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Crop Science 42:1191-1201 (2002)
© 2002 Crop Science Society of America

CROP ECOLOGY, MANAGEMENT & QUALITY

Effects of Planting Date, Genotype, and Their Interactions on Sunflower Yield

I. Determinants of Oil-Corrected Grain Yield

Abelardo J. de la Vega*,a and Antonio J. Hallb

a Advanta Semillas S.A.I.C., Ruta Nac. 33 Km 636, CC 294, (2600) Venado Tuerto, Argentina
b IFEVA, Facultad de Agronomía, Universidad de Buenos Aires/CONICET, Av. San Martín 4453, (1417) Buenos Aires, Argentina

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


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Sunflower (Helianthus annuus L.) yields are strongly reduced when normal sowing dates are delayed. The objectives of this study were to investigate the physiological bases of the sowing date (S), genotype (G), and G x S interaction effects on sunflower yield, and to contribute to the formulation of ideotype-based selection strategies for improving yield at late plantings. Nine hybrids differentially adapted to northern and central Argentina were evaluated during two seasons in October (normal) and December (late) planting dates at Venado Tuerto, Argentina. Yield was defined as the product of total biomass and harvest index. Sowing date accounted for most of the yield variation. The G x S interaction, in turn, accounted for a portion of the total variability three times higher than the contribution of G. Both S and G x S interaction effects on yield mostly involved the variation of attributes and processes expressed postanthesis. Biomass differences between planting dates were the dominant determinant of the S effect on yield. The genotype-specific responses for harvest index were the dominant determinant of the G x S interaction, and were mostly associated with changes in the rate of harvest index increase. Variations in biomass and harvest index were strongly associated with the amount of intercepted radiation during grain filling which, in turn, was associated to duration of grain filling and green leaf area. Canopy stay green proved to be associated with adaptation to late planting dates. This indirect selection criterion appears to be a more reliable attribute for use in breeding for adaptation to late plantings than some other genotype characteristics linked to yield.

Abbreviations: A, anthesis • DAA, days after anthesis • E, emergence • G, genotype • HI, harvest index • LAI, leaf area index • PM, physiological maturity • Qd, fractional radiation interception • RUE, radiation use efficiency • S, sowing date • Y, year


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
SUNFLOWER IS ONE OF THE four most important annual crops in the world grown for edible oil (Putt, 1977). It is successfully grown over a widely scattered geographical area and is considered a crop adapted to a wide range of environmental conditions (Beard and Geng, 1982). However, numerous studies have shown that oil yield in sunflower is reduced when normal spring sowing dates are delayed in both temperate (Robinson, 1970; Unger, 1980; Beard and Geng, 1982; Miller et al., 1984) and subtropical (Bange et al., 1997; Patil et al., 1989) environments. The observed lower yields associated with late plantings have been variously hypothesized as due to warmer temperatures during the early growth period, which promotes excessive early stem growth (Beard and Geng, 1982) and reduce time to flowering (Andrade, 1995; d'Andria et al., 1995), and to cooler temperatures and reduced incident radiation postanthesis, which affects the dynamics of grain filling (Andrade, 1995; Bange et al., 1997).

Because of double cropping practices or adverse weather at optimum planting times, sunflower is frequently planted late. The formulation of breeding strategies to improve yield at late sowing dates would be facilitated by identification of attributes and processes underlying the observed yield reductions. A genotype x sowing date (G x S) interaction found for yield (Robinson, 1970; Beard and Geng, 1982; Miller et al., 1984) suggests the existence of genetic variability for traits related to specific adaptation to late plantings. Developing an understanding of the biophysical bases of these interactions could lead to the identification of opportunities for genetic improvement to overcome this constraint to production (Basford and Cooper, 1998).

