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Published online 6 May 2005
Published in Crop Sci 45:1052-1057 (2005)
© 2005 Crop Science Society of America
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PLANT GENETIC RESOURCES

Performance of Crosses among French and Spanish Maize Populations across Environments

R. A. Malvara,*, P. Revillaa, A. Butróna, B. Gouesnardc, A. Boyatc, P. Soengasa, A. Álvarezb and A. Ordása

a Misión Biológica de Galicia, Spanish Council for Scientific Research, Apartado 28, 36080 Pontevedra, Spain
b Estación Experimental de Aula Dei, Spanish Council for Scientific Research, Apartado 202, 50080 Zaragoza, Spain
c INRA Centre de Montpellier, Unité Mixte de Recherches Diversité et Génome de Plantes Cultivées, Domaine de Melgueil, 34130 Mauguio, France

* Corresponding author (rmalvar{at}mbg.cesga.es)


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Heterotic patterns among European maize (Zea mays L.) populations are strongly affected by genotype x environment (GE) interactions and no single heterotic pattern has been identified so far that is not influenced by GE interaction. The objectives of this work were to study (i) the mean performance and stability of the heterotic patterns ‘Humid Spain x Southern France’ and ‘Dry Spain x Humid Spain’ and (ii) the influence of some environmental and genotypic covariates on G and E main effects and their interaction. We studied the GE interaction for grain yield in eight environments using Sites Regression (SREG) and factorial regression models. The biplot obtained from the SREG model allowed visual cultivar evaluation. The factorial regression model incorporated genotypic and environmental covariates that enhanced biological interpretation of GE interaction. The heterotic patterns Humid Spain x Southern France and Dry Spain x Humid Spain had similar mean performance across environments, but the former, represented by the cross Lazcano x Millette du Lauragais, was more stable. Effects of G, E, and GE for grain yield were mainly due to earliness, vigor effects, and/or environmental factors related to cold stress. An adequately long vegetative cycle along with early vigor had a great influence on the mean grain yield performance of Lazcano x Millette du Lauragais. Additionally, its intermediate number of days to silking and tolerance to temperature stresses could be related to its stability. Breeding for tolerance to temperature stresses could render more stable maize genotypes.

Abbreviations: E, Environmental main effects • G, Genotype main effects • GE, Genotype x environment • GGE, G plus GE interaction • SREG, Sites Regression Model


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
MOST MAIZE HYBRIDS grown in Europe correspond to the U.S. heterotic pattern Reid x Lancaster, or the heterotic pattern European flint x U.S. dent (Moreno-González, 1988; Misevic, 1989; Ordás, 1991; Sinobas and Monteagudo, 1996; Garay et al., 1996a, 1996b). European maize is used as a source of adaptation in construction of local breeding populations, although selection for early maturing dent x dent hybrids could yield adapted dent germplasm (Moreno-González et al., 1997).

European maize breeding populations are mostly flint types. Flint maize supplies adaptation to European conditions, while American dent maize contributes high yield potential. Local flint maize germplasm provides characteristics such as early vigor, earliness, resistance to stem lodging, and relative resistance to drought stress. In addition, flint kernels possess a better potential than other kernels for developing high quality flours. Therefore, the high quality flour producers flint x flint maize hybrids could be a good alternative to the most common hybrids used in Europe, European flint x American dent, mostly in regions where earliness is required.

Heterotic patterns have been found among maize germplasms from different European countries (Misevic, 1989; Ordás, 1991; Radovic and Jelovac, 1995; Revilla et al., 2002). A diallel among six French and six Spanish local populations was evaluated at four locations and 2 yr and analyzed following the Analysis II of Gardner and Eberhart (1966) (unpublished). Different heterotic patterns were obtained for each location, although they could be grouped in two main heterotic patterns, Humid Spain x Dry Spain and Humid Spain x Southern France. Additionally, the year x population interaction was significant for grain yield and clearly affected the ranking of populations (unpublished data). Heterotic patterns among European maize populations are strongly affected by GE interactions and no single heterotic pattern has been identified so far that is not subject to GE effects. Therefore, to select the most suitable heterotic pattern across environments, it would be advisable not only to consider the G and E main effects but also the GE interaction effects, to choose high- and stable-grain yielding population crosses. Multiparametric models, AMMI (Additive Main Effects and Multiplicative Interaction) (Gauch and Zobel, 1988), SREG (Crossa and Cornelius, 1997), and factorial regression (Denis, 1988) are suitable for interpreting the response of genotypes to different environments. Among models proposed in studying GE interaction (Crossa, 1990; Brancourt-Hulmel et al., 1997), the SREG model (Crossa and Cornelius, 1997) has been chosen for the present study because it simultaneously considers G and GE interaction effects for visualization of cultivar performance across different environments, allowing interpretations in terms of mean performance and stability of genotypes.

