International Soil Carbon Network (ISCN) Database v3-1

Nave, L. E.; Johnson, K. D.; Ingen, Catharine van; Agarwal, Deborah; Humphrey, Marty; Beekwilder, Norman

doi:10.17040/iscn/1305039

Cited by 9 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our framework is based on data compiled by the ISCN (Nave et al, 2016(Nave et al, ) (years 1944(Nave et al, -2014, which is a database with 19,292 observations for soil N (percent by weight in an oven-dried sample) at different soil horizon-based depths (Figure 1). We also compiled 21 environmental F I G U R E 1 Flow diagram of the methodological steps that we used to predict soil N spatial variability, associated uncertainty, and quantify correlations with total N deposition and biomass.…”

Section: Methodsmentioning

confidence: 99%

“…The overarching goal of the present work is to provide quantitative information of the spatial distribution (at 5-km spatial grids) of total soil N (percent by weight of organic and inorganic N in an oven-dried sample) from the International Soil Carbon Network (ISCN; Nave et al, 2016) and associated uncertainties across the conterminous United States (CONUS). We developed a machine learning framework based on DSM to predict soil N and associated uncertainties in a threedimensional space (i.e., latitude, longitude, and six soil depths).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial variability and uncertainty of soil nitrogen across the conterminous United States at different depths

et al. 2022

View full text Add to dashboard Cite

Soil nitrogen (N) is an important driver of plant productivity and ecosystem functioning; consequently, it is critical to understand its spatial variability from local‐to‐global scales. Here, we provide a quantitative assessment of the three‐dimensional spatial distribution of soil N across the United States (CONUS) using a digital soil mapping approach. We used a random forest‐regression kriging algorithm to predict soil N concentrations and associated uncertainty across six soil depths (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) at 5‐km spatial grids. Across CONUS, there is a strong spatial dependence of soil N, where soil N concentrations decrease but uncertainty increases with soil depth. Soil N was higher in Pacific Northwest, Northeast, and Great Lakes National Ecological Observatory Network (NEON) ecoclimatic domains. Model uncertainty was higher in Atlantic Neotropical, Southern Rockies/Colorado Plateau, and Southeast NEON domains. We also compared our soil N predictions with satellite‐derived gross primary production and forest biomass from the National Biomass and Carbon Dataset. Finally, we used uncertainty information to propose optimized locations for designing future soil surveys and found that the Atlantic Neotropical, Pacific Northwest, Pacific Southwest, and Appalachian/Cumberland Plateau NEON domains may require larger survey efforts. We highlight the need to increase knowledge of biophysical factors regulating soil processes at deeper depths to better characterize the three‐dimensional space of soils. Our results provide a national benchmark regarding the spatial variability and uncertainty of soil N and reveal areas in need of a better representation.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial variability and uncertainty of soil nitrogen across the conterminous United States at different depths

et al. 2022

View full text Add to dashboard Cite

show abstract

“…If all soil data adhered to a common template, or data model, with uniform tables and column names, then it would be trivial to append the data from one study with the data from a second. Unfortunately, due to the diversity of soil types and methodologies, as well as ever evolving measurement technologies, we feel that this is impractical for soil research data, although several valiant efforts are underway to do this (Nave et al, 2016;Lawrence et al, 2020). In practice, researchers will continue to develop their own data tables and internal conventions that make sense for their experimental structure, location, and measurement type.…”

Section: Community Toolsmentioning

confidence: 99%

Comment on bg-2021-323

2022

View full text Add to dashboard Cite

“…DSMW (FAO et al, 2012), complemented soil information, and eventually superseded DSMW. ISRIC-World Soil Information 60 further used the soil profiles from Harmonized continental SOTER-derived database (SOTWIS) (van Engelen et al, 2005) and the International Soil Carbon Network (ISCN) (Nave et al, 2015) to supplement the data missing area of HWSD and constructed WISE30sec.…”

Section: Introduction 30mentioning

confidence: 99%

On the magnitude and uncertainties of global and regional soil organic carbon: A comparative analysis using multiple estimates

Lin

Dai

Mishra

et al. 2022

Preprint

View full text Add to dashboard Cite

Abstract. Globally, soil is one of the largest terrestrial carbon reservoirs, with soil organic carbon (SOC) regulating overall soil carbon dynamics. Robust quantification of SOC stocks in existing global observation-based estimates avails accurate predictions in carbon climate feedbacks and future climate trends. In this study, we investigated global and regional SOC estimates, based on five widely used global gridded SOC datasets (HWSD, WISE30sec, GSDE, SoilGrids250m, and GSOCmap), a regional permafrost dataset from Mishra et al. (UM2021), and a global-scale soil profile database (the World Soil Information Service soil profile database, WoSIS) reporting measurements of a series physical and chemical edaphic attributes. Our comparative analyses show that the magnitude and distribution of SOC varies widely among datasets, with certain datasets showing region-specific robustness. At the global scale, the magnitude of SOC stocks simulated by GSDE, GSOCmap, and WISE30sec are comparable, while estimates of SoilGrids250m and HWSD are at the upper and lower ends, respectively. Global SOC stocks ranged from 577–1171 Pg C and 1086–2678 Pg C at 0–30 cm and 0–100 cm depth. The spatial distribution of SOC stocks varies greatly among datasets, especially in the northern circumpolar and Tibetan Plateau permafrost regions. In general, the UM2021 and WISE30sec perform better in the northern circumpolar permafrost regions, and GSDE performs better in China. SOC stocks estimated by different datasets also show large variabilities across different soil layers and biomes. Overall, GSOCmap performs well at 0–30 cm depth, while SoilGrids250m and GSDE perform better at multiple depths. Among the five gridded global datasets, SoilGrids250m exhibits a more consistent spatial pattern and depth distribution with WoSIS. Large uncertainties in existing global gridded SOC estimates are generally derived from soil sampling density, diverse sources and mapping methods for soil datasets. We call for future efforts for standardizing soil sampling efforts, cross-dataset comparison, proper validation, and overall global collaboration to improve SOC estimates. The data are available at https://doi.org/10.6084/m9.figshare.20220234 (Lin et al., 2022).

show abstract

International Soil Carbon Network (ISCN) Database v3-1

Cited by 9 publications

References 0 publications

Spatial variability and uncertainty of soil nitrogen across the conterminous United States at different depths

Spatial variability and uncertainty of soil nitrogen across the conterminous United States at different depths

Comment on bg-2021-323

On the magnitude and uncertainties of global and regional soil organic carbon: A comparative analysis using multiple estimates

Contact Info

Product

Resources

About