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Study Design

Sites were selected to represent the range of conditions found in the region. Site selection was based on four stratifying elements hypothesized to impact productivity: housing age, prior land use (i.e. the land use immediately prior to residential development), coarse vegetation density and built structure density.

Turfgrass ANPP

The parcels selected for sampling ranged in size from 0.19 acres to 4.0 acres, with turfgrass covering varying proportions of the parcel. At a random location within each parcel covered with turfgrass, a 0.5 x 0.5 m square microplot was established for sampling turfgrass production. To estimate aboveground annual production on the turfgrass plots, we quantified clippings, as well as stubble, thatch, and moss production over a full growing season (April 1-November 15).

Clippings. The grass microplots were clipped by hand once weekly or biweekly during the growing season, and clippings were collected using a portable vacuum cleaner (Falk 1980). In order to replicate as closely as possible the conditions on residential plots in situ, plots were clipped to the average height of the rest of the homeowner’s lawn during each mowing event. Mowing height thus varied from parcel to parcel, with an average height of 8.4 cm (minimum 4.5 cm and maximum 15.0 cm). Clippings were dried for 48 hours at 75oC and weighed. A small subsample (roughly 0.1 g) of the clippings was taken from each weekly clipping collection and composited together into one site-specific sample for each parcel in each collection year. This composited clipping sample was dried and ground in a Wiley mill to pass a 40 mesh screen and analyzed for C and N content (Carlo-Erba CN analyzer).

Thatch and stubble biomass. Thatch and stubble biomass were found using a sequential coring technique (Falk 1976, Falk 1980). At 2-month intervals during the growing season (April, June, August, October), three cores (5-cm by 10-cm deep) were collected from randomly-located sites on locations within the parcel covered with turfgrass. For each core, the live and dead organic matter in the thatch and stubble components were sorted, dried, and weighed.

Moss biomass. Moss percent cover was recorded for each of the sequential cores, and moss biomass was dried and weighed.

Total ANPP. Total turfgrass production was calculated annually as the sum of live + dead production as described by Falk (1976, 1980) and Qian et al. (2003). Using this approach:

ANPP = Annual clipping production + (maximum stubble biomass * stubble turnover rate) + (maximum thatch biomass * thatch turnover rate) + (maximum moss biomass * moss turnover rate). Turnover rate = (maximum biomass – minimum biomass)/ maximum biomass (Equation 2).

The dry weight of the clippings and stubble were converted to g C by multiplying by 45.85%, the mean %C from the composite clipping samples described above. Thatch was converted to g C by multiplying thatch biomass by 57.35%. This value is based the average %C of the clippings, adjusted for the higher lignin content in the thatch compared to the shoots (Qian et al. 2003). %C of 43% was used to convert moss biomass to g C (Vingani et al. 2004).

Soil respiration

Permanent chamber bases (10 cm diameter, 2 per plot) were established within each turfgrass microplot. At each weekly site visit, soil respiration rates were measured in situ using a Li-Cor 8100 Automated Soil CO2 Flux System. Soil temperature (at 0 cm, 5 cm, and 10 cm depths) and moisture measurements were also taken weekly. Grass was not clipped within the respiration chamber collars: as a result, the measured CO2 flux is a combination of the belowground heterotrophic and autotrophic respiration, as well as the dark respiration of the turfgrass. A correction factor was used to subtract the dark respiration of the turfgrass inside the soil respiration collars. The corrected soil respiration value is thus a measure of soil and root respiration only. To develop annual estimates of soil respiration, a statistical model was developed for each study parcel, relating soil temperature at 5 cm depth to instantaneous soil respiration. This model was then used together with daily estimates of soil temperature to aggregate an annual soil respiration value for each of the study parcels in each measurement year.

Soil physical/chemical properties

Cores were sorted to remove coarse roots and rocks (> 2 mm). The roots and rocks were dried at 105C, weighed and set aside. Rock volume was determined by mass and an assumed density of 2.7 g/cm3. Subsamples of homogenized soil from each depth interval were analyzed for soil dry weight and percent moisture (48 hrs at 105 oC). Bulk density (BD) was calculated as BD = (Total Dry Mass - Rock Mass) / (Total Volume - Rock Volume). Soil texture was obtained by the hydrometer method (Gee and Bauder 1986). Total C and N were obtained by flash-combustion / oxidation using a Thermo Finnigan Flash EA 1112 elemental analyzer (0.06% C and 0.01% N detection limits). For all data, the density of C in a unit area (1 m2) was calculated as C = CfBD(1-d2mm)V, where C is carbon density, d2mm is the fraction of material larger than 2 mm diameter, BD is bulk density, Cf is the fraction by mass of organic C, and V is the volume of the soil core (Post et al. 1982).

Lawn management

A survey asking homeowners to describe their lawn care practices was administered in 2007. Results from the survey were used to confirm that the homeowners had used standard lawn management practices.

References

Falk, J.H. 1976 Energetics of a suburban lawn ecosystem Ecology 57 141 150

Falk, J.H. 1980 The primary productivity of lawns in an urban environment J. Appl. Ecol. 17 689 696

Gee, G. W. and J. W. Bauder. 1986. Particle size analysis. Pages 383-411 in A. Klute, editor. Methods of Soil Analysis, part 1. Physical and mineralogical methods, Second Edition. American Society of Agronomy, Madison, WI.

Qian YL, Bandaranayake W, Parton WJ, Mecham B, Harivandi MA, Mosier AR. 2003. Long‐term effects of clipping and nitrogen management in turfgrass on soil organic carbon and nitrogen dynamics: The CENTURY model simulation. Journal of Environmental Quality. 32(5):1694-700.