Combining genotype, environment and attribute variables in regression models for predicting the cell-means of multi-environment cultivar trials

 The main objectives of this study were: (1) to develop models which combine variables of genotype, environment and attribute in regression models (GEAR) for increasing the accuracy of predicted cell-means of the genotype×environment two-way table, and (2) to compare GEAR models with the additive main effects and multiplicative interaction (AMMI) model. GEAR models were developed by regressing the observed values on principal components of genotypes (PCG) and environments (PCE). Genetic and environmental attributes were also added to the GEAR models. GEAR and AMMI models were applied to multi-environment trials of triticale (trial 1), maize (trial 2) and broad beans (trial 3). The random data-splitting and cross-validation procedure was used and the root mean square-predicted difference (RMSPD) was computed to validate each model. GEAR models increased the accuracy of predicted cell-means. Attribute variables, such as soil pH, rainfall, altitude and class of genotype, did not improve the best GEAR model of trial 1, but they increased the predictive value of other models. Two iterations of the computer program further refined the best GEAR model. Based on the RMSPD criterion, GEAR models were as good as, or better than, some AMMI truncated models for predicting cell-means. The approximate accuracy gain factors (GF) of the best GEAR model over the raw data were 2.08, 3.02 and 2.22, for trials 1, 2 and 3, respectively. The GF of the best AMMI model were 1.74, 2.28 and 2.32 for trials 1, 2 and 3, respectively. The analysis of variance of the predicted cell means showed that the genotype×environment interaction (GEI) variance was reduced by about 20% in trial 1 and 81% in trial 2. A bias associated with the predicted cell reduced the GEI variability. Advantages of using GEAR models in muti-environment cultivar trials are that they: (1) increase the precision of cell-mean estimates and (2) reduce the GEI variance and increase trait heritability. Received: 15 August 1997 / Accepted: 28 October 1997     

Stability of Sweet Potato Cultivars to Alternaria Leaf and Stem Blight Disease

Alternaria leaf petiole and stem blight is an economically important disease of sweet potato ( Ipomoea batatus L.) in tropical and sub-tropical environments. Published research on cultivar resistance to the sweet potato disease is limited. To evaluate cultivar reaction and stability to the disease, multi-location and replicated experiments were established in 12 environments in Uganda. Disease severity (area under disease progress curves – AUDPC), and cultivar root yield were also assessed. Significant differences (P < 0.001) in AUDPC were detected among cultivars. Mean AUDPC ranged from 46.3 (Araka Red) to 78.4 (New Kawogo) across locations and seasons and the genotypes Araka Red and Tanzania had the lowest disease values. The location and season effects accounted for 67.1% and 7.5% of the total variance of AUDPC recorded among cultivars. The ranking of cultivars based on predicted AUDPC from Additive Main Effect and Multiplicative Interactive model (AMMI) showed that the NASPOT 1, the susceptible check, and New Kawogo were most susceptible to the disease in 11 of the 12 environments. Low and stable disease was consistently recorded and predicted on NASPOT 3 and the landrace cultivars Tanzania, Dimbuca, and Araka Red across environments. These results suggest that landrace cultivars had relative stability to the disease and wide adaptation across environments. These results suggest that AMMI statistical model and other multivariate techniques can be utilized for prediction of Alternaria disease stability in these locations.     

Potential gain from selection for yield stability in two grain sorghum populations

Summary Maximum yield under highly unpredictable environments should be associated with selection of genotypes with superior performance across good and poor environments. Several stability parameters have been proposed to identify superior genotypes over a wide range of environments. None of these has been used as selection criteria, however, because of their low heritability. The objective of the study presented here was to compare the relative efficiency of predicted gain from indirect selection among three stability parameters: the coefficient of regression (b), deviation from regression (S _d ² ), and principal components scores (PC) from the AMMI model; two indices including mean yield and a stability parameter; and three indices involving yield at the best, the worst, and an intermediate environment. Two hundred S₁ families from each of two sorghum populations (TP24D and KP9B) were evaluated at four dry-land evironments over 2 years. The low heritability estimates and the low genetic correlation between the various stability parameters and mean yield resulted in their low relative efficiency as indirect selection criteria for high yield across environments. However, when the parameters were combined with mean yield over all to create indices, the relative efficiency increased for all the environments. In terms of resource allocation, these indices were not as efficient as mean productivity, rank summation, and selection index that involved fewer environments in their estimation.Contribution no. 9820 of Agricultural Research Division, Univ. of Neb. and no. 92-203-J of Kansas Exp. Stn.     

