scholarly journals THE USE OF AMMI MODEL FOR INTERPRETING GENOTYPE × ENVIRONMENT INTERACTION IN DURUM WHEAT

2017 ◽  
Vol 54 (5) ◽  
pp. 670-683 ◽  
Author(s):  
REZA MOHAMMADI ◽  
MOHAMMAD ARMION ◽  
ESMAEIL ZADHASAN ◽  
MALEK MASOUD AHMADI ◽  
AHMED AMRI
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Air Temperature ◽  
Heading Date ◽  
Principal Component ◽  
High Yield ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Breeding Lines ◽  
Ammi Model

SUMMARYDurum wheat (Triticum durum) is one of the most important cereal crops in the Mediterranean region; however, its cultivation suffers from low yield due to environmental constrains. The main objectives of this study were to (i) assess genotype × environment (GE) interaction for grain yield in rainfed durum wheat and to (ii) analyse the relationships of GE interaction with genotypic/meteorological variables by the additive main effects and multiplicative interaction (AMMI) model. Grain yield and some related traits were evaluated in 25 durum wheat genotypes (landrace, breeding line, old and new varieties) in 12 rainfed environments differing in winter air temperature. The AMMI analysis of variance indicated that the environment had highest contribution (84.3% of total variation) to the variation in grain yield. The first interaction principal component axis (IPCA1) explained 77.5% of GE interaction sum of squares (SS), and its effect was 5.5 times greater than the genotype effect, indicating that the IPCA1 contributed remarkably to the total GE interaction. Large GE interaction for grain yield was detected, indicating specific adaptation of genotypes. While the postdictive success method indicated AMMI-4 as the best model, the predictive success one suggested AMMI-1. The AMMI biplot analysis confirmed a rank change interaction among the locations, indicating the presence of strong and unpredictable rank-change location-by-year interactions for locations. In contrast to landraces and old varieties, the breeding lines with high yield performance had high phenotypic plasticity under varying environmental conditions. Results indicated that the GE interaction was associated with the interaction of heading date, plant height, rainfall, air temperature and freezing days.

scholarly journals Unlocking the Genetic Diversity within A Middle-East Panel of Durum Wheat Landraces for Adaptation to Semi-arid Climate

Agronomy ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 233 ◽  
Author(s):  
Salam Abu-Zaitoun ◽  
Kottakota Chandrasekhar ◽  
Siwar Assili ◽  
Munqez Shtaya ◽  
Rana Jamous ◽  
...  
Keyword(s):  
Middle East ◽  
Grain Yield ◽  
Durum Wheat ◽  
Core Collection ◽  
Heading Date ◽  
Principal Component ◽  
Kernel Weight ◽  
Physiological Adaptation ◽  
Environment Interaction ◽  
Semi Arid

Drought is the major environmental factor limiting wheat production worldwide. Developing novel cultivars with greater drought tolerance is the most viable solution to ensure sustainable agricultural production and alleviating threats to food-security. Here we established a core-collection of landraces and modern durum wheat cultivars (WheatME, n = 36), from the Middle East region (Jordan, Palestine and Israel) aiming at unlocking the genetic and morpho-physiological adaptation to semi-arid environment conditions. Interestingly, genetic analysis of the WheatME core-collection could not distinguish the landraces according to their country of origin. Field-based evaluation of the core-collection conducted across range of contrasting environmental conditions: Til-Palestine, Bet-Dagan-Israel and Irbid-Jordan with annual precipitation of 500 mm, 360 mm and 315 mm, respectively. The Til environment showed highest grain yield while the Irbid environment showed the lowest values. Analysis of variance showed a significant Genotype × Environment interaction for plant phenology traits (plant height and heading date) and productivity traits (1000-kernel weight, and grain yield). Principal component analysis showed three main cultivar groups: High yielding lines (modern durum cultivars, and landraces), tall late flowering landraces, and landraces with high grain weight. This knowledge could serve as basis for future breeding efforts to develop new elite cultivars adapted to the Mediterranean Basin’s semi-arid conditions.

