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Macropore flow in structured soils is an important process in relation to the transport of water, contaminants and nutrients in the soil. A close relation exists between hydraulic conductivity K(h) near saturation and the potential of macropore flow. At the same time, tracer breakthrough experiments in the laboratory can determine the degree of macropore flow. In this study, we aim to investigate a direct link between tracer breakthrough characteristics and soil hydraulic properties (SHPs) of structured soils, which may explain spatial variation of solute transport in soils. We used SHPs and tracer breakthrough characteristics for 71 undisturbed topsoil columns (19 cm height, 20 cm diameter) from Denmark. We defined K10 (near-saturated hydraulic conductivity) as K(h) at a matric potential (h) of −10 cm and used logarithmic transformation, logK10. On the same soil columns, we calculated the 5%, 25%, and 50 % arrival times (AT) as the percentage of the cumulative relative mass of tracer percolating through the bottom of the soil column. The regression analyses proved significant positive relation between logK10 and 5% AT, 25% AT, and 50 % AT. The saturated hydraulic conductivity appeared to be less critical for the shape of the tracer breakthrough curves. In addition, the 5% AT, 25% AT, and 5 0%AT correlated with soil pF values at 1.7, 2.0, and 2.5 (volumetric water content at h equal to −100 cm, −300 cm, and −500 cm, respectively) showing significant negative correlations. Linking SHPs with tracer breakthrough characteristics on large intact columns thus proves highly useful for characterizing soil macropore functions.

Litter decomposition is a critical process in carbon cycling, which can be affected by land use. The relationship between litter decomposition and soil properties under different land uses remains unclear. Litter decomposition can be quantified by the Tea Bag Index (TBI), which includes a decomposition rate k and a stabilization factor S. Our objective was to investigate linkages between TBI and soil physicochemical and gas transport properties and land use. We buried three pairs of tea bags in 20 sites (covering cropland, grassland, heathland, and forest land uses) in a transect from the western to the eastern coast of the Jutland peninsula, Denmark. The tea bags were retrieved after 90 d and TBI was determined. Disturbed and undisturbed (100 cm 3 soil cores) samples were collected from each site. Thereafter, clay content, organic carbon (OC), bulk density (ρ b), pH, electrical conductivity (EC), oxalate-extractable phosphorus (P ox), aluminum (Al ox), and iron (Fe ox) content, soil water content, gas diffusivity (D p/D 0), and air permeability (k a) at −10 kPa were measured. Results showed that grasslands had the highest k and S among four land uses, and agricultural soils (croplands and grasslands) exhibited higher TBI values than seminatural soils (forest and heathland). The prediction of S was better than that of k based on multiple linear regression analysis involving soil physicochemical properties. Clay content and OC were not strong predictors. Including D p/D 0 and k a improved the prediction of S, and finally, the inclusion of land use enhanced the prediction of both k and S. The different trends between two distinct land-use groups can be attributed to pH, P ox, and ρ b.

The rapid warming of the Arctic is changing the conditions for agricultural activity in southwest Greenland markedly, which necessitates studies of the physical properties of the soil resource. Soil water repellency (SWR) is a soil property that changes soil functional behaviour across a soil-specific range in water-content (W). Although SWR occurs worldwide, it has not been studied in sub-arctic pasture and grass fields. Thus, the aim was to examine the prevalence of SWR in South Greenland and to establish pedotransfer functions for SWR on soil properties that are faster to measure than SWR, i.e. soil organic fractions, texture and soil water retention. This study included 145 soil samples from 22 sub-arctic agricultural fields distributed across three areas of South Greenland, with broad distributions in texture (clay: 0.017–0.194 kg kg−1) and organic carbon (OC) (0.009–0.241 kg kg−1) contents. The degree of soil water repellency (SWR) as a function of water content (SWR-W curve) was measured from oven-dry conditions to the water-content at which the soil became hydrophilic (WNON), and total SWR (SWRAREA) was calculated as the integrated trapezoidal area (SWRAREA) of the SWR-W curve. A total of 99% and 98% of the soil samples were water-repellent and extremely water repellent at their maximum SWR, respectively. Among the three soil organic fractions (OC, Loss-on-ignition at 550 °C, and 225 °C (LOI550 and LOI225)), LOI550 was the best predictor of SWRAREA and WNON (both with r2 = 0.93). Multiple linear regressions including clay content increased r2adj to 0.92 and 0.95 with OC and LOI550, respectively. The Campbell-Shiozawa (CS) model was fitted to the soil–water retention curves (pF 3.0–6.9), and the inverse slope (−α−1) of the CS model exhibited a high correlation to both SWRAREA (r2adj of 0.87) and WNON (r2adj of 0.93), thus suggesting that soil water retention governs SWR. Lastly, it was shown that the coefficient of proportionality between OC and both SWRAREA and WNON for these sub-arctic soils coincided with pasture soils from New Zealand.

Soil aeration is a key parameter for sustainable and productive agriculture. The intensification of agricultural activity in Greenland involves land use (LU) and LU change, affecting the soil–air phase. The combined effects of natural compaction (bulk density, ρ b), texture (texture uniformity index; TUI), and LU on the soil–air phase of subarctic soils are not well known. This study aims to identify and compare the main drivers for air-filled porosity (ε) and soil-structure changes within and across sites in Greenland and Denmark. We analyzed comprehensive data sets of ε, relative gas diffusivity D p/D o), and air-permeability (k a) measured on intact soil samples from South Greenland (pasture) and Denmark (cultivated, urban, and forest). The mechanical robustness of the air phase was evaluated by linear models of ε as a function of ρ b (H-model). The ratio of k a to D p/D o served as a soil-structure index (Ω); the latter significantly correlated to TUI. The Greenlandic pasture soils did not show signs of well-developed soil structure (low Ω-values), whereas low H-values suggested the soils were mechanically robust compared to similar-textured cultivated soils. The soil–air characteristic curve (ε vs. pF) was parameterized, and the moisture control parameter was accurately predicted by TUI and LU (R 2 =.95). Overall, the ρ b was found to control the air-phase functions within a field. However, considering changes in ε-levels across different fields, texture, LU, and other environmental factors became statistically more relevant than ρ b. A modeled response surface for changes in ε with soil conditions may, in perspective, be useful for better-predicting gas transport in soil, both within and across fields.

