Soil data recency: The foundation for harmonizing soil data across time.

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.

Soil organic matter quality influences mineralization and GHG emissions in cryosols: a field-based study of sub- to high Arctic.

Arctic soils store large amounts of labile soil organic matter (SOM) and several studies have suggested that SOM characteristics may explain variations in SOM cycling rates across Arctic landscapes and Arctic ecosystems. The objective of this study was to investigate the influence of routinely measured soil properties and SOM characteristics on soil gross N mineralization and soil GHG emissions at the landscape scale. This study was carried out in three Canadian Arctic ecosystems: Sub-Arctic (Churchill, MB), Low-Arctic (Daring Lake, NWT), and High-Arctic (Truelove Lowlands, NU). The landscapes were divided into five landform units: (1) upper slope, (2) back slope, (3) lower slope, (4) hummock, and (5) interhummock, which represented a great diversity of Static and Turbic Cryosolic soils including Brunisolic, Gleysolic, and Organic subgroups. Soil gross N mineralization was measured using the (15) N dilution technique, whereas soil GHG emissions (N2 O, CH4 , and CO2 ) were measured using a multicomponent Fourier transform infrared gas analyzer. Soil organic matter characteristics were determined by (1) water-extractable organic matter, (2) density fractionation of SOM, and (3) solid-state CPMAS (13) C nuclear magnetic resonance (NMR) spectroscopy. Results showed that gross N mineralization, N2 O, and CO2 emissions were affected by SOM quantity and SOM characteristics. Soil moisture, soil organic carbon (SOC), light fraction (LF) of SOM, and O-Alkyl-C to Aromatic-C ratio positively influenced gross N mineralization, N2 O and CO2 emissions, whereas the relative proportion of Aromatic-C negatively influenced those N and C cycling processes. Relationships between SOM characteristics and CH4 emissions were not significant throughout all Arctic ecosystems. Furthermore, results showed that lower slope and interhummock areas store relatively more labile C than upper and back slope locations. These results are particularly important because they can be used to produce better models that evaluate SOM stocks and dynamics under several climate scenarios and across Arctic landscapes and ecosystems.

Controlled soil warming powered by alternative energy for remote field sites.

Experiments using controlled manipulation of climate variables in the field are critical for developing and testing mechanistic models of ecosystem responses to climate change. Despite rapid changes in climate observed in many high latitude and high altitude environments, controlled manipulations in these remote regions have largely been limited to passive experimental methods with variable effects on environmental factors. In this study, we tested a method of controlled soil warming suitable for remote field locations that can be powered using alternative energy sources. The design was tested in high latitude, alpine tundra of southern Yukon Territory, Canada, in 2010 and 2011. Electrical warming probes were inserted vertically in the near-surface soil and powered with photovoltaics attached to a monitoring and control system. The warming manipulation achieved a stable target warming of 1.3 to 2 °C in 1 m(2) plots while minimizing disturbance to soil and vegetation. Active control of power output in the warming plots allowed the treatment to closely match spatial and temporal variations in soil temperature while optimizing system performance during periods of low power supply. Active soil heating with vertical electric probes powered by alternative energy is a viable option for remote sites and presents a low-disturbance option for soil warming experiments. This active heating design provides a valuable tool for examining the impacts of soil warming on ecosystem processes.

Repeat-pulse 13CO2 labeling of canola and field pea: implications for soil organic matter studies.

Both the quantity and quality of plant residues can impact soil properties and processes. Isotopic tracers can be used to trace plant residue decomposition if the tracer is homogeneously distributed throughout the plant. Continuous labeling will homogeneously label plants but is not widely accessible because elaborate equipment is needed. In order to determine if the more accessible repeat-pulse labeling method could be used to trace plant residue decomposition, this labeling procedure was employed using (13)CO(2) to enrich field pea and canola plants in a controlled environment. Plants were exposed weekly to pulses of 33 atom% (13)CO(2) and grown to maturity. The distribution of the label throughout the plant parts (roots, stem, leaves, and pod) and biochemical fractions (ADF and ADL) was determined. The label was not homogeneously distributed throughout the plant; in particular, the pod fractions were less enriched than other fractions indicating the importance of continuing labeling well into plant maturity for pod-producing plants. The ADL fraction was also less enriched than the ADF fraction. Because of the heterogeneity of the label throughout the plant, caution should be applied when using the repeat-pulse method to trace the fate of (13)C-labeled residues in the soil. However, root contributions to below-ground C were successfully determined from the repeat-pulse labeled root material, as was (13)C enrichment of soil within the top 15 cm. Canola contributed more above- and below-ground residue C than field pea; however, canola was also higher in ADF and ADL fractions indicating a more recalcitrant residue.