Comparison of Joint Regressing Analysis (JRA) and additive main effect and multiplication interaction (AMMI) model in the study of GXE interaction in soybean

2008 ◽  
Vol 20 (1) ◽  
Author(s):  
CO Aremu ◽  
DK Ojo ◽  
OA Oduwaye ◽  
JO Amira
Keyword(s):  
Gxe Interaction ◽  
Main Effect ◽  
Ammi Model ◽  
Regressing Analysis

scholarly journals Provenance by Site Interaction of Pinus densiflora in Korea

2008 ◽  
Vol 57 (1-6) ◽  
pp. 131-139 ◽  
Author(s):  
In-Sik Kim ◽  
Hae-Yun Kwon ◽  
Keun-Ok Ryu ◽  
Wan Yong Choi
Keyword(s):  
Regression Model ◽  
Pinus Densiflora ◽  
Interaction Model ◽  
Height Growth ◽  
Seed Transfer ◽  
Gxe Interaction ◽  
Main Effect ◽  
Ammi Model ◽  
Interaction Term ◽  
Component Scores

Abstract Thirty-six provenances of Pinus densiflora were evaluated for stability and adaptability for height growth at 11 test sites in Korea. The data were obtained from measurements at age 6 and analyzed using linear regression model and AMMI (additive main effect and multiplicative interaction) model. There was significant provenance by site interaction effect (p < 0.011). The interaction term explained 7.1% of total variation. While the regression model accounted for 15.8% of GxE interaction term, the AMMI model accounted for 74.9% with four PCA values. Most of the provenances were not significantly different from the unity (b =1.0), except for Inje (1), Jungsun (4), Bongwha (5), Koryung (26), Hamyang (30) and Seoguipo (36). Adaptability of provenances to the test sites was estimated with mean height growth and first AMMI component scores (IPCA 1). Inje (1), Bongwha (5), Taean (20) and Seoguipo (36) were specifically adapted to the high yielding environments. Considering the first and second AMMI components (IPCA 1 and IPCA 2, respectively) scores, Whachun (2), Samchuk (10), Joongwon (14) and Buan (29) provenances were more stable than others. The implication of GxE interaction was discussed in view of seed transfer and delineation of seed zones.

scholarly journals Interaksi Genotipe x Lingkungan Hasil dan Komponen Hasil 14 Genotipe Tomat di Empat Lingkungan Dataran Rendah

2015 ◽  
Vol 43 (1) ◽  
pp. 59
Author(s):  
Suprayanti Martia Dewi ◽  
Sobir , ◽  
Muhamad Syukur
Keyword(s):  
Stability Analysis ◽  
Block Design ◽  
Fruit Weight ◽  
Genotype X Environment Interaction ◽  
Environment Interaction ◽  
Genotype X Environment ◽  
Gxe Interaction ◽  
Superior Genotype ◽  
Ammi Model ◽  
The Stability

Genotype x environment interaction (GxE) information is needed by plant breeders to assist the identification of superior genotype. Stability analysis can be done if there is a GxE interaction, to show the stability of a genotype when planted in different environments. This study aimed to estimate the effects of genotype x environment interaction on yield and yield components of fruit weight per plant as well as to look at the stability of 14 tomato genotypes at four lowland locations. The study was conducted at four locations, namely Purwakarta, Lombok, Tajur and Leuwikopo. Experiments at each location was arranged in a randomized complete block design with three replications. Stability analysis was performed using the AMMI model. Fruit weight, fruit diameter, number of fruits per plant and total fruit weight per plant characters showed highly significant genotype x environment interactions. Variability due to the effect of GxE interaction based on a AMMI2 contributed by 88.50%. IPBT3, IPBT33, IPBT34, IPBT60 and Intan were stable genotypes under AMMI model.<br />Keywords: AMMI, multilocation trials

Simultaneous Use of AMMI Model and Yield for Stability Analysis of Wheat Genotypes Evaluated Under Central Zone of the India

2020 ◽  
Vol 2 (6) ◽  
Author(s):  
Ajay Verma ◽  
Gyanendra Pratap Singh
Keyword(s):  
Central Zone ◽  
High Yield ◽  
First Year ◽  
Wheat Genotypes ◽  
Gxe Interaction ◽  
Ammi Analysis ◽  
Stable Performance ◽  
Ammi Model ◽  
Simultaneous Use ◽  
High Degree

