Linking the social, economic, and agroecological: a resilience framework for dairy farming
Bibliographic record
Abstract
Agriculture is a major economic driver in Aotearoa-New Zealand (New Zealand), led by export earnings from dairy farming. Dairying is uniquely exposed to climatic-and nonclimatic socioeconomic stressors, which have their greatest effects on production and yield. The growing need to consider these and other changes is accelerating efforts aimed at ensuring greater resilience, adaptability, and flexibility within the industry. To gain insight into these dynamics at the farm-level, a resilience-based assessment framework was piloted with three different types of dairy farming systems, following extensive drought on the east coast of the North Island. Using a participatory and bottom-up approach, the framework was used to qualitatively explore the potential significance of varying social, economic, and agroecological attributes between high-input, low-input, and organic systems, and their implications for resilience. The "lock in trap" of highly intensive systems, although profitable in the near term, may be less resilient to climate shocks because these are likely to occur in conjunction with changing market and financial risks. Low-input systems are less dependent, in particular, on fossil fuels and are associated with higher levels of farmer satisfaction and well-being. Organic farming provides ecological benefits, and the financial premium paid to farmers may act as a short-term buffer. The framework provides insight into the current context at the farm level and can draw out individual perspectives on where to target interventions and build resilience. Results demonstrate the potential of in-depth qualitative assessments of resilience, which can usefully complement quantitative metrics. The framework can be used as the basis for further empirical assessment and inform the design of similar approaches for cross-sector comparative analysis, large-N surveys, or modelling. Furthermore, the preliminary characterization of resilient farm-systems has the potential to contribute to broader sustainability frameworks for agriculture and can inform strategic adaptation planning in the face of climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".