Biplot analysis of genotype-environment interaction in durum wheat using the AMMI model

2006 ◽  
Vol 54 (4) ◽  
pp. 459-467 ◽  
Author(s):  
E. Farshadfar ◽  
J. Sutka
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Field Experiments ◽  
Block Design ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Biplot Analysis ◽  
Randomized Block Design ◽  
Total Variability ◽  
Ammi Model

The genotype by environment (GE) interaction is a major problem in the study of quantitative traits because it complicates the interpretation of genetic experiments and makes predictions difficult. In order to quantify GE interaction effects on the grain yield of durum wheat and to determine stable genotypes, field experiments were conducted with ten genotypes for four consecutive years in two different conditions (irrigated and rainfed) in a completely randomized block design with three replications in each environment. Combined analysis of variance exhibited significant differences for the GE interaction, indicating the possibility of stable entries. The results of additive main effect and multiplicative interaction (AMMI) analysis revealed that 12% of total variability was justified by the GE interaction, which was six times more than that of genotype. Ordination techniques displayed high differences for the interaction principal components (IPC1, IPC2 and IPC3), indicating that 92.5% of the GE sum of squares was justified by AMMI1, AMMI2 and AMMI3, i.e. 4.5 times more than that explained by the linear regression model. The results of the AMMI model and biplot analysis showed two stable genotypes with high grain yield, due to general adaptability to both rainfed and irrigated conditions, and one with specific adaptation.

scholarly journals Genotype and environment interaction of drought tolerant rice genotypes evaluated in drought prone areas of Bangladesh

2016 ◽  
Vol 14 (1) ◽  
pp. 23-30
Author(s):  
RR Majumder ◽  
TR Hore ◽  
MA Kader ◽  
TL Aditya
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Principal Component ◽  
Field Trials ◽  
High Yield ◽  
Environment Interaction ◽  
Growth Duration ◽  
Yield Data ◽  
Wide Range ◽  
Rice Genotypes

Field trials were conducted using ten rice genotypes including four national and international check varieties under drought prone rainfed environments of Rangpur, Nilphamari, Kurigram and Lalmonirhat districts of Bangladesh in Transplant Aman 2012 season to evaluate the growth duration and grain yield stability as well as adaptability. The experiment used randomized complete block design with three replications. Growth duration and Grain yield data were subjected to Additive Main effects and Multiplicative Interaction (AMMI) analysis. The mean growth duration of 10 genotypes over four environments showed that the longest growth duration was found for IR83383-B-B-129-4 (115.6 days) and the lowest growth duration was found for BRRI dhan56 (Ck) (113.4 days). IR82635-B-B-145-1 produced maximum 4.28 t/ha grain yield followed by 4.28 t/ha in IR82589-B-B-84-3 and the lowest grain yield was recorded for IR83376-B-B-130-2 with 3.24 t/ha. It was evident from the AMMI 1 biplot analysis that IR83377-B-B-93- 3, IR83383-B-B-129-4, IR82635-B-B-145-1, IR82635-B-B-75-2 and BRRI dhan56 (Ck) generally exhibited high yield with high main (additive) effects showing positive first principal component of the interaction (IPCA1), but IR82635-B-B-145-1 being the overall best. Hence, IR82635-B-B-145-1 was identified as specially adapted to Lalmonirhat and this environment was considered as the wide range suitable environment for the genotype. Lalmonirhat could be regarded as a good selection site for rice improvement due to stable yields. IR83377-B-B-93-3, IR82635-B-B-145-1, IR83383-B-B-129-4, IRRI 123 (Ck), BRRI dhan57 (Ck) and BINA dhan7 (Ck) were hardly affected by the G × E interaction and would perform well across a wide range of environments. In AMMI 2 biplot, IR82635-B-B-145-1 and IRRI 123 (Ck) was more responsive since they were away from the origin whereas other genotypes were nearer to the origin and hence they were more stable to different environments. IR83383-B-B-129-4 was very close to the origin, so it was more stable to different environments. Similarly Rangpur Sadar and Lalmonirhat showed similar potentiality over IR83383-B-B-129-4. The study it revealed that IR83377-B-B-93-3, IR83383-B-B-129-4, IR82635-B-B-145-1, IR82635-B-B-75-2 and IR82589-B-B-84-3 has the potentiality to show drought tolerance for the regions and might be suitable for releasing as a variety after conducting proper yield evaluation trials and Distinction, Uniformity and Stability (DUS) tests.J. Bangladesh Agril. Univ. 14(1): 23-30, June 2016

