Nitrogen Fertigation Concentration and Timing of Application Affect Nitrogen Nutrition, Yield, Firmness, and Color of Apples Grown at High Density
Bibliographic record
Abstract
A randomized complete block, split-plot experiment with six replicates was established and maintained for the first six fruiting seasons (1999 to 2004) in a high-density apple [ Malus sylvestris (L.) Mill var. domestica (Borkh.) Mansf.] orchard on M.9 rootstock planted in Apr. 1998. This report assesses responses to six main-plot fertigation treatments, each containing three tree subplots of five different cultivars (Ambrosia, Cameo, Fuji, Gala, and Silken). Fertigation treatments were a factorial combination of two nitrogen (N) rates and three N application timings. N was applied at low (28 mg N/L) or high (168 mg N/L) concentrations daily at 0 to 4, 4 to 8, or 8 to 12 weeks after full bloom (wafb). Under greater N inputs, all cultivars had increased midsummer leaf and harvested fruit N concentrations, decreased fruit firmness, and in heavy crop years, decreased percent red color. Annual yield of all cultivars was significantly increased by N rate in a single year, but their cumulative yields were not different between treatments as a result of rate or timing. Altering the timing of N application within 12 wafb only affected leaf and fruit tissue N concentration. Leaf N was higher after 4 weeks of fertigation any time, although concentrations declined over the growing season, reaching minimum values around harvest. Fruit N was increased by fertigation 4 to 12 wafb. Yield, fruit firmness, and color were unaffected by fertigation timing. Critical fruit quality issues for ‘Gala’ and ‘Silken’ were small fruit size, for Ambrosia low fruit numbers, and for ‘Cameo’ soft fruit. ‘Fuji’, which achieved high yield and leaf N concentration and firm fruit, had poor red color regardless of N treatments.
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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) | 0.001 | 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 | 0.001 |
| 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".