Printer Friendly

Can spring wheat-growing megaenvironments in the northern Great Plains be dissected for representative locations or niche-adapted genotypes?

GENOTYPE X ENVIRONMENT INTERACTION (GEI) refers to the differential response of genotypes to differing environments. It reduces the association between phenotypic and genotypic values. In plant breeding, GEI is critical if it is associated with significant genotypic rank change (Baker 1988): the commonly known "crossover interaction" (COI). In the presence of COI, selections from one environment may perform poorly in another. This complicates the process of genotypic selection and the identification of superior genotypes for a range of environments in breeding programs. It also makes it difficult for farmers to choose the best cultivar for the unpredictable upcoming year.

One approach to COI is to characterize the environments in terms of the way they influence the relative performance of the genotypes and to identify environmental groupings with negligible COI. Although several statistical models have been proposed to study GEI (DeLacy et al., 1996), only a few of them are known to be capable of distinguishing between significant genotypic rank change (COI) and no genotypic rank change. Two types of models are defined as being suitable for grouping sites within which there is negligible COI (Crossa et al., 2002): the shifted multiplicative model (SHMM) by Seyedsadr and Cornelius (1992) and the sites regression model (SREG) by Cornelius et al. (1996). Crossa et al. (1993) used the SHMM model with an associated cluster method in a study of multilocation trials and confirmed that such a model is able to identify subsets of sites with statistically negligible genotypic rank change. Yan et al. (2000), Yah and Hunt (2001), and Crossa et al. (2002) demonstrated that the application of SREG model with the use of biplots of primary and secondary effects can identify groups of sites with low COI.

Considering that the need to grow different varieties in different environments is mainly due to the existence of GEI, Gauch and Zobel (1996, 1997) defined megaenvironments as a portion of a crop species' growing region with a homogenous environment causing some genotypes to perform similarly. Yan et al. (2001) suggested that if the GEI patterns identified in biplots of the SREG analyses are repeatable over years, megaenvironments can be identified. If such patterns are not repeatable then tested environments belong to a single megaenvironment.

Padbury et al. (2002) categorized the agroecoregions of the land resources of the northern Great Plains and identified five ecoregions in Alberta. For the purpose of cultivar recommendation, however, spring wheat growing regions in Alberta have traditionally been divided into six subregions on the basis of the soil type, geographical, and climatic factors (Alberta seed guide at:; verified 17 January 2006). These divisions are henceforth referred to as "agro-climatic zones." Each year, multilocation data from regional variety testing in the province are summarized on the basis of this geographical classification. This method of summarizing the data gives the impression that each agroclimatic zone is uniform in terms of relative performance of cultivars throughout the region. However, a great amount of within-zone variability is commonly observed and these zones do not truly reflect differential spring wheat adaptations in Alberta. Recently, a new approach has been taken, whereby yearly multilocation data are summarized on the basis of yielding ability of individual sites, regardless of their geographical location. Yang et al. (2005) described a performance-based approach to identify groups of sites with similar yielding ability (isoyield groups), which might not necessarily be contiguous, and recommended the use of this approach for choosing appropriate genotypes for a given environment with a known target yield.

We report here on a study of a dataset of 22 yr of multilocation regional spring wheat variety trials in Alberta, using SHMM and SREG models. This study was undertaken to (i) identify groups of sites with statistically negligible genotypic rank change for each of the 22 yr, (ii) investigate whether there is a repeatable GEI pattern which may lead to the identification of wheat growing megaenvironments, as described by Gauch and Zobel (1996 and 1997), and (iii) identify responsive sites representative of the average varietal performance over the years.


Locations and Genotypes

Long-term grain yield data from the regional hard-red spring wheat variety trials from 1981 to 2002 in Alberta were analyzed. Every year some 10 to 22 genotypes, including newly released varieties or nominated wheat genotypes from different breeding programs in the Canadian Prairies, in addition to check cultivars, were grown in a randomized complete block design with three or four replications at each of 13 to 32 locations throughout the province. The main objective of these trials was to determine the relative performance of the genotypes included in different parts of the province as a guide for producer variety selection. Genotypes were quite diverse for agronomic performance, particularly for disease resistance, lodging tolerance, and earliness. Locations and genotypes varied each year, resulting in highly unbalanced year x genotype and year x location data, but the yearly genotype x location data were balanced. Only trials with a coefficient of variation smaller than 15% and locations repeated at least twice were included for the analysis, giving a total of 472 variety trials. This included 64 wheat genotypes and 47 locations in 22 yr. The locations were distributed over the six agroclimatic zones and nine soil classification groups (Table 1). Plot sizes were different in locations and years. Grain yield per plot data was converted to tonnes per hectare before analyses. Each year's data were subjected to a combined analysis of variance, by SAS PROC GLM (SAS Institute, 1999), with genotypes and locations considered fixed, to estimate the genotype (G) and location (L) main effects and the genotype x location (GL) interaction. Adjusted least square means were computed and used in subsequent SHMM and SREG analyses.

SHMM Analysis

A SHMM model (Seyedsadr and Cornelius, 1992) with a cluster method, as described by Crossa et al. (1993), was used to study the associations among sites for each of the 22 yr of regional wheat variety trials. This was done to identify subsets of sites in each year with reduced frequencies of COI. Furthermore, we examined whether or not there was a repeatable pattern in clusters across years. The SHMM model with t multiplicative or bilinear terms (SHM[M.sub.t] is given by:

[[bar.y].sub.ij.] = [beta] + [t.summation over k=1] [[lambda].sub.k][[alpha].sub.ik][[gamma].sub.jk] + [[bar.[epsilon].sub.ij.]

where [[bar.y].sub.ij.] is the mean of the ith cultivar in the jth environments; [beta] is the shift parameter; [[lambda].sub.k] ([[lambda].sub.1] [greater than or equal to] [[lambda].sub.2] [greater than or equal to] ... [greater than or equal to] [[lambda].sub.t]) are scaling constants (singular values) that allow the imposition of orthonormality constraints on the singular vectors for cultivars, [[alpha].sub.ik] = ([[alpha].sub.1k], ..., [[alpha].sub.gk]) and sites, [[gamma].sub.jk] = ([[gamma].sub.1k], ..., [[gamma].sub.ek]), such that [[summation].sub.i] [[alpha].sup.2.sub.ik] = [[summation].sub.j][[gamma].sup.2.sub.jk] = 1 and [[summation].sub.i][[alpha].sub.ik] [[alpha].sub.ik'] = [[summation].sub.j][[gamma].sub.jk] [[gamma].sub.jk'] = 0 for k [not equal to] k'; [[alpha].sub.ik] and [[gamma].sub.jk] for k = 1, 2, 3, ... are called "primary," "secondary," "tertiary,." .. etc. effects of cultivars and sites, respectively; [[bar.[epsilon]].sub.ij.] is the residual error assumed to be normally and independently distributed with the mean being zero and the variance being ([[sigma].sup.2]/r, where [[sigma].sup.2] is the pooled error variance from the combined ANOVA; and r is the number of replications. The distances for all possible pairs of sites were calculated as the residual sum of squares after SHMMI was fitted to the data from all the site pairs. Dendrograms were constructed using complete linkage clustering methods as implemented in SAS PROC CLUSTER (SAS Institute 1999). For the dendrograms generated for each of the 22 yr, frequencies of COI in each cluster of sites were calculated using a modification of the Azzalini-Cox test for improved sensitivity (Cornelius et al., 1992). In each of the yearly dendrograms, a cut-off point was selected to group sites into subsets with an average COI frequency of less than 10%. This was often the third or forth fusion level in dendrograms. A summary table was then made to represent the association of sites with other sites in each of the agroclimatic zones and the overall association of each site with other sites in Alberta. For this purpose, the association of each site with the sites in each zone was calculated as the number of times this site was clustered together with other sites within the same agroclimatic zones, divided by the total number of site occurrences of the given zone. These values were then averaged over agroclimatic zones to represent the overall association of each site with other sites in the province.

