Drought resistance of bermudagrass (Cynodon spp.) ecotypes collected from different climatic zones

Environmental and Experimental Botany 85 (2013) 22–29

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Drought resistance of bermudagrass (Cynodon spp.) ecotypes collected from different climatic zones Yi Zhou, Christopher J. Lambrides ∗ , Shu Fukai The University of Queensland, School of Agriculture and Food Sciences, Qld 4072, Australia

a r t i c l e

i n f o

Article history: Received 1 March 2012 Received in revised form 19 July 2012 Accepted 30 July 2012 Keywords: Soil water content Evapotranspiration Canopy temperature Root biomass Turfgrass

a b s t r a c t The objectives of this paper were to (1) evaluate drought resistance of a large number of bermudagrass ecotypes collected from different climatic zones of regional Australia and compare their performance to commercial cultivars, (2) describe the mechanisms of drought resistance observed, and (3) investigate the relationship between geographic origins of the ecotypes and their drought resistance. Fifty-two genotypes of bermudagrass were evaluated in two field experiments using lysimeters 40 cm deep. The grasses were grown in well-watered conditions and then a drought treatment was imposed by withholding water and excluding rainfall using a portable rain-out shelter. Two criteria were used to select for drought resistance, i.e. survival period (SP), defined as the number of days after water was withheld to the stage when 100% leaf firing had occurred and Days50 defined as the days required to reach 50% green cover. These experiments suggested that genotypes with superior drought resistance had lower stomatal conductance in the earlier phases of the dry-down period as suggested by less water use and higher canopy temperature depression. Lower water use during the early stage of dry-down resulted in more soil available water at the end of the drought period to extend green-leaf cover. There was no correlation between root dry matter and survival period/Days50 . We also found some ecotypes performed better in drought conditions than popular commercial cultivars. There was no relationship between drought resistance and geographic origins, suggesting that drought resistant ecotypes could be obtained from any climatic zone sampled in this study. Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved.

1. Introduction Drought resistance of turfgrasses can be defined as the ability to survive under an unfavourable water deficit (Beard, 1973). Generally, plants exhibit two types of drought resistance either dehydration avoidance or dehydration tolerance (Chaves et al., 2003), but turfgrasses most commonly employ dehydration avoidance mechanisms (Zhou et al., 2012). By definition, dehydration avoiders are able to keep high water potential when exposed to drought conditions by decreasing water loss and/or extracting more water deeper in the soil profile (Levitt, 1980).

Abbreviations: SP, survival period; ET, evapotranspiration; SWC, soil water content; RWC, relative water content; DAWW, days after water was withheld; GC, green cover; CTD, canopy temperature depression; CY, clipping yield; WUE, water use efficiency; WUEr , relative water use efficiency; RDM, root dry matter; VPD, vapor pressure deficit; VDW, verdure dry weight. ∗ Corresponding author at: The University of Queensland, School of Agriculture and Food Sciences, Rm N310, Hartley Teakle Building (Blg 83), Slip Rd St. Lucia, Brisbane, Queensland 4072, Australia. Tel.: +61 7 3365 1103/1518; fax: +61 7 3365 1177. E-mail addresses: [email protected] (Y. Zhou), [email protected] (C.J. Lambrides), [email protected] (S. Fukai).

Several studies have demonstrated that turfgrasses have the capacity to avoid drought by conserving water. Zhao et al. (1994) and Fernandez and Love (1993) studied 10 and 25 commercial turfgrasses cultivars in pot conditions (26 and 30 cm depth), respectively, and both found that the low evapotranspiration (ET) rate in a drying cycle was positively correlated to two criteria of drought resistance, i.e. turf colour and relative water content (RWC) in the drought period. Furthermore, low ET rates of resistant cultivars of Kentucky bluegrass (Poa pratensis L.) during drought, was explained by lower stomatal conductance at the beginning of stress in a 40 cm pot study (Wang and Huang, 2003). Other studies have indicated that turfgrasses are able to capture more water to avoid dehydration (Levitt, 1980). Field studies of tall fescue (Festuca arundinacea Schreb.) and Kentucky bluegrass showed that genotypes with higher quality and less leaf firing in drought conditions were found to have larger root length density and greater water extraction deeper in the soil profile when compared to drought susceptible genotypes (Carrow, 1996a; Bonos and Murphy, 1999). As a C4 grass, bermudagrasses (Cynodon spp.) are adapted to hot, dry climatic regions and include several taxa of the genus Cynodon (L.) Rich. For bermudagrasses, both lower ET and greater soil water extraction at depth have been reported as drought resistance mechanisms. A previous 40 cm lysimeter study (Zhou et al.,

