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Published online 27 October 2005
Published in Crop Sci 45:2443-2453 (2005)
© 2005 Crop Science Society of America
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CROP BREEDING, GENETICS & CYTOLOGY

Differential Adaptation of CIMMYT Bread Wheat to Global High Temperature Environments

M. Lillemob,*, M. van Ginkela, R. M. Trethowana, E. Hernandeza and J. Crossaa

a International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico DF, Mexico
b Dep. of Plant and Environmental Sciences, Norwegian Univ. of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway

* Corresponding author (morten.lillemo{at}umb.no)


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
A good understanding of the target environment and the extent of genotype x environment (G x E) interaction is essential for all cereal breeding programs. Differential adaptation of bread wheat (Triticum aestivum L.) to various heat-stressed environments around the world was analyzed by cumulative cluster analysis of locations and genotypes in 9 yr of CIMMYT's High Temperature Wheat Yield Trial (HTWYT). The grouping pattern of yield-testing environments could largely be explained by the temperature at different growth stages and relative humidity at booting. A clear distinction was observed between sites with heat stress and more temperate locations, and the heat-stressed environments could be grouped into sites experiencing high temperature throughout the season and sites with more specific terminal heat stress. In addition, dry and humid heat-stressed locations tended to differentiate. The ability of individual locations to predict yield in different heat-stressed environments was studied by the shifted multiplicative model (SHMM) site clustering method, and identified locations like Tandojam (Pakistan), which associated well with both heat-stressed and temperate environments. The good ability of the January planting date in Ciudad Obregon (Mexico) to predict yield performance in many heat-stressed environments was also confirmed. Genotypes grouped according to their relative performance in different locations, and specific adaptation to the various types of heat-stressed environments was apparent. However, a subset of genotypes was identified that showed stable, and high yield across all types of environments, both heat-stressed and temperate.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
BREAD WHEAT is a widely adapted crop that can be grown in many different environments. Although it is best adapted to cool or temperate growing conditions, it is grown in many areas of the world where heat stress is a major yield-limiting factor, especially at the end of the season. These areas include lowland central and peninsular India, the lowland Terai of Nepal, Bangladesh, Thailand, southern China, Nigeria, Sudan, the Bolivian lowlands, and parts of Brazil and Paraguay (Dubin and Rajaram 1996).

These heat-stressed environments have been defined as a separate megaenvironment within CIMMYT's bread wheat breeding program to better target germplasm development. The tropical lowlands, which comprise most of the heat-stressed areas, represent 9 million hectares of wheat production and can be split into lowland humid areas (e.g., Bangladesh, eastern India, Terai of Nepal, and lowland Bolivia and Paraguay) and lowland dry areas (e.g., central and peninsular India, Nigeria and Sudan). The most important disease constraints are Helminthosporium Leaf Blight (HLB) caused by Bipolaris sorokiniana (Sacc.) Shoemaker and leaf rust caused by Puccinia triticina Eriks. = P. recondita Roberge ex Desmaz. f. sp. tritici (Eriks. & E. Henn.) D.M. Henderson (Dubin and van Ginkel, 1990). HLB is mostly confined to the humid tropical areas, whereas leaf rust is important to all areas (Dubin and Rajaram 1996). Advanced breeding lines targeted for heat-stressed areas are annually distributed to international cooperators through the HTWYT.

Crop environments can be characterized in terms of the way they influence the relative performance or rank of genotypes. One useful method for this purpose is the SHMM, which identifies subsets of locations with minimal internal crossover interaction (Cornelius et al., 1992; Crossa et al., 1993). However, this method requires balanced data sets where the same locations and genotypes are repeated over years. To analyze multienvironment trials where the composition of genotypes changes from year to year, DeLacy et al. (1996b) developed a cumulative cluster analysis based on the incremental sum of squares (ISS) or Ward's strategy (Ward, 1963). ISS has a strong clustering property that tends to minimize the growth of large groups (DeLacy et al., 1996a). A cumulative analysis can be performed by averaging the distance matrices over years and then eliminating rows and columns with empty cells (DeLacy et al., 1996b).

