Unleashing the sequestration potential of soil organic carbon under climate and land use change scenarios in Danish agroecosystems

Future global climate changes are expected to increase soil organic carbon (SOC) decomposition. However, the combined effect of C inputs, land use changes, and climate on SOC turnover is still unclear. Exploring this SOC-climate-land use interaction allows us to understand the SOC stabilization mechanisms and examine whether the soil can act as a source or a sink for CO2. The current study estimates the SOC sequestration potential in the topsoil layer of Danish agricultural lands by 2038, considering the effect of land use change and future climate scenarios using the Rothamsted Carbon (RothC) model. Additionally, we quantified the loss vulnerability of existing and projected SOC based on the soil capacity to stabilize OC. We used the quantile random forest model to estimate the initial SOC stock by 2018, and we simulated the SOC sequestration potential with RothC for a business-as-usual (BAU) scenario and a crop rotation change (LUC) scenario under climate change conditions by 2038. We compared the projected SOC stocks with the carbon saturation deficit. The initial SOC stock ranged from 10 to 181 Mg C ha-1 in different parts of the country. The projections showed a SOC loss of 8.1 Mg C ha-1 for the BAU scenario and 6 Mg C ha-1 after the LUC adoption. This SOC loss was strongly influenced by warmer temperatures and clay content. The proposed crop rotation became a mitigation measure against the negative effect of climate change on SOC accumulation, especially in sandy soils with a high livestock density. A high C accumulation in C-saturated soils suggests an increase in non-complexed SOC, which is vulnerable to being lost into the atmosphere as CO2. With these results, we provide information to prioritize areas where different soil management practices can be adopted to enhance SOC sequestration in stable forms and preserve the labile-existing SOC stocks.

Visible-Near-Infrared Spectroscopy can predict Mass Transport of Dissolved Chemicals through Intact Soil

The intensification of agricultural production to meet the growing demand for agricultural commodities is increasing the use of chemicals. The ability of soils to transport dissolved chemicals depends on both the soil’s texture and structure. Assessment of the transport of dissolved chemicals (solutes) through soils is performed using breakthrough curves (BTCs) where the application of a solute at one site and its appearance over time at another are recorded. Obtaining BTCs from laboratory studies is extremely expensive and time- and labour-consuming. Visible–near-infrared (vis–NIR) spectroscopy is well recognized for its measurement speed and for its low data acquisition cost and can be used for quantitative estimation of basic soil properties such as clay and organic matter. In this study, for the first time ever, vis–NIR spectroscopy was used to predict dissolved chemical breakthrough curves obtained from tritium transport experiments on a large variety of intact soil columns. Averaged across the field, BTCs were estimated with a high degree of accuracy. So, with vis-NIR spectroscopy, the mass transport of dissolved chemicals can be measured, paving the way for next-generation measurements and monitoring of dissolved chemical transport by spectroscopy.

Three-dimensional imaging of active acid sulfate soil using a DUALEM-21S and EM inversion software

One of the major environmental issues in Finland is the presence of large tracts of acid sulfate soil (ASS) landscapes along the coast. Accurately identifying the distribution of ASS sediments, and in particular soil pH, is essential for developing targeted management strategies. One approach is the use of digital soil mapping (DSM) with various ancillary information. Although electromagnetic (EM) induction data has shown potential in mapping ASS, few studies have been conducted to map the spatial distribution of pH at different depths. In this study, a DUALEM-21S was used to collect apparent soil electrical conductivity (EC_a) data across a 23-ha field near Vaasa, which lies along the western coast of Finland. A quasi-3D inversion algorithm was used to calculate the estimated true electrical conductivity (σ - mS m^-1). A calibration relationship was developed between σ and incubation-pH measured at various depths from topsoil (0-0.2 m), subsurface (0.2-0.4 m) and subsoil (e.g. 0.4-0.6 and 1.8-2 m) using an artificial neural network (ANN) model. The performance of the ANN model was good given the large R² values for calibration (0.72) and validation (0.65). It was concluded that the combination of EC_a data and quasi-3D inversion algorithm (in EM4Soil) was able to map the spatial distribution of incubation-pH associated within an ASS landscape. The approach has the potential to be applied across the coastal areas of Finland and elsewhere to map incubation-pH and identify active-ASS areas and thereby improve the management of these areas.

