Application of Climate-Based Index Insurance Across Terrestrial and Aquatic Production Systems in Australia
Bibliographic record
Abstract
Frost, drought, and marine heatwaves (MHWs) are growing threats to the financial sustainability of both agriculture and aquaculture. In Australia’s wheat sector, frost and drought are leading climate risks. Farmers often delay sowing to avoid frost, increasing vulnerability to heat and drought, which can significantly reduce yield. Meanwhile, drought during key growth stages can sharply impact production and income. In aquaculture, rising sea surface temperatures (SSTs) are driving more frequent MHWs, particularly affecting temperature-sensitive species like Atlantic salmon. Prolonged exposure to SSTs above 18°C can cause mass mortality events, as seen in the 2019 Newfoundland die-off, which resulted in the loss of 2.6 million fish and USD 5.5 million in damages. To address these risks, we developed and tested targeted index insurance solutions using forty years of simulation data focused on 22 wheat farms and forty years of climate data for five salmon farming sites. For wheat, the Heating Degree Day Temperature Minimum Call Option (HDDTmin) policy enabled optimal sowing while protecting against the financial effects of frost. The policy improved returns, especially in high frost-risk zones. For drought, a dualtrigger index insurance policy based on rainfall deficits provided increasing payouts as conditions became drier. It reduced income volatility on 95% of farms and lifted long-term income by 21%. In aquaculture, a Cooling Degree Day Temperatures Maximum Call Option (CDD Tmax) policy index triggered payouts when MHW conditions intensified, offering rapid financial support for losses or to assist in adaptation schemes. These findings demonstrate that index insurance is a scalable, data-driven tool for managing climate risk. It enables timely payouts, reduces financial volatility, and enhances long-term resilience across land and sea-based food systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".