Previously, we successfully used SHMM and ISS classification methods to analyze the relationships among international testing sites for the Semi-Arid Wheat Yield Trial (SAWYT; targeted for dryland environments) (Trethowan et al., 2001), the Elite Spring Wheat Yield Trial (ESWYT; targeted for irrigated environments) (Trethowan et al., 2003), and the High Rainfall Wheat Yield Trial (HRWYT; targeted for high rainfall environments) (Lillemo et al., 2004). The objective of the present study was to evaluate the associations among test locations and genotypes in 9 yr of the HTWYT nursery and to associate this with environmental variables to provide a better understanding of G x E interaction in the high temperature wheat growing areas of the world.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Locations and Genotypes
Genotypes comprising the HTWYT nurseries 1 to 9 (1992–2000) were bred in Mexico for high temperature environments by shuttle breeding as described by Braun et al. (1996). Segregating materials were shuttled between the Centro de Investigaciones Agrícolas del Noroeste (CIANO) (27°23' N and elevation 38 m above sea level) near Ciudad Obregon, a dry, irrigated site in northwestern Mexico, and CIMMYT's high rainfall research station at Atizapan, Toluca in the central Mexican Highlands (19°16' N and elevation 2640 m above sea level). Leaf rust (caused by P. triticina) and stem rust (caused by P. graminis Pers.:Pers.) are the prevalent diseases at CIANO and stripe rust (caused by P. striiformis Westend.), Septoria tritici Roberge in Desmaz., leaf rust, Fusarium head blight (caused by Fusarium spp.), BYDV (Barley yellow dwarf virus), and intermittent water-logging are common at Toluca. The environment in Cd. Obregon provides good screening conditions for tolerance to heat stress, as the temperature increases steadily from January (normal planting date is in mid November) till the end of July, reaching maximum temperatures around 40°C. Lines are selected for inclusion in the HTWYT on the basis of their yield performance across two to 3 yr at Cd. Obregon using both optimal planting dates (sown in November) and late planting dates in January and February. The late planting dates cause heat stress during flowering and grainfilling.

The HTWYT nursery is assembled each year from seed increased under fungicide control at Mexicali, a disease free site located in northwestern Mexico, and distributed globally on request to international collaborators. Each trial consisted of between 30 and 50 entries and was planted using local agronomic practices. Two-replicate {alpha}-lattice designs were used (Barreto et al., 1997), and the composition of lines varied from year to year, representing the most recently developed germplasm for heat-stressed environments. In the statistical analyses, genotypes were considered as fixed effects and replicates and subblocks within replicates as random effects. Adjusted means were calculated for each location and used in all subsequent analyzes to examine site clustering or grouping. Data from 101 locations and a total of 233 individual yield trials were used for the analysis. A complete list of the locations is presented in Table 1. Except for the cumulative cluster analysis, all other statistical analyses of the yield data were performed by SAS 8.1 (SAS Institute Inc., Cary, NC, USA).


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  		Table 1. List of locations used in the analysis of HTWYTs 1 through 9 and their latitude, longitude, altitude, and frequency of occurrence.

 
Cumulative Cluster Analysis
The cumulative cluster analysis was based on the ISS clustering method recommended by DeLacy and Cooper (1990) and used by Abdalla et al. (1996), Trethowan et al. (2001)(2003) and Lillemo et al. (2004). Dissimilarities between sites and genotypes were measured by squared Euclidean distance (SED) and column standardized before clustering analysis. The statistical software package SEQRET (DeLacy et al., 1998) was used for conducting the cumulative cluster analysis for both locations and genotypes. The most representative entries for each cluster in the resulting dendrograms were identified as those with the smallest sum of SED to other entries.

SHMM Clustering
The SHMM clustering procedure (Crossa et al., 1993) was used to examine the associations among sites for each of the 9 yr of the HTWYT and to identify groups of sites with reduced crossover-interaction (COI). The methods and models were the same as previously outlined in Trethowan et al. (2001)(and 2003) and Lillemo et al. (2004). Fusion levels retaining about 70% of the sum of squares were chosen as cut-off points to determine site clusters, and each site's associations with other sites were calculated as the number of times (in pair-wise comparisons) it clustered together with other sites divided by the total number of possible clusters. A summary table was made by adding associations across years, using the procedure described by Trethowan et al. (2001)(2003) and Lillemo et al. (2004).

