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Record W6989853848

Can Enhanced Efficiency N Fertilizers Mitigate N2O Emissions without Compromising Crop Yield?

2022· dissertation· en· W6989853848 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsNitrous oxideNitrificationCrop yieldSoil waterMoistureCropCanolaWater content
DOInot available

Abstract

fetched live from OpenAlex

Optimizing N fertilizer management is imperative to minimize nitrous oxide (N2O) emissions from croplands and to lower the environmental footprint of canola. This field study evaluated the effects of enhanced efficiency N fertilizers (EENF; fertilizers containing urease inhibitors and/or nitrification inhibitors) when applied in the fall vs. in the spring at the soil-test recommended rate vs. a reduced rate (70% of soil-test rate) on agronomic and environmental performances and compared to the conventional practice in dryland canola (Brassica napus L.) over 2 years in Saskatoon. Also, a soil incubation study investigated the N2O reduction potentials from urea-treated soils with and without Nitrapyrin (nitrification inhibitor) throughout a freeze-thaw event. Results from the field study showed the marginal differences in yield, seed N, and crop nitrogen use efficiency (NUE) between conventional urea and EENFs. Under the prolonged drought in 2021, crop NUE was improved with N application in the fall or at a reduced rate although crop yield remained low across N treatments. The modification of N source, rate, and timing did not influence N2O emissions, indeed, N2O emissions was primarily controlled by soil moisture conditions. Dry conditions during the growing season in 2020 and 2021 contributed to low emissions. The different soil moisture conditions and thaw intensity during the spring thaw periods affected N2O production where the fall N application produced higher cumulative emissions during the spring thaw and annually compared to the spring N application in Year 1, but the opposite results were observed in Year 2. Yield-scaled N2O emissions were exceedingly high across N treatments. In 2020 and 2021, the provincial average yield did not provide positive economic returns on EENF investments. Results from the incubation study demonstrated that soil moisture, temperature, and N levels had a greater impact on regulating N2O production than Nitrapyrin. Fertilized soils regardless of N formulations produced higher N2O than non-fertilized soils. High soil moisture and temperature conditions favoured denitrification and magnified the intensity of emissions. Based on these results, yield gains and N2O mitigation may not always be achieved by applying EENFs to soils in dry years.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.177
Teacher spread0.168 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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