Variabilidad, tendencia y eventos extremos en los rendimientos agrícolas a nivel de partidos en la provincia de Buenos Aires
Bibliographic record
Abstract
Climate variability is the main determinant of fluctuations in the productive and economic results of agriculture. Extreme events are expected to occur with greater frequency and intensity in the future. In this scenario, the effects of climate variability on agricultural production are of special interest. This study analyzes the time series of yields at the county level of the main crops in the province of Buenos Aires, in the period 2000/01-2020/21. The trend and occurrence of extreme values ””of wheat, corn and soybean yields are identified. The frequencies of extreme values ””are related to the phases of the ENSO -El Niño-Southern Oscillation phenomenon, for each crop year. Yields show significant positive trends in 78%, 46% and 30% of the counties for wheat, corn and soybean, respectively. There is a significant relationship between the frequencies of extreme yield values ””and the ENSO phases, this relationship being more important in summer crops. In particular, there is a relative frequency of extremely low or very low yields of 38 and 41%, in second consecutive La Niña crop years, for corn and soybean, respectively. While the frequencies of extremely low or very low yields in neutral or El Niño crop years are between 0% - 3%. Regarding the economic value of production, the differences between obtained vs. expected values, accumulated in the period, are positive values of +3285 and +872 million USD in “El Niño” years for the north and south regions, respectively, and negative values of -3387 and -388 million USD, in “La Niña” years for both regions, respectively. The results provide evidence on the potential value of ENSO-based seasonal forecasts for agriculture. However, it is necessary to deepen the analysis of the effects of ENSO and other seasonal phenomena on yields. It is also necessary more information about the attitudes of the farmers in the Pampas and the different management practices that can be adjusted based on these forecasts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".