Associations among international CIMMYT bread wheat yield testing locations in high rainfall areas and their implications for wheat breeding.
To better target germplasm adapted to different environmental conditions, various agroecological zones or megaenvironments have been defined which represent similar biotic and abiotic stresses, cropping system requirements, and consumer preferences (Rajaram et al., 1994; Rajaram and van Ginkel, 2001). One of these major megaenvironments is characterized by average cropping season rainfall above 500 mm. Representative regions include high rainfall sites in West Asia and North Africa (WANA), the central highlands of eastern and central Africa, the southern cone and Andean highlands of South America, and the highlands of central Mexico. The total area in developing countries exceeds 8 million hectares.
Spring bread wheat (Triticum aestivum L.) germplasm targeted to this high rainfall mega-environment is developed by shuttling segregating generations between two contrasting environments in Mexico; a fully irrigated, high-yield potential site located near Ciudad Obregon in north-western Mexico and a high-rainfall, high disease incidence site at Toluca in the Central Mexican Highlands (Braun et al., 1996). Since 1992, advanced bread wheat lines targeted to this megaenvironment have been distributed globally through the HRWYT, following yield testing at Toluca.
The selection of superior genotypes is generally complicated by the presence of genotype x environment (G x E) interactions, whereby the relative yields of genotypes vary across different environments. A useful way to deal with G x E interactions in a breeding program is to characterize the crop environments in terms of the way they influence the relative performance of genotypes. Pattern analysis, as defined by Williams (1976), is the joint use of classification and ordination methods. Many models have been proposed for extraction and interpretation of grouping patterns (see DeLacy et al. (1996a), for review). The shifted multiplicative model (SHMM) is a clustering method that identifies subsets of locations with negligible crossover interaction, i.e., locations that give the same relative ranking of genotypes (Cornelius et al., 1992; Crossa et al., 1993). However, it requires balanced data sets where the same locations and genotypes are repeated over years. Another, highly recommended classification method for multienvironment trials is the incremental sum of squares (ISS) or Ward's strategy (Ward, 1963), which searches to minimize the incremental sum of squares within each group. ISS has a strong clustering property which tends to minimize the growth of large groups and produces groups of relatively even size (DeLacy et al., 1996a). In multienvironmental trials, where the composition of genotypes change from year to year, but many of the same locations are repeated, a cumulative analysis can be performed by averaging the environmental distance matrices over years and then eliminating rows and columns with empty cells (DeLacy et al., 1996b).
DeLacy et al. (1994) used pattern analysis based on ISS to examine associations among environments over time for the International Spring Wheat Yield Nursery (ISWYN), targeted to all the worlds spring bread wheat growing areas. Recently, we have used both SHMM and ISS classification methods to analyze the relationships among international testing sites for the Semi-Arid Wheat Yield Trial (SAWYT) (Trethowan et al., 2001) and the Elite Spring Wheat Yield Trial (ESWYT) targeted to irrigated environments (Trethowan et al., 2003).
Our objective in the present study was to evaluate the associations among test locations where the HRWYT nursery was grown and to attempt to explain the underlying causes of these associations.
MATERIALS AND METHODS
Locations and Genotypes
Genotypes yield-tested in HRWYTs 1 to 8 (1992-1999) were bred in Mexico for high-rainfall environments by shuttle breeding as described by Braun et al. (1996). Segregating materials were shuttled between the Centro de Investigaciones Agricolas del Noroeste (CIANO) (27[degrees]23'N and elevation 38 m above sea level) near Ciudad Obregon, a dry, fully irrigated site in northwestern Mexico, and CIMMYT's research station at Atizapan, Toluca, in the central Mexican Highlands (19[degrees]16'N and elevation 2640 m above sea level). Leaf rust [caused by Puccinia recondita Roberge ex Desmaz. f. sp. tritici (Eriks. & E. Henri.) D.M. Henderson] and stem rust (caused by P. graminis Pers.:Pers.) are the prevalent diseases at CIANO and stripe rust (caused by P. striiformis Westend.), Septoria blotch (caused by Septoria tritici Roberge in Desmaz.), leaf rust, Fusarium head blight (caused by Fusarium graminearum Schwabe), Barley yellow dwarf virus (BYDV), and intermittent water-logging are common at Toluca. The final yield trials for selecting germplasm to enter the HRWYT nursery were conducted across 2 to 3 yr at the research station in Toluca.
Each year the HRWYT nursery was assembled from seed increased under fungicide application at Mexicali, a disease free site located in northwestern Mexico and distributed globally upon request to international collaborators. Each trial consisted of between 30 and 50 entries and was planted on the basis of local agronomic practices. Two-replicate [alpha]-lattice designs were used (Barreto et al., 1997). The composition of lines varied from year to year, representing newly developed germplasm, and a local check variety representing the best locally adapted germplasm was included at each site each year. The local check varied among locations and in some instances changed between years at the same location. Genotypes were considered as fixed effects and replicates and subblocks within replicates as random effects. Adjusted means were calculated and used in all subsequent SHMM and cumulative cluster analyzes to examine site clustering or grouping. A total of 187 location years or individual yield trials were used for the analysis and are listed in Table 1. Except for the cumulative cluster analysis, all other statistical analyses of the yield data were performed with SAS 8.1 (SAS, 1999).
