Soil organic carbon (SOC) has a major impact on soil quality, and increasing SOC is of growing interest as a climate change mitigation strategy. However, previous work has shown that changes in SOC may vary with short-term climate cycles. This study aimed to investigate the relationship between precipitation, potential evapotranspiration (PET) and SOC at a greater spatial and temporal extent than previously done in Saskatchewan, Canada. Using data from the Prairie Soil Carbon Balance project, change in SOC was assessed as a function of mean growing season precipitation (May to August) and PET using generalized additive models. For change in SOC content and stock, rates of SOC change decreased with increased total precipitation and increased with increased PET. These trends reflect differences in the response of both plant growth (C inputs) and decomposition rates (C outputs) in different soil–climate conditions. These results confirmed that SOC can be expected to fluctuate with short-term climate variation. Hence, such relationships must be considered when assessing SOC changes due to management or estimating future SOC under a changing climate.

Remote sensing (RS) has been widely applied to map soil salinity in landscapes where salinity exhibits strong spatial contrasts, characterized by high electrical conductivity (EC) values. However, its effectiveness in regions dominated by low EC values remains less understood, particularly in irrigated agroecosystems where salinization processes differ from natural dryland settings. This study evaluated RS-based models within the Riverhurst Irrigation District, Saskatchewan, where both irrigation induced salinity and naturally occurring salinity occur, and where the majority of EC values fall within the 0-2 dS/m range. Vegetation and salinity indices derived from 30 m Landsat 8 imagery, together with geomorphometric variables from a 5 m LiDAR-derived digital elevation model, were used to model soil salinity for three depth intervals (0-30, 30-60, and 60-90 cm) using Random Forest (RF) and support vector machine (SVM). Model evaluation on independent test dataset showed that the SVM outperformed RF, achieving a higher coefficient of determination (R2) of 0.77 and a lower root mean square error (RMSE) of 0.48, compared to RF (R2 = 0.66, RMSE = 0.51).

Strong evidence exists that management practices can increase soil organic matter (SOM); however, climate is also known to affect SOM content. The objective of this study was to investigate the relationship between precipitation and SOM. Precipitation and SOM data from southeastern Saskatchewan were highly correlated ( r = 0.91, p < 0.01), with SOM contents declining during drier periods, indicating SOM is responsive to short-term climate cycles. Assessing soil organic carbon (SOC) stock changes must account for climate variability to avoid ascribing either SOC losses due to lower precipitation or gains due to higher precipitation to management practices.

There is an ongoing demand for region-specific soil organic carbon estimates to support sustainable land management and inform carbon credit programs. The Prairie Pothole Region is prominent agricultural area that extends through Canada and the United States, and features a significant number of wetlands commonly referred to as prairie potholes. The contribution of these wetlands to landscape-level soil organic carbon storage is complex and may not be consistent across the region as influenced by several environmental and management factors. This study reviews existing literature to identify the main factors that contribute to variability in soil organic carbon stocks in prairie pothole wetlands. Soil organic carbon stock data from 10 studies in the Prairie Pothole Region were summarized through a meta-analysis. Variable importance and regression analyses were used to assess which factors explain variability in soil organic carbon. Wetland class explained up to 26.6

In the digital age, soil data have become crucial for understanding the role of soil in agricultural systems, biodiversity, carbon sequestration, ecosystem services, and sustainability, thus guiding decision-making. However, diverse data collection methods and fragmented soil data management practices complicate creation of a unified soil database from varied datasets. In Ontario, Canada, data fragmentation across different institutions hinders access and use of this vital soil data for spatial and temporal analyses. Moreover, the latest update in the National Pedon Database is dated back to 2011, underscoring the need for centralized provincial data warehouses for systematic soil information access and analysis. This study addressed these challenges by developing and implementing an end-to-end, multidimensional soil data curation framework that integrates diverse soil data genres and sources in Ontario, enhancing the database updating process. Applying minimum inclusion criteria, data from 13 sources across four different data genres (fixed depth, topsoil, profile, and peatland), comprising 14 145 observation sites and their respective layers, were integrated into an SQLite database. Despite dense sampling in southern Ontario, data gaps increased with depth. Harmonization focused on key attributes such as depth, soil organic carbon, and texture yielded 6335 unique sites and 28 134 layers, including 5460 profiles, 178 topsoil, and 697 fixed depth sites. Using a scalable and transparent soil data “hyperlooping” framework, integrated with platforms like KNIME, R, and SQL, this comprehensive database supports enhanced digital soil assessment and mapping in Ontario, and beyond.

