Published November 10, 2014

Journal of Environmental Quality

Short Communications

Sources of Variation in Home Lawn Soil Nitrogen Dynamics Noelle G. Martinez, Neil D. Bettez, and Peter M. Groffman*

U

rban land in the United States, or those areas with

Abstract

a density of 1600 people per km2, increased by 47% from 1982 to 1997 (Fulton et al., 2001). This increase was driven by a population increase of only 17%, suggesting that residential land use is occupying increasingly larger parcels. Increases in urban land are particularly marked in coastal areas, such as in the Chesapeake Bay watershed, where urbanized land area is predicted to increase by 80% by 2030 (Goetz et al., 2004). Much of the open land in urban areas is decorative rather than functional; that is, green spaces attached to houses rather than arable land for agriculture. Turfgrass lawns dominate these green spaces and cover 1.9% of the United States and 10% of the state of Maryland (Milesi et al., 2005). Lawns are frequently fertilized (Law et al., 2004; Osmond and Hardy, 2004), leading to concerns about the movement of fertilizers from lawns into receiving waters where they can cause eutrophication (Conley et al., 2009). In the Chesapeake Bay, nitrate (NO3-), a highly mobile byproduct of fertilizer use, is the prime cause of eutrophication (Boesch et al., 2001). There is also concern about release of nitrous oxide (N2O), a greenhouse gas that has a global warming potential 300 times greater than CO2 (Prather et al., 1995), associated with lawn fertilizer use. Studies of nitrogen cycling in lawns have produced varied and conflicting results. While several studies in the western United States have found that fertilized lawns emit high amounts of N2O (Kaye et al., 2004; Bijoor et al., 2008; Hall et al., 2008; Townsend-Small et al., 2011), a study done as part of the U.S. NSF-funded urban long-term ecological research project in Baltimore, the Baltimore Ecosystem Study (BES), found that N2O fluxes in lawns were similar to native forests, that NO3leaching was surprisingly low (but still significant), and that retention of added 15N was high in lawns relative to forests (Groffman et al., 2009; Raciti et al., 2008; 2011a). There is a clear need for a mechanistic understanding of soil N dynamics in lawns so that the factors controlling the variation in lawn environmental performance between different studies and regions can be determined. It has been hypothesized that variation in the environmental performance of lawns is related to variation in human management

Urban, suburban, and exurban lawns are an increasingly important ecosystem type in the United States. There is great concern about the environmental performance of lawns, especially nitrate (NO3−) leaching and nitrous oxide (N2O) flux associated with nitrogen (N) fertilizer use. Previous studies of lawn N dynamics have produced conflicting results, with some studies showing high NO3− leaching and N2O flux and others showing lower losses and high retention and cycling of N inputs. We hypothesized that this variation is caused by differences in lawn management and soil properties that control root and soil organic matter (SOM) dynamics that influence N cycling processes. We tested these hypotheses by making measurements of soil NO3−, root biomass, rates of potential net N mineralization and nitrification, N2O flux, and SOM levels in samples from the front and backyards of residential homes in suburban and exurban neighborhoods with contrasting soil types in the Baltimore metropolitan area. There were no differences between front and backyards, between suburban and exurban neighborhoods, or between different soil types. Further, there were no significant relationships between root biomass, SOM, soil NO3- levels, and N2O fluxes. These results suggest that lawns have uniformly high rates of plant productivity that underlies high levels of SOM and N retention in these ecosystems across the Baltimore metropolitan area.

Copyright © American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. 5585 Guilford Rd., Madison, WI 53711 USA. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

N.G. Martinez, Univ. of New Mexico, Dep. of Biology, 167 Castetter Hall, Albuquerque, NM 87131-0001; N.D. Bettez and P.M. Groffman, Cary Institute of Ecosystem Studies, 2801 Sharon Turnpike, Millbrook, NY 12545. Assigned to Associate Editor Amy Townsend-Small.

J. Environ. Qual. 43:2146–2151 (2014) doi:10.2134/jeq2014.03.0103 Received 4 Mar. 2014. *Corresponding author ([email protected]).

Abbreviations: BES, Baltimore Ecosystem Study; SOM, soil organic matter.