甘蔗品种主要性状的基因型与环境及其互作效应分析

用AMMI模型双标图对国家第六轮甘蔗品种区域试验5个试点的12个甘蔗品种试验数据进行分析,研究甘蔗区试中不同品种的产量稳定性问题。结果表明,参试品种的6个产量性状在品种间和地点间差异显著,品种与地点的互作效应差异显著;FN30、YG16蔗茎产量和含糖量高,稳定性强,属于高产、稳产性较好的品种。AMMI模型很好地解释了甘蔗品种产量性状的基因型效应、环境效应和GE互作效应。     

AMMI adjustment for statistical analysis of an international wheat yield trial

Summary Multilocation trials are important for the CIMMYT Bread Wheat Program in producing high-yielding, adapted lines for a wide range of environments. This study investigated procedures for improving predictive success of a yield trial, grouping environments and genotypes into homogeneous subsets, and determining the yield stability of 18 CIMMYT bread wheats evaluated at 25 locations. Additive Main effects and Multiplicative Interaction (AMMI) analysis gave more precise estimates of genotypic yields within locations than means across replicates. This precision facilitated formation by cluster analysis of more cohesive groups of genotypes and locations for biological interpretation of interactions than occurred with unadjusted means. Locations were clustered into two subsets for which genotypes with positive interactions manifested in high, stable yields were identified. The analyses highlighted superior selections with both broad and specific adaptation.     

Grain Yield and Charcoal Rot Resistance Stability in Common Beans under Terminal Drought Conditions

Charcoal rot (Macrophomina phaseolina) is a major disease of beans (Phaseolus vulgaris L.) in Mexico. The use of germplasm combining high‐yield stability with resistance to drought and charcoal rot could reduce damage from this disease. In this study, we compared the Eberhart and Russell method and the Additive Main Effect and Multiplicative Interaction (AMMI) model plus biplot analysis for measuring grain yield (GY) and charcoal rot resistance (CHRR) stabilities in 98 F_{8 : 10} recombinant inbred lines (RILs) derived from a cross between bean adapted to the tropics (BAT) 477 (resistant) × Pinto UI‐114 (susceptible). Experiments were conducted from 2007 to 2009 in Isla, Cotaxtla, Río Bravo and Díaz Ordaz, México, under irrigated or terminal drought conditions. anova detected significant differences (P ≤ 0.05) in GY and CHRR among environments, genotypes and genotype × environment interactions (GEI). Most RILs showed good responses to unfavourable environments based on GY (48) and CHRR (40). AMMI anova s for both traits showed that all sources of variation in the model accounted for approximately 49% of the total squared sum. For the first principal component (PC1), we found 13 RILs that were stable for GY, and for the second (PC2), we found 9 that were stable for GI. For CHRR, we detected 14 stable RILs (PC1) and eight (PC2). Biplot analysis showed the largest vectors for Díaz Ordaz (irrigated and drought, 2008), where the highest and most variable GYs were detected. The shortest vectors were found in Isla (drought, 2007) and Río Bravo (irrigated and drought, 2008), where the lowest and least variable GY were found. We found differential responses of RILs to locations, years and soil humidity conditions as well as significant GEI based on GY and CHRR. The two methods were complementary, and both gave us information to select stable, high‐yield germplasm associated with resistance to charcoal rot disease.     

Genetic analyses using GGE model and a mixed linear model approach,and stability analyses using AMMI bi-plot for late-maturity alpha-amylase activity in bread wheat genotypes

Low falling number and discounting grain when it is downgraded in class are the consequences of excessive late-maturity α-amylase activity (LMAA) in bread wheat (Triticum aestivum L.). Grain expressing high LMAA produces poorer quality bread products. To effectively breed for low LMAA, it is necessary to understand what genes control it and how they are expressed, particularly when genotypes are grown in different environments. In this study, an International Collection (IC) of 18 spring wheat genotypes and another set of 15 spring wheat cultivars adapted to South Dakota (SD), USA were assessed to characterize the genetic component of LMAA over 5 and 13 environments, respectively. The data were analysed using a GGE model with a mixed linear model approach and stability analysis was presented using an AMMI bi-plot on R software. All estimated variance components and their proportions to the total phenotypic variance were highly significant for both sets of genotypes, which were validated by the AMMI model analysis. Broad-sense heritability for LMAA was higher in SD adapted cultivars (53%) compared to that in IC (49%). Significant genetic effects and stability analyses showed some genotypes, e.g. ‘Lancer’, ‘Chester’ and ‘LoSprout’ from IC, and ‘Alsen’, ‘Traverse’ and ‘Forefront’ from SD cultivars could be used as parents to develop new cultivars expressing low levels of LMAA. Stability analysis using an AMMI bi-plot revealed that ‘Chester’, ‘Lancer’ and ‘Advance’ were the most stable across environments, while in contrast, ‘Kinsman’, ‘Lerma52’ and ‘Traverse’ exhibited the lowest stability for LMAA across environments.     

