Response of high density apple orchards on coarsetextured soil to form of potassium applied by fertigation
Bibliographic record
Abstract
This study tested the effects of fertigated potassium sources on orchard cation status. A randomized, complete block experimental design was maintained from 2000 to 2003, in a high density ‘Jonagold’/M.9 apple (Malus × domestica Borkh.) orchard planted in 1993 on a loamy sand. Seven K-fertigation treatments included annual application of either no K (control), 15 g K/tree as either potassium chloride or potassium magnesium sulphate (KMag) or 30 g K/tree as potassium chloride, KMag, potassium sulphate or potassium thiosulphate, applied daily during 6 wk midsummer to six replicate, four-tree plots. Fertigated K-forms did not affect yield, but increased soil K after 3 yr to 30-cm depth beneath the drip emitters. This increased leaf and fruit K concentrations. Fruit K/Ca ratio was also increased by K-fertigation. A high incidence of bitter pit at harvest was unaffected by fertigating K, but rather was associated with low harvest fruit Ca concentration and large fruit size. KMag increased soil Mg availability, but leaf and fruit Mg concentrations were slightly affected, indicating the difficulties of improving apple Mg status when co-applying K. Leaf and fruit Ca concentrations were minimally affected by treatments. Soil Ca declined slightly after 3 yr of K fertigation. Key words: Bitter pit, calcium, chloride, magnesium, Malus × domestica, sulphate, thiosulphate
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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 | 0.000 | 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".