Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran

The genotype × environment (GE) interaction influences genotype selection and recommendations. Consequently, the objectives of genetic improvement should include obtaining genotypes with high potential yield and stability in unpredictable conditions. The GE interaction and genetic improvement for grain yield and yield stability was studied for 11 durum breeding lines, selected from Iran/ICARDA joint program, and compared to current checks (i.e., one durum modern cultivar and two durum and bread wheat landraces). The genotypes were grown in three rainfed research stations, representative of major rainfed durum wheat-growing areas, during 2005–09 cropping seasons in Iran. The additive main effect and multiplicative interaction (AMMI) analysis, genotype plus GE (GGE) biplot analysis, joint regression analysis (JRA) (b and S²di), six stability parameters derived from AMMI model, two Kang’s parameters [i.e., yield-stability (YSi) statistic and rank-sum], GGE distance (mean performance + stability evaluation), and two adaptability parameters [i.e., TOP (proportion of environments in which a genotype ranked in the top third) and percentage of adaptability (Ad)] were used to analyze GE interaction in rainfed durum multi-environment trials data. The main objectives were to (i) evaluate changes in adaptation and yield stability of the durum breeding lines compared to modern cultivar and landraces (ii) document genetic improvement in grain yield and analyze associated changes in yield stability of breeding lines compared to checks and (iii) to analyze rank correlation among GGE biplot, AMMI analysis and JRA in ranking of genotypes for yield, stability and yield-stability. The results showed that the effects due to environments, genotypes and GE interaction were significant (P < 0.01), suggesting differential responses of the genotypes and the need for stability analysis. The overall yield was 2,270 kg ha^?1 for breeding lines and modern cultivar versus 2,041 kg ha^?1 for landraces representing 11.2 % increase in yield. Positive genetic gains for grain yield in warm and moderate locations compared to cold location suggests continuing the evaluation of the breeding material in warm and moderate conditions. According to Spearman’s rank correlation analysis, two types of associations were found between the stability parameters: the first type included the AMMI stability parameters and joint regression parameters which were related to static stability and ranked the genotypes in similar fashion, whereas the second type consisted of the rank-sum, YSi, TOP, Ad and GGED which are related to dynamic concept of stability. Rank correlations among statistical methods for: (i) stability ranged between 0.27 and 0.97 (P < 0.01), was the least between AMMI and GGE biplot, and highest for AMMI and JRA and (ii) yield-stability varied from 0.22 (between GGE and JRA) to 0.44 (between JRA and AMMI). Breeding lines G8 (Stj3//Bcr/Lks4), G10 (Ossl-1/Stj-5) and G12 (modern cultivar) were the best genotypes in terms of both nominal yield and stability, indicating that selecting for improved yield potential may increase yield in a wide range of environments. The increase in adaptation, yield potential and stability of breeding lines has been reached due to gradual accumulation of favorable genes through targeted crosses, robust shuttle breeding and multi-locational testing.     

Genotype x environment interaction for durum wheat grain yield and selection for drought tolerance in irrigated and droughted environments in Iran

Durum wheat is grown in the Mediterranean region under stressful and variable environmental conditions. In a 4-year-long experiment, 14 genotypes [including 11 durum breeding lines, two durum (Zardak) and bread (Sardari) wheat landraces, and one durum (Saji) newly released variety] were evaluated under rainfed and irrigated conditions in Iran. Several selection indices [i.e. stress tolerance index (STI), drought tolerance efficiency (DTE), and irrigation efficiency (IE)] were used to characterize genotypic differences in response to drought. The GGE biplot methodology was applied to analyze a three-way genotype-environment-trait data. Combined ANOVA showed that the year effect was a predominant source of variation. The genotypes differed significantly (P < 0.01) in grain yield in the both rainfed and irrigated conditions. Graphic analysis of the relationship among the selection indices indicated that they are not correlated in ranking of genotypes. The two wheat landraces and the durum-improved variety with high DTE had minimum yield reduction under drought-stressed environments. According to STI, which combines yield potential and drought tolerance, the “Saji” cultivar followed by some breeding lines (G11, G8, and G4) performed better than the two landraces and were found to be stable and high-yielding genotypes in drought-prone rainfed environments. The breeding lines G8, G6, G4, and G9 were the efficient genotypes responding to irrigation utilization. In conclusion, the identification of the durum genotypes (G12, G11, and G4) with high yield and stability performance under unpredictable environments and high tolerance to drought stress conditions can help breeding programs and eventually contribute to increasing and sustainability of durum production in the unpredictable conditions of Iran.     

