The carbon budget and greenhouse gas balance of annual and perennial crops for dairy feed
Bibliographic record
Abstract
Dairy production emits significant amount of greenhouse gases (GHG) to the atmosphere. Nevertheless, cultivation of hay and corn for dairy feed have the potential to partially mitigate GHG emission from dairy production by sequestering carbon in soil as soil organic matter. The present study investigated the net ecosystem carbon budget (NECB) and greenhouse gas balance (GHGB) of a hay and corn field grown side-by-side over three years in Elora, Ontario, Canada, as an indicator of net sequestered carbon. The NECB of the two crops were determined using measurements of net ecosystem exchange (NEE) and carbon in plant and applied manure. The greenhouse gas balance (GHGB) were determined using the NECB plus the total nitrous oxide (N2O) fluxes. On average over the three study years, NECB of hay (7 ± 51 g C m−2 yr−1) was significantly lower than corn (154 ± 79 g C m−2 yr−1) indicating that corn was a larger carbon source than hay. The three-year average GHGB of 796 and 127 g CO2-eq m−2 yr−1 for corn and hay, respectively, indicated that corn was a larger emitter of GHG than hay. The NECB was the more dominant factor than N2O emissions in influencing the outcome of the annual GHGB. In conclusion, hay has a larger potential than corn in sequestering carbon and mitigating GHG emission.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".