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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.

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In a long-term experiment set up in Martonvásár (N 47°21′, E 18°49′), Hungary in 1960 on a humous loam soil of the chernozem type, the effect of five crop production factors in increasing maize yields was studied in seven treatments. The factors studied were soil cultivation, fertilisation, plant density, variety and weed control. All the factors had a favourable and an unfavourable level. Yield data recorded over 42 years were evaluated using analysis of variance and stability analysis. The highest yield (8.59 t ha −1 ) was obtained when all the production factors were favourable and lowest (2.09 t ha −1 ) when these factors were unfavourable. When only one factor was unfavourable and all the other factors were favourable the following yields were obtained (t ha −1 ): soil tillage: 8.32, fertilisation: 5.21, genotype: 4.98, plant density: 6.31 weed control: 7.01. The crop production factors contributed to the increase in maize yield in the following ratios (%): fertilisation 30.6, variety 32.6, plant density 20.2, weed control 14.2, soil cultivation 2.4. The highest value of the coefficient of variation (CV%) was obtained when all the production factors were at the unfavourable level (45.7%) and when weed control or fertilisation were unfavourable (36.6% and 34.8%, respectively), while the lowest value was recorded when all the factors were favourable (19.5%). The significant treatment × year interaction could be attributed principally to treatments in which weed control, fertilisation, genotype or all the factors were unfavourable. The regression coefficient of linear regression analysis provided a satisfactory characterisation of the stability of the treatments in different environments, while the distance between the straight lines expressed the yield differences between the treatment pairs. The AMMI (Additive Main Effect and Multiplicative Interaction) model proved to be a valuable approach for understanding agronomic treatment × environment interactions and assessing the mean performance and yield stability of treatments.

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The effect of sowing date, N fertilisation and genotype on the grain yield and yield stability of maize was studied between 1991 and 2006 in a long-term N fertilisation experiment set up on chernozem soil in Martonvásár, Hungary. The N treatments (0, 60, 120, 180 and 240 kg ha −1 ) represented the main plot of the three-factor, split-split-plot experiment, with the sowing date (early, optimum, late, very late) in the sub-plots and hybrids from different maturity groups in the sub-sub-plots. The highest yields were obtained for the early and optimum sowing dates (8.712 and 8.706 t ha −1 ). Compared with the optimum sowing date, a delay of ten or twenty days led to yield losses of 5% and 12.5%, respectively. In the late and very late sowings and in years with unfavourable weather conditions, yield increments were only observed up to an N rate of 60 kg ha −1 , while in the early and optimum sowings and in favourable years yield increments were significant up to 120 kg ha −1 N. Yield stability was smallest in the early and very late sowings, in the control and for high N rates, and in the early and late maturity hybrids. It can be concluded that high yields and yield stability are not mutually exclusive.

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Cereal Research Communications
Authors: N. Pržulj, M. Mirosavljević, P. Čanak, M. Zorić, and J. Boćanski

46 : 59 – 73 . Rao , P.S. , Reddy , P.S. , Rathore , A. , Reddy , B.V. , Panwar , S. 2011 . Application GGE biplot and AMMI model to evaluate sweet sorghum

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To assess the stability and yield performance of safflower genotypes and to identify subregions within Iran, a set of experiments was conducted at six locations during 2003–2005. AMMI model analysis and some stability parameters derived from the grain yield were used. AMMI analysis showed differences between genotypes and environments and the GE interaction was highly significant, indicating that the agro-climatic environmental conditions were different, and that there was a differential response of the genotypes to the environments. The first two IPCA components of the GE interaction explained 51.5% of the GE interaction. According to the AMMI model, G16 was the most superior genotype in 15 out of 18 environments. The biplot of IPCA1 and IPCA2 showed that the six locations represent different environments, and mega-environments in Iran were identified for safflower breeding programmes. Due to the great fluctuation observed when selecting genotypes through stability parameters, it was not possible to distinguish stable genotypes clearly. In addition, when calculating these parameters high yield performance is not considered. So the Yield and Stability Index (YSI) can be recommended as a new approach to facilitate genotype selection, where genotypes with low values of YSI are the best. According to YSI the genotypes G16, G2, G9 and G1 can be selected. These genotypes were also selected using the AMMI model.

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Superior grain quality is the main goal of rice breeders because of its high commercial value. Progress in selection for grain quality with yield in harsh environments is markedly affected by environmental variation. The genotype by environmental (G × E) interaction influence on grain quality was analyzed in this study, comprised of 17 rice hybrids grown in six location- year environments. The objective of this study was to examine the influence of G × E interaction for grain quality in hybrid rice by using AMMI model. Results of the trial revealed that grain quality was highly influenced by environmental factors and brings out the suitability of specific genotype to specific location/season through the biplot. On the other hand, external environmental variables can be regressed on the environmental scores to lead to a useful biological interpretation of the interaction effects, which is not possible in additive effect models. The implications of these results for rice hybrids on grain quality in varied environmental location are discussed.