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.

Pattern analysis of genotype-by-environment interaction for grain yield in durum wheat

2009 ◽  
Vol 147 (5) ◽  
pp. 537-545 ◽  
Author(s):  
R. MOHAMMADI ◽  
A. AMRI ◽  
R. HAGHPARAST ◽  
D. SADEGHZADEH ◽  
M. ARMION ◽  
...  
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Pattern Analysis ◽  
Genotype By Environment Interaction ◽  
Interaction Matrix ◽  
Environment Interaction ◽  
Yield Data ◽  
Breeding Lines ◽  
Cold Environments ◽  
Genotype By Environment

SUMMARYPattern analysis, cluster and ordination techniques, was applied to grain yield data of 20 durum wheat genotypes grown in 19 diversified environments during 2005–07 to identify patterns of genotype (G), environment (E) and genotype-by-environment (G×E) interaction in durum multi-environment trials (METs). Main effects due to E, G and G×E interaction were highly significant, and 0·85 of the total sum of squares (SS) was accounted for by E. Of the remaining SS, the G×E interaction was almost 12 times the contribution of G alone. The knowledge of environmental and genotype classification helped to reveal several patterns of G×E interaction. This was verified by ordination analysis of the G×E interaction matrix. Grouping of environments, based on genotype performance, resulted in the separation of different types of environments. Pattern analysis confirmed the cold and warm mega-environments, and allowed the discrimination and characterization of adaptation of genotypes. However, several patterns of G×E interaction in Iran's regional durum yield trials were further discerned within these mega-environments. The warm environments tended to be closer to one another, suggesting that they discriminate among durum genotypes similarly, whereas cold environments tended to diverge more. The dwarf and early maturing breeding lines from ICARDA with low to medium yields and high contribution to G×E interaction were highly adapted to warm environments, whereas the tall and later maturing genotypes with low to high yields were highly adapted to the cold environments of Iran.

scholarly journals Analysis of genotype-environment interaction and yield stability of Thai upland rice (Oryza sativa L.) genotypes using AMMI model

2020 ◽  
pp. 362-370
Author(s):  
Shams Shaila Islam ◽  
Jakarat Anothai ◽  
Charassri Nualsri ◽  
Watcharin Soonsuwon
Keyword(s):  
Principal Component Analysis ◽  
Grain Yield ◽  
Upland Rice ◽  
Oryza Sativa L ◽  
Principal Component ◽  
Vital Role ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Ammi Model ◽  
Rice Genotypes

Genotype-environment interaction and stability analysis has been important for plant breeders and plays a vital role in identifying genotypes that are stable or unstable in a given environment. The experiments in this research were conducted to determine the effects of genotype, environment and genotype-environment interaction on grain yield using the AMMI statistical model, and to recognize the most stable rice genotypes among ten genotypes in southern Thailand’s provinces of environments in Songkhla, Satun and Phatthalung. Highly significant differences were shown from the combined analysis for environments with grain yields, revealing that environments were different and indicated change ability between the genotypes and their interactions. The average grain yield assessment of the tested genotypes was around the environments where genotype G8 (Nahng Kian) had the highest grain yield 6234.11 kg/ha. AMMI biplot of the Interaction Principal Component Analysis (IPCA) scores visualized 90.7% for IPCA1 and 9.3% for IPCA2 with the genotypes and environments for grain yield. In the AMMI stability value method, G8 (Nahng Kian) was the most stable genotype followed by the genotypes G2 (Mai Tahk) and G10 (Hawm Jet Ban) Songkhla, Satun and Phatthalung environments.

