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Methods to evaluate wheat cultivar testing environments and improve cultivar selection protocols
Abstract
Analysis of cultivar by environment (C × E) interaction can improve efficiency of crop breeding efforts. Variety selection and recommendation based on wheat (Triticum aestivum L.) yield testing trials could possibly benefit from this type of analysis as well. The objectives of the present work were to evaluate methods to identify relevant testing environments and improve the predictive value of data from wheat cultivar yield trials in the eastern US. The data come from 32 site years of winter wheat yield trials conducted in Virginia. Biplot analysis revealed that all current testing sites were relevant and that most performed similarly within a year. The degree of relationship or dissimilarity among environments was also evaluated using straight-line distance between observations in variable space measured as the squared Euclidean distance (ED). Analysis using the ED method revealed that all environments contained the centroid and were thus representative testing environments, similar to results from the biplot analysis. Biplots were effective at identifying cultivars and testing locations that were major sources of C × E interaction. Biplots and best linear unbiased predictions (BLUPs) were used to compare cultivar performance across environments. In a separate evaluation, the ED from the centroid to a cultivar mean was used to weight past relative yields for that cultivar and increased the predictability of future yield of a cultivar in three of four seasons. Weighting by ED decreased the number of site years needed to develop confidence in the yield stability of a particular cultivar from six to three. Utilizing the BLUPs for future grain yields, predictive ability of future performance after 1 year was 40% better and overall was 25% better than that achieved by weighting with ED. Overall the BLUPS method of estimating future performance was more accurate and more reliable than weighting with ED.
... Previous studies have analyzed GEI to improve crop breeding and selection of high-yielding and stable varieties [22][23][24][25]. Generally, the interaction between the genotype and environment had made it challenging to find superior and more stable genotypes [26][27][28]. To achieve stable yield production, the development of genotypes with a consistent high yield in various environments (E) along with good grain quality is inevitable [29,30]. ...
... To achieve stable yield production, the development of genotypes with a consistent high yield in various environments (E) along with good grain quality is inevitable [29,30]. The best way to overcome this problem is to assess genotypes across a diverse set of environments over several years under different conditions [26,27,31]. ...
Salinity is a major abiotic stress affecting cereal production. Thus, tritipyrum (x. Tritipyrum), a potential novel salt-tolerant cereal, was introduced as an appropriate alternative for cereal production. The purposes of this study were to evaluate agronomic traits, yield, and yield stability of eight primary tritipyrum lines, five promising triticale lines, and four bread wheat varieties and to screen a stable yielding line. The experiments were conducted in randomized complete block designs with three replicates in three locations during four growing seasons. Analysis of variance in each environment and Bartlett’s test for the variance homogeneity of experimental errors were made. Subsequently, separate experiments were analyzed as a combined experiment. The stability of grain yield was analyzed according to Eberhart and Russell’s regression method, environmental variance, Wrick’s ecovalance, Shokla’s stability variance, AMMI, and Tai methods. Genotype × environment interactions (GEI) and environments were significant for the agronomic traits. Stability analysis revealed that combined primary tritipyrum line (Ka/b)(Cr/b)-5 and triticale 4115, 4108, and M45 lines had good adaptability in all environments. The results of the AMMI3 model and pattern analysis showed that the new cereal, tritipyrum, had the most stable response in various environments. The tritipyrum line (Ka/b)(Cr/b)-5 had the best yield performance and general adaptability. Based on Tai’s method, the contribution of spike number to the stability of grain yield over different environments was higher than that of other yield components. Also, tritipyrum lines demonstrated higher stability compared with wheat and triticale. Totally, M45 triticale and tritipyrum (Ka/b)(Cr/b)-5 lines were the most stable genotypes with high grain yield. Complementary agronomic experiments may then release a new grain crop of triticale and a new pasture line of combined primary tritipyrum for grain and forage. Moreover, the combined tritipyrum line can be used in bread wheat breeding programs for producing salt-tolerant wheat cultivars.
... Genotype x environment interaction (GEI) hinders selection of the best genotypes due to confounding results for the genetic differences between wheat genotypes. Therefore, much attention has been made to analysing GEI (Haile et al. 2007;Gunjača et al., 2007;Thomason and Philips, 2006;Mardeh et al., 2006;Hoffman et al., 2005;Pepo and Györi, 2005;Yan et al., 2002). Wheat breeders try to select genotypes responsive to favourable environments for grain yield and other important wheat traits (Fufa et al., 2005). ...
... Tovarnik (chernozem) and Osijek (eutric cambisol) have more fertile soils that Požega (pseudogley). Differences between tested locations were also reported by Yan et al. (2005), Thomason and Philips (2006) and Marić et al. (2007). Lower density showed more stability on more fertile soils (Tovarnik and Osijek); wile higher density were more stabile on less fertile soils (Požega and Nova Gradiška). ...
The objectives of this paper were to examine influence of different testing environments (different soil type) and sowing rates oil formation of winter wheat hectolitre weight and, after the stability analysis, to identify most stabile genotypes and locations. Research work was conducted on 14 winter wheat genotypes and four testing locations. On each location genotypes were sown in two sowing rates - 330 and 600 germinable seeds m(-2) Statistical analysis showed that genotypes, locations, sowing rates and genotype x environment interaction had highly significant (p <= 0.01) influence on hectolitre weight. Two genotypes Divana and Lucija - were identified as stabile genotypes with high hectolitre weight.
... This analysis helps determine whether the target cropping region is homogeneous or should be divided into different mega-environments (Yan and Rajcan 2002; Casanoves et al. 2005; Samonte et al. 2005; Dardanelli et al. 2006). It also helps evaluate test environments (Yan and Rajcan 2002; Blanche and Myers 2006; Thomason and Phillips 2006) that effectively identify superior genotypes within a megaenvironment . A mega-environment is defined as a group of locations that consistently share the same best genotype(s) (Yan et al. 2000; Yan and Kang 2003). ...
... Further genetic gains in grain yield and stability can be achieved by improving cold tolerance and tolerance to major biotic stresses. In this study, GGE biplot methodology, as has been shown to be very effective for analysing GE interaction data (Yan et al. 2000; Yan and Rajcan 2002; Yan and Kang 2003; Bhan et al. 2005; Malvar et al. 2005; Voltas et al. 2005; Thomason and Phillips 2006; Yan and Tinker 2006), allowed a meaningful and useful summary of GE data and assisted in examining the natural relationships and variations in genotype performance among various testing environments. Analysis by the GGE biplot approach in the present research also identified 2 rainfed mega-environments. ...
Integrating yield and stability of genotypes tested in unpredictable environments is a common breeding objective. The main goals of this research were to identify superior durum wheat genotypes for the rainfed areas of Iran and to determine the existence of different mega-environments in the growing areas of Iran by testing 20 genotypes in 4 locations for 3 years via GGE (genotype+genotype-by-environment) biplot analysis. Stability of performance was assessed by the Kang's yield-stability statistic (YSi) and 2 new methods of yield-regression statistic (Ybi) and yield-distance statistic (Ydi).The combined analysis of variance showed that environments were the most important source of yield variability, and accounted for 76% of total variation. The magnitude of the GE interaction was ∼10 times the magnitude of the G effect. The GGE biplot suggested the existence of 2 durum wheat mega-environments in Iran. The first mega-environment consisted of environments corresponding to 'cold' locations (Maragheh and Shirvan) and a moderately cold location (Kermanshah), where 'Sardari' was the best adapted cultivar; the second mega-environment comprised 'warm' environments, including the Ilam and Kermanshah locations, where the recommended breeding lines G16 (Gcn//Stj/Mrb3), G17 (Ch1/Brach//Mra-i), and G18 (Lgt3/4/Bcr/3/Ch1//Gta/Stk) produced the highest yields. Ranking of genotypes based on GGE was found to be highly correlated with that based on the statistics YSi and Ybi. The discriminating power v. the representative view of the GGE biplot identified Kermanshah as the location with the least discriminating ability but greater representation, suggesting the possible of testing genotypes adapted to both warm and cold locations at the Kermanshah site. The results verified that the statistics YSi and Ybi were highly correlated (r≤0.94**) and could be a good alternative for GGE biplot analysis for selecting superior genotypes with high-yielding and stable performance.
... Components of variance (%) using multiple regression analysis for the six locations and years were calculated. Genotypes were considered fixed effects, whereas environment and year were assumed to be random effects (Salarpour et al., 2020;Thomason & Phillips, 2006). ...
Barley production is severely affected by drought caused by the unpredictable Mediterranean weather patterns, which include uneven rainfall and extreme temperatures. This leads to a decrease in crop yield. However, to tackle this issue, landraces and wild species are crucial sources of variation for stress adaptive traits. By incorporating these traits into improved varieties, we may see an increase in yield and stability under drought conditions. Seventy‐six quantitative traits loci (QTLs) identified traits were mapped using recombinant inbred lines (RIL) population Arta × Harmal‐2//Esp/1808‐4L, evaluated at six dry and semi‐dry areas over 3 years. The study investigated traits such as grain yield, biological yield, harvest index, kernel weight, seed per head, days to heading, kernel filling duration, growth vigour, growth habit, lodging and plant height. Numerous QTLs were discovered that are associated with various phenotypic traits related to grain yield, kernel yield, duration of filling period and days to heading. For areas with less than 250 mm/annum of rainfall, QTLs were identified on chromosome 2H for biological yield, days to heading, and kernel weight, on 1H for harvest index, and on 2H, 4H, and 5H for kernel weight. For semi‐dry areas with rainfall less than 450 mm, QTLs were found on chromosome 6H for grain yield, 2H and 5H for kernel weight, 1H and 6H for seed per head, and 2H for days to heading. Notably, these QTLs significantly explain more than 10% of phenotypic variation. The 2H chromosome was found to have the most important QTL and pleiotropic effect for yield and its components, such as kernel weight, days to heading, and biological yield. The cross Arta/Harmal was adapted, and mechanisms were developed to cope with drought stress, reflected by the significant and positive correlation of biological yield and harvest index with grain yield. Chromosomes 1H, 2H, 4H and 5H harbour more than 60% of mapped QTLs for dry areas. It is worth noting that the QTLs mentioned earlier, along with the kernel weight QTLs (QKW 1.5, QKW2.7b, QKW4.1, QKW6.7, QKW6.9), have consistently exhibited positive effects on crop yield in semi‐dry and dry areas, making them potential candidates for breeding drought‐tolerant crops. Genomic co‐localisation of the QTL for Arta/Harmal population suggested that selection for drought through linked markers can be an option for drought tolerance selection for barley in dry areas.
... The GGE biplot was plotted To investigate the best genotypes in each environment [30]. This kind of data biplot allows us to identify the best genotypes in each environment and detect stable genotypes in all environments [31]. ...
Knowledge about the extent of genotype in environment interaction is helpful for farmers and plant breeders. This is because it helps them choose the proper strategies for agricultural management and breeding new cultivars. The main contribution of this paper is to investigate genotype on environmental interaction using the GGE biplot method (Genotype and the Genotype-by-Environment) in ten canola cultivars. The experimental design was a randomized complete block design (RCBD) with three replications to assess the stability of grain yield of ten canola cultivars in five regions of Iran, including Birjand, Karaj, Kashmar, Sanandaj, and Shiraz, within two agricultural years of 2016 and 2017. The results of combined ANOVA illustrated that the effects of the environment, genotype × environment, and genotype were highly significant at 1%. Variance Analysis showed that three environmental impacts, genotype, and interaction of genotype in the environment effect, produced 68.44%, 18.63%, and 12.9% of the total variance. The GGE biplot graphs were constructed using PCA. The first principle component (PC1) explained 65.3%, and the second (PC2) explained 18.8% of the total variation. The research examined polygon diagrams to identify two top genotypes and four mega-environments. Also, the appropriate genotypes for each environment were diagnosed. Using the GGE biplot, it was possible to make visual comparisons and identify superior genotypes in canola. Accordingly,. The results obtained from graphical analysis indicated that Licord, Hyola 401 and Okapi genotypes showed the highest yield and were selected as the most stable genotypes. Also, Karaj region was chosen as a experimental region where the screening of genotypes was very suitable. Based on the ranking of the genotypes in the most suitable region (Karaj), Okapi genotype was selected as the desired genotype. In examining the heatmap drawn between the genotypes and the investigated environments, a lot of similarity between the genotypes of Sarigal, Hyola 401 and Okapi was observed in the investigated environments. The GGE biplot graphs enabled the detection of stable and superior environments and the grouping of cultivars and environments based on grain yield. The results of this research can be used both for extension and for future breeding programs. Our results provide helpful information about the canola genotypes and environments for future breeding programs.
