Above and Below Ground Interactions in Monoculture and Intercropping of Onion and Lettuce in Greenhouse Conditions
Bibliographic record
Abstract
Intercropping has been seen as an advantageous strategy in sustainable agriculture. Plants however interact with one another both above and below ground with members of the same species (intraspecific) or members of a different species (interspecific) for nutrients, water and light. It is therefore essential to understand these interactions when intercropped. The objective was to examine the above and below ground interactions between onion and lettuce in monocrop and intercrop systems. We examined the various possible interactions (no competition, above ground, below ground, or full) using a full factorial randomized design under greenhouse conditions. Onion yield was highest in intraspecific above ground competition and lowest in below ground and full interspecific competition with lettuce. Dry weight of onions in above ground competition with lettuce was significantly greater than that of the control group. Fresh weight of lettuce leaves were highest in below ground and full interspecific competition treatments. The hectare model and yield results suggest that there is strong below ground competitive effect between onion and lettuce in intercrop. Asymetric interspecific facilitation was found: facilitation by onion led to increased lettuce yield but a negative effect of lettuce on onion yield was observed. Knowledge of competitive interactions between component crops can have several applications in sustainable agricultural as it helps to match the most efficient species under specific conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".