Genetic Variability of Traffic Tolerance and Surface Playability of Bermudagrass (Cynodon spp.) under Fall Simulated Traffic Stress

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Shehbaz Singh Department of Horticulture and Landscape Architecture, Oklahoma State University, 358 Agricultural Hall, Stillwater, OK 74078, USA

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Shuhao Yu Department of Horticulture and Landscape Architecture, Oklahoma State University, 358 Agricultural Hall, Stillwater, OK 74078, USA

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Mingying Xiang Department of Horticulture and Landscape Architecture, Oklahoma State University, 358 Agricultural Hall, Stillwater, OK 74078, USA

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Charles H. Fontanier Department of Horticulture and Landscape Architecture, Oklahoma State University, 358 Agricultural Hall, Stillwater, OK 74078, USA

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Yanqi Wu Department of Plant and Soil Sciences, Oklahoma State University, 371 Agricultural Hall, Stillwater, OK 74078, USA

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Dennis L. Martin Department of Horticulture and Landscape Architecture, Oklahoma State University, 358 Agricultural Hall, Stillwater, OK 74078, USA

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Anmol Kajla Department of Horticulture and Landscape Architecture, Oklahoma State University, 358 Agricultural Hall, Stillwater, OK 74078, USA

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Abstract

Bermudagrasses (Cynodon spp.) are the most preferred turfgrass species for athletic fields in the southern and transition zones of the United States. Developing and using bermudagrasses with superior traffic tolerance and surface playability under trafficked conditions benefits turfgrass managers, athletes, and sport organizations. A 2-year field study was conducted in Stillwater, OK, to quantify the genetic variability of traffic tolerance and surface playability from a population composed of two commercially available and 87 experimental interspecific hybrid bermudagrasses under fall simulated traffic stress. The experiment design was a randomized complete block design with three replications. Plots were subjected to 60 simulated cleat traffic events for 6 weeks in the fall of 2019 and 2020 using a Baldree traffic simulator. Bermudagrasses were evaluated for turfgrass quality (TQ), normalized difference vegetation index (NDVI), fall percent green cover (FPGC), shear strength (SS), and surface hardness (SH) after 3 and 6 weeks of traffic. Spring green-up percent green cover (SGPGC) was evaluated in the spring of 2020 and 2021. Except for SH, significant entry effects were found for all parameters and reliability estimates were moderate to high (i2 = 0.49 to 0.68) under simulated trafficked conditions. Experimental entries 17-4200-19X13, 17-4200-19X9, 17-4200-36X19, 17-5200-4X11, 18-7-2, 18-7-6, 18-8-2, 18-8-3, 18-8-7, 18-9-2, OSU1101, and OSU1664, and TifTuf® had excellent traffic tolerance. Entries 18-8-7, OSU1101, OSU1675, TifTuf®, and Tahoma 31® demonstrated high SS. There was a large group of entries that had consistent early spring green-up across both years, including Tilin#5, 18-9-8, OKC1221, OSU1257, OSU1318, OSU1337, OSU1406, OSU1439, OSU1651, OSU1675, Tahoma 31®, and TifTuf®. OSU1101 was the entry ranking in the top statistical grouping most often throughout the study. Findings illustrated the possibility of improving traffic tolerance and SS through breeding and using phenotypic selection could reliably select bermudagrass genotypes with improved traffic tolerance and SS in the transition zone.

Bermudagrasses (Cynodon spp.) are the predominant warm-season turfgrasses for athletic fields use in the southern and transition zones of the United States (Beard 1973). The widespread use of bermudagrass is due to its dense canopy, relative ease of establishment, wide mowing height tolerance range, tolerance to heat and traffic stress, and excellent recuperative capacity (Christians 2011). Athletic field facilities are widespread in schools, communities, and colleges throughout the United States, with an estimated 25 million students and 20 million community-based youth playing sports annually (Micheli et al. 2000). Traffic stress is one of the primary stresses on athletic fields, as it can lead to various types of damage, including wear, soil compaction, rutting, or soil displacement and divoting (Carrow and Petrovic 1992). Soil compaction causes a decline in porosity, and an increase in bulk density, if excessive, can lead to reduced growth of turfgrasses. Divoting refers to removing turfgrass from a surface due to vehicular or foot actions that closely relate to the shear resistance (Carrow and Petrovic 1992; Trappe et al. 2011a). Athletic fields often subjected to repeated traffic stress from equipment and athletes in certain patterns can lead to multiple types of damage occurring simultaneously, further injuring the turfgrass and increasing playability variations within the field (Kowalewski et al. 2013; Straw et al. 2018).

Maintenance of a high-quality playing surface is critical for reducing sports-related injuries, rehabilitation costs, litigation, etc. However, not all facilities have the financial resources to maintain playing surfaces at an exceptional level, highlighting the need for turfgrass varieties that can withstand heavy traffic and provide a safe and reliable environment for athletes, even under less-than-ideal maintenance conditions. It is critical to understand the magnitude of genetic factors in traffic tolerance for breeders to select appropriate breeding and selection strategies for bermudagrass improvement (Hallauer and Miranda 1981). Similar to broad-sense heritability (H2), reliability (i2) estimates the proportion of genetic variance contributing to total observed phenotypic variation within a population with different breeding backgrounds, instead of using only a reference population for broad-sense heritability estimates (Bernardo 2002). Grimshaw et al. (2018) reported the heritability of three fine fescue (Fescue spp.) species as a clone was between 0.49 to 0.6 for traffic tolerance in St. Paul, MN, USA. Limited information is available regarding the genetic information of traffic tolerance in bermudagrass. Understanding the genetic basis of traffic tolerance would provide valuable information to breeders seeking to improve bermudagrass varieties in the future.

