GENOTYPE-ENVIRONMENT INTERACTION VARIANCES IN YIELD TRIALS OF FALL RYE

Estimates of genotype by environment interaction variances were obtained from the western Canada Co-operative fall rye tests grown in 1963–1967. All first-order interactions and the second-order interactions were significantly greater than zero at the 0.05 level of probability. Although the estimate of cultivar by year interaction variance was relatively small, it accounted for 40% of the variance of a cultivar mean when only three years of testing were considered. However, testing in 20 locations for three years with four replicates could detect yield differences of approximately 10% of the mean of the highest yielding cultivar. If further reduction of the yield difference detectable is desired, more locations should be included in the test.

Download Full-text

Genotype by environment interaction for yearling weight in Nellore cattle applying reaction norms models

Animal Production Science ◽

10.1071/an17048 ◽

2018 ◽

Vol 58 (11) ◽

pp. 1996

Author(s):

S. Ribeiro ◽

J. P. Eler ◽

V. B. Pedrosa ◽

G. J. M. Rosa ◽

J. B. S. Ferraz ◽

...

Keyword(s):

Genetic Correlation ◽

Environmental Gradient ◽

Genotype By Environment Interaction ◽

Reaction Norms ◽

Breeding Value ◽

Environment Interaction ◽

Genotype By Environment ◽

Genotype Environment Interaction ◽

Nellore Cattle ◽

Gradient Variation

In the present study, a possible existence of genotype × environment interaction was verified for yearling weight in Nellore cattle, utilising a reaction norms model. Therefore, possible changes in the breeding value were evaluated for 46 032 animals, from three distinct herds, according to the environmental gradient variation of the different contemporary groups. Under a Bayesian approach, analyses were carried out utilising INTERGEN software resulting in solutions of contemporary groups dispersed in the environmental gradient from –90 to +100 kg. The estimates of heritability coefficients ranged from 0.19 to 0.63 through the environmental gradient and the genetic correlation between intercept and slope of the reaction norms was 0.76. The genetic correlation considering all animals of the herds in the environmental gradient ranged from 0.83 to 1.0, and the correlation between breeding values of bulls in different environments ranged from 0.79 to 1.0. The results showed no effect of genotype × environment interaction on yearling weight in the herds of this study. However, it is important to verify a possible influence of the genotype × environment in the genetic evaluation of beef cattle, as different environments might cause interference in gene expression and consequently difference in phenotypic response.

Download Full-text

Evaluation of sugarcane genotypes with respect to sucrose yield across three crop cycles using GGE biplot analysis

Experimental Agriculture ◽

10.1017/s0014479721000144 ◽

2021 ◽

pp. 1-13

Author(s):

Aliya Momotaz ◽

Per H. McCord ◽

R. Wayne Davidson ◽

Duli Zhao ◽

Miguel Baltazar ◽

...

Keyword(s):

Block Design ◽

Genotype By Environment Interaction ◽

Environment Interaction ◽

Gge Biplot ◽

Genotype By Environment ◽

Biplot Analysis ◽

Genotype Environment Interaction ◽

Area 5 ◽

Randomized Complete Block Design ◽

Main Effects

Summary The experiment was carried out in three crop cycles as plant cane, first ratoon, and second ratoon at five locations on Florida muck soils (histosols) to evaluate the genotypes, test locations, and identify the superior and stable sugarcane genotypes. There were 13 sugarcane genotypes along with three commercial cultivars as checks included in this study. Five locations were considered as environments to analyze genotype-by-environment interaction (GEI) in 13 genotypes in three crop cycles. The sugarcane genotypes were planted in a randomized complete block design with six replications at each location. Performance was measured by the traits of sucrose yield tons per hectare (SY) and commercial recoverable sugar (CRS) in kilograms of sugar per ton of cane. The data were subjected to genotype main effects and genotype × environment interaction (GGE) analyses. The results showed significant effects for genotype (G), locations (E), and G × E (genotype × environment interaction) with respect to both traits. The GGE biplot analysis showed that the sugarcane genotype CP 12-1417 was high yielding and stable in terms of sucrose yield. The most discriminating and non-representative locations were Knight Farm (KN) for both SY and CRS. For sucrose yield only, the most discriminating and non-representative locations were Knight Farm (KN), Duda and Sons, Inc. USSC, Area 5 (A5), and Okeelanta (OK).

