Measurement and modelling of tillage and water erosion within intensive potato production systems of northwestern New Brunswick, Canada
Bibliographic record
Abstract
In Canada, there is growing acceptance that tillage erosion is a serious form of soil degradation and a threat to the sustainability of agriculture across the country. To date, the potential for tillage erosion within potato (Solanum tuberosum L.) production systems has not been investigated. To address this issue, field experiments were conducted in northwestern New Brunswick to generate tillage translocation and erosivity values for primary, secondary and "tertiary" (i.e., field operations conducted during planting, hilling and harvesting) tillage implements commonly used for potato production. The potential for tillage erosion was equally high for the mouldboard plough, chisel plough and offset disc, and larger than that for the vibrashank. Surprisingly, tertiary field operations moved soil further and were more erosive than primary and secondary tillage operations, alone or combined. Overall, the risk of tillage erosion during the production of potatoes is considerably greater than that for other major cropping systems in Canada. Water erosion is also a serious problem within the potato producing regions of Atlantic Canada. However, to date, no previous studies have looked at the impact of both tillage and water erosion on total soil erosion within potato production. Using repeated-measurements of the fallout radionuclide cesium-137 (137Cs), annual soil losses between 1990 and 2005 at a New Brunswick benchmark site were 13.6 Mg ha-1 yr-1, with approximately half of the mapped field having soil losses greater than the tolerable soil loss limit of 6 Mg ha-1 yr-1. A new Directional Tillage Erosion Model (DirTillEM) was used to account for the apparent effect of tillage direction and field boundaries on soil redistribution at this field site. Overall, DirTillEM predictions improved relationships between 137Cs redistribution and estimated soil erosion over those determined by two previously published wate
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".