As a direct selection factor for traffic tolerance, turfgrass green cover is the percentage of playing surface covered with green turfgrass and has been widely used in evaluating traffic tolerance. According to previous studies, substantial variations were found for canopy response under simulated traffic stress among bermudagrass entries. In a study conducted in Texas, variations among 17 bermudagrass cultivars were reported in terms of a percent reduction in verdure after a prescribed traffic treatment (Beard et al. 1981). Thoms et al. (2011) determined ‘Tifway’ and ‘Riviera’ have greater traffic tolerance than ‘Patriot’ based on the percent green cover assessment under simulated traffic conditions. Trappe et al. (2011b) investigated 42 bermudagrasses in the summer and fall season for traffic tolerance using a turf performance index (TPI) by counting the number of entries ranked at the top statistical group throughout the study. The cultivars Celebration, PremierPro, Contessa, and Barbados, and some experimental entries, were considered to have relatively good traffic tolerance over both seasons. A separate study conducted in Oklahoma determined Riviera, ‘OKC1134’ (hereafter referred to as NorthBridge®), and ‘OKC1119’ (hereafter referred to as Latitude 36®) as the most traffic tolerant among 40 bermudagrasses (Segars 2013). ‘DT-1’ (hereafter referred to as TifTuf®) was shown to have improved traffic tolerance compared with ‘Tifway’ under 6 weeks of 12 traffic-simulated events (Kowalewski et al. 2015). However, we are unaware of the genetic variability of traffic tolerance and whether variations in traffic tolerance are heritable.

In addition to traffic tolerance, SS and SH are critical components of athletic field performance, as they provide traction and safety to athletes (Brosnan et al. 2014; Dickson et al. 2018; McNitt et al. 1997). Traction is the interaction forces associated with the player and the turfgrass surface, which enables the player to appropriately grip the playing surface to prevent possible falls or slips (Canaway and Bell 1986). As a critical aspect of athletic field performance, traction can be divided into linear and rotational traction (McNitt et al. 1997). Rotational traction receives more attention as athletes make frequent horizontal moves during games, and SS has been commonly used to measure the traction provided by turfgrass to the foot when making horizontal changes of direction (Rogers et al. 1998). Dickson et al. (2018) found that the SS decreased rapidly when the soil water content was more than 0.30 m3⋅m−3 on Tifway bermudagrass. Goddard et al. (2008) reported that bermudagrass cultivars Riviera and Tifway showed greater shear resistance only under high-traffic conditions, and using crumb rubber can significantly increase shear resistance under both low- and high-traffic conditions. Previous studies have mainly focused on the influence of playing surface conditions on SS and how cultural practices can increase SS in bermudagrass. However, limited information is available regarding the genetic variability of SS within bermudagrass under traffic stress.

Bermudagrass is the most used warm-season turfgrass species in the transitional climatic zones. Nonetheless, its vulnerability to winterkill in these regions leads to substantial expenses for resodding athletic fields (Taliaferro et al. 2004). Therefore, athletic field managers require bermudagrass with excellent winter survivability to avoid these costs and maintain high-quality playing surfaces. Bermudagrass winter survivability is a quantitative trait and is often influenced by other stress factors (Yu et al. 2022). Previous studies showed drought stress injury influences the spring green-up in bermudagrass (DeBoer et al. 2020; Yu et al. 2023). Similarly, Schwartz et al. (2009) also reported the spring green-up in zoysiagrass (Zoysia spp.) could be influenced by stresses, such as disease caused by Bipolaris spp., glufosinate herbicide application, and mole cricket (Scapteriscus spp.) damage. Injury caused by traffic stress in the fall could affect bermudagrass spring green-up. Little information is available regarding the potential impact of traffic stress on genetic variability of spring green-up. Thus, further research is needed to better understand this potential impact and its implications for bermudagrass breeding. Accordingly, the objectives of this study were to 1) evaluate the traffic tolerance and surface playability differences among a large set of bermudagrass experimental entries in their ability to tolerate fall simulated traffic stress, and 2) quantify the genetic variability and estimate the reliability of bermudagrasses traffic tolerance and surface playability under fall simulated traffic stress.

Materials and Methods

Plant materials, site descriptions, establishment, and plot maintenance.

There were 89 bermudagrass entries, including two commercially available cultivars and 87 putative interspecific hybrid [Cynodon dactylon (L.) Pers. × Cynodon transvaalensis Burtt-Davy] experimental selections developed by the Oklahoma State University (OSU) turfgrass breeding program. Cultivars Tahoma 31® and TifTuf® were selected for this study because of their growing popularity for use in athletic fields. The experiment was conducted in a randomized complete block design with three replications.

All plant materials were transplanted into a field trial on 5 Jun 2019 at the OSU Turfgrass Research Center in Stillwater, OK. To promote the establishment, twenty-one 2.5-cm-diameter plugs were transplanted at 0.3-m spacing within a 2.4 × 1.2-m plot. After transplanting, oxadiazon (Ronstar G; Bayer, Cary, NC, USA) was applied at a 2.24 kg⋅ha−1 a.i. for pre-emerge weed control. Based on the soil test, fertilizers 25–0–10 (N–P2O5–K2O) and 10–20–10 (Agri-Nutrients, Inc., Catoosa, OK, USA) were applied at 4.8 g⋅m−2 N after transplanting and on 21 Jun 2019. From 1 Jul 2019 to 5 Aug 2019, Urea (46–0–0) was applied weekly at 4.8 g⋅m−2 N for 6 weeks to promote rapid establishment. On 3 Mar 2020, oxadiazon (Ronstar G; Bayer) was applied at a 5.6 kg⋅ha−1 a.i. for preemergence weed control. Diammonium phosphate (18–46–0) (4.8 g⋅m−2 N) was applied in May 2020 to ensure sufficient P for optimal plant growth. Similar to 2019, weekly urea (4.8 g⋅m−2 N) was applied from June to August in 2020. Irrigation was applied as necessary to prevent wilting. Plots were mowed at 2.5 cm using a triplex reel mower (TR330; Jacobsen Corporation, Racine, WI, USA) with clippings returned starting 5 weeks after planting and three times per week during the growing seasons in 2019 and 2020. Five-day running average daily air temperature during the study period was recorded by Stillwater Mesonet station.

