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Advances in Sustainable Turfgrass Management

A special issue of Grasses (ISSN 2813-3463).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 9850

Special Issue Editors


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Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
Interests: agriculture; artificial intelligence; crop protection; remote-sensing
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Special Issue Information

Dear Colleagues,

Green areas such as gardens, parks, sport facilities, and farmhouses play a key role within urban, peri-urban, and recreational–rural contexts. The presence of plants can mitigate the negative anthropic impact and give to citizens comfortable areas where they can spend their free time and relax. Turfgrass management is the major issue in green areas’ maintenance; thus, there is increasing interest in the definition and application of sustainable strategies for this task. Challenges around this topic are, for instance, exhaust gas local emission and noise pollution reduction, together with the precise application of inputs (fertilizers, herbicides, and so on). Turfgrass care can also be improved using innovative smart and autonomous technologies for mowing, watering, pest control, fertilization, and many other operations.

In this Special Issue, all contributions regarding innovative management strategies, technologies, machines, and products as well as new cultivars for turfgrasses’ sustainable management are welcome, including applications in sport fields, municipalities, and sub-urban areas. We hence invite experts and researchers who can provide relevant original research, reviews, and opinion pieces on the topics of this Special Issue.

Dr. Marco Fontanelli
Dr. Mino Sportelli
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Grasses is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • precise turfgrass management (PTM)
  • GHG reduction
  • non-chemical weed control
  • UAV and/or UTV for turf care
  • sustainable pest management in turf
  • precise input application
  • use of biostimulants
  • use of innovative turf species

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Published Papers (5 papers)

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Research

Jump to: Review

9 pages, 255 KiB  
Communication
Urban Landscapes: Turfgrass Benefits
by Alex J. Lindsey, Marco Schiavon, J. Bryan Unruh and Kevin Kenworthy
Grasses 2025, 4(1), 3; https://doi.org/10.3390/grasses4010003 - 7 Jan 2025
Viewed by 567
Abstract
Recently, turfgrass has received scrutiny from the public in many parts of the United States due to the misconception that it has limited benefits and has negative impacts on the environment. These negative impacts are often associated with water and chemical usage during [...] Read more.
Recently, turfgrass has received scrutiny from the public in many parts of the United States due to the misconception that it has limited benefits and has negative impacts on the environment. These negative impacts are often associated with water and chemical usage during turfgrass maintenance. Even with these ill-advised concerns, turfgrass remains an important component of urban landscapes. Contrary to public opinion, turfgrass has numerous environmental, ecological, economical, social, and societal benefits. This review paper summarizes and highlights the benefits of turfgrass systems. Full article
(This article belongs to the Special Issue Advances in Sustainable Turfgrass Management)
19 pages, 2468 KiB  
Article
Water Conservation Practices and Nitrogen Fertility for the Reduction of Greenhouse Gas Emissions from Creeping Bentgrass Putting Greens
by Kristina S. Walker and Katy E. Chapman
Grasses 2024, 3(3), 221-239; https://doi.org/10.3390/grasses3030016 - 18 Sep 2024
Viewed by 774
Abstract
Irrigation practices that conserve water use have the potential to reduce greenhouse gas (GHG) emissions but may adversely affect turfgrass appearance. The purpose of this study was to identify irrigation practices and N fertilizers that will decrease carbon dioxide (CO2), methane [...] Read more.
Irrigation practices that conserve water use have the potential to reduce greenhouse gas (GHG) emissions but may adversely affect turfgrass appearance. The purpose of this study was to identify irrigation practices and N fertilizers that will decrease carbon dioxide (CO2), methane (CH4,), and nitrous oxide (N2O) emissions while evaluating turfgrass color and quality. In both years, supplemental rainfall (SRF) soil moisture content was higher than business as usual (BAU) irrigation and syringing (SYR). Higher soil moisture led to increased fluxes in both soil CO2 and soil N2O. In 2017, the SRF fluxed lower soil CO2 as soil moisture reached levels that restricted respiration. Soil moisture was also an important predictor of soil N2O flux with BAU and SRF having higher soil N2O fluxes. SRF produced the greenest turf from May to July, whereas SRY and SRF produced the greenest turf from August to October in 2016. Both BAU and SRF had the greenest turf in 2017. BAU had the highest turfgrass quality ratings in 2016 followed by SRF and SRY, respectively, whereas in 2017 SRF and SRY had higher turfgrass quality ratings. When adopting water conservation practices to reduce GHG emissions, soil moisture content and site-specific rainfall should be closely monitored to prevent overwatering. Full article
(This article belongs to the Special Issue Advances in Sustainable Turfgrass Management)
Show Figures