Best Linear Unbiased Prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis

Multilocation trials are often used to analyse the adaptability of genotypes in different environments and to find for each environment the genotype that is best adapted; i.e. that is highest yielding in that environment. For this purpose, it is of interest to obtain a reliable estimate of the mean yield of a cultivar in a given environment. This article compares two different statistical estimation procedures for this task: the Additive Main Effects and Multiplicative Interaction (AMMI) analysis and Best Linear Unbiased Prediction (BLUP). A modification of a cross validation procedure commonly used with AMMI is suggested for trials that are laid out as a randomized complete block design. The use of these procedure is exemplified using five faba bean datasets from German registration trails. BLUP was found to outperform AMMI in four of five faba bean datasets.     

Genotype by environment interaction for 450-day weight of Nelore cattle analyzed by reaction norm models

Genotype by environment interactions (GEI) have attracted increasing attention in tropical breeding programs because of the variety of production systems involved. In this work, we assessed GEI in 450-day adjusted weight (W450) Nelore cattle from 366 Brazilian herds by comparing traditional univariate single-environment model analysis (UM) and random regression first order reaction norm models for six environmental variables: standard deviations of herd-year (RRMw) and herd-year-season-management (RRMw-m) groups for mean W450, standard deviations of herd-year (RRMg) and herd-year-season-management (RRMg-m) groups adjusted for 365-450 days weight gain (G450) averages, and two iterative algorithms using herd-year-season-management group solution estimates from a first RRMw-m and RRMg-m analysis (RRMITw-m and RRMITg-m, respectively). The RRM results showed similar tendencies in the variance components and heritability estimates along environmental gradient. Some of the variation among RRM estimates may have been related to the precision of the predictor and to correlations between environmental variables and the likely components of the weight trait. GEI, which was assessed by estimating the genetic correlation surfaces, had values < 0.5 between extreme environments in all models. Regression analyses showed that the correlation between the expected progeny differences for UM and the corresponding differences estimated by RRM was higher in intermediate and favorable environments than in unfavorable environments (p < 0.0001).     

Variation in kafirin and alcohol-soluble glutelin chromatograms of sorghum inbred lines revealed by reversed-phase high-performance liquid chromatography

Summary Separations of kafirin and alcohol soluble glutelin proteins by reversed-phase high-performance liquid chromatography (RP-HPLC) from 7 inbreds and one hybrid of sorghum [Sorghum bicolor (L.) Moench] and one source of Johnsongrass [Sorghum halapense (L.) Pers.] were compared. Objectives were to assess the stability of protein profiles for seed sources produced at different locations and in different environments to examine the potential of RP-HPLC to provide genotypic profiles for sorghum. Analyses of variance data showed that levels of variation due to environments and locations were small; the majority of variation (93%) was among genotypes. Associations among inbreds revealed by multivariate and cluster analysis showed similarity with those that would be expected on the basis of pedigree. A chi-square analysis showed no deviation in the hybrid profile from the expected 21 ratio of peaks from the female and male inbred parents, respectively. Improvements in the ability to correctly assign common peaks are necessary before associations among numerous sorghum genotypes can be reliably demonstrated by analysis of data from reversed-phase high-performance liquid chromatography (RP-HPLC).     

among-Environment Heteroscedasticity and Genetic Autocorrelation: Implications for the Study of Phenotypic Plasticity

