Simulation of the Occurrence of Water Runoff in Soils Under Cultivation of Industrial Tomato Irrigated by Center Pivot
Bibliographic record
Abstract
The tomato crop is almost totally irrigated. Among the irrigation methods utilized, mechanized sprinkling by center pivot stands out in tomato cultivation. A cultural treatment used in the tomato is the synchronization of the irrigations with the applications of the pesticides since with the leaf wetting the plants become unprotected and susceptible to diseases. In an attempt to reduce pesticide applications, growers seek to increase the time between irrigations, however, there are limitations, inherent to the soil and the irrigation system itself. The objective of this work was to simulate the soil water runoff tendency for irrigation management in the tomato crop, simulating three different types of soils (sandy, medium and clayey), three declines (0, 5 and 10%), and two types of deflectors (I-Wob and Spray). For this, four pivot sizes (25, 50, 75 and 100 ha) were defined and the methodology of maximum allowable precipitation estimated by the Newton-Raphson numerical technique was used to verify the different runoff conditions. The results showed that clayey soils are more susceptible when compared to medium and sandy soils, to surface runoff. Pivots of 100, 75 and 50 ha present greater susceptibility to runoff, with 25 ha being the best suitability for infiltration capacity in both soils. There is a percentage reduction of the maximum allowable rainfall of 40.74 % (±1.54) when the terrain is plan and pass to have 5% inclination and 22.99% (±1.47) between 5 and 10 %. I-Wob type deflectors have a better distribution of application, a consequently better relation with the maximum allowable precipitation intensity and less possibility of the surface runoff.
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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 | 0.001 |
| 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".