Water repellency (WR) significantly affects the hydraulic behavior of soils. Although WR often is regarded as a phenomenon with implications for dry soils, it is prevalent at water contents (w) exceeding the wilting point water content. Because the measurement of the WR–w relationship is laborious, alternative more time-efficient methods are desirable to estimate parameters of the WR-w curve. Using 32 high organic carbon (OC) soils from Denmark and South Greenland, we characterized the water vapor sorption isotherms (WSIs), investigated the interrelated effects of OC and clay contents on WSIs and the WR–w relationship, and further evaluated if parameters of the WR-w curve may be derived directly from WSIs. The samples exhibited OC and clay contents ranging from 0.014 to 0.369 kg kg–1 and from 0.02 to 0.16 kg kg–1, respectively. The WSIs measured for relative humidity (RH) values between 3 and 93%, were strongly hysteretic, were OC dependent, and could be accurately characterized with the Guggenheim, Anderson, and de Boer model. Further, the WRarea and wnon parameters, derived from WR measured for several w, were well estimated with linear regressions based on OC content and multiple linear regressions based on OC and clay contents. Estimations for WRarea and wnon based on the WSI parameter wm-a were superior to OC and clay content. Finally, we established mathematical expressions that estimate WRarea or wnon from any air-dry w obtained from either the desorption or adsorption isotherms between 10 and 90% RH.

Current farm systems rely on the use of Plant Protection Products (PPP) to secure high productivity and control threats to the quality of the crops. However, PPP use may have considerable impacts on human health and the environment. A study protocol is presented aiming to determine the occurrence and levels of PPP residues in plants (crops), animals (livestock), humans and other non-target species (ecosystem representatives) for exposure modelling and impact assessment. To achieve this, we designed a cross-sectional study to compare conventional and organic farm systems across Europe. Environmental and biological samples were/are being/will be collected during the 2021 growing season, at 10 case study sites in Europe covering a range of climate zones and crops. An additional study site in Argentina will inform the impact of PPP use on growing soybean which is an important European protein-source in animal feed. We will study the impact of PPP mixtures using an integrated risk assessment methodology. The fate of PPP in environmental media (soil, water and air) and in the homes of farmers will be monitored. This will be complemented by biomonitoring to estimate PPP uptake by humans and farm animals (cow, goat, sheep and chicken), and by collection of samples from non-target species (earthworms, fish, aquatic and terrestrial macroinvertebrates, bats, and farm cats). We will use data on PPP residues in environmental and biological matrices to estimate exposures by modelling. These exposure estimates together with health and toxicity data will be used to predict the impact of PPP use on environment, plant, animal and human health. The outcome of this study will then be integrated with socio-economic information leading to an overall assessment used to identify transition pathways towards more sustainable plant protection and inform decision makers, practitioners and other stakeholders regarding farming practices and land use policy. EEA Balcarce Fil: Silva, Vera. Wageningen University and Research. Soil Physics and Land Management Group; Países Bajos. Fil: Alaoui, Abdallah. University of Bern. Institute of Geography. Centre for Development and Environment; Suiza. Fil: Schlünssen, Vivi. Aarhus University. Department of Public Health; Dinamarca. National Research Centre for the Working Environment; Dinamarca. Fil: Vested, Anne. Aarhus University. Department of Public Health; Dinamarca. Fil: Graumans, Martien. Radboud Institute for Health Sciences; Países Bajos. Fil: Van Dael, Maurice. Radboud Institute for Health Sciences; Países Bajos. Fil: Trevisan, Marco. Università Cattolica del Sacro Cuore. Department for Sustainable Food Process; Italia. Fil: Suciu, Nicoleta. Università Cattolica del Sacro Cuore. Department for Sustainable Food Process; Italia. Fil: Mol, Hans. Wageningen University and Research. Wageningen Food Safety Research; Países Bajos. Fil: Beekmann, Karsten. Wageningen University and Research. Wageningen Food Safety Research; Países Bajos. Fil: Figueiredo, Daniel. Utrecht University. Institute for Risk Assessment Sciences; Países Bajos. Fil: Harkes, Paula. Wageningen University and Research. Soil Physics and Land Management Group; Países Bajos. Fil: Hofman, Jakub. Masaryk University. Faculty of Science. Research Centre for Toxic Compounds in the Environment; Republica Checa. Fil: Kandeler, Ellen. University of Hohenheim. Institute of Soil Science and Land Evaluation; Alemania. Fil: Abrantes, Nelson. University of Aveiro. Centre for Environmental and Marine Studies and Department of Environment and Planning; Portugal. Fil: Campos, Isabel. University of Aveiro. Centre for Environmental and Marine Studies and Department of Environment and Planning; Portugal. Fil: Martínez, María Ángeles. Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas; España. Fil: Pereira, Joana Luísa. Centre for Environmental and Marine Studies and Department of Environment and Planning; Portugal. Fil: Goossens, Dirk. Wageningen University and Research. Soil Physics and Land Management Group. Países Bajos. KU Leuven Department of Earth and Environmental Sciences; Belgica. Fil: Gandrass, Juergen. Institute of Coastal Environmental Chemistry; Alemania. Fil: Debler, Freya. Institute of Coastal Environmental Chemistry; Alemania. Fil: Huerta Lwanga, Esperanza. Wageningen University and Research. Soil Physics and Land Management Group; Países Bajos. Fil: Jonker, Marlot. Dutch Mammal Society; Países Bajos. Fil: Van Langevelde, Frank. Wageningen University and Research. Wildlife Ecology and Conservation Group; Países Bajos. Fil: Sorensen, Martin. Aarhus University. Department of Animal Science; Dinamarca. Fil: Wells, Jerry. Wageningen University and Research. Host-Microbe Interactomics, Animal Sciences Group; Países Bajos. Fil: Boekhorst, Jos. Wageningen University and Research. Host-Microbe Interactomics, Animal Sciences Group; Países Bajos. Fil: Huss, Anke. Utrecht University. Institute for Risk Assessment Sciences; Países Bajos. Fil: Mandrioli, Daniele. Ramazzini Institute. Cesare Maltoni Cancer Research Center; Italia. Fil: Sgargi, Daria. Ramazzini Institute. Cesare Maltoni Cancer Research Center; Italia. Fil: Nathanail, Paul. Land Quality Management; Reino Unido. Fil: Nathanail, Judith. Land Quality Management; Reino Unido. Fil: Tamm, Lucius. Research Institute of Organic Agriculture; Suiza. Fil: Fantke, Peter. Technical University of Denmark. Department of Technology, Management and Economics; Dinamarca. Fil: Mark, Jennifer. Research Institute of Organic Agriculture; Suiza. Fil: Grovermann, Christian. Research Institute of Organic Agriculture; Suiza. Fil: Larsen, Ana Frelih. Ecologic Institute; Alemania. Fil: Herb, Irina. Ecologic Institute; Alemania. Fil: Chivers, Charlotte Anne. University of Gloucestershire. Countryside and Community Research Institute; Reino Unido. Fil: Mills, Jane. University of Gloucestershire. Countryside and Community Research Institute; Reino Unido. Fil: Alcon, Francisco. Universidad Politécnica de Cartagena; España. Fil: Contreras, Josefina. Universidad Politécnica de Cartagena; España. Fil: Baldi, Isabelle. Bordeaux University. INSERM U1219; Francia. Fil: Pasković, Igor. Institute of Agriculture and Tourism, Department of Agriculture and Nutrition; Croacia. Fil: Matjaz, Glavan. University of Ljubljana. Biotechnical Faculty; Eslovenia. Fil: Norgaard, Trine. Aarhus University. Department of Agroecology; Dinamarca. Fil: Aparicio, Virginia Carolina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Fil: Ritsema, Coen. Wageningen University and Research. Soil Physics and Land Management Group; Países Bajos. Fil: Geissen, Violette. University and Research. Soil Physics and Land Management Group; Países Bajos. Fil: Scheepers, Paul. Radboud Institute for Health Sciences; Países Bajos.