A Baldree traffic simulator was created by modifying a self-propelled core aerifier (ProCore 648; The Toro Company, Bloomington, MN, USA). Coring heads of the aerifier unit were replaced by six spring-loaded metal plates fitted with screw-in plastic cleats of 1.27-cm size as described by Kowalewski et al. (2013). One traffic event created ∼678 cleat marks per square meter. Once plots had reached 100% visually assessed green cover, traffic was applied twice per day from Monday through Friday, totaling 10 traffic events per week from 16 Sep to 26 Oct in 2019 and from 7 Sep to 16 Oct in 2020, a total of 60 traffic events each year.

Data collection.

During the traffic period each year, data were taken weekly after traffic applications. Visual TQ was assessed using the National Turfgrass Evaluation Program (NTEP) rating scale, with 1 representing completely dead or dormant turfgrass, 9 being outstanding turfgrass, and 6 being minimally acceptable turfgrass (Morris and Shearman 2000). NDVI was measured using a handheld multispectral crop canopy sensor (CS-45 RapidSCAN; Holland Scientific, Lincoln, NE, USA) from ∼1 m in height. Images were collected using a digital camera (Powershot G5; Canon, Tokyo, Japan) mounted on a lightbox (Richardson et al. 2001). In spring, images were taken on 28 Mar 2020 and on 16 Apr 2021 for spring green-up evaluation when soil temperatures at 5 cm depth were 12.5 and 12.8 °C, respectively. The images were analyzed using ImageJ version 1.52a software (Wayne Rasband, National Institutes of Health, Bethesda, MD, USA) using a custom macro and the color threshold feature with hue, saturation, and brightness settings of 50 to 120, 0 to 250, and 120 to 230, respectively. The software counts the number of green pixels in the image and then divides the green pixel with the total pixel of the image to estimate the percent green cover (PGC). Hereafter, PGC is referred to as FPGC for fall and SGPGC for spring green-up. The measurements of TQ, NDVI, and FPGC represent the traffic tolerance.

A customized Turf-Tec SS tester (Turf-Tec International, Tallahassee, FL, USA) was used to measure SS. The SS tester was modified according to Canaway’s trolley-mounted rotational device to obtain consistent data (Canaway and Bell 1986). The Turf-Tec SS tester (3.4 kg) was loaded with additional weight (45.35 kg), resulting in a total weight of 48.75 kg for the apparatus. The cleated foot was used to obtain SS data. The SS values were obtained on all plots at three random locations using a 40° angle of rotation to obtain the maximum rotational traction (McNitt et al. 1997). SH was measured using a Clegg impact soil tester (Turf-Tec International, Tallahassee, FL, USA) with a 2.25-kg missile to obtain maximum deceleration (Gmax) on the impact at the surface due to gravity. As per American Society for Testing and Materials (ASTM) standards (ASTM 2010) with modification, a 2.25-kg missile dropped from 46 cm at four random spots within each plot was used to measure SH.

Data analysis.

Due to the limited computation power for SAS to analyze a large number of data collected during the study and consider the similar variation of data, only 3 weeks and 6 weeks of traffic data were subjected to statistical analysis. Analysis of variance (ANOVA) for TQ, NDVI, FPGC, SGPGC, SS, and SH was conducted under trafficked conditions using the MIXED procedure within SAS 9.4 (SAS Institute Inc., Cary, NC, USA) with repeated measurements. TYPE III method of moments estimation was selected for variance components estimate (Dong et al. 2015). The reliability (i2) for SGPGC was calculated by the equation adopted from Hallauer (1970): i2 = σG2/(σG2 + σGR2/R + σGY2/Y + σE2/RY) and the reliability (i2) for SGPGC and for TQ, NDVI, FPGC, SS, and SH was calculated by: i2 = σG2/(σG2 + σGY2/Y + σGR2/R + σGDY2/DY + σE2/RDY), where σG2 is the variance of entry, σGY2 is the variance of entry × year, σGR2 is the variance of entry × replication, σGDY2 is the variance of entry × date within the year, σE2 is the error variance, R is the number of replications, Y is the number of years, and D is the number of rating dates. Once significant (P < 0.05) entry-by-year and entry-by-date interactions were identified, means of entries were separated within each date using Fisher’s protected least significant difference test at the P = 0.05 significance level. Pearson correlation coefficients among TQ, NDVI, FPGC, SGPGC, SS, and SH were calculated using CORR procedure in SAS. TPI was calculated as the number of times (dates) each entry appeared in the top statistical group for each parameter across each date for both years (Engelke et al. 1995).

Results and Discussion

Traffic tolerance.

The ANOVA and estimation of variance components for TQ, NDVI, FPGC, are given in Table 1. Highly significant (P < 0.0001) effects were detected for all sources of variation except the year for TQ, NDVI, and FPGC and the effect of entry-by-date within year was not significant for NDVI. Highly significant (P < 0.0001) entry effects were detected for TQ, FPGC, and NDVI, indicating the genetic component played a significant role in traffic tolerance. The entry-by-year interactions were highly significant (P < 0.0001) for TQ, FPGC, and NDVI, suggesting that entries did not consistently respond to traffic stress over the 2 years. Highly significant (P < 0.0001) entry-by-date within year interactions were found for FPGC and TQ, indicating that entries responded differently at traffic and soil compaction levels since traffic and soil compaction together resulted in more injury to turfgrass (Jiang et al. 2003).

Table 1.

Variance components and reliability estimates of fall percent green cover (FPGC), turfgrass quality (TQ), normalized difference vegetation index (NDVI), shear strength (SS), surface hardness (SH), and spring green-up percent green cover (SGPGC) for 89 bermudagrasses under simulated trafficked conditions.

Table 1.

