ScienceDirect. Evaluation of Genotype Environment Interaction in Rice Based on AMMI Model in Iran

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1 Available online at ScienceDirect Rice Science, 2017, 24(3): Evaluation of Genotype Environment Interaction in Rice Based on AMMI Model in Iran Peyman SHARIFI 1, Hashem AMINPANAH 1, Rahman ERFANI 2, Ali MOHADDESI 3, Abouzar ABBASIAN 4 ( 1 Department of Agronomy and Plant Breeding, Rasht Branch, Islamic Azad University, Rasht , Iran; 2 Rice Research Institute of Iran, Agricultural Research Education and Extension Organization, Amol , Iran; 3 Rice Research Station of Iran, Agricultural Research Education and Extension Organization, Tonekabon , Iran; 4 Young Researchers and Elite Club, Rasht Branch, Islamic Azad University, Rasht , Iran) Abstract: Identification of high-yielding stable promising rice lines and determination of suitable areas for rice lines would be done by additive main effects and multiplicative interaction (AMMI) model. Seven promising rice genotypes plus two check varieties Shiroudi and 843 were analyzed using a randomized complete block design with three replications in three consecutive years (2012, 2013 and 2014). Homogenous error variance was indicated in the nine environments for grain yield. The combined analysis of variance indicated significant effects of environment, genotype and genotype environment (GE) interactions on grain yield. The significant effect of GE interaction reflected on the differential response of genotypes in various environments and demonstrated that GE interaction had remarkable effect on genotypic performance in different environments. The application of AMMI model for partitioning the GE interaction effects showed that only the first two terms of AMMI were significant based on Gollob s F-test. The lowest AMMI-1 was observed for G7, G2 and G6. G7 and G6 had higher grain yield. According to the first eigenvalue, which benefits only the first interaction principal component scores, G1, G6, G2 and G9 were the most stable genotypes. The values of the sum of first two interaction principal component scores could be useful in identifying genotype stability, and G6, G5 and G2 were the most dynamic stable genotypes. AMMI stability value introduced G6 as the most stable one. According to AMMI biplot view, G6 was high yielding and highly stable genotype. In conclusion, this study revealed that GE interactions were an important source of rice yield variation, and its AMMI biplots were forceful for visualizing the response of genotypes to environments. Key words: biplot; grain yield; GE interaction; multi-environment trial; stability Rice is one of the staple foods for a large proportion of the world s population and nearly 90% of world s rice is produced in Asia. In Iran, rice is one of the most important crop and the preferred staple food for the majority of people (Nasiri and Pirdashti, 2003). Therefore, recommendation for released varieties from promising lines with static or dynamic stabilities is very important for sustainable agriculture and food security of smallholder farmers. Iran is the 20th producer of rice in the world, with an average production of 2.3 million tons in 2014 and average yield of 4.3 t/hm 2 (FAO, 2016). The performance of any character is a combined result of the genotype (G) of the variety, the environment (E) and the interaction between genotype and environment (GE). To evaluate the consistency of rice grain yield and develop genotypes that respond optimally and consistently across years and geographic regions, it is necessary to research on yield stability and GE interactions (Blanche et al, 2009). GE interactions exist when the responses of two genotypes to different levels of environmental stress are not consistent. A Received: 3 October 2016; Accepted: 17 February 2017 Corresponding author: Peyman SHARIFI (Peyman.sharifi@gmail.com; kadose@yahoo.com) Copyright 2017, China National Rice Research Institute. Hosting by Elsevier B V This is an open access article under the CC BY-NC-ND license ( Peer review under responsibility of China National Rice Research Institute

2 174 Rice Science, Vol. 24, No. 3, 2017 good and efficient genotype must remain acceptable in different years and environmental conditions (Hill, 1975). Better understanding of GE interactions and stability in crops was used as a decision tool, particularly at the final stage of variety introduction process, to generate essential information on pattern of adaptation in breeding lines, screen new varieties for release, and determine the recommendation domains for released varieties (Yan and Kang, 2003). GE interaction was quantified using several procedures based on evaluation of genotypes under multiple environments. These methods divided into univariate and multivariate stability statistics. The most widely used univariate methods are based on regressing the mean value of each genotype on the environmental index or marginal means of environments (Yates and Cochran, 1938; Finlay and Wilkinson, 1963; Eberhart and Russell, 1966). Multivariate analysis of GE interaction is an alternative and complementary method for evaluating genotype stability (Crossa, 1990). Additive main effects and multiplicative