Impacts of Dredging and Brush Cutting of Paired Agricultural Drainage Ditches on GHG Emissions and Nutrient Filtration Capacity
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".