Basic statistics and boxplots of FPGC, TQ, and NDVI collected at 3 weeks and 6 weeks after initiating traffic treatment in 2019 and 2020 were reported in Figs. 13. At the end of the study in 2019, FPGC ranged from 18.8 to 45.6, TQ ranged from 2.0 to 6.0, and NDVI ranged from 0.38 to 0.57 (Table 2, Figs. 13). At the end of the study in 2020, FPGC ranged from 22.6 to 59.6, TQ ranged from 1.3 to 6.3, and NDVI ranged from 0.42 to 0.64 (Table 2, Figs. 13). After 6 weeks of traffic in both years, decreases in population means were observed in FPGC, TQ, and NDVI. Except for TQ, more severe decreases were observed in 2019 than in 2020. It is likely that the smaller reduction of FPGC and NDVI in 2020 were due to a more mature bermudagrass stand compared with 2019. However, in 2019, the population means of TQ, FPGC, and NDVI were numerically higher after 3 weeks of traffic compared with 2020. For FPGC, entries 17-4200-36X19, 17-5200-4X11, 18-8-2, 18-8-3, 18-8-7, and OSU1101 were ranked at the top statistical group in each rating according to the TPI (Table 2). For NDVI, entries 17-5200-3X23, 17-5200-4X11, 18-7-6, 18-8-2, 18-8-3, 18-9-2, OSU1101, and OSU1664 ranked at the top statistical group in each rating. Entries 17-4200-19X13, 17-4200-19X9, 18-7-2, OSU1101, and OSU1318 were ranked at the top statistical group for TQ in each rating.

Fig. 1.
Fig. 1.

Boxplot analysis of fall percent green cover. The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the fall percent green cover (%). Descriptive statistics are on top of each boxplot.

Citation: HortScience 59, 1; 10.21273/HORTSCI17488-23

Fig. 2.
Fig. 2.

Boxplot analysis of turfgrass quality. The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the turfgrass quality (1 to 9 scale). Descriptive statistics are on top of each boxplot.

Citation: HortScience 59, 1; 10.21273/HORTSCI17488-23

Fig. 3.
Fig. 3.

Boxplot analysis of normalized difference vegetation index (NDVI). The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the NDVI (0 to 1 scale). Descriptive statistics are on top of each boxplot.

Citation: HortScience 59, 1; 10.21273/HORTSCI17488-23

Table 2.

Mean turfgrass quality (TQ), fall percent green cover (FPGC), normalized difference vegetation index (NDVI), shear strength (SS), surface hardness (SH) of last rating of each year and turf performance index (TPI) of testing parameters of 89 bermudagrasses under simulated trafficked conditions.

Table 2.
Table 2.

TifTuf was the top standard performer regarding traffic tolerance under trafficked stress. The TPI of TifTuf for TQ, FPGC, and NDVI was 8, whereas the TPI of Tahoma 31 for the respective parameters was 2. Although TifTuf had the highest FPGC among all entries in 2019, experimental entries 17-4200-19X13, 17-4200-19X9, 17-4200-36X19, 17-5200-4X11, 18-7-2, 18-7-6, 18-8-2, 18-8-3, 18-8-7, 18-9-2, OSU1101, and OSU1664 showed improved consistent traffic tolerance regarding greater accumulated TPI through the rating in 2019 and 2020. One experimental entry, OSU1101, was the only entry with consistent top performance for FPGC, NDVI, and TQ in each rating, suggesting improvement in traffic tolerance.

Due to highly significant entry (P < 0.0001) effects, the reliability for FPGC, TQ, and NDVI under trafficked conditions was moderate to high (i2 = 0.49 to 0.61) (Table 1), indicating that 49% to 61% of the observed variation in traffic tolerance was attributed to the genetic factor under trafficked stress. Under nonstressed conditions, high broad-sense heritability of TQ was found for African bermudagrass (C. transvaalensis Burtt-Davy) (H2 = 0.74) (Kenworthy et al. 2006) and general appearance of common bermudagrass [C. dactylon (L.) Pers.] (H2 = 0.93) (Wofford and Baltensperger 1985). Under trafficked stress conditions, the TQ reliability of this population was i2 = 0.53, similar to TQ reliability (i2 = 0.61) reported by Yu et al. (2023) under drought stress conditions. The decline in reliability is likely due to entry-by-stress interactions. The reliability of NDVI (i2 = 0.49) was lower than the reliability of FPGC (i2 = 0.61). According to the model developed by Bell et al. (2002), NDVI captures variations of both color and percent live cover. It is likely that the lower NDVI reliability was due to the variance of color changes caused by traffic stress that were not captured by digital image analysis.

Besides investigating canopy response, several studies have focused on understanding the physiological response under traffic stress. For instance, Wei et al. (2022) found electrolyte leakage, malondialdehyde content, and free proline content significantly increased when bermudagrass was subjected to traffic stress. In Kentucky bluegrass (Poa pratensis L.), activities of antioxidant enzymes ascorbate peroxidase, catalase, and superoxide dismutase decrease after traffic stress (Pease et al. 2022). In addition, Pease et al. (2020) investigated the leaf anatomy of Kentucky bluegrass and observed differences in intercellular void space (IVS) between traffic-sensitive and traffic-tolerant cultivars, that cultivars with larger (IVS) are generally more traffic tolerant. However, using physiological and leaf anatomy traits is less desirable, as parameters in breeding and selecting traffic-tolerant cultivars due to measurements are time-consuming. Phenotypic recurrent selection in the most commonly used approach of most breeding programs and knowing the reliability of traffic tolerance is the foundation of developing traffic-tolerant bermudagrass. To enhance the selection, more research is needed to dissect traffic tolerance into more specific traits that can be used as an indirect selection factor and ultimately identify quantitative trait loci and develop molecular markers in selecting traffic-tolerant bermudagrass without testing under trafficked stress.

Shear strength and SH.

The ANOVA, estimation of variance components, and reliability for SS and SH are given in Table 1. For SS, effects of entry-by-replication, entry-by-year, and date within year were significant (P < 0.05) and the entry effect was highly significant (P < 0.0001), indicating genetic factor plays a major role in SS. Similarly, significant (P < 0.05) replication, date within year, entry-by-replication, and entry-by-year effects were found for SH. No significant entry effect was found for SH, suggesting SH was not controlled by genetic factors and efforts should focus on cultural practices to alleviate soil compaction.