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(Law et al., 2004; Osmond and Hardy, 2004; Fissore et al., 2012; Qin et al., 2013; Larson and Brumand, 2014;) associated with socioeconomic and local neighborhood factors (Zhou et al., 2009; Blaine et al., 2012; Cook et al., 2012; Fraser et al., 2013; Polsky et al., 2014) as well as with inherent soil factors. A visually obvious source of variation is the difference between more highly managed front lawns, which may serve aesthetic purposes in a neighborhood context, and much more variably managed backyards. This variation is thought to arise from inherent or imposed (e.g., by a homeowner association) desires to have a neat, green front lawn that may lead homeowners to apply fertilizer at higher rates to front yards (Cook et al., 2012; Zhou et al., 2009). There are also obvious differences in lawn size between urban, suburban, and exurban neighborhoods that should be associated with differences in fertilization practices and soil properties (Law et al., 2004; Fraser et al., 2013). Soil properties that control the growth and movement of roots and the accumulation of organic matter are also likely important controllers of lawn N dynamics (Petrovic, 1990; Easton et al., 2007). Raciti et al. (2011b) observed surprisingly high levels of organic C and N deep (to 1 m) in the profile of lawn soils in Baltimore and suggested that this accumulation of organic matter may play a role in the high N retention (Raciti et al., 2008), low nitrification (Raciti et al., 2011a), N2O flux, and NO3- leaching (Groffman et al., 2009) discussed above. However, the factors underlying the high organic-matter levels, and the role that this plays in influencing N losses, are not clear (Selhorst and Lal, 2012). There is particular interest in the factors controlling nitrification, which is the key process regulating both hydrologic and gaseous losses from ecosystems (Aber et al., 1989; Galloway et al., 2003). In this study, we addressed several of the uncertainties that have emerged from recent research on N dynamics in lawns. We made measurements of soil NO3-, root biomass, rates of potential net N mineralization and nitrification, N2O flux, and soil organic matter (SOM) levels in samples from the front and backyards of residential homes in suburban and exurban neighborhoods with contrasting soil types in the Baltimore metropolitan area. Our objectives were to determine (i) whether N cycling differs between front and backyard lawns in neighborhoods of different population and housing density, (ii) whether nitrification is regulated by root biomass and SOM content, and (3) whether high root biomass explains SOM accumulation at depth in the soil profile. We anticipated higher nitrification rates, soil NO3- levels, and N2O fluxes in front-yard lawns. In lawns that have higher nitrification rates, we expected to see low amounts of roots and SOM. Finally, we projected positive correlations between root biomass and SOM, especially at depth in the soil profile. Our approach was to conduct a random sampling to determine if our hypothesized differences between front and backyard lawns and neighborhoods of different population and housing density existed. These differences could then be used to guide future sampling programs that encompass long-term differences in management.

Materials and Methods The BES research is focused on the Gwynns Falls watershed (39°15¢ N, 76°30¢ W; ~170 km2), which spans a rural–urban

gradient in the Baltimore metropolitan area (Doheny, 1999). The climate is humid subtropical, with warm summers (average maximum of 30°C) and cool winters (mean minimum of 4°C) and approximately 1000 mm of rainfall that is evenly distributed throughout the year (Duncan et al., 2013). A fraction of the watershed is natural forest dominated by tulip poplar (Liriodendron tulipifera L.) and oaks, primarily chestnut (Quercus montana Willd.) scarlet (Quercus coccinea Münchh.), and white (Quercus alba L.) (Groffman et al., 2006). Urban lawns consist primarily of Kentucky bluegrass (Poa pratensis L.), tall fescue (Festuca arundinacea Schreb.), fine fescue (Festuca spp.), and white clover (Trifolium repens L.). The BES study areas are located on the Piedmont Plateau, which is underlain by igneous and metamorphic rocks that cause variation in soil fertility (Froelich et al., 1980). Highfertility soils (e.g., Legore series, a fine-loamy, mixed, mesic, Ultic Hapludalf ) are found over mafic rocks and weathered minerals from amphibolites, diabase, or other basic igneous rocks (NRCS, 1998). Lower-fertility soils (e.g., Manor series, a coarse-loamy, micaceous, mesic Typic Dystrudepts) are found over acid crystalline rocks such as gneiss and micaceous schist. The high-fertility soils have higher pH, better water retention capacity, and higher N availability (Groffman et al., 2006). Atmospheric N deposition in the Baltimore area is estimated at 1.1 g N m−2 yr−1 (Bettez and Groffman, 2013). We sampled in three watersheds in June 2011 within the Baltimore metropolitan area that provided a contrast in soil type and density. Glyndon (81 ha) and Dead Run 5 (192 ha) are suburban subwatersheds of the Gwynns Falls dominated by small, residential parcels with an average housing age of 25 yr (Grove et al., 2006). Soils in the Glyndon watershed are dominated by the low-fertility Manor and Glenelg (fine-loamy, mixed, semiactive, mesic Typic Hapludult) series while Dead Run 5 is characterized by the higher-fertility Legore series soils. Cranberry Branch is an 850 ha watershed located in exurban Carroll County, MD. Housing age in this watershed is similar to Glyndon and Dead Run, but parcels are larger (0.4 ha). Soils in Cranberry Branch are dominated by the low-fertility Glenelg series. Residential parcels (five per watershed) were selected for sampling by walking through neighborhoods and requesting permission from homeowners. Parcels were picked by randomly selecting a subdivision within each watershed, knocking on doors, and requesting permission to sample. Within each parcel, front and backyard sampling locations under turfgrass were randomly chosen from areas not within close proximity to underground power lines and pipes. Areas of excessive slope and shade were avoided. We did not have any prior knowledge about differences in management practices such as fertilization or irrigation among neighborhoods, individual properties, or between front and backyards before choosing sites. This was a random sampling to determine if such differences existed. Undisturbed 1-m soil cores (2 per parcel—one in the front and one in the backyard) were extracted using a 3.3-cm diameter soil corer. Cores were enclosed in plastic sleeves with end caps and stored at 4°C until processing (within 1 wk). Cores were sliced in half so that soil profile characteristics would be visible and undisturbed allowing for assessment of horizon depths and evidence of profile disturbance. This sampling design produced 30 samples allowing for comparison of four soil depths (n = 30),

