Analysis of Cultivar X Environment Interactions for Kansas Growing Wheat Using Regression, Variance Component, and Clustering Methods

The effectiveness of targeting and predicting maize (Zea mays.L) hybrid performance is difficult when the magnitude of genotype x environment (GE) interaction and yield prediction cannot be interpreted and is only based on genotypes (G) and GE means. The traditional analysis of variance (ANOVA) is not sufficient in predicting and giving information into the patterns of genotypes and environments that give rise to GE interaction. The objectives of this study were to show the usefulness of G plus GE interaction (GGE) using the properties of GGE biplot based on the site regression (SREG) model analysis of a biplot in predicting yield performance and stability of early to intermediate maturing hybrids (EIHYB) grown in southern Africa. The SREG analysis model was based on regional trial data of EIHYB from three seasons (2005 - 2007) across 30 environments under four different management practices: well fertilized/rain fed conditions, managed nitrogen stress, managed drought stress, and managed low pH stress. GGE biplots were constructed using the first two principal components (PC1 and PC2) derived from singular value decomposition of environment-centered multi-environmental trials. The PC1 scores of the hybrids and the environments were plotted against their respective PC2 scores to effectively show mean performance and stability for grain yield across years and environments; discriminativeness vs. representativeness of test locations across the years and which-won-where. The SREG model showed that maize hybrids were under major environmental and GE interactions. In spite of large variation from year to year maize hybrids responded positively to better environmental conditions relative to grain yield performance and key environmental patterns could be established.

Genotype x environment (GxE) interactions can reduce the accuracy of a model to predict the performance of an animal and have an undesirable influence if not accounted for when estimating breeding values. Consequently, identification of these GxE is necessary when considering a turkey breeding program. Reranking based on the genetic prediction of turkey egg production, fertility, and hatchability in different seasons was indicative of a potential GxE interaction. Quantification of the GxE interactions was based on the genetic correlation estimated when traits were expressed in different seasons. Egg production was expressed as the percentage of days with an egg produced; fertility represented the proportion of hatched eggs that contained a fertile embryo; and hatchability was defined as the percentage of fertile eggs that produced a live bird. Variance components and heritability for egg production, fertility, and hatchability were estimated using ASReml. The heritability (h(2)) of egg production was calculated to be 0.32 for both lines with the phenotypic and genetic variance, 141.3 and 45.58 (percent days with egg produced)(2) and 118.3 and 38.35 (percent days with egg produced)(2) for female and male lines, respectively. The h(2) estimates for fertility were 0.08 in both lines with and of 293.3%(2) and 24.03%(2), and 576.9%(2) and 48.43%(2) for female and male lines, respectively. The hatchability h(2), and estimates were 0.09, 267.1%(2), and 24.44%(2), respectively, for the female line and 0.15, 582.2%(2), and 90.01%(2) for the male line, respectively. Based on an animal model, the variance components were used to calculate estimated breeding values for each trait. The annual fluctuation in estimated breeding values resulted in the need to evaluate egg number, fertility, and hatchability as 2 traits, summer and winter lay. The correlation between the 2 traits was less than unity (female line: r(egg production) = 0.76, r(fertility) = -0.20, r(hatchability) = 0.75 and male line: r(egg production) = 0.86, r(fertility) = 0.19, r(hatchability) = 0.68) suggesting a GxE interaction, and animals will significantly rerank in genetic predictions for these reproductive phenotypes in different seasons of lay. Egg production, fertility, and hatchability in turkeys could be considered as 2 distinct traits in an animal model based on season of lay.

Three sets of regional six-row barley (Hordeum vulgare L.) trial data, representing cultivar x location x year, were grouped for locations based on the similarity of genotype x environment (GE) interaction. Locations were selected from each group (cluster) so that the structure of the GE interaction generated by the subsets of the locations would be approximately similar to that of the whole set (all locations). The purpose of this paper is to determine the number of locations where the GE interaction structure generated by these selected locations would be fairly consistent over years. Two statistics were used to measure the success of the selected locations: (1) the ratio of GE mean square (MS) associated with the selected location set relative to that associated with the best set (which gives the highest GE interaction MS) and (2) the rank correlation between the cultivar means averaged over the selected locations and those based on the entire data set. The results show that, for eastern Canada, 10-13 locations based on the cluster method can achieve a fairly consistent GE interaction structure over years.

