Opportunities to reduce nitrous oxide emissions from horticultural production systems in Canada
Bibliographic record
Abstract
Horticultural systems, specifically vegetable production systems, are considered intensive agricultural systems as they are characterized by high nitrogen (N) fertilizer application rate, frequent tillage, and irrigation operations. Accordingly, horticultural production in temperate climates is prone to N losses — mainly during post-harvest (during fall and winter) or pre-plant (spring) periods — such as N2O emissions and nitrate leaching. The risk for N losses is linked to low crop N use efficiency (NUE) combined with a narrow C:N and high N content of crop residues. Here we reviewed the studies conducted in Canada and similar climates to better understand the risk of N2O emission and potential agronomic management strategies to reduce N2O emissions from horticultural systems. Current knowledge on N2O emissions from horticultural systems indicate that increasing crop NUE, modifying the amount, type, time, and rate of N fertilizer inputs, and adopting cover crops in crop rotations are some of the effective approaches to decrease N2O emissions. However, there is uncertainty related to the efficiency of the existing N2O mitigation strategies due to the complex interactions between the factors (soil characteristics, type of plant species, climatic conditions, and soil microbial activity) responsible for N2O production from soil. Little research on N2O emissions from Canadian horticultural systems limits our ability to understand and manage the soil N2O production processes to mitigate the risk of N2O emissions. Thus, continuing to expand this line of research will help to advance the sustainability of Canadian horticultural cropping systems.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".