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front vs. backyards (n = 15), and three different neighborhoods (n = 10). Soil cores were cut into four sections (0–10, 10–30, 30–70, and 70–100 cm) consistent with previous studies in our region (Raciti et al., 2011a; 2011b) and hand sifted for 10 min to remove surface vegetation and litter, large roots (live and dead), and rocks. Roots were dried at 105°C then weighed. Soil moisture was determined by drying at 105°C, and soil organic matter content was determined by loss on ignition (450°C for 4 h). Bulk density was calculated by dividing the rock-free dry weight of the soil in each core section by the volume of each section. Inorganic N (NH4+ and NO3−) was extracted using 2 M KCl and analyzed colorometrically using a flow injection analyzer. Subsamples of hand-sifted, ambient-moisture soil were set aside to determine rates of potential net N2O production, potential net N mineralization, nitrification, and microbial respiration using methods described by Raciti et al. (2011a). Samples (10 g) were placed into 1-L mason jars and sealed with lids with septa that allow for gas sampling. Jars (including empty blanks) were placed into cardboard boxes and stored at room temperature. After 10 d, gas samples were removed from the jars and transferred to evacuated glass vials via needle and syringe. Concentrations of N2O and CO2 were determined by electron capture and thermal conductivity gas chromatography, respectively. Mason jars were then opened, and inorganic N (NH4+ and NO3−) was extracted and analyzed as described above. Potential net N mineralization was calculated as the accumulation of inorganic N over the 10-d incubation. Potential net nitrification was calculated as the accumulation of NO3− over the 10-d incubation. Microbial respiration was calculated from the accumulation of CO2 over the 10-d incubation. Potential net N2O production was calculated from the accumulation of N2O over the 10-d incubation. Three-way analysis of variance (ANOVA) was used to evaluate the effects of lawn location (front and backyard), watershed (Cranberry Branch [exurban], Glyndon [suburban with lowfertility soils], and Dead Run 5 [urban with high-fertility soils]), and depth on the key response variables (bulk density, SOM content, extractable NO3−, potential net N mineralization and nitrification rates, microbial respiration, N2O production, and root mass) (PROC GLM, SAS 1988, release 6.03, SAS Institute). Correlation analysis (PROC CORR) was used to test for relationships amongst measured variables.

Results Nearly all variables showed significant declines with depth (Table 1; Fig. 1 and 2). Production of N2O was an exception however, as this variable showed very little variation with depth. Soil NO3−, root mass, potential net nitrification, and potential net mineralization rates declined more sharply with depth than SOM (Table 1). Location (front vs. backyard) had no effect on any variable. Bulk density, SOM, root mass, potential N mineralization and nitrification, NO3− pool size, microbial respiration, and N2O production were all quite similar in front and backyards (Fig. 1). Watershed (urban, suburban, and exurban) did not have a statistically significant effect (p < 0.05) on any variable. Bulk density, SOM, root mass, potential N mineralization and nitrification, NO3− pool size, microbial respiration, and N2O production were all quite similar in the Glyndon, Dead Run 5, and Cranberry Branch watersheds (Fig. 2). There were no significant relationships between nitrification or N2O and SOM or root biomass. There was a significant correlation (r = 0.82, p = 0.0013) between N2O production and soil moisture (Fig. 3).

