Emissies naar lucht uit de landbouw,1990-2018 : Berekeningen met het model NEMA
Bibliographic record
Abstract
In the Netherlands, agricultural activities are a major source of gaseous emissions of ammonia (NH3), nitrogen oxide (NO), nitrous oxide (N2O), methane (CH4), non-methane volatile organic compounds (NMVOC), CO2 from lime fertilisers and particulate matter (PM10 and PM2.5). The emissions were calculated using the National Emission Model for Agriculture (NEMA). In 2018, NH3 emissions from livestock manure, fertiliser and other sources on farms and hobby farms, from private use and from manure application in terrestrial ecosystems amounted to 118.0 million kg NH3, 2.2 million kg less than in 2017. This decrease was due mainly to the reduction in the size of the dairy herd. Emissions of N2O in 2018 were 20.5 million kg, 0.5 million kg less than in 2017. NO emissions in 2018 amounted to 22.3 million kg, 0.6 million kg less than in 2017. CH4 emissions decreased from 503 to 484 million kg due to the smaller dairy herd. Emissions of NMVOC amounted to 93 million kg in 2018, down from 98 million kg in 2017. Emissions of particulate matter PM10 decreased in 2018 from 6.2 to 5.9 million kg. PM2.5 emissions remained constant at 0.6 million kg. Based on new data for several factors which are described in this report, emission figures have been updated for a number of years in the time series since 1990. NH3 emissions from livestock manure have fallen by two thirds since 1990, mainly as a result of lower nitrogen excretion rates of livestock and the introduction of low-emission manure application. Emissions of N2O and NO decreased over this period by 40% and 33% respectively, less markedly than the NH3 reduction because of higher emissions from manure injection (compared with surface spreading manure) and a shift from excretion on pasture to excretion in animal houses. CH4 emissions decreased by 18% between 1990 and 2018 due to a decrease in livestock numbers and increased feed use efficiency of dairy cattle. PM10 emissions increased by 19% in the same period due to laying poultry farms switching from housing systems with liquid manure to systems with solid manure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".