Soil organic carbon (SOC) sequestration assessment requires accurate and effective tools for measuring baseline SOC stocks. An emerging technique for estimating baseline SOC stocks is predictive soil mapping (PSM). A key challenge for PSM is determining sampling density requirements, specifically, determining the economically optimal number of samples for predictive soil mapping for SOC stocks. In an attempt to answer this question, data were used from 3861 soil organic carbon samples collected as part of routine agronomic soil testing from a 4702 ha farming operation in Saskatchewan, Canada. A predictive soil map was built using all the soil data to calculate the total carbon stock for the entire study area. The dataset was then subset using conditioned Latin hypercube sampling (cLHS), both conventional and stratified by slope position, to determine the total carbon stocks with the following sampling densities (points per ha): 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8. A nonlinear error function was then fit to the data, and the optimal number of samples was determined based on the number of samples that minimized soil data costs and the value of the soil carbon stock prediction error. The stratified cLHS required fewer samples to achieve the same level of accuracy compared to conventional cLHS, and the optimal number of samples was more sensitive to carbon price than sampling costs. Overall, the optimal sampling density ranged from 0.025 to 0.075 samples per hectare.

Despite the increasing use of portable, low-cost spectrometers in estimating soil properties, there is lack of documentation regarding the factors contributing to the lower performance of these spectrometers when compared to conventional ones. This study investigates potential factors influencing performance of the Nanoquest, a low-cost spectrometer, in estimating soil organic carbon (SOC) and total nitrogen (TN). To conduct the study, five different models (cubist, partial least squares regression, support vector machines, random forest, and generalised boosted models) were tested for the estimation SOC and TN and a fivefold cross-validation analysis was conducted for model hyperparameter optimization. The Nanoquest achieved a Lin's concordance correlation coefficient (CCC) value of 0.84 and an R 2 value of 0.74 for SOC. For TN, CCC values of 0.86 and an R 2 value of 0.78 were obtained. To understand the impact of the spectral range and spectral resolution on SOC and TN estimation, the ASD spectra were digitally resampled to match the Nanoquest spectral range and resolution. This resampling resulted in a slight decrease in model performance for the spectral range and a more pronounced decrease for the spectral resolution.

Shallow groundwater consumption via phreatophytic transpiration and resulting vegetation-linked groundwater table (GWT) fluctuation is a typical soil hydrological process in wetland riparian areas. However, upland and riparian land use alterations may further influence the shallow GWT fluctuation, temporally and spatially. In this multi-year field study, we investigated whether introducing short rotation willow (SRW) positively or negatively affects the shallow GWT, soil water availability, and soil health on marginal riparian lands of the Prairie Pothole Region (PPR). We compared the impact of SRW on these parameters to two common land uses: annual crop (AC) and pasture (PA). Depth to GWT was monitored via data loggers from 28 wells in two semi-arid PPR sites. The GWT depth varied by land use practices only in site B (p < 0.001; PA > SRW = AC) but not significantly in site A (p = 0.325), and the patterns were inconsistent between sites. In GWT depth prediction, the performance of Artificial Neural Network (ANN) was better than Autoregressive Integrated Moving Average (ARIMA) models but was inconsistent alike with field observations. The GWT depth responded to seasonal precipitation and potential evapotranspiration (ET) patterns. The monthly GWT fluctuations peaked between June and August due to increased precipitation, while they were lower during May and September with reduced precipitation; however, these variations were not significant (p > 0.05). Higher precipitation and lower potential ET throughout the wet year (i.e., in 2014) significantly (p < 0.05) raised GWT (i.e., decreased depth to GWT) under all land uses, and vice versa. Our study indicated that planting SRW in marginal riparian land of the PPR would not negatively impact shallow GWT or soil water availability. Moreover, the SRW plantation could also help manage soil salinity without severely depleting the soil's nutrient pools or diminishing soil quality and health indicator parameters measured during the first rotation.

