Coffee crops adaptation to climate change in agroforestry systems with rubber trees in southern Brazil
Bibliographic record
Abstract
Adaptation to climate change is a strategy for crops to cope with the scenario of rising temperatures worldwide. In the case of Coffea arabica L., the use of agroforestry systems (AFS) with woody species is a promising practice to reduce excessive heat during the day. This study aimed to 1) evaluate air temperature changes that occur in an AFS of coffee and double alleys of rubber trees (Hevea brasiliensis Müell. Arg.) and 2) carry out an analysis of future warming scenarios by comparing the cultivation of Arabic coffee in full sun and in an AFS of double alleys of rubber trees. The microclimatic variables were measured between two rows of coffee trees at 1.0 m of height from June 2016 to June 2018. The results indicate that the AFS with double alleys of rubber trees spaced 16 m apart had an average temperature reduction from 1.4 to 2.5 °C from 10h00 to 16h00. The study also simulated temperature increases of 1.7, 2.6, 3.1, and 4.8 °C from 2018 to 2099, according to scenarios predicted by the Intergovernmental Panel on Climate Change (IPCC), and the impact in coffee production in Paraná State, Brazil. Using the climatic generator PGECLIMA_R, simulations suggest a progressive reduction of traditional areas suitable for open-grown coffee in the state. Production conditions can be maintained through the AFS, since the systems attenuate mean temperatures by 1-2 °C. We conclude that the AFS of coffee and rubber trees contribute to coffee crop adaptations to a future warmer environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".