Although the HRWYT entries were developed for high rainfall conditions with more than 500 mm of rain during the cropping season, many collaborators grew them at locations with lower rainfall, with consequently lower yield levels. Some collaborators in dry areas also grew the nursery under irrigation.
The SHMM clustering procedure (Crossa et al., 1993) was used to examine the associations among sites for each of the eight years of HRWYT and to identify groups of sites with reduced COI. The methods were the same as previously outlined in Trethowan et al. (2001 and 2003). The SHMM model for the mean of the ith genotype (i = 1, 2, ... g) in the jth site (j = 1, 2, ... s) ([[bar]y.sub.ij]) can be represented [[bar]y.sub.ij] = [beta] + [[SIGMA].sup.t.sub.k=1] [[lambda].sub.k] [[alpha].sub.ik] [[gamma].sub.jk] + [[bar][epsilon].sub.ij]. (Seyedsadr and Cornelius, 1992), where [beta] is the shift parameter; [[lambda].sub.k]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 the singular values (scale parameters) that allow imposition of orthonormality constraints on the singular vectors for genotypes, [[alpha].sub.1k], ..., [[alpha].sub.gk] and for sites, [[gamma].sub.1k], ..., [[alpha].sub.sk], such that [[SIGMA].sub.i][[alpha].sup.2.sub.ik] = [[SIGMA].sub.j] [[gamma].sup.2.sub.jk] and [[SIGMA].sub.i][[alpha].sub.ik][[alpha].sub.ik'] = [[SIGMA].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," ..., effects of the ith genotype and the jth site, respectively; [[bar][epsilon].sub.ij.] is the residual error.
The distances for all possible pairs of sites were calculated, and dcndrograms constructed using the complete linkage (farthest neighbor) clustering method. The third fusion level was selected as an arbitrary cut-off point to determine site clusters, and each site's associations with other sites were calculated as the number of times (in pair vise 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 and 2003).
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) and Trethowan et al. (2001 and 2003). Dissimilarities between sites were measured by squared Euclidean distance (SED), and distance matrices were calculated for each year for all sites with at least two years of data. An across-year distance matrix was constructed by averaging distances from each year where data was available for any site-by-site comparison. Since clustering algorithms require a complete distance matrix with values for all site-by-site comparisons, sites contributing empty cells were subsequently removed to end up with a complete distance matrix. The statistical software package SEQRET (DeLacy et al., 1998) was used for conducting the cumulative cluster analysis. The most representative site for each cluster in the resulting dendrogram was identified as that with the smallest sum of SED to other sites.
A summary of the dendrogram results for individual years of the SHMM cluster analysis was made to examine the association of various sites with different geographical regions (Table 2). The frequency at which a site clusters together with other sites in different geographical regions is expressed as a fraction of the total number of possible groupings. Because of the inherent uncertainty of associations based on only a few years of data, only sites with data from at least 4 yr are considered in the following discussion, but data from all locations was used to make the summary in Table 2.
Generally, two types of sites were identified: a group of locations highly associated with other sites around the world and two very distinct locations with very poor associations with other sites. Locations like for example Marcos Juarez in Argentina (54%), Kentziko Thermi in Greece (54%), Bethlehem in South Africa (52%), and other sites with high associations with other sites on the global level, can be considered as good predictors of global yield performance for the high rainfall megaenvironment. The temperature profiles of these sites are shown in Fig. 1, and they all share the same characteristics: Relatively low temperature during the vegetative growth stages and a marked increase in temperature toward maturity.
[FIGURE 1 OMITTED]
On the other hand, the remarkably low associations with other sites shown for Sta. Catalina in Ecuador (6%) and Toluca in Mexico (13%) indicate that yield trial data from these sites are irrelevant for predicting global performance within this mega-environment. The temperature profiles of these sites are also different in that the temperature is either stable or decreasing toward maturity (Fig. 1)
Cumulative Cluster Analysis
Since most locations only planted a few of the eight HRWYTs, a complete distance matrix could only be made for 20 of the 46 locations that planted at least 2 yr of the nursery (Fig. 2). On the basis of the average SED to other locations, the two most representative sites in the resulting dendrogram were the Iranian yield testing location at Bayecola and Bethlehem in South Africa.
[FIGURE 2 OMITTED]
Environmental information about the locations in the cumulative cluster analysis is listed in Table 3. Climatic data has been obtained from the nearest occurring meteorological station that could be found in various databases, bearing in mind that for many locations these are only rough estimates of the actual conditions. The data were included to detect major trends among the different groups and precaution should therefore be used when interpreting data from individual sites. Most collaborators who planted the HRWYT also reported data on days to maturity and diseases. If a disease is not listed, it does not necessarily mean that it did not occur, only that the collaborator did not report data for that disease.
The biggest group in the cumulative cluster analysis comprised more than half of the sites, and they were all characterized by increasing temperature toward maturity. The most prevalent disease in this group was leaf rust, but also stripe rust, powdery mildew, and Septoria blotch were frequently reported. For the other groups, none of the environmental aspects could be clearly associated with the groupings.
Generally, there are many external or environmental factors that influence the yield ranking of cultivars from site to site. The most common are latitude, altitude, planting date, cultural management, daylength, temperature, water availability, diseases, and specific abiotic stresses such as low pH.