Sustainable soil resource management depends on reliable soil information, often derived from ‘legacy soil data’ or a combination of old and new soil data. However, the task of harmonizing soil data collected at different times remains a largely unexplored in the literature. Addressing this challenge requires incorporating the temporal dimension into mathematical and statistical models for spatio-temporal soil studies. This study aimed to create a comprehensive framework for harmonizing soil data across various time. We assessed the integration of historical and recent soil data, ranging from 4 to 48 years old data, using soil data recency analysis. To achieve this, we introduced an ‘age of data’ attribute, calculating the time difference between soil survey years and the present (e.g., 2022). We applied three machine learning models - Decision Trees (DT), Random Forest (RF), Gradient Boosting (GBM) - to a dataset containing 6339 sites and 28,149 depth-harmonized layers. The results consistently demonstrated robust performance across models, RF outperforming with an R-squared value of 0.99, RMSE of 1.41, and a concordance of 0.97. Similarly, DT and GBM also showed strong predictive power. Terrain-derived environmental covariates played a more important role than land use and land cover (LULC) change in predicting soil data recency. While LULC change showed soil organic carbon concentration variability across the different depths, it was a less important factor. Anthropogenic factors, such as LULC change and normalized difference vegetation index (NDVI), were not primary determinants of soil data recency. Variations in soil depth had no impact on predicting soil data recency. This study validated that terrain-derived covariates, especially elevation factors, effectively explain the quality of older soil data when predicting current soil attributes using the soil data recency concept. This approach has the potential to enhance real-time estimates, such as carbon budgets, and we emphasize its importance in global earth system models.

Drainage leads to trade-offs between crop production efficiency and wetland conservation, with complex impacts on ecosystem services. In North America’s Prairie Pothole Region, wetland drainage is widespread, often to increase the available land for cultivation, prevent crop loss due to flooding, and manage soil salinity. Wetlands are known for providing key ecosystem services such as improved water quality, flood mitigation, and carbon storage. There is limited research on how changes to soil hydrology and soil redistribution through wetland drainage can impact soil carbon storage and persistence in this region. This research evaluates factors that contribute to soil carbon storage in drained prairie pothole wetland based on 33 drained wetlands in Saskatchewan, Canada. These analyses showed regional differences in the response of soil carbon storage to drainage that are driven by environmental factors such as annual precipitation, temperature, and wetland permanence. We observed increasing soil carbon storage from the Dark Brown to Black soil zones, as well as with longer wetland pond permanence. The sampling depth used for calculating soil carbon storage was especially important when comparing geographically across the soil zones as the Black soil zone had greater soil carbon stored at depth. Soil carbon was also intensively monitored over 2 years following installation of surface drainage across a wetland complex (8 drained wetlands) where water was partially directed to a consolidation wetland. We further assessed changes in soil carbon dynamics and protection from microbial decomposition based on three soil organic matter fractions, ATR-FTIR for organic matter functional groups, and phospholipid fatty acid analysis to understand the microbial community abundance and structure. After 2 years following drainage, ephemeral wetlands with short pond permanence were found to be most sensitive to drainage and the only wetland class with decreases in soil carbon. The temporary and seasonal wetland classes showed no significant differences in soil carbon content but there were changes in the organic matter with depth due to soil redistribution during drainage implementation. Jointly, this research provides region-specific estimates of soil carbon storage in drained prairie pothole wetlands that can be used to inform wetland soil carbon management in cultivated fields.

