Thaw hazards on hillslopes in the permafrost regions of the Qinghai-Tibet Plateau (QTP) are widespread and pose a severe threat to regional landscapes and infrastructure safety. Their occurrence has become more frequent due to climate warming and intensified human activities. However, a comprehensive understanding of their spatial distribution and future change is currently lacking. This study compiled a large dataset of thaw hazards on hillslopes on the QTP from literature, as well as non-thaw hazards identified via visual interpretation. Nine advanced machine/deep learning models and 18 predictors were employed to assess the susceptibility and spatial variability of thaw hazards on hillslopes in the permafrost regions of the QTP. The SHapley Additive Explanations (SHAP) model revealed that NDVI, precipitation seasonality, mean annual precipitation, and mean annual ground temperature are key environmental predictors. Model evaluation results indicate that the area under the receiver operating characteristic curve (AUC) values of the Random Forest, Bagging, XGBoost, and Gradient Boosting models all exceeded 0.95, demonstrating excellent predictive performance. Therefore, this study selected the statistical ensemble means of the four models' prediction results to reduce potential biases inherent in individual models. The results show that high and very high susceptibility areas account for 5.53% of the permafrost region, mainly concentrated in the central and northeastern parts of the QTP, while low and very low susceptibility areas account for 89.39%, mainly located in the southern region. Future scenario predictions indicate that low and very low susceptibility areas would slightly expand, whereas high and very high susceptibility areas would decrease in extent. This study provides an important basis for establishing a more comprehensive inventory of thaw hazards on hillslopes in the QTP, which is of significance for geological disaster mapping in the cold regions of the Northern Hemisphere.

Accurate determination of stem water content is crucial for understanding tree water use, predicting drought stress and wildfire risk, and optimizing irrigation management. Time Domain Reflectometry (TDR) has emerged as a leading method for measuring stem water content due to its non-destructive, rapid, and continuous measurement capabilities. However, TDR applications face two critical challenges: (1) inconsistent measurement parameters across studies, making data comparison difficult, and (2) species-specific calibration requirements that limit model generalization. To address these challenges, we established the first standardized TDR-measured stem water content database, compiling data from 19 calibration studies (1990-2025) covering 33 tree species and 23 calibration models. The database includes comprehensive metadata on species characteristics (e.g., sapwood type, wood density, stem diameter), probe geometric parameters (e.g., length, diameter, spacing), and measurement indicators (e.g., apparent dielectric permittivity, water content). In addition, we developed improved calibration models through machine learning models, with the Multilayer Perceptron achieving optimal performance (R2=0.84, RMSE=0.071 cm3 cm−3, MAE=0.053 cm3 cm-3. This database provides a foundation for standardized TDR applications in stem water measurement and supports tree hydraulic research, drought prediction, and irrigation management. The database is publicly available through GitHub, and we call for continued data contributions to improve model accuracy.

The below-ground component of the Earth’s critical zone is crucial to human activities and underpins numerous chemical, physical and biological processes. However, previous studies primarily concentrated on below-ground temperatures (BGT) until depths up to 3 m and periods shorter than 70 years; few studies have globally analyzed the historical spatiotemporal variability of BGT beyond those ranges. The objective of this study was to investigate BGT anomalies (ΔBGT) between depths of 0–42 m during 1850–2100 using model outputs from CMIP6. The results show a three-stage accelerating warming pattern (1850–2014): weak pre-1943 warming (0.02 °C decade⁻¹, depth-average), mid-century stagnation, and post-1984 acceleration (0.33 °C decade⁻¹, depth-average) for depth mean of 0.05∼1.75 m. Future mean warming rises ∼1.7 times from SSP1‑2.6 (2.08 °C) to SSP5‑8.5 (3.45 °C), with maximum of warming mean expanding 2.6 times. Asymmetric BGT extremes drive elevated subsurface heat risk under high emissions. A robust seasonal hierarchy reversal occurs (DJF‑ to JJA‑dominated), with winter BGT most sensitive to radiative forcing. ΔBGT amplifies strongly from 60°N, and enhances in high‑altitude/coastal regions under high emissions. Heterogeneous bottom boundary condition placement (BBCP) is an important structured uncertainty source in multi-model BGT analysis, introducing non-physical sampling artifacts in ensemble-mean vertical profiles. Despite inter‑model heterogeneity, the multi‑model ensemble yields physically consistent depth‑attenuated warming, providing an ensemble-constrained reference for subsurface thermal change investigation. By 2100, low-moderate emission scenarios (e.g., SSP1‑2.6, SSP2‑4.5) will slow BGT warming. This study can provide insightful understanding of the overlooked BGT and inform future model intercomparison projects and ensemble mean analysis.

