Integrated effects of anaerobic soil disinfestation and beneficial microbes in strawberry production
Bibliographic record
Abstract
Fruit rot diseases, including anthracnose fruit rot and Botrytis fruit rot caused by Colletotrichum acutatum and Botrytis cinerea, respectively, pose major challenges to sustainable strawberry production in the United States. Organic and small-scale growers require non-chemical alternatives to soil fumigation due to health, regulatory, or technical constraints. This study evaluated anaerobic soil disinfestation (ASD), beneficial bacteria (Bacillus velezensis IALR619 and TerraGrow-a commercial product), and their combinations as integrated strategies to manage fruit rot, suppress weeds, and enhance fruit quality. A split-plot field trial was conducted over two growing seasons (2022/23-2023/24) at the Hampton Roads AREC in Virginia Beach, VA. ASD significantly reduced AFR, BFR, and overall fruit rot incidence compared to non-fumigated and Pic-Clor 60-fumigated controls. Post-plant inoculation with B. velezensis IALR619, TerraGrow, or TerraGrow + Oxidate 5.0 further decreased AFR incidence. ASD also reduced weed density and improved postharvest fruit firmness, total soluble solids, and juice pH. B. velezensis IALR619 inoculation enhanced fruit firmness compared to uninoculated controls. The beneficial microbes with ASD offer a promising alternative to synthetic fumigation, reducing fungicide usage by organic growers, small farms, and resource-limited growers, while also improving strawberry fruit quality.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".