Maintaining soil organic carbon (SOC) is critical for global food security as it is essential for soil functions that sustain crop yields. There has been an increase in predictive soil mapping, which when combined with extensive crop yield datasets, enables a better understanding of crop yield and SOC relationships. This study focused on updating maps of SOC content in Saskatchewan using recently digitized historical SOC datasets and predictive soil mapping, and using the maps to examine the relationship between SOC and crop yield. A database of 5014 SOC values was used to map SOC contents using a Random Forest model and a range of environmental covariates. The final SOC model had a R2 of 0.48, root mean square error of 0.98%, concordance correlation coefficient of 0.67, and a bias of 0.12%. The relationship between mapped SOC values and crop yield data, with 100,000-200,000 records depending on crop type, was then assessed using a linear mixed effects model after normalizing the data by rural municipality to remove broad-scale climate effects. Overall, an increase in SOC by 1% led to an increase on average of 263 kg ha-1 for wheat (Triticum aestivum L.), 293 kg ha-1 for barley (Hordeum vulgare L.), 133 kg ha-1 for canola (Brassica napus L.), and 135 kg ha-1 for field peas (Pisum sativum L.). These results show that increasing SOC was associated with greater yields for four major crops in Saskatchewan, with the largest gains occurring when the initial SOC contents are lower. Soil organic carbon directly increases crop yields. Yield response to soil organic carbon shows diminishing returns. Yield response to soil organic carbon is likely region and crop specific.

There is increasing interest in soil organic carbon (SOC) sequestration as a climate change mitigation strategy. There is a need to estimate the quantity of SOC sequestered historically due to no-till, and the remaining sequestration potential in Saskatchewan. To answer this question, predictive soil mapping results were linked with the Century model to predict SOC stock change over time to a depth of 20 cm considering three different future climate change scenarios. Climate scenarios included low, moderate, and high amounts of climate change and included estimated changes to monthly minimum, average, and maximum temperature, total monthly precipitation, and average monthly relative humidity at an 800 m x 800 m resolution. Historically, the modelled average SOC gain for Saskatchewan was 2.8 Mg ha-1. Future potential simulated SOC was lower over the next 30 years, with average SOC gains estimated to range from 1.4 to 1.7 Mg ha-1 by 2054 and 2.3 to 3.1 Mg ha-1 by 2100. There is also unequal spatial distribution of SOC stock gain potential, with the northern grain growing regions showing lower future potential. The predicted future gains will be at a lower rate than in the past with carbon sequestration rates dropping from 0.06 to less than 0.02 Mg ha-1 year-1. Additional management practices such as improved residue management and the introduction of crop varieties with increased below ground carbon inputs and more stable residues should be explored to offset the diminishing SOC returns from no-till.

Short rotation willow (SRW) is a land management strategy involving the cultivation of rapidly growing, biomass-rich herbaceous-woody plants. This practice holds promise for renewable energy production, water quality preservation, carbon sequestration, greenhouse gas (GHG) mitigation, enhancement of soil extracellular enzyme activities (EEAs), and promotion of overall soil health. The rapid growth of SRW demands substantial water and nutrient resources, posing concerns when cultivated in marginal riparian lands within the Prairie Pothole Region (PPR), potentially leading to alterations in groundwater table (GWT) depth fluctuations, elevated soil salinity levels, and disruptions to biogeochemical cycles. Hence, this study comprehensively evaluated the effects of establishing SRW as a degraded marginal riparian land use practice in the PPR and attempted to answer several vital questions in the field and microcosm scale on soil hydrology, salinity, nutrients, soil organic carbon (SOC), GHG emissions, and EEAs involved in biogeochemical cycling. In a field experiment, the effects of SRW were evaluated by measuring the depth to GWT, groundwater and soil electrical conductivity (EC), macronutrients (N, P, K, and S), and SOC content in different fractions and chemical compositions during the first rotation (3-year cycle) compared with adjacent annual crop and pasture in two semi-arid PPR sites. In a microcosm experiment, GHG (CO2, CH4, and N2O) emissions and EEAs [beta-glucosidase (BG), N-acetyl glucosaminidase (NAG), and alkaline phosphatase (AP)] were measured in intact soil cores treated with declining water tables and different groundwater salinity levels. No consistent land use impacts on GWT or soil EC were observed between sites. Land use in site B significantly impacted GWT depth, implying site-specific factors, such as topography and soil characteristics, may be dominant over land use effects. Under SRW, the levels of macronutrients in the soil varied but did not significantly reduce the overall nutrient content of the soil. Total SOC was highest in pasture; light fraction organic carbon and particulate organic carbon followed a similar land use pattern, i.e., pasture > SRW = annual crop. Land uses affected GHG emissions significantly in the order of pasture > annual crop = SRW. GHG emission varied with salinity and GWT but there was no interaction with land use practices. Soil EEAs were significantly impacted by different land uses, i.e., pasture > annual crop = SRW, suggesting that the effects resulted from associated SOC. Our microcosm experiment suggests that the SRW land use practice holds promise as a sustainable Nature-Based Solution for enhancing climate resiliency in PPR. It exhibits a lower global warming potential compared to annual crop and pasture. Therefore, widespread implementation of the SRW land use practice in degraded marginal land could help mitigate the effects of climate change in the region.

