<i>Determining Environmental Benefits and Economic Costs of Different Manure Handling Strategies in Quebec’s Dairy Production using Farm Simulation</i>
Bibliographic record
Abstract
Abstract. The recent consolidation of Quebec‘s dairy farms is such that the industry expects an increase in free-stall dairies under liquid manure management at the expense of tie-stall dairies under solid manure management. This transition could however have implications on greenhouse gases (GHG), so that dairy producers must consider strategies such as enclosed storage and manure incorporation for limiting their emissions to help control global warming. To assess the overall cost-effectiveness of different combinations of housing, manure management, and mitigation measures for a representative dairy farm in two regions with contrasted climate (Southwestern and Eastern Quebec), a farm-scale, optimization model (N-CyCLES) was used. Housing and manure management types did not significantly affect the farm net income (FNI) in both regions. Nevertheless, free-stall barns and solid manure management systems needed more N imports since they were respectively associated with greater N volatilization and slower release of elements into the soil. For these reasons, tie-stall barns and liquid manure systems generally had lower N balance and GHG production. A covered manure storage lessened manure volume and volatilization, which reduced fertilizer and manure spreading costs, increased crop sales and FNI, and enhanced N and GHG balances. Manure incorporation increased soil management costs, but reduced N and GHG footprints by decreasing use of N-based fertilizers and N2O emissions caused by manure application. Consequently, the transition towards free-stall dairies with liquid systems seems advantageous from the economic and environmental point of views, and using covered manure storage would be economically viable to further reduce GHG emissions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".