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CROP BREEDING, GENETICS & CYTOLOGY

Defining Sunflower Selection Strategies for a Highly Heterogeneous Target Population of Environments

^a Advanta Semillas S.A.I.C., Ruta Nac. 33 Km 636, CC 559, (2600) Venado Tuerto, Argentina
^b CSIRO Plant Industry, Queensland Bioscience Precinct, 306 Carmody Rd., St. Lucia, QLD 4067, Australia

^* Corresponding author (avega{at}waycom.com.ar)

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Genotype x environment (G x E) interactions can be a major impediment to genetic progress in sunflower (Helianthus annuus L.) breeding for Argentina. Previous studies revealed that northern and central environments show repeatable differences in genotype discrimination, suggesting some G x E interactions could be accommodated by selecting for specific adaptation. In this study, a trial dataset of 10 hybrids grown over 46 environments was used to validate this megaenvironment definition, to determine the value of division of the sunflower region of Argentina, and to define optimal testing strategies to balance resources between subregions. Pattern analysis confirmed the northern and central megaenvironments. Subdivision of the target region and the testing resources increased the within-subregion genotype to G x E interaction ratios and did not decrease trial repeatabilities. The genetic correlation between target region and its subregions was 0.36. In contrast to studies for barley in Canada, the calculated ratios of correlated response in a subregion to indirect selection in the undivided target region relative to direct response in the subregion demonstrate that division of the sunflower region is 3x more effective than selecting for broad adaptation to the undivided target region. The unpredictable G x E interactions within subregions should be accommodated by selecting for broad adaptation. In the northern subregion, there is scope to redefine testing strategies by replacing years with locations with no cost in performance predictability. Testing resources can be balanced based on the market value of the two subregions.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
APPROXIMATELY 1.9 MILLION hectares of sunflower are grown in Argentina between latitudes 26°S (Chaco) and 39°S (southern Buenos Aires). This growing region includes subtropical (northern) and temperate (central and southern) climates and a wide range of planting dates, from July to December. Analyses of large-scale multienvironment trials have identified that large and regional G x E interactions can be a major impediment to genetic progress in breeding for this highly heterogeneous target population of environments (de la Vega et al., 2001; Chapman and de la Vega, 2002). Understanding the magnitude, repeatability, and predictability of such G x E interactions is needed to accommodate their effects through appropriate selection strategies aimed at exploiting broad and/or specific genotype-adaptation patterns (Basford and Cooper, 1998 and references therein).

Observation and interpretation of patterns of genotype adaptation in multienvironment trials provide the stimulus for investigations into the magnitude, repeatability, and predictability of G x E interactions. The variance components estimated from the combined analysis of variance can be used to measure the relative magnitude of G and G x E interaction variance components and to predict the response to selection (Cooper and DeLacy, 1994). If the predictable portion of G x E interactions (e.g., genotype by location [G x L] interaction) explains a large portion of variability of the system under study, pattern analysis (clustering and ordination) (Williams, 1976) can be used to group environments into subregions on the basis of similarity of cultivar performance (Cooper and DeLacy, 1994).

Conversion of G x L interaction into G effect that can contribute to selection response is the main reason for the subdivision of large breeding target environments (Comstock and Moll, 1963). If genotype x subregion (G x S) interaction is large in comparison to the G effect, then subdivision will be effective in increasing the G variance in the subregions relative to the original undivided area (Atlin et al., 2000). However, a fair and straightforward comparison between selecting for specific adaptation to the identified subregions and selecting for broad adaptation to the undivided target region implies the absence of substantial differences in costs between the two strategies. This limitation presents breeders with a dilemma. While the division of the target region into more homogeneous subregions could increase genetic variance and the G to G x E interaction ratio within-subregion, this division of testing resources could also result in loss of precision in the estimation of genotype means within the smaller subregions (Atlin et al., 2000).

