Tomato growth, yield, and quality response to mixed chemical–organic fertilizers and grafting treatments in a high tunnel environment
Bibliographic record
Abstract
Tomato (Solanum lycopersicum L.) is a major vegetable crop world-wide and grown in high tunnels in many regions. This study investigates the use of two fertilizer sources, chemical and integrated (composted poultry manure plus urea) fertilizers, and grafting on growth, nitrate-N content, yield, and fruit quality of tomato grown in high tunnels in northwestern Washington. Grafting treatments consisted of ‘Panzer’ tomato grafted on one of three tomato rootstocks ‘Estamino’, Maxifort’, ‘DRO138TX’, or non-grafted (control). Application of chemical fertilizer increased number of leaves per plant, plant height, and cumulative fresh biomass of pruned suckers relative to tomato plants grown with the integrated fertilizer treatment. Grafted tomato plants had greater plant growth than non-grafted plants throughout the growing season. There was no significant difference between fertilizer treatments on nitrate-N concentration in plant tissue or fresh petiole sap; however, grafted plants contained higher levels of nitrate-N than non-grafted plants. Total and marketable fruit weight and number did not differ due to fertilizer source, but total and marketable fruit weight was higher for grafted plants than for non-grafted plants in 2016. There was no significant effect due to fertilizer source on fruit firmness, water content, pH, titratable acidity, and β-carotene; however, total soluble solids (TSS) and lycopene content were higher for fruit grown with integrated fertilizer in 2016. Grafting enhanced water content of tomato fruit in 2015, and TSS (°Brix) in 2016.
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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 | 0.001 |
| 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".