SREG Analysis

The yearly multilocation data were subjected to analyses by the SREG model with two principle components (Cornelius et al., 1996; Yah et al., 2000). The SREG model is:

[[bar.y].sub.ij.] = [[mu].sub.j] + [t.summation over k=1] [[lambda].sub.k] [[alpha].sub.ik] [[gamma].sub.jk] + [[bar.[epsilon]].sub.ij.]

where [[mu].sub.j] is the site mean and other terms are as defined in the SHMM model.

It should be noted that in the SREG model, only the main effects of genotypes (G) plus genotype x location (GL) are absorbed into the bilinear terms but in the SHMM model, all effects, G, L, and GL are absorbed into the bilinear terms.

The genotype main effect (G) plus genotype x location (GL) biplots (henceforth referred to as GGL biplots) were constructed by use of the SREG model (Cornelius et al., 1996; Yah et al., 2000) with the first two principle components (PCs). From the yearly GGL biplots, using the graphical method described by Yah et al. (2000) and Crossa et al. (2002), subsets of sites with common winning (highest yielding) genotypes were identified to classify each year's testing sites into subsets with negligible COI.

The SREG and SHMM analyses, like other GEI analyses, are based on a common assumption that individual [[bar.[epsilon]].sub.ij.] values are normally and independently distributed (NID). The consequence of failures to NID assumptions often result in a larger pooled error, thereby rendering the GEI analysis less sensitive. Conversely, error variances often differ considerably over test environments. Such heterogeneity from pooling error variances across environments tends to increase levels of significance of the F test. Thus, the net outcome is that the two opposing effects often offset one another (Cochran and Cox 1957, Ch. 14).

The SAS codes used for the SHMM and SREG analyses and graphing the GGL biplots are provided by CIMMYT Biometrics Group and are available at: english/wps/biometrics/index.htm; verified 10 February 2006.


The average yields of sites across years ranged from 0.81 to 8.91 Mg [ha.sup.-1] (Table 1). For each site only the maximum and minimum of the yearly average yield and the across year average are presented. The across year variation in yield for every location is remarkable. The substantial among-site variation within years and within geographical regions is also notable. Results of the yearly analyses of variance provide a snapshot of the overall magnitudes of G, L, and GL variance terms in each of the 22 yr (Table 2). Location was always the main contributor to yield variation across years, accounting for 67 to 98% of the (G + L + GL) variance. In addition, the GL component was greater than the G component in all years.


In this research, SHMM with one multiplicative term (SHM[M.sub.1]) was used, in conjunction with cluster analysis as introduced by Crossa et al. (1993). In this method, if SHM[M.sub.1] does not give an adequate fit, the next step is to move down the branches of the tree until groups of sites (clusters) are found to which SHM[M.sub.1] provides an adequate fit. We estimated the relative frequencies of COI (Cornelius et al., 1992) for each group of sites in each of the yearly dendrograms and set an arbitrary limit of 10% COI to determine the cut-off point where SHM[M.sub.1] provides an adequate fit. Dendrograms from the yearly SHMM cluster analysis were used to study the association among the various sites. Only the SHMM dendrogram generated for the year 1998 is presented as an example provided in Fig. 1. Results indicate that the application of the SHMM model, in conjunction with cluster analyses, did classify sites in groups with smaller frequencies of COI. In each of the yearly dendrograms, the frequency of COI decreased by moving down the branches. However, clustering did not generally correspond with the provincial agroclimatic, soil, or geographical classification patterns nor with the yielding ability of the sites. Moreover, clustering did not, by and large, follow a repeatable pattern over the years.

Results of the SHMM cluster analysis for individual years are summarized in Table 3. This table is provided to examine the association of various sites with different spring wheat growing agroclimatic zones in Alberta. To avoid bias estimates due to sites with only a few years of data, only sites that were planted in at least 40% (9 yr) of the regional yield trials were examined. The frequency at which each site clustered with other sites in different agroclimatic zones is expressed as the ratio of number of times that each site was grouped with other sites in each zone over the total number of occurrence of sites in a given zone. In general, there appears to be no indication that sites within each of the agroclimatic zones necessarily have higher associations with sites from the same zone. In most cases, sites had high associations with sites from a different zone or low associations with sites from the same zone. This indicates that the agroclimatic classification may not necessarily identify groups of sites with low COI and may not be an ideal criterion for summarizing data from multilocation yield trials for variety recommendation purposes.

Site associations obtained from the summary of SHMM clusters (Table 3) were averaged over the agroclimatic zones to examine the overall long-term association of each individual site with other sites in the province. This identified sites with relatively high and (or) low associations with other sites in Alberta. A group of sites in different agroclimatic zones had relatively higher associations with other sites in Alberta over time. For example, Lethbridge (0.25), Oyen (0.26), Strathmore (0.21), in zone 1, Lacombe (0.22) in zone 4, and Beaverlodge (0.25), Fort St. John (0.24), and Fort Vermillion (0.22) in zones 5 and 6 had high values relative to other sites. These sites showed high overall associations, in terms of yearly genotypic ranking, with other sites in the entire province and may be considered generally good predictors of relative wheat variety performances for the spring wheat growing areas in Alberta. On the other hand, the remarkably low associations with other sites shown for Vermilion (0.10), Fair View (0.10), Kelsey (0.12), Keg River (0.12), Irricana (0.13), and Bow Island (0.13) indicate that yield trial data from these sites may be irrelevant to those from other sites in the province in terms of yearly genotypic ranking.


The relatively large contribution of L to yield variation, which is irrelevant to cultivar evaluation and megaenvironment investigation, justifies the application of SREG model as it focuses on G and GL but discards L that is irrelevant for genotype rankings. The large GL relative to G indicates the possible existence of different megaenvironments (Yan et al., 2000). Application of the SREG model is especially beneficial given the large contribution of L to the total variation. The SREG model with two principal components was used in all years as they accounted for a large portion of GE.

