Short- and long-term drought impacts on the U.S. beef value chain
Bibliographic record
Abstract
Abstract Climate change is expected to increase the frequency and severity of extreme weather events such as droughts. While droughts are known to reduce crop yields, their impacts on livestock systems are more complex and evolve over time. Producers may respond to drought conditions by liquidating herds, including breeding stock, which alters supply dynamics throughout the meat value chain. These shifts may occur through multiple, overlapping channels, including increased feed costs and direct physiological effects on animals such as heat stress. We estimate the short- and long-term effects of drought-related environmental stress on the U.S. beef value chain, using the Drought Severity and Coverage Index as a composite measure of realized drought conditions. We interpret our findings as capturing the total effect of drought conditions on livestock market dynamics. Results show that producer prices increase significantly with a lag following drought events, while livestock futures markets respond earlier. Retail beef prices are also affected, but the impact on consumers is dampened and more delayed. These findings suggest that drought-related supply disruptions are a key contributor to rising U.S. beef prices.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".