Figure 1

Figure 1
<p>Total rainfall (mm) and mean air temperatures (°C) during the growing season (May–October) of 2016 and 2017. Total rainfall is indicated by a bar chart (left axis) and mean air temperature is indicated by a line graph (right axis). Weather data were collected by the Grand Forks International Airport (Grand Forks, ND, USA).</p>
Full article ">Figure 2
<p>Canopy temperature by irrigation regime (SRF = supplemental rainfall, SYR = syringing, and BAU = business as usual) in 2016 (<b>a</b>) and 2017 (<b>b</b>). * Means are significantly different at the 0.05 level according to Fisher’s protected LSD <span class="html-italic">t</span>-test.</p>
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<p>Soil moisture content (%) by irrigation treatment in the 2016 (<b>a</b>) and 2017 (<b>b</b>) growing seasons. BAU, business as usual; SRF, supplemental rainfall; SYR, syringing.</p>
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<p>Carbon dioxide (CO<sub>2</sub>) emissions by fertilizer source in 2016 (<b>a</b>) and 2017 (<b>b</b>). Notes: a = urea &gt; unfertilized control; b = milorganite and urea &gt; unfertilized control; c = milorganite &gt; urea; d = milorganite &gt; urea and unfertilized control; e = milorganite &gt; unfertilized control. Means are significantly different at the 0.05 level according to Fisher’s protected LSD <span class="html-italic">t</span>-test.</p>
Full article ">Figure 5
<p>Carbon dioxide (CO<sub>2</sub>) emissions by fertilizer rate in 2016 (<b>a</b>) and 2017 (<b>b</b>). Notes: a = 147 kg N ha<sup>−1</sup> yr<sup>−1</sup> &gt; unfertilized control (0 kg N ha<sup>−1</sup> yr<sup>−1</sup>); b = 294 kg N ha<sup>−1</sup> yr<sup>−1</sup> &gt; unfertilized control (0 kg N ha<sup>−1</sup> yr<sup>−1</sup>); c = 147 and 294 kg N ha<sup>−1</sup> yr<sup>−1</sup> &gt; unfertilized control (0 kg N ha<sup>−1</sup> yr<sup>−1</sup>); d = 294 kg N ha<sup>−1</sup> yr<sup>−1</sup> &gt; 147 kg N ha<sup>−1</sup> yr<sup>−1</sup>; e = 294 kg N ha<sup>−1</sup> yr<sup>−1</sup> &gt; 147 kg N ha<sup>−1</sup> yr<sup>−1</sup> and unfertilized control (0 kg N ha<sup>−1</sup> yr<sup>−1</sup>). Means are significantly different at the 0.05 level according to Fisher’s protected LSD <span class="html-italic">t</span>-test.</p>
Full article ">Figure 6
<p>Carbon dioxide (CO<sub>2</sub>) emissions by irrigation regime in 2016 (<b>a</b>) and 2017 (<b>b</b>). Notes: a = SYR &gt; SRF &gt; BAU; b = SYR and SRF &gt; BAU; c = BAU &gt; SRF; d = SRF &gt; SYR &gt; BAU; e = SRF &gt; SYR &gt; BAU; f = SRF &gt; BAU; g = BAU and SRF &gt; SYR; h = SRF &gt; SYR and BAU; i = BAU &gt; SRF and SYR; j = BAU &gt; SRF; k = SRF &gt; BAU &gt; SYR; l = BAU &gt; SYR &gt; SRF; m = BAU and SYR &gt; SRF; n = SYR &gt; BAU; o = SYR &gt; SRF. Means are significantly different at the 0.05 level according to Fisher’s protected LSD <span class="html-italic">t</span>-test.</p>
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<p>Nitrous oxide (N<sub>2</sub>O) emissions by fertilizer source in 2016 (<b>a</b>) and 2017 (<b>b</b>). Notes: a = milorganite &gt; urea; b = milorganite and urea &gt; unfertilized control; c = milorganite &gt; unfertilized control; d = milorganite &gt; urea &gt; unfertilized control; e = urea &gt; unfertilized control; f = urea &gt; milorganite and unfertilized control. Means are significantly different at the 0.05 level according to Fisher’s protected LSD <span class="html-italic">t</span>-test.</p>
