Soluble and particulate nitrogen losses from tile drained fields in Southern Quebec, Canada
Bibliographic record
Abstract
Eutrophication and cyanobacteria blooms are a growing problem in Missisquoi Bay of Lake Champlain in southern Quebec, and these are largely attributed to non-point source phosphorus and nitrogen (N) pollution from agricultural land in the surrounding watersheds. Residual soil N left after crop harvest contains soluble and particulate forms of N that are at risk of being transported from tile drained agricultural fields to waterways. This study aimed to find the sources of soluble N (mainly nitrate; NO3-N) and particulate organic N (PON) that are susceptible to loss, and the transport pathways by which they move to surface water through tile drained agricultural fields. Water samples were collected at the tile drainage outlet of fields with a clayey and a sandy soil during fall 2010, spring and fall 2011 and spring 2012. There was 1.3 times greater NO3-N concentration and 1.1 fold higher PON concentration in tile drainage water from sandy soil than clayey soil and electrical conductivity measurements indicated that preferential flow was the main pathway for PON loss from clayey soil. Using a dual stable isotopes of δ15N and δ18O of NO3 -N coupled with a mixing model, inorganic NH4 fertilizer was found to be the most important contributor to the NO3-N pool in tile drainage water within two weeks of fertilizer application; however, microbially-processed NO3-N was the main source (40 to 49% of NO3-N in tile drainage water) when crops were not growing in the field. Sources of PON in tile drainage water were manure N (47%) and plant residue N (20%) from topsoil layer of the clayey soil, while soil organic N (SON) contributed 94% of PON lost from the topsoil of the sandy soil. More specifically, the PON pool contained N-rich soil organo-mineral complexes from the top soil layer that reached the tile drains by preferential flow pathways. Decreasing NH4 inputs from fertilizer and allocating sufficient N credits to manure and legume residue inputs could reduce the buildup of NO3-N and organic N, thereby reducing NO3-N and PON losses from these sources. I conclude that source fingerprinting techniques using stable isotope tracers are an effective way of assembling information on the susceptibility of N inputs to loss and transport pathways, which need to be considered when choosing best management practices to reduce non-point source N pollution from the agricultural sector.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".