Table 4 summarizes the results of the SREG analyses of the multilocation yearly data. Both PC1 and PC2, derived from subjecting the multilocation data of each year to singular value decomposition, were significant (p < 0.01) for all years. The PC1 accounted for 28 to 63% and PC2 for 14 to 28% of the total G + GL. In total, PC1 and PC2 together accounted for 45 to 83% of the G + GL, which make up a given GGL biplot. Therefore, the SREG model with the first two PCs was the most predictively accurate member of its model family in all years. The correlation coefficient for PC1 scores with the genotypic main effects was always greater than 0.80 (p < 0.01), except for years 1992 and 1997 with r = 0.72 (p < 0.01) and r = 0.69 (p < 0.01), respectively (Table 4).

For each year, a symmetrically scaled GGL biplot of PC1 and PC2 scores was generated to graphically approximate the location-centered yield data for all years. Only biplots for years 1998 to 2001 are presented in Fig. 2. In each biplot, following the graphical method described by Yan et al. (2000, 2001) and Crossa et al. (2002), markers of the responsive genotypes were connected to generate a polygon, within which the markers of all other genotypes are contained. The responsive genotypes are those farthest away from the origin of the biplot. These are either the best or the poorest genotypes at some or all locations and can be used to identify COI groups. A perpendicular line, starting from origin, was drawn to intersect each side of the polygon. This divided the biplot into several sectors. In each sector, the responsive genotype at the vertex of the polygon was the best-performing genotype at sites included between the two corresponding perpendiculars (Yan et al., 2000). In 2000, for example (Fig. 2), AC Superb was the best performer in LCM, ILT, VMY, DLT, IRC, STM, OYN, STP, TRC, KLM, CST, BLG, OTN, and SD2, while 5600HR was the best performer in FVM, FKT, and FSJ (see Table 1. for the list of abbreviations). Vertex genotypes without any sites in their sectors were not the highest yielding genotypes in any site and genotypes within the polygon were less responsive than the vertex genotypes.


Yan et al. (2000) and Crossa et al. (2002) noted that if the site PC1 scores from the SREG model have the same sign, then PC1 presents a noncrossover GL interaction. In both studies (Yan et al., 2000 and Crossa et al., 2002), tests of the subsets of responsive genotypes and sites in the respective sector in GGL biplots were used to identify subsets of winning genotype(s) and sites with low or non-COI. We applied the same strategy to identify yearly COI patterns on the basis of the winning genotypes and sites in the GGL biplots. In 1998, for example (Fig. 2), AC Elsa was the winner genotype and Roblin was the worst genotype in IRC, STM, BLG, CTR, and WLK. On the contrary, Roblin was the winning genotype and AC Elsa was the worst genotype in the sites located in the opposite sector with negative primary effect i.e., BKS. Therefore, cultivars Roblin vs. AC Elsa and sites IRC, STM, BLG, CTR, and WLK with positive primary effects and BKS with negative primary effect and located in opposite sectors had a clear COI pattern. The responsive genotypes at the vertices of yearly biplots along with the sites included in the sectors formed the COI subsets of genotypes and sites. The yearly COI groups are summarized in Table 5. Within each subset of sites, the frequency of COI is minimal for the responsive genotypes of that subset. However, site groupings across years did not follow a repeatable pattern, indicating that GEI patterns were inconsistent across years and therefore cannot be explained within geographical boundaries. Any repetition in site groupings seems to be random and did not follow any set pattern.

A near perfect correlation between the genotypic main effects and the primary effects in the SREG analyses in most years (Table 4) allows for the evaluation of a given site's discriminating ability and responsiveness. Moreover, it allows for the examination of genotypes for their yielding ability and stability (Yan et al., 2001; Crossa et al., 2002). In 2001, for example (Fig. 2), location ILT was the most discriminating, indicated by the longest distance from the origin, but because of its large PC2 score, genotypic differences observed at ILT may not exactly reflect the genotypic differences in average yield over all sites. In the same year, TRC and FSJ were not as discriminating as ILT, but genotypic differences at TRC and FSJ were highly consistent with those averaged over sites, because it had a near-zero PC2 score. Sites ODS, LCM, VGL, and TRC were repeatedly selected as the best testing sites over the years. These sites were selected on the basis of their large PC1 (most discriminating) and close to zero PC2 scores (most representative of average performance) and were identified as the best sites for the genotypic performances across years. On the other hand, in some years there were sites with distinct COI patterns with all other sites and with the average. An example can be seen in Fig. 2 for year 1999 where CTR with a negative PC1 score has a clear COI with all other sites.

With regards to genotypes, an ideal genotype is one with a large PC1 (high average yield) and near zero PC2 scores (stable across most sites). In 1998, with near perfect correlation between PC1 and genotypic main effect (r = 0.94; p < 0.01), AC Barrie had the highest PC1 (average yield) and near zero PC2 scores (most stable) and can be considered the ideal genotype for recommendation across the province in that year. Un like the GEI patterns that were not repeatable, there seems to be some repeatability for the genotypes that were selected as ideal genotypes in the GGL biplots. Varieties Neepawa and Katepwa in early 1980s, Laura in late 1980s, CDC Teal and AC Barrie in 1990s, and AC Superb in early 2000s were varieties that were repeatedly selected as high yielding and stable genotypes for the spring wheat growing areas in Alberta. It is noteworthy that these cultivars were chosen by prairie farmers in Canada and were generally grown on vast hectarages throughout the prairies during and following their testing purposes. For example AC Barrie was still grown on 25% of the Canadian prairie wheat growing area in 2004 (Canadian Wheat Board, pers comm.).


Genotype x environment interaction, especially when associated with genotypic rank change, is a complex phenomenon and may be influenced by many external environmental factors. While some of the environmental factors such as soil type, seeding date, altitude, and latitude are dependant on management and (or) location (fixed effects in statistical terminology), many other environmental factors, such as temperature, rainfall, and disease pressure can randomly differ in type and (or) magnitude from year to year. The random nature of the year-dependent factors causes yearly variations in the pattern of external environmental factors to be generally unpredictable. These unpredictable environmental factors, influencing the GEI, are highly variable in the wheat growing areas of the Canadian prairies (located at >49[degrees]N), with strong year-to-year fluctuations in weather patterns, which is typical of agricultural lands located in the higher latitudes of the Northern hemisphere.

Considering that the need to grow different cultivars in different environments is due to the existence of GEI, Gauch and Zobel (1996) defined a megaenvironment as a portion of a crop species' growing region with a homogeneous environment that causes some genotypes to perform similarly. They later presented a methodology for identifying megaenvironments, which was based on the winning genotypes in different environments (Gauch and Zobel, 1997). Taking a similar strategy, Yah et al. (2000) identified yearly "which-won-where" patterns in GGL biplots from SREG analysis to identify subsets of sites with common winning genotypes. They identified a repeatable pattern across years and concluded that the wheat growing areas in Ontario can be divided into two megaenvironments. Crossa et al. (2002) discussed that such grouping of sites, using SHMM and (or) SREG models can identify subsets of sites with low-level COI for multilocation data. In the present study, the yearly multilocation data were subjected to the SHMM cluster analysis. Furthermore, following the graphical method based on the GGL biplots (Yan et al., 2000; Crossa et al., 2002), the yearly which-won-where patterns were outlined.