SREG Biplot
The SREG model as described by Crossa and Cornelius (1997) was used to construct a biplot of locations and genotypes across years to get a better visualization of the G x E interaction. This analysis was done on locations and genotypes that were included in the cumulative cluster analyses, and to minimize the number of missing data points, only yield data from HTWYT 7–9 was used. The interpretation of biplots from the SREG model is with respect to the variation due to the main effects of cultivar (G) and G x E, (G + G x E). An ideal cultivar should have large primary effects (high mean yield) and near-zero secondary effects (more stable) and an ideal site should have large primary effects (high power to discriminate cultivars) and small secondary effects. Such properties tend to occur if the primary effects of cultivars are highly correlated with the cultivar means.

The cosine of the angle between two site (or cultivar) vectors approximates the phenotypic correlation of yield performance of the two sites (or cultivars). An angle of zero indicates a correlation of +1; an angle of 90° (or –90°), a correlation of 0; and an angle of 180°, a correlation of –1. Furthermore, the cultivar scores for the first multiplicative component of the SREG model will usually be closely associated with the cultivar main effects.

Climatic Data
Daily climatic data for the HTWYT locations in the cumulative cluster analysis was obtained from the National Climatic Data Center, National Oceanic and Atmospheric Administration, Asheville, NC, USA. For each location the closest meteorological station within a range of 100 km and at similar altitude was chosen. The minimum, maximum, and mean temperature and relative humidity were calculated at each site each year for the following approximate growth stages: tillering, spike initiation, booting, milk stage (GF1), and dough stage (GF2), according to the procedure used by Reynolds et al. (2003). As an approximation, the three first growth stages were given the same amount of thermal time each, and their duration was calculated as a third of the number of day degrees from crop emergence to anthesis. Similarly, GF1 and GF2 were assumed to occupy equal parts of the day degrees from anthesis to maturity.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Associations among Sites
To identify heat environments that are similar in the way they discriminate among genotypes, a cumulative cluster analysis was conducted across all 9 yr of yield data for the HTWYT nursery. Since many locations planted only a subset of the nine yield trials, a complete distance matrix could only be made for 32 of the 50 locations that were represented by at least 2 yr of data (Fig. 1) . On the basis of mean SED, the two most representative sites in the resulting dendrogram were Bhairahwa in Nepal and the January planting at Cd. Obregon in Mexico.



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  		Fig. 1. Dendrogram of the cumulative cluster analysis of locations. The most representative site for each cluster is indicated by a star (*), and the most representative site across all locations is indicated by two stars (**).

 
The sites clustered into five distinct groups (E1–E5), which could be distinguished by different temperature profiles during the growing season (Fig. 2) and relative humidity at the booting stage (Table 2). The first four groups all experience heat stress at some growth stages, whereas E5 mostly consists of sites at higher latitudes and with a more temperate climate. Highest mean growing season temperatures were found for sites in E2 and E3; sites in E2 are generally hot and dry, whereas the climate in E3 can be described as hot and humid; the latter includes all the sites that reported HLB as an important disease. The severity of the heat stress in E2 and E3 is also indicated by the relatively low yield levels compared to other sites. E1 and E4 have relatively moderate temperatures during the vegetative growth stage but experience terminal heat stress during grainfilling. Generally, locations in E4 have slightly higher temperatures than E1, especially during spike initiation and booting.



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  		Fig. 2. Mean temperature at each growth stage for the five groups of the cumulative cluster analysis of locations. Definitions of the growth stages are given in Materials and Methods.

 

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  		Table 2. Summary of environmental data for locations in the cumulative cluster analysis. Numbers presented are averages across years.

 
A SHMM analysis for each of the 9 yr was conducted to examine associations among sites and to identify good predictor sites for global yield performance. Table 3 summarizes the site associations in the five groups of the cumulative cluster analysis, for those locations that planted the nursery in at least 4 yr. Tandojam (Pakistan), Dharwad (India), the January planting date at Cd. Obregon and Indore (India) showed the best overall associations with global yield rank. Dera Ismail Khan (Pakistan) showed a remarkably low association with global yield performance. Dharwad (India), Amphur Muang (Thailand), and the January planting date at Cd. Obregon associated best with yield ranking in heat-stressed environments; all three sites associated closely with sites in E2 of the cumulative cluster analysis. Dera Ismail Khan (Pakistan) showed a low association with heat-stressed environments, but not surprisingly (together with Aleppo, Syria), it associated well with the same nonheat-stressed environments identified in the cumulative cluster analysis.


