What does it take to build resilience against droughts in food value chains?
Bibliographic record
Abstract
Although the impacts of climate change are increasingly challenging food production efforts around the globe, evidence from past studies suggests that adapting and building food systems’ resilience to climate change is not a trivial task. In this study, we adopted a multi-stakeholder perspective by using qualitative and quantitative data to examine the process of building resilience in food value chains against droughts. Through a transdisciplinary process engaging practitioners from different activities of four key Swiss food value chains, we identified measures to build resilience in these value chains and their respective barriers, and determined key stakeholders to facilitate the implementation of the measures. We further complemented the results of the study with a quantitative survey of 832 Swiss farmers aimed at more deeply understanding the barriers from an agricultural perspective. The measures proposed by the practitioners for building resilience in the value chains are primarily focused on production activity and are aimed at avoiding production disruptions and mitigating farmers’ economic losses. Although some of these measures (e.g., irrigation, amassing stocks of animal feed) can be implemented by farmers themselves, other measures (e.g., compensation through pricing changes, flexibility in quality requirements) require interventions from other stakeholders, including post-production actors (processors, retailers) and consumers. However, our results indicate that such implementation is hindered by conflicting interests, the uneven exposure of actors to droughts, and a lack of motivation by the actors to act beyond securing their own operational needs. We conclude that a value chain approach based on collaboration is essential for building food system resilience against droughts and that research on motivations to enable such collaborations deserves more attention in resilience design and research.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| 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".