Association between Yield and Carbon Isotope Discrimination Value in Different Organs of Durum Wheat Under Drought

Carbon isotope discrimination (Δ) has been proposed as a selection criterion for transpiration efficiency and grain yield in drought‐prone environments for several C³ species, including cereals. Δ analysis, however, has mainly been concerned with grain or culm tissues and little work has been devoted to other organs. The objective of this study was to describe Δ variation in different organs and to examine the relationships between Δ and grain yield across environments. Six durum wheat genotypes with contrasted grain Δ were cultivated under rainfed conditions during three successive years at Montpellier (South of France). Δ was measured on flag leaf, stalk, awns, chaff and rachis sampled at anthesis and maturity, and on mature grain. Higher genotypic variation and closer correlation with yield were noted for grain Δ compared to other plant parts. The genotype ranking across years was more consistent for grain Δ than for other organs. Consequently, the grain seems the most effective plant part for Δ analysis in durum wheat under Mediterranean conditions. The study of Δ variation in other organs may be useful, however, to evaluate the contribution of those organs to grain filling and final yield according to environmental conditions.     

应用GGE双标图分析我国春小麦的淀粉峰值粘度

将原始数据减去各试点均值后形成的数据集中只含基因型主效G和基因型与环境互作效应GE, 合称GGE. 对GGE做单值分解, 以第一和第二主成分近似, 按第一和第二主成分值将所有品种和试点绘于同一平面图即形成GGE双标图. 以其分析我国春麦区10个试点20个品种淀粉糊化特性的峰值粘度, 结果表明铁春1号在大部分试点峰值粘度表现较好,     

Specific and General Adaptation and Yield Stability of Wheat (Triticum aestivum L.) Genotypes

Mean grain yield performance of 12 wheat and one triticale genotypes were measured at four locations over four consecutive years, using a randomized complete block design with four replications. The genotypes used were commercial cultivars and advanced lines from different wheat breeding projects located in different areas in Iran. Two locations were in semiarid regions and the other two locations in the temperate zones. The combined analysis of variance indicated highly significant genotype-environment (GE) interactions. From combinations of locations and years three sets of environments were generated. Set I and set II, each, consisted of eight environments (two locations and four years) representing semiarid and temperature environments, respectively. Set III consisted of 16 environments including both semiarid and temperate conditions. Set I and set II were used to measure specific adaptation of the genotypes while set III was employed for measuring general adaptation. The methods of Eberhart and Russell (1966) were used for partitioning GE interactions. The mean square associated with the heterogeneity of regression was highly significant under all sets of environments. These observations indicated that a major part of GE interaction could be accounted for by differences in the regression of the individual genotypes. All the genotypes had significant regression mean square under set I, set II, and set III environments, with the exception of two genotypes under set II. However, mean yields, regression coefficients, and the mean squares associated with deviation from regression greatly varied over the sets of environments. Only three genotypes, a commercial cultivar and two new advanced line, were identified as having specific adaptation and yield stability to semiarid environments. Among all the genotypes, only a commercial cultivar was identified as adapted and stable to temperate conditions. Two of the three genotypes which were adapted to semiarid environments also showed general adaptation to set III environments. However, the mean yield of these two genotypes under semiarid conditions (set I) were significantly greater than their respective mean yields under set III environments. Thus, wider adaptability was compensated by lower mean yield. The present study indicates that, while a wide range of environments is necessary and recommended for measuring general adaptation reactions and yield stability of various genotypes, one should not ignore the possibility of finding some genotypes with specific adaptation to specific environments and thus maximizing yield production. Stable genotypes with general of specific adaptation should be utilized in breeding projects in order to develop even more desirable lines.     

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.     

Effect of Glaucousness on Carbon Isotope Discrimination and Grain Yield in Durum Wheat

The effect of glaucousness on yield and transpiration efficiency was studied in durum wheat under Mediterranean conditions. Two groups of 16 genotypes, with contrasting glaucousness, were compared over 2 years for flag leaf and kernel carbon isotope discrimination, residual transpiration, specific leaf dry weight, biomass and grain yield. Significantly higher carbon isotope discrimination and lower specific leaf dry weight were noted in the glaucous lines. No significant effect of glaucousness was found on residual transpiration. Glaucous genotypes also exhibited higher grain yield, which was strongly correlated to the carbon isotope discrimination values. Results suggest that glaucousness has a negative effect on transpiration efficiency, which could be related to modifications of leaf energy balance. Higher stomatal conductance, reflected by the high carbon isotope discrimination, could mainly explain the higher grain yield found in the glaucous lines.     

