Mega-environments Research Articles

Envirotype-based delineation of environmental effects and genotype × environment interactions in Indian soybean (Glycine max, L.)

Soybean is a rainfed crop grown across a wide range of environments in India. Its grain yield is a complex trait governed by many minor genes and influenced by environmental effects and genotype × environment interactions. In the current investigation, grain yield data of different sets of 41, 30 and 48 soybean genotypes evaluated during 2019, 2020 and 2021, respectively across 19 locations and twenty years’ data on 19 different climatic parameters at these locations was used to study the environmental effects on grain yield, to understand the genotype × environment interactions and to identify the mega-environments. Through analysis of variance (ANOVA), it was found that predominant portion of the variation was explained by environmental effects (E) (53.89, 54.86 and 60.56% during 2019, 2020 and 2021, respectively), followed by genotype × environment interactions (GEI) (31.29, 33.72 and 28.82% during 2019, 2020 and 2021, respectively). Principal Component Analysis (PCA) revealed that grain yield was positively associated with RH (Relative humidity at 2 m height), FRUE (Effect of temperature on radiation use efficiency), WSM (Wind speed at 2 m height) and RTA (Global solar radiation based on latitude and Julian day) and negatively associated with VPD (Deficit of vapour pressure), Trange (Daily temperature range), ETP (Evapotranspiration), SW (Insolation incident on a horizontal surface), n (Actual duration of sunshine) and N (Daylight hours). Identification of mega-environments is critical in enhancing the selection gain, productivity and varietal recommendation. Through envirotyping and genotype main effect plus genotype by environment interaction (GGE) biplot methods, nineteen locations across India were grouped into four mega-environments (MEs). ME1 included five locations viz., Bengaluru, Pune, Dharwad, Kasbe Digraj and Umiam. Eight locations—Anand, Amreli, Lokbharti, Bidar, Parbhani, Ranchi, Bhawanipatna and Raipur were included in ME2. Kota and Morena constitutes ME3, while Palampur, Imphal, Mojhera and Almora were included in ME4. Locations Imphal, Bidar and Raipur were found to be both discriminative and representative; these test locations can be utilized in developing wider adaptable soybean cultivars. Pune and Amreli were found to be high-yielding locations and can be used in large scale breeder seed production.

Stability Analyses for Yield and Its Components in Little Millet (Panicum sumatrense L.)

Linear regression model of Eberhart and Russell is used to identify high yielding stable little millet genotypes suitable across environments. Pooled analysis of variance revealed significant genetic variability among the little millet genotypes for yield and yield attributing traits. Significant variability among environments confirms the heterogeneity in the locations for the traits. Significant genotype x environment interaction for all the traits indicated differential response of the genotypes for the traits in different locations. Among genotypes BL-6, LMNDL-4, LMNDL-3, OLM 203, VS 13, VS 15, VS 19, VS 25 and VS 6, with a regression coefficient near to unity and non-significant deviation from regression, were considered to be highly stable and suitable to all environments for fodder yield. For grain yield VS10 and VS 19 genotypes recorded regression coefficient near to unity with non-significant deviation from regression were considered to be stable in all the environments. GGE biplot model were used to evaluate stability for important traits; fodder yield and grain yield and test location representativeness in little millet genotypes. The testing locations were partitioned into two mega environments (ME) for fodder yield. ME1 was represented by Nandyal and Vizianagaram with OLM 203 as the winning genotype, while in ME 2 which was represented by Perumallapalle with LMNDL 4 performed well. Genotype VS 10 was stable for fodder yield in all the environments.For grain yield over pooled locations, BL 6 performed well at Perumallapalle location while LMNDL 5 performed well at Nandyal. Genotype VS 6 was the best genotype for Vizianagaram. The genotype VS 19 was near to origin and it was considered as stable for all the environments.

Exploring the trait‐yield association patterns in different oat mega‐environments of Canada

