Drivers of genotype by environment interaction in radiata pine as indicated by multivariate regression trees

2015 ◽  
Vol 353 ◽  
pp. 21-29 ◽  
Author(s):  
Washington J. Gapare ◽  
Miloš Ivković ◽  
Katharina J. Liepe ◽  
Andreas Hamann ◽  
Charlie B. Low
Keyword(s):  
Multivariate Regression ◽  
Regression Trees ◽  
Radiata Pine ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Multivariate Regression Trees ◽  
Genotype By Environment

scholarly journals Pattern of genotype by environment interaction for radiata pine in southern Australia

2014 ◽  
Vol 72 (3) ◽  
pp. 391-401 ◽  
Author(s):  
Miloš Ivković ◽  
Washington Gapare ◽  
Huixiao Yang ◽  
Gregory Dutkowski ◽  
Peter Buxton ◽  
...  
Keyword(s):  
Radiata Pine ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment

scholarly journals Genetic Parameters and Genotype by Environment Interaction in Radiata Pine for Growth and Wood Quality Traits in Australia

2010 ◽  
Vol 59 (1-6) ◽  
pp. 113-124 ◽  
Author(s):  
Brian. S. Baltunis ◽  
W. J. Gapare ◽  
H. X. Wu
Keyword(s):  
Genetic Correlation ◽  
Genetic Parameters ◽  
Radiata Pine ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Tree Diameter ◽  
Genotype By Environment ◽  
Branch Angle ◽  
Stem Straightness ◽  
Branch Size

Abstract The phenotypic response of genotypes across different environments can be quantified by estimating the genotype by environment interaction (GxE). In a practical sense, GxE means that the relative performance of genotypes does not remain constant under all test conditions. Genetic parameters and genotype by environment interactions for wood density, growth, branching characteristics and stem straightness were investigated in eight radiata pine progeny trials derived from a second generation breeding population in Australia. Five trials were on the mainland, while three trials were in Tasmania. Generally, ĥ2 for density > branch angle > stem straightness > tree diameter > branch size; and significant ĥ2 was observed for all traits and at all trials with only two exceptions. Genetic correlations were estimated among the five traits, and a large negative genetic correlation observed between wood density and tree diameter indicated that a selection strategy should be developed in dealing with this adverse genetic correlation in advanced generations of breeding for radiata pine. Interactions for density, branch angle, and stem straightness were small within the two regions. Overall, branch angle had the least GxE, followed by density and stem straightness. Growth traits (tree diameter and branch size) tended to be the most interactive with substantial GxE present. Genotype by regional interactions (Mainland versus Tasmania) revealed that density and branch angle had the least interactions (ȓB = 0.98 and ȓB = 0.95, respectively). Branch size and tree diameter had the highest interactions among the two regions (ȓB = 0.55 and ȓB = 0.63, respectively). Within Tasmania, only branch size and tree diameter had a sizable interaction within the three sites. In contrast, there was little interaction for tree diameter among the Mainland trials. Branch size in the Mainland trials had a similar size of interaction as in Tasmania. Further research is recommended in identifying the cause of GxE for tree diameter and branch size in radiata pine across the entire radiata pine estate in Australia.

scholarly journals Bayesian Additive Regression Trees for Genotype by Environment Interaction Models - AMBARTI

2021 ◽  
Author(s):  
Danilo Augusto Sarti ◽  
Estevão Batista Prado ◽  
Alan Inglis ◽  
Antônia Alessandra Lemos dos Santos ◽  
Catherine Hurley ◽  
...  
Keyword(s):  
Regression Trees ◽  
R Package ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Real World Data ◽  
Data Set ◽  
Genotype By Environment ◽  
Additive Regression ◽  
Fully Bayesian ◽  
Bayesian Additive Regression Trees

We propose a new class of models for the estimation of Genotype by Environment (GxE) interactions in plantbased genetics. Our approach, named AMBARTI, uses semiparametric Bayesian Additive Regression Trees to accurately capture marginal genotypic and environment effects along with their interaction in a fully Bayesian model. We demonstrate that our approach is competitive or superior to the traditional AMMI models widely used in the literature via both simulation and a real world data set. Furthermore, we introduce new types of visualisation to properly assess both the marginal and interactive predictions from the model. An R package that implements our approach is available at https://github.com/ebprado/ambarti .

