Tillage erosion as an important driver of in‐field biomass patterns in an intensively used hummocky landscape

Öttl, Lena Katharina; Wilken, Florian; Auerswald, K.; Sommer, Michael; Wehrhan, Marc; Fiener, Peter

doi:10.1002/ldr.3968

Cited by 24 publications

(35 citation statements)

References 85 publications

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“…Additionally, Yoo et al (2005) developed a geomorphic-carbon transport model to understand the potential for carbon to be sequestered in upland landscapes. At decadal timescales, the SPEROS-C model (Van Oost et al, 2005) has simulated the effects of fluvial and tillage erosion to understand how soil and carbon redistribute in arable land (Dlugoß et al, 2012;Öttl et al, 2021). Recently, landscape evolution models have been shown to accurately predict 2-dimensional SOC redistribution patterns in grasslands (Hancock & Wells, 2021) and complex 3-dimensional SOC patterns in agricultural fields (Yan et al, 2019).…”

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confidence: 99%

A Landscape Evolution Modeling Approach for Predicting Three‐Dimensional Soil Organic Carbon Redistribution in Agricultural Landscapes

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A Landscape Evolution Modeling Approach for Predicting Three‐Dimensional Soil Organic Carbon Redistribution in Agricultural Landscapes

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“…A separate approach to assessing region‐wide erosion, the Daily Erosion Project (DEP; Cruse et al., 2006; Gelder et al., 2018), uses the physically based Water Erosion Prediction Project (WEPP) model (Laflen & Flanagan, 2013), which integrates topography, precipitation, soil property, and crop data to provide daily estimates of the mass of soil eroded from hillslopes in Iowa, Nebraska, Minnesota, Wisconsin, and Kansas at the scale of ∼90 km 2 watersheds. However, RUSLE and WEPP do not incorporate tillage or gully erosion in their model predictions, which can be important drivers of soil transport (Govers et al., 1996; S. Li et al., 2008; Öttl et al., 2021; Papiernik et al., 2009; J. Poesen, 2018; J. W. Poesen et al., 1996; Thaler, Larsen, et al., 2021; Valentin et al., 2005; Van Oost et al., 2006). NRI estimates of soil loss have been generated every 5 years from 1982 to 2017 and temporal trends suggest that erosion rates have generally been decreasing since 1982 (U.S. Department of Agriculture, 2018).…”

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confidence: 99%

“…However, RUSLE and WEPP do not incorporate tillage or gully erosion in their model predictions, which can be important drivers of soil transport (Govers et al, 1996;S. Li et al, 2008;Öttl et al, 2021;Papiernik et al, 2009;J. Poesen, 2018;J.…”

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Rates of Historical Anthropogenic Soil Erosion in the Midwestern United States

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Conventional agricultural practices have accelerated soil erosion rates, resulting in widespread soil degradation throughout the world's agricultural regions (Montgomery, 2007b). Soil degradation diminishes soil fertility by removing organic matter and nutrients (Pimentel, 2006), which, without countervailing practices such as fertilization and genetic crop enhancements, leads to reductions in crop yields (Lal, 2004;Tilman et al., 2002). Fertilizer use, however, does not fully restore the productivity of eroded soils (Fenton et al., 2005), and because fossil fuels are required to generate the energy required for fertilizer production, the use of fertilizers to increase yields in degraded soils is not sustainable (Montgomery, 2007a). Further, soil erosion leads to increased agricultural production costs (Pimentel et al., 1995) and negative offsite effects such as increased sedimentation and nutrient export to downstream waterbodies (Tilman et al., 2002). In the United States, recognition of the high costs of soil erosion in the early twentieth century led to the development and implementation of soil conservation practices (Bennett, 1948). Field trials have demonstrated the efficacy of soil conservation efforts (Pimentel et al., 1976;Steiner, 1987), but it is unclear whether the advent of soil conservation practices and policies has led to a reduction of region-wide soil erosion rates in the U.S.

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“…Generally, the spatial distribution of soil types and soil properties in hummocky ground moraines mainly results from lateral topsoil translocation (including SOC) by tillage erosion, as has been demonstrated across continents [27][28][29][30][31]. These soil landscapes have been intensively studied under aspects of erosion feedbacks on crop biomasses and yields [31][32][33][34], as well as on carbon dynamics [35] and greenhouse gas fluxes [27,36,37]. The landscape is characterized by a large number of closed depressions (kettle holes) which act as ultimate sediment traps of transported topsoil material; hence SOC.…”

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A Parsimonious Approach to Estimate Soil Organic Carbon Applying Unmanned Aerial System (UAS) Multispectral Imagery and the Topographic Position Index in a Heterogeneous Soil Landscape

Wehrhan

Sommer

2021

Remote Sensing

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Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils’ SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R²) = 0.91; root mean square error (RMSE) = 0.11% and R² = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R² = 0.88, RMSE = 0.07%; R² = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.

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Tillage erosion as an important driver of in‐field biomass patterns in an intensively used hummocky landscape

Cited by 24 publications

References 85 publications

A Landscape Evolution Modeling Approach for Predicting Three‐Dimensional Soil Organic Carbon Redistribution in Agricultural Landscapes

A Landscape Evolution Modeling Approach for Predicting Three‐Dimensional Soil Organic Carbon Redistribution in Agricultural Landscapes

Rates of Historical Anthropogenic Soil Erosion in the Midwestern United States

A Parsimonious Approach to Estimate Soil Organic Carbon Applying Unmanned Aerial System (UAS) Multispectral Imagery and the Topographic Position Index in a Heterogeneous Soil Landscape

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