Greenhouse gas emissions are affected by land use type in two agroforestry systems: Results from an incubation experiment
Bibliographic record
Abstract
Abstract In order to better understand factors affecting greenhouse gas (GHG) emissions in Canadian agroforestry systems, we conducted a laboratory incubation study to assess N 2 O, CO 2 and CH 4 emissions from soils in response to land use (forestland vs. cropland) and agroforestry system type (hedgerow vs. shelterbelt) in central Alberta, Canada. Emissions of N 2 O were lower in soils from forestland than cropland, and forest soils acted as a net sink of atmospheric CH 4 while cropland soils were weak sources of CH 4 . However, the forest soil had higher CO 2 emission rates than the cropland soil within both agroforestry systems. Soil CH 4 oxidation was higher in soil from hedgerow (consisted of natural forest vegetation) than from shelterbelt system (planted forest vegetation), while the former also had lower N 2 O emissions. Overall, soil CO 2 emissions were significantly higher from hedgerow than from shelterbelt systems. Emissions of N 2 O were positively related with gross nitrification rates and soil pH, and negatively related with gross N immobilization rates. The CO 2 emissions were positively related with water‐soluble organic C contents, while CH 4 emissions were positively related with clay content, but negatively with gross N immobilization rates and soil organic C content. The global warming potential was higher in forestland soil than in cropland soil within agroforestry systems, and higher in forestland soil of the hedgerow compared to that in shelterbelts. Our results suggest that we need to select land uses or agroforestry systems that have a higher potential of mitigating GHG emissions from soils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".