Effect of wood-based biochar on soil quality, small fruit yield and quality in southern Quebec, Canada
Bibliographic record
Abstract
The small fruit market (grapes, strawberries, blueberries and raspberries) is valued at more than $100 million per year in Quebec, Canada. Farmers seek to produce small fruits with high yield and quality. Wood-based biochar could be used as a soil amendment to improve soil quality, which may promote small fruit growth. The objectives of this research were to (1) determine if wood-based biochar can increase the yield and quality of grape, strawberry, blueberry and raspberry in southern Quebec, (2) present a simple evaluation system to compare fruit quality in biochar plots versus control plots, and (3) determine if wood-based biochar application resulted in a long-term improvement of soil physical and chemical properties in a vineyard in southern Quebec. It was hypothesized that wood-based biochar will boost the yield of grape, strawberry, blueberry and raspberry, and improve quality parameters like average fruit weight, fruit firmness, colour, juice pH, total soluble solids (TSS), total phenolic content (TPC) and antioxidant activity, for optimal fruit quality. Furthermore, it was hypothesized that that soil physical and chemical quality improvements will be detectable several years after applying wood-based biochar. The field trials for grapes, strawberries, blueberries and raspberries were established on commercial farms in southern Quebec. Plots received biochar (n=5 per site) and no biochar (n=5 per site) in April to May 2013. Small fruit yield was assessed throughout the fruit harvest period and samples were collected during September to October 2013 for fruit quality evaluation. Soil samples were collected from the vineyard in July 2018 to assess the long-term impact of wood-based biochar on soil quality. Average fruit weight of strawberry was significantly (P<0.05) greater with the wood-based biochar application, possibly due to plant-available nutrients supplied by biochar, but there was no effect of biochar on the yield or quality of other small fruits. Grapes and raspberries had good fruit quality, similar to published ranges, in both biochar-amended and control plots. Strawberry TSS and TPC values were suboptimal due to cold weather condition and air exposure during fruit storage. Large blueberry fruit size in biochar-amended and control plots suggests that the crop should be profitable when sold as fresh blueberry. Among soil physico-chemical properties, only soil bulk density, extractable magnesium (Mg) and extractable boron (B) concentrations were significantly (P<0.05) affected by the application of wood-based biochar applied five years earlier. Wood-based biochar application rates were considered to be too low to affect the small fruits growth, but still contributed to alleviate soil compaction by reducing soil bulk density and reduce nutrient loss by absorbing some nutritive elements (e.g., Mg and B) after five years. Overall, wood-based biochar is not expected to be an effective soil amendment to improve small fruit yield and quality in southern Quebec, Canada.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".