Predicting nitrogen fertilizer requirements of potatoes in Atlantic Canada with soil nitrate determinations
Bibliographic record
Abstract
Nitrogen greatly affects potato ( Solanum tuberosum L.) yield, but excess N can result in environmental degradation. In this study soil nitrate (NO3-N) content was determined pre-plant to predict fertilizer N requirements of potatoes in Atlantic Canada and in mid-season to adjust N fertilization during the growing season. Soil NO3-N contents were measured to a 0.30-m depth in spring prior to planting at four on-farm sites in each of 3 yr (1995 to 1997) in the upper St. John River Valley of New Brunswick, Canada. Mid-season soil NO3-N contents at a 0–0.30 m depth were also determined (32–47 days after planting) at two sites in three N treatments in 1995 (0, 50, and 250 kg N ha-1) and in four N treatments in 1996 and 1997 (0, 50, 100, and 250 kg N ha-1). The yield response of potatoes to six rates of N fertilization (0–250 kg N ha-1) with and without supplemental irrigation was used to determine the economically optimum N application (Nop). The pre-plant spring soil NO3-N test alone could not adequately predict the N requirements of potatoes in Atlantic Canada; the Nop and relative yield were poorly correlated (0.07 < R2< 0.52) with spring soil NO3-N content. The mid-season soil NO3-N test, however, could be used to determine the need for supplemental N fertilizer; NO3-N content correlated well (0.44 < R2< 0.68) with the relative yield for total and marketable yield. We suggest a critical mid-season value of 80 mg NO3-N kg-1 soil for marketable yield, above which additional N application might not be necessary. Key Words: N fertilizer, nitrate, Nop, relative yield, Solanum tuberosum, critical value
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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 | 0.000 | 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".