Genotype × environment interaction and yield stability in a cowpea-based cropping system

Twenty-four cowpea genotypes were evaluated under sole cropping or additive series intercropping with sorghum from 2004 to 2005 at four sites representative of the Guinea and Sudan savannah ecologies in Ghana. The aim was to determine whether cowpea breeding programs that emphasize selection under sole-crop conditions have the potential to produce cultivars that are effective under additive series intercropping. Genotype × cropping systems interaction was significant for days to 50% flowering but not for grain yield, biomass and other studied traits. Genotypic yield reaction to cropping systems indicated that bridging the yield gap between sole cropping and intercropping systems is best addressed by agronomic interventions that reduce stress on intercrop cowpea rather than by selecting for specifically adapted genotypes for intercropping. Significant genotype × environment interactions were observed for all traits when data was pooled over cropping systems. Partitioning of the genotype × environment interaction variance indicated that days to 50% flowering was dominated by heterogeneity of genotypic variance, whereas genotype × environment interactions for grain yield and biomass was mainly due to imperfect correlations. Large differences in genotypic yield stability were observed as estimated by the among-environment variance, regression of yield on the environmental index, Kataoka’s index, and by partitioning of genotype × environment interaction sum of squares into components attributable to each genotype. The results suggest that in regions where genotype × environment interaction for yield frequently causes re-ranking across environments, genotypes with the least contribution to the interaction sum of squares are likely to be most productive. On the whole, the results support the contention that breeding under sole-crop conditions has the potential to produce cultivars effective under intercropping conditions.     

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.     

Genotype by environment interactions (GEIs) for barley grain yield under salt stress condition

Changes in the relative genetic performance of genotypes across environments are referred to as genotype × environment interactions (GEIs). GEIs can affect barley breeding improvement for salt tolerance because it often complicates the evaluation and selection of superior genotypes. The present study evaluated the GEIs over 60 barley genotypes for yield components and grain yield in six salinity environments in North Delta, Egypt. Data were analyzed using the additive main effects and multiplicative interaction (AMMI) and Tai’s stability parameters. GEIs effects on yield explained 20.3, 20.1, 14.6, and 33.0% of the total variation besides, the first two principal components account for 67.3, 56.3, 64.3, and 83.7% of the explained variance in the four sets, respectively. Six genotypes namely G-4, G-7, G-20, G-34, G-36, and G-39 were found to be most stable and high yielding across environments (GY >2.00 t ha^-1), and located close to zero projection onto the AEC ordinate. Tai’s stability parameters demonstrated that these genotypes were more responsive to the environmental changes. The genotypes G-50 and G-53 showed perfect/static stability (α = -0.95, -0.91, respectively). In contrast, the genotype; G-36 had α = 0 and λ = 1.10, indicating parallel with the environmental effects followed by G-44. Overall, we found that GEIs for grain yield are highly significant in all sets, suggesting that responded differently across environments. This interaction may be a result of changes in genotypes’ relative performance across environments, due to their differential responses to various abiotic factors.     

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.     

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 × environment interactions for seed yield in rainfed winter safflower (Carthamus tinctorius L.) multi-environment trials in Iran

Evaluation of genotype × environment interaction (GEI) is an important component of the variety selection process in multi-environment trials. The objectives of this study were first to analyze GEI on seed yield of 18 spine safflower genotypes grown for three consecutive seasons (2008–2011) at three locations, representative of rainfed winter safflower growing areas of Iran, by the additive main effects and multiplicative interaction (AMMI) model, and second to compare AMMI-derived stability statistics with several stability different methods, and two stability analysis approaches the yield-stability (Ysi) and the GGE (genotype + genotype × environment) biplot that are widely used to identify high-yielding and stable genotypes. The results of the AMMI analysis showed that main effects due to genotype, environment, and GEI as well as first six interaction principle component axes (IPCA1 to 6) were significant (P < 0.01). According to most stability statistics of AMMI analyses, genotypes G5 and G14 were the most stable genotypes across environments. According to the adjusted stability variance (s²), the high-yielding genotype, G2, was unstable due to the heterogeneity caused by environmental index. Based on the definition of stable genotypes by regression method (b = 1, S _d ²  = 0), genotypes G11, G9, G14, G3, G12 and G13 had average stability for seed yield. Stability parameters of Tai indicated that genotype G5 had specific adaptability to unfavorable environments. The GGE biplot and the Ysi statistic gave similar results in identifying genotype G2 (PI-209295) as the best one to release for rainfed conditions of Iran. The factor analysis was used for grouping all stability parameters. The first factor separated static and dynamic concepts of stability, in which the Ysi and GGED (i.e., the distance from the markers of individual genotypes to the ideal genotype) parameters had a dynamic concept of stability, and the other remaining parameters had static concept of stability.     

