Under-seeding potato with nurse crops in eastern Canada: challenges and opportunities
Bibliographic record
Abstract
Soil erosion can be a serious issue in eastern Canada during the 3–5 wk that it takes for potato (Solanum tuberosum L.) to emerge under the cool, humid climatic conditions with frequent heavy rainfall events. Seeding a fast-growing nurse crop at the same time as the potato crop can hold the soil particles in place, reduce surface crusting, and increase water infiltration. The objective of this study, conducted in Prince Edward Island and in New Brunswick in 2017, was to evaluate the effects of under-seeding potato with barley (Hordeum vulgare L.) and winter rye (Secale cereale L.) on marketable potato yield, nitrate dynamics during the growing season, and soil moisture content. Nurse crop growth was terminated mechanically (hilling), with a selective herbicide, or with a nonselective herbicide. Yield increases ranging from 9% to 91% were observed when nurse crop growth was terminated using a nonselective herbicide at both sites. Inconsistent results were obtained when a mechanical method or a selective herbicide were used, with marketable yield reduced at one site and no effect on yield at another site. There was a trend toward higher soil nitrate contents under the control than under the nurse crop treatments, though it was not consistent across sampling times. Results demonstrated that there are circumstances under which nurse crops can be successfully integrated into a potato-based system and provided future hypotheses to test. Potential confounding factors that can impact the nurse crop efficiency are discussed.
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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 | 0.001 |
| Science and technology studies | 0.004 | 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".