Abstract
Traffic resistance of turfgrasses is an essential indicator of urban recreational and sports turf quality (TQ). In our study, four turfgrass species were investigated for their wear resistance. A self-made traffic simulator was used to determine the wear resistance of the study turf area in a 2-year field trial (2019–20). The experimental plots were established using a randomized block design with three replicates. The morphological characteristics, soil physical properties, and physiological indices of the grasses were analyzed. Using the acquired quantitative data, we set the turf cover index (TCI), the turf quality index (TQI), and the shoot density index (SDI) as the wear tolerance index, and assessed the correlations among these morphological characteristics, soil physical properties, physiological indices, and wear tolerance. ‘Lanyin III’ zoysiagrass and ‘Tifgreen’ hybrid bermudagrass provided relatively greater wear tolerance, followed by ‘Qingdao’ zoysiagrass and common bermudagrass after 12 weeks of traffic exposure in 2019 and 2020. Traffic changes the soil physical properties and affects the physiological metabolism of turfgrasses. Leaf morphology characteristics and the mechanical strength of these grasses were related significantly to TCI, TQI, and SDI, and most physiological responses and soil properties correlated significantly with TCI and TQI. Our findings of the correlations among physiological responses, soil properties, leaf morphology, and wear tolerance will allow grass breeders to evaluate their breeding procedures more efficiently.
A major criterion for turfgrass breeding selection is high wear tolerance, withstanding forces that can crush plant leaves, stems, crowns, or roots. Among the selection schemes, wear treatment has been commonly adopted in turf plots for progeny testing by most breeders (Sampoux et al., 2013). Traffic is considered the most common and devastating stress against sports turfs (Wei et al., 2022). The term “traffic stress” usually encompasses wear and soil compaction (Dest and Ebdon, 2017). Soil compaction can damage soil physical properties and inhibit turfgrass root growth as well as visual quality (Turgeon, 2005). A past study proved that warm-season turfgrass species have a greater tolerance to wear compared with cool-season ones (Shearman and Beard, 1975). In terms of warm-season turfgrass species, zoysiagrass (Zoysia japonica) and bermudagrass (Cynodon dactylon) demonstrate stronger wear tolerance (Zhang et al., 2021). On the other hand, the potential physiological mechanisms regarding the difference in wear tolerance among these species remain unknown. The relative wear tolerance in various species differs across research studies (Haselbauer et al., 2012; Lulli et al., 2012; Sheikh Mohamadi et al., 2017).
Wear tolerance in grass stems and leaves is reinforced by their morphological traits. Of all such traits, cell wall components are of the greatest importance, especially in the number of sclerenchyma fibers. Lignin and cellulose contents ensure structural stability as the main constituent of vascular integrity and tensile strength in the tissues among various grass species (Godlewska and Ciepiela, 2020; Maksup et al., 2020). Vincent (1991) verified the linear relation between leaf strength and sclerenchyma and lignin amounts resulting from identical mechanical properties of lignified fibers among various grass species. According to these results, a relationship exists between wear tolerance and anatomic and morphological plant features, including the total cell wall content, the number of sclerenchyma fibers, tiller density, tiller dry weight, width and tensile strengths of leaves, and shoot density (Głąb et al., 2015; Seo et al., 2015). This relation was verified by Dowgiewicz et al. (2011) for Agrostis sp. Similar conclusions were derived by Zhang et al. (2004), who noted a remarkable relationship between tensile strength and cross-sectional area and the numerous principal vascular bundles in forage grass leaves. Turfgrass wear tolerance depends on specific physiological parameters, such as improved acid detergent fiber (Canaway, 1981), sucrose phosphate synthase and sucrose synthase activities, leaf moisture (Jabbari and Rohollahi, 2020), density (Pornaro et al., 2016), leaf angle (Kowalewski et al., 2015), leaf width (Głąb et al., 2015), and silica concentration (Trenholm et al., 2004).
The relationship between antioxidant defense mechanisms and plant tolerance to stresses has also been reported previously (Sukweenadhi et al., 2017). Electrons leaking from the electron transport chains of chloroplasts react with oxygen to generate reactive oxygen species (ROS), such as hydrogen peroxide. Excessive ROS damages the cell membrane through lipid peroxidation, proteins, or nucleic acids. Diverse antioxidant metabolites and enzymes in the plant can respond to ROS. Superoxide dismutase (SOD) is a major indicator of plant resilience that significantly affects plant resistance to adverse environments, such as the first line of defense for scavenging ROS (Rohollahi et al., 2018). Traffic stress damages plant leaves, destroys the leaf cell membrane structure, intensifies the peroxidation in membrane lipids, and increases malondialdehyde (MDA) content—a cytotoxic end product of unsaturated fatty acid oxidation (Sheikh Mohamadi et al., 2017). Total soluble sugars are the critical substances in the carbohydrate metabolism of turfgrass cells that are closely related to photosynthesis (Yang et al., 2018), exerting significant effects on tissue and organ establishment, which is beneficial to enhance the resistance of turfgrasses. Proline and osmoregulatory substances can also be used as reservoirs for energy and ammonia sources under traffic stress because they participate directly in plant metabolism after the stress is lifted (Moreno-Galván et al., 2020).
Our study aims 1) to evaluate the differences among four turfgrass species in terms of leaf and canopy morphologies and wear tolerance, 2) to evaluate how traffic stress affects the soil physical properties and physiological metabolism among these four turfgrass species, and 3) to assess the correlations among their morphological characteristics, soil physical properties, physiological indexes, and wear tolerance. We expect our results to provide grass breeders with a range of indicators of turf morphological characteristics, soil physical properties, and turf physiological metabolism associated with the wear tolerance of these grasses to assist in selecting optimal or best-suited turfgrass species for a given location.