Discussion Front and backyards showed no differences across all watersheds. We expected that front yards receive more fertilizer and would therefore have higher soil NO3− levels, nitrification rates, and N2O production. This expectation was based on assumptions about the aesthetic role of front lawns, that is, we expected inherent or imposed (e.g., by a homeowner association) desires to have a neat, green front lawn to lead homeowners to apply higher rates of fertilizer to front yards (Zhou et al., 2009; Cook et al., 2012). We also expected backyards to have higher bulk density than front yards due to the recreational role of backyards, that is, we expected that these areas get more foot, vehicle, and pet traffic that might lead to soil compaction. Our objective was to conduct a random sampling to determine if such differences existed. These differences could then be used to guide future sampling programs that encompass long-term differences in management. We hypothesized that the front vs. backyard comparison would be such a strategy, but it was not. Other factors that should be considered in future programs as drivers of either homogeneity or heterogeneity include atmospheric deposition,

Table 1. Bulk density, soil organic matter, extractable NO3−, potential net N mineralization, net nitrification, microbial respiration, N2O production, and root mass of 15 front and backyard lawns at four depths. Values are mean ± standard error of 15 front and 15 backyard samples (pooled due to lack of significant differences between front and backyards) taken in three different watersheds or neighborhoods (n = 30). Variable Bulk density (g cm−3) Soil organic matter‡ (%) NO3− (mg N kg−1) Potential net N mineralization (mg N kg−1 d−1) Potential net nitrification (mg N kg−1 d−1) Microbial respiration (mg C kg−1 d−1) Potential N2O production (mg Nkg−1 d−1) Root mass (mg kg−1)

Depth 0–10 cm

10–30 cm

30–70 cm

70–100 cm

0.79 ± 0.03b† 7.0 ± 0.3a

1.26 ± 0.12a 4.6 ± 0.7b

1.25 ± 0.02a 3.7 ± 0.6bc

0.79 ± 0.04b 3.3 ± 0.3c

9.4 ± 1.1a 4.6 ± 1.4a 0.31 ± 0.04a 10.2 ± 0.9a 1.8 ± 0.04a 49.8 ± 11.4a

2.6 ± 0.3b 1.3 ± 0.4a 0.09 ± 0.02 b 4.5 ± 0.4b 1.7 ± 0.05a 3.0 ± 1.1b

0.8 ± 0.1c 1.3 ± 0.9a 0.03 ± 0.02b 4.4 ± 0.4b 1.8 ± 0.02a 1.0 ± 0.5b

0.7 ± 0.3c 3.5 ± 2.2a 0.03 ± 0.03b 4.7 ± 0.5b 1.8 ± 0.05a 0.3 ± 0.1b

† Different lowercase letters indicate statistically significant (p < 0.05) differences between depths. ‡ Differences in soil organic matter between 10 to 30- and 70 to 100-cm depths significant only at p < 0.10. 2148

Journal of Environmental Quality

Fig. 1. Extractable nitrate (A), potential net N mineralization (B), potential net nitrification (C), and N2O production (D) in front and backyard samples at four depths. Values are mean ± standard error of 15 front and 15 backyard samples taken in three different neighborhoods (n = 15). There were no significant differences between front and backyard samples at any depth for any variable.

Fig. 2. Extractable nitrate (A), potential net N mineralization (B), potential net nitrification (C), and N2O production (D) in three different watersheds or neighborhoods at four depths. Values are mean ± standard error of 5 front and 5 backyard samples taken in each neighborhood (n = 10). There were no significant differences between watersheds or neighborhoods at any depth for any variable.

which is elevated in this region (Bettez and Groffman, 2013); previous land-use history, which has been shown to be a strong driver of soil C and N pools and processes (Raciti et al., 2011a; 2011b); pets, which have been shown to be significant sources of N and P in residential landscapes (Baker et al., 2001); and trees, which can strongly influence water and nutrient fluxes in these landscapes (Peters et al., 2010).

Uniformly high biological N cycling may underlie the lack of variation between front and backyard lawns in our sites. Soil organic matter was relatively high at all sites at 10 cm (7%), and nitrification rates were relatively low, as has been found in our previous lawn studies in Baltimore (Raciti et al., 2011a; 2011b). These results suggest that even if front yards are more heavily fertilized than backyards, NO3− levels and N2O production

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Fig. 3. Nitrous oxide production vs. soil moisture over all sites and depths (n = 120).

