Soil Organic Carbon Stocks and Dynamics in Cultivated Prairie Pothole Wetlands
Bibliographic record
Abstract
The Prairie Pothole Region requires stronger evidence to improve our understanding of how soil organic carbon varies across wetlands. The contribution of prairie pothole wetlands to soil carbon storage in agroecosystems is an important consideration for conservation, soil carbon reporting, and environmental policies. The aim of this research is to refine soil organic carbon estimates for prairie pothole wetlands by accounting for variability associated with environmental and land management factors. Through a meta-analysis of studies from the region, climate, hydrology, parent material, and land management were identified as key variables for explaining wetland soil organic carbon. Data gaps in specific climate regimes (ecoregions) and wetland land management practices were recognized as being needed to accurately estimate soil organic carbon stocks in prairie pothole wetlands. Soil sampling for undrained and drained wetlands in cultivated fields across the Saskatchewan portion of the Prairie Pothole Region was completed to refine soil carbon stock estimates. The stock change factors for cultivation and drainage were also calculated. Ecoregion and wetland type significantly influenced soil organic carbon storage in both undrained and drained cultivated wetlands. The impact of land management was also recognized in the undrained wetlands and with drainage, further emphasizing the opportunity for sustainable agricultural practices to promote soil carbon sequestration in these wetlands. To accomplish this research, methods were developed to classify wetlands with open-source remote sensing technology and predictive models. The outputs from these workflows enabled wetland class assignment with adequate prediction accuracies and improved the extrapolation of soil organic carbon measurements from sampling points within the wetland to the entire wetland area. A microcosm experiment was also conducted to investigate how dynamic soil salinity affects wetland carbon cycling and greenhouse gas emissions. The experiment results showed decreasing wetland salinity contributed to increases in greenhouse gas emissions. This understanding provides a foundation for how we can expect landscape-scale soil salinity changes to affect wetland soil organic carbon stocks. The data from this research can be applied to broader research and modeling of soil carbon and wetland ecosystem services across the Prairie Pothole Region. The findings have enhanced regional soil carbon stock estimates and can support decision-makers in the region to develop wetland management practices that support carbon sequestration and sustainability in agricultural prairie landscapes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".