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.     

Combining ability and heterosis under pest epidemics in a broad-based global wheat-breeding population

Wheat breeders rarely apply population improvement schemes or select parental sources according to combining ability and heterotic patterns. They rely on pedigree selection methods for breeding new cultivars. This experiment was undertaken to assess the advantages of using diallel crosses to define combining ability and understand heterosis in a broad‐based wheat‐breeding population across different environments affected by yellow rust. Sixty‐four genotypes derived from a full diallel mating scheme were assessed for grain yield in two contrasting growing seasons at two locations for two consecutive years. Parental genotypes showed significant combining ability for grain yield that was affected by yellow rust and genotype‐by‐environment (GE) interactions, both of which affected heterosis for grain yield. Significant GE interactions suggested that decentralized selection for specific environments could maximize the use of this wheat germplasm. Cultivar effects and specific heterosis were the most important factors influencing grain yield. Some crosses capitalized on additive genetic variation for grain yield. This research shows the power of available quantitative breeding tools to help breeders choose parental sources in a population improvement programme.     

Contribution of Weather Variables to Genotype X Environment Interaction in Grain Sorghum1

Genotype × environment (GE) interactions are a major problem in plant breeding programs that involve testing in diverse environments. These interactions can reduce progress from selection. Few studies have characterized the effects of weather variables on GE interactions in sorghum (Sorghum bicolor [L.] Moench). The present investigation estimated the contribution of environmental index, (?, or mean yield of all cultivars in jth environment minus ?. xor overall mean yield for all cultivars and all environments), rainfall, minimum and maximum temperature, and relative humidity, to GE interaction. Yield means of 5 full-season and 10 medium-season grain sorghum hybrids grown during 1986—1988 at four locations were used in the study. The GE interaction was significant and partitioned into σ²_i, components assignable to each genotype. Weather variables (covariates) were used to remove heterogeneity from the GE interaction. The remainder of the GE interaction variance was partitioned into variance components (s²_i) assignable to each genotype. In both maturity groups, the environmental index removed most, although non-significant, heterogeneity from the GE interaction sums of squares. Of all weather variables, preseason and seasonal rainfall contributed most to the GE interaction sums of squares.     

Interpreting genotype × environment interactions for durum wheat grain yields using nonparametric methods

The objective of this study was to compare nonparametric stability procedures and apply different nonparametric tests for genotype × environment (G × E) interactions on grain yields of 15 durum wheat genotypes selected from Iran/ICARDA joint project grown in 12 environments during 2004–2006 in Iran. Results of nonparametric tests of G × E interaction and a combined ANOVA across environments indicated the presence of both crossover and noncrossover interactions, and genotypes varied significantly for grain yield. In this study, high values of TOP (proportion of environments in which a genotype ranked in the top third) and low values of sum of ranks of mean grain yield and Shukla’s stability variance (rank-sum) were associated with high mean yield. The other nonparametric stability methods were not positively correlated with mean yield but they characterized a static concept of stability. The results of correlation analysis indicated that only TOP and rank-sum methods would be useful for simultaneous selection for high yield and stability. These two methods identified lines Mrb3/Mna-1, Syrian-4 and Mna-1/Rfm-7 as genotypes with dynamic stability and wide adaptation. According to static stability parameters, the genotypes 12A-Mar8081 and 19A-Mar8081 with lowest grain yield were selected as genotypes with the highest stability.     

GGE Biplot Analysis of Genotype by Environment Interaction in Field Pea (<Emphasis Type="Italic">Pisum sativum</Emphasis> L.) Genotypes in Northwestern Ethiopia