Identification of the causal factors of the GE interaction and quantification of unexplained variation are very important for identifying factors affecting stability and adaptation to specific environmental conditions (Epinat-Le Signor et al., 2001). The factorial regression model incorporates genotypic and environmental covariates (Denis, 1988) that enhance biological interpretation of G, E, and GE interaction effects (Baril et al., 1995; Brancourt-Hulmel et al., 1997).

The SREG and factorial regression models have been considered as complementary (Baril, 1992; Van Eeuwijk et al., 1995). The SREG model reveals which genotype won in each environment and factorial regression tries to explain G, E, and GE interaction in terms of the variation showed by certain environmental and genotypic covariates.

The objectives of this work were to estimate (i) the mean performance and stability of the heterotic patterns Humid Spain x Southern France and Dry Spain x Humid Spain and (ii) the influence of some environmental and genotypic covariates on G, E, and GE interaction.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Genetic Materials
Six Spanish and six French local maize populations (Table 1, Fig. 1), representing local maize germplasm from the principal maize growing areas in both countries, were crossed in a complete diallel without reciprocals in 1999 using paired rows with 15 plants per row. Five sets of paired rows were used for each cross, each plant was used once as male or female. Also, each population was multiplied to obtain seed under the same environmental conditions. About 50 ears were obtained for most crosses and populations. Pollinated ears from each of the five pairs of rows for each cross were harvested and shelled in bulk.


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  		Table 1. Accession name, number, and origin of flint corn populations crossed in a diallel design and evaluated during 2000 and 2001 in two French and two Spanish locations.

 


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  		Fig. 1. Approximate area of origin of the Spanish and French maize populations crossed in a diallel mating design and evaluated during 2000 and 2001 in four locations: 1. Pontevedra, 2. Zaragoza, 3. Mauguio, and 4. Saint Martin de Hinx.

 
Experimental Design
The 12 parental populations and their 66 F1 crosses were planted in Pontevedra (northwest of Spain) (lat. 42°24' N, long. 8°38' W, 20 m above sea level), Zaragoza (eastern Spain) (lat. 41°44' N, long. 0°47' W, 250 m above sea level), Mauguio (southeast of France) (lat. 43°37' N, long. 4°0' W, 13 m above sea level), and Saint Martin de Hinx (southwest of France) (lat. 43°34' N, long. 1°16' W, 65 m above sea level) in 2000 and 2001 (Fig. 1). Three hybrid checks were also included to fit a 9 x 9 triple partially balanced lattice design. Because of the lack of seed of two commercial hybrids for the second year, only the commercial hybrid check Dunia was common to all environments.

Each experimental unit consisted of two rows spaced 0.80 m apart, with 25 two-plant hills spaced 0.21 m apart. Plots were overplanted and thinned, obtaining a final density of approximately 60000 plants ha–1. Data taken for each plot were early vigor (from 1 = poor to 9 = high), silking date (days from sowing to silking for about 50% of plants), root lodging (% of plants leaning more than 30° from the vertical), grain yield (Mg ha–1 at 140 g H2O kg–1), and grain moisture (g H2O kg–1). Early vigor was recorded only at the two Spanish locations. Early vigor, days to silking, root lodging, and grain moisture averaged across environments were considered as genotypic covariates.