Basic statistics and boxplots of SS collected at 3 weeks and 6 weeks in 2019 and 2020 are reported in Fig. 4. Traffic stress consistently decreased the SS each year, which is consistent with previous reports (Dunn et al. 1994). At the end of the study in 2019, SS ranged from 14.1 to 18.7 Nm and at the end of the study in 2020, SS ranged from 15.8 to 21.9 Nm. At the end of the study, the mean SSs in 2019 and 2020 were 16.8 and 19.0 Nm, respectively. On Kentucky bluegrass (P. pratensis L.) turfgrass, SS below 10 Nm may be considered unacceptable for sports turf (Stier et al. 1999). Later, Dickson et al. (2018) suggested that SS of hybrid bermudagrass should be at least 18 Nm to avoid poor surface-playing conditions at sports events, indicating the growing emphasis on safety standards for turf as well as the need for turfgrass cultivars with improved SS. In the current study, five entries, OSU1101, 18-9-1, 18-8-7, OSU1406, and OSU1675 had acceptable SS at the end of 6 weeks of traffic in the first year (Table 2). In the second year, only 13 entries had unacceptable SS (18 Nm) (Table 2). Entries OSU1101, 18-9-1, 18-8-7, OSU1406, and OSU1675 achieved acceptable SS shortly after establishment, showing potential to maintain acceptable SS shortly after resodding for sports events. Comparing SS data collected over 2 years, Tahoma 31, TifTuf, 18-8-7, OSU1101, and OSU1675 were consistently ranked at the top statistical group among all ratings. OSU1101 was the only experimental entry that showed numerically greater SS than Tahoma 31 and TifTuf. Generally, players wanted more traction than less traction (Aldous et al. 2005). On the contrary, turfgrass with excessive SS used on athletic fields could trap an athlete’s foot and may cause potential injury or in some cases have been associated with increased fatigue after events (Aldous et al. 2005).

Fig. 4.
Fig. 4.

Boxplot analysis of shear strength. The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the shear strength (Nm). Descriptive statistics are on top of each boxplot.

Citation: HortScience 59, 1; 10.21273/HORTSCI17488-23

There is a trend of increasing SH with more traffic stress applied, especially in 2020 (Fig. 5). In 2019, at the end of the study, SH ranged from 51.6 to 63.7 Gmax and in 2020, SH ranged from 64.9 to 99.2 Gmax. Because of the nonsignificant entry effect, the entry variance component estimation was negative and considered as zero. Therefore, the reliability for SH was zero and therefore, no TPI of SH was reported.

Fig. 5.
Fig. 5.

Boxplot analysis of surface hardness. The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the surface hardness (Gmax). Descriptive statistics are on top of each boxplot.

Citation: HortScience 59, 1; 10.21273/HORTSCI17488-23

Because of a highly significant (P < 0.0001) entry effect, the reliability of SS was i2 = 0.57. The moderate SS indicated the possibility of improving SS through recurrent selection. Several studies have reported that SS was controlled by turfgrass cultivars. Richardson et al. (2019) reported positive correlation among root length, root surface area, and root volume and the rotational resistance measurements of perennial ryegrass (Lolium perenne L.) and bermudagrass mixtures under shade conditions. Sancar et al. (2023) indicated that bermudagrass SS was positively correlated with shoot density. Segars et al. (2022) observed a difference in rhizome growth pattern (bunch-like vs. intermingling) exists in bermudagrass. It is likely the bermudagrass genotypes with intermingling patterns may provide more shear resistance compared with the bunch-like pattern genotypes. In addition, the dry weight of rhizome was positively correlated (r2 = 0.67) to sod tensile strength, a trait that may have similarities to SS (Segars et al. 2022). Rhizomes serve as the major carbohydrate storage organ of bermudagrass (Schiavon et al. 2016). In fall, bermudagrass genotypes with more carbohydrates stored in the rhizome may provide extra energy to recover from traffic stress while still maintaining great SS due to the significant correlations between traffic tolerance and SS identified in this study. There is a trend that the SS of bermudagrass decreased when subjected to more traffic events (Fig. 4), which may explain that more carbohydrate stored in the rhizome has been used to recover from traffic stress that decreases the SS. However, we are unaware of the relationship between SS and rhizome structures and productions of bermudagrass. Research is needed to understand whether rhizome structures and productions influence SS and information could be used as an indirect factor in developing and selecting high SS bermudagrass.

Spring green-up.

According to the ANOVA, highly significant (P < 0.0001) effects were found for all sources of variations except for entry-by-replication (Table 1). Because of a highly significant (P < 0.0001) entry effect, the entry effect variance component was the largest among all sources of variation and the reliability of SGPGC was high (i2 = 0.68). Basic statistics and boxplots of SGPGC collected on 28 Mar 2020 and on 16 Apr 2021 are reported in Fig. 6. In 2020, the SGPGC ranged from 22.4 to 85.7 and in 2021, SGPGC ranged from 9.7 to 67.9. Although SGPGC was collected when soil temperatures were above 12.5 °C, there was a decreased population mean SGPGC in 2021 due to a winter storm in Feb 2021 that delayed spring green-up (Fig. 7).

Fig. 6.
Fig. 6.

Boxplot analysis of spring green-up percent green cover. The x-axis is environment: 2020 and 2021. The y-axis represents the spring green-up percent green cover (%). Descriptive statistics are on top of each boxplot.

Citation: HortScience 59, 1; 10.21273/HORTSCI17488-23

Fig. 7.
Fig. 7.

Five-day running average daily air temperature (Tavg) during the experimental period. Data were obtained from the Oklahoma Mesonet – Stillwater site, located at Stillwater, OK, USA.

