Effects of Within-row Intercropping with Snow Peas on Yield and Quality of Cherry Tomatoes in an Organic High Tunnel Production System
Bibliographic record
Abstract
Intercropping can increase land use efficiency in high tunnel crop production, but it may also lead to decreases in yield and quality of main crops due to the potential competition for resources. This study evaluated the agronomic viability of intercropping snow pea ( Pisum sativum L., ‘Ho Lan Dou’) with cherry tomato ( Solanum lycopersicum L. var. cerasiforme ‘Sarina hybrid’) without additional inputs of water and fertilizers on peas in an organic high tunnel production system under Southern Ontario climate conditions in Guelph, Ontario, Canada (lat. 43.5 °N, long. 80.2 °W) during 2015 and 2016. In each 80-cm-wide bed, the tomato crops were planted alternately in double rows spaced 30 cm apart, with in-row spacing of 110 cm, which resulted in a planting density of ≈24,000 plants/ha. The snow pea seeds were sown between the tomato plants (i.e., within the same beds as tomatoes) in holes (two seeds per hole), with four rows in each bed and in-row holes spaced 10 cm and at least 25 cm away from the tomato plants, which resulted in a seeding rate of ≈650, 000 seeds/ha. The same amount of water or fertilizer was applied to the intercropping and nonintercropping plots based on the needs of the cherry tomato plants. Plant growth, fruit yield, and quality were compared between tomato plants with and without intercropping. Intercropping with snow peas did not affect total marketable fruit yield, unmarketable fruit percentage, fruit quality traits (e.g., individual fruit weight, soluble solids content, dry matter content, and postharvest water loss), or early-stage plant growth of the cherry tomato. Therefore, it is at least an agronomical possibility to intercrop snow peas with cherry tomatoes on the same beds without additional inputs of water and fertilizer on snow peas in an organic high tunnel system. The additional yield of pea shoots or pods in the intercropping treatment also increased economic gross returns in the high tunnels, although the economic net return might vary with the costs of seeds and labor involved in snow pea growing.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".