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Record W7052695619

Soluble and particulate nitrogen losses from tile drained fields in Southern Quebec, Canada

2013· dissertation· en· W7052695619 on OpenAlexfundaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2013
Typedissertation
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsnot available
FundersMcGill University
KeywordsTile drainageDrainageTileSurface runoffParticulatesManureHydrology (agriculture)Soil waterEutrophication
DOInot available

Abstract

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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; NO 3 -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 NO 3 -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 δ 15 N and δ 18 O of NO 3 -N coupled with a mixing model, inorganic NH 4 fertilizer was found to be the most important contributor to the NO 3 -N pool in tile drainage water within two weeks of fertilizer application; however, microbially-processed NO 3 -N was the main source (40 to 49% of NO 3 -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 ii 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 NH 4 inputs from fertilizer and allocating sufficient N credits to manure and legume residue inputs could reduce the buildup of NO 3 -N and organic N, thereby reducing NO 3 -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.iii Resume L'eutrophisation et les proliférations de cyanobactérie sont un problème croissant dans la Baie Missisquoi du Lac Champlain dans le sud du Québec.Celles-ci peuvent être largement imputées a une pollution en phosphore et azote (N) d'origine diffuse, provenant de terres agricoles dans les basins versants s'y déversant.L'azote résiduel du sol, qui demeure après la récolte, comprend des formes soluble et particulaires qui risquent d'être transportées des champs équipés d'une système de drainage souterrain vers les cours d'eau.Cette étude tenta d'identifier les sources d'azote soluble (principalement les nitrates; NO 3 -N) et d'azote organique particulaire (AOP) qui sont vulnérables aux pertes, et les voies de transport par lesquelles elles se rendent des champs agricoles équipés de systèmes de drainage souterrains aux eaux de surface.Des échantillons d'eau furent prélevés à l'exutoire du système de drainage souterrain de champs aux sols argileux ou sablonneux, à l'automne 2010, au printemps et à l'automne 2011, et au printemps 2012.Les concentrations en NO 3 -N et en AOP furent 1.3 et 1.1 plus élevées, respectivement, dans l'eau de drainage provenant du sol sablonneux que du sol argileux, Un suivi de l'électroconductivité du sol indiqua que l'écoulement préférentiel fut la principale voie des pertes en AOP dans le sol argileux.Le suivi d'isotopes stables (δ 15 N et δ 18 O) du NO 3 -N du sol et des eaux de drainage, en combinaison avec un modèle de combinaison, démontra que, dans les deux semaines après son épandage, l'engrais inorganique à base de NH 4 contribua le plus au stock de NO 3 -N des eaux de drainage souterraines.Cependant, le NO 3 -N transformé par les microbes fut la principale source (40 à 49%) du iv NO 3 -N dans les eaux de drainage, lorsque les cultures étaient absentes.Or, 47% et 20% de l'AOP dans les eaux de drainage provint, respectivement, d'azote de fumier et d'azote des résidus de cultures ayant leur origine dans la couche arable du sol argileux, tandis que l'azote organique de la couche arable du sol sablonneux contribua 94% de l'AOP perdu.Plus particulièrement, le stock d'AOP de la couche arable contenait des complexes organominéraux riches en azote, qui se sont rendus au drains par des voies préférentielles d'écoulement.Une diminution des apports en NH 4 provenant d'engrais, et une prise en compte des crédits d'azote associés au fumier et aux résidus de plantes légumineuses, pourrait réduire l'accumulation de NO 3 -N et d'azote organique, réduisant ainsi les pertes en NO 3 -N et AOP provenant de ces sources.Les techniques d'empreinte isotopique on donc permis de faire un suivi efficace des intrants azotées tout en générant de nouvelles connaissances sur la vulnérabilité des intrants azotées aux voies de perte et de transport.Celles-ci devront être considérées lors du choix et de la mise en œuvre de pratiques de gestion optimales dans le secteur agricole visant à réduire la pollution azotée diffuse v Preface and contribution of authors This thesis is composed of four chapters, preceded by a general introduction explaining the context of this research.Chapter 1 is a literature review that summarizes the body of knowledge surrounding this thesis.Chapters 2, 3 and 4 are

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.008
GPT teacher head0.216
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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