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42 results for “turfgrass”
Data from: Carbon accumulation of cool season sports turfgrass species in distinctive soil layers
<p>Carbon sequestered by turfgrasses may contribute to reducing atmospheric CO<sub>2 </sub>levels, to improved soil health and to increased turfgrass quality. Therfore in a field study conducted in the Netherlands, the amount of soil C accumulated by nine cool season turfgrass monocultures and 12 mixtures of turfgrass species during the first three years of establishment was analysed and compared. Thatch, mat and other soil layers and the layers were sampled and thickness of these layers was quantified. From these samples, dry matter, C and N concentrations, and CN ratio were measured.</p> <p>The study was conducted on a 3 years old turfgrass field of the turfgrass seed company DLF. The site was located in the Netherlands (51°32´N, 4°20´E), on a sandy soil (Hortic Anthrasol as described in the FAO/Unesco soil map of the world (2006)). The monocultures consisted of different varieties of the (sub)species <em>Lolium perenne (lp), Poa pratensis (Pp), Festuca arundinacea (Fa), Festuca rubra commutata (Frc), Festuca rubr trichophylla (Frt), Festuca rubra rubra (Frr), Festuca ovina duriuscala (Fod), Festuca ovina vulgaris (Fov), Agrostis stolonifera (As). </em>Varieties were treated as replicates per (sub)species, which resulted in some variation in the number of replicates, as not all species were available in the same number of varieties.<em> </em>Varieties of the<em> (s</em>ub)species and mixtures were on the market as commercial turfgrass seeds. </p> <p>In 2016 a soil profile sampler with a depth of 20 cm, a horizontal length of 10 cm and a width of 2 cm was used to take an undisturbed soil profile in each plot and the thickness of each layer, thatch, matt and remainder soil, was measured using the protocol as described in Evers et al. (2024). Plant biomass in the plots was quantified by taking cores of the top 20 cm of the soil with a core sampler (diameter 28 mm). Cores were divided into thatch, mat, the remainder soil till 10 cm depth, and 10-20 cm depth, respectively, based on the earlier measurement of layer thicknesses in the field. Sediment of each section was then carefully washed out with tap water, after which the remaining below-ground (dead and living) plant biomass was dried at 65°C until stable weight and weighed. Total C and N analyses were carried out at the General Instrumentation Department of Radboud University with a Vario Micro Cube Element Analyzer (Elementar, Langenselbold, Germany), from which C and N concentrations (in % of dry matter or in mg cm<sup>-3</sup> C from total plant biomass in a layer) and CN ratios were calculated.</p> <p>Statistical analyses were carried out using the open source program R version 3.5.2 (2018-12-20). Differences in thickness of thatch and mat as well as differences in the C accumulation and C- and N concentration in thatch, mat and soil layers between (sub)species of turfgrasses in were based on the calculated means per species. Normality of residuals and the equality of variances was checked with diagnostic plots and Levene’s test, respectively. Non-normal and heteroscedastic data were either log transformed in linear models from the car package, or general least square (gls) models using varIdent from the nlme package were used. All data were further analyzed with ANOVA-type3 from the car package, followed by the Tukey post hoc test of the emeans package. Correlations between thatch and mat thickness were analyzed with linear regression models in R of the ggplot package. Similar procedures were performed for correlation between thatch, mat or soil thickness and C accumulation as well as for the correlation between C concentration and N concentration on C accumulation in a particular layer.</p>
Data from: Validating a practical methodology for thatch - mat - soil distinction in turfgrass soils
<p><span>We described a <a name="_Hlk163223211"></a>practical method for the distinction of thatch, mat and soil layers in turfgrass soils, being a combination of visually and manually observable characteristics. For two widely different turfgrass species, we analyzed total organic matter (TOM) and dry bulk density (ρd) in thin slices of 6 mm (between 0 and 10 cm soil depth), resulting in clear patterns for both soil properties with increasing depth. Statistical analysis of TOM patterns resulted in similar boundary depths between calculated and observed layers, validating our practical method for the distinction of thatch, mat and soil layers as a reliable method. </span><span>Furthermore, we characterized thatch, mat and soil layer by different TOM fractions. <span>TOM was fractionalized into three distinctive and functional pools of organic matter: (1) visible organic matter (VOM), consisting of mainly non-decomposed plant structures, (2) decomposed organic matter (DOM), consisting of mainly decomposed plant structures with its associated microbial biomass, and (3) soil organic matter (SOM), being the background value or recalcitrant native organic matter in a soil and its local microbial biomass.