Visible and near-infrared (vis-NIR) spectroscopy enables rapid and cost-effective soil characterization, supporting the estimation of soil chemical, physical, and biological properties. Most applications, however, focus on using spectroscopy to predict individual soil properties and augment conventional laboratory analyses, while the potential of spectral information to study soil variation in space remains largely unexplored. Latent variables derived from the dimensionality reduction of spectral data offer a compact representation of soil variability, capturing multiple soil properties simultaneously. When spatially predicted, these latent variables can provide a means to investigate soil-landscape relationships in a spatially explicit context.This study aimed to (i) model the spatial variation of latent variables derived from compressed topsoil vis-NIR spectral data across Denmark at 10 m resolution using a digital soil mapping approach; (ii) identify the drivers, in relation to SCORPAN factors, controlling their spatial variation; and (iii) examine how the predicted latent variables represent the soil-landscape relationships across Denmark. An earlier study compressed the data using principal component analysis, kernel principal component analysis, shallow autoencoders, and convolutional autoencoders. To predict the latent variables in space, we used 32 predictors comprising harmonized environmental layers representing climate, relief, and parent material. A weighted machine learning algorithm was used to model each latent variable, with bootstrap resampling providing pixel-level uncertainty estimates. Overall, climate was the main driver across all methods, followed by topography and parent material, mainly the extent of clay till deposits.By integrating compressed spectral data with diverse spatial predictors, we captured complex, non-linear soil-landscape relationships. The resulting high-resolution maps offer a spatial description of soil variation in relation to soil-forming factors and spectral signature. Our maps can be used to improve point-based predictions of soil properties or provide enhanced covariates to support digital soil mapping of soil properties and classes.

The soil-gas diffusivity (ratio of gas diffusion coefficients in soil and pure air), Dp/D0, controls the mobility of gases in variably saturated soils, including aeration, emission and uptake of greenhouse gases. Previous soil-gas diffusivity studies have focused on lower-organic soils. Here, Dp/D0 was measured on intact soil cores at six different soil-water matric potentials between -30 and -1000 cm H2O (pF between 1.5 to 3) on 127 Danish peat top soils (12-52% SOC). The SOC level was not found to be main control of Dp/D0 for peat soils. Dp/D0 versus soil-air content (ε) curves varied as much within a narrow SOC interval (e.g., 30-36% SOC) as for the whole range. In contrast to previous observations for lower-organic soils, it was found that at each pF level, Dp/D0 increased linearly with ε, the slope, the Penman pore continuity index P, increased linearly with pF. Previous Dp/D0 models developed for lower-organic soils (typically < 4% SOC) failed to describe Dp/D0 for peat soils. A soil-water characteristic curve-dependent model (Buckingham-Burdine-Campbell, BBC) well predicted Dp/D0 for peat soils. A modified WLR model with inputs of actual and effective air-filled porosity (ε*, taken as ε around pF3) was developed. The BBC and modified WLR models were successfully validated against independent data for high-organic soils representing different climate zones and organic matter quality. This work provides a better understanding of and models for gas diffusion in high-organic soils, setting a platform for including soil-air phase properties when evaluating soil functions, security, and capital.

Soil organic carbon (SOC) is an important soil health indicator. The SOC directly influences major soil functions such as nutrient cycling, fertility, soil structure, and water and air regulation. As a result, it is important to understand both the soil’s storage capacity and the stability of this carbon. Several methods have been developed to assess distinct SOC pools based on different physical, biological, and thermal definitions. Rock Eval 6 thermal analysis partitions SOC into four operational fractions (S1 to S4), which serve as proxies for organic compounds of increasing thermal stability. Thus is S1 the most labile and easily decomposable, S2 and S3 reflecting progressively more stable organic carbon pools, and the residual refractory carbon measured as S4. Although Rock Eval 6 provides accurate and reproducible estimates of thermally defined SOC fractions, the high initial investment cost and low analytical throughput limit its application for large scale monitoring. Despite the promising application of soil diffuse reflectance spectroscopy for predicting various soil properties, a comprehensive evaluation of this method for estimating Rock Eval 6 derived SOC fractions is lacking. In this presentation, we evaluate the feasibility of vis-NIR spectroscopy as a non-destructive and cost-effective alternative method to predict Rock Eval 6 SOC fractions (S1 to S4). A total of 131 soil samples were collected from lowland areas across Denmark under different land-use types. Partial least squares regression and interval partial least squares regression were applied to relate soil reflectance spectra measured between 400 and 2500 nm to thermally defined SOC fractions. Our results indicate that vis-NIR spectroscopy can reliably estimate thermally defined SOC fractions derived from Rock Eval 6 analysis, with R² values of 0.79, 0.81, 0.78, and 0.53 for S1, S2, S3 and S4, respectively. Among the individual fractions, S2 was estimated with the highest accuracy, while S1 and S3 showed moderate predictive performance and S4 exhibited lower accuracy. Based on these statistical parameters, we conclude that vis-NIR spectroscopy is a feasible and rapid tool for estimating thermally defined SOC fractions.

