The effect of daytime temperature, planting density and intercropping on oats and peas
Bibliographic record
Abstract
Oats and peas are grown throughout Canada and around the world as a nutritious feed for cattle. Commonly, farmers grow oats and peas together in the same field (i.e., intercropped) to produce forage that is higher yielding and more nutritious than individually-grown oats or peas. Given the importance of oats and peas to the cattle industry, investigating and understanding how climatic factors and growing methods affect yields is important.\nPrevious studies have shown that greatest yields are achieved when daytime temperatures are between 15°C and 20°C, oats and peas are intercropped, and a high planting density is used. However, these three factors have not been investigated together in one experiment before. Therefore in this project, the interacting effect of daytime temperatures, planting densities and cropping methods were studied. Using a climate-controlled greenhouse, oats and peas were grown together and apart at three different planting densities and daytime temperatures.\nContrary to previous studies, I found that temperatures above 20°C positively affected oat and pea yields. However, leaf count and plant height data indicate that these plants were simply maturing earlier.\nAs expected, planting density positively affected yields in both species. Individual yield data shows that per-plant yields did not vary with density. This indicates that plant growth was not nutrient limited. Thus, increases in yield with planting density are almost wholly attributable to the increased number of plants per area.\nFinally, I found that intercropping oats and peas together resulted in yields intermediate to sole-cropped oats and peas. No difference in individual plant weights, leaf counts or heights were observed between plants that were sole or intercropped. Thus, it might be that intercropping oats with peas does not positively affect yields when plants are not nutrient limited, and that increased yields obtained when these two species are intercropped results primarily from an increase in planting density.\nTo validate these observations, I recommend repeating this study with a larger number of replicates and harvesting all treatments at the same stage of maturity.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".