Quality of Ethiopian Durum Wheat Lines in Two Diverse Environments

In Ethiopia, durum wheat is largely used for production of local fermented and flat bread. Two diverse environments (Motta and Adet) were used to evaluate 15 durum wheat genotypes for grain yield and quality traits. The mean flour protein content of genotypes ranged from 10.1 % to 12.5 % and 6.7 % to 8.1 % at Motta and Adet respectively. The mean mixograph development time was 4 min at Motta and 2.8 min at Adet and SDS (Sodium dodecyl sulphate) sedimentation ranged between 10.7 and 32.3 ml across locations. Flour protein content was correlated negatively with mixograph development time and positively with vitreous kernels and single‐kernel hardness at both environments. Mixograph development time was selected to predict the gluten strength. Flour protein content, SDS sedimentation and seed weight were included in a stepwise regression. A prediction model was compiled that explained 69 % of the variation for mixograph development time.     

The Effectiveness of HA-GGE Biplot Application in Analyzing the Data from the National Cotton Cultivar Regional Trial in the Yangtze River Valley

A large number of regional crop trials have demonstrated the ubiquitous existence of genotype × environment interactions (G×E), which make it complicated to select superior cultivars and identify the ideal testing sites. The GGE (genotype main effect plus genotype × environment interaction) biplot is the most powerful statistical and graphical displaying tool available for regional crop trial dataset analysis. The objective of the present study was to demonstrate the effectiveness of the biplot in evaluating the high and stable yields of candidate cultivars simultaneously, and in delineating the most adaptive planting region, analyzing trial location discrimination ability and representativeness, and identifying the ideal cultivar and trial locations. The lint cotton yield dataset with nine experimental genotypes and 17 test locations (three replicates in each) was collected from the national cotton regional trial in the Yangtze River Valley (YaRV) in 2012. The results showed that: (1) the effects of genotype (G), environment (E), and genotype × environment interaction (G×E) were significant (P < 0.01) for lint cotton yield. Differences among environments accounted for 78.7% of the treatment total variation in the sum of squares, whereas the genotype main effect accounted for 8.7%, and the genotype × environment interaction accounted for 12.6%. (2) The “ideal cultivar” and “ideal location” view of the HA-GGE biplot identified Zhongcj408 (G2) and Nannon12 (G9) as the best ideal genotypes; Cixi in Zhejiang Province and Jiangling in Hubei Province were the most ideal locations.(3) The “which-won-where” view of the biplot outlined the adaptive planting region for each experimental cultivar. (4) The “similarity among locations” view clustered the trial locations into four groups, among of which the two outlier locations, Shehong (SH) and Chengdu (QBJ), located in Shichuan Basin in the upper reaches of YaRV, were clustered in one group, whereas the Nanyang (NY) of Henan Province at the northern edge of YaRV was singled out as a sole group. Such location clustering results implied an apparent association with the geographical environment.     

AMMI analysis of rainfed lowland rice (Oryza sativa) trials in Nigeria

The additive main effects and multiplicative interaction (AMMI) model is used to analyse the grain yield data of 13 rice genotypes grown in 12 rainfed lowland rice environments. The trials were organized by the International Network for Genetic Evaluation of Rice in Africa (INGER-Africa) and conducted in Nigeria. Main effects due to environments (E), genotypes (G) and G × E interaction were found to be significant (P = 0.001). Cross validation analysis suggested that an AMMI model with one interaction principal component axis (IPCA) was most useful predictively, whereas Gollobs’ test declared two components, IPCA1 and IPCA2, statistically significant (P = 0.01). The IPCAl, however, accounted for most (47.8%) of the G × E sum of squares. Correlation and regression analysis, and relative scatter of genotype and environment points on the AMMI biplot suggest that the interaction partitioned in IPCA1 resulted from differences in the days to flowering among the genotypes. The paper discusses these in relation to the occurrence of Fe toxicity at the test sites and varietal tolerance to the stress.     

Parametric and nonparametric measures of phenotypic stability in linseed (Linum usitatissimum L.)