interaction (AMMI) model is a popular extension of ANOVA for studying GE interaction (Gauch, 1992). This method extracts genotype and environment main effects and uses interaction principal components (IPCs) to explain patterns in the GE interaction or residual matrix, which provides a multiplicative model (Romagosa and Fox, 1993). The AMMI model combines ANOVA for main effects of the genotype and environment with principal components analysis of GE interactions (Zobel et al, 1988; Gauch and Zobel, 1996). Several AMMI parameters were introduced for studying the stability of genotypes across multi environments. AMMI stability value (ASV), developed by Purchase et al (2000) to quantify and rank the genotypes based on their yield stability, is established upon the first and the second IPC scores for each genotype. This method is comparable with the methods used by Shukla (1972) and Eberhart and Russell (1996). Sabaghnia et al (2008) reported ASV as good dynamic criteria stability for detecting stable genotypes. ASV and modified ASV (MASV) parameters are reliable statistics for GE interaction description and simultaneous selection of yield and stability (Karimizadeh et al, 2012). Samonte et al (2005) used AMMI parameters to assess stability of rice grain yield. Bose et al (2014b) also analyzed AMMI parameters and stability of 17 early maturing rice genotypes over four seasons, and found that the first two IPCs cumulatively explained 93.76% of the total interaction effects. Tarang et al (2013) evaluated ten rice genotypes for three years using AMMI methods and characterized two stable lines based on the smallest distance from the offset of coordinates in AMMI vectors. Bose et al (2014a) used AMMI analysis for evaluating 12 rice genotypes and determined stable ones from three years tests, the first three AMMIs (AMMI-1 to AMMI-3) being highly significant. Tariku et al (2013) evaluated 16 rainfed lowland rice genotypes at three locations of eight environments and indicated the GE interaction is partitioned among the first four IPCs, which cumulatively captured 91.13% of the total GE interactions. The practical use of different statistical methods is to explain GE interaction, thereby facilitate variety recommendation decision. To achieve better economic benefits, identification of rice varieties with wider adaptability and stability is important for variety recommendation from promising lines. The main objectives of the present study are to identify more high-yielding stable promising lines and determine the areas where rice lines would be adapted by AMMI model. The novelty of this research is the promising lines identified, which had desirable qualitative characteristics and high yield stability, can be introduced as released varieties. MATERIALS AND METHODS Experimental design and rice materials Seven promising rice genotypes, generated from a crossing program among five rice cultivars (Shiroudi, Khazar, Surinam, Deylamani and Amol 3) plus two check varieties Shiroudi and 843 (Table 1), were analyzed using a randomized complete block design with three replications in three consecutive years (2012 to 2014). Three locations that differed in altitude along the temperature gradient in northern Iran were selected for field trials (Tonekabon, -20 m above the sea level (ASL); Amol, 76 m ASL; and Sari, 132 m ASL). Table 2 provides a detailed description of the test stations, soil types, the maximum and the minimum temperatures, and precipitation for each environment. The combinations of year and location were indicated as an environment and used in analysis. Initially, 30-day-old seedlings were transplanted at

3 Preeti Rekha TALUKDAR, et al. Diversity in Aromatic Rice 175 Table 1. Pedigrees related to genotypes. Genotype Parent Origin G1 Shiroudi Khazar Iran G2 IR (A8948) (Surinam Deylamani) Iran G3 IR (A37632) (Amol 3 Number 3) Iran G4 IR (A37632) (Amol 3 Number 3) Iran G5 IR (A37632) (Amol 3 Number 3) Iran G6 IR (A37632) (Amol 3 Number 3) Iran G7 IR (A37632) (Amol 3 Number 3) Iran G8 843 (check variety) Iran G9 Shiroudi (check variety) Iran all the locations in 25 cm 25 cm plant intervals in an area of 20 m 2 (5 m 4 m). The period of planting in all locations and growing seasons was April August. The recommended dosages (75 kg/hm 2 ) of P and K and 50% of total N dosage (90 kg/hm 2 ) were applied at the time of transplanting. The remaining 50% of N was divided and top-dressed at 30 and 60 d after transplanting. The crop was treated using normal cultural practices. Weeds were controlled by two times hand-weeding. Neither herbicides nor insecticides were used in the trials. The crop was harvested at full maturity from 5 m 2 plots (80 plants per plot) from the middle two rows of each plot, leaving aside guard rows on either side of the plot. The grain yield was adjusted for 14% moisture. Statistical analysis For genotypic yields across environment trials, prediction assessment was conducted using the AMMI method (Gabriel, 1978). The AMMI model was as follows: Yij i j n k k 1 ik where, Y ij is the yield of ith genotype in jth environment over all replications, μ is the grand mean, α i is the ith genotype mean deviation (genotype mean minus grand mean), β j is the jth environment mean deviation, λ k is