Estimating the soil particle size distribution (PSD) from visible-near infrared (vis-NIR) reflectance spectra is conventionally limited to predicting discrete soil fractions (e.g., sand, silt, and clay). This approach presents significant challenges: it requires the harmonization of data from different classification systems and, by reducing the PSD to a few values, fails to reflect the entire variation in soil texture. To address these gaps, we present a novel physics-informed neural network (PINN) for the direct estimation of the continuous PSD from vis-NIR reflectance measurements. The PINN learns a continuous, differentiable, and non-parametric representation of the cumulative PSD by integrating both measurements and physical constraints imposed during training. This approach eliminates the need for harmonization and interpolation of measurements originating from different soil texture classification systems and allows the model to be trained on datasets with varying numbers of measurements per sample. Performance evaluation on 30% of the 2777 studied samples showed that the PINN achieved an RMSE of 6.77% and an R 2 of 0.97 in predicting the cumulative PSD fraction. For the texture fractions, the model achieved RMSE values of 4.72%, 3.06%, and 2.75% for sand, silt, and clay, respectively. A comparison with a physics-agnostic (i.e., physics-uninformed) version of the model revealed that both approaches performed similarly in terms of RMSE and R 2. However, the physics-agnostic model violated physical constraints even in data-rich scenarios. In contrast, the PSD obtained from the PINN maintained its physical integrity even under data sparsity conditions and consistently produced non-negative, monotonically increasing predictions that sum to 100% at the largest particle size.

In arable production and environmental planning, accurate soil maps are crucial for informed decision-making. However, creating precise digital soil maps is challenging due to regional and field spatial variations in soil texture caused by diverse pedogenic factors. This research focuses on evaluating a Danish three-layer root zone (0-120 cm depth) soil map (AC map). Over the last decade, the AC map has served as a key input in diverse applications including soil texture, bulk density, content of organic matter, soil profile classification, maximum rooting depth, and hydraulic parameters across three horizons. Validation was conducted by using two datasets: a dependent dataset comprising data from the Danish textural database and Danish profile database and an independent dataset that never have been used for AC map creation. The dependent dataset contained 37,948 points for the A horizon (0-30 cm), 5892 for the B horizon (35-75 cm), and 1136 for the C horizon (70-120 cm), while the independent dataset included 4879 observation points for the A horizon. For dependent datasets, nested k-fold cross-validation and for independent datasets, all of datasets were applied to calculate mean root mean square error (RMSE) and mean bias error (MBE), alongside difference tests (D-values), cumulative mass functions (CMFs), and residual regression kriging (RRK) comparing measured and predicted values. Results showed that the clay content in the A horizon was well-predicted with low D-values and a low bias in contrast to the horizons in the subsoil that were less good predicted. The independent dataset (A horizon) gave higher ME and D-values than the dependent data, which was expected, but the validation also showed that the AC map remains accurate, unbiased, and operationally reliable soil maps on regional scale and has served as a reliable data source in regional hydrological modelling studies conducted over the past decades in Denmark.