A number of environmental, agronomic and engineering applications require knowledge of the Atterberg limits (liquid limit, LL; plastic limit, PL) and the plasticity index, PI of soils. The tedious and costly nature of standard experimental methods, as well as challenges with measurement repeatability motivated the development of regressions as well as more sophisticated techniques to estimate the Atterberg limits from other properties such as clay content, cation exchange capacity (CEC), and soil specific surface area. The amount of water adsorbed to particle surfaces at relative humidity (RH) < 95% is intimately linked to these soil properties, which suggests that hygroscopic water content (wh) may be a better predictor of the Atterberg limits. The present study (i) proposes regression models that estimate the LL, PL, and PI from wh at different relative humidity values ranging from 10 to 90% and considering water sorption hysteresis, and (ii) compares the performance of the models to other models that comprise clay, silt and organic carbon contents and CEC. For model development, wh was measured by water adsorption and desorption for 168 soil samples that varied widely in terms of geographic origin, clay mineralogy, and soil organic carbon content. The LL and PL were determined with the drop cone penetrometer and rolling device, respectively. Regression models were developed for both sorption directions for nine RH values between 10 and 90%. For 44 independent soil samples, the models estimated LL, PL and PI accurately (e.g., for desorption wh measured at 90% RH, RMSE and r2 values were 6.43% & 0.89; 3.95% & 0.83 and 6.69% & 0.79, respectively). There was no clear effect of sorption direction on the estimation accuracy. The wh determined at higher RH tended to better estimate the Atterberg limits compared to that measured at lower RH. The wh models were superior in estimating LL and PL compared to models that were based on clay content and organic carbon or CEC. For the PI, the models based on CEC performed slightly better than the wh models. Thus, a single measure of wh can provide reliable estimates of the Atterberg limits and PI.

The reduction of permafrost areas and a prolonged vegetation period, both due to the ongoing climate change, open up new possibilities for agricultural activities in the circumpolar region. Presently, not much is known about the physical and functional properties of subarctic agricultural soils, making it challenging to evaluate soil impacts from increased agricultural activity. This study aims at assessing the mechanical properties of sandy Greenlandic pasture soils regarding a potential future land-use change. The compression curves of 210 undisturbed soil core samples of three pasture fields in South Greenland and, as comparison, four intensively cultivated agricultural fields in Denmark were generated by uniaxial confined compression tests (UCCT). Four soil mechanical properties were determined: the precompression stress ( σ pc ) as a measure of soil strength, the compression index ( C c ) as a measure of compressibility, the swelling index ( C s ) as a measure of resilience to compaction and the rebound ( Δ e ) after a relaxation time of 60 s as a measure of short-time recovery from compaction. The Greenlandic pasture soils exhibited significantly higher σ pc than the Danish cultivated soils despite their significantly lower initial bulk density, ρ b . The C c negatively correlated with ρ b , and the Greenlandic soils showed higher C c values than the Danish soils. The C s showed a reciprocal correlation with ρ b and, partly due to lower ρ b of the Greenlandic soils, higher C s than the Danish soils. The South Greenlandic sandy pasture soils showed compression and rebound characteristics on level with Danish loamy cultivated soils, likely due to a high content of particulate organic matter (non-degraded organic matter, including grass root-mat residues).

Larger pores allowing macropore flow can rapidly transport contaminants as well as nutrients from the soil surface to the subsoil and groundwater. Modeling of the involved macropore flow is complex due to large number of required parameters. One way to deal with this difficulty is the precise evaluation of the soil hydraulic conductivity function using robust pedotransfer functions for the near-saturated part. Aiming to develop pedotransfer functions to estimate saturated and unsaturated hydraulic conductivities [Ks and K(h)] as well as water contents at specific pressure heads, we used Gaussian Process Regression (GPR), a non-parametric machine learning model, to obtain van Genuchten (1980) retention and conductivity parameters for Danish soils based on soil texture and organic matter. We defined K10 as K(h) at − 10 cm pressure head. The difference between the logarithm of Ks and K10 [log(Ks) − log(K10)] was denominated log(Kjump) and was assumed to convey the potential degree of macropore flow considering only soil hydraulic properties. Macropore flow in Denmark was evaluated through the developed PTFs for log(Ks) and log(K10) with an average RMSE of 0.635 and 0.594 (for K in cm d−1) under a confidence interval obtained by bootstrapping. Dynamics of pressure head in an initially saturated soil column with a zero-flux top boundary condition and a 2 m deep groundwater level were simulated using the Hydrus-1D model. The result of this modeling showed that soils in the eastern part of Denmark had a higher likelihood to experience macropore flow. Finally, using the spatial distribution of meteorological data, a macropore flow map of Denmark was produced. Precipitation was a dominating factor on macropore flow in sandy clay loam and sandy loam soils when compared to sandy and loamy sand soils. Finally, maps of relative risk classes of macropore flow qualitatively differentiated land areas in Denmark for vulnerability of nutrient loss and groundwater contamination.

