Economic response of potato to nitrogen rate, timing of nitrogen application, cultivar, and irrigation
Bibliographic record
Abstract
Three multiyear studies were conducted in Manitoba, Canada to evaluate the effect of nitrogen (N) fertilizer rate (ranging from 0 to 225 or 0 to 240 kg N ha−1) and its interactions with timing of N application (preplant, split application), cultivar (Russet Burbank (RB), Glacier Fryer (GF), Umatilla Russet (UR)), and moisture regime (irrigated, nonirrigated) on the yield and net revenue (NR) of potato ( Solanum tuberosum L.). Based on soil test N, all sites were expected to be N-responsive, with soil test N at most sites ranging from 24 to 45 kg NO3-N ha−1 to 60 cm and measuring 70 and 117 kg NO3-N ha−1 to 60 cm at the remaining two sites. Linear and quadratic coefficients of N and irrigation were significant for yield and NR. However, the NR curves for N inputs were relatively flat, and the NRs were only slightly less than the optimal NR within the vicinity of the optimum. Split N applications performed similarly to preplant N, and GF performed better than RB or UR; however, GF optimal economic N rates were about 55% higher than the optimal economic N rates of RB and UR cultivars. Optimal economic N rate for the best potato practices ranged from 157 to 216 kg N ha−1, depending on the studies; or averaging at about 188 kg N ha−1. Adoption of these best N management practices will improve profitability and N use efficiency in potato production and reduce negative environmental impacts.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".