Soil water repellency (SWR) is a natural process and affects water dynamics from nano to ecosystem scales. However, the spatial distribution of SWR at the ecosystem scale, as well as the underlying drivers across diverse habitats, land uses and soil textures, remain underexplored. This study presents a comprehensive survey of SWR in Denmark and its predicted spatial distribution, using approximately 7,500 samples. We used digital soil mapping methods (Quantile Random Forest model) to map and identify the relationship between SWR and various environmental variables, including vegetation (via satellite imagery), soil properties (texture and soil organic carbon), and landforms (slope and wetness index). The predicted maps at 10 m resolution revealed that SWR varies across different land uses and vegetation types, with higher values in areas of natural vegetation (e.g., heathlands and coniferous forests) compared to grasslands and croplands (mostly hydrophilic). The analysis also identified soil organic carbon, Sentinel band 3 (Green band − Chlorophyll absorption) and soil texture as key drivers of spatial variation in SWR at the national extent. We found that soil texture influences SWR intensity, which generally decreases as clay content increases across most land use types, except for heathlands. While the predicted maps provided valuable insights into SWR distribution and its environmental drivers, further research is needed to explore the spatio-temporal dynamics of SWR within each habitat, particularly in relation to soil moisture changes. This study highlights the potential of combining machine learning and remote sensing to provide crucial spatial information for managing water resources and enhancing ecosystem resilience in the face of climate change.

Soil water repellency (SWR) significantly impacts water infiltration and soil health, influencing ecological processes across various habitats. Although the mechanisms behind SWR remain partially unclear, it is influenced by both soil and biological properties. While several studies have examined SWR in agricultural soils, fewer studies have focused on natural habitats. This study examines the relationships between soil properties (electrical conductivity (EC), pH, and total carbon (TC)), prokaryotic communities, and potential SWR (measured by the molarity of ethanol droplet test, 60°C pretreatment) in 1153 soil samples spanning 33 habitat types across Denmark. Using path model analysis, we show that both biotic and abiotic factors contribute significantly to SWR. A model including pH, EC, TC, and prokaryotic community composition (β-diversity) could explain ~50% of the variation in SWR, with β-diversity and TC being the most important properties. Furthermore, we reveal distinct variations in SWR across habitat types, which cover a wide range of SWR, from not water repellent to strongly water repellent. Prokaryotic α-diversity was negatively correlated to the degree of SWR, and we found a clear gradient in β-diversity from the highest to the lowest degree of SWR. The degree of SWR was divided into five classes, and we identified 69 genera indicating one or a combination of the SWR classes, which could potentially be used as indicators of the degree of SWR. This research underscores the importance of including the microbial communities in studies examining SWR. In perspective, the observed relations between SWR and soil prokaryotic diversity and community composition also imply that SWR could become a key biophysical indicator of soil health.

Detailed soil maps are an essential tool for water resource, land management and agricultural planning. However, due to different soil processes and variation in geology, soil textural properties vary in space at different scales challenging the development of accurate soil maps. Despite the advances in sampling and efforts to produce accurate maps, uncertainties remain at many scales. Therefore, by implementing error evaluation methods such as comparing predictions and observations, it is possible to understand the performance of digital soil maps. The aim of this work was to obtain the uncertainties of soil categories from a Danish national soil map (AC map) based on measured contents of soil texture and soil organic matter. The AC maps describes the horizon characteristics (soil texture, soil organic matter, soil horizon depths and bulk density) at three depths corresponding to the A (0-30 cm), B (30-70 cm), and C (70-120 cm) horizon. In addition, it combines information on soil classification (soil texture), geology at a depth of ca. 1.5 meters as well as the national geological region. The map has a spatial resolution of 250 meters in the A and B horizons and 500 meters in the C horizon. The analysis is based on 38,000 textural points for the A horizon, 7,000 points for the B horizon, and almost 1,700 points for the C horizon. Considering mean and medians of the content of clay and organic matter together with the observed data, the AC map was validated. The uncertainties of the AC map, statistical correlation coefficient (R2), root mean square error (RMSE), and the Nash-Sutcliffe efficiency (NSE) were used. The results showed that the AC map has an acceptable performance when predicting the clay content in the A horizon (R2 = 0.97, RMSE = 1.15%, and NSE = 0.91), B horizon (R2 = 0.95, RMSE = 1.85%, and NSE= 0.8), and C horizon (R2 = 0.74, RMSE = 5.32%, and NSE = 0.41, respectively. The performance of the prediction of organic matter content in the A horizon (R2 = 0.84, RMSE = 0.39%, and NSE = 0.65) and B horizon (R2 = 0.66, RMSE = 0.44%, and NSE = 0.32) horizons was acceptable as well. Also, the result of validation showed that the highest residual errors of AC maps for clay content and organic matter content has been related loamy (>15% clay) and peat soils. In conclusion, the AC maps, with its optimal accuracy especially in the A and B horizons, can be a suitable tool for use at variable scales in the analysis of crop growth and nitrate leaching modelling studies. Keywords: Soil texture, digital soil maps, soil variability, soil organic matter, clay, soil properties

