Nitrate and pesticide leaching from a processing carrot production system in Nova Scotia
Bibliographic record
Abstract
Environmental concerns associated with the continuous application of manure on farmland include potential contamination of surface and subsurface aquatic ecosystems. Our objective was to explore the potential environmental impact of utilizing liquid hog manure (LHM) as a fertilizer in carrot (Daucus carota L.) production. The effects of LHM (169 kg N ha-1; 50% assumed to be available), inorganic fertilizer (IF) (NPK; 70-80-100 kg ha-1) treatments and herbicide (linuron) application on the quality of drainage water from a carrot production system in Nova Scotia were examined. Nitrate-N (NO3−-N) (1 mg NO3−-N L-1 ≈ 4.5 NO3− L-1) concentrations resulting from two applications of 70 t ha-1 of LHM and 70 kg N ha-1 as ammonium nitrate (34-0-0) applied to tile-drained plots were monitored over 2 yr. The average NO3−-N concentrations in the drainage discharge were slightly greater for the LHM treatment (14.4 mg L-1) compared to the IF treatment (12.9 mg L-1), but the difference was not statistically significant (P > 0.05). Most drainage and NO3−-N losses occurred outside the growing season (November-April), but average NO3−-N concentrations in the drainage discharge exceeded 10 mg L-1 both during and outside the growing season. Flow rates of drainage discharge were not significantly affected by the fertility treatments. Less than 0.1% of the linuron herbicide applied to the test plots leached into the drainage water over the study period. Carrot yields were unaffected by the fertility treatments. We conclude that LHM is potentially useful source of nutrients for carrot with minimal harm to the environment, under the rates and management practices used in this study. However, excessive rates of application and/or long-term (>10 yr) repeated applications of manure may negatively impact water quality. Key words: Daucus carota, amendment, leaching, manure, yield
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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 | 0.001 | 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) | 0.000 | 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".