Hybrid Phenology Modeling for Predicting Temperature Effects on Tree Dormancy
This work addresses the need for accurate and interpretable phenology modeling for fruit tree dormancy prediction, which is incremental as it combines existing methods rather than introducing a new paradigm.
The authors tackled the problem of inconsistent predictions in phenology modeling due to structural discrepancies across biophysical models by integrating a neural network with conventional models, resulting in a hybrid approach that consistently outperformed both traditional and machine learning models in predicting cherry tree blooming dates across multiple countries.
Biophysical models offer valuable insights into climate-phenology relationships in both natural and agricultural settings. However, there are substantial structural discrepancies across models which require site-specific recalibration, often yielding inconsistent predictions under similar climate scenarios. Machine learning methods offer data-driven solutions, but often lack interpretability and alignment with existing knowledge. We present a phenology model describing dormancy in fruit trees, integrating conventional biophysical models with a neural network to address their structural disparities. We evaluate our hybrid model in an extensive case study predicting cherry tree phenology in Japan, South Korea and Switzerland. Our approach consistently outperforms both traditional biophysical and machine learning models in predicting blooming dates across years. Additionally, the neural network's adaptability facilitates parameter learning for specific tree varieties, enabling robust generalization to new sites without site-specific recalibration. This hybrid model leverages both biophysical constraints and data-driven flexibility, offering a promising avenue for accurate and interpretable phenology modeling.