A combined analysis with three parametricand two nonparametric measures to assess G × E interactions and stability analyses toidentify stable genotypes of linseed across18 environments in Ethiopia wereundertaken. The combined analysis ofvariance for environments (E), genotypes(G) and G × E interaction was highlysignificant (p<0.01), suggestingdifferential responses of the genotypes andthe need for stability analysis. Theparametric stability measures ofcoefficient of variability and thestability variance showed that R12-N10D wasthe most stable genotype, whereascultivars' superiority measure indicatedChilalo to be the most stable cultivar.Like most of the parametric methods, thenon-parametric measures revealed thatR12-N10D had the smallest changes in ranksand thus was the most stable genotype incontrast to R12-D24C, which was unstableand the lowest yielder. A comparison of thefive stability measures showed that thecoefficient of variability, stabilityvariance and variance of ranks were similarin assessing the relative stability of thegenotypes, whereas cultivars' superioritymeasure deviated from the others. Thestability variance and variance of rankswere significantly rank correlated, andwere the best in determining thecomparative stability of linseed genotypes.The coefficient of variability was alsorelatively better than the cultivar'ssuperiority measure. Further studies ofrepeatability tests are, however, needed todetermine the best methods. The stabilitystatistics generally identified R12-N10D,followed by Chilalo, as the most stablevarieties, whereas R12-D24C and R11-M20Gwere the least stable varieties.     

Assessment of mungbean genotypes for durable resistance to Yellow Mosaic Disease: Genotype × Environment interactions

Yellow mosaic disease (YMD) is the major constraint of mungbean for realizing high productivity worldwide. Moreover, management of disease using YMD‐resistant genotypes is the simplest approach. Therefore, based on a preliminary screening of 220 genotypes during the year 2010 and 2011 at 17 locations, a set of 25 genotypes was further selected to evaluate at six locations over 2 years for identification of more stable resistant genotypes. The genotype and genotype × environment (GGE) analysis indicated that the genotypes and environment effects were significant (P < 0.001) for YMD incidence. Interestingly, the GGE biplot analysis successfully accounted for 74.71 per cent of the total variation with three genotypes (ML 818, ML 1349 and IPM 02‐14) showing high degree of resistance and stability over the locations. Notably, a strong positive association was observed between disease reaction and temperature, relative humidity and rainfall. As crop is grown in diverse growing environments, aforementioned genotypes can be used as stable/durable sources for future breeding programme to develop YMD‐resistant cultivars.     

Analysis of genotype-by-environment interaction for grain yield of rainfed durum wheat genotypes in warm winter areas of Iran

This study was performed for pattern analysis of genotype-by-environment (GE) interaction on 20 durum wheat genotypes grown in 15 testing environments during 2004–06 in Iran. Combined analysis of variance showed significant genotypes (G), environments (E), and GE interactions (P < 0.01), with environmental main effects being the predominant source of variation, followed by GE interaction. The results showed various patterns of genotype responses to different environment groups and assisted in structuring the durum wheat testing locations with identification of two major-environment groups with high genotype discrimination ability. The locations (Gachsaran and Ilam) corresponding to warm and semi-arid aresa were similar in genotype discrimination and showed no association with the other testing locations (Gonbad, Moghan, and Khoramabad) representing the Mediterranean area, indicating they differ in rankings of genotypes. The top-yielding genotypes, G13, G14 and G9, were highly adapted to warm and semi-arid environments, but those corresponding to the Mediterranean area had a high ability to discriminate the genotypes G16, G11, and Saimareh. The stability and adaptability of specific genotypes were assessed by plotting their nominal grain yields at specific environments in an ordination biplot, which aided in the identification of environment groups. Appropriate check genotypes for all environments or for specific environments were also identified. Pattern analysis allowed a sensible and useful summarization of GE interaction data set and helped to facilitate selecting superior genotypes for target-growing sites.     

