Toward inventory‐based estimates of soil organic carbon in forests of the United States

Abstract

Soil organic carbon (SOC) is the largest terrestrial carbon (C) sink on Earth; this pool plays a critical role in ecosystem processes and climate change. Given the cost and time required to measure SOC, and particularly changes in SOC, many signatory nations to the United Nations Framework Convention on Climate Change report estimates of SOC stocks and stock changes using default values from the Intergovernmental Panel on Climate Change or country-specific models. In the United States, SOC in forests is monitored by the national forest inventory (NFI) conducted by the Forest Inventory and Analysis (FIA) program within the U.S. Department of Agriculture, Forest Service. The FIA program has been consistently measuring soil attributes as part of the NFI since 2001 and has amassed an extensive inventory of SOC in forest land in the conterminous United States and southeast and southcentral coastal Alaska. That said, the FIA program has been using country-specific predictions of SOC based, in part, upon a model using SOC estimates from the State Soil Geographic (STATSGO) database compiled by the Natural Resources Conservation Service. Estimates obtained from the STATSGO database are averages over large map units and are not expected to provide accurate estimates for specific locations, e.g., NFI plots. To improve the accuracy of SOC estimates in U.S. forests, NFI SOC observations were used for the first time to predict SOC density to a depth of 100 cm for all forested NFI plots. Incorporating soil-forming factors along with observations of SOC into a new estimation framework resulted in a 75% (48 ± 0.78 Mg/ha) increase in SOC densities nationally. This substantially increases the contribution of the SOC pool, from approximately 44% (17 Pg) of the total forest ecosystem C stocks to 56% (28 Pg), in the forest C budget of the United States.

Soil Organic Carbon Across Mexico and the Conterminous United States (1991–2010)

“…Our SOC estimates across forested areas are comparable to those reported on studies (Bolaños González et al, ; Domke et al, ). However, our results show high uncertainty across areas dominated with high SOC values (e.g., >1 g/cm 2 , some northern and tropical forests, peatlands, and other black soils dominated areas) and across higher latitudes (Figure ), as documented in previous studies (Tian et al, ).…”

Carbon stocks and stock change on federal forest lands of the United States

“…4, top row). However, the recent move toward directly basing litter and soil organic carbon estimates on FIA plot data (Domke et al 2016(Domke et al , 2017 is more apparent (Fig. 4, middle and bottom rows).…”

“…The Forest Service is continually improving this process of obtaining carbon estimates from forest inventory (e.g., see Domke et al. , ). One result of these updates is that on average over 98% of ecosystem carbon on forest plots is estimated according to different conversion factors or models today (U.S. EPA ) relative to similar scope summaries from almost a decade ago (U.S. EPA , Heath et al.…”

“…Many of the carbon or biomass fields in the FIADB tables that explicitly provide carbon quantities by ecosystem pool, especially in the condition tables, predate most-current estimates . The Forest Service is continually improving this process of obtaining carbon estimates from forest inventory (e.g., see Domke et al 2016Domke et al , 2017. One result of these updates is that on average over 98% of ecosystem carbon on forest plots is estimated according to different conversion factors or models today (U.S. EPA 2018) relative to similar scope summaries from almost a decade ago (U.S. EPA 2010).…”

Networking our science to characterize the state, vulnerabilities, and management opportunities of soil organic matter

“…While there are robust, broadly consistent methods for accounting for and predicting future C stocks in forest aboveground biomass, there is less consensus on methods for assessing belowground SOC. Long-term monitoring (Johnson & Todd, 1998;McLaughlin & Phillips, 2006), experimental manipulation (Edwards & Ross-Todd, 1983;Gundale et al, 2005), expert review (Jandl et al, 2007;Lal, 2005), quantitative synthesis ( These applications may be aided by promising advances in digital soil mapping (Mansuy et al, 2014;Mishra & Riley, 2015) and spatially explicit soil carbon assessments (Domke et al, 2017;Soil Survey Staff 2013).…”