Genotype-environment interactions and phenotypic stability analyses of linseed in Ethiopia

Seed yield of 10 linseed genotypes, tested in a randomized block design with four replications across 18 environments of Ethiopia was analysed using different stability models. The objectives were to assess genotype‐environment (G‐E) interactions, determine stable genotypes, and to compare the stability parameters. Year by location and location variability were the dominant source of interactions. The stability analyses identified ‘R12‐N10D’, ‘Chilalo’ and ‘P13611’ב10314D’ as more stable genotypes, while ‘R11‐N1266’, ‘R10‐N27G’ and ‘R12‐D24C’ were specifically adapted to some environments. The highly significant rank correlations found among the deviations from regression, additive main effects multiplicative interaction, stability values, coefficients of determination, and stability variances indicated their close similarity and effectiveness in detecting stable genotypes over a range of Ethiopian environments.     

Genotype by environment interactions in barley (<Emphasis Type="Italic">Hordeum</Emphasis><Emphasis Type="Italic"> vulgare</Emphasis> L.): different responses of landraces,recombinant inbred lines and varieties to Mediterranean environment

Genotype-by-environment interactions (GEIs) can affect breeding progress because they often complicate the evaluation and selection of superior genotypes. This drawback can be reduced by gaining insights into GEI processes and genotype adaptation. Here, we have studied the GEIs over a set of 24 barley genotypes that were grown across six environments (location-by-year combinations) in Sardinia, Italy. Three groups of genotypes were analysed: barley landraces (LANs), recombinant inbred lines (RILs), and commercial varieties (VARs). The additive main effects and multiplicative interaction (AMMI) model was used for data analysis, and results evidenced no significant differences in grain yield averages for the 24 genotypes. However, there was a relevant GEI for yield mainly between two of the six environments (one characterised by warm pre-anthesis period and high spring rainfalls, and the other characterised by opposite features) and two groups of genotypes (VAR and LAN). Moreover, a negative trade-off between yield levels of genotypes was seen when the barley genotypes were grown in the contrasting environments. Overall, intermediate GEI levels were seen for RILs in comparison to LANs and VARs, and some of the RILs provided valuable yield levels (e.g. RILs 23 and 52). The results thus show the potential usefulness of LANs as a genetic resource for breeding, e.g. to obtain genotypes adapted to Mediterranean environments, such as the RILs analysed in this study. Most of the actual work was carried out when the first author was a PhD student in ‘Agro-meteorology and ecophysiology of agricultural and forest systems’ and she was affiliated to Dipartimento di Scienze Agronomiche e Genetica Vegetale Agraria, Università degli Studi di Sassari, Via E. de Nicola, Sassari 07100, Italy.     

Impact of different environments in Europe on yield and quality of sugar beet genotypes

Sugar beet (Beta vulgaris L.) yield and quality are determined by genotype and environment. This study aimed at analysing the relative importance of the environment for yield and quality of sugar beet genotypes and at assessing parameters which could give essential improvement for beet quality if included as additional selection criteria. For that purpose, root yield and quality (sugar, K, Na, amino N, total soluble N, betaine, glutamine, invert sugar, raffinose) of 9 sugar beet genotypes were investigated in 52 environments (25 sites in 2003 and 27 sites in 2004) in randomised field trials across Europe.The environment accounted for about 80% of the total variance for all parameters. Effects of the tested genotypes were larger for the content of betaine (8.5%) and sugar (7.6%) than for other parameters (1–5%). With the exception of invert sugar and amino N, the genotype by environment interaction was about 3% and thereby lower than the main effect of genotypes. Interactions resulted in an increase of the differences between genotypes which can be used to select genotypes in the most discriminating environments. The response of genotypes in sugar content was contrasting to other parameters and points to a physiological limit for sugar storage at about 20%. As no crossover interaction occurred for root yield or any quality parameter, there seemed to be no specific suitability of the tested genotypes to certain environmental stress conditions. This is probably due to the fact that the harvested beetroot is a vegetative storage organ and has no growth stages susceptible to unfavourable environmental conditions such as flowering and grain filling which are important for final yield in cereals. Invert sugar showed the largest relative differences between genotypes which were strongly enhanced in southern and some south-eastern European environments. Because of its importance during processing, considering invert sugar in breeding could improve technical quality for processing considerably for those extreme environments.     

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.     