... In this type of diagram, genotypes that are in a section with one or more specific traits show good performance relative to that trait. Also, genotypes close to the origin of the graph do not respond well to trait changes [7,17]. According to the multidimensional diagram drawn in Karaj region, Armaverski, Azargol, Master, Favorite and Gabur genotypes were the most distant from the origin and were identified as superior genotypes. ...
In order to investigate the genotype-trait interaction in the form of a randomized complete block design (RCBD) on ten sunflower genotypes in four regions of Karaj, Birjand, Firoozabad and Arak in the cropping years 2018–2019. The results of combined analysis of variance at the probability level of 0.01 showed that the genotypes were significantly different in terms of the studied traits. The effect of genotype × environment was also significantly different in all traits except plant height, leaf width and grain yield. Genotype × year × environment was also significant in all traits except plant height and stem diameter. The results of comparison of the mean by Duncan method also identified Azargol, Zaria and Armaverski genotypes as genotypes with favorable rank. Armaverski and Azargol genotypes were identified as desirable genotypes in all studied regions in the study of polygon diagrams. Genotype ranking chart based on ideal genotype also introduced Zargol and Azargol genotypes as genotypes with suitable yield and desirability. The grouping diagram of genotypes in terms of traits also grouped the studied genotypes in Karaj, Birjand and Firoozabad regions into four groups in terms of traits and the studied genotypes in Arak region into three groups.
... Hence in this investigation, visual observations of AMMI biplot analysis enable to identify genotypes and testing environments that exhibited major sources of GE interaction as well as those that were stable. Similar results were reported in wheat by Thomason and Phillips [18]. ...
In Sudan, grain sorghum (Sorghum bicolor (L.) Moench), is the most important cereal crop, in terms of total acreage, production and consumption. One hundred and twenty S1 families were taken at random from an advanced random mating Gezira sorghum population (G S P-1) developed and improved for six cycles using S1 family selection, at Rain-fed Crop Research Centre for Arid and Semi-Arid areas (RCRCASA) in the University of Gezira, Wad Medani Sudan. The study was conducted during two seasons (2004-2005) to study genetic variability in the population (GSP-1) at four rain-fed areas in Sudan namely; Gedarif University farm at northern Gedarif environment (2004), Gedarif Research Station at northern Gedarif environment (2005), Rahad Scheme rain-fed at marginal Gedarif environment (2004) and Kasamoor North east Gedarif (2005). The design used was a modified Randomized Complete Block Design (RCBD) with two replications nested within six blocks. Stability was estimated for the 120 families yield (Kgh-1). The combined analysis over environments revealed significant differences between environments, which indicated that four environments are contrasting for evaluating the genotypes. In average over environments the genotypes have shown G×E interaction was not significant for yield, indicating relative ranking of the genotypes remained constant and yield was stable over all environments. The mean was1448Kgh-1. The Additive Main Effect and Multiplicative Interaction (AMMI) stability analysis with the first principal components (PCA1) axes for grain yield identified stable families as the families with a lower absolute PCA1 score which were 101, 95, 93, 96, 94, 103, 97, 102, 99, 100, 104, 98 respectively, would produce a lower absolute GE interaction effect and would have a less variable yield across the four Gedarf studied environments. These could provide a good source for sorghum improvement in Gedarif rain-fed area
... Some varieties are referred to as standards (Vanessa, Tiffany, Casanova, etc.) and serve for monitoring the impact of various agro-technical and environmental conditions on the crop (climate specifics of the season, different cultivation methods, location changes, etc.) as well as for orientation when introducing new varieties in the assortment [1]. Plant breeding lasts for 10-15 years from selection to the point where the variety can be put on the market [5,6]. Barleys appropriate for malting, after the varietal recognition takes place, are then tested for their technological indicators preferably during a statistically relevant period of 3-5 years [1]. ...
To ensure the demanded quality for malting barley, each season a number of quality indicators have to be determined and evaluated. Depending on the variety, location and growing conditions (precipitation, temperature, soil type, and agro-technical measures), the achieved quality indicators have to fulfil a certain theoretical value to set the variety into an end-purpose group. End purpose can be designated as feed or malting. However, based on their intended end-use and in respect to their characteristics, barley varieties in Croatia can also be classified as dual, namely malting-feed varieties. Most important parameters that have to be analyzed every year include total protein content, soluble proteins, moisture, malt extract, extract difference, friability, viscosity, Kolbach index, wort color, wort pH. For malting barley varieties, these values have to be within the set and recommended values, taken from different literature sources. The aim of this work was to present a five-year long, continuous examination of all mentioned quality control indicators. All indicators are prone to variations due to the variety, location and growing conditions, so a five-year continuous study can provide mean values for the tested indicators important for growers, maltsters and brewers. Expressed variations can be noted in protein content. Namely, 2018 resulted with the lowest protein content in comparison to other years, for almost all varieties.
... The BLUP method was used to predict the breeding values of DH lines for GY and STS, using the WOMBAT tool (Meyer 2007). Environment (normal and drought stress) and year were considered as fixed effects, and DH lines were assumed to be random effects (Thomason and Phillips 2006). ...
Water shortage and drought stress in the reproductive stage of wheat (Triticum aestivum L.) considerably affect grain yield (GY). Mapping genes for drought tolerance assists in selection for drought improvement. In the present study, we evaluated breeding values based on best linear unbiased predictions (BLUPs) and drought tolerance/susceptibility indices (DT/SIs), and identified marker–phenotype associations in 220 doubled haploid (DH) lines. The DH lines were evaluated for GY and 1000-grain weight (TGW) under drought stress and well-watered conditions at the heading stage in 2015 and 2016. The linkage map comprised 1333 SSR, DArT and SNP markers with an average density of 2.18 markers per cM. The BLUPs were significantly correlated with GY of the lines. Significant correlations were found between stress tolerance score (STS) and drought response index, yield index, yield stability index, geometric mean productivity and stress tolerance index. The lines DH_R295 and DH_R360 had the highest breeding values for GY and STS. Major QTLs, one main effect and eight epistatic, were identified for the DT/SIs. A major QTL was identified for STS-GY, which explained 11.39% of the STS-GY variation in 2015. This QTL was co-located with QTLs for yield index-GY and yield stability index-GY within the BS00066932_51–gwm0314b marker interval (48 cM on 3B). The search for gene annotation showed that BS00066932_51 overlapped with protein-encoding genes. In conclusion, the QTL-linked markers help genotype selection for the improvement of drought tolerance, and they are good candidates for use in genomic selection.
... Many important traits in sweetpotato are sensitive to environmental change as evidenced in several studies (Naskar & Singh, 1992;Manrique & Hermann, 2000;Grüneberg et al., 2005;Osiru et al., 2009;Niringiye et al., 2014b). It is therefore important to quantify the GEI and determine the stability of the different genotypes through the application of appropriate statistical analyses to multi-locational and multi-seasonal trials (Thomason & Philips, 2006). The additive main effects and multiplicative interaction (AMMI) (Gauch, 2006) is the model of choice when main effects and interactions are both important (Zobel et al., 1988) and can be used to identify both superior and stable genotypes (Crossa, 1990). ...
Alternaria leaf petiole and stem blight (Alternaria spp.) is an important sweetpotato (Ipomoea batatas (L.) Lam.) disease in Uganda. Severity of the disease varies with environment, with higher disease levels recorded under high moisture and humidity conditions. To breed for resistance to this disease, germplasm that is resistant must be identified through multi-locational trials. This study was conducted to evaluate selected sweetpotato genotypes for stable resistance to Alternaria blight across sites and seasons. Thirty sweetpotato genotypes from different agro-ecological zones of Uganda and the National Sweetpotato Program were evaluated for resistance to Alternaria blight using fungicide treatment and Alternaria blight pathogen inoculation at Namulonge and Kachwekano over three seasons. There were highly significant differences among the genotypes for Alternaria blight severity with higher disease levels at Kachwekano than Namulonge. Genotypes Shock, Silk Luwero and the resistant check Tanzania had the lowest Alternaria severity and were therefore the most resistant while NASPOT 1 and NASPOT 7 had the highest severity values and were the most susceptible. Improved cultivars were more susceptible than the landraces. Genotypes Tanzania and Namusoga and environment Namulonge 2015B were the most stable for Alternaria blight. Treatment with fungicide resulted in variable reductions in Alternaria blight severity among genotypes across seasons and sites with NASPOT 1 having the lowest percentage reduction of 40.8% between the Alternaria inoculated and fungicide treated plots. Kigaire recorded the highest percentage disease reduction of 63.6%. Those genotypes with acceptable performance for Alternaria blight may be used as parents in breeding new genotypes with improved performance.
... Genotype by environment interactions have proved to be highly significant in most genotype x environment studies (Hristov et al. 2010;Johansson et al. 2000;Koppel and Ingver 2010;Malik et al. 2011;Malik 2012;Rozbicki et al. 2015;Thomason and Phillips 2006;Williams et al. 2008). Although, in some cases the interaction was neglected in comparison to both genetic and environmental effects (Laidig et al. 2017). ...
Technological (processing performance and end-product) and nutritional quality of wheat is in principle determined by a number of compounds within the wheat grain, including proteins, polysaccharides, lipids, minerals, heavy metals, vitamins and phytochemicals, effecting these characters. The genotype and environment is of similar importance for the determination of the content and composition of these compounds. Furthermore, the interaction between genotypes and the cultivation environment may play a significant role. Many studies have evaluated whether the genotype or the environment plays the major role in determining the content of the mentioned compounds. An overall conclusion of these studies is that except for compounds encoded by single major genes, importance of certain factors mainly depend on how wide environments and how diverse cultivars are within these comparative studies. Comparing environments all over, e.g. across Latin America, ends up with a high significance of the environment while large studies including genotypes of wide genetic background result in a significant role for the genotype. In addition, for some technological properties and components, genotype has a higher effect (e.g. grain hardness and gluten proteins), while environment influences stronger on others (e.g. protein and mineral content).Content and concentration of proteins, but also to some extent of starch, some non-starch polysaccharides and lipids, are essential in determining the technological quality of a wheat flour. For nutritional quality of the flour, the majority of the compounds are together the important determinant. Thus an increased understanding of environmental effects is essential. As to how the environment is influencing the content of the compounds, there are some differences. The protein content and composition is strongly affected by environmental factors influencing nitrogen availability and cultivar development time. However, these two factors are impacted by a range of environmental (temperature, precipitation, humidity/sun hours, etc.) and agronomic (soil properties, crop management practices such as seeding density, nitrogen fertilizer application timing and amount, etc.) components. Thus, to understand the interplay between the various environmental and agronomic factors impacting the technological quality of a wheat flour, modeling is a useful tool. Several other compounds, including minerals and heavy metals, are to a higher extent determined by site specific variation, resulting in similar rankings of entries across locations, although the total content is varying among years. The bioactive compounds and vitamins are a part of the defense mechanisms of plants and thus there is a variation in these compounds depending on prevailing biotic and abiotic stresses (heat, drought, excess rainfall, nutrition, diseases and pests). Thus, even for nutritional quality of wheat, incorporating all compounds of relevance in the evaluation would benefit from modeling tools.
... Genotype by environment interactions have proved to be highly significant in most genotype x environment studies (Hristov et al. 2010;Johansson et al. 2000;Koppel and Ingver 2010;Malik et al. 2011;Malik 2012;Rozbicki et al. 2015;Thomason and Phillips 2006;Williams et al. 2008). Although, in some cases the interaction was neglected in comparison to both genetic and environmental effects (Laidig et al. 2017). ...
Technological (processing performance and end-product) and nutritional quality of wheat is in principle determined by a number of compounds within the wheat grain, including proteins, polysaccharides, lipids, minerals, heavy metals, vitamins and phytochemicals, effecting these characters. The genotype and environment is of similar importance for the determination of the content and composition of these compounds. Furthermore, the interaction between genotypes and the cultivation environment may play a significant role. Many studies have evaluated whether the genotype or the environment plays the major role in determining the content of the mentioned compounds. An overall conclusion of these studies is that except for compounds encoded by single major genes, importance of certain factors mainly depend on how wide environments and how diverse cultivars are within these comparative studies. Comparing environments all over, e.g. across Latin America, ends up with a high significance of the environment while large studies including genotypes of wide genetic background result in a significant role for the genotype. In addition, for some technological properties and components, genotype has a higher effect (e.g. grain hardness and gluten proteins), while environment influences stronger on others (e.g. protein and mineral content).
