Simple SummaryIntroducing new technologies in the agricultural and livestock field does not always lead to straightforward on-farm activities. Smart glasses for augmented reality are a new technology that may assist workers in many operations, allowing them to visualize, in the glasses’ lens, diverse information related to a single subject (e.g., animal, plant, feed stock, machinery) or to receive assistance in real-time through video-calls. Using commercially available smart glasses, we explored their potential usefulness in livestock farms. The device was tested using all the functions available in different conditions, both in laboratory and open field environments. The results obtained highlighted the important contribution to assist workers in on-farm daily activities, thanks to the clear and rapid data visualization and to the good quality of audio-video streaming. Specifically, smart glasses enable real time file consulting, data collection, data sharing and remote assistance, all done while working hands-free. AbstractThe growing interest in Augmented Reality (AR) systems is becoming increasingly evident in all production sectors. However, to the authors’ knowledge, a literature gap has been found with regard to the application of smart glasses for AR in the agriculture and livestock sector. In fact, this technology allows farmers to manage animal husbandry in line with precision agriculture principles. The aim of this study was to evaluate the performances of an AR head-wearable device as a valuable and integrative tool in precision livestock farming. In this study, the GlassUp F4 Smart Glasses (F4SG) for AR were explored. Laboratory and farm tests were performed to evaluate the implementation of this new technology in livestock farms. The results highlighted several advantages of F4SG applications in farm activities. The clear and fast readability of the information related to a single issue, combined with the large number of readings that SG performed, allowed F4SG adoption even in large farms. In addition, the 7 h of battery life and the good quality of audio-video features highlighted their valuable attitude in remote assistance, supporting farmers on the field. Nevertheless, other studies are required to provide more findings for future development of software applications specifically designed for agricultural purposes.

A life cycle assessment (LCA) methodology was used to evaluate the cumulative energy demand and the related environmental impact of three large-power stand-alone photovoltaic (PV) irrigation systems ranging from 40 kWp to 360 kWp. The novelty of this analysis is the large power of these systems as the literature up to now is restricted to modeled PV pumping systems scenarios or small power plants, where the size can be a critical factor for energy and environmental issues. The analysis shows that the yearly embodied energy per unit of PV power ranged from 1306 MJ/kWp to 1199 MJ/kWp depending of the PV generator size. Similarly, the related yearly carbon dioxide impacts ranged from 72.6 to 79.8 kg CO2e/kWp. The production of PV modules accounted for the main portion (about 80%) of the primary energy embodied into the PV irrigation system (PVIS). The outcomes of the study also show an inverse trend of the energy and carbon payback times respect to the PV power size: In fact, energy payback time increased from 1.94, to 5.25 years and carbon payback time ranged from 4.62 to 9.38 years. Also the energy return on investment depends on the PV generator dimension, ranging from 12.9 to 4.8. The environmental impact of the stand-alone PV systems was also expressed in reference to the potential amount of electricity generated during the whole PV life. As expected, the largest PVIS performs the best result, obtaining an emission rate of 45.9 g CO2e per kWh, while the smallest one achieves 124.1 g CO2e per kWh. Finally, the energy and environmental indicators obtained in this study are strongly related to the irrigation needs, which in turn are influenced by other factors as the type of cultivated crops, the weather conditions and the water availability.

a b s t r a c tThe multivariate statistical approach is one of the most common techniques applied in livestock classification, where quantitative and qualitative variables are used throughout the statistical analysis to obtain farms descriptions. The aim of this study was to divide dairy farms on the bases of farm size, mechanization level, energy profile and availability of building and facilities. A population of 285 conventional dairy cow farms located in the south of Italy was involved in this project. Using the principal component analysis and the k-means cluster analysis allowed to obtain 3 different groups. Results showed a repartition where 156 farms were located in cluster 2 ''semi-intensive, low structural and mechanized farms'', 110 farms in cluster 1 ''semi-intensive, high structural and mechanized farms'', and 19 farms were positioned in cluster 3 characterized by ''intensive, high structural and mechanized farms. Larger farms are equipped with a wide number of appliances, holding higher level of power installed, but when reported to the number of raised heads or to the cultivated land area as indices, larger farms resulted more efficient and utilized less power per unit.

The setting up of innovative irrigation water management might contribute to the mitigation of negative issues related to climate change. Our hypothesis was that globe artichoke irrigated with a traditionally drip system could be converted to an innovative water management system based on precision irrigation techniques and on evaporative cooling application in order to improve crop physiological status with positive impacts on earliness, total heads yield and water saving. Over two experiments carried out at plot- and field-scale, two irrigation management systems, differing in type and application time, were compared: (i) conventional, and (ii) canopy-cooling. Plant physiological status at a weekly sampling interval and the head atrophy incidence (as the ratio of the total primary heads collected) were monitored. We also recorded and determined heads production, and yield components. In both experiments, throughout the application period of evaporative cooling (three months), canopy-cooling showed the lowest value of leaf temperature and the highest photosynthesis values compared with the conventional one (+3 °C and -30%, respectively). The physiological advantage gained by the crop with evaporative cooling has led to a higher production both in terms of total yield (+30%), and in terms of harvested first order heads that from an economic viewpoint are the most profitable for farmers. At farm-scale, the canopy-cooling treatment resulted in a higher earliness (35 days) and water productivity (+36%) compared with conventional one. Our findings show that by combining evaporative cooling practice with precision irrigation technique the heads yield can be optimized also leading to a relevant water saving (-34%). Moreover, the study proved that canopy-cooling set up might be a winning strategy in order to mitigate climatic changes and heat stress conditions.

Dairy cattle farms are continuously developing more intensive systems of management, which require higher utilization of durable and non-durable inputs. These inputs are responsible for significant direct and indirect fossil energy requirements, which are related to remarkable emissions of CO 2 . This study focused on investigating the indirect energy requirements of 285 conventional dairy farms and the related carbon footprint. A detailed analysis of the indirect energy inputs related to farm buildings, machinery and agricultural inputs was carried out. A partial life cycle assessment approach was carried out to evaluate indirect energy inputs and the carbon footprint of farms over a period of one harvest year. The investigation highlights the importance and the weight related to the use of agricultural inputs, which represent more than 80% of the total indirect energy requirements. Moreover, the analyses carried out underline that the assumption of similarity in terms of requirements of indirect energy and related carbon emissions among dairy farms is incorrect especially when observing different farm sizes and milk production levels. Moreover, a mathematical model to estimate the indirect energy requirements of dairy farms has been developed in order to provide an instrument allowing researchers to assess the energy incorporated into farm machinery, agricultural inputs and buildings. Combining the results of this two-part series, the total energy demand (expressed in GJ per farm) results in being mostly due to agricultural inputs and fuel consumption, which have the largest share of the annual requirements for each milk yield class. Direct and indirect energy requirements increased, going from small sized farms to larger ones, from 1302-5109 GJ·y −1 , respectively. However, the related carbon dioxide emissions expressed per 100 kg of milk showed a negative trend going from class <5000 to >9000 kg of milk yield, where larger farms were able to emit 48% less carbon dioxide than small herd size farm (43 vs. 82 kg CO 2 -eq per 100 kg Fat-and Protein-Corrected Milk (FPCM)). Decreasing direct and indirect energy requirements allowed reducing the anthropogenic gas emissions to the environment, reducing the energy costs for dairy farms and improving the efficient utilization of natural resources.