Climate Change, Uncertainty, and Adaptation: The Case of Irrigated Agriculture in the Murray–Darling Basin in Australia
Bibliographic record
Abstract
Climate change is likely to have substantial effects on irrigated agriculture. Extreme climate events, such as droughts, are likely to become more common. These patterns are evident in median projections of climate change for the Murray–Darling Basin in Australia. Understanding climate change effects on returns from irrigation involves explicit representation of spatial changes in natural stocks (i.e., water supply) and their temporal variability (i.e., frequency of drought states of nature) and the active management responses to capital stocks represented by mitigation and alternative adaptation strategies by state of nature. A change in the frequency of drought will induce a change in the allocation of land and water between productive activities. In this paper, a simulation model of state‐contingent production is used to analyze the effects of climate change adaptation and mitigation. In the absence of mitigation, climate change will have severe adverse effects on irrigated agriculture in the Basin. However, a combination of climate mitigation and adaptation through changes in land and water use will allow the maintenance of agricultural water use and environmental flows. Le changement climatique risque d’avoir des répercussions considérables sur l’agriculture irriguée. Les phénomènes climatiques extrêmes, tels que les sécheresses, risquent de devenir plus fréquents. Ces phénomènes sont mis en évidence dans les projections médianes du changement climatique établies pour le bassin de Murray–Darling, en Australie. Pour comprendre les répercussions du changement climatique sur le rendement des cultures irriguées, il faut disposer d’une représentation explicite des changements spatiaux qui touchent les stocks naturels (c.‐à‐d. l’approvisionnement en eau) et de leur variabilité temporelle (c.‐à‐d. les états de la nature de la fréquence de la sécheresse) et assurer une gestion active des stocks de capital grâce à des stratégies d’atténuation et d’adaptation selon l’état de la nature. Une variation de la fréquence des sécheresses entraînera une modification de l’allocation des terres et de l’eau entre les activités de production. Dans le présent article, nous avons utilisé un modèle de simulation états‐contingences pour analyser les répercussions des stratégies d’atténuation du changement climatique et d’adaptation à ce changement. En l’absence de stratégies d’atténuation, le changement climatique aura des répercussions défavorables sur l’agriculture irriguée dans le Bassin. Toutefois, des stratégies d’atténuation combinées à des stratégies d’adaptation comprenant des changements dans l’utilisation des terres et de l’eau permettront de maintenir l’utilisation de l’eau à des fins agricoles et les débits environnementaux.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".