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Soil salinity is one of the major environmental constraints in increasing agricultural crop production, especially wheat production in India. Screening of diverse germplasm in representative growing conditions is prerequisite for exploring traits with stable expression imparting salinity tolerance. A study was undertaken during 2011–2012 for characterizing wheat germplasm in three environments representing growing conditions of crop in Northern parts of India, estimating inter-relationship among traits and evaluating stability of trait conferring salinity tolerance. Significant value of mean square for observed trait across the environments signified presence of large variability in genotypes. Significant yield reduction was recorded in almost all genotypes in saline environment compared to non-saline condition. Ratio of potassium and sodium ion in leaf tissue (KNA); a key salt tolerance traits was found to be significantly correlated with biomass, SPAD value and plant height. Due to the presence of significant genotype × environment interaction (G × E) for KNA, additive main effect and multiplicative interaction (AMMI) model was utilized to study stability of KNA among genotypes and environments. IPCA1 and IPCA2 were found to be significant and explained more than 99 per cent of variation due to G × E. KRICHAUFF was having maximum trait value with specific adaptation while DUCULA 4 and KRL 19 were having general adaptability. AMMI2 biplot revealed high stability of Kharchia 65 and KRL 99 across environments. E1 (timely sown, non-saline soil) recorded maximum site mean while E2 (timely sown, sodic soil) was having minimum interaction with genotypes (AMMI1 = 1.383). Thus, our studies suggest that AMMI model is also useful for estimating adaptability of traits other than yield utilized for breeding salt tolerant wheat varieties.

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Genotype by environment interaction distorts genetic analysis, changes relative ranking of genotypes and a major obstruction for varietal release. AMMI model is a quick and relevant tool to judge environmental behaviour and genotypic stability in comparison to ANOVA, multiplicative model and linear regressions. We evaluated 19 barley genotypes grown at 08 diverse locations to identify discriminating environments and ideal genotypes with dynamic stability. In AMMI ANOVA, the locations and genotype by environment interaction exhibited 66% and 14.7% of the total variation. The initial first two principal components showed significant interaction with 36.0 and 28.4% variation, respectively. AMMI1 biplot showed that the environments Bawal, Ludhiana and Durgapura were high yielding with high IPCA1 scores and located far away from the biplot origin. However, in AMMI1and AMMI2 biplots the locations Hisar, Ludhiana, Karnal, Bathinda and Modipuram were found suitable with low IPCA2 scores. Yield stability index (YSI) was highly useful with ASV ranks and the genotypes DWRB150 and BH1013 and checks BH902, DWRUB52 and DWRB101 were selected for high grain yield and wider adaptability across the locations.

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-environment interaction in durum wheat using the AMMI model — Acta Agronomica Hungarica 54: 459–467 Sutka J. Biplot analysis of genotype-environment interaction in durum wheat using the AMMI model

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Cereal Research Communications
Authors: S. L. Krishnamurthy, S. K. Sharma, D. K. Sharma, P. C. Sharma, Y. P. Singh, V. K. Mishra, D. Burman, B. Maji, B. K. Bandyopadhyay, S. Mandal, S. K. Sarangi, R. K. Gautam, P. K. Singh, K. K. Manohara, B. C. Marandi, D. P. Singh, G. Padmavathi, P. B. Vanve, K. D. Patil, S. Thirumeni, O. P. Verma, A. H. Khan, S. Tiwari, M. Shakila, A. M. Ismail, G. B. Gregorio, and R. K. Singh

Genotype × environment (G × E) interaction effects are of special interest for identifying the most suitable genotypes with respect to target environments, representative locations and other specific stresses. Twenty-two advanced breeding lines contributed by the national partners of the Salinity Tolerance Breeding Network (STBN) along with four checks were evaluated across 12 different salt affected sites comprising five coastal saline and seven alkaline environments in India. The study was conducted to assess the G × E interaction and stability of advanced breeding lines for yield and yield components using additive main effects and multiplicative interaction (AMMI) model. In the AMMI1 biplot, there were two mega-environments (ME) includes ME-A as CARI, KARAIKAL, TRICHY and NDUAT with winning genotype CSR 2K 262; and ME-B as KARSO, LUCKN, KARSA, GOA, CRRI, DRR, BIHAR and PANVE with winning genotypes CSR 36. Genotypes CSR 2K 262, CSR 27, NDRK 11-4, NDRK 11-3, NDRK 11-2, CSR 2K 255 and PNL 1-1-1-6-7-1 were identified as specifically adapted to favorable locations. The stability and adaptability of AMMI indicated that the best yielding genotypes were CSR 2K 262 for both coastal saline and alkaline environments and CSR 36 for alkaline environment. CARI and PANVEL were found as the most discernible environments for genotypic performance because of the greatest GE interaction. The genotype CSR 36 is specifically adapted to coastal saline environments GOA, KARSO, DRR, CRRI and BIHAR and while genotype CSR 2K 262 adapted to alkaline environments LUCKN, NDUAT, TRICH and KARAI. Use of most adapted lines could be used directly as varieties. Using them as donors for wide or specific adaptability with selection in the target environment offers the best opportunity for widening the genetic base of coastal salinity and alkalinity stress tolerance and development of adapted genotypes. Highly stable genotypes can improve the rice productivity in salt-affected areas and ensure livelihood of the resource poor farming communities.

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