0098-8472/$ – see front matter Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.envexpbot.2012.07.008

Y. Zhou et al. / Environmental and Experimental Botany 85 (2013) 22–29

2009) of 8 bermudagrass genotypes including 4 commercial cultivars and 4 Australian ecotypes found that the genotypes that maintained green cover for longer periods after water was withheld were characterized by lower ET and higher canopy temperature in the early stage of drought resulting in more available soil water in the later stages. Qian and Fry (1997) reported that in polyvinyl chloride (PVC) containers (27 cm depth), ‘Midlawn’ bermudagrass and the two other warm-season turfgrass species buffalograss (Buchloe dactyloides (Nutt.) Engelm.) and zoysiagrass (Zoysia japonica Steud.) were able to maintain higher leaf water potential than ‘Mustang’ tall fescue at 40 days without watering because they had lower ET rate from 0 to 10 days. Moreover, water extraction studies using 10 bermudagrass genotypes in deep lysimeters (150 cm depth) conducted by Hays et al. (1991) showed that root mass at 30, 60, 90, and 150 cm depth of soil profile was positively correlated with turf quality during drought stress, but this only occurred in one of the two tests. Generally, bermudagrasses are considered to have superior drought resistance to other warm-season turfgrass species such as zoysiagrass, St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze] and centipedegrass [Eremochloa ophiuroides (Munro.) Hack.] (Carrow, 1995, 1996b; Qian and Fry, 1997). Wild ecotypes can potentially have better drought resistance than commercial cultivars and intra-specific variation within the genus Cynodon has largely been neglected. Some previous studies of bermudagrass have indicated that wild ecotypes may be an excellent source of drought resistance. For example, genotypes selected from the breeding program at Oklahoma State University had higher visual quality than commercial cultivars during a 60-day water deficit period (Hays et al., 1991), and also a wild ecotype collected from Biloela, Queensland, Australia had longer survival period than 4 commercial varieties widely used in Australia (Zhou et al., 2009). The previous drought studies in bermudagrass described above did not include large numbers of genotypes. Despite the enormous range in genetic variation reported among bermudagrass ecotypes (Taliaferro, 2003), there are no comprehensive studies describing the mechanisms of drought resistance using a large number of genotypes collected from a wide range of environments. Bermudagrasses are adapted and distributed to most parts of Australia (Sharp and Simon, 2002). Early records show that it was widely distributed in Australia at the time of European settlement and was found in remote areas not disturbed by man. Beehag (1992) considers bermudagrass to be indigenous to Australia but not endemic. Recent molecular evidence from phylogenic analysis of chloroplast genomes suggests that bermudagrass may have existed in Australia for up to 500 years (Jewell et al., 2012). Beard and Sifers (1997) showed that the US bermudagrass varieties developed from sub-tropical environments had the best drought resistance compared to the ones selected under temperate conditions. But there have been no studies to illustrate the relationship between drought resistance of bermudagrass ecotypes and their geographic origins in other parts of the world. The objectives of this study were to (1) evaluate the drought resistance of a large number of bermudagrass ecotypes collected from different climatic zones of regional Australia and compare their performance to commercial cultivars and (2) to describe the mechanisms of drought resistance observed, and (3) investigate the relationship between geographic origins of the ecotypes and their drought resistance.

