Evaluation of a New Generation of Coated Fertilizers to Reduce the Leaching of Mineral Nutrients and Greenhouse Gas (N2O) Emissions
Bibliographic record
Abstract
The increased use of fertilizers in agriculture and forest and horticulture nurseries contributes to the pollution of water resources and greenhouse gas emissions. The objective of this study is to evaluate a new generation of fertilizers coated with new biodegradable polymers in terms of physical quality, release kinetics, and their effect on reducing nitrate leaching and N2O emissions and compare them to uncoated fertilizers (Urea, monoammonium phosphate (MAP), and KCl) having the same mineral nutrient concentration. In a peat-based substrate, the release of mineral nutrients was similar in both types of fertilizer. Two hours after application, Urea released 34% more urea than Biodrix N, the difference disappearing after one day. The leaching of cumulative ammonium nitrogen after 20 days was reduced by 40% and 26% respectively by Aminaex and Biodrix N compared to Urea. In a peat-based substrate containing 30% (v/v) of compost, the cumulative nitrate leaching was reduced by 54% by Biodrix N and by 41% by Aminaex compared to Urea. The highest average N2O flux was observed on the first day for Urea, whereas for Aminaex and Biodrix N, N2O emissions increased on the third day, reaching a peak of efflux on day 10. A 10-day delay of the N2O efflux emissions and a longer period of emissions were observed in treatments containing Aminaex and Biodrix N compared to Urea. Cumulative N2O efflux was 142, 154, and 171 mg m−2, respectively, for Urea, Aminaex, and Biodrix N over a 20-day period. These new biodegradable polymer-coated nitrogen fertilizers can reduce mineral nutrient leaching in the event of heavy rainfall and lower maximum N2O emissions in comparison with conventional nitrogen sources.
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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) | 0.000 | 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 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".