As the global human population and associated anthropogenic activities rapidly increase, so does the areal extent of dis-turbed soils. Regulatory frameworks must incorporate reclamation criteria and management options for these disturbed soils, requiring consistent descriptions and interpretations. Many human-altered soils cannot be classified using the current Cana-dian System of Soil Classification (CSSC), thus an Anthroposolic order is proposed. Anthroposols are soils that are highly modified or constructed by human activity, with one or more natural horizons removed and replaced, added to, or signif-icantly modified. Disturbed horizons are anthropic in origin and contain materials significantly modified physically and/or chemically by human activities. Three great groups are defined by the presence of anthropogenic artefacts and organic carbon content. Eight subgroups are based on the amount of organic material, thickness of horizons, material composition, hydro-logic regime, and presence of permafrost. Traditional phases and modifiers are used as in the CSSC. The proposed classification has been revised from the original publication in 2012 after field testing and discussion among soil scientists across Canada. This revised classification is proposed for inclusion in the revised CSSC, to account for the very large and expanding aerial extent of disturbed soils in Canada, and to remain current with other global soil taxonomy systems.

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The major drivers of soil variation in Saskatchewan at scales finer than the existing soil maps are parent material variance, slope position, and salinity. There is therefore a need to generate finer-scale parent material maps as part of updating soil maps in Saskatchewan. As spatially referenced soil point data are lacking in Saskatchewan, predictive soil mapping methods that disaggregate existing soil parent material maps are required. This study focused on investigating important environmental covariates to use in parent material disaggregation, particularly bare soil composite imagery (BSCI). Synthetic point observations were generated using an area-proportional approach based on existing soil survey polygons and a random forest model was trained with those synthetic observations to predict parent material classes. Including BSCI as environmental covariates increased model accuracy from 0.38 to 0.52 and the model Kappa score from 0.19 to 0.35 compared with models where it was not included. Models that included training points from all locations, regardless of whether BSCI was available, and included BSCI as environmental covariates had similar results to the BSCI model with an accuracy of 0.48 and a Kappa value of 0.30. Based on these results, BSCI is an important covariate for parent material disaggregation in the Saskatchewan Prairies. Future work to disaggregate soil classes based on slope position and salinity, and to combine those methods with parent material disaggregation is needed to generate detailed soil maps for the Canadian Prairies.

Wetland soil types, which can be distinguished based on calcium carbonate content, vary in their effect on ecosystem functions like phosphorus retention, salinity contributions, and greenhouse gas forcing. These soil types may be predictively mapped with machine learning models that use terrain derivatives calculated from high-resolution digital elevation models. Soil profiles from three Saskatchewan study sites were classified into three functional categories-upland, calcareous wetland, or noncalcareous wetland-and used to train random forest models for predictive soil mapping. Multiple terrain derivatives were included as predictor variables to capture local- and landscape-scale morphometry and hydrology influences, including five derivatives developed for this study. Models were developed at three spatial resolutions: 2, 5, and 10 m, and tested via internal cross-validation and independent validation with datasets from previous studies. Predictive accuracies were highest when mapping at 2 m resolution (independent validation accuracy range = 64%-100%) but also successful when mapping at 5 and 10 m resolutions (independent validation accuracy range = 63%-100%); however, visual inspection determined that the maps generated at 10 m resolution were less detailed and occasionally featured questionable discontinuous soil distributions. Three of the five terrain derivatives developed for this study were among the most important predictor variables (first, second, and 10th most important). Models trained using only data from a specific site had slightly better performance than models trained using data from all sites, except in regions where training data were lacking.