</span></span></p> <p><span><span>We distinguished thatch, mat and soil layer based on visual and manual observable characteristics of the layers and a protocol as described in Evers et al. (2024) https://doi.org/10.1002/its2.148. This study was conducted on a well-established turfgrass demonstration field with monoculture plots of turfgrass varieties (turfgrass seed company DLF; Moerstraten, the Netherlands; 51°32'27'' N 4°20'54" E) 3.5 years after sowing, reflecting the result of organic matter accumulation over these initial years of turfgrass establishment. The climate regime was marine with cool to medium summer temperatures and mild winters (Cfb/Cfa according to the Köppen-Geiger climate classification system (Peel, et al., 2007)). The field was built on a sandy soil (Hortic Anthrasol as described in the FAO/UNESCO soil map of the world (2006)). Sampling of the soil took place in June 2016 before the field was sown. We tested our methodology with two turfgrass species, slender creeping red fescue (<em>Festuca rubra trichophylla </em>(Frt), variety Beudin of DLF) <span>as an example of a spreading turfgrass</span><em> </em><span>and perennial ryegrass (<em>Lolium perenne </em></span>(Lp)<em>,</em> variety Duparc of DLF) as an example of a bunch-type grass, as these two species were expected to differ widely in thatch and mat depth. The individual plot size for each variety was approximately 1 m<sup>2</sup> (0.8 x 1.2 m). Plots of each species were sampled at the end of February 2020. A subplot of 0.25 m<sup>2</sup> (0.5 m x 0.5 m) in the center of one plot per species was selected, to avoid contamination with other varieties (at least 90% pure monoculture), and it was marked with a metal frame. From the 25 cells of 5 x 5 cm in this frame, nine evenly dispersed cells were chosen to take a set of soil samples of 10 cm depth, using a core sampler with 2.8 cm diameter, for thatch-mat and mat-deeper soil boundary observation and TOM and <em>ρ</em><sub>d</sub> analyses. A<span>nother set of nine samples was taken next to the previous cells for VOM analyses. </span>Every fresh 10 cm soil core was first photographed (Canon Powershot S5 camera, 24 megapixels; Canon Europe, Amstelveen, the Netherlands), judged on thatch-mat and mat-deeper soil boundaries following the method described in supplementary information, and then sliced into 14 subsamples of 6 mm each plus a 16 mm subsample at the bottom, starting to measure just below the green canopy with an accurate ruler (Sola HK ¼ W12, EU-accuracy class 3), for analyzing TOM and <em>ρ</em><sub>d</sub>.in every subsample. TOM and <em>ρ</em><sub>d</sub> were analyzed after samples were dried at 105°C for 24 h. VOM was analyzed after a<span>ll sediment per slice was carefully washed off with tap water in a fine sieve (approximately 600 µm (27 mesh)), after which the remaining (dead and living) plant biomass, mainly roots and rhizomes, was dried at 65 °C for at least 48 h. SOM was determined separately in the bulk soil of the study site and is 2.6% of the dry matter. DOM was calculated via subtraction of VOM and SOM from TOM</span></span></span></p> <p><span>Based on the analyzed TOM content of the 14 slices per nine replicates, the boundaries of distinctive layers were calculated. For this, we rescaled the TOM results per replicate via the normalized function <em>F </em>(χ) = (χ-χ<sub>min</sub>)/(χ<sub>max</sub>-χ<sub>min</sub>) into values between 0 and 1 to overcome scale differences between replicates but keeping distributions the same. Per turfgrass species, the best fitted line, i.e., the smallest <em>rse</em>, and its 95%-confidence interval through all 135 points was iteratively calculated by non-linear least square regression with the nls function of R (version 3.5.2; 2018-12-20). For the fitting of the statistical models, either a logistic function (<em>F </em>(χ) = α/(1+e<sup>-(βχ+γ)</sup>) or a bell-shaped Gaussian function (<em>G </em>(χ) = αe<sup>-((χ-β)^2/2γ^2)</sup>) was used, based on the best fit for the respective turfgrass species. The characteristics of these mathematical functions were used to explain the TOM-dynamics in the soil. To this end, the turning points of the curves were calculated by finding where the first derivative of both functions equals zero, i.e., solving <em>F’</em> (χ) = 0 and <em>G’ </em>(χ) = 0 respectively. The inflection points of each curve were determined by analyzing the second derivative, i.e., identifying the points where the second derivative <em>F’’</em> (χ) or <em>G’’</em> (χ) = 0, indicating changes in concavity. The inflection points and turning points indicated changes in TOM content in the turfgrass soil profile as likely boundaries between distinctive soil layers. </span><span>Differences in parameters between distinctive soil layers and turfgrass species were determined based on the calculated means of parameters per nine replicates of soil slices Normality of residuals and the equality of variances was checked with diagnostic plots and Levene’s test, respectively. Normally distributed means were compared with one-way ANOVA for a 3-layered soil system, followed by either Tukey post hoc tests in case of equality of variance, or by the Games-Howell post hoc test in case of no equality of variance, or with a one sample t-test for a 2-layered soil system. </span></p>
