Simulation of management practices to reduce nitrogen losses to water and air on well-drained grass-based dairy farms in derogation
Notice bibliographique
Résumé
A greater understanding of nitrogen (N) flows between productive use in dairy farms and environmental losses can inform regulation, support policy, and manage expectations around delivery of good water and air quality. Here, we use the €riN-Moorepark dairy system model (MDSM) model that was built to simulate N flows and losses for Irish grass-based dairy farms to quantify N flows and losses for a well-drained dairy farm at derogation (i.e., stocked at the maximum allowable rate of 250 kg N ha −1 ) in Ireland and then propose alternate scenarios that can minimize environmental losses. The model is capable of simulating nitrate (NO 3 − ), ammonia (NH 3 ), nitrous oxide (N 2 O), and dinitrogen (N 2 ) emissions from urine, dung, slurry, dairy-soiled water, and fertilizer N under a range of management practices. Specifically, we propose alternative systems around derogation by varying inorganic (200 or 225 kg N ha −1 ) and organic (170, 230, 268, 340, 430 kg N ha −1 ) fertilizer rates; substituting calcium ammonium nitrate (CAN) with protected urea (i.e., urea + Agrotain) and implementing restrictive grazing for vulnerable time periods when losses are the greatest (e.g., October–February or September–February). The €riN-MDSM estimated that at derogation the farm N surplus was 241.3 kg N ha −1 , leached loads were 46.6 kg N ha −1 (target for good water quality outcomes is 30 kg N ha −1 ), and gaseous emissions were 127.3 kg N ha −1 . Better environmental outcomes were observed by reducing stocking rates to 170 kg N ha −1 and 230 kg N ha −1 , decreasing leaching losses by 21.5% and 6.4%, respectively. Further reductions in inorganic fertilizer to 200 kg N ha −1 decreased leaching by 27.5% and NH 3 emissions by 31%. By substituting CAN fertilizer with protected urea, NH 3 emissions decreased by 5.2% from derogation baseline. Further reductions were possible by varying stocking rates and fertilizer rates together. Restrictive grazing significantly decreased NO 3 − leaching to groundwater, with reductions from derogation equivalent to 38.3% and 28.8% for 170 kg N ha −1 and 230 kg N ha −1 stocking rates, respectively. Further reductions in inorganic fertilizer to 200 kg N ha −1 resulted in a 44.3% decrease in NO 3 − leaching to groundwater (∼30 kg N ha −1 target) and a 24% decrease in NH 3 emissions to air. Future systems need to consider a combination of reduced fertilizer rates, restricted grazing, and use of protected urea to minimize N losses, especially during high rainfall periods. Although the results observed demonstrate opportunities to reduce N losses from grazing systems from a combination of reduced fertilizer rates, restricted grazing, and use of protected urea, the effects of these mitigations must also be considered in terms of economic cost to farms. On that basis, future system trials should monitor flows and losses from the implementation of such mitigations in addition to the economic effects to chart a way forward for better water and air outcomes while maintaining the profitability of Irish dairy farms.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».