Download Full-text

Association of single-nucleotide polymorphisms at the Delta locus with genotype by environment interaction for sensory bristle number in Drosophila melanogaster

Genetics Research ◽

10.1017/s0016672302005621 ◽

2002 ◽

Vol 79 (3) ◽

pp. 211-218 ◽

Cited By ~ 11

Author(s):

GRETCHEN L. GEIGER-THORNSBERRY ◽

TRUDY F. C. MACKAY

Keyword(s):

Standard Deviation ◽

Single Nucleotide Polymorphisms ◽

Genotype By Environment Interaction ◽

Stabilizing Selection ◽

Environment Interaction ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Genotype By Environment ◽

Bristle Number ◽

The Mean

The nature of forces maintaining variation for quantitative traits can only be assessed at the level of individual genes affecting variation in the traits. Identification of single-nucleotide polymorphisms (SNPs) associated with variation in Drosophila sensory bristle number at the Delta (Dl) locus provides us with the opportunity to test a model for the maintenance of variation in bristle number by genotype by environment interaction (GEI). Under this model, genetic variation is maintained at a locus under stabilizing selection if phenotypic values of heterozygotes are more stable than homozygotes across a range of environments, and the mean allelic effect is much smaller than the standard deviation of allelic effects across environments. Homozygotes and heterozygotes for two SNPs at Dl, one affecting sternopleural and the other abdominal bristle number, were reared in five different environments. There was significant GEI for both bristle traits. Neither condition of the model was satisfied for Dl SNPs exhibiting GEI for sternopleural bristle number. Heterozygotes for the abdominal bristle number SNPs were indeed the most stable genotype for two of the three environment pairs exhibiting GEI, but the mean genotypic effect was greater than the standard deviation of effects across environments. Therefore, this mechanism of GEI seems unlikely to be responsible for maintaining the common bristle number polymorphisms at Dl.

Download Full-text

Multi-Environment Trial Analysis of Food Barley in Ethiopia Using AMMI and GGE Biplot Methods

Journal of Plant Breeding and Genetics ◽

10.33687/pbg.006.03.2579 ◽

2018 ◽

Vol 6 (3) ◽

pp. 75-85 ◽

Cited By ~ 2

Author(s):

Girma Fana ◽

Diriba Tadese ◽

Hiwot Sebsibe ◽

Ramesh P.S. Verma

Keyword(s):

Genotype By Environment Interaction ◽

Environment Interaction ◽

Gge Biplot ◽

Lower Yield ◽

Genotypic Effect ◽

Genotype By Environment ◽

Trial Analysis ◽

Ammi Model ◽

The Mean ◽

The Ideal

Food barley released varieties were tested in 2012 for performance across major environments in Ethiopia consisting of 12 varieties Diribe, Tilla, Abbay, Biftu, Defo, Dinsho, Mulu, Setegn, Misiratch, Basso, Mezezo and local checks over six locations Gergera, Estayish, Shambu, Arjo, Robe and Sinana. The objective was to determine genotype by environment interaction using AMMI and GGE biplot, compare the two models for identifying the adaptable and stable genotypes. Sinana was identified as the high yielding environment and MULU the high yielding variety with mean yields of 3466.31 and 3137.67 kg/ha, respectively. The mean yield at Estayish was lower (1535 kg/ha) than other environments whereas lower yield (2212.16 kg/ha) was also obtained from the variety DINSHO. The AMMI analysis of Variance indicated that 47% of the total sum of squares is attributed to the Environmental effect, 8% to the genotypic effect and 25% to the interaction. The first three principal components of the GEI explained 81% of the variation. Genotypes Basso, Biftu and Setegn were the most stable whereas Diribe was unstable. Variety Mulu was identified as the winner genotype by AMMI model whereas Diribe was identified as the winner by the GGE model. GGE model better explains the which-won-where scenario and hence preferred to AMMI model. The discriminating and representative view of the GGE biplot depicted that Sinana and Shambu are discriminating environments whereas Sinana, Estayish and Gergera are representative environments. Therefore, Sinana is the ideal environment for discriminating genotypes and representing other environments for selecting ideal genotypes.

Download Full-text

Simultaneous selection of yield and yield stability in chickpea genotypes using the GGE biplot technique

Acta Agronomica Hungarica ◽

10.1556/aagr.61.2013.3.2 ◽

2013 ◽

Vol 61 (3) ◽

pp. 185-194 ◽

Cited By ~ 1

Author(s):

E. Farshadfar

Keyword(s):

Grain Yield ◽

Principal Component ◽

Environment Interaction ◽

Gge Biplot ◽

Yield Data ◽

Genotype By Environment ◽

Growing Seasons ◽

Genotype Environment Interaction ◽

The Mean ◽

Selection Of

GGE biplot analysis is an effective method, based on principal component analysis (PCA), to fully explore multi-environment trials (METs). It allows visual examination of the relationships among the test environments, genotypes and the genotype-by-environment interactions (G×E interaction). The objective of this study was to explore the effect of genotype (G) and the genotype × environment interaction (GEI) on the grain yield of 20 chickpea genotypes under two different rainfed and irrigated environments for 4 consecutive growing seasons (2008–2011). The yield data were analysed using the GGE biplot method. The first mega-environment contained environments E1, E3, E4 and E6, with genotype G17 (X96TH41K4) being the winner; the second mega-environment contained environments E5, E7 and E8, with genotype G12 (X96TH46) being the winner. The E2 environment made up another mega-environment, with G19 (FLIP-82-115) the winner. The mean performance and stability of the genotypes indicated that genotypes G4, G16 and G20 were highly stable with high grain yield.