Nonparametric phenotypic stability analysis in advanced barley (<Emphasis Type="Italic">Hordeum vulgare</Emphasis> L.) genotypes

The development of genotypes with adaptation to a wide range of environments is one of the most important goals of plant breeding programs. In order to compare nonparametric stability measures and to identify promising high-yield and stable barley (Hordeum vulgare L.), 20 barley genotypes selected from the Iran/ICARDA joint project and grown in nine environments during 2009-11 in Iran. Four nonparametric statistical tests of significance for genotype × environment (GE) interaction and 10 nonparametric measures of stability were used to identify stable genotypes in nine environments. Results of nonparametric tests of G×E interaction (Kubinger, Hildebrand, and Kroon/ Laan) and a combined ANOVA across environments, indicated the presence of both crossover and non-crossover interactions. Also, only TOP and rank-sum values were positively associated with high yield. Thus, in the simultaneous selection for high yield and stability, only the rank-sum and TOP methods were useful in terms of the principal component analysis results, and correlation analysis of nonparametric stability statistics and yield. According to these stability parameters (TOP and rank-sum), three genotypes (G13, G12, and G17) were the most stable for grain yield. The results also revealed that based on nonparametric test results, stability could be classified into three groups, according to agronomic and biological concepts of stability.     

Performance Evaluation of Malt Barley: from Malting Quality and Breeding Perspective

Lack of suitable malt barley varieties that exhibit high yielding, stable performance, and good malting quality is the major factor among several production constraints contributing to low productivity of malt barley in the North Gondar Zone. The present study was done to evaluate and recommend the best performing varieties in the major potential areas of North Gondar. The experiment was conducted at three locations for two consecutive years (2015 and 2016) during the main cropping season using twelve improved varieties. The design was randomized complete block design with three replications. Analysis of variance and GGE [genotype main effect (G) and genotype-by-environment interaction (GE)] biplot analysis were conducted following their respective procedures. Combined analysis of variance revealed a highly significant difference (P < 0.01) among genotypes, environments, and genotype-by-environment interaction for grain yield, most agronomic and malt quality traits. All the varieties had acceptable malt quality traits. The variety IBON-174/03 was found to be the highest yielding and the most stable variety across environments. According to the polygon view of biplot analysis, the varieties were spread across four sections and the test environments spread across two sections. Among the six test environments, D and C were more discriminating and F and B were less discriminating. Test environments F, E, and A were found to be more representative of the mega-environment than D. Considering early maturity, malt quality, grain yield, and stability performance; it is recommended to use the variety IBON-174/03 for production in the study areas and in similar areas.     

Comparison of shifted multiplicative model,rank correlation,and biplot analysis for clustering winter wheat production environments

Categorization of locations with similar environments helps breeders to efficiently utilize resources and effectively target germplasm. This study was conducted to determine the relationship among winter wheat (Triticum aestivum L.) yield testing locations in South Dakota. Yield trial data containing 14 locations and 38 genotypes from 8 year were analyzed for crossover genotype (G) × environment (E) interactions according to the Azzalini-Cox test. G × E was significant (P < 0.05) and contributed a small proportion of variation over the total phenotypic variation. This suggested that for efficient resource utilization, locations should be clustered. The data were further analyzed using the Shifted Multiplicative Model (SHMM), Spearman’s rank correlation and GGE biplot to group testing locations based on yield. SHMM analysis revealed four major cluster groups in which the first and third had three locations, with four locations in each of the second and fourth groups. Spearman rank correlations between locations within groups were significant and positive. GGE biplot analysis revealed two major mega-environments of winter wheat testing locations in South Dakota. Oelrichs was the best testing location and XH1888 was the highest yielding genotype. SHMM, rank correlation and GGE biplot analyses showed that the locations of Martin and Winner in the second group and Highmore, Oelrichs and Wall in the third group were similar. This indicated that the number of testing locations could be reduced without much loss of grain yield information. GGE biplot provided additional information on the performance of entries and locations. SHMM clustered locations with reduced cross-over interaction of genotype × location. The combined methods used in this study provided valuable information on categorization of locations with similar environments for efficient resource allocation. This information should facilitate efficient targeting of breeding and testing efforts, especially in large breeding programs.     

