Spray-on-mulch Technology for Intensively Grown Irrigated Apple Orchards: Influence on Tree Establishment, Early Yields, and Soil Physical Properties
Bibliographic record
Abstract
Use of and interest in organic mulches for both integrated fruit production (IFP) and organic fruit production is increasing given recent efforts to reduce pesticide inputs and improve soil health. A series of four experiments was conducted in the southern interior of British Columbia over 5 years to investigate the use of a spray-on-mulch (SOM) slurry, comprised primarily of recycled waste newsprint fiber, as an effective method to control excessive weed competition and enhance tree establishment and performance. In four experiments, ‘Gala’, ‘Granny Smith’, ‘Ambrosia’, and ‘Honeycrisp’ apple ( Malus × domestica ) trees on ‘Malling 9’ (‘M.9’) rootstock were exposed to a series of treatments including a glyphosate check, SOM waste paper, SOM over an organic underlay, SOM incorporated with dichlobenil or tackifier, SOM over black landscape fabric, rowcover cloth, or polyethylene plastic. SOM provided superior weed control in comparison with the glyphosate check treatment, a standard orchard practice in many modern orchards in North America. SOM application over compost, paper, and especially over cloth barriers were found to be more effective weed barriers than SOM alone. In comparison with glyphosate checks, SOM improved tree growth during tree establishment. Although the addition of dichlobenil provided season-long weed control, tree growth was diminished in comparison with SOM alone and remained similar to that of the glyphosate checks. There was little or no benefit of including a 2.5% tacking agent to help improve SOM integrity and long-term surface stability. When applied to bearing 4-year-old trees, SOM provided similar tree vigor as glyphosate checks over four growing seasons. The addition of landscape fabric, plastic, or cloth underlay material in combination with SOM improved tree vigor in formative years, but this benefit diminished over time. SOM-treated trees had greater cumulative yields over glyphosate checks after 3 years of production. SOM provided significant temperature moderation during the summer and winter months and provided moisture conservation during the summer. There were few SOM effects on plant nutrient status.
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 | 0.000 |
| 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 | 0.001 | 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".