Additive Main Effects And Multiplicative Interaction Biplot Analysis Research Articles

AMMI and GGE biplot analysis for genotype × environment interactions affecting the yield and quality characteristics of sugar beet.

Sugar beet, an important sugar crop, contributes significantly to the world's sugar production. However, genotype-environment interactions (GEI) often affect the quality characteristics of sugar beet. Hence, understanding the effects of GEI on sugar beet quality can aid in identifying high-quality genotypes that can adapt to different environments. Traditional variance analysis can only be used to examine the yield of a variety and not its specific adaptability to specific conditions. Therefore, more comprehensive analytical methods are required to evaluate the characteristics of the variety under specific environments. Additive main effects and multiplicative interaction (AMMI) and genotype main effect and genotype × environment interaction (GGE) biplot models can be employed to comprehensively evaluate different varieties and address the drawbacks associated with a single evaluation method. Moreover, these models also allow us to explore new varieties more objectively and comprehensively. In this study, the adaptability and stability of 16 sugar beet varieties, in terms of yield and sugar content, were evaluated using AMMI and GGE biplot analysis in seven pilot projects undertaken in 2022. In the assessment of a small but significant proportion of the total GEI variance for the two qualitative traits (yield and sugar content), 80.58% of the variance was explained by the cumulative contribution of IPC1, IPC2, and IPC3. AMMI and GGE biplots clearly highlighted that KWS4207 (G3) exhibited high and stable quality. They also demonstrated that the experiments in Jalaid Banner (Inner Mongolia) (E7) were the most representative. Together, the results suggested that the comprehensive application of AMMI and GGE biplot analysis allowed for a more comprehensive, scientific, and effective evaluation of sugar beet varieties across different regions. The findings offer a theoretical basis for sugar beet breeding and could guide the rational design of experiments for testing new varieties of sugar beet.

Yield performance and stability analysis of three cultivars of Gayo Arabica coffee across six different environments

Abstract The three cultivars of Gayo Arabica coffee (GAC) are distributed widely in the Gayo Highlands, Aceh Province, Indonesia, between 900 and 1,700 m above sea level (masl). The difference in altitude affects the yield and quality of coffee, and Arabica coffee has biennial bearing characteristics, so there are always annual yield fluctuations. This study aimed to determine the yield performance and stability level of the three GAC cultivars, Gayo 1, Gayo 2, and Gayo 3 (G3), across six different environments. The study used a randomized complete block design with three replications. The six environmental conditions are the combinations of two altitudes (900 and 1,500 masl) and 3 years of production (2019, 2020, and 2021). The estimation of coffee yields is based on the fruit value, which is calculated by the number of productive branches per tree, nodes per branch, berries per node, and the weight of a single cherry. An investigation of the genotype-by-environmental interaction using a combined analysis of variance and the yield performance and stability analysis performed using the Eberhart and Russell method, AMMI (additive main effects and multiplicative interaction) and GGE (genotype + genotype × environment) biplot analysis. Results showed that the yield performance of GAC was significantly affected by the interaction between genotype and environment. G3 was an ideal cultivar because it had high yield performance, was stable, and could adapt to broader environments in Gayo Highland. Therefore, G3 deserves high priority for Arabica coffee development in that region.

