Impacts of Dredging and Brush Cutting of Paired Agricultural Drainage Ditches on GHG Emissions and Nutrient Filtration Capacity
Notice bibliographique
Résumé
Agriculture provides many beneficial and essential ecosystem services. Along with these \nbeneficial services, the conversion of natural ecosystems into heavily modified agricultural \necosystems is also a source of disservice, including being a major source of global greenhouse \ngas (GHG) emissions and pollution of downstream waterways due to increased nutrient runoff. \nCarbon (C), Nitrogen (N) and Phosphorus (P) applied to agricultural fields as fertilizer are a \nsource of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions. Nutrient \nrunoff can lead to excess P in surface water bodies causing algae blooms, and excess N can lead \nto excess nitrate (NO3) in rural groundwater (GW) wells. There is a need to establish beneficial \nmanagement practices (BMPs) to take into account agriculture-derived pollution with all \nagricultural practices. This thesis informs the development of BMPs by examining the \nenvironmental pollution aspects of both: 1) GHG emissions; and 2) nutrient export; resulting from \nthe common practices of brush cutting and dredging of ditches to enhance drainage. \nRiparian vegetation in agricultural drainage ditches has been shown to decrease \ninsolation, which decreases soil and water temperatures. This vegetation also hinders drainage \nby restricting flow, thus raising water levels, which decreases CO2 emissions and increases CH4 \nand N2O emissions. However, no previous studies have examined in detail the effects of \nremoving drainage ditch vegetation. This study examines the GHG emissions from four ditch \nmicroplots in the South Nation Watershed in southern Ontario, Canada following the removal \nof riparian vegetation from two microplots. The trials took place over three field seasons, and \nthe intervention methods were selected to observe the effects of brush cutting and of dredging \non GHG emissions between years. The Control Shrub and Control Tree microplot sites were \nleft unaltered. The Brush Cut Shrub and Brush Cut Tree sites were brushed in Spring 2018 and \nDredged in Fall of 2018, with observations at all sites taking place over 2018-2020 growing \nseasons. Brushing increased CO2 emissions at the treed site but had little effect on the shrub \nsite. Dredging decreased CH4 emissions. \nRiparian vegetation has also been shown to obstruct the path for water flow, decreasing \nwater velocities and raising water levels, which increases the ability of ditches to filter and retain \nnutrients. Simultaneously with the GHG research above, this study also examines the N, P, and C \nv \nexport from two adjacent watersheds within the South Nation Watershed following the removal \nof riparian vegetation from one of them. The trials took place over two field seasons and the \nintervention methods were selected to observe the effects of brush cutting and dredging on N and \nP export over two years. The southern watershed (Brush Cut) was brushed + dredged in 2018 \nand the northern watershed (Control) was left intact before flow monitoring took place in 2019 \nand the Fall of 2020. Tile drain discharge containing DOC, N and P, occurred during the Spring \nand Fall when the water table is higher, but was not observed during the summer. Brush cutting \nand dredging increased hydraulic outflow and reduced or eliminated NO3 retention capacity of \nagricultural drainage ditches by 320% in 2019 and 68% in Fall 2020. This increase in NO3 \nexport may negatively affect rural water supplies. Lack of O2 and increased retention of DOC \nand SO4 in the Control watershed suggests that significant NO3 reduction occurred. Differences \nin P export between Brush Cut and Control in 2019 were small. There are more signs of P \ntransformation in the Control watershed, but brush cutting and dredging may not significantly \naffect eutrophication. \nThis thesis will help inform stakeholders about the environmental geochemical costs and \nbenefits of brushing and dredging so that they can develop BMPs that minimize GHG production \nand maximize nutrient filtration. Future research is needed to determine how many years the \neffects of these intervention methods remain, and also determine other environmental impacts \nsuch as their effects on biodiversity.
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 | 0,000 | 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 ».