Non-growing season nitrous oxide fluxes from an agricultural soil as affected by application of liquid and composted swine manure
Bibliographic record
Abstract
Kariyapperuma, K. A., Furon, A. and Wagner-Riddle, C. 2012. Non-growing season nitrous oxide fluxes from an agricultural soil as affected by application of liquid and composted swine manure. Can. J. Soil Sci. 92: 315–327. Agricultural soils have been recognized as a significant source of anthropogenic nitrous oxide (N 2 O) emissions, an important greenhouse gas and contributor to stratospheric ozone destruction. Application of liquid swine manure (LSM) has been reported to increase direct N 2 O emissions from agricultural soils. Composting of LSM with straw under forced aeration has been suggested as a mitigation practice for emissions of N 2 O. In cold climates, up to 70% of total annual soil N 2 O emissions have been observed during winter and spring thaw. Non-growing season soil N 2 O emissions after field application of composted swine manure (CSM) versus LSM have not been directly compared in past studies. A 2-yr field experiment was conducted at the Arkell Research Station, Ontario, Canada, as a part of a larger study to evaluate composting as a mitigation strategy for greenhouse gases (GHGs). The objectives were to quantify and compare non-growing season N 2 O fluxes from agricultural soils after fall application of LSM and CSM. Nitrous oxide fluxes were measured using the flux-gradient method. Compared with LSM, CSM resulted in 57% reduction of soil N 2 O emissions during February to April in 2005, but emissions during the same period in 2006 were not affected by treatments. This effect was related to fall and winter weather conditions with the significant reduction occurring in the year when soil freezing was more pronounced. Compared with LSM, CSM resulted in a reduction of 37% (CO 2 -eq) of estimated N 2 O emissions per liter of treated manure and of 50% in the emission factor for the non-growing season.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".