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compound at The University of Queensland, St. Lucia, Brisbane, Australia. We used 52 genotypes including 4 commercial cultivars, Wintergreen (W. Green), Grand Prix (G. Prix), CT2 and Legend and 48 ecotypes collected from regional Australia (Supplementary Fig. S1) from four different climatic zones based on the Köppen classification (Pratley, 1988) (Supplementary Fig. S1 and Table 1). CT2 was selected from a breeding program in Falbrook, California, USA. All grasses were planted in PVC pots (40 cm deep × 10 cm diameter) lined with a plastic bag to create a closed lysimeter. The soil type was a University of California (UC) Mix (sandy soil) (Baker and Chandler, 1957). A chemical analysis of UC Mix indicated the content of N, P and K to be 310, 87, and 251 mg kg−1 respectively. Each pot contained 4700 g UC mix. Volumetric moisture content at field capacity determined by the method of Richardson and Siccama (2000) was 25.1%. Three replicates of each genotype were arranged in a completely randomised design with 8 rows and 16 columns. The lysimeters were placed in a large wooden box (40 cm high) sitting on a bench (0.92 m × 2.0 m) and tightly packed so they were touching. The outer surfaces of the box were covered with sisalation (Aluminium coated plastic) to reflect incoming solar radiation. A 0.8 mm thick clear plastic roof was constructed and placed over the pots to exclude precipitation during rainfall events. On 10th October 2008, grasses were established by using three plugs per lysimeter and then propagated further until a plateau or equilibrium density was reached (Biran et al., 1981). After 2 months, all the grasses were clipped with scissors to 2 cm from soil level every 6 days. Clippings were removed and discarded. Grasses were fertilised with soluble liquid fertilizer flowfeed (Grow Force, Brisbane, QLD, Australia) equivalent to 50 kg N ha−1 month−1 . When all canopies were established (i.e. 100% coverage) on 21st January 2009, the experiment commenced and data were recorded. The ‘well watered’ treatment occurred at the beginning of the experiment when all the grasses were watered to field capacity every 3 days for a period of 18 days. After 18 days the drought treatment was applied where no further water was provided. Survival period (SP), defined as the number of days after water was withheld (DAWW) to the stage when 100% leaf firing had occurred, was recorded for each grass. Digital images were obtained with a PowerShot SX1 IS camera, (Canon Inc., Tokyo, Japan) positioned 10 cm vertically above the lysimeter in uniform sunlight at 0, 6, 12, 18, 21, 24, 28 until 31 DAWW when most grasses had little green leaf. Colour charts (Royal Horticultural Society, London, UK) were used to adjust different light intensities that may have existed when photos were taken. In order to calculate the percentage of green cover during dry-down, digital image analysis was conducted using SigmaScan Pro (v5.0, SPSS Inc., Chicago, IL) after the method of Richardson et al. (2001) and Karcher and Richardson (2005). Scatter plots of the percent green cover (GC) versus DAWW indicated a strong nonlinear relationship in every pot. Furthermore, the data fit very well to a sigmoid variable slope model. GC =

k 1 + ea+bt

(1)

2.1. Experiment 1

where t is DAWW, a and b are parameters of the curve and k is the maximum green cover at 0 DAWW in this experiment. For each experimental unit, the sigmoid variable slope model was significantly fit to the change of green cover during drought stress and the coefficient of determination (R2 ) was higher than 90% in each case. Using the following equations Days50 (days required to reach 50% green cover) for each grass was calculated as the time at which the rate of decrease in GC is a maximum (Tipton, 1984).

The response of bermudagrass genotypes to drought stress were investigated in lysimeter experiments conducted in an open

1 k k= 2 1 + ea+b∗(Days50 )

2. Materials and methods

(2)

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Y. Zhou et al. / Environmental and Experimental Botany 85 (2013) 22–29

Table 1 Rainfall and mean temperature of summer, winter and annual of the four Australian climatic zones where the 52 genotypes in this study were collected. The classification of climatic zone was after the Köppen system (Pratley, 1988). Data were summarized from Bureau of Meteorology (2005). The distribution of the climatic zones 1, 2, 3 and 4 was provided in Supplementary Fig. S1. Climatic zone