COTTON GERMPLASM CHARACTERIZATION FOR DROUGHT TOLERANCE BASED ON MORPHO-PHYSIOLOGICAL AND FIBER QUALITY PARAMETERS

Drought tolerance is a quantitative trait that is exceedingly challenging to breed, especially for allotetraploids like cotton. The scenario of limited water resources necessitates developing droughttolerant cultivars that conserve significant irrigation water throughout the summer. Therefore, the presented study used a design to statistically analyze the morphological, physiological, and fiber quality parameters linked with drought tolerance, which is a comprehensive method for choosing better genotypes from the available cotton germplasm. Measuring these parameters ensued for plants grown under field conditions. The germplasm comprised 150 cotton genotypes studied at two water regimes, i.e., regular and water-stressed conditions for two consecutive seasons of 2015–2016 and 2016–2017. Data recording ran for different morpho-physiological and fiber quality parameters. Significant differences occurred for all the treatments, genotypes, and Genotype × Environment interaction for all the morphological, physiological, and fiber quality parameters under study. Additive Main effects and Multiplicative Interaction (AMMI) analysis and AMMI biplot analysis helped analyze the results, which revealed that the cotton genotypes FH-900, FH-901, FH-312, AS-1, AS-2, AS-3, RH510, RH-627, AR-2, AR-9, BH-118, BH-175, SLH-74, CIM-1100, CIM-598, and MM-58 were drought tolerant and ranked highest concerning stress condition. Moreover, correlation studies distinguished the relationship between relevant traits concerning drought tolerance.

AMMI and GGE Biplot Analysis of Yield and Related Traits among Selected Mini-core Pigeonpea (Cajanus cajan L. Millsp.) Accessions

Pigeon pea [Canjanus cajan (L.) Millsp.] is an orphan crop that has remained greatly unimproved in quality and quantity in Africa even though it has great potential of supplying food to the growing population. Its yield is affected by various biotic and abiotic factors and a lack of varieties with broad adaptability to a range of environments. It is therefore important to select accessions that are stable in different environments for yield and yield components. A randomized complete block design layout was used to carry out a multi-locational trial using 107 pigeon pea accessions. Data was taken for the total number of pods per plant at maturity, pod length, seed weight, seeds per pod, and seed yield per plant. Additive main effects and multiplicative interaction (AMMI) with genotype and genotype by environment interaction (GGE) biplot were used to interpret the data. The mean squares for the PCA explained by the first two principal components account for 100% of the G x E interaction. The results revealed that the differences in the yield and yield components across the three locations were mostly due to the genotypes of the pigeon pea and to some extent the genotype by environment interaction. Six accessions were observed to be high-yielding and stable across all three environments.

GENOTYPE BY ENVIRONMENT INTERACTION AND STABILITY ANALYSIS OF ETHIOPIAN COMMERCIAL DURUM WHEAT (Triticum turgidum L.) CULTIVARS IN SOUTHERN ETHIOPIA

<p class="abstrakinggris">The use of improved varieties with wider adaptability and stability is necessary to maximize the productivity of durum wheat. However, due to genotype by environment interaction (GEI) effect, superior genotypes in one environment could be inferior in other environments. Hence, a multi-environmental trial (MET) was commenced to determine the magnitude of GEI effect and identify stable durum wheat genotypes across environments. The MET was conducted using nine durum wheat cultivars in randomized completely block design with three replications across four contrasting locations in 2020 crop seasons. The result of combined analysis of variance showed the presence of significant differences among the main effects; environments, genotypes, and GEI effects for grain yield. The additive main effects and multiplicative interaction (AMMI) combined ANOVA for main effects of environment, genotype, and GEI revealed highly significant differences among cultivars with 66.2%, 26.4%, and 7.3% share of sum square variation, respectively, of the total variation. AMMI and genotype plus genotype by environment (GGE) Bi-plot analysis identified the cultivars Fetan, Denbi, and Mangudo as high yielders and adaptive to the favourable locations. AMMI stability value and yield stability index identified Mangudo as the most stable and adaptive cultivar across locations. The AMMI Bi-plot analysis showed that the first two interaction principal component analysis (IPCAs) captured 90.45% of the total interaction sum of squares (ISS), where IPCA1 took 55.61% and IPCA2 accounted for 34.84% of GEI effects. This study identified Mangudo as the most stable cultivar with acceptable yield while Fetan was the top yielder genotype across locations.</p>

Genotype × environment interactions of potato tuber quality characteristics by AMMI and GGE biplot analysis

