The development and adoption of conservation tillage systems on the Canadian Prairies
Bibliographic record
Abstract
One of the major agricultural innovations on the Canadian Prairies over the last 40 years has been the introduction of conservation tillage (CT). Conservation tillage-a system that includes minimum and zero tillage (ZT) -was introduced as an alternative to traditional (conventional) tillage (TT) to control soil degradation and to promote agricultural sustainability. The development and adoption of CT systems involved pioneer farmers, engineers, scientists, and farmer associations. By the end of the 1970s, CT started to take shape on the Prairies, but for a number of economic, technical, political and social reasons, the adoption of CT did not occur on any major scale before the 1990s. Today, more than 75% of the Prairie's cropland is under some form of CT with more than 50% under ZT. In this paper, the factors behind the development and adoption of conservation tillage technology on the Prairies in the period between 1930 and 2011 are reviewed. Then, some of the benefits of the adoption of CT on the Prairies are highlighted. The data show that CT and ZT became profitable for the majority of farmers during and after the 1990s, and that the increased use of CT contributed to the dramatic decrease in the area under summerfallow and to the increase in the area sown to canola and pulse crops. These changes contributed to the reduction of all forms of land degradation and to decreases in agricultural greenhouse gas (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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".