Forecasting tree crop yield with limited data - a macadamia case study

Macadamia yield forecast models were trained with a large set of commercial yield data (10 years, 1,156 records). Predictors included remote sensing and weather data, aggregated spatially to macadamia block boundaries, and temporally to quarterly intervals. Errors were typically around 23% at the block level, and 10% at the region level. Much of the yield variability yield was predicted even for orchards excluded from training data. At least 400-500 training data points were needed to minimize error. Best results were obtained with a fusion of weather and remote sensing data, aggregated over 8 quarterly periods from 2 years before harvest.