The model of correlated response to selection (Falconer, 1989), wherein measurements made on the same genotypic attribute in different environments are treated as correlated traits, can be used to determine the relative merits of selecting for specific versus broad adaptation. Atlin et al. (2000) adapted this model to determine the effect of subdividing a target region by considering yield in the undivided region and a subregion as correlated traits. If the objective is to improve performance in the subregion 1 (S1), selection may be undertaken directly in S1 or indirectly in the undivided target region (TR). The relative effectiveness of the two strategies depends on the genetic correlation between genotype performance in S1 and TR (r_g) and the repeatability or broad-sense heritability (H) in the subregion (H_S1) and the target region (H_TR). The predicted ratio of correlated response (CR) in S1 to indirect selection in TR relative to direct response (DR) in S1 may be used to determine if division of the target region is likely to increase response to selection and can be calculated as follows (Atlin et al., 2000):

[1]

If CR/DR is less than 1.0, then division of the target region would result in an increase in the response to selection. The genetic correlation between the undivided region and its subregions can be estimated using the variance components derived from a (G x S) mixed model applied to a trial dataset (Atlin et al., 2000) using the formula:

[2]

where ²_g is the genotypic variance component and ²_gs the G x S interaction component of variance. Direct selection is likely to be effective when H_S1 is high, although the testing resources were less than those of the undivided target region, and when there is substantial G x E interaction associated with the effect of the subregions (²_gs) on cultivar relative performance, causing r_g to be low.

Pattern analysis of a reference set of 10 single-cross sunflower hybrids grown over 21 environments (3 yr) in Argentina (de la Vega et al., 2001) has revealed that northern (subtropical) and central (temperate) environments show repeatable differences in genotype discrimination for oil yield. These results suggest that dividing the sunflower target region of Argentina and exploiting specific adaptation to each subregion is a breeding strategy potentially useful to increase selection response. However, the existing analysis does not demonstrate that the relative merit of this strategy is greater than selecting for broad adaptation to the undivided target region.

Apart from the strong differences observed between northern and central environments in the manner in which they influence the relative performance of sunflower hybrids, large G x Y, and genotype x location x year (G x L x Y) interactions for oil yield within regions were found for balanced data over 3 yr (de la Vega et al., 2001) and for unbalanced data over 8 yr (Chapman and de la Vega, 2002). These within-region interactions make it difficult to ensure that a multienvironment testing regime to test new and current hybrids across a small number of locations and years will adequately sample each megaenvironment target population of environments.

In this study, we have applied variance component analysis, pattern analysis, and correlated response to selection analysis to an extended dataset consisting of 46 trials (7 yr) with the following objectives: (i) to validate the subregions identified by de la Vega et al. (2001); (ii) to determine if subdivision of the sunflower target region of Argentina into central and northern subregions is likely to improve selection response; and (iii) to evaluate different testing strategies (i.e., number of locations, years, and replicates) aimed at accommodating the effects of the G x E interactions within-subregion. Differences between subregions in terms of the relative size of the G x L, G x Y, and G x L x Y interaction components of variance for oil yield would suggest that different selection strategies should be designed for each subregion. These strategies are compared and discussed in the framework of the objectives of a breeding program.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Trial Dataset
A reference set (Fox and Rosielle, 1982) of 10 sunflower single-cross hybrids (Table 1) was evaluated in 46 central, northern, and managed (Cooper et al., 1995) environments of Argentina (Table 2). The hybrids comprising the reference set of genotypes were selected from the Advanta Semillas testing program based on their contrasting relative performance across environments for oil yield (de la Vega et al., 2001). The set includes commercial hybrids widely planted in the central region (Contiflor 15, Contiflor 9, and TC 2001), commercial hybrids widely planted in the northern region (Morgan 734 and Aguará), and experimental hybrids that showed patterns of adaptation different from the commercial hybrids. These hybrids represent a wide range of genetic diversity according to their genetic origin and to restriction fragment length polymorphism (RFLP) molecular marker analyses (A. Leon, Advanta Argentina, Balcarce, unpublished data).

The locations range from 23°S to 37°S, and are evenly distributed between subtropical (northern) and temperate (central) sunflower-growing environments. Five of the 46 environments were managed environments. In V27, V29, V22, V23, and VI9, the normal October planting date at a central location (Venado Tuerto) was delayed until December, and in VI9 the day length was artificially extended to 15.5 h with low intensity (ca. 40 µmol PAR m^–2 s^–1) mixed incandescent and fluorescent lighting during the whole crop cycle. This represents an average extension of 2 h per day in comparison to the natural photoperiod during flowering and grain filling. In the remaining 41 trials, planting took place within the normal sowing window at each location (i.e., ca. mid-October in central locations and ca. mid-August to mid-September in northern locations).