Yield trials frequently have both significant main effects and a significant genotype X environment (GE) interaction. Traditional statistical analyses are not always effective with this data structure: the usual analysis of variance (ANOVA), having a merely additive model, identifies the GE interaction as a source but does not analyze it; principal components analysis (PCA), on the other hand is a multiplicative model and hence contains no sources for additive genotype or environment main effects; and linear regression (LR) analysis is able to effectively analyze interaction terms only where the pattern fits a specific regression model. The consequence of fitting inappropriate statistical models to yield trial data is that the interaction may be declared nonsignificant, although a more appropriate analysis would find agronomically important and statistically significant patterns in the interaction. Therefore, agronomists and plant breeders may fail to perceive important interaction effects. This paper compares the above three traditional models with the additive main effects and multiplicative interaction (AMMI) Model, in an analysis of a soybean [Glycine max (L.) Merr.] yield trial. ANOVA fails to detect a significant interaction component, PCA fails to identify and separate the significant genotype and environment main effects, and LR accounts for only a small portion of the interaction sum of squares. On the other hand, AMMI analysis reveals a highly significant interaction component that has clear agronomic meaning. Since ANOVA, PCA, and LR are sub‐cases of the more complete AMMI model, AMMI offers a more appropriate first statistical analysis of yield trials that may have a genotype X environment interaction. AMMI analysis can then be used to diagnose whether or not a specific sub‐case provides a more appropriate analysis. AMMI has no specific experimental design requirements, except for a two‐way data structure.

Genotype × environment (GE) interactions are a challenge to plant breeders because they cause difficulties in selecting genotypes evaluated in diverse environments. When GE interaction is significant, its cause, nature, and implications must be carefully considered. No information is available on the contribution of weather variables and environmental index (Ȳ.j or mean yield of all cultivars in jth location minus Ȳ.. or overall mean yield for all cultivars and all locations) to GE interaction for yield in maize (Zea mays L.). The objective of this study was to determine the effect of an environmental index, maximum and minimum temperatures, rainfall for the growing season, preseason rainfall, and relative humidity (covariates) on GE interaction for yield. Seventeen hybrids grown in 12 environments (4 locations × 3 yr) were studied. The GE interaction was significiant and was partitioned into σ2i (stability variance) components assignable to each genotype (hybrid). Heterogeneity (non‐additivity) due to each covariate was removed from the GE interaction, and the remainder of the GE interaction variance was partitioned into s2i components assignable to each genotype. Environmental index accounted for 9.61% of the GE interaction sum of squares (P = 0.10). This was the largest amount of heterogeneity removed from the GE interaction by any single covariate. Rainfall during the growing season removed 1.4% of the GE interaction sum of squares and preseason rainfall removed 1.1% of the GE interaction sum of squares as heterogeneity. Minimum temperature, maximum temperature, and relative humidity removed a negligible amount of heterogeneity from the GE interaction. The 17 hybrids evaluated in this study were differentially affected more by differential fertility and/or cultural practices (environmental index) than by weather factors. Seven of the 17 hybrids had unstable performance across the 12 environments.

The genotype x environment (GE) interaction is a major challenge to plant breeders as it complicates testing and selection of superior genotypes and consequently reduces gains from selection. This chapter introduces and compares different statistical models to handle GE interaction by applying them to the durum wheat breeding program in Iran as an example. The results indicate significant crossover GE interaction suggesting the need for applying appropriate analysis for the exploitation and/or the minimization of GE interaction in multi-environment trials (MET) data. The test locations differed in their discriminative ability and representativeness. Highly significant correlations were found between univariate and multivariate statistical models in ranking genotypes for stability and for integrating yield with stability performances, indicating that they can be used interchangeably. Evaluation of genotypes based on multiple traits data identified parental germplasm for earliness, short stature, high grain weight and high grain yield. The proposed statistical analysis can assist in increasing the efficiency of breeding program through (a) selection of the most discriminate locations, (b) identifying superior genotypes based on both strategies dealing with exploitation and minimization of GE interaction and (c) exploring significant genetic gains in yield and yield stability.

