Long-term effects of crop rotation, stubble management and tillage on soil phosphorus dynamics
Bibliographic record
Abstract
The effects of various management practices on soil phosphorus (P) dynamics were investigated in a field experiment in New South Wales, Australia, during 24 years of different crop rotation, stubble management, and tillage treatments. Topsoil samples collected at the beginning of the trial and after 6, 12, 18, and 24 years were analysed for resin-extractable P, inorganic and organic P, and total P. According to the calculated P input–output budget, 9–14 of the 20 kg P/ha added as superphosphate annually remained in the system, depending on the treatment. The measured increase in total P in 0–0.20 m did not differ between treatments, showing an accumulation rate of only 9 ± 2 kg P/ha.year. These results suggest a loss of 4 ± 2 kg P/ha.year, presumably into lower soil layers. Resin-extractable P at 0–0.10 m increased by 1.7 kg P/ha.year, irrespective of the treatment. The increase in total P after 24 years was almost completely accounted for by the increase in total extractable inorganic P. Changes in organic P paralleled changes in organic carbon, with a significant loss in treatments with stubble burning (wheat–lupin rotation and continuous wheat), and a significant accumulation in a wheat–subterranean clover rotation with stubble retention and direct drilling. We conclude that on the time scale of this experiment, the dynamics of carbon and organic P are closely linked.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".