More than 50% of the world's irrigated farmland depends on flood irrigation, which is often inefficient and environmentally unsustainable. Increasing the resource use efficiency while reducing environmental impacts is critical for ensuring the sustainability of these systems. In this study, a novel surface irrigation practice, namely, ridge-furrow planting with selective furrow irrigation (RFSFI), was compared with traditional flat cultivation with border irrigation (TFCBI). Multiyear field experiments were conducted at two representative sites in the North China Plain from 2017-2023 to assess the effects on the wheat agronomic performance, the resource use efficiency, and the carbon footprint (CF). Compared with TFCBI, RFSFI significantly increased the tiller number (13.7-16.8%), leaf area index (7.8-11.3%), and aboveground biomass (15.2-35.9%) (P < 0.05) but decreased the plant height, which led to an improved canopy structure and greater interception of the photosynthetically active radiation (7.1-14.4%). These changes contributed to higher grain yield and net income levels. RFSFI also increased the input water productivity by 6.0-23.3%, the nitrogen agronomic efficiency by 10.7-37.4%, and the radiation use efficiency by 6.0-23.3% (P < 0.05). Furthermore, RFSFI increased the carbon use efficiency by 25.7% and the carbon sustainability index by 91.2%, whereas the CF decreased by 15.2% (P < 0.05). These results demonstrate that RFSFI constitutes a readily scalable and economically viable strategy for sustainably implementing flood-irrigated wheat systems globally, particularly in regions facing land and water constraints.

Historically, soil acidification has not been a widespread concern in Western Canadian cropping systems, although pH declines have been documented and may be accelerated in current systems because of significant increases in fertilizer applications over the last 20 years. This study quantified the long-term liming effects and nutrient management on soil acidification (pH), N stocks, marketable yields, and crop uptake of N, P, and cations in alfalfa–brome forage, wheat, and oats in a cereal–forage rotation at the University of Alberta Breton Plots. Grain protein content and N surplus were also evaluated for wheat and oats. Nutrient treatments included (1) Check (no fertilizer), (2) Manure, (3) NPKS, and (4) PKS fertilizers, where S denotes sulfur, each with and without lime applications. Alfalfa–brome yields, N, P, and cation uptake increased significantly with liming under NPKS and PKS treatments and were linked to higher N uptake in subsequent wheat and oat crops. In the Check and Manure treatments, liming had no effects on marketable yields, N, or cation uptake in all crops, or grain protein and N surplus in cereals. Soil acidification rates were greater in NPKS and PKS than in Manure and the Check treatments, regardless of liming. Cation exports in alfalfa–brome exceeded those in cereal crops and are associated with increased H+ release by alfalfa roots, and reduced soil base saturation capacity, especially in the NPKS and PKS treatments. Current findings suggest that soil acidification rates have likely increased with diversified crop rotations and fertilizer use, coinciding with conservation tillage.

