The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships
This content will become publicly available on June 29, 2024
- Award ID(s):
- 2242034
- NSF-PAR ID:
- 10446667
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Nature
- Volume:
- 618
- Issue:
- 7967
- ISSN:
- 0028-0836
- Page Range / eLocation ID:
- 981 to 985
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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