Residential household yard care practices along urban-exurban gradients in six climatically-diverse U.S. Metropolitan areas

Dexter H. Locke, Colin Polsky, J. Morgan Grove, Peter M. Groffman, Kristen C. Nelson, Kelli L. Larson, Jeannine Cavender-Bares, James B. Heffernan, Rinku Roy Chowdhury, Sarah E. Hobbie, Neil D. Bettez, Sharon J. Hall, Christopher Neill, Laura Ogden, Jarlath O’Neil-Dunne

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Residential land is expanding in the United States, and lawn now covers more area than the country’s leading irrigated crop by area. Given that lawns are widespread across diverse climatic regions and there is rising concern about the environmental impacts associated with their management, there is a clear need to understand the geographic variation, drivers, and outcomes of common yard care practices. We hypothesized that 1) income, age, and the number of neighbors known by name will be positively associated with the odds of having irrigated, fertilized, or applied pesticides in the last year, 2) irrigation, fertilization, and pesticide application will vary quadratically with population density, with the highest odds in suburban areas, and 3) the odds of irrigating will vary by climate, but fertilization and pesticide application will not. We used multi-level models to systematically address nested spatial scales within and across six U.S. metropolitan areas—Boston, Baltimore, Miami, Minneapolis-St. Paul, Phoenix, and Los Angeles. We found significant variation in yard care practices at the household (the relationship with income was positive), urban-exurban gradient (the relationship with population density was an inverted U), and regional scales (city-to-city variation). A multi-level modeling framework was useful for discerning these scale-dependent outcomes because this approach controls for autocorrelation at multiple spatial scales. Our findings may guide policies or programs seeking to mitigate the potentially deleterious outcomes associated with water use and chemical application, by identifying the subpopulations most likely to irrigate, fertilize, and/or apply pesticides.

Original languageEnglish (US)
Article numbere0222630
JournalPloS one
Issue number11
StatePublished - Nov 1 2019

Bibliographical note

Funding Information:
This research is supported by the Macro-Systems Biology Program (US NSF) under Grants EF-1065548, -1065737, -1065740, -1065741, -1065772, -1065785, -1065831, and -121238320 and the NIFA McIntire-Stennis 1000343 MIN-42-051. The work arose from research funded by grants from the NSF LTER program for Baltimore (DEB- 0423476, DEB-1027188); Phoenix (BCS-1026865, DEB-0423704, DEB-9714833, DEB-1637590, DEB-1832016); Plum Island, Boston (OCE-1058747 and 1238212); Cedar Creek, Minneapolis?St. Paul (DEB- 0620652); and Florida Coastal Everglades, Miami (DBI-0620409). Edna Bailey Sussman Foundation, Libby Fund Enhancement Award and the Marion I. Wright ?46 Travel Grant at Clark University, The Warnock Foundation, the USDA Forest Service Northern Research Station, Baltimore and Philadelphia Field Stations, and the DC-BC ULTRA-Ex NSF-DEB-0948947 also provided support. This work was supported by the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the National Science Foundation DBI-1052875. Anonymous reviewers supplied constructive feedback that helped to improve this paper. The findings and opinions reported here do not necessarily reflect those of the funders of this research.

Publisher Copyright:
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.


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