GGE叠图法─分析品种×环境互作模式的理想方法

本文介绍一种分析作物区域试验结果的方法－ＧＧＥ叠图法。首先，将原始产量数据减去各地 点的平均产量，由此形成的数据集只含品种主效应Ｇ和品种－环境互作效应ＧＥ，合称为ＧＧＥ。对ＧＧＥ 作单值分解，并以第一和第二主成分近似之。按照第一和第二主成分值将各品种和各地点放到一个平 面图上即形成ＧＧＥ叠图。借助于辅助线，可以直观回答以下问题：（１）什么是某一特定环境下最好的 品种；（２）什么是某一特定品种最适合的环境；（３）任意两品种在各环境下的表现如何；（４）试验中品 种×环境互作的总体模式是怎样的；（５）什么是高产、稳产品种；（６）什么是有利于筛选高产、稳产品 种的环境。     

Identifying high yielding stable winter wheat genotypes for irrigated environments in Central and West Asia

Improved winter wheat (Triticum aestivum L.) cultivars are needed for the diverse environments in Central and West Asia to improve rural livelihoods. This study was conducted to determine the performance of elite winter wheat breeding lines developed by the International Winter Wheat Improvement Program (IWWIP), to analyze their stability across diverse environments, and to identify superior genotypes that could be valuable for winter wheat improvement or varietal release. One hundred and one advanced winter wheat breeding lines and four check cultivars were tested over a 5-year period (2004–2008). Grain yield and agronomic traits were analyzed. Stability and genotypic superiority for grain yield were determined using genotype and genotype × environment (GGE) biplot analysis. The experimental genotypes showed high levels of grain yield in each year, with mean values ranging from 3.9 to 6.7 t ha⁻¹. A set of 25 experimental genotypes was identified. These were either equal or superior to the best check based on their high mean yield and stability across environments as assessed by the GGE biplot analysis. The more stable high yielding genotypes were ID800994.W/Falke, Agri/Nac//Attila, ID800994W/Vee//F900K/3/Pony/Opata, AU//YT542/N10B/3/II8260/4/JI/Hys/5/Yunnat Esskiy/6/KS82W409/Spn and F130-L-1-12/MV12. The superior genotypes also had acceptable maturity, plant height and 1,000-kernel weight. Among the superior lines, Agri/Nac//Attila and Shark/F4105W2.1 have already been proposed for release in Kyrgyzstan and Georgia, respectively. The findings provide information on wide adaptation of the internationally important winter wheat genotypes, and demonstrate that the IWWIP program is enriching the germplasm base in the region with superior winter wheat genotypes to the benefit of national and international winter wheat improvement programs.     

Using GGE Biplot Analysis to Evaluate Interrelationships between Yield and Yield Components of Oat Genotypes in Different Growing Seasons

The aim of this study was to evaluate oat genotypes for grain yield and yield components in both 2014-2015 and 2015-2016 growing seasons using GGE biplot analysis. Experiments were laid out according to lattice design. Biomass at harvest, grain yield, number of grain per panicle, weight of grain per panicle, flag leaf width, flag leaf height, plant height, stem diameter, thousand kernel weight, time of panicle emergence, harvest index, panicle length, and spikelet per panicle were evaluated for 56 oat genotypes. GGE biplot graphics of the first and second years explained 54.4 and 55% of total variation, respectively. Grain yield, number of grains per panicle, and weight of grain per panicle were closely and positively associated in both growing seasons. Recently registered cultivar Sari and lines 26, 29, and 30 were found as promising genotypes for Çanakkale conditions. Traits of genotype at different growing seasons can be visually studied using different genotype-by-trait (GT) biplots.     

