Multi-environment Yield Trials Research Articles

Detection of superior rice genotypes and yield stability using AMMI and MTSI models

Multi-environment yield trials and multi-trait stability analyses are essential to identify the superior genotype. The aim of this research was to assess the adaptation and stability of rice genotypes using additive main effect and multiplicative interaction (AMMI) and genotype plus genotype by environment (GGE) biplot analysis and multi-trait stability index (MTSI). In this study, 20 rice genotypes were evaluated for three seasons at the same location during 2020–21. Based on AMMI analysis, the genotypes G22 (Vandana), G32 (IC-0098989), G34 (IC-0135769), G60 (ADT 36), G61 (ADT 37), and G64 (ADT 45) were found to have high yield and were more stable. GGE analysis revealed that the high-yielding and stable genotypes were G22 (Vandana), G34 (IC-0135769), G60 (ADT 36), and G64 (ADT 45). The genotypes G65 (ADT 48), G60 (ADT 36), G38 (IC-0135529), and G33 (IC-0124198) were observed to be stable based on the Multi Trait Stability Index (MTSI). Based on the above, G60 (ADT 36) could be concluded as stable across all seasons, not only based on yield but also other important agronomic traits.

REMATTOOL-R: a smart tool for identifying superior maize genotypes from multi-environment yield trials

ABSTRACT Breeders routinely evaluate many experimental hybrids that may be of different maturities. In maize (Zea mays L.), days to 50% anthesis and percent grain moisture content are used as proxies for relative maturity. The lack of an easy-to-use statistical tool that gives yield potential of all entries in a trial while classifying them into different relative maturity categories in a single visualization makes it difficult to quickly assess superior genotypes. We report on a tool called REMATTOOL-R to aid breeders in visualizing and assessing the relationship between yield and certain agronomic traits, viz., days to anthesis, percent harvest grain moisture content, and number of harvested plants, and help them in advancing experimental hybrids to the next stage. REMATTOOL-R uses either Best Linear Unbiased Estimators (BLUEs) or Best Linear Unbiased Predictors (BLUPs) of yield and agronomic traits from multilocation trials to perform various computations. The various computations produce graphical and tabular visualizations of the relationship between grain yield and days to anthesis, moisture content, and number of harvested plants that can be used to support selection decisions by the breeder. REMATTOOL-R output tables show entries with at least 5% higher yield than the check varieties in the trial. REMATTOOL-R is a robust, simple, user-friendly, and easily comprehensible tool, convenient for identifying superior genotypes during all the trial stages of a maize breeding program. REMATTOOL-R will be useful to breeders and researchers in related disciplines.

Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat

Key messageSparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget.Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1–9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder’s advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs.

Optimizing the setup of multienvironmental hybrid wheat yield trials for boosting the selection capability.

The accuracy of genomic prediction increases with increasing heritability, and thus the challenge of optimizing the design of multienvironment yield trials under a limited budget arises. With this in mind, we aimed to find the best of several options to sparsely distribute a fixed number of plots across different environments to increase the accuracy of hybrid performance prediction. We used a comprehensive published genomic and phenotypic data set of 1,604 winter wheat (Triticum aestivum L.) hybrids and compared several commonly used biometric models for phenotypic data analysis in a resampling study to identify the one that most accurately estimated the hybrid performance in different imbalanced trials. Our results showed that when using information about genotypic relationships, genotypic values were more strongly associated with the reference values than when this information was ignored. In addition, a balanced environmental sampling resulted in an adequate characterization of each environment and increased the accuracy for estimating the hybrid performance. One promising design involved dividing the genotypes into equally sized subgroups that were tested in a subset of environments, with the constraint that the subgroups overlapped with respect to the environments. This scenario appears to be particularly appropriate, as it provided both high accuracies in the estimates of genotypic values and had low variability resulting from the data sample used. Thus, we were able to clearly demonstrate the utility for optimizing the design of multienvironment hybrid wheat yield trials in times of genomic selection.