Soil water content, matric potential, thermal properties, and electrical conductivity are fundamental and interrelated properties required by a variety of applications in soil science, hydrology, agriculture, and engineering. However, the measurements of the properties are affected by the temporal and spatial variability of soil due to employment of a variety of sensors, which hinders the research and modeling of coupled water, heat and solute transport. In addition, the laborious, costly and time-consuming sensor optimization is always a challenge for traditional sensor development. The objective of this study was to develop a multifunctional sensor integrating heat pulse, time domain reflectometry and porous ceramic matrix and optimize the sensor with COMSOL based numerical simulations. COMSOL simulated ceramic properties (e.g., thermal conductivity, volumetric heat capacity, dielectric permittivity, electrical conductivity) and soil properties (e.g., thermal conductivity and volumetric heat capacity) with different scenarios of sensor dimensions (e.g., the radius and length of the ceramic and extended rod length) were systematically evaluated and verified with experimental data. Our results show that the optimal radius and length of the ceramic are 18 mm and 40 mm, respectively, and the optimal rod length extended out of the ceramic is 50 mm. The optimized results indicate low estimation errors for dielectric permittivity (±1%), electrical conductivity (±1%), thermal conductivity (±2%), and volumetric heat capacity (±1%) of the ceramic as well as thermal conductivity (±3%) and volumetric heat capacity (±1%) of soil. The new multifunctional sensor can provide accurate measurement and modeling of soil hydrothermal properties.

Soil pH and nutrient stratification, as well as spatial variability, pose a problem for diversified no-till systems in the Canadian Northern Great Plains (CNGP). Therefore, we conducted a review aimed at (1) summarizing the evolution of soil pH stratification and variability from conventional to no-till systems, (2) reviewing the contributing factors to soil pH and nutrient stratification, (3) discussing the implications of soil pH stratification on pest management, and (4) deliberating on challenges and opportunities for managing stratified pH in CNGP no-till systems. Reviewed literature indicates that historical tillage practices have redistributed soil downslope, exposing carbonate-rich or acidic subsoils on convex slopes and depositing organic matter in lower positions, contributing to spatial pH variability. The shift to no-till production has intensified cropping systems and led to soil pH and nutrient stratification resulting from seeding and fertilizer application practices, and, to a lesser extent, from crop residue and manure management practices. Soil pH stratification and variability may affect herbicide efficacy, weed pressure, and the incidence of soil-borne diseases, as well as their management. Changes to standard soil sampling practices and fertilizer recommendations are key to managing soil pH and nutrient stratification. Liming plus strategic tillage are potential remedial solutions, but the cost–benefit trade-offs associated with these interventions remain uncertain.

Study region: Iran, characterized by diverse climatic conditions, including arid, semi-arid, and humid subtropical regions, where ET0 dynamics vary significantly due to climatic differences. Study focus: Reference evapotranspiration (ET0) is a fundamental component of hydrological modelling and plays a critical role in agricultural water management. Reliable ET0 predictions are essential for optimizing irrigation systems and estimating water demand. This study evaluates the potential of ERA5-Land reanalysis data, in combination with a Random Forest (RF) machine learning model, to predict daily and 8-day ET0 across these diverse climatic conditions. Daily ET0 values were calculated using the FAO-56 Penman-Monteith model and validated against groundbased observations from 50 weather stations (2008-2017). The RF model was trained using ERA5-Land climatic variables (air temperature, relative humidity, and ET0 from ERA5-Land) along with the day of the year (DOY). New hydrological insights for the region: Results demonstrated a high correlation between ERA5Land temperature estimates and observed station data (Pearson correlation coefficient, r = 0.97; Root Mean Square Error, RMSE = 2.77 degrees C), while relative humidity showed a weaker agreement (Normalized Root Mean Square Error, NRMSE = 21 %). The RF model outperformed traditional approaches in arid and semi-arid regions, achieving NRMSE values of 25 % and 28 %, respectively, with a 60 % improvement over humid regions. At the 8-day scale, predictive accuracy improved further (RMSE = 6.05 mm/8 days, r = 0.99). Beyond model performance, this study provides new insights into the spatiotemporal variability of ET0 across different climatic zones. The findings indicate that temperature is the dominant climatic factor driving ET0 variability, while relative humidity exhibits higher uncertainty, particularly in humid regions. Seasonal trends highlight notable summer ET0 peaks exceeding 30 mm/day in arid zones, emphasizing the need for climate-adaptive irrigation strategies. The proposed methodology is computationally efficient, requiring minimal input variables, and demonstrates robust and scalable performance for large-scale ET0 estimation. These findings provide a cost-effective solution for water resource management, drought monitoring, and climate change adaptation, particularly in data-scarce regions.