Ten field pea genotypes were evaluated in randomized complete block design with four replications for three consecutive years (2010-2012) main cropping seasons at four locations in each year. The objectives were to determine magnitude of genotype by environment interaction and to identify stable field pea genotype with high grain yield to be released as a cultivar to producer for Northwestern Ethiopia. The GGE [genotype main effect (G) and genotype by environment interaction (GE)] biplot graphical tool was used to analyze yield data. The combined analysis of variance revealed a significant difference (P<0.01) among genotypes, environments and genotype-by-environment interaction for grain yield. The average environment coordinate biplot revealed that EH99005-7 (G2) was the most stable and the highest yielding genotype. Polygon view of GGE-biplot showed the presence of three mega-environments. The first section includes the test environments E1 (Adet 2010), E3 (Debretabor 2010), E5 (Adet 2011), E6 (Motta 2011), E7 (Debretabor 2011), E8 (Dabat 2011), E9 (Adet 2012) and E12 (Dabat 2012) which had the variety G1 (EH99009-1) as the winner; the second section contains the environments E4 (Dabat 2010), E10 (Motta 2012) and E11 (Debretabor 2012) with G2 as the best grain yielder and the third section contains the E2 (Motta 2010) with G4 (Tegegnech X EH90026-1-3-1) as the best grain yielder. The comparison GGE- biplot of field pea genotypes with the ideal genotype showed that G2 was the closest genotype for the ideal cultivar. Among the twelve environments, E2, E6 and E10 were more discriminating and E3, E9 and E12 were less discriminating. Genotype EH99005-7 was the most stable and the highest yielding genotype. As a result it is released officially for Northwestern Ethiopia. Therefore, it is recommended to use genotype EH99005-7 for wider cultivation in Northwestern Ethiopia and similar areas.     

High yielding spring bread wheat germplasm for global irrigated and rainfed production systems

Global wheat (Triticum aestivum L.) production must increase 2% annually until 2020 to meet future demands. Breeding wheat cultivars with increased grain yield potential, enhanced water-use efficiency, heat tolerance, end-use quality, and durable resistance to important diseases and pests can contribute to meet at least half of the desired production increases. The remaining half must come through better agronomic and soil management practices and incentive policies. Analyses of the recent International Yield Trials indicate that grain yields of the best new entries were usually 10% higher than the local checks globally, as well as within a country across sites. Variation in yield across sites within a country/region underline the role of genotype × environment (GE) interaction and provides opportunities to select for stable genotypes, which is not often done. The lack of proper analysis undermines proper utilization of germplasm with high yield potential and stability in the national wheat breeding programs. Some of the best performers in irrigated areas were amongst the best in semiarid environments, reinforcing the fact that high yield potential and drought tolerance can be improved simultaneously. The best performing lines often had genotypic base of widely adapted genotypes Kauz, Attila, Baviacora, and Pastor, with genetic contributions from other parents including synthetic wheat. We recommend within country multilocation analysis of trial performance for a crop season to identify lines suiting particular or different locations within a country. The immediate feedback on GE interaction will also help in breeding lines for countries having substantial variation across locations and years.     

Environmental and genetic determination of protein content and grain yield in durum wheat under Mediterranean conditions

The unpredictability of the Mediterranean climate causes fluctuations in wheat yield and quality, but offers the opportunity for obtaining high‐quality durum wheat in terms of grain protein content. Twenty‐five durum wheat genotypes were grown under irrigated and rainfed conditions at each of two latitudes in Spain during 1998 and 1999. Differences between latitudes in grain protein content and chlorophyll content in the flag leaf were attributable to nitrogen fertilization management. Cycle length until anthesis was less affected by the environment than grain‐filling duration, and was longer under irrigated conditions than in the rainfed sites. A negative asymptotic curve was the best equation to fit the relationship between yield and protein content, suggesting that yield improvements in fertile environments may be attained with negligible reductions in protein content. ‘Jabato’, ‘Waha’, ‘Lagost‐3’, ‘Massara‐1’ and ‘Vit?on’ showed medium to high yield, yield stability and high protein content. Chlorophyll content in the flag leaf, measured at anthesis with the soil‐plant analysis development (SPAD) portable field unit, may be useful for the fast and cheap detection of durum wheat genotypes with high grain protein content in drought‐stressed Mediterranean environments.     

Analysis of adaptation of barley,triticale, durum and bread wheat under Mediterranean conditions