The impact of among-environment heteroscedasticity and genetic autocorrelation on the analysis of phenotypic plasticity is examined. Among-environment heteroscedasticity occurs when genotypic variances differ among environments. Genetic autocorrelation arises whenever the responses of a genotype to different environments are more or less similar than expected for observations randomly associated. In a multivariate analysis-of-variance model, three transformations of genotypic profiles (reaction norms), which apply to the residuals of the model while preserving the mean responses within environments, are derived. The transformations remove either among-environment heteroscedasticity, genetic autocorrelation or both. When both nuisances are not removed, statistical tests are corrected in a modified univariate approach using the sample covariance matrix of the genotypic profiles. Methods are illustrated on a Chlamydomonas reinhardtii data set. When heteroscedasticity was removed, the variance component associated with the genotype-by-environment interaction increased proportionally to the genotype variance component. As a result, the genetic correlation r(g) was altered. Genetic autocorrelation was responsible for statistical significance of the genotype-by-environment interaction and genotype main effects on raw data. When autocorrelation was removed, the ranking of genotypes according to their stability index dramatically changed. Evolutionary implications of our methods and results are discussed.     

Environment characterization as an aid to wheat improvement: interpreting genotype-environment interactions by modelling water-deficit patterns in North-Eastern Australia

Genotype-environment interactions (GEI) limit genetic gain for complex traits such as tolerance to drought. Characterization of the crop environment is an important step in understanding GEI. A modelling approach is proposed here to characterize broadly (large geographic area, long-term period) and locally (field experiment) drought-related environmental stresses, which enables breeders to analyse their experimental trials with regard to the broad population of environments that they target. Water-deficit patterns experienced by wheat crops were determined for drought-prone north-eastern Australia, using the APSIM crop model to account for the interactions of crops with their environment (e.g. feedback of plant growth on water depletion). Simulations based on more than 100 years of historical climate data were conducted for representative locations, soils, and management systems, for a check cultivar, Hartog. The three main environment types identified differed in their patterns of simulated water stress around flowering and during grain-filling. Over the entire region, the terminal drought-stress pattern was most common (50% of production environments) followed by a flowering stress (24%), although the frequencies of occurrence of the three types varied greatly across regions, years, and management. This environment classification was applied to 16 trials relevant to late stages testing of a breeding programme. The incorporation of the independently-determined environment types in a statistical analysis assisted interpretation of the GEI for yield among the 18 representative genotypes by reducing the relative effect of GEI compared with genotypic variance, and helped to identify opportunities to improve breeding and germplasm-testing strategies for this region.     

Seedling and adult stage resistance to net form of net blotch (NFNB) in spring barley and stability of adult stage resistance to NFNB in Morocco

This study was conducted to identify stable resistance to net form of net blotch (NFNB) in spring barley in Moroccan environments. Seedling resistance to NFNB was evaluated by inoculating 336 barley genotypes with two NFNB isolates LDNH04Ptt-19 and TD-10 in the greenhouse. These genotypes were evaluated for adult plant resistance to NFNB under seven environments in Morocco in 2015 and 2016. The disease severity was estimated at GS 77–87 on barley leaves using a double-digit scale. To investigate stability of resistance, 149 barley genotypes were subjected to AMMI analysis. At the seedling stage, differential responses of barley genotypes to different NFNB isolates were identified, whereas genotypes had variable stability to NFNB resistance at the adult stages. Five genotypes, AM-68, AM-95, AM-250, AM-267 and AM-322, were resistant to both NFNB isolates at the seedling stage. There were significant (p < .001) effects of genotype (G) and G × E interaction on NFNB severity for barley genotypes at the adult stage. The principal components, IPCA1 and IPCA2, accounted for 48.4% and 18.7% variation for NFNB severity, respectively. The AMMI stability values (ASVs) ranged from 0.01 to 15.5, and fifty-nine barley genotypes had stable responses (ASV ≤ 0.05) across all seven environments. Specifically, two stable genotypes, AM-187 and AM-244, had lower mean NFNB severities across all environments, suggesting a quantitative resistance in these genotypes. Divergent environmental responses of NFNB severity were measured in Sidi El Ayedi 2015 and Sidi Allal Tazi 2016, suggesting that these environments may be suitable to capture resistance to diverse pathotypes. These stable genotypes are valuable resources for introgression of both qualitative resistance and quantitative resistance to NFNB in future.     

Use of the Maximum Likelihood Method in the Analysis of Phenotypic Stability

The stability variance is an important estimator of phenotypic stability of genotypes. It may be estimated by method of moments and by maximum likelihood. We demonstrate by Monte Carlo simulation that, given a sufficient number of environments, maximum likelihood estimates (MLE's) are slightly better if ranking of genotypes is the experimenter's major aim. A likelihood ratio test is available for different hypotheses.     