Roadside turfgrass seed mixtures: models and figures
<div> <div> <div> <div> <p>Roadsides in urban areas are often seeded with turfgrass mixtures to provide ground cover and reduce weed abundance. Designing mixtures to withstand exposure to biotic and abiotic stress is challenging. Research from managed and natural ecosystems have shown that increasing plant species richness and diversity can increase groundcover and suppress weed cover, but it is unclear whether such relationships hold in roadside environments. Our objective was to determine the effect of seeded turfgrass species richness on ground cover and weed suppression alongside roadsides in diverse regions in Minnesota. We tested six turfgrass species in monocultures, two-way mixtures, some three-way mixtures, and a single six-way mixture at seven sites seeded in the fall of 2018, and seven sites seeded in the fall of 2019. Seeded turfgrass, weed, and bare soil coverage was measured at each site over two growing seasons. There was a positive relationship between turfgrass species richness and turfgrass cover, and this interaction effect increased over time. We found that increasing turfgrass species richness reduced bare soil coverage. Turfgrass cover was also more consistent across research sites (i.e., greater spatial stability) with increasing species richness. Our results show that positive relationships between plant species richness and groundcover hold in highly disturbed and managed roadside environments. These findings can improve the design of seed mixtures for roadsides and in other ecological contexts where vegetative cover is important.</p> </div> </div> </div> </div>
Synthetic Turfgrass Dataset
<p><em>Synthetic Turfgrass Dataset created in blender, top view of a golf course surface.</em></p> <p> </p> <table> <tbody> <tr> <td><strong>Image Type</strong></td> <td><strong>Folder name</strong></td> <td><strong>Count</strong></td> </tr> <tr> <td>RGB Training Images</td> <td><strong>Images</strong></td> <td>6510</td> </tr> <tr> <td> <p>Mono 8bit Annotation images:</p> <p>- Class grass, Value :30</p> <p>- Class divot anomaly, Value: 10</p> </td> <td><strong>ImageLables</strong></td> <td>6510</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Turfgrass Divot Dataset (Synthetic ) for divot detection object detection system
<p>The dataset provided below has been synthetically created using Blender. A fundamental analysis on this data was conducted utilizing the YOLO V3 object detection technique to identify divots or areas of damage.</p> <p>Used for paper: </p> <p>Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection</p> <p>https://github.com/stevefoy/Turfgrass-Divot-Object-Detection</p> <pre><span>@inproceedings</span>{<span>IMVIP2024</span>, <span>author</span> = <span><span>{</span>Stephen Foy and Simon McLoughlin<span>}</span></span>, <span>title</span> = <span><span>{</span>Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection<span>}</span></span>, <span>booktitle</span> = <span><span>{</span>Irish Machine Vision and Image Processing Conference (IMVIP)<span>}</span></span>, <span>year</span> = <span><span>{</span>2024<span>}</span></span> }</pre> <p><strong>Contents of the Zip File</strong>:</p> <ul> <li> <p><strong>synthDivot_416x416 Folder</strong>:</p> <ul> <li>Train and validation subfolders</li> <li>1200 RGB PNG images</li> <li>Corresponding masks for each image</li> <li>Bounding box data in YOLO <code>.txt</code> format</li> </ul> </li> <li> <p><strong>synthDivot_608x608 Folder</strong>:</p> <ul> <li>Train and validation subfolders</li> <li>1200 RGB PNG images</li> <li>Bounding box data in YOLO <code>.txt</code> format</li> </ul> </li> </ul> <p> </p> <p> </p>
Roadside turfgrass seed mixtures: models and figures
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Canopy Interception and Throughfall of Turfgrass
<p>Datasets including precipitation (rain_mm) and throughfall (rain_zoy and rain_bent) for two turfgrass species.</p> <p>Example python notebook for filtering data is included based on date range.</p>
Data from: Development of microsatellite markers for buffalograss (Buchloë dactyloides; Poaceae), a drought-tolerant turfgrass alternative
Premise of the study: Buchloë dactyloides is an important component of Great Plains prairies and a popular drought-tolerant turfgrass alternative in North America. This species comprises an autopolyploid series, and microsatellite primers were developed in order to understand the distribution of genetic variation among cytotypes and across its large geographic range. Methods and Results: Fifteen microsatellite loci were designed and successfully amplified in six B. dactyloides populations. Within-population genetic diversity was comparatively high, consistent with B. dactyloides' life history. Allelic variation at 13 loci was consistent with cytotype established in chromosome-counted samples. Conclusions: This variable, interpretable set of loci allows for the determination of multi-locus genotype in B. dactyloides individuals of varying cytotype. Data such as these from a range-wide sample set can provide important insights for germplasm conservation and crop improvement in this ecologically and economically important species.
