RaCAbib.bib

@article{soil_survey_staff_kellog_2014,
	title = {Kellog {Soil} {Survey} {Laboratory} {Methods} {Manual}; {Soil} {Survey} {Investigations} {Report} {No}. 42; {Version} 5.0 (2014)},
	url = {https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb1253872.pdf},
	author = {{Soil Survey Staff}},
	year = {2014},
	keywords = {Kellog Soil Survey Laboratory Methods Manual Inves},
	file = {Attachment:C\:\\Users\\Skye.Wills\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles\\o3taif5e.default\\zotero\\storage\\C3NFIMBS\\Nrcs Nssc Kssl - 2014 - Kellog Soil Survey Laboratory Methods Manual Soil Survey Investigations Report No. 42 Version 5.0 (2014).pdf:application/pdf}
}

@article{nusser_design_1998,
	title = {{DESIGN} {AND} {ESTIMATION} {FOR} {INVESTIGATING} {THE} {DYNAMICS} {OF} {NATURAL} {RESOURCES}},
	volume = {8},
	issn = {1051-0761},
	url = {http://doi.wiley.com/10.1890/1051-0761(1998)008[0234:DAEFIT]2.0.CO;2},
	doi = {10.1890/1051-0761(1998)008[0234:DAEFIT]2.0.CO;2},
	number = {2},
	journal = {Ecological Applications},
	author = {Nusser, S. M. and Breidt, F. J. and Fuller, W. A.},
	month = may,
	year = {1998},
	keywords = {environmental monitoring, environmental statistics, multi, phase estimation, stage design, survey sampling, two},
	pages = {234--245}
}

@article{mikhailova_comparing_2016,
	title = {Comparing soil carbon estimates in glaciated soils at a farm scale using geospatial analysis of field and {SSURGO} data},
	volume = {281},
	issn = {00167061},
	url = {http://www.sciencedirect.com/science/article/pii/S0016706116302774},
	doi = {10.1016/j.geoderma.2016.06.029},
	abstract = {Soil carbon is a key soil property related to ecosystem services and it is often used in soil carbon content estimates at various scales. Uncertainties in soil carbon estimates often arise from variability in field, laboratory, and/or geospatial data at a farm scale. The objectives of this study were to quantify and compare levels of soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TC) for a 147-hectare field site in upstate New York based on three alternative analysis procedures: a) using carbon concentrations reported by the Soil Survey Geographic (SSURGO) spatial databases for each soil map unit (SMU) present at the field site and applying that value across each SMU; b) averaging the carbon contents of soil cores collected within a specific SMU boundary and applying the averaged value across each SMU; and c) interpolating carbon contents across the field site based on the individual soil cores. Maps of SOC, SIC, and TC contents based on the interpolated core samples were different from maps created by applying averaged core results or SSURGO values across the SMUs. Differences in the magnitudes and spatial distributions of carbon can be attributed to several factors. For example, SSURGO soil carbon values are frequently measured for a selected pedon(s) from a “type location” and not from the actual study location. These “type locations” can be located far from study sites and even in different states. Also, SSURGO soil carbon values may overestimate the actual contents when compared to systematic field measurements because the SSURGO values at lower depths are often extrapolated from upper soil horizons. Such extrapolations affect inorganic carbon to a much greater extent than organic carbon, therefore better agreement is observed in the present study between SOC estimates from the SSURGO database and field measurements. Because regional and/or global carbon estimates are rarely made with detailed field data due to the high costs of field and laboratory measurements, additional field sampling is needed to constrain and improve these estimates and also to assess the potential variability present.},
	journal = {Geoderma},
	author = {Mikhailova, E.A. and Altememe, A.H. and Bawazir, A.A. and Chandler, R.D. and Cope, M.P. and Post, C.J. and Stiglitz, R.Y. and Zurqani, H.A. and Schlautman, M.A.},
	year = {2016},
	pages = {119--126}
}

@article{adhikari_linking_2016,
	title = {Linking soils to ecosystem services — {A} global review},
	volume = {262},
	issn = {00167061},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0016706115300380},
	doi = {10.1016/j.geoderma.2015.08.009},
	journal = {Geoderma},
	author = {Adhikari, Kabindra and Hartemink, Alfred E.},
	month = jan,
	year = {2016},
	pages = {101--111}
}

