Management impacts on nitrous oxide emissions and nitrogen cycling gene abundances in an Okanagan Valley sweet cherry (Prunus avium) orchard soil
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".