Growth Performance and AMMI Yield Stability Analysis of Five New Maize Hybrid Populations

Multi-environment yield trials are essential in an estimation of genotype by environment (GE) interaction and identification of superior genotypes in the final selection cycles. The objective of this study was to evaluate yield stability of five hybrid maize in three locations using AMMI (Additive Main Effects and Multiplicative Interaction) method. A randomized block design with three replications was applied to the experiment at each site. The genotypes tested were UNIB CT5, UNIB CT8, UNIB CT9, UNIB CT13, and UNIB CT14. The hybrids were single-crosses from selected pairs of S6 gamma irradiated mutant parental lines. The hybrids were cultivated in three different locations with different agroclimatic. They were Air Duku village, district of Curup, Rejang Lebong (rainy season of the year of 2014); Kandang Limun village, district of Muara Bangkahulu, Bengkulu (wet season of the year of 2015) and Medan Baru village, district of Muara Bangkahulu, Bengkulu (dry season of the year of 2015). The results showed that among five newly developed hybrids tested, CT8 and CT9 were the most prospective genotype for Ultisol for a dry and wet season, respectively. Based on the postdictive success and predictive success methods, the model used (AMMI 2) was able to explain 89% interaction-influenced variation. The genotypes found stable in three locations based on AMMI analyses was UNIB CT14. Three hybrids were considered specific adaptation. They were UNIB CT9 for rainy season of Kandang Limun, UNIB CT8 for dry season of Medan Baru and UNIB CT13 for rainy season of Curup. UNIB CT5 did not adapt to any of the three environments tested.

Assessment of Flue-Cured Tobacco Recombinant Inbred Lines under Multi-Environment Yield Trials

Cross-over interaction is always a major concern for plant breeders when recommending a crop cultivar for different agroecologies. Hundred flue-cured tobacco recombinant inbred lines (RILs) derived from F4:5/F4:6/F4:7 populations along with three parental checks were evaluated to determine genotype by environment (GE) interaction. The experiments were conducted for three consecutive years (2012–2013, 2013–2014 and 2014–2015) at two different locations i.e., Mardan (plain area) and Mansehra (hilly area), Pakistan using alpha lattice design with three replications in each environment. Six distinct environments were generated in combination of three years and two locations. Results obtained from additive main effect and multiplicative interaction (AMMI) analysis revealed that 46.5% of phenotypic variation in yield was contributed by environmental effects while 45.0% was explained by GE interaction. First four principal components (PCs) were significant and cumulatively explained 89.9% of variation due to GE interaction. For instance, based on AMMI-1 model, genotype G11 was identified as high yielding (2669 kg ha-1 ) followed by G86 (2586 kg ha-1 ) and G28 (2563 kg ha-1 ). Likewise, in AMMI-2 model, G11, G5 and G56 were identified as most stable genotypes. Generally, performance of inbred lines at Mardan was consistent. Mansehra had the most discriminating and erratic environments over years for FCV lines. Genotypes G11 and G86 appeared as high yielding elite tobacco lines possessing dynamic stability. The mentioned FCV lines were superior to standard checks in yield and stability; hence could be recommended for diverse environments. This study puts emphasis on the significance of conducting multi-environment yield trials to screen not only best performing lines but also to reduce breeding cycles for new tobacco cultivars.

GGE Biplot Analysis of Multi-Environment Yield Trials in Soybean Promising Lines

Soybean in Indonesia is grown in diverse agro-ecological environments. The performance of soybean yield often varies due to significant genotype × environment interaction (GEI), therefore the yield stability of performance is an important consideration in the breeding program. The aim of the research was to exploring the GEI pattern and yield stability of soybean promising lines in the tropics using GGE (Genotype and Genotype by Environment Interaction) biplot method. A total of 16 soybean promising lines were evaluated in ten environments during 2016 growing season. The experiment was arranged in a randomized completely block design with four replicates. The analysis of variance revealed that environments (E) explained the highest percentage of variation (51.45%), meanwhile the genotypes (G) and genotype × environment interactions (GEI) contributed for 3.24%, and 14.59% of the total variation, respectively. Seed yield of 16 soybean promising lines ranged from 2.41 to 2.83 t.ha-1 with an average of 2.74 t.ha-1. Joint effects of genotype and interaction (G+GE) which was partitioned using GGE biplot analysis showed that the first two components were significant, explaining 60.88% (37.89% PC1 and 22.98% PC2) of the GGE sum of squares. Indonesia can be divided into at least four putative mega environments for soybean production. The GGE biplot identified G10 as high yielding and stable promising line, thus recommended to be developed in multi-environment in tropical regions of Indonesia.