In each environment a randomized complete block design with three or four replicates was used to test the 10 genotypes. The trials were over-planted and thinned to 47 600 pl. ha^–1. A plot size of three or four rows x 6 m and inter-row spacing of 0.70 m was used except in PA9 (four rows x 6 m and an inter-row spacing of 0.52 m) and in VI9 (three rows x 3 m and an inter-row spacing of 0.70 m). Nutrient deficiencies were prevented with fertilization when necessary. Weeds and insect pests were controlled chemically. Plot data of grain yield were determined by hand harvesting of 3.99 m² in 44 trials (central row, discarding the border plants), 5.93 m² in PA9 (two central rows, discarding the border plants) or 2.10 m² in VI9 (central row; there was no extra spacing between plots). Grain oil concentration was determined on a 10 g oven-dried achene sample by nuclear magnetic resonance (Granlund and Zimmerman, 1975). Oil yield was calculated as the product of grain yield and grain oil concentration. Analyses of variance were conducted to estimate the genotypic components of variance for oil yield and their standard errors for each individual trial using GenStat 7.1.

Pattern Analysis
Complementary principal component analyses (PCA) were conducted on the environment-centered (GGE biplot) (Cooper and DeLacy, 1994; Yan and Hunt, 2002) and double-centered (AMMI2 biplot) (Gauch, 1988) genotype x trial arrays of best linear unbiased predictors (BLUPs; Robinson, 1991) for oil yield. The BLUPs were calculated from restricted maximum likelihood (REML) (Patterson and Thompson, 1975) using GenStat 7.1, following the procedures discussed by Cooper and DeLacy (1994). The environment-centered and double-centered matrices were standardized within-trial to unit variance before PCA (Fox and Rosielle, 1982; Cooper and DeLacy, 1994). The principal components of the squared Euclidean distance matrices were estimated using a singular value decomposition procedure in GenStat 7.1. Biplots (Gabriel, 1971) of the first two principal components were constructed from these analyses to examine correlations between environments in the manner they influence the relative performance of the genotypes.

The GGE biplot displays the G and G x E interaction effects of the multienvironment trial dataset, which is the relevant information for cultivar evaluation and selection (Yan and Hunt, 2002), and can be interpreted as follows (Kroonenberg, 1997; Chapman et al., 1997): Genotypes located near the origin might either have all their values close to the environment means, given that the data were environment centered, or their variability is located in another dimension. Similarly, environments close to the origin may have little variability across genotypes or may not fit well in two dimensions. Entries that are close together are similar in performance across environments, while adjacent environments are similar in the way they discriminate among genotypes. For any particular environment, genotypes can be compared by projecting a perpendicular from the genotype symbols to the environment vector; i.e., genotypes that are further along in the positive direction of the environment vector are higher yielding, and vice versa. The cosine of the angle between any two environment vectors approximates their correlation with equality if the fit is perfect (Kroonenberg, 1997). Thus, acute angles between any two vectors indicate positive associations; i.e., they influence the genotypic relative performance in a similar manner; 90° angles indicate no association; and angles greater than 90° indicate negative associations.

The AMMI2 biplot displays only the G x E interaction effects, thus being less effective than the GGE biplot for cultivar selection, as a genotype interacting positively (or negatively) with an environment does not necessarily perform well (or poorly) in that environment. However, the AMMI2 biplot allows a better separation of the environments, which is the main target of a pattern analysis in the context of this study. For classification, a hierarchical agglomerative clustering method with incremental sum of squares (Ward, 1963) as the fusion criterion was utilized; i.e., the reference hybrids were grouped on the basis of their genotype-specific responses to the testing environments.

Correlated Response to Selection
A G x S analysis of variance was conducted for the entire dataset to examine partitions of the G and G x E interaction components of variance for oil yield. In this analysis, locations were classified as either northern-type or central-type, according to the results of pattern analysis. The phenotypic observation y_mjkli on hybrid i in replicate l of location j, year k, and subregion m was modeled according to Atlin et al. (2000) as:

[3]

where µ is the grand mean; s_m the effect of subregion m; (l/s)_mj the effect of location j nested within subregion m; (y/s)_mk the effect of year k nested within subregion m; (ly/s)_mjk the effect of the interaction between location j and year k nested within subregion m; (r/sly)_mjkl the effect of replicate l nested within the subregion-location-year combination mjk; g_i the genotypic effect of the hybrid i; (gs)_im the interaction effect for hybrid i and subregion m; (gl/s)_mij the interaction effect for hybrid i and location j nested within subregion m; (gy/s)_mik the interaction effect between hybrid i and year k nested within subregion m; (gly/s)_mijk the interaction effect between hybrid i, location j and year k nested within subregion m; and _imjkl is the residual effect for hybrid i in replicate l of subregion-location-year combination mjk (experimental error). The effect of the subregion was considered fixed. All other terms were set to random. The REML analysis in GenStat 7.1 was used to estimate variance components and their standard errors for the random terms in the model.

