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Model uncertainty obscures major driver of soil carbon/Reply
Tao et al.1 applied a linear mixed-effects model to this dataset that included CUE, mean annual temperature (MAT), soil depth and random effects and explained 55% of the variation in the log-transformed SOC (Fig. 2a and Extended Data Table 1 in Tao et al.1). To address uncertainties in model structu...
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Published in: | Nature (London) 2024-03, Vol.627 (8002), p.E1-E6 |
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Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
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Summary: | Tao et al.1 applied a linear mixed-effects model to this dataset that included CUE, mean annual temperature (MAT), soil depth and random effects and explained 55% of the variation in the log-transformed SOC (Fig. 2a and Extended Data Table 1 in Tao et al.1). To address uncertainties in model structure and parameters that impede robust model predictions, the authors used a comprehensive model-data-assimilation approach to calibrate a selection of 23 parameters of a SOC model based on a global dataset of SOC measurements. [...]Tao et al.1 approximated C inputs to the soil using NPP from predictions of a land surface model. [...]future research efforts should be allocated towards investigating several mechanisms of SOC stabilization and loss, rather than solely focusing on CUE. |
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ISSN: | 0028-0836 1476-4687 |
DOI: | 10.1038/s41586-023-06999-1 |