Development of a conceptual framework to evaluate organic fertilisers : assessment on soil quality and agronomic, environmental and economics aspects
Bibliographic record
Abstract
Organische stof wordt algemeen beschouwd als een belangrijke factor om de kwaliteit van de bodem van landbouwgrond te handhaven en te verbeteren.Er bestaat echter vooralsnog geen systemisch kader om de bodemchemische, -fysische, -biologische en economische aspecten te evalueren met betrekking tot de toepassing van organische bemestingsproducten.Mede als gevolg van de transitie van een lineaire economie naar een circulaire economie zullen veel nieuwe organische producten op de markt komen die voortkomen uit de be-en verwerking van verschillende organische reststromen, zoals slib van afvalwater, mestoverschot en voedselresten.Dit rapport geeft een overzicht voor de karakterisering van zowel de organische meststoffen, alsmede de impact op de bodemkwaliteit met daaraan gekoppeld de agronomische, milieukundige, gezondheid en economische aspecten.Ten slotte worden de belangrijkste kennislacunes en ontbrekende methoden vermeld om de duurzaamheidsaspecten van nieuwe organische meststoffen in kaart te brengen.Dergelijke informatie is relevant zowel voor agrariërs ten aanzien van gebruik van organisch meststoffen als voor financiers en grondeigenaren ten aanzien van de kwaliteit van de bodem als voor beleidsmakers ten aanzien van wet-en regelgeving ten aanzien van toelating.Organic matter is widely recognised as an important factor in maintaining and improving soil quality in agricultural land.However, there is no systemic framework or approach to quantify soil's chemical-, physical-, biological-and economic aspects.Furthermore, due to the introduction of the circular economy, many new organic fertiliser products are becoming available.These products are derived from several organic waste streams, such as sewage-sludge, surplus manure and food-waste.This report describes an approach that can be used to evaluate the effect of applying organic fertilisers on the impact on soil quality, agronomy, the environment and human-health.Finally, the main knowledge gaps and missing methods to assess sustainability aspects of new organic fertilisers are mentioned.Such information is relevant both for farmers (who might ask: "what will I get?"), financers and landlords (who might ask: "what is the effect on land value?"), as well as for policy makers (who might ask: "how could legislation aspects be dealt with?").
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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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".