Lead Authors:
Kate Lajtha, Oregon State University
Vanessa L. Bailey, Pacific Northwest National Laboratory
Contributing Authors:
Karis McFarlane, Lawrence Livermore National Laboratory
Keith Paustian, Colorado State University
Dominique Bachelet, Oregon State University
Rose Abramoff, Lawrence Berkeley National Laboratory
Denis Angers, Agriculture and Agri-Food Canada
Sharon A. Billings, University of Kansas
Darrel Cerkowniak, Agriculture and Agri-Food Canada
Yannis G. Dialynas, University of Cyprus (formerly at Georgia Institute of Technology)
Adrien Finzi, Boston University
Nancy H. F. French, Michigan Technological University
Serita Frey, University of New Hampshire
Noel P. Gurwick, U.S. Agency for International Development
Jennifer Harden, U.S. Geological Survey and Stanford University
Jane M. F. Johnson, USDA Agricultural Research Service
Kristofer Johnson, USDA Forest Service
Johannes Lehmann, Cornell University
Shuguang Liu, Central South University of Forestry and Technology
Brian McConkey, Agriculture and AgriFood Canada
Umakant Mishra, Argonne National Laboratory
Scott Ollinger, University of New Hampshire
David Paré, Natural Resources Canada, Canadian Forest Service
Fernando Paz Pellat, Colegio de Postgraduados Montecillo
Daniel deB. Richter, Duke University
Sean M. Schaeffer, University of Tennessee
Joshua Schimel, University of California, Santa Barbara
Cindy Shaw, Natural Resources Canada, Canadian Forest Service
Jim Tang, Marine Biological Laboratory
Katherine Todd-Brown, Pacific Northwest National Laboratory
Carl Trettin, USDA Forest Service
Mark Waldrop, U.S. Geological Survey
Thea Whitman, University of Wisconsin, Madison
Kimberly Wickland, U.S. Geological Survey
Science Lead:
Melanie A. Mayes, Oak Ridge National Laboratory
Review Editor:
Francesca Cotrufo, Colorado State University
Federal Liaison:
Nancy Cavallaro, USDA National Institute of Food and Agriculture


12.4.1 United States

Scientists have used several approaches to estimate U.S. SOC stocks. These stocks may be aggregated in specific land areas such as geopolitical boundaries (i.e., states) or Land Resource Regions, or they may be grouped by soil-order or land-cover classes (Guo et al., 2006; Wills et al., 2014). Most efforts have developed estimates for the conterminous United States (CONUS), but results vary based on methods and assumptions. Guo et al. (2006) estimated SOC stocks for CONUS as between 30 and 150 Pg (0 to 2 m in depth) by soil order using the State Soil Geographic database (STATSGO; USDA Soil Conservation Service 1993) and another 23 to 94 Pg C stock as inorganic carbon within the top 2 m of surface. Compared with CONUS, fewer studies have estimated soil carbon stocks for Alaska. Mishra and Riley (2012) estimated stocks in Alaska as 77 Pg C, an update from the value of 48 Pg estimated by Bliss and Maursetter (2010). The U.S. Geological Survey (USGS) calculated CONUS SOC storage as 77.4 Pg C from the Soil Survey Geographic (SSURGO) database, developed by the USDA Natural Resources Conservation Service (NRCS). This information is supplemented with data from the Digital General Soil Map of the United States (STATSGO2; catalog.data.gov/dataset/u-s-general-soil-map-statsgo2-for-the-united-states-of-america; Sundquist et al., 2009; see Table 12.1).

Table 12.1. Estimates of Soil Carbon Storage in the Conterminous United States in Different Land-Use Classesa–d

Land Cover Soil Organic Carbon (from RaCAe) Soil Organic Carbon (Bliss et al., 2014) Soil Organic Carbon (Sundquist et al., 2009) Soil Organic Carbon (Other Estimates)
Forests and Woodlands 20 13. 25.1 28f
Agriculture 13 13.4 27.4d
Shrublands 5.6 9.7
Urban 3.3 1.9g
Wetlands 14 8.9 13.5h – 11.5i
Rangelands (+ Pasture) 19 12.3 11.2d
Totals 65 57.2j 73.4

a Storage measured in soil down to 1 m in depth.
b All values are in petagrams of carbon (Pg C).
c No total is given for “Other Estimates” values because the values do not represent all land-use classes and some land-use classes likely overlap (e.g., urban is partially accounted for in agriculture [see d] and developed; range estimates likely include some agricultural land).
d “Agriculture” is listed in Sundquist et al. (2009) as “agriculture and developed”; “rangelands and pasture” is listed as “other” and includes all grasslands.
e RaCA, U.S. Department of Agriculture’s Rapid Carbon Assessment.
f Domke et al. (2017).
g Pouyat et al. (2006).
h From the Second State of the Carbon Cycle Report (SOCCR2), Ch. 13: Terrestrial Wetlands
i Nahlik and Fennessy (2016).
j Total soil profile of carbon is 73 Pg.

