Genetic Advance ◽  
Chip Colour

Heritability and Genetic Advance Estimation From Parental Lines of Hybrid Rice

2012 ◽  
Vol 3 (7) ◽  
pp. 11-13
Author(s):  
Arpita Shrivastava ◽  
◽  
D. K. Mishra D. K. Mishra ◽  
G. K. Koutu G. K. Koutu ◽  
S. K. Singh S. K. Singh
Keyword(s):  
Hybrid Rice ◽  
Genetic Advance ◽  
Parental Lines

Comparison of Expected Genetic Advance with Selection on the Basis of Clone and Polycross Progeny‐Test Performance in Alfalfa 1

Crop Science ◽  
1971 ◽  
Vol 11 (1) ◽  
pp. 88-91 ◽  
Author(s):  
R. R. Hill ◽  
M. W. Pedersen ◽  
L. J. Elling ◽  
R. W. Cleveland ◽  
J. H. Graham ◽  
...  
Keyword(s):  
Test Performance ◽  
Progeny Test ◽  
Genetic Advance

Genetic Evaluation and Bi-plot Analysis in Okra Hybrids for Yield, Quality Components and YVMV Resistance

2018 ◽  
Vol 5 (04) ◽  
Author(s):  
SS SOLANKEY ◽  
ANIL K SINGH
Keyword(s):  
Genetic Divergence ◽  
Principal Component ◽  
F1 Hybrids ◽  
Genetic Advance ◽  
Economic Traits ◽  
Cross Combination ◽  
Plot Analysis ◽  
Eigen Value ◽  
Coefficient Of Variability ◽  
Yield Quality

Fifty one okra F1 hybrids (using 17 lines as female and 3 testers as male parent) were evaluated in RCBD design during two different consecutive seasons (summer and rainy). Phenotypic coefficient of variability (PCV) was higher than genotypic coefficient of variability (GCV) for all studied character exhibiting environmental effects on the expression of characters. Heritability (h2b) along with genetic advance per cent of mean was found highest for character YVMV (86.95% and 150.61%). All the 51 okra hybrids were grouped into 4 distinct clusters in which Cluster II was the largest cluster having 28 F1s (54.90% of total F1s) followed by Cluster I with 14 F1s (27.45% of total F1s). Out of the major 6 PCs, 4 principal components (PC1, PC2, PC3 and PC4) accounted with proportionate values of 22.61, 17.22, 11.87 and 10.63%, respectively and contributed 62.33 % of the cumulative variation having Eigen value more than one. Moreover, based on PCs and genetic divergence in Cluster I and Cluster IV for plant height, YVMV and number of fruit per plant is important to identify the best cross combination (Arka Abhay × Arka Anamika) in okra. Therefore, the best cross combinations for improvement in various economic traits can be recommended on the basis of genetic divergence and principal component analysis in okra.

Study onGenetic Variability, Heritability and Genetic Advance for Seed Yield and Component Traits in Rice

2017 ◽  
Vol 4 (03) ◽  
Author(s):  
PUNIT KUMAR ◽  
VICHITRA KUMAR ARYA ◽  
PRADEEP KUMAR ◽  
LOKENDRA KUMAR ◽  
JOGENDRA SINGH
Keyword(s):  
Grain Yield ◽  
Plant Height ◽  
Seed Yield ◽  
Flag Leaf ◽  
Grain Weight ◽  
Major Influence ◽  
Genetic Advance ◽  
Component Traits ◽  
Biological Yield ◽  
1000 Grain Weight

A study on genetic variability, heritability and genetic advance for seed yield and component traits was made in 40 genotypes of riceduring kharif 2011-2012 at SHIATS, Allahabad. The analysis of variance showed highly significant differences among the treatments for all the 13 traits under study.The genotypes namely CN 1446-5-8-17-1-MLD4 and CR 2706 recorded highest mean performance for panicles per hill and grain yield. The highest genotypic and phenotypic variances (VG and VP) were recorded for spikelets per panicle (3595.78 and 3642.41) followed by biological yield (355.72 and 360.62) and plant height (231.48 and 234.35).High heritability (broad sense) coupled with high genetic advance was observed for plant height, flag leaf length, panicles per hill, tillers per hill, days to maturity, spikelet’s per panicle, biological yield, harvest index, 1000 grain weight and grain yield, indicating that selection will be effective based on these traits because they were under the influence of additive and additive x additive type of gene action. Highest coefficient of variation (PCV and GCV) was recorded for tillers per hill (18.42% and 17.23%), panicle per hill (19.76 % and 18.68%), spikelet’s per panicle (34.30 and34.07 %), biological yield (28.31 % and 28.12 %), 1000 grain weight (15.57 % and 15 31 %) and grain yield (46.66% and 23.54 %), indicating that these traits are under the major influence of genetic control, therefore the above mentioned traits contributed maximum to higher grain yield compared to other traits, indicating grain yield improvement through the associated traits.