AbstractThis article presents a graphical method to visually analyze the trait–yield association (TYA) patterns based on data from multi‐location, multi‐year crop variety trials, exemplified using oat data from trials conducted across Canada from 2017 to 2022. Each year a new set of 60–66 oat (Avena sativa L.) breeding lines were tested in replicated yield trials at 9–11 locations, and data for yield, key agronomic and quality traits, and crown rust scores were collected at all or some of the locations. Pearson correlation coefficient was calculated between yield and each trait for each trial. The correlation coefficients from different locations and years were arranged in a TYA × trial (TYT) two‐way table. This table was subjected to singular value decomposition, and the resulting first two principal components were used to generate a TYT biplot. The TYT biplot revealed three oat mega‐environments (MEs) in Canada, consistent with the conclusion from previous ME analyses, indicating that each ME had its characteristic TYA patterns. It was found that yield was consistently and positively correlated with crown rust (Puccinia coronata var. avenae) resistance, test weight, kernel weight, and groat content in ME1 (the crown rust‐prone regions of Ontario); yield was correlated positively with plant height but negatively with oil content in ME2 (the northern regions of eastern Canada). Interestingly, crown rust resistance was found to contribute negatively to yield in ME2. No strong and consistent TYAs were found in ME3 (the Canadian prairies). Different types of TYAs have different uses in genotypic selection.

The Sustainability Index and Other Stability Analyses for Evaluating Superior Fe-Tolerant Rice (Oryza sativa L.)

Rice (Oryza sativa L.) is an important agricultural commodity in Indonesia. The combination of stability analysis on yields was considered accurate in selecting superior genotypes. The objectives of this study were as follows: identify the effects of genotypes, the environment, and their interactions (GEIs) on the yields of Fe-tolerant rice; select superior genotypes (stable and high yields) under diverse environment conditions in Indonesia; and determine the mega-environments (MEs) and representative environments for Fe-tolerant rice development. Fifteen genotypes of Fe-tolerant rice were used for this study. Field experiments were conducted at six experimental fields in Indonesia using a randomized block design with two replications. A combined analysis of variance (ANOVA) was used to determine the effect of genotypes, the environment, and their interactions on Fe-tolerant rice yields. Parametric, non-parametric, AMMI (additive main effects and multiplicative interaction), GGE biplot, and SI (sustainability index) measurements were used to determine the superior genotypes. GGE biplot was also used to determine MEs and representative environments. The measurement results showed that genotypes, the environment, and their interactions significantly affected rice yields with contributions of 13.30%, 35.78%, and 50.92%, respectively. One superior Fe-tolerant rice genotype (stable and high yield) was selected based on all measurements, namely G4 (B14316E-KA-4). In this experiment, two MEs were generated. Lampung was identified as a representative environment for the development of superior genotypes. The results of this study can be used as a consideration in the release and development of new superior varieties of Fe-tolerant rice in Indonesia.

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

The methods utilized to analyze genotype by environment interaction (GEI) and assess the stability and adaptability of genotypes are constantly changing and developing. In this regard, often instead of depending on a single analysis, it is better to use a combination of several methods to measure the nature of the GEI from various dimensions. In this study, the GEI was investigated using different methods. For this purpose, 18 sugar beet genotypes were evaluated in randomized complete block design in five research stations over 2 years. The additive effects analysis of the additive main effects and multiplicative interaction (AMMI) model showed that the effects of genotype, environment and GEI were significant for root yield (RY), white sugar yield (WSY), sugar content (SC), and extraction coefficient of sugar (ECS). The multiplicative effect's analysis of AMMI into interaction principal components (IPCs) showed that the number of significant components varies from one to four in the studied traits. According to the biplot of the mean yield against the weighted average of absolute scores (WAAS) of the IPCs, G2 and G16 for RY, G16 and G2 for WSY, G6, G4, and G1 for SC and G8, G10 and G15 for ECS were identified as stable genotypes with optimum performance. The likelihood ratio test showed that the effects of genotype and GEI was significant for all studied traits. In terms of RY and WSY, G3 and G4 had high mean values of the best linear unbiased predictions (BLUP), so they were identified as suitable genotypes. However, in terms of SC and ECS, G15 obtained high mean values of the BLUP. The GGE biplot method classified environments into four (RY and ECS) and three (WSY and SC) mega-environments (MEs). Based on the multi-trait stability index (MTSI), G15, G10, G6, and G1 were the most ideal genotypes.

Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations

AbstractGenotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype‐specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega‐environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single‐step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank‐change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME.