The Arctic is warming at twice the global average, which may impact agricultural production in Greenland. Therefore knowledge of the functional properties of Greenlandic soil resources is necessary. The relative soil gas diffusivity [the soil gas diffusion coefficient (Dp)–free-air diffusion coefficient (Do) ratio] is the chief parameter controlling gas transport in soils. Predictions of Dp/Do are needed to estimate root zone aeration and terrestrial greenhouse gas fluxes. We used existing models to analyze the Dp/Do of soils from Greenlandic fields and a pore connectivity index (Cip) to infer their degree of structural development and identify the main parameters controlling Dp/Do. In total, 201 × 3 intact 100-cm3 soil samples were sampled across six fields with clay and organic C contents of 0.016 to 0.089 and 0.016 to 0.105 kg kg−1, respectively. The Dp/Do was measured with the one-chamber nonsteady-state method at soil water potentials between –10 and –1,000 cm H2O. Accurate determination of total porosity (Φ) was ensured by calibrating a particle density model on 129 samples. The soils exhibited a less developed structure and highly tortuous pore networks, resulting in low Dp/Do as a function of air-filled porosity (ε). Density-corrected models with air saturation (ε/Φ) reduced the RMSE. Furthermore, Cip at –1,000 cm H2O soil water potential increased linearly with dry bulk density (ρb), suggesting that ρb is a key controller of Dp/Do, which is important for planning cultivation practices for Southern Greenlandic soils. Lastly, we found that an air saturation >35% is required for adequate soil aeration.

Phosphorus (P) is one of the most important plant nutrients, and farmers regularly apply P as mineral fertilizer and with animal manures. Typically, reactions with amorphous aluminum and iron oxides or carbonates retain P in the soil. However, if P additions exceed the soil’s ability to bind them, P may leach from soil to surface waters, where it causes eutrophication. The phosphorus sorption capacity (PSC) is thus an inherent soil property that, when related to bound P, can describe the P saturation of the soil. Detailed knowledge of the spatial distribution of the PSC is therefore important information for assessing the risk of P leaching from agricultural land.In weakly acidic soils predominant in Denmark, the PSC depends mainly on the oxalate-extractable contents of aluminum and iron. In this study, we aimed to map PSC in four depth intervals (0 – 25; 25 – 50; 50 – 75; 75 – 100 cm) for Denmark using measurements of oxalate-extractable aluminum and iron from 1,623 locations.We mapped both elements using quantile regression forests. Predictions of oxalate-extractable aluminum had a weighted RMSE of 13.9 mmol kg-1. For oxalate-extractable iron, weighted RMSE was 33.5 mmol kg-1.We included depth as a covariate and therefore trained one model for each element. For each element in each depth interval, we predicted the mean prediction value as well as 100 quantiles ranging from 0.5% to 99.5% in 1% intervals. The maps had a 30.4 m resolution. We then calculated PSC by convoluting the prediction quantiles of the two elements, using every combination of quantiles, in order to obtain the prediction uncertainty for PSC.Oxalate-extractable aluminum was roughly normal distributed, while oxalate-extractable iron had a large positive skew. The age and origin of the parent material had a large effect on oxalate-extractable aluminum, and soil-forming processes such as weathering and podzolization had clear effects on the distribution in depth. Meanwhile, organic matter, texture and wetland processes were the main factors affecting oxalate-extractable iron, so much so that they obscured any trends with depth.The weighted RMSE of the predicted PSC was 19.1 mmol kg-1. PSC was highest in wetland areas and lowest in young upland deposits, such as aeolian deposits and the loamy Weichselian moraines of eastern Denmark. The sandy glaciofluvial plains and Saalian moraines of western Denmark had intermediate PSC. In most cases, PSC was highest in the top soil, but in the sandy soils of western Denmark, PSC was highest in the depth interval 25 – 50 cm due to podzolization.

Glucose (C6H12O6) is common in soils and widely used as a model substrate to trace the turnover of dissolved organic carbon. Yet, challenges and limitations of extraction and direct quantification of glucose in divergent soils are poorly documented. Here we used a simplified procedure for extraction and enzymatic glucose quantification with 18 soils representing different pH, clay and organic carbon (OC) contents. Extracts were reacted for 30 min (20°C) with glucose oxidase, peroxidase, and o-dianisidine. The reaction was terminated with H2SO4 and oxidized o-dianisidine was measured spectrophotometrically (540 nm). Recovery of glucose from mineral soils was not significantly affected by soil pH and OC, but decreased to 96% forincreasing clay contents (up to 45% clay). Extraction of soil in water (1:5, w/v) for 0.5 min was sufficient for sandy soils, whereas clayey soils were extracted for 5 min with benzoic acid (0.1%, w/v) as antimicrobial agent. Assays with organic soils (20% and 29% OC) showed that enzymatic glucose quantification was impeded presumably by extractable humic substances. It was possible to address this limitation with matrix-matched standard curves, but results from organic soils should be interpreted with care. The procedure had a detection limit of ~1.5 µg g−1 soil and was applicable to small (1 g) soil samples allowing to trace the recovery and dynamics of glucose in time-course experiments. The procedure could also be applied to native soil samples in which case references for colorimetric background are prepared by adding H2SO4 to the soil extract prior to the addition of assay reagents.

Standard measurement methods for soil clay content, including the hydrometer and pipette methods, are laborious and difficult to repeat. This has motivated the application of proxy methods such as estimation of clay content from soil water vapor sorption isotherms. Previous studies have revealed that vapor sorption-based clay estimates are inaccurate for soils dominated by swelling or highly weathered non-swelling clays such as kaolinite. In this study, we evaluate an existing vapor sorption-based clay estimation model for soils dominated by either illite (IL), smectite (SM), or kaolinite (KA) clay minerals, and propose modifications to account for high SM and KA contents. Compared to the pipette method, the clay content obtained from the existing water sorption model at 50% relative humidity (RH) was accurate for the IL samples, but significantly overestimated (RMSE = 23.7%; ME = 19.7%) or underestimated (RMSE = 28.8%; ME = −17.0%) clay content for SM and KA rich soils, respectively. The proposed modification involves correcting the estimated clay content with a “slope factor”, S for RH values ranging from 30 to 90% for both adsorption and desorption. For SM samples, S averaged 0.76 and 0.72 for adsorption and desorption, respectively, and for KA samples, S ranged from 2.26 to 1.19 and followed a polynomial relationship with RH. Comparison of the estimated clay content from the modified models showed markedly improved estimation of the measured clay content (for RH of 90%, RMSE = 8.0%; ME = 2.6% for SM samples and RMSE = 9.6%; ME = −5.7% for KA samples).

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 20 cm in height and 20 cm 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 0.08 kg kg − 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 ≤ 50 μm). 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.

