Improving the DNDC Model for Estimating Decomposition and Carbon Dioxide Emissions from Biosolids and Manure-Amended Fields
Notice bibliographique
Résumé
<b>Highlights</b> <list list-type=bullet><list-item> DNDCv.CAN was modified to include a new manure C pool with its own decomposition rate </list-item><list-item> The improved model better simulated decomposition of organic amendments and CO<sub>2</sub> emissions </list-item><list-item> A modified temperature function alleviated the early season CO<sub>2</sub> over-prediction for biosolids </list-item><list-item> The simulation of decomposition of cattle manure was also improved but not so drastically as for biosolids </list-item><list-item> The model enhancements increase confidence for simulating manure and biosolid management </list-item></list> <b><sc>Abstract.</sc></b> The objective of this study was to improve and validate the Denitrification and Decomposition (DNDC) model for simulating CO<sub>2</sub> emissions from land application of biosolids and manure. A separate manure C pool was added to the DNDC framework to disaggregate manure decomposition from the soil organic matter pools. Decomposition rates of biosolids were estimated using measurements of organic material. The effect of soil temperature on soil organic matter decomposition was also improved using an arctangent function. Data collected from two climatically distinct sites in Montreal (Quebec) and Truro (Nova Scotia) in 2017–2019 with continuous corn was used to test the model in simulating crop yield, soil temperature and moisture, soil CO<sub>2</sub> fluxes amended by biosolids (mesophilic anaerobically digested, composted, and alkaline-stabilized biosolids), urea, and unfertilized control. Data from the third site was used to verify the model with solid cattle manure (SCM) and inorganic fertilizer (IF) applied to a corn-soybean rotation field in Harrow, Ontario (2012-2015). Both default and improved models were calibrated using the data from IF for the Ontario site, and soil surface spread treatments for Montreal and Nova Scotia while SCM, control and soil-incorporated treatments were used for validation. Crop yields were well simulated by the improved model [relative root mean squared error rRMSE (4.1 – 30.1%) for all the three sites. The improved DNDC model (Wilmott d coefficient 0.72 ≤ d ≤ 0.96) outperformed the default version (0.61 ≤ d ≤ 0.9) in simulating CO<sub>2</sub> fluxes across all the three sites. Similarly, the statistical results showed that the model effectively simulated both soil temperature (d ≥ 0.88) and moisture (0.53 ≤ d ≤ 0.91) across the sites. The addition of an independent biosolids/manure C pool in DNDC resulted in more reasonably simulated decomposition rates for alkalized and composted biosolids to better match observed CO<sub>2</sub> emissions. The modified temperature function alleviated the over-prediction of CO<sub>2</sub> emissions shortly after biosolid application and greatly improved the timing of emissions during the growing season. The revised model also better differentiated the CO<sub>2 </sub>emissions between biosolids types and urea treatments. These model enhancements will enable us to simulate best management practices for integrated crop-livestock-manure management systems, optimizing nutrient cycling across farm systems to enhance the sectors' sustainability-profitability and resiliency, minimizing reliance on nitrogen (N) fertilizer derived from fossil fuels.
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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 | 0,000 | 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 ».