1 2 3 4

Classification

Temperate-Mediterranean Sub-tropical with warm summer and cool winter Sub-tropical with hot summer and mild winter Tropical rainy

Mean temperature (◦ C)

Rainfall (mm) Summer

Winter

Annual

Summer

Winter

Annual

25–50 100–200 200–600 800–1200

200–400 100–200 50–100 50–200

500–800 500–800 600–1000 1000–2400

21–24 21–24 24–27 24–27

12–15 6–9 12–15 16–21

18–21 12–15 21–24 21–27

And Days50 = −

a b

(3)

Lysimeters were weighed every 3 days until 18 days of treatment in well-watered condition and 28 DAWW in drought condition to estimate soil water content (SWC). Daily vapor pressure deficit (VPD) during the whole experiment was calculated using the method described by Allen et al. (1998). The average VPD in the well-watered condition (0–18 days after experiment started) and drought condition (18–58 days after experiment started) of this experiment was 1.21 and 1.15 KPa, respectively (Fig. 2). Infrared thermometer (IR) gun (Model MI-N 15+, Mikron Infrared Inc., CA, USA) was used to measure canopy temperature. The IR gun held vertically at a fixed distance of 3–5 cm above the canopy was moved around every part of the grass sward. Temperature data was captured every 20 ms over a 5 s period and stored, averaged and recorded automatically. Canopy temperature depression (CTD) was calculated using canopy temperature minus the air temperature. CTD was determined 10 times from 0 to 25 DAWW on clear, still, sunny days between 11 a.m. and 2 p.m. Leaf RWC was determined at 0, 4, 11, 17 and 23 DAWW according to the method of Turner (1981) on the basis of the following equation: RWC =

FW − DW × 100% TW − DW

(4)

where FW is leaf fresh weight, DW is leaf dry weight after being dried at 60 ◦ C for 4 days, and TW is turgid weight of leaves after soaking in water for 4 h at room temperature (20 ◦ C) while exposed to artificial light. All the grasses were clipped to 2 cm and clipping yield (CY) was determined every 6 days until 18 days of treatment in well-watered condition and 6 DAWW in drought conditions when the growth was reduced greatly. Water use efficiency (WUE) defined here as clipping yield production per unit of water evapotranspired was estimated by the linear regression of cumulative CY on cumulative ET using data points collected from 6, 12 and 18 days of treatment in well-watered condition (WUEww ) and 6 DAWW during water deficit (WUEdr ) (Zhou et al., 2012). Relative water use efficiency (WUEr ) gave the relative difference in WUE of grasses grown under irrigated and drought conditions where, WUEr =

WUEdr × 100% WUEww

Fig. 1. Daily vapor pressure deficit (VPD) in the period of two field experiments (experiment 1 and 2) conducted at the St. Lucia campus of The University of Queensland in 2009. The arrow indicates the time when water was withheld.

from Queensland. Twenty-two ecotypes collected from Queensland (Supplementary Fig. S1) and 4 commercial cultivars used in experiment 1 were included in experiment 2. For experiment 2, the location, lysimeter size, soil type, rainout shelter, experimental design, planting time, management during canopy establishment and drought treatment were the same as experiment 1. Data collection started 20th February 2009, green cover was determined at 6, 12, 21, 29, 32, 36 and 45 DAWW, survival period and Days50 were determined as described above. At 4, 10, 21, 25, 30, 33 and 37 DAWW, lysimeters were weighed to estimate SWC. The average VPD in the well-watered condition (0–10 days after experiment started) and drought condition (10–58 days after experiment started) was 1.22 and 1.05 KPa, respectively (Fig. 1). CTD was also determined on clear sunny days 7 times between 0 and 31 DAWW. In this experiment clipping yield was only collected during the drought period, therefore, only WUEdr was calculated. After each grass had 0% green cover, they were dissected to measure verdure dry weight, total RDM and RDM below 20 cm of soil profile as described above.

