Blueberry Production Trends in North America, 1992 to 2003, and Predictions for Growth
Bibliographic record
Abstract
Blueberry production area in North American increased 30% from 1992 to 2003 to 239,818 acres (97,054 ha); most of this increase occurred in Canada. During this period, lowbush blueberry ( Vaccinium angustifolium ) area increased 33% and highbush 24%. In the United States, the area planted to highbush, which includes northern ( V. corymbosum ) and southern highbush ( Vaccinium sp.) and rabbiteye ( V. ashei ) blueberries, increased from 48,790 acres (19,745 ha) to 55,898 acres (22,622 ha) from 1992 to 2003, a 15% increase. In 2003, the midwestern region of the U.S. accounted for 35% of the area of highbush blueberries planted. The southern, northeastern, and western regions accounted for 29%, 19%, and 13% of the planted area, respectively. Specific states in the U.S. that had considerable growth from 1992 to 2003 were California, Mississippi, North Carolina, Oregon, and Washington. In Canada, the area planted to highbush blueberries increased 105% to 11,010 acres (4456 ha). Commercial blueberry plantings in Mexico were estimated at 70 acres (28.3 ha) in 2003. In the U.S., total lowbush area increased 6% in 10 years, with Maine accounting for 97% of the area planted. In Canada, lowbush area increased 57% since 1992 with 37% and 34% of the total area present in Quebec and Nova Scotia, respectively. The blueberry industry is still projected to grow considerably in the next 5 to 10 years. Highbush blueberries in the U.S. are expected to increase in area planted by 14% and 31% in the next 5 and 10 years, respectively. In Canada, planted area of highbush blueberries is expected to increase by 22% in 5 years and 26% in 10 years. If projections are correct, planted area in Mexico will increase by almost 30-fold in 10 years. The managed area of lowbush blueberries is expected to increase by 5% to 10% over the next 10 years. Data on typical yields, types of cultivars grown, markets, proportion of machine harvest, major production problems, and changes in production practices are presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".