FIGURE 7 in Taxonomy and morphology of plant-parasitic nematodes associated with turfgrasses in North and South Carolina, USA
FIGURE 7. Micrographs of Hemicycliophora thienemanni, H. conida, Hemicaloosia graminis from turfgrasses in NC and SC. All scale bars =20µm. A. Female esophageal region of H. thienemanni. B. Vulva region of H. thienemanni. C. Female tail of H. thienemanni. D. Female esophageal region of H. conida. E,F. Vulva region of H. conida. G. Female esophageal region of Hemicaloosia graminis. H. Vulva region of Hemicaloosia graminis. I. Female tail of Hemicaloosia graminis. J. Male esophageal region of Hemicaloosia graminis. K. Male tail of Hemicaloosia graminis.
FIGURE 4 in Taxonomy and morphology of plant-parasitic nematodes associated with turfgrasses in North and South Carolina, USA
FIGURE 4. Micrographs of Belonolaimus longicaudatus and Dolichodorus heterocephalus from turfgrasses in NC and SC. All scale bars=20µm. A. Pharyngeal region of B. longicaudatus. B. Vulva region of B. longicaudatus. C. Female tail of B. longicaudatus. D,E. Male tails of B. longicaudatus. F. Pharyngeal region of D. heterocephalus. G. Vulval region of D. heterocephalus. H. Female tail of D. heterocephalus. I. Male tails of D. heterocephalus.
FIGURE 1 in Taxonomy and morphology of plant-parasitic nematodes associated with turfgrasses in North and South Carolina, USA
FIGURE 1. Micrographs of Pratylenchus penetrans, Tylenchorhynchus claytoni and Filenchus cylindricus from turfgrasses in NC and SC. Scale bars: A, B, J=50 μm; C-I, K-M=20μm. A,B. Entire body of P. penetrans. C. Pharyngeal region of P. penetrans. D,E. Female tails of P. penetrans. F. Male tail of P. penetrans. G. Pharyngeal region of T. claytoni. H. Female tail of T. claytoni. I. Male tail of T. claytoni. J. Entire body of F. cylindricus. K. Pharyngeal region of F. cylindricus. L. Vulval region of F. cylindricus. M. Female tail of F. cylindricus.
FIGURE 6 in Taxonomy and morphology of plant-parasitic nematodes associated with turfgrasses in North and South Carolina, USA
FIGURE 6. Micrographs of Hemicriconemoides chitwoodi, H. wessoni, Paratylenchus goldeni and Aphelenchoides myceliophagus from turfgrasses in NC and SC. Scale bars: A, D=50µm; B, C, E–M=20µm. A. Entire body of H. chitwoodi. B. Pharyngeal region of H. chitwoodi. C. Vulva and tail region of H. chitwoodi. D. Entire body of H. wessoni. E. Pharyngeal region of H. wessoni. F. Vulva and tail region of H. wessoni. G. Pharyngeal region of P. g o l d e n i. H. Vulva and tail region of P. goldeni. I. Pharyngeal region of A. myceliophagus. J. Vulval region of A. myceliophagus. K. Pharyngeal region of A. myceliophagus. L. Female tail of A. myceliophagus. M. Male tail of A. myceliophagus.
FIGURE 3 in Taxonomy and morphology of plant-parasitic nematodes associated with turfgrasses in North and South Carolina, USA
FIGURE 3. Micrographs of J2 of Meloidogyne graminis, M. naasi, Heterodera sp. and Cactodera sp. from turfgrasses in NC and SC. Scale bars: A, E, I, L=50µm; B–D, F–H, J, K, M–O=20µm. A. Entire body of M. graminis. B. Pharyngeal region of M. graminis. C,D. Tail of M. graminis. E. Entire body of M. naasi. F. Pharyngeal region of M. naasi. G,H. Tails of M. naasi. I. Entire body of H. sp. J. Pharyngeal region of H. sp. K. Tail of H. sp. L. Entire body of C. sp. M. Pharyngeal region of C. sp. N,O. Tails of C. sp.