... Plant breeding takes up 10-15 years from selection to the point where the variety can be placed on the market with increased yield and resistance to disease, better overall quality or some other economic advantage. 6,7 In the case of brewing and WM barleys, after the variety has been recognized, it is subjected to the examination regarding the stability of its technological indicators during a statistically relevant period (3-5 years). After that, the influence of agro-technical and environmental conditions on these properties is also to be verified. ...
The aim of this paper was to assess the technological quality of three novel Croatian spring barley varieties and lines intended for brewing and distilling. Two tested lines Osk.5.45/2-15 and Osk.5.33/23-15 and one variety Pivarac were developed at the Agricultural Institute Osijek, while the used control sample was the recognised whisky barley variety Grace. The quality of starting barley and final malts were assessed. The results indicate that the tested varieties/lines of spring barley have the potential to become recognised as whisky malt varieties. In order to confirm the obtained results, further monitoring should be employed during the statistically relevant period. The tested quality values the OSK.5.33/23-15 has shown were the closest to the recommended values for whisky malts. In all tested varieties β-glucans content should be reduced which would consequently improve the F/C extract difference and friability values and increase the fermentability and extract yield during fermentation.
... Developing plant cultivars with broader adaptation that have stable agronomic performance across locations and environments is often desired (Thomason and Phillips, 2006). This simplifies seed production by limiting the number of cultivars needed to fit multiple geographic regions without sacrificing yield potential and productivity. ...
Yield and agronomic data from a regional soft red winter wheat (Triticum aestivum L.) nursery—consisting of 604 advanced breeding lines (ABLs) and 36 testing locations over a 21‐yr period—were evaluated to understand recent genetic gains in wheat and determine the impact of selection location and environment on cultivar performance and adaptation. Relative mean yield improvement of ABLs with respect to historical cultivar AGS 2000 was 106 kg ha⁻¹ yr⁻¹ (1.58 bu acre⁻¹ yr⁻¹), equating to an annual genetic gain of 1.6%. Yield gains for wheat during this timespan were attributed to an increase in both yield potential and stability across environments. However, a strong tradeoff (r = −0.36, p < 2.2 × 10⁻¹⁶) was observed between yield potential and stability. Additionally, distance between selection and evaluation environments was significantly correlated with yield, with yield decreasing as distance between locations increased. Advanced breeding lines had a +221, +126, and −29.6 kg ha⁻¹ yr⁻¹ (+3.29, +1.88, and −0.44 bu acre⁻¹) yield difference over the location mean when the selection location was within, adjacent, and nonadjacent to the trial location zone, respectively. Advanced breeding lines in general performed poorly in production environments west of their selection site. Based on data analyzed, elevation and latitude are significant geographic parameters to consider when determining optimal selection location for a production environment. Meanwhile, change in growing degree days between selection and evaluation location had a stronger influence on yield than precipitation. Findings demonstrate the importance and benefits of breeder collaborations and multienvironment testing on crop improvement, which will be needed to maximize yield gains in the 21st century.
... However, the yield is the combined effects of G, E, and GEI; only G and GEI are pertinent and concurrently significant in genotype evaluation (Yan et al., 2007). When selecting genotypes across contrasting environments for yield stability, plant breeders look for a non-crossover type of GEI or preferably the absence of GEI for general adaptation (Thomason and Phillips, 2006). Nevertheless, GEI is important for exploitation by GGE biplot to target suitable genotypes to a specific environment or group of environments. ...
Cowpea is an important food crop with high nutritional and socio-economical values in South Sudan. However, the lack of improved varieties is one of the main production constraints. This study was undertaken to assess the yield stability performance of improved cowpea genotypes across six environments in South Sudan in 2014 and 2015. Nine genotypes were evaluated in a randomized complete block design with three replications. Genotype and genotype x environment biplot analysis method was used to determine yield stability. Highly significant (p less than 0.001) genotype x environment interaction effect was detected for seed yield. IT90K-277-2 had the highest while ACC004 had the lowest grain yield. Palotaka was as highly discriminating and repeatable environment compare to the other testing sites. IT07K-211-1-8 and Mading Bor II were the most responsive genotypes, while IT90K-277-2 was the most stable high yielding genotype across the test environments and can be grown by farmers across the region.
... The GGE bi-plot procedure (Yan and Tinker, 2006) consists of a set of bi-plot explanation approaches, whereby important questions regarding genotype evaluation and test-environment evaluation can be visually addressed. Increasingly, plant breeders and other agronomists have found GGE bi-plots were useful in mega-environment analysis (Dardanellia et al., 2006), genotype evaluation (Voltas et al., 2005;Kang et al., 2006), test-environment evaluation (Thomason and Phillips, 2006), trait-association and trait-profile analyses (Ober et al., 2005), and ...
The present study was conducted on thirty-six common beans (Phaseolus vulgaris L.) Genotypes across six contrasting environments defined for its different soil fertility status and located at the southern Ethiopia. The genotypes were arranged in 6 x 6 triple lattice design and executed for two successive main cropping seasons with the objectives to evaluate yield performance of common bean genotypes and identification of mega environments. GGE (i.e., G = genotype and GE = genotype by environment, interaction) bi-plot methodology was used for graphical presentation of yield data after subjecting the genotypic means of each environment to GGE Bi-plot software. The first two principal components (AXIS 1 and AXIS2) were used to display a two-dimensional GGE bi-plot. Thus, genotypic AXIS1 scores >0 classified the high yielding genotypes while AXIS2 scores <0 identified low yielding genotypes. Unlike genotypic AXIS1, genotypic AXIS2, scores near zero showed stable genotypes whereas large AXIS2 scores classified the unstable ones. The environmental AXIS1 were related to crossover nature of GEI while AXIS2 scores were associated with non-cross over GEI. The six test environments in the southern region were divided in to two distinct mega environments (Mega-1 and 2). Mega-1 constituted GOHF13, ARMF12 and ARLF13 while genotype 14 (SCR10) being the best winner, on the other hand, Mega-2 contained GOHF12 and while common bean genotype 20(SCR17) being the best winner. The results of this study indicated that breeding for specific adaptation should be taken as a breeding strategy in southern region to exploit positive GEI to increase production and productivity of common bean.
... (genotype 9 environment) interaction (GGE) biplots (Yan and Kang 2003;Yan and Tinker 2006). Plant breeders have found GGE biplots to be useful in the evaluation of test environments (Yan and Rajcan 2002;Blanche and Myers 2006;Thomason and Phillips 2006;Srinivasa rao et al. 2011). The GGE provides both additive and multiplicative effects represented by principal component analysis (PCA) (Yan et al. 2000). ...
The dearth of proper delineation for energy sorghum cultivation has led to a prerequisite for evaluation and identification of test environments for the newly developed lines. This becomes of vital importance as the biomass yield is highly influenced by genotype and environmental (G × E) interactions. Several agronomic traits were considered to assess the biomass yield and the combined analysis of variance for G (genotype), L (location) and interaction effect of G × L. The variations in the yield caused by the interaction of G × L are very essential to acquire knowledge on the specific adaptation of a genotype. Thus, the multi-location trials conducted across locations and years have helped to identify the stable environments with specific adaptation for biomass sorghum. The presence of close association between the test locations suggested that the same information about the genotypes could be obtained from fewer test environments, and hence the potential to reduce evaluation costs. The two genotypes—IS 13762 and ICSV 25333—have shown stable performance for biomass traits across all the locations, in comparison with CSH 22SS (check). The top ten entries with stable and better performance for fresh biomass yield, dry biomass yield, grain yield and theoretical ethanol yield were ICSV 25333, IS 13762, CSH 22SS, IS 25302, IS 25301, IS 27246, IS 16529, DHBM2, ICSSH 28 and IS 17349.
... Biplots of AMMI model are effective at identifying cultivars and testing locations that are major sources of G × E interaction. Biplots and best linear unbiased predictions (BLUPs) are the best tool to compare cultivar performance across environments (Thomason and Phillips 2006). The high accumulated per cent value of explanation of the sum of squares on the first two axes of interaction, the adaptability and stability of rice genotypes can be graphically interpreted, considering only biplots with the first two axes of the GE interaction. ...
Rice (Oryza sativa L.) is the most import cereal crop in the South Asian countries and unfortunately, it is sensitive
to salinity. Breeding for salt tolerant varieties is the cost-effective way of addressing this problem. The development
and dissemination of these high yielding and salinity tolerant varieties to different agro-ecological zones of the
country involves conducting multi-location trials. In the current study, one such trial was conducted using 44
genotypes which were tested across seven salt stress environments during Kharif, 2014. The data recorded for
days to 50% flowering and grain yield were analyzed through both Genotype and Genotype × Environment
interaction (GGE) and Additive Main Effects and Multiplicative Interactions(AMMI) analyses. GGE biplots
accounted for 92.5% and 87.5% of the interaction variance, whereas AMMI biplots could explain 95.7% and
88.5% for days to 50% flowering and grain yield, respectively. The location Aligarh (ENV6) was found the most
discriminating for both the characters. Furthermore, it was found the most favorable environment. For days to
50% flowering, two mega environments were identified while for grain yield one mega-environment through
GGE biplot and three through AMMI biplot could be demarcated. Both AMMI and GGE have led to similar
conclusions with minor differences. However, the GGE biplot was found comparatively more advantageous
over AMMI. The genotypes RP 5898-18-5-2-1-1, Bulk 18, NDRK 50043, CSR 11-121, CSR 23, RP 5898-38-7-
2-1-1 and CSR 55 were found stable with above average yields across saline and sodic soils. These rice lines
could be used for commercial cultivation for improve the productivity in salt affected soils.
... The ISTLs were derived from crosses between NIPTLs, four cultivars of hexaploid Iranian bread wheat ('Omid', 'Roshan', 'Flat', and 'Niknejad'), and two tetraploid wheat cultivars ('Shotordandan' and 'Cathlicum'). Five plants were randomly selected from each plot to measure the plant height (cm), spike length (cm), number of tillers per plant, number of fertile tillers per plant, number of grains per spike, 1000-grain to be the fixed effects, and the lines (ISTL and NIPTL) was considered to be the random effect (Thomason and Phillips, 2006). ...
This is the first report of secondary tritipyrum recombinant inbred lines derived from Iranian wheat cultivars (Triticum durum Desf./Triticum aestivum L. × Thinopyrum bessarabicum Savul. & Rayss). An experiment was performed under normal (1 dS m⁻¹) and saline (12 dS m⁻¹) conditions during the 2013 to 2015 growing seasons in Kerman, Iran (29.48° N, 57.64° E) to evaluate the potential of tritipyrum, a new salt-tolerant cereal. The genotypes examined were 13 non-Iranian primary tritipyrum lines (NIPTLs, 2n = 6x = 42; AABBEbEb), 144 new Iranian secondary tritipyrum lines (ISTLs) derived by crossing NIPTLs with four Iranian bread wheat (Triticum aestivum L.) cultivars, two tetraploid wheat varieties, and six Iranian wheat cultivars. The agronomic traits were measured and the results showed spikes in the weight per plant and 1000-grain weight that can be used as index traits for NIPTLs and ISTLs for selection under normal as well as saline conditions. Although the Smith–Hazel and Pesek–Baker indices indicated that the best response of the ISTLs to selection was related to the number of sterile florets and grains per spike under normal conditions, under saline conditions, the most effective trait was the number of fertile tillers. The breeding value for the NIPTLs and ISTLs were the best linear unbiased prediction values calculated using the results of the selection indices. The ISTLs crosses ‘Roshan’ × (Az/b), ‘Niknejad’ × (Ka/b)(Cr/b), and ‘Omid’ × (Ka/b) (Cr/b) showed the highest average breeding value and are suitable for use in breeding programs for new ISTLs that are salt-tolerant cultivars with high yield potential in saline soil and brackish water.
