Effects of nitrogen fertilization on fruit yield and quality of processing tomatoes
Bibliographic record
Abstract
Four processing tomato (Lycopersicon esculentum Mill.) cultivars (CC337, H9230, H9492 and H9553) were field-grown in southwestern Ontario using five N fertilization rates in each of 4 yr (0, 50, 100, 150 and 200 kg N ha-1 in 1999 and 0, 100, 150, 200 and 250 kg N ha-1 in 2000, 2001 and 2002). Total fruit yield increased linearly as N rate was increased except in 2001, which was a dry year. Responses of marketable yield to fertilizer N rate were dependent on cultivar and the year. In years when sufficient soil water was available, N fertilizer rates of 200 kg ha-1 or higher were required to produce the maximum marketable yield for the four cultivars. In the dryer years, the response to fertilizer N rate was cultivar dependent, and the application of 150 to 200 kg N ha-1 was sufficient to maximize marketable yield. Fertilizer N above the rate required for maximum marketable yield increased green fruit yield at harvest. Nitrogen rate did not affect the soluble solids (SS) content, firmness, size or colour of marketable fruit. Differences in yield and fruit quality were noted among cultivars. H9553 was the highest-yielding cultivar. H9230 had the largest fruit size. CC337 generally had the best red fruit colour and the lowest percent SS. H9492 generally had the highest percent SS. To attain maximum marketable yields, it appears that N rates should be adjusted based on cultivars, anticipated rainfall levels and availability of irrigation. Key words: Lycopersicon esculentum, processing tomato, nitrogen fertilization, yield, quality
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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 | 0.000 | 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) | 0.001 | 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".