Joint regression analysis and AMMI model applied to oat improvement

2012 ◽  
Author(s):  
A. Oliveira ◽  
T. A. Oliveira ◽  
S. Mejza
Keyword(s):  
Regression Analysis ◽  
Ammi Model ◽  
Joint Regression Analysis

A comparison between joint regression analysis and the AMMI model: a case study with barley

2012 ◽  
Vol 82 (2) ◽  
pp. 193-207 ◽  
Author(s):  
Dulce G. Pereira ◽  
Paulo C. Rodrigues ◽  
Stanislaw Mejza ◽  
João T. Mexia
Keyword(s):  
Regression Analysis ◽  
Ammi Model ◽  
Joint Regression Analysis

Genotype–environment interaction of lovegrass forage yield in the semi-arid region of Argentina

2001 ◽  
Vol 137 (3) ◽  
pp. 329-336 ◽  
Author(s):  
M. A. IBAÑEZ ◽  
M. A. DI RENZO ◽  
S. S. SAMAME ◽  
N. C. BONAMICO ◽  
M. M. POVERENE
Keyword(s):  
Regression Analysis ◽  
Arid Region ◽  
Interaction Effects ◽  
Sum Of Squares ◽  
Forage Yield ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Ammi Model ◽  
Semi Arid Region ◽  
Semi Arid

Genotype–environment interaction and yield stability were evaluated for 19 genotypes of lovegrass (Eragrostis curvula). The study was conducted in the central semi-arid region of Argentina. Three locations and two growing seasons in combination generated six environments. Genotypic responses and stability of yield under variable environments were investigated. The genotype–environment interaction was analysed by three methods: regression analysis, AMMI and principal coordinates analysis (PCO). Analysis of variance showed that effects of genotype, environment and genotype–environment interaction were highly significant (P < 0·01). The genotypes accounted for 20% of the treatment sum of squares, with environment responsible for 65% and interaction for 14·5%. The biplot indicated that there was partial agreement between the AMMI and regression model. However the scatter point diagrams obtained from PCO analysis revealed only limited agreement with the results obtained by the regression analysis and the AMMI model. The results show that the AMMI model as a whole explained twice as much of the interaction sum of squares as did regression analysis and was more adequate than PCO analysis in quantifying environment and genotype effects for forage yield. AMMI analysis of the genotype–environment interaction effects showed that there were responses characteristic of a particular location. This type of association implies some predictability of genotype–environment interaction effects on forage yield production when differential responses across genotypes are associated with locations. Environmental factors may contribute to the interpretations of genotype–environment interaction. However in the semi-arid region, where fluctuations in growing conditions are unpredictable, additional research is required to obtain an integration of interaction analysis with external environmental (or genotypic) variables.

scholarly journals PEMANFAATAN ANALISIS REGRESI DAN AMMI UNTUK EVALUASI STABILITAS HASIL GENOTIPE PADI DAN PENGARUH INTERAKSI GENETIK DAN LINGKUNGAN

2015 ◽  
Vol 22 (1) ◽  
pp. 21 ◽  
Author(s):  
Yuni Widyastuti
Keyword(s):  
Multivariate Analysis ◽  
Regression Analysis ◽  
Primary Component ◽  
Statistical Analyses ◽  
Additive Effects ◽  
Average Effect ◽  
Ammi Model ◽  
The Stability ◽  
Average Index

<div data-canvas-width="788.5">Some statistical analyses were employed to depict responses of genotype (G) on environment (E). Regression analysis</div><div data-canvas-width="802.6966666666665">reflects the average index of G x E to calculate the genotype responses to heterogenous environment. Regression deviation</div><div data-canvas-width="802.6916666666664">was employed to count the stability of the result obtained through such a method, which, further be developed to test</div><div data-canvas-width="802.7233333333332">the average effect of G x E through a combination of additive effects of multivariate analysis with multiplication effect</div><div data-canvas-width="802.6350000000001">on the primary component (AMMI model). Regression analysis showed that Ciherang, Hibrindo R1, IH806 hybrid was</div><div data-canvas-width="339.73166666666657">more superior than others in terms of stability and</div><div data-canvas-width="462.62666666666667">product adaptability, whereas IH805, IH808, and IH809, are specific</div><div data-canvas-width="424.0566666666667">for locations Batang, Jember, Ngawi, and Madiun, respectively.</div>

Comparison of locations used in cotton-breeding trials

1992 ◽  
Vol 32 (6) ◽  
pp. 739 ◽  
Author(s):  
ER Williams ◽  
DJ Luckett ◽  
PE Reid ◽  
NJ Thomson
Keyword(s):  
Regression Analysis ◽  
The Other ◽  
Cultivar Differences ◽  
Yield Data ◽  
Cotton Breeding ◽  
Eastern Australia ◽  
Discrimination Criterion ◽  
The Mean ◽  
Joint Regression Analysis ◽  
The Stability

Cotton-breeding trials are conducted annually throughout the commercial growing regions of eastern Australia. Accumulated yield data for the period 1974-85 were assembled into an incomplete cultivar x location x year table. This table was then analysed in order to compare test locations. The method involved analysing cultivar x location tables separately for each year, using symmetric joint regression analysis. Results were then collected into location x year tables and further analysed. Four criteria for comparing test locations were developed. The discrimination criterion is important when locations are evaluated in terms of their ability to display cultivar differences. The representation criterion measures the ability of a location to mirror the relative performance of cultivars over all locations. The other 2 criteria are concerned with the mean yield at test locations and the stability of location yields over years. Based on the 4 criteria, preferred test locations are recommended.

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