Limited effect of thermal pruning on wild blueberry crop and its root-associated microbiota
Bibliographic record
Abstract
Thermal pruning was a common pruning method in the past but has progressively been replaced by mechanical pruning for economic reasons. Both practices are known to enhance and maintain high yields; however, thermal pruning was documented to have an additional sanitation effect by reducing weeds and fungal diseases outbreaks. Nevertheless, there is no clear consensus on the optimal fire intensity required to observe these outcomes. Furthermore, fire is known to alter the soil microbiome as it impacts the soil organic layer and chemistry. Thus far, no study has investigated into the effect of thermal pruning intensity on the wild blueberry microbiome in agricultural settings. This project aimed to document the effects of four gradual thermal pruning intensities on the wild blueberry performance, weeds, diseases, as well as the rhizosphere fungal and bacterial communities. A field trial was conducted using a block design where agronomic variables were documented throughout the 2-year growing period. MiSeq amplicon sequencing was used to determine the diversity as well as the structure of the bacterial and fungal communities. Overall, yield, fruit ripeness, and several other agronomical variables were not significantly impacted by the burning treatments. Soil phosphorus was the only parameter with a significant albeit temporary change (1 month after thermal pruning) for soil chemistry. Our results also showed that bacterial and fungal communities did not significantly change between burning treatments. The fungal community was dominated by ericoid mycorrhizal fungi, while the bacterial community was mainly composed of Acidobacteriales, Isosphaerales, Frankiales, and Rhizobiales. However, burning at high intensities temporarily reduced Septoria leaf spot disease in the season following thermal pruning. According to our study, thermal pruning has a limited short-term influence on the wild blueberry ecosystem but may have a potential impact on pests (notably Septoria infection), which should be explored in future studies to determine the burning frequency necessary to control this disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".