Effects of full inversion tillage during pasture renewal on soil and plant cadmium concentrations: a case study in New Zealand
Bibliographic record
Abstract
Context Cadmium (Cd) accumulation is a concern in permanent pasture soils, as it can lead to increased Cd uptake by plants. Aims This study aimed to quantify the effect of full inversion tillage (FIT or ploughing deeper than 30 cm), used during pasture renewal, on the redistribution of Cd within the soil profile and on plant Cd concentration. Methods Two field trials (Trial 1, Alfisol; Trial 2, Andisol) were established in New Zealand using contrasting tillage practices (FIT; SIT, shallow tillage; and NT, no tillage) to sow turnips as summer forage crops, followed by autumn re-sowing of perennial ryegrass/white clover pasture. Key results In the Alfisol, no measurable differences (P > 0.05) in soil and plant Cd were detected among the tillage treatments. In the Andisol, FIT decreased (P < 0.05) total (0.25 mg/kg) and extractable soil Cd (0.013 mg/kg) in the 0–5 cm depth, compared to pre-tillage (0.42 and 0.031 mg/kg, respectively). Moreover, at this soil depth, FIT achieved a 52% lower (P = 0.034) extractable soil Cd concentration than the ST treatment. In addition, the subsequent new pasture had lower (P = 0.007) average Cd concentration following FIT compared to ST (0.03 vs 0.05 mg/kg). Conclusions We demonstrated that the use of FIT during pasture renewal is a potential solution to reduce topsoil Cd concentration. Implications The FIT is more effective in soil where total soil Cd concentration or its degree of vertical stratification with depth is relatively high.
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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 | 0.001 |
| 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".