The combined effects of thermal pruning, fungicide, and fertilizer applications on lowbush blueberry
Bibliographic record
Abstract
Wild lowbush blueberries ( Vaccinium angustifolium Ait. and Vaccinium myrtilloides Michx.) yields are highly variable since they depend on several factors, some of which can be influenced by growers with different agricultural practices. The primary objective of this research was to evaluate the effects of three key management practices on the yield of lowbush blueberry, as well as on related variables, including the number and height of stems, the nutrient status of the plants, and the presence of crop pests. Over 4 years, 12 combinations of three practices were tested and replicated four times at two sites in Normandin (Quebec, Canada). The practices included (i) type of pruning (mechanical or thermal), (ii) use of fungicide (with or without), and (iii) application of fertilizer (mineral, organic, or none). Thermal pruning did not enhance fruit yield or any other evaluated parameters compared to mechanical pruning. Throughout the years, fungicide applications caused a yield gain of about 212 kg ha −1 year −1 , a decrease in disease rate ( Sphaerulina leaf spot), and an increase in stem density when combined with mineral fertilizer. Mineral fertilizer also reduced the incidence of Sphaerulina leaf spot, improved the plant nutrient status, and caused a gain in yield of about 853 kg ha −1 year −1 . Organic fertilization improved fruit yield by about 691 kg ha −1 year −1 . Finally, the results indicated that applying fungicide increased the export of macronutrients in harvested fruits, highlighting the necessity for long-term monitoring of nutrients.
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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) | 0.001 | 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 | 0.000 |
| 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".