As Canada's vast Boreal Plains are extensively managed, predictive soil mapping could be used as an effective tool to generate high-resolution soil information for the region to inform sustainable resource management. This study aimed to investigate the use of multi-temporal remote sensing data and terrain derivatives to map soil types in the region. A method of constraining subgroup and great-group soil-type predictions based on the predictions at higher-order levels (great-group and order, respectively) was tested. Sentinel time series median values obtained by using Google Earth Engine were tested in combination with first- and second-order digital elevation model derivatives for use as predictor variables in the predictive models. A recursive feature selection process was implemented to reduce the number of predictor variables used in model training. Soil classes were predicted at the order, great-group, and subgroup levels and two approaches were tested. In the first approach, models were unconstrained based on previous predictions. In the second approach, models were constrained to predict only soil great-group classes that occur within the predicted soil order for a given location and similarly predict only soil subgroup classes that occur within the predicted soil great group for a given location. Determined through independent validation testing, the most probable predicted soil maps had overall accuracies ranging from 42% to 68% and kappa scores ranging from 0.33 to 0.48. Overall, the constrained models had the best performance of the approaches tested.

There is increasing demand for more detailed soil maps to support fine-scale land use planning, soil carbon management, and precision agriculture in Saskatchewan. Predictive soil mapping that incorporates a combination of environmental covariates provides a cost-effective tool for generating finer resolution soil maps. This study focused on mapping soil properties for multiple soil horizons in Saskatchewan using historical legacy soil data in combination with remote sensing band indices, bare soil composite imagery, climate data, and terrain attributes. Mapped soil properties included soil organic carbon content (SOC), total nitrogen, cation exchange capacity (CEC), electrical conductivity (EC), inorganic carbon (IOC), sand and clay content, and total profile soil organic carbon stocks. For each of these soil properties, a recursive feature elimination was undertaken to reduce the number of features in the overall model. This process involved iteratively removing features such that random forest out-of-bag error was minimized. Final random forest models were built for each property and evaluated using an independent test dataset. Overall, predictive models were successful for SOC (R2 = 0.71), total nitrogen (R2 = 0.65), CEC (R2 = 0.46), sand content (R2 = 0.44) and clay content (R2 = 0.55). The methods used in this study enable mapping of a greater geographic region of Saskatchewan compared to those previously established that relied solely on bare soil composite imagery.

Prairie Pothole Region wetlands are integral in reducing nutrient mobility in prairie watersheds. Wetland soil CaCO3 content likely plays an important role in wetland phosphorus retention capacity. Accurate predictions of the spatial distributions of wetlands with CaCO3-enriched soils would allow for further study on these relationships and prioritized wetland conservation efforts to encourage this ecosystem service. Solute accumulations (including CaCO3) within wetlands are largely determined by the wetland’s topographic position and relationship with groundwater. These characteristics were estimated by predicting spill channel connections between wetlands using LiDAR-derived digital elevation models. Spatial distributions of solute-rich wetlands were predicted with a simple decision tree model that predicts wetlands as either fresh or solute-rich based on approximated hydrologic characteristics (Strahler order and terminal status) determined from the predicted active spill channel networks. The model was trained and tested using measurements of wetland pond water and soil electrical conductivity (i.e., solute-richness) in three study areas within the Saskatchewan Prairie Pothole Region. The model achieved acceptable predictive accuracies based on training and independent validation tests. The proposed methodologies could be incorporated into more complex multivariate models for the purposes of predictive soil mapping or hydrologic studies in the Prairie Pothole Region.