Summary Yield data obtained from a comparative small grain cereals trial, grown for five consecutive growing seasons at a total of 23 environments in Cyprus, were subjected to regression analysis. Within each environment, yield trials consisted of a standard set of three cultivars or elite lines of barley, triticale, durum and bread wheat. The regression coefficient (b) of crop mean on the environmental index (I) and the mean square deviation from regression (sd²) were calculated for each crop. Each crop tended to have its own characteristic value of sd² and its magnitude was an excellent indicator of specific crop-environment interaction. The causes of large sd², for two of the four crops, were the susceptibilith of barley to lodging, when favourable conditions were encountered at high yielding environments, and triticale dependence on late season precipitation. Durum wheat and triticale had an average response to different yielding environments (b>1.19) and both were significantly different from those of bread wheat (1.08) and barley (0.54). Hence, barley, bread and durum wheat are specifically adapted to low, average and high yielding Mediterranean environments, respectively. The cultivation of triticale at the expence of durum wheat is not feasible. Furthermore, interactions between crops and environments demonstrated by the regression parameters, should constitute the basis for decision making, regarding crop adaptation in a region. The average yield in all environments should not be considered as a proper criterion for adaptation. In this study, triticale had a similar mean grain yield (3,842 kg/ha) to that of bread wheat, but was significantly higher yielding than barley or durum wheat (5 and 7%, respectively).     

Durum wheat varieties in N‐deficient environments and organic farming: a comparison of yield,quality and stability performances

Stability and reliability of yield and quality for 15 durum wheat genotypes (old and modern) were evaluated in a 5‐year experiment (southern Italy) in organic farming. Genotypes were grown at two N levels (0 and 80 kg/ha), with the aim of evaluating ‘genotype × environment’ (GE) interactions and their role on genotype selection in N‐limited environments. Several approaches to stability were used, within the frame of mixed models and additive main effects and multiplicative interaction analysis, and their validity in stressful conditions and organic farming was discussed. Especially for protein and gluten content, results indicate high environmental variability and the presence of crossover ‘N × environment’ interactions, which supports the need for specific breeding programmes in N‐deficient environments. The average response was strongly affected by N availability (on average, yield was 2.95 and 3.42 t/ha, protein content was 11.6% and 12.85%, gluten content was 8.55% and 9.92%, respectively, at 0 and 80 kg N/ha), and few genotypes gave high yield and quality at both fertilization levels. Only ‘Gargano’ and ‘Fortore’ showed a good inter‐year stability at both N levels. The old cultivars gave minimal responsiveness to increased N input, but gave good results in limiting conditions, indicating that they may play a role in organic farming.     

Evaluation of Genotype × Environment Interactions in Chinese Spring Wheat by the AMMI Model, Correlation and Path Analysis

An understanding of the characteristics of crop varieties and advanced lines could help improve their cultivation and to further enhance their potential. The objectives of this study were to estimate the genotype (G), environment (E) and genotype × environment (GE) interactions on the grain yield of Chinese spring wheat genotypes in 2000 and 2001 by the additive main effects and multiplicative interaction (AMMI) model, and to evaluate the relationships between yield and its components by correlation and path analysis. Grain yield varied from 3.9 to 5.2 t ha^?1, among which SW8188 had the highest yield performance, followed by 58769‐6 and Chuannong 16. Three interaction principal components (IPC) accounted for a total of 79.99 % and 72.96 % of the interactions with 41.05 % and 52.08 % for the corresponding degrees of freedom in 2000 and 2001, respectively. When IPC3 was significant, the stability coefficient D_i was more useful in the evaluation of the stability of each genotype. The estimates of D_i in the 2 years indicated that the D_i values varied between genotypes and years. The D_i values ranged from 1.804 to 14.665 and 2.497 to 12.481 in 2000 and 2001 respectively. The suitable locations (environments) for all genotypes were characterized. These results would be useful for improving the Chinese spring wheat cultivation and improvement.     

Yielding ability, yield stability, and quality of exotic durum wheat germplasm in Sicily

A set of 20 accessions of durum wheat (Triticum turgidum L., durum group), which mostly included exotic landraces from various Mediterranean countries, and four Italian improved varieties were evaluated for grain yield in five environments and for quality traits in two environments of Sicily. Glutenin composition was also assessed electro-phoretically on six seeds per entry. The cultivars differed (P ≤ 0.01) for yield, protein content and sodium dodecyl sulphate (SDS-sed) sedimentation volume, in the presence of significant (P ≥ 0.001) genotype-environment interaction effects. These effects were large for yield and moderately large for protein content relative to genotype main effects following estimation of variance components. Most exotic cultivars yielded comparably with, and some of them showed greater stability than, the best yielding Italian variety 'Simeto. Some exotic cultivars combined outstanding yield, protein content and SDS-sedimentation values and represent therefore extremely valuable germplasm sources to broaden the local genetic base. The glutenin composition LMW-2 and HMW 7 + 8 was positively associated with gluten quality. Six entries showed heterogeneous electrophoretic patterns, suggesting the presence of different genotypes within the cultivar.