Olive oil production is among the most significant pillars of crop production, especially in the Mediterranean region. The management risks undertaken throughout the olive oil production chain can be minimized using smart tools and applications. This review addressed the influence of the fruit fly of Bactrocera oleae (B. oleae) or Dacus oleae on the quality and antioxidant activity of the olives and their products based on the most recent literature data. Furthermore, in this review, we focused on the latest research achievements in remote sensor systems, features, and monitoring algorithms applied to remotely monitor plant diseases and pests, which are summarized here. Thus, this paper illustrates how precision agriculture technologies can be used to help agricultural decision-makers and to monitor problems associated with integrated pest management for crops and livestock, achieving agricultural sustainability. Moreover, challenges and potential future perspectives for the widespread adoption of these innovative technologies are discussed.

In a world grappling with a growing population and shifting climate patterns, ensuring safe and sustainable food production has emerged as a paramount challenge. Extreme climate events, such as late spring frosts (LSFs), have a detrimental impact on productivity, plant growth, and consequently, crop yield. Similarly, viticulture is intricately linked to weather and climate conditions. Frost risk can be a significant issue in viticulture, potentially causing major economic damages with yield losses affecting vast areas or even entire territories from a single event. Chilling temperatures (ranging from 0 to 15 °C) and freezing conditions (below 0 °C) present unique challenges for vineyards, occurring when temperatures deviate from their usual range. These temperature fluctuations can significantly impair viticulture, affecting grapevines and diminishing both the quality and quantity of the grape harvest. In recent years, frost events have become more frequent and severe while winegrowers use various techniques to combat frost. This article aims to summarize the negative effects of extreme frost conditions in a changing climate on grapes and wine production and provide novel solutions and adaptation strategies, including sensing analysis tools, to help vineyards mitigate these impacts and ensure sustainable production.

Effective data governance and management are necessary but challenging prerequisites for creating value from data assets. Findability, accessibility, interoperability, and reusability are guiding principles for data owners in managing and archiving datasets, known as the FAIR Principles. Dataspaces provide an infrastructure for heterogeneous, multi-source data integration and cross-organizational data sharing that would benefit from FAIR compliance. In this paper, we propose semantics as an approach to ensure data FAIRness, enabling machine-aided discovery and reuse of data in different formats and structures.
We conduct a systematic literature review to translate the overarching principles into ten concrete methods that can be implemented using semantic technologies. In addition, we analyze three mature dataspace initiatives for their adherence to the FAIR Principles and describe their specific implementation. In summary, we argue that semantics provide a common and infrastructure-independent foundation for data management in emerging dataspaces.

Climate change is one of the major environmental and socio-economic challenges facing sustainable wine production. Saint Emilion, a world-renowned wine-growing region, is witnessing firsthand a series of weather events related to frost waves, hailstorms, rising temperatures, and shifts in rainfall patterns. Accordingly, through the European Commission-funded AgriDataValue project, the implementation of natural solutions based on fine-scale modeling of climatic variables is being sought. Saint Emilion has been chosen as the study area due to the presence of existing on-field solutions adopted by winegrowers and supported by the Saint-Emilion wine council to mitigate the effects of climate change. These solutions include improving soil health and fertility using grass strips and enhancing landscape features and hedge planting. A salt-dispersing balloon system was also adopted to combat hailstorms. Additionally, Saint-Emilion has a large network of weather stations and data that are key for better understanding climatic events and for modeling the efficiency of active and passive solutions. However, given the costly nature of the latter, strategic risk-based planning is needed. For this purpose, MeteoFrance DRIAS and outputs of the CMIP6 model are used. Data is obtained in NetCDF format, then rasterized for subsequent downscaling. By transposing NetCDFs to raster and then points, the gridded rasters become a virtual weather station network and hence can be considered as a mesh of “spatial weather stations.” Through specific Bayesian kriging techniques using semi-variograms, the initial 100 km grid is downscaled to 1 km. Following the establishment of the climate indicators, exposure is determined. This framework embodies the first elements of the IPCC risk analysis framework (IPCC, 2014; 2020). As the studied climatic risks will show different spatial-temporal gradients, different grape types and plantations will be affected to different degrees. Hence, a GIS-based exposure analysis is conducted based on their setting with respect to the risk at different horizons. At this resolution, the evolution of the studied risks can be tracked at a fine scale and at different time horizons and climatic scenarios, namely SSP2-4.5 and SSP5-8.5. Accordingly, with this long-term planning aspect and its climate-informed nature, a strategic planning of solutions in areas of different risk levels can be made following a prioritized approach, thus ensuring both short-term and long-term efficiency.

The adoption of digital technologies in agriculture offers significant potential to enhance productivity, sustainability, and resilience. This paper presents initial insights derived from an ongoing study aimed at evaluating the challenges and opportunities for farmers in adopting data-driven solutions. During the initial phases, the study utilized an online workshop with 46 participants from various agri-stakeholder groups to conduct a comprehensive PESTLE analysis, exploring the political, economic, social, technological, legal, and environmental factors influencing the adoption of data-driven solutions in agriculture. Key findings indicate that cohesive governmental policies, innovative business models, and targeted educational initiatives are essential for fostering digital transformation in agriculture. Political and regulatory challenges, such as aligning cross-border data-sharing frameworks and ensuring consistent policy implementation, must be addressed. High costs and technical knowledge requirements are substantial economic barriers, particularly for smallholder farmers, necessitating tailored financial support and cooperative business models. Socially, the digital divide and trust issues in data security highlight the need for equitable infrastructure distribution and trust-building measures. Technological advancements like IoT and blockchain offer opportunities but require robust data governance and cybersecurity frameworks. From a legal perspective, simplifying regulatory compliance and clarifying data ownership are crucial for facilitating adoption. While digital agriculture practices support sustainability goals, environmental risks such as electronic waste and increased carbon footprint need careful management. The insights gathered from diverse agri-stakeholders provide an understanding of the factors impacting digital transformation in agriculture. This research contributes to understanding the specific barriers faced by farmers in adopting data-driven solutions and offers actionable recommendations for creating inclusive and effective data-driven agricultural initiatives.