(5)

Once the survival period was determined, the grasses were cut into 3 sections: verdure (all plant matter above soil height and below clipping height) (Fry and Huang, 2004), roots (including rhizomes) in the top 20 cm of the soil profile and roots below 20 cm. Roots were washed free of soil using a high pressure shower, dried in an oven at 60 ◦ C, and weighed to determine root dry matter (RDM). 2.2. Experiment 2 Experiment 2 was conducted to check the repeatability of the results from experiment 1, especially for the ecotypes collected

2.3. Statistics In both experiments, a cluster analysis of observations was undertaken for survival period and Days50 using the Ward linkage method (Hair, 2010), while similarities were measured by Squared Euclidean Distance (Minitab Inc., State College, PA, USA) to classify all the genotypes into groups. After that, group was included as a factor in an analysis of variance to provide group means for several attributes. For characteristics such as GC, SWC and CTD measured in different time periods for the same experimental unit, repeated measures was applied using Mixed Models of SAS (SAS Institute Inc., Cary, NC, USA). In addition, principal component analysis (PCA) was used to transform a number of observed variables

Y. Zhou et al. / Environmental and Experimental Botany 85 (2013) 22–29

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Fig. 2. Grouping of genotypes after cluster analysis using two criteria survival period and Days50 of (a) 52 genotypes in experiment 1 and (b) 26 genotypes in experiment 2. Dendrograms were constructed with Ward linkage and Squared Euclidean distance.

to a smaller number of artificial variables (called principal components) to account for most of the variance in the observed variables. Therefore, attributes having significant genotypic variation were used for PCA in each experiment.

3. Results For each experiment, a cluster analysis using data of SP and Days50 classified the genotypes into distinct groups (Fig. 2). Groups 1–4 in experiment 1 and Group A–C in experiment 2 represent rankings of drought resistance from highest to lowest and the difference between groups was significant (Table 2). For example in experiment 1, the survival period and Days50 of Group 1 was more than 10 days and 3 days longer, respectively, than Group 4. Likewise, in

experiment 2 Group A had 16 and 2 days longer survival period and Days50 , respectively, than Group C. The change of green cover with time of each group gave good fit to the sigmoid variable slope model. The variation in green cover among groups was small at the beginning, larger in the middle stage of drought but narrowed at the end when green cover approached 0% (Fig. 3). Grasses within the drought resistant groups (i.e. Group 1 and Group A) kept higher green cover than others during the drought stress period. For example, the green cover of Groups 1 and A were about 40% and 30% higher than Groups 4 and C at 24 and 32 DAWW, respectively. In the early stage of drought, SWC was not significantly different among groups, but subsequently, the variation increased and became significant (Fig. 4). SWC of drought resistance Group 1 was higher than others in the middle stage of drought, but at 28 DAWW

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Table 2 Traits measured in two field experiments (a) experiment 1 and (b) experiment 2 conducted at the St. Lucia campus of The University of Queensland. Traits included survival period (SP), Days50 , relative water content (RWC) at 23 days after water withheld (DAWW), verdure dry weight (VDW), total root dry matter (RDM) including rhizomes, RDM below 20 cm of soil profile and water use efficiency (WUE) in drought and well-watered conditions of drought resistance groups in both experiments. Within a column, group means followed by the same letter are not significantly different based on standard error (P = 0.05). (a)

WUE

Groups

SP (days)

Days50 (days)

RWC at 23 DAWW

VDW (g)

Total RDM (g)

RDM below 20 cm (g)

Drought (mg mm−1 )

Well-watered (mg mm−1 )

Relative (%)

1 2 3 4 P-value

38.6a 33.8b 29.8c 26.9d <0.001

23.3a 21.0b 20.5c 19.3d <0.001

69.9a 59.6b 55.6c 48.5d <0.001

10.51a 11.11a 10.71a 11.84a 0.162

5.28a 5.70a 6.05a 5.46a 0.241

1.91b 2.04b 2.30a 1.64c 0.019

12.01a 11.00b 11.20b 9.90c 0.012

8.81a 8.49a 8.62a 8.24a 0.065

131.1a 130.4a 130.3a 118.8b 0.041

Well-watered (mg mm−1 )

Relative (%)