Variability and Correlations in Grain Sorghum Genotypes (Sorghum bicolor [L.] Moench) Under Drought Conditions at Different Stages of Growth

Twenty-two genotypes of grain sorghum were grown under drought conditions by omitting one irrigation during stages of before flowering period, kernel filling period, and physiological maturity period at Assiut Univ. Farm in 1987 and 1988 seasons. The results obtained revealed that considerable variation existed among genotypes for all the studied traits. The most effective moisture stress treatment in reducing grain yield, panicle weight and plant height was during flowering stage. While 1000-kernel weight was much affected by moisture stress during grain filling period. The genotype x year interaction (σ²_gy) was large compared to genotype x irrigation treatment (σ²_gl) indicated that genotypes responded differently when they were grown from year to year. The genotypic variance (σ²_g) for all traits were large reflecting the importance of genetic variability. Both phenotypic and genotypic correlations among traits showed that plant height and 1000-kernel weight were highly correlated with grain yield, while leaf area index was low associated with plant height.     

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.     

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.     

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.     

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.     

Characterization of genotype by environment interactions in soybean breeding programmes of southeast Europe

Southeast Europe is Europe's major production area of soybean of maturity groups (MG) 0, I and II, but genotype by environment interaction (GEI) for soybean traits in this region is still not characterized. The objective of this study was to characterize GEI in soybean grown in Croatia for seed yield, protein and oil content. Fourteen soybean cultivars of MG 0 and I were evaluated at 15 environments. In the combined anova , genotypes, environments and GEI were significant. All three seed traits were more affected by year than by location and seed yield and protein content were more sensitive to environmental changes than oil content. Genotype by environment interaction is generally of less importance than effects of genotypes and years, especially for oil content. High‐yielding genotypes had values of regression coefficient (b) close to 1, indicating that they were not responsive to extreme environments, either positive or negative. Means and b values were not correlated for yield and negatively correlated for protein and oil content, probably because of low‐input practice in the region. The absence of recognizable differences in means, b values and principal component scores of ammi analysis between two MGs for all traits indicated that soybean cultivars of MG 0 may be recommended for growing in the region because they responded similarly to environmental changes as do full‐season genotypes of MG I.     

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.     

Effects of Early and Late Harvest on Agronomic Performance and Stability of Late Blight Resistant (R-gene Free) Potato Genotypes

Late blight is an important constraint to potato production and genotype resistance is an effective disease control mesure. Ten late blight resistant potato genotypes (R-gene free) were assessed for yield performance and stability at early (90 days) and late harvest (120 days) at two locations in Kenya during two years. Significant differences (P ≤ 0.05) in area under disease progress curves (AUDPC) were detected among potato genotypes. Resistant genotypes free of R-genes had significantly (P ≤ 0.05) higher yield at late than early harvest, perhaps due to increased tuber bulking period. The rank of genotypes for AUDPC, late blight resistance, and tuber yield varied across seasons and locations (environment). Additive main effects and multiplicative interaction (AMMI) analysis of tuber yield and late blight resistance resulted in significant (P ≤ 0.05) effects of genotypes (G) and environments (E). The proportion of genotypic variance was larger than the environmental variance and the G × E interactions. For tuber yield, the G, E, and G × E interactions accounted for 42.9, 39.6 and 17.5%; and 53.4, 29.7, and 16.9% at early and late harvests, respectively. For AUDPC, G, E, and G × E accounted for 80.2, 5.0, and 14.8%; as well as 82.3, 4.6, and 13% for early and late harvests, respectively. The resistance of potato genotypes without R-genes varied. Selective deployment of resistant genotypes can improve potato tuber yield.     

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.     

Analysis of the genotype-by-environment interaction of spring barley tested in the Nordic Region of Europe: Relationships among stability statistics for grain yield

Variation in agronomic and quality characteristics was investigated in 220 Nordic spring barley cultivars across distinct environments (6 locations during 3 years) in the Nordic Region of Europe. The objectives of this research were to determine the importance of the genotype by environment interaction in all characteristics evaluated and to establish the relationship among different stability statistics for grain yield. Combined analysis of variance across locations indicated that both environments and genotype by environment interactions influenced significantly the cultivar phenotypes for all characteristics, irrespective of their type (row number) or earliness. The first two interaction principal component axes of the additive-main-effects-and-multiplicative-interaction (AMMI) model accounted together between 35% and 75% of the total genotype-by-environment interaction for all characteristics. Grain yield was, on average, higher in 2-row than in 6-row cultivars, which were significantly earlier in heading and grain maturity than the former. However, in some of the most northern locations, 6-row barley cultivars significantly outyielded on average 2-row barley lines. The genotype by location interaction variance (σ² _GL) accounted by each genotype was significantly associated to the deviation from regression (Tai's λ) while the coefficient of regression (β) was significantly correlated to the IPCA1 and IPCA2 of the AMMI model in 2-row, 6-row and early barley cultivars. This revised version was published online in August 2006 with corrections to the Cover Date.