@article{batjes_total_1996,
	title = {Total carbon and nitrogen in the soils of the world},
	volume = {47},
	issn = {1351-0754},
	url = {http://doi.wiley.com/10.1111/j.1365-2389.1996.tb01386.x},
	doi = {10.1111/j.1365-2389.1996.tb01386.x},
	number = {2},
	journal = {European Journal of Soil Science},
	author = {BATJES, N.H.},
	month = jun,
	year = {1996},
	pages = {151--163}
}

@incollection{bliss_distribution_2014,
	address = {Cham},
	title = {Distribution of {Soil} {Organic} {Carbon} in the {Conterminous} {United} {States}},
	url = {http://link.springer.com/10.1007/978-3-319-04084-4_9},
	booktitle = {Soil {Carbon}},
	publisher = {Springer International Publishing},
	author = {Bliss, Norman B. and Waltman, Sharon W. and West, Larry T. and Neale, Anne and Mehaffey, Megan},
	year = {2014},
	note = {DOI: 10.1007/978-3-319-04084-4\_9},
	pages = {85--93},
	file = {Attachment:C\:\\Users\\Skye.Wills\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles\\o3taif5e.default\\zotero\\storage\\T2ACWQ9N\\Bliss et al. - 2014 - Distribution of Soil Organic Carbon in the Conterminous United States.pdf:application/pdf}
}

@article{fennessy_carbon_2016,
	title = {Carbon storage in {US} wetlands},
	url = {http://digital.kenyon.edu/biology_publications/90},
	journal = {Nature Communications},
	author = {Fennessy, Siobhan and Nahlik, Amanda},
	month = dec,
	year = {2016}
}

@article{guo_analysis_2006,
	title = {Analysis of {Factors} {Controlling} {Soil} {Carbon} in the {Conterminous} {United} {States}},
	volume = {70},
	issn = {1435-0661},
	url = {https://www.soils.org/publications/sssaj/abstracts/70/2/601},
	doi = {10.2136/sssaj2005.0163},
	number = {2},
	journal = {Soil Science Society of America Journal},
	author = {Guo, Yinyan and Gong, Peng and Amundson, Ronald and Yu, Qian},
	year = {2006},
	keywords = {Guo60},
	pages = {601},
	file = {Attachment:C\:\\Users\\Skye.Wills\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles\\o3taif5e.default\\zotero\\storage\\NZ8SC2RC\\Guo et al. - 2006 - Analysis of Factors Controlling Soil Carbon in the Conterminous United States.pdf:application/pdf}
}

@article{jobbagy_vertical_2000,
	title = {{THE} {VERTICAL} {DISTRIBUTION} {OF} {SOIL} {ORGANIC} {CARBON} {AND} {ITS} {RELATION} {TO} {CLIMATE} {AND} {VEGETATION}},
	volume = {10},
	issn = {1051-0761},
	url = {http://doi.wiley.com/10.1890/1051-0761(2000)010[0423:TVDOSO]2.0.CO;2},
	doi = {10.1890/1051-0761(2000)010[0423:TVDOSO]2.0.CO;2},
	number = {2},
	journal = {Ecological Applications},
	author = {Jobbágy, Esteban G. and Jackson, Robert B.},
	month = apr,
	year = {2000},
	keywords = {belowground processes and global change, carbon content extrapolation, climate, deep soil, depth profiles, ecosystem allocation, plant functional types, root distributions, soil, soil carbon storage, soil gradients, vegetation relationships},
	pages = {423--436}
}

@article{lal_soil_2004,
	title = {Soil {Carbon} {Sequestration} {Impacts} on {Global} {Climate} {Change} and {Food} {Security}},
	volume = {304},
	url = {http://science.sciencemag.org/content/304/5677/1623},
	number = {5677},
	journal = {Science},
	author = {Lal, R.},
	year = {2004}
}

@book{leemans_millennium_2003,
	title = {Millennium {Ecosystem} {Assessment}: {Ecosystems} and human well-being: a framework for assessment},
	url = {http://library.wur.nl/WebQuery/wurpubs/wever/326575},
	author = {Leemans, R and Groot, R.S.},
	year = {2003}
}

