Annual Bloom-time Phosphorus Fertigation Affects Soil Phosphorus, Apple Tree Phosphorus Nutrition, Yield, and Fruit Quality
Bibliographic record
Abstract
A randomized, complete block, split-plot experimental design with six replicates was established and maintained annually for the first five fruiting seasons (1999 to 2003) in a high-density apple [ Malus sylvestris (L) Mill var. domestica (Borkh.) Mansf] orchard on M.9 rootstock planted in Apr. 1998. Main plot treatments involved eight different nutrient regimes, each containing three tree subplots of each of five different cultivars (Ambrosia, Cameo, Fuji, Gala, and Silken). This report compares a +phosphorus (P) treatment, involving annual fertigation at bloom time of 20 g P/tree as ammonium polyphosphate (10N–15P–0K), to a −P treatment. Both treatments also received nitrogen, potassium, and boron nutrients through fertigation. Drip fertigation of P increased 2 M KCl-extractable P to 0.4-m depth within 0.5-m distance of the drippers. Leaf and fruit P concentrations were consistently increased by the +P treatment with few differences among cultivars. P-fertigated trees also had a 20% increase in cumulative yield overall cultivars during the first five fruiting seasons. Standard fruit quality measurements, including fruit size, soluble solids concentration, titratable acidity, and red coloration were unaffected by P application. However, reductions in incidence of water core at harvest, increased resistance to browning, and elevated antioxidant content of harvested fruit measured in some years imply a role for P in apple membrane stability. The cumulative results indicate that applications of 20 g P as ammonium polyphosphate annually at bloom would be advantageous for apples receiving adequate fertigated applications of nitrogen, potassium, and boron. Best apple performance was associated with leaf P concentrations above 2.2 mg·g −1 dry weight and fruit P concentrations between 100 and 120 mg·kg −1 dry weight.
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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 | 0.001 | 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".