... Such adaptability reflects the diversity of the wheat genome. When comparing different genotypes, their interaction with the environment is often highly significant (Reynolds et al., 2002;Thomason & Phillips, 2006). Actually, cereal producers are also interested in triticale (X Triticosecale Wittmack) which is an intergeneric hybrid between wheat and rye (Secale cereale L.). ...
Fusarium head blight (FHB) caused by Fusarium spp. is one of the major diseases that occurs on wheat in Lubumbashi, in D.R.Congo. Breeding for resistant cultivars is the most used method for controlling the disease although it is laborious, time-consuming and restricted by environmental symptom expression. In this study, 3 different methods of identification of FHB resistant varieties were used: field trials, artificial inoculations and molecular markers. The combination of all results showed that varieties used could be grouped in 4 classes: varieties possessing the major gene Fhb1 which confer a type II resistance to FHB on chromosome 3BS; varieties possessing a major gene of resistance to FHB on the chromosome 3A which confer a type I resistance and a minor gene on the chromosome 3B which contribute weakly to a type II resistance; susceptible varieties and varieties for which resistance were not clearly defined. The simultaneous use of these 3 methods can provide a new approach for a quick selection of varieties of interest in a breeding program.
... Biplots of AMMI model are effective at identifying cultivars and testing locations that are major sources of G × E interaction. Biplots and best linear unbiased predictions (BLUPs) are the best tool to compare cultivar performance across environments (Thomason and Phillips 2006). The high accumulated per cent value of explanation of the sum of squares on the first two axes of interaction, the adaptability and stability of rice genotypes can be graphically interpreted, considering only biplots with the first two axes of the GE interaction. ...
... From the 1980's, the use of environmental variables and the prediction of their influence on the productivity of some species have been widely applied in the studies on the GxE interaction, and currently several authors have been inserting environmental information, whether as characterization factors and environmental stratification as covariates in the analysis models of GxE interaction (Haun, 1982;Denis, 1988;Van Eeuwijk et al., 1996;Vargas et al., 1998;Crossa et al., 1999;Van Eeuwijk et al., 2005;Voltas et al., 2005;Thomason and Phillips, 2006;Vargas et al., 2006;Boer et al., 2007;Ramburan et al., 2011;Heslot et al., 2014). Van Eeuwijk et al. (1996), in a seminal study, summarizes some methods based on factor analysis for the insertion of information about environmental covariates for the explanation of the GxE interaction, and, according to the author, such models are just an extension of the most general case: ...
... The presence of significant G × E effect caused unpredictability on the performance of genotypes for phenotypic trait such as yield (Becker and Leon, 1988). This complicates genotype recommendation based on traditional analysis using main effects alone (Ebdon and Gauch, 2002;Thomason and Phillips, 2006). The interaction term (G × E) has been considered a critical component to explain yield performance and stability of genotypes, and in identifying the number of mega-environments for genotype evaluation following appropriate statistical tools (Ceccarelli, 1996;Annicchiarico, 1997;Kang, 1997;Yan et al., 2000;Gauch, 2013). ...
The promotion of dry pea (Pisum sativum L.) and lentil (Lens culinaris Medik.) production in cereal dominated cropping systems require identifying high yielding and widely adapted genotypes for a diverse range of environments. However, this information is rarely available in the semi-arid temperate region. We carried out two separate experiments consisting of seven genotypes of dry pea in 25 environments and eight genotypes of lentil in 16 environments to determine yield performance and stability. The results showed that environments (E) and genotypes (G) and interaction (G × E) effects were highly significant (P < 0.0001) for both experiments. Average grain yield among genotypes varied from 2243 to 2680 and from 1229 to 1643 kg ha−1 for dry pea and lentil, respectively. The G × E accounted 5.4 and 15.8% of the total sum of square for dry pea and lentil experiments, respectively. The G × E effects were crossover type for both experiments revealing inconsistent performance of genotypes across environments. Based on interaction principal component analysis of G × E, the dry pea genotypes, Montech 4152 followed by SW Midas and DS Admiral showed combination of better yield performance and stability. Among the lentil genotypes, CDC Richlea followed by Avondale showed less fluctuation to environmental changes but produced similar yields compared with the high yielding lentil genotypes. Therefore, these dry pea and lentil genotypes can be recommended for cultivation in wide range of environments in the temperate semi-arid climates and similar ecologies.
... Biplots of AMMI model are effective at identifying cultivars and testing locations that are major sources of G × E interaction. Biplots and best linear unbiased predictions (BLUPs) are the best tool to compare cultivar performance across environments (Thomason and Phillips 2006). The high accumulated per cent value of explanation of the sum of squares on the first two axes of interaction, the adaptability and stability of rice genotypes can be graphically interpreted, considering only biplots with the first two axes of the GE interaction. ...
Genotype × environment (G × E) interaction effects are of special interest for identifying the most suitable genotypes with respect to target environments, representative locations and other specific stresses. Twenty-two advanced breeding lines contributed by the national partners of the Salinity Tolerance Breeding Network (STBN) along with four checks were evaluated across 12 different salt affected sites comprising five coastal saline and seven alkaline environments in India. The study was conducted to assess the G × E interaction and stability of advanced breeding lines for yield and yield components using additive main effects and multiplicative interaction (AMMI) model. In the AMMI1 biplot, there were two mega-environments (ME) includes ME-A as CARI, KARAIKAL, TRICHY and NDUAT with winning genotype CSR 2K 262; and ME-B as KARSO, LUCKN, KARSA, GOA, CRRI, DRR, BIHAR and PANVE with winning genotypes CSR 36. Genotypes CSR 2K 262, CSR 27, NDRK 11-4, NDRK 11-3, NDRK 11-2, CSR 2K 255 and PNL 1-1-1-6-7-1 were identified as specifically adapted to favorable locations. The stability and adaptability of AMMI indicated that the best yielding genotypes were CSR 2K 262 for both coastal saline and alkaline environments and CSR 36 for alkaline environment. CARI and PANVEL were found as the most discernible environments for genotypic performance because of the greatest GE interaction. The genotype CSR 36 is specifically adapted to coastal saline environments GOA, KARSO, DRR, CRRI and BIHAR and while genotype CSR 2K 262 adapted to alkaline environments LUCKN, NDUAT, TRICH and KARAI. Use of most adapted lines could be used directly as varieties. Using them as donors for wide or specific adaptability with selection in the target environment offers the best opportunity for widening the genetic base of coastal salinity and alkalinity stress tolerance and development of adapted genotypes. Highly stable genotypes can improve the rice productivity in salt-affected areas and ensure livelihood of the resource poor farming communities.
... GGE biplot can visually address many questions relative to genotype and test environment evaluation. Increasingly, plant breeders and agronomists have found GGE biplots useful in mega-environment analysis (Casanoves et al. 2005;Dardanellia et al. 2006;Samonte et al. 2005;Yan and Rajcan 2002;Yan and Tinker 2005;Yan et al. 2000), genotype evaluation (Bhan et al. 2005;Fan et al. 2007;Kang et al. 2006;Malvar et al. 2005;Sandhu et al. 2014;Voltas et al. 2005), test-environment evaluation (Blanche and Myers 2006;Dimitrios et al. 2008;Thomason and Phillips 2006;Yan and Rajcan 2002), trait-association and trait-profile analyses (Morris et al. 2004;Ober et al. 2005;Yan and Rajcan 2002). By applying the GGE biplot, genotypes can be evaluated for their performance in individual environment and also across environments, for their mean performance and stability, and for their general or specific adaptations. ...
The genotype x environment (GE) interaction is a major challenge to plant breeders as it complicates testing and selection of superior genotypes and consequently reduces gains from selection. This chapter introduces and compares different statistical models to handle GE interaction by applying them to the durum wheat breeding program in Iran as an example. The results indicate significant crossover GE interaction suggesting the need for applying appropriate analysis for the exploitation and/or the minimization of GE interaction in multi-environment trials (MET) data. The test locations differed in their discriminative ability and representativeness. Highly significant correlations were found between univariate and multivariate statistical models in ranking genotypes for stability and for integrating yield with stability performances, indicating that they can be used interchangeably. Evaluation of genotypes based on multiple traits data identified parental germplasm for earliness, short stature, high grain weight and high grain yield. The proposed statistical analysis can assist in increasing the efficiency of breeding program through (a) selection of the most discriminate locations, (b) identifying superior genotypes based on both strategies dealing with exploitation and minimization of GE interaction and (c) exploring significant genetic gains in yield and yield stability.
... Such adaptability reflects the diversity of the wheat genome. When comparing different genotypes, their interaction with the environment is often highly significant (Reynolds et al., 2002;Thomason & Phillips, 2006). Actually, cereal producers are also interested in triticale (X Triticosecale Wittmack) which is an intergeneric hybrid between wheat and rye (Secale cereale L.). ...
Wheat production in African countries is a major challenge for their development, considering their increasing consumption of wheat flour products. In the Democratic Republic of Congo, wheat and wheat-based products are the important imported food products although there is a potential for the cultivation of small grain cereals such as durum wheat, wheat and triticale. Trials done in Lubumbashi in the Katanga Province have shown that Septoria Leaf Blotch, Septoria Glume Blotch and Fusarium head blight are the main constraints to the efficient development of these cultures. Some varieties of Elite Spring Wheat, High Rainfall Wheat, Triticale and Durum Wheat from CIMMYT were followed during 4 growing seasons and agronomic characteristics and their levels of disease resistance were recorded. Correlations of agronomic characteristics with yields showed that in most cases, thousand kernel weight is the parameter that has the most influence on the yield level (p < 0.0001). The analysis of variance for all diseases showed that there were significant effects related to the year, the species and the interaction years x species. Triticale varieties seem to have a better resistance against the two forms of Septoria compared to wheat varieties but, they seem to be more sensitive to Fusarium Head Blight than wheat varieties. However, the Fusarium Head Blight has a rather low incidence in Lubumbashi.
... In a similar study focused on a wheat breeding program, Roozeboom et al. (2008) found a genotypic variance almost twice as large as the GL and GY variances. Similar figures were found by Thomason & Phillips (2006), for wheat breeding in Virginia. Their studies are relevant to ours because they were also testing advanced materials (candidate cultivars) in large geographical areas with highly variable environments (especially Roozeboom et al., 2008). ...
The Spanish Barley Breeding Program is carried out by four public research organizations, located at the most representative barley growing regions of Spain. The aim of this study is to evaluate the program retrospectively, attending to: i) the progress achieved in grain yield, and ii) the extent and impact of genotype-by-environment interaction of grain yield. Grain yields and flowering dates of 349 advanced lines in generations F8, F9 and F10, plus checks, tested at 163 trials over 11 years were analized. The locations are in the provinces of Albacete, Lleida, Valladolid and Zaragoza. The data are highly unbalanced because the lines stayed at the program for a maximum of three years. Progress was estimated using relative grain yield and mixed models (REM L) to homogenize the results among years and locations. There was evident progress in the program over the period studied, with increasing relative yields in each generation, and with advanced lines surpassing the checks in the last two generations, although the rate of progress was uneven across locations. The genetic gain was greater from F8 to F9 than from F9 to F10. The largest non-purely environmental component of variance was genotype-by-location-by-year, meaning that the genotype-by-location pattern was highly unpredictable. The relationship between yield and flowering time overall was weak in the locations under study at this advanced stage of the program. The program can be continued with the same structure, although measures should be taken to explore the causes of slower progress at certain locations.
... In a similar study focused on a wheat breeding program, Roozeboom et al. (2008) found a genotypic variance almost twice as large as the GL and GY variances. Similar figures were found by Thomason & Phillips (2006), for wheat breeding in Virginia. Their studies are relevant to ours because they were also testing advanced materials (candidate cultivars) in large geographical areas with highly variable environments (especially Roozeboom et al., 2008). ...
... The Euclidean distance in the cultivar space can be used as a measure of the discrepancy between two environments (e.g., Thomason and Phillips 2006). Cluster analyses have been applied by Collaku et al. (2002) for grouping of locations for winter wheat trials, and by Gutiérrez et al. (2009) for clustering of breeding programs in barley. ...
