Hybrid Phenology Modeling for Predicting Temperature Effects on Tree Dormancy

2025-01-28

AI Summary

To address the poor generalizability of traditional phenological models and the limited interpretability of purely data-driven approaches, this study proposes a mechanism-guided hybrid phenological model that integrates biophysical mechanisms—such as Chill Portion and Forcing—with a lightweight neural network, enabling parameter adaptation under mechanistic constraints. Trained jointly on long-term cherry blossom observations from Japan, South Korea, and Switzerland, the model achieves zero-calibration generalization across cultivars and regions without site-specific reparameterization. Empirical evaluation shows that its mean absolute error in flowering date prediction is 32% lower than that of purely physical or purely machine learning baselines, significantly improving both accuracy and interpretability. This work establishes a novel paradigm for climate-sensitive fruit tree phenology forecasting—one that balances mechanistic fidelity with robust, cross-context generalizability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Temperature Prediction
• Phenology of Trees
• Machine Learning Limitations

Innovation

• Hybrid Model
• Neural Networks
• Cherry Tree Phenology