Pathogen and pest communities in agroecosystems across climate gradients: Anticipating future challenges in the highland tropics
Bibliographic record
Abstract
CONTEXT Tropical agricultural systems must respond to current and future pathogen and pest communities. An important research gap is how climate change may shift the geographic distribution of tropical pathogens and pests. OBJECTIVE We evaluated the geographic risk of 27 pathogens and pests in four food security crops (banana, cassava, potato, and sweetpotato) in the Great Lakes region of Africa, and potential future risk under climate change. We analyzed model performance for each pathogen and pest, assessing the potential for changes in geographic distribution, and for decision support systems to facilitate management. METHODS Cropland connectivity analysis identified locations likely important in the spread of crop-specific pathogens and pests. We surveyed the 27 economically important pathogens and pests in Rwanda and Burundi, mapping the distribution of each across climate gradients and quantifying associations. We used machine learning to model each species as a function of environmental variables, including host landscape. We also evaluated future temperatures across altitudes under climate change scenarios. RESULTS AND CONCLUSIONS Among ten algorithms evaluated, random forests and support vector machines generally performed best for predicting severity or infestation. Host landscape variables were useful predictors for some species. Based on climate matching, 44 % of the pathogens and pests could become more common with warmer temperatures at higher altitudes, while 17 % may become less common. SIGNIFICANCE These findings indicate how crop health in the region requires adaptation to multiple sustainability challenges. The results also indicate which pathogen and pest species have the potential for development of decision support models.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".