Forecasting tree phenology in a climate change context


Temperature is, with high certainty, the most important driver of spring phenology in deciduous tree crops from temperate climates. Rising temperatures are therefore expected to affect the timing of early-season development stages, with implications for the trees’ exposure to climate-related hazards. Global warming may cause increasing risk of spring frost damage, insufficient chill accumulation, low pollination rates and irregular fruit development. In a changing climate, risk profiles change slowly but steadily, gradually modifying production prospects. The gradual nature of such changes makes it difficult for growers to differentiate between actual trends and the background noise produced by natural variability. Tree phenology models aim to support growers in selecting appropriate cultivars for their orchards and to project climate change impacts on fruit production. Fulfilling this objective requires reasonably accurate models, which have long been in short supply. In particular for chill accumulation, which greatly influences the timing of spring phases, researchers have used a wide range of models that assume wildly varying temperature responses. Further uncertainties derive from the choice of heat model, as well as from open questions about the relationship between chill and heat accumulation. Providing accurate guidance for climate change adaptation amid these uncertainties remains a challenge. To provide accurate guidance on climate change adaptation, phenology modelers need to ensure that they use state-of-the-art models and apply protocols to validate models under climatic conditions that correspond to a warmer future. Such validation is, however, challenged by our limited ability to predict future conditions and by inevitable gaps in our understanding of tree dormancy. Modelers should therefore adopt effective approaches for assessing risks and communicating uncertainties. We present tools and approaches that contribute to develop valid phenology models and give insights on strategies for integrating uncertainties into a phenology risk assessment framework that can produce actionable adaptation advice for growers.