Twelve rice varieties were cultivated in inland hydromorphic lowland over a four year-season period in tropical rainforest ecology to study the genotype x environment (GxE) interaction and yield stability and to determine the agronomic and environmental factors responsible for the interaction. Data on yield and agronomic characters and environmental variables were analyzed using the Additive Main Effect and Multiplicative Interaction (AMMI), Genotype and Genotype x Environment Interaction, GGE and the yield stability using the modified rank-sum statistic (YSi). AMMI analysis revealed environmental differences as accounting for 47.6% of the total variation. The genotype and GxE interaction accounted for 28.5% and 24% respectively. The first and second interaction axes captured 57% and 30% of the total variation due to GXE interaction. The analysis identified ‘TOX 3107’ as having a combination of stable and average yield. The GGE captured 85.8%of the total GxE. ‘TOX 3226-53-2-2-2’ and ‘ITA 230’ were high yielding but adjudged unstable by AMMI. These two varieties along with ‘WITA 1’ and ‘TOX 3180-32-2-1-3-5’ were identified with good inland swamp environment, which is essentially moisture based. The two varieties (‘TOX 3226-53-2-2-2’ and ‘ITA 230’), which were equally considered unstable in yield by the stability variance, ?2i, were selected by YSi in addition to ‘TOX 3107’, ‘WITA 1’, ‘IR 8’ and ‘M 55’. The statistic may positively complement AMMI and GGE in selecting varieties suited to specific locations with peculiar fluctuations in environmental indices. Correlation of PC scores with environmental and agronomic variables identified total rainfall up to the reproductive stage, variation in tillering ability and plant height as the most important factors underlying the GxE interaction. Additional information from the models can be positively utilized in varietal development for different ecologies.

Growth performance and stability of hybrid poplar clones in simultaneous tests on six sites

Quinoa is a highly adaptable crop due to its considerable genetic variability, making it an important trait for cultivation under different soil and climatic conditions. To achieve crop-wide adaptation, it is essential to identify variability based on morpho-agronomic differences and genotype x environment (GxE) interaction. This study aimed to characterize eight quinoa progenies in Brazil and Colombia. The experiments in Brazil were conducted in an irrigated area of the Fazenda Água Limpa, Universidade de Brasília, at 1,100 m, on two dates: March to July 2018 and May to August 2019. In Colombia, experiments were carried out in Santander de Quilichao and Popayán at 1,100 and 1,800 m, respectively. The treatments consisted of five progenies selected in Brazil, one from Colombia, and two from Ecuador. The experiments followed a complete randomized block design, with eight progenies and four replications. For statistical analysis, the F test was used with p≤0.01 and p≤0.05. Means were grouped by the Scott-Knott test. The AMMI (Additive Main effects and Multiplicative Interaction) analysis was performed, combining analysis of variance and analysis of principal components, to adjust the main effects of genotypes (G) and environments (E) and the GxE interaction. Significant differences were found at p≤0.01 and p≤0.05 for environments, genotypes, and the interaction of GxE. The progenies with wide adaptation to environments were BRX2, BRX5, BRX6 (selected from BRS Syetetuba) and PRIX (selected from Piartal), with average yields above 3,151.95 kg/ha. The genotypes showed differences at the same location in different periods, expressing the need to carry out selection for specific periods and locations. Genotypes BRX5 and BRX6 showed high agronomic potential in all evaluated environments, being promising for future genetic improvement programs.