Genotype, Environment, and GXE Interaction Effect on Some Selected Traits of Taro (<i>Colocasia esculenta</i> (L.) Schott)

The experiment was conducted to evaluate the nature and the extent of the genotype and environment interactions, to select stable and high yielding taro genotypes for fresh storage corm yield, and related traits to identify the most representative and discriminating environment in southwest Ethiopian regions. The study was tested across four environments (Jimma, Agaro, Gera, and Metu) for two cropping seasons in southwest Ethiopia. Nine selected and promising genotypes and one standard check (Denu) were evaluated by using RCBD that replicated three times. The important data were collected from all tested location and analyzed using different statistical software’s. The analyzed result showed significant differences (p<0.01) for genotype, environment, and genotype by environment interactions (GEI) effects for all the traits evaluated exceptions to the root length. It also revealed that the extent of the mean square of the environment was more than those of the genotype and GEI for all traits targeted and indicated the uniqueness of the test environment. The genotypes 053 and 133 were identified as an ideal genotypes being high yield and wider adaptability hence nominated for release and then for production. The GGE bi-plot also identified Agaro-2 and Gera-2 were the most ideal environment for the evaluated of taro genotypes for their important useable traits. Four mega-environments (MGE) were identified for taro breeding; where environments Agaro-22, Gera-2, and Gera-1 combined into MGE-1, environments Metu-1, and Jimma-1 fell into a separate MGE-2, and environments Jimma-2 and Agaro-1 pooled into MGE-3, and Metu-2 separated into MGE-4, respectively.

STABILITY IN YIELD PERFORMANCE EVALUATION OF SELECTED COTTON (Gossypium hirsutum) GENOTYPES IN VARIABLE AGRO-ENVIRONMENTS

Yield constancy is a crucial characteristic for a variety to become popular among growers. To study this aspect, the present trial was carried out at seven locations in the cotton belt of Punjab during 2020-21. Twenty-five upland cotton strains from different breeding stations were tested along with the standard variety CIM-602. The main objective was to choose super-yielding plus stable strains. Maximum variability due to environments (65%) followed by GEI (22.8%) was observed. The first two interaction principal components (IPC) were squeezed with 72.3% of cumulative variability due to GEI. The analysis of additive main effects and multiplicative interaction (AMMI) diagnosed AMMI5 as an appropriate model. Strain CKC-5 gave maximum mean yield (1812kg ha-1) and winner in all AMMI models. Test sites were split into two mega environments (ME). ENT7 (CRS Faisalabad) site was bearing the highest mean seed cotton yield of (2532kg ha-1) with the biggest (52.18) IPC1 score. The correlation between sites and IPC1 scores was (0.68) as recorded by AMMI analysis. AMMI1 ranks depicted that (PCI2) CIM-875 bears yield advantage of (29.09%) at ENT6 (Vehari) site over (trial winner strain CKC-5) due to micro adaptations. Genotype Selection Index (GSI) discriminated strain BS-J5 as yielder cum stable one with the least GSI value. Approval of this strain for general cultivation from the respective forum may boost cotton production in the province.

Delineation of Genotype X Environment Interaction for Grain Yield in Spring Barley under Untreated and Fungicide-Treated Environments

Barley (Hordeul vulgare L.) is the fourth most important cereal crop based on production and cultivated area. Biotic stresses, especially fungal diseases in barley, are devastating, incurring high possibilities of absolute yield loss. Identifying superior and stable yielding genotypes is crucial for accompanying the increasing barley demand. However, the identification and recommendation of superior genotypes is challenging due to the interaction between genotype and environment. Hence, the present investigation was aimed at evaluating the grain yield of different sets of spring barley genotypes when undergoing one of two treatments (no treatment and fungicide treatment) laid out in an alpha lattice design in six to seven locations for five years, through additive main effects and multiplicative interaction (AMMI), GGE biplot (genotype + genotype X environment), and stability analysis. The combined analysis of variance indicated that the environment was the main factor that contributed to the variation in grain yield, followed by genotype X environment interaction (GEI) effects and genotypic effects. Ten mega environments (MEs) with five MEs from each of the treatments harboured well-adapted, stable yielding genotypes. Exploiting the stable yielding genotypes with discreet use of the representative and discriminative environments identified in the present study could aid in breeding for the improvement of grain yield in spring barley genotypes.

Comprehensive Stability Analysis of Wheat Genotypes through Multi-Environmental Trials

