Tree Effects on Coffee Leaf Rust at Field and Landscape Scales
Bibliographic record
Abstract
Although integrating trees into agricultural systems (i.e., agroforestry systems) provides many valuable ecosystem services, the trees can also interact with plant diseases. We demonstrate that a detailed understanding of how plant diseases interact with trees in agroforestry systems is necessary to identify key tree canopy characteristics, leaf traits, spatial arrangements, and management options that can help control plant diseases at different spatial scales. We focus our analysis on how trees affect coffee leaf rust, a major disease affecting one of the world's most significant crop commodities. We show that trees can both promote and discourage the development of coffee leaf rust at the plot scale via microclimate modifications in the understory. Based on our understanding of the role of tree characteristics in shaping the microclimate, we identify several canopy characteristics and leaf traits that can help manage coffee leaf rust at the plot scale: namely, thin canopies with high openness, short base height, horizontal branching, and small, dentate leaves. In contrast, at the edge of coffee farms, having large trees with high canopy volume and small, thick, waxy leaves is more useful to reduce throughflow wind speeds and intercept the airborne dispersal of urediniospores, an important consideration to control disease at the landscape scale. Seasonal pruning can help shape trees into the desired form, and trees can be spatially arranged to optimize desired effects. This case study demonstrates the added value of combining process-based epidemiology studies with functional trait ecology to improve disease management in agroforestry systems.
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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 | 0.000 |
| 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) | 0.001 | 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 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".