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<p>Nitrous oxide (N<sub>2</sub>O) emissions by fertilizer rate in 2016 (<b>a</b>) and 2017 (<b>b</b>). Notes: a = 294 kg N ha<sup>−1</sup> yr<sup>−1</sup> &gt; 147 kg N ha<sup>−1</sup> yr<sup>−1</sup>; b = 294 kg N ha<sup>−1</sup> yr<sup>−1</sup> and 147 kg N ha<sup>−1</sup> yr<sup>−1</sup> &gt; unfertilized control (0 kg N ha<sup>−1</sup> yr<sup>−1</sup>); c = 294 kg N ha<sup>−1</sup> yr<sup>−1</sup> &gt; 147 kg N ha<sup>−1</sup> yr<sup>−1</sup> &gt; unfertilized control (0 kg N ha<sup>−1</sup> yr<sup>−1</sup>); d = 294 kg N ha<sup>−1</sup> yr<sup>−1</sup> &gt; unfertilized control (0 kg N ha<sup>−1</sup> yr<sup>−1</sup>); e = 294 kg N ha<sup>−1</sup> yr<sup>−1</sup> &gt; 147 kg N ha<sup>−1</sup> yr<sup>−1</sup> and unfertilized control (0 kg N ha<sup>−1</sup> yr<sup>−1</sup>). Means are significantly different at the 0.05 level according to Fisher’s protected LSD <span class="html-italic">t</span>-test.</p>
Full article ">Figure 9
<p>Nitrous oxide (N<sub>2</sub>O) emissions by irrigation regime in 2016 (<b>a</b>) and 2017 (<b>b</b>). Notes: a = BAU &gt; SYR and SRF; b = SYR and SRF &gt; BAU; c = SYR &gt; SRF and BAU; d = SRF &gt; SYR and BAU; e = BAU &gt; SYR; f = SRF &gt; BAU &gt; SYR; g = SRF and BAU &gt; SYR; h = SRF &gt; SYR. Means are significantly different at the 0.05 level according to Fisher’s protected LSD <span class="html-italic">t</span>-test.</p>
Full article ">Figure 10
<p>The effects of the irrigation regime ((<b>a</b>,<b>b</b>); SRF = supplemental rainfall, SYR = syringing, BAU = business as usual) and fertilizer ((<b>c</b>,<b>d</b>); MILH = milorganite high, 294 kg N ha<sup>−1</sup> yr<sup>−1</sup>, MILL = milorganite mow, 147 kg N ha<sup>−1</sup> yr<sup>−1</sup>, UREH = urea high, 294 kg N ha<sup>−1</sup> yr<sup>−1</sup>, UREL = urea low, 147 kg N ha<sup>−1</sup> yr<sup>−1</sup>, UNTC = unfertilized control, 0 kg N ha<sup>−1</sup> yr<sup>−1</sup>) on turfgrass color (NDVI = normalized difference vegetation index, −1 to 1) in 2016 and 2017. Datapoints on the graphs represent means by date. * Means are significantly different at the 0.05 level according to Fisher’s protected LSD <span class="html-italic">t</span>-test. Plots were fertilized the first week of every month from May to October with a high N rate of 49 kg N ha<sup>−1</sup>, a low N rate 24.5 kg N ha<sup>−1</sup>, or an unfertilized control of 0 kg N ha<sup>−1</sup> per application.</p>
Full article ">Figure 11
<p>The effects of irrigation regime ((<b>a</b>,<b>b</b>); SRF = supplemental rainfall, SYR = syringing, BAU = business as usual) and fertilizer ((<b>c</b>,<b>d</b>); MILH = milorganite high, 294 kg N ha<sup>−1</sup> yr<sup>−1</sup>, MILL = milorganite low, 147 kg N ha<sup>−1</sup> yr<sup>−1</sup>, UREH = urea high, 294 kg N ha<sup>−1</sup> yr<sup>−1</sup>, UREL = urea low, 147 kg N ha<sup>−1</sup> yr<sup>−1</sup>, UNTC = unfertilized control, 0 kg N ha<sup>−1</sup> yr<sup>−1</sup>) on turfgrass quality (1–9 visual scale; where 1 = completely brown dead turf, 6 = minimally acceptable turf, and 9 = optimum uniformity, density, and greenness) in 2016 and 2017. Datapoints on the graphs represent means by date. * Means are significantly different at the 0.05 level according to Fisher’s protected LSD <span class="html-italic">t</span>-test. Plots were fertilized the first week of every month from May to October with a high N rate of 49 kg N ha<sup>−1</sup>, a low N rate 24.5 kg N ha<sup>−1</sup>, or an unfertilized control of 0 kg N ha<sup>−1</sup> per application.</p>