Pattern analysis of genotype × environment interaction for striga resistance and grain yield in African sorghum trials

The parasitic weed Striga hermonthica (Del.) Benth. seriously limits sorghum [Sorghum bicolor (L.) Moench] production in Sub-Saharan Africa. As an outbreeder, S. hermonthica is highly variable with an extraordinary capacity to adapt to different hosts and environments, thereby complicating resistance breeding. To study genotype x environment (G x E) interaction for striga resistance and grain yield, nine sorghum lines, 36 F₂ populations and five local checks were grown under striga infestation at two locations in both Mali and Kenya. Mean squares due to genotypes and G x E interaction were highly significant for both sorghum grain yield and area under striga severity progress curve(ASVPC, a measure of striga emergence and vigor throughout the season). For grain yield, the entry x location-within-country interaction explained most of the total G x E while for ASVPC, entry x country and entry x location-within-country interactions were equally important. Pattern analysis (classification and ordination techniques) was applied to the environment-standardized matrix of entry x environment means. The classification clearly distinguished Malian from Kenyan locations for ASVPC, but not for grain yield. Performance plots for different entry groups showed differing patterns of adaptation. The ordination biplot underlined the importance of entry x country interaction for ASVPC. The F₂ derived from the cross of the striga-resistant line Framida with the striga-tolerant cultivar Seredo was the superior entry for both grain yield and ASVPC, underlining the importance of combining resistance with tolerance in striga resistance breeding. The observed entry x country interaction for ASVPC may be due to the entries' different reactions to climatic conditions and putative differences in striga virulence in Mali and Kenya. This revised version was published online in August 2006 with corrections to the Cover Date.     

基于HA-GGE双标图的甘蔗试验环境评价及品种生态区划分

采用遗传力校正的GGE双标图(heritability adjusted GGE,HA-GGE),分析基因型(G)、环境(E)、基因型与环境互作效应(GE)对产量变异的影响,对14个试验点的分辨力、代表性和理想指数进行分析,并对这些试验点的生态区进行划分。结果表明,甘蔗试验环境对产量变异的影响大于基因型和基因型与环境互作;互作因素中以环境×基因型的互作效应最大,基因型×年份的互作效应最小。广东遂溪(E3)和广西崇左(E6)为最理想试验环境,对筛选广适性新品种和鉴别理想品种的效率最高;福建福州(E1)、福建漳州(E2)、广东湛江(E4)、云南保山(E11)、云南临沧(E13)、云南瑞丽(E14)为理想试验环境;广西百色(E5)、广西河池(E7)、海南临高(E10)、云南开远(E12)为较理想试验环境;广西来宾(E8)、广西柳州(E9)为不太理想的试验环境。根据HA-GGE双标图分析结果,可将我国甘蔗生态区划分为3个,即以广西百色、河池、来宾和柳州为代表的华南内陆甘蔗品种生态区,以云南保山、开远、临沧、瑞丽为代表的西南高原甘蔗品种生态区,涵盖福建福州、漳州、广东湛江、遂溪、广西崇左等试点的华南沿海甘蔗品种生态区。     

Impact of grain morphology and the genotype by environment interactions on test weight of spring and winter wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Wheat (Triticum aestivum L.) market grades and prices are determined in part by test weight (TW). Millers value high TW because it is typically associated with higher flour extraction rates and better end-use quality. Test weight is expected to be influenced by other directly quantifiable grain attributes such as grain length (GL), grain width (GW), shape, single-grain-density (SGD), thousand-grain-weight (TGW), and packing efficiency (PE). The objectives of this study were to: (1) determine the primary morphological grain attributes that comprise TW measurements for winter and spring wheat classes; and (2) determine TW stability and genotype and genotype × environment interactions (GEIs) of the attributes that comprise TW. A market class representative group of 32 hard spring and 24 hard winter wheat cultivars was grown at several locations in South Dakota in 2011 and 2012. A regularized multiple regression algorithm was used to develop a TW model and determine what grain attribute reliably predicts TW. A GGE biplot was used for stability and GEI analyses whereas a linear mixed model was used for variance analyses. Data were collected on eight grain traits: TW, SGD, TGW, protein concentration, GW, GL, shape, size, and PE. Observations showed that in both spring and winter wheat, SGD accounted for over 90% of the phenotypic variation of TW. Cultivars with stable and high TW were identified in both wheat classes. Apart from TW; significant (p?<?0.0001) genotype, environment, and GEI variances were observed for GW and SGD, a more direct measure of which could help improve genetic gain for TW.     