The average particle density (r d ) is a fundamental soil property, used for calculating the total porosity. Traditional r d measurement by pycnometer method is tedious and time-consuming. In this study, visible–near-infrared (vis–NIR) spectroscopy and a simple two-compartment linear and curvilinear pedotransfer function only requiring knowledge of soil organic matter content (OM) were tested and compared as alternative, indirect, rapid, and cost-effective methods. Soil r d was measured by water pycnometer on 179 soils representing a wide range of OM (0.002–0.767 kg kg -1 ), whereas soil spectra were measured on air-dry samples by vis–NIR spectroscopy. The r d models were developed using partial least squares regression with leave-one-out-cross-validation using vis–NIR spectral data, and a simple two-compartment pedotransfer function, r d = A(OM) + B(1 − OM) using the OM content. Predictive abilities of these two methods were tested using three different datasets: (i) minerals soils (OM < 0.1 kg kg -1 ), (ii) organic soils (OM > 0.1 kg kg -1 ), and (iii) all soils. Calibrating the two-compartment pedotransfer function for the entire dataset gave expected values for the individual particle densities of OM (A = 1.244 g cm -3 ) and mineral particles (B = 2.615 g cm -3 ). The vis–NIR spectroscopy model successfully predicted soil r d for the entire dataset (R 2 = 0.87, RMSECV = 0.10 g cm -3 ), with a poorer performance than the two-compartment linear model (R 2 = 0.96, RMSE = 0.06 g cm -3 ). using only the mineral soils data did not suffice to obtain realistic and accurate vis–NIR spectroscopy (R 2 = 0.62, RMSECV = 0.02 g cm -3 ) or OM based (R 2 = 0.80, RMSE = 0.02 g cm -3 ) models for r d , illustrating the importance of the wide range of OM content considered in the present study.

Glyphosate [N-(phosphonomethyl) glycine] is the active ingredient in Roundup, which is the most used herbicide around the world. It is a non-selective herbicide with carboxyl, amino, and phosphonate functional groups, and it has a strong affinity to the soil mineral fraction. Sorption plays a major role for the fate and transport of glyphosate in the environment. The sorption coefficient (Kd) of glyphosate, and hence its mobility, varies greatly among different soil types. Determining Kd is laborious and requires the use of wet chemistry. In this study, we aimed to estimate Kd using basic soil properties, and visible near-infrared spectroscopy (vis–NIRS). The latter method is fast, requires no chemicals, and several soil properties can be estimated from the same spectrum. The data set included 68 topsoil samples collected across the South Island of New Zealand, with clay and organic carbon (OC) contents ranging from 0.001 to 0.520 kg kg−1 and 0.021 to 0.217 kg kg−1, respectively. The Kd was determined with batch equilibration sorption experiments and ranged from 13 to 3810 L kg−1. The visible near-infrared spectra were obtained from 400 to 2500 nm. Multiple linear regression was used to correlate Kd to oxalate extractable aluminium and phosphorous and pH, which resulted in an R2 of 0.89 and an RMSE of 259.59 L kg−1. Further, interval partial least squares regression with ten-fold cross-validation was used to predict Kd by vis–NIRS, and an R2 of 0.93 and an RMSECV of 207.58 L kg−1 were obtained. Thus, these results show that both basic soil properties and vis–NIRS can predict the variation in Kd across these samples with high accuracy and hence, that glyphosate sorption to a soil can be determined with vis–NIRS.

Dry bulk density (BD) is a key soil physical property, characterizing the level of soil compaction and controlling flow and transport of fluids and solutes. In this study, predictive BD models were developed from 2462 BD measurements of mineral soils representing both top- and subsoil horizons across Denmark. Three types of BD models were compared: (i) single-parameter based on soil organic carbon (SOC), (ii) multi-parameters based on SOC, soil texture and depth, and using multiple linear regression (MLR), and (iii) visible–near infrared spectroscopy (vis–NIRS) based, using the spectral information and partial least squares regression (PLSR). Also, three machine learning techniques (random forest (RF), regression rules (RR), and artificial neural networks (ANN)) were applied for multi-parameter and vis–NIRS based models to potentially improve predictions. BD models were calibrated on 70% of dataset and validated on the remaining 30%. Single-parameter models with SOC had the lowest predictive potential and were not able to predict BD above 1.62 g cm−3. Machine learning using RF, RR and ANN on soil properties resulted in significantly higher prediction accuracies (RMSE ≤ 0.11 g cm−3 and R2 ≥ 0.60) than MLR (RMSE = 0.12 g cm−3 and R2 = 0.41) and SOC-based models. The relative importance of variables was obtained from the multi-parameter models with SOC and clay as the most important variables. The prediction accuracies MLR and vis–NIRS did not differ but were better than SOC-based models, suggesting that vis–NIRS could be a cost- and time-efficient alternative to pedotransfer functions that generally require determination of one or more soil properties for developing the calibration models. Decomposing the spectral data into principal components and using selected components as inputs for RF, RR and ANN improved the model performances only for RF models. Instead and in perspective, combining techniques by supplementing spectral data with readily available soil information and using machine learning techniques may further improve BD predictions.

The mobilization and transport of colloid particles in soils can have negative agronomic and environmental effects. This work investigates the controls of particle release and transport from undisturbed soil columns sampled from an agricultural, loamy field with clay and silt contents of 0.05 to 0.14 and 0.07 to 0.16 kg kg-1, respectively. Forty-five soil columns (20 × 20 cm) were collected from the field and exposed to a constant irrigation of 10 mm h-1 for 8 h. The accumulated mass of particles in the outflow from each column was highly correlated (r = 0.88) with the volumetric mass of fines (MFvol). The MFvol is defined as the sum of clay and fine silt (vol represents both the particle source available for mobilization and leaching and an indicator of soil structure. The particle release process showed two linear particle release rates. Although the two particle release rates were distinctly different, both were strongly correlated with MFvol. The difference between the two rates was related to the degree of preferential flow characterized by the 5% arrival time of an applied tracer pulse. Soil columns with a longer 5% arrival time (less preferential flow) showed a distinct difference between the two rates, whereas soil columns with a short 5% arrival time and fast water transport showed resemblance between the two particle release rates. Thus, the combined effects of particle source, type, and pathways (via soil structure and compaction) need consideration to understand and predict particle transport dynamics through intact topsoil.

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.

