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CSIRO Plant Industry, Cotton Research Unit, Locked Bag 59, Narrabri, NSW 2390, Australia
* Corresponding author (warwick.stiller{at}csiro.au).
Received for publication December 16, 2002.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Abbreviations: ACRI, Australian Cotton Research Institute • ALT, CSIRO Advanced Lines Trials • ET, evapotranspiration • GDD, growing degree days • G x E, genotype x environment (interaction) • MMD, mean maturity date • WUE, water use efficiency
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Clearly it is important to maximize WUE under dryland conditions. Gutstein (1969) previously obtained WUE values for dryland cotton ranging from 3.30 to 4.35 kg lint ha–1 mm–1, which were equal to the best reported for irrigated cotton. Hearn (1994) reviewed agronomic WUE of cotton, showing published WUE ranging from 1.32 to 3.76 kg lint ha–1 mm–1, with a mean of 2.33 kg lint ha–1 mm–1. Previous studies at this site, the Australian Cotton Research Institute (ACRI), Narrabri, have shown agronomic WUE of 2.27 kg lint ha–1 mm–1 under irrigation (Hodgson et al., 1990). Currently, there is a need for additional studies to identify the WUE of dryland cotton.
Short season length, probability of rainstorms, depletion of soil moisture, and in-season damage by insects are among the prime factors that limit cotton production worldwide. In an attempt to mitigate and/or avoid the consequences of these limiting factors, plant breeders have developed earlier-maturing cultivars (Richmond and Ray, 1966). Early maturity and high yields in upland cotton are correlated when rain is not timely or does not occur during the growing season and the crop is required to survive and yield on stored soil moisture. This earliness–yield relationship in cotton reverses when adequate rain occurs during the growing season (Quisenberry and Roark, 1976). Quisenberry (1980) reported that care must be taken when earliness is used to select genotypes for moisture stress environments. Under conditions where soil moisture is less predictable, phenological plasticity (indeterminacy) may be more beneficial than earliness (Turner, 1986).
The okra leaf trait has been shown to demonstrate increased physiological WUE (Pettigrew et al., 1993). Given the desire to optimize maturity and other traits for special-purpose dryland cultivars, it is important to establish if there are differences in the performance of cultivars with different maturity and leaf type and if interactions across sites and stress levels occur. These genotype x environment (G x E) interactions play a key role in developing strategies for crop improvement. Numerous methods for analyzing and exploiting G x E interactions have been proposed (Byth et al., 1976; Eberhart and Russell, 1966; Finlay and Wilkinson, 1963; Gauch, 1988; Yates and Cochran, 1938). Cockerham (1963) reported change in genotype ranking across environments as the genetic correlation across environments, which was later described by Gail and Simon (1985) as crossover interaction.
The objectives of this research are to evaluate relationships between morphological and phenotypic characteristics such as leaf type and maturity with performance under dryland conditions. This information will assist in developing breeding strategies for water stress situations. Six seasons of cultivar trials comparing irrigated and dryland treatments at three sites were conducted. Additional experiments at one location compared Australian and Texan cultivars varying in maturity and leaf shape for yield and WUE. These studies were part of a larger program aimed at developing improved cultivars for dryland systems.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Cultivars
Ten cultivars that were grown in all six seasons were selected from the ALT. Table 1 presents the cultivars included in the ALT and diverse cultivar experiments. Cultivars Siokra 1-4, Siokra L23, CS 50, and Sicot 189 were common to all experiments.
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-design (Patterson et al., 1978; Williams, 1986; Williams and John, 1989). Plots in most experiments were three or four rows of 15-m length, with 1 m between all rows. At Biloela, dryland experiments had two row plots in a single-skip configuration, as is standard in the region. With this configuration, the crop is only planted on two out of every three rows, i.e., two rows 1 m apart and then 2 m till the next two crop rows. Sites were either cropped to wheat (Triticum aestivum L.) two winters previous or to grain sorghum [Sorghum bicolor (L.) Moench] the previous summer. Nitrogen fertilizer was applied, usually as NH3, at rates between 60 and 80 kg N ha–1 for dryland experiments and 140 to 180 kg N ha–1 for irrigated experiments, depending on cropping rotation. Cotton was planted with a cone seeder on rows 1 m apart to achieve a plant population of 120000 per hectare. Furrow irrigation (totaling 300 to 600 mm over the season) was applied as required. Agronomic inputs were applied according to the Cotton Pest Management Guide (Shaw, 1999).