Yield Results and Stability Analysis from the Sorghum Regional Biomass Feedstock Trial

Sorghum [Sorghum bicolor (L.) Moench] is one of four herbaceous dedicated bioenergy crops the U.S. Department of Energy identified as critical to annually produce one billion tons of dry biomass. Of these four crops, sorghum is unique as it is a drought-tolerant, annual crop established from seed that is readily tractable to genetic improvement. The purpose of this study was to assess the yield potential and stability of sorghums grown across diverse production environments in the USA. For this study, six sorghum genotypes (one cultivar, five hybrids) were grown in yield trials in seven locations in six states for 5 years (2008–2012). Variation in dry and fresh yield was attributable to not only genotypes, but also to the effects of year, location, and year × location. Even with the highest yielding genotype, environmental conditions were a major factor in determining the yield in a given year. This variability affects the consistency of the biomass supply for ethanol production. In general, the southeastern USA had the highest mean yields for fresh weight and dry weight, indicating that this area may be the most reliable for biomass production. A significant variation was detected among genotypes for fresh weight, dry weight, moisture content, and brix, revealing that sufficient variation within sorghum exists for continued improvement and that certain hybrids are more tractable for biomass/bioenergy production. With dedicated bioenergy sorghum germplasm and proper production environments, sorghum will be a valuable tool in the goal of the sustainable production of one billion tons of dry biomass each year in the USA.     

THE CONTRIBUTION OF NEW MUTATIONS TO GENOTYPE-ENVIRONMENT INTERACTION FOR FITNESS IN DROSOPHILA MELANOGASTER

Many studies have documented the existence of genotype-environment interaction (GEI) for traits closely related to fitness in natural populations. A type of GEI that is commonly observed is changes in the fitness ranking of genetic groups (families, clones, or inbred lines) in different environments. We refer to such changes in ranking as crossing of reaction norms for fitness. A common interpretation of crossing of reaction norms for fitness is that selection favors different alleles in the different environments (i.e., that “trade-offs” exist). If this is the case, selection could maintain genetic variation, and even lead to reproductive isolation between subpopulations using different environments. Even if the same alleles are favored in every environment, however, deleterious mutations that vary in the magnitude of their effect depending on environment could cause reaction norms for fitness to cross. If deleterious mutations with environment-dependent effects are responsible for maintaining much of the variation leading to crossing of reaction norms for fitness in natural populations, it should be possible to observe crossing of reaction norms for fitness among otherwise genetically identical lines bearing newly arisen spontaneous mutations. We examined the contribution of new mutations to GEI for fitness in Drosophila melanogaster. Eighteen lines were derived from a common, highly inbred base stock, and maintained at a population size of 10 pairs for over 200 generations, to allow them to accumulate spontaneous mutations. Because of the small population size of the lines, selection against mildly deleterious mutations should have been relatively ineffective. The lines were tested for productivity (number of surviving adult progeny from a standard number of parents) in five different environmental treatments, comprising different food media, temperatures, and levels of competition. The lines showed highly significant GEI for productivity, owing largely to considerable changes in ranking in the different environments. We conclude that mutations that are deleterious on average, but whose quantitative effects depend on environment, could be responsible for maintaining much of the variation leading to crossing of reaction norms for fitness that has been observed in samples of D. melanogaster from the wild.     

Integration of statistical and physiological analyses of adaptation of near-isogenic barley lines

Seven near-isogenic barley lines, differing for three independent mutant genes, were grown in 15 environments in Spain. Genotype x environment interaction (G x E) for grain yield was examined with the Additive Main Effects and Multiplicative interaction (AMMI) model. The results of this statistical analysis of multilocation yield-data were compared with a morpho-physiological characterization of the lines at two sites (Molina-Cano et al. 1990). The first two principal component axes from the AMMI analysis were strongly associated with the morpho-physiological characters. The independent but parallel discrimination among genotypes reflects genetic differences and highlights the power of the AMMI analysis as a tool to investigate G x E. Characters which appear to be positively associated with yield in the germplasm under study could be identified for some environments.     

