Establishing Irrigation Levels Targeting Higher Content of Lycopene and Water Use Efficiency in Tomato
Bibliographic record
Abstract
The demand for healthier foods has been increasing worldwide. Associated with this trend, it is crucial to optimize the use of inputs for ensuring the sustainability of production. The fruits of tomato are important sources of minerals, vitamins, and especially of carotenoids such as the lycopene. This carotenoid plays biological activities that are crucial such as the antioxidant function, besides its proven action in the prevention of cancers and degenerative diseases. The irrigation seems to play a fundamental role in the biosynthesis of lycopene. Thus, it is fundamental to establish levels of irrigation that might provide higher content of lycopene, productivity, and efficiency in the use of water in the production of tomato. The objective of this study was to establish adequate levels of irrigation for the obtainment of higher content of lycopene, productivity of fruits, and higher efficiency in the use of water in the production of salad tomato. For this, two experiments were carried in different cropping seasons. The treatments consisted of the application of four irrigation depths, corresponding to 50, 100, 150 and 200% of the tomato evapotranspiration. The estimates of maximum productivity corresponded to the application of the irrigation depth of 112% ETc, while the maximum content of lycopene and the higher efficiency in the use of water corresponded to 50% ETc. The irrigation depth of 100% ETc is recommended as the best irrigation depth to obtain higher content of lycopene, productivity, and efficiency in the use of water jointly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 teacher head, 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".