Farm-level vulnerability to climate change in the Eastern Bay of Plenty, New Zealand, in the context of multiple stressors
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».