Adapting to climate change on the farm: Experiences of small-scale ecological farmers in two regions of China
Bibliographic record
Abstract
While ecological practices are widely recognized as effective strategies for addressing climate change, their adoption among smallholders is significantly influenced by the experiences of early adopters such as small-scale ecological farmers. Despite this important factor in adoption, few empirical studies have examined how small-scale ecological farmers experience climate change and evaluate the effectiveness of their ecological approaches. Drawing on data from in-depth interviews, farm visits and surveys with 28 ecological farmers in China, we develop an integrative analytical framework that uses farmers’ own narratives to examine how they perceive, are impacted by, and respond to climate change at the farm level. We found that beyond direct impacts on crop yield and quality, ecological farm productivity was undermined as climate shocks disrupt agroecosystems, damage farm facilities, and pose health risks to farm workers. In response, farmers apply diverse ecological practices alongside socio-economic measures to build resilience. While the paper demonstrates the adaptive value of ecological practices, it also reveals that their broader and successful adoption among smallholders is contingent upon overcoming substantial economic, social and institutional barriers. The study highlights the potential of bottom-up, farmer-led initiatives and advocates for targeted policies and services tailored specifically to the needs of ecological farmers.
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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 | 0.002 | 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 | 0.000 | 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".