AMMI analysis had observed highly significant effects of environment (E), GxE interaction and genotypes (G) during 2018-19 and 2019-20 years of study. Suitability of PBW822, HI8811 & HI8713 genotypes as compared to HD3345 by WAASB measure for first year. Superiority index found HD3345, PBW822 & NIDW1158 as of stable performance with high yield. PRVG measures settled for HI8811, GW322 & HI 8737 and MHPRVG considered HI8811, HI8713 & GW322 wheat genotypes. All negative values of correlations exhibited by SI measure whereas WAASB measure exhibited direct relationships as well as negative values with SI, PRVG, MHPRVG and yield. WAASB measure observed suitability of GW513, HI1636 & MACS6747 wheat genotypes for the second year. Superiority index found GW513, HI1636 & HI1544 as of stable performance along with high yield. PRVG as well as MHPRVG measures observed suitability of GW513, HI1636, & MP1361 while HD3377 as unstable wheat genotype. SI measure had expressed only indirect relations of high degree with other measures except of positive values with yield, PRVG and MHPRVG. Measure WAASB had exhibited direct relations with most of measures along with negative correlation for SI, yield, PRVG and MHPRVG values. Stability measures by simultaneous use of AMMI analysis and average yield of genotypes would be more meaningful as compared to measures based either on the AMMI or yield only.

scholarly journals GENOTYPE-LOCATION INTERACTION OF INDICA RICE USING AMMI MODEL

2011 ◽  
Vol 24 (2) ◽  
pp. 09-18
Author(s):  
M. J. Hasan ◽  
M. U. Kulsum ◽  
M. S. Hossain ◽  
M. M. Billah ◽  
A Ansary
Keyword(s):  
Interaction Effect ◽  
Indica Rice ◽  
Average Yield ◽  
Phenotypic Stability ◽  
Positive Interaction ◽  
G X E Interaction ◽  
Main Effect ◽  
Days To Maturity ◽  
Ammi Model ◽  
Rice Genotypes

Phenotypic stability of 12 rice genotypes for plant height, days to maturity and yield were assessed at five different locations through regression and deviation from regression using Additive Main Effect and Multiplicative Interaction (AMMI) model. The result showed highly significant genotypic and G x E interaction. The G x E interaction influenced the relative ranking of the genotypes tested, BR1A/BR827R, Teea, BRRI dhan33 and Mayna showed low interaction effect with score nearest to zero with above average yield. While two genotypes BRRI hybrid dhan4 and Heera995 exhibited high positive interaction effect, gave mean grain yield around 5 ton/ha and was better suited to favorable environments. Similarly AMMI characterized the environments and identified Satkhira as a favorable environment for the better expression of the trait studied.DOI: http://dx.doi.org/10.3329/bjpbg.v24i2.17001

scholarly journals Stability analysis of bread wheat landraces and lines using biometrical genetic models

Genetika ◽  
2018 ◽  
Vol 50 (2) ◽  
pp. 449-464
Author(s):  
Fatemeh Bavandpori ◽  
Jafar Ahmadi ◽  
Sayyed Hossaini
Keyword(s):  
Bread Wheat ◽  
Block Design ◽  
Gge Biplot ◽  
Breeding Programs ◽  
Biplot Analysis ◽  
Genotype Effect ◽  
Randomized Complete Block Design ◽  
Main Effect ◽  
Ammi Analysis ◽  
Ammi Model

In order to evaluate yield stability of twenty genotypes of bread wheat, an experiment was conducted in randomized complete block design (RCBD) with three replications under irrigated and rainfed conditions in Razi University of Kermanshah for three years (2011-2013). Combined analysis of variance showed highly significant differences for the GEI. Stability determined by AMMI analysis indicated that the first two AMMI model (AMMI1-AMMI2) were highly significant (P<0.01). The GEI was three times higher than that of the genotype effect. The results of Biplot AMMI2 showed that, genotypes WC-47359, WC-47472, WC-4611, WC-47388 and WC-47403 had general adaptability. Based on the ASV and GSI, the genotypes number WC-47403 and WC-47472 revealed the highest stability. GGE biplot analysis of yield displaying main effect G and GEI justified 57.5 percent of the total variation. The first two principal components (PC1 and PC2) were used to create a 2-dimensional GGE biplot and explained 34.3, 23.2 of GGE sum of squares (SS), respectively. Genotypes WC-47403, PISHGAM2 exhibited the highest mean yield and stability. Based on the results obtained the best genotypes were WC-47403, PISHGAM2, WC-4968, WC-47472 and WC-47528 for breeding programs.