The center row of the three row plots, the center two rows of the four row plots, and both rows of the two row plots were machine-harvested with a spindle picker and the seed cotton weighed. A subsample of approximately 400 g of seed cotton was taken from each plot. Subsamples were ginned to determine lint percentage and lint yields. A lint sample from each plot was evaluated for quality using a Spinlab High Volume Instrument (HVI) model 900 (Zellweger Uster, Knoxville, TN). Fiber characters measured were upper half mean length (cm), strength (g tex–1), and micronaire value.
The soil types across the three sites were as follows:
SiteUSDA classification (Soil Survey Staff, 1996)Australian classification (Isbell, 1996) ACRI Typic Haplustert Self mulching vertosol; very fine (clay > 60%) Dalby Typic Haplotorrert Self mulching vertosol; very fine (clay > 60%) Biloela Entic Haplustert Gray-brown crusty verto- sol; clayey (clay < 45%)
Self-mulching Vertosols are typical of the soils used for cotton production in Australia (Ward et al., 1999). These soils have a high water-holding capacity, greater than 190 mm to a depth of 1500 mm (Chan and Hodgson, 1981), which is beneficial for dryland crops although they can be prone to waterlogging (Hodgson, 1982; Hodgson and Chan, 1982).
Diverse Cultivar Experiments
The diverse cultivar experiments were located at ACRI. The cultivars were arranged in randomized blocks with three replicates. The number of cultivars varied between seasons (Table 1). In the 1996–1997 season, each experiment was grown under both dryland and irrigated conditions. This was achieved by growing each experiment side by side with 8 m of planted buffer between them. Plot size was three rows by 15 m. Crop management and yield measurements were as for ALT experiments described above.
As the crop began to mature, four successive hand harvests were taken from 1 m of row in each plot of the diverse cultivar experiments to determine cultivar maturity. The mean maturity date (MMD), a weighted mean harvest date based on the successive hand harvests and calculated by the formula given by Christidis and Harrison (1955), was estimated for each cultivar. The MMD is not a commonly used method of calculating crop maturity, partly due to the complexity of the calculation. Richmond and Ray (1966) examined various methods for calculating crop maturity and concluded that MMD, which gave the highest heritability values, was considered to be the most discriminating and most reliable.
Agronomic water use of three cultivars (Tamcot HQ95, Siokra L23, and Sicot 189) was measured in each experiment by weekly neutron attenuation measurements (Model 503 DR, CPN Corp., Martinez, CA), with 12 measurements taken to a depth of 160 cm. A modified Ritchie (1972) model as described by Tennakoon (2000) was used to estimate the daily and total evapotranspiration (ET). This procedure uses a daily basis water balance to extrapolate between each weekly neutron attenuation measurement. Total ET was summed from daily water balance calculations. The coefficients used were as measured for Australian cotton fields by Cull et al. (1981). Agronomic WUE was calculated as lint yield divided by cumulative ET summed from planting to the MMD for each cultivar (kg lint ha–1 mm–1).
Climate
Rainfall, solar radiation, and temperature were recorded at ACRI, Dalby Airport, and Biloela Research Station.
Table 2 shows monthly rainfall and mean or total growing degree days (GDD), recorded at each site in all six seasons. Growing day degrees are calculated as heat units, base 12°C, for each month (Constable and Shaw, 1988). Long-term records indicate that Biloela is the hotter and wetter site while Dalby is slightly cooler and wetter than Narrabri. Considering the variability of rainfall and temperature at each site, the 6-yr data set was a good representation of the three climates.
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Statistical Analysis
Traits were compared using analysis-of-variance techniques with the Genstat 5 package (Lawes Agricultural Trust, IACR, Rothamsted, UK). In the case of lint yield, residual maximum likelihood (REML) analysis was also used to reduce row and column variation within the data.
Data from the diverse cultivar experiments were analyzed to explore the association between lint yield and crop maturity using Genstat 5 and hierarchical regression techniques. The first regression used pooled data across experiments to give an overall regression, the second allowed for a different intercept for each season, and the final allowed for different intercepts and a different slope for each season.