Full article ">
10 pages, 2631 KiB  
Communication
A New Method for Hybrid Bermuda Grass (Cynodon dactylon × C. transvaalensis Burtt.-Davy) Vegetative Propagation
by Simone Magni, Giuliano Sciusco, Lisa Caturegli, Mino Sportelli, Tommaso Federighi, Marco Fontanelli, Alberto Minelli, Joseph Scott McElroy and Marco Volterrani
Grasses 2024, 3(1), 1-10; https://doi.org/10.3390/grasses3010001 - 23 Dec 2023
Viewed by 2116
Abstract
Hybrid Bermuda grasses (Cynodon dactylon × C. transvaalensis Burtt.-Davy) represent one of the greatest contributions to the growing quality of turfgrass in the warm season and transition zone areas of the world. Hybrid Bermuda grass production relies on vegetative propagation from sod [...] Read more.
Hybrid Bermuda grasses (Cynodon dactylon × C. transvaalensis Burtt.-Davy) represent one of the greatest contributions to the growing quality of turfgrass in the warm season and transition zone areas of the world. Hybrid Bermuda grass production relies on vegetative propagation from sod or sprigs. In the past, efforts have focused on improving the technique of stolonizing (or sprigging) for establishment in new areas. Such propagation requires bulk harvesting and planting of all rhizomes and stolons. We have developed a novel method of propagation and establishment from a single node harvested from greenhouse grown stolons. Despite a stolon fraction bearing a single node being suitable for effectively propagating a warm-season turfgrass, the technique has been held as economically impractical until now. Our method has been developed to obtain the multiplication of plant material in soilless conditions by harvesting single-node sprigs, propagation of plants from the single nodes, and transplant of single plants in the field. The investigation aimed to identify values for method set-up. Indeed, node and internode size variability with differential between maximum diameters is crucial for discrimination. For Patriot Bermuda grass stolons, nodes exhibited a maximum diameter of 2.43 ± 0.46 mm, while internodes had a maximum diameter of 1.54 ± 0.16 mm. Based on these findings, a 2 mm sieve was selected, achieving an optimal ratio between the node fraction and internode residues. The sieve yielded 87% of node fractions and only 1% of internodes from the initial mix, demonstrating its efficacy. Further results for the transplanting phase indicated that a double release resulted in an average success rate of 98.8%, with only 6.9% blank cells when using a single release. The average was 149 plants per tray over 160 cells, representing a 93.1% success rate. These results underscore the efficiency and acceptability of the overall propagation process in alignment with market references. Full article
(This article belongs to the Special Issue Advances in Sustainable Turfgrass Management)
Show Figures

