The carbon budget and greenhouse gas balance of annual and perennial crops for dairy feed
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| 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 it