© 2015 Wolters Kluwer Health, Inc. Preferential flowand transport throughmacropores affect plant water use efficiency and enhance leaching of agrochemicals and the transport of colloids, thereby increasing the risk for contamination of groundwater resources. The effects of soil compaction, expressed in terms of bulk density (BD), and organic carbon (OC) content on preferential flow and transport were investigated using 150 undisturbed soil cores sampled from 15 15-mgrids on two field sites. Both fields had loamy textures, but one site had significantly higher OC content. Leaching experiments were conducted in each core by applying a constant irrigation rate of 10mmh-1 with a pulse application of tritium tracer. Five percent tritium mass arrival times and apparent dispersivities were derived from each of the tracer breakthrough curves and correlated with texture, OC content, and BD to assess the spatial distribution of preferential flow and transport across the investigated fields. Soils fromboth fields showed strong positive correlations between BD and preferential flow. Interestingly, the relationships between BD and tracer transport characteristics were markedly different for the two fields, although the relationship between BD and macroporosity was nearly identical. The difference was likely caused by the higher contents of fines and OC at one of the fields leading to stronger aggregation, smaller matrix permeability, and a more pronounced pipe-like pore system with wellaligned macropores.

Soil texture and soil organic carbon (OC) influencethe bacterial microenvironment and also controlherbicide sorption. A field-scale exploratory study wasconducted to investigate the potential interaction betweensoil texture parameters, herbicides, and soil bacterialrichness and diversity. Glyphosate and bentazon wereused to evaluate the herbicidal effect on bacterial communityunder different conditions created by clay and OCgradients in a loamy field. Metabarcoding by highthroughputsequencing of bacterial rDNA was used toestimate bacterial richness and diversity using OTUs,abundance-based coverage (ACE), Shannon diversityindex, and phylogenetic diversity. In general, bacterialrichness and diversity increased after bentazon applicationand decreased after glyphosate application. Therewas no significant effect for field locations with Dextern (the ratio between clay and OC) values below 4.04 (themedian of the values in the field study). The correlationcoefficient (r) between bacterial richness and clay decreasedafter bentazon application, but increased afterglyphosate application. Correlations between Dexter nand bacterial indices followed the same pattern, decreasingafter bentazon application and increasing after glyphosateapplication. This indicated that the specific chemicalnature of individual herbicides affected bacterial communities.This study reinforced the importance of includingsoil physical and chemical characteristics to explainthe influence of pesticides on the variation in soil bacterialcommunities in agroecosystems.

In intensely cultivated regions, it is crucial to have knowledge of the leaching potential related to pesticides in agricultural production. This is especially true in countries, like Denmark, that base its drinking water supply on untreated groundwater. Since fluazifop-P-butyl (FPB) is applied to control perennial and annual weed grasses in agricultural fields, the objective of this study was to evaluate leaching of its two degradation products – fluazifop-P (FP; free acid; (R)-2-(4-(5-trifluoromethyl-2-pyridyloxy)phenoxy)propionic acid) and TFMP (5-(trifluoromethyl)-2(1H)-pyridinone) – through an agricultural field consisting of loamy soil. Drainage and groundwater samples were collected over a five-year period following four spring/summer applications of FPB, and analysed for both FP and TFMP. FP was only detected once in groundwater, whereas TFMP within the first year after the first and fourth application was detected in concentrations exceeding the value of 0.1 µg L-1 in 100% and 24% of the drainage samples and 9% and 14% of the groundwater samples, respectively. Detections of TFMP up to 18 months after application were obtained both in the drainage and groundwater. What differentiated the first and fourth FPB applications from the two others were heavy precipitation events within one week of FPB application, which resulted in rapid transport of TFMP through the discontinuities in the soil and contributed to relatively high TFMP detections in drainage and groundwater. This study indicated that pesticide degradates like TFMP, often being more soluble than the pesticide, have a relatively high leaching potential especially associated with heavy precipitation events shortly after the application. Hence, such pesticide degradates should like in Denmark be considered “relevant” meaning that the EU value for drinking water applies to them, having its leaching potential regulatory assessed based on high quality estimations of their persistence, and be exposed to an assessment of the risk to consumers of drinking contaminated groundwater.

Although soil microorganisms play a pivotal role in delivering various ecosystem functions and services, we are only beginning to understand how microbial communities are shaped by various soil physical properties and environmental factors. In this study, we integrate metabarcoding of bacteria and fungi and soil physical measurements at 60 locations on a 15 × 15 m grid in an agricultural field to: (1) characterize the spatial variation of microbial richness and diversity, and (2) identify the soil physical parameters that are most strongly correlated with them. The study field is located in Silstrup Denmark, extends over 1.69 ha, and slopes 1-2° to the north. Soil samples for 454-pyrosequencing were extracted by pushing 50-mL sterilized plastic vials into the soil. Bulk soil samples were used for the measurement of pH, organic carbon, and soil texture. Intact 100 cm3 soil cores, collected in triplicate, were used for measuring the soil water characteristic curve. Across the field, bacterial richness (number of OTUs) and diversity indices (Shannon diversity and phylogenetic diversity) showed strong spatial autocorrelations. Fungal number of OTUs was quite similar over the field and thus no spatial autocorrelation was observed for them. Fungal Shannon diversity slightly increased from south to north, with spatial autocorrelation for distances larger than 100 m. The ratio of clay to organic carbon (n) was found to be the best predictor of bacterial richness and diversity indices. Neither organic carbon nor clay content was significantly correlated with fungal richness and diversity indices. For soil structural parameters, soil water retention in the pF range 5 to 6.8 (parameter B) was significantly correlated with both bacterial and fungal Shannon diversities. Amount of macropores (> 30 μm) and total porosity (ϕ) were only significantly correlated with fungal Shannon diversity. These results suggest that variation in microbial communities is not random but strongly related with variations in organic carbon, clay content, and soil water characteristics at the field scale. Although soil microorganisms play a pivotal role in delivering various ecosystem functions and services, we are only beginning to understand how microbial communities are shaped by various soil physical properties and environmental factors. In this study, we integrate metabarcoding of bacteria and fungi and soil physical measurements at 60 locations on a 15 × 15 m grid in an agricultural field to: (1) characterize the spatial variation of microbial richness and diversity, and (2) identify the soil physical parameters that are most strongly correlated with them. The study field is located in Silstrup Denmark, extends over 1.69 ha, and slopes 1–2° to the north. Soil samples for 454-pyrosequencing were extracted by pushing 50-mL sterilized plastic vials into the soil. Bulk soil samples were used for the measurement of pH, organic carbon, and soil texture. Intact 100 cm3 soil cores, collected in triplicate, were used for measuring the soil water characteristic curve. Across the field, bacterial richness (number of OTUs) and diversity indices (Shannon diversity and phylogenetic diversity) showed strong spatial autocorrelations. Fungal number of OTUs was quite similar over the field and thus no spatial autocorrelation was observed for them. Fungal Shannon diversity slightly increased from south to north, with spatial autocorrelation for distances larger than 100 m. The ratio of clay to organic carbon (n) was found to be the best predictor of bacterial richness and diversity indices. Neither organic carbon nor clay content was significantly correlated with fungal richness and diversity indices. For soil structural parameters, soil water retention in the pF range 5–6.8 (parameter B) was significantly correlated with both bacterial and fungal Shannon diversities. Amount of macropores (> 30 μm) and total porosity (ϕ) were only significantly correlated with fungal Shannon diversity. These results suggest that variation in microbial communities is not random but strongly related with variations in organic carbon, clay content, and soil water characteristics at the field scale.

