Phosphorus management in balanced agricultural systems
Bibliographic record
Abstract
Abstract. The practice of large phosphorus (P) additions to agricultural land has resulted in an increased depletion of limited mineable rock phosphate resources, P accumulation in soils with an increased risk for P losses, and intensified eutrophication and deterioration of water quality in recipient water bodies. A number of measures have been used to reach balance between P inputs and outputs in agricultural systems, with the goal of achieving improved P use efficiency, sustained high crop yields and reduced P losses. This paper discusses how this goal may be achieved. Results from a Swedish long‐term fertility experiment combined with results of a P leaching study using a selection of soils from the fertility experiment are used to evaluate the effects of a balanced P system on yields, soil P levels and P leaching. Three P fertilizer application strategies are compared (zero P, replacement P, and a treatment where surplus P fertilization was used to achieve a rapid increase in the soil P status). The replacement P strategy appeared to be the most sustainable system but P fixation in this system must be accounted for. When surplus P rates were applied, increased crop yields were counterbalanced by poorer use efficiency and P accumulation in soil. Topsoil P content was a poor predictor of P leaching. Instead, balancing P inputs and outputs represents a first step in the management of P losses, but additional, site‐specific measures are required to counteract site‐specific factors responsible for P losses.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".