@article{lefevre_soil_2017,
	title = {Soil organic carbon: the hidden potential.},
	url = {https://www.cabdirect.org/cabdirect/abstract/20173155458},
	journal = {Soil organic carbon: the hidden potential.},
	author = {Lefèvre, C. and Rekik, F. and Alcantara, V. and Wiese, L.},
	year = {2017},
	keywords = {air pollutants, air pollution, atmosphere, carbon sequestration, climate change, ecosystem services, emissions, global warming, greenhouse gases, infiltration, mineralization, nutrients, organic carbon, porosity, productivity, soil fertility, soil organic matter, soil structure, soil types, sustainability, temperature, water availability}
}

@article{post_soil_1982,
	title = {Soil carbon pools and world life zones},
	volume = {298},
	issn = {0028-0836},
	url = {http://www.nature.com/doifinder/10.1038/298156a0},
	doi = {10.1038/298156a0},
	number = {5870},
	journal = {Nature},
	author = {Post, Wilfred M. and Emanuel, William R. and Zinke, Paul J. and Stangenberger, Alan G.},
	month = jul,
	year = {1982},
	pages = {156--159}
}

@incollection{robinson_soil_2012,
	title = {Soil {Natural} {Capital} and {Ecosystem} {Service} {Delivery} in a {World} of {Global} {Soil} {Change}},
	url = {https://books.google.com/books?hl=en&lr=&id=bHMoDwAAQBAJ&oi=fnd&pg=PA41&ots=1fLBwxsKsW&sig=JH8NyNF4QAiKYmPNNd0gOcSGk4Y#v=onepage&q&f=false},
	booktitle = {Soils and {Food} {Security}},
	author = {Robinson, D. A. and Emmett, B.A. and Reynolds, B and Rowe, E.C. and Spurgeon, D. and Keith, A.M. and Lebron, I. and Hockley, N},
	year = {2012}
}

@article{sequeira_predicting_2014,
	title = {Predicting soil bulk density for incomplete databases},
	volume = {213},
	issn = {00167061},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0016706113002498},
	doi = {10.1016/j.geoderma.2013.07.013},
	journal = {Geoderma},
	author = {Sequeira, Cleiton H. and Wills, Skye A. and Seybold, Cathy A. and West, Larry T.},
	month = jan,
	year = {2014},
	pages = {64--73}
}

@book{soil_science_division_staff_soil_2017,
	address = {Washington, D.C.},
	title = {Soil {Survey} {Manual}: {USDA} {Handbook} 18},
	url = {https://www.nrcs.usda.gov/wps/portal/nrcs/detailfull/soils/ref/?cid=nrcs142p2_054262},
	publisher = {Government Printing Office},
	author = {{Soil Science Division Staff}},
	editor = {Ditzler, C. and Scheffe, K. and Monger, H.C.},
	year = {2017}
}

@book{soil_survey_staff_natural_resources_conservation_service_gridded_2013,
	title = {Gridded {Soil} {Survey} {Geographic} ({gSSURGO}) {Database}},
	url = {https://datagateway.nrcs.usda.gov/},
	urldate = {2013-01-15},
	author = {Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture.},
	year = {2013}
}

@book{soil_survey_staff_rapid_2016,
	title = {Rapid {Carbon} {Assessment}: {Methodology}, {Sampling}, and {Summary}},
	url = {https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcs142p2_052841&ext=pdf},
	abstract = {The RaCA project was designed to capture the range and total amount of soil carbon across the CONUS. The project initially emphasized soil organic carbon (SOC) stocks, or the amount of SOC in a volume (area and depth) of soil. Staff at the National Soil Survey Center (NSSC) developed the concept and NRCS soil scientists at the field soil survey offices executed the project. A multi-level stratified random sampling scheme was created to maximize geographical and spatial sample coverage, to maximize the number of conditions represented, and to give a framework for aggregating information into regional areas.},
	urldate = {2017-11-29},
	author = {{Soil Survey Staff} and Loecke, Terrance},
	year = {2016}
}

@book{stan_development_team_stan:_2014,
	title = {Stan: {A} {C}++ library for probability and sampling},
	abstract = {@article\{stan2014stan, title=\{Stan: A C++ library for probability and sampling\}, author=\{Stan Development Team and others\}, journal=\{Online: http://mc-stan. org\}, year=\{2014\} \}},
	author = {{Stan Development Team}},
	year = {2014}
}