Materials and Methods
Plant material and experimental site
This research was performed at the Teaching and Research Base at South China Agricultural University, Guangzhou, China (lat. 23.30N, long. 113.81E), with ‘Tifgreen’ hybrid bermudagrass (Cynodon dactylon × C. transvaalensis), common bermudagrass (C. dactylon), ‘Lanyin III’ zoysiagrass (Zoysia japonica), and ‘Qingdao’ zoysiagrass (Z. japonica). The native soil type of Guangzhou was Lateritic red clay soil, with an organic matter of 14.5 g⋅kg–1 and an average pH of 6.3. The sand used for this research varied in particle size, ranging from very coarse (9.6%) to coarse (19.5%) to medium (34.9%) to fine (19.7%). The experimental areas were tilled to an approximate depth of 10 cm and were leveled immediately before planting. The plots were seeded on 18 July 2018. Seeds of bermudagrass (‘Tifgreen’ hybrid bermudagrass and common bermudagrass) and zoysiagrass (‘Lanyin III’ zoysiagrass and ‘Qingdao’ zoysiagrass) were hand-broadcast and cultivated in plots at seeding rates of 40 g⋅m–2 (bermudagrass) and 30 g⋅m–2 (zoysiagrass). The plots were irrigated to maintain the soil humidity content at 80% field capacity or greater to avoid any visible drought stress during plant establishment. Weed control included 2-methyl-4-chlorophenoxyacetic acid applications at labeled rates to control broadleaf weeds, and grassy weeds were removed manually. A 10N–5P2O5–10K2O fertilizer was applied at 15 g⋅m–2 once every 2 weeks, beginning at seeding, and once per month from October to June. All turfgrass species were maintained at a cutting height of 4 cm and were mowed when needed using a reel-type mower.
The study region experiences a subtropical monsoon climate, with annual mean evaporation, temperature, and precipitation of 1450.5 mm, 23.4 °C, and 1786.8 mm, respectively, and snowfall and frost-free periods of 365 d (1981–2020). During the research period, the annual total precipitation amount reached 465.3 mm in 2019 and 500.1 mm in 2020. The annual mean temperatures reached 27.5 °C and 28.1 °C in 2019 and 2020, respectively.
Traffic simulation
The experimental plots (1.5 × 7.5 m) were constructed with three replicates using a randomized split-plot design using four cultivars of nontraffic (CK) and traffic treatments. The total number of plots was 24. As indicated by Brosnan et al. (2005), measuring unmowed spaced plants is not the most dependable method for predicting mowed turf stand turfgrass performance all the time. Plant material type was determined per laboratory analysis from the field trials to obtain plants grown under typical exploitative circumstances.
Traffic treatments were applied as a strip in replicates with a self-made traffic simulator (STS). The STS has five parts—a roller, steel plate, spike, bearing, and frame, all of which are steel. The steel plate and spikes are removable and easy to replace. The dimensions of the drum are 21 cm in diameter, 100 cm in length, 1.2 cm thick, weighing 80 kg, with a bearing in the middle of the drum with a 5-cm diameter and a length of 104 cm. The frame outside the drum is connected to an SR1Z-80 microtiller (Xinyuan Inc., Guangzhou, China) to drive the traffic simulator, with a maximum engine power of 4.2 kW. The unit pressure of STS was 1.91 MPa. Traffic processing was performed annually from the middle of June to late September. Six passes were conducted during the twice-weekly (Monday and Thursday) applications. The experiment lasted for 12 weeks for both years.
Measurements of wear tolerance
Measurement of soil physical properties
The dry bulk density was calculated by collecting soil samples using metal cylinders with an ≈100-cm3 volume (diameter, 5.02 cm; length, 5.05 cm), and six replicated samples were collected for each plot. The samples were collected in the 0- to 15-cm soil layer. The core samples were gathered from Sept. 2019–20, weighed, and dried at 105 °C until a constant weight was reached. Total porosity was measured according to the particle density and bulk density results. Particle density was measured using a pycnometric technique. The mean particle density value reached 2.648 g⋅cm–3 in the 0- to 10-cm soil layer. The soil samples received a heating treatment of 105 °C in an oven (model 101-0; Jinpin Instrument Inc., Shanghai, China) for 48 h to determine the soil water content. Soil surface hardness was measured using a soil impact instrument (Clegg 083; SD Instrument, London, UK).
Measurement of physiological indexes
Relative water content.
Membrane permeability.
Leaf SOD activity.
The 3-mL reaction solution encompassed 13 mm methionine, 63 mm r-nitro blue tetrazolium chloride, 1.3 mm riboflavin, 50 mm phosphate-buffered saline (pH 7.8), and 50 mL of the enzyme extract. The reaction solution was cultured for 10 min under fluorescent light (80 mmol⋅m–2⋅s–1). The absorbance was measured at 560 nm by spectrophotometry (Ultraviolet-5200; Metash Inc., Shanghai, China) (Chen et al., 2009).
MDA content.
A fresh leaf sample (0.2 g) was subjected to homogenization in 1.5 mL 5% trichloroacetic acid and then centrifuged at 14,000 gn for 25 min. The aliquot (0.5 mL) of the supernatant was mixed with 1 mL 20% trichloroacetic acid containing 0.5% thiobarbituric acid and heated at 100 °C for 30 min, followed by quick cooling and centrifugation at 10,000 gn for 10 min. The supernatant was collected, and its absorbance was measured at 532 and 600 nm. Deducting the nonspecific absorbance (600 nm), MDA concentration was measured at an extinction coefficient of 155 mm–1⋅cm–1 (Heath and Packer, 1968).