the singular value for IPC axis k, γ ik is the ith genotype eigenvector value for IPC axis k, δ jk is the jth environment eigenvector value for IPC axis k, and ε ij is the error term. The eigenvalue (EV) stability parameter of AMMI (Zobel et al, 1998) was calculated according to the expression: EV N 2 in / n1 n In this formula, γ in is the genotype eigenvector for jk ij Table 2. Description of environmental factors in three locations in Iran. Location Soil type Year T max T min Precipitation (mm) Tonekabon Silty clay loam Amol Silt loam Sari Silt loam T max, Maximum temperature; T min, Minimum temperature. axis n, and N is the number of IPCs that were retained in the AMMI procedure via different F-test. The sum of IPCs scores (SIPC) parameter is expressed as (Sneller et al, 1997): SIPC N.5 n n1 0 in Where, λ n is the eigenvalue of the IPC analysis axis n. In this equation, N = 1 for SIPC1; and for SIPCF, N was the number of IPC that were retained in the AMMI model. ASV was calculated as described by Purchase et al (2000) as follows: SSIPC1 1 2 ASV IPC IPC2 2 SSIPC2 Where SSIPC1/SSIPC2 is the weight given to the IPC1 value by dividing the IPC1 sum of square by the IPC2 sum of square. The larger IPC score, either negative or positive, the more specifically adapted a genotype is to certain environments. Smaller ASV scores indicate a more stable genotype across environments. For effective interpretation of GE via AMMI model, a new parameter MASV is introduced as below formula (Adugna and Labuschange, 2002): MASV N 1 n1 SSIPCn SSIPC n1 2 IPC IPC 2 n1 In this parameter, all significant IPCs were used. Environmental index (EI) is obtained as the mean of all varieties at the jth environment minus the grand mean and genotypic index (GI) is obtained as the mean of all environments of the ith genotype minus the grand mean. The IRRISTAT software was used for combined analysis of variance, and AMMI analysis and the n

4 176 Rice Science, Vol. 24, No. 3, 2017 Table 3. ANOVA for AMMI model and Gollob s F-test and average root mean square predictive difference. S.O.V. a df SS MS Proportion Noise G ** 0.30 b 0.11 d E ** 0.29 b 0.11 d GE ** 0.41 b 0.62 d Component ** 0.49 c Component ** 0.24 c Component c Component c Component c Component c Component c Residual c Error G, Genotype; E, Environment; GE, Genotype environment interaction; SS, Sum of squares; MS, Mean square. **, Significant at the 0.01 level. a Predicted by the SAS program with repeating times splitting data; b Calculated by dividing on total SS of G, E and GE; c Calculated by dividing on SS of GE; d Calculated by [(df MS Error)/SS]. graphs were drawn by new Developed SAS Program (Akbarpour et al, 2014). RESULTS Analysis of variance and mean comparison Homogenous error variance was indicated in the nine environments for grain yield, and therefore, combined analysis of variance was carried out across environments. The combined analysis of variance indicated that the main effects of random environments and fixed genotypes were significant for grain yield (Table 3). The results revealed that 29% of the total sum of squares was attributable to environmental effects, 30% to genotype effects and 41% to GE interaction effects. All of the source additive effects were significant (P < 0.01). Analysis of variance for grain yield of rice genotypes in AMMI model and related Gollob s F-test also indicated that GE interaction was statistically significant (P < 0.001) (Table 3). The application of AMMI model for partitioning the GE interaction effect showed that only first two terms of AMMI were significant based on Gollob s F-test (Gollob, 1968). In this study, the proportion of IPC1 (49%) to the interaction sum of squares was far greater than that of IPC2 (24%) (Table 3). IPC3 was considered as noise, since its mean of square was not significant, and it described only 10% of the total sum of squares and did not help in prediction of validation observations. Mean yield performance along with rank of genotypes across environments indicated that the genotypes have high variation around the mean yield ( kg/hm 2 ) (Table 4). The range of grain yield was from kg/hm 2 (G3) to kg/hm 2 (G7). G1, G6, G7, G8 and G9 produced higher grain yield, while G2, G3, G4 and G5 had the lowest grain yield across environments. AMMI stability The lowest values of AMMI-1 were observed for G7, G2 and G6, which indicated a higher stability of these genotypes in all of the environments (Table 4). Between these genotypes, G7 and G6 had higher grain yield in all environments. The highest AMMI-1 belonged to G5, followed by G1, G4, G9, G3 and G8 in decreasing order. Therefore, these genotypes were more specifically adapted to certain environments. G6 were also a stable genotype according to AMMI-2. Overall, according to AMMI-3, G8, G7, G5 and G3 had the lowest values (Table 4). Several AMMI stability parameters are presented in Table 5. According to the EV4 stability statistic, a genotype with lower EV4 is considered to be more Table 4. Average yield and first three AMMI parameters for nine rice genotypes. Genotype Grain yield (kg/hm 2 ) Rank AMMI-1 AMMI-2 AMMI-3 G G G G G G G G G AMMI-1, AMMI-2 and AMMI-3 are the first three interaction principal component environments, respectively.