Many of the papers cited above have not specifically examined the physiological bases of yield response to late sowing or attempted to relate these to a selection strategy. A useful framework to investigate environmental and genotypic effects on crop performance defines yield as the product of total biomass produced and the fraction of that biomass partitioned to harvestable yield (harvest index, HI) (Charles-Edwards, 1982). Total biomass produced will depend on incident radiation, canopy fractional interception, and the efficiency with which intercepted radiation is converted into biomass (radiation use efficiency, RUE). Bange et al. (1997) used this physiological framework to investigate the effect of sowing date (S) on sunflower yield performance. They found that changes in both biomass accumulation and harvest index were crucial in determining yield reductions associated with late planting dates in a subtropical environment. Biomass accumulation was mostly influenced by the amount of intercepted radiation rather than by radiation use efficiency. The observed reductions in harvest index were associated with a shortening in grain filling duration and a reduction in the daily rate of harvest index increase (Bange et al., 1998). These authors did not find a significant G x S interaction for yield, possibly because the hybrids they used were similar in terms of genetic background (Alan Scott, 1999, personal communication). This feature of their work precludes the analysis of positive components of the G x S interactions, a potentially useful source of indirect selection strategies.

Pattern analysis of a multienvironment trial (de la Vega et al., 2001) showed strong differences between normal and delayed planting dates in a central environment of Argentina (Venado Tuerto), in terms of the G x S interaction for oil yield exhibited by a reference set of genotypes. Cluster analysis also revealed three genotype groups of different adaptation pattern, i.e., central, northern, and broadly adapted hybrids, which showed contrasting specific responses to late sowing dates.

This paper reports results of experiments and analyses performed within the physiological framework described above, using nine differentially adapted sunflower hybrids (three hybrids per genotype group) grown at Venado Tuerto with normal (October) and delayed (December) planting dates during 2 yr. Variations in grain yield (corrected for oil synthesis costs) associated with S and G x S interaction were analyzed in terms of oil-corrected biomass (dry matter), harvest index, and the determinants of these variables. The overall objective was to enhance the current understanding of the physiological determinants of S and G x S interaction effects for sunflower in a temperate environment. The results can contribute to the formulation of ideotype-based selection strategies for improving yield at late plantings.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Cultural Details
Crops of a reference set of nine sunflower hybrids were grown on a deep coarse loam soil (Typic Hapludoll) with supplementary irrigation at the Advanta Semillas Research Center, Venado Tuerto, Argentina (33° 41' S, 61° 57' W). Crops (population density 4.76 plant m-2) were sown in October (S1, normal) and December (S2, late) during the 1996-1997 (Exp. 1) and 1998-1999 (Exp. 2) seasons. The hybrids composing the reference set of genotypes (Table 1) were selected from the Advanta Argentina testing program based on their contrasting relative performance in oil yield across environments and also because they represent a wide range of genetic diversity according to RFLP (restricted fragment length polymorphism) molecular marker analyses (A. León, Advanta Argentina, Balcarce, unpublished data). Planting date and genotype constituted the main plots and subplots of split plot trials with three replications. A plot size of at least 8 rows x 6 m and interrow spacing of 0.70 m was used. Planting dates were Exp. 1: S1: 28 October, S2: 14 December; Exp. 2: S1: 22 October, S2: 19 December.


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  		Table 1. Agronomic characters and genotype grouping (by pattern analysis) of the sunflower hybrids evaluated in Venado Tuerto during 1996–1997 and 1998–1999 seasons, using October (S1) and December (S2) planting dates (mean values for two trials per sowing date). All genotypes are single-cross hybrids, developed by the sunflower breeding program of Advanta Argentina, except Morgan 734, provided by Dow-Morgan Argentina.

 
The station is situated in the temperate Pampean region, with mean annual rainfall of 931 mm. Table 2 summarizes environmental conditions during the experiments. The seasonal patterns shown by the environmental variables registered can be considered representative of the mean environment of Venado Tuerto. Daily incident radiation (MJ m-2 d-1) was estimated (Ångström, 1924; Prescott, 1940) from sunshine hours measured at a meteorological station 1 km from the plots and estimated radiation receipt above the atmosphere by means of a relationship developed from 902 data points measured for incident radiation and sunlight duration at a station 150 km from Venado Tuerto, between 1982 and 1985. In S2, photoperiod was shorter and daily incident radiation was lower during anthesis and grain filling than in S1 (Table 2). In 1998-1999, incident radiation was lower and rainfall was higher during grain filling than in 1996-1997 (Table 2).