Results from crop variety trials may vary between geographical regions because of differences in climate and soil types. Results are usually presented at regional level. To evaluate the importance of the regions used in the Swedish variety trial programs, we examined which regions produced similar levels of yield and similar ratios in yield between cultivars; the amount by which variance could be reduced by division into regions or clusters of regions; and the amount of trials per region and year, replicates per trial, and trials per year required in order to fulfill specifications on the precision of results. Yield data from spring barley and winter wheat trials performed during 1997–2006 were studied using cluster analysis and variance component estimation. The objectives were (1) to discuss the effects of regions on precision when the number of trials has decreased; (2) to demonstrate the method; and (3) to report the results obtained. In spring barley, clusters of regions produced different levels of yield, but similar yield ratios between cultivars. In winter wheat, clusters of regions giving different yield ratios were identified. When the option of a single analysis was compared with that of region-wise analysis, the reduction in variance with the former, due to the larger number of trials, outweighed the reduction in variance with the latter due to decreased random interaction between trials and cultivars.
... The GGE biplot methodology (Yan et al., 2000;Yan, 2001Yan, , 2002Yan and Kang, 2003;Yan and Tinker, 2006) consists of a set of biplot interpretation methods, whereby important questions regarding genotype evaluation and test-environment evaluation can be visually addressed. Increasingly, plant breeders and other agronomists have found GGE biplots useful in mega-environment analysis (Yan and Rajcan, 2002;Casanoves et al., 2005;Samonte et al., 2005;Yan and Tinker, 2005b;Dardanellia et al., 2006), genotype evaluation (Bhan et al., 2005;Malvar et al., 2005;Voltas et al., 2005;Kang et al., 2006), test-environment evaluation (Yan and Rajcan, 2002;Blanche and Myers, 2006;Thomason and Phillips, 2006), trait-association and trait-profi le analyses (Yan and Rajcan, 2002;Morris et al., 2004;Ober et al., 2005), and heterotic pattern analysis (Yan and Hunt, 2002;Narro et al., 2003;Andio et al., 2004;Bertoia et al., 2006). The legitimacy of GGE biplot analysis was, however, recently questioned by Gauch (2006), who concluded that, for GED analyses, AMMI analysis was either superior or equal to GGE biplot analysis. ...
The use of genotype main effect (G) plus genotype by environment (GE) interaction (G+GE) biplot analysis by plant breeders and other agricultural researchers has increased dramatically during the past 5 yr for analyzing multi-environment trial (MET) data. Recently, however, its legitimacy was questioned by a proponent of Additive Main Effect and Multiplicative Interaction (AMMI) analysis. The objectives of this review are: (i) to compare GGE biplot analysis and AMMI analysis on three aspects of genotype-by-environment data (GED) analysis, namely mega-environment analysis, genotype evaluation, and test-environment evaluation; (ii) to discuss whether G and GE should be combined or separated in these three aspects of GED analysis; and (iii) to discuss the role and importance of model diagnosis in biplot analysis of GED. Our main conclusions are: (i) both GGE biplot analysis and AMMI analysis combine rather than separate G and GE in mega-environment analysis and genotype evaluation, (ii) the GGE biplot is superior to the AMMI1 graph in mega-environment analysis and genotype evaluation because it explains more G+GE and has the inner-product property of the biplot, (iii) the discriminating power vs. representativeness view of the GGE biplot is effective in evaluating test environments, which is not possible in AMMI analysis, and (iv) model diagnosis for each dataset is useful, but accuracy gain from model diagnosis should not be overstated.
... The GGE biplot methodology (Yan et al., 2000;Yan, 2001Yan, , 2002Yan and Kang, 2003;Yan and Tinker, 2006) consists of a set of biplot interpretation methods, whereby important questions regarding genotype evaluation and test-environment evaluation can be visually addressed. Increasingly, plant breeders and other agronomists have found GGE biplots useful in mega-environment analysis (Yan and Rajcan, 2002;Casanoves et al., 2005;Samonte et al., 2005;Yan and Tinker, 2005b;Dardanellia et al., 2006), genotype evaluation (Bhan et al., 2005;Malvar et al., 2005;Voltas et al., 2005;Kang et al., 2006), test-environment evaluation (Yan and Rajcan, 2002;Blanche and Myers, 2006;Thomason and Phillips, 2006), trait-association and trait-profi le analyses (Yan and Rajcan, 2002;Morris et al., 2004;Ober et al., 2005), and heterotic pattern analysis (Yan and Hunt, 2002;Narro et al., 2003;Andio et al., 2004;Bertoia et al., 2006). The legitimacy of GGE biplot analysis was, however, recently questioned by Gauch (2006), who concluded that, for GED analyses, AMMI analysis was either superior or equal to GGE biplot analysis. ...
The use of genotype main effect (G) plus genotype‐by‐environment (GE) interaction (G+GE) biplot analysis by plant breeders and other agricultural researchers has increased dramatically during the past 5 yr for analyzing multi‐environment trial (MET) data. Recently, however, its legitimacy was questioned by a proponent of Additive Main Effect and Multiplicative Interaction (AMMI) analysis. The objectives of this review are: (i) to compare GGE biplot analysis and AMMI analysis on three aspects of genotype‐by‐environment data (GED) analysis, namely mega‐environment analysis, genotype evaluation, and test‐environment evaluation; (ii) to discuss whether G and GE should be combined or separated in these three aspects of GED analysis; and (iii) to discuss the role and importance of model diagnosis in biplot analysis of GED. Our main conclusions are: (i) both GGE biplot analysis and AMMI analysis combine rather than separate G and GE in mega‐environment analysis and genotype evaluation, (ii) the GGE biplot is superior to the AMMI1 graph in mega‐environment analysis and genotype evaluation because it explains more G+GE and has the inner‐product property of the biplot, (iii) the discriminating power vs. representativeness view of the GGE biplot is effective in evaluating test environments, which is not possible in AMMI analysis, and (iv) model diagnosis for each dataset is useful, but accuracy gain from model diagnosis should not be overstated.
... The GGE biplot methodology (Yan et al., 2000; Yan, 2001, 2002; Yan and Kang, 2003; Yan and Tinker, 2006) consists of a set of biplot interpretation methods, whereby important questions regarding genotype and test-environment evaluation can be visually addressed. Even more, plant breeders and other agronomists have found GGE biplots useful in test environment evaluation (Yan and Rajcan, 2002; Blanche and Myers, 2006; Thomason and Phillips, 2006). The aim of this work was to determine which environment of those typically concerned by asparagus breeders is most useful for genotype selection during asparagus breeding programmes. ...
Asparagus is a perennial crop which remains in production for at least 10 years. Therefore, the appropriate election of cultivars is crucial for asparagus growers. The aim of this work was to determine which environment is most desirable for enhancing asparagus clonal hybrids selection. Thirty four asparagus clonal hybrids and two testers were planted in a complete randomized block design. Total yield was evaluated for each hybrid in four environments conformed by combinations between age of culture and type of production. Data were subjected to an ANOVA and broad sense heritability was calculated for each environment. GGE biplot methodology was also used. The second productive season (for blanched and green production) was the best test environment and the most powerful to discriminate genotypes. Selection in this productive season would reduce time and costs in asparagus cultivars evaluation.
Pearl millet is cultivated under the most adverse agro-climatic conditions challenged by low and erratic rainfall, high mean temperature, high potential evaporation, and infertile and shallow soils with poor water holding capacity resulting in the huge temporal and spatial variation in its productivity and unstable production. The objective of this chapter is to review the strategies for achieving higher productivity and greater stability in India and Africa and to suggest future approaches and necessary interventions in pearl millet breeding meet the forthcoming challenges to provide higher and stable yields. The major strategies for enhancing yield included strategic use of germplasm resources and cultivar development (mostly hybrids) with targeted traits as per the requirement of production ecologies. On the other hand, the approach to augmenting stability has been improving genetic resistance to diseases, increasing tolerance to abiotic stresses, and addressing regional adaptation. Following the adoption of high-yielding, disease-resistant and drought-tolerant cultivars and crop production technology, pearl millet productivity has been consistently increasing in India. In view of increasing demand of pearl millet grain and stover in future, the higher yields are to be targeted. Pearl millet cultivation is likely to become more challenging because of predicted intense drought stress, rise in temperature, and greater disease incidences in sub-Sahara Africa and South Asia; yields must be increased at a much faster rate with greater stability in challenging agro-ecologies. Speed breeding and molecular-marker assisted breeding are going to play a very important role to enhance genetic gains in future. Heterotic grouping of hybrid parental lines would be an important strategy to increase the magnitude of heterosis on a long-term basis. Mainstreaming the bio-fortification is essentially needed to amalgamate higher productivity with nutritional traits to address both energy and micronutrient malnutrition issues.
Two multi-year field trials were conducted to evaluate boxwood cultivars for their susceptibility to the blight pathogens Calonectria pseudonaviculata and C. henricotiae in northern Germany. Fifteen cultivars were included in the first trial from 2007 to 2012, and 46 cultivars in the second trial from 2014 to 2017. Both trials were done in a naturally infested field, supplemented with infected plant tissue added to the soil before planting. Each cultivar had three replicate hedge sections with ten plants per section and they were assessed annually for blight severity expressed as proportion of leaves blighted and fallen. Blight severity varied significantly among years (P < 0.0001) and cultivars (P < 0.05) within each trial. In the first trial, mean severity ranged from 0.03 to 0.11 for the most resistant cultivars and 0.35 to 0.96 for the most susceptible ones. Similarly, in the second trial, mean severity ranged from 0.06 to 0.27 and 0.71 to 0.97 for the most resistant and susceptible cultivars, respectively. ‘Suffruticosa’ was consistently the most susceptible cultivar, followed by ‘Marianne’, ‘Myosotidifolia’, ‘Raket’ and ‘Morris Midget’. ‘Herrenhausen’ was the most resistant cultivar, followed by B. microphylla var. japonica, B. microphylla var. koreana, ‘Green Mound’, ‘Faulkner’, and ‘Winter Beauty’. This study provides field data showing the performance of boxwood cultivars under different levels of disease pressure in an area where C. henricotiae was dominant. This knowledge will help boxwood growers and gardeners to choose less susceptible cultivars and help plant breeders to select for disease resistance.
Introduction and Objective: Selection of desirable hybrids compared to other maize hybrids
(Zea mays L.) is one of the methods used to achieve high grain yield in maize. Also, the most
important morphological features affecting grain yield can be used in the selection and
introduction of genotypes.
Material and Methods: This study was conducted to investigate the relationship between
different traits with grain yield and the selection of the most important morphological
characteristics affecting the grain yield of corn hybrids for genotype selection. The experiment
was conducted as a randomized complete block design (RCBD) with three replications in the
research farm of Islamic Azad University, Karaj Branch in the cropping years of 2018-2019 on
12 commercial single cross corn hybrids.
Results: The results of combined analysis of variance showed that the genotypes had a
significant difference in the probability level of 0.01 in terms of agronomic traits. The effect of
year × genotype was also significant in ear length, grain width, grain length, grain thickness,
1000-grain weight and grain yield. Based on the results of Duncan method comparison,
KSC704 and KSC707 genotypes were selected as the highest ranked hybrids. In comparison
with the mean of genotype × year in terms of grain yield, SC302 hybrid and KSC701 and
KSC706 hybrids in the second crop year were identified as top-ranked genotypes. Factor
analysis by Verimex method introduced four factors that explained 73% of the variance of the
data and were named as grain characteristics, ear characteristics, plant height and ear length.
The results of correlation analysis between traits also showed a positive and significant
correlation between ear length trait with number of rows per ear and grain yield. Also, the
number of rows per ear had a positive and significant correlation with grain width and grain
length. Graphic analysis based on polygonal view of KSC707, KSC706, KSC260, KSC705 and
SC604 genotypes were more superior to other hybrids studied. In the genotype ranking chart,
KSC707 hybrid was identified as the ideal genotype, which was more favorable than other
genotypes in terms of studied traits. The correlation diagram between the traits showed a
positive and significant correlation of most of the traits with the yield trait, based on which the
traits of grain width, 1000-grain weight, grain length, ear length, number of rows per ear and
grain yield had a positive and significant correlation. They were together. Based on the
grouping diagram of genotypes according to the studied traits, genotypes were grouped into four
parts.
Conclusion: In general, KSC707 genotype was identified as the optimal genotype in terms of
the studied traits. The results of this study, which was evaluated in two cropping years, indicate
that these genotypes can be used in breeding programs to increase yield.
