Annual, Off-season Raspberry Production in Warm Season Climates
Why this work is in the frame
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Bibliographic record
Abstract
There is increasing interest in red raspberry ( Rubus idaeus ) production worldwide due to increased demand for both fresh and processed fruit. Although the United States is the third largest raspberry producer in the world, domestic demand exceeds supply, and the shortage in fresh market raspberries is filled by imported fruit from Canada during July and August, and from Mexico and Chile during November through May. The raspberry harvest season is well defined and the perishability of the fruit limits postharvest storage. Winter production of raspberry in tropical and subtropical climates could extend the harvest season and allow off-season fruit production during periods of high market prices. The objective of the current study was to examine growth and yield of red raspberry cultivars grown in an annual winter production system in Florida and Puerto Rico. Long cane cultivars were purchased from a nursery in the Pacific northwestern U.S. in 2002 (`Heritage' and `Tulameen'), 2003 (`Tulameen' and `Willamette'), and 2004 (`Tulameen' and `Cascade Delight') and planted in raised beds in polyethylene tunnels in December (Florida) or under an open-sided polyethylene structure in January-March (Puerto Rico). In Florida, harvest occurred from ∼mid-March through the end of May, while in Puerto Rico, harvest occurred from the end of March through early June (except in 2002, when canes were planted in March). Yields per cane varied with cultivar, but ranged from ∼80 to 600 g/cane for `Tulameen', 170 to 290 g/cane for `Heritage', 135 to 350 g/cane for `Willamette', and ∼470 g/cane for `Cascade Delight'. Economic analysis suggests that, at this point, returns on this system would be marginal. However, increasing cane number per unit area and increasing pollination efficiency may increase yields, while planting earlier would increase the return per unit. The key to success may hinge on developing a system where multi-year production is feasible in a warm winter climate.
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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 | 0.001 |
| 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 it