(b)

WUE

Groups

SP (days)

Days50 (days)

A B C P-value

50.0a 42.2b 34.2c <0.001

26.9a 24.5b 24.1b 0.004

RWC at 23 DAWW

VDW (g)

Total RDM (g)

RDM below 20 cm (g)

Drought (mg mm−1 )

9.76a 8.46b 10.25a 0.002

4.57a 3.25b 3.74b 0.028

2.01a 1.32b 1.39b 0.014

9.19a 6.98b 6.25c 0.001

Fig. 3. Per cent green cover of bermudagrasses grown in two field experiments at the St. Lucia campus of The University of Queensland. The figure shows group means for (a) 4 groups in experiment 1 and (b) 3 groups in experiment 2. Within one time period, group means followed by the same letter are not significantly different based on standard error at P = 0.05 ns indicates not significantly different.

Fig. 4. Volumetric soil water content in soil profiles for bermudagrasses grown in two field experiments at the St. Lucia campus of The University of Queensland. The figure shows group means for (a) 4 groups in experiment 1 and (b) 3 groups in experiment 2. Within one time period, group means followed by the same letter are not significantly different based on standard error at P = 0.05. Asterisk (*) indicates a significant difference between groups and ns indicates no significant difference between groups at the same time period.

Y. Zhou et al. / Environmental and Experimental Botany 85 (2013) 22–29

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in experiment 2, Group B was significantly smaller than others (Table 2). The difference in total RDM was not significant among groups in experiment 1, but for RDM below 20 cm, Group 4 was significantly lower than other groups, about 0.6 g smaller than Group 3 which was the highest (Table 2). While in experiment 2, Group A had considerably higher total RDM as well as RDM below 20 cm compared to Groups B and C, with the difference being about 1 g and 0.7 g, respectively (Table 2). For both experiments, drought resistant Groups 1 and A had higher WUEdr than others, about 2 and 3 mg mm−1 higher than drought susceptible Groups 4 and C which were the lowest groups (Table 2). WUEww and WUEr were calculated only in experiment 1, and it was found that there was no significant difference between groups for WUEww , while Group 4 had considerably smaller WUEr , nearly 50% lower than other groups (Table 2). At the genotypic level, survival period and Days50 were significantly correlated to each other, and both were positively correlated to SWC and CTD in the early stage of dry-down and negatively correlated to VDW and CTD in the late stage of drought in both experiments (Fig. 6). No significant correlation between RDM and SP/Days50 was observed in either experiment. In addition, WUEdr and WUEr in experiment 1 were significantly correlated with survival period and Days50 (Fig. 6a).

4. Discussion 4.1. Mechanisms of drought resistance

Fig. 5. Canopy temperature depression of bermudagrasses grown in two field experiments at the St. Lucia campus of The University of Queensland. The figure shows group means for (a) 4 groups in experiment 1 and (b) 3 groups in experiment 2. Within one time period, group means followed by the same letter are not significantly different based on standard error at P = 0.05 ns indicates no significant difference between groups at the same time period.

when drought resistance Group 4 lost green colour completely, SWC of Group 4 was more than 10% higher than other groups, and Group 1 was also significantly higher than Group 2 and 3. There was no significant difference in SWC among Groups 1, 2 and 3 after the survival period (Fig. 4a) and a similar observation was made in experiment 2 (Fig. 4b). The difference in CTD among groups was not significant in the early stage of drought, e.g. 0 and 3 DAWW and in the middle stage, e.g. 15 DAWW in experiment 1 (Fig. 5a), but it was significant in other periods. In both experiments, at the beginning of the drought treatment the drought resistant groups had considerably higher CTD than drought susceptible groups, the difference being more than 1 ◦ C, while by the end of dry-down the CTD of the drought resistant groups was the lowest, and the difference went up to about 3 ◦ C (Fig. 5). In experiment 1, RWC of grasses in the drought resistant groups was higher than the other groups at 23 DAWW, and the variation was greater than 20% (Table 2). There were small differences in verdure dry weight (VDW) among groups in experiment 1, but