scholarly journals Agronomic characterization of high-yielding irrigated barley accessions in the Cerrado

2017 ◽  
Vol 52 (2) ◽  
pp. 84-94 ◽  
Author(s):  
Ricardo Meneses Sayd ◽  
Renato Fernando Amabile ◽  
Fábio Gelape Faleiro ◽  
Ana Paula Leite Montalvão ◽  
Mateus Costa Coelho
Keyword(s):  
Grain Yield ◽  
Selection Process ◽  
Block Design ◽  
Heading Date ◽  
Environmental Parameters ◽  
Federal District ◽  
High Yield ◽  
Environment Interaction ◽  
Experimental Conditions ◽  
Genotypic And Phenotypic Correlations

Abstract: The objective of this work was to estimate the genetic, phenotypic, and environmental parameters of 113 barley accessions (Hordeum vulgare), previously selected based on high yield, in an irrigated production system in the Cerrado (Brazilian savanna), for use in breeding programs in Brazil. The experiment was conducted in 2013 at two sites in the Federal District, using a randomized complete block design with three replicates. The evaluated traits were: grain yield, kernel plumpness, thousand grain weight, plant height, lodging, and days to heading. Significant differences were observed between environment and accessions for all studied traits, as well as genotype x environment interaction. The MCU 3832 PI 402310, MCU 3484 PI 401962, CI 08053 Custer, MCU 3634 PI 402112, and MCU 3816 PI 402294 accessions stood out regarding grain yield and kernel plumpness. It is possible to obtain high selection gains with high heritability values. However, indirect selection is not recommended in the experimental conditions due to the low genotypic and phenotypic correlations obtained. It is also possible to select barley accessions with high yield and early heading date simultaneously, an important factor in the genotype selection process for future experiments. The accessions from Colombia are the most adapted to the Cerrado conditions.

scholarly journals Yield stability of performance in multi-environment trials of barley (Hordeum vulgare L.) genotypes

2013 ◽  
Vol 61 (3) ◽  
pp. 787-793 ◽  
Author(s):  
Naser Sabaghnia ◽  
Mohtasham Mohammadi ◽  
Rahmatollah Karimizadeh
Keyword(s):  
Principal Component ◽  
Genotype By Environment Interaction ◽  
Yield Stability ◽  
High Yield ◽  
Environment Interaction ◽  
Linear Regression Models ◽  
Hordeum Vulgare L ◽  
Stability Parameters ◽  
Environmental Variance ◽  
Breeding Lines

Fourteen new breeding lines obtained from the barley breeding programs, cultivar Izeh and one local check genotype were evaluated for yield stability at eleven environments. The combined analysis of variance indicated the significance of the environments, genotypes and genotype by environment interaction. According to the environmental variance (EV) and coefficient of variation (CV), genotypes G2, G12, G13 and G14 while based on Wi, P, PP and SH parameters genotypes G4, G10 and G12 were the most stable ones. Regarding both PI and MSPI parameters, genotypes G2, G10 and G11 were the most stable. According to coefficients of three linear regression models, genotypes G1, G6 and G8 were more responsive and had specific adaptability to favorable environments. Considering most of stability parameters, genotypes G4 (3 393 kg ha−1) G12 (3 440 kg ha−1) can be recommended as the most stable genotype with regard to both stability and yield. In this study, high values of DI were associated with high mean yield, but the other stability methods were not positively correlated with mean yield. The results of principal component analysis and correlation analysis indicated that EV, CV, ER, and DI stability parameters would be useful for simultaneously selecting for high yield and stability.