FIGURE 2 in Taxonomy and morphology of plant-parasitic nematodes associated with turfgrasses in North and South Carolina, USA
FIGURE 2. Micrographs of Hoplolaimus galeatus, Scutellonema brachyurum and Helicotylenchus dihystera from turfgrasses in NC and SC. All scale bars =20 μm. A. Pharyngeal region of H. galeatus. B. Vulval region of H. galeatus. C. Female tail of H. galeatus. D,E. Male tails of H. galeatus. F. Pharyngeal region of S. brachyurum. G. Vulval region of S. brachyurum. H. Female tail of S. brachyurum. I. Pharyngeal region of H. dihystera. J. Vulva and tail region of H. dihystera.
FIGURE 5 in Taxonomy and morphology of plant-parasitic nematodes associated with turfgrasses in North and South Carolina, USA
FIGURE 5. Micrographs of Mesocriconema curvatum, M. xenoplax, M. sphaerocephala and Ogma floridense from turfgrasses in NC and SC. All scale bars=20µm. A. Pharyngeal region of M. curvatum. B. Pharyngeal region of M. xenoplax. C. Pharyngeal region of M. sphaerocephala. D,G. Female tail of M. xenoplax. E. Pharyngeal region of O. floridense. F. Female tail of M. curvatum. H. Female tail of M. sphaerocephala. I. Body with anastomoses of M. sphaerocephala. J. Vulva and tail region of O. floridense.
FIGURE 9 in Taxonomy and morphology of plant-parasitic nematodes associated with turfgrasses in North and South Carolina, USA
FIGURE 9. Micrographs of Paratrichodorus allius, Paratrichodorus minor from turfgrasses in NC and SC (Scale bars: A=100µm; B-F= 20µm). A. Female entire body of P. allius. B. Female esophageal region of P. allius. C. Female tail region of P. allius. D. Female esophageal region of P. m i n o r. E. Vulva and tail region of P. m i n o r. F. Female tail region of P. m i n o r.
FIGURE 8 in Taxonomy and morphology of plant-parasitic nematodes associated with turfgrasses in North and South Carolina, USA
FIGURE 8. Micrographs of Xiphinema americana, X. bakeri, X. chambersi, Longidorus paralongicaudatus from turfgrasses in NC and SC. All scale bars =20µm. A. Female esophageal region of X. americana. B. Vulva region of X. americana. C,H. Female esophageal region of X. bakeri. D. Vulva region of X. bakeri. E. Female esophageal region of X. chambersi. F. Vulva region of X. chambersi. G. Female tail region of X. americana. I. Female tail region of X. bakeri. J. Female tail region of X. chambersi. K. Female esophageal region of L. paralongicaudatus. L. Vulva region of L. paralongicaudatus. M,N. Female tail region of L. paralongicaudatus.
Fig. 1 in New Turfgrass Host Associations and Regional Detections for the Flea Beetle Chaetocnema minuta Melsheimer (Coleoptera: Chrysomelidae) in the Southwestern United States
Fig. 1. Adult and larva of Chaetocnema minuta isolated from damaged creeping bentgrass in southern Utah (photo: A. Van DYke).
An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small‐plot turfgrass research
<p>Collection of multispectral imagery from an aerial sensor is a means to obtain plot-level vegetation index (VI) values; however, post-capture image processing and analysis remain a challenge for small-plot researchers. An ArcGIS Pro workflow of two task items was developed with established routines and commands to extract plot-level VI values (Normalized Difference VI, Ratio VI, and Chlorophyll Index-Red Edge) from multispectral aerial imagery of small-plot turfgrass experiments. Users can access and download task item(s) from the ArcGIS Online platform for use in ArcGIS Pro. The workflow standardizes the processing of aerial imagery to ensure repeatability between sampling dates and across site locations. A guided workflow saves time with assigned commands, ultimately allowing users to obtain a table with plot descriptions and index values within a .csv file for statistical analysis. The workflow was used to analyze aerial imagery from a small-plot turfgrass research study evaluating herbicide effects on St. Augustinegrass [<em>Stenotaphrum secundatum</em> (Walt.) Kuntze] grow-in. To compare methods, index values were extracted from the same aerial imagery by TurfScout, LLC and were obtained by handheld sensor. Index values from the three methods were correlated with visual percentage cover to determine the sensitivity (i.e., the ability to detect differences) of the different methodologies.</p>
Data from: Development of microsatellite markers for buffalograss (Buchloë dactyloides; Poaceae), a drought-tolerant turfgrass alternative
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