Water and solute transport in agricultural soils predicted by volumetric clay and silt contents

Solute transport through the soil matrix is non-uniform and greatly affected by soil texture, soil structure, and macropore networks. Attempts have been made in previous studies to use infiltration experiments to identify the degree of preferential flow, but these attempts have often been based on small datasets or data collected from literature with differing initial and boundary conditions. This study examined the relationship between tracer breakthrough characteristics, soil hydraulic properties, and basic soil properties. From six agricultural fields in Denmark, 193 intact surface soil columns 20cm in height and 20cm in diameter were collected. The soils exhibited a wide range in texture, with clay and organic carbon (OC) contents ranging from 0.03 to 0.41 and 0.01 to 0.08kgkg(-1), respectively. All experiments were carried out under the same initial and boundary conditions using tritium as a conservative tracer. The breakthrough characteristics ranged from being near normally distributed to gradually skewed to the right along with an increase in the content of the mineral fines (particles ≤50μm). The results showed that the mineral fines content was strongly correlated to functional soil structure and the derived tracer breakthrough curves (BTCs), whereas the OC content appeared less important for the shape of the BTC. Organic carbon was believed to support the stability of the soil structure rather than the actual formation of macropores causing preferential flow. The arrival times of 5% and up to 50% of the tracer mass were found to be strongly correlated with volumetric fines content. Predicted tracer concentration breakthrough points as a function of time up to 50% of applied tracer mass could be well fitted to an analytical solution to the classical advection-dispersion equation. Both cumulative tracer mass and concentration as a function of time were well predicted from the simple inputs of bulk density, clay and silt contents, and applied tracer mass. The new concept seems promising as a platform towards more accurate proxy functions for dissolved contaminant transport in intact soil.

Digital mapping of soil organic carbon contents and stocks in Denmark

Estimation of carbon contents and stocks are important for carbon sequestration, greenhouse gas emissions and national carbon balance inventories. For Denmark, we modeled the vertical distribution of soil organic carbon (SOC) and bulk density, and mapped its spatial distribution at five standard soil depth intervals (0-5, 5-15, 15-30, 30-60 and 60-100 cm) using 18 environmental variables as predictors. SOC distribution was influenced by precipitation, land use, soil type, wetland, elevation, wetness index, and multi-resolution index of valley bottom flatness. The highest average SOC content of 20 g kg(-1) was reported for 0-5 cm soil, whereas there was on average 2.2 g SOC kg(-1) at 60-100 cm depth. For SOC and bulk density prediction precision decreased with soil depth, and a standard error of 2.8 g kg(-1) was found at 60-100 cm soil depth. Average SOC stock for 0-30 cm was 72 t ha(-1) and in the top 1 m there was 120 t SOC ha(-1). In total, the soils stored approximately 570 Tg C within the top 1 m. The soils under agriculture had the highest amount of carbon (444 Tg) followed by forest and semi-natural vegetation that contributed 11% of the total SOC stock. More than 60% of the total SOC stock was present in Podzols and Luvisols. Compared to previous estimates, our approach is more reliable as we adopted a robust quantification technique and mapped the spatial distribution of SOC stock and prediction uncertainty. The estimation was validated using common statistical indices and the data and high-resolution maps could be used for future soil carbon assessment and inventories.

