(164) The Influence of Mulch Applications on Vegetation Dynamics in Organically Managed Highbush Blueberry
Bibliographic record
Abstract
Within-row weed management of highbush blueberry ( Vaccinium corymbosum L.) is reliant upon herbicide applications. However, in organic production, herbicides are typically not permitted and alternative methods must be used. The impact of thick (25-cm) mulch applications on weed pressure in an organic production system was initiated at a commercial operation in Nova Scotia, Canada, during 2005. A split-plot experimental design was used with five blocks (replications), six treatments, and five plants per split plot (cv. Duke). The whole-plot factor consisted of mulch/fertility treatments and included: i) control (no amendment); ii) ammonium sulphate fertilizer (30 kg·ha -1 N); iii) pelletized poultry manure (60 kg·ha -1 N); iv) pine needles (80 t·ha -1 ); v) horse manure and sawdust compost (550 t·ha -1 ); and vi) seafood waste compost (360 t·ha -1 ). The split-plot factor consisted of level of hand weeding (–/+). Weed control was assessed by sampling percent ground cover and weed shoot biomass in three 0.25-m 2 quadrats in nonweeded subplots. Blueberry leaf N content, plant canopy volume, and berry yield (fresh weight and number) were recorded. The manure/sawdust compost and pine needle treatments had the lowest weed biomass and percent ground cover values, thereby providing the best weed control. Weed shoot biomass, blueberry leaf N, plant canopy volume, and berry yield were greatest in the seafood waste compost treatment. Results from this preliminary study indicate the potential of using these groundcover treatments to improve organic cultural management practices.
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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 | 0.000 |
| 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) | 0.001 | 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".