agriIQ produces a transparent, auditable score for Australian agricultural conditions. This page documents how every score is calculated, what data feeds it, and the known limitations. For the complete institutional methodology paper, download the PDF below.
The national composite score is the simple arithmetic mean of the seven dimension scores, equally weighted.
Why equal weighting? Equal weighting avoids subjective bias about which dimension matters most. Different lenders weight dimensions differently based on their portfolio — a cotton-heavy book cares more about seasonal conditions, while a diversified national portfolio may weight credit conditions more heavily. By providing equal-weight composites alongside individual dimension scores, users can apply their own judgement.
Future versions may offer configurable dimension weights for Enterprise users.
Conditions support agricultural operations and lending activity
Some conditions challenging; warrants monitoring and closer review
Significant headwinds; elevated risk requiring active management
Reflects the financial health of Australian farm operations using ABARES production data, farm cash income indicators, and cost-of-production estimates. Higher scores indicate stronger profitability conditions.
Normalisation: Current indicators are compared against historical ranges and mapped to a 0-100 scale. Improving cost-to-income ratios and rising farm output values increase the score.
Tracks spot and indicator prices for eight key agricultural commodities. The dimension score aggregates price direction and magnitude of change across the commodity basket.
Normalisation: Price levels and recent trends (30-day direction, percentage change) are scored. Rising prices across multiple commodities lift the score; declining prices lower it.
Assesses the current growing conditions across Australia. Combines recent rainfall against historical averages, soil moisture levels, and climate driver signals (SOI, ENSO phase, IOD).
Normalisation: Rainfall is scored as a ratio of recent 30-day totals to 12-month historical averages. Soil moisture is banded (Very Dry < 10%, Dry 10-20%, Adequate 20-30%, Wet >= 30%). Climate drivers adjust the baseline: a strong La Nina phase lifts the score, while El Nino conditions lower it.
Measures cost pressures facing farm operations including fuel, fertiliser, chemicals, and labour. Lower input cost growth results in a higher score (less pressure on margins).
Normalisation: PPI quarter-on-quarter and year-on-year changes are mapped inversely: falling input costs score highly, while rapidly rising costs score poorly. The score represents margin protection, not absolute cost levels.
Tracks the health of Australian agricultural export markets. Considers total export values, commodity-specific export volumes, and trade flow indicators.
Normalisation: Export values are compared against historical ranges. Growing export values and improving trade balances increase the score. Trade disruptions or declining volumes lower it.
Reflects the cost and availability of agricultural credit. Considers the RBA cash rate, agricultural lending rates, and the spread between them.
Normalisation: Lower cash rates and lending rates produce higher scores. The rate trend direction (falling vs rising) is also factored in. A stable low-rate environment scores well; a rapidly tightening environment scores poorly.
Monitors biosecurity risk across animal and plant health. Active biosecurity alerts for foot-and-mouth disease, lumpy skin disease, varroa mite, fall armyworm, and other threats are tracked per commodity.
Normalisation: The score starts at 100 (no alerts) and decreases based on the number and severity of active biosecurity incidents. Only confirmed alerts are counted; informational mentions do not affect the score.
Australia is divided into 57 agriculturally relevant SA4 regions (ABS Statistical Area Level 4). Each region receives a daily composite score based on localised conditions.
When a lending team uploads a portfolio of agricultural exposures, each exposure is scored individually, then aggregated into portfolio-level metrics.
The agriIQ data pipeline runs automatically every day. All times are approximate and in AEST.
| Time (AEST) | Process |
|---|---|
| ~6:00 AM | 18 data ingesters run sequentially |
| ~6:10 AM | AI narrative generation begins (98 narratives in 4 batches) |
| ~6:30 AM | Proactive alerts sent to subscribed users |
| ~6:35 AM | Portfolio risk scores updated |
| ~7:00 AM Mon | Weekly email digest |
Score Explainability Layer
Structured decomposition showing contributing factors, data sources, and directional impact for each dimension score.
Narrative Confidence Indicators
AI-generated narratives now carry a confidence classification (High/Medium/Low) based on data freshness and source coverage.
Data Transparency & Fair Use Policy
Published policy statement clarifying that agriIQ scores regional conditions, not individual creditworthiness.
Climate Stress Index
SA4 sub-regional pages now include heat stress days, evaporation, and vapour pressure deficit from SILO DataDrill.
SILO Rainfall Integration
Authoritative SILO DataDrill rainfall data from Long Paddock (QLD Government) added to SA4 scoring.
SA4 Sub-Regional Scoring
57 SA4 statistical areas now scored individually with localised rainfall, soil moisture, and climate data.
Initial Release
Seven-dimension scoring framework launched with 18 data ingesters across 14 government and industry sources.
High Impact Group Pty Ltd. (2026). agriIQ Australian Agricultural Conditions Scoring Methodology (v2.0). Retrieved from https://www.agriiq.com.au/methodology