Potato is the most important non-grain crop worldwide, whose quality characteristics are always affected by genotype × environment interaction (GEI). Knowledge about the influence of GEI on potato quality could be very important to develop new or existing potato genotypes with high quality characteristics coupled with stability at varying environmental conditions. In this research, for the first time, additive main effects and multiplicative interaction (AMMI) and genotype (G) main effects plus genotype × environment (E) interaction (GGE) biplot analyses were used to dissect GEI in potato quality traits by evaluating six potato genotypes (Arizona, Cayenne, Generosa, Laudine, Salad Blue and Vogue) over four locations and two consecutive years (eight environments). Pooled ANOVA revealed a P ≤ 0.001 significance of ANOVA components for all traits under study, with G that attributed the highest variation on dry matter, citric acid, DPPH and washability, and E that contributed more on total phenolics, vitamin C, reducing sugars ad FRAP. After assessing a small but significant proportion to total variance of GEI for all qualitative traits, it was found that almost all of the variance was explained by the cumulative contribution of IPC1 and IPC2 for AMMI and by PC1 and PC2 for GGE. From AMMI and GGE biplots clearly emerged that, despite qualitative performances of genotypes fluctuated considerably across the environments, Salad Blue was the most suitable genotype for dry matter, total phenolics, vitamin C and FRAP throughout the 8 environments due to its above-average mean values and stability. On the contrary, no clear differentiations were found between environments, indicating that the choice of the “ideal” environment depended on the quality trait. Our results suggested the importance of AMMI and GGE analysis in identifying qualitatively high and stable genotypes over multi-environmental trials. This approach could be useful in providing scientific strength for the setup of potato protected geographical identifications.

SNP Based Trait Characterization Detects Genetically Important and Stable Multiple Stress Tolerance Rice Genotypes in Salt-Stress Environments.

Soil salinity is a major constraint to rice production in coastal areas around the globe, and modern high-yielding rice cultivars are more sensitive to high salt stress, which limits rice productivity. Traditional breeding programs find it challenging to develop stable salt-tolerant rice cultivars with other stress-tolerant for the saline environment in Bangladesh due to large yield variations caused by excessive salinity fluctuations during the dry (boro) season. We examined trait characterization of 18 advanced breeding lines using SNP genotyping and among them, we found line G6 (BR9621-B-1-2-11) (single breeding line with multiple-stress-tolerant QTL/genes) possessed 9 useful QTLs/genes, and two lines (G4:BR9620-2-7-1-1 and G14: IR 103854-8-3-AJY1) carried 7 QTLs/genes that control the desirable traits. To evaluate yield efficiency and stability of 18 rice breeding lines, two years of field experiment data were analyzed using AMMI (additive main effect and multiplicative interaction) and GGE (Genotype, Genotype Environment) biplot analysis. The AMMI analysis of variance demonstrated significant genotype, environment, and their interaction, accounting for 14.48%, 62.38%, and 19.70% of the total variation, respectively, and revealed that among the genotypes G1, G13, G14, G17, and G18 were shown to some extent promising. Genotype G13 (IR 104002-CMU 28-CMU 1-CMU 3) was the most stable yield based on the AMMI stability value. The GGE biplot analysis indicates 76% of the total variation (PC1 48.5% and PC2 27.5%) which is performed for revealing genotype × environment interactions. In the GGE biplot analysis, genotypes were checked thoroughly in two mega-environments (ME). Genotype G14 (IR103854-8-3-AJY1) was the winning genotype in ME I, whereas G1 (BR9627-1-3-1-10) in ME II. Because of the salinity and stability factors, as well as the highest averages of grain yield, the GGE and AMMI biplot model can explain that G1 and G13 are the best genotypes. These (G1, G6, G13, G14, G17, and G18) improved multiple-stress-tolerant breeding lines with stable grain yield could be included in the variety release system in Bangladesh and be used as elite donor parents for the future breeding program as well as for commercial purposes with sustainable production.