Both the SHMM analysis and the cumulative cluster analysis point to a common environmental feature of sites with good predictability of global yield performance; they all have increasing temperature toward maturity. However, this study does not provide any data to explain whether there is any direct relationship between temperature profile and the ability to predict global yield performance. Different temperature profiles mostly reflect differences in planting dates, and are also driven by daylength variation, rainfall, and solar radiation. It is likely that this association could also reflect some other underlying characteristics of the sites. Although disease data is scarce for some of the locations with good predictive ability, those locations all have favorable conditions for leaf rust, which is globally among the most important biotic stress factors in wheat production.
Except for Marcos Juarez, which did not occur in the cumulative cluster analysis, all globally good predictors identified from the SHMM summary grouped into group two of the cumulative cluster analysis. The general similarity of these sites and their high association with global performance, as indicated from both analyses, make them good indicators for the identification of germplasm with broad adaptability. On the other hand, Toluca and Sta. Catalina, which were identified from the SHMM analysis as poor predictors of global yield performance, clustered into separate groups of the cumulative cluster analysis, which indicates the more specific adaptability required for these sites.
Sta. Catalina, located in the highlands of Ecuador, has a high incidence of stripe rust, and is characterized by a different, more virulent race composition (Broers and Danial, 1994). There are also indications that the soil at this site is infected with root lesion nematodes (Pratylenchus thornei Sher and Allen; Trethowan, pers. comm.). A similarly low association between Sta. Catalina and other international yield trial sites was also found in the analysis of the ESWYT nursery (Trethowan et al., 2003).
Toluca's low association with global yield performance is also in accordance with earlier findings (Braun et al., 1992). Apart from cooler conditions and shorter days during grain filling with occasional night frosts, Toluca experiences other extremes; about 850 mm of rain falls during the growing season and serious disease epidemics such as stripe rust, Septoria blotch, BYDV, Fusarium head scab, and late-arriving leaf rust occur. Soils are frequently water logged, and crop lodging and preharvest sprouting are constraints.
It is therefore not surprising that the very special conditions at these two locations resulted in a different yield ranking of lines compared with other global test sites which have more favorable conditions for wheat cultivation. Although such extreme locations can be excellent for disease screening (e.g., stripe rust in Sta. Catalina and stripe rust, Septoria blotch and Fusarium head scab in Toluca), it is clear from this analysis that their yield data is not relevant for identification of genotypes with global adaptation. Nevertheless, it is likely that the high rainfall germplasm has benefited from several generations of selection at Toluca, since this ensures photoperiod insensitivity (being part of the Cd. Obregon-Toluca breeding shuttle), and good resistance to the most important diseases and abiotic stresses for high rainfall areas around the world (Braun et al., 1992; Campuzano 1997; Rajaram and van Ginkel, 2001). Further work should aim to identify more representative yield testing locations in Mexico for the high rainfall breeding material.
This is the first study of the relationships among yield testing sites for the high-rainfall germplasm since the partition of the CIMMYT breeding material into megaenvironments and the establishment of the HRWYT nursery. This paper shows that within the high-rainfall megaenvironment, there are at least two subgroups. The largest subgroup consists of autumn- and/or early spring-planted sites characterized by increasing temperature as the crop approaches maturity and generally high association with global yield performance. The second, and smaller subgroup, consists of spring-planted sites with either stable or decreasing temperature in the later stages of development and low association with global yield performance.
