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Record W7119425611

Management impacts on nitrous oxide emissions and nitrogen cycling gene abundances in an Okanagan Valley sweet cherry (Prunus avium) orchard soil

2025· other· en· W7119425611 on OpenAlexaff
Katherine-Faye Karen Jansen

Bibliographic record

VenuecIRcle (University of British Columbia) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMicrocosmNitrificationOrchardCompostSoil carbonNitrous oxideCyclingNitrogen
DOInot available

Abstract

fetched live from OpenAlex

Agricultural soil management contributes to nitrous oxide (N₂O) emissions. The use of organic and inorganic amendments, and nitrification inhibitors can influence N₂O production by altering microbial nitrogen–cycling functional genes and metabolism. This thesis includes one in–field study and two microcosm studies aimed at identifying best management practices based on the impacts of organic amendments, nitrogen source, and the nitrification inhibitor 3,4–dimethylpyrazole phosphate (DMPP) on N₂O emissions, physicochemical properties, and abundances for total bacteria, archaea, and six nitrogen–cycling genes in a sweet cherry orchard soil in the Okanagan. Field plot treatments were bare, compost (CMP), and woodchip (WC); soil and gas sampling occurred in June and August (2020). The I1 and I2 microcosms contained soil from the field site and were incubated for 33 to 38 days. I1 had seven treatments varying NH₄⁺ (A), NO₃⁻ (N), and DMPP (I), and I2 had five treatments varying NH₄⁺, compost (C), and DMPP (I). Compost–treated field plots produced more N₂O, had a higher percent carbon and nitrogen, and lower CN ratio than WC plots. The higher CN ratio and proportion of insoluble carbon in WC plots likely increased nitrogen use efficiency and assimilation, and decreased denitrification–N₂O. In the I1 experiment the ANI treatment produced the most N₂O-N, while the uninhibited/inhibited pairs were comparable (A≈AI, N≈NI). Closed microcosms may have encouraged low oxygen, excess NH₄⁺ and NO₂⁻/NO₃⁻ in ANI, and, with low cation exchange capacity (CEC), may have facilitated co-denitrification and abiotic–N₂O production. In the I2 experiment, compost increased the N₂O-N produced in the presence of DMPP (ACI>AI), but N₂O-N was comparable between the uninhibited/inhibited pairs (A≈AI, AC≈ACI). The inhibitor DMPP may be ineffective if NO₃⁻ is available from sources besides nitrification (ACI/AI). Adding compost increased CEC and carbon, aiding NH₄⁺ adsorption and potentially assimilation, preventing drastically elevated N₂O-N compared to NH₄⁺ alone (AC≈A). In conclusion, high-CN organic matter with more insoluble carbon may reduce fertilizer and irrigation–induced N₂O in–field but DMPP needs in–field testing in the Okanagan with different soil types and management practices to identify ideal application rates and methods.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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
Published2025
Admission routes1
Has abstractyes

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