Sorption is considered one of the most important processes controlling pesticidemobility in agricultural soils. Accurate predictions of sorption coefficients are needed for reliable risk assessments of groundwater contamination from pesticides. In this work, we aim to estimate the glyphosate sorption coefficient, Kd, from easily measurable soil properties in two loamy, agricultural fields in Denmark: Estrup and Silstrup. Forty-five soil samples in Estrup and 65 in Silstrup were collected fromthe surface in a rectangular grid of 15 × 15-mfromeach field, and selected soil properties and glyphosate sorption coefficients were determined. Multiple linear regression (MLR) analyses were performed using nine geo-referenced soil properties as variables to identify the parameters related with glyphosate sorption. Scenarios considered in the analyses included: (i) each field separately, (ii) both fields together, and (iii) northern and southern sections of the field in Silstrup. Considering correlations with all possible sets of the same nine geo-referenced properties, a best-four set of parameters was identified for each model scenario. The best-four set for the field in Estrup included clay, oxalate-extractable Fe, Olsen P and pH, while the best-four set for Silstrup included clay, OC, Olsen P and EC.When the field in Silstrup was separated in a northern and southern section, the northern section included EC, and oxalate-extractable Fe, Al and P,whereas the southern part included pH, clay, OC and Olsen P. The best-four set for both fields together included clay, sand, pH and EC. Thus, the most common parameters repeated in the best-four sets included clay and pH as also reported previously in the literature, but in general, the composition of the best-four set differed for each scenario, suggesting that different properties control glyphosate sorption in different locations and at different scales of analysis. Better predictions were obtained for the best-four set for the field in Estrup (R2 = 0.87) and for both fields (R2 = 0.70), while the field in Silstrup showed a lower predictability (R2 = 0.36). Possibly, the low predictability for the field in Silstrup originated from opposing gradients in clay and oxalate-extractable Fe across the field. Also,whereas a lower clay content in Estrup may be the limiting variable for glyphosate sorption, the field in Silstrup has a higher clay content not limiting the sorption, but introducing more variability in Kd due to changes in othersoil properties.

The recently presented Dexter et al. (2008) threshold (ratio of clay to organic carbon (OC) of 10 kg/kg-1) is a good indicator for soil functional properties. However, the conventional analysis of OC and clay are costly and time consuming, thus an alternative method to quantify OC, clay or clay/OC ratio directly would be valuable. Visible near infrared spectroscopy (vis-NIRS) is a cost-effective method for soil analysis and was tested here for the prediction of clay/OC ratio. Soil samples from two agricultural fields in Denmark (N=115) were analyzed. Partial Least Squares regression (full cross-validation) was performed on 80% randomly selected samples to correlate soil spectra with OC, clay and clay/OC. The robustness of calibration models was tested on the remaining 20% of the samples. The soil from the two study sites vary greatly presenting clay/OC ratio between 1.20 and 10.43. Successful calibration results for OC, clay and clay/OC ratio were generated. The OC content was predicted with a RMSEP of 0.23%, R2 of 0.96, whereas the prediction of clay resulted in RMSEP=0.94%, and R2=0.94. The most successful predictive ability reported was of the clay/OC ratio (RMSEP=0.42, and R2=0.97) with the most important absorption bands related to both clay minerals (1421, 1910 and 2206 nm – OH bonds and 429, 720 nm – Fe oxides) and organic carbon (1730, 2160 nm and 2310 nm). The results of this study show that vis-NIRS can provide very successful and direct determination of Dexter index on a field scale through its correlation to both OC and clay

The large spatial heterogeneity in soilphysico-chemical and microbial parameters challengesour ability to predict and model pesticide leaching fromagricultural land. Microbial mineralization of pesticidesis an important process with respect to pesticide leachingsince mineralization is the major process for the completedegradation of pesticides without generation ofmetabolites. The aim of our study was to determinefield-scale variation in the potential for mineralizationof the herbicides glyphosate, bromoxyniloctanoate,diflufenican, and bentazone and to investigate whetherthis variation can be predicted by variations in basic soilparameters. Sixty-five soil samples were sampled froman agricultural, loamy field in Silstrup, Denmark, from a60×165mrectangular grid. The mineralization potentialof the four pesticides was determined using a 96-wellmicroplate 14C-radiorespirometric method. Initial mineralizationrates were determined using first-order kineticsfor glyphosate and bromoxyniloctanoate and zeroorderkinetics for diflufenican and bentazone. Themineralization rates of the four pesticides varied betweenthe different pesticides and the different soil samples,but we could not establish correlations between thepesticide mineralization rates and the measured soilparameters. Only the glyphosate mineralization ratesshowed slightly increasing mineralization potentials towardsthe northern area of the field, with increasing clayand decreasing OC contents. The mineralization potentialsfor glyphosate and bentazone were compared with9-years leaching data from two horizontal wells 3.5 mbelow the field. The field-scale leaching patterns, however,could not be explained by the pesticide mineralizationdata. Instead, field-scale pesticide leaching mayhave been governed by soil structure and preferentialflow events.