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  		Table 2. Mean environmental data for the different crop developmental phases in each trial. Anthesis and physiological maturity dates used for defining the mean crop stages represent the average for all hybrids at each trial. Crop phase A represents the 15-d interval centered around the mean date of full anthesis.

 
Phenology
Crop phenology was followed from emergence (E) to physiological maturity (PM). Anthesis (A) was defined as the time at which 50% of the plant population reached R-5.5 (Schneiter and Miller, 1981). Physiological maturity (time at which maximum grain weight is achieved) was determined from the dynamics of grain growth (Ploschuk and Hall, 1995). Samples (3 grains from 5 plants or 5 grains from 3 plants per plot) were harvested every 3 to 4 d from the intermediate portion of the floral disc, dried at 70°C for at least 48 h, and weighed.

Oil-Corrected Grain Yield and Its Determinants
Yield and yield determinants at physiological maturity were determined by hand harvesting of total aboveground biomass of 3.99 m2 (one central row, discarding the border plants). Grain oil concentration was determined on 10-g, oven-dried achene samples by nuclear magnetic resonance (Granlund and Zimmerman, 1975) with a Newport Analyzer (Newport-Oxford Instruments Ltd, Newport Pagnell, Buckinghamshire, England). All grain (achene, includes kernel and husk) yield data is presented at 110 g kg-1 moisture.

Dynamics of aboveground dry matter accumulation was followed from emergence (Exp. 1) or flowering (Exp. 2) to maturity by taking samples of two adjacent plants per experimental unit every week. Results for the first year showed that S and G x S interaction effects were mostly determined by attributes and processes expressed in postanthesis. In the second year, measurements were concentrated on the grain filling phase, with leaf area index and total biomass at anthesis used as an integrative measure of crop growth in the previous phase. The plants were separated into organs and stems were split lengthwise into four sections to hasten drying. Plant material was dried in a glasshouse at 50 to 60°C (day) and 40°C (night) for more than 5 d before weighing. In a crop of oil-rich grain such as sunflower, genotypic and environmental effects on seed oil content may confound the interpretation of trial results expressed as biomass. Consequently, crop biomass in postanthesis and grain yield measurements were corrected for energy expended in oil synthesis by means of the production values given by Penning de Vries et al. (1983) and assuming that the biomass of nongrain organs contained 25 mg g-1 lipids. After discounting a mass of lipid equivalent to 25 mg g-1 of the nonoil portion of the grain, the remaining lipid in the grain was assumed to be replaced by a 97.5:2.5 (w/w) carbohydrate:lipid mixture. The results are termed oil-corrected biomass and oil-corrected grain yield in this paper. Although sunflower grain has a much higher proportion of protein than the remaining crop organs, no equivalent correction for protein synthesis costs was made, because most of the protein in the grain is derived from nitrogen taken up by the crop before the grain begins to grow rapidly (Hall et al., 1995). Harvest index (HI) was determined for each sample taken after anthesis as the ratio of oil-corrected grain dry matter to oil-corrected above ground dry matter.

Radiation Interception
Leaf area index (LAI) dynamics were followed using measurements of maximum leaf width to determine leaf area of individual leaves (Pereyra et al., 1982) on three randomly selected plants per plot at weekly intervals from emergence (Exp. 1) or anthesis (Exp. 2) to physiological maturity. Fractional daily radiation interception (Qd) was estimated from LAI values according to Orgaz et al. (1992). The combined use of the Pereyra et al. (1982) and Orgaz et al. (1992) functions was verified by direct measurements of canopy interception using the technique of Trápani et al. (1992) for these genotypes over a wide range of LAI (0.5–4.3), and found appropriate, i.e., the combined functions produced data indistinguishable (P = 0.10) from test data.