Comparative mapping of soil physical-chemical and structural parameters at field scale to identify zones of enhanced leaching risk

Preferential flow and particle-facilitated transport through macropores contributes significantly to the transport of strongly sorbing substances such as pesticides and phosphorus. The aim of this study was to perform a field-scale characterization of basic soil physical properties like clay and organic carbon content and investigate whether it was possible to relate these to derived structural parameters such as bulk density and conservative tracer parameters and to actual particle and phosphorus leaching patterns obtained from laboratory leaching experiments. Sixty-five cylindrical soil columns of 20-cm height and 20-cm diameter and bulk soil were sampled from the topsoil in a 15-m × 15-m grid in an agricultural loamy field. Highest clay contents and highest bulk densities were found in the northern part of the field. Leaching experiments with a conservative tracer showed fast 5% tracer arrival times and high tracer recovery percentages from columns sampled from the northern part of the field, and the leached mass of particles and particulate phosphorus was also largest from this area. Strong correlations were obtained between 5% tracer arrival time, tracer recovery, and bulk density, indicating that a few well-aligned and better connected macropores might change the hydraulic conductivity between the macropores and the soil matrix, triggering an onset of preferential flow at lower rain intensities compared with less compacted soil. Overall, a comparison mapping of basic and structural characteristics including soil texture, bulk density, dissolved tracer, particle and phosphorus transport parameters identified the northern one-third of the field as a zone with higher leaching risk. This risk assessment based on parameter mapping from measurements on intact samples was in good agreement with 9 yr of pesticide detections in two horizontal wells and with particle and phosphorus leaching patterns from a distributed, shallow drainage pipe system across the field.

Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: the case study of Denmark

Soil organic carbon (SOC) is one of the most important carbon stocks globally and has large potential to affect global climate. Distribution patterns of SOC in Denmark constitute a nation-wide baseline for studies on soil carbon changes (with respect to Kyoto protocol). This paper predicts and maps the geographic distribution of SOC across Denmark using remote sensing (RS), geographic information systems (GISs) and decision-tree modeling (un-pruned and pruned classification trees). Seventeen parameters, i.e. parent material, soil type, landscape type, elevation, slope gradient, slope aspect, mean curvature, plan curvature, profile curvature, flow accumulation, specific catchment area, tangent slope, tangent curvature, steady-state wetness index, Normalized Difference Vegetation Index (NDVI), Normalized Difference Wetness Index (NDWI) and Soil Color Index (SCI) were generated to statistically explain SOC field measurements in the area of interest (Denmark). A large number of tree-based classification models (588) were developed using (i) all of the parameters, (ii) all Digital Elevation Model (DEM) parameters only, (iii) the primary DEM parameters only, (iv), the remote sensing (RS) indices only, (v) selected pairs of parameters, (vi) soil type, parent material and landscape type only, and (vii) the parameters having a high impact on SOC distribution in built pruned trees. The best constructed classification tree models (in the number of three) with the lowest misclassification error (ME) and the lowest number of nodes (N) as well are: (i) the tree (T1) combining all of the parameters (ME=29.5%; N=54); (ii) the tree (T2) based on the parent material, soil type and landscape type (ME=31.5%; N=14); and (iii) the tree (T3) constructed using parent material, soil type, landscape type, elevation, tangent slope and SCI (ME=30%; N=39). The produced SOC maps at 1:50,000 cartographic scale using these trees are highly matching with coincidence values equal to 90.5% (Map T1/Map T2), 95% (Map T1/Map T3) and 91% (Map T2/Map T3). The overall accuracies of these maps once compared with field observations were estimated to be 69.54% (Map T1), 68.87% (Map T2) and 69.41% (Map T3). The proposed tree models are relatively simple, and may be also applied to other areas.

Spatial soil zinc content distribution from terrain parameters: a GIS-based decision-tree model in Lebanon

Heavy metal contamination has been and continues to be a worldwide phenomenon that has attracted a great deal of attention from governments and regulatory bodies. In this context, our study proposes a regression-tree model to predict the concentration level of zinc in the soils of northern Lebanon (as a case study of Mediterranean landscapes) under a GIS environment. The developed tree-model explained 88% of variance in zinc concentration using pH (100% in relative importance), surroundings of waste areas (90%), proximity to roads (80%), nearness to cities (50%), distance to drainage line (25%), lithology (24%), land cover/use (14%), slope gradient (10%), conductivity (7%), soil type (7%), organic matter (5%), and soil depth (5%). The overall accuracy of the quantitative zinc map produced (at 1:50.000 scale) was estimated to be 78%. The proposed tree model is relatively simple and may also be applied to other areas.