Early-Season Avocado Yield Forecasting in New Zealand Using Sentinel-1, Sentinel-2, and Climate Variables

Reliable pre-season yield forecasting remains a persistent challenge in avocado (Persea americana Mill.) production, due to strong year-to-year variation and alternate bearing. This study developed an early-season, block-level yield forecasting framework combining multi-temporal Sentinel-2 optical indices, Sentinel-1 SAR structural indices, TerraClimate variables and historical yield over a two-year lagged predictor window ending before the target season begins. The framework was applied to 252 commercial orchard blocks in the Bay of Plenty and Northland regions of New Zealand and evaluated on five production seasons (2020–2024). Five machine learning approaches, XGBoost, CatBoost, LightGBM, LSTM, and the tabular foundation model TabICL, were compared using Leave-One-Year-Out (LOYO) cross-validation. TabICL gave the strongest performance (RMSE = 2.80 ± 0.47 t ha−1, MAE = 2.12 ± 0.31 t ha−1, R2 = 0.85 ± 0.04). Extending the predictor window from one to two years, excluding historical yield information, reduced TabICL’s mean RMSE by approximately 1.32 t ha−1. SHAP analysis identified actual evapotranspiration and yield from two seasons earlier as the strongest predictors, with canopy structure and temperature also contributing. A pre-season yield map for 2024 shows the framework’s potential to support harvest scheduling, labour planning, and supply forecasting. Combining multi-source satellite time series with a biologically informed two-year yield lag offers a practical approach to early-season avocado yield forecasting, with potential for other perennial fruit crops.