Identification of Elite Pigeonpea Genotypes against Fusarium Wilt and Sterility Mosaic Disease through AMMI and GGE Biplot Analysis

Background: Pigeonpea is an important legume crop in the world. The diseases Fusarium Wilt (FW) and Sterility Mosaic Disease (SMD) causes a complete yield loss i.e. up to 100% in susceptible pigeonpea genotypes. Amidst of these conditions the selection of stable resistant genotypes against these diseases under varying environmental conditions is the primary management choice for minimizing yield losses (Sharma et al., 2012; Bhaskar et al., 2016). Methods: Fifty pigeonpea genotypes were evaluated against Fusarium wilt and SMD in artificial epiphytotic conditions during four Kharif seasons i.e., 2017, 2018, 2019 and 2020. Additive main effects and multiplicative interaction (AMMI) analysis was used to decipher the interaction between genotype (G) and environment (E) for FW and SMD in pigeonpea. Result: Analysis revealed that in both the diseases IPCA1 and IPCA2 collectively contributed more than 80% interaction. AMMI biplots revealed that Kharif- 2019 shows positive effect to FW disease and negative effect to SMD. The AMMI model integration with GGE biplot, identified stable and resistant genotypes (1, 6, 7, 9, 11, 14, 20, 25, 21, 30, 33, 35, 42, 44, 46, 47, 48 and 49) to both FW and SMD based on their performance across diverse environments. These genotypes will be referred for pigeonpea FW and SMD disease resistance breeding programmes.

Genotype × environment interaction and stability analyses of grain yield in rainfed winter bread wheat

Abstract The genotype × environment (GE) interaction analysis is fundamental in crop breeding programs to guide selection and for recommendation of high performing and stable genotypes for breeding objectives. This study aimed at quantifying the GE interaction effects and determines grain yield stability among winter bread wheat genotypes under rainfed conditions of Iran. Twenty-four winter wheat genotypes were evaluated under nine test locations using a randomized complete blocks design with four replications during three cropping seasons (2019–21). The additive main effects and multiplicative interaction (AMMI) model and several parametric and nonparametric stability statistics were applied for analysis of grain yield data collected from the experiments. AMMI analysis of variance for grain yield revealed significant effects (p < 0.01) for genotype, environment, and GE interaction. The environment was the main source of variation and accounted for 83.5% of the total yield variation, followed by GE (6.5%) and genotype (1.0%) effects. The AMMI biplot analysis indicated the genotypes G3, G23, G22, G10, and G19 as high yielding with stability performance across environments. Genotypes G14, G13, G20, and G9 showed large positive interaction with the environments featuring the highest rainfall during growing season, while genotypes G7, G6, and G21 had a large positive interaction with environments with low rainfall. Spearman’s rank correlation analysis revealed that the AMMI stability value, Shukla’s stability variance (σ2i), Wricke’s ecovalence (W2i), coefficient of determination (R2i), variance in regression deviations (S2di), and nonparametric statistic of S2(i) were not correlated with mean yield in tested genotypes, showing they are related to static/biological concept of stability. In contrast, the genotypic superiority index (Pi) and regression coefficient (bi) were significantly correlated (p < 0.01) with mean yield and corresponded to dynamic/agronomic concept of stability. These findings suggest that selection of genotypes should be considered based on selection objectives of using the various stability parameters described here. In conclusion, the selected genotypes in this study should be recommended as new cultivars or parental lines for grain yield and stability improvement under rainfed conditions of Iran or similar agro-ecologies.

