Optimizing Pathogen Control through Mixed Cocoa–Plantain Agroecosystems in the Ecuadorian Coastal Region
Bibliographic record
Abstract
Mixed production systems play a vital role in the economic sustainability and ecological balance of agroecosystems. Cocoa and plantain are key crops in Ecuador but face phytosanitary challenges, like witches’ broom and black sigatoka diseases, especially when cultivated under monocropping systems. Combining habitat manipulation with adaptive pathogen management (APM) strategies can mitigate these challenges, but their efficacy in mixed cropping systems remains unclear. This study investigates disease and pest incidence in mixed cocoa–plantain systems during the establishment phase, focusing on the impact of spatial arrangements. Mixed agroecosystems showed a lower witches’ broom incidence in cocoa than monocultures. Whereas, in plantain, there was a consistent black sigatoka incidence across spatial arrangements but a lower infection rate per leaf within mixed systems. We found varied nematode populations with monocultures hosting the highest root damage due to phytoparasitic nematodes. Weevil populations were also influenced by spatial arrangements with monocultures among the highest. Overall, mixed agroecosystems influence disease and pest incidence, potentially hindering pathogen spread. Plantain–cocoa associations reduce disease incidence in cocoa but may not affect the overall incidence of black sigatoka in plantain, at least during the establishment phase. Continued monitoring is crucial for understanding the long-term impacts and microclimatic effects on pest populations that could offer sustainable pest management strategies, reducing the reliance on chemical pesticides.
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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) | 0.000 | 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".