The NRCS’s recent RaCA project captures information on the carbon content of soils across CONUS at a relatively uniform point in time (Soil Survey and Loecke 2016). A secondary goal was to capture SOC stocks in different kinds of soils and land uses. For this assessment, RaCA collected 144,833 samples from the upper 1 m of 32,084 soil profiles at 6,017 randomly selected locations across the United States. Independently developed soil groups for each RaCA region were combined with land-use, land-cover information, yielding an estimate of the total carbon stock across CONUS of 65 Pg C (see Figure 12.2). Different estimates of soil carbon pools are expected to differ; individual soil and land-cover classes have different levels of uncertainties surrounding their carbon pool estimates, and errors can include land-classification differences and different ways of aggregating sparse data. For example, Domke et al. (2017) used the USDA Forest Service’s Forest Inventory and Analysis (or FIA) data to project SOC density in CONUS forest types and parts of Alaska and compared regional projections to those from RaCA. These modeled SOC density projections were substantially smaller than those of RaCA for most NRCS Land Resource Regions, at times by more than a factor of three.


Figure 12.2: Rapid Carbon Assessment (RaCA) of Soil Organic Carbon (SOC) Stock Values

Figure 12.2: Data are in megagrams (Mg) of carbon per hectare (ha) to 100 cm. Soil group strata and land use and land cover (LULC) strata were linked together into a LULC-Soil Group Combination, designated as “LUGR.” Prepared using the geometric mean of pedon stocks according to RaCA methodology. [Figure source: Reprinted from U.S. Department of Agriculture Natural Resources Conservation Service, Soil Survey Staff, RaCA project. Prepared by Skye Wills, 2016]


Carbon storage in interior CONUS wetlands are assessed (see Ch. 13: Terrestrial Wetlands) using a combination of NRCS SSURGO data and the U.S. Fish and Wildlife Service’s (USFWS) National Wetland Inventory. These estimates of the upper 1 m indicate that terrestrial wetlands store about 13.6 Pg C, a value very similar to that of Nahlik and Fennessy (2016), who reported a value of 11.5 Pg. Storage of carbon in CONUS saline wetlands is significantly lower. Estimates of tidal wetland soil stocks along the freshwater-to-saline transition area plus the seagrass soil stocks are 0.8 Pg C for “blue carbon” ecosystems (see Ch. 15: Tidal Wetlands and Estuaries). Given that more than half the historical U.S. wetland area has been lost due to anthropogenic activities, further loss of wetland soils represents a key vulnerability that could result in a net transfer of carbon from the soil to the atmosphere.

12.4.2 Mexico

The most recent estimate of soil carbon stocks in Mexico is reported to a depth of only 30 cm. According to Jobbágy and Jackson (2000), the top 20 cm of soil typically represents 40% of total soil carbon stocks averaged across vegetation communities in Mexico. At 9.13 Pg C in the top 30 cm, this reported SOC stock is 73% of the country’s total terrestrial stock (CONAFOR 2010), but a conservative estimate of SOC stocks to 1 m in depth might be 18 Pg C, assuming that the top 30 cm represents about half the total soil carbon stocks. However, this estimate remains highly uncertain as acquisition of field data to fill data gaps (e.g., bulk density measurements) and spatial extrapolation methods continue to evolve (de Jong et al., 2010). For example, simply using different versions of land-cover maps for spatially extrapolating mean SOC values results in significant differences for semitropical low forests and mangroves (Paz Pellat et al., 2016). Despite these issues, almost half (48%) of Mexico’s SOC appears to be contained in forests, especially the dry deciduous, semi-evergreen, and oak forests (see Tables 12.2, this page, and 12.3). Furthermore, grazing lands accounted for 23% of the total SOC stock, mostly due to their extensive area. Finally, despite the relatively low soil carbon density of shrublands, they were extensive enough to account for 7% of the total SOC stock (Paz Pellat et al., 2016).