Citation: HortScience 59, 1; 10.21273/HORTSCI17488-23

TifTuf consistently had the highest SGPGC mean across 2 years (Table 2). Under nontrafficked conditions, TifTuf often has delayed spring green-up compared with cultivar Tahoma 31 (NTEP 2017). Wei et al. (2022) discovered that under trafficked stress, bermudagrass leaf soluble sugar contents significantly increased compared with the nontrafficked control. The study conducted by Schiavon et al. (2016) provides evidence for a correlation between the soluble sugar content in stolons and the winter survivability of TifTuf. These findings support the assumption that the translocation of sugars into stolons during late fall may contribute to the improved winter survivability of TifTuf.

In addition to standard cultivars Tahoma 31 and TifTuf, experimental entries Tilin#5, 18-9-8, OKC1221, OSU1257, OSU1318, OSU1337, OSU1406, OSU1439, OSU1651, and OSU1675 consistently ranked in the top statistical group in each year (Table 2). Combining all traffic tolerance, surface playability, and winter survivability parameters, the TPI of OSU1101 was 16 and the TPI of TifTuf, 17-4200-19X9, 18-8-2, and 18-8-7 was 14 (Table 2). With the exception of TifTuf, none of these entries achieved rankings in the top statistical groups for SGPGC. This suggests that, despite their consistent excellent performance in terms of traffic tolerance and surface playability, they could still be susceptible to winterkill when used in the transition zone.

Spring green-up is not traditionally considered a trait related to traffic tolerance and surface playability. However, bermudagrass on athletic fields can be susceptible to winterkill when used in the transition zone and winterkill could be worsened if subjected to traffic stress in the fall. It has been reported that bermudagrasses subjected to multiple stressors (e.g., drought stress) can exhibit delayed spring green-up in some entries (Yu et al. 2022, 2023). In the present study, the SGPGC reliability under traffic stress was i2 = 0.68, suggesting under current traffic stress, genetic factors control spring green-up phenotypic expression. Similar results were reported in common bermudagrass and African bermudagrass (Guo et al. 2017; Kenworthy et al. 2006; Stefaniak et al. 2009). The cold winter in 2020 presumably created more selection pressure on winter survivability, increasing the environmental variances (Yu et al. 2022). In the meantime, our results suggest that entries having good traffic tolerance and high SS did not have early spring green-up. Future bermudagrass improvement efforts could focus on combining traffic tolerance, SS, and early spring green-up.

Correlation analysis.

Because of highly significant (P < 0.0001) entry-by-year interaction, data were separated by year for Pearson correlation analysis. Highly positive correlations were found among traffic tolerance parameters (r = 0.83 to 0.94) (Table 3) in 2019 and (r = 0.72 to 0.82) (Table 4) 2020. Highly significant (P < 0.0001) correlations were found between SS and traffic tolerance parameters in both years. Consistent with TPI results, entries ranked high in traffic tolerance usually ranked at the top statistical group in SS, suggesting selection for high SS entries could use traffic tolerance as an indirect selection factor. It is understandable that SH was negatively correlated (P < 0.0001) with traffic tolerance parameters because traffic and soil compaction cause more severe injury to bermudagrass (Jiang et al. 2003). Except for SS and TQ, negative correlations were found between SS and traffic tolerance parameters in 2019. However, in 2020, negative correlations between SH and traffic tolerance parameters were highly significant (P < 0.0001), indicating the potential for soil compaction to increase over time with negative consequences for turf performance. Negative correlations were found between SS and SH, but the only highly significant (P < 0.0001) correlation was found in 2020, consistent with the observation of decreased SS with increased SH in 2020. Compared with the SH data collected in 2019 and 2020, SH from traffic stress is a driving factor influencing traffic tolerance in 2020 but not in 2019. Therefore, a long-term traffic stress study is warranted to understand the effect of excessive soil compaction on traffic tolerance and SS. No correlations were observed between traffic tolerance parameters and SGPGC besides TQ, suggesting traffic tolerance and winter survivability are independent traits. Based on the moderate to high reliability of traffic tolerance and winter survivability, it is possible to combine these two traits.

Table 3.

Pearson correlation coefficients between the traffic tolerance, surface playability, and winter survivability traits of 89 bermudagrass entries in 2019.

Table 3.
Table 4.

Pearson correlation coefficients between the traffic tolerance, surface playability, and winter survivability traits of 89 bermudagrass entries in 2020.

Table 4.

Conclusions

This is the first study reporting the genetic variability and reliability of SS, SH, TQ, and NDVI in response to simulated fall traffic. Results showed moderate to high reliability for traffic tolerance, SS, and SGPGC in bermudagrass. Moderate to high reliability indicated that bermudagrass traffic tolerance and SS are mostly controlled by genetic factors and improvement can be achieved through breeding and phenotypic selection. In addition, this study identified bermudagrass genotypes with improved traffic tolerance and SS that can be used in athletic fields or other highly trafficked areas, particularly those requiring early green-up for spring events.

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    • Export Citation
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  • Fig. 1.

    Boxplot analysis of fall percent green cover. The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the fall percent green cover (%). Descriptive statistics are on top of each boxplot.

  • Fig. 2.

    Boxplot analysis of turfgrass quality. The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the turfgrass quality (1 to 9 scale). Descriptive statistics are on top of each boxplot.

  • Fig. 3.

    Boxplot analysis of normalized difference vegetation index (NDVI). The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the NDVI (0 to 1 scale). Descriptive statistics are on top of each boxplot.

  • Fig. 4.

    Boxplot analysis of shear strength. The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the shear strength (Nm). Descriptive statistics are on top of each boxplot.

  • Fig. 5.

    Boxplot analysis of surface hardness. The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the surface hardness (Gmax). Descriptive statistics are on top of each boxplot.

  • Fig. 6.

    Boxplot analysis of spring green-up percent green cover. The x-axis is environment: 2020 and 2021. The y-axis represents the spring green-up percent green cover (%). Descriptive statistics are on top of each boxplot.

  • Fig. 7.

