Future chill risk assessment using chillR

E. Luedeling
Temperate fruit and nut trees in warm locations are threatened by climate change, because they may fail to satisfy their chilling requirements in a warmer future. This risk is difficult to quantify, because 1) limited validity of chilling requirement estimates across locations makes site-specific assessments necessary; 2) the effective chill accumulation period is often unknown; 3) advanced chill metrics, in particular Chill Portions, are difficult to compute; and 4) many researchers and orchard managers are poorly equipped for state-of-the-art climate risk analysis. The chillR package for R helps overcome these challenges. Functions contained in this open-source package simplify many computational tasks involved in chill quantification, such as filling gaps in daily or hourly temperature records and producing hourly temperature curves from daily data. chillR contains representations of common chill (and heat) models, which can easily be applied to temperature records. Users can also define additional temperature response functions. chillR can delineate temperature response phases based on long-term phenology records (through partial least squares regression), as well as quantify chill (and heat) accumulation during these phases. Evaluation of the relative sensitivity of phenological dates to variation in accumulated heat and chill allows assessing a cultivar's susceptibility to warming. Finally, chillR makes use of a weather generator to produce typical chill distributions for climate scenario ensembles to facilitate climate risk assessment. chillR's functions are demonstrated using a temperature record from Klein-Altendorf, Germany, and cherry bloom data recorded there. Risk analysis for an ensemble of 63 climate scenarios forecasts no major changes in chill accumulation over the coming decades in this location.
Luedeling, E. (2020). Future chill risk assessment using chillR. Acta Hortic. 1280, 225-232
DOI: 10.17660/ActaHortic.2020.1280.31
winter chill, climate risk assessment, dynamic model, PLS regression, temperate trees

Acta Horticulturae