Climate Change and the Economics of Farm Management in the Face of Land Degradation: Dryland Salinity in Western Australia
Bibliographic record
Abstract
Projected changes in climate would affect not only the profitability of agriculture, but also the way it is managed, including the way issues of land conservation are managed. This study provides a detailed analysis of these effects for an extensive dryland farming system in south‐west Australia. Using a whole‐farm linear programming model, with discrete stochastic programming to represent climate risk, we explore the consequences of several climate scenarios. Climate change may reduce farm profitability in the study region by 50% or more compared to historical climate. Results suggest a decline in the area of crop on farms, due to greater probability of poor seasons and lower probability of very good seasons. The reduced profitability of farms would likely affect the capacity of farmers to adopt some practices that have been recommended to farmers to prevent land degradation through dryland salinization. In particular, establishment of perennial pastures (lucerne or alfalfa, Medicago sativa), woody perennials (“oil mallees”, Eucalyptus spp.), and salt‐tolerant shrubs for grazing (“saltland pastures”, Atriplex spp.) may become slightly more attractive in the long run (i.e., relative to other enterprises) but harder to adopt due to their high establishment costs in the context of lower disposable income.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".