Use of Principal Coordinate Analysis for Measuring GE Interactions in Rain-Fed Durum Wheat Genotypes

Genotype × environment interactions complicate selection of superior genotypes for narrow and wide adaptation. Multienvironment yield trials of twenty durum wheat genotypes were conducted at five locations of Iran (Gachsaran, Gonbad, Moghan, Ilam and Khorram abad) over four years (2009-2013). Combined ANOVA of yield data of the twenty environments (year/location combined) revealed highly significant differences among genotypes and environments as well as significant genotype-environment interaction indicated differential performance of genotypes over test environments. The GE interaction was examined using multivariate analysis technique as principal coordinate analysis (PCOA). According to grand means and total mean yield, test environments were grouped into two main groups as high mean yield (H) and low mean yield (L). There were eleven H test environments and nine L test environments which analyzed in the sequential cycles. For each cycle, both scatter point diagram and minimum spanning tree plot were drawn. The identified most stable genotypes with dynamic stability concept and based on the minimum spanning tree plots and centroid distances were G12 (3342 kg ha-1), G10 (3470.3 kg ha-1), G5 (3203.0 kg ha-1), and G1 (3263.5 kg ha-1), and therefore could be recommended for unfavorable or poor conditions. Genotypes G10 (3470.3 kg ha-1) and G9 (3404.2 kg ha-1) were located several times in the vertex positions of high cycles according to the principal coordinates analysis (PCOA) and therefore could be recommended for favorable or rich conditions. Finally, the results of principal coordinates analysis in general confirmed the breeding value of the genotypes, obtained on the basis of the yield stability evaluation.

GGE-Biplot Analysis of Multi-Environment Yield Trials of Common Bean (Phaseolus vulgaris L.) in the southern Ethiopia

The present study was conducted on thirty-six common beans (Phaseolus vulgaris L.) Genotypes across six contrasting environments defined for its different soil fertility status and located at the southern Ethiopia. The genotypes were arranged in 6 x 6 triple lattice design and executed for two successive main cropping seasons with the objectives to evaluate yield performance of common bean genotypes and identification of mega environments. GGE (i.e., G = genotype and GE = genotype by environment, interaction) bi-plot methodology was used for graphical presentation of yield data after subjecting the genotypic means of each environment to GGE Bi-plot software. The first two principal components (AXIS 1 and AXIS2) were used to display a two-dimensional GGE bi-plot. Thus, genotypic AXIS1 scores >0 classified the high yielding genotypes while AXIS2 scores <0 identified low yielding genotypes. Unlike genotypic AXIS1, genotypic AXIS2, scores near zero showed stable genotypes whereas large AXIS2 scores classified the unstable ones. The environmental AXIS1 were related to crossover nature of GEI while AXIS2 scores were associated with non-cross over GEI. The six test environments in the southern region were divided in to two distinct mega environments (Mega-1 and 2). Mega-1 constituted GOHF13, ARMF12 and ARLF13 while genotype 14 (SCR10) being the best winner, on the other hand, Mega-2 contained GOHF12 and while common bean genotype 20(SCR17) being the best winner. The results of this study indicated that breeding for specific adaptation should be taken as a breeding strategy in southern region to exploit positive GEI to increase production and productivity of common bean.

GGE Biplot Analysis of Multi-environment Yield Trials for Wheat in Northern India

GGE Biplot Analysis of Multi-environment Yield Trials for Wheat in Northern India

Genotypic variability and stability of fibre yield components in kenaf (<i>Hibiscus cannabinus</i> L.)

