Abstract

Flowering in apple occurs at the conclusion of bud dormancy, a protective mechanism that enables trees to survive the potentially damaging cold conditions of winter and resume growth when temperatures become more favourable. As the entrance and progression of dormancy are closely linked to seasonal temperatures, higher winter averages and unpredictable weather events present a significant challenge to maintaining yield and quality in a changing climate. The FruitFlow project is a research partnership between France, Spain and Germany with the aim of developing climate-smart technologies to predict and promote flowering and attenuate the negative impact of global warming on fruit production in apple and peach. Here we present the results of the first two work packages which both used the French apple core collection of 240 diverse cultivars. Firstly, WP1 - The prediction of flowering date using leaf NIRS, where we show both lab-based NIRS measurements on ground leaf tissue and field-based NIRS using a portable device and intact leaves can predict flowering date of the following season using partial least squares (PLS). Secondly, WP2 – GWAS on flowering date to identify genes underlying dormancy control and flowering. Following construction of a genotypic dataset consisting of a 480K apple SNP array and capture sequencing of over 500 flowering-related genes, we identified a strong QTL on chromosome 9 associated to flowering data. We also present a potential role for an underlying candidate gene in redox-mediated dormancy control. Finally, we demonstrate how flowering predictions based on NIRS can be used in GWAS to achieve similar results to the directly measured phenotype, which could have useful implications for genetic studies on difficult to measure traits.