Long-term Yield of Horticultural Crops in Wisconsin in Relation to Seasonal Climate in Comparison with Southern Ontario, Canada
Bibliographic record
Abstract
The relationship between long-term weather and yield of 11 horticultural crops and one field crop in Wisconsin was determined for a 55-year period (1950–2005). The relationships among weather parameters and yield in Wisconsin were also compared with associations between weather and yields in Ontario, Canada, from a previous study. The number of days in a growing season with maximum temperatures 30 °C or greater (hot days) was negatively correlated with yields of beet for canning ( r 2 = 0.15), green pea ( r 2 = 0.16), onion ( r 2 = 0.08), and sweet corn for processing ( r 2 = 0.16) in Wisconsin. Hot days were also negatively correlated with yield of green pea ( r 2 = 0.16) in Ontario, Canada. Growing season precipitation in Wisconsin was positively correlated with yields of beet for canning ( r 2 = 0.18) and green pea ( r 2 = 0.09). An increase in yields of beet for canning in Wisconsin and green pea from Ontario was also observed with an increase in number of days with rainfall during the growing season ( r 2 = 0.12 and 0.15, respectively). Monthly minimum and maximum temperatures and hot days had an effect on vegetable yields in Wisconsin. A high number of days with precipitation in May and July was associated with yields of most vegetables and grain corn in Wisconsin. These results indicated the importance of the total and frequency of seasonal precipitation and the negative effect of exposure of crops to extreme temperatures on yields of vegetable crops.
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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 | 0.001 |
| 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) | 0.001 | 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 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".