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  		Table 3. Summary of associations with environment groups for individual sites in the SHMM analysis that planted at least four yield trials. The associations are expressed as the number of times a site clustered with other sites divided by the maximum number of possible clusters. Percentages are given in parentheses. The environment groups are explained in Fig. 1 and Table 2.

 
Comparison of the late planting times in Cd. Obregon reveals that the January planting is much better associated with global yield performance than the February planting. Whereas the January planting associated with other sites in 110 of 180 possible groupings, the February planting only associated with other sites in 46 of 134 possible groupings (Table 3). This difference is highly significant (Z = 4.89 and p < 0.001 for a binomial probability test).

Associations among Genotypes
Many genotypes were repeated in two or more years, and it was possible to construct a complete distance matrix of 27 genotypes across years (Fig. 3) . The overall most representative genotypes based on mean SED were OASIS/SKAUZ//4*BCN (CID 122462, SID 4) and KAUZ*2/MNV//KAUZ (CID 65989, SID 12), both derivatives of the CIMMYT line Kauz (BCN is also a Kauz line). The genotypes grouped into five distinct clusters (G1–G5), with different environmental responses for yield. Of the other phenotypic parameters tested, significant differences among the genotype groups were found for resistance to stem rust, stripe rust and HLB, days to heading and the length of the grainfilling period (Table 4).



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  		Fig. 3. Dendrogram of the cumulative cluster analysis of genotypes. The most representative line for each cluster is indicated by a star (*), and the most representative line across all locations is indicated by two stars (**).

 

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  		Table 4. Data summary for genotypes in the cumulative cluster analysis.

 
G x E Interaction
The G x E interaction can be illustrated by the response of the standardized values of yield for the five genotype groups across the five environment groups (Fig. 4) . Generally, G1 and G2 showed the highest yield and less variation among environments, and can thus be regarded as the most widely adapted genotypes in this study. The genotypes in G3 showed very specific adaptability to E1, whereas the genotypes in G5 were specifically adapted to E4. The two rust susceptible and very early heading varieties from Bangladesh in G4 performed best in the first three environments, but showed very low yield performance in E4 and E5. Not unexpectedly, the genotype yield rank in the nonheat environment E5 was related to the level of stripe rust infection, a cool climate disease. However, no association was evident between the level of resistance to HLB and adaptation to E3, the humid heat-stress environment where HLB is expected to occur. The genotypes best adapted to this environment came from G3 and possessed good leaf rust resistance and a relatively early heading date.



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  		Fig. 4. Standardized yield response for five genotype groups across five groups of environments. Environment and genotype group memberships are given in Fig. 1 and 3.

 
The general response pattern of genotypes across environments was confirmed in the SREG biplot of yield data across the last 3 yr of the HTWYT nursery (Fig. 5) . This biplot contains the G + (G x E) effects, and genotype and environment vectors with the same direction have positive G + (G x E), whereas genotype and environment vectors in the opposite directions have negative interaction. The overall good and stable yield performance of genotypes in G2 (encircled with a dashed line in Fig. 5) across environments is apparent. Except for one outlier, they all clustered together and showed a positive correlation with the location vectors. The overall representativeness of locations like Indore and the January planting at Cd. Obregon, identified from the SHMM analysis and Bhariahwa from the cumulative cluster analysis was also confirmed (underlined in Fig. 5). Tandojam, the overall best predictor of global yield performance (Table 3), did not plant the last 3 yr of the nursery and was not included in the SREG analysis.



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  		Fig. 5. SREG biplot of yield across HTWYTs 7 through 9. Location vectors (1–32) are presented as arrows and genotype vectors (1–27) as points. The corresponding location groups and genotype groups from the cumulative cluster analyses are shown in parentheses, and given in Tables 2 and 4, respectively. Locations identified as being good predictors of global yield performance are underlined, and the group of genotypes with stable and high yield across locations circled with a dashed line.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The grouping patterns of locations and genotypes as demonstrated in this study of the HTWYT nursery have been based on yield data alone, and then we have attempted to use external factors like weather data and information on major diseases to explain these patterns. Environments in the HTWYT could be discriminated on the basis of growth stage temperature and relative humidity at booting. Clearly, there is a distinction between sites with heat stress (E1–E4) and more temperate locations (E5), and the heat-stressed environments can be grouped into sites experiencing high temperature throughout the season (E2 and E3) and sites with more specific terminal heat stress (E1 and E4). In addition, dry and humid heat-stressed locations (E2 and E3) tend to differentiate.

