Short-Term Impact of Elemental Sulfur on Cranberry Nutrition and Crop Performance
Bibliographic record
Abstract
Cranberry (Vaccinium macrocarpon Ait.) is an ammophilous plant grown on acid soils (pH 4.0 - 5.5). Elemental sulfur is commonly applied at a recommended rate of 1120 kg S ha−1 per pH unit to acidify cranberry soils, potentially impacting the plant mineral nutrition. The general recommendation may not fit all conditions encountered in the field. Our objective was to develop an equation to predict the sulfur requirement to reach pHwater of 4.2 to tackle nitrification in acidic cranberry soils varying in initial pH values, and to measure the effect of elemental sulfur on the mineral nutrition and the performance of cranberry crops. A 3-yr experiment was designed to test the effect of elemental sulfur on soil and tissue tests and on berry yield and quality. Four S treatments (0, 250, 500 and 1000 kg S ha−1) were established on three duplicated sites during two consecutive years. We ran soil, foliar tissue, berry tissue tests, and measured berry yield, size, anthocyanin content (TAcy), Brix, and firmness. Nutrients were expressed as centered log ratios to reflect nutrient interactions. Results were analyzed using a mixed model. Soil Ca decreased while soil Mn and S increased significantly (p ≤ 0.05). Sulfur showed no significant effects on nutrient balances in uprights. The S impacted negatively berry B balance, and positively berry Mn and S balances. A linear regression model relating pH change to S dosage and elapsed time (R2 = 0.53) showed that to reach pHwater of 4.2 two years after S application, 250 - 1000 kg S ha−1 could be applied depending on initial soil pH value. The stratification of surface-applied elemental S in the soil profile should be further examined in relation to plant rooting and nutrient leaching.
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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 | 0.001 |
| 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 | 0.000 | 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".