This study shows a new methodological proposal for wine farm management, as a result of the progressive development of the technological innovations and their adoption. The study was carried out in Italy involving farmers, workers, or owners of wine farms who are progressively introducing or using precision agriculture technologies on their farm. The methodology proposed was divided in four stages (1. understanding the changes in action; 2. identifying the added value of Smart Farming processes; 3. verifying the reliability of new technologies; 4. adjusting production processes) that can be applied at different levels in vine farms to make the adoption of precision agriculture techniques and technologies harmonious and profitable. Data collection was carried out using a participant-observer method in brainstorming sessions, where the authors reflected on the significance of technology adoption means and how to put them in practice, and interviews, questionnaire surveys, diaries, and observations. Moreover, project activities and reports provided auxiliary data. The findings highlighted the issues of a sector which, although with broad investment and finance options, lacks a structure of human, territorial, and organizational resources for the successful adoption of technological innovations. The work represents a basis for the future development of models for strategic scenario planning and risk assessments for farmers, policymakers, and scientists.

The present research aimed to evaluate whether two sensors, optical and laser, could highlight the change in olive trees’ canopy structure due to pruning. Therefore, two proximal sensors were mounted on a ground vehicle (Kubota B2420 tractor): a multispectral sensor (OptRx ACS 430 AgLeader) and a 2D LiDAR sensor (Sick TIM 561). The multispectral sensor was used to evaluate the potential effect of biomass variability before pruning on sensor response. The 2D LiDAR was used to assess its ability to discriminate volume before and after pruning. Data were collected in a traditional olive grove located in Tenute di Cesa Farm, in the east of Tuscany, Italy, characterized by a 4x6 m planting layout and by developed plants. LiDAR data were used to measure canopy volumes, height, and diameter, and the generated point cloud was studied to assess the difference in density between treatments. Ten plants were selected for the study. To validate the LiDAR results, manual measurements of the canopy height and diameter dimensions of the plants were taken. The pruning weights of the monitored plants were obtained to assess the correlation with the canopy characterization data. The results obtained showed that pruning did not affect the results of the multispectral sensor, and the potential variation in canopy density and porosity did not lead to different results with this instrument. Plant volumes, height, and diameters calculated with the LiDAR sensor correlated well with the values of manual measurements, while volume differences between before and after pruning obtained good correlations with pruning weights (Pearson correlation coefficient: 0.66–0.83). The study of point cloud density in canopy thickness and height showed different shapes before and after pruning, especially in the former case. Correlations between point cloud density obtained from LiDAR and multispectral sensor results were not statistically significant. Even if more studies are necessary, the results obtained can be of interest in pruning management.

The aims of agricultural land management change continuously, reflecting shifts in wider societal priorities. Currently, these include addressing the climate crisis, promoting environmental sustainability, and supporting the livelihoods of rural communities while ensuring food security. Working toward these aims requires information on the character of agricultural land and how dynamic processes influence it. Remote and near-surface sensing data are important sources of information on the characteristics of soils, plants, water, topography, and related processes. Sensing data are collected, analysed, and used in decision-making by specialists in multiple domains connected to land management. While progress has been made to connect the use of sensing data across agricultural and environmental applications under the umbrella of integrated sustainable land management, archaeological and heritage uses of these data remain largely disconnected. This creates barriers to accounting for the impacts of past human activities on contemporary agricultural landscapes through the alteration of soils, topography, and plant communities. In parallel, it hinders the creation of knowledge about the archaeological features which form an essential part of the heritage of agricultural landscapes. The ipaast-czo project explores the potential of a coordinated approach across all these domains, which would reduce these barriers and provide benefits by better integrating information generated using sensing. To do so, both conceptual and practical barriers to developing shared practices and how these might be overcome were considered. In this study, a conceptual framework designed to create a shared understanding of how agricultural landscapes work and enable collaboration around their management was proposed. This framework treats present-day rural agricultural landscapes as Critical Zones: complex entities shaped by long-term human–environment interactions including contemporary farming. Practitioners in precision agriculture and archaeological remote and near-surface sensing, as well as users of these data, were engaged using workshops and interviews. The relationships between practitioners’ objectives, data requirements for their applications, and their perceptions of the benefits and disadvantages of changing working practices were interrogated. The conceptual framework and assessment of practical benefits and challenges emerging from this work provide a foundation for leveraging shared sensing data and methods for long-term integrated sustainable land management.

Remote and near surface sensing data are widely used in archaeology and heritage management for feature discovery, change detection and monitoring, as an input to predictive modelling, and in the planning process. While global and regional datasets are widely used for some purposes, data are regularly acquired specifically for archaeological projects because of the very high spatial resolution required for feature detection and assessments of archaeological significance and the need for data on subsurface features. The sensing data collected for archaeology cover limited areas and only a few types of sensors, known to produce data efficiently, are regularly employed. Precision agriculture is beginning to produce large quantities of varied sensing data across extensive landscape areas. This situation creates an opportunity to adapt and reuse precision agricultural data for archaeology and heritage work, extending covering and enhancing our understanding of archaeology in contemporary agricultural landscapes. Equally, there is potential for coordinated data collection, collecting data once for multiple applications, and to add value through analyses which bring together perspectives from multiple related domains to model long-term processes in anthropogenic soil systems. This article provides a high-level overview of policy and technological developments which create the potential for sensing data reuse, coordinated data collection, and collaborative analyses across archaeological, agricultural, and agri-environmental applications while underscoring the structural barriers which, at present, constrain this potential. It highlights examples where the development of interoperable data and workflows can promote tighter integration of archaeology and cultural heritage management with sustainable agricultural land management and support integrated decision making.

The present research had two aims. The first was to evaluate the effect of height and exposure on the vegetative response of olive canopies’ vertical axis studied through a multispectral sensor and on the qualitative and quantitative product characteristics. The second was to examine the relationship between multispectral data and productive characteristics. Six olive plants were sampled, and their canopy’s vertical axis was subdivided into four sectors based on two heights (Top and Low) and two exposures (West and East). A ground-vehicle-mounted multispectral proximal sensor (OptRx from AgLeader^®) was used to investigate the different behaviours of the olive canopy vegetation index (VI) responses in each sector. A selective harvest was performed, in which each plant and sector were harvested separately. Product characterisation was conducted to investigate the response of the products (both olives and oils) in each sector. The results of Tukey’s test (p > 0.05) showed a significant effect of height for the VI responses, with the Low sector obtaining higher values than the Top sector. The olive product showed some height and exposure effect, particularly for the olives’ dimension and resistance to detachment, which was statistically higher in the upper part of the canopies. The regression studies highlighted some relationships between the VIs and product characteristics, particularly for resistance to detachments (R² = 0.44–0.63), which can affect harvest management. In conclusion, the results showed the complexity of the olive canopies’ response to multispectral data collection, highlighting the need to study the vertical axis to assess the variability of the canopy itself. The relationship between multispectral data and product characteristics must be further investigated.