Consistency of long-term marketable yield of carrot and onion cultivars in muck (organic) soil in relation to seasonal weather
Bibliographic record
Abstract
To identify carrot and onion cultivars that provide consistent marketable yields, we tracked the yields of five fresh market carrot [(Daucus carota L. subsp. sativus (Hoffm.) Arcang.] and six onion (Allium cepa L.) cultivars for at least 13 yr. Relationships between long-term weather variables and marketable yields were also investigated. The effects of cultivar, year and cultivar × year interactions on yield of carrots and onions were assessed. Cultivar and year had significant effects on carrot and onion yields, while the interaction was significant in only one of four data sets of carrot yield. Carrot cv. Cellobunch (95.4 t ha–1) and onion cv. Corona (74.1 t ha–1) had the highest mean marketable yields over the years studied. There was a slight positive correlation between mean yield of the assessed carrots and maximum temperatures in September (r = 0.44). Mean carrot yield was also somewhat negatively correlated with total rainfall in July (r = –0.43) and with number of days with rain in August (r = –0.43) and September (r = –0.44). Most onion cultivars showed stronger relationships between marketable yield and various weather patterns. Marketable yield of onions increased with an increase in the number of days with rainfall in June (r = 0.57). The mean marketable yield of the six onion cultivars decreased in relation to temperatures ≥30°C in June (r = –0.55) and August (r = –0.53). The mean yield of all the onions in the trials was negatively correlated (r = –0.78) with growing degree days (base 5°C, May to August). The results indicated that the data from long-term cultivar trials can be used to identify cultivars that yield well despite seasonal variations in weather. Key words: Daucus carota, Allium cepa, temperature, rainfall
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".