The implementation of operational climate service prototypes, which encompasses the co-design and delivery of real-time actionable products with/to stakeholders, contributes to efficiently leveraging operational climate predictions into actionable climate information by providing practical insight on the actual use of climate predictions. This work showcases a general guideline for implementing an operational climate service based on subseasonal predictions

Climate services are one of the tools that can support the agriculture sector to address the impacts of climate change on agricultural production systems, not only considering climatic aspects but also social needs. This work describes the knowledge co-production journey of the EU-funded project MED-GOLD to create an end-to-end climate service for wine sector users.

CSIndicators is an R package that gathers generalised methods for the flexible computation of climate-related indicators. Each method represents a specific mathematical approach which is combined with the possibility of selecting a flexible time period to define the indicator.

The potential increase in the adoption value of seasonal forecasts is spotlighted in this paper by introducing observation-forecast blending for wine-sector indicators over the Iberian Peninsula. The predictions of six bioclimatic indicators (temperature and precipitation based) considered highly important from the perspective of wine-sector users were prepared for each month of the growing season and combined with previous observations as the indicator period progresses.

Environmental and economic costs demand a rapid transition to more sustainable farming systems, which are still heavily dependent on chemicals for crop protection. Despite their widespread application, powdery mildew (PM) and downy mildew (DM) continue to generate serious economic penalties for grape and wine production. To reduce these losses and minimize environmental impacts, it is important to predict infections with high confidence and accuracy, allowing timely and efficient intervention.

Context and purpose of the study – Climate change is having an unprecedented impact on the wine industry, which is one of the major agricultural sectors around the world. Global warming, combined with the variation in rainfall patterns and the increase in frequency of extreme weather events, is significantly influencing vine physiology and exposing, more frequently, plants to severe biotic and abiotic stresses.

The phenological evolution is a crucial aspect of grapevine growth and development. Accurate detection of phenological stages can improve vineyard management, leading to better crop yield and quality traits. However, traditional methods of phenological tracking such as on-site observations are time-consuming and labour-intensive. This work proposes a scalable data-driven method to automatically detect key phenological stages of grapevines using satellite data.

Plasmopara viticola, the causal agent of downy mildew (DM), and Uncinula necator, the causal agent of powdery mildew (PM), are two of the main phytopathogenic microorganisms causing major economic losses in the primary sector, especially in the wine sector, by wilting bunches and leaves with a consequent decrease in the photosynthetic rate of the plant and in the annual yield. Currently, the most widespread methods for planning spraying are based on the 3-10 rule, which states that the first application should take place when: (i) the air temperature is greater than 10°C; (ii) shoots are equal or greater than 10 cm; and (iii) a minimum of 10 mm rainfall within 24–48 hours has occurred, or at the beginning of the bud break with periodic applications according to the manufacturer’s instructions.