An environment was defined as the combination of a year and a location. Environmental covariates considered during the growing period were average daily temperature, mean daily minimum temperatures, mean daily maximum temperatures, absolute minimum temperature, absolute maximum temperature, total rainfall, and mean daily rainfall (Table 2). Those parameters were estimated from daily data for maximum and minimum temperatures and rainfall. A meteorological station equipped with a maximum-minimum thermometer and a rainfall collector was placed in each location for gathering climatic data.


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  		Table 2. Values for the environmental covariates{dagger}.

 
Sites Regression
The sites regression model used was (Cornelius et al., 1996; Crossa and Cornelius, 1997):

where Yij is the mean grain yield of genotype i in the environment j, n ranges from 1 to r, with r = number of principal components (PCs) required to approximate the original data. ßj is the environmental main effect. {xi}*in and {eta}*jn are the ith genotype and the jth environmental symmetrical scaling scores for PCn, respectively. {xi}*in and {eta}*jn were obtained from the original scores for PCn, {xi}in and {eta}jn, as defined in Yan et al. (2000). This type of scaling has the property that genotype scores and environmental scores have the same unit for each PCn (Yan, 2002). In the SREG model, PC analysis was made on residuals of an additive model in which environments are the only additive terms. Therefore, the term {Sigma}{xi}*in{eta}*jn contains the variation due to genotypes and the GE interaction. A two-dimensional biplot of the first two PCs (Gabriel, 1971) called GGE biplot (genotype plus GE interaction) (Yan et al., 2000) was generated. Genotypes and environments were displayed in the same plot. Each genotype and environment was defined by the scores of genotype and environment on the first two PCs, respectively. Analyses were made by a SAS (SAS, 2000) program for graphing GE and GGE biplots developed by Burgueño et al. (2004).

Factorial Regression
The general form for a factorial regression model with K genotypic and H environmental covariates is (Denis, 1980, 1988):

where {rho}k and {delta}h are the regression coefficients of genotypic (Gik), and environmental covariates (Ejk), respectively; {alpha}i and ßj are the residuals of genotype and environmental main effects, respectively; {theta}kh is the regression coefficient of the cross-product of covariates Gik and Ejh; and {alpha}'ih and ß'jk are the genotype i and environment j specific regression coefficient of Ejh and Gik, respectively. The term {epsilon}ij is the residual interaction effect. All other sources of variation were considered fixed. The covariates and their order in the factorial regression model for grain yield data were obtained by performing a stepwise regression on genotype covariates and a second stepwise regression on environmental covariates (Denis, 1988). After standardization of covariates, factorial regression analyses were performed by the computer package INTERA (Decoux and Denis, 1991). All terms were tested with the residual experimental error.


   RESULTS AND DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES
 
SREG Analyses
The E accounted for 51.3% of the total variation for grain yield (Table 3), while G accounted for 29.9%, and the GE interaction accounted for 18.8% (Table 3). The first six significant PCs obtained after singular value decomposition of grain yield data explained 97.3% of GGE variation, with the first and second PCs explaining 66.4 and 11.2%, respectively (data not shown). Therefore, a biplot of PC1 and PC2 represents 77.6% of the GGE.


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  		Table 3. Analysis of variance for the factorial regression model including two environmental and four genotypic covariates from trials conducted at two French and two Spanish locations during 2000 and 2001.

 
In the SREG biplot (Fig. 2), the axes movement obtained by using the average environment coordination, which forces the abscissa to present the genotype main effect and, consequently, makes more interpretable the biplot in terms of mean performance and stability of genotypes (Yan, 2002), was so slight that original axes were not replaced. The most suitable genotype across environments would have high score for PC1 and a near zero score for PC2 (Yan et al., 2000; Crossa et al., 2002). PC1 represents a noncrossover GE interaction. A genotype with a larger PC1 score has a greater average grain yield, and its performance varies across environments in direct proportion to the environment PC1 scores. The two-dimensional biplot showed that crosses Lazcano x Rastrojero, and Tuy x Rastrojero, representing the heterotic pattern Dry Spain x Humid Spain defined by Ordás (1991), and Lazcano x Millette du Lauragais, representing the heterotic pattern Humid Spain x Southern France, had high average grain yield (Fig. 2). On the contrary, populations per se had, in general, negative PC1 scores, suggesting poorer average performance than crosses in which heterosis effects are expressed. The PC2 represents disproportionate genotype grain yield differences across environments. Among population crosses with good average performance, Lazcano x Millette du Lauragais had a PC2 score closer to zero, meaning that this population cross has a proportionate response across environments. Therefore, Lazcano x Millette du Lauragais appeared as a high- and stable-grain yielding population cross. Although genotype-focused scaling is the most suitable method for visualizing the interrelationship and comparing among genotypes, symmetric scaling has intermediate characteristics between the genotype and the environment focused scaling methods and it is preferred for visualizing the "which-won-where" outcome (Yan, 2002).



