Optimal Housing and Manure Management Strategies to Favor Productive and Environment-Friendly Dairy Farms in Québec, Canada: Part II. Greenhouse Gas Mitigation Methods
Bibliographic record
Abstract
Abstract. Several strategies are available for mitigating greenhouse gas (GHG) emissions associated with dairy manure management in barns, storage units, and fields. For instance, incorporation of manure into the soil, solid-liquid separation, composting, enclosed manure storage, and anaerobic digestion have been identified as good options. However, these strategies are not widely adopted in Canada because clear information on their effectiveness to abate the whole-farm GHG footprint is lacking. Better information on the most cost-effective options for reducing on-farm GHG emissions would assist decision making for dairy producers and foster adoption of the most promising approaches on Canadian dairies. In this context, whole-farm modeling provides a tool for evaluating different GHG abatement strategies. An Excel-based linear optimization model (N-CyCLES) was used to assess the economics and the nutrient and GHG footprints of two representative dairy farms in Québec, Canada. The farms were located in regions with contrasting climates (southwestern and eastern Québec). The model was developed to optimize feeding, cropping, and manure handling as a single unit of management, considering the aforementioned mitigation options. Greenhouse gas emissions from the different simulated milk production systems reached 1.27 to 1.85 kg CO2e kg-1 of corrected milk, allowing GHG reductions of up to 25% compared to the base system described in Part I. Solid-liquid separation had the greatest GHG mitigation potential, followed by the digester-like strategy involving a tight cover for gas burning. However, both options implied a decrease in farm net income. Manure incorporation into the soil and composting were associated with high investment relative to their GHG abatement potential. The most cost-effective option was using a loose cover on the manure storage unit. This approach lessened the manure volume and ammonia-N volatilization, thereby reducing fertilizer and manure spreading costs, increasing crop sales and profit, and enhancing the whole-farm N and GHG footprints. Consequently, covering the manure tanks appears to be an economically viable practice for Québec dairy farms. Keywords: Anaerobic digestion, Composting, Dairy cow, Farm net income, Greenhouse gas emission, Incorporation, Nutrient footprint, Solid-liquid separation, Storage cover, Whole-farm model.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".