Evaluation of denitrification and decomposition from three biogeochemical models using laboratory measurements of N <sub>2</sub> , N <sub>2</sub> O and CO <sub>2</sub>
Notice bibliographique
Résumé
Abstract. Biogeochemical models are essential for the prediction and management of nitrogen (N) cycling in agroecosystems, but the accuracy of the denitrification and decomposition sub-modules is critical. Current models were developed before suitable soil N2 flux data were available, which may have led to inaccuracies in how denitrification was described. New measurement techniques, using gas chromatography and isotope-ratio mass spectrometry (IRMS), have enabled the collection of more robust N2, N2O and CO2 data. We incubated two arable soils – a silt-loam and a sand soil – for 34 and 58 d, respectively, with small field-relevant changes made to control factors during this period. For the silt-loam soil, seven treatments varying in moisture, bulk density and NO3- contents were included, with temperature changing during the incubation. The sandy soil was incubated with and without incorporation of litter (ryegrass), with temperature, water content and NO3- content changing during the incubation. The denitrification and decomposition sub-modules of DeNi, Coup and DNDC were tested using the data. No systematic calibration of the model parameters was conducted since our intention was to evaluate the general model structure or “default” model runs. Measured fluxes generally responded as expected to control factors. We assessed the direction of modeled responses to control factors using three categories: no response, a response in the same direction as measurements or a response in the opposite direction to measurements. DNDC responses were 14 %, 52 % and 34 %, respectively. Coup responses were 47 %, 19 % and 34 %, respectively. DeNi responses were 0 %, 67 % and 33 %, respectively. The magnitudes of the modeled fluxes were underestimated by Coup and DNDC and overestimated by DeNi for the sandy soil, while there was no general trend for the silt-loam soil. None of the models was able to determine litter-induced decomposition correctly. To conclude, the currently used sub-modules are not able to consistently simulate the denitrification and decomposition processes. For better model evaluation and development, we need to design better experiments, take more frequent measurements, use new or updated measurement techniques, address model complexity, add missing processes to the models, calibrate denitrifier microbial dynamics, and evaluate the anaerobic soil volume concept.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| 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 ».