High Tunnels Can Promote Growth, Yield, and Fruit Quality of Organic Bitter Melons (Momordica charantia) in Regions with Cool and Short Growing Seasons
Bibliographic record
Abstract
There is a potentially large market for locally produced organic bitter melons ( Momordica charantia L.) in Canada, but it is a great challenge to grow this warm-season crop in open fields (OFs) due to the cool and short growing season. To test the feasibility of using high tunnels (HTs) for organic production of bitter melons in southern Ontario, plant growth, fruit yield and quality, and pest and disease incidence were compared among three production systems: OF, HT, and high tunnel with anti-insect netting (HTN) at Guelph in 2015. The highest marketable fruit yield was achieved in HTN (≈36 t·ha −1 ), followed by HT (≈29 t·ha −1 ), with the lowest yield obtained in OF (≈3 t·ha −1 ). Compared with OF, there were several other benefits for bitter melon production in HT and HTN: increased plant growth, advanced harvest timing, reduced pest numbers and disease incidence, and improved fruit quality traits such as increased individual fruit weight and size, and reduced postharvest water loss. In addition to higher yield, HTN had fewer insect pests and disease incidence compared with HT. The results suggest that HTs can be used for organic production of bitter melon in southern Ontario and regions with similar climates. Also, the addition of anti-insect netting to HTs is beneficial to production if combined with an effective pollination strategy.
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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) | 0.002 | 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".