Figure 1

Figure 1
<p>Single-node propagation method conceptual diagram.</p>
Full article ">Figure 2
<p>Different phases of the proposed single node propagation method realized both in greenhouse and in field.</p>
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<p>Success rate of Bermuda grass sprig transplantation comparing hand-transplanting and single and double release.</p>
Full article ">Figure 4
<p>Turf cover over time. Turf development from plugs up to 100% established in eight weeks.</p>
Full article ">
20 pages, 1352 KiB  
Article
The Effects of Fertilizer Sources and Site Location on Greenhouse Gas Emissions from Creeping Bentgrass Putting Greens and Kentucky Bluegrass Roughs
by Katy E. Chapman and Kristina S. Walker
Grasses 2023, 2(2), 78-97; https://doi.org/10.3390/grasses2020008 - 5 May 2023
Cited by 3 | Viewed by 1789
Abstract
Understanding greenhouse gas (GHG) emissions from turfgrass allows managers to make cultural management decisions to reduce GHG emissions. The objective of this study was to evaluate fertilizer source [urea (URE), polymer-encapsulated urea (POL), and milorganite (MIL)] and site location (green, wet rough, and [...] Read more.
Understanding greenhouse gas (GHG) emissions from turfgrass allows managers to make cultural management decisions to reduce GHG emissions. The objective of this study was to evaluate fertilizer source [urea (URE), polymer-encapsulated urea (POL), and milorganite (MIL)] and site location (green, wet rough, and dry rough) on GHG [carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)] emissions. Greenhouse gas data, soil temperature, soil moisture, canopy greenness, and turfgrass quality were collected. High soil temperature and moisture were correlated with soil CO2 and N2O flux. The wet rough fluxed more soil CH4 across the 2-year study. The POL fluxed the highest amount of soil CO2, while POL and MIL fluxed the largest amount of soil N2O on the wet rough. Milorganite and POL increased canopy greenness in both roughs during the spring. On the green, URE produced greater canopy greenness in the spring and fall. Our results indicate that when soil moisture and temperature are high, turfgrass managers should employ methods of reducing soil temperatures that do not increase soil moisture to reduce GHG emissions. Under warm and wet conditions, gaseous losses of GHGs are accelerated with slow-release fertilizers. Full article
(This article belongs to the Special Issue Advances in Sustainable Turfgrass Management)
Show Figures

Figure 1

Figure 1
<p>2013 Carbon dioxide (CO<sub>2</sub>) and Nitrous oxide (N<sub>2</sub>O) flux using soil temperature (°C) and soil moisture (%) as predictors of CO<sub>2</sub> and N<sub>2</sub>O flux. The black dots represent the raw data and the grid represents the model relating soil temperature and moisture as predictors of CO<sub>2</sub> and N<sub>2</sub>O flux using the parameter estimates from a regression analysis. Parameter estimates for CO<sub>2</sub> were significant at the 0.0001 level. Parameter estimate for the 2013 CO<sub>2</sub> flux Intercept is −510, Slope of Temperature is 29, Slope of Moisture is 15. Parameter estimate for the 2014 CO<sub>2</sub> flux Intercept is −190, Slope of Temperature is 35, Slope of Moisture is 7.2. Parameter estimate for the 2013 N<sub>2</sub>O flux Intercept is −0.063, Slope of Temperature is 0.0022, Slope of Moisture is 0.0022. Parameter estimate for 2014 N<sub>2</sub>O flux is the Intercept is 0.50, Slope of Temperature is 0.0013, Slope of Moisture is 0.00013.</p>
Full article ">Figure 2
<p>2013 and 2014 Carbon dioxide (CO<sub>2</sub>) flux for each site location by fertilizer treatment (A) Green (B) Wet Rough (C) Dry Rough (1) 2013 (2) 2014. MIL = Milorganite; POL = Polymer encapsulated urea; URE = Urea; UNT = unfertilized control. Notes: <sup>a</sup> POL &gt; UNT; <sup>b</sup> UNT &gt; URE; <sup>c</sup> POL &gt; MIL = URE = UNT; <sup>d</sup> MIL &gt; POL = UNT; <sup>e</sup> MIL &gt; POL = URE; <sup>f</sup> POL &gt; URE; <sup>g</sup> URE &gt; POL; <sup>h</sup> MIL = POL = URE &gt; UNT; <sup>i</sup> URE &gt; UNT; <sup>j</sup> UNT = URE &gt; POL = MIL; <sup>k</sup> MIL = URE &gt; POL; <sup>l</sup> MIL = POL = UNT&gt;URE; <sup>m</sup> UNT &gt; POL = MIL; <sup>n</sup> POL = UNT &gt; MIL; <sup>o</sup> MIL &gt; URE; <sup>p</sup> POL &gt; UNT = URE; <sup>q</sup> UNT &gt; POL; letters do not represent LSD (least significant difference) notations. * Means are significantly different at the 0.05 according to the LSD.</p>
Full article ">Figure 3
<p>2013 and 2014 Nitrous oxide (N<sub>2</sub>O) flux for each site location by Fertilizer treatment. (A) Green (B) Wet Rough (C) Dry Rough (1) 2013 (2) 2014. MIL=Milorganite; POL = Polymer encapsulated urea; URE = Urea; UNT = unfertilized control. Notes: <sup>a</sup> POL &gt; UNT; <sup>b</sup> UNT &gt; URE; <sup>c</sup> POL &gt; MIL = URE = UNT; <sup>d</sup> MIL &gt; POL = UNT; <sup>e</sup> MIL &gt; POL = URE; <sup>f</sup> POL &gt; URE; <sup>g</sup> URE &gt; POL; <sup>h</sup> MIL = POL = URE &gt; UNT; <sup>i</sup> URE &gt; UNT; <sup>j</sup> UNT = URE &gt; POL = MIL; <sup>k</sup> MIL = URE &gt; POL; <sup>l</sup> MIL = POL = UNT &gt; URE; <sup>m</sup> UNT &gt; POL = MIL; <sup>n</sup> POL = UNT &gt; MIL; <sup>o</sup> MIL &gt; URE; <sup>p</sup> POL &gt; UNT = URE; <sup>q</sup> UNT &gt; POL; <sup>r</sup> MIL &gt; UNT; <sup>s</sup> POL &gt; MIL = URE &gt; UNT; <sup>t</sup> POL &gt; MIL &gt; UNT; <sup>u</sup> URE &gt; UNT = POL; <sup>v</sup> MIL &gt; URE = UNT; <sup>w</sup> MIL &gt; URE = POL = UNT; <sup>x</sup> MIL &gt; POL; <sup>y</sup> URE &gt; UNT = MIL; <sup>z</sup> POL &gt; UNT = MIL; letters do not represent LSD (least significant difference) notations. * Means are significantly different at the 0.05 according to the LSD.</p>
Full article ">