The SHMM analysis identified sites that associated well with overall global yield ranking, thereby facilitating the identification of representative or key yield testing locations. The methods applied in this study are not only relevant to international wheat breeding but can be used to analyze any breeding program, regardless of crop species, as long as a sufficient number of yield trials are sown at representative locations throughout the target area.
Table 1. Summary of locations used in the analysis of HRWYT's 1-8 and their latitude, longitude, altitude and frequency of occurrence. Region Site Country North America Charlottetown, PEI Canada Saskatoon, SK Canada Swift Current, SK Canada Apizaco, Tlaxcala Mexico Calpulalpan, Tlaxcala Mexico Cd. Obregon Mexico Chapingo, Texcoco Mexico El Bajio, Celaya Mexico El Batan, Texcoco Mexico Ex-Hacienda El Copal, Irapuato Mexico Juchitepec Mexico La Barca Mexico Montecillo, Texcoco Mexico Atizapan, Toluca Mexico Tepatitlan, Jalisco Mexico Zaragoza Mexico Plains, GA United States Central America Labor Ovalle, Quezaltenango Guatemala Zamorano Honduras Andes San Benito, Cochabamba Bolivia Tibaitata, Bogota Colombia Sta. Catalina, Cutuglagua Ecuador Cusco Peru Southern Cone La Ballenera, Miramar Argentina Marcos Juarez Argentina Parana, Entre Rios Argentina Pergamino Argentina San Miguel de Tucuman Argentina Apucarana Brazil Bela Vista Do Paraiso Brazil Cruz Alta Brazil Londrina Brazil Palotina Brazil Passo Fundo Brazil Pelotas Brazil Graneros, Rancagua Chile Hidango, Litueche Chile La Platina, Santiago Chile Quilamapu, Chillan Chile Capitan Miranda Paraguay Don Esteban, Young Uruguay El Cardo, Young Uruguay Erro, Dolores Uruguay La Estanzuela Uruguay San Patricio, Young Uruguay Europe Domaine Du Chaumoy, Le Subdray France Ferme De Loudes, Castelnaudary France Lectoure, Castillon-la-Bataille France Orgerus France Kentziko Thermi, Thessaloniki Greece Tolentino Italy Vollebekk, [Angstrom]s Norway Elvas Portugal Alameda Del Obispo, Cordoba Spain Finca La Carrerada, Lleida Spain La Mojonera, Toledo Spain Rancho De La Merced, Cadiz Spain Torregrossa/Belloc, Lleida Spain Kharkov Ukraine Odessa Ukraine Ustymovska Ukraine West Asia and North Africa Guelma Algeria Baku Azerbaijan Komombo, Aswan Egypt Araghi Mahaleh, Gorgan Iran Bayecola, Sari Iran Darab Iran Karaj, Tehran Iran Mazraeh Nemoneh, Gorgan Iran Moghan, Ardabil Iran Al-Tuwaitha, Baghdad Iraq Tel Hadya, Aleppo Syria Beja, Tunis Tunisia Aydin Turkey Bornova, Izmir Turkey Edirne Turkey Menemen, Izmir Turkey Samsun Turkey Bishkek Kyrgyzstan Southern and Eastern Africa Munanira Burundi Holetta, Addis Ababa Ethiopia Njoro Kenya Bembeke Malawi Bethlehem South Africa Houthaaldoorns, Lichtenburg South Africa Langgewens, Gouda South Africa Moredou, Gouda South Africa Lyamungo Tanzania Simba-Tilotanga, Lyamungo Tanzania Mbala Zambia Harare Zimbabwe South Asia New Delhi India Islamabad Pakistan Mingora, Swat Pakistan Quota Pakistan East Asia Harbin China Hongxinglong China Lanzhou, Gansu China Kitami, Kunneppu Japan Dan Phung Viet Nam Oceania Lincoln, Christchurch New Zealand Region Site Latitude North America Charlottetown, PEI 46[degrees]20'N Saskatoon, SK 52[degrees]9'N Swift Current, SK 50[degrees]17'N Apizaco, Tlaxcala 19[degrees]25'N Calpulalpan, Tlaxcala 19[degrees]35'N Cd. Obregon 27[degrees]23'N Chapingo, Texcoco 19[degrees]17'N El Bajio, Celaya 20[degrees]32'N El Batan, Texcoco 19[degrees]31'N Ex-Hacienda El Copal, Irapuato 20[degrees]45'N Juchitepec 19[degrees]6'N La Barca 20[degrees]15'N Montecillo, Texcoco 19[degrees]26'N Atizapan, Toluca 19[degrees]16'N Tepatitlan, Jalisco 20[degrees]51'N Zaragoza 28[degrees]33'N Plains, GA 32[degrees]28'N Central America Labor Ovalle, Quezaltenango 14[degrees]52'N Zamorano 14[degrees]0'N