Wheat crop contributes to a major portion of the agriculture economy of Nepal. It is ranked as the third major cereal crop of the country even though, it faces terminal heat stress which speeds up the grain filling rate and shortens the filling period, causing reduction in grain weight, size, number and quality losses. We can minimize this loss through a genotypic selection of high-yielding lines by understanding the genotype-environment interaction. The objective of this research is to obtain a high yielding line with a stable performance across the environments. In order to do so, we conducted an experiment using eighteen elite wheat lines and two check varieties in alpha-lattice design with two replications in different environments viz. irrigated and terminal heat stress environment from November 2019 to April 2020. The analysis of variance revealed that genotype, environment and their interaction had a highly significant effect on the yield. Furthermore, the which-won–where model indicated specific adaptation of elite lines NL-1179, NL-1420, BL-4407, NL-1368 to the irrigated environment and Bhirkuti to the terminal heat-stressed environment. Similarly, the mean-versus-stability study indicated that elite lines BL-4407, NL-1368, BL-4919, NL-1350, and NL-1420 had above-average yield and higher stability whereas elite lines Gautam, NL-1412, NL-1376, NL-1387, NL-1404, and N-1381 had below-average yield and lower stability. The ranking of elite lines biplot, PC1 explaining 73.6% and PC2 explaining 26.4% of the interaction effect, showed the rank of elite line, NL-1420 > NL-1368> NL-1350 > other lines, close to the ideal line. On the basis of the obtained results, we recommend NL-1420 with both the high yield and stability is suited across both the environments, while NL-1179 and Bhirkuti is adapted specifically for irrigated and terminal heat stress environment, respectively.
Plant breeding programs use multi-environment trial (MET) data to select superior lines, with the ultimate aim of increasing genetic gain. Selection accuracy can be improved with the use of advanced statistical analysis methods that employ informative models for genotype by environment interaction, include information on genetic relatedness and appropriately accommodate within-trial error variation. The gains will only be achieved, however, if the methods are applied to suitable MET datasets. In this paper we present an approach for constructing MET datasets that optimizes the information available for selection decisions. This is based on two new concepts that characterize the structure of a breeding program. The first is that of “contemporary groups,” which are defined to be groups of lines that enter the initial testing stage of the breeding program in the same year. The second is that of “data bands,” which are sequences of trials that correspond to the progression through stages of testing from year to year. MET datasets are then formed by combining bands of data in such a way as to trace the selection histories of lines within contemporary groups. Given a specified dataset, we use the A-optimality criterion from the model-based design literature to quantify the information for any given selection decision. We demonstrate the methods using two motivating examples from a durum and chickpea breeding program. Datasets constructed using contemporary groups and data bands are shown to be superior to other forms, in particular those that relate to a single year alone.
Field experiment carried out in the seasons 2011/2012 and 2012/2013 in Abu
Ghraib Research Station/ Agricultural Research Office. Experiment included 16
genetic entrance from ICARDA in addition to the local variety (D7) for comparison.
Treatments were distributed in (RCBD) design with three replications. Significant
differences were observed among all genotypes and studied traits. ICARDA-14 and
local variety, gave higher values (84 and 85.11 cm) for plant height, local variety and
ICARDA -16 (9.5 and 8.940 cm) for spike length, ICARDA-8 and 12 (20.57 and
20.5) for spikelet number/spike, ICARDA-9 and 16, (70.2 and 66.32) for grain
number/sike, ICARDA -6 and 2 gave the highest weight of 1000 seed, (41.18 and 38.76
g), while ICARDA -12 and 3 were superior in spikes number/m2 (527.5 and 490.2),
grain greater for ICARDA -12 and 3 (5.187 and 4.787 t / h). The highest biological
yield was for each ICARDA-1 and the local variety, which were superior in dry
weight also (19.95 and 17.61 t / ha) but the highest harvesting index was for
ICARDA-8 (38.06 and 35.32%) in both seasons, respectively.
Pasta wheat (Triticum turgidum var. durum Desf.), or “candeal” wheat, as usually called in Argentina, is a cereal crop of long tradition in southern Buenos Aires province, where it is grown exclusively for supplying the demands of regional as well as national semolina and pasta industries. Although near the end of the decade of ´sixties´ and beginning of ´seventies´ the production of this cereal crop reached substantial levels which allowed the country to participate in the international market, at present, to guarantee the grain volumes required to operate throughout the year (about 250.000 t), the candeal chain depends on pre-sowing cultivation contracts held with producers.
The wideness of cultivation area, and the diversity of soils where it´s sown, along with year-on-year fluctuations of weather factors, usually bring about troubles of luck of consistency in the industrial quality of harvested grain which complicate the operability of the processing industry, whose protocols procedures require high homogeneity in the quality of the raw material. In addition to this, there are the quality variations due to the spectrum of varieties in use, and their different response to fluctuations imposed by environment. This is why, for assisting the production sector in making decisions for allowing stabilize quality levels throughout the time, it becomes necessary the implementation of studies to deeply explore the way different variables integrated in grain and semolina quality, are affected by environmental factors, and by the complex relationships governing the response of genotypes to it.
Within this context, in the present thesis work the effects of environment, genotype and genotype-environment interactions (GE) on six quality attributes of interest for industry, three on the grain, test weight, vitreousness and protein content, and three on semolina, yellow index, and gluten quantity and quality, were analyzed on five commercial cultivars of pasta wheat, sown during three crop cycles in four locations belonging to Sub Humid Central South and Semiarid South West sub-regions of the candeal producing area.
Outcomes of the study revealed the existence of wide differences in relative contribution of environment, genotype and GA interactions for the six attributes analyzed, with environment effects prevailing on the three variables measured on grain and semolina gluten content, and a greater impact of genotype on yellow index and gluten strength.
Concerning GE interactions, they were particularly important for grain protein and wet gluten contents, while for the rest, they were smaller, and generally associated with changes which were in proportion to environmental fluctuations. Cultivar cycle length from emergence to heading, and in second place, yield and grain size and shape, were the variables which influenced the most the differential response of varieties to environment, with exception of gluten index where response sensitivity showed to be mainly related with variety intrinsic gluten strength.
Among sub-regions, Semiarid South West, stood out consistently with high levels of grain protein, wet gluten content and yellow index, and values of test weight and vitreousness also satisfactory and stable between seasons, which can be considered a strength for this region considering its characteristic lower yield levels. Within Central South, where yields were generally higher, grain quality was satisfactory to the same extent, with La Dulce standing out with consistently high values of test weight, where Barrow showed more difficulty to satisfy the demands imposed by Grade 1 of the trading standard and inconsistency among seasons. The latest, in contrast, showed the highest levels of vitreousness, even in cases when grain protein was in the intermediate-range values.
Despite the differences in agro-climatic conditions, and in the yield and quality of harvested grain, evidence gathered in this study revealed that, concerning quality, tested locations represented a single mega-environment of response, where quality levels exhibited by genotypes were, in general, proportionate to environmental changes, and sporadic episodes of water deficit or excess and/orhigh temperatures during grain filling, were the most likely causes of alterations in genotypes´ behavior.
Cold is the most important abiotic factor that affect rice yield in Chile, which can alter the phenology and physiology of the rice at seedling stage. With the aim to increase the accuracy for cold tolerance evaluation in Chilean Rice Breeding Program of the Instituto de Investigaciones Agropecuarias (INIA), 109 experimental lines were evaluated to cold tolerance using morphological and physiological traits, at seedling stage. Cold treatment was achieved by placing seedlings at 5 °C on dark for 72 h and evaluations were made after seven days recovery. Leaf chlorosis based on the standard evaluation system scale (SES), Chlorophyll content (Chl), Malondialdehyde concentration (MDA) and maximum quantum yield of Photosystem II (Fv/Fm) were evaluated. Best linear unbiased prediction (BLUP) for all traits and multivariate analysis were made in order to determine the cold tolerant genotypes. Variability in cold tolerance among experimental lines was described by principal component and cluster analysis of BLUPs for all traits. The broad sense heritability calculated for SES scale was the highest (0.54), while for Fv/Fm was the lowest (0.10). Genotypes with high cold tolerance were Quila 242002 and Quila 241304, while more susceptible genotypes were Quila 64117, Quila 260312 and Quila 241607. The results suggest that the BLUPs and multivariate analysis allow adequate clustering of rice genotypes according to the degree of their cold tolerance. Finally, we suggest that SES scale and Chl content were the most suitable traits to evaluate cold tolerance for the rice genotypes studied and for the conditions evaluated.
Mohammadi, R. and Amri, A. 2012. Analysis of genotype x environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods. Can. J. Plant Sci. 92: 757-770. Multi-environment trials (MET) are conducted annually throughout the world in order to use the information contained in MET data for genotype evaluation and mega-environment identification. In this study, grain yield data of 13 durum and one bread wheat genotypes grown in 16 diversified environments (differing in winter temperatures and water regimes) were used to analyze genotype by environment (GE) interactions in rain-fed durum MET data in Iran. The main objectives were (i) to investigate the possibility of dividing the test locations representative for rain-fed durum production in Iran into mega-environments using the genotype main effect plus GE interaction (GGE) biplot model and (ii) to compare the effectiveness of the GGE-biplot and several non-parametric stability measures (NPSM), which are not well-documented, for evaluating the stability performance of genotypes tested and the possibility of recommending the best genotype(s) for commercial release in the rain-fed areas of Iran. The results indicate that the grain yield of different genotypes was significantly influenced by environmental effect. The greater GE interaction relative to genotype effect suggested significant environmental groups with different top-yielding genotypes. Warm environments differed from cold environments in the ranking of genotypes, while moderate environments were highly divergent and correlated with both cold and warm environments. Cold and warm environments were better than moderate environments in both discriminating and representativeness, suggesting the efficiency and accuracy of genotype selection would be greatly enhanced in such environments. According to the NPSM, genotypes tend to be classified into groups related to the static and dynamic concepts of stability. Both the GGE-biplot and NPSM methods were found to be useful, and generally gave similar results in identifying high-yielding and stable genotypes. In contrast to NPSM, the GGE-biplot analysis would serve as a better platform to analyze MET data, because it always explicitly indicates the average yield and stability of the genotypes and the discriminating ability and representativeness of the test environments.
Pearl millet [Pennisetum glaucum (L.) R. Br.] is grown under a wide range of environmental conditions in India. The All India Coordinated Pearl Millet Improvement Project (AICPMIP) has the responsibility of testing and releasing pearl millet cultivars adapted to such conditions. As a part of this process, AICPMIP has divided the entire pearl millet growing regions into three different zones (A 1, A, and B) based on the rainfall pattern and local adaptation of the crop. This study was conducted to define the presently used test locations into possible mega-environments and to identify essential test locations for cost-effective evaluation of pearl millet cultivars. Grain yield data of different sets of 34 to 45 medium-maturity pearl millet hybrids tested at 29 to 34 locations during 2006 to 2008 were analyzed using genotype main effects and genotype x environment interaction biplot method. Two distinct pearl millet mega-environments with consistent grouping of locations across the years and corresponding to AICPMIP's designated A and B zones were identified. No such consistent grouping of locations corresponding to AICPMIP's designated A 1 zone was, however, observed. Based on the discriminating ability, uniqueness, and research resources, 13 locations were identified as essential test locations for evaluation across the two mega-environments. Testing at these locations appeared to provide good coverage of the whole pearl millet growing areas of India. Based on these findings, it is suggested to conduct initial yield trials at identified 13 locations across all the pearl millet growing zones represented by two mega-environments followed by testing of selected hybrids with specific adaptation in their respective adaptation zones.
El objetivo del presente estudio fue identifi car
ambientes representativos y discriminatorios para seleccionar
genotipos de arroz mediante el Biplot GGE. Se recurrió a la
base de datos del proyecto de arroz periodo 2001-2009. Se
analizaron: rendimiento de grano (toneladas/hectárea) y la
proporción de granos enteros, de manera individual, y mediante
un índice de selección (rendimiento + granos enteros).
La información fue analizada mediante el programa Biplot
GGE. A cada Biplot generado se le determinó la distancia en
milímetros entre localidades verdaderas y la ideal; posteriormente
las distancias fueron estandarizadas. Además se estimó
la capacidad discriminatoria y representatividad de las localidades.
