Contribution of land use practices to GHGs in the Canadian Prairies crop sector
Bibliographic record
Abstract
The global crop sector is estimated to contribute about 10.4% of global GHGs annually. The Canadian crop sector is assessed as adding about 6.5% to total national emissions. These estimates over report the impact of farming as they ignore the complex interaction of cropping with the environment and the role land use, land use change and forestry (LULUCF) play in sequestering carbon. This study quantifies the contribution of land use to GHG emissions and removals in the Canadian Prairies crop sector between 1985 and 2016. The modeling effort explores how different farming practices (i.e., conventional tillage (CT), minimum tillage (MT), zero tillage (ZT), summerfallow, crop rotations, and residue retention) and input usage rates (i.e., fertilizer and fuel) affect GHG emissions in different soil climate zones and provinces in the Prairies region. The adoption of sustainable practices led to an 80% decline in GHG emissions in the crop sector between 1985 and 2016. Since 2005, the baseline for Canada's Paris commitment, sectoral emissions dropped 53%, more than is required to meet the 2030 target. Most promising, the crop sector was a net GHG sink between 2013 and 2016 in Alberta and between 2006 and 2016 in Saskatchewan. As positive as these developments have been, more can be done by directing research to identify options for reducing GHGs in Manitoba (which made only minimal improvements as farmers there faced conditions requiring continuous use of conventional tillage practices), to explore better nitrogen management (a major continuing source of GHG from cropping) and by searching for low carbon transport options.
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.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".