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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.

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.

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).

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 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.

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.

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.