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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0.001). To evaluate the pairs of cultivars that contributed to the crossover G x E interaction, the crossover point was estimated also using the procedure of Singh et al. (1999). The crossover between Siokra L23 and Siokra 1-4 with Sicala V-2 and Sicot 189 all occurred within the dryland environmental range (417–1360 kg lint ha–1). This highlights that Siokra L23 and Siokra 1-4 yielded relatively better under dryland conditions than they did under irrigated conditions. No cultivars crossed over Siokra V-15, indicating that it was the highest-yielding cultivar across all environments. Disease is also a factor that contributes to variable yields under irrigated conditions. In particular, Verticillium wilt (Verticillium dahliae Kleb.) can be severe under irrigated conditions, resulting in yield reductions of up to 20% (Allen, 1992). However, the disease is not favored by the drier conditions of dryland cotton (Ranney, 1973). Verticillium wilt incidence was not assessed in these experiments, so we cannot discount that the irrigation x cultivar interaction was influenced to some degree by the susceptibility of Siokra 1-4 and Siokra L23 to that disease. However, CS 50 and DP16 are also very susceptible to Verticillium wilt, and these cultivars yielded relatively the same to worse, respectively, under dryland conditions compared with irrigated (Fig. 1D). In addition, the interaction with Siokra 1-4 and Siokra L23 is very important because it is a direct comparison of the two production systems regardless of whether the interaction is influenced by disease susceptibility, WUE, or both.
Okra Leaf
There was a significant (P 0.05) effect of leaf type across these experiments (okra leaf, 819 kg lint ha–1; normal leaf, 745 kg lint ha–1). Okra leaf cultivars were the highest yielding in most dryland experiments, and Siokra V-15 was the highest-yielding cultivar overall. This finding disagrees with the conclusion of Meredith and Wells (1986) that okra leaf types yield 5% less than normal leaf types. It has been documented that the okra leaf trait has greater photosynthesis per unit leaf area and higher WUE (Baker and Myhre, 1968; Pettigrew et al., 1993). Our yield data, cultivar interaction, and agronomic WUE data support the conclusion that the okra leaf trait was useful for dryland environments.
Crop Maturity
Figure 2
shows the positive association between crop maturity (MMD) and lint yield. These data suggest that for every day delay in maturity, there was an increase of 34.4 kg lint ha–1. This agrees closely with other published data from Bange and Milroy (2004), which indicate a 34 kg lint ha–1 increase. The regression analysis showed that all slopes were statistically equal but with different intercepts (yield levels). This relationship is strengthened by the cultivars originating from Texas, which were all earlier maturing and lower yielding. However, there was also a range in maturity amongst these cultivars for which the relationship held. In addition, Siokra S-101, an early maturing CSIRO cultivar, also fitted the relationship in having lower yield than CSIRO full-season cultivars. Obviously, the optimum season length would vary with production environment: Shorter-season areas would require earlier-maturing cultivars.
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Agronomic Water Use Efficiency
Agronomic WUE of three cultivars—Tamcot HQ95, Siokra L23, and Sicot 189—are shown in Table 5. These three cultivars represent a wide maturity range, from very early (Tamcot HQ95) to late (Siokra L23 and Sicot 189). They may also differ in adaptation; Tamcot HQ95 developed in Texas had unknown adaptation to Australian conditions while the other two varieties were developed by CSIRO at ACRI (Table 1). Mean agronomic WUE across all cultivars and experiments was 2.54 kg lint ha–1 mm–1, corresponding closely with the value of Hearn (1994) from a review of literature. Sicot 189 used significantly (P 0.001) more water (mm ET) than either Tamcot HQ95 or Siokra L23; however, Siokra L23 and Sicot 189 yielded significantly more than Tamcot HQ95. This resulted in very highly significant differences (P 0.001) between cultivars for WUE, with Siokra L23 having 11 and 25% higher WUE than Sicot 189 and Tamcot HQ95, respectively. Each of these cultivars used a different strategy under the limited water environment. The early maturity of Tamcot HQ95 allowed it to escape late-season water stress. However, early maturity also limits root growth and exploration of the soil (Ludlow and Muchow, 1990). This resulted in lost opportunity to utilize later rainfall and hence lower yield as shown in Table 5. Sicot 189, with full-season maturity, maintained water uptake for a longer time and possibly also through greater rooting volume. This allowed Sicot 189 to produce the equal highest yield. Siokra L23, also with full-season maturity, had the equal lowest water use and the equal highest yield and therefore used water more efficiently (Table 5).
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0.001) higher WUE than the early maturing cultivar. The okra leaf cultivar also had significantly (P 0.001) higher WUE than either of the normal leaf cultivars. Although this data is not conclusive, we suggest that the combinations of later maturity and okra leaf (as represented by Siokra L23) are desirable traits for increased agronomic WUE in a dryland cultivar.