Genotype x environment interaction of crossover type: detecting its presence and estimating the crossover point

Genotype-environment interaction (GEI) introduces inconsistency in the relative rating of genotypes across environments and plays a key role in formulating strategies for crop improvement. GEI can be either qualitative (i.e., crossover type) or only quantitative (i.e., non-crossover type). Since the presence of crossover-type interaction has a strong implication for breeding for specific adaptation, it is important to assess the frequency of crossover interactions. This paper presents a test for detecting the presence of crossover-type interaction using the response-environment relationship and enumerates the frequency of crossovers and estimation of the crossover point (CP) on the environment axis, which serves as a cut-off point for the two environments groups where different/specific selections can be made. Sixty-four barley lines with various selection histories were grown in northern Syria and Lebanon giving a total of 21 environments (location-year combinations). Linear regression of the genotypic response on the environmental index represented a satisfactory model, and heterogeneity among regressions was significant. At a 5% level of significance, 38% and 19% of the pairs showed crossover interactions when the error variances were considered heterogeneous and homogeneous, respectively, implying that an appreciable number of crossovers took place in the case of barley lines responding to their environments. The CP of 1.64 t/ha, obtained as the CP of regression lines between the genotype numbers 19 and 31, provided maximum genotype x environment-group interaction. Across all environments, genotype nos. 59 and 12 stood first and second for high yield, respectively. The changes in the ranks of genotypes under the groups of environments can be used for selecting specifically adapted genotypes. Received: 25 January 1999 / Accepted: 16 March 1999     

Choosing probe genotypes for the analysis of genotype-environment interaction in winter wheat trials

Genotype-environment interaction was analyzed in French multi-environment wheat (Triticum aestivum L.) trials using probe genotypes and bi-additive factorial regression. Probe genotypes are specific genotypes in which the comparisons of yield components to reference values describe the most-important environmental factors that limited grain yield. The time-period until flowering was described by the deviation of kernel number from a threshold number while the grain-filling period was described by the reduction of thousand-kernel weight from a potential value. The aim of this paper was to determine the convenient number and the characteristics of probe genotypes to include in wheat breeding trials. Two sets of genotypes were used to model genotype-environment interaction: set 1 with 12 varieties tested in 18 environments and set 2 with ten lines tested in 14 environments. Set 2 was used for validation. Seven probe genotypes described the environments by providing environmental covariates, namely differences in yield components, for further analysis of interaction in set 1 and set 2. Interaction was modelled with bi-additive factorial regressions including differences in yield components. Several rounds of models were fitted to determine the optimal number of probe genotypes (i.e. environmental covariates) to introduce. From the seven probe genotypes, all the possible combinations including one to seven genotypes were studied. Significance of the combinations was tested with critical values obtained from simulations through 1,000 random permutations. Taking into account the information available on the probe genotypes, one would think that two, three or four probe genotypes would be sufficient, otherwise the number should reach four or five genotypes. In all cases, these numbers will provide models more-parsimonious than the classical AMMI model. The important information to be known on the probe genotypes prior their first multilocation experiment is: interaction pattern, earliness, and differences in yield component. Tested for the first time, a quadruplet is better than a triplet because the probability of choosing complementary genotypes increases with their number.     

Sensitivity of Resistance to Net Blotch in Barley

The aim of this study was to demonstrate various methods of analysing terminal net blotch, Pyrenophora teres Drechs. f. teres Smedeg., severity data from 15 spring barleys, Hordeum vulgare L., grown in Finnish official variety trials in five environments. The analyses have been developed and used principally by plant breeders for assessing crop yield, but lend themselves to use by plant pathologists. Pyrenophora teres is the major barley phytopathogen in Finland and improved resistance to it is sought. Joint regression analysis (JRA) and an additive main effects and multiplicative interaction (AMMI) model were used to investigate the data. Statistically significant genotype by environment (GE) interaction for resistance was indicated, and this included qualitative (crossover) interactions among genotypes over environments. A stable, non-sensitive, response to net blotch over environments, combined with a low mean score for terminal severity of the disease characterized the six-row barley 'Thule' which showed statistically significant crossover interaction only with 'Tyra'. 'Kustaa' exhibited the lowest mean terminal net blotch severity, but was relatively sensitive to net blotch. 'Arve' exhibited severe terminal net blotch in all environments, was relatively sensitive to environment and exhibited no crossover interaction with other genotypes. AMMI analysis appeared to represent a useful method for analysing these disease severity data, facilitating the selection of useful sources of resistance. Plots of AMMI-adjusted mean net blotch severities against first principal component axis (PCA) scores were informative for differentiating genotype response over environments, and are therefore potentially useful to plant pathologists and barley breeders seeking to gauge and subsequently improve the resistance status of barley to net blotch.