Climate Sensitivity and the Value of Agricultural Production in the Brazilian Northeast: An Approach Using Spatial Panel Data
Bibliographic record
Abstract
This work analyzes the sensitivity of agricultural production in relation to changes in precipitation and temperature levels in the Northeast region of Brazil. For that purpose, data from 2006 to 2016 were used for 952 municipalities in the area. The econometric methodology derived from Kunwar and Bohara (2017) and is applied to the production value of municipalities in the Brazilian Northeast, where it is assumed that the production value is also impacted by the interactions between precipitation and temperature. Thus, time and space data are used, applied to a spatial econometric methodology. The results suggest that the agricultural production of the municipalities of the Brazilian Northeast is spatially autocorrelated. There is indication that there are municipalities with high (or low) levels of production, which have neighbors with these same characteristics. It was also verified that, from 2006 to 2011, the agricultural production was more sensitive to changes in temperature levels than to changes in average precipitation. However, after the years 2012, 2013, 2015 and 2016, agricultural production has become more sensitive to changes in the precipitation levels. It was also noted that in the analyzed period there was an increase in the average sensitivity of the agricultural production in relation to the precipitation levels, while the average temperature sensitivity showed a decrease.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".