Soil erosion is a critical form of land degradation, posing a significant threat to soil health and ecosystem productivity. Accurate monitoring of soil erosion is essential for robustly estimating erosion rates to better plan and develop effective mitigation management strategies. While there have been extensive studies and remarkable progress, existing soil erosion monitoring methods mainly focus on disparate spatial scales, ranging from point and plot scales to slope, watershed, and regional levels. The absence of quantitative techniques for monitoring soil erosion at medium to large scales represent a significant impediment to characterizing and understanding its spatiotemporal dynamics. This study aims to provide a comprehensive review of existing soil erosion monitoring methods and to investigate the potential of Interferometric Synthetic Aperture Radar (InSAR) technology to address this critical gap. A comprehensive analysis of soil erosion processes and monitoring approaches is presented, evaluating the applicability of InSAR for monitoring large-scale soil erosion. Our analysis reveals that over 50 % of InSAR-based soil erosion monitoring studies have been conducted in China (37.2 %) and Italy (14.0 %). The findings indicate that InSAR offers the capability to acquire continuous, large-scale ground elevation deformation data with millimeter-level precision. Its successful application in monitoring urban subsidence and landslides suggests its potential for analyzing erosion at medium to large scales. The time-series data provided by InSAR are invaluable for elucidating the temporal evolution of erosion processes. Despite challenges related to atmospheric disturbances, noise, and maintaining phase coherence, further development of InSAR techniques for soil erosion studies is warranted. Future studies should prioritize refining the efficiency and accuracy of methodologies designed to address the complexities of soil erosion, thereby providing innovative tools and a robust scientific foundation for improved monitoring and management practices.

Thaw-induced mass movements are common geomorphological phenomena induced by permafrost degradation, which has profound implications for soil health and ecosystem stability. Despite significant changes in soil properties caused by thaw-induced mass movements, a thorough understanding and quantitative assessments of this phenomenon is relatively scarce. This study systematically quantified the effects of thaw-induced mass movements on various soil parameters by analyzing soil structural, hydraulic, chemical, and thermal properties at different locations of a typical thaw-induced mass movements in the permafrost region of the Qinghai-Tibetan Plateau. This study also investigated the changes in soil texture, water content, pH, organic carbon content and erodibility at different soil depths. Our findings showed that soil particles became finer and bulk density increased downslope. Concurrently, soil aggregate stability and erodibility also increased downslope. Soil water content and organic matter content both displayed a consistent downslope decrease. At the toe of the mass movement, soil hydraulic properties were significantly lower, with the lowest water holding capacity, along with reduced saturated hydraulic conductivity and field water capacity (i.e., 57.89% and 54.03% of the control, respectively). In addition, the pattern and rate of heat transfer were directly affected by the changes in soil parameters. As soil water content decreased, soil thermal conductivity and volumetric heat capacity showed an exponential and linear decrease, respectively. This study provides important data for understanding permafrost degradation and soil ecosystem response through quantitative analysis, and confirms the effects of thaw-induced mass movements on regional soil parameters.