Drained agricultural peat soils are hotspots for biogenic CO2 emissions, contributing to elevated atmospheric CO2 levels. Due to microbial mineralization, the organic carbon (OC) content of these soils transitions to that of mineral soils, but it remains unclear how the residual OC content controls the rate of CO2 emission. This hinders the integration of soils with 6-12% OC into national greenhouse gas inventories. Based on a comprehensive laboratory study with organic soils from 103 sites in Denmark, we show that area-scale CO2 emissions from soils with >6% OC are not controlled by OC content and OC density, i.e., that soil OC content (wt/wt) is a poor predictor of area-specific CO2 emissions. The empirical data suggest that CO2 emission factors for 6-12% and >12% OC soils should be considered the same. On the other hand, the data also suggest that disaggregation of emission factors for soils with even higher OC contents is not necessary. We conclude that a global underestimation of CO2 emissions from 6-12% OC soils occurs in countries with large proportions of organic soils in transition from organic to organo-mineral soils due to agricultural management. Refining CO2 emission estimates for 6-12% OC soils is critical for the accuracy of national inventories, but also for recognizing the climate benefits of emerging initiatives to rewet drained organic soils.

Macropore flow in structured soils is an important process determining the transport of water, contaminants, and nutrients in the soil. Therefore, we also expect a close connection between hydraulic conductivity (k(h)) near saturation and the potential of macropore flow. In combination with measurements of soil hydraulic properties (SHPs), tracer breakthrough characteristics can be used to get an insight into the understanding of macropore flow in structured soils. In this study, we aim to investigate if a direct link exists between tracer breakthrough characteristics and SHPs of structured soils, which may partly explain the dynamics and the spatial variation of solute transport in soils. We hypothesize that a direct relationship exists between the characteristics of breakthrough curves (BTCs) and the near-saturated k(h) of the soil. We used SHPs and tracer breakthrough characteristics for 71 undisturbed topsoil columns (20 cm height, 20 cm diameter) sampled from eight different sites in Denmark. We defined k[10] (near-saturated hydraulic conductivity) as k(h) at a matric potential (h) of -10 cm. On the same soil columns, based on the tracer breakthrough experiment, we calculated the 5%, 25%, and 50% arrival times (ATs) as the percentage of the cumulative relative mass of the tritium tracer leaching through the soil column. Linear mixed models (LMMs) effectively captured the linear relationships among variables. However, applying a machine learning method (Gradient Boosting Decision Trees, GBDT) further clarified the importance of predictors by capturing nonlinear threshold effects and key interactions among soil hydraulic properties. Although the overall predictive accuracy of GBDT was slightly lower compared to LMM, both methods consistently highlighted k[10] as the most influential predictor, emphasizing its key role in preferential flow dynamics. We conclude that linking SHPs with tracer breakthrough characteristics on large intact columns is highly useful for characterizing soil macropore functions.

Agricultural activity on drained lowlands is a common practice in Denmark and there are suggestions to rewet some of them for climate mitigation purposes. Rewetting those lowlands might result in a change in microbial community composition. This study investigates the current prokaryotic diversity and community composition in soil samples from cultivated lowlands to provide the baseline for monitoring changes after rewetting. Furthermore, variations in soil properties between sites are examined, and the properties driving differences in prokaryotic diversity and community composition are identified. In total, 116 samples were collected from field sites across Denmark that were categorized as one of four different land-use types: Crop, Grass, Fallow, and Other. Soil properties were selected to cover chemical (soil water repellency, pH, electrical conductivity), hydrological (depth to ground-water table, soil water content at field capacity (-100 hPa)), nutrient-related (total nitrogen, organic carbon, carbon-to-nitrogen-ratio, fractions of pyrolizable and residual organic matter), and structural (total porosity, pore size distribution index) functions of the soil. Soil samples exhibited significant variations in their chemical and physical properties, including pH ranging from 2.02 to 7.55, organic carbon ranging from 3 g 100g-1 to 50 g 100g-1, soil water repellency ranging from 71.27 mN m-1 (hydrophilic) to 33.85 mN m-1 (very strongly hydrophobic), and total porosity ranging from 51% to 95%. Soil samples clustered according to soil class (mineral, organo-mineral, organic, highly organic) but not according to land-use type (crop, grass, fallow, other). Prokaryotic alpha diversity, measured as Shannon’s diversity index (H), ranged from 4.16 to 5.89 across samples and could best be predicted by pH, followed by total porosity, fraction of pyrolizable carbon, and pore size distribution index. The pH alone explained 36% of the variation in H between samples. Hierarchical clustering identified three prokaryotic clusters highly correlated with pH. A weak correlation was found between differences in community composition (beta diversity) and geographic distance (r = 0.15, p < 0.001). However, pH was also the main driver of beta diversity, explaining 11% of the variation. At the same time, models including additional variables only had marginally better explanatory power. In conclusion, pH was the predominant driver of prokaryotic alpha and beta diversity across land-use types in lowland soils.