Andes San Benito, Cochabamba 17[degrees]30'S Tibaitata, Bogota 4[degrees]4'N Sta. Catalina, Cutuglagua 0[degrees]22'S Cusco 13[degrees]24'S Southern Cone La Ballenera, Miramar 38[degrees]8'S Marcos Juarez 32[degrees]42'S Parana, Entre Rios 31[degrees]50'S Pergamino 33[degrees]56'S San Miguel de Tucuman 26[degrees]48'S Apucarana 23[degrees]36'S Bela Vista Do Paraiso 23[degrees]0'S Cruz Alta 28[degrees]38'S Londrina 23[degrees]22'S Palotina 24[degrees]17'S Passo Fundo 28[degrees]15'S Pelotas 31[degrees]52'S Graneros, Rancagua 34[degrees]3'S Hidango, Litueche 34[degrees]6'S La Platina, Santiago 33[degrees]27'S Quilamapu, Chillan 36[degrees]31'S Capitan Miranda 27[degrees]17'S Don Esteban, Young 32[degrees]40'S El Cardo, Young 32[degrees]41'S Erro, Dolores 33[degrees]43'S La Estanzuela 34[degrees]20'S San Patricio, Young 33[degrees]41'S Europe Domaine Du Chaumoy, Le Subdray 47[degrees]2'N Ferme De Loudes, Castelnaudary 43[degrees]30'N Lectoure, Castillon-la-Bataille 44[degrees]50'N Orgerus 48[degrees]50'N Kentziko Thermi, Thessaloniki 40[degrees]38'N Tolentino 43[degrees]15'N Vollebekk, [Angstrom]s 59[degrees]40'N Elvas 38[degrees]54'N Alameda Del Obispo, Cordoba 37[degrees]53'N Finca La Carrerada, Lleida 41[degrees]35'N La Mojonera, Toledo 39[degrees]58'N Rancho De La Merced, Cadiz 36[degrees]43'N Torregrossa/Belloc, Lleida 41[degrees]35'N Kharkov 50[degrees]0'N Odessa 46[degrees]27'N Ustymovska 49[degrees]22'N West Asia and North Africa Guelma 36[degrees]28'N Baku 40[degrees]5'N Komombo, Aswan 23[degrees]8'N Araghi Mahaleh, Gorgan 36[degrees]55'N Bayecola, Sari 36[degrees]42'N Darab 29[degrees]10'N Karaj, Tehran 35[degrees]50'N Mazraeh Nemoneh, Gorgan 36[degrees]30'N Moghan, Ardabil 38[degrees]15'N Al-Tuwaitha, Baghdad 34[degrees]0'N Tel Hadya, Aleppo 36[degrees]1'N Beja, Tunis 36[degrees]44'N Aydin 36[degrees]42'N Bornova, Izmir 38[degrees]27'N Edirne 41[degrees]40'N Menemen, Izmir 38[degrees]40'N Samsun 41[degrees]18'N Bishkek 42[degrees]54'N Southern and Eastern Africa Munanira 2[degrees]55'S Holetta, Addis Ababa 9[degrees]3'N Njoro 0[degrees]25'S Bembeke 14[degrees]10'S Bethlehem 28[degrees]11'S Houthaaldoorns, Lichtenburg 26[degrees]10'S Langgewens, Gouda 33[degrees]17'S Moredou, Gouda 33[degrees]18'S Lyamungo 3[degrees]14'S Simba-Tilotanga, Lyamungo 3[degrees]13'S Mbala 8[degrees]53'S Harare 17[degrees]43'S South Asia New Delhi 28[degrees]33'N Islamabad 33[degrees]45'N Mingora, Swat 34[degrees]36'N Quota 30[degrees]12'N East Asia Harbin 45[degrees]41'N Hongxinglong 46[degrees]43'N Lanzhou, Gansu 36[degrees]6'N Kitami, Kunneppu 43[degrees]47'N Dan Phung 21[degrees]5'N Oceania Lincoln, Christchurch 43[degrees]38'S Region Site Longitude North America Charlottetown, PEI 63[degrees]0'W Saskatoon, SK 106[degrees]36'W Swift Current, SK 107[degrees]50'W Apizaco, Tlaxcala 98[degrees]09'W Calpulalpan, Tlaxcala 98[degrees]38'W Cd. Obregon 109[degrees]56'W Chapingo, Texcoco 98[degrees]53'W El Bajio, Celaya 100[degrees]49'W El Batan, Texcoco 98[degrees]50'W Ex-Hacienda El Copal, Irapuato 101[degrees]20'W Juchitepec 98[degrees]52'W La Barca 102[degrees]33'W Montecillo, Texcoco 98[degrees]54'W Atizapan, Toluca 99[degrees]51'W Tepatitlan, Jalisco 102[degrees]39'W Zaragoza 100[degrees]55'W Plains, GA 85[degrees]30'W Central America Labor Ovalle, Quezaltenango 91[degrees]30'W Zamorano 87[degrees]2'W Andes San Benito, Cochabamba 66[degrees]6'W Tibaitata, Bogota 74[degrees]12'W Sta. Catalina, Cutuglagua 78[degrees]32'W Cusco 71[degrees]52'W Southern Cone La Ballenera, Miramar 57[degrees]55'W Marcos Juarez 62[degrees]7'W Parana, Entre Rios 60[degrees]31'W Pergamino 60[degrees]33'W San Miguel de Tucuman 65[degrees]12'W Apucarana 51[degrees]23'W Bela Vista Do Paraiso 51[degrees]12'W Cruz Alta 53[degrees]36'W Londrina 51[degrees]10'W Palotina 53[degrees]50'W Passo Fundo 52[degrees]25'W Pelotas 52[degrees]21'W Graneros, Rancagua 70[degrees]42'W Hidango, Litueche 71[degrees]47'W La Platina, Santiago 70[degrees]38'W Quilamapu, Chillan 71[degrees]55'W Capitan Miranda 55[degrees]49'W Don Esteban, Young 57[degrees]23'W El Cardo, Young 