In situ nitrogen mineralization and nutrient release by soil amended with black soldier fly frass fertilizer
Bibliographic record
Abstract
Although black soldier fly frass fertilizer (BSFFF) is effective on crop performance, information on nitrogen (N) mineralization and nutrient release capacity of soils amended with BSFFF is lacking. This study utilized field incubation experiments to investigate the ammonification, nitrification, microbial populations, and quantities of nutrients released by soils amended with BSFFF and commercial organic fertilizer (SAFI) for a period equivalent to two maize cropping seasons. For the control treatment, no BSFFF or SAFI was added. Results indicated that most of the N in BSFFF amended soils was available in the ammonium form, while soils treated with SAFI had higher nitrate concentration. The BSFFF amended soils experienced shorter net immobilization periods of N (30-60 days) compared to SAFI treated soils (60-95 days). Increased rates of mineralization (3-10 times) and nitrification (2-4 times) were observed in soils treated with BSFFF during the second season of application. The BSFFF treated soils showed significantly higher N, phosphorus, and magnesium release than the control. Repeated application of BSFFF led to increased N release by three-folds in the soil. Furthermore, soil amendment with BSFFF increased the populations of bacteria and fungi, reduced soil acidity, and increased phosphorus (two-folds) and magnesium (two-four-folds) release than SAFI treated soils. Our findings highlight the crucial role of BSFFF in improving soil health by addressing the challenges of soil acidity, phosphorus fixation and nutrient mining, which is characteristic of most tropical soils.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 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".