Data from: Shade tree traits and microclimate modifications: Implications for pathogen management in biodiverse coffee agroforests
Bibliographic record
Abstract
Diversified coffee agroforests modify microclimate conditions in comparison to monocultures, impacting the success of significant plant pathogens, such as Hemileia vastatrix, which causes coffee leaf rust (CLR). However, research is often limited to the dichotomous analysis of shaded agroforestry systems or unshaded monocultures, often overlooking the nuanced effect of shade tree trait diversity. Our study aims to determine the cumulative effects of shade tree canopy architectural characteristics and leaf functional traits in biodiverse agroforests on microclimate modifications and CLR incidence. We measured plot-level microclimate conditions (air temperature, relative humidity, leaf wetness duration, throughfall kinetic energy) in three single-stratum and two double-strata shade tree canopy treatments, including Erythrina poeppigiana, Terminalia amazonia, and Chloroleucon eurycyclum. Commonly reported canopy characteristics and leaf traits were compared to average microclimate conditions and CLR incidence levels. We found that shade tree trait expression significantly explained most microclimate conditions, and that two key shade tree traits (canopy openness, leaf area) significantly explain CLR incidence levels (R2 = 0.211, p = 0.036). Our results highlight the differences in microclimate conditions and CLR incidence among biodiverse agroforests, as well as the important explanatory power of shade tree traits. Specific effects of shade tree traits on pathogen dynamics can cirectly inform agroforestry system design (i.e. shade tree species selection) and sustainable coffee farm management practices (i.e. pruning practices).
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".