5 Preeti Rekha TALUKDAR, et al. Diversity in Aromatic Rice 177 Table 5. Values of AMMI stability parameters for nine rice genotypes. Genotype EV1 EV2 EV4 SIPC2 ASV MASV GI E EI G E G E G E G E G E G E G E G E G E EV, Eigenvector; SIPC, Sum of interaction principal component scores; ASV, AMMI stability value; MASV, Modified AMMI stability value; GI, Genotypic index; E, Environment; EI, Environmental index; E1, Tonekabon in 2012; E2, Amol in 2012; E3, Sari in 2012; E4, Tonekabon in 2013; E5, Amol in 2013; E6, Sari in 2013; E7, Tonekabon in 2014; E8, Amol in 2014; E9, Sari in stable. Therefore, G7 was the most stable genotype, which had relatively high yield, whereas G8 and G1 were the most unstable genotypes. According to EV1, which benefits only IPC1 score, G1, G6, G2 and G9 were the most stable genotypes, and based on EV2 (IPC1-IPC2), G8, G2 and G6 were the most stable genotypes (Table 5). The values of the SIPC2 parameter could be useful in identifying genotype stability, and G6, G5 and G2 were the most stable genotypes, whereas G1, G3, G4, G7, G8 and G9 were the most unstable genotypes. It is interesting that G6, as stable genotype according to SIPC2 parameter, had high mean grain yield (Table 5). According to ASV, G6 and G2 were as the most stable genotypes, whereas the most unstable genotypes were G5, G1, G3, G4 and G9 (Table 5). G2, G7, G8 and G6 were the most stable according to MASV. Considering the mean ranks of all of the AMMI stability parameters, G2, G6 and G8 were the most stable genotypes. All of these stable genotypes, except for G2, had higher yield. The environmental and genotypic indices are presented in the Table 5. Environmental index directly reflects the environment by negative and positive values. Among the nine environments, E1 followed by E7 and E9 recorded the highest and positive environmental index for grain yield. Therefore, these environments appeared to be the most favorable for particular conditions. Negative values of environmental index, which were observed in E8, E6, E3, E2 and E5, indicated the unfavorable nature of that particular condition. The highest positive value of genotypic index was revealed in G7, G8, G9, G6 and G1, indicating the favorability of these genotypes, while the others were identified as unfavorable genotypes. AMMI biplot stability Genotype and environment additive main effects against their respective first multiplicative term (IPC1) are depicted as points on a plane in AMMI-1 biplot (Fig. 1). G2, G6 and G7, with their relative IPC1 scores close to zero, had less response to the interaction and showed general adaptation to the test environments. G5 demonstrated large positive IPC1 score and found better adaption to environment E6 with larger and same sign IPC1 score (Fig. 1). G6 and G7, with the highest mean yield over the test environments (Table 4) and low IPC1 scores (Fig. 1) were considered as the most stable genotypes with relatively less variable yield performance across environments. E8 and E9 combined larger main effects with larger interaction effects (Fig. 1). This indicated that the relative ranking of genotypes was unstable at this year, making it less predictable environment for rice production and evaluation compared to the remaining test environments. AMMI-2 biplot was generated using genotypic and environmental scores of the first two AMMI multiplicative components to cross-validate the IPC1 Fig. 1. AMMI-1 model biplot for grain yield of nine rice genotypes in nine environments. E1, Tonekabon in 2012; E2, Amol in 2012; E3, Sari in 2012; E4, Tonekabon in 2013; E5, Amol in 2013; E6, Sari in 2013; E7, Tonekabon in 2014; E8, Amol in 2014; E9, Sari in 2014.

