Fertilization efficiency of thirty marketed and experimental recycled phosphorus fertilizers
Bibliographic record
Abstract
Recycling phosphorus (P) from waste streams like sewage sludge, animal manures or food industry by-products is required to sustain soil fertility without depleting non-renewable P resources. Several technologies are available for P recovery, leading to fertilizers differing in P solubility and bioavailability. In this study, thirty fertilizers obtained through different technologies were tested to assess if their fertilization efficiency was equivalent to mineral soluble fertilizer applied as triple superphosphate (TSP). The main selection criteria were (1) ensuring a wide chemical diversity, and (2) choosing products already on the market or at a late stage of development, to encompass a representative selection of current and future recycled fertilizers. The products were classified according to their organic carbon content and neutral ammonium citrate (NAC), and the main P species of each fertilizer was determined through K-edge and L2,3-edge X-ray absorption near edge structure spectroscopy (XANES). Three pot experiments with wheat, barley and ryegrass were conducted in three growing substrates with contrasting properties, all within a pH range of 5.8-6.9. Fertilizers containing ammonium magnesium phosphate, monoammonium phosphate, monocalcium phosphate, and dicalcium phosphate type species as dominant P species showed a similar fertilization efficiency to TSP, while fertilizers with hydroxyapatite, tricalcium phosphate, phytic acid or iron phosphates as their main P species had lower fertilization efficiencies. We conclude that while the trend towards high-efficiency, refined inorganic recycled P fertilizers is positive, lower-performing, mostly unrefined fertilizers must be assessed in light of their long-term P supply potential and additional benefits to soil health owing to their content of organic matter and other nutrients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".