Data Analysis
The nonlinear routine of TBLCURVE (Jandel TBLCURVE, 1992) was used to fit piecewise bilinear regression models to individual grain weight vs. time (Exp. 1 and 2) and HI vs. time (Exp. 1 and Exp. 2 S1) relationships to estimate the rates and durations of grain filling and HI increase. A conditional model (Ploschuk and Hall, 1995) was used with a first stage where:

[1]
for DAA < C, and a second stage where:

[2]
where a and b are the intercept and the slope, respectively, of the linear regression corresponding to the first stage, DAA is days after anthesis, and the constant C is the unknown breakpoint of the function indicating the end of grain filling or HI increase. Functions were fitted to data from all replications per treatment. The lag phase of HI increase was derived from Eq. [1] (HI = 0, corresponding to the intercept of the constant linear HI increase vs. time relationship on the DAA axis; Chapman et al., 1993). In Exp. 2 S2, storm damage immediately after physiological maturity of some hybrids precluded fitting the full bilineal model, so Eq. [1] was fitted to the first 4 values to estimate the lag phase of HI increase.

LAI dynamics were described by fitting fourth-order polynomials to LAI estimates as a function of time from sowing. The fitted functions were used to estimate Qd and accumulated intercepted radiation during the emergence–anthesis (Exp. 1) and anthesis–physiological maturity (Exp. 1 and 2) phases. Radiation use efficiency (RUE, g MJ-1 m-2) was estimated for pre- and postanthesis phases as the slope of the function (y = bx) that describes oil-corrected biomass vs. accumulated intercepted radiation relationships.

Pooled analysis of variance over 2 yr (Y), and two planting dates (S), based on a fixed effects linear model, was performed to separate Y, S, G, and their interaction effects for all characters recorded. We considered Y as fixed because the years sampled are not a random sample of the environmental conditions at Venado Tuerto, since 1996-1997 and 1998-1999 were classified as neutral and Niña years, respectively, in terms of the El Niño Southern Oscillation effect. The statistical significance of Y, S, and Y x S was tested against the pooled error (a) (i.e., Y x S x rep), and that of G, G x S, G x Y, and G x S x Y, against the pooled error (b) (i.e., residual error). Error terms were homogeneous across years (F test, P < 0.05). The partitioning of the treatment sums of squares was used as a measure of the relative contribution of each source of variation to the total variability for each attribute. Correlations among traits were computed using hybrid means for S1 and S2 across 1996-1997 and 1998-1999 seasons.


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES
 
Oil-Corrected Grain Yield
Combined analysis of variance showed that sowing date affected oil-corrected grain yield (Table 3), which was greater in S1 (Table 4). The partitioning of the treatment sum of squares revealed that S was the major source of variation for oil-corrected grain yield in these experiments (Table 3). Even though HI was lower in S2 (Tables 3 and 4), the marked differences between planting dates for total biomass at PM (Tables 3 and 4) were the dominant determinant of the S effect on oil-corrected grain yield (Fig. 1) . The increase in biomass between A and PM ({Delta}biomassA-PM) accounted for a larger proportion of the differences in biomassPM between S1 and S2 than biomassA (Table 5).


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  		Table 3. Partitioning of the treatment sums of squares and significances derived from the pooled analysis of variance for oil-corrected grain yield and its primary and secondary components of nine sunflower hybrids grown in four environments (Venado Tuerto, two seasons and two planting times). Values in the body of the table are in percent of total SS after removal of residuals.

 

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  		Table 4. Mean values for oil-corrected grain yield and its primary and secondary components in October (S1) and December (S2) planting dates at Venado Tuerto, during 1996–1997 and 1998–1999 seasons (mean values for nine hybrids).

