Rate and timing of nitrogen fertilization of Russet Burbank potato: Yield and processing quality
Bibliographic record
Abstract
Split fertilizer N application is thought to improve fertilizer N use efficiency of potato, primarily by reducing NO3 leaching losses. This study evaluated the effects of the rate and timing of N fertilization on yield and processing quality of Russet Burbank potato under rain-fed production. Trials conducted in 1999–2001 included different fertilizer N rates (0–160 kg N ha-1 in 1999 and 0–200 kg N ha-1 in 2000 and 2001) applied either at planting according to normal grower practice, or at hilling, the latest time that granular fertilizer can practically be applied. Tuber total and marketable yield, size distribution, specific gravity, fry colour and tuber concentrations of NO3, sucrose and glucose were measured. Increasing rates of N fertilization increased tuber yield and tuber size, increased tuber NO3 concentration and decreased tuber specific gravity, but had little effect on tuber sugar concentrations or fry colour. Season-to-season variations in total tuber yield responses to N fertilization rate were attributed primarily to variation in soil N supply. Timing of N application had little effect on tuber yield, size distribution or processing quality under adequate soil moisture conditions. However, under dry soil conditions, split N application reduced tuber yield and tuber size. Key words: Solanum tuberosum, tuber specific gravity, tuber nitrate concentration, fry colour
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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) | 0.001 | 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".