57[degrees]40'W Erro, Dolores 58[degrees]5'W La Estanzuela 57[degrees]41'W San Patricio, Young 57[degrees]36'W Europe Domaine Du Chaumoy, Le Subdray 2[degrees]20'E Ferme De Loudes, Castelnaudary 2[degrees]0'E Lectoure, Castillon-la-Bataille 0[degrees]2'E Orgerus 1[degrees]42'E Kentziko Thermi, Thessaloniki 22[degrees]57'E Tolentino 13[degrees]30'E Vollebekk, [Angstrom]s 10[degrees]47'E Elvas 7[degrees]9'W Alameda Del Obispo, Cordoba 4[degrees]47'W Finca La Carrerada, Lleida 0[degrees]45'W La Mojonera, Toledo 4[degrees]45'W Rancho De La Merced, Cadiz 6[degrees]9'W Torregrossa/Belloc, Lleida 0[degrees]45'E Kharkov 36[degrees]25'E Odessa 30[degrees]42'E Ustymovska 34[degrees]20'E West Asia and North Africa Guelma 7[degrees]28'E Baku 48[degrees]5'E Komombo, Aswan 32[degrees]47'E Araghi Mahaleh, Gorgan 54[degrees]20'E Bayecola, Sari 53[degrees]13'E Darab 55[degrees]13'E Karaj, Tehran 50[degrees]58'E Mazraeh Nemoneh, Gorgan 53[degrees]30'E Moghan, Ardabil 48[degrees]18'E Al-Tuwaitha, Baghdad 45[degrees]0'E Tel Hadya, Aleppo 36[degrees]56'E Beja, Tunis 9[degrees]8'E Aydin 26[degrees]45'E Bornova, Izmir 27[degrees]14'E Edirne 26[degrees]34'E Menemen, Izmir 27[degrees]4'E Samsun 36[degrees]20'E Bishkek 74[degrees]36'E Southern and Eastern Africa Munanira 29[degrees]34'E Holetta, Addis Ababa 38[degrees]30'E Njoro 36[degrees]0'E Bembeke 34[degrees]26'E Bethlehem 28[degrees]11'E Houthaaldoorns, Lichtenburg 26[degrees]10'E Langgewens, Gouda 18[degrees]40'E Moredou, Gouda 19[degrees]2'E Lyamungo 37[degrees]53'E Simba-Tilotanga, Lyamungo 37[degrees]53'E Mbala 31[degrees]22'E Harare 31[degrees]5'E South Asia New Delhi 77[degrees]16'E Islamabad 73[degrees]6'E Mingora, Swat 72[degrees]26'E Quota 67[degrees]1'E East Asia Harbin 126[degrees]37'E Hongxinglong 131[degrees]33'E Lanzhou, Gansu 103[degrees]53'E Kitami, Kunneppu 143[degrees]42'E Dan Phung 105[degrees]38'E Oceania Lincoln, Christchurch 172[degrees]30'E Region Site Altitude m North America Charlottetown, PEI 15 Saskatoon, SK 497 Swift Current, SK 825 Apizaco, Tlaxcala 2408 Calpulalpan, Tlaxcala 2580 Cd. Obregon 38 Chapingo, Texcoco 2249 El Bajio, Celaya 1765 El Batan, Texcoco 2249 Ex-Hacienda El Copal, Irapuato 1750 Juchitepec 2670 La Barca 1517 Montecillo, Texcoco 2254 Atizapan, Toluca 2640 Tepatitlan, Jalisco 1900 Zaragoza 350 Plains, GA 400 Central America Labor Ovalle, Quezaltenango 2407 Zamorano 805 Andes San Benito, Cochabamba 2730 Tibaitata, Bogota 2550 Sta. Catalina, Cutuglagua 3050 Cusco 2900 Southern Cone La Ballenera, Miramar 30 Marcos Juarez 110 Parana, Entre Rios 110 Pergamino 65 San Miguel de Tucuman 460 Apucarana 845 Bela Vista Do Paraiso 618 Cruz Alta 473 Londrina 540 Palotina 340 Passo Fundo 684 Pelotas 12 Graneros, Rancagua 479 Hidango, Litueche 296 La Platina, Santiago 629 Quilamapu, Chillan 217 Capitan Miranda 200 Don Esteban, Young 96 El Cardo, Young 80 Erro, Dolores 45 La Estanzuela 81 San Patricio, Young 80 Europe Domaine Du Chaumoy, Le Subdray 170 Ferme De Loudes, Castelnaudary 170 Lectoure, Castillon-la-Bataille 78 Orgerus 120 Kentziko Thermi, Thessaloniki 10 Tolentino 140 Vollebekk, [Angstrom]s 90 Elvas 280 Alameda Del Obispo, Cordoba 110 Finca La Carrerada, Lleida 360 La Mojonera, Toledo 220 Rancho De La Merced, Cadiz 20 Torregrossa/Belloc, Lleida 200 Kharkov 170 Odessa 34 Ustymovska 104 West Asia and North Africa Guelma 228 Baku 28 Komombo, Aswan 142 Araghi Mahaleh, Gorgan 5 Bayecola, Sari 3 Darab 1100 Karaj, Tehran 1321 Mazraeh Nemoneh, Gorgan 1011 Moghan, Ardabil 1343 Al-Tuwaitha, Baghdad 62 Tel Hadya, Aleppo 292 Beja, Tunis 150 Aydin 60 Bornova, Izmir 10 Edirne 48 Menemen, Izmir 10 Samsun 10 Bishkek 813 Southern and Eastern Africa Munanira 2120 Holetta, Addis Ababa 2390 Njoro 2165 Bembeke 1560 Bethlehem 1687 Houthaaldoorns, Lichtenburg 1477 Langgewens, Gouda 91 Moredou, Gouda 82 Lyamungo 1280 Simba-Tilotanga, Lyamungo 1750 Mbala 1668 Harare 1480 South Asia New Delhi 228 Islamabad 683 Mingora, Swat 1000 Quota 1600 East Asia Harbin 172 Hongxinglong 75 Lanzhou, Gansu 1517 Kitami, Kunneppu 196 Dan Phung 10 Oceania Lincoln, Christchurch 11 HRWYT nursery Region