 


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  		Fig. 1. Relationships between oil-corrected grain yield and (A) oil-corrected biomass at physiological maturity, and (B) harvest index, for nine sunflower hybrids grown in October (S1) and December (S2) sowing dates at Venado Tuerto during 1996-1997 and 1998-1999. Dashed arrows highlight some contrasting genotypic responses to S2, which underlie G x S interactions (Genotype 1, triangles; Genotype 9, circles). Genotype groups derived from cluster analysis of a multienvironment trial (de la Vega et al., 2001), i.e., G1: Northern adapted, G2: Central adapted, G3: broadly adapted. See Table 1 for genotype group membership.

 

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  		Table 5. Correlations, based on hybrid means, among sunflower traits measured in October and December planting dates across two seasons (1996–1997 and 1998–1999).

 
The effect of G on oil-corrected grain yield was significant, but it accounted for a portion of the total variability almost three times lower than the contribution of the G x S interaction (Table 3). This is consistent with the strong crossover interaction found between planting dates (see dashed arrows in Fig. 1) and the negative association previously found between October and December planting dates at Venado Tuerto in terms of the manner in which they influence the hybrid relative performance for oil yield (de la Vega et al., 2001). This kind of G x S interaction indicates that yield gains under delayed planting dates would have been unlikely to occur if selection had been done at normal planting dates and vice versa. The G x S interaction for oil-corrected grain yield also accounted for a larger portion of the treatment sums of squares than the unpredictable interactions (i.e., G x Y and G x S x Y, Allard and Bradshaw, 1964). This suggests that positive components of this interaction could be used in a selection strategy that utilizes traits related to specific adaptation to late plantings.

Oil-Corrected Biomass at Anthesis
Oil-corrected biomassA was greater in S1 than in S2 (Tables 3 and 4). In Exp. 1, a positive association (P < 0.05) between oil-corrected biomassA and accumulated intercepted radiation between emergence and anthesis was found (data not shown). Radiation interception in this phase depends on the dynamics of LAIE-A and the time to anthesis. Both S and G (Table 3) effects on oil-corrected biomassA were strongly determined by the genotypic and environmental variability for time to anthesis (Table 5), rather than by RUEE-A. In S2, mean LAIA was slightly higher than in S1 (Tables 3 and 4). The faster rate of LAI generation between emergence and anthesis observed in S2 (e.g., Fig. 2) , possibly attributable (Rawson and Hindmarsh, 1982; Sadras and Hall, 1988) to higher temperatures during this period and higher mean incident radiation (Table 2), resulted in a higher daily mean intercepted radiationE-A in S2 than in S1. However, this effect was not sufficient to counteract the reduction in the time elapsed between emergence and anthesis in S2 (i.e., 11 d; Tables 3 and 4), which determined a lower accumulated intercepted radiationE-A in S2 than in S1 (Tables 3 and 4).



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  		Fig. 2. Leaf area index during the crop life cycle for Genotypes 1, 2, and 9 in October (S1) and December (S2) planting dates at Venado Tuerto, 1996-1997. See Table 1 for the genotype codes. Vertical arrows show anthesis (A) and physiological maturity (PM) in S1 (right arrow) and S2 (left arrow). Vertical bars indicate standard deviations.

 
The actual values of RUEE-A obtained in this study (Table 4) are similar to those reported by other authors (Trapani et al., 1992; Chapman et al., 1993; Steer et al., 1993; Hall et al., 1995; Bange et al., 1997), and did not differ among planting dates and genotypes across planting dates (Table 3). The decline in post- relative to preanthesis RUE (Table 4; Fig. 3) is also consistent with previous reports (Trapani et al., 1992; Steer et al., 1993; Bange et al., 1997).



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  		Fig. 3. Relationship between accumulated oil-corrected biomass and accumulated intercepted radiation from emergence to physiological maturity for Genotypes 1, 2, and 9 in October (S1) and December (S2) planting dates at Venado Tuerto, 1996-1997. See Table 1 for the genotype codes. Vertical arrows show anthesis for S1 (right arrow) and S2 (left arrow). Vertical and horizontal bars indicate standard deviations.

