Water management strategies to enhance fruit solids and yield of drip irrigated processing tomato
Bibliographic record
Abstract
Processing tomato (Lycopersicon esculentum Mill.) cv. H9553 was used to investigate the effects of water management strategies on fruit yield, quality and solids production in southwestern Ontario over a 3-yr period (2003-2005). Treatments included four levels of drip irrigation (1.2, 1.0, 0.8 and 0.5 of potential crop evapotranspiration, ETc) during the growing season, three preharvest water cutoff times (4, 3 and 2 wk preharvest) and an unirrigated treatment. Irrigation generally increased total and marketable fruit yield, increased the average fruit weight and reduced green fruit yield and blossom-end rot when compared with the unirrigated treatment. Percent fruit solids were reduced, but total solid yields (t ha-1) were increased by irrigation. In a dry year (2005), fruit and total solid yields increased with irrigation water level but were not affected by the preharvest water cutoff time. In wetter years, the irrigation regime that applied the least water (0.5 ETc) reduced the amount of water applied to the crop while maintaining high yields and fruit quality. Fruit maturity, colour, firmness and the amount of culled fruit were not influenced by either the irrigation water level or the preharvest water cutoff time. The irrigation regime that applied the least water when used in combination with an early preharvest water cutoff appeared to counteract the reduction in percent fruit solids associated with irrigation. Some reduction in yield may occur with this irrigation regime and rainfall may interfere with implementation of this strategy. Key words: Lycopersicon esculentum, yield, blossom-end rot
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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) | 0.000 | 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) | 0.000 | 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".