Effects of long-term tillage, terminating no-till and cropping system on organic C and N, and available nutrients in a Gleysolic soil in Québec, Canada
Bibliographic record
Abstract
Abstract Some biological and chemical properties of a Gleysol were examined after 24 years of soil tillage (chisel plough – CP, mouldboard plough – MP, no-till – NT) and that of ploughing the 24-yr NT (P-NT) once, in two cropping systems (conventional – CONV, organic – ORG) applied over 4 years (2007–2010) of a long-term experiment (autumn 1987–autumn 2011) at La Pocatière, Québec, Canada. The 0–10, 10–20 and 20–30 cm soil depths were sampled in autumn 2011 after a maize trial. Tillage affected light fraction organic carbon (LFOC), light fraction organic nitrogen (LFON) and mineralizable N (Nmin) in soil, with the lowest LFOC, LFON and Nminvalues in the MP treatment. No-till had lower soil pH than the other tillage systems in the 10–20 and 20–30 cm soil depths. Tillage affected the amounts of nitrate-N in 0–10 and 10–20 cm soil depths, with the lowest amounts for MP (4.3 kg nitrate-N/ha) compared with NT (7.2 or 8.5 kg nitrate-N/ha) or CP (7.7 kg nitrate-N/ha). The P-NT had no negative impact on organic C and N, or available nutrients in soil. Cropping system had no effect on soil organic C and N, available nutrients or pH. Findings suggest that long-term NT or CP may result in greater storage of organic C and N in soil and improve available nutrients compared with MP. Ploughing 25-year-old NT plots redistributed available nutrients in the profile but had no negative effect on soil organic C or N.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".