scholarly journals Yield stability of rapeseed genotypes under drought stress conditions

2019 ◽  
Vol 79 (01) ◽  
Author(s):  
Mehdipour Sara ◽  
Rezaeizad Abbas ◽  
Azizinezhad Reza ◽  
Etminan Alireza
Keyword(s):  
Genotype X Environment ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Gxe Interaction ◽  
Cropping Season ◽  
Moisture Regimes ◽  
Ammi Model ◽  
Gxe Interactions ◽  
Cropping Seasons ◽  
Poor Stability

Genotype by Environment (GxE) interactions of 29 rapeseed genotypes in normal irrigation and irrigation cut off from flowering and silique formation stages have been worked out from the data recorded during three cropping seasons. Combined variance analysis showed a significant variation for year (cropping season), moisture regimes, genotype, genotype x moisture regimes and genotype x year interactions. Results of AMMI model analysis showed that three first genotype x environment principal components (PC) were significant at 1% level of probability and fourth PC at 5% level. These four components explained 35.6, 24.4, 18.4 and 14.8 per cent of the GxE sum of squares, respectively. According to AMMI2 biplot analysis, genotypes such as L155, Neptune, Elvise, Jerry, GkGabriella, Sw102, GKH0224, Julius, GKH3705 and Sarigol were positioned in the center of the biplot so had the least GxE interaction and showed the most general compatibility. Based on simultaneous selection, winter type of genotypes namely, GKH2624, SW102, HW118, GKH3705, Wpn6 and L72 were identified as high yielding and stable whereas, spring genotypes namely, Zabol10, Dalgan, Jerome and Hyola4815 were identified as low yielding with poor stability.

scholarly journals Model selection in additive main effect and multiplicative interaction model in durum wheat

Genetika ◽  
2012 ◽  
Vol 44 (2) ◽  
pp. 325-339 ◽  
Author(s):  
Naser Sabaghnia ◽  
Rahmatollah Karimizadeh ◽  
Mohtasham Mohammadi
Keyword(s):  
Durum Wheat ◽  
Interaction Model ◽  
Principal Component ◽  
Wheat Genotype ◽  
Ge Interaction ◽  
Stability Parameters ◽  
Data Set ◽  
Group Iii ◽  
Main Effect ◽  
Ammi Model

The study included data set of 20 durum wheat genotype across 15 rain-fed environments. A combined analysis of variance showed that the genotypes differed significantly for seed yield and GE (year ? location) interaction. Cross validations procedure and four various F-tests including FGollob, FRatio, FGH1 and FGH2 are used for testing the GE interaction principal component analysis (IPCA) axes and indicated that two, four, six or seven axes could be significant. According to EV1, D1, AMGE1 and SIPC1 parameters, genotypes G3, G7 and G17 were the most stable genotypes while based on EV4, D4, SIPC4 and AMGE4 parameters, genotype G13 was the most stable genotype. The hierarchical clustering showed that the twenty one studied the AMMI stability parameters and mean yield could be divided into four distinct groups. Group III contains mean yield, SIPC4, SIPC6 and SIPC8 which were computed from four, six or eight IPCAs. In conclusion, G13 (DON-MD 81- 36) was found to be the most stable genotype as well as high mean yield performance (2592.45 kg ha-1) and so is recommended for commercial release in semi-arid areas of Iran. Also, the SIPC-based stability parameters of the AMMI model was found to be useful in detecting the yield stability of the genotypes studied.

scholarly journals Choosing components in the additive main effect and multiplicative interaction (AMMI) models

2006 ◽  
Vol 63 (2) ◽  
pp. 169-175 ◽  
Author(s):  
Carlos Tadeu dos Santos Dias ◽  
Wojtek Janusz Krzanowski
Keyword(s):  
Simulated Data ◽  
User Needs ◽  
Data Set ◽  
Multiplicative Interaction ◽  
Main Effect ◽  
Ammi Model

The additive main effect and multiplicative interaction (AMMI) models allows analysts to detect interactions between rows and columns in a two-way table. However, there are many methods proposed in the literature to determine the number of multiplicative components to include in the AMMI model. These methods typically give different results for any particular data set, so the user needs some guidance as to which methods to use. In this paper we compare four commonly used methods using simulated data based on real experiments, and provide some general recommendations.

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