Forcing experiment and model integration to validate an olive tree phenological model

Abstract

Accurate assessment of chill and heat requirements is a fundamental challenge in phenological modeling of perennial fruit species, especially in the current climate change setting. Such models are typically calibrated and validated on the basis of long-term phenological and temperature records, without the integration of forcing experiments to highlight endodormancy release dates. Here we investigated olive tree flowering date modeling by combining a statistical approach and forcing experiments on the Picholine Marocaine cultivar with the aim of developing an olive phenological model for accurate assessment of chill and heat requirements. We initially assessed the endodormancy release date using a forcing experiment in 2021 and 2022 by comparing floral bud fresh weights under natural field conditions and after forcing conditions in a climate-controlled growing chamber. Subsequently, we used the PhenoFlex framework to calibrate and validate the model based on 31 years of flowering date and temperature data. We obtained an optimal model following nine calibrations with distinct season onset dates, and the latter were similar to the chilling onset date determined through a previous partial least squares regression approach. We also used endodormancy release dates to improve our model calibration by fitting the predicted flowering date to the observed value according to the recalibrated chill and heat requirement values. Finally, we validated the model based on six years of flowering date and temperature data. Our findings highlighted that the model calibrated solely via phenological records was less accurate than that calibrated based on a combination of phenological records and forcing experiment data. Our investigations broaden the scope for future applications of phenological olive tree modeling to accurately assess chill and heat requirements, which is currently a challenging issue facing olive growing in Mediterranean areas under climate warming.