District Scale GHG Emission Indicators for Canadian Field Crop and Livestock Production
Bibliographic record
Abstract
The three main farm products from Canadian agriculture, i.e., proteins, vegetable oils, and carbohydrates, account for 98% of the land in annual crops in Canada. The intensities and efficiencies of these field crops in relation to their Greenhouse Gas (GHG) emissions were assessed for their value as land use change indicators. To facilitate spatial comparisons, this assessment was carried out at the Ecodistrict (ED) scale. The Unified Livestock Industry and Crop Emissions Estimation System (ULICEES) model was modified to operate at the ED scale, and used to quantify the GHG emission intensity of protein. GHG emissions were also calculated for plant products not used for livestock feed. The livestock GHG emissions and GHG-protein intensities estimated using ED scale inputs to ULICEES were reasonably close to GHG-protein intensities generated by the version of ULICEES driven by provincial scale census data. Carbohydrates were split into two groups, i.e., whether or not they supported livestock. Annual farm product data at 5-year intervals were used to generate GHG emissions from all farm operations. The range of GHG emissions from all farm operations in Western Canada was from 42 to 54 Mt CO2e between in 1991 and 2011, while GHG emissions from livestock ranged from 22 to 34 Mt CO2e over the same period. The Eastern Canadian GHG emissions from all farm operations declined gradually from 24 to 22 Mt CO2e over the period, with most of the eastern GHG emissions being from livestock. Ruminant livestock accounted for most of the livestock GHG emissions, particularly in the west. Provincial scale GHG emission efficiencies of the four farm product groups were assessed on a per-unit of GHG emissions basis for 2006. The most GHG-efficient province for protein was Ontario, whereas the most GHG-efficient province for all three plant products was Saskatchewan. The coastal provinces were the least GHG-efficient sources of all four farm product groups.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".