Genotype × environment interactions and underlying environmental factors for winter wheat in Ontario

1999 ◽  
Vol 79 (4) ◽  
pp. 497-505 ◽  
Author(s):  
H. M. Haji ◽  
L. A. Hunt
Keyword(s):  
Environmental Factors ◽  
Winter Wheat ◽  
Grain Yield ◽  
Minimum Temperature ◽  
Principal Component ◽  
Evaluation Process ◽  
Grain Filling ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Ge Interaction

Many studies have dealt with the problem of varying genotype performance in different environments (GE interaction). Little is known, however, of the underlying causes of GE interaction. This study was undertaken to identify some of the environmental factors that are associated with GE interactions in winter wheat in Ontario, Canada.Grain yields were obtained from the 1992/1993 and 1993/1994 winter wheat (Triticum aestivum L. em. Thell.) recommendation trials in Ontario. In each year, trials were grown at nine sites. Variance components for the second-order interaction between genotypes × sites × years were considerably larger (22% of the phenotypic variance) than the first-order interactions between genotypes × years and genotypes × sites, which, respectively, accounted for only 5% of the total variation, suggesting that the genotypes were responding differentially to environmental factors at the various sites. Both linear and non-linear components of the regression of genotype grain yields on site mean grain yield were significant. Grain yield of many genotypes was associated with both the lowest and the mean minimum temperature in January and with precipitation during the vegetative stage (May) in 1992/1993 and during the grain-filling period (July) in 1993/1994. Interaction principal component analysis scores for the environment were associated with January minimum temperature in both years although winter survival differences were only apparent in 1992/1993.The results indicate that January temperatures, together with moisture supply before anthesis, are associated with some of the GE interaction noted with winter wheat in Ontario. Both of these aspects, but in particular January temperatures, should thus be taken into account when evaluating cultivar performance trials. Cultivars should be exposed to low January temperatures at some sites during the evaluation process, and weather records from evaluation sites should be examined to ensure that this has occurred. Key words: Genotype × environment interaction, wheat (winter), temperature, grain yield, stress, moisture

scholarly journals Analysis of genotype × environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods

2012 ◽  
Vol 92 (4) ◽  
pp. 757-770 ◽  
Author(s):  
Reza Mohammadi ◽  
Ahmed Amri
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Environment Interaction ◽  
Parametric Methods ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Yield Data ◽  
Stability Performance ◽  
Genotype Environment Interaction ◽  
Non Parametric

Mohammadi, R. and Amri, A. 2012. Analysis of genotype × environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods. Can. J. Plant Sci. 92: 757–770. Multi-environment trials (MET) are conducted annually throughout the world in order to use the information contained in MET data for genotype evaluation and mega-environment identification. In this study, grain yield data of 13 durum and one bread wheat genotypes grown in 16 diversified environments (differing in winter temperatures and water regimes) were used to analyze genotype by environment (GE) interactions in rain-fed durum MET data in Iran. The main objectives were (i) to investigate the possibility of dividing the test locations representative for rain-fed durum production in Iran into mega-environments using the genotype main effect plus GE interaction (GGE) biplot model and (ii) to compare the effectiveness of the GGE-biplot and several non-parametric stability measures (NPSM), which are not well-documented, for evaluating the stability performance of genotypes tested and the possibility of recommending the best genotype(s) for commercial release in the rain-fed areas of Iran. The results indicate that the grain yield of different genotypes was significantly influenced by environmental effect. The greater GE interaction relative to genotype effect suggested significant environmental groups with different top-yielding genotypes. Warm environments differed from cold environments in the ranking of genotypes, while moderate environments were highly divergent and correlated with both cold and warm environments. Cold and warm environments were better than moderate environments in both discriminating and representativeness, suggesting the efficiency and accuracy of genotype selection would be greatly enhanced in such environments. According to the NPSM, genotypes tend to be classified into groups related to the static and dynamic concepts of stability. Both the GGE-biplot and NPSM methods were found to be useful, and generally gave similar results in identifying high-yielding and stable genotypes. In contrast to NPSM, the GGE-biplot analysis would serve as a better platform to analyze MET data, because it always explicitly indicates the average yield and stability of the genotypes and the discriminating ability and representativeness of the test environments.

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