Performance of cowpea varieties under <Emphasis Type="Italic">Striga gesnerioides</Emphasis> (Willd.) Vatke infestation using biplot analysis

Striga gesnerioides (Willd) Vatke, is a major destructive parasitic weed of cowpea (Vigna unguiculata (L.) Walp.) which causes substantial yield reduction in West and Central Africa. The presence of different virulent races within the parasite population contributes to significant genotype × environment interaction, and complicates breeding for durable resistance to Striga. A 3-year study was conducted at three locations in the dry savanna agro-ecology of Nigeria, where Striga gesnerioides is endemic. The primary objective of the study was to identify cowpea genotypes with high yield under Striga infestation and yield stability across test environments and to access suitability of the test environment. Data collected on grain yield and yield components were subjected to analysis of variance (ANOVA). Means from ANOVA were subjected to the genotype main effect plus genotype × environment (GGE) biplot analysis to examine the multi-environment trial data and rank genotypes according to the environments. Genotypes, environment, and genotypes × environment interaction mean squares were significant for grain yield and yield components, and number of emerged Striga plants. The environment accounted for 35.01%, whereas the genotype × environment interaction accounted for 9.10% of the variation in grain yield. The GGE biplot identified UAM09 1046-6-1 (V7), and UAM09 1046-6-2 (V8), as ideal genotypes suggesting that these genotypes performed relatively well in all study environments and could be regarded as adapted to a wide range of locations. Tilla was the most repeatable and ideal location for selecting widely adapted genotypes for resistance to S. gesnerioides.     

Genetic variation and environmental stability of grain mineral nutrient concentrations in <Emphasis Type="Italic">Triticum dicoccoides</Emphasis> under five environments

Nineteen wild emmer wheat [Triticum turgidum ssp. dicoccoides (Körn.) Thell.] genotypes were evaluated for the grain concentrations of phosphorous (P), potassium (K), sulfur (S), magnesium (Mg), calcium (Ca), zinc (Zn), manganese (Mn), iron (Fe) and cooper (Cu) under five different environments in Turkey and Israel. Each mineral nutrient has been investigated for the (1) genotype by environment (G × E) interactions, (2) genotype stability, (3) correlation among minerals and (4) mineral stability. Among the macronutrients analyzed, grain concentrations of Ca (range 338–2,034 mg kg^?1) and S (range 0.18–0.43%) showed the largest variation. In the case of micronutrients, the largest variation was observed in the grain Mn concentration (range 13–87 mg kg^?1). Grain concentrations of Fe and Zn also showed important variation (range 27–86 and 39–115 mg kg^?1, respectively). Accessions with higher nutrient concentrations (especially Zn and Fe) had also greater grain weight, suggesting that higher grain Zn and Fe concentrations are not necessarily related to small grain size or weight. Analysis of variance showed that environment was the most important source of variation for K, S, Ca, Fe, Mn and Zn, explaining between 44 and 78% of the total variation and G × E explained between 20 and 40% of the total variation in all the minerals, except for S and Zn where its effect accounted for less than 16%. Genotype was the most important source of variation for Cu (explaining 38% of the total variation). However, genotype effect was also important for Mg, Mn, Zn and S. Sulfur and Zn showed the largest heritability values (77 and 72%, respectively). Iron exhibited low heritability and high ratio value between the G × E and genotype variance components \( \left( {\sigma_{\text{GE}}^{2} /\sigma_{G}^{2} } \right) \), suggesting that specific adaptation for this mineral could be positively exploited. The wild emmer germplasm tested in the current study revealed some outstanding accessions (such as MM 5/4 and 24-39) in terms of grain Zn and Fe concentrations and environmental stability that can be used as potential donors to enhance grain micronutrient concentrations in wheats.     

