Effect of land management practices and environmental parameters on growing season denitrification rates under dairy crop rotations in Atlantic Canada
Bibliographic record
Abstract
Denitrification losses from agricultural land have been identified as a significant nitrogen (N) loss pathway that contributes to poor utilization of applied N. Higher losses have been reported when inorganic fertilizer N is substituted with an organic N source such as livestock manure. This research examines the relationships between denitrification rates, land management practices and soil processes when using spring applied liquid dairy manure (LDM) as principal N source. Mean daily denitrification rates (DDRs) in a perennial hayfield (PH) rotation and a corn–soybean–wheat (CSW) rotation with or without tillage (T and NT respectively) ranged between 0.9 and 27.0 g N ha−1 day−1. Mean, seasonal DDRs in the PH rotation were significantly lower in three out of six seasons when compared with the CSW-T and (or) CSW-NT rotation. When averaged across the six season study period, the mean DDR in PH of 4.4 g N ha−1 day−1 was also significantly lower than CSW-T and NT (7.6 and 8.1 g N ha−1 day−1, respectively). There were no significant effects of tillage in the CSW rotations in any of the six growing seasons. When treatment and growing season data were combined, a positive relationship between water-filled pore space (WFPS) and DDR indicated a threshold of approximately 40% WFPS for onset of significant denitrification. Similarly, the relationship between soil nitrate levels and denitrification rates in the population data set demonstrated that NO3− began to limit DDR below 2–5 mg N kg−1. This 6-year study of denitrification losses suggests a primary effect of WFPS, a secondary effect of O2 consumption as reflected by soil respiration, and nitrate limiting only at relatively low concentrations. Environmental variables were more consistent drivers of denitrification in three manure-fed crop rotations typical of Atlantic Canada dairy operations than were land management decisions and practices.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".