Soluble sugar content.
Dry leaf powder (20 mg) was soaked four times for 15 min with 90% (v/v) ethanol (20 mL). Upon centrifugation at 3000 gn for 10 min, the supernatants were gathered and combined, and the final volume was diluted to 40 mL. Then, 2 mL of the supernatant was collected in a glass tube. Ultimately, 1 mL of 18% phenol solution and 5 mL of concentrated sulfuric acid were supplemented. The mixture was stirred uniformly, and its absorption was measured at 490 nm with a spectrophotometer (Buysse and Merckx, 1993).
Free proline content.
The free proline content was determined as described by Bates et al. (1973). For this purpose, fresh leaf samples (500 mg) were homogenized in 10 mL sulfosalicylic acid (3% w/v). Next, the extract was screened through Whatman No. 2 filter paper, to which 2 mL aliquot, 2 mL acid ninhydrin, and 2 mL glacial acetic acid were introduced for boiling at 100 °C for 1 h. Then, 2 mL of toluene was retrieved from the mixture and stirred completely for 15 to 20 s. Absorbance was then measured at 520 nm in the toluene blank, and the upper layer was isolated from the aqueous phase.
Leaf morphology characteristics, mechanical strength, and cell wall components
The response variables for leaf morphology characteristics were collected before traffic treatment. The measurements for leaf morphology covered the length, width, and angle of leaves. Leaf length and leaf width were evaluated with an electronic digital caliper with 0.001-mm readability. Leaf angle was calculated on a 1- to 4-oint scale, with 1 point equal to a leaf-to-sheath angle of 0° to 22.5°; 2 points, 22.5° to 45°; 3 points, 45° to 67.5°; and 4 points, 67.5° to 90° (Brosnan et al., 2005).
For mechanical strength, leaf stretching resistance, erect stem stretching resistance, and root system stretching resistance were used for the CK leaves as the experimental material. Each grass species had a normal growth of three inverted leaves, an erect stem, and a root system of comparable length, which as fastened with double-sided tape at both the ends at ≈1 cm and was clamped to both the sides of the tensiometer clamps (HDE-500; Haibao Instruments Inc., Guangzhou, China) to take readings when pulled off. To avoid the effect of clamps on turfgrass leaves and the root system at both ends, only turfgrass leaves and the root system broken in the middle were counted during the test. A total of 15 replicates for each data item were counted for each species.
The cell wall components include the leaf cellulose content, leaf lignin content, and leaf silica content. Leaf cellulose content and leaf lignin content were calculated using leaf tissues per Goering and Van Soest (1970). Leaf silica content was calculated using leaf tissues per He et al. (2016).
To determine the anatomic structure of leaves, grass leaves from the same part were taken and cut into 0.5-cm segments and fixed with formalin, acetic acid, and alcohol for 7 d. The paraffin slices were then dyed with safflower solid green. The corneum, vascular bundle, and alveolar cells were photographed using an Olympus BX43 biological microscope.
Statistical analyses
All results are the averages of two years, 2019–20. Data analysis was performed with the statistical package Statistica (version 10.0; StatSoft Inc., Tulsa, OK). Analysis of variance was conducted to determine the overall significant differences of the cultivars. Upon discovering significant differences, treatment methods were separated by Fisher’s least significant difference test at a significance level of P < 0.05. SPSS (Version 22.0; SPSS Inc., Chicago, IL) was used to conduct statistical analyses. Student’s t test indicated no interactions (P > 0.05) between the two different years (2019 and 2020). The correlation among physiological responses, soil properties, leaf morphology, mechanical strength, cell wall component, and wear tolerance was calculated by Spearman’s test at two significance levels, P < 0.05 and P < 0.01. Linear or quadratic polynomial regression curves were fitted to the relationship of physiological responses, soil properties, leaf morphology, mechanical strength, cell wall components, and turf indexes. The selection of regression models was based on the coefficient of determination R2.
Results
Turf cover and quality.
Wear tolerance in the turfgrass cultivars was assessed based on turf characteristics, including the TCI, TQI, and SDI (Table 1). TC in the tested cultivars exhibited significant (P < 0.05) differences between the untreated and control plots. Four cultivars exhibited a high TC, with coverage of more than 90%. STS compaction lowered the TC of all cultivars. The highest TC on compaction was observed in ‘Lanyin III’ zoysiagrass and ‘Tifgreen’ hybrid bermudagrass, with a TCSTS of more than 70%. Taking TCI into consideration, the best cultivar exhibiting the lowest TCI was ‘Lanyin III’ zoysiagrass (0.11). The most vulnerable cultivar was common bermudagrass (TCI of 0.18). The TQ in the untreated plots of the 4 cultivars was 7.33 to 7.50. The assessment of the TQ on STS compaction suggested lowered TQ for all cultivars, as demonstrated by the traffic treatment. The TQI of all tested cultivars was less than zero. All four cultivars had a relatively low TQI of less than 0.2. The poorest quality was observed in common bermudagrass cultivars (TQI = 0.18), whereas the highest quality was observed in ‘Lanyin III’ zoysiagrass (TQI = 0.13). The shoot density was assessed as an essential factor associated with TQ and TC. The shoot density for tested turf cultivars range between 0.87 and 1.88 shoots/cm2. The lowest shoot density was noted in ‘Qingdao’ zoysiagrass (average, 0.87 shoots/cm2). Perhaps because of the differences in the species, the shoot density for the two bermudagrass species was significantly greater than that for two zoysiagrass species (P < 0.05). As for most cultivars, STS compaction lowered the shoot density by 39% on average. The SDI for these cultivars was more than 0.3.