@book{united_states_department_of_agriculture_natural_resources_conservation_service_national_2007,
	title = {National {Resources} {Inventory}},
	url = {https://www.nrcs.usda.gov/wps/portal/nrcs/main/national/technical/nra/nri/},
	number = {42},
	urldate = {2017-08-15},
	author = {United States Department of Agriculture, Natural Resources Conservation Service, Natural Resources Convervation Service},
	year = {2007}
}

@book{soil_survey_staff_soil_2014
	edition = {5.0},
	series = {Soil {Survey} {Invetigations} {Report}},
	title = {Soil {Survey} {Laboratory} {Methods} {Manual} {\textbar} {NRCS} {Soils}},
	url = {https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb1253871.pdf},
	number = {42},
	urldate = {2017-08-28},
	author = {Soil Survey Staff},
	year = {2014}
}

@book{soil_survey_staff_rapid_2017,
	title = {Rapid {Carbon} {Assessment} ({RaCA}) {\textbar} {NRCS} {Soils}},
	url = {https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_054164},
	urldate = {2017-08-28},
	publisher = {Natural Resources Conservation Service, National  Soil Survey Center,},
	author = {Soil Survey Staff, National Resources Assessment.},
	year = {2017}
}

@article{ahlbrandt_geologic_nodate,
	title = {Geologic and {Paleoecoogic} {Studies} o the {Nebraska} {Sand} {Hills}},
	url = {https://pubs.usgs.gov/pp/1120a-c/report.pdf#page=9},
	author = {Ahlbrandt, T.S. and Fryberger, S.G.},
	file = {Attachment:C\:\\Users\\Skye.Wills\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles\\o3taif5e.default\\zotero\\storage\\HNTWMBTC\\Unknown - Unknown - Geologic and Paleoecoogic Studies o the Nebraska Sand Hills.pdf:application/pdf}
}

@book{r_core_team_r_2017,
	title = {R},
	isbn = {3-900051-14-3},
	abstract = {A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.},
	author = {{R Core Team}},
	year = {2017},
	keywords = {data analysis, r, software, statistical analysis}
}

@article{ggplot2_development_team_ggplot2_2012,
	title = {Ggplot2-0.9.0},_2013
	abstract = {Version 0.9.0 of the ggplot2 package contains a number of changes that provide a user with more flexibility and greater ease of use in the construction of a ggplot. The two most evident improvements from a user's perspective are: (i) the help pages have been expanded consid- erably, with many new examples; and (ii) the computing time has been reduced significantly. Several new geoms are introduced, as well as a few new stat\_ functions.},
	journal = {Production},
	author = {{Ggplot2 Development Team}},
	year = {2012}
}

@article{beaudette_algorithms_2013,
	title = {Algorithms for quantitative pedology: {A} toolkit for soil scientists},
	issn = {00983004},
	doi = {10.1016/j.cageo.2012.10.020},
	abstract = {Soils are routinely sampled and characterized according to genetic horizons, resulting in data that are associated with principle dimensions: location (x, y), depth (z), and property space (p). The high dimensionality and grouped nature of this type of data can complicate standard analysis, summarization, and visualization. The "aqp" (algorithms for quantitative pedology) package was designed to support data-driven approaches to common soils-related tasks such as visualization, aggregation, and classification of soil profile collections. In addition, we sought to advance the study of numerical soil classification by building on previously published methods within an extensible and open source framework. Functions in the aqp package have been successfully applied to studies involving several thousand soil profiles. The stable version of the aqp package is hosted by CRAN (http://cran.r-project.org/web/packages/aqp), and the development version is hosted by R-Forge (http://aqp.r-forge.r-project.org). © 2012.},
	journal = {Computers and Geosciences},
	author = {Beaudette, D. E. and Roudier, P. and O'Geen, A. T.},
	year = {2013},
	keywords = {Aggregation of soil data, Numerical soil classification, Soil classification, Soil data, Soil profile, Soil survey, Visualization}
}

@book{gelman_bayesian_2014,
	title = {Bayesian {Data} {Analysis}},
	isbn = {978-85-7811-079-6},
	abstract = {Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein−protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-α-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM- GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 {\textbackslash}AA for the interface backbone atoms) increased from 21\% with default Glide SP settings to 58\% with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63\% success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40\% of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.},
	author = {Gelman, Andrew and Carlin, John B. and Stern, Hal S. and Dunson, David B. and Vehtari, Aki and Rubin, Donald D.},
	year = {2014},
	pmid = {25246403},
	note = {DOI: 10.1017/CBO9781107415324.004},
	keywords = {icle}
}