Genotype by environment interaction analysis of wheat (Triticum aestivum L.) grain yield under rain-fed conditions in Zambia

Understanding genotype by environment interaction (GEI) is important for crop improvement because it aids in the recommendation of cultivars and the identification of appropriate production environments. The objective of this study was to determine the magnitude of GEI for the grain yield of wheat grown under rain-fed conditions in Zambia by using the additive main effects and multiplicative interaction (AMMI) model. The study was conducted in 2015/16 at Mutanda Research Station, Mt. Makulu Research Station and Golden Valley Agricultural Research Trust (GART) in Chibombo. During2016/17, the experiment was performed at Mpongwe, Mt. Makulu Research Station and GART Chibombo, Zambia. Fifty-five rain-fed wheat genotypes were evaluated for grain yield in a 5 × 11 alpha lattice design with two replications. Results revealed the presence of significant variation in yield across genotypes, environments, and GEI indicating the differential performance of genotypes across environments. The variance due to the effect of environments was higher than the variances due to genotypes and GEI. The variances ascribed to environments, genotypes, and GEI accounted for 45.79%, 12.96%, and 22.56% of the total variation, respectively. These results indicated that in rain-fed wheat genotypes under study, grain yield was more controlled by the environment than by genetics. AMMI biplot analysis demonstrated that E2 was the main contributor to the GEI given that it was located farthest from the origin. Furthermore, E2 was unstable yet recorded the highest yield. Genotype G47 contributed highly to the GEI sum of squares considering that it was also located far from the origin. Genotypes G12 and G18 were relatively stable because they were situated close to the origin. Their position indicated that they had minimal interaction with the environment. Genotype 47 was the highest-yielding genotype but was unstable, whereas G34 was the lowest-yielding genotype and was unstable.

AMMI and GGE Biplot Analysis for G×E Interaction of Wheat Genotypes under Different Irrigation and Sowing Condition

Background: AMMI analysis showed that genotype, environment and genotype-environment interaction had a highly significant variation for 20 wheat genotypes analyzed over four environments. ASV ranking revealed G15 (RVW-4275) as a stable genotype while G3 (RVW-4263) and G9 (RVW-4269) as unstable genotypes. GGE biplot analysis for environment interrelationship revealed that E1 (Irrigated timely sown), E2 (Restricted irrigation timely sown) were correlated forming one group and E3 (Irrigated late sown), E4 (Restricted irrigation late sown) were correlated forming another group. Polygon view showed that G9 (RVW-4269) was found stable and better performing in E1, G12 (RVW-4272) was stable under the E2 environment and G3 (RVW-4263) was stable in E3. Ideal genotype graph with concentric circles having ideal genotype at the center and genotypes G12(RVW-4272), G18(RVW-4278), G13(RVW-4273), G11(RVW-4271), G10 (RVW4270) present in a concentric circle close to the center can to considered as stable and desirable genotypes.Methods: In the present study the plant material comprised of 20 wheat genotypes. These genotypes were randomly allocated in different replication under different environmental condition. The field trial was evaluated at four different environments viz., E1- Irrigated timely sown, E2- Restricted irrigation is timely sown (RI- 2 irrigation), E3- Irrigated late sown, E4- Restricted irrigation late sown during Rabi season of 2016-2017 at research farms, college of agriculture, Gwalior, MP. The genotype main effects and genotype × environment interaction effects (GGE) model and additive main effects and multiplicative interaction (AMMI) model were two statistical approaches used to determine stable genotype in R software.Result: Highly significant difference was seen for genotype and G×E interaction in our study, revealing that genotype yield output was highly impacted by G×E. In all four environments and G3, G9 as unstable genotypes in all four environments, ASV ranking revealed G15 as a stable genotype. For further breeding, these genotypes G12, G18, G13, G11, G2, G10 and G15 may be used to grow genotypes adapted to conditions of partial irrigation or drought stress.

