Fifty years of sustainable no-tillage agriculture in the semi-arid Canadian Prairies
Bibliographic record
Abstract
Fifty years of sustainable no-tillage agriculture in the semi-arid Canadian Prairies Agriculture collapsed in the Canadian Prairies during the multi-year drought of 1926-1934. Two changes to local agriculture practice became critical in the recovery of top-soil and agricultural yield. One was abandonment of summer fallow, the other was adoption of no-tillage techniques pioneered in this region. We have obtained soil samples from commercial fields in cereal production (up to one century), from long-term experimental field-plots at research stations, from undisturbed prairies, and from secondary grasslands converted from agriculture. The data provides a chronosequence of fields about 40 years in continuous no-tillage, to contrast against fields in traditional tillage, against secondary grasslands, and undisturbed native prairie. For all samples, we measured free-living nitrogen fixation capacity (15N) in the laboratory, aggregate size distribution, microbial nitrogen fixing community (nif gene), and both bacteria (16S DNA) and eukaryote (18S DNA) diversity. We reconstructed eukaryote community structure and food web structure for the fields. Our results indicate that despite decades of continuous no-tillage, free-living nitrogen fixing capacity remains far below undisturbed prairies, but improved from ploughed fields. Soil aggregate size distribution remains lower in continuous no-tillage, but grasslands contain more larger-sized aggregates enabling more nitrogen fixation. Biodiversity indices follow a pattern of reduced diversity with increased disturbance from agriculture. Biodiversity improves with years into no-tillage or abandonment to secondary grasslands. Overall, we had anticipated a greater recovery of biodiversity, food web complexity, and of free-living nitrogen fixation in decades old continuous no-tillage fields, compared to continuous tillage. Nonetheless, the region of Canadian prairies in no-tillage has been resilient to cyclical droughts, and has accumulated soil organic carbon since adoption of no-tillage. The results are significant because about 95% of the area in cereal crops (~10 million Ha) is in no-tillage, and significant soil organic matter has accumulated in the agroecosystem to contribute to carbon storage to mitigate climate change.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".