scholarly journals Genetic advance on tandem selection of several soybean genotypes

2021 ◽  
Vol 672 (1) ◽  
pp. 012018
Author(s):  
M S Poerwoko ◽  
dan N Sjamsijah
Keyword(s):  
Genetic Advance ◽  
Soybean Genotypes ◽  
Selection Of

scholarly journals Trait Diversity, Heritability and Genetic Advance in Selected Germplasm Lines of Tef [Emgmstis tef(Zucc.) Trotter]

Hereditas ◽  
2000 ◽  
Vol 133 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Kebebew Assefa ◽  
Seyfu Ketema ◽  
Hailu Tefera ◽  
Tiruneh Kefyalew ◽  
Fufa Chundera
Keyword(s):  
Genetic Advance ◽  
Trait Diversity

Evaluating natural variation, heritability, and genetic advance of photosynthetic traits in rice ( Oryza sativa )

2021 ◽  
Author(s):  
Liana G. Acevedo‐Siaca ◽  
Robert Coe ◽  
W. Paul Quick ◽  
Stephen P. Long
Keyword(s):  
Oryza Sativa ◽  
Natural Variation ◽  
Genetic Advance ◽  
Photosynthetic Traits

scholarly journals Genetic worth of multiple sets of cowpea breeding lines destined for advanced yield testing

Euphytica ◽  
2021 ◽  
Vol 217 (2) ◽  
Author(s):  
Patrick Obia Ongom ◽  
Christian Fatokun ◽  
Abou Togola ◽  
Oluwaseye Gideon Oyebode ◽  
Mansur Sani Ahmad ◽  
...  
Keyword(s):  
Seed Weight ◽  
Genetic Variance ◽  
Principal Component ◽  
Performance Tests ◽  
Genetic Advance ◽  
Breeding Lines ◽  
Lattice Design ◽  
Mean And Variance ◽  
Genotypic Coefficient Of Variation ◽  
Location Interaction

AbstractThe objective of this study was to determine genetic potentials in eight sets of cowpea lines for grain yield (GY), hundred seed weight (HSDWT) and days to 50% flowering (DT50FL). A total of 614 F6 genotypes constituting the sets, grouped by maturity, were evaluated across two locations in Northern Nigeria, in an alpha lattice design, two replications each. Data were recorded on GY, HSDWT and DT50FL.Variance components, genotypic coefficient of variation (GCV), and genetic advance (GA) were used to decode the magnitude of genetic variance within and among sets. Genetic usefulness (Up) which depends on mean and variance to score the genetic merits in historically bi-parental populations was applied to groups of breeding lines with mixed parentage. Principal component analysis (PCA) was used to depict contribution of traits to observed variations. GY and DT50FL explained the variance within and between sets respectively. Genotypes were significantly different, although genotype-by-location and set-by-location interaction effects were also prominent. Genetic variance (δ2G) and GCV were high for GY in Prelim2 (δ2G = 45,897; GCV = 19.58%), HSDWT in Prelim11 (δ2G = 7.137; GCV = 17.07%) and DT50F in Prelim5 (δ2G = 4.54; GCV = 4.4%). Heritability varied among sets for GY (H = 0.21 to 0.57), HSDWT (H = 0.76 to 0.93) and DT50FL (H = 0.20 to 0.81). GA and percentage GA (GAPM) were high for GY in Prelim2 (GAPM = 24.59%; GA = 269.05Kg/ha), HSDWT in Prelim11 (GAPM = 28.54%; GA = 4.47 g), and DT50F in Prelim10 (GAPM = 6.49%; GA = 3.01 days). These sets also registered high values of genetic usefulness, suggesting potential application in non-full sib populations. These approaches can be used during preliminary performance tests to reinforce decisions in extracting promising lines and choose among defined groups of lines.

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