Assessing the agronomic potential of linseed genotypes by multivariate analyses and association mapping of agronomic traits

High prices of fish oil make linseed attractive for aquaculture and animal feed. To ensure a constant supply of linseed, the development of stable cultivars is of strategic importance. In this study, 35 linseed genotypes were evaluated in five Chilean environments (E) from 2009 to 2012. The additive main effect and multiplicative interaction analysis (AMMI), genotype (G) plus genotype by environment (GE) interaction (GGE) biplot analysis and three stability parameters were tested with the aim of identifying adapted genotypes for the development of linseed cultivars. An association mapping (AM) analysis was also conducted for four agronomic traits and the stability of the associated markers was evaluated using the QQE (QTL main effect and QTL by environment interaction) approach. Combined analysis of variance for yield, seeds per boll (SPB), plant height (PH) and days to flowering (DTF) were significant for G, E and GE (P < 0.001). The combined stability analysis identified some Canadian, Argentinean and Chilean accessions to be the best adapted and highest yielding genotypes. Coancestry analysis indicated that crossing Canadian and Chilean genotypes could maximize transgressive segregation for yield. Significant associations for DTF, PH and SPB explained up to 59 % of the phenotypic variation for these traits. The QQE and AM analyses were consistent in identifying marker LGM27B as the most stable and significant across all environments with the largest effect in reducing DTF. The combined application of the stability, AM and QQE analyses could accelerate the development of marketable linseed cultivars adapted to Southern Chile.     

Genotype by environment interaction components underlying variations in root,sugar and white sugar yield in sugar beet (<Emphasis Type="Italic">Beta vulgaris</Emphasis> L.)

The success of plant breeding programs depends on the ability to provide farmers with genotypes with guaranteed superior performance in terms of yield across a range of environmental conditions. We evaluated 49 sugar beet genotypes in four different geographical locations in 2 years aiming to identify stable genotypes with respect to root, sugar and white sugar yields, and to determine discriminating ability of environments for genotype selection and introduce representative environments for yield comparison trials. Combinations of year and location were considered as environment. Statistical analyses including additive main effects and multiplicative interactions (AMMI), genotype main effects and genotype?×?environment interaction effects (GGE) models and AMMI stability value (ASV) were used to dissect genotype by environment interactions (GEI). Based on raw data, root, sugar and white sugar yields varied from 0.95 to 104.86, 0.15 to 20.81, and 0.09 to 18.45 t/ha across environments, respectively. Based on F-Gollob validation test, three interaction principal components (IPC) were significant for each trait in the AMMI model whereas according to F ratio (F_R) test two significant IPCs were identified for root yield and sugar yield and three for white sugar yield. For model diagnosis, the actual root mean square predictive differences (RMS PD) were estimated based upon 1000 validations and the AMMI-1 model with the smallest RMS PD was identified as the most accurate model with highest predictive accuracy for the three traits. In the GGE biplot model, the first two IPCs accounted for 60.52, 62.9 and 64.69% of the GEI variation for root yield, sugar yield and white sugar yield, respectively. According to the AMMI-1 model, two mega-environments were delineated for root yield and three for sugar yield and white sugar yield. The mega-environments identified had an evident ecological gradient from long growing season to intermediate or short growing season. Environment-focused scaling GGE biplots indicated that two locations (Ekbatan and Zarghan) were the most representative testing environments with discriminating ability for the three traits tested. Environmentally stable genotypes (i.e. G21, G28 and G29) shared common parental lines in their pedigree having resistance to some sugar beet diseases (i.e. rhizomania and cyst nematodes). The results of the AMMI model were partly in accord with the results of GGE biplot analysis with respect to mega-environment delineation and winner genotypes. The outcome of this study may assist breeders to save time and costs to identify representative and discriminating environments for root and sugar yield test trials and creates a corner stone for an accelerated genotype selection to be used in sweet-based programs.