A excepción de Alanje, las localidades más apropiadas
para rendimiento (Soná, Barú), no fueron las mismas para
granos enteros (Tonosí, Barú, Divisa). El índice de selección
identifi có las localidades apropiadas para seleccionar (Tonosí,
Alanje, Calabacito, Soná, Barú). Todas las localidades fueron
efectivas en su capacidad discriminatoria para rendimiento. Se
encontraron diferencias en representatividad, siendo Calabacito
y Changuinola, las de mayor y menor representatividad,
respectivamente. Las localidades presentaron similar capacidad
discriminatoria y representatividad para granos enteros.
Al integrar rendimiento más granos enteros, se hizo posible
separar las localidades más discriminatorias (Remedios, Tanara,
Alanje) y las más representativas (Calabacito, Tonosí,
Barú). Las implicaciones prácticas de este trabajo es que nos
permitirá priorizar la investigación en aquellas localidades
más apropiadas para identifi car genotipos superiores.
Multi-environment trials (MET) are commonly conducted in plant breeding programs to identify superior genotypes and to determine mega-environments in a targeted region. The main objectives of the study were to graphically analyze MET data from rainfed barley (Hordeum vulgare L.) and interpret genotype-by-environment (GE) interaction using GGE biplot methodology and Kang's two parameters, i.e., yield-stability statistic (YSi) and rank-sum (RS). The analyses were performed on grain yield data of 18 rainfed barley genotypes, derived from an Iran/ICARDA joint project, grown at three representative rainfed barley locations in warm areas of Iran during three cropping seasons (2007–2009). Combined ANOVA indicated that the environment accounted for 87.8% of total variation, followed by GE interaction. The large magnitude of the GE interaction relative to genotypic effect suggested the possible existence of sub-environmental groups for the genotypes. Collective analyses of yearly and combined GGE biplots were used to identify high-yielding genotypes and their areas of adaptation, and suggested the existence of two rainfed barley mega-environments. The “discriminating power vs. the representative view” of the GGE biplot identified Moghan as the location with high discriminating ability and greater representativeness, suggesting the possibility of testing genotypes adapted to warm rainfed areas at this location. The YSi and RS statistics as well as GGE biplot gave similar results for simultaneously selecting for high yield and stability. Based on the simultaneous selection index, the genotypes G9 (ChiCm/An57//Albert/3/Alger/Ceres362-1-1) and G13 (Onslow/Arda2) could be recommended for commercial release in warm rainfed areas of Iran. The study also indicated that YSi and RS were as efficient as the GGE biplot method in selecting high-yielding and stable genotypes under variable environments.
Although Cambisols are the predominant soil type in Central Europe, especially in less favoured mountain areas, the long-term sustainability of winter wheat production on such soils has not been examined. In this paper, the yield of winter wheat over 50 years of farmyard manure, N, P and K fertilizer application (12 treatments altogether) was analysed in the Lukavec Crop Rotation Experiment (LCRE), which was established in 1955 in a potato-growing area (mean annual precipitation and temperature 686mm and 6.8°C, respectively).In the unfertilized control, low plant available P, K and Mg concentrations were recorded after 50 years. The annual yield growth (AYG) of grain was negative in the control as well as in low N treatments and positive in the 46kgNha−1 treatment. The mean AYG ranged from 7.1 to 72.8kgha−1 following the application of 46 to 121kgNha−1, respectively. In the first decade of the experiment, the increase in grain yield per 1kg of applied N was 7.3kgha−1 while in the last decade it was 27.1kgha−1. The mean grain yield of long-straw and short-straw varieties was 3.9 and 4.7tha−1, respectively. In the control, the grain yields were 4.3, 3.2 and 2.4tha−1 after root crops, legumes and cereals, respectively.To summarize the 50 years’ results of winter wheat production in the LCRE, grain yield was the most affected by mineral fertilizers, followed by the effect of variety, the preceding crop and farmyard manure application. The long-term sustainability of winter wheat production on low productive sandy-loamy Cambisols can be achieved only by adequate application of N, P and K fertilizers. High year-to-year variation in grain yield stresses the necessity of long-term studies in crop research, which are able to separate real trends from inter-annual fluctuations.
Whether production of winter wheat is stable without any fertilizer input on Greyic Phaeozem in the Czech Republic still remains unsolved. Phaeozems represent 3% of soils in Europe and they are particularly common in wet steppe regions. In this paper, the yield of winter wheat over 50 years of farmyard manure (FYM), N, P and K fertilizer application (12 treatments altogether) was analyzed in the Čáslav Crop Rotation Experiment (CCRE), which was established in 1955 in a sugar beet growing area (mean annual precipitation and temperature 555 mm and 8.9 °C, respectively).In the unfertilized control treatment, low plant available P and suitable K concentrations were recorded after 50 years. The annual yield growth (AYG) of grain was positive, even in the control treatment without any fertilizer input. The mean AYG ranged from 24.9 kg ha−1 in the control treatment to 73.6 kg ha−1 following the application of 119 kg N ha−1. In the first two decades of the experiment, there was no significant effect of treatment on grain yield. The increase in grain yield per 1 kg of applied N was 20.1 kg ha−1 in the last decade of the experiment. The mean grain yields of long-straw and short-straw varieties were 3.8 and 5.5 t ha−1, respectively. In the control, the grain yields were 4.8, 4.1 and 3.3 t ha−1 after legumes, root crops and cereals, respectively.To summarize the 50 years of results of winter wheat production in the CCRE, grain yield was most influenced by variety, followed by mineral fertilizers, the preceding crop and only moderately by FYM application. The highest grain yields were recorded in treatments with the highest N application rate. The long-term stability of winter wheat production on Greyic Phaeozem can be expected under a crop rotation system with legumes and root crops, even without any fertilizer input.
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.
In this paper we present the analysis of yield data from a broad cross-section of crop variety evaluation programmes (CVEP) conducted in Australia. The main sources of variety by environment interaction are ‘non-static’ interactions, namely those linked to seasonal influences. These contributed an average of 41·3% of the total variance. In contrast the static component accounts for only 5·3% of the total.
We develop methods for determining the relative accuracy of CVEP based on selection of newly promoted entries. The accuracy of the current testing regimes for the Australian CVEP under study is determined. The accuracy of alternative schemes, with different numbers of years of testing, numbers of locations per year and numbers of replicates per trial is also examined. Cost effective methods for improving the accuracy of CVEP are discussed.
Partial least squares (PLS) and factorial regression (FR) are statistical models that incorporate external environmental and/or cultivar variables for studying and interpreting genotype × environment interaction (GEI). The Additive Main effect and Multiplicative Interaction (AMMI) model uses only the phenotypic response variable of interest; however, if information on external environmental (or genotypic) variables is available, this can be regressed on the environmental (or genotypic) scores estimated from AMMI and superimposed on the AMMI biplot. The objectives of this study with two wheat [Triticum turgidum (L.) var. durum] field trials were (i) to compare the results of PLS, FR, and AMMI on the basis of external environmental (and cultivar) variables, (ii) to examine whether procedures based on PLS, FR, and AMMI identify the same or a different subset of cultivar and/or environmental covariables that influence GEI for grain yield, and (iii) to find multiple FR models that include environmental and cultivar covariables and their cross products that explain a large proportion of GEI with relatively few degrees of freedom. Results for the first trial showed that AMMI, PLS, and FR identified similar cultivar and environmental variables that explained a large proportion of the cultivar × year interaction. Results for the second wheat trial showed good correspondence between PLS and FR for 23 environmental covariables. For both trials, PLS and FR complement each other and the AMMI and PLS biplots offered similar interpretations of the GEI. The FR analysis can be used to confirm these results and to obtain even more parsimonious descriptions of the GEI.
A systematic comparison between two cultivar evaluation and recommendation systems, i.e., the balanced and replicated performance trials conducted in small plots at a small number of locations, and the unbalanced and non‐replicated on‐farm trials conducted in large strips on many farms, is lacking. This study was initiated to investigate the usefulness of the two contrasting systems in cultivar evaluation and the relationships between them. Yield data from Ontario winter wheat ( Triticum aestivum L.) strip trials and performance trials for 1998 to 2000 were analyzed by mixed models. For all 3 yr, results from the two systems were highly correlated, both in terms of the best linear unbiased predictors (BLUP) and for the t ‐values of BLUP. Cultivars judged to be superior (or inferior) by one system were never judged to be inferior (or superior) by the other. Thus, both on‐farm strip trials and replicated small‐plot trials provide valid data for effective cultivar evaluation. On the basis of t ‐statistics, which measure cultivar reliability, cultivars can be classified into superior ( t ≥ 2), inferior ( t ≤ −2), and intermediate or inadequately tested (−2 < t < 2). Two cultivars can be regarded as different in reliability if their t ‐values differ by ≥3. The evaluation power of strip trials for a cultivar depends on the number of trials in which the cultivar is tested; a cultivar may not be adequately evaluated if it is tested in fewer than 20 trials.
This note considers a restricted maximum likelihood (REML) procedure for mixed models with multiplicative terms that was recently suggested by B. J. Gogel, B. R. Cullis and A. P. Verbyla [ibid. 51, 744-749 (1985)]. It discusses an extension to certain heteroscedastic mixed models, which are of interest for plant breeders.
The French INRA wheat (Triticum aestivum L. em Thell.) breeding program is based on multilocation trials to produce high-yielding, adapted lines for a wide range of environments. Differential genotypic responses to variable environment conditions limit the accuracy of yield estimations. Factor regression was used to partition the genotype-environment (GE) interaction into four biologically interpretable terms. Yield data were analyzed from 34 wheat genotypes grown in four environments using 12 auxiliary agronomic traits as genotypic and environmental covariates. Most of the GE interaction (91%) was explained by the combination of only three traits: 1,000-kernel weight, lodging susceptibility and spike length. These traits are easily measured in breeding programs, therefore factor regression model can provide a convenient and useful prediction method of yield.
A systematic comparison between two cultivar evaluation and recommendation systems, i.e., the balanced and replicated performance trials conducted in small plots at a small number of locations, and the unbalanced and non-replicated on-farm trials conducted in large strips on many farms, is lacking. This study was initiated to investigate the usefulness of the two contrasting systems in cultivar evaluation and the relationships between them. Yield data from Ontario winter wheat (Triticum aestivum L.) strip trials and performance trials for 1998 to 2000 were analyzed by mixed models. For all 3 yr, results from the two systems were highly correlated, both in terms of the best linear unbiased predictors (BLUP) and for the t-values of BLUP. Cultivars judged to be superior (or inferior) by one system were never judged to be inferior (or superior) by the other. Thus, both on-farm strip trials and replicated small-plot trials provide valid data for effective cultivar evaluation. On the basis of t-statistics, which measure cultivar reliability, cultivars can be classified into superior (t greater than or equal to 2), inferior (t less than or equal to -2), and intermediate or inadequately tested (- 2 < t < 2). Two cultivars can be regarded as different in reliability if their t-values differ by greater than or equal to3. The evaluation power of strip trials for a cultivar depends on the number of trials in which the cultivar is tested; a cultivar may not be adequately evaluated if it is tested in fewer than 20 trials.
Multiplicative statistical models such as the additive main effects and multiplicative interaction model (AMMI), the genotypes regression model (GREG), the sites regression model (SREG), the completely multiplicative model (COMM), and the shifted multiplicative model (SHMM) are useful for studying patterns of yield response across sites and estimating realized cultivar responses in specific environments. Traditionally the series of multiplicative terms is truncated at some point beyond which further terms are believed to have little statistical Significance or predictive value. Shrinkage estimators have been advocated as a model fitting method superior to model truncation. In this study, by data splitting and cross validation, we evaluated the predictive accuracy of (i) truncated multiplicative models, (ii) shrinkage estimators of multiplicative models, (iii) Best Linear Unbiased Predictors (BLUP) of the cell means based on a two-way random effects model with interaction, and (iv) empirical cell means in one wheat [durum (Triticum turgidum L. var. durum) and bread (Triticum aestivum L.)] and four maize (Zea mays L.) cultivar trials, with and without adjustment for replicate differences within environments. Shrinkage estimates of multiplicative models were at least as good as the better choice of truncated models fitted by least squares or BLUPs. Shrinkage estimation yields potentially better estimates of cultivar performance than do truncated multiplicative models and eliminates the need for cross validation or tests of hypotheses as criteria for determining the number of multiplicative terms to be retained. If random cross validation is used to choose a truncated model, data should be adjusted for replicate differences within environments.