Soil acidity is associated with nutrient deficiencies and phytotoxicity, and is detrimental to crop productivity due to its effects on plant roots and soil biota. Liming is an effective strategy for managing acidity in agricultural fields. Liming increases soil pH, changing soil chemistry, leading to shifts in community composition and activity of soil biota. However, it is not fully understood how liming impacts different groups of soil biota, their interactions and soil biological health. This review summarizes liming effects on soil biota i.e., plant roots, bacteria, archaea, fungi, nematodes, and earthworms. Reviewed literature indicates that liming increases abundance, diversity and activity of most bacterial species. Liming often increases the abundance of nematodes and earthworms, but some studies showed no liming effects on these communities. Application of lime improves mycorrhizal colonization of plant roots. Liming mostly reduces abundance, biomass and activity of most fungi which tend to favor acidic soils. However, liming effects on fungi were inconsistent; in some studies, liming reduced fungal abundance and the fungal to bacteria ratio, while in other studies, liming showed no impact on both abundance and the ratio. Some variations in liming effects on soil biota were due to differences in duration of liming, liming material used and site-specific environmental characteristics. Overall, this review highlights the complex and variable impacts liming has on soil biota, emphasizing the importance of considering species-specific responses, soil type and environmental conditions when implementing liming strategies. Understanding these dynamics is crucial for optimization of soil health and crop productivity in acidic soils.

Global studies highlight widespread soil phosphorus (P) depletion, affecting soil health, crop yields, and carbon emissions, while field studies emphasize soil P accumulation and legacy effects driving downstream pollution. However, how various natural and anthropogenic factors shape soil P availability at the watershed scale, especially in cold-climate agricultural breadbaskets, remains unclear. This study applied a process-based model to a large agricultural watershed in western Canada and simulated biogeochemical, hydrological, and crop growth processes, to assess influence of soil moisture, soil temperature, and snowmelt on spatiotemporal soil P dynamics. The model was calibrated and validated against canola crop yield, streamflow, soil temperature, and soil P for 1990-2016. Analyses revealed three distinct patterns of soil P trends across regions: increasing, declining, and stationary. Despite similar fertilizer rates, differences in long-term soil P trends were primarily driven by soil moisture availability. Soil moisture-limited regions (11.3-65.85 mm) exhibited soil P accumulation due to constrained plant uptake (14.87-16.09 kg P/ha), whereas moisture-sufficient counties (47-63 mm) showed net P depletion or equilibrium through enhanced crop uptake. Climate-driven shifts, including earlier (~2 weeks) and more frequent snowmelt, along with increased soil temperature phase-change cycles, enhanced winter P mobilization via mineralization and potential freeze-thaw processes but were insufficient to offset accumulation driven by moisture-limited growing-season uptake. However, P depletion pattern may differ in low-water-use cropping systems, where lower evapotranspiration rates can help conserve soil moisture, resulting in more soluble P available for plant uptake. Overall, the interplay of soil moisture, soil temperature, and snowmelt in canola cropping systems suggests that growing-season, moisture-driven plant P uptake dominates long-term soil P trends, whereas winter processes are secondary. Effective management of soil P and mitigation of downstream impacts in cold-region agricultural systems should therefore prioritize strategies accounting for soil moisture, crop type, as well as soil temperature, and snowmelt dynamics.

Abstract Soil acidity is one of the major constraints limiting crop production worldwide. About 50% of the global arable land is acidic. Liming remains an effective strategy for soil acidity amelioration and improvement of soil fertility. The objective of this review was to summarize information on liming effects on soil physical and chemical properties in the North American and European contexts. We reviewed how different lime products influence soil pH and various soil processes that contribute to soil physical and chemical health. Our findings were that, when applied at appropriate rates, liming materials generally increased soil pH, cation exchange capacity (CEC), exchangeable calcium and magnesium, nutrient availability and reduced toxicities of aluminum (Al), manganese (Mn), and heavy metals. Many studies showed that liming modifies soil properties and processes both in the short‐ and long‐term. While most studies reported improvements in nutrient availability, there were some differences in liming impacts on phosphorus (P) and potassium (K), mostly due to differences in soil type and composition. Liming improves structural properties including aggregate stability, soil friability, porosity, and water infiltration. Knowledge about liming impacts on soil physical and chemical properties is essential for optimizing liming rates to enhance soil health and improve productivity. Future studies should explore liming effects on CEC, associations of P and K with cations supplied by liming (e.g., Ca2+), and use of some waste materials as lime alternatives.