In rainfed areas, due to variable environmental factors, improving the yield stability of the introduced cultivars along with increasing yield should be considered. The main aim of this study is to obtain high-yield wheat genotypes that are stable and adaptable to cold climatic conditions in Iran. For this purpose, 25 wheat genotypes were evaluated in a randomized complete blocks design with three replications during three cropping seasons (2013-2016) under supplementary irrigation and rainfed conditions. PBSTAT-GE software was used for genotype × environment interaction (GEI) analysis and comprehensive sustainability analysis. The results showed that G5, G14, G16 and G18 genotypes had good stability and general adaptation based on parametric and non-parametric stability statistics. Combined analysis of variance based on the Additive Main Effect and Multiplicative Interaction (AMMI) model showed that GEI is significant in the term of grain yield. Also, the ratios between the sum of squares G, GE and IPC1 showed that the AMMI is suitable for data analysis. GGE biplot analysis identified five mega-environments (MEs), in which ME I including E1, E2, E3, E4, E5, E6, and G7, G5, G14, G13, G16, G18, G20 being the superior ME I genotypes. According to AMMI and GGE biplote stability methods, lines G20, G18, G13, G16, G14 and Saein cultivar (G5) can be considered as desirable genetic resources in wheat production programs under variable environments in Iran, due to having the appropriate combination of yield and stability.

Multi-trait selection for mean performance and stability of maize hybrids in mega-environments delineated using envirotyping techniques.

Under global climate changes, understanding climate variables that are most associated with environmental kinships can contribute to improving the success of hybrid selection, mainly in environments with high climate variations. The main goal of this study is to integrate envirotyping techniques and multi-trait selection for mean performance and the stability of maize genotypes growing in the Huanghuaihai plain in China. A panel of 26 maize hybrids growing in 10 locations in two crop seasons was evaluated for 9 traits. Considering 20 years of climate information and 19 environmental covariables, we identified four mega-environments (ME) in the Huanghuaihai plain which grouped locations that share similar long-term weather patterns. All the studied traits were significantly affected by the genotype × mega-environment × year interaction, suggesting that evaluating maize stability using single-year, multi-environment trials may provide misleading recommendations. Counterintuitively, the highest yields were not observed in the locations with higher accumulated rainfall, leading to the hypothesis that lower vapor pressure deficit, minimum temperatures, and high relative humidity are climate variables that -under no water restriction- reduce plant transpiration and consequently the yield. Utilizing the multi-trait mean performance and stability index (MTMPS) prominent hybrids with satisfactory mean performance and stability across cultivation years were identified. G23 and G25 were selected within three out of the four mega-environments, being considered the most stable and widely adapted hybrids from the panel. The G5 showed satisfactory yield and stability across contrasting years in the drier, warmer, and with higher vapor pressure deficit mega-environment, which included locations in the Hubei province. Overall, this study opens the door to a more systematic and dynamic characterization of the environment to better understand the genotype-by-environment interaction in multi-environment trials.

Mega-environment analysis to assess adaptability, stability, and genomic predictions in grain sorghum hybrids

Multi-environment trials (MET) are fundamental for assessing genotype-by-environment interaction (GxE) effects, adaptability and stability of genotypes and provide valuable information about target regions. As such, a MET involving grain sorghum hybrid combinations derived from elite inbred lines adapted to diverse sorghum production regions was developed to assess agronomic performance, stability, and genomic-enabled prediction accuracies within mega-environments (ME). Ten females and ten males from the Texas A&M and Kansas State sorghum breeding programs were crossed following a factorial mating scheme to generate 100 hybrids. Grain yield, plant height, and days to anthesis were assessed in a MET consisting of ten environments across Texas and Kansas over two years. Genotype plus Genotype-by-block-of-environment biplot (GGB) assessed ME, while the "mean-vs-stability" view of the biplot and the Bayesian Finlay–Wilkinson regression evaluated hybrid adaptability and stability. A genomic prediction model including the GxE effect was applied within ME to assess prediction accuracy. Results suggest that grain sorghum hybrid combinations involving lines adapted to different target regions can produce superior hybrids. GGB confirmed distinct regions of sorghum adaption in the U.S. Further, genomic predictions within ME reported inconsistent results, suggesting that additional effects rather than the correlations between environments are influencing genomic prediction of grain sorghum hybrids.

Optimizing the test locations and replicates in multi‐environmental cotton registration trials in southern Xinjiang, China

AbstractMulti‐environment trials are routinely conducted around the world to select superior cultivars; the numbers of replicates and locations remains insufficiently studied despite their effects on trial efficiency and cost. The objective of the present study was to compensate for the mentioned lack by dealing with the specific case of cotton (Gossypium hirsutum L.) in arid conditions of China and by estimating the optimal numbers of locations and/or replicates in a more comprehensive way than implemented so far, that is, optimizing with regard jointly to three criteria, namely, genotype ranking, location evaluation, and environment investigation. Trial heritability and the optimal numbers of locations and replicates were estimated through adapted genotype plus genotype × environment biplot analysis from data of cotton variety registration trials in the fringes of the Taklimakan Desert in Southern Xinjiang in China over the 2011–2020 period when three replicate trials were generally conducted in nine locations. Despite the identification of two mega‐environments (MEs) through location grouping biplot analysis, our study showed robust results of genotype ranking, correlation among test locations, and location grouping that were not affected by reducing the number of replicates. It was concluded that two replicates (instead of three) were sufficient for reliable genotype evaluation, test location evaluation, and ME classification in the studied trial scheme. The implied savings justifies similar study on other schemes.

