Nitrate Leaching and Potato Yield under Varying Plow Timing and Nitrogen Rate
Bibliographic record
Abstract
Core Ideas Fall plowing created more post–fall‐plowing nitrate leaching than spring plowing. Fall or spring plowing did not influence post–potato‐harvest nitrate leaching and potato yield. Increasing fertilizer N input to potato crops increased post–potato‐harvest nitrate leaching. Increasing fertilizer N input to potato crops suppressed potato yield. Excessive nitrate leaching from potato (Solanum tuberosum L.) production has been linked to groundwater nitrate contamination and eutrophication in receiving surface water. This study was conducted to assess the effects on nitrate leaching and potato yield of delaying red clover (Trifolium pratense L.) plowing from late fall to spring in a barley (Hordeum vulgare L.)–red clover–potato rotation and varying additions of fertilizer N to potato crops in Prince Edward Island, Canada, between 2014 and 2017. The experiment used a split‐plot arrangement in a completely randomized design with three replications. A stainless steel lysimeter was installed in each plot to collect soil water to test nitrate concentrations in leachate. Nitrate leaching occurred primarily between late fall, after the potato harvest, and in spring. Delaying forage plowing from late fall to spring reduced post–fall‐plowing nitrate leaching but had no influence on either post–potato‐harvest nitrate leaching or potato yield. Increasing fertilizer N input to the potato crops was shown to not only increase the risk of excessive nitrate leaching but also to suppress potato yield. Higher levels of soil N supplies from the mineralization of the plowed‐down red clover and soil organic matter to the potato crops in combination with a shorter than ideal growth period for the long‐season Russet Burbank potato appear to create overfertilizing situations, resulting in excessive post–potato‐harvest nitrate leaching and suppressed yields. Adequately accounting for soil N supplies is critical for enhancing potato productivity while minimizing nitrate leaching.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".