In Ethiopia, Andean sugar beans are low seed yielding and unstable in productivity. Therefore, 16 advanced Andean sugar bean genotypes were evaluated for seed yield performance using 4x4 triple lattice design at nine locations in the 2013 and 2014 Meher cropping seasons to decide the stability of genotypes over environments. Additive main effects and multiplicative interaction (AMMI) and Genotype plus Genotype x Environment (GE) interaction (GGE) models were used to analyze the data. Mean seed yield performance of genotypes ranged from 1261.28 -2095.30 kg ha -1 . DAB 37, DAB 175, DAB 178, DAB 179, DAB 180, DAB 182, SARBYT-15, KG-11-48, and Cranscope were high seed yielding genotypes whereas genotypes viz., DAB 177, DAB 181, DAB 137, DAB 197, DAB 214, DAB 196, and F8 Drought line-37 were low seed yielding genotypes. All sources of variations viz., genotype (G), environment (E), and genotype x environment interaction (GEI) effects were highly significant (p < 0.01). They represented 9.97%, 67.88% and 22.15% variations in the treatment, respectively. As the GEI effect was highly significant, it is necessary to consider mean seed yield performance and stability together when selecting high seed yielding genotypes. PC1 and PC2 were highly significant (p < 0.01) and together accounted for nearly 70% variations in the GEI. AMMI1, GGE scatter, GGE comparison, and GGE ranking biplots identified DAB 177 as stable high yielding genotypes across environments. However, stable high seed yielding genotypes identification of GGE comparison biplot was superior to others. Environment focusing scaled vector view of GGE biplot revealed repeatability of GEI pattern over years. Thus, SARBYT-15 was selected as ideal genotype for mega-environment consisting of Alem Tena, Melkasa, Areka, and Haramaya. DAB 179 was selected for Jimma, Assossa, Miesso, and Sirinka. DAB 181 was selected for Arsinegelle. As a result, both widely and specifically adapted Andean sugar bean genotypes were recommended for verification and release for their adaptation agroecologies of Ethiopia.

The choice and recommendation of a variety for commercial cultivation are influenced by genotype x environment interaction (GEI)). The complication of genotype by environment interaction (GEI) is that usually involves layout of trials in various seasons, making it difficult to identify the genotype adapted to different environments. Twenty sugarcane clones and four standard checks were evaluated under three environments within the tropical climate. Additive Main Effects and Multiplicative Interaction (AMMI) model was applied to assess the extent of genotype x environment (GE) interaction and also the stability of sugarcane clones across the environments. The significant difference was observed by AMMI analysis among the tested clones and environments. The sum of the first two principal components conferred to 63.6 per cent of the total of G x E interaction. In the present study, the genotypes G24 (Co 88025), G23 (CoV 94101) and G20 (Co 16001) recorded in high mean yield and higher Principal Component Analysis (PCA) scores; hence, these materials specifically suited to the favorable locations. Since the genotypes Co 15021(G19), Co 0240 (G3), and Co 13001(G7) were near the center point of the axes and hence were influenced by the environment. These clones recorded higher cane yield and stability and suitable for cultivation in different environments. The utilization of the AMMI model made it easy for the visual comparison and identification of exceedingly superior genotypes for every set of environments.

Seed yields of 14 soybean genotypes were evaluated in four locations i.e. Adana, Şanlıurfa, Antalya and İzmir under second crop conditions through summer seasons from 2014 to 2016. The aims of research are to estimate the stability parameters in terms of seed yield of 14 soybean genotypes by used different stability analysis methods across eleven environmental conditions and to study interrelationship among these stability methods. The analysis of variance for seed yield revealed that the genotypes and the environments as well as the genotype x environment interactions (GEI) were statistically significant at P&amp;lt;0.01. Environmental effects were contributed of 51.04% to the total sum of squares whereas GEI and genotype effects were 20.8% and 2.59%, respectively. According to most of stability methods, BATEM 223, BATEM 306, BATEM 317 and KASM 02 were determined to be stable genotypes. These genotypes demonstrated superior adaptability with high yield performances in many environments. Results of correlation analysis indicated that seed yield was positively and significantly correlated with Di2 (P&amp;lt;0.01), Si(6) (P&amp;lt;0.05) and TOP (P&amp;lt;0.01) and showed a negative and significant correlation with Pi (P&amp;lt;0.01) and RS (P&amp;lt;0.01). In addition, the coefficient of regression (bi) had positively significant associated with CVi, αi (P&amp;lt;0.01) and Ri2 (P&amp;lt;0.05).

