Prendre soin des sols en production de pomme de terre au Canada – partie 1 – travail du sol et amendements
Bibliographic record
Abstract
Caring for soil in potato production in Canada -Part 1 -Tillage and soil amendmentsPotato production has been associated with environmental issues such as increased nitrogen leaching and soil degradation especially during the wet periods when ground cover and crops are absent.To address these issues, Agriculture and Agri-Food Canada (AAFC) scientists, in collaboration with academia, growers, and other industry professionals, examined how various regenerative soil management practices, such as tillage and soil amendments (manure and compost) can enhance potato productivity and soil health, while reducing nitrate leaching.Under the Living Laboratory Atlantic project, the team compared primary non-inversion shallow tillage regime (PNIST), which leaves more crop residue on the soil surface, with conventional moldboard plow.Compared to moldboard plow, after only one growing season of adopting PNIST, the team found improvement in soil structure (the soil was more stable and less prone to erosion), amount of active carbon (the fraction of soil carbon readily available as a food and energy for the soil microbial community), and available nitrogen.While potato yields were comparable between the two tillage regimes, the PNIST helps enhance the environmental sustainability of potato production through improvements in soil health.Adding manure and compost to the soil is another effective way to regenerate soil health.The team demonstrated that one single application of cow manure (using a moderate rate of 20 metric tons per hectare) increased total and marketable potato yield by 28 and 26%, respectively, and soil nitrogen supply by an average of 44%.When amounts of available conventional organic amendments are limited, producers can use alternative amendments.This team found that applying willow chips to the soils after potato harvest increased soil carbon and soil stability (making it less prone to erosion) and added potassium to the soil.They also
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".