Colloids are potential carriers for strongly sorbing chemicals in macroporous soils, but predicting the amount of colloids readily available for facilitated chemical transport is an unsolved challenge. This study addresses potential key parameters and predictive indicators when assessing colloid dispersibility and transport at the field scale. Samples representing three measurement scales (1-2 mm aggregates, intact 100 cm3 rings, and intact 6283 cm3 columns) were retrieved from the topsoil of a 1.69 ha agricultural field in a 15 m × 15 m grid (65 locations) to determine soil dispersibility as well as 24 comparison parameters including textural, chemical, and structural (e.g. air permeability) 8 soil properties. The soil dispersibility was determined (i) using a laser diffraction method on 1-2 mm aggregates equilibrated to an initial matric potential of -100 cm H2O, (ii) using an end-over-end shaking on 6.06 cm (diam.) × 3.48 cm (height) cm intact soil rings equilibrated to an initial matric potential of -5 cm H2O, and (iii) as the accumulated amount of particles leached from 20 cm × 20 cm intact soil columns after 6.5 hr (60 mm accumulated outflow). At all three scales, soil dispersibility was higher in samples collected from the northern part of the field where the greatest leaching of pesticides was observed in a horizontal well at ~ 3.5 m depth during a 9-year monitoring program. This suggests that the three dispersibility methods used are all relevant for field-scale mapping of areas with enhanced risk of colloid-facilitated transport. Subsequently, using multiple linear regression (MLR) analyses, soil dispersibility was predicted at all three sample scales from the 24 measured, geo-referenced parameters to produce sets of only a few promising indicator parameters for evaluating soil stability and particle mobilization on field scale. The MLR analyses at each scale were separated in predictions using all, only north, and only south locations in the field. We found that different independent variables were included in the regression models when the sample scale increased from aggregate to column level. Generally, the predictive power of the regression models was better on the 1-2 mm aggregate scale than on the intact 100 cm3 and 20 cm × 20 cm scales. Overall, results suggested that different drivers controlled soil dispersibility 1 at the three scales and the two sub-areas of the field. Predictions of soil dispersibility and the risk of colloid-facilitated chemical transport will therefore need to be highly scale- and area-specific.

Soil macropores often control fluid flow and solute transport, and quantification of macropore 19 characteristics including their temporal and spatial variability is essential to predict soil hydraulic and 20 hydrogeochemical functions. In this study, we measured air and solute transport properties (air permeability 21 (ka) at in-situ water content and -20 hPa of matric potential, and time taken to recover 5% of the applied tracer 22 in the outflow – 5% arrival time) and linked these to direct macropore visualization and quantification by X-ray 23 CT-scanning (e.g. macroporosity, macroporosity of the restricting layer, length density) across a field (Silstrup, 24 Denmark) with natural gradients in texture and density. We observed a high positive correlation between CT-25 inferred macroporosity (carefully corrected for rock artifacts) and the other macropore characteristics and a 26 high intra-field ka variability (4.66−78.10 μm2 at -20 hPa), and 5% arrival time (0.07−2.36 h). Both ka and 5% 27 arrival time were positively correlated with macroporosity (R2 = 0.77 for ka (-20 hPa) and 0.59 for 5% arrival 28 time) and macroporosity of the restricting layer (R2 = 0.80 for ka (-20 hPa) and 0.70 for 5% arrival time). 29 Negative correlations with soil bulk density were found for ka, 5% arrival time, and macroporosity, suggesting 30 that density is the main driver of functional soil structure and gas and solute transport at the site. Linking gas 31 transport and tracer experiments with X-ray CT-based visualization and quantification was found to be a 32 powerful method to understand field-scale variations in gas and solute transport as influenced by soil structure 33 and density. Soil macropores often control fluid flow and solute transport, and quantification of macropore characteristics including their variability in space and time are essential to predict soil hydraulic and hydrogeochemical functions. In this study, measurements of air and solute transport properties and direct macropore visualization by X-ray CT scanning were carried out on 17 large (19-cm diam.; 20-cm length) undisturbed soil columns sampled across a field site (Silstrup, Denmark) with natural gradients in texture and density. Air permeability (ka) at in-situ water content and -20 hPa of matric potential, and 5% arrival time of tracer (time taken to recover 5% of the applied tracer in the outflow) were measured and linked to macropore characteristics (macro-porosity, macro-porosity of the restricting layer, number of macropores, branches, node density, and length density) quantified via X-ray CT. There was a high positive correlation between the CT-inferred macro-porosity carefully corrected for rock artifacts and the other macropore characteristics. High variability in ka (4.66 to 78.10 µm2 at -20 hPa), and 5% arrival time (0.07 to 2.36 h) were observed. Both ka and 5% arrival time were strongly positively correlated with macro-porosity (R2 = 0.77 for ka at -20 hPa; R2 = 0.59 for 5% arrival time) and macro-porosity of the restricting layer (R2 = 0.80 for ka at -20 hPa; R2 = 0.70 for 5% arrival time). High negative correlations with soil bulk density were found for ka, 5% arrival time, and macro-porosity, suggesting that density is the main control of functional soil structure and gas and solute transport at the Silstrup site. Linking gas transport and chemical tracer experiments with X-ray CT based visualization and quantification of macro-porosity was found to be a powerful method to understand field scale variations in gas and solute transport properties as influenced by soil structure and density.

The characterization of soil pore space geometry is important for explaining fluxes of air, water, and solutes through soil and understanding soil hydrogeochemical functions. X-ray computed tomography (CT) can be applied for this characterization, and in this study CT-derived parameters were used to explain water, air, and solute transport through soil. Forty-five soil columns (20 by 20 cm) were collected from an agricultural field in Estrup, Denmark, and subsequently scanned using a medical CT scanner. Nonreactive tracer leaching experiments were performed in the laboratory along with measurements of air permeability ( K a ) and saturated hydraulic conductivity ( K sat ). The CT number of the matrix (CT matrix ), which represents the moist bulk density of the soil matrix, was obtained from the CT scans as the average CT number of the voxels in the grayscale image excluding macropores and stones. The CT matrix showed the best relationships with the solute transport characteristics, especially the time by which 5% of the applied mass of tritium was leached, known as the 5% arrival time ( t 0.05 ). The CT-derived macroporosity (pores >1.2 mm) was correlated with K a and log 10 ( K sat ). The correlation improved when the limiting macroporosity (the minimum macroporosity for every 0.6-mm layer along the soil column) was used, suggesting that soil layers with the narrowest macropore section restricted the flow through the whole soil column. Water, air, and solute transport were related with the CT-derived parameters by using a best subsets regression analysis. The regression coefficients improved using CT matrix , limiting macroporosity, and genus density, while the best model for t 0.05 used CT matrix only. The scanning resolution and the time for soil structure development after mechanical activities could be factors that increased the uncertainty of the relationships. Nevertheless, the results confirmed the potential of X-ray CT visualization techniques for estimating fluxes through soil at the field scale.