 
Oil-Corrected Biomass at Physiological Maturity
The reduction in {Delta}oil-corrected biomassA-PM observed in S2 was associated with a reduction in the duration of grain filling (Table 5; Fig. 4A) , and a corresponding reduction (Tables 3 and 4) in the amount of radiation intercepted during this period (Table 5). S accounted for a very high portion of the variability observed for accumulated intercepted radiationA-PM (Table 3), a trait strongly associated with oil-corrected grain yield (Table 5). The reduction of 37.9% in the mean accumulated intercepted radiationA-PM observed in S2 was determined (in roughly equivalent proportions) by reductions in the duration of grain filling (22.4%) and in the daily incident radiation (20.8%; Table 1).



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  		Fig. 4. Relationships between (A) {Delta}oil-corrected biomass between anthesis and physiological maturity and (B) harvest index and the time elapsed between anthesis and physiological maturity, for nine sunflower hybrids grown in October (S1) and December (S2) sowing dates at Venado Tuerto during 1996-1997 and 1998-1999. Dashed arrows highlight some contrasting genotypic responses to S2, which underlie G x S interactions (Genotype 1, triangles; Genotype 9, circles). For genotype groups (i.e., G1, G2, G3) see legend of Fig. 1.

 
Variations in mean RUEA-PM, which was lower in S2 than in S1 (Table 4), may have also contributed to the observed reduction in {Delta}oil-corrected biomassA-PM (Table 5). This difference appears to be mostly determined by the results of Exp. 2 (i.e., significant Y x S interaction; Table 3). However, the total number and variability of data points used to estimate RUE were smaller and greater, respectively, in post- than in preanthesis (Fig. 3). Caution therefore must be exercised when considering possible S, G, and G x S interaction effects for RUEA-PM.

Some evidence for a G effect on RUEA-PM in S2 is found in the association between this trait and seed set (Fig. 5) . This is consistent with the findings of Sadras et al. (1991), who suggested that sink size relative to source activity may impose a control on photosynthetic activity of crops with low seed set.



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  		Fig. 5. Relationship between radiation use efficiency from anthesis to physiological maturity and percentage of seed set for 9 sunflower hybrids grown in December (S2) sowing date at Venado Tuerto during 1996-1997 and 1998-1999. *Significant at P = 0.05.

 
Beard and Geng (1982) hypothesized that characters measured at harvest were highly dependent on early growth and, consequently, the observed lower yields associated with late planting would be due to the unfavorable environmental conditions during the early growth period. Although the observed reduction in time to anthesis in S2 determined a reduction of 12.7% in oil-corrected biomassA (Table 4), the stability of mean RUEE-A across planting dates found in this study, which is consistent with the results of Bange et al. (1997), as well as the achievement of a similar LAIA between sowing dates, do not support the Beard and Geng hypothesis. Rather, these results suggest that the S effects on oil-corrected grain yield mostly involve the variation of attributes and processes expressed postanthesis (i.e., a reduction of 62.6% in {Delta}oil-corrected biomassA-PM was observed in S2).

The lack of significant G x S interaction for total intercepted radiationE-A, LAIA, RUEE-A, and oil-corrected biomassA (Table 3) is consistent with the notion (see above) that changes in hybrid relative performance for oil-corrected grain yield among environments are associated with attributes and processes expressed postanthesis (e.g., Fig. 4). Figure 2, which shows the LAI vs. time relationships for three hybrids of contrasting responses for oil-corrected grain yield, further illustrates this point. Genotypes 1 and 2 showed very similar responses to S2 in terms of rate of LAI generation, LAImax, and time to anthesis. Conversely, the responses of both hybrids in postanthesis for leaf senescence and duration of grain filling were strongly contrasting. Genotype 9 and 2 showed similar responses to S2 in terms of duration of grain filling, but Genotype 9 intercepted less than 40% of the incident radiation at physiological maturity (contrast with around 60% for Genotype 2) (Fig. 6) .



