Effects of the ENSO on the Variability of Precipitation and Air Temperature in Agricultural Regions of Mato Grosso State
Bibliographic record
Abstract
The analyze of the El Niño, La Niña, and Neutral years phenomena and their influence on the temporal distribution of precipitation and air temperature is of great importance in agricultural systems, with the view to adapt crop management in order to reduce the risks of losses, optimizing rainwater and contributing to food security. The aim of this paper was to characterize variations in annual, monthly, and dekads rainfall in normal years and in those in the two extreme ENSO events in the municipalities of Tangará da Serra, Rondonópolis, and Sinop, in Mato Grosso state. Historic data were used, from INMET and ANA, covering 1970 to 2016. Probable annual precipitation was determined via the gamma distribution. In the three municipalities studied, the period considered as rainy falls between October and April and the dry season falls between May and September. The average annual rainfall for the municipalities is 1800, 1900, and 1500 mm, for Tangará da Serra, Sinop, and Rondonópolis, respectively. The effects of the ENSO, besides causing a 100 mm reduction in average annual precipitation, also cause little summers (“veranicos”) in the months of November and February. The municipalities of Tangará da Serra, Rondonópolis, and Sinop presented high levels of rainfall in Neutral years. The effects of the ENSO reduce rainfall levels but increase the number of rainy days. The Neutral years are more suitable to agriculture at regions of Mato Grosso State, followed by El Niño years, with concentrated rainy period and La Niña, with higher occurrence of veranicos, that maybe mitigated with use of irrigations systems.
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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 | 0.001 |
| 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) | 0.000 | 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 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".