New citizen science initiative enhances flowering onset predictions for fruit trees in Great Britain

Abstract

Accurately predicting flowering phenology in fruit tree orchards is crucial for timely pest and pathogen treatments and for the introduction of managed pollinators. Making predictions requires large datasets of flowering dates, which are often limited to single locations. Consequently, the resulting phenology predictions are not representative across larger geographic areas. Citizen science may offer a solution to this data gap, with millions of biological records across a wide range of taxa recorded annually. Here, a new citizen science platform called ‘FruitWatch’ is introduced, monitoring the flowering dates of fruit trees in Great Britain. The objectives of this study are to assess the suitability of FruitWatch submissions to (i) detect latitudinal variation in flowering onset dates, (ii) parameterize existing phenology modelling frameworks, and (iii) make predictions of flowering onset dates across Great Britain for a single year. Using data for four cultivars from 2022, linear models reveal significant latitudinal delays in flowering onset of as much as 1.49 ± 0.63 days per degree latitude further north (Pear ‘Conference’), with significant delays also seen in Cherry ‘Stella’ (1.39 ± 0.48 days) and Plum ‘Victoria’ (1.22 ± 0.18 days). FruitWatch informed phenology modelling frameworks performed well for predicting flowering onset, with root mean square error values of predictions from validation datasets ranging between 4.6 (‘Victoria’) and 8.0 (‘Conference’) days. The parameterized models also provided realistic flowering onset predictions across Great Britain in 2022, with earlier flowering dates predicted in warmer areas. These findings demonstrate the potential of citizen science data to offer growers cultivar- and location-specific phenology predictions to help inform orchard management.

Acknowledgements

The authors would like to thank Ajay Kumar, for his advice and support developing www.fruitwatch.org, the many University of Reading students and staff who contributed to the testing of FruitWatch and all the members of the public who have submitted records. This project was funded by BBSRC (Grant number: BB/T508895/1) and Waitrose Agronomy Group as part of the Waitrose Collaborative Training Partnership. This work was supported in part by Oracle Cloud credits and related resources provided by the Oracle for Research program (Grant number: 16366771).

Data availability

The data that support the findings of this study are openly available in The University of Reading Data Archive. Available at: https://doi.org/10.17864/1947.000524

Conflict of interest statement

The authors declare no conflict of interest.

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