Wood-based biochar for small fruit production in southern Quebec, Canada
Bibliographic record
Abstract
The market for small fruits (grapes, blueberries, strawberries, and raspberries) is valued at more than $100 million CAD per year in Quebec, Canada. Wood-based biochar is an amendment that improves soil quality, which may boost small fruit growth and production. The objective of this research was to determine if wood-based biochar could increase the yield and quality of grape, blueberry, strawberry, and raspberry in southern Quebec. We evaluated the fruit yield as well as the quality parameters like average fruit weight, fruit firmness, color, juice pH, total soluble solids, total phenolic content, and antioxidant activity. Field trials were established on commercial farms with grape, blueberry, strawberry, and raspberry production systems in plots that received wood-based biochar and no biochar in spring (April–May). Small fruits were harvested at their ripening stage for yield and quality evaluation in the fall (July–October). Biochar application did not improve yield and quality parameters of small fruits, except it gave a marginally higher yield of grape (3.7 vs. 4.4 t·ha −1 , P = 0.08) and a higher average fruit weight of strawberry (11.9 vs. 13.2 g, P < 0.05). There was no difference in the quality of the fruit from biochar-amended and control plots in the first year of biochar application, possibly because the quality parameters are affected more by weather conditions, handling, and storage than by soil quality. This work suggests that wood-based biochar (<5 t·ha −1 ) has a limited first-year impact on small fruit yield and quality in southern Quebec, Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".