Effect of manure application methods on nutrient and metal mobilization with snowmelt flooding in a manured agricultural land
Notice bibliographique
Résumé
Accumulation of phosphorus (P), nitrogen (N), and metal(loid)s with manure applications to agricultural lands and their subsequent losses via runoff and leaching pose a potential risk of water quality deterioration. In the Canadian prairies, snowmelt flooding contributes to the majority of annual runoff. Injection of liquid swine manure (LSM) has been well documented as a manure application method to reduce nutrient loss with rainfall-runoff; however, its effectiveness in reducing snowmelt-driven nutrient and metal(loid) runoff has not been studied to our knowledge. This thesis examined the (a) release of P and metal(loid)s [zinc (Zn), manganese (Mn), iron (Fe), magnesium (Mg), calcium (Ca), and arsenic (As)] to floodwater under simulated snowmelt flooding, and (b) losses of P, N, Zn, Mn, Fe, Mg, and Ca to snowmelt runoff from field plots, with LSM injection and surface application. LSM was applied in the Fall of 2021 to four replicated field plots with three treatments: manure injected, surface-applied, and unmanured (control). For the simulated snowmelt study, intact soil columns were extracted from each plot after two weeks of manure application. The columns were flooded with ultrapure water at 4±1 °C to simulate snowmelt flooding. Porewater and floodwater samples were extracted for 8 consecutive weeks and analyzed for dissolved reactive P (DRP), pH, and metal(loid)s concentrations. For the field study, snowmelt was collected from installed boxes in each plot in the 2022 spring for 10 days when the temperature was above 0 °C and snowmelt was present. The snowmelt samples were analyzed for DRP, nitrate-N, metals (Zn, Mn, Fe, Mg, and Ca), and pH. Significant differences in DRP concentrations in porewater or floodwater were not observed among the three treatments in the simulated snowmelt study; however, concentrations of DRP in floodwater increased (1.5-fold and 5-fold in porewater and floodwater, respectively) under prolonged flooding conditions irrespective of the treatment. Metal concentrations in porewater or floodwater did not show significant differences among the three treatments in the simulated snowmelt study, with the exception of porewater Mg concentration on day 0 and floodwater Zn concentration on day 7. In the field, the DRP concentrations in snowmelt were significantly higher in surface-applied manure treatment than in injected and control treatments from day 7 to 8. The concentrations of nitrate-N or any of the metals were not significantly different among the treatments. However, DRP, nitrate-N, and metal concentrations increased with time during the snowmelt period. There was a dramatic spike in nitrate-N concentration towards the end of the sampling period, where the increase in nitrate-N concentration was 44-fold compared to day 1. We also found significant correlations and positive linear relationships between snowmelt volume and DRP and metals loads, suggesting that snowmelt volume was the main driving factor of DRP and metal (Zn, Mn, Fe, and Ca) loss with snowmelt. The results of this research suggest that the management practices should be focused not only on reducing concentrations of nutrients and metals in snowmelt but also on reducing the snowmelt volume and snowmelt flooding duration.
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 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 | 0,000 |
| 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 | 0,001 | 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 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 ».