Options for Improved Phosphorus Cycling and Use in Agriculture at the Field and Regional Scales
Bibliographic record
Abstract
Soil phosphorus (P) cycling in agroecosystems is highly complex, with many chemical, physical, and biological processes affecting the availability of P to plants. Traditionally, P fertilizer recommendations have been made using an insurance‐based approach, which has resulted in the accumulation of P in many intensively managed agricultural soils worldwide and contributed to the widespread water quality issue of eutrophication. To mitigate further environmental degradation and because future P fertilizer supplies are threatened due to finite phosphate rock resources and associated geopolitical and quality issues, there is an immediate need to increase P use efficiency (PUE) in agroecosystems. Through cultivar selection and improved cropping system design, contemporary research suggests that sufficient crop yields could be maintained at reduced soil test P (STP) concentrations. In addition, more efficient P cycling at the field scale can be achieved through agroecosystem management that increases soil organic matter and organic P mineralization and optimizes arbuscular mycorrhizal fungi (AMF) symbioses. This review paper provides a perspective on how agriculture has the potential to utilize plant and microbial traits to improve PUE at the field scale and accordingly, maintain crop yields at lower STP concentrations. It also links with the need to tighten the P cycle at the regional scale, including a discussion of P recovery and recycling technologies, with a particular focus on the use of struvite as a recycled P fertilizer. Guidance on directions for future research is provided. Core Ideas There is an urgent need to increase P use efficiency in agroecosystems. Crop yields could be maintained at lower than recommended soil test P concentrations. Both the quantity and quality of organic matter influence P availability. Further research on ability of organic P to supply P to crops is needed. Struvite has the potential to fill an important niche in P recycling.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".