The structures underpinning vulnerability: examining landscape-society interactions in a smallholder coffee agroforestry system
Bibliographic record
Abstract
Abstract Smallholder farmers dependent on rain-fed agriculture are particularly vulnerable to extreme climate events and, therefore, it is necessary to identify adaptive measures that would increase farmer resilience to these shocks. The management options in a low-input system, like forest coffee ( Coffea arabica ), are limited and there are several factors out of farmers’ control driving their vulnerability to changing climatic conditions. These can relate to social structures and landscape factors, which can interact to reduce farmers’ adaptive capacity, creating a state of contextual vulnerability. We explored the potential synergies of this interaction across elevation, patch area and shade management gradients for smallholder coffee farms around the UNESCO Yayu Coffee Forest Biosphere Reserve in Ethiopia before, during and immediately following the 2015/16 El Niño. We documented a dramatic collapse in coffee yields across all farms, resulting in coffee incomes 29.5% ± 18.0% and 19.5% ± 10.0% of 2014 incomes in 2015 and 2016, respectively. We identified farms at elevations between 1500 and 1600 m with canopy openness between 40% and 45% as being consistently low yielding over our study period. We found these farmers had the highest rates of income diversification and, therefore, were already exhibiting adaptive capacity. Farmers with the largest income losses were spatially concentrated between 1600 and 1700 m, located in larger patch areas with lower canopy openness. Farmers at this elevation have access to poor infrastructure, restrictions on shade management and reported higher dependence on income from coffee, indicating an interaction of biotic and social factors exacerbating their vulnerability. Unfortunately, due to a nationally declared state of emergency, we were unable to survey farmers on the adaptive measures they undertook; therefore, we are limited in assessing their resilience. However, we do show the importance of considering both biotically and socially-mediated influences for assessing smallholder vulnerability, particularly barriers to diversifying incomes.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".