Turf characteristics. Results are given as averages for the years 2019 and 2020.
Soil physical property responses.
Soil surface hardness of the four turfgrasses displayed different degrees of increase after traffic treatment, all exhibiting significant differences (P < 0.05) from the corresponding CK treatment (Fig. 1). The turf surface hardness of the two zoysiagrass species was significantly (P < 0.05) less relative to that of the two bermudagrass species in the CK condition for the four turfgrasses. Under the traffic conditions, the turf surface hardness of the four turfgrasses differed significantly (P < 0.05). Compared with the CK treatment corresponding to each grass species, the increase in surface hardness of the lawns of ‘Tifgreen’ hybrid bermudagrass, common bermudagrass, ‘Lanyin III’ zoysiagrass, and ‘Qingdao’ zoysiagrass was 45.75%, 80.93%, 27.62%, and 45.24%, respectively. In contrast, the increase in soil surface hardness of the common bermudagrass lawn was much greater than that of the other grass species.
Response of soil surface hardness, bulk density, total porosity, and soil water content of four turfgrass species—‘Tifgreen’, common bermudagrass, ‘Lanyin III’ zoysiagrass, and ‘Qingdao’ zoysiagrass—under traffic stress. Different lowercase letters indicate significant differences in data from different nontraffic (CK) treatments with the same measurement at the 0.05 level. Different uppercase letters indicate that the data of different traffic treatments of the same measurement exhibited significant differences at the 0.05 level. The asterisk indicates significant differences in data of the same CK treatment and traffic treatment of the same measurement at the 0.05 level.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Response of soil surface hardness, bulk density, total porosity, and soil water content of four turfgrass species—‘Tifgreen’, common bermudagrass, ‘Lanyin III’ zoysiagrass, and ‘Qingdao’ zoysiagrass—under traffic stress. Different lowercase letters indicate significant differences in data from different nontraffic (CK) treatments with the same measurement at the 0.05 level. Different uppercase letters indicate that the data of different traffic treatments of the same measurement exhibited significant differences at the 0.05 level. The asterisk indicates significant differences in data of the same CK treatment and traffic treatment of the same measurement at the 0.05 level.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Response of soil surface hardness, bulk density, total porosity, and soil water content of four turfgrass species—‘Tifgreen’, common bermudagrass, ‘Lanyin III’ zoysiagrass, and ‘Qingdao’ zoysiagrass—under traffic stress. Different lowercase letters indicate significant differences in data from different nontraffic (CK) treatments with the same measurement at the 0.05 level. Different uppercase letters indicate that the data of different traffic treatments of the same measurement exhibited significant differences at the 0.05 level. The asterisk indicates significant differences in data of the same CK treatment and traffic treatment of the same measurement at the 0.05 level.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
The soil water content in the four turfgrasses exhibited different degrees of decrease after the traffic treatment, all of which were significantly different (P < 0.05) from the results of the corresponding CK treatment (Fig. 1). The soil water content of the two zoysiagrass species was significantly less (P < 0.05) compared with that of the two bermudagrass species in the CK condition for the four turfgrasses. The soil water content of ‘Tifgreen’ hybrid bermudagrass was significantly greater (P < 0.05) compared with that of the other grass species under traffic conditions. Compared with the corresponding CK treatments of each grass species, the soil water content of ‘Tifgreen’ hybrid bermudagrass, common bermudagrass, ‘Lanyin III’ zoysiagrass, and ‘Qingdao’ zoysiagrass decreased by 15.39%, 25.26%, 17.65%, and 30.61%, respectively.
The soil capacity of the four turfgrasses ranged from 1.25 to 1.35 g⋅cm–3 without traffic (CK), with no significant differences among them (P > 0.05). Soil capacity in the four turfgrasses exhibited different degrees of increase after traffic treatment, all of which demonstrated significant differences from the corresponding CK treatment (P < 0.05) (Fig. 1). The soil capacity of ‘Lanyin III’ zoysiagrass was significantly less than that for the other grasses (P < 0.05). In comparison with the corresponding CK treatments in each grass species, the increase in the soil capacity of ‘Tifgreen’ hybrid bermudagrass, common bermudagrass, and ‘Qingdao’ zoysiagrass was similar, with increases of 26.49%, 27.90%, and 29.12%, all of which were greater than the 17.23% value of ‘Lanyin III’ zoysiagrass.
The total soil porosity of the four turfgrasses ranged from 48.00% to 52.00% without traffic (CK) condition, and none of them exhibited significant differences (P > 0.05). The total soil porosity for the four turfgrasses demonstrated different magnitudes of decrease after the traffic treatment. All exhibited substantial differences relative to the corresponding CK treatment (P < 0.05). The total soil porosity of ‘Lanyin III’ zoysiagrass was significantly greater than that of the other grasses (P < 0.05), and the total soil porosity of ‘Qingdao’ zoysiagrass was not significantly different from that of ‘Tifgreen’ hybrid bermudagrass and common bermudagrass (P > 0.05). Compared with the CK treatment corresponding to each grass species, the total soil porosity of ‘Tifgreen’ hybrid bermudagrass, common bermudagrass, ‘Lanyin III’ zoysiagrass, and ‘Qingdao’ zoysiagrass decreased by 24.88%, 28.97%, 16.02%, and 27.76%, respectively.
Physiological responses.
The relative water content of all grass species displayed different degrees of decrease after the traffic treatment. ‘Tifgreen’ hybrid bermudagrass (91.3%) and ‘Lanyin III’ zoysiagrass (91.7%) had significantly greater decreases (P < 0.05) than that of common bermudagrass (89.2%).