English

Undertaking a multi-environment trial prior to releasing a high yielding and stable varieties for a specific environment is a major step in plant breeding. Therefore, the objective of this work was to study the effect of Genotype × Environment Interaction (GEI) and evaluate the adaptability and stability of sixteen large white common bean genotypes. Additive main effects and multiplicative interaction (AMMI) and genotype main effect and genotype by environment interaction (GGE) biplot models were used. The experimental design was 4 × 4 triple lattice across environments. AMMI analysis of variance showed environments that explained the greater proportion (72.42%) of the total variation, followed by GEI (10.75%) and genotype (2.32%). This indicates the possibility of selecting stable genotypes. AMMI biplot analysis revealed that the first and second interaction axes captured 42.62 and 26.77% of the total variation due to GEI. GGE model showed that the nine environments used for the study belonged to two mega-environments. AMMI stability value (ASV), AMMI and GGE biplot identified one common genotype, G14 (SAA 2) that was the overall best in performance in relation to yield and stability. This suggests that for reliability and optimum result it is better to combine the result of two or more analytical tools for yield and stability in recommendation genotype for verification and release. Key words: Biplot, genotype × environment interaction (GEI), grain yield, stability, additive main effects and multiplicative interaction (AMMI), AMMI stability value (ASV).

Phenotyping and Genotype × Environment Interaction of Resistance to Leaffolder, Cnaphalocrocis medinalis Guenee (Lepidoptera: Pyralidae) in Rice.

Rice leaffolder, Cnaphalocrocis medinalis is one of the key foliage feeding insects of great concern throughout Asia as it results in significant yield losses. High visibility of damage is triggering farmers to apply toxic pesticides for its management. Therefore, it is vital to identify new stable sources of resistance for leaffolder. Phenotyping of 160 recombinant inbred lines (RILs) of a cross between a resistant parent, W1263 and a susceptible parent, TN1 using a rapid field screening method for three seasons resulted in identification of nine RILs as stable sources of resistance to rice leaffolder. Phenotypic frequency distributions were found continuous indicating that the resistance is a quantitative trait governed by polygenes. Phenotypic data for three seasons were analyzed using Genotype and Genotype × Environment Interaction (GGE) analysis for identification of stable resistant lines. Additive main effect and multiplicative interaction (AMMI) analysis showed that 86.41% of the total sum of square of damaged leaf area was attributed to genotype (GEN) effect; 0.48% to environment (ENV) effects and 5.68% to genotype by environment (G × E) interaction effects. Damage area, damage score and leaf length showed very high broad-sense heritability across three environments. However, leaf width had low heritability indicating higher environment influence. Phylogenetic analysis grouped these 160 RILs and parents into five clusters based on resistant reaction. AMMI and GGE biplot analysis revealed that stable genotypes G8 (MP114) and G3 (MP108) with lower damage area and damage score can be utilized in developing cultivars with leaffolder resistance.

One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis

To improve multi-environmental trial (MET) analysis, a compound method—which combines factor analytic (FA) model with additive main effect and multiplicative interaction (AMMI) and genotype main effect plus genotype-by-environment interaction (GGE) biplot—was conducted in this study. The diameter at breast height of 36 open-pollinated (OP) families of Pinus taeda at six sites in South China was used as a raw dataset. The best linear unbiased prediction (BLUP) data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data. The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot. BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method. AMMI analysis identified that two datasets had highly significant effects on the site, family, and their interactions, while BLUP data had a smaller residual error, but higher variation explaining ability and more credible stability than raw data. GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation, test-environment evaluation, and genotype evaluation. In addition, BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components. Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.

Selection of superior genotype of sweet-potato in Indonesia based on stability and adaptability

