Effects of Fall vs. Spring Plowing Forages on Nitrate Leaching Losses to Groundwater
Bibliographic record
Abstract
Abstract High levels of nitrate leaching losses from potato rotation systems have caused concerns for both drinking water quality and aquatic ecosystem protection in Prince Edward Island ( PEI ). Paired‐field experiments were carried out in commercial fields to evaluate the potential of delayed plowing forages within potato rotation from fall to spring on reducing nitrate leaching at two separate sites in PEI during 2010 and 2013. Monitoring showed that fall plowing resulted in elevated tile‐drain nitrate concentrations compared to spring plowing, probably mainly due to crop residue mineralization during fall which was hastened by earlier herbicidal termination of forage (i.e., herbicidal killing) at Site 1. A similar trend was also observed regarding nitrate concentrations of shallow groundwater at Site 2 during the forage phase. The practice of delaying the plowing of forages and/or associated earlier herbicidal termination of forage until spring reduces forage‐phase nitrate leaching loss by 20 to 61%, and should therefore be encouraged for nitrate mitigation. The study also demonstrated that only a small fraction (9.6 to 22%) of the fall plow‐down forages decayed during the forage phase and a large portion was retained in the soil into the next season. Growers should consider accounting for some of the carried‐over N for the subsequent crops regardless implementing fall or spring plowing.
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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 | 0.000 | 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) | 0.001 | 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".