Genetic correlation between the overall target region and its constituent subregions was calculated according to Eq. [2], using the variance components derived from mixed model [3]. The predicted ratio between CR and DR in central and northern subregions was calculated using Eq. [1]. Repeatabilities on a trial mean basis within the target region and within each subregion were calculated as:

[4]

where ²_g is the G variance component, ²_gl is the G x L interaction variance component, ²_gy is the G x Y interaction variance component, ²_gly is the G x L x Y interaction variance component, ² is the experimental error variance component, and l, y, and r are a given number of locations, years, and replicates, respectively. The variance components were derived from a linear model for the analysis of genotypes, locations, years, and replicates, with all factors considered random (Atlin et al., 2000). To be consistent with the assumption of no increase of testing resources in the specific adaptation option, H_SNorth and H_SCentral were calculated using half of the total plot number (by reducing number of locations) used to calculate H_TR. However different criteria to define resource allocation could be used in a breeding program. With the aim of comparing testing strategies based on an uneven distribution of resources, CR/DR ratios were estimated for a range of selection strategies by setting the number of replicates to three (r = 3) and years to two or four (y = 2, 4) in Eq. [4] and varying the number of locations between central and northern regions within a total of 20 locations for the undivided target region.

Multienvironment trial structures within-subregion were compared by substituting the components of variance obtained for oil yield into Eq. [4], considering three-replicate trials and changing the number of locations (1–15) and years (1–6). Varying the number of years and locations was used to test potential selection strategies aimed at reducing the number of years of testing by increasing the number of locations. Surface plots were used to graphically display the repeatability response to different location-year combinations in each subregion.

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
A strong across-environment variation was found for oil yield and its components grain yield and grain-oil concentration (Table 2). The estimates of the trial G variance component for oil yield (Table 2) showed a greater variation than trial-mean oil yield, although the two variables were positively correlated (r = 0.68, P < 0.0001). In general, mean values and genotypic components of variance for oil yield across northern environments were lower than across central environments.

The first and the second principal components of the GGE biplot accounted for 57% of the G and G x E interaction variation (Fig. 1A). The environment vectors covered a wide range of Euclidean space, indicating the existence of strong G x E interactions among the 46 environments evaluated. The first principal component appears to be associated with adaptation to the central environments, while the second principal component appears to be related to adaptation to northern environments. On average, the angle between central and northern environments is around 90°, which indicate that both regions are not associated in terms of their genotype discrimination effects. Broadly adapted hybrids, such as GV23146, tend to be at the top right hand quadrant of the diagram. Specifically adapted hybrids to the central and the northern regions tend to be at the bottom right and top left quadrants of the GGE biplot, respectively.

View larger version (26K): [in this window] [in a new window]   Fig. 1. GGE (A) and AMMI2 (B) biplots of the first and second principal components for oil yield of 10 sunflower hybrids grown in 46 trials. Genotypes are represented by points and environments by vectors. Black and open circles identify central- and northern-type environments, respectively. In B, symbols indicate genotype groups with members of a similar response pattern at the 4-group level for oil yield. See Table 2 for environment codes.

When normal October planting dates in the central location of Venado Tuerto were delayed until December (trials V27, V29, V22, V23), the trials associated positively with the northern environments for oil yield (Fig. 1A and 1B). Planting dates determine predictable G x E interactions, since this factor is under the control of the researcher or the farmer. Consequently, December plantings at Venado Tuerto were classified as northern-type environments for the analysis of correlated response to selection. This was confirmed when the photoperiod was extended to 15.5 h in this environment (trial VI9), as genotypes reverted to responses similar to those of a normal planting date in central environments.

Cluster analysis of oil yield showed that the reference genotypes could be separated into four groups of different response patterns across environments (superimposed on Fig. 1B). This truncation retained about 60% of the G x E sums of squares. Genotype Group 1 interacted positively with the central environments. Group 4 is composed by one genotype that showed relatively broad adaptation; i.e., high across-environment average oil yield revealed by a positive interaction with almost all environments in the GGE biplot of Fig. 1A. Genotype Groups 2 and 3 showed on average an improved relative performance for oil yield in the northern subregion, but contrasting relative responses in the direction of the second principal component of the AMMI2 biplot (Fig. 1B).

