Understanding the Fertilizer Management Impacts on Water and Nitrogen Dynamics for a Corn Silage Tile‐Drained System in Canada
Bibliographic record
Abstract
Effective management of dairy manure is important to minimize N losses from cropping systems, maximize profitability, and enhance environmental sustainability. The objectives of this study were (i) to calibrate and validate the DeNitrification‐DeComposition (DNDC) model using measurements of silage corn ( Zea mays L.) biomass, N uptake, soil temperature, tile drain flow, NO 3 − leaching, N 2 O emissions, and soil mineral N in eastern Canada, and (ii) to investigate the long‐term impacts of manure management under climate variability. The treatments investigated included a zero‐fertilizer control, inorganic fertilizer, and dairy manure amendments (raw and digested). The DNDC model overall demonstrated statistically “good” performance when simulating silage corn yield and N uptake based on normalized RMSE (nRMSE) < 10%, index of agreement ( d ) > 0.9, and Nash–Sutcliffe efficiency (NSE) > 0.5. In addition, DNDC, with its inclusion of a tile drainage mechanism, demonstrated “good” predictions for cumulative drainage (nRMSE < 20%, d > 0.8, and NSE > 0.5). The model did, however, underestimate daily drainage flux during spring thaw for both organic and inorganic amendments. This was attributed to an underestimation of soil temperature and soil water under frequent soil freezing and thawing during the 2013–2014 overwinter period. Long‐term simulations under climate variability indicated that spring applied manure resulted in less NO 3 − leaching and N 2 O emissions than fall application when manure rates were managed based on crop N requirements. Overall, this study helped highlight the challenges in discerning the short‐term climate interactions on fertilizer‐induced N losses compared with the long‐term dynamics under climate variability. Core Ideas A new tile drainage mechanism implemented in DNDC was successfully evaluated. DNDC performed well in simulating silage corn biomass, N uptake, and soil inorganic N. Soil temperature and tile drainage was sometimes underestimated in the overwinter period. Seasonal N losses were better simulated than nonseasonal N losses. Long‐term simulation indicated lower N leaching and N 2 O for spring than fall manure.
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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 | 0.001 | 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 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".