For each year, in both models, testing sites fell into several subsets, often two or three major groups of sites. The pattern of the site groupings, however, was not consistently repeatable in terms of sites which grouped together, and such groupings varied over years. These groupings generally did not correspond with traditional wheat growing area divisions in Alberta, nor with the classification of agroecosystems of the land resources of northern Great Plains (Padbury et al., 2002). Quite often sites in the southeast corner of the province (area 1), grouped with sites in the northern part of the province (area 6). This indicated that GEI patterns were inconsistent over the years, mainly because of complex, highly variable, and unpredictable year effects in the northern Great Plains which accounted for an extremely large portion of GEL In four year interval combined analyses (results not shown), while location seems to be the main contributor to total variation, year x location accounted for 21 to 40% of the total sums of squares.

It can therefore be concluded that the classification of spring wheat growing areas in Alberta and defining spring wheat growing megaenvironments, as defined by Gauch and Zobel (1997), on the basis of GEI patterns, seems to be unrealistic, mainly because of the lack of repeatability of such patterns over years. Rather it should be regarded as a single megaenvironment with unpredictable COI pattern. Results are congruent with findings of other studies suggesting that there is little or no repeatability of site groupings across years in this region of North America (Yang et al., 2005). As an alternative to classification of sites into megaenvironments as subsets of sites with minimal COI (Gauch and Zobel, 1997), Yang et al. (2005) introduced the term "isoyield environments" in their study of multilocation field pea data from Alberta. They recommended site groupings based on yielding ability of sites. Results of the present study indicate that COI patterns are independent of yielding ability of sites for spring wheat. This suggests that genotypic rank change may occur within a group of sites with nonsignificant yield differences. Under such a situation, identification of genotypes well adapted to a wide range of environments with different levels of yielding ability can be recommended. This is not only important to breeders wishing to identify best performing genotypes in a range of environments but also to producers wishing to choose varieties with a reduced risk for the unpredictable year.

The SREG model is known to explain what is commonly called genotype main effects in terms of a non-crossover GEI (Yan and Hunt, 2001) and has been used to identify superior cultivars and test environments facilitating the identification of such cultivars (Yan et al., 2000). Results of the SREG analysis in the present study pointed to sites Olds, Lacombe, Vegreville, and Trochu as being the most discriminating sites, representative of the average genotypic performance across the province over the years.

Moreover, the SHMM model has also been used to study the association among testing sites in multilocation--year trials on the basis of the frequency that sites grouped together in negligible COI groups (e.g., Lillemo et al., 2004; Trethowan et al., 2001) and to identify sites with high association with other sites. The summary of SHMM clusters in the present study identified the testing sites Lethbridge, Oyen, Strathmore, Lacombe, Beaverlodge, Fort St. John, and Fort Vermilion as the most representative sites in Alberta with overall high association with other sites in the province. It is concluded that although there is no predictability for the GEI patterns, some sites are better for variety testing in terms of their discriminating ability and (or) being representative of average variety performance. This indicates that the regional varietal testing in Alberta and other regions of the northern Great Plains may be conducted at a fewer locations with better discriminating ability and (or) more representative of the average performance.

There were differences between results obtained from the two models in terms of classifying sites with reduced COI. This was largely because the GGL biplots site groupings are mainly on the basis of similar winning genotypes (Yan et al., 2000), while in the SHMM model the overall response of all genotypes determines the COI groups. While differences were notable between the two models, our conclusion of the absence of repeatability in site groupings is independent of the statistical models.

With near perfect correlation between PC1 and genotypic yield in most years, GGL biplots can also be used to identify the high yielding and stable genotypes (Crossa et al., 2002). These genotypes with large PC1 (higher average yield) and near zero PC2 (more stable) were repeatedly selected over the years. Varieties Neepawa and Katepwa in the early 1980s, Laura in the late 1980s, CDC-Teal and AC-Barrie in the 1990s, and Superb in the early 2000s were the varieties that were repeatedly selected as ideal genotypes (high yielding and stable) across all locations. These varieties were also the most popular spring wheat cultivars grown by farmers across the province during the respective time periods. As an example of this phenomenon, AC Barrie was still grown on 25% of wheat-growing land in the Canadian Prairies in 2004, and Superb was already the second most popular cultivar at 14% of the wheat-land (Canadian Wheat Board, pers. Comm.). This may indicate that variety selection by plant breeders and (or) farmers on the basis of genotypic main effects have resulted in selection for wide adaptation over random events and hence a greater popularity of such genotypes.

Genetic similarity of the high yielding and stable genotypes over years may suggest that this genetic background needs to be maintained while introducing new genetic diversity in germplasm development programs. Katepwa is a backcross derivative of Neepawa (coefficient of parentage = 0.98), and AC Barrie is also derived from crosses involving Neepawa (coefficient of parentage = 0.79). Neepawa has relatively less genetic similarity with Laura (coefficient of parentage = 0.37) and CDC Teal (coefficient of parentage = 0.44). The genetic similarity among the stable varieties indicate that maintaining the genetic background of the high yielding and stable cultivars while introducing new genetic diversity through crossing may result in further development of well-adapted varieties for the Canadian prairies.

In some years, groups of one or two sites with clear COI with all other sites were identified in both SHMM and SREG model analyses. Examples of such sites can be seen in SHMM dendrogram of year 1998 (Fig. 1) and in the GGL biplot of the same year (Fig. 2; 1998). In both models site BKS has shown a clear COI pattern with all other sites. If these sites had repeatedly shown the clear COI pattern with other sites in the province, a group(s) of marginal sites in Alberta could have been identified. However, as it seems that this is a randomly occurring phenomenon for only a few sites and only in a few years, it can be concluded that this marginal COI pattern is also year-dependant. For the purpose of variety selection and recommendation, inclusion of these sites may result in bias estimates of genotypic performance. It can therefore be recommended that in the analysis of yearly multilocation regional variety tests, data from such sites be dropped before the analysis.

Implications for Future Regional Cultivar Evaluation

Analysis using the SHMM cluster method and the GGL biplots of the SREG model indicated that GEI effects do not follow a repeatable pattern in Alberta. Although COI was frequently observed within the yearly data, megaenvironments cannot be identified because of lack of repeatability and therefore unpredictability of the GEI patterns. It can be concluded that the spring wheat growing regions in Alberta, and on a greater scale, in the northern Great Plains, belong to a single megaenvironment with inconsistent GEI patterns.

Under the highly variable and unpredictable year effect, typical of the Canadian prairies in the higher latitudes of the northern Great Plains, variety selection should focus on selecting for wide adaptation to random events and not on selection for specific adaptation. This is mainly because the random year effect makes it impossible to select for a specific environment. Selection for general adaptation and yield stability should be feasible through selection for genotypic main effects from multilocation--year data. Despite the lack of repeatability in GEI patterns over years, repeated selection of the same genotypes and selection of genotypes with high genetic similarity, over years, as the best performing genotypes (high yield and stable) was notable. This indicated that the only way to deal with the unpredictable GEI is through selection for wide adaptation.