6 IPC2 178 Rice Science, Vol. 24, No. 3, 2017 interaction pattern of the nine rice genotypes within nine environments (Fig. 2). In this case, G1, G3, G4, G5 and G8 expressed either positively or negatively high interactive behavior and contributed more to the exhibited GE interaction. Genotype-environment affinity was depicted as orthogonal projections of the genotypes on the environmental vectors to identify the best genotypes with respect to environments. The best genotypes with respect to environment E5 were G1 and G7. G3 and G4 were better adapted to environments E7 and E4, respectively. Similarly, G2, G6, G8 and G9 were better adapted to E8, whereas G5 was the best adapted to the environment E9 (Fig. 2). DISCUSSION The significant effects of genotypes and environments in the present study exhibited the outward appearance of variability in genotypes and diversity of growing conditions at different environments. The significant effects of GE interaction reflected on the differential response of genotypes in various environments. This demonstrated that GE interaction was highly significant and had remarkable effect on genotypic performance in different environments. Therefore, it was possible to proceed and calculate stability parameters. The GE was accounted for 41% of total sum of squares and was higher than the genotype effect (30%) and environment (29%), which suggests the possible existence of different environment groups. It is very common for mega-environmental trials data to embody a mixture of crossover and noncrossover types of GE. The large magnitude of GE IPC1 Fig. 2. AMMI-2 model biplot for IPC1 vs IPC2 for nine rice genotypes in nine environments. E1, Tonekabon in 2012; E2, Amol in 2012; E3, Sari in 2012; E4, Tonekabon in 2013; E5, Amol in 2013; E6, Sari in 2013; E7, Tonekabon in 2014; E8, Amol in 2014; E9, Sari in interaction in present study causes more dissimilarity in the genetic systems which control the physiological processes conferring yield stability in different environments. This result revealed that there was a differential yield performance among genotypes across test environments due to the presence of GE interaction. The relative contributions of GE interaction effects for grain yield in this study were similar to the findings in other studies (Saied, 2010; Tariku et al, 2013). In contrast to the results of present study, Khatun et al (2015) indicated that environment, genotype and GE interaction effects accounted for 23.60%, 16.27% and 24.89% of the total sum of squares of rice grain yield, respectively. The proportion of genotype, environment and GE interaction were reported to be 8.8%, 45.0% and 18.0% by Mostafavi et al (2014); 17.8%, 55.4% and 26.7% by Samonte et al (2005) and 10.52%, 34.09% and 34.78% by Tariku et al (2013), respectively. Katsura et al (2016) indicated that there are significant genotype, environment and GE interaction effects on yield, accounting for 24.8%, 20.2% and 28.2% of the total variation, respectively. According to the EV4 stability statistic, G7 was the most stable genotype, which had relatively high yield, whereas according to EV1, which benefits only IPC1 scores, G1, G6, G2 and G9 were the most stable genotypes. Different numbers of IPCs in EV computation resulted in relatively different conclusions in identification of the most stable genotypes. It is important to notice that EV1 parameter is based on only 49% of GE interaction variability, while EV4 is based on AMMI models that explain 92% of sum of squares. G6, as a high-yielding genotype with the lowest ASV score, was the most stable. The high interactions of G5 with environments were also confirmed by high ASV, suggesting unstable yield of this genotype across environments. According to the results of ANOVA for AMMI model and F-test approximated by Gollob s tests, the interactions of the nine rice genotypes across the nine environments were best predictable by the first two IPCs. This indicated that the existence of differential yield responses among the current released rice genotypes across the test environments was due to the presence of significant GE interaction effect. Due to significance of the first two IPCs in the present study, it seems that ASV is useful and adequate for determining the stable genotypes, and it facilitates the interpretation of GE interaction and identification of superior genotypes.

