GrapevineXL reliably predicts multi-annual dynamics of vine water status, berry growth, and sugar accumulation in vineyards

Abstract

Climate and water availability greatly affect each season’s grape yield and quality. Using models to accurately predict environment impacts on fruit productivity and quality is a huge challenge. We calibrated and validated the functional-structural model, GrapevineXL, with a data set including grapevine seasonal midday stem water potential (Ψxylem), berry dry weight (DW), fresh weight (FW), and sugar concentration per volume ([Sugar]) for a wine grape cultivar (Vitis vinifera cv. Cabernet Franc) in field conditions over 13 years in Bordeaux, France. Our results showed that the model could make a fair prediction of seasonal Ψxylem and good-to-excellent predictions of berry DW, FW, [Sugar] and leaf gas exchange responses to predawn and midday leaf water potentials under diverse environmental conditions with 14 key parameters. By running virtual experiments to mimic climate change, an advanced veraison (i.e. the onset of ripening) of 14 and 28 days led to significant decreases of berry FW by 2.70% and 3.22%, clear increases of berry [Sugar] by 2.90% and 4.29%, and shortened ripening duration in 8 out of 13 simulated years, respectively. Moreover, the impact of the advanced veraison varied with seasonal patterns of climate and soil water availability. Overall, the results showed that the GrapevineXL model can predict plant water use and berry growth in field conditions and could serve as a valuable tool for designing sustainable vineyard management strategies to cope with climate change.

Acknowledgements

The authors thank Nabil Girollet (INRAE, Bordeaux, France) and Pierre Gay (Université de Bordeaux) for assistance in setting and running the model on the computing facilities MCIA (Mésocentre de Calcul Intensif Aquitain, on the cluster of Curta) of the University of Bordeaux. W.Y. thanks the Chinese Scholarship Council (CSC) for supporting his study in INRAE, France. We are grateful to multiple interns who assisted with data collection. This research was supported partly by National Key R&D Program of China (grant numbers 2021YFE0109500,2019YFD1000100), CAS Youth Interdisciplinary Team (JCTD-2022-06), and National Natural Science Foundation of China (grant number 31860527). Research was conducted as part of the LIA INNOGRAPE International Associated Laboratory.

Author contributions

W.Y.: data curation, formal analysis, investigation, visualization, methodology, writing-original draft; J.Z.: methodology, software, writing-review and editing; C.vL.: data collection, resources, investigation, writing-review and editing; Z.D.: conceptualization, funding acquisition, supervision, writing-review and editing; G.A.G.: conceptualization, supervision, writing-review and editing.

Data availability

All data supporting the findings of this study are available within the paper and within its supplementary materials published online.

Conflict of interest statement

None declared.

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