During the experiment, the SOD activity of the four turfgrasses without traffic treatment (CK) were maintained at 93.00 to 96.00 U⋅g–1 with no significant differences (P > 0.05), whereas the SOD activity for all grass species exhibited different degrees of increase after the traffic treatment. Under traffic stress, the SOD activity of ‘Tifgreen’ hybrid bermudagrass (122.64 U⋅g–1) and ‘Lanyin III’ zoysiagrass (125.40 U⋅g–1) was significantly greater than those of common bermudagrass (101.22 U⋅g–1) and ‘Qingdao’ zoysiagrass (103.45 U⋅g–1) (P < 0.05). On the other hand, the SOD activity increased by 28.53% and 31.23%, respectively, for ‘Tifgreen’ hybrid bermudagrass and ‘Lanyin III’ zoysiagrass, which was much greater than that for common bermudagrass and ‘Qingdao’ zoysiagrass (7.31% and 9.67%, respectively).
The leaf MDA content of the four turfgrasses without traffic treatment CK remained at 3.00 to 4.00 µmol⋅g–1, and the values were not significantly different (P > 0.05). In contrast, the leaf MDA content of all grasses demonstrated various degrees of increase after the traffic treatment. All exhibited significant differences (P < 0.05) from the corresponding CK treatment (Fig. 2). The leaf MDA content of the four turfgrasses differed significantly (P < 0.05) under traffic stress, with the greatest difference noted in common bermudagrass (17.48 µmol⋅g–1) and the least difference seen in ‘Lanyin III’ zoysiagrass (9.03 µmol⋅g–1).
Response of relative water content, leaf electrolyte leakage, superoxide dismutase activity, leaf malondialdehyde content, soluble sugar content, and leaf-free proline (Pro) content of four turfgrass species—‘Tifgreen’, common bermudagrass, ‘Lanyin III’ zoysiagrass, and ‘Qingdao’ zoysiagrass—under traffic stress. Different lowercase letters indicate significant differences in data in different nontraffic (CK) treatments with the same measurement at the 0.05 level. Different uppercase letters indicate significant differences in data in different traffic treatments of the same measurement at the 0.05 level. The asterisk indicates significant differences in data with the same CK treatment and traffic treatment of the same measurement at the 0.05 level. FW, fresh weight.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Response of relative water content, leaf electrolyte leakage, superoxide dismutase activity, leaf malondialdehyde content, soluble sugar content, and leaf-free proline (Pro) content of four turfgrass species—‘Tifgreen’, common bermudagrass, ‘Lanyin III’ zoysiagrass, and ‘Qingdao’ zoysiagrass—under traffic stress. Different lowercase letters indicate significant differences in data in different nontraffic (CK) treatments with the same measurement at the 0.05 level. Different uppercase letters indicate significant differences in data in different traffic treatments of the same measurement at the 0.05 level. The asterisk indicates significant differences in data with the same CK treatment and traffic treatment of the same measurement at the 0.05 level. FW, fresh weight.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Response of relative water content, leaf electrolyte leakage, superoxide dismutase activity, leaf malondialdehyde content, soluble sugar content, and leaf-free proline (Pro) content of four turfgrass species—‘Tifgreen’, common bermudagrass, ‘Lanyin III’ zoysiagrass, and ‘Qingdao’ zoysiagrass—under traffic stress. Different lowercase letters indicate significant differences in data in different nontraffic (CK) treatments with the same measurement at the 0.05 level. Different uppercase letters indicate significant differences in data in different traffic treatments of the same measurement at the 0.05 level. The asterisk indicates significant differences in data with the same CK treatment and traffic treatment of the same measurement at the 0.05 level. FW, fresh weight.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
The soluble sugar content of the four turfgrasses remained in the range of 0.75% to 0.85% without traffic treatment (CK), and no significant differences were observed (P > 0.05). On the other hand, the soluble sugar content in all grass species exhibited different increases after the traffic treatment (Fig. 2). Under traffic stress, the soluble sugar content of common bermudagrass and ‘Qingdao’ zoysiagrass was significantly less than that of ‘Tifgreen’ hybrid bermudagrass and ‘Lanyin III’ zoysiagrass. When compared with the CK treatment corresponding to each grass species, the increase in the soluble sugar content of ‘Tifgreen’ hybrid bermudagrass, common bermudagrass, ‘Lanyin III’ zoysiagrass, and ‘Qingdao’ zoysiagrass was 102.80%, 73.22%, 111.48%, and 100.00%, respectively.
The leaf free proline content of the four turfgrasses remained in the range of 28.00 to 30.00 µg⋅g–1 without any traffic treatment (CK), with no significant differences observed (P > 0.05). In contrast, the leaf free proline content of the four turfgrass species demonstrated different degrees of increase after the traffic treatment, with significant differences from the corresponding CK treatment (P < 0.05) (Fig. 2). Under traffic stress, the leaf free proline content of ‘Lanyin III’ zoysiagrass was significantly greater (P < 0.05) compared with that for other grass species, reaching 156.51 µg⋅g–1, whereas that of common bermudagrass was significantly less than that of other species, with only 95.18 µg⋅g–1 of leaf free proline.
The leaf electrolyte leakage of the four turfgrasses without traffic treatment (CK) remained at 16.50% to 17.50%, with no significant differences observed (P > 0.05). In contrast, the leaf relative EC of the four turfgrass species exhibited different degrees of increase after the traffic treatment. Significant differences were observed (P < 0.05) between the corresponding CK treatment (Fig. 2). Under traffic stress, the relative EC of common bermudagrass (55.23%) was significantly greater (P < 0.05) compared with that for the other grass species, whereas that of ‘Lanyin III’ zoysiagrass (43.10%) was substantially less than that of the other species.