Site number (1-8) North America Charlottetown, PEI 1, 2, 6 Saskatoon, SK 6 Swift Current, SK 3 Apizaco, Tlaxcala 7, 8 Calpulalpan, Tlaxcala 7 Cd. Obregon 7, 8 Chapingo, Texcoco 3, 7 El Bajio, Celaya 6 El Batan, Texcoco 2, 3, 5 Ex-Hacienda El Copal, Irapuato 8 Juchitepec 5 La Barca 1 Montecillo, Texcoco 7, 8 Atizapan, Toluca 2, 3, 5, 6, 7, 8 Tepatitlan, Jalisco 1, 2 Zaragoza 6 Plains, GA 4 Central America Labor Ovalle, Quezaltenango 4, 5 Zamorano 5, 7 Andes San Benito, Cochabamba 2, 4, 6 Tibaitata, Bogota 1 Sta. Catalina, Cutuglagua 1, 2, 4, 5, 6 Cusco 2 Southern Cone La Ballenera, Miramar 7 Marcos Juarez 4, 6, 7, 8 Parana, Entre Rios 1, 5, 6, 8 Pergamino 2, 3, 7, 8 San Miguel de Tucuman 6 Apucarana 2 Bela Vista Do Paraiso 1, 7 Cruz Alta 1, 4, 5 Londrina 2, 5 Palotina 4, 8 Passo Fundo 7 Pelotas 1 Graneros, Rancagua 5 Hidango, Litueche 4, 5, 7 La Platina, Santiago 2 Quilamapu, Chillan 1, 7, 8 Capitan Miranda 2, 4, 5 Don Esteban, Young 5 El Cardo, Young 7 Erro, Dolores 6 La Estanzuela 1, 2, 4, 5 San Patricio, Young 2 Europe Domaine Du Chaumoy, Le Subdray 2 Ferme De Loudes, Castelnaudary 1, 2 Lectoure, Castillon-la-Bataille 3 Orgerus 1, 2 Kentziko Thermi, Thessaloniki 1, 2, 4, 5, 6 Tolentino 1 Vollebekk, [Angstrom]s 2 Elvas 1, 2, 4, 5, 6 Alameda Del Obispo, Cordoba 4 Finca La Carrerada, Lleida 5 La Mojonera, Toledo 1 Rancho De La Merced, Cadiz 4, 5, 6 Torregrossa/Belloc, Lleida 2 Kharkov 4, 5 Odessa 5 Ustymovska 6, 7 West Asia and North Africa Guelma 1 Baku 5 (sown twice) Komombo, Aswan 6 Araghi Mahaleh, Gorgan 7 Bayecola, Sari 1, 2, 3, 7 Darab 1, 2 Karaj, Tehran 1, 2 Mazraeh Nemoneh, Gorgan 2 Moghan, Ardabil 7 Al-Tuwaitha, Baghdad 6 Tel Hadya, Aleppo 5, 6, 7 Beja, Tunis 1, 4 Aydin 8 Bornova, Izmir 8 Edirne 8 Menemen, Izmir 4, 5 Samsun 7 Bishkek 7 Southern and Eastern Africa Munanira 3 Holetta, Addis Ababa 6 Njoro 5, 6 Bembeke 5 Bethlehem 1, 2, 3, 5, 6, 7, 8 Houthaaldoorns, Lichtenburg 3, 5 Langgewens, Gouda 3 Moredou, Gouda 2, 4 Lyamungo 1, 2, 3 Simba-Tilotanga, Lyamungo 6, 7 Mbala 1 Harare 6 South Asia New Delhi 8 Islamabad 6, 7, 8 Mingora, Swat 6, 7 Quota 6 East Asia Harbin 1 Hongxinglong 1 Lanzhou, Gansu 2 Kitami, Kunneppu 3, 4 Dan Phung 4 Oceania Lincoln, Christchurch 4, 5 Table 2. Summary of regional associations for individual sites in the SHMM analysis that planted at least 50% of the yield trials. Number of groupings with the region/Total number of groupings Sta. Marcos Sites Toluca Catalina Juarez Parana Country Mexico Ecuador Argentina Argentina Number of yield trials 6 5 4 4 North America 2/21 1/16 6/17 5/16 Central America 2/3 0/3 1/2 0/2 Andes 1/6 0/5 2/4 1/5 Southern Cone 4/33 3/31 10/19 7/19 Europe 0/19 2/28 4/10 9/16 North Africa and West Asia 5/21 0/18 9/14 10/15 Southern and Eastern Africa 1/20 0/16 7/9 10/13 South Asia 2/7 0/3 6/7 4/5 East Asia 0/2 1/5 0/2 0/2 New Zealand 0/1 0/2 1/1 1/1 Total 17/133 7/127 46/85 47/94 % 13 6 54 50 Number of groupings with the region/Total number of groupings La Kentziko Sites Pergamino Estanzuela Thermi Country Argentina Uruguay Greece Number of yield trials 4 4 5 North America 6/19 4/11 8/16 Central America 1/1 1/3 0/3 Andes 1/3 2/8 2/10 Southern Cone 10/17 13/23 15/31 Europe 8/9 12/24 14/23 North Africa and West Asia 4/14 12/15 12/18 Southern and Eastern Africa 8/11 5/11 12/16 South Asia 1/4 0/0 3/3 East Asia 1/2 1/5 1/5 New Zealand 0/0 1/2 1/2 Total 40/80 51/102 68/127 % 50 50 54 Number of groupings with the region/Total number of groupings Sites Elvas Bayecola Bethlehem Country Portugal Iran South Africa Number of yield trials 5 4 7 North America 4/16 6/17 11/30 Central America 1/3 0/1 1/3 Andes 3/10 1/5 2/8 Southern Cone 18/31 12/22 21/39 Europe 9/23 9/15 18/25 North Africa and West Asia 12/18 9/12 13/26 Southern and Eastern Africa 5/16 4/13 10/16 South Asia 0/3 2/2 4/7 East Asia 1/5 1/4 2/4 New Zealand 1/2 0/0 1/1 Total 54/127 44/91 83/159 % 43 48 52 Table 3. Summary of environmental data for locations in the cumulative cluster analysis. Group Site name Country Season 1 San Benito Bolivia January-May 1 Toluca Mexico June-October 2 Darab Iran December-May 2 Elvas Portugal