Download Full-text

Analysis of Genotype × Environment Interaction for milk production in dairy cow

Scientific Messenger of LNU of Veterinary Medicine and Biotechnology ◽

10.32718/nvlvet8905 ◽

2018 ◽

Vol 20 (89) ◽

pp. 27-34

Author(s):

S. S. Kramarenko ◽

N. I. Kuzmichova ◽

A. S. Kramarenko

Keyword(s):

Milk Yield ◽

Genotype By Environment Interaction ◽

State Enterprise ◽

Environment Interaction ◽

Genotype By Environment ◽

Genotype Environment Interaction ◽

Lactation Curve ◽

Study Results ◽

Milk Performance ◽

Calving Season

Genotype by environment interaction was studied with 526 lactation milk records of Red Steppe dairy cows maintained at State Enterprise “Breeding reproducer “Stepove” (Mykolayiv region, Ukraine). The analyses in this study were based on the milk yields of cow per 1st–10th month (M1–M10) and per 305 day for complete lactations (Y305). We tested the hypotheses that milk performance were influenced by the sire (factor “Sire”), by number of lactation (factor “NoL”), by of cow’s year of born (factor “Generation”) and by the season of calving (factor “SoC”). The data were analysed with the “Variance components” and the “ANOVA/MANOVA” modules of statistical software STATISTICA (StatSoft Inc, USA). Experimental cows originated from five sires. The effect of the sire was significantly expressed in milk yield from the 2nd to 7th month of lactation (in all cases: P < 0.001–0.024) and Y305 (P = 0.011). The 12-year period studied (year of cow’s birth from 2001 to 2011) was classified into four periods as follows: G1 – 2001–2003, G2 – 2004–2006, G3 – 2007–2009 and G4 – 2010–2011. Year of birth (factor “Generation”) had significant (in all cases: P < 0.001–0.044) effect on all traits studied (but not on M7–M8). All cows were divided according to the season of calving (SoC): winter (December to February), spring (March to May), summer (June to August) and autumn (September to November). The production of milk for M1–M2, M4–M8 and M10 (but not for 305 day lactation) statistically differed according to the season of calving (in all cases: P < 0.05). From the study results, a significant relationship was found between the milk yield and lactation number, with the maximum milk yield occurring in the third lactation cows (pattern 1 < 2 < 3 = 4+). Milk yields from the M1 to M6 month of lactation (in all cases: P < 0.001–0.017) and Y305 (P < 0.001) were statistically different between cows according to the number of lactation. Cow’s lactation number, year of birth and calving season causes differences in the shape and persistency of lactation curve. Genotype by environment interactions for lactation number and cow’s year of birth can be result in re-ranking of sire between the different environments.

Download Full-text

Using AMMI approach to delineate genotype by environment interaction and stability of barley (Hordeum vulgare L.) genotypes under northern Indian shivalik hill conditions

Indian Journal of Genetics and Plant Breeding (The) ◽

10.31742/ijgpb.80.3.15 ◽

2020 ◽

Vol 80 (03) ◽

Author(s):

Om Prakash Yadav ◽

A. K. Razdan ◽

Bupesh Kumar ◽

Praveen Singh ◽

Anjani K. Singh

Keyword(s):

Hordeum Vulgare ◽

Grain Yield ◽

Principal Component ◽

Genotype By Environment Interaction ◽

Environment Interaction ◽

Hordeum Vulgare L ◽

Genotype By Environment ◽

Biplot Analysis ◽

Principal Component Axis ◽

Ammi Analysis

Genotype by environment interaction (GEI) of 18 barley varieties was assessed during two successive rabi crop seasons so as to identify high yielding and stable barley varieties. AMMI analysis showed that genotypes (G), environment (E) and GEI accounted for 1672.35, 78.25 and 20.51 of total variance, respectively. Partitioning of sum of squares due to GEI revealed significance of interaction principal component axis IPCA1 only On the basis of AMMI biplot analysis DWRB 137 (41.03qha–1), RD 2715 (32.54qha–1), BH 902 (37.53qha–1) and RD 2907 (33.29qha–1) exhibited grain yield superiority of 64.45, 30.42, 50.42 and 33.42 per cent, respectively over farmers’ recycled variety (24.43qha–1).

Download Full-text