Development of hydrologic processes in the DNDC model to explore beneficial management for reducing nutrient losses from cropping systems
Bibliographic record
Abstract
Promoting sustainable agricultural practices that maintain or increase crop yields while limiting negative anthropogenic influences on the environment is an important global research initiative. Biophysical agricultural models are effective science-based management tools for assessing sustainability provided they are frequently updated with our current understanding of the many interlinked environmental processes. In this thesis the widely used DeNitrification DeComposition (DNDC) model was compared to the more hydrologically complex Root Zone Water Quality Model (RZWQM2) to determine which processes were sufficient for simulating water and nitrogen dynamics. Based on these findings a new quasi-2D sub-model for tile drainage, improved water flux, root growth dynamics, and a deeper and heterogeneous soil profile were implemented in DNDC. Simulation of soil water storage, daily water flow and nitrogen loading to tile drains was greatly improved post-development. The revised model was then used to investigate fertilizer management options for reducing N losses over a multi-decadal horizon at locations in eastern Canada and the U.S. Midwest. The assessment helped to distinguish which fertilizer practices are effective in reducing N losses over a long-term time horizon. In addition, modelling methodologies were assessed for simulating the impacts of climate change on cropping systems. The DNDC model proved to be a useful tool for characterizing the feedbacks between climate, soil, crop and management that are critical for accurately assessing crop system behavior
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".