Efficiency of Controlled‐Release Urea for a Potato Production System in Quebec, Canada
Bibliographic record
Abstract
Proper N management is essential to prevent N losses and ensure high potato (Solanum tuberosum L.) yield and quality. Controlled‐release urea (CRU) could increase nitrogen use efficiency (NUE) by matching the release of N with potato N uptake. This 3‐yr study conducted in Quebec, Canada, compared four treatments, namely an unfertilized control (0 N), calcium ammonium nitrate (CAN) application at rates of 150 kg N ha−1 (150CAN) and 200 kg N ha−1 (200CAN), and CRU application at a rate of 150 kg N ha−1 (150CRU). The effects of treatments were assessed on potato yield, specific gravity, NUE, and chlorophyll meter readings (CMR) for two cultivars (Goldrush and Chieftain) along with soil nitrate adsorbed on anion exchange membranes (NO3AEMs). Marketable yield and the yield of jumbo and medium size classes significantly increased with N fertilization up to 150 kg N ha−1, and yields were higher with 150CRU than with 150CAN or 200CAN. Nitrogen fertilization increased CMR, but had no effect on specific gravity. The CRU continually released more nitrate during the growing season as indicated by higher NO3AEMs values, and had higher NUE compared with other treatments. Relative yield (RY) was significantly related by linear‐plateau functions to CMR and NO3AEMs measured 40 to 50 d after planting, hence providing the critical values of 41.9 for CMR and 15 μg cm−2 d−1 for NO3AEMs, above which the probability of a yield response to additional N is low. Controlled‐release urea is a promising N source for increasing the yield and NUE of potatoes produced in eastern Canada.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".