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  		Fig. 6. Relationships between the proportion of the incident radiation which is intercepted by the crop (Qd) and time after full anthesis for Genotypes 1, 2, and 9 in October (S1) and December (S2) planting dates at Venado Tuerto, 1996-1997. See Table 1 for the genotype codes. Vertical arrows indicate physiological maturity.

 
Harvest Index
Harvest index was slightly greater in S1 than in S2 (Tables 3 and 4). As with {Delta}Oil-corrected biomassA-PM, HI was strongly influenced by the accumulated intercepted radiationA-PM (Table 5) and the duration of grain filling (Fig. 4B). The partitioning of the treatment sums of squares revealed that the contribution of HI to the mean differences between sowing dates for oil-corrected grain yield was low, but it accounted for most of the G x S interaction observed for this attribute (Table 3). Dashed arrows in Fig. 1B and 4B highlight the strongly contrasting genotypic responses to planting date in terms of HI (see Table 1), which determined the G x S interaction found for this trait (Tables 1 and 3).

The effects of S, G, and G x S on HI were analyzed in terms of the rate and duration of the linear phase of the function that describes HI increase (e.g., Fig. 7) . Both the S and G x S interaction effects for HI were mostly associated with changes in the daily rate of HI increase, rather than with changes in the duration of the linear phase of HI increase (Fig. 7). Mean values of daily HI increase were 0.0125 d-1, 0.0131 d-1 and 0.0117 d-1 for Exp. 1 S1, Exp. 2 S1, and Exp. 1 S2, respectively (Table 4). These values are similar to those found by Bange et al. (1998) (mean 0.0125 d-1) and larger than that used by Chapman et al. (1993) in the simulation model QSUN (0.0113 d-1). In Exp. 1, the daily rate of HI increase was higher in S1 than in S2 (Tables 3 and 4). This is consistent with the observations of Bange et al. (1998), who found a reduction in the slope of the daily linear HI increase associated to late planting dates. These authors hypothesized that the low temperatures registered during grain filling would be underlying this response. In this study, the planting date effect on the grain filling period would neither be determined by temperature (daily mean temperature S1: 23.2°C, S2: 22.7°C, average 2 yr) nor by water availability (irrigation). Incident radiation (daily mean S1: 20.7 MJ m-2, S2: 16.4 MJ m-2), photoperiod (daily mean S1: 14.7 h, S2: 13.5 h) (de la Vega et al., 2001), or their interaction seem to be more likely candidate causes underlying the observed late-planting effect on grain filling duration and daily rate of HI increase.



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  		Fig. 7. Bilineal relationships between harvest index and time after full anthesis for Genotypes 1, 2, and 9 in October (S1) and December (S2) planting dates at Venado Tuerto, 1996-1997. See Table 1 for the genotype codes.

 
A significant G x S interaction was also found for the daily HI increase (Table 3). This suggests that the response of the dynamics of HI increase to environment may contribute to the observed G x S interaction for yield. It also suggests caution should be exercised when assuming that the rate of HI increase is relatively stable across genotypes and environments, and thus can be used with some confidence in simulation models (Chapman et al., 1993; Bange et al., 1998; Bindi et al., 1999). Figure 7 shows the HI vs. time relationships for three genotypes of contrasting responses for rate and duration of linear HI increase in S1 and S2. Genotype 1, which improved its relative performance in S1, showed a strong reduction in the rate of HI increase in S2, while Genotype 9, which showed a contrasting relative performance across environments in comparison to Treatment 1, increased the rate of HI increase in this environment.

The changes in duration of the lag phase of linear HI increase between sowings (Tables 3 and 4; Fig. 7) are consistent with the findings of Bange et al. (1998). These authors hypothesized that these differences were inversely related to temperature. In the present study, temperature differences (Table 2) cannot explain these differences. Analyses of (stem + receptacle) biomass dynamics for the anthesis–physiological maturity interval showed contrasting patterns in S1 and S2 for all genotypes (data not shown). In S1, the biomass of this component continued to increase for about 10 d after anthesis; in S2, no postanthesis increase was observed. We hypothesize that these differences in stem + receptacle biomass dynamics between sowing dates, together with faster leaf senescence in S2 (Fig. 2) underlie the shorter lag phase for HI increase (Fig. 7) found in S2. A clear definition of the timing of onset of HI increase is required for the modeling of crop growth and yield, and a greater understanding of the genotypic and environmental attributes that determine the duration of the lag phase is needed.

