The escalating global population and climate change necessitate sustainable livestock production methods to meet rising food demand. Precision Livestock Farming (PLF) integrates information and communication technologies (ICT) to improve farming efficiency and animal health. Unlike traditional methods, PLF uses machine learning (ML) algorithms to analyze data in real time, providing valuable insights to decision makers. Dairy farming in diverse climates is challenging and requires well-designed structures to regulate internal environmental parameters. This study explores the application of the Facebook-developed Prophet algorithm to predict indoor temperatures in a dairy farm over a 72 h horizon. Exogenous variables sourced from the Open-Meteo platform improve the accuracy of the model. The paper details case study construction, data acquisition, preprocessing, and model training, highlighting the importance of seasonality in environmental variables. Model validation using key metrics shows consistent accuracy across different dates, as the mean absolute percentage error on daily base ranges from 1.71% to 2.62%. The results indicate excellent model performance, especially considering the operational context. The study concludes that black box models, such as the Prophet algorithm, are effective for predicting indoor temperatures in livestock buildings and provide valuable insights for environmental control and optimization in livestock production. Future research should explore gray box models that integrate physical building characteristics to improve predictive performance and HVAC system control.

The benefits of ecosystem services provided by urban green systems have been highlighted in research on spatial and landscape planning, and the need has emerged for an integrated approach to urban green planning aiming at increasing climate mitigation and urban resilience. Research indicates that plant selection and substrate management are vital for optimizing the most important performance of green roofs, like building thermal insulation, urban heat reduction, air quality improvement, and stormwater management. In Mediterranean climates, it is essential to investigate sustainable management solutions for green roofs like the growth potential of native, low-maintenance forbs adapted to thermal and water stress on specific substrates. Medicinal species may be suitable, provided that interactions with pollutants are controlled. This study evaluates the performance of Melissa officinalis and Hypericum perforatum on experimental green roof modules under controlled conditions, comparing chemical fertilization and three different treatments