Temperate fruit trees enter a dormant state and resume growth development once cultivar-specific chill requirements (CR) are fulfilled. Sustainable fruit production requires crucial knowledge of the CR of the varieties to predict the adaptability of crops to future climate conditions. Experimental methodologies based on exposing shoots to forcing conditions, and statistical methods based on analysing long-term phenology records, such as Partial Least Square regression (PLS), are commonly used to estimate CR. To demonstrate the importance of unifying methodologies, we compared CR estimations from six approaches that included bud weight increase (30 % and 50 %), different percentages of bud break (30 %, 50 % and 90 %) after forcing conditions, and PLS regression. The CR of four almond and four apricot cultivars were experimentally and statistically determined in Murcia, South-Eastern Spain. Comparisons between approaches revealed that the choice of the method had a significant effect on the estimates. Large variation in CR estimates was found between PLS regression and the experimental methodologies in some late-flowering cultivars. To assess the adaptation potential of a certain cultivar to future climate conditions based on the CR estimates obtained through the different methodologies, we compared the probability of fulfilling cultivar-specific CR's under two global warming scenarios. Projections revealed a marked decreasing trend of chill availability in the region, which is already threatening the cultivation of genotypes with high CR. This study demonstrates that the variability of methodologies used to quantify CR has an important impact on assessing the probability of a successful adaptation. CR obtained by a certain methodology suggest that insufficient chill might be an increasingly common situation for some cultivars, potentially resulting in production risks. Conversely, other methodologies appear to indicate a more favourable performance for the same cultivar. Implementing standardized methods and metrics is essential to ensure more reliable information regarding the prospects for future crop adaptation

This work was funded by PRIMA, a program supported under H2020, the European Union's Framework programme for research and innovation (“AdaMedOr” project; grant number PCI2020–112113 of the Spanish Ministry of Science and Innovation). Authors acknowledge the Spanish Ministry of Science and Innovation the support of projects “Almond breeding assisted by genomic tools (AlmBredOmics)” and “Apricot breeding (APRIBRED)”

Peer reviewed