Sweet-potato (Ipomoea batatas [L.] Lam.) is the 3rd most important root and tuber plant in the world, after potatoes and cassava. The knowledge of the structure of Genotype × Environment interaction (GEI) could assist in the development of cultivars of sweet-potato. The objective of this research was to select superior sweet-potato genotypes in Indonesia based on Finlay-Wilkinson regression, additive main effect and multiplicative interaction (AMMI) analysis, AMMI stability value (ASV), and Genotype plus Genotype × Environment (GGE) biplot methods. Genetic materials evaluated in this research were 10 Indonesian superior genotypes developed by the Plant Breeding and Seed Technology Lab., Faculty of Agriculture, University of Padjadjaran, Bandung. The research was arranged in randomized block design, with 16 genotypes as treatments. Data analysis of the yield characters was performed by using combined variance analysis: Finlay-Wilkinson regression, ASV, AMMI, and GGE Biplot. The GEI significantly influenced the tested genotypes at three sites. However, they did not show any significant differences in the GEI. Ayamurasaki, Beniazuma, Awachy2, 15(112), Awachy4, Awachy5, 80(109), 54(160), 35(180) were specific adaptation genotypes of marginal land based on Finlay-Wilkinson analysis; whereas, Rancing, AC Putih, Ayamurasaki, Naruto Kinotoki, Awachy1, Awachy2, 15(112), Awachy4, 57(97), 80(109), 54(160), 68(120) and 35(180) performed as stable genotypes based on ASV. Ayamurasaki, Naruto Kinotoki, Awachy1, Awachy2, 57(97), 80(109) and 68(120) were stable genotypes based on AMMI analysis. However, based on GGE Biplot analysis Ayamurasaki, Awachy1, 80 (109) and 68 (120) were stable genotypes. ASV analysis provided effective stable genotype selections of 87.50% greater than other stability methods. Ayamurasaki, Awachy1, 80(109) and 68(120) were stable genotypes based on AMMI, ASV, and GGE biplot analysis.

Stability of oil palm (Elaeis guineensis Jacq.) progenies on yield and yield Components across environments using AMMI analysis

Southern Thailand is annually affected by climate change, which impacts on yield performance of oil palm. Therefore, effect of climate change on adaptability, cultivation and yield of oil palm in various planting areas was investigated. This research aimed to evaluate the adaptability of six oil palm progenies (cross numbers 110, 118, 119, 130, 132 and 137) at 5 year-old-plants grown in three environmental areas of southern Thailand: Nakhon Si Thammarat, Phatthalung and Songkhla provinces. The experiment was arranged in a completely randomized design with five replications per treatment (1 tree/replicate) in each environment during 2013-2014. The additive main effects and multiplicative interaction (AMMI) model was used to analyze the stability of yield and yield components. The results showed that the variances attributed to environment (E), genotype (G) and their interactions (G x E) were highly significant. The yield of each progeny depended on growth location. The AMMI biplot analysis showed that progeny 110 was the most stable genotype based on yield and yield components in all the environments. The highest yield of fresh fruit bunches in Phatthalung, Songkhla and Nakhon Si Thammarat provinces were obtained in progeny 130 (403.16 kg/palm/year), progenies 132 and 137 (303.20 and 297.96 kg/palm/year), and progeny 119 (283.52 kg/palm/year), respectively. This indicates that the suitability of an oil palm progeny in general depends on the specific environment for planting.

Stability Parameters and AMMI Analysis of Quinoa ( Chenopodium quinoa Willd.)

SCREENING for stable genotype entails estimating the genotype (G)×environment (E) interaction (GEI) in multi-environmental trials (MET). Quinoa is a nutritionally rich crop as a source of vitamins, minerals and essential amino acids. It has been introduced to many countries in diverse regions worldwide. We evaluated five genotypes of quinoa under ten environments including irrigated and rain-fed conditions across Egypt. We used several stability parameters as well as additive main effects and multiplicative interaction (AMMI) analysis to determine the best genotype for each environment/location across Egypt. Based on AMMI analysis of variance, the sum of squares (SS) of E, G, and GEI explained ≈ 78%, 14%, 8%, respectively, of the treatment sum of squares. The SS of interaction principal components analysis axis1 (IPCA1) and IPCA2 explained 75 and 18%, respectively. KVL-SRA3 was the most stable genotype according to ecovalence value (Wi), to deviation from regression coefficient value (S2di) of Eberhart and Russell and to IPCA1, IPCA2 and AMMI stability value (ASV). Regalona was the most unstable genotype based on the same parameters. These results were visualized using AMMI biplot analysis, which revealed that KVL-SRA3 was widely adapted to all environments unlike Regalona that was poorly adapted to most environments. The Spearman’s rank correlation among different stability parameters was significantly variable for both the five-quinoa genotypes and the ten investigated environments. Our results indicated that most stability parameters were consistent with AMMI parameters in identifying stable genotypes with some exceptions according to the concept of each of stability parameter (agronomic or biological). This study is an important step to open doors for the adoption of an extraordinary nutritional crop in Egypt.