@article{rstudio_team_rstudio_2016,
	title = {{RStudio}: {Integrated} {Development} for {R}},
	issn = {0022541X},
	doi = {10.1007/978-81-322-2340-5},
	abstract = {RStudio Team (2015). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com/.},
	journal = {[Online] RStudio, Inc., Boston, MA URL http://www. rstudio. com},
	author = {RStudio Team, -},
	year = {2016}
}

@article{schoeneberger_field_2012,
	title = {Field {Book} for {Describing} and {Sampling} {Soils}},
	url = {https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_052523.pdf},
	journal = {Natural Resources Conservation Service, National  Soil Survey Center,},
	author = {Schoeneberger, P and Wysocki, D and Benham, E and Soil Survey Staff},
	year = {2012},
	file = {Attachment:C\:\\Users\\Skye.Wills\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles\\o3taif5e.default\\zotero\\storage\\9FHPDRZR\\Unknown - 2012 - Field Book for Describing and Sampling Soils.pdf:application/pdf}
}

@article{hoffman_no-u-turn_2014,
	title = {The {No}-{U}-{Turn} {Sampler}: {Adaptively} {Setting} {Path} {Lengths} in {Hamiltonian} {Monte} {Carlo}},
	volume = {15},
	url = {http://www.jmlr.org/papers/volume15/hoffman14a/hoffman14a.pdf},
	abstract = {Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by first-order gradient information. These features allow it to converge to high-dimensional target distributions much more quickly than simpler methods such as random walk Metropolis or Gibbs sampling. However, HMC's performance is highly sensitive to two user-specified parameters: a step size and a desired number of steps L. In particular, if L is too small then the algorithm exhibits undesirable random walk behavior, while if L is too large the algorithm wastes computation. We introduce the No-U-Turn Sampler (NUTS), an extension to HMC that eliminates the need to set a number of steps L. NUTS uses a recursive algorithm to build a set of likely candidate points that spans a wide swath of the target distribution, stopping automatically when it starts to double back and retrace its steps. Empirically, NUTS performs at least as efficiently as (and sometimes more efficiently than) a well tuned standard HMC method, without requiring user intervention or costly tuning runs. We also derive a method for adapting the step size parameter on the fly based on primal-dual averaging. NUTS can thus be used with no hand-tuning at all, making it suitable for applications such as BUGS-style automatic inference engines that require efficient " turnkey " samplers.},
	journal = {Journal of Machine Learning Research},
	author = {Hoffman, Matthew D and Gelman, Andrew},
	year = {2014},
	keywords = {adaptive Monte Carlo, Bayesian inference, dual averaging, Hamiltonian Monte Carlo, Markov chain Monte Carlo},
	pages = {1593--1623},
	file = {Attachment:C\:\\Users\\Skye.Wills\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles\\o3taif5e.default\\zotero\\storage\\BIPM26XU\\Hoffman, Gelman - 2014 - The No-U-Turn Sampler Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.pdf:application/pdf}
}

@article{fry_completion_2011,
	title = {Completion of the 2006 national land cover database for the conterminous {United} {States}},
	issn = {0099-1112},
	abstract = {National Land Cover Database 2006 (NLCD2006) is a 16-class land cover classification scheme that has been applied consistently across the conterminous United States at a spatial resolution of 30 meters. NLCD2006 is based primarily on the unsupervised classification of Landsat Enhanced Thematic Mapper+ (ETM+) circa 2006 satellite data. NLCD2006 also quantifies land cover change between the years 2001 to 2006. The NLCD2006 land cover change product was generated by comparing spectral characteristics of Landsat imagery between 2001 and 2006, on an individual path/row basis, using protocols to identify and label change based on the trajectory from NLCD2001 products. It represents the first time this type of 30 meter resolution land cover change product has been produced for the conterminous United States. A formal accuracy assessment of the NLCD2006 land cover change product is planned for 2011. Generation of NLCD2006 products helped to identify some issues in the NLCD2001 land cover and percent developed imperviousness products only (there were no changes to the NLCD2001 percent canopy). These issues were evaluated and corrected, necessitating a reissue of NLCD2001 products (NLCD2001 Version 2.0) as part of the NLCD2006 release. A majority of the NLCD2001 updates occurred in coastal mapping zones where NLCD2001 was published prior to the completion of the National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program (C-CAP) 2001 land cover products. NOAA C-CAP 2001 land cover has now been seamlessly integrated with NLCD2001 land cover for all coastal zones. NLCD2001 percent developed imperviousness was also updated as part of this process.},
	journal = {Photogrammetric Engineering and Remote Sensing},
	author = {Fry, Joyce a. J A and Xian, George and Jin, Suming and Dewitz, Jon a. J A and Homer, Collin G and Yang, Limin and Barnes, Christopher a. and Herold, Nathaniel D and Wickham, James D},
	year = {2011}
}