might not be elevated due to high rates of plant productivity that underlie high levels of SOM and N retention. We expected differences between the Glyndon, Cranberry Branch, and Dead Run 5 watersheds due to differences in soil type. Groffman et al. (2006) found that differences in the two main soil types in the Baltimore area affect forest soil N dynamics with a less intensive sampling regime than was employed here. We therefore expected lawns developed on the more nutrientrich soils that underlie the Dead Run 5 watershed to have higher rates of nitrification and N2O production than lawns developed on the more nutrient-poor soils in the Glyndon and Cranberry Branch watersheds. However, while the difference in soil type causes significant differences in soil N cycling in forests, these differences are not expressed in lawns. The vegetation change from forest to lawn clearly overwhelms the natural soil controls. We saw no evidence of imported topsoil or profile mixing in any of our soil cores, which were taken in clear plastic tubes allowing for inspection of soil profile characteristics down to 1-m depth. Again, the high rates of plant productivity by grass that produce high levels of SOM and N retention appear to dominate over differences in soil type. The other watershed effect we expected to observe was between the large lots in Cranberry Branch and the smaller lots in Glyndon and Dead Run 5. Law et al. (2004) suggested that people manage small lots more intensively than large lots in the Baltimore area. However, this does not appear to result in differences in soil N cycle processes. Previous studies in the Baltimore region have found differences between lawns and forest and among lawns of different age or land-use history with similar sampling intensity as in this study (Raciti et al., 2011a, 2011b). We therefore have confidence that the lack of differences that we observed between front and backyards and in different neighborhoods suggest that there is homogeneity of soil C and N dynamics in lawns in this region. Not surprisingly, most variables declined with depth, consistent with other lawn studies in Baltimore (Raciti et al., 2011a; 2011b). More surprising was the lack of relationship between root biomass and other variables. We had hypothesized that the high levels of SOM at depth reported by Raciti et al. (2011b) were driven by high-root biomass. However, root biomass declined very sharply with depth, much more sharply than SOM, so there does not appear to be a direct link between the two. High SOM at depth may be a result of leaching of dissolved organic carbon from the surface that gets adsorbed on 2150

soil particles at depth. Interestingly, much of the SOM at depth appears to be labile (at least in these laboratory incubations of disturbed soil) as microbial respiration declined with depth much less sharply than many other variables. We also expected to find negative correlations between root biomass and nitrification, soil NO3−, and N2O production. Mechanistically, root biomass could serve as an index of plant uptake capacity or of the supply of carbon to support microbial immobilization, both of which should reduce N-cycle variables. However, we did not observe a relationship between root biomass and N cycling, likely because the rates of nitrification and N2O production in our sites were quite low. Possibly, the N cycle is so tightly controlled by plants and microbes in these lawns that it is not possible to see relationships between roots and specific processes. We observed positive relationships between potential N2O production and soil moisture. Nitrous oxide is produced by denitrification and nitrification, both of which are stimulated by soil moisture, especially denitrification, which is an anaerobic process. These results suggest that naturally wet or irrigated lawns might produce substantial amounts of N2O. This control may explain the high rates of N2O flux that have been observed in studies in the western United States, where lawns are routinely irrigated (Kaye et al., 2004; Bijoor et al., 2008; Hall et al., 2008; Townsend-Small et al., 2011).

Conclusions These results suggest that there is a marked uniformity in N cycling in home lawns across the Baltimore metropolitan region. We observed very similar rates of multiple processes in front and backyards across a wide range of soil and housing-density conditions. The results support previous research in the Baltimore area and elsewhere that suggests that lawns have surprisingly conservative and consistent N cycling. These previous studies were able to demonstrate significant differences between lawns and native forests with a similar sampling frequency as used in this study, suggesting that our methods are capable of determining subtle differences within the urban landscape and that N cycling within lawns is quite homogeneous. Our study did not include any obviously overfertilized or compacted sites that might function as hotspots of N export to water and air. Additionally, this study did not include measurements of actual NO3− leaching or runoff. Still, our sites were representative of lawns in the region, and our results suggest that these ecosystems have significant potential for N cycling and retention. Further work to elucidate the specific mechanisms of this retention, including the development of management practices to sustain and improve it, is clearly warranted.

Acknowledgments This research was supported by the NSF Long-Term Ecological Research (LTER) (DEB-0423476) and Research Experiences for Undergraduates (REU) (DEB-244101) programs and by a grant from the National Institute of General Medical Sciences award number 5T34GM008751. We thank four anonymous reviewers and Associate Editor Amy Townsend-Small for excellent suggestions for revision and Lisa Martel, Kate Shepard, Dan Dillon, and Erin Mellinthin for help with field sampling and laboratory analysis.

Journal of Environmental Quality

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Sources of variation in home lawn soil nitrogen dynamics.

Urban, suburban, and exurban lawns are an increasingly important ecosystem type in the United States. There is great concern about the environmental p...
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