The variance component analysis showed a high G x S interaction to G ratio in the undivided target region (Table 3). As a consequence, an increase in G variance component and a decrease in the ratio of total G x E interaction (i.e., G x L + G x Y + G x L x Y) to G are observed when the target region is divided into subregions. Calculated repeatabilities were 0.45 for the undivided target region (for a testing strategy of 10 locations, 1 yr, and 4 replicates) and 0.80 and 0.40 for the central and northern subregions, respectively (5 locations, 1 yr, and 4 replicates per subregion). For a selection strategy of 20 locations, 3 yr, and 4 replicates, the calculated repeatabilities were 0.73, 0.95, and 0.73 for the undivided target region, the central subregion, and the northern subregion, respectively. The genetic correlation between the undivided target region and its constituent subregions was 0.36. The calculated CR/DR ratios for a set of testing strategies (Table 4) demonstrate that, on average, division of the sunflower region in central and northern subregions is approximately three times more effective in terms of selection response than selecting for broad adaptation to the undivided target region.

View larger version (26K): [in this window] [in a new window]   Fig. 2. Average (A) and individual (B) predicted ratios of correlated response in a subregion to indirect selection in the undivided target region (CR/DR) for different selection strategies consisting of 2 or 4 yr, 3 replicates, and a variable number of testing locations, using the variance components estimates for sunflower oil yield in the central, northern, and undivided target regions of Argentina. Arrows in Fig. 2A show different strategies for division of testing resources.

View larger version (52K): [in this window] [in a new window]   Fig. 3. Surface plots of predicted hybrid-mean repeatability for oil yield in the central (A) and northern (B) subregions as the number of locations and years of testing in multienvironment trials are changed (assumes three replicates for each location-year combination), based on the within-subregion components of variance given in Table 3. Black triangles indicate repeatability estimates for 1-yr or one-location testing combinations assuming one replicate per trial.

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

The almost lack of overlap between central and northern environments observed in the biplots of Fig. 1 indicates that subdivision and selection for specific adaptation to the identified subregions is a potential strategy to increase selection response. However, according to the relative performance of hybrid GV23146 across northern and central environments (Fig. 1A), it can be suggested that selection for broad adaptation to the undivided target region is possible, and that both strategies have to be compared in terms of yield gains predicted on the basis of correlated response to selection. Subdivision of the sunflower target region of Argentina into northern and central subregions strongly increased the ratio of within-subregion G to total G x E interaction variance components (Table 3). According to the calculated repeatabilities, the equal division of the testing resources did not result in loss of precision in the estimation of the within-subregion genotype means relative to the estimation of the genotype means for the undivided target region. This, together with a low genetic correlation between the target region and the subregions, resulted in low CR/DR ratios (Table 4). These estimates do not assert that it is impossible to recombine some of the components of specific adaptation to the identified subregions in a way that will contribute to an improvement in broad adaptation, which is the main target of a breeder. However, they strongly support the notion that dividing the sunflower target region of Argentina in central and northern subregions and exploiting specific adaptation to both megaenvironments would result in increased response to selection.

Different criteria to define resource allocation could be used in a breeding program. Division on the basis of minimizing the average CR/DR ratio across subregions and according to the relative market size of the subregions can lead to opposite solutions, as the subregion that shows the lowest hybrid-mean repeatability (i.e., the northern region) is also the one that currently has the lowest market value. However, the CR/DR baseline of Fig. 2A is broad, suggesting that investment in testing resources can be balanced based on the relative market value of the two regions.

These results contrast with those published by Atlin et al. (2000) for barley in Canada, where the lack of local adaptation suggested that programs that test broadly are likely to outperform ones that are narrowly targeted. In our study, the program target region includes temperate and subtropical areas, which strongly differ in the environmental attributes affecting the expression of crop yield (Chapman and de la Vega, 2002). It is also relevant that, although the genotypes composing the reference set used in this study represent the available germplasm, they were selected from previous trials on the base of their contrasting environment-specific responses and consequently constitute a more accurate bioassay for indirect characterization of the environment than would a completely random sample of genotypes.

