Stage-Aware Early Warning forAustralian Winter Crop Yield
Forecast yield shortfalls using 38 years of daily weather data (1989–present) and soil properties across 6 Australian regions. Run models directly in your browser.
6 Australian Regions
NSW, QLD, SA, TAS, VIC, WA
5 Major Crops
Wheat, Barley, Canola, Oats, Lupins
ML-Powered Models
Ridge, ElasticNet, HistGradientBoosting
Uncertainty Quantified
Conformal prediction intervals
82,008
Data Points
Daily weather records
966
Observations
Region × Crop × Year
5
Forecast Windows
May-Jun through May-Oct
4830
Total Rows
Expanded panel dataset
About the Project
A stage-aware early-warning system built for Australian winter crop management.
What We Predict
The system forecasts Australian winter crop yield shortfalls — when actual yields fall below the expected linear trend. We predict both the continuous yield shortfall (t/ha) and classify the risk of low-yield events for 5 major crops across 6 states.
Why It Matters
Australian agriculture faces increasing climate variability. Early warnings of yield shortfalls give farmers, policymakers, and supply chain operators critical lead time (12–28 weeks before harvest) to make informed decisions about planting, irrigation, and market strategy.
Data Sources
Daily weather data from the Australia Silo network (1989–present, 82,008 records) combined with 10 soil attributes across 2 depth layers (topsoil 0–30cm, subsoil 30–100cm). Yield data from AustCropRrt covering 966 unique region-crop-year observations.
Methodology
Stage-aware feature engineering captures weather conditions during critical crop growth stages. Two model families: history-free (weather + soil only) and operational (with lagged yield history). The operational model achieves R² = 0.730 for the May-Oct window.
Data Timeline & Splits
Forecast Windows
Five lead-time windows from May, each providing progressively more information as the growing season advances.
May–Jun
Earliest forecast — 12 weeks before harvest
May–Jul
Early season outlook
May–Aug
Mid-season prediction
May–Sep
Late season forecast
May–Oct
Pre-harvest — most accurate
Model Families
Two task types, multiple algorithms — all evaluated across 5 forecast windows with rolling-origin cross-validation.
Regression — Yield Prediction
Predict expected yield (t/ha) and yield shortfall (t/ha)
Ridge
Linear model with L2 regularization
Elastic Net
Combined L1 + L2 regularization
HistGradientBoosting
Gradient-boosted decision trees
Classification — Low-Yield Risk
Classify whether yield will fall below crop-specific thresholds
Logistic Regression
Linear classifier for low-yield risk
HistGradientBoosting
GBDT classifier for risk prediction