Dehydration avoidance by decreasing water loss during the early-middle period of drought, was the main mechanism of drought resistance observed in these studies where root depth was limited to 40 cm. The grass genotypes in the most drought resistant groups were characterised by leaving greater moisture in the soil profile in the early stage of dry-down. Therefore, the genotypes that consumed less water at the beginning had more available soil water towards the end of the drought period to delay leaf firing. This dehydration avoidance result was consistent with our previous study of a small number of bermudagrass genotypes (Zhou et al., 2009), however in the study presented here we show the same result with a large number of genotypes. The lower water use of the drought resistant genotypes in the early stages of the dry period was probably because of lower stomatal conductance suggesting greater sensitivity of stomatal aperture to water deficit in these grasses. The reduction of stomatal conductance is an adaptation by plants that decreases the loss of water when exposed to drought (Levitt, 1980). In our study, measurements of canopy temperature were used to indicate stomatal conductance given that stomatal closure can lead to increases in canopy temperature (Jackson et al., 1981). Warmer canopies as a result of lower stomatal conductance have been also reported previously for turfgrass species (Blonquist et al., 2009; Bonos and Murphy, 1999). The high moisture status as measured by RWC of the leaves of the drought resistant grasses was most likely an outcome of lower stomatal conductance during the early stages of drought. Maintenance of RWC is essential to provide turgor for cell enlargement and growth (Hsaio, 1973). The study of 6 creeping bentgrass cultivars during water deficit by McCann and Huang (2008) also reported that RWC was positively correlated with visual traits such as turf quality (r = 0.91). Higher WUEr of drought resistance genotypes suggested that they could require less water to produce a certain amount of canopy biomass when drought occurred. Continued leaf growth during soil drying as a result of reduced water use could contribute to the maintenance of turf quality and drought resistance (Aronson et al., 1987). Similarly, Zhou et al. (2012) also found that WUEr was positively

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Y. Zhou et al. / Environmental and Experimental Botany 85 (2013) 22–29

Fig. 7. Linear regression between two field experiments (experiment 1 and 2) conducted at the St. Lucia campus of The University of Queensland. The regressions were for 26 genotypes (n = 26) that were in common to both experiments. The regressions provided are for two criteria of drought resistance used in this study i.e. survival period and Days50 .

group, partly because it consumed more water in the early stage of dry-down, and partly because it could not extract as much water as other groups did in the late stage of drought, possibly due to smaller root biomass below 20 cm of soil profile. Conversely, Group 3 was also a drought susceptible group consuming more water in the early stage of dry-down, possibly due to its greater root biomass below 20 cm of soil profile. These observations of root biomass suggest that for experiments conducted in 40 cm lysimeters too much or too little root biomass at depth may contribute to greater susceptibility to drought although water use during the early drought period remained the dominant characteristic affecting drought resistance in these experiments. 4.2. Tools for selecting drought resistance and performance of selected drought resistant bermudagrasses

Fig. 6. Plot of eigenvectors (a) experiment 1 and (b) experiment 2. for several traits measured in two field experiments grown at the St. Lucia campus of The University of Queensland. Traits included survival period (SP), Days50 , canopy temperature depression (CTD) in the early, middle and late stage of water deficit, soil water content (SWC), relative water content (RWC), verdure dry weight, total root dry mater (RMD) and RDM below 20 cm of soil profile, water use efficiency in well-watered condition (WUEww ) and drought condition (WUEdr ) and relative WUE (WUEr ). The first two components explained 54.8% and 59.1% of the total variation for experiment trait 1 and 2, respectively. Eigenvector plots can be interpreted as follows. (1) vectors with a narrow angle between them indicates a positive correlation exists between the traits, the narrower the angle the greater the correlation; (2) trait vectors perpendicular to each other indicates no correlation between the traits; (3) trait vectors in opposing directions indicates a negative correlation exists between the traits.

correlated to SP using 4 warm-season turfgrass species including bermudagrass. While the results showed that there was no significant relationship between drought resistance and root biomass at the genotypic level (Fig. 6), group differences were revealed (Table 2). For example, Group 4 in experiment 1 was the most drought susceptible

