Assessing The Impacts of Tillage, Manure Management, and Crop Rotation on Crop Growth and Soil Carbon Dynamics in Eastern Canada
Notice bibliographique
Résumé
Highlights DNDCv.CAN was modified to simulate inverted soil & residue litter C deposition below the plow layer depth. The model reflects moldboard plowing with higher SOC at depth from residue burial. The DNDC framework enhancements captured tillage effects, with model outputs closely aligning with measured data. DNDC modifications offer scientific foundation for investigating soil carbon sequestration. Abstract. The effects of tillage-induced soil mixing and buried crop residues on soil organic carbon (SOC) changes at depth are inadequately represented in most models, especially when combined with different types of manure application. The objectives of this study were to improve the performance of the DeNitrification DeComposition (DNDC) model for assessing the impact of tillage (moldboard versus chisel plowing) combined with manure application on SOC sequestration at depth in cereal monoculture and cereal-perennial forage systems on a silty clay soil at Normandin, Quebec, and a corn-soybean-wheat rotation on a sandy loam soil at the Laval University agronomic research station in St-Augustin-de-Desmaures, Quebec. The two studies provided a comprehensive data suite of soil characteristics, crop yields, and soil organic carbon stocks across multiple depths under a combination of tillage and nutrient management practices. The DNDC model framework was enhanced by including soil inversion under moldboard plowing with allocation of crop residues to deep soil layers where they decompose more slowly. At Normandin, the model performed well in simulating barley yields under moldboard-manure management as per the average relative error (ARE = 4.7%) and the normalized root-mean-squared error (NRMSE = 7.1%) but demonstrated some challenges in simulating interannual hay yield variations perhaps due to the challenges in simulating winter kill. The accuracy of cumulative SOC simulations (0-10, 0-20, 0-30, and 0-50 cm) closely aligned with observed data, varying with fertilizer and tillage management, with (NRMSE) values ranging from 4.7% to 26.9% under forage rotation, and from 3.1% to 14.5% in barley monocropping, respectively in 2002 and 2010. As expected, the measured and modelled liquid dairy manure (LDM) in the hay rotations had higher SOC than the monoculture barley with dairy manure. DNDC effectively captured the trends in measured SOC stocks, showing significantly higher values in the deeper soil layers (0-30 to 0-50 cm) under moldboard plow and crop rotation compared to the top layers here chisel plow showed more SOC. Conversely, the effect of nutrient source on soil carbon was evident, with LDM maintaining higher SOC levels than mineral fertilizers under rotation, although the difference was less significant in the cereal monoculture. At the Quebec site, DNDC demonstrated excellent performance in estimating wheat yields (ARE of 0.6% and -7.6%), corn grain yields (ARE of 7.5% and 1.2%) and with moderate performance for soybean yields ARE of -13.2% and -11.9%) under calibration and validation, respectively. Overall, DNDC captured the carbon stock differences among management systems (ARE was -2%, and -8% for the calibration and validation, respectively) in the top 15 cm for the 8th year of the Quebec trial (2016). This study demonstrates the importance of assessing the impacts of management practices and manure application on SOC content across the entire soil profile, considering buried residues and tillage inversion.. These tillage applications are not usually considered in crop models but are important for the site specific and regional evaluation of strategies to promote soil carbon sequestration
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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) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».