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  		Fig. 2. The GGE biplot based on maize yield of 12 populations and their crosses in eight environments. Crosses among populations were designated by the letters assigned to their population parents: A, B, C, D, E, F, G, H, I, J, K, and L for Tuy, Viana, Lazcano, Basto/Rastrojero, Rastrojero, Enano levantino/Hembrilla, Bade, Lacaune, Esterre, Ain, Millette du Lauragais, and Millete Montagne Noire, respectively.

 
Factorial Regression Analyses
Factorial regression analyses showed that, among environmental variables, only the means of maximum and minimum temperatures significantly affected the E component of grain yield variability (Table 3). Mean minimum and maximum temperatures explained 76 and 6% of E variation, respectively; while the significant residual effect explained 18% of E variation. Mean minimum temperatures had significant and positive effects ({delta}TMIN) on grain yield, as expected, because the minimum temperatures at these locations frequently fall below the requirements of maize. A practical conclusion is that the main environmental stress is cold temperatures and, therefore, a breeding program for cold tolerance is justified for this area (Revilla et al., 2000).

All genotypic covariates had significant effects on G variation for grain yield (Table 3). Days to silking explained 39% and early vigor 20% of the variation for G, but the residual was also significant and explained 36% of the G variation. Therefore, other variables should have been included to achieve a better understanding of the variation among genotypes. However, the proportion of G explained by the genotype covariates in this study (64%) was larger than that in the study (45.7%) by Butrón et al. (2004). This is probably because genotypes in our study were more variable in maturity than those used by Butrón et al. (2004). The importance of days to silking, with a positive regression coefficient on grain yield, agrees with the idea that later maturing germplasm usually yields more than early maturing germplasm. The coefficient of regression of grain yield on early vigor ({rho}V) was also positive because early vigor supplies adaptation to cool springs that is the main limiting environmental factor for maize in most European regions (Revilla et al., 2000). Therefore, in Europe the great influence of early vigor on G effects justifies the search for heterotic patterns among European maize materials. Kernel moisture had a small, but significant negative effect ({rho}K) on grain yield. However, if the regression was made with kernel moisture as the only independent variable, it would explain a larger proportion of variability and the coefficient would become positive (data not shown). Materials with a longer growing cycle have higher moisture and grain yield than earlier maturing materials at a given harvest time. But, when days to silking are included in the model as covariable, differences in moisture among materials are mainly due to kernel type. Flint maize usually yields less and has more kernel moisture than dent maize at a given harvest time.

The factorial regression model explained 60% of the GE interaction sums of squares for maize grain yield, although the residual GE was significant (Table 3). Previous authors have not been able to reduce residual interaction to nonsignificant levels (Hébert et al., 1995; Biarnès-Dumoulin et al., 1996; Epinat-Le Signor et al., 2001; Butrón et al., 2004). Most of the components of the interaction were significant, but with small contributions to the sums of squares for GE variation (Table 3). The eight GE covariate cross-products explained a small portion of sums of squares for the interaction (20%), but six covariate cross-products were significant (Table 3).

The regression coefficient of grain yield on the covariate cross-product mean maximum temperatures x days to silking was positive ({theta}TMAX-S). Long-cycle materials were favored by high mean maximum temperatures that accelerated maize growth, allowing them to adapt to places with short growing seasons. Nevertheless, high maximum temperatures could be stressful for short-cycle materials that are perfectly adapted to those conditions.