Heat pulse (HP) is the most widely used transient technique determining soil thermal properties (STPs) in unfrozen conditions, yet its application to frozen soils introduces significant challenges. At high subfreezing temperatures (-5 to 0 degrees C), the HP measurements induce thawing and refreezing of ice, dynamically altering the frozen soil thermal properties (FSTPs) being measured. Conventional analytical solutions fail to account for these phase change effects, leading to substantial errors in estimation. Although various approaches have been developed to improve FSTPs determination, achieving accurate measurements remain challenging. This study employed a COMSOL-based numerical model to solve heat conduction equations incorporating latent heat and compared the results with that obtained with traditional analytical solutions. The results revealed that analytical solutions consistently underestimate frozen soil thermal conductivity (FSTC) at temperatures above-3 degrees C, even with optimized heating strategies. Numerical simulations demonstrated that phase transition parameters critically influence temperature evolution, particularly above-5 degrees C, and the COMSOL improved FSTC estimates between-4 and 0 degrees C, though the performance depended on heating strategies. To facilitate parameter selection, linear regression models were derived for phase transition interval (Delta Tp, R2 = 0.37) and phase transition point (Tpc, R2 = 0.30). These advancements enhance the accuracy of HP-measured FSTPs, providing a more reliable approach for cold-region researches and applications.

Long-term agricultural management practices affect soil health, but identifying measurable soil properties that reflect soil health (soil health indicators – SHIs) is a challenge. Five long-term (>40 years) crop rotations with varying cropping frequency, crop diversity, fertility and lime treatments were sampled as part of the Soil Heath Institute's North American Project to Evaluate Soil Health Measurements (NAPESHM) at the University of Albert Breton Plots research site in 2019, and additional samples were collected in 2020 for additional indicator measurements. Many soil health frameworks such as the Cornell Assessment of Soil Health (CASH) generate soil health scores based on key SHIs identified from a large, multivariate database using principal component analysis (PCA). For our dataset, we adopted the PCA-based approach with the following questions in mind: 1) Were the key SHIs that explain a large portion of the total dataset variance also the key SHIs that were most sensitive to the rotation, fertilizer and lime management? 2) Were the key SHIs identified with PCA associated with soil health, soil fertility or inherent soil characteristics? We identified seven key SHIs with PCA that explained 86.8% of the database variance. Results of a permutational MANOVA suggested that crop rotation and fertilizer management significantly influenced the total variance of the identified key SHIs. Four of the seven identified key SHIs primarily reflected soil health and three SHIs primarily reflected soil fertility and/or inherent soil properties but were also positively associated with soil health at this site. Overall, the PCA-based approach used to develop the site-specific soil health score (SSpeSHS) proved to be a helpful screening tool for the identification of key SHIs that are sensitive to soil-health-promoting management practices from a large dataset.

Spatial variability in soil pH is a major contributor to within-field variations in soil fertility and crop productivity. An improved understanding of the spatial variability of soil pH within agricultural fields is required to determine liming requirements for precision farming. This study with the use of proximal sensors, firstly assessed the spatial pattern of soil pH and how it can be used to determine site-specific, spatially variable lime requirements. Secondly, the effects of soil pH on soil concentrations of nitrate nitrogen (N0(3)-N), phosphorus (P), potassium (K), sulfur (SO4-S), calcium (Ca), magnesium (Mg), soil organic matter (SOM), aluminum (Al), and manganese (Mn) were assessed in three study fields in central Alberta, Canada. Soil pH varied between 4.5 and 7.5 across all field sites. The field-scale coefficient of variation (CV %) for soil pH, Al and Mn ranged between 4.39 and 7.50 %, 7.33-13.72 %, and 7.33-13.72 % across the three sites. The other soil properties showed low, moderate, and high variability, with field-scale CVs ranging between 6.39 and 17.70 % for SOM and 24.33-91.39 % for SO4-S. Soil pH exhibited positive correlations with both Ca and Mg, across all fields. Negative correlations were observed between soil pH and Al across all fields. A principal component analysis (PCA) was performed for all soil parameters and two principal components accounted for 50%, 54.9%, and 76.8% of the total variance in field 1, field 2, and field 3, respectively. Geostatistical semivariance indicated a strong spatial dependence of all chemical parameters across fields. Large regions within a field were strongly acidic (pH < 5.5) and required lime applications ranging from 0 to 6 t ha(-1). We conclude that proximal soil sensors can be calibrated to soil properties, enabling variable rate lime recommendations on spatially variable fields for the management of soil acidity.