In the analysis of some years, one or two testing sites were identified with clear COI pattern with all other locations. Since this marginal response of some sites in some years seems to be randomly occurring with no consistent pattern, it is recommended that the yearly data from such locations be removed before summarizing the data, to avoid unwanted bias estimates of the varietal performance. Such marginal sites can be identified each year, by the use of models capable of identifying COI patterns such as SHMM and (or) SREG. Moreover, identification of sites with a greater predictability of the average varietal performance suggests that regional trials may be conducted at fewer locations. Such sites would generally be more discriminating and more representative of the average varietal performance in the region.


The dataset used in this research contained data collected and compiled by many scientists who conducted regional spring wheat variety trials across the province of Alberta over the years 1981 through 2002. It is impossible to name all the scientists involved. However, their hard work and dedication is duly acknowledged. We also gratefully acknowledge research grants provided by the Alberta Agricultural Research Institute and the Alberta Crop Industry Development Fund, and the wheat producers' check-off fund managed by the Western Grains Research Foundation.


Baker, R.J. 1988. Tests for crossover genotype-environmental interactions. Can. J. Plant Sci. 68:405-410.

Cochran, W.G., and G.M. Cox. 1957. Experimental designs. 2nd ed. John Wiley & Sons, New York.

Cornelius, P.L., M. Seyedsadr, and J. Crossa. 1992. Using the shifted multiplicative model to search for "separability" in crop cultivar trials. Theor. Appl. Genet. 84:161-172.

Cornelius, P.L., J. Crossa, and M.S. Seyedsadr. 1996. Statistical tests and estimators of multiplicative models for genotype-by-environment interaction, p. 199-234. In M. S. Kang and H. G. Gauch (ed.) Genotype-by-environment interaction. CRC Press, Boca Raton, FL.

Crossa, J., P.L. Cornelius, and W. Yan. 2002. Biplots of linear-bilinear models for studying crossover genotype x environment interaction. Crop Sci. 42:619-33.

Crossa, J., P.L. Cornelius, M. Seyedsadr, and P. Byrne. 1993. A shifted multiplicative model cluster analysis for grouping environments without genotypic rank change. Theor. Appl. Genet. 85:577-586.

DeLacy, I.H., K.E. Basford, M. Cooper, J.K. Bull, and C.G. McLaren. 1996. Analysis of multi-environment trials- an historical perspective. p. 39-124. In M. Cooper and G. L. Hammer (ed.) Plant adaptation and crop improvement. CAB International. Walingford, UK.

Gauch, H.G., and R.W. Zobel. 1996. AMMI analysis of yield trials. p. 1-40. In M. S. Kang and H. G. Gauch (ed.) genotype-by-environment interaction. CRC Press, Boca Raton, FL.

Gauch, H.G., and R.W. Zobel. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37:311-326.

Lillemo, M., M. van Ginkel, R.M. Trethowan, E. Hernandez, and S. Rajaram. 2004. Associations among international CIMMYT bread wheat yield testing locations in high rainfall areas and their implications for wheat breeding. Crop Sci. 44:1163-1169.

Padbury, G., S. Waltman, J. Capiro, G. Coen, S. McGinn, D. Mortensen, G. Nielsen, and R. Sinclair. 2002. Agroecosystems and land resources of the northern great plains. Agron. J. 94:251-261.

SAS Institute. 1999. SAS/STAT user's guide. V. 8.0. SAS Institute., Cary, NC.

Seyedsadr, M., and EL. Cornelius. 1992. Shifted multiplicative models for nonadditive two-way tables. Comm. Statist. B. Simul. Comp. 21: 807-832.

Trethowan, R.M., J. Crossa, M. van Ginkel, and S. Rajaram. 2001. Relationships among bread wheat international yield testing locations in dry areas. Crop Sci. 41:1461-1469.

Yan, W., and L.A. Hunt. 2001. Interpretation of genotype x environment interaction for winter wheat yield in Ontario. Crop Sci. 41:19-25.

Yan, W., P.L. Cornelius, J. Crossa, and L.A. Hunt. 2001. Two types of GGE biplots for analyzing multi-environment trial data. Crop Sci. 41:656-663.

Yan, W., L.A. Hunt, Q. Sheng, and Z. Szlavnics. 2000. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 40:597-605.

Yang, R.C., S.F. Blade, J. Crossa, D. Stanton, and M.S. Bandara. 2005. Identifying isoyield environments for field pea production. Crop Sci. 45:106-113.

Abbreviations: GEI, genotype by environment interaction; COI, crossover interaction; SREG, site regression model; SHMM, shifted multiplicative model.

Alireza Navabi, Rong-Cai Yang, James Helm, and Dean M. Spaner *

A. Navabi, R-C. Yang and D. Spaner, Department of Agricultural, Food, and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada; J. Helm, Field Crop Development Centre, Second Floor, Agriculture Building, 5030- 50 Street, Lacombe, AB, T4L 1W8, Canada. Received 24 June 2005. * Corresponding author (dean.spaner@
Fig. 1. Dendrogram resulting from a shifted multiplicative model
cluster analysis of sites for Alberta hard-red spring wheat regional
trials in 1998. Numbers on clusters represent the frequency of
crossover interaction as a ratio of total possible number of crossover
interaction in each cluster (%), determined using the Azzalini-Cox
test (Baker, 1988). For sites, areas, and soil zones see Table 1.

Yield                                    Between
(Mg [ha.sup.-1])   Soil    Area   Site   Clusters

5.77               GW       5     FSJ      3.9
6.00               PB       4     LCM     13.7
4.05               PB       4     WLK     15.6
3.99               GW       5     SD2
2.43               DG       5     FKT     10.4
5.19               DG       1     DLT
2.87               BN       1     OYN
5.09               BN       2     IRC
2.09               PB       3     VGL
3.67               DB       1     STM
4.02               DG       5     BLG
4.85               TB       2     TRC      4.6
4.02               PB       4     CST
1.62               PB       4     CLR
4.41               PB       5     VMY
4.58               DG       6     FVM
4.59               BN       R     BKS

Table 1. Average, minimum, and maximum grain yiels of sites growing
regional hard-red spring wheat variety trials over the years 1981 to
2002 in six agroclimatic zones in Alberta.