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

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).

Exploring genotype‐by‐environment interaction in sweet sorghum under tropical environments

2021 ◽  
Author(s):  
Vander Fillipe Souza ◽  
Pedro César de Oliveira Ribeiro ◽  
Indalécio Cunha Vieira Júnior ◽  
Isadora Cristina Martins Oliveira ◽  
Cynthia Maria Borges Damasceno ◽  
...  
Keyword(s):  
Sweet Sorghum ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Tropical Environments

Genotype by environment interaction on phenology and synchronous maturity of mungbean

2021 ◽  
Author(s):  
Siti Marwiyah ◽  
Willy Bayuardi Suwarno ◽  
Desta Wirnas ◽  
Trikoesoemaningtyas xxx ◽  
Surjono Hadi Sutjahjo
Keyword(s):  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment

scholarly journals Genotype and environment interaction of sweetpotato varieties

2019 ◽  
Vol 44 (3) ◽  
pp. 501-512
Author(s):  
S Sultana ◽  
HC Mohanta ◽  
Z Alam ◽  
S Naznin ◽  
S Begum
Keyword(s):  
Tuber Yield ◽  
Agricultural Research ◽  
Genotype By Environment Interaction ◽  
Yield Potential ◽  
Environment Interaction ◽  
Dry Matter Content ◽  
Genotype By Environment ◽  
Discriminating Power ◽  
Study Results ◽  
Main Effect

The article presents results of additive main effect and multiplicative interaction (AMMI) and genotype (G) main effect and genotype by environment (GE) interaction (G × GE) biplot analysis of a multi environmental trial (MET) data of 15 sweetpotato varieties released from Bangladesh Agricultural Research Institute conducted during 2015–2018. The objective of this study was to determine the effects of genotype, environment and their interaction on tuber yield and to identify stable sweetpotato genotypes over the years. The experimental layout was a randomized complete block design with three replications at Gazipur location. Combined analysis of variance (ANOVA) indicated that the main effects due to genotypes, environments and genotype by environment interaction were highly significant. The contribution of genotypes, environments and genotype by environment interaction to the total variation in tuber yield was about 60.16, 10.72 and 12.82%, respectively. The first two principal components obtained by singular value decomposition of the centred data of yield accounted for 100% of the total variability caused by G × GE. Out of these variations, PC1 and PC2 accounted for 71.5% and 28.5% of variability, respectively. The study results identified BARI Mistialu- 5, BARI Mistialu- 14 and BARI Mistialu- 15 as the closest to the “ideal” genotype in terms of yield potential and stability. Varieties ‘BARI Mistialu- 8, BARI Mistialu- 11 and BARI Mistialu- 12’ were also selected as superior genotypes. BARI Mistialu- 3 and BARI Mistialu- 13 was comparatively low yielder but was stable over the environment. Among them BARI Mistialu-12, BARI Mistialu-14 and BARI Mistialu-15 are rich in nutrient content while BARI Mistialu-8 and BARI Mistialu-11 are the best with dry matter content and organoleptic taste. Environments representing in 1st and 3rd year with comparatively short vectors had a low discriminating power and environment in 2nd year was characterized by a high discriminating power. Bangladesh J. Agril. Res. 44(3): 501-512, September 2019

EFFECTS OF GROWTH INHIBITION BY HYDROCORTISONE ACETATE ON VARIANCE IN CHICKENS

1970 ◽  
Vol 12 (3) ◽  
pp. 627-634
Author(s):  
J. S. Gavora ◽  
G. C. Hodgson
Keyword(s):  
Body Weight ◽  
External Environment ◽  
Hormonal Treatment ◽  
Genotype By Environment Interaction ◽  
Future Research ◽  
Environment Interaction ◽  
Internal Environment ◽  
Hydrocortisone Acetate ◽  
Genotype By Environment ◽  
Treatment Interaction

Traditionally genotype by environment interaction studies have dealt with changes in external environment. In this experiment an attempt was made to alter internal environment and keep external environment constant. Cockerels from each of six different commercial stocks were injected with 0,1,2 and 4 mgs hydrocortisone acetate per 100 gms body weight at 14 days of age. This type of hormonal treatment was shown to release additional variability in growth without producing any stock-treatment interaction at the level of means. The results indicate a possible new avenue for future research.

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