Leaf morphology characteristics, mechanical strength, and cell wall components.
Significant differences in leaf stretching resistance, erect stem stretching resistance, and root system stretching resistance were noted among all tested cultivars (P < 0.05). ‘Lanyin III’ zoysiagrass exhibited the highest value and common bermudagrass demonstrated the lowest value. Differences were observed among leaf length, leaf angle, and leaf width in the four turfgrass species. Leaf width and leaf angle of the two zoysiagrass species were significantly greater than those of the two bermudagrass species (P < 0.05). No leaf width and leaf angle differences were noted between common bermudagrass and ‘Tifgreen’ hybrid bermudagrass.
The determined leaf anatomy features depended on the turfgrass species and cultivar. The anatomic structure of the leaves of the four turfgrass species is shown in Fig. 3, in which the lignified cell wall and nucleus are red, and the parenchyma cells and cytoplasm are green. The leaf cellulose content (21.43%) and leaf silica content (0.20%) of ‘Lanyin III’ zoysiagrass were highest among all tested cultivars, whereas common bermudagrass had the lowest values. The leaf lignin content of ‘Qingdao’ zoysiagrass, common bermudagrass, and ‘Tifgreen’ hybrid bermudagrass was significantly less than ‘Lanyin III’ zoysiagrass (10.15%) (Table 2).
Leaves were fixed with formalin, acetic acid, and alcohol for 7 d, then paraffin slices were dyed with safflower solid green. The lignified cell wall and nucleus are red; parenchyma cells and cytoplasm are green. COM, common bermudagrass; III, ‘Lanyin III’ zoysiagrass; QD, ‘Qingdao’ zoysiagrass; TG, ‘Tifgreen’ hybrid bermudagrass.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Leaves were fixed with formalin, acetic acid, and alcohol for 7 d, then paraffin slices were dyed with safflower solid green. The lignified cell wall and nucleus are red; parenchyma cells and cytoplasm are green. COM, common bermudagrass; III, ‘Lanyin III’ zoysiagrass; QD, ‘Qingdao’ zoysiagrass; TG, ‘Tifgreen’ hybrid bermudagrass.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Leaves were fixed with formalin, acetic acid, and alcohol for 7 d, then paraffin slices were dyed with safflower solid green. The lignified cell wall and nucleus are red; parenchyma cells and cytoplasm are green. COM, common bermudagrass; III, ‘Lanyin III’ zoysiagrass; QD, ‘Qingdao’ zoysiagrass; TG, ‘Tifgreen’ hybrid bermudagrass.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Leaf anatomic characteristics. Results are presented as the averages of 2 years (2019–20).
Correlations among physiological responses, soil properties, leaf morphology, mechanical strength, cell wall components, and wear tolerance.
Tables 3 and 4 depict Pearson’s correlation coefficients between the TQIs and grass morphology and soil properties. Interestingly, no correlation was noted between soil water content and any determined parameter observed. Leaf morphology characteristics and mechanical strength had significant correlations with TCI, TQI, or SDI (P < 0.05), indicating that this parameter had the greatest impact on grass wear tolerance. The TCI is calculated from the TC of trafficked plots vs. nontraffic sites. It was highly significantly (P < 0.01) and correlated with only three traits: soluble sugar content, electrolyte leakage, and relative water content (Table 4). The regression models representing these relationships are presented in Fig. 4. When compared with TCI, TQI is based on the turf’s visual aspect. It considers turf color, uniformity, and leaf texture, apart from turf coverage. The TQI was highly significantly (P < 0.01) and correlated with five traits: leaf lignin content, leaf silica content, bulk density, total porosity, and proline (Tables 3 and 4). The regression models representing these relationships are presented in Fig. 5. Most physiological responses and soil properties were related significantly to TCI and TQI. In general, the correlation coefficients of physiological responses and soil properties were greater for TCI compared with those for TQI. And the correlation coefficients of leaf morphology characteristics and mechanical strength were greater for TCI compared with those for TQI.
Regression models for the relationship between turf cover index (TCI) and three traits: soluble sugar content (SSC), leaf electrolyte leakage (EL), and relative water content (RWC). Data were fitted by linear and nonlinear regressions in the form of y = ax + b and y = ax2 + bx + c, whichever provided the larger R2 value.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Regression models for the relationship between turf cover index (TCI) and three traits: soluble sugar content (SSC), leaf electrolyte leakage (EL), and relative water content (RWC). Data were fitted by linear and nonlinear regressions in the form of y = ax + b and y = ax2 + bx + c, whichever provided the larger R2 value.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Regression models for the relationship between turf cover index (TCI) and three traits: soluble sugar content (SSC), leaf electrolyte leakage (EL), and relative water content (RWC). Data were fitted by linear and nonlinear regressions in the form of y = ax + b and y = ax2 + bx + c, whichever provided the larger R2 value.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Regression models for the relationship between turf quality index (TQI) and five traits: leaf cellulose content (LCC), leaf silica content (LSC), bulk density (BD), total porosity (TP), and leaf-free proline content (Pro). Data were fitted by linear and nonlinear regressions in the form of y = ax + b and y = ax2 + bx + c, whichever provided the larger R2 value.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Regression models for the relationship between turf quality index (TQI) and five traits: leaf cellulose content (LCC), leaf silica content (LSC), bulk density (BD), total porosity (TP), and leaf-free proline content (Pro). Data were fitted by linear and nonlinear regressions in the form of y = ax + b and y = ax2 + bx + c, whichever provided the larger R2 value.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Regression models for the relationship between turf quality index (TQI) and five traits: leaf cellulose content (LCC), leaf silica content (LSC), bulk density (BD), total porosity (TP), and leaf-free proline content (Pro). Data were fitted by linear and nonlinear regressions in the form of y = ax + b and y = ax2 + bx + c, whichever provided the larger R2 value.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Correlations among leaf morphology, mechanical strength, cell wall components, and wear tolerance.