December-June 2 Capitan Miranda Paraguay June-October 2 Kentziko Thermi Greece December-June 2 Bethlehem South Africa August-December 2 Karaj, Teheran Iran December-May 2 Parana Argentina July-November 2 Pergamino Argentina July-December 2 Lyamungo Tanzania May-October 2 Bayecola, Sari Iran December-May 2 La Estanzuela Uruguay July-December 3 Tepatitlan Mexico July-November 3 Charlottetown, PEI Canada May-September 3 Sta. Catalina Ecuador January-July 4 Orgerus France March-July 4 Ferme De Loudes France November-July 4 Londrina Brazil April-August 4 El Batan Mexico June-October Season Season rainfall temp. Group Site name mm [degrees]C 1 San Benito 559 17.8 1 Toluca 847 13.7 2 Darab irrigated 13.3 2 Elvas 381 12.6 2 Capitan Miranda 677 18.0 2 Kentziko Thermi 252 12.3 2 Bethlehem irrigated 14.3 2 Karaj, Teheran irrigated 10.3 2 Parana 343 15.7 2 Pergamino 397 15.1 2 Lyamungo 808 17.7 2 Bayecola, Sari 330 10.8 2 La Estanzuela 534 16.1 3 Tepatitlan 674 20.4 3 Charlottetown, PEI 409 14.8 3 Sta. Catalina 761 13.2 4 Orgerus 262 12.1 4 Ferme De Loudes 310 14.8 4 Londrina 435 18.5 4 El Batan 561 16.7 Temp. Source profile ([double Yield Group Site name ([dagger]) dagger]) Mg/ha 1 San Benito D 5 2.5 1 Toluca D 6 6.0 2 Darab I 2 7.0 2 Elvas I 2 2.9 2 Capitan Miranda I 1 2.7 2 Kentziko Thermi I 1 3.8 2 Bethlehem I 1 3.6 2 Karaj, Teheran I 1 6.5 2 Parana I 1 2.0 2 Pergamino I 3 3.5 2 Lyamungo I 3 3.3 2 Bayecola, Sari I 2 7.2 2 La Estanzuela I 1 5.2 3 Tepatitlan D 1 4.1 3 Charlottetown, PEI D 1 3.7 3 Sta. Catalina S 1 3.3 4 Orgerus I 1 4.9 4 Ferme De Loudes I 1 6.1 4 Londrina S 4 3.7 4 El Batan D 6 3.8 Days to Diseases reported Group Site name maturity ([section]) 1 San Benito -- 1 Toluca 139 LR, YR, SFD 2 Darab 156 LR, YR, ST, SFD 2 Elvas 181 LR, PM, ST 2 Capitan Miranda -- LR, PM 2 Kentziko Thermi 191 PM 2 Bethlehem 155 LR, YR, PM 2 Karaj, Teheran 173 LR, YR 2 Parana 123 LR 2 Pergamino -- LR, ST, SFD 2 Lyamungo -- LR, YR, PM 2 Bayecola, Sari 180 LR, YR, PM, ST, SFD 2 La Estanzuela -- LR, ST 3 Tepatitlan 109 LR 3 Charlottetown, PEI 110 PM, SFD 3 Sta. Catalina -- YR, SFD 4 Orgerus -- 4 Ferme De Loudes -- PM 4 Londrina -- LR 4 El Batan 123 LR, YR ([dagger]) Tendencies over the three last months of the growing season: D = Decreasing temperature, S = Stable temperature, I = Increasing temperature ([double dagger]) These are the sources used for obtaining long-term averages for rainfall and temperature: 1. World Climate at: http://www.worldclimate.com; verified 12 March 2004. 2. Weather Base at: http://www.weatherbase.com/; verified 12 March 2004. 3. Worldwide Bioclimatic Classification System at: http://www.globalbioclimatics.org/data/indice1_1.html; verified 12 March 2004. 4. Major World Crop Areas and Climatic Profiles at: http://www.usda.gov/oce/waob/jawf/profiles/mwcacp2.htm; verified 12 March 2004. 5. Monthly Climate Profiles at: http://www.bestplaces.net/html/climatewld1.asp; verified 12 March 2004. 6. CIMMYT experimental stations. ([section]) LR = Leaf rust, YR = Stripe rust, PM = Powdery mildew, ST = Septoria tritici, SFD = Severe foliar diseases.
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. The authors are thankful to Tom Payne for providing valuable information about the international yield testing locations, and to Jose Crossa for advise on the use of statistical methods.
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M. Lillemo, * M. van Ginkel, R. M. Trethowan, E. Hernandez, and S. Rajaram
M. Lillemo, Dep. of Plant and Environmental Sciences, Agricultural Univ. of Norway, P.O. Box 5003, N-1432 As, Norway; M. van Ginkel, R.M. Trethowan, E. Hernandez, and S. Rajaram, Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico DF, Mexico. Received 10 Mar. 2003. * Corresponding author (firstname.lastname@example.org).
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|Title Annotation:||Crop Breeding, Genetics & Cytology|
|Author:||Lillemo, M.; van Ginkel, M.; Trethowan, R.M.; Hernandez, E.; Rajaram, S.|
|Article Type:||Author Abstract|
|Date:||Jul 1, 2004|
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