Multi-environment yield trials (METs) are essential to reduce the challenges of the genotypes × environment interaction (G × E) in identification of promising genotypes. Therefore, this study estimated genetic variability, broad-sense heritability (h2b), genetic advances (GA) and repeatability as well as stability of core and bast fibres yield of 33 kenaf genotypes evaluated across six locations. The varieties were grown in an experiment laid out in randomized complete block design with three replications in 2016. Mature plants were harvested, retted and dried. Core fibre (CF) and bast fibre (BF) were weighed and data collected were subjected to analysis of variance (ANOVA) separately and across locations. Genetic variances, broad-sense heritability (h2b), genetic advance (GA) and repeatability were estimated for both traits. The G × E was assessed using joint regression analysis. Highly significant variation existed for both core and bast fibre yields among the genotypes in each of the locations. Phenotypic parameters were higher than those of the genotypes for both core fibre weight (CRW) and bast fibre weight (BSW). Broad-sense heritability was 82% for CRW and 66% for BSW while GA was 53.7% and 8.8% for CRW and BSW, respectively. Combined ANOVA showed significant differences for genotypes, environments, and G × E for CRW and BSW (p<0.001). Partitioning of G × E showed that only Gen × Env. (linear) was significant (P<0.05). Genotypic variance was 85% of the phenotypic variance for CRW, but only 32% for BSW. Hence, environmental effect was more on the CRW than BSW. Inheritance of the CRW and BSW is genetic because of their high heritability. Genetic advance and repeatability were higher for CRW than BSW. Based on high heritability with high GA for CRW, gene action governing inheritance of CRW is additive. Both additive and non-additive gene actions are responsible for BSW because its high heritability but low GA. Progress can therefore be made in selection for CRW than BSW in kenaf improvement programmes. Linear response of genotypes on environments accounted for the major part of total variation for the two types of fibre yield. Genotypes A-60-282-15, AC-313-293, AU-2452-5A and Cuba 108 are adapted to favourable environments while AU-2452-43, AU-24526, 2QQ 171 and AU-7192 are adapted to less favourable environments both in CF and BF yields.Keywords: Bast fibre, Core fibre, Genotype, Gene action, Regression analysis, Stability

Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize.

The ability to predict the agronomic performance of single-crosses with high precision is essential for selecting superior candidates for hybrid breeding. With recent technological advances, thousands of new parent lines, and, consequently, millions of new hybrid combinations are possible in each breeding cycle, yet only a few hundred can be produced and phenotyped in multi-environment yield trials. Well established prediction approaches such as best linear unbiased prediction (BLUP) using pedigree data and whole-genome prediction using genomic data are limited in capturing epistasis and interactions occurring within and among downstream biological strata such as transcriptome and metabolome. Because mRNA and small RNA (sRNA) sequences are involved in transcriptional, translational and post-translational processes, we expect them to provide information influencing several biological strata. However, using sRNA data of parent lines to predict hybrid performance has not yet been addressed. Here, we gathered genomic, transcriptomic (mRNA and sRNA) and metabolomic data of parent lines to evaluate the ability of the data to predict the performance of untested hybrids for important agronomic traits in grain maize. We found a considerable interaction for predictive ability between predictor and trait, with mRNA data being a superior predictor for grain yield and genomic data for grain dry matter content, while sRNA performed relatively poorly for both traits. Combining mRNA and genomic data as predictors resulted in high predictive abilities across both traits and combining other predictors improved prediction over that of the individual predictors alone. We conclude that downstream "omics" can complement genomics for hybrid prediction, and, thereby, contribute to more efficient selection of hybrid candidates.

Application and relative importance of spatial analysis for replicated multi-environment yield trials in sugarcane (Saccharum Spp.)

Data obtained from multi-environment yield trials of sugarcane are usually analyzed using traditional models that detect only those variations existed between plots or test genotypes. However, blocking does not control spatial heterogeneity existed within and between trials. This investigation was conducted to evaluate the application and relative importance of spatial analysis for sugarcane multi-environment trials conducted under different designs. Yield data obtained from 20 yield trials were analyzed using classical linear and autoregressive models on the basis of mixed model. Results of the within trial analysis indicated both Akaike information criterion (AIC) and relative efficiency selected the classical linear model as best model in most of the cases. The anisotropic autoregressive model was the best model in modeling cane and sugar yields data of plant cane crop trials which received poor management practices and should be applied under these circumstances. When we analyze the combined multi-environment trials data, the heteroscedastic anisotropic autoregressive model showed better adjustments for cane and sugar yield data modeled across locations, while heteroscedastic linear model was the best model for cane and sugar yields data modeled over crop years. Thus, the heteroscedastic isotropic autoregressive model is recommended to be applied in sugarcane METs data analysis. Moreover, our investigation insures the applicability of the spatial analysis for data obtained from yield trials conducted under simple lattice design.

