Ecological controls on N <sub>2</sub> O emission in surface litter and near-surface soil of a managed grassland: modelling and measurements
Notice bibliographique
Résumé
Abstract. Large variability in N2O emissions from managed grasslands may occur because most emissions originate in surface litter or near-surface soil where variability in soil water content (θ) and temperature (Ts) is greatest. To determine whether temporal variability in θ and Ts of surface litter and near-surface soil could explain this in N2O emissions, a simulation experiment was conducted with ecosys, a comprehensive mathematical model of terrestrial ecosystems in which processes governing N2O emissions were represented at high temporal and spatial resolution. Model performance was verified by comparing N2O emissions, CO2 and energy exchange, and θ and Ts modelled by ecosys with those measured by automated chambers, eddy covariance (EC) and soil sensors on an hourly timescale during several emission events from 2004 to 2009 in an intensively managed pasture at Oensingen, Switzerland. Both modelled and measured events were induced by precipitation following harvesting and subsequent fertilizing or manuring. These events were brief (2–5 days) with maximum N2O effluxes that varied from < 1 mgNm−2h−1 in early spring and autumn to > 3 mgNm−2h−1 in summer. Only very small emissions were modelled or measured outside these events. In the model, emissions were generated almost entirely in surface litter or near-surface (0–2 cm) soil, at rates driven by N availability with fertilization vs. N uptake with grassland regrowth and by O2 supply controlled by litter and soil wetting relative to O2 demand from microbial respiration. In the model, NOx availability relative to O2 limitation governed both the reduction of more oxidized electron acceptors to N2O and the reduction of N2O to N2, so that the magnitude of N2O emissions was not simply related to surface and near-surface θ and Ts. Modelled N2O emissions were found to be sensitive to defoliation intensity and timing which controlled plant N uptake and soil θ and Ts prior to and during emission events. Reducing leaf area index (LAI) remaining after defoliation to half that under current practice and delaying harvesting by 5 days raised modelled N2O emissions by as much as 80 % during subsequent events and by an average of 43 % annually. Modelled N2O emissions were also found to be sensitive to surface soil properties. Increasing near-surface bulk density by 10 % raised N2O emissions by as much as 100 % during emission events and by an average of 23 % annually. Relatively small spatial variation in management practices and soil surface properties could therefore cause the large spatial variation in N2O emissions commonly found in field studies. The global warming potential from annual N2O emissions in this intensively managed grassland largely offset those from net C uptake in both modelled and field experiments. However, model results indicated that this offset could be adversely affected by suboptimal land management and soil properties.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».