Seven years of spatial crowdsourcing in viticulture: lessons learned from the monitoring of vine water status with the Apex-vigne project

Abstract

Regional-scale vineyard monitoring is crucial for addressing climate change adaptation, notably water stress. While crowdsourcing offers a promising solution for collecting data at this large spatial scale, its true effectiveness, including participant mobilization and robustness against sampling biases, remains under-documented. This paper provides a critical analysis of crowdsourcing’s potential and limitations for regional vineyard monitoring, using the seven-year Apex-Vigne project as a case study. Apex-Vigne monitors vine water status via a simple, calculated indicator, iG-Apex, derived from weekly vine shoot growth observations contributed by industry stakeholders (winegrowers and advisors) through a mobile application. The analysis focused on the spatio-temporal distribution of data collected in Metropolitan France, specifically within a 49,500 km² Mediterranean study zone (2019–2025). The project’s capacity to generate regional-scale information was assessed by mapping iG-Apex values, and key scientific challenges were identified. Over seven seasons, Apex-Vigne successfully gathered 31,141 observations from over 887 contributors on 13,896 fields. Observations were collected following three main use cases scenarios according to the specific interests of contributors: on-farm experimentation at within-field level, field monitoring at farm level and reference field monitoring at regional level. The data volume proved sufficient to spatialize vine water status and illustrate temporal dynamics at the regional level. These results demonstrate crowdsourcing’s potential as a new source of information for regional decision support in viticulture. The study also highlights scientific challenges raised by crowdsourcing projects in viticulture. Social sciences are needed to understand contributors’ motivations and new data sciences approaches are to be explored to limit the influence of sampling biases or to automatically identify observations with atypical behaviour.

References

Text Brunel, G., Pichon, L., Taylor, J., & Tisseyre, B. (2019). Easy water stress detection system for vineyard irrigation management. In Precision Agriculture 2019 – Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019 (pp. 935–942). https://doi.org/10.3920/978-90-8686-888-9_115

Christakakis, P., Papadopoulou, G., Mikos, G., Kalogiannidis, N., Ioannidis, D., Tzovaras, D., & Pechlivani, E. M. (2024). Smartphone-based citizen science tool for plant disease and insect pest detection using artificial intelligence. Technologies, 12(7). https://doi.org/10.3390/technologies12070101

Hamon, B., Thibault, J., Tissot, C., Parker, A., & Quénol, H. (2024). Identification of the best viticultural areas by spatial optimisation. Application in New Zealand South Island in the context of climate change. OENO One, 58(3), 1–11. https://doi.org/10.20870/oeno-one.2024.58.3.8031

Hofmann, M., Volosciuk, C., Dubrovský, M., Maraun, D., & Schultz, H. R. (2022). Downscaling of climate change scenarios for a high-resolution, site-specific assessment of drought stress risk for two viticultural regions with heterogeneous landscapes. Earth System Dynamics, 13(2), 911–934. https://doi.org/10.5194/esd-13-911-2022

Mason, K., & Isaacs, R. (2021). Regional variation in captures of male Paralobesia viteana (Lepidoptera: Tortricidae) in monitoring traps in Michigan is not due to geographical variation in male response to pheromone. Environmental Entomology, 50(4), 795–802. https://doi.org/10.1093/ee/nvab033

Minet, J., Curnel, Y., Gobin, A., Goffart, J.-P., Mélard, F., Tychon, B., et al. (2017). Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach. Computers and Electronics in Agriculture, 142, 126–138. https://doi.org/10.1016/j.compag.2017.08.026

Pellegrino, A., Lebon, E., Simonneau, T., & Wery, J. (2005). Towards a simple indicator of water stress in grapevine (Vitis vinifera L.) based on the differential sensitivities of vegetative growth components. Australian Journal of Grape and Wine Research, 11(3), 306–315. https://doi.org/10.1111/j.1755-0238.2005.tb00030.x

Pichon, L., Brunel, G., Zhang, Y., & Tisseyre, B. (2022). Towards a regional mapping of vine water status based on crowdsourcing observations. Oeno One, 56(2), 279–290. https://doi.org/10.20870/oeno-one.2022.56.2.5442

Pichon, L., Laurent, C., Payan, J.-C., & Tisseyre, B., (2023). Observation of shoot growth: a simple and operational decision-making tool for monitoring vine water status in the vineyard. OENO One 57, 235–244. https://doi.org/10.20870/oeno-one.2023.57.1.5481

Sgubin, G., Swingedouw, D., Dayon, G., García de Cortázar-Atauri, I., Ollat, N., Pagé, C., & van Leeuwen, C. (2018). The risk of tardive frost damage in French vineyards in a changing climate. Agricultural and Forest Meteorology, 250-251, 226–242. https://doi.org/10.1016/j.agrformet.2017.12.253

Acknowledgments

The authors gratefully acknowledge the financial support of #Digitag ANR-16-CONV-0004 and the Région Occitanie, which funded the ImApex and Iconic projects that enabled this work.