Characterizing Phosphorus Transport to Agricultural Tile Drains in Vermont
Bibliographic record
Abstract
Dairy agroecosystems use subsurface or tile drainage to improve trafficability and crop yield. One of the main concerns of tile drainage is that it acts as a major source of non-point phosphorus (P) pollution because of preferential flow pathways (PFP). PFP are large conduits in the soil that allow rapid movement of water. This reduces contact time infiltrate has with deeper soil horizons, which are not saturated with P and that could act as a sink for this nutrient. Also, PFP are responsible for channeling manure applied at the surface to tile drains. The rate of tile drain installation is likely to increase as rainfall patterns leading up to and during the growing season are altered by climate change. Climate change is also expected to increase rainfall intensity and periods of drought, furthering the need to understand the role of PFP activity on P transport.There has been much work done on P transport and PFP in tile drained landscapes in the US Midwest, however there is a lack of data and analysis in the Northeast. In the Lake Champlain Basin (LCB) agriculture is a significant contributor of P and the role of tile drainage is still unclear. In addition, tile drain data in the LCB is from New York and Quebec while there is limited published edge of field (EoF) studies in Vermont. There are also many dairy farms in Vermont that use tile drainage and apply manure near the end of the growing season, and the impact of P loading to the lake from these practices is unknown. This thesis aims to provide analysis of P transport in tile drained landscapes in Vermont. Data collected for this study comes from three tile drains from two fields, monitored using typical EoF methods. Field sites in this study are typical of the region in terms of soils (Covington and Panton clays) and best management practice usage (i.e. manure injection, light tillage, and cover cropping). A high temporal resolution dataset of rainfall and tile discharge metrics (i.e. flow and P) were used in the analysis. PFP activity was assessed using a four-component hydrograph separation technique using two methods, electrical conductivity end-member unmixing and hydrograph recession analysis. A unique rainfall intensity analysis was performed to forecast the impacts of climate change on P transport. Results show that most of the P export occurred during the non-growing season, however drought played an important role in seasonal export. Peak P concentrations coincided with events post manure injection in the fall, and in the spring with events post cover crop termination and post planting. The rainfall pulse analysis showed tile drain P loading was higher during events because of higher intensity rainfall pulses. Future work should aim to better understand the role of manure application method and timing, as well as how antecedent moisture impacts subsequent P transport.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".