Productiveness Response and Quality of Fruits of Tomato Under Different Levels of Fertilizers and Irrigation
Bibliographic record
Abstract
The mineral fertilizing and the supply of water seem to influence the content of lycopene in the fruits of tomato, thus it is crucial to elucidate the ideal levels of fertilizers and the adequate management of irrigation for this crop. Hence, the study had an objective to evaluate the efficiency of different levels of mineral fertilizing and the effect of irrigation management on the productivity as well as on the lycopene content in the fruits of tomato. For this, two experiments in Winter/Spring and Summer/Autumn were carried out. The treatments consisted of the combination of different levels of mineral fertilizersing with nitrogen, phosphorous, and potassium along with two levels of irrigation. The productivity of fruits, the production of large fruits, the lycopene content, and the efficiency in the use of nutrients, were evaluated. With the application of 120% of the recommended dose of fertilizing, the productivity of large fruits was maximum. The efficiency in the use of nitrogen, phosphorous, and potassium was maximum with the application of 120, 121 and 50% of the recommended dose of fertilizing, respectively, and 100% of the irrigation depth. The maximum content of lycopene was obtained, by applying 200% of the fertilizing dose and 50% of the irrigation depth. The best combination for the production of large fruits, higher lycopene content, and higher efficiency in the use of nitrogen, phosphorous, and potassium, was the application of 120% of the dosage of fertilizing and 100% of the irrigation depth.
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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".