Review

Jump to: Research

8 pages, 266 KiB  
Review
Grasscycling: A Key Practice for Sustainable Turfgrass Management
by Cristina Pornaro, Alberto Novello, Micheal Fidanza and Stefano Macolino
Grasses 2022, 1(1), 45-52; https://doi.org/10.3390/grasses1010005 - 12 Dec 2022
Cited by 3 | Viewed by 2870
Abstract
For aesthetic considerations, grass clippings are removed from lawns during mowing. When turfgrass clippings are returned, this practice is called “mulching” or grasscycling. Thus, grasscycling has increasingly become a standard practice for low-input lawns managed under a simpler maintenance system, and grasscycling has [...] Read more.
For aesthetic considerations, grass clippings are removed from lawns during mowing. When turfgrass clippings are returned, this practice is called “mulching” or grasscycling. Thus, grasscycling has increasingly become a standard practice for low-input lawns managed under a simpler maintenance system, and grasscycling has many environmental benefits. Primarily, grasscycling facilitates an increase in soil nitrogen content and soil carbon sequestered by the turfgrass ecosystem. Several studies reported that grasscycling positively influences turfgrass colour and quality. When clippings are returned, turfgrass colour and quality can be maintained with a lower amount of fertilisation than turfgrass with clipping removal. Together with these positive effects, grasscycling practices can contribute to an increase of thatch in the turfgrass sward, while its influence on weed invasion is still questionable. This grasscycling practice can result in a maintenance cost-savings and represent a low-input approach to turfgrass management in terms of nutrients returned and utilised by the turfgrass, and with carbon (C) emissions mitigated and C sequestered. The unwelcome appearance linked to grass clipping residues and vegetation on the turfgrass canopy can be easily obviated by the use of machinery that delivers clippings forcefully toward the ground to incorporate them into the verdure or by using mowers that produce clippings small enough to be returned and quickly decomposed. Full article
(This article belongs to the Special Issue Advances in Sustainable Turfgrass Management)
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