                                 Data        Northern
Location                 Abrev   sets   latitude ([dagger])   Altitude

                                number                           m

Lethbridge (rainfed)      DLT     19      49[degrees]37'        928
Oyen                      OYN     17      51[degrees]27'        777
Strathmore                STM     15      51[degrees]3'         963
Vulcan                    VCN      8      50[degrees]31'        990
Warner                    WNR     12      49[degrees]16'       1010
Acme                      ACM     11      51[degrees]30'        905
Drumheller                DMR     11      51[degrees]28'        687
Irricana                  IRC     10      51[degrees]19'        932
1Yochu                    TRC     18      51[degrees]49'        870
Castor                    CTR      3      52[degrees]13'        769
Kelsey                    KLS     10      52[degrees]8'         670
Killam                    KLM      2      52[degrees]51'        674
Ohaton                    OTN      4      53[degrees]           668
Provost                   PVT     12      52[degrees]22'        666
St. Paul                  STL      8      54[degrees]           586
Stettler                  SLR      5      52[degrees]18'        823
Vegreville                VGL     14      53[degrees]31'        639
Vermilion                 VML     10      53[degrees]22'        618
AWP Carstairs             CST      4      51[degrees]34'       1090
Calmar                    CLR      4      53[degrees]16'        720
Ellerslie                 ELS     13      53[degrees]34'        670
Lacombe                   LCM     20      52[degrees]28'        847
Olds                      ODS     12      51[degrees]46'       1040
Stony Plain               STP      3      53[degrees]32'        704
West Lock                 WLK     14      54[degrees]9'         652
Beaver Lodge              BLG     21      55[degrees]12'        745
Dawson Creek (BC)         DSC     16      55[degrees]40'        654
Donnelly                  DNL      4      55[degrees]43'        602
Fairview                  FVW     10      56[degrees]4'         670
Falher                    FLR      6      55[degrees]43'        582
Fort Kent                 FKT      3      54[degrees]18'        548
Fort St. John (BC)        FSJ     21      56[degrees]15'        673
Laclabiche                LCB      4      54[degrees]46'        567
Manning                   MNG      8      56[degrees]52'        480
Sarda (1)                 SDI      3      55[degrees]43'        582
Sarda (2)                 SD2      4      55[degrees]43'        582
Smokey lake               SMK      2      54[degrees]16'        680
Valley View               VVW      5      55[degrees]4'         762
Vimy                      VMY      4      54[degrees]4'         659
Fort Vermilion            FVM     17      58[degrees]22'        282
Buffalo Head              BHD     10      58[degrees]3'         335
Keg River                 KGR     10      57[degrees]48'        427
Worsley                   WSL      9      56[degrees]30'        640
High Level                HLV     11      58[degrees]37'        338
Bow Island                BLD      9      49[degrees]43'        816
Brooks                    BKS     19      50[degrees]33'        746
Lethbridge (irrigated)    ILT     16      49[degrees]37'        928

                                                     Across-year yield
                                     Soil zone
Location                 Area   ([double dagger])   Avg.    Min.   Max.

                                                         Mg [ha.sup.-1]

Lethbridge (rainfed)       1          DB             3.1    0.9     5.2
Oyen                       1          BN             2.6    1.2     6.2
Strathmore                 1          DB             4.2    2.0     8.9
Vulcan                     1          DB             3.1    1.8     5.0
Warner                     1          BN             3.1    1.7     4.5
Acme                       2          TB             4.4    2.2     5.9
Drumhetler                 2          DB             4.4    2.8     5.5
Irricana                   2          BN             3.7    1.9     5.0
1Yochu                     2          TB             4.2    2.1     5.5
Castor                     3          DN             2.2    1.6     2.7
Kelsey                     3          DN             3.1    1.4     6.5
Killam                     3          DN             3.5    2.7     4.4
Ohaton                     3          DN             3.3    2.9     4.0
Provost                    3          DB             3.3    2.2     5.7
St. Paul                   3          GR             2.6    2.2     2.9
Stettler                   3          TB             3.8    3.2     4.3
Vegreville                 3          PB             3.5    2.0     4.7
Vermilion                  3          TB             3.6    2.7     4.8
AWP Carstairs              4          PB             4.6    3.7     5.3
Calmar                     4          PB             2.1    1.2     3.0
Ellerslie                  4          PB             3.4    2.5     4.3
Lacombe                    4          PB             5.3    2.0     7.2
Olds                       4          PB             4.5    2.8     5.7
Stony Plain                4          PB             4.4    2.7     5.6
West Lock                  4          PB             3.7    1.9     5.3
Beaver Lodge               5          DG             3.4    1.9     6.2
Dawson Creek (BC)          5          GW             3.8    2.3     5.8
Donnelly                   5          GW             3.1    2.2     3.6
Fairview                   5          GW             3.5    1.5     5.1
Falher                     5          DG             2.9    2.5     4.0
Fort Kent                  5          DG             2.6    2.4     3.2
Fort St. John (BC)         5          GW             3.8    1.2     5.7
Laclabiche                 5          DG             2.7    2.1     3.3
Manning                    5          GW             2.5    1.7     3.7
Sarda (1)                  5          GW             4.2    3.1     5.4
Sarda (2)                  5          GW             4.7    3.9     5.9
Smokey lake                5          GW             3.0    2.8     3.2
Valley View                5          GW             2.1    0.9     2.4
Vimy                       5          PB             4.4    3.4     5.7
Fort Vermilion             6          DG             3.7    1.0     5.3
Buffalo Head               6          GW             3.1    0.8     4.4
Keg River                  6          GW             3.8    3.1     4.7
Worsley                    6          GW             3.1    1.8     4.0
High Level                 6          GW             2.6    1.3     4.0
Bow Island                 R          BN             4.9    3.2     7.5
Brooks                     R          BN             5.2    3.4     7.0
Lethbridge (irrigated)     R          DB             4.3    2.0     5.8

([dagger]) Geographical data is for the closest meteorological station
obtained from Environment Canada web-site at:
http://www; verified 17 January 2006.

([double dagger]) Soil Zones are: black (BK), brown (BN), dark brown
(DB), dark gray (DG), deep black (PB), dry brown (DN), gray (GR),
and gray woode (GW ). R indicates experiment under irrigation.

Table 2. Genotype (G), location (L), and genotype x location
(GL) variance components for the Alberta hard-red spring
wheat regional trials, 1981 to 2002, presented as the ratios of
sums of squares.

                                  Sums of
                                 (% of G +
Year   Source   df ([dagger])     L + GL)

1981     L            27           87.3
         G            14            2.2
         GL          378           10.5
1982     L            22           88.0
         G            13            5.2
         GL          286            6.7
1983     L            21           83.5
         G            12            5.1
         GL          252           11.4
1984     L            13           67.0
         G            10            3.9
         GL          130           29.1
1985     L            23           98.0
         G            10            0.3
         GL          230            1.7
1986     L            28           95.5
         G             7            1.0
         GL          196            3.5
1987     L            31           93.6
         G             9            2.3
         GL          279            4.2
1988     L            29           84.6
         G             9            4.9
         GL          261           10.5
1989     L            30           94.4
         G            11            1.2
         GL          330            4.3
1990     L            31           95.1
         G            12            1.4
         GL          372            3.6
1991     L            29           91.7
         G            16            1.1
         GL          464            7.2

                                  Sums of
                                 (% of G +
Year   Source         df          L + GL)

1992   L              17           91.6
       G              16            1.3
       GL            272            7.0
1993   L              25           88.9
       G              15            2.3
       GL            375            8.9
1994   L              13           88.9
       G              14            4.0
       GL            182            7.0
1995   L              12           90.2
       G              16            2.2
       GL            192            7.6
1996   L              13           94.3
       G              14            1.3
       GL            182            4.5
1997   L              15           88.9
       G              16            2.3
       GL            240            8.8
1998   L              16           93.6
       G              17            2.0
       GL            272            4.4
1999   L              18           92.5
       G              21            2.6
       GL            378            4.9
2000   L              19           88.6
       G              18            4.0
       GL            342            7.4
2001   L              12           85.6
       G              21            4.9
       GL            252            9.5
2002   L              6            78.1
       G              19           10.2
       GL            114           11.7

([dagger]) df is degrees of freedom.

