Response of Apple to Fertigation of N and K under Conditions Susceptible to the Development of K Deficiency
Bibliographic record
Abstract
A split-plot experimental design was imposed in the year of planting and maintained for the first five growing seasons in a high density apple orchard on M.9 rootstock planted at 1.5 m (within row) × 4 m (between row) in a loamy sand soil susceptible to K deficiency when drip-irrigated. Four N-K fertigation treatments involving low (N1) and high (N2) rates of N combined with 0 (K0) or 15 g K/tree per year (K1) were applied in five replicated and randomized main plot units. Subplots consisted of three-tree plots of each of the apple cultivars Gala, Fuji, Fiesta and Spartan. Soil solution monitoring indicated the maintenance of distinctly different soil solution N and K concentrations in the respective N-K treatments during the study. The most important plant response was prevention of the development of K deficiency by the K1-fertigation treatment. Fertigation of 15 g K/tree generally increased leaf K, fruit K and Mg concentrations, fruit size and yield and fruit titratable acidity and red coloration at harvest for all cultivars. K fertigation also decreased leaf Mg and B concentrations, fruit N, P and Ca concentration and fruit firmness. In addition to leaf K concentrations <1%, K deficiency was associated with fruit K concentrations <100 mg/100 g fresh weight and soil solution K concentration <5 mg·L -1 . Increasing the rate of fertigated N when growth was constrained by K deficiency increased leaf N and Mn and decreased leaf P and B, but had no effect on tree vigor or fruit production and quality.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".