The interaction of covariates mean of maximum temperatures and early vigor accounted for 2% of the GE interaction. The negative regression coefficient of grain yield on mean maximum temperature x early vigor ({theta}TMAX-E) showed that the interaction effect of vigorous materials increased with lower means of maximum temperatures, while the interaction effect of less vigorous materials increased as higher maximum temperatures increased (Table 3). That was expected since maize development is optimum at temperatures about 25°C, but that is less obvious for genotypes with higher vigor at early stages because they are better adapted to suboptimal temperatures.

The mean maximum temperatures x lodging covariate cross-product explained 7% of the sums of squares for GE interaction. The negative regression coefficient of grain yield on the cross-product mean maximum temperatures x lodging ({theta}TMAX-L) suggested that the acceleration of maize growth because of higher means of maximum temperatures could be unfavorable for hybrids susceptible to lodging because of faster senescence that favors grain yield losses due to dropped ears. On the other hand, hybrids less susceptible to lodging could be favored by warmer conditions because those conditions enhance the production of assimilates that confer stem strength and are used for producing grain.

Mean minimum temperatures x silking had a negative effect ({theta}TMIN-S) on grain yield that accounted for 7% of GE variation. Higher minimum temperature benefited shorter season, earlier silking materials, while full season later silking materials better tolerated cooler mean minimum temperatures. Full season materials are typically planted earlier in the season when cooler temperatures occur than are shorter season earlier silking materials. Natural selection would favor tolerance to cooler mean temperatures in the full season materials.

The regression coefficient of grain yield on the cross-product mean of minimum temperatures x early vigor ({theta}TMIN-E) was significant and positive, although it became negative after removing any other effect on the GE variation for grain yield (data not shown). Therefore, the significant contribution of this cross product to the GE interaction should not be considered because it has been altered by possible correlations among environmental and/or genotypic covariates.

The variability for GE was similarly explained by covariate cross-products (20%), the interaction of environmental covariates with the residual genotype variation (22%), and the interaction of genotypic covariates with the residual environmental variation (18%). There were significant genotype-specific responses to the means of minimum and maximum temperatures, which could not be explained by differences in any of the genotypic covariates used, suggesting the presence of genetic variability for temperature stress tolerance among European populations. Grain yield also showed significant differences among environment-specific responses to days to silking, early vigor, lodging, and kernel moisture (Table 3). The G, E, and GE effects for grain yield were mainly due to earliness, vigor effects, or environmental grain yield limiting factors, agreeing with other authors (Argillier et al., 1994; Epinat-Le Signor et al., 2001; Butrón et al., 2004). However, in the present study, the yield limiting factors detected were the means of minimum and maximum temperatures, while in other studies the water balance, mean temperature, mean minimum temperature, and percentage of air humidity were cited as important yield limiting factors (Argillier et al., 1994; Epinat-Le Signor et al., 2001; Butrón et al., 2004).


   CONCLUSIONS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES
 
The heterotic patterns Humid Spain x Southern France and Dry Spain x Humid Spain had similar mean performance across environments, but the former, represented by the cross Lazcano x Millette du Lauragais, was more stable. The G, E, and GE effects for grain yield were mainly due to earliness, vigor effects or environmental factors related to cold stress. An adequate cycle of at least 68 d from sowing to flowering along with high early vigor had a great influence on the good grain yield of the Lazcano x Millette du Lauragais cross. In addition, the intermediate number of days to silking and some tolerance to temperature stresses may have contributed to its stability. Breeding for tolerance to temperature stresses could provide more stable maize genotypes for European production.


   ACKNOWLEDGMENTS
 
The authors thank Dr. Moro for his invaluable help in the statistical processing of results and to Dr. Denis for facilitating the access to the statistical package INTERA.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Research supported by the Committee for Science and Technology of Spain (Project Cod. AGL2001-3946), the Ministry of Science and Technology (Ref. HF1999-0138), the Ministère de l'Education Nationale et de la Recherche, and the Excma. Diputación Provincial de Pontevedra, Spain.

Received for publication May 14, 2004.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 


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