The ratios of within-subregion G x L interaction to G variance components (Table 3) suggest that further subdivision is unlikely to result in increased response to selection. The REML analysis revealed that the highest portion of the total G x E interaction observed within regions was accounted for in the G x L x Y interaction. A portion of the G x L x Y and G x Y interactions could be characterized, but not predicted. This is the case of many repeatable G x E interactions determined by patterns of water availability (Cooper, 1999; Chapman et al., 2000; Atlin et al., 2001). The variable and unpredictable nature of these interactions must be accommodated by selecting for broad adaptation. This strategy requires extensive testing regimes that ensure that each crop target subregion is adequately sampled.

The larger ratios of G x L x Y and G x Y interactions to G (Table 3) and the lower trial G variance components (Table 2) observed in the northern subregion determine that to achieve repeatabilities of more than 0.80, trials would need to be conducted over at least 4 yr. Conversely, in the central subregion there is scope to redefine testing strategies by replacing years with locations at no cost in ability to predict genotype performance. This would allow acceleration of genetic progress in this particular sunflower-growing subregion. A point to note is that, if the number of years of testing is increased, the estimated CR/DR ratios for the northern and central regions decrease and increase, respectively (Fig. 2B). This is because H_North shows a higher relative increase than H_TR when the number of years is increased, while H_Center shows an opposite relative response.

Northern and broadly adapted hybrids can be classified as early maturity (Group 2) or late maturity (Group 3 and 4). These groups had contrasting relative performance in the direction of the second principal component of Fig. 1B, which discriminates between years in which the early hybrids show a positive G x E interaction component; i.e., 1997–1998 and 2002–2003 (trials RE8, MA8, VO3, CH3), and years of opposite relative performance pattern. This contrasting relative performance could be accounting for a portion of the substantial G x Y interaction observed in the northern region ( as large as the G effect, Table 3) and implies that the sample of the northern subregion differs from year to year. Analysis of private trial datasets (data not shown) suggests that the rainfall excesses during grain filling associated with the effect of the warm phase of El Niño Southern Oscillation (e.g., seasons 1997–1998 and 2002–2003) could be promoting an improvement in the relative performance of the early hybrids.

These findings suggest a possible refinement of the subregion strategy. December plantings in the central location Venado Tuerto (V22, V23, V27, V29) expose sunflower crops to low radiation during grain filling (de la Vega and Hall, 2002) and discriminate among genotypes in a way similar to the 1997–1998 and 2002–2003 (which are also cloudy during grain-filling), promoting an improvement in the relative performance of the northern-adapted early maturity hybrids (Fig. 1B). This suggests that this managed environment is a good surrogate of northern environments like the 1997–1998 or 2002–2003 seasons. This issue requires study to determine if the apparently useful subregion selection strategy can be improved to deliver further benefits in the northern subregion.

	ACKNOWLEDGMENTS

 
This research was supported by Advanta Semillas S.A.I.C. The authors thank Aldo Martínez, Sergio Solián, Carlos Ghanem, Daniel Kennedy, Ney Flores, César Sánchez, and Hugo Baravalle for collaborating in the field experiments and data management; Dr. Martín Grondona for his statistical advice; and Drs. Antonio Hall, Chris Lambrides, and Greg Rebetzke for their comments on the manuscript.

Received for publication March 1, 2005.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 

