Farm-level vulnerability to climate change in the Eastern Bay of Plenty, New Zealand, in the context of multiple stressors
Bibliographic record
Abstract
Climate change research is undergoing a monumental shift, from an almost exclusive focus on mitigation, and the reduction of greenhouse gases, to adaptation, and identifying the ways in which nations, communities and sectors might best respond to the reality of a changing climate. Vulnerability assessments are now being employed to identify the conditions to which socio ecological systems are exposed-sensitive and their capacity to adapt. Work has been conducted across a range of geographical locations and systems as diverse as healthcare and mining. There are however, few examples of analyses incorporating an assessment of the multiple climatic and non-climatic stressors to which agricultural producers are exposed. This thesis examines farm-level vulnerability to climate change of agricultural producers from the Eastern Bay of Plenty, New Zealand. The study area has a diverse agricultural economy, founded upon pastoral farming (dairy and drystock) and kiwifruit. This dependence on agricultural production, and the likely influence of expected changes in climatic conditions in the future provided a unique setting in which to develop a place-based case study exploring vulnerability to future climatic variability and change. Using a mixed methods approach, including semi-structured interviews and temporal analogues, a conceptual framework of farm-level vulnerability was developed and applied. The application of the framework was conducted through an empirical study that relied on engagement with and insights from producers who identified current exposure-sensitivity and adaptive capacity. It is shown that pastoral farmers and kiwifruit growers are exposed-sensitive to a range of climatic and non-climatic conditions that affect production, yields and farm income and returns. It demonstrates that producers have in turn, developed a range of short- and long-term adaptive strategies in order to better manage climatic conditions. It shows that these responses are varied, and are not made in response to climatic conditions alone, illustrating the need to consider other, multiple stimuli. An assessment of future vulnerability is presented, based on the empirical work and the identification of those drivers of vulnerability that are likely to be of concern and that will shape the capacity of farmers and growers to respond to climatic variability and change. The thesis as a whole not only provides a place-based case study on the vulnerability of farmers and kiwifruit growers in eastern New Zealand, but also demonstrates the need to engage with producers in order to develop an understanding of the complex ways in which climatic conditions interact with non-climatic stimuli beyond the farm-gate to influence vulnerability to climatic variability and change, both now and in the future.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".