MétaCan
Menu
Back to cohort
Record W2567118442

Legacy Effects of Long-term Manure Applications on Soil-derived Nitrous Oxide Emissions

2017· dissertation· en· W2567118442 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUniversity Library - University of Saskatchewan (University of Saskatchewan) · 2017
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Soil, Plant Science
Canadian institutionsnot available
FundersAgriculture and Agri-Food Canada
KeywordsNitrous oxideTerm (time)ManureEnvironmental scienceAgronomyEcologyPhysicsBiology
DOInot available

Abstract

fetched live from OpenAlex

Termination of the manure application treatments at the Dixon long-term manure research site in Humboldt, Saskatchewan provided a unique opportunity to explore how a change in management regime to annual urea applications would affect nitrous oxide (N2O) emissions.My hypotheses were that long-term manure applications would produce a legacy (or priming) effect that would result in enhanced N2O emissions following the changeover to a more readily available nitrogen source and that this effect would be relatively short-lived.The impacts of long-term manure application and change in fertility management in the sub-humid prairies of Saskatchewan has not been investigated in great depth, this work provided an opportunity for greater insight into changes in N transformation and gaseous N loss from a manured agroecosystem.Nitrous oxide fluxes associated with the long-term manure and fertilizer application from the Dixon site were measured during a 37-month period (i.e., from May 2011 to June 2014).In addition, denitrification enzyme activity (DEA) was measured in a subset of the plots starting in June 2011 and continuing three times per year (i.e., prior to and after the spring fertilizer application and again in early fall).Treatment-induced N2O emissions for the various historical amendment treatments indicate that past management can result in considerable N being lost from the system as N2O.Indeed, summed over the three-year post-manure period (i.e., from fertilizer application in 2011 through the 2014 spring thaw), N2O-N losses accounted for 2% to 6% of the total applied fertilizer-N.Moreover, under environmental conditions that optimize denitrification, N2O-N losses can be even greater.For example, high DEAs coupled with warm moist soil conditions resulted in large N2O emission events following the spring 2013 fertilizer application and during the 2014 spring thaw.As a result, cumulative annual N2O-N losses in 2013/14 were much greater than those in previous yearswith emissions from the liquid swine manure (LSM)amended plots ranging from 3% to 15% of applied N.These data support my earlier hypothesis that long-term applications of manure-N canespecially at high application rates and following frequent application -produce a 'priming' effect that exacerbates N2O emission when a more available form of N (e.g., urea fertilizer) is applied to the soil.Moreover, this priming effect appears to be relatively long-livedpersisting in the soil more than four and a half years after the last manure application.In any given year, however, the impact of the priming effect on Without their guidance I wouldn't have been able to excel in my studies and research.I would also like to thank my committee members Drs.Diane Knight and Bobbi Helgason.Dr. Helgason was paramount to my understanding of the complicated world of soil microbes and the different methods of analysis.This extends to Sarah Kuzmicz of the soil micro-lab and AAFC who provided technical help, as well as laboratory analysis used in this study, and to Hannah Konschuh who filled in that role while Sarah was away on leave.They all provided the kindness and reassurance to keep me on track and focused to finish.I would also like to extend my thanks to the lab group in 5E19 (Darin, Frank, Mark, Sharon, Dwayne, and Amanda) as well as all the summer students (especially Nicolas and Zheng, Adam and Kelsey), who helped me complete my field and lab research.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.003
Open science0.0040.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.171
Teacher spread0.165 · 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