Agricultural activity in drained lowlands accelerates peat decomposition and greenhouse gas emissions. Rewetting is increasingly adopted across Europe to mitigate these emissions, but its effects on soil microbial communities remain poorly understood. We examined prokaryotic communities and network structure in Danish lowland soils designated for potential rewetting to improve our understanding of these communities and their drivers. We included less-studied soil properties linked to hydrology and structure (soil water content at field capacity, van Genuchten pore-size distribution index (n), and soil water repellency (SWR)) in addition to common properties such as pH and organic carbon (OC). We analysed 113 soil samples across land-use types (grass, fallow, crop, other) spanning mineral to organic soils with gradients in pH (2.0-7.6), OC (0.025-0.499 kg kg-1), SWR (33.9-71.3 mN m-1), and soil structure (n: 1.1-1.3). Prokaryotic alpha diversity (Shannon-Wiener index: 2.9-5.7) was best predicted by pH, followed by porosity and n. Together, pH, porosity, n, and OC accounted for 24.5% of the variance in community composition. Hierarchical clustering identified three prokaryotic clusters strongly aligned with pH. Network analysis revealed marginal differences when comparing samples from fallow and grass, while complexity increased progressively across clusters. Interestingly, high-pH soils showed the highest alpha diversity but the least complex networks, while low-pH soils showed the opposite. In conclusion, soil pH emerged as the dominant driver of prokaryotic communities in Danish lowlands, but hydrological and structural properties also played important roles. Network complexity provided complementary insights into ecosystem organisation beyond diversity alone.

Macropore transport is an important process of phosphorus (P) loss from tile-drained agricultural land to surface waters where P inputs may cause accelerated eutrophication. Many laboratory experiments or plot studies have shown that P loss by macropore transport increases with increasing concentrations of mobilizable P in the topsoil. However, operational models that quantify the risk of P losses by macropore transport based on typically available information on soil properties, including P status and soil hydrological properties, are currently lacking. This study has collated and analyzed comprehensive existing data from standardized column-leaching experiments with 193 topsoils from different locations in Denmark. In addition to general physical and chemical soil properties including soil P pools, water, and P transport were measured on the large undisturbed soil columns. This data has been used to investigate relationships between P loss and soil properties under varying degrees of macropore transport. Specifically, we have used two statistical methods to analyze relationships between variables and to explore predictive models – multiple linear mixed models (MLMM) and structural equation modeling (SEM). The latter technique allows for testing complex causal relationships among observed and latent variables. Our SEM approach has so far yielded rather poor model fits, and the model structures for estimating the loss of dissolved and particulate P from the columns were characterized by low significance. This was partly due to missing data. In contrast, different MLMM fitted the measured dissolved and particulate P losses satisfactorily. Water-extractable P and saturated hydraulic conductivity were the most important variables for estimating dissolved P losses, while colloid mobilization in soils and tritium leaching breakthrough time explained particulate P losses to a large degree. Our initial statistical analyses show that P loss in dissolved and particulate form from large columns under macropore runoff scenarios can be reasonably explained by soil properties that are typically mapped in Denmark. This approach could bridge empirical and mechanistic modeling and facilitate mapping the risk of P loss by macropore transport.

