In the dairy cattle sector, the evaluation of the effects induced by heat stress is still one of the most impactful and investigated aspects as it is strongly connected to both sustainability of the production and animal welfare. On the other hand, more recently, the possibility of collecting a large dataset made available by the increasing technology diffusion is paving the way for the application of advanced numerical techniques based on machine learning or big data approaches. In this scenario, driven by rapid change, there could be the risk of dispersing the relevant information represented by the physiological animal component, which should maintain the central role in the development of numerical models and tools. In light of this, the present literature review aims to consolidate and synthesize existing research on the physiological consequences of heat stress in dairy cattle. The present review provides, in a single document, an overview, as complete as possible, of the heat stress-induced responses in dairy cattle with the intent of filling the existing research gap for extracting the veterinary knowledge present in the literature and make it available for future applications also in different research fields.

Wearable collar technologies have become integral to the advancement of precision livestock farming, revolutionizing how dairy cattle are monitored in terms of their behaviour, health status, and productivity. These devices leverage cutting-edge sensors, including accelerometers, RFID tags, GPS receivers, microphones, gyroscopes, and magnetometers, to provide non-invasive, real-time insights that enhance animal welfare, optimize resource use, and support decision-making processes in livestock management. This systematized review focuses on analyzing the sensors integrated into collar-based systems, detailing their functionalities and applications. However, significant challenges remain, including the high energy consumption of some sensors, the need for frequent recharging, and limited parameter coverage by individual devices. Future developments must focus on integrating multiple sensor types into unified systems to provide comprehensive data on animal behaviour, health, and environmental interactions. Additionally, advancements in energy-efficient designs, longer battery life, and cost-reduction strategies are essential to enhance the practicality and accessibility of these technologies. By addressing these challenges, wearable collar systems can play a pivotal role in promoting sustainable, efficient, and responsible livestock farming, aligning with global goals for environmental and economic sustainability. This paper underscores the transformative potential of wearable collar technologies in reshaping the livestock industry and driving the adoption of innovative farming practices worldwide.

Precision Livestock Farming (PLF) relies on several technological approaches to acquire, in the most efficient way, precise and real-time data concerning production and welfare of individual animals. In this regard, in the dairy sector, PLF devices are being increasingly adopted, automatic milking systems (AMSs) are becoming increasingly widespread, and monitoring systems for animals and environmental conditions are becoming common tools in herd management. As a consequence, a great amount of daily recorded data concerning individual animals are available for the farmers and they could be used effectively for the calibration of numerical models to be used for the prediction of future animal production trends. On the other hand, the machine learning approaches in PLF are nowadays considered an extremely promising solution in the research field of livestock farms and the application of these techniques in the dairy cattle farming would increase sustainability and efficiency of the sector. The study aims to define, train, and test a model developed through machine learning techniques, adopting a Random Forest algorithm, having the main goal to assess the trend in daily milk yield of a single cow in relation to environmental conditions. The model has been calibrated and tested on the data collected on 91 lactating cows of a dairy farm, located in northern Italy, and equipped with an AMS and thermo-hygrometric sensors during the years 2016–2017. In the statistical model, having seven predictor features, the daily milk yield is evaluated as a function of the position of the day in the lactation curve and the indoor barn conditions expressed in terms of daily average of the temperature-humidity index (THI) in the same day and its value in each of the five previous days. In this way, extreme hot conditions inducing heat stress effects can be considered in the yield predictions by the model. The average relative prediction error of the milk yield of each cow is about 18% of daily production, and only 2% of the total milk production.

Circular economy plays a key role in increasing the sustainability of the agricultural sector, given the countless possibilities of transforming crop residues and recycling precious resources. The maize cultivation process produces a significant amount of residual organic materials, commonly left on the field, as a soil conditioner and source of nutrients even if some parts, such as the cob, play a minor role in these actions. The solutions for the valorization of this remnant depend on economic and environmental factors and the evaluation of the environmental performances of the processes in a life-cycle perspective is important to compare the overall sustainability of the valorization alternatives, maximizing their environmental added value. This work reports the results of Life Cycle Analysis, from cradle-to-gate of corn cob valorized as a raw material in two scenarios: corn cob pellet and corn cob abrasive grits to use as blasting or finishing media. A comparative study has been performed with two products available on the market and with the same functions. The results show that cob-based products have lower impact than those currently used. The work provides indication for evaluating the benefits of turning agricultural wastes in natural-based materials and intends to promote circular economy processes in agriculture production.

Computer vision is rapidly transforming the field of dairy farm management by enabling automated, non-invasive monitoring of animal health, behavior, and productivity. This review provides a comprehensive overview of recent applications of computer vision in dairy farming management operations, including cattle identification and tracking, and consequently the assessment of feeding and rumination behavior, body condition score, lameness and lying behavior, mastitis and milk yield, and social behavior and oestrus. By synthesizing findings from recent studies, we highlight how computer vision systems contribute to improving animal welfare and enhancing productivity and reproductive performance. The paper also discusses current technological limitations, such as variability in environmental conditions and data integration challenges, as well as opportunities for future development, particularly through the integration of artificial intelligence and machine learning. This review aims to guide researchers and practitioners toward more effective adoption of vision-based technologies in precision livestock farming.

In urban areas, a considerable proportion of energy demand is allocated to buildings. Since rooftops constitute one-fourth of all urban surfaces, an increasing amount of attention is paid to achieving the most efficient shapes and component designs compatible with every climate and urban context, for rooftops of varying sizes. In this study, three types of rooftop technologies, namely insulated, green roof, and rooftop greenhouse, are evaluated for energy and thermal performance using computer simulations. Water surface exposure, absorption, and intrusion are the three important factors in the calculation of hygrothermal models that impact energy consumption and building envelope performance; however, a few studies are specifically focused on providing realistic results in multi-dimensional hygrothermal models and the assessment of the impact of moisture in roofing solutions. This paper aims at evaluating the performance of three different roofing technologies through a two-dimensional hygrothermal simulation in software WUFI. To accomplish this, a precise localized microclimate model of a complex urban context on the scale of a neighborhood was employed to evaluate the cooling and heating loads of the buildings, the impact of the water content in the green roof on the thermal behavior of the roof surface, and the feasibility of designing a building with nearly zero cooling needs. A two-story building in the city center of Bologna, Italy is modelled. Simulation results have shown that during the cooling period, the performance of the designed rooftop greenhouse is the most effective by 50% reduction in cooling loads. Besides, the impact of moisture in green roofs has been detected as a negative factor for thermal and energy performance of the building in the Mediterranean climate. The results ultimately highlighted the capability of passively-designed rooftop greenhouses to create a building with nearly zero cooling needs.