Table 3. Summary of regional association for individual sites with
each group of sites in agroclimatic zones and on average in the
shifted multiplicative model analysis that planted at least 40%
of the Alberta hard-red spring wheat regional yield trials during
the years 1981 to 2002.

                               Association with zone ([double dagger])
zone-testing site ([dagger])      1         2       3       4

Zone 1
Lethbridge                      0.33      0.38    0.32    0.26
Oyen                            0.30      0.40    0.32    0.35
Strathmore                      0.24      0.38    0.20    0.20
Warner                          0.21      0.24    0.14    0.20
Zone 2
Acme                            0.24      0.10    0.22    0.15
Drumheller                      0.21      0.16    0.17    0.26
Irricana                        0.27      0.10    0.15    0.22
Trochu                          0.34      0.16    0.18    0.24
Zone 3
Kelsey                          0.10      0.12    0.11    0.18
Provost                         0.14      0.16    0.14    0.16
Vegreville                      0.30      0.26    0.22    0.20
Vermilion                       0.09      0.12    0.09    0.19
Zone 4
Ellerslie                       0.14      0.16    0.20    0.15
Lacombe                         0.29      0.32    0.29    0.28
Olds                            0.20      0.20    0.17    0.19
West Lock                       0.14      0.22    0.17    0.22
Zone 5-6
Beaver Lodge                    0.40      0.26    0.31    0.35
Buffalo Head                    0.26      0.16    0.17    0.18
Dawson Creek (BC)               0.23      0.18    0.12    0.30
Fairview                        0.11      0.16    0.15    0.12
Fort St. John (BC)              0.41      0.30    0.28    0.31
Fort Vermilion                  0.33      0.24    0.31    0.22
Keg River                       0.14      0.18    0.12    0.14
Worsley                         0.16      0.10    0.14    0.30
High Level                      0.27      0.20    0.18    0.23
Bow Island                      0.13      0.14    0.35    0.12
Brooks                          0.21      0.22    0.23    0.11
Lethbridge                      0.20      0.26    0.17    0.23

                                    Association with
                                 zone ([double dagger])
zone-testing site ([dagger])    5-6       R     Average

Zone 1
Lethbridge                      0.42    0.23      0.25
Oyen                            0.42    0.20      0.26
Strathmore                      0.23    0.20      0.21
Warner                          0.25    0.20      0.17
Zone 2
Acme                            0.23    0.20      0.15
Drumheller                      0.20    0.23      0.17
Irricana                        0.19    0.05      0.13
Trochu                          0.16    0.20      0.19
Zone 3
Kelsey                          0.15    0.20      0.12
Provost                         0.17    0.23      0.14
Vegreville                      0.22    0.16      0.19
Vermilion                       0.11    0.09      0.10
Zone 4
Ellerslie                       0.22    0.20      0.14
Lacombe                         0.30    0.14      0.22
Olds                            0.21    0.18      0.16
West Lock                       0.22    0.14      0.15
Zone 5-6
Beaver Lodge                    0.38    0.18      0.25
Buffalo Head                    0.17    0.20      0.16
Dawson Creek (BC)               0.31    0.16      0.16
Fairview                        0.15    0.07      0.10
Fort St. John (BC)              0.35    0.11      0.24
Fort Vermilion                  0.34    0.20      0.22
Keg River                       0.15    0.14      0.12
Worsley                         0.26    0.16      0.14
High Level                      0.28    0.18      0.18
Bow Island                      0.15    0.05      0.13
Brooks                          0.18    0.07      0.14
Lethbridge                      0.21    0.09      0.16

([dagger]) R: experiment under irrigation.

([double dagger]) Associations are presented as the ratio of number
of times that each site grouped together with sites in each zone over
the number of occurrence of sites in the respected zone.

Table 4. Contribution of the first two principal components (PC) to
genotype (G) plus genotype x location (GL) effects, test statistics and
the coefficient of correlation between genotypic main effects and the
first principal component for the Alberta hard-red spring wheat
regional trials, 1981 to 2002.

             % of G + GL         df

Year      PC1   PC2   Total   PC1   PC2

1981      43    19     63     40     38
1982      63    16     78     34     32
1983      47    15     62     32     30
1984      52    19     70     22     20
1985      46    15     61     32     30
1986      49    26     75     34     32
1987      50    17     67     39     37
1988      49    17     66     37     35
1989      38    25     63     40     38
1990      38    22     60     42     40
1991      30    26     56     44     42
1992      47    15     62     32     30
1993      28    17     45     39     37
1994      44    19     63     26     24
1995      29    28     57     27     25
1996      34    23     58     26     24
1997      39    20     59     30     28
1998      45    15     60     32     30
1999      49    14     63     38     36
2000      42    17     59     36     34
2001      45    18     63     32     30
2002      61    23     83     24     22
Average   44    19     63     --     --

          MS SREG ([dagger])
                                r ([double dagger])
Year        PC1      PC2       (PC1: [[alpha].sub.ik])

1981        1.9      0.8                0.95
1982        2.7      4.7                0.97
1983        2.2      0.8                0.96
1984        1.0      0.4                0.98
1985        0.7      0.2                0.85
1986        1.4      0.8                0.89
1987        1.4      0.5                0.98
1988        2.3      0.8                0.93
1989        0.8      0.6                0.86
1990        1.1      0.7                0.95
1991        1.0      0.9                0.83
1992        1.9      0.6                0.72
1993        1.3      0.9                0.94
1994        1.0      0.5                0.99
1995        0.9      1.0                0.96
1996        0.9      0.6                0.80
1997        1.2      0.7                0.69
1998        1.3      0.4                0.94
1999        2.1      0.7                0.97
2000        1.8      0.8                0.99
2001        1.9      0.8                0.98
2002        1.4      0.6                0.95
Average     --       --                 --

([dagger]) PC1 and PC2 means squares are all significant at p < 0.01
except PC2 mean square of 1985 that was significant at p < 0.05

([double dagger]) r = coefficient of correlation between PC1 and
genotypic main effect. All r values were significant at p < 0.01.

Table 5. Subsets of sites with negligible crossover interaction
identified in the genotype main effect plus genotype x location biplots
in the site regression model analyses of individual years for the
Alberta hard-red spring wheat regional trials, 1981 to 2002.

