Yield of Leafy Greens in High Tunnel Winter Production in the Northwest United States
Bibliographic record
Abstract
Season extension structures like high tunnels make it possible to produce cold-tolerant crops during winter months for both a longer cropping season and a winter market season. The effects of location and planting date on the fresh yield of several cultivars of Asian greens ( Brassica rapa L.), lettuce ( Lactuca sativa L.), and spinach ( Spinacia oleracea L.) were examined at Moscow, ID/Pullman, WA, and Vancouver, WA, a cold temperate climate and a mild marine climate, respectively. In Winter 2005–06, 20 cultivars were evaluated and in Winter 2006–07 a subset of 12 cultivars were evaluated. Location impacted yield, and higher yields overall were attained at Vancouver than at Moscow/Pullman, likely as a result of more consistent, warmer soil and air temperatures as well as increasing irradiance in February and March at Vancouver. Asian green cultivars had the highest overall yield resulting from faster growth compared with spinach and lettuce cultivars at both locations. Although most lettuce cultivars grew throughout the winter, further research is needed to identify the most suitable cultivars, seeding dates, and planting densities to optimize winter production of this crop and for Asian greens and spinach. Planting date influenced yields with the highest yields obtained for the third planting date for all trials except at Moscow/Pullman in the second year. Overall, this research suggests that it is possible to grow many cold-tolerant cultivars of Asian greens, spinach, and lettuce in a high tunnel during the winter months in both mild and cold temperate northern climates.
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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".