Potato Response to Nitrogen Sources and Rates in an Irrigated Sandy Soil
Bibliographic record
Abstract
Management strategies to reduce N losses to the environment from potato ( Solanum tuberosum L.) production while maintaining yields depend on selecting the right N source and rate. A 5‐yr (2008‐2012) field experiment was conducted on an irrigated sandy soil in Quebec, Canada, to examine the effect of N fertilizer source and rate on total (TY) and marketable tuber yield (MY), total plant N accumulation (vines + tubers), specific gravity, culls (unmarketable tubers), and apparent fertilizer N recovery (ANR). The treatments included an unfertilized control, and three N sources [ammonium nitrate (AN), ammonium sulfate (AS), and polymer‐coated urea (PCU)] applied at four rates (60, 120, 200, and 280 kg N ha −1 ). The PCU was applied 100% at planting and the AN and AS were applied 40% at planting and 60% at hilling. The TY and MY increased with N rate up to 200 kg N ha −1 , but were similar among the N sources. On average, total plant N accumulation and ANR were greater for AN and PCU than AS. However in 2008, when there was a greater risk of N loss due to high rainfall, total plant N accumulation and ANR were greater for PCU than AN and AS. Tuber specific gravity and culls were influenced by N rate, but the response was dependent on soil and climatic conditions. Results suggest that, under humid conditions with irrigation, a one‐time application of PCU in potato production can minimize the risk of N loss without reducing tuber yield and quality.
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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) | 0.001 | 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".