Zinc and boron nutrition management in fertigated high density apple orchards
Bibliographic record
Abstract
An experimental high density apple (Malus × domestica Borkh.) block (1666 trees ha-1) on M.9 rootstock was planted in 1992 and maintained until 1996 as a randomized, replicated split-plot experiment with 5 N-K fertigation treatments, each with subplots containing four apple cultivars (Gala, Fuji, Fiesta, and Spartan). Management of Zn and B nutrition varied throughout the experiment ranging from no application (1992–1993) to foliar applications (1994) to fertigation of 3.5 g Zn tree-1 and 0.34 g B tree-1 during the growing season in 1995–1996. Deficient concentrations of Zn and B were measured in leaves and "blossom-blast" B deficiency symptoms were observed within 2 yr without applications of Zn or B . Foliar application of both nutrients increased their respective leaf concentrations and ameliorated B-deficiency symptoms. Zinc-fertigation in 1995–1996 failed to improve leaf Zn concentration. In contrast, B-fertigation at the same time readily increased root zone soil solution B concentrations and increased leaf B concentrations to values within the sufficient-optimum range for apple. Generally, cultivars responded similarly to B and Zn-treatments although, relative to other cultivars, Spartan had higher concentrations of Zn and B in leaves and Fuji had high leaf B. Key words: Fertigation, leaf boron and zinc, Malus × domestica Borkh., soil solution boron
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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 | 0.001 | 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".