Ecophysiological crop modeling to match almond cultivars planting to Israel's climate
Research team: Sperling. O (ARO, Co-PI)
Research Funding: Chief Scientist, Israel Ministry of Agriculture and Rural Development
Team and Students: Faina Khoroshevsky and Snir Thasha
​
Developing a Decision Support System for Matching Deciduous Tree Varieties to Climate-Adapted Growing Areas in Israel
There is currently a partial mismatch between deciduous tree species and their growing habitats in Israel—an issue that is expected to intensify due to climate change and global warming. Our recent studies indicate that the alignment between tree species, particularly in deciduous orchards, and their environmental growing conditions is often suboptimal. This misalignment directly reduces yield potential and limits the long-term resilience and profitability of orchard systems. We have identified three major climatic constraints that limit optimal crop productivity: a lack of synchrony among fruiting varieties, which affects pollination efficiency. Early flowering under warm winter conditions leads to frost susceptibility or misaligned fruit development. Delayed flowering in advanced spring seasons can interfere with fruit set and the timing of harvest. To address these challenges, we propose the development of an advanced Decision Support System (DSS) that will help guide the climate-adapted selection and spatial planning of deciduous tree varieties, with a focus on almonds as a model crop. This DSS will be based on a novel bioclimatic growth model, integrating two chilling models commonly used for deciduous trees—the Persian model and the Dynamic model—to predict flowering and fruiting behavior under both current and future climatic scenarios. The system will enable informed, location-specific recommendations for planting decisions, thus improving yield reliability and ecological suitability. Deciduous trees have evolved complex physiological mechanisms to survive and reproduce in their native climates. However, these conditions are rapidly shifting, and agricultural systems must adapt accordingly. Yield estimation in such systems is complex, influenced by multiple environmental, genetic, and management factors. Among the earliest and most critical indicators of potential yield is flowering, particularly the timing and density of flowering and its synchronization with pollinators. This project will address these challenges by developing innovative methods for processing and analyzing data from multiple orchard plots located along a climatic gradient across Israel. The final product will be a spatial decision support tool for variety selection and site-specific planting recommendations, designed to optimize flowering synchronization and crop load under dynamic climate conditions.