Fiber Quality
Fiber length is particularly important in a dryland cultivar in Australia as stress during the elongation phase can reduce fiber length (Hearn, 1976). On average, between 14 and 20% of dryland cotton falls into the price discount range for fiber length (<2.77 cm) each season compared with a negligible amount of irrigated cotton (Queensland Cotton, personal communication, 2002; Auscott Ltd., personal communication, 2002). The price discount currently received by growers for cotton that has fiber length of 2.69 and 2.62 cm is 11 and 21% respectively (Auscott Ltd, personal communication, 2002).
Table 6 presents the average fiber length, strength, and micronaire for each cultivar grown under both irrigated and dryland conditions. Fiber lengths of all cultivars were above the discount range although with small samples in these experiments, fiber lengths are usually longer than obtained at the commercial scale. All cultivars had significantly (P 0.001) reduced fiber length under dryland conditions, the reductions varying between 0.1 and 0.18 cm. Siokra V-15 had the longest fiber under dryland conditions and the equal longest fiber under irrigated conditions while CS 8S had the shortest fiber under both conditions. There was a significant (P 0.01) irrigation x cultivar interaction for fiber length although this interaction accounted for less than 2.5% of the variation within the stratum. The cultivar CS 50 is an example of this interaction, having the equal second longest fiber under irrigated conditions and the equal third shortest fiber under dryland conditions (Table 6). We conclude that cultivars with genetically longer fiber are desirable to avoid price discounts.
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0.001) increased under dryland conditions (Table 6). There was a significant (P 0.001) irrigation x cultivar interaction for micronaire, with some cultivars having a larger increase in micronaire than others (particularly Siokra 1-1 and Siokra 1-4). However, all of the cultivars had dryland micronaire within the commercially acceptable range of 3.5 to 4.9 (Auscott Ltd, personal communication, 2002).
Selection Environment
Experimental evidence from a number of crops in different geographical areas suggests that when different cultivars or breeding lines are tested in a sufficiently large environmental range, G x E interactions of the crossover type are common. If there are G x E interactions of the crossover type, and the selection and the target environments lie at opposite sides of the crossover point, breeding lines developed in favorable conditions are not likely to perform well in difficult environments (Ceccarelli, 1996). In contrast, Rosielle and Hamblin (1981) concluded that selection for tolerance to stress would generally result in reduced yield under favorable conditions and selection for high yield potential should increase yield under both stress and nonstress conditions.
Until recently, all lines in our breeding program were evaluated under irrigated conditions until they had reached an advanced stage, at which time they were also tested under dryland conditions. There was no selection under dryland conditions. Many researchers have reported on the best environments for selection, but conclusions vary. Quisenberry et al. (1980) concluded that selection within early generation populations was most effective when performed at locations where environmental components do not limit yield, i.e., selection for yield potential as suggested by Rosielle and Hamblin (1981). Other researchers such as Arboleda-Rivera and Compton (1974) reported success when selecting only under water stress conditions. Our data indicate that both strategies may have a role in identifying superior dryland cultivars. The high yield of Siokra V-15 under both dryland and irrigated conditions supports the strategy of selecting under nonlimiting environments. However, the crossover interaction of Siokra L23 and Siokra 1-4 suggests that improvements can be made by selecting lines under dryland conditions; lines that may not necessarily have the highest yield under irrigated conditions. We conclude that to exploit the possibility of selecting better dryland types, selection should occur at an earlier stage in the breeding program, before testing for adaptation at multiple sites.
The significant site interactions shown in Table 4 indicate that a larger number of dryland testing sites in representative locations may be required. For dryland sites, correlation coefficients between cultivar ranking for an individual season and the overall mean cultivar rank for all six seasons were 0.71, 0.52, 0.71, 0.56, 0.65, and 0.39 for the 1993–1994, 1994–1995, 1995–1996, 1996–1997, 1997–1998, and 1998–1999 seasons, respectively. Correlation coefficients for all two-season mean rank combinations with overall mean cultivar rank ranged from 0.61 to 0.89, indicating that data from two consecutive seasons could be sufficient to identify cultivar ranking. This, combined with cultivar x site interactions, indicates that an increased number of testing sites might reduce the number of seasons required to identify long-term cultivar ranking.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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ACKNOWLEDGMENTS |
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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