Canopy Stay Green as a Secondary Trait Associated with Adaptation to Late Sowing
Figures 2, 6, and 7 show that some hybrids of similar patterns of relative performance for oil-corrected grain yield (i.e., Genotypes 2 and 9) showed contrasting responses in terms of primary (i.e., biomass and HI) and secondary (i.e., Qd, incident radiation, RUE, rate, and duration of HI increase) yield determinants. This suggests that some degree of compensation among attributes is underlying the genotype-specific responses for oil-corrected grain yield within the same genotype groups, increasing the difficulty of identification of common indicators for an ideotype-based selection strategy. However, it was found that the hybrids that improve their relative performance in S2 maintain the duration of grain filling and the green leaf area for a longer time than the hybrids that improve their relative performance in S1 (e.g., Genotypes 2 and 9 in Fig. 7). Austin (1993) postulated that to be of value in assessing a trait in a population of plants, a screening test must satisfy a number of criteria, as follows: there must be heritable variation in the character; it should be possible to assess the character simply, rapidly and inexpensively; and there must be an appreciable genetic correlation between the attribute and yield under field conditions. It is concluded that canopy stay green (defined as green leaf area duration during grain filling), which is an easy-to-assess trait that was correlated to oil-corrected grain yield in S2 (Fig. 8) , serves as a putative indicator of adaptation to late planting dates. Oil-corrected grain yield was also strongly (r = 0.72; P < 0.05) correlated with accumulated Qd over the A-PM interval. However, time from anthesis to 50% of LAI at anthesis (i.e., the variable plotted in Fig. 8) is more closely related to the kind of observation a breeder might make in inspecting his trials.



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  		Fig. 8. Relationship between oil-corrected grain yield and the time elapsed between full anthesis and the moment when leaf area index falls to 50% of that at anthesis (an estimator of canopy stay green) for 9 sunflower hybrids grown in December (S2) sowing date at Venado Tuerto during 1996-1997 and 1998-1999. **Significant at P = 0.01.

 

   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES
 
The experiments described here, in contrast to those of Bange et al. (1997), show that G x S interactions can account for a portion of total oil-corrected yield variability almost three times higher than the contribution of G, provided a reference set of hybrids with a sufficiently diverse genetic background is examined. These results have further served to establish the importance of attributes and processes affecting crop performance in the postanthesis phase in determining both S and G x S effects, and point to the parts played by postanthesis biomass accumulation and HI changes in determining S and G x S effects, respectively. Changes in HI were associated with variations in rate of HI increase, an attribute that has sometimes (Bange et al., 1998; Bindi et al., 1999) been regarded as relatively stable across environments and genotypes. Finally, a strong association between improved relative performance for late sowing and canopy stay green has been established, which should allow for indirect selection to improve crop adaptation to this constraint. This finding may be particularly important in view of the compensations between effects of late sowing on different yield determinants (e.g., rate of HI increase and radiation interception) exhibited by the various hybrids. In a companion paper (de la Vega and Hall, 2002), we describe the use of another approach to analyze S and G x S effects on crop yield components for the same set of hybrids.


   ACKNOWLEDGMENTS
 
We would like to thank Advanta Semillas (Steven Quast, Alan Scott, Ricardo Siciliano) for making this research possible. We also thank Carlos Ghanem, Aldo Martínez, Sergio Solián, Daniel Kennedy, Ney Flores, and César Sánchez for collaborating in the field experiments, and Dr. Martín Grondona for his help in the statistical analyses.

Received for publication April 2, 2001.


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




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