An official website of the United States government
Soil respiration that releases CO 2 into the atmosphere roughly balances the net primary productivity and varies widely in space and time. However, predicting its spatial variability, particularly in intensively managed landscapes, is challenging due to a lack of understanding of the roles of soil organic carbon (SOC) redistribution resulting from accelerated soil erosion. Here we simulate the heterotrophic carbon loss (HCL)—defined as microbial decomposition of SOC—with soil transport, SOC surface redistribution, and biogeochemical transformation in an agricultural field. The results show that accelerated soil erosion extends the spatial variation of the HCL, and the mechanical-mixing due to tillage further accentuates the contrast. The peak values of HCL occur in areas where soil transport rates are relatively small. Moreover, HCL has a strong correlation with the SOC redistribution rate rather than the soil transport rate. This work characterizes the roles of soil and SOC transport in restructuring the spatial variability of HCL at high spatio-temporal resolution.
more »
« less
A Landscape Evolution Modeling Approach for Predicting Three‐Dimensional Soil Organic Carbon Redistribution in Agricultural Landscapes
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.
more »
« less
- Award ID(s):
- 1653191
- PAR ID:
- 10375659
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Biogeosciences
- Volume:
- 127
- Issue:
- 2
- ISSN:
- 2169-8953
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Soil is the source of the vast majority of food consumed on Earth, and soils constitute the largest terrestrial carbon pool. Soil erosion associated with agriculture reduces crop productivity, and the redistribution of soil organic carbon (SOC) by erosion has potential to influence the global carbon cycle. Tillage strongly influences the erosion and redistribution of soil and SOC. However, tillage is rarely considered in predictions of soil erosion in the U.S.; hence regionwide estimates of both the current magnitude and future trends of soil redistribution by tillage are unknown. Here we use a landscape evolution model to forecast soil and SOC redistribution in the Midwestern United States over centennial timescales. We predict that present‐day rates of soil and SOC erosion are 1.1 ± 0.4 kg ⋅ m−‐2 ⋅ yr−‐1and 12 ± 4 g ⋅ m−2 ⋅ yr−1, respectively, but these rates will rapidly decelerate due to diffusive evolution of topography and the progressive depletion of SOC in eroding soil profiles. After 100 years, we forecast that 8.8 (+1.9/−2.1) Pg of soil and 0.17 (+0.03/−0.04) Pg of SOC will have eroded, causing the surface concentration of SOC to decrease by 4.4% (+0.9/−1.1%). Model simulations that include more widespread adoption of low‐intensity tillage (i.e., no‐till farming) determine that soil redistribution, SOC redistribution, and surficial SOC loss after 100 years would decrease by ∼95% if low‐intensity tillage is fully adopted. Our findings indicate that low‐intensity tillage could greatly decrease soil degradation and that the potential for agricultural soil erosion to influence the global carbon cycle will diminish with time due to a reduction in SOC burial.more » « less
-
Soil erosion in agricultural landscapes reduces crop yields, leads to loss of ecosystem services, and influences the global carbon cycle. Despite decades of soil erosion research, the magnitude of historical soil loss remains poorly quantified across large agricultural regions because preagricultural soil data are rare, and it is challenging to extrapolate local-scale erosion observations across time and space. Here we focus on the Corn Belt of the midwestern United States and use a remote-sensing method to map areas in agricultural fields that have no remaining organic carbon-rich A-horizon. We use satellite and LiDAR data to develop a relationship between A-horizon loss and topographic curvature and then use topographic data to scale-up soil loss predictions across 3.9 × 105km2of the Corn Belt. Our results indicate that 35 ± 11% of the cultivated area has lost A-horizon soil and that prior estimates of soil degradation from soil survey-based methods have significantly underestimated A-horizon soil loss. Convex hilltops throughout the region are often completely denuded of A-horizon soil. The association between soil loss and convex topography indicates that tillage-induced erosion is an important driver of soil loss, yet tillage erosion is not simulated in models used to assess nationwide soil loss trends in the United States. We estimate that A-horizon loss decreases crop yields by 6 ± 2%, causing $2.8 ± $0.9 billion in annual economic losses. Regionally, we estimate 1.4 ± 0.5 Pg of carbon have been removed from hillslopes by erosion of the A-horizon, much of which likely remains buried in depositional areas within the fields.more » « less
-
Abstract Terrestrial cosmogenic nuclides (TCN) are widely employed to infer denudation rates in mountainous landscapes. The calculation of an inferred denudation rate (Dinf) from TCN concentrations is typically performed under the assumptions that denudation rates were steady during TCN accumulation and that soil chemical weathering negligibly impacted soil mineral abundances. In many landscapes, however, denudation rates were not steady and soil composition was significantly impacted by chemical weathering, which complicates interpretation of TCN concentrations. We present a landscape evolution model that computes transient changes in topography, soil thickness, soil mineralogy, and soil TCN concentrations. We used this model to investigate TCN responses in transient landscapes by imposing idealized perturbations in tectonically (rock uplift rate) and climatically sensitive parameters (soil production efficiency, hillslope transport efficiency, and mineral dissolution rate) on initially steady‐state landscapes. These experiments revealed key insights about TCN responses in transient landscapes. (a) Accounting for soil chemical erosion is necessary to accurately calculateDinf. (b) Responses ofDinfto tectonic perturbations differ from those to climatic perturbations, suggesting that spatial and temporal patterns inDinfare signatures of perturbation type and magnitude. (c) If soil chemical erosion is accounted for, basin‐averagedDinfinferred from TCN in stream sediment closely tracks actual basin‐averaged denudation rate, showing thatDinfis a reasonable proxy for actual denudation rate, even in many transient landscapes. (d) Response times ofDinfto perturbations increase with hillslope length, implying that response times should be sensitive to the climatic, biological, and lithologic processes that control hillslope length.more » « less
-
Croplands have been the focus of substantial investigation due to their considerable potential for sequestering carbon. Understanding the potential for soil organic carbon (SOC) sequestration and necessary management strategies will be enabled with accurate process‐based models. Accurately representing crop growth and agricultural practices will be critical for realistic SOC modeling. The MEMS 2 model incorporates a current understanding of SOC formation and stabilization, measurable SOC pools, and deep SOC dynamics and is seen as a highly promising tool to inform management intervention for SOC sequestration. Thus far, MEMS 2 has been developed to represent grasslands. In this study, we further developed MEMS 2 to model annual grain crops and common agricultural practices, such as irrigation, fertilization, harvesting, and tillage. Using four Ameriflux sites, we demonstrated an accurate simulation of crop growth and development. Model performance was strong for simulating aboveground biomass (index of agreement [d] range of 0.89–0.98) and green leaf area index (dfrom 0.90 to 0.96) across corn, soybean, and winter wheat. Good agreement with observations was also achieved for net ecosystem CO2exchange (dfrom 0.90 to 0.96), evapotranspiration (dfrom 0.91 to 0.94), and soil temperature (dof 0.96), while discrepancy with the available soil water content data remain (dfrom 0.14 to 0.81 at four depths to 100 cm). While we will continue model testing and improvement, MEMS 2 (version 2.14) has now demonstrated its ability to effectively simulate the growth of common grain crops and practices.more » « less