Correlations between soybean seed quality traits using a genome-wide association study panel grown in Canadian and Ukrainian mega-environments

Improvement of soybean [ Glycine max (L.) Merr.] seed quality traits in addition to agronomic traits requires a detailed understanding of correlations between these traits. The objective of this study was to determine the correlations between seed compositions in soybeans grown in Canadian and Ukrainian mega-environments (MEs). The correlations between seed quality traits and agronomic traits were also studied. A genome-wide association study panel consisting of 184 soybean accessions was used for the study. The panel was grown in three Ontario field locations and two Ukrainian locations for 2 years, from 2018 to 2019. A total of 18 traits were measured and analyzed. The Pearson's correlation coefficients ( r) were calculated, and the genotype-by-trait biplots were generated to analyze the linear correlations between the traits. The well-documented negative correlations between protein and oil, as well as oil and the amino acids Lys, Cys, Met, and Thr, were confirmed. In addition, a positive correlation was observed between stearic acid and palmitic acid, while linolenic acid and oleic acid concentrations were negatively correlated. Sucrose was positively correlated with linolenic acid and raffinose and negatively with protein and the four amino acids. Most of the agronomic traits had positive correlations with each other, while there was no strong linear association detected between agronomic traits and the seed quality traits in either ME. The results of this study suggest that improvement of yield and other agronomic traits through breeding may be possible in both Canada and Ukraine without affecting the important seed quality traits.

Identification of quantitative trait loci associated with seed quality traits between Canadian and Ukrainian mega-environments using genome-wide association study.

Identifying QTL associated with soybean seed quality traits from a diverse GWAS panel cultivated in Canadian and Ukrainian mega-environments may facilitate future cultivar development for foreign markets. Understanding the complex genetic basis of seed quality traits for soybean in the mega-environments (MEs) is critical for developing a marker-assisted selection program that will lead to breeding superior cultivars adapted to specific regions. This study aimed to analyze the accumulation of 14 soybean seed quality traits in Canadian ME and two seed quality traits in Ukrainian ME and identify associated ME specific quantitative trait loci (QTLSP) and ME universal QTL (QTLU) for protein and oil using a genome-wide association study (GWAS) panel consisting of 184 soybean genotypes. The panel was planted in three locations in Canada and two locations in Ukraine in 2018 and 2019. Genotype plus genotype-by-environment biplot analysis was conducted to assess the accumulation of individual seed compounds across different locations. The protein accumulation was high in the Canadian ME and low in the Ukrainian ME, whereas the oil concentration showed the opposite trends between the two MEs. No QTLU were identified across the MEs for protein and oil concentrations. In contrast, nine Canadian QTLSP for protein were identified on various chromosomes, which were co-located with QTL controlling other traits identified in the Canadian ME. The lack of common QTLU for protein and oil suggests that it may be necessary to use QTLSP associated with these traits separately for the Canadian and Ukrainian ME. Additional Ukrainian data for seed compounds other than oil and protein are required to identify novel QTLSP and QTLU for such traits for the individual or combined Canadian and Ukrainian MEs.

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.

GGE-BIPLOT Analysis of Durum Wheat Yield Trials

Durum wheat (Triticum durum L.) is a vital crop in the rain-fed areas of Turkey. In this study, the grain yield of twenty durum wheat genotypes (sixteen advanced lines with four checks) tested across 8 rain-fed environments during the 2008-2009 cropping season was evaluated using GGE (Genotype (G) Main Effect + Genotype by Environment Interaction (GEI)) Biplot Approach. Environment (E) captured most (83 %) of the grain yield (GY) variation, while the portions attributed to G and GEI were only 6 and 11 %, respectively. In addition, most of the testing Es were highly correlated. According to GGE-Biplot analysis, ‘Which won where’ pattern partitioned the testing Es into three mega-environments (ME): the first ME with six Es with G13 (the highest yielder) as the winning genotype; the second ME encompassed one environment (E1, Konya) with G4 (the lowest yielder) as the winning genotype, and the last ME represented by one location (E6, Altintas) with G10 (the higher yielder) as the winning genotype. GGE-Biplot analysis showed that although the Durum Wheat Yield Trials were conducted in many environments, outcomes alike can be obtained from one or two representatives of each ME. On the other hand, no correlation of these MEs with their geographic location was observed. In conclusion, the presence of cross-over GEI underscores that efforts should be given to identify specifically adapted genotypes rather than broadly adapted ones tested on multi-environment trials (METs).

