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Record W6991007814

EVALUATING THE NITROUS OXIDE MITIGATION POTENTIAL OF ENHANCED EFFICIENCY NITROGEN FERTILIZER PRODUCTS IN A SASKATCHEWAN IRRIGATED CEREAL PRODUCTION SYSTEM

2022· dissertation· en· W6991007814 on OpenAlexfundaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldEngineering
TopicPolymer-Based Agricultural Enhancements
Canadian institutionsnot available
FundersClimate Change and Emissions Management CorporationInternational Plant Nutrition InstituteMinistry of Agriculture - Saskatchewan
KeywordsNitrous oxideFertilizerGreenhouse gasIrrigationAmmonium nitrateNitrateNitrificationNitrogenCrop yieldNutrient
DOInot available

Abstract

fetched live from OpenAlex

Nitrogen fertilizers added to agricultural field crops are a significant source of nitrous oxide (N2O) emissions from Canadian soils. Irrigated cropping systems are of particular concern due to intensive management and higher fertilizer rates corresponding to higher yield potential, which result in higher N2O emissions. Fall fertilizer applications are also at risk of greater N2O-N loss due to the length of time between application and crop uptake in the subsequent growing season. Enhanced efficiency nitrogen fertilizers (EENFs) can be used to mitigate environmental losses that contribute to greenhouse gas (GHG) emissions by slowing the release rate of N. Nitrous oxide emissions experience spatial and temporal variability and are highly dependent on management practice, thus, it is important to evaluate mitigation techniques across different geographies and cropping systems. \nOver the course of two growing seasons and the subsequent spring thaw periods, fall and spring applications of conventional fertilizers (CF) and EENFs were evaluated in spring wheat under irrigation in south-central Saskatchewan. Nutrient supply rate of nitrate (NO3-) and ammonium (NH4+) were measured using PRS® probes and N2O emissions were collected from non-steady state vented chambers that were placed both on and off the fertilizer bands. Treatments included an unfertilized check, two conventional N sources (urea and anhydrous ammonia), a polymer-coated urea (ESN), two nitrification inhibitors (eNtrench, N-Serve), a dual−action urease inhibitor (Limus), and a dual (nitrification + urease) inhibitor (SuperU). \nIn this study, the supply rate of NO3- and NH4+ from EENFs was consistent with the mode of action of the product. Polymer-coated urea and products containing a urease inhibitor (UI) reduced the supply rate of NH4+ compared to CFs and products containing a nitrification inhibitor (NI) reduced the supply rate of NO3-. Interestingly, increased supply rates of bioavailable N were observed in all treatments over the winter when the soil was frozen. Unsurprisingly, the greatest N2O emissions fluxes corresponded with the spring melt periods and the period shortly following spring fertilization. Up to 75% of the annual, cumulative N2O flux occurred during the spring thaw. Enhanced efficiency nitrogen fertilizer N2O emission reductions were inconsistent when applied in the fall, whereas, spring applications of EENFs were much more consistent at reducing N2O emissions. Fall-applied SuperU (U/NI) and eNtrench (NI) reduced N2O emissions compared to untreated urea but only in the second field season. Spring-applied SuperU (U/NI), eNtrench (NI) and Limus (DAUI) consistently and significantly reduced N2O emissions across both field seasons (78-99%). The PCU (ESN) and AA-based NI (N-Serve) successfully reduced N2O emissions (43% and 68%, respectively) in the second field season only. Although environmental benefits are clear, specifically from using EENFs in a spring application of N, an agronomic benefit of increased yield was not observed in any of the N source treatments or application timings.

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 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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.186
Teacher spread0.179 · 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 teacher head, not a consensus.

Study designBench or experimental
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
Published2022
Admission routes2
Has abstractyes

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