Regression on environmental mean was investigated for measuring production stability of grain and straw yields of 80 lines of oats ( Avena sativa L.) tested in 24 field environments. Mean squares for heterogeneity among regressions suggested that the regression parameter was not heritable for grain yield, but may be heritable for straw yield. The regression lines for straw yield tended to converge at an environmental yield level below that normally used for oat production. Therefore, selection with use of mean yields alone would save cultivars that are superior at all yield levels. This situation was attributed to a high correlation between regression coefficients and mean yields.
Analyses were conducted on direct and cube‐root scales of measurement. The cube‐root scale was chosen to reduce the heterogeneity of error mean squares obtained from the 24 individual environments, but also was found to reduce the significance of the mean squares for heterogeneity among regressions. The effect of a transformation of the data on heterogeneity and usefulness of regression coefficients is discussed.
The model, Yij = μ1 + β1Ij + δij, defines stability parameters that may be used to describe the performance of a variety over a series of environments. Yij is the variety mean of the ith variety at the jth environment, µ1 is the ith variety mean over all environments, β1 is the regression coefficient that measures the response of the ith variety to varying environments, δij is the deviation from regression of the ith variety at the jth environment, and Ij is the environmental index.
The data from two single-cross diallels and a set of 3-way crosses were examined to see whether genetic differences could be detected. Genetic differences among lines were indicated for the regression of the lines on the environmental index with no evidence of nonadditive gene action. The estimates of the squared deviations from regression for many hybrids were near zero, whereas extremely large estimates were obtained for other hybrids.
Large genotype x environment (g x e) interaction variances for yield relative to those for genotypes have been recognized for wheat cultivars in Queensland. The utility of a linear model to explain these interactions was examined by yield-testing 100 different wheat cultivars at nine different environments, including four locations and three years, in south-eastern Queensland. The linear model was found to explain less than 40% of the total g x e interaction and to give only a general indication of cultivar responses to different environments. Selection strategies to identify widely adapted cultivar involving several parameters (mean cultivar yield over all environments, the g x e interaction for each cultivar and the regression coefficient for each cultivar), singly and in combination, were evaluated. Greater selection differentials were found in most environments when selection was practiced for high mean yield across all environments when the yield of each cultivar in each environment was expressed as a percentage of the environment mean yield.
Multienvironment trials (MET) are conducted every year for all major crops throughout the world, and best use of the information contained in MET data for cultivar evaluation and recommendation has been an important issue in plant breeding and agricultural research. A genotype main effect plus genotype x environment interaction (GGE) biplot based on MET data allows visualizing (i) the which-won-where pattern of the MET, (ii) the interrelationship among test environments, and (iii) the ranking of genotypes based on both mean performance and stability. Correct visualization of these aspects, however, requires appropriate singular-value (SV) partitioning between the genotype and environment eigenvectors. This paper compares four SV scaling methods. Genotype-focused scaling partitions the entire SV to the genotype eigenvectors; environment-focused scaling partitions the entire SV to the environment eigenvectors; symmetrical scaling splits the SV symmetrically between the genotype and the environment eigenvectors; and equal-space scaling splits the SV such that genotype markers and environment markers take equal biplot space. It is recommended that the genotype-focused scaling be used in visualizing the interrelationship and comparison among genotypes and the environment-focused scaling be used in visualizing the interrelationship and comparison among environments. All scaling methods are equally valid in visualizing the which-won-where pattern of the MET data, but the symmetric scaling is preferred because it has all properties intermediate between the genotype- and the environment-focused scaling methods.
Cultivar evaluation and mega-environment identification are among the most important objectives of multi-environment trials (MET). Although the measured yield is a combined result of effects of genotype (G), environment (E), and genotype x environment interaction (GE), only G and GE are relevant to cultivar evaluation and mega-environment identification. This paper presents a GGE (i.e., G + GE) biplot, which is constructed by the first two symmetrically scaled principal components (PC1 and PC2) derived from singular value decomposition of environment-centered MET data. The GGE biplot graphically displays G plus GE of a MET in a way that facilitates visual cultivar evaluation and mega-environment identification. When applied to yield data of the 1989 through 1998 Ontario winter wheat (Triticum aestivum L.) performance trials, the GGE biplots clearly identified yearly winning genotypes and their winning niches. Collective analysis of the yearly biplots suggests two winter wheat mega-environments in Ontario: a minor mega-environment (eastern Ontario) and a major one (southern and western Ontario), the latter being traditionally divided into three subareas. There were frequent crossover GE interactions within the major mega-environment but the location groupings were variable across years. It therefore could not be further divided into meaningful subareas. It was revealed that in most years PC1 represents a proportional cultivar response across locations, which leads to noncrossover GE interactions, while PC2 represents a disproportional cultivar response across locations, which is responsible for any crossover GE interactions. Consequently, genotypes with large PC1 scores tend to give higher average yield, and locations with large PC1 scores and near-zero PC2 scores facilitates identification of such genotypes.
The question of choice of selection criterion when lines are grown in stress and non‐stress environments is examined from a theoretical standpoint in this paper. Tolerance to stress is defined as the difference in yield between stress and non‐stress environments, while mean productivity is the average yield in stress and non‐stress environments. Equations are developed for the genetic correlations of tolerance and mean productivity with one another and with yields in stress and non‐stress environments in terms of the ratio of genetic variances and the genetic correlations between yields in stress and non‐stress environments. These equations show that selection for tolerance to stress will generally result in a reduced mean yield in non‐stress environments and a decrease in mean productivity. Selection for mean productivity will generally increase mean yields in both stress and non‐stress environments. Tolerance and mean productivity show negative genetic correlations whent he genetic variance in stress environmentsi s less than the genetic variance in non‐stress environments. This result provides an explanation for the positive correlations often reported between regression coefficient stability and mean productivity; a line with high tolerance to stress normally would have a low regression coefficient stability and genetic variances in stress environments are generally lower than in non‐stress enviornments.
This research was undertaken to obtain information about the type of yield trial environment that will foster maximum progress from selection. Theory and parameter estimates from five crops that are relevant to the question of the optimum nursery environment for yield testing are reported. The five crop species were barley, Hordeum vulgare L.; wheat, Triticum aestivum L.; oats, Avena sativa L.; soybean, Glycine max (L.) Merr.; and flax, Linum usitatissimum L. Analyses of variance of yield data (a separate analysis for each year and location) reconstructed from information in the annual reports of Uniform Nursery Trials were the sources of our parameter estimates. We defined y as the value of a genotype relative to a test environment, y as the value of a genotype relative to the entire population of environments in which a selected genotype would be used, and H as σ2y/(σ2y + σ2e/n) where σ2y is the variance of y, σ2e, is plot error variance and n is the number of replications in the field trial comparison used as the basis for selection. We then found the proper measure of test environment value to be r√H where r is the correlation between y and y. This is because r√H reflects genotype ✕ environment interaction as well as ✕2e and n, appropriately. The single environment variance analyses available to us provided estimates of ✕2y and ✕2e but not of r. In general, both ✕2y and ✕2e were larger when the nursery yield level was higher. Estimates of √H from the data of low, intermediate and high yielding trials were not greatly different. The indicated advantage (given n = 3) of favorable (high yield) environments over intermediate environments were 4% and 7% for wheat and soybeans, respectively, but in the case of barley, oats and flax, there were no indications that high yield environments were superior. Our results indicate that if any class of environments is eventually established as substantially superior for testing purposes, it will be because the correlation between y and y is comparatively high for that class of environments.
Plant breeding trials produce quantities of data and finding the useful information within that data has historically been a major challenge of plant breeding. A recently developed graphical data summary, called GGEbiplot, can aid in data exploration. GGEbiplot is a Windows application that performs biplot analysis of two-way data that assume an entry X tester structure. GGEbiplot analyzes the data and outputs the results as an image, and it also produces an interactive show of the data. It allows interactive visualization of the biplot from various perspectives. A multienvironment trial data set, in which cultivars are entries and environments are testers, was used to demonstrate the functions of GGEbiplot. These include but are not limited to: (i) ranking the cultivars based on their performance in any given environment, (ii) ranking the environments based on the relative performance of any given cultivar, (iii) comparing the performance of any pair of cultivars in different environments, (iv) identifying the best cultivar in each environment, (v) grouping the environments based on the best cultivars, (vi) evaluating the cultivars based on both average yield and stability, (vii) evaluating the environments based on both discriminating ability and representativeness, and (viii) visualizing all of these aspects for a subset of the data by removing some of the cultivars or environments. GGEbiplot has been applied to visual analysis of genotype X environment data, genotype X trait data, genotype X marker data, and diallel cross data.
A method for the analysis of genotype environment interaction in large data sets is presented and applied to yield data for 49 wheat cultivars grown in each of 63 international environments. Pattern analysis using numerical classification defined separately groups of cultivars and groups of environments, based on similarities in yield performance. The group structure for cultivars was interpreted in terms of similarities and differences in cultivar mean yield and/or cultivar yield response patterns across environments. In addition, the cultivar groups reflected differences in genetical and selectional origin. Environment groups largely reflected differences in the average mean yield of the set of cultivars, but some groups showed differences in response patterns related to differential rust incidence.The cultivar and environment groupings were superimposed on the original data matrix, reducing it to a 100 cell 1010 matrix of group means. The efficiency of the reduction process was measured by comparing the variation retained in the reduced matrix with the total variation available in the original data matrix. Further study of the information retained by the 1010 matrix was made by plotting cultivar group yields and cultivar group interaction effects against an environment group index. The reduction process achieved a size reduction of 97 per cent with the loss of only 18 per cent of the total variation available in the original unreduced matrix. Partitioning was used to identify the nature of this loss. However, the information retained in the reduced matrix maintained the integrity of the cultivar group yield response patterns and allowed comparison of cultivars on a group basis across the environmental range. This reduced greatly the complexity of analysis of cultivar performance and interaction patterns, and simplified the identification and specification of differences in response among cultivars.
The stability and genotypic mean of four traits, grain yield, grain protein content, alveograph W and bread volume, were evaluated
in three multi-location trials, each covering two years. The stability of each genotype was evaluated by environmental variance
(s2
E), interaction variance (s2
W) and variance of the ranks of the phenotypic values corrected for the genotypic effect (s2
R). The bootstrap method was used to study correlations between the genotypic mean and the three stability statistics and to
calculate their accuracy. The repeatability of the stability statistics was measured by correlations between the values obtained
in each of the two years. In addition, theoretical smaller trials were generated by random sampling and the stability values
calculated were correlated with those of the original trial. Environmental variance appears to be usable both for yield and
for quality traits, but there is a risk of counter-selecting a high genotypic mean of W. Whatever the trait and statistic
envisaged, stability is poorly repeatable and its evaluation requires several years and a large number of locations per year
to minimise sampling and environmental effects.
The identification of relevant but diverse environments for the assessment of the relative performance of wheat cultivars was developed by using conventional and pattern analysis procedures. The utility of weighting environments on proximity to a hypothetical most frequently encountered environment, to improve the quality of the predictive inference of relative cultivar performance, was also assessed. There was an increase in the agreement of the relative yield of cultivars obtained in different years using this technique.
Grain yield and grain protein content are two very important traits in bread wheat. They are controlled by genetic factors, but environmental conditions considerably affect their expression. The aim of this study was to determine the genetic basis of these two traits by analysis of a segregating population of 194 F(7) recombinant inbred lines derived from a cross between two wheat varieties, grown at six locations in France in 1999. A genetic map of 254 loci was constructed, covering about 75% of the bread wheat genome. QTLs were detected for grain protein-content (GPC), yield and thousand-kernel weight (TKW). 'Stable' QTLs (i.e. detected in at least four of the six locations) were identified for grain protein-content on chromosomes 2A, 3A, 4D and 7D, each explaining about 10% of the phenotypic variation of GPC. For yield, only one important QTL was found on chromosome 7D, explaining up to 15.7% of the phenotypic variation. For TKW, three QTLs were detected on chromosomes 2B, 5B and 7A for all environments. No negative relationships between QTLs for yield and GPC were observed. Factorial Regression on GxE interaction allowed determination of some genetic regions involved in the differential reaction of genotypes to specific climatic factors, such as mean temperature and the number of days with a maximum temperature above 25 degrees C during grain filling.
Statistics for Experimenters Genotype  environmental interactions for wheat yields and selection for widely adapted wheat genotypes
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Polytechnic Institute and State University.