Stability on Maize Hybrids Based on GGE Biplot Graphical Technique

In this study, 12 maize hybrids were planted and evaluated to determine the effect of genotype and genotype-environment interaction (GEI) base GGE (genotype plus genotype-by-environment) using a Graphical biplot technique in four research stations (Arak, Birjand, Shiraz and Karaj) within two years using a Randomized Complete Blocks Design (RCBD). The combined analysis of variance showed that the effects of the environment, genotype and genotype-environment interaction (GEI) were significant in the one percent probability level. GGE biplot results indicated that the first and second principal components (PC1 and PC2) explained more than 83% of the grain performance variation. Simultaneous study of grain performance and hybrid stability using the biplot of average environment coordinates showed that the KSC705 genotype had the highest yield and stability. Polygon view divided the studied areas into two mega-environments (MEs) and identified the best genotypes in each mega-environment (ME). In the first mega-environment (ME1), the Karaj and Shiraz with KSC706 and KSC400 genotypes were detected, and were the best; and in the second mega-environment (ME2), Arak and Birjand with KSC704 and KSC707 genotypes performed better. The biplot graph for the correlation between the genotypes categorized the studied hybrids into four groups positively related to each other based on the angles between vectors. The KSC704 and KSC707 genotypes were desirable in the yield in Shiraz and Karaj and KSC706 were in Arak and Birjand. Additionally, Arak-Birjand, Karaj-Shiraz showed a positive and significant correlation. Birjand and Karaj had most genotype interaction with each other.

Genomic prediction for grain yield in a barley breeding program using genotype × environment interaction clusters

AbstractGenotype × environment interaction (GEI) is one of the key factors affecting breeding value estimation accuracy for agronomic traits in plant breeding. Measures of GEI include fitting prediction models with various kernels to capture the variance resulting from GEI, and characterizing trials into megaenvironment (ME) clusters within which breeding values can be estimated to remove the main GEI effects. However, many of the current approaches require observations of common genotypes across all trials, which is unavailable in most breeding programs. Our study introduces two methods that can be implemented on unbalanced data to categorize trials into clusters, where both need a correlation matrix between trials: one estimated via a factor analytic (FA) model and another estimated via weather variables. The methods were tested using empirical barley (Hordeum vulgare L.) yield data in a commercial breeding program from 102 trials over 5 yr spread across multiple locations in Australia. Leave‐one‐year‐out cross‐validation achieved comparable predictive accuracies using either trials or clusters as the observed variable in GEI FA models (max. 0.45), which was higher than the accuracy achieved using the non‐GEI model (0.37). In the random cross‐validations, accuracies achieved within clusters (0.42–0.64) were mostly comparable with those achieved in the full population (0.62). In the within‐cluster validations, higher predictive accuracies were achieved when the training population was from the same cluster (mean 0.22) than outside of the cluster (mean 0.16). Our proposed methods of characterizing multienvironment trials into clusters provides a novel way to define training populations by reducing the variance resulting from GEI and could be implemented in any plant breeding program.

Oat mega‐environments in Canada

AbstractGenotype × environment interaction (GE) is a perennial problem in plant breeding and must be dealt with. Dealing with GE starts with differentiation of repeatable GE from unrepeatable GE in a target region. Repeatable GE can be used by dividing the target region into mega‐environments (MEs) and breeding ME‐specific cultivars, and unrepeatable GE must be accommodated by testing adequately within a ME. This study applied LG (location‐grouping) biplot analysis to several datasets from multiyear oat (Avena sativa L.) variety trials conducted at locations across Canada. Analysis showed that the oat growing regions in Canada can be divided into three MEs: the crown rust (Puccinia coronata Corda f. sp. avenae Eriks.) prone regions in southern and eastern Ontario (ME1), other regions in eastern Canada (ME2), and the Canadian Prairies (ME3). In addition, two sub‐MEs existed within ME2. Latitude was shown to be the main factor for the ME differentiation. The results suggest that oat variety trials should be conducted and cultivar recommendation be made according to MEs, as opposed to by administrative regions that are currently in place.