The chemical species of trace elements (TEs) in agricultural soils is highly variable under diverse conditions, requiring tools with clear resolution and minimal disturbance for exploration. A novel surgical (316L) stainless steel (SS) lysimeter with a 5 μm pore size was developed to collect field soil solutions. The size-resolved distribution of TEs were characterized into total (nitric acid digestion), particulate (0.45-5 μm), dissolved (<0.45 μm), colloidal (1 kDa to 0.45 μm), and mainly ionic (<1 kDa) fractions in the lysimeter soil solutions. Total concentrations of TEs (dry weight basis) in acid digested Gray Luvisolic soils were analyzed. Most TEs in lysimeter soil solutions were present in particulate phases, relevant to their geochemical affinities and occurrences in soil minerals. Among dissolved fractions, As, Ba, Co, Li, Mn, Tl, and V existed as mainly ionic species in the soil solutions. Copper, Pb, Al, Th, and U showed variable associations with dissolved organic matter (DOM) and/or inorganic colloids among agricultural treatments. Inorganic NPKS or NKS fertilizer applications with lower pH (5.25-5.74) enhanced mobility and potential bioavailability of Ba, Co, Li, Mn, and Pb present in mainly ionic species, compared with other locations (pH 5.82-6.37). Manure application exhibited a dual effect, potentially increasing bioavailability for As, Tl, and V due to probably enhanced cation exchange capacity (CEC), while also facilitating specific adsorption of Cu and U on DOM, potentially reducing their bioavailability depending on DOM molecular weight. Colloidal and ionic Al and Th concentrations were higher in forest soils than agricultural soils, with extremely low potential bioavailability of Th attributed to strong precipitation with inorganic colloids and adsorption on DOM. The lysimeter sampling and size fractionation method provided a clear insight into agricultural effects on TE distributions and enhancing understanding of agricultural soil health in terms of TE bioavailability in situ.

Spring barley (Hordeum vulgare L.), being a cold-tolerant crop, may not benefit as much from a warmer climate and a lengthening of the growing season due to climate change though its suitable production area could expand further north. The objectives of this study were to assess the impact of climate change on barley yields across Canada for both current production regions and potential northern crop expansion regions in the future. Three crop models (DeNitrification and DeComposition, Decision Support System for Agrotechnology Transfer, and Simulateur mulTIdisciplinaire pour les Cultures Standard) and 18 climate scenarios (1981-2100) were used to simulate the effect of climate change on potential (non-water and non-nitrogen limited) and rainfed (non-nitrogen limited) spring barley yields for 32 locations across Canada. For the currently planted spring barley regions characterized by a humid summer in Eastern Canada (growing season precipitation >500 mm), potential and rainfed yields were projected to slightly increase in the future (<+0.2 t ha(-1)). In western regions where precipitation amount is lower (growing season precipitation <500 mm), changes in the potential yield varied slightly (-0.1 to +0.2 t ha(-1)), while the rainfed yield was projected to increase (0.2-1.0 t ha(-1)) mainly due to a reduction in water stress under elevated CO2. Finally, in northern regions where future expansion may occur, projected yield increases were generally large (up to 2.8 t ha(-1) for potential yield), but the risk of crop failure usually remained high. Our findings suggest that future climate change will present both opportunities and regionalized risks for spring barley producers with the potential to further expand barley production northward.