Nitrogen release from five organic fertilizers commonly used in greenhouse organic horticulture with contrasting effects on bacterial communities
Bibliographic record
Abstract
Organic fertilization in greenhouses relies on organic fertilizers with low carbon/nitrogen ratio. Nitrogen (N) availability thus depends on an efficient mineralization driven by microbial communities. However, data on the mineralization rate of such fertilizers are scarce, and their improper use can lead to either N deficiency, or N losses to the environment. Consequently, better knowledge of N availability following organic fertilization is crucial for the development of sustainable greenhouse organic horticulture. We investigated the effect of pelleted poultry manure (PM) and blood (BM), feather (FM), alfalfa (AM), and shrimp (SM) meals on N availability and bacterial communities in a peat-based organic growing medium and a mineral soil. Nitrogen and carbon (C) pools were measured periodically over a 52 wk incubation experiment. Bacterial communities were characterized by sequencing the regions V6–V8 of the 16S rRNA gene on the high-throughput Illumina MiSeq platform, 4 wk after the start of the incubation. Nitrogen mineralization plateaued for the mineral soil and the peat substrate at, respectively, 41% and 63% of applied N for PM, 56%–93% (BM), 54%–81% (FM), 34%–53% (AM), and 57%–73% (SM). Organic fertilizers supported markedly contrasted bacterial communities, closely linked to soil biochemical properties, especially mineral N, pH, and soluble C. Alfalfa meal promoted the highest Shannon diversity index in the mineral soil, whereas SM and PM increased it in the peat-based growing medium. Our results quantified the mineralization and highlighted the impact on bacterial communities of commonly used organic N fertilizers in conditions relevant to organic greenhouse horticulture.
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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 | 0.001 |
| 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".