The suitability of regression analysis for studying the phenotypic stability of grain yield was investigated using a collection of 220 Nordic barley lines. Linear regression explained 26-52% of the genotype x environment (GE) interactions in different groupings of the material. The regression coefficient, b i , measures the yield response of the i-th genotype to improved environmental conditions. Deviations from regression, S di (2) , have been used to estimate Tai's stability parameter, λ i , which is a measure of the phenotypic yield stability in the agronomic sense. Repeatability of b i , λ i , and grain yield was studied by means of correlations between estimates obtained in each experimental year. Yield had the highest repeatability, with correlations between years ranging from 0.57 to 0.85. In this study, regression coefficients and λ i -values were not repeatable, i.e. genotypes reacted differentially to the yearly climatic variations. Six-rowed (6r) barleys had higher responsiveness, but lower mean yields, than two-rowed (2r) barleys. This is partly due to the history of selection of 6r-barleys, which mainly originate from regions with low potential yield levels, i.e. Finland and Norway. In general, responsiveness and stability were not correlated with yield. The highest-yielding lines had b i ≈1. The response pattern of the different types of barleys used in this study show that responsiveness can be changed by recombination.

Considering the continued and unpredictable variation of climatic condition, the yield stability has become an important topic in wheat breeding. This study evaluated the grain yield of 11 winter wheat varieties with different genetic and ecologic origin during three years, to compare the effects of genotype, year and genotype × year interactions and to determine their stability and reliability for cultivar recommendations. The climatic conditions during the three years had the highest contribution (80.03%) to the yield variability of wheat, while the varieties had a lower influence (11.41%), and the variety x year interaction contributed only with 8.56% to the total variation. In 2020, the climatic conditions were significantly more favorable than in 2018–2019, allowing obtaining yield increases of 33.82-44.20%. Also, in 2018 there was a higher favorability of the growing conditions compared to that of 2019, associated with significant yield increases of 7.75%. The highest yield stability was observed in Boema and Galio varieties, on the background of a non-crossover genotype x environment (GE) interaction. The stability of these varieties has been associated with different yield levels, above the experience mean in Galio and below the experience mean in Boema, respectively. In Akteur and Josef varieties, the yield was strongly influenced by the GE interaction, being associated with lower levels than the experience mean. On the background of high occurrence probabilities (0.75–0.95) of unfavorable environmental conditions for wheat crop, Galio and Laurenzio varieties would have the highest yield reliability, associated with values of 5173–6328 kg/ha (Galio) and 5101–5979 kg/ha (Laurenzio). As such, given the results of all stability parameters, it turns out that the Romanian varieties showed a good adaptation to the local ecological conditions compared to most of the foreign varieties.

In peanuts (Arachis hypogaea L.) a high degree of phenotypic similarity among components of multiline cultivars is necessary in order to meet the demands of industry. To achieve this, the compositional scheme for peanut multilines has utilized sibling lines composited between the F4 and F8 generations. As a result, the alleged enhancement of stability may be reduced due to a lack of genetic heterogeneity among component lines, which abates an important advantage of multiline cultivars. This study was conducted to examine the relationship among sibling components of two peanut multilines and to determine if the multilines perform in a more stable manner across environments than any single component. Partitioning of genotype ✕ environment (GE) interaction sum of squares, regression analyses, and stability variances were used to assess stability over environments (2 years, 4 locations) for two multilines and their four components. For one multiline, three of four component lines were not significantly different from the multiline for yield nor did deviations from regression and stability variances differ among components. Conversely, components of the second multiline displayed significant variability for yield, regression coefficients and deviations from regression. However, the multiline performed in a stable manner with relatively high yields and low deviations from regression. Mean yields, regression coefficients, and deviations from regression were shown to be useful parameters in the formation of a multiline. Joint regression analysis and GE partitioning techniques were useful in determining the nature and origin of the GE among groups of late generation segregates. Although additional studies are needed, these data indicate that the compositional scheme for peanut multilines is a feasible method to circumvent GE effects. This study, however, suggests that pure lines of peanuts can be identified that are as stable across environments as multilines.