A separate grouping of dry and humid locations was also reported by Reynolds et al. (1998) in a previous study of the International Heat Stress Genotypes Experiment between 1990 and 1994. However, in our study, the distinction only becomes apparent at the fourth fusion level of the cumulative cluster analysis (Fig. 1); the standardized yield response diagram (Fig. 4) also did not reveal much genotypic yield difference between E2 and E3. The main difference between dry and humid heat environments in terms of adaptation of genotypes was thought to be the presence of HLB in the humid areas. However, this analysis failed to show any association between HLB resistance and relative yield in E3. The relatively similar yield ranking of the dry and humid heat areas revealed in our study may reflect lack of variation for HLB resistance in the germplasm tested. The genotypes in the HTWYT were bred in Mexico without selection for HLB resistance, as the nursery is primarily targeted to dry lowland tropical areas (Dubin and Rajaram 1996).

The good overall association of the January planting date at Cd. Obregon in Mexico with global yield performance in heat-stressed environments supports the findings of previous studies (Reynolds et al., 1992; DeLacy et al., 1994) and may explain why CIMMYT wheat germplasm adapts well to many heat-stressed areas around the world. On the other hand, planting in Cd. Obregon in February, which results in higher terminal heat stress, is poorly associated with global performance. To improve the efficiency and effectiveness of wheat breeding for heat-stressed environments, germplasm could be sourced from or selected at locations such as Tandojam in Pakistan and Indore and Dharwad in India (Table 3). Tandojam may provide information on broader adaptation as it also associated well with temperate yield-testing locations.

The cumulative cluster analysis of genotypes in the HTWYT showed a clear distinction among the genotype groups in terms of adaptation to different environments, but the available data on diseases and earliness (Table 4) poorly interpret the G x E, as illustrated by the standardized yield response diagram in Fig. 4. Unfortunately, data on physiological parameters such as canopy temperature depression (CTD), which has been demonstrated to be an important determinant of heat tolerance (Reynolds et al., 1998; Ayeneh et al., 2002), was not available for analysis. It must be pointed out that the genotypes comprising the HTWYT were selected on the basis of performance under late planting in Cd. Obregon, and hence they all possess some heat tolerance. The differentiation of genotypes would probably have been different if a substantial number of heat-sensitive genotypes were included in the nursery.

Studies have shown that temperature sensitivity during the spike primordial stage contributes significantly to observed G x E interaction for yield in heat-stressed environments (Fischer, 1985; Reynolds et al., 2003). It has further been demonstrated that high temperature during booting significantly reduces the number of grains per spike but does not affect grain weight (Shpiler and Blum, 1986; Dawson and Wardlaw, 1989). This is also supported by our study; genotype TKW measurements did not vary significantly among environments (data not shown). Genotypes in G2 and G4 retained high relative yield in the most severely heat-stressed environments E2 and E3, which may reflect better physiological tolerance of these genotypes to higher temperatures during the spike primordial stage.

Earliness is an important factor for adaptation to heat-stress since early maturing varieties escape late-occurring heat stress (He and Rajaram 1994). This finding is partly supported by our study and may explain the good performance of the very early-heading varieties from Bangladesh from G4 in E1 to E3 (Fig. 4), despite their susceptibility to leaf rust, stem rust, and HLB (Table 4). Similarly, the very late-heading genotypes in G1 performed relatively poorly in E2 and E3 compared with the other less heat-stressed environments.


   ACKNOWLEDGMENTS
 
This study was conducted during the first author's stay at CIMMYT in Mexico, which was made possible by a grant from the Research Council of Norway. Ky Mathews is acknowledged for her assistance in extracting the daily climatic data, and Tom Payne for providing valuable information about the international yield testing locations. Special thanks are given to all of CIMMYT's collaborators in national programs who planted the trials and reported the data that was used in the analysis.

Received for publication November 16, 2004.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 




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