Responses of Fruit Yield and Quality of Processing Tomato to Drip‐Irrigation and Fertilizers Phosphorus and Potassium
Bibliographic record
Abstract
Water and nutrient management are essential to achieve high yield and desirable quality attributes in processing tomato (Lycopersicon esculentum Mill.). A 4‐yr field study (2006–2009) was conducted to assess effects of contrasting water management (drip‐irrigation vs. nonirrigation), fertilizer P (0, 30, 60, and 90 kg P ha−1), and K (0, 200, 400, and 600 kg K ha−1) on yields and quality of processing tomato when the optimum N rate of 270 kg N ha−1 was applied. Compared with nonirrigation, drip irrigation increased marketable fruit yield by 127%, total fruit yield by 66%, and fruit size by 32%, while it decreased soluble solids content (SSC) by 19% and lycopene content by 8%, with no effects on dry biomass of stems and leaves (DBSL). Phosphorus addition had no effects on marketable yield and SSC, but increased the DBSL and lycopene content to maximum values at 60 kg P ha−1. Fertilize K rate affected all examined variables but the lycopene content. Increasing K rates from 0 to 200 kg K ha−1 increased marketable fruit yield by 10% and total fruit yield by 9%, but fruit size declined by 3%. Increasing K rates from 200 to 600 kg K ha−1, however, had no effects on yield and fruit size. Fertilizer K rate had no effects on SSC with nonirrigation, but resulted in a linear increase in SSC with drip‐irrigation. The results suggested that, with optimum N supply, K application is required to increase fruit yield and quality of drip irrigated processing tomato.
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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) | 0.001 | 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".