Temporal Rainfall Variations Induce Forecast Errors in Rainfed Agriculture in the Brazilian State of Ceará, Brazil
Bibliographic record
Abstract
The research aims to: a) assess the instabilities associated with rainfall and the variables that define the production of rice, beans, cassava and corn in the state of Ceará between 1945 and 2020; b) estimate models that can be used to make projections of harvested areas, yields and prices for these crops between 1945 and 2020; c) assess the impact of rainfall on the estimated forecasting models; d) assess how rainfall affects the likelihood of farmers making forecasts of the variables that define agricultural production. Rainfall data was obtained from the National Centers for Environmental Information (NOAA). Crop yield data came from the Brazilian Institute of Geography and Statistics (IBGE). Instabilities were measured by the coefficients of variation. ARIMA models (autoregressive, integrated and moving average model) were used to make the forecasts. The hypothesis that the residuals generated by the models are influenced by annual rainfall was tested. The results showed high instabilities in annual rainfall, which spread to the variables that define crop yields. Parsimonious and robust adjustments were obtained from a statistical point of view and it was shown that the errors generated, including their magnitudes, in the models used to forecast all the variables that define bean and corn yields, harvested areas and rice yields, as well as cassava yields, are influenced by annual rainfall in Ceará between 1945 and 2020.
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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 | 0.000 | 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".