High tunnels without anti-insect netting benefit organic cherry tomato production in regions with cool and short growing seasons
Bibliographic record
Abstract
The potential market for locally produced organic cherry tomatoes (Solanum lycopersicum var. cerasiforme) is large in Canada, but it is challenging 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 improving organic production in southern Ontario, plant growth, fruit yield and quality, and pest and disease incidence were compared for ‘Sarina hybrid’ cherry tomato among three production systems: OF, HT, and high tunnel with anti-insect netting (HTN) in Guelph, ON, in 2015 and 2016. Averaged over the 2 yr, the highest marketable fruit yield was achieved in HT (≈70 t ha−1), followed by HTN (≈50 t ha−1), with the lowest yield obtained in OF (≈24 t ha−1). Compared with OF, increased plant growth, extended harvest period, reduced pest numbers, and increased proportion of marketable fruits all led to the higher marketable yields in HT and HTN. Under HT vs. OF, fruit quality attributes, such as soluble solids content and post-harvest water loss, were better in 2015 and similar in 2016. Although the insect netting reduced pest incidence and disease infection, fruits harvested from HTN had lower yield, smaller size, and lower soluble solids content than those from HT in both years. Therefore, HT can be recommended for organic production of cherry tomatoes in southern Ontario.
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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 | 0.001 | 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".