    Five-day running average daily air temperature (Tavg) during the experimental period. Data were obtained from the Oklahoma Mesonet – Stillwater site, located at Stillwater, OK, USA.

  • Aldous DE, Chivers IH, Kerr R. 2005. Player perceptions of Australian Football League grass surfaces. Int Turfgrass Soc Res J. 10:318326.

    • Search Google Scholar
    • Export Citation
  • ASTM. 2010. Annual book of ASTM standards. Vol. 15.07. End use products. Standard test methods for measuring impact-attenuation characteristics of natural playing surface systems using lightweight portable apparatus. ASTM, West Conshohocken, PA.

  • Beard JB. 1973. Turfgrass: Science and Culture. Prentice-Hall, NJ, USA.

  • Beard JB, Batten SM, Almodares A. 1981. An assessment of wear tolerance among bermudagrass cultivars for recreational and sports turf use. Progress Report-Texas Agricultural Experiment Station (USA).

  • Bell GE, Martin DL, Wiese SG, Dobson DD, Smith MW, Stone ML, Solie JB. 2002. Vehicle-mounted optical sensing: An objective means for evaluating turf quality. Crop Sci. 42:197201. https://doi.org/10.2135/cropsci2002.1970.

    • Search Google Scholar
    • Export Citation
  • Bernardo R. 2002. Breeding for quantitative traits in plants. Stemma Press, Woodbury, MN, USA.

  • Brosnan JT, Dickson KH, Sorochan JC, Thoms AW, Stier JC. 2014. Large crabgrass, white clover, and hybrid bermudagrass athletic field playing quality in response to simulated traffic. Crop Sci. 54:18381843. https://doi.org/10.2135/cropsci2013.11.0754.

    • Search Google Scholar
    • Export Citation
  • Canaway P, Bell MJ. 1986. An apparatus for measuring traction and friction on natural and artificial playing surfaces. J Sports Turf Res Inst. 62:211214.

    • Search Google Scholar
    • Export Citation
  • Carrow RN, Petrovic AM. 1992. Effects of traffic on turfgrasses, p 295–330. In: Waddington DV, Carrow RN, Shearman RC (eds). Turfgrass. Vol. 32. Agronomy Monograph. ASA, CSSA, and SSSA.

  • Christians NE. 2011. Fundamentals of turfgrass management (4th ed). John Wiley & Sons, Inc., Hoboken, NJ, USA.

  • DeBoer EJ, Karcher DE, McCalla JH, Richardson MD. 2020. Effect of late-fall wetting agent application on winter survival of ultradwarf bermudagrass putting greens. Crop Forage Turfgrass Manag. 6:e20035. https://doi.org/10.1002/cft2.20035.

    • Search Google Scholar
    • Export Citation
  • Dickson KH, Sorochan JC, Brosnan JT, Stier JC, Lee J, Strunk WD. 2018. Impact of soil water content on hybrid bermudagrass athletic fields. Crop Sci. 58:14161425. https://doi.org/10.2135/cropsci2017.10.0645.

    • Search Google Scholar
    • Export Citation
  • Dong H, Thames S, Liu L, Smith MW, Yan L, Wu YQ. 2015. QTL mapping for reproductive maturity in lowland switchgrass populations. BioEnergy Res. 8:19251937. https://doi.org/10.1007/s12155-015-9651-9.

    • Search Google Scholar
    • Export Citation
  • Dunn JH, Minner DD, Fresenburg BF, Bughrara SS. 1994. Bermudagrass and cool‐season turfgrass mixtures: Response to simulated traffic. Agron J. 86:1016. https://doi.org/10.2134/agronj1994.00021962008600010003x.

    • Search Google Scholar
    • Export Citation
  • Engelke MC, Lehman VG, Mays C, Colbaugh PF, Reinert JA, Knoop WE. 1995. ‘CATO’ creeping bentgrass - adaptation and potentials. Texas Turfgrass Research. Texas Agricultural Experimental Station, Texas AgriLife Extension Service, College Station, TX. PR-5113:3135.

    • Search Google Scholar
    • Export Citation
  • Goddard MJ, Sorochan JC, McElroy JS, Karcher DE, Landreth JW. 2008. The effects of crumb rubber topdressing on hybrid Kentucky bluegrass and bermudagrass athletic fields in the transition zone. Crop Sci. 48:20032009. https://doi.org/10.2135/cropsci2007.07.0405.

    • Search Google Scholar
    • Export Citation
  • Grimshaw AL, Qu Y, Meyer WA, Watkins E, Bonos SA. 2018. Heritability of simulated wear and traffic tolerance in three fine fescue species. HortScience. 53:416420. https://doi.org/10.21273/hortsci12450-17.

    • Search Google Scholar
    • Export Citation
  • Guo Y, Wu YQ, Moss JQ, Anderson JA, Zhu L. 2017. Genetic variability for adaptive, morphological, and reproductive traits in selected cold‐hardy germplasm of common bermudagrass. Crop Sci. 57:8288. https://doi.org/10.2135/cropsci2016.05.0369.

    • Search Google Scholar
    • Export Citation
  • Hallauer AR. 1970. Genetic variability for yield after four cycles of reciprocal recurrent selections in maize. Crop Sci. 10:482485. https://doi.org/10.2135/cropsci1970.0011183X001000050007x.

    • Search Google Scholar
    • Export Citation
  • Hallauer AR, Miranda JB. 1981. Quantitative genetics in maize breeding. Iowa State University Press, Ames, IA, USA.

  • Jiang Y, Carrow RN, Duncan RR. 2003. Effects of morning and afternoon shade in combination with traffic stress on seashore paspalum. HortScience. 38:12181222. https://doi.org/10.21273/HORTSCI.38.6.1218.

    • Search Google Scholar
    • Export Citation
  • Kenworthy KE, Taliaferro CM, Carver BF, Martin DL, Anderson JA, Bell GE. 2006. Genetic variation in Cynodon transvaalensis Burtt-Davy. Crop Sci. 46:23762381. https://doi.org/10.2135/cropsci2006.02.0075.