7 Preeti Rekha TALUKDAR, et al. Diversity in Aromatic Rice 179 Nonetheless, we also used MASV which benefits three IPCs and identified G7, G8 and G6 as stable genotypes. The results of present study, similar to previous reports, exhibited that the most accurate model for AMMI can be predicted by using the first two IPCs (Gauch and Zobel, 1996; Yan et al, 2000; Yan and Rajcan, 2002; Nayak et al, 2008; Akter et al, 2015). The factors like germplasm diversity, environmental conditions and crop type will affect the complexity degree of the best predictive model (Crossa et al, 1990). Plant breeders have used firstly the stability concept for identifying a genotype with constant yield in different environments (static concept), but later simultaneous considering of both mean yield and stability is proposed (dynamic concept). The ASV and MASV benefit dynamic concept of stability and could be useful for simultaneous selection of yield and stability (Dehghani et al, 2010). Therefore, based on these stability parameters, G6 (a high-yielding promising line) could be introduced as dynamic stable genotype. In order to identify a rice genotype with specific or relatively broader adaptation, studies on the magnitude and patterns of GE interaction effect are very important. Thus, the interaction of nine rice genotypes with the nine environments scattering over the first two AMMI multiplicative components of genotypes and environments visualized the pattern of affinity between the genotypes and the environments. The distances from the biplot origin are indicative of the amount of interaction that was exhibited by genotypes over environments or environments over genotypes (Yan and Kang, 2003). G2 and G6, which located near the biplot origin, were less responsive than the vertex genotypes, and they demonstrated low interactive action over environments. This revealed that these two genotypes exhibited lower inconstancy to the changes in the growing environment. Based on environments proximity to the origin, E2 relatively exhibited lesser genotype discriminative ability and proved to be more characteristic of the average environment than the remaining environments. On the other hand, E8, E5, E9 and E6, by the longest distance to the biplot origin, demonstrated higher genotypes discriminating ability and they were less representative of the average environment. CONCLUSIONS The present study indicated that rice grain yield was non-liable to significant fluctuations of environment conditions. Analysis of variance for the AMMI model of grain yield indicated that genotypes, environments, GE interaction and AMMI components 1 and 2 were significant. Thus, both yield and IPC1 and IPC2 scores should be taken into account simultaneously to utilize the useful effect of GE interactions and to make recommendation of the genotypes more accurate. It showed that the GE interaction was an important source of rice yield variation and its biplots were powerful for visualizing the response patterns of genotypes and environments. G7 ranked first in grain yield but tended to be unstable. According to biplot view and AMMI stability parameters including ASV, MASV, SIPC2 and EV2, G6 [Number 126 from IR (A37632) (Amol 3 Number 3)] was high yielding and highly stable genotype. REFERENCES Adugna W, Labuschagne M T Genotype-environment interactions and phenotypic stability analyses of linseed in Ethiopia. Plant Breeding, 121(1): Akbarpour O, Dehghani H, Sorkhi B, Gauch H G Evaluation of genotype environment interaction in barley (Hordeum Vulgare L.) based on AMMI model using developed SAS program. J Agric Sci Technol, 16: Akter A, Hasan M J, Kulsum M U, Rahman M H, Paul A K, Lipi L F, Akter S Genotype environment interaction and yield stability analysis in hybrid rice (Oryza sativa L.) by AMMI biplot. Bangl Rice J, 19(2): Becker H C, Léon J Stability analysis in plant breeding. Plant Breeding, 101(1): Blanche S B, Utomo H S, Wenefrida I, Myers G O Genotype environment interactions of hybrid and varietal rice cultivars for grain yield and milling quality. Crop Sci, 49(6): Bose L K, Jambhulkar N N, Pande K, Singh O N. 2014a. Use of AMMI and other stability statistics in the simultaneous selection of rice genotypes for yield and stability under direct-seeded conditions. Chil J Agric Res, 74(1): 1 7. Bose L K, Jambhulkar N N, Singh O N. 2014b. Additive main effects and multiplicative interaction (AMMI) analysis of grain yield stability in early duration rice. J Anim Plant Sci, 24(6): Crossa J Statistical analysis of multilocation trials. Adv Agron, 44: Crossa J, Gauch H G, Zobel R W Additive main effects and multiplicative interactions analysis of two international maize cultivar trials. Crop Sci, 30(3): Dehghani H, Sabaghpour S H, Ebadi A Study of genotype environment interaction for chickpea yield in Iran. Agron J, 102(1): 1 8. Eberhart S A, Russell W A Stability parameters for

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