Consequences of potassium, magnesium sulphate fertilization of high density Fuji apple orchards
Bibliographic record
Abstract
Neilsen, G. H. and Neilsen, D. 2011. Consequences of potassium, magnesium sulphate fertilization of high density Fuji apple orchardsConsequences of potassium, magnesium sulphate fertilization of high density Fuji apple orchards. Can. J. Soil Sci. 91: 1013–1027. Three annual broadcast fertilizer treatments of 0, 100 or 200 kg K ha –1 as K, Mg sulphate (KMag) were applied in a randomized complete block design with six replicate multi-tree plots. The study was undertaken for three successive growing seasons in eight commercial apple orchards of fruiting ‘Fuji’/M.9 (Malus × domestica Borkh.) located on coarse-textured soils typical of the Okanagan valley fruit production region of southern British Columbia. After 3 yr, KMag fertilization increased the amount and proportion of exchangeable K and Mg at the 0–10 and 10–20 cm depth. Soil exchangeable Ca was generally decreased in the surface 0–10 cm layer although the Ca/Σ[Ca + Mg + K] ratio decreased over the surface 20 cm. During the study, application of KMag fertilizer often increased leaf and fruit K concentrations, minimally affected leaf Mg and sometimes decreased fruit Ca concentration by years 2 and 3 and had little effect on fruit K/Ca or Mg/Ca ratio. KMag fertilization was effective in an orchard of marginal K nutritional status, increasing cumulative yield, fruit size and red colouration, implying an economic response to the K contained in the fertilizer. KMag fertilization was effective for maintaining leaf Mg concentration, but could not be relied upon to increase deficient leaf Mg. There were no widespread declines in fruit Ca concentration, nor increases in Ca-related harvest disorders after 3 yr of KMag fertilization, despite declines in exchangeable Ca and Ca/Σ[Ca + Mg + K] in surface soil layers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".