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Abstract Soil erosion diminishes agricultural productivity by driving the loss of soil organic carbon (SOC). The ability to predict SOC redistribution is important for guiding sustainable agricultural practices and determining the influence of soil erosion on the carbon cycle. Here, we develop a landscape evolution model that couples soil mixing and transport to predict soil loss and SOC patterns within agricultural fields. Our reduced complexity numerical model requires the specification of only two physical parameters: a plow mixing depth,Lp, and a hillslope diffusion coefficient,D. Using topography as an input, the model predicts spatial patterns of surficial SOC concentrations and complex 3D SOC pedostratigraphy. We use soil cores from native prairies to determine initial SOC‐depth relations and the spatial pattern of remote sensing‐derived SOC in adjacent agricultural fields to evaluate the model predictions. The model reproduces spatial patterns of soil loss comparable to those observed in satellite images. Our results indicate that the distribution of soil erosion and SOC in agricultural fields can be predicted using a simple geomorphic model where hillslope diffusion plays a dominant role. Such predictions can aid estimates of carbon burial and evaluate the potential for future soil loss in agricultural landscapes.
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Soil organic carbon is not just for soil scientists: measurement recommendations for diverse practitioners
Abstract Soil organic carbon (SOC) regulates terrestrial ecosystem functioning, provides diverse energy sources for soil microorganisms, governs soil structure, and regulates the availability of organically bound nutrients. Investigators in increasingly diverse disciplines recognize how quantifying SOC attributes can provide insight about ecological states and processes. Today, multiple research networks collect and provide SOC data, and robust, new technologies are available for managing, sharing, and analyzing large data sets. We advocate that the scientific community capitalize on these developments to augment SOC data sets via standardized protocols. We describe why such efforts are important and the breadth of disciplines for which it will be helpful, and outline a tiered approach for standardized sampling of SOC and ancillary variables that ranges from simple to more complex. We target scientists ranging from those with little to no background in soil science to those with more soil‐related expertise, and offer examples of the ways in which the resulting data can be organized, shared, and discoverable.
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- PAR ID:
- 10452984
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecological Applications
- Volume:
- 31
- Issue:
- 3
- ISSN:
- 1051-0761
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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