How management practices influence vegetative and reproductive plant traits of wild lowbush blueberry species
Bibliographic record
Abstract
Optimizing agricultural practices is an effective way to increase fruit productivity in commercial wild lowbush blueberry ( Vaccinium angustifolium Aiton; Vaccinium myrtilloides Michx) fields, but results from northern Quebec (Canada) are scarce. In this study, we assessed the effect of the main crop management practices, namely pruning method (mechanical and thermal), fungicide (with and without), and fertilization (mineral, organic, and without) on key vegetative and reproductive plant traits of both wild blueberry species. The experiment was conducted from fall 2016 to fall 2018, when the combination of pruning, fungicide, and fertilizing was applied. Results show that fertilizer application was the main management practice affecting vegetative and reproductive plant traits followed by fungicide application effects during pruning years only. Mineral fertilizer improved plant traits to a greater extent than organic fertilizer during the pruning phase only, and no significant differences in the second year after application (harvesting phase) suggest a delayed but similar final effect of organic fertilizer. Results also showed that V. myrtilloides produces taller stems with more leaves compared to V. angustifolium, whereas V. angustifolium produces more flower buds, a key reproductive plant trait. Results also highlight the fact that V. angustifolium needs both fertilizer and fungicide to keep leaves on the stem during late summer, whereas V. myrtilloides needs either fertilizers or fungicides. This study also shows that pruning method has no significant effect on any of the measured plant traits. However, we believe that long-term studies are still needed to assess the impact of pruning method over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".