GGE biplot analysis of multi-environment yield trials of rice produced in a temperate climate

Low temperature is one of the main environmental constraints for rice (Oryza sativa L.) grain production yield. It is known that multi-environment studies play a critical role in the sustainability of rice production across diverse environments. However, there are few studies based on multi-environment studies of rice in temperate climates. The aim was to study the performance of rice plants in cold environments. Four experimental lines and six cultivars were evaluated at three locations during three seasons. The grain yield data were analyzed with ANOVA, mixed models based on the best linear unbiased predictors (BLUPs), and genotype plus Genotype × Environment interaction (GGE) biplot. High genotype contribution (> 25%) was observed in grain yield and the interaction between genotype and locations was not very important. Results also showed that 'Quila 241319' was the best experimental line with the highest grain yield (11.3 t ha-1) and grain yield stability across the environments; commercial cultivars were classified as medium grain yield genotypes.

GGE Biplot Analysis of Multi-environment Yield Trials of Durum Wheat (Triticum turgidum Desf.) Genotypes in North Western Ethiopia

This experiment was done to identify the most stable durum wheat genotype(s) as well as desirable environment(s) for durum wheat ( Triticum turgidum var. durum Desf.) research in north western Ethiopia. Grain yield performance of the tested genotypes were evaluated at four locations (Adet, Debretabor, Gaint and Simada) using randomized complete block design with three replication for two consecutive years (2010 and 2011). Combined analysis of variance showed that grain yield was significantly affected by environments (E), genotypes (G) and GE interactions. The first two principal components (PC1 and PC2) were used to create a two - dimensional GGE biplot and explained 45.67% and 32.71 % of the total sums of squares of GE interaction, respectively. The ‘which - won where’ feature of the GGE biplot suggested that the existence of three durum wheat mega

Parametric stability analyses of multi-environment yield trials in Triticale (xTriticosecale wittmack)

One of the main goals of Triticale (xTriticosecale Wittmack) Breeding Program of Turkey is to improve high yielding and stable genotypes across environments. In this study, 16 parametric stability methods were used to evaluate the genotype x environment interaction (GEI) in 9 (4 officially registered varieties and 5 advanced lines) triticale (xTriticosecale Wittmack) genotypes. The genotypes were evaluated for grain yield at 4 different locations for 3 years in rain-fed areas of Turkey. The testing locations have different climatic and edaphic conditions providing the conditions necessary for the assessment of stability. A combined analysis of variance, parametric stability statistics and rank correlations among them were determined. Significant differences were detected between genotypes and their GEIs. Different parametric stability statistics were used to determine stability of the studied genotypes. The level of association among the statistics was assessed using Spearman?s rank correlation. Rank-correlation coefficients between yield and some parametric stability statistics were highly significant. Genotypes mean yield was significantly correlated to the parametric stability statistics Pi (r = 0.95**), PCA1 (r = 0.87**) and Di (r = 0.98**). A principal component analysis based on rank correlation matrix was performed for grouping the different parametric stability statistics studied. In conclusion, based on most parametric stability statistics, the genotype G8 was found to be the most stable and high yielding. This genotype is, therefore, recommended for release as a cultivar for rain-fed areas of Turkey.

EVALUATION OF EXPERIMENTAL MAIZE HYBRIDS TESTED IN MULTI- LOCATION TRIALS USING AMMI AND GGE BIPLOT ANALYSES

Multi-environment yield trials are essential in estimation of genotype by environment (GE) interaction and identification of superior genotypes in the final selection cycles. The objective of this study was to evaluate stability and adaptability of grain yield of experimental maize hybrids by GGE (Genotype and Genotype by Environment Interaction) biplot and AMMI (Additive Main Effects and Multiplicative Interaction) analyses. This study comprised 19 experimental NS (Novi Sad) maize hybrids tested across twelve environments in northern Serbia in a randomized complete block design. The ANOVA (Analysis Of Variance) indicated significant effects of genotypes (G), environments (E) and their interaction (GE). On the same time, the highest percentage of variation was explained by E (77.83%) while G and GE effects together explained the rest of variation (<30%). Joint effects of genotype and interaction (G+GE) was partitioned using GGE biplot analysis where the first two components were significant, explaining 62.40% (44.34% PC1 and 18.06% PC2) of the GGE sum of squares. The two principal components in AMMI analysis were significant, explaining 53.99% (30.87% PC1 and 23.12% PC2) of interaction variation. Stability of analysed genotypes was similar in both methods. It is concluded that there is no large difference between the AMMI and GGE biplot analyses in evaluation of experimental maize hybrids in different climatic conditions and that both methods can be used equally successfully.

