Climate adaptation turning points in cacao production in the Peruvian Amazon
Bibliographic record
Abstract
Cacao farmers in Peru aim to have an economic activity that delivers sustained income and allows them to achieve an acceptable quality of life. Their main challenges comprise drought, pests and diseases. For farmers, droughts of two weeks during the harvest season are critical, because they reduce the yield that could be harvested and commercialized. Drought stress also turns cacao trees more vulnerable to attacks of pests and diseases. \nThe climate conditions where farmers perceive undesirable conditions were identified at a precipitation 32°C in the study area located in San Martin. Further critical climatic variables are: precipitation of the driest month, maximum temperature of the warmest month, as well as precipitation of the driest quarter and mean temperature of the driest quarter. \nIn San Martin cacao already faces current maximum temperatures that exceed its optimum range and reach an average maximum of 32°C during the warmest month of the year. Climate projections show a clear increasing trend in temperature for the future. Models project an average of 34.8°C for 2050 under RCP 4.5 and up to 37.9°C by 2070 under RCP 8.5. Some localities in the study area also receive precipitation lower than 100 mm/ month during the driest month and the driest quarter under current conditions. Although there is variability among projections and RCP, models project a decrease in precipitation for the driest month and driest quarter under RCP 8.5. \nThe suitability models show that the distribution range of cacao is mostly projected to remain suitable. Areas that may gain in suitability are located along a narrow NNW - SSE stripe along the Andes at higher elevations. In contrast, losses are projected along the Andean foothills and lower Amazon basin, especially towards 2070. Diseases follow a similar geographic trend. However, for some diseases the loss of suitable areas is prominent along large areas at the eastern Andean slopes and towards the lower Amazon. Insects’ responses vary under future scenarios. Carmenta sp. maintains large suitable areas in San Martin and Monalonion sp. gains in suitable areas under RCP 8.5. \nThis work shows the added value of integrated approaches, especially by adding performance thresholds and incorporating stakeholders’ perceptions into ecological modelling. In addition, to the author’s knowledge, this work is the first to model cacao together with varied cacao pests and diseases in a single modelling exercise. \nThere is a diversity of stakeholders in the Peruvian cacao sector, who require practical information on climate change impacts on cacao, its pests and diseases, as well as adaptation possibilities. Thanks to scientific platforms as well as stakeholder networks, there is an opportunity and momentum to share the results of this work.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".