Quantifying spatial and vertical variations in soil C:N relationships in permafrost-affected landscapes
Permafrost regions are experiencing rapid changes that affect carbon (C) and nitrogen (N) cycles, with implications for vegetation dynamics and gas exchanges with the atmosphere. Soil C:N ratio is a key indicator of organic matter quality, yet spatial estimates of N stocks and C:N ratios lag behind...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Geoderma |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0016706125002563 |
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| author | Joshua O. Minai Julie D. Jastrow Roser Matamala Chien-Lu Ping Gary J. Michaelson Nicolas A. Jelinski |
| author_facet | Joshua O. Minai Julie D. Jastrow Roser Matamala Chien-Lu Ping Gary J. Michaelson Nicolas A. Jelinski |
| author_sort | Joshua O. Minai |
| collection | DOAJ |
| description | Permafrost regions are experiencing rapid changes that affect carbon (C) and nitrogen (N) cycles, with implications for vegetation dynamics and gas exchanges with the atmosphere. Soil C:N ratio is a key indicator of organic matter quality, yet spatial estimates of N stocks and C:N ratios lag behind those for C. We used quantile regression forests to compare direct and indirect digital soil mapping approaches for predicting soil C:N ratios at 0–30, 30–60, and 60–100 cm depths across a latitudinal transect in Alaska. The indirect approach – deriving C:N from separately predicted C and N stocks – outperformed direct mapping for the surface layer (0–30 cm), while direct mapping was marginally better at greater depths. However, prediction accuracy decreased with depth for both methods. Temperature and topography were the most important predictors. Both approaches overestimated low and underestimated high C:N ratios, with direct mapping showing greater bias. Our results underscore the challenges of modeling C:N ratios in heterogeneous, data-sparse permafrost soils, but also suggest that indirect mapping holds promise if supported by more extensive datasets. |
| format | Article |
| id | doaj-art-d15ff4e6d8a347df99a589a63785f8bb |
| institution | Kabale University |
| issn | 1872-6259 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geoderma |
| spelling | doaj-art-d15ff4e6d8a347df99a589a63785f8bb2025-08-20T04:00:34ZengElsevierGeoderma1872-62592025-08-0146011741810.1016/j.geoderma.2025.117418Quantifying spatial and vertical variations in soil C:N relationships in permafrost-affected landscapesJoshua O. Minai0Julie D. Jastrow1Roser Matamala2Chien-Lu Ping3Gary J. Michaelson4Nicolas A. Jelinski5Argonne National Laboratory, Lemont, IL, United States; Corresponding author.Argonne National Laboratory, Lemont, IL, United StatesArgonne National Laboratory, Lemont, IL, United StatesUniversity of Alaska Fairbanks, Palmer, AK, United StatesUniversity of Alaska Fairbanks, Palmer, AK, United StatesUniversity of Minnesota Twin Cities, St. Paul, MN, United StatesPermafrost regions are experiencing rapid changes that affect carbon (C) and nitrogen (N) cycles, with implications for vegetation dynamics and gas exchanges with the atmosphere. Soil C:N ratio is a key indicator of organic matter quality, yet spatial estimates of N stocks and C:N ratios lag behind those for C. We used quantile regression forests to compare direct and indirect digital soil mapping approaches for predicting soil C:N ratios at 0–30, 30–60, and 60–100 cm depths across a latitudinal transect in Alaska. The indirect approach – deriving C:N from separately predicted C and N stocks – outperformed direct mapping for the surface layer (0–30 cm), while direct mapping was marginally better at greater depths. However, prediction accuracy decreased with depth for both methods. Temperature and topography were the most important predictors. Both approaches overestimated low and underestimated high C:N ratios, with direct mapping showing greater bias. Our results underscore the challenges of modeling C:N ratios in heterogeneous, data-sparse permafrost soils, but also suggest that indirect mapping holds promise if supported by more extensive datasets.http://www.sciencedirect.com/science/article/pii/S0016706125002563Permafrost SoilsC:N ratioDigital Soil MappingQuantile Regression Forests |
| spellingShingle | Joshua O. Minai Julie D. Jastrow Roser Matamala Chien-Lu Ping Gary J. Michaelson Nicolas A. Jelinski Quantifying spatial and vertical variations in soil C:N relationships in permafrost-affected landscapes Geoderma Permafrost Soils C:N ratio Digital Soil Mapping Quantile Regression Forests |
| title | Quantifying spatial and vertical variations in soil C:N relationships in permafrost-affected landscapes |
| title_full | Quantifying spatial and vertical variations in soil C:N relationships in permafrost-affected landscapes |
| title_fullStr | Quantifying spatial and vertical variations in soil C:N relationships in permafrost-affected landscapes |
| title_full_unstemmed | Quantifying spatial and vertical variations in soil C:N relationships in permafrost-affected landscapes |
| title_short | Quantifying spatial and vertical variations in soil C:N relationships in permafrost-affected landscapes |
| title_sort | quantifying spatial and vertical variations in soil c n relationships in permafrost affected landscapes |
| topic | Permafrost Soils C:N ratio Digital Soil Mapping Quantile Regression Forests |
| url | http://www.sciencedirect.com/science/article/pii/S0016706125002563 |
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