Year                 Wining genotype{s} {subset of sites}

1981   Columbus, Katepwa ([dagger]), Neepawa {WNR, ODS ([dagger]), VML,
         PVT}, BW537 {TRC, FSJ, WLK, FLR, MNG, OYN}, Chinook {KLS, VVW}
1982   Katepwa ([dagger]), Neepawa ([dagger]), Columbus
         {PVT ([dagger]), ODS, LCM, BKS, DLT, WNR, VVW, DMR, TRC, ILT,
         BLG, KLS, FVM, WLK, BLD, ACM, ELS, WSL}, Benito
         {VCN, STM}, Manitou, Columbus {DSC, FSJ}
1983   Katepwa, Neepawa ([dagger]) {TRC, DMR, MNG, BKS, WNR, KGR, VML,
         LCM ([dagger]), ODS, STM, DLT, OYN, FLR}, Sinton {FVM, PVT,
         FSJ, ACM, KLS, ELS, BHD}, Manitou {HLV}
1984   BW85, Katepwa, Kenyon ([dagger]) {ODS ([dagger]), BLG, FVW, ILT,
         WSL, VML, DSC, KGR, BKS}, Conway {BLD, PVT}, Leader {HLV, ELS,
1985   Neepawa ([dagger]), Katepwa {PVT, ODS ([dagger]), WNR, FVM, BLG,
         LCM, ELS, STL, BHD, ILT}, Lancer {WLK, OYN, VVW, FLR, FSJ},
         Park {DLT, HLV}, Leader {WSL}
1986   Katepwa ([dagger]) {WLK, PVT, ODS ([dagger]), TRC, DSC, VCN,
         BLG, FLR, ELS}, Columbus {BKS, STM, BLD, OYN, WNR, DLT, DMR,
         FSJ, FVW, MNG, VVW, BHD, ACM, HLV, LCB, FVM}, Park {KLS,
         ILT, WSL}
1987   Laura ([dagger]) [A1]{SMK ([dagger]), BLD, FVM, BLG, DSC, BLD,
         WSL, VGL ([dagger]), TRC, PVT, WNR, FVM, OYN, BHD, LCB, VVW,
         VCN, STM, DLT, FLR, ILT, SCM, VML, HLV, BKS}, Katepwa {FSJ,
         LCM, ODS, WLK, ELS}, Roblin {KGR}
1988   Laura ([dagger]) {ILT ([dagger]), FVM, MNG, BLD, KLS, ACM, VGL,
         CNN, BHD, STL, ACM, HLV, LCB, DSC, BLC, SMK, ODS, WLK}, Leader
         {TRC, FSJ, VML, WNR}, Roblin {KGR, FLR, WSL, DMR, ELS, STM}
1989   Roblin, CDC McKawa ([dagger]) {TRC, BLD ([dagger]), WLK, ELS,
         ACM, KGR, KLS, BKS, ODS}, Laura {VGL, IRC, DSC, DMR, DNL,
         FSJ, LCM, ILT, FUM, DLT, HLV, WNR, DMR}, Lancer, Leader
         {VCN, BHD, STL}
1990   Laura ([dagger]) {STL, STM, IRC, DNL, KLS ([dagger]), DLT, FSJ},
         AC Minto {LCM, BLD, DMR, VML, ODS, FVM, TRC, PVT, SLR, ELS,
         OGL, WSL, BGR, BKS}, Lancer {BHD, HLV, CLR, ILT, OYN, MNG,
         WNR, WLT}, Park, PT900 {ACM}
1991   CDC Teal, CDC Merlint {HLV ([dagger]), DLT, ACM, KGR, STL, WNR,
         IRC, PVT, BHD, DNL, FVM, ILT, LCM, WLK, DMR}, Minto {FSJ, MNG,
         BLG, WSL}, PT900 {ELS, BKS, VML, KLS}, Lancer {SLR, VCN}
1992   CDC Teal {ACM, DMR, LCM, BKS, DNL, OYN, PVT, ILT1, Roblin {KGR,
         STM ([dagger]), SLR, TRC}, Merlin {BLG, DSC, WNR, FVW},
         Lancer {CLR, FSJ}
1993   Grandin ([dagger]) {DMR, VML, BKS, WNR, STM, ILT ([dagger]),
         FVM, OYN, BLG, LCM, DLT, SLR, VCN}, Laura {FSJ, VGL, IRC, ACM,
         STL, BHO, ELS, HLV}, Park {PVT, ODS, TRC, KGR}
1994   AC Barrie ([dagger]), Grandin {TRC, OYN, BKS, ODSt, FSJ, DLT,
         LCM, STL, MNG, VGL}, Laura {WLK, DSC, BLG}
1995   Laura ([dagger]) {SD1, BLG ([dagger]), ELS, VGL, FVM, WLK, DLT,
         TRC}, Grandin {SD2, OYN, LCM, FSJ1, Pasqua {BKS}
1996   Elsa, Laura {IRC, CST, WLK, TRC, VGL, BLG, FSJ}, Cadillac {FVM,
         OSC, OYN, LCM ([dagger]), STM, DLT}, Eatona {BKS}
1997   Intrepid {VMY, WLK, LCM ([dagger]), STM, CST}, Roblin {CTR},
         McKenzie {TRC, BKS, IRC, DLT}, Laura {VGL, FVM, BLG, OTN,
         FSJ, TRC, DSC}
1998   AC Barrie ([dagger]) {CST, TRC ([dagger]), VGL, LCM, OYN, VMY},
         Elsa {FVM, CTR, WLK, BLG, STM, IRC ([dagger]), McKenzie
         {FSJ, SD2, FKT, DLT}
1999   5600HR {SD1, FSJ, SD2, FMV, LCM ([dagger])}, AC
         Abbey ([dagger]), McKenzie {TRC, VGL, OTN, DSC, STM, CST, OYN,
         VMY, FKT, DLT], Eatona {BLG}, AC Cadillac {CTR}
2000   Superb ([dagger]) {SDI, LCM, ILT, VMY, DLT, IRC, STM, OYN, STP,
         TRC, KLM ([dagger]), CST, BLG, OTN, SD2, BKS, VGL}, 5600HR
         {FVM, FKT, FSJ}
2001   Superb ([dagger]) {TRC ([dagger]) BLG, FSJ ([dagger]), STP,
         IRC, DLT, LCM, ILT, OTN1, Majestic {FVM, DSC, VGL, KLM}
2002   Elsa {LCM, TRC, FSJ}, Superb {BLG, DSC}, CDC Bounty
         {DLT ([dagger])], AC Barrie {ILT}

([dagger]) Ideal genotypes and testing sites in each year.
COPYRIGHT 2006 Crop Science Society of America
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2006 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Navabi, Alireza; Yang, Rong-Cai; Helm, James; Spaner, Dean M.
Publication:Crop Science
Geographic Code:1USA
Date:May 1, 2006
Previous Article:Nonparametric methods for interpreting genotype x environment interaction of lentil genotypes.
Next Article:Kernel size variation in naked oat.

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters