Impacts of Climate Change and Different Crop Rotation Scenarios on Groundwater Nitrate Concentrations in a Sandy Aquifer
Bibliographic record
Abstract
Nitrate in groundwater is a major concern in agricultural sub-watersheds. This study assessed the impacts of future climate and agricultural land use changes on groundwater nitrate concentrations in an agricultural sub-watershed (Norfolk site) in southern Ontario, Canada. A fully integrated hydrologic model (HydroGeoSphere) was used in combination with the root zone water quality model (RZWQM2) (shallow zone) to develop water flow and nitrate transport models. Three climate change models and three crop rotations (corn-soybean rotation, continuous corn, corn-soybean-winter wheat-red clover rotation) were used to evaluate the potential impact on groundwater quality (nine predictive scenarios). The selected climate change scenarios yielded less water availability in the future period than in the reference period (past conditions). The simulated nitrate nitrogen (Nitrate-N) concentrations were lower during the future period than the reference period. The continuous corn land use scenario produced higher Nitrate-N concentrations compared to the base case (corn-soybean rotation). However, the best management practices (BMP) scenario (corn-soybean-winter wheat-red clover rotation) produced significantly lower groundwater nitrate concentrations. BMPs, such as the one examined herein, should be adopted to reduce potential negative impacts of future climate change on groundwater quality, especially in vulnerable settings. These findings are important for water and land managers, to mitigate future impacts of nutrient transport on groundwater quality under a changing climate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 source (direct Gemma or distilled Codex), 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".