APIS-P Operational Group: Beekeeping for the Prediction of Fungal Infections and Harvest Estimation

The expected outcome of R1 is to determine whether beehives can be used as a practical tool for the early detection of downy mildew and botrytis spores in vineyards. To this end, monitored beehives will be installed, and samples of pollen, honey, and vine health will be analyzed to identify the presence of pathogens. For winegrowers and wineries, the added value would be having an early indication of fungal pressure before damage becomes visible in the vineyard. This can help improve vineyard monitoring, allow for earlier intervention, reduce losses, and better adjust treatments. In practice, users will be able to utilize the hive data to support decision-making regarding plant health and as a complement to traditional visual monitoring. Furthermore, it will allow them to determine whether this signal can be used to evaluate the effectiveness of applied treatments and track the evolution of risk throughout the growing season. This would facilitate more proactive and less reactive vineyard management.

The expected outcome of R2 is to compare three methods for monitoring pollen and spores in vineyards: manual reference collectors, automatic collectors, and biomonitoring using beehives. The practical objective is to determine whether beehives and automatic systems can serve as an alternative to, or complement, traditional methods, which are slower and more expensive. For wineries and grape growers, the main benefit would be having more agile monitoring systems that require less specialized labor and offer faster response times. If the results are positive, users will be able to choose more efficient tools to control pathogen pressure, monitor the biodiversity of the environment, and make decisions with lower operating costs. It will also allow them to assess whether these solutions provide sufficiently reliable information for routine use on the farm. This will enable them to decide which system is most useful in terms of cost, speed, and reliability.

The expected outcome of R3 is to improve downy mildew and botrytis risk prediction models by combining meteorological data, vineyard phenological stage, spore counts, and data from beehives. The practical benefit for winegrowers will be access to risk alerts more closely aligned with the specific conditions of each plot, allowing for timely treatment and avoiding unnecessary interventions. This can translate into fewer pesticide applications, lower costs, less environmental impact, and better crop protection. In practice, wineries will be able to use these models as a decision-support tool to plan treatments, monitor critical periods, and subsequently assess the effectiveness of the implemented strategy. Their application can make integrated pest management more precise and improve crop profitability.

The expected outcome of R4 is to demonstrate whether beehives can act as useful bioindicators of both vineyard production and health risks. The relationship between grapevine pollen collected by bees, grape and wine production, and the appearance of pathogenic spores and disease symptoms will be analyzed. For the end user, this could offer a dual opportunity: early warning of health problems and an additional source of information to anticipate the vineyard's productive performance. If this relationship is confirmed, winegrowers and wineries will be able to use hive data to strengthen crop monitoring, better refine their management decisions, and plan the harvest season with greater confidence. This would provide producers with a simple and cost-effective tool for anticipating risks and opportunities, thereby increasing their ability to anticipate problems and improve technical control of the vineyard.

The expected outcome of R5 is the development of a preliminary model for estimating the grape harvest based on pollen collected by bees during flowering. The main benefit for wineries and grape growers would be to have an additional tool for anticipating the harvest with more information and further in advance. This can improve labor planning, logistics, winery capacity, and sales forecasts. It can also help adjust decisions during the harvest if deviations from expectations are detected. In practice, users will be able to leverage the data collected from the hives to support production forecasting, complementing other agronomic indicators and reducing uncertainty in harvest management. If successful, the system can become a practical aid for improving organization and reducing estimation errors. Furthermore, it can provide an objective basis for making early business decisions.

The expected outcome of R6 is to identify the presence of microplastics of environmental origin in samples collected from the beehive and assess their potential incorporation into the production system. For wineries and producers, the main value lies in having access to useful information on environmental pollution, which until now has not been routinely monitored in the field. This can help strengthen environmental quality monitoring, detect potential sources of pollution, and improve the environmental traceability of production. In practice, the results can be used to support sustainability strategies, enhance product image, and anticipate future market demands or regulations regarding environmental quality and food safety. It can also offer a commercial advantage by strengthening transparency and product differentiation for customers and value chains.

The expected outcome of R7 is to better characterize the biodiversity of the vineyard's surroundings and identify potential sources of pathogens through the analysis of pollen and spores collected in pollen traps and beehives. For the end user, this means a better understanding of which plant and fungal species are present around the plot, how they can influence the crop, and from which areas inoculum pressure may originate. This information can be very useful for improving monitoring, guiding preventative measures, and better understanding the relationship between landscape and plant health. In practice, wineries and winegrowers will be able to use these results to strengthen their management strategy, support vineyard protection decisions, and better assess the environment as a productive and health factor. Furthermore, it can help design more selective interventions and reduce unnecessary treatments based on the actual risk. This can translate into a better balance between production, prevention, and sustainability.

RD1 aims to maximize the visibility of the APIS project and keep winegrowers, wineries, beekeepers, cooperatives, government agencies, and the general public informed from the outset. The expected outcome is clear and continuous dissemination of the project's objectives, progress, and benefits through the website, social media, webinars, press releases, graphic materials, and audiovisual content. For the end user, the added value lies in easy access to useful information about new tools for monitoring vineyard health, reducing risks, and improving decision-making. This dissemination will also allow other wineries, regions, and organizations to learn about the experience and assess its application. In practice, professionals will be able to follow the project's evolution, identify solutions transferable to their farms, and anticipate innovation opportunities with an impact on costs, sustainability, and competitiveness.

RD2 is designed to transfer the solutions and results obtained in APIS to the sector in a useful and applicable way. The expected outcome is that winegrowers, wineries, beekeepers, and technicians will have practical materials to understand how to use biomonitoring with beehives, sensors, and risk models in vineyard management. To this end, technical data sheets, posters, audiovisual materials, articles, and field demonstration workshops will be developed. The main benefit for the end user is transforming the project's results into concrete tools to improve phytosanitary monitoring, adjust treatments, reduce costs, and make more informed decisions. In practice, this transfer will allow them to learn firsthand which solutions work, how to implement them, and what advantages they can offer in terms of productivity, sustainability, and the ability to anticipate crop diseases and risks.