Soil organic matter (SOM) is an important component of ecosystem carbon stocks. Generally, SOM found in mineral and organo-mineral soils can be categorised into two fractions: particulate organic matter (POM) and mineral-associated-organic matter (MAOM), both of which contain soil organic carbon (SOC). Understanding the relationship between SOC and SOM fractions provides insight into SOM decomposition and SOC storage potential. Here we show an intriguingly tight relationship between the fraction of SOC in SOM (denoted as f OC $$ {f}_{\mathrm{OC}} $$ ), habitat and soil physical properties, as well as SOC stored in POM and MAOM. This opens up new ways to predict spatial variations in the distribution of POC and MAOC using more widely available f OC $$ {f}_{\mathrm{OC}} $$ data as a covariate. By compiling 14 datasets and 9503 measurements from across Europe and globally we analysed f OC $$ {f}_{\mathrm{OC}} $$ across mineral and organic soils, which fell between 0.38 and 0.58, consistent with variation in carbon of major plant components. f OC $$ {f}_{\mathrm{OC}} $$ followed a habitat gradient with lowest median values in Seagrass sediments (0.36 ± 0.09) and Permafrost habitats, followed by croplands (0.47 ± 0.08) and a maximum in semi-natural habitats (e.g., neutral, acid and calcareous grasslands) (0.56 ± 0.07), with differences between broadleaved (0.50 ± 0.087) and coniferous woodlands (0.53 ± 0.07) which were driven by overall organic matter content. The data show a tight link between vegetation carbon and the contents of SOC and SOM across various habitats, which could be used to inform agricultural soil management, improved land-use planning (e.g., woodlands), and tracking climate-related SOC targets.

The soil water retention curve (SWRC) is essential for describing water and energy exchange processes at the interface between the solid earth and the atmosphere. Despite its importance, measuring the SWRC using standard laboratory methods is challenging and time-consuming. This paper presents a novel physics-informed neural network (PINN) approach for developing pedotransfer functions (PTFs) to predict continuous SWRCs based on soil texture, organic carbon content, and dry bulk density. In contrast to conventional parametric PTFs developed for specific SWRC models, the PINN learns a non-specific form of the SWRC by effectively integrating both measurements and physical constraints into the training process. This approach allows the estimated SWRC to maintain its physical integrity from saturation to oven-dry conditions, even in scenarios with sparse data. The new approach is particularly effective for tackling the challenges encountered in developing PTFs on large SWRC datasets, which often have an imbalance towards the wet-end and include numerous samples with limited and unevenly distributed measurements. We compared the performance of the PINN with that of a conventional physics-agnostic neural network using a dataset of 4200 soil samples. While both networks performed similarly at the wet-end where data are abundant, the PINN excelled at the dry-end where data are sparse and unevenly distributed, achieving a normalized RMSE of 0.172 compared to 0.522 for the conventional neural network. The SWRC derived from the PINN is differentiable with respect to the matric potential and can be seamlessly integrated into the governing equations of water flow in the unsaturated zone.

Organic-rich agricultural soils, including drained peatlands, are hotspots for biogenic CO2 emissions. Due to microbial mineralisation, the organic carbon (OC) content of these soils transitions to that of mineral soils, but it remains unclear how the residual OC content controls the rate of CO2 emission. Here we show that area-scaled CO2 emissions from topsoils with >6% OC are not controlled by OC content and OC density in a comprehensive laboratory incubation experiment. National greenhouse gas inventories assign area-scaled CO2 emission factors to soils with >12% OC, while soils with 6-12% OC are mostly disregarded or treated with lower emission factors. In this respect, our results suggest that CO2 emissions from organic soils could be underestimated by up to 40% in the Danish national inventory submission to the United Nations Framework Convention on Climate Change (UNFCCC). We conclude that global underestimation of area-scaled CO2 emissions from 6-12% OC soils occurs in countries with large proportions of organic soils in transition from organic to organo-mineral soils due to agricultural management. Refining CO2 emission estimates for 6-12% OC soils is critical for the accuracy of national inventories, but also for recognising the climate benefits of initiatives to rewet drained organic soils.