Correlations among physiological responses, soil properties, and wear tolerance.
The SDI correlated significantly (P < 0.01) with eight traits: leaf stretching resistance, root system stretching resistance, leaf length, leaf width, leaf angle, leaf cellulose content, soil surface hardness, and MDA (Tables 3 and 4). The regression models representing these relationships are presented in Fig. 6.
Regression models for the relationship between shoot density index (SDI) and eight traits: leaf stretching resistance (LSR), root system stretching resistance (RSSR), leaf length (LL), leaf width (LW), leaf angle (LA), leaf cellulose content (LCC), soil surface hardness (SSH), and leaf malondialdehyde (MDA) content. Data were fitted by linear and nonlinear regressions in the form of y = ax + b and y = ax2 + bx + c, whichever provided the larger R2 value.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Regression models for the relationship between shoot density index (SDI) and eight traits: leaf stretching resistance (LSR), root system stretching resistance (RSSR), leaf length (LL), leaf width (LW), leaf angle (LA), leaf cellulose content (LCC), soil surface hardness (SSH), and leaf malondialdehyde (MDA) content. Data were fitted by linear and nonlinear regressions in the form of y = ax + b and y = ax2 + bx + c, whichever provided the larger R2 value.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Regression models for the relationship between shoot density index (SDI) and eight traits: leaf stretching resistance (LSR), root system stretching resistance (RSSR), leaf length (LL), leaf width (LW), leaf angle (LA), leaf cellulose content (LCC), soil surface hardness (SSH), and leaf malondialdehyde (MDA) content. Data were fitted by linear and nonlinear regressions in the form of y = ax + b and y = ax2 + bx + c, whichever provided the larger R2 value.
Citation: HortScience 57, 4; 10.21273/HORTSCI16453-21
Discussion
Głąb et al. (2015) believed that the TCI and TQI should be measured according to the TC and TQ in trafficked plots associated with nontrafficked sites. This measurement follows a visual score. TCI and TQI values range between –1 and 1. Positive values are noted in a treated plot with less TC compared with the control. Negative values, which are scarcely reported by practice, prove greater TC on traffic simulation. The TCI and TQI belong to dimensionless numbers and used as efficient tools to judge the wear tolerance for different species or cultivars. Following TCI, wear tolerance can be ranked as follows: ‘Lanyin III’ zoysiagrass > ‘Tifgreen’ hybrid bermudagrass > ‘Qingdao’ zoysiagrass > common bermudagrass. Similarly, ‘Lanyin III’ zoysiagrass and ‘Tifgreen’ hybrid bermudagrass had a better TQI rating compared with ‘Qingdao’ zoysiagrass and common bermudagrass.
Our cumulative findings verify the correlation of wear resistance indexes with leaf dimensions, including the leaf width, leaf length (for the SDI), leaf lignin content, and leaf silica content (for the TQI), erect stem stretching resistance, and root system stretching resistance (for all three indexes). Greater leaf width and leaf angle values suggest greater turfgrass wear tolerance, which was partially verified by Zhang et al. (2004), who reported a significant positive relation between tensile strength and the cross-sectional area of forage cultivars of Lolium perenne and Poa pratensis. In our research, the mechanical properties (i.e., leaf, stem, and root systems) of both zoysiagrass species seemed significantly greater compared with those of bermudagrass (P < 0.05), indicating that zoysiagrass had better mechanical strength, greater plant protection when subjected to abrasion stress, and greater resistance to wear and tear. From this perspective, zoysiagrass is more resistant to traffic than bermudagrass, as also proved previously (Liu et al., 2019).
Lulli et al. (2011) suggested lignin—the main component used to determine tissue strength—as a predictor for C4 turfgrass wear resistance at the breeding stage. A similar finding was reported by Mackinnon et al. (1988), who studied L. perenne cultivars and observed that cultivars with a greater leaf strength also exhibited high sclerenchyma wall percentages. Research on the tensile properties for forage grasses has revealed that the number of fibers and the degree of lignification of leaf tissues are essential to the mechanical strength of leaves (Zhang et al., 2018). In general, turfgrasses with high leaf lignin and lignocellulose contents are more resistant to traffic exposure. The lignin and cellulose in the cell wall determine the resilience and stiffness of turfgrass, and the more resilient and stiffer the turfgrass, the better it can counteract some of the abrasion damage in the face of traffic stress, thereby protecting it. In our study, we observed that the leaf lignin and cellulose contents of two zoysiagrass species were greater. Significantly, the leaf lignin and cellulose contents of ‘Lanyin III’ zoysiagrass were significantly greater than those of the other three grass species, indicating that the former could offset more wear-and-tear damage from traffic than the latter given the same traffic intensity.
In contrast, the leaf tensile strength, erect stem tensile strength, and root tensile strength of ‘Lanyin III’ zoysiagrass reached 12.32 MPa, 21.72 MPa, and 21.62 MPa, respectively. This finding indicates that turfgrasses with greater leaf lignin and cellulose contents have a stronger tensile strength, reflecting stronger traffic resistance. In addition, silica is widely distributed in plant cells (Doblin et al., 2010), is usually tested for its involvement in turfgrass wear resistance (Trenholm et al., 2004), and has proved to be decisive in this phenomenon.
The root system’s common response to increasing bulk density resulting from intensive traffic should be a reduced length, a centralized root biomass on the upper layer, and a reduced rooting depth (Lipiec et al., 2003). As illustrated by the research of Głąb and Szewczyk (2014), traffic simulation with Brinkman traffic simulator (BTS) has a significant impact on soil physical properties. The physical composition of the soil is directly altered, which indirectly affects the growth of aboveground plants. Soil bulk density and soil porosity can reflect soil compactness, and soil porosity and infiltration rate are closely related (Essien, 2011). Under moderate-intensity traffic conditions, with an increase in the traffic treatment time, soil particles were squeezed together, bulk density increased, and soil porosity decreased. Our study results show that the soil capacity of four warm-season turfgrasses increased and their porosity decreased after traffic treatment. The bulk density of ‘Lanyin III’ zoysiagrass and common bermudagrass increased the least (17.23%) and the most (29.12%), respectively, among the four warm-season turfgrass species. Total porosity of ‘Lanyin III’ zoysiagrass and common bermudagrass decreased the least (16.02%) and the most (28.97%), respectively, among the four warm-season turfgrass species (Fig. 1). Thus, ‘Lanyin III’ zoysiagrass and common bermudagrass have stronger and lower soil protection capabilities, respectively. Gray (1998) noted that plant roots offer soil stability and protect it from erosion. The tensile strength of plant roots correlated positively with soil protection capability (Gobinath et al., 2020). We observed that the root tensile strength of ‘Lanyin III’ zoysiagrass and common bermudagrass were greatest (21.62 MPa) and lowest (12.45 MPa), respectively, among the four warm-season turfgrass species. Compared with other turfgrass species, ‘Lanyin III’ zoysiagrass, with its greater root tensile strength and stronger soil protection capability, anchors the soil from moving outward and reduces the possibility of slipping, protecting it from erosion. The soil properties correlated significantly with the TCI and the TQI (Fig. 4). Traffic exposure caused soil compaction, which compressed the space for air and nutrients in the soil, and restricted the activity of the turfgrass root system, thereby affecting the biomass of its aboveground growing portions. Moreover, the increase in the soil capacity of grass by our STS also had a cumulative effect, and the quality of the grass continued to decline.
Traffic stress damages the leaves, stems, and crowns through horizontal and vertical compaction, possibly leading to leaf relative water content loss (Samaranayake et al., 2008). In addition, relative water content loss was possibly caused by an increased evapotranspiration rate and decreased water absorption, corresponding to soil compaction (Han et al., 2008). Membrane permeability has a close relationship with cell membrane composition and physiological status (Siddiqui et al., 2016). An increase in electrolyte leakage can be attributed to cell membrane damage by ROS, particularly when subjected to severe traffic stress. ROS possibly damages the cell membrane by lipid peroxidation and exerts additive effects on electrolyte leakage. The difference of electrolyte leakage in species is related to antioxidant activity (Kimball et al., 2017), leaf structure, and cell wall composition (Moore et al., 2008). In our research, the SOD activity in all four turfgrasses demonstrated different degrees of increase under traffic stress. In terms of SOD activity, ‘Lanyin III’ zoysiagrass and ‘Tifgreen’ hybrid bermudagrass exhibited the best performance under traffic stress, and both of them were more tolerant to traffic, thus indicating that ‘Lanyin III’ zoysiagrass and ‘Tifgreen’ hybrid bermudagrass could better eliminate excessive free radicals and negative oxygen ions under traffic stress. Total soluble sugars are critical substances involved in cellular carbohydrate metabolism of turfgrass and are closely related to photosynthesis as well. They are the most critical factor affecting the growth of turfgrass cells. Soluble sugar content accumulation is associated with a plant’s ability to withstand stress, contributing to postdamage recuperation (Niemi et al., 2017).
Furthermore, as soluble substances, they can be used to reduce intracellular osmotic potential and regulate the balance between reservoir sources, thereby exerting significant effects on the establishment of tissues and organs, which is conducive to enhancing the resistance of turfgrass. The greater the soluble sugar content, the greater the regenerative ability of plants (Khoshkholghsima and Rohollahi, 2015). As an osmoregulatory substance, proline can preserve the energy and ammonia sources under traffic stress and participate directly in plant metabolism after the pressure is removed (Wei et al., 2020). In our study, the soluble sugar content and proline content of four warm-season turfgrasses displayed different degrees of increase under moderate traffic stress. The total soluble sugar and free proline contents of ‘Lanyin III’ zoysiagrass reached 1.72% and 156.51 µg⋅g–1, both of which were greater than those of the other three turfgrasses, indicating that ‘Lanyin III’ zoysiagrass had more osmotic regulators under traffic stress compared with the other species. This finding suggests that, under traffic stress, more osmoregulatory substances are involved in maintaining normal physiological metabolism and incurring resistance to the adverse effects of traffic stress.
Conclusion
‘Lanyin III’ zoysiagrass and ‘Tifgreen’ hybrid bermudagrass provided relatively greater wear tolerance capabilities, followed by ‘Qingdao’ zoysiagrass and common bermudagrass after 12 weeks of traffic exposure in 2019 and 2020. Traffic exposure changes the soil’s physical properties and affects the physiological metabolism of turfgrasses. Leaf morphology characteristics and mechanical strength were all significantly related to the TCI, TQI, SDI, and most physiological responses, whereas soil properties correlated significantly with the TCI and TQI. Understanding the association of physiological responses, soil properties, and leaf morphology with wear tolerance will enable grass breeders to evaluate the breeding procedure more efficiently.
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