Pattern analysis of multi-environment yield trials in barley (Hordeum vulgare L.)

Pattern analysis, cluster and ordination techniques were applied to grain yield data of 24 cultivars of 2- and 6-rowed barley (Hordeum vulgare L.) grown in 26 environments in Turkey during 2004-2008 to identify patterns of genotype (G), environment (E) and genotype × environment interaction (GEI) in barley multi-environment trials (METs). Analysis of variance showed that 86.9% of the total sum of squares was accounted for by E. Of the remaining sum of squares, the contribution of GEI was almost 9 times that of the contribution of G alone. Knowledge of environmental and cultivar classification helped to reveal several patterns of GEI. This was verified by ordination analysis of the GEI matrix. Grouping environments based on cultivar performance resulted in the separation of different types of environments. Pattern analysis confirmed 2 mega-environments in the highest similarity level and allowed the discrimination and characterization of barley cultivar adaptation. The high-yielding environments (Eskişehir and Konya; first mega-environment) tended to be closer to one another, suggesting that they discriminate among barley cultivars similarly, whereas low-yielding environments tended to be more diverse (Afyon and Uşak; second mega-environment). Cultivars with similar patterns in performance were separated into 5 clusters. The two 6-rowed (Kıral-97 and Çetin-00) and two 2-rowed barley cultivars (Şahin-91 and Aydan hanım) with low to medium yields (3.60-3.84 t ha^{-1}) contributed greatly to GEI and were highly adapted to high-yielding environments. The tall and later maturing 2-rowed barley cultivars (Karatay-94, İnce-04, Kalaycı-97, Özdemir-05, Tokak 157/37, and Keser) with high yields (4.35-4.18 t ha^{-1}) were highly adapted to most of the environments studied.

GGE-biplot Analysis of Multi-environment Yield Trials of Barley (Hordeium vulgare L.) Genotypes in Southeastern Ethiopia Highlands

GGE-biplot Analysis of Multi-environment Yield Trials of Barley (Hordeium vulgare L.) Genotypes in Southeastern Ethiopia Highlands

Comparison of parametric and non-parametric methods for analysing genotype×environment interactions in safflower (Carthamus tinctoriusL.)

SUMMARYGenotype×environment interaction (GEI) is a major factor in the development of stable and high-yielding safflower cultivars under rain-fed conditions. In order to quantify GEI effects on the seed yield of 17 safflower genotypes and to identify stable genotypes, multi-environment yield trials (multi-environment trials (MET)) were conducted for four consecutive years in 33 different environments (year–location combinations) during 2003–06. The results indicated that GEI was significant using the Hildebrand (1980) procedure for non-crossover interaction (no change in genotypic rank) and by the Azzalini &amp; Cox (1984) and the De Kroon &amp; Van Der Laan (1981) tests for crossover interaction (change in genotypic rank). The rank-interaction was not significant when assessed using the Bredenkamp (1974) method. Fifteen univariate stability methods measuring different aspects of stability were used to determine stable genotypes. A principal component analysis based on rank correlation matrix separated those methods based on a dynamic concept of yield stability (change in genotypic performance corresponds to the predicted level for each environment) from those which are based on a static one (the lowest changes in genotypic performance across environments). The methods could be grouped into three distinct classes: (i) those which were associated with yield level and the dynamic concept of stability; (ii) those which were associated with environmental variance, which represents static stability; and (iii) those which were grouped with non-parametric stability statistics, which also represent static stability. The superiority index of cultivar performance, coefficient of regression, Rank-Sum (sum of ranks of yield and stability variance) and TOP (number of environments at which the cultivar occurred in the top third of the ranks) defined the dynamic stability.In this MET, the genotype PI-537598 was identified as the genotype with the highest yield and high stability of yield.