Soil water repellency (WR) is ubiquitous across Greenlandic cultivated fields, which may constrain agricultural production. Fine-grained glacial rock flour (GRF) is available in the surrounding landscape, which could serve as a soil amendment. We tested whether the application of GRF (rates of 0, 50, 100, 300, and 500 ton ha(-1)) reduced the WR across two field trials in South Greenland. The field trials, Upernaviarsuk (UP) and South Igaliku (SI), differed in clay (UP: 0.05-0.11 kg kg(-1); SI: 0.03-0.05 kg kg(-1)) and organic carbon (OC) contents (UP: 0.04-0.13 kg kg(-1); SI: 0.01-0.03 kg kg(-1)). We measured WR across gravimetric water contents (W) from oven-dry to the W where WR ceased (W-NON) to obtain whole WR-W curves. Most soils became hydrophilic around air-dry conditions at application rates of >= 300 ton ha(-1), likely due to increased clay:OC ratios. Application rates of >= 300 ton ha(-1) generally reduced the trapezoidal integrated area of the WR-W curve (WRAREA), W-NON,W- and WR after heat treatments at 105 degrees C (WR105) and 60 degrees C (WR60). The WR105 was significantly reduced in both fields at 500 ton ha(-1), while WR60 was significantly reduced in UP at application rates of >= 300 ton ha(-1). The GRF effects were masked by texture and OC variations. Normalizing WRAREA to the water vapor sorption isotherms (utilizing the Campbell-Shiozawa model) revealed that GRF consistently reduced the normalized WRAREA. The SI field showed the largest reduction in the normalized WRAREA, likely due to its lower OC and clay contents. Thus, GRF could reduce WR across two Greenlandic field trials.

The warming climate is rapidly changing the circumpolar region, presenting new opportunities and challenges for agricultural production in South Greenland. The warming climate is projected to increase the frequency of drought periods, but little is known about the soil-water retention (SWR) and the plant available water (PAW) of the agricultural soils in the region. This study aimed to measure the SWR and PAW of Greenlandic agricultural soils and evaluate the effect of organic carbon (OC) and clay (CL) content using pedotransfer functions based on OC and CL. The study included 464 South Greenlandic agricultural soil samples from 20 fields with a wide distribution in clay (0.016-0.184 kg kg(-1)) and OC contents (0.006-0.254 kg kg(-1)). Pedotransfer functions were successfully developed for estimating the gravimetric water content (w) at five soil-water potentials (-1500, -100, -30, -10, and -5 kPa) and PAW. The OC content was the primary variable governing the gravimetric water content at each soil-water potential, evidenced by R-2 values consistently above 0.80. The effect of OC on the gravimetric water content at -1500 kPa was close to the range reported in the literature, but OC effects were markedly higher between -100 and -5 kPa. Overall, this study highlights a substantial effect of OC on the PAW as a 1% increase in OC increased PAW by more than 4%, which is almost twice the value of a recent meta-study. Our study highlights the potentially dominating effects of organic matter on soil-water balance and availability in high-latitude agriculture.

The particle density (rho(s)) is a fundamental physical property needed for calculating the soil porosity and phase distributions. While rho(s) is often estimated using soil organic matter (SOM) content and particle size distribution, the specific densities of each soil component remain unclear in a subarctic agricultural setting. This study aimed to evaluate the rho(s) of soils from Southwest Greenland using a three-compartment model (3CM) based on the mixing ratio of SOM derived from loss-on-ignition, mineral particles <20 mu m (FC), and mineral particles >= 20 mu m (CC). We further evaluated the accuracy of the 3CM against pedotransfer functions (PTFs) and visible near-infrared (vis-NIR) spectroscopic models. A total of 324 soil samples from 16 Greenlandic agricultural fields were investigated, covering a wide range in SOM content (0.021-0.602 kg kg(-1)) and clay content (0.020-0.185 kg kg(-1)). Despite their high SOM content, the Greenlandic soils exhibited relatively high rho(s) (1.936-3.044 Mg m(-3)), which together with a large SOM/organic carbon ratio of 2.16 indicated a high SOM density of 1.493 Mg m(-3). The 3CM fit on all soils indicated FC and CC densities of 3.047 and 2.713 Mg m(-3), respectively, while a subset of soils (n = 203) from the same geological setting resulted in FC and CC densities of 2.738 and 2.731 Mg m(-3). Prediction accuracy of the 3CM (RMSE = 0.067 Mg m(-3)) was similar to PTFs (RMSE = 0.068-0.070 Mg m(-3)) and better than vis-NIR spectroscopic models (RMSE = 0.091 Mg m(-3)).