VineColD: an integrative database for global historical tracing and real-time monitoring of grapevine cold hardiness

Abstract

Cold hardiness is a crucial physiological parameter that determines the survival of grapevines during the dormant season. Accurate modeling and large-scale prediction of grapevine cold hardiness are essential for assessing the potential geographic distribution of grapevine cultivation, quantifying the impact of climate change on grapevine habitats, and ensuring the sustainability of the grape and wine industries in cool climate regions worldwide. However, until now, no comprehensive database has been available. In this research, we combined advanced automated machine learning techniques with extensive historical and current weather data to create an integrative database for grapevine cold hardiness: VineColD (https://cornell-tree-fruit-physiology.shinyapps.io/VineColD/). We developed the NYUS.2.1 model, an automated machine learning-based system for predicting grapevine cold hardiness and in this study, applied it to global historical weather data from 17,985 curated weather stations spanning 30{degrees} to 55{degrees} in both hemispheres from 1960 to 2024, resulting in the development of an integrative grapevine cold hardiness database and monitoring system. VineColD integrates both a global historical dataset and a daily updated regional cold hardiness system, offering a comprehensive resource to study grape cold hardiness for 54 grapevine cultivars. The platform provides multiple download options, from single-station data to complete datasets, and the interactive multi-functional R Shiny application facilitates data analysis and visualization. VineColD delivers critical insights into the impact of climate change on grapevine cultivation and supports a range of analytical functions, making it a valuable tool for grape growers and researchers.

Plant phenotyping relevance

ブドウの耐寒性という植物生理形質を予測する機械学習モデルを開発し、全球データベースと監視プラットフォームとして提供しており、形質推定手法と再利用可能な基盤が研究の中心である。

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