SP and Days50 were used as the criteria to select for drought resistance among bermudagrass ecotypes in this study. In previous research aimed at selecting for drought resistance among turfgrasses, SP or Days50 has also been applied (Karcher et al., 2008; Richardson et al., 2008; Steinke et al., 2011; Zhou et al., 2009). Use of an infrared thermometer to measure CTD is rapid, accurate, nondestructive and able to handle large numbers (Jackson et al., 1981). The high correlation between CTD and SP or Days50 (Fig. 6) in the present study suggested that an infrared thermometer was an effective tool to select for drought resistance in bermudagrass. Similarly, Emekli et al. (2007) also reported that CTD measured by infrared thermometer was associated with variation in water use of bermudagrass. As presented here the methods to screen for drought resistance were highly repeatable as indicated by the close correlations observed between experiments 1 and 2 (Fig. 7). In addition, these results were consistent with our previous studies. For example, the drought resistance of ecotype 81-1 (Fig. 2 and Table 2) had been demonstrated by Zhou et al. (2009) who found that the ecotype 81-1 had drought resistance greater than 4 commercial cultivars used in both studies. A number of additional ecotypes in this study had superior drought resistance compared to commercial types. The most drought resistant ecotype 700 survived 14% longer than Wintergreen, one of the most drought resistant commercial cultivars in experiment 1. Bermudagrass has an enormous range of

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genetic variation, particularly for plant type, rhizomes and stolons (Taliaferro, 2003), and therefore it is likely other ecotypes can be identified with superior drought resistance to those used in this study. 4.3. Relationship between drought resistance and geographic origins The ecotypes used in this study were collected from a range of climatic zones (Supplementary Fig. S1 and Table 1), however, there was not a clear relationship between drought resistance and their geographic origin in the experiments presented here. Genotypes from drought resistant and drought susceptible groups could be identified from every climatic zone. Various adverse environmental factors from collection sites such as water logging or deficit, soil acidity, specific mineral deficiencies or toxicities, in addition to effects of climate could have a considerable influence on characterisation (Frankel, 1989). Therefore, traits associated with drought resistance such as stomatal closure and small canopies could also be selected for by exposure to many environmental factors other than drought. 5. Conclusions The experiments reported here suggest that dehydration avoidance was the main mechanism of drought resistance employed by the bermudagrasses in this study. Genotypes with superior drought resistance had lower stomatal conductance in the earlier stage of dry-down period as suggested by less water use and higher canopy temperature depression. Ecotypes with drought resistance better than commercial types were identified and could be used for further evaluation. Importantly, drought resistance of ecotypes was not associated with their geographic origins. These studies will be indicative of bermudagrass performance in shallow soil profiles that are typical of many urban environments. Acknowledgements This project was supported by the Australian Research Council (ARC) linkage project (EcoTurf; ID: LP0775239). The assistance of the Central Glasshouse Service Unit of The University of Queensland is also acknowledged. We also acknowledge our industry partners Jimboomba Turf Group Pty Ltd, South East Queensland Council of Mayors and Department of Agriculture, Fisheries and Forestry. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.envexpbot. 2012.07.008. References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration-guidelines for computing crop water requirements, Irrigation and Drainage Paper No. 56. FAO, Rome. Aronson, L.J., Gold, A.J., Hull, R.J., 1987. Cool-season turfgrass responses to drought stress. Crop Science 27, 1261–1266. Baker, K.F., Chandler, P.A., 1957. The U.C. System for Producing Healthy ContainerGrown Plants through the Use of Clean Soil, Clean Stock and Sanitation. University of California, Berkeley. Beard, J.B., 1973. Turfgrass: Science and Culture. Prentice-Hall, Englewood Cliffs, N.J. Beard, J.B., Sifers, S.I., 1997. Genetic diversity in dehydration avoidance and drought resistance within the Cynodon and Zoysia species. In: Proceedings International Turfgrass Research Conference, Vol. 8, pp. 603–610.

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