Table 12.2. Soil Organic Carbon Distribution in Mexico by FAO FRAa Classesb

FAO FRA Classesa Area in Millions of Hectares Petagrams of Carbon
Forestlands 65 4.3
Other Forestlands 20 0.6
Other Lands 108 4.1
Planted Forest 0.33 < 0.01
Totals 194 9.1

a Global Forest Resources Assessment (FRA) of the United Nations Food and Agriculture Organization (FAO).
b From Paz Pellat et al. (2016).

Table 12.3. Soil Organic Carbon Distribution in Mexico for Vegetation Types with Top Five Highest Total Soil Carbon Estimatesa

Vegetation Types (Top Five) Area in Millions of Hectares Teragrams of Carbon Percent of Total
Grazing Lands 50 2,115 23
Deciduous Dry Forest 14 690 8
Desert Microphyll Shrub 22 600 7
Medium Semi-Evergreen Forest 5 570 6
Oak Forest 11 564 6

a From the National Institute for Statistics and Geography of Mexico for 2007 (from Paz Pellat et al., 2016).

At the national scale, CO2 fluxes from mineral soils to the atmosphere were estimated as 30.2 Tg CO2 per year, mostly from deforestation of secondary oak, pine-oak, and tropical dry forests (de Jong et al., 2010). About 10% of Mexico’s land is strongly affected by soil erosion, with about 36% remaining stable (Bolaños-González et al., 2016).

Temperate forests in Mexico are potential areas of carbon sequestration because about 10% of total GHG emissions in Mexico are attributed to land-use change from opening new areas to cultivation and logging. Tropical forests in Mexico also experience much of the same pressures of land-use change, but they occur over stronger gradients of precipitation. Land-use change from forest to pasture appears to interact strongly with precipitation. For example, dry tropical forest conversion to pasture may increase SOC (3.7% at 788 mm per year), yet this same land-use change appears to decrease SOC as precipitation increases (–0.2% at 2,508 mm per year; –2.2% at 4,725 mm per year; Campo et al., 2016). Mangroves in Mexico have the highest density of soil carbon (364 Mg C per hectare), located throughout Mexico’s extensive coastline and riverine systems. A variety of disturbances affect mangroves and, as in many parts of the world, include erosion, increasing sea level change, and salt intrusion (Gilman et al., 2008). Due to the difficultly in sampling these soils, few estimates are available, especially if attempting to quantify this stock to the bottom of the organic layer. Nevertheless, the Gulf of Mexico region generally has the highest carbon stocks (1,300 Mg C per hectare) of SOC compared with those of the other regions in Mexico (100 to 1,100 Mg per hectare; Herrera Silveira et al., 2016).

12.4.3 Canada

Canada has a total land area of 998.5 megahectares (Mha) that contains 72.2 gigatons of carbon (Gt C) to a depth of 30 cm (Tarnocai 1997). The total of 55.2 Mha of land currently used for agriculture contain about 4.14 Gt C to a depth of 30 cm and 5.5 Gt to 1 m. As about 80% of agricultural land is located in the Canadian Prairies, most (approximately 88%) SOC is also found in Prairie soils, which are mostly carbon-rich Chernozemic soils developed under grassland. Tarnocai (1997) estimated a total of 262.3 Pg C in soils within the tundra, forest, and agricultural regions of Canada. Over half the carbon (147.1 Pg C; Tarnocai 2006) is in organic (peat) soils, some of which are affected by permafrost. Total soil carbon estimates for Canada likely will increase as knowledge of deep carbon stocks in permafrost soils increases (Hugelius et al., 2014). For example, Kurz et al. (2013) estimated that soils in Canada’s boreal forest region alone contain 208 Pg C, which is about 80% of the Tarnocai (1997) estimate of the total carbon stocks in Canada. Of this 208 Pg, the majority (137 Pg) of the boreal soil carbon stocks are in the deep organic soils of the country’s extensive peatlands, and the remainder (71 Pg) are in upland forest soils that often have thick organic soil horizons (42 to 55 Mg C per hectare; estimated from Letang and de Groot 2012) that overlay the mineral soil (Kurz et al., 2013; see Table 12.4).

