Climate Change and the Coffee Industry in Brazil
Bibliographic record
Abstract
This article discusses the impacts that climate change will have on the Brazilian coffee industry throughout the 21st century; particularly how coffee plants will respond to changes in temperature and precipitation and how the incidence of coffee leaf rust could change. Historical and current conditions of Brazil’s coffee industry and the importance of its position in the global coffee industry are introduced. The current climate of Brazil’s major coffee producing regions are summarized. Context is provided on the origin and spread of coffee leaf rust as well as its mode of infection. Syntheses of climate models in Brazil are used to assess the effects climate change will have on coffee production, including climate’s effects on coffee plants as well as the incidence of coffee leaf rust. Rising average annual temperatures and more variable precipitation patterns could hamper coffee plant growth and increase coffee leaf rust incidence. Rising levels of carbon dioxide may increase coffee plant growth, leading to greater yields. Solutions to mitigate damage to the Brazilian coffee industry, proposed by previous researchers, are reviewed and discussed. These include expansion into high altitude regions, shade systems, sun systems, fungal predators of coffee leaf rust, scientific advancement of crops and financial protections for farmers.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".