@article{wills_quantifying_2013,
	title = {Quantifying {Tacit} {Knowledge} about {Soil} {Organic} {Carbon} {Stocks} {Using} {Soil} {Taxa} and {Official} {Soil} {Series} {Descriptions}},
	volume = {77},
	issn = {0361-5995},
	url = {https://www.soils.org/publications/sssaj/abstracts/77/5/1711},
	doi = {10.2136/sssaj2012.0168},
	language = {en},
	number = {5},
	urldate = {2018-02-16},
	journal = {Soil Science Society of America Journal},
	author = {Wills, Skye and Seybold, Cathy and Chiaretti, Joe and Sequeira, Cleiton and West, Larry},
	year = {2013},
	pages = {1711}
}

@article{kern_spatial_1994,
	title = {Spatial {Patterns} of {Soil} {Organic} {Carbon} in the {Contiguous} {United} {States}},
	volume = {58},
	issn = {0361-5995},
	url = {https://www.soils.org/publications/sssaj/abstracts/58/2/SS0580020439},
	doi = {10.2136/sssaj1994.03615995005800020029x},
	language = {en},
	number = {2},
	urldate = {2018-02-16},
	journal = {Soil Science Sociefty of America Journal},
	author = {Kern, Jeffrey S.},
	year = {1994},
	pages = {439}
}

@book{u.s._department_of_agriculture_natural_resources_conservation_service_national_nodate,
	title = {National soil survey handbook, title 430-{VI}.},
	url = {https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054242},
	author = {U.S. Department of Agriculture, Natural Resources Conservation Service}
}

@article{wickham_split_2011,
    title = {The Split-Apply-Combine Strategy for Data Analysis},
    author = {Wickham, Hadley},
    journal = {Journal of Statistical Software},
    year = {2011},
    volume = {40},
    number = {1},
    pages = {1--29},
    url = {http://www.jstatsoft.org/v40/i01/},
}

@manual{Baptiste_gridExtra_2017,
    title = {gridExtra: Miscellaneous Functions for "Grid" Graphics},
    author = {Baptiste, Auguie},
    year = {2017},
    note = {R package version 2.3},
    url = {https://CRAN.R-project.org/package=gridExtra},
  }
  
@manual{Jeppson_ggmosaic_2017,
    title = {ggmosaic: Mosaic Plots in the 'ggplot2' Framework},
    author = {Jeppson, Haley and Hofmann, Heike  and Cook, Di},
    year = {2017},
    note = {R package version 0.1.2},
    url = {https://CRAN.R-project.org/package=ggmosaic},
  }
  
@incollection{ellert_measuring_2008,
  abstract = {Ellert, B.H., Janzen, H.H., VandenBygaart, A.J., Bremer, E. (2008) Measuring change in soil organic carbon storage. In Carter, M.R., and E.G. Gregorich (ed.) Soil Sampling and Methods of Analysis, 2nd ed., CRC Press, Boca Raton, FL. pp 25–38.},
  address = {Boca Raton, FL},
  author = {{Ellert B.H.} and {Janzen H.H.} and {VandenBygaart A.J.} and {Bremer E.}},
  booktitle = {Soil Sampling and Methods of Analysis},
  edition = {2nd},
  editor = {Carter, M.R. and Gregorich, E.G},
  pages = {25--38},
  publisher = {CRC Press},
  title = {{Measuring change in soil organic carbon storage}},
  year = {2008}
  }