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

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.

AMMI and GGE biplot analyses of genotype-environment interaction in spring barley lines

Yield stability depends on the resistance of varieties and hybrids to stressful environmental factors. Assessment of genotype-environment interaction helps breeders select the best genotypes for submission to the state variety trials. The article presents results of AMMI (Additive Main effects and Multiplicative Interaction) and GGE (Genotype plus Genotype-Environment interaction) biplot analyses of the grain yield data in eight promising spring barley lines bred at the Plant Production Institute nd. a V.Ya. Yuryev of NAAS and two standard varieties in 2012–2015. The objective of this study was to determine the effect of genotype, environment and their interaction for grain yield and identify stable and performance genotypes. The experimental layout was randomized complete block design with four replications. The analysis of variance on grain yield data showed that the mean squares of environments, genotypes and genotype-environment interaction (GEI) accounted for 85.8, 8.1 and 6.1 % of treatment combination sum of squares, respectively. To find out the effects of GEI on grain yield, the data were subjected to AMMI and GGE biplot analysis. The AMMI model presented greater efficiency by retaining most of the variation in the first two main components, 95.7 %, followed by the GGE biplot model, 82.9 %. Lines 09-837 (G8) and 08-1385 (G9) presented an elevated grain yield and stability as determined by the AMMI and GGE biplot methodologies. These lines named as “Avgur” and “Veles” were submitted to the state variety trial. The results finally indicated that AMMI and GGE biplot are informative methods to explore stability and adaptation pattern of genotypes in practical plant breeding and in subsequent variety recommendations.

Genotype × Environment Interaction and Yield Stability Analysis in Hybrid Rice (Oryza sativa L.) By AMMI Biplot

CORRECTION: Due to a number of formatting and layout issues, the PDF of this paper was replaced on 10th October 2016. The page numbers of this article have changed from 79-86 to 83-90.Assessing the adaptability and stability of promising rice genotypes is one of the important steps for accurate evaluation. This study determined the genotype × environment interaction (GEI) and stability performance of 12 promising rice genotypes in four environments during 2009 Aman season. The experiment used randomized complete block design with three replications. Yield stability and adaptability of yield performance were analyzed by combined analysis and additive main effects and multiplicative interaction (AMMI) model. The environment, genotype main effects, and the GEI were all highly significant (P<0.001). The study indicated that the tested genotypes, such as BRRHA G1 (5.47 tha-1), G2 (5.68 tha-1), G3 (6.29 tha-1) and G4 (5.27 tha-1) had higher average yields, which indicated these genotypes adapted to favourable environments (E1 and E3). Whereas the environment, E3 could be regarded as a more stable site for high yielding hybrid rice improvement than the other locations. Based on AMMI biplot analysis, genotypes BRRI1A/BRRI827R (G1), IR58025A/BRRI10R (G2), BRRI 10A/BRRI 10R (G3) and BRRI hybrid dhan1 (G4) have higher average mean yields with high main (additive) effects and positive IPCA1 score, among them BRRI 10A/BRRI10R (G3) being the overall best. Locations E1 and E3 could be regarded as a good selection site for rice hybrid improvement due to stable yields.Bangladesh Rice j. 2015, 19(2): 83-90