• Atlin, G.N., R.J. Baker, K.B. McRae, and X. Lu. 2000. Selection response in subdivided target regions. Crop Sci. 40:7–13.[Abstract/Free Full Text]
• Atlin, G.N., M. Cooper, and Å. Bjornstad. 2001. A comparison of formal and participatory breeding approaches using selection theory. Euphytica 122:463–475.[CrossRef]
• Basford, K.E., and M. Cooper. 1998. Genotype x environment interactions and some considerations of their implications for wheat breeding in Australia. Aust. J. Agric. Res. 49:153–174.
• Basford, K.E., P.M. Kroonenberg, and M. Cooper. 1996. Three-mode analytical methods for crop improvement programs. In Cooper, M., Hammer, G.L. (ed.) Plant adaptation and crop improvement. CAB Internatl., ICRISAT & IRRI, Wallingford, UK, p. 291–305.
• Chapman, S.C., and A.J. de la Vega. 2002. Spatial and seasonal effects confounding interpretation of sunflower yields in Argentina. Field Crops Res. 73:107–120.
• Chapman, S.C., J. Crossa, and G.O. Edmeades. 1997. Genotype by environment effects and selection for drought tolerance in tropical maize. I. Two-mode pattern analysis of yield. Euphytica 95:1–9.[CrossRef]
• Chapman, S.C., M. Cooper, D. Butler, and R. Henzell. 2000. Genotype by environment interactions affecting grain sorghum. I. Characteristics that confound interpretation of hybrid yield. Aust. J. Agric. Res. 51:197–207.
• CIMMYT. 1989. An account of how priorites are set among megaenvironments from a breeding perspective. Internal Document Number 17. Mexico DF.
• Comstock, R.E., and R.H. Moll. 1963. Genotype-environment interaction. In Hanson, W.D., Robinson, H.F. (ed.) Statistical genetics and plant breeding. Publ. 982. Natl. Acad. of Sciences–Natl. Res. Council, Washington, DC, p. 164–194.
• Cooper, M. 1999. Concepts and strategies for plant adaptation research in rainfed lowland rice. Field Crops Res. 64:13–34.
• Cooper, M., and I.H. DeLacy. 1994. Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multienvironment experiments. Theor. Appl. Genet. 88:561–572.[CrossRef][Web of Science]
• Cooper, M., D.R. Woodruff, R.L. Eisemann, P.S. Brennan, and I.H. DeLacy. 1995. A selection strategy to accommodate genotype-by-environment interaction for grain yield of wheat: Managed-environments for selection among genotypes. Theor. Appl. Genet. 90:492–502.[Web of Science]
• de la Vega, A.J., and A.J. Hall. 2002. Effects of planting date, genotype, and their interaction on sunflower yield. I. Determinants of oil-corrected grain yield. Crop Sci. 42:1191–1201.[Abstract/Free Full Text]
• de la Vega, A.J., S.C. Chapman, and A.J. Hall. 2001. Genotype by environment interaction and indirect selection for yield in sunflower. I. Two-mode pattern analysis of oil and biomass yield across environments in Argentina. Field Crops Res. 72:17–38.[CrossRef]
• Eisemann, R.L., M. Cooper, and D.R. Woodruff. 1990. Beyond the analytical methodology–better interpretation and exploitation of genotype-by-environment interaction in breeding. In Kang, M.S (ed.) Genotype-by-environment interaction and plant breeding. Louisiana State Univ., Baton Rouge, LA, p. 108–117.
• Falconer, D.S. 1989. Introduction to quantitative genetics, 3rd ed. Longman Scientific & Technical, Burnt Mill, Harlow, England.
• Fox, P.N., and A.A. Rosielle. 1982. Reference sets of genotypes and selection for yield in unpredictable environments. Crop Sci. 22:1171–1175.[Abstract/Free Full Text]
• Gabriel, K.R. 1971. The biplot-graphical display of matrices with applications to principal component analysis. Biometrika 58:453–467.[Abstract/Free Full Text]
• Gauch, H.G. Jr. 1988. Model selection and validation for yield trials with interaction. Biometrics 44:705–715.[CrossRef][Web of Science]
• Gauch, H.G., and R.W. Zobel. 1997. Identifying megaenvironments and targeting genotypes. Crop Sci. 37:311–326.[Abstract/Free Full Text]
• Granlund, M., and D.C. Zimmerman. 1975. Effect of drying conditions on oil content of sunflower (H. annuus L.) seeds as determined by wide-line nuclear magnetic resonance (NMR). North Dakota Acad. Sci. Proc. 27(Part 2):128–132.
• Kroonenberg, P.M. 1997. Introduction to biplots for G x E tables. Research Report #51. Centre for Statistics. The University of Queensland, Brisbane, Qld 4072 Australia.
• Patterson, H.D., and R. Thompson. 1975. Maximum likelihood estimation of components of variance. In Proc. of the 8th Internatl. Biometrics Conf., p. 197–207.
• Robinson, G.K. 1991. That BLUP is a good thing: The estimation of random effects. Stat. Sci. 6:15–51.
• Ward, J.H. 1963. Hierarchical grouping to optimize an objective function. J. Amer. Stat. Assoc. 58:236–244.[CrossRef]
• Williams, W.T. 1976. Pattern analysis in agricultural science. Elsevier Scientific Publishing Company, Amsterdam, The Netherlands.
• Yan, W., and L.A. Hunt. 2002. Biplot analysis of multienvironment trial data. In Kang, M.S (ed.) Quantitative genetics, genomics and plant breeding. CAB Internatl., Wallingford, UK, p. 289–303.

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