Modelling nitrogen leaching in Prince Edward Island under climate change scenarios
Bibliographic record
Abstract
Projected climate change in Canada and its impact on crop yield and production have been studied, but the impacts on soil and water quality are less well known. The objective of this study was to model and evaluate the potential impacts of climate change on soil nitrogen (N) leaching in Prince Edward Island. Residual soil nitrogen (RSN), the quantity of inorganic soil N at the time of harvest, was calculated from an annual N budget, based on Census of Agriculture data. RSN was "added" to the soil in the fall and subject to leaching until the start of the next growing season. Water and N movement in and through the soil were calculated with a modified version of the Versatile Soil Moisture Budget. The provincial averages of RSN and N leaching under historic (1971–2000) climate and management conditions were calculated to be 30.8 kg N ha-1 and 27.9 kg N ha-1, i.e. , 91% of the RSN was lost via leaching. With no changes in agricultural practices, N leaching under four climate change (2040–2069) scenarios remained very similar (± 1%) to that simulated under historic climatic conditions. With agricultural intensification, in response to climate change and economic conditions, RSN levels increased to 35.7 kg N ha-1 and estimates of soil N leaching increased by 5 to 30% beyond historic levels. Key words: Residual soil nitrogen, versatile soil moisture budget, climate change impacts, agricultural adaptation, water contamination
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".