Table 12.4. Estimates of Soil Carbon Storage in Canadaa–b

Land Cover Soil Organic Carbon
Organic (Peat) Soils 147.1c, 137e
Agriculture 5.5d
Boreal Forest Region 208e, f
Upland Forest Soils 71e
Total 262.3c, g

a Storage measured in soil down to 1 m in depth.
b Values in petagrams.
c Tarnocai (2006).
d Tarnocai (1997).
e Kurz et al. (2013).
f Note that this overlaps with estimates of organic peat soil carbon.
g Columns do not add up due to overlap in categories.

Canadian forest soil carbon research over the last decade has focused on understanding the dynamics of SOC as influenced by 1) mosses (Bona et al., 2013, 2016); 2) forest composition and soil taxonomy (Laganiere et al., 2015; Shaw et al., 2008, 2015); 3) invasive earthworms (Cameron et al., 2015); 4) response to temperature changes (Laganiere et al., 2015; Smyth et al., 2011); 5) response to wildfire, specifically in peatlands (Granath et al., 2016; Kettridge et al., 2015); and 6) recovery patterns (Ward et al., 2014). Under development is a national peatland carbon modeling system (Webster et al., 2016) that will fill information gaps previously identified, including a peatland-type map; landscape-scale modeling of forested, treed, and nontreed peatland types; water table fluctuation in response to climate change; and CH4 fluxes (Shaw et al., 2016). Eventually, responses to permafrost thaw, wildfire, and anthropogenic disturbances will be included (Shaw et al., 2016; Webster et al., 2016). Several new spatial products and databases have improved the understanding of relationships among vegetation types (Beaudoin et al., 2014; Thompson et al., 2016) and changes in disturbance-type patterns (Hermosilla et al., 2016), improving accuracy and enhancing the ability to scale up and integrate results from fine-scale to landscape-scale studies reporting national GHG emissions.

The 55.7 Mha of land that currently are used for agriculture in Canada are estimated to contain about 4.3 Pg C to a depth of 30 cm and 6.6 Pg C to 1 m using the Canadian Soil Information Service (CanSIS) National Soil Database. As of 2013, Canadian agricultural land removed 11 Tg CO2 per year, an amount which represents about 2% of the total national GHG emissions (ECCC 2015). This is due largely to a reduction in the use of summer fallow lands and increased adoption of no-till practices in the Canadian Prairies. However, this value has declined from the reported 13 Tg in 2005 because changes in SOC stocks and fluxes tend to reach equilibrium at some point after a change in conditions.

12.4.4 Arctic and Boreal Ecosystems

Arctic and boreal ecosystems cover about 22% of the global land surface (Chapin et al., 2000) and contain 1,035 ± 150 Pg C in the upper 3 m of surface soil (Hugelius et al., 2014), amounts which equal about 33% of the total global surface SOC pool (Jobbágy and Jackson 2000; Schuur et al., 2015). The presence of permafrost and waterlogged soils in boreal and Arctic soils has allowed the accumulation of large quantities of carbon in this biome (McGuire et al., 2009; see Ch. 11: Arctic and Boreal Carbon for more details). Deep soils (>3 m in depth) contain significant stocks estimated between 210 ± 70 Pg C and 456 ± 45 Pg C, particularly in carbon-rich Pleistocene-age sediments called “yedoma” found in unglaciated parts of Alaska and Siberia, as well as in their alluvial deposits (Hugelius et al., 2014).

The changing disturbance regime can strongly affect soil carbon storage and flux. Permafrost thaw (Schuur et al., 2015) is tied to changes in the timing, frequency, and severity of wildfires (Chapin et al., 2010; Kasischke et al., 2010), plant community composition (Mann et al., 2012), and alterations in the hydrological cycle (Jorgenson et al., 2001, 2010; Roach et al., 2013). Thaw will affect both storage and fluxes of carbon as the climate continues to warm. An estimated 5% to 15% of the terrestrial permafrost carbon pool is thought to be vulnerable to decomposition and release to the atmosphere, based on a synthesis of experimental studies, ecosystem models, and expert assessments (Schuur et al., 2015). Carbon loss from peatlands has shown large responses to water table fluctuations (Waddington et al., 2015), wildfire events (Turetsky et al., 2011), and permafrost thaw (Jones et al., 2017; Wisser et al., 2011). Key uncertainties as to the future of carbon storage in Arctic and boreal regions include the extent to which plant community productivity will respond to elevated CO2 (McGuire et al., 2009), whether landscapes will become wetter or drier in the future (Schuur et al., 2015), the magnitude of winter fluxes (Commane et al., 2017), and the extent of the permafrost carbon feedback (Schaefer et al., 2011; Schuur et al., 2015).

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