    • Search Google Scholar
    • Export Citation
  • Kowalewski AR, Schwartz BM, Grimshaw AL, Sullivan DG, Peake JB, Green TO, Clayton HM. 2013. Biophysical effects and ground force of the Baldree traffic simulator. Crop Sci. 53:22392244. https://doi.org/10.2135/cropsci2013.02.0118.

    • Search Google Scholar
    • Export Citation
  • Kowalewski AR, Schwartz BM, Grimshaw AL, Sullivan DG, Peake JB. 2015. Correlations between hybrid bermudagrass morphology and wear tolerance. HortTechnology. 25:725730. https://doi.org/10.21273/HORTTECH.25.6.725.

    • Search Google Scholar
    • Export Citation
  • McNitt AS, Middour RO, Waddington DV. 1997. Development and evaluation of a method to measure traction on turfgrass surfaces. J Test Eval. 13:99107.

    • Search Google Scholar
    • Export Citation
  • Micheli LJ, Glassman R, Klein M. 2000. The prevention of sports injuries in children. Clin Sports Med. 19:821834. https://doi.org/10.1016/S0278-5919(05)70239-8.

    • Search Google Scholar
    • Export Citation
  • Morris KN, Shearman RC. 2000. NTEP turfgrass evaluation guidelines. National Turfgrass Evaluation Program [Online]. http://www.ntep.org/pdf/ratings.pdf.

  • National Turfgrass Evaluation Program (NTEP). 2017. Progress Report NTEP No. 18-12. 2013. National Bermudagrass Test. National Turfgrass Evaluation Program. Beltsville, MD, USA.

  • Pease BW, Thoms AW, Arora R, Christians NE. 2020. Intercellular void space effects on Kentucky bluegrass traffic tolerance. Agron J. 112:34503455. https://doi.org/10.1002/agj2.20242.

    • Search Google Scholar
    • Export Citation
  • Pease BW, Thoms AW, Arora R, Christians NE. 2022. Antioxidant enzyme responses of Kentucky bluegrass to simulated athletic traffic stress. Int Turfgrass Soc Res J. 14:215224. https://doi.org/10.1002/its2.59.

    • Search Google Scholar
    • Export Citation
  • Richardson MD, Karcher DE, Purcell LC. 2001. Quantifying turfgrass cover using digital image analysis. Crop Sci. 41:18841888. https://doi.org/10.2135/cropsci2001.1884.

    • Search Google Scholar
    • Export Citation
  • Richardson MD, Mattina G, Sarno M, McCalla JH, Karcher DE, Thoms AW, Sorochan JC. 2019. Shade effects on overseeded bermudagrass athletic fields: II. Rooting, species composition, and traction. Crop Sci. 59:28562865. https://doi.org/10.2135/cropsci2019.05.0311.

    • Search Google Scholar
    • Export Citation
  • Rogers JN III, Vanini JT, Crum JR. 1998. Simulated traffic on turfgrass topdressed with crumb rubber. Agron J. 90:215221. https://doi.org/10.2134/agronj1998.00021962009000020017x.

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Shehbaz Singh Department of Horticulture and Landscape Architecture, Oklahoma State University, 358 Agricultural Hall, Stillwater, OK 74078, USA

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Shuhao Yu Department of Horticulture and Landscape Architecture, Oklahoma State University, 358 Agricultural Hall, Stillwater, OK 74078, USA

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Mingying Xiang Department of Horticulture and Landscape Architecture, Oklahoma State University, 358 Agricultural Hall, Stillwater, OK 74078, USA

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Charles H. Fontanier Department of Horticulture and Landscape Architecture, Oklahoma State University, 358 Agricultural Hall, Stillwater, OK 74078, USA

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Yanqi Wu Department of Plant and Soil Sciences, Oklahoma State University, 371 Agricultural Hall, Stillwater, OK 74078, USA

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Dennis L. Martin Department of Horticulture and Landscape Architecture, Oklahoma State University, 358 Agricultural Hall, Stillwater, OK 74078, USA

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Anmol Kajla Department of Horticulture and Landscape Architecture, Oklahoma State University, 358 Agricultural Hall, Stillwater, OK 74078, USA

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Contributor Notes

Funding for this project was provided in part by the Oklahoma Department of Agriculture, Food, and Forestry Specialty Crop Block Grant. We thank Bradley Battershell for his guidance in the construction of the Baldree traffic simulator.

S.Y. is the corresponding author. E-mail: shuhao.yu@okstate.edu.

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  • Fig. 1.

    Boxplot analysis of fall percent green cover. The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the fall percent green cover (%). Descriptive statistics are on top of each boxplot.

  • Fig. 2.

    Boxplot analysis of turfgrass quality. The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the turfgrass quality (1 to 9 scale). Descriptive statistics are on top of each boxplot.

  • Fig. 3.

    Boxplot analysis of normalized difference vegetation index (NDVI). The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the NDVI (0 to 1 scale). Descriptive statistics are on top of each boxplot.

  • Fig. 4.

    Boxplot analysis of shear strength. The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the shear strength (Nm). Descriptive statistics are on top of each boxplot.

  • Fig. 5.

    Boxplot analysis of surface hardness. The x-axis is environment: 2019_3T (3 weeks of 30 traffic events in 2019), 2019_6T (6 weeks of 60 traffic events in 2019), 2020_3T (3 weeks of 30 traffic events in 2020), and 2020_6T (6 weeks of 60 traffic events in 2020). The y-axis represents the surface hardness (Gmax). Descriptive statistics are on top of each boxplot.

  • Fig. 6.

    Boxplot analysis of spring green-up percent green cover. The x-axis is environment: 2020 and 2021. The y-axis represents the spring green-up percent green cover (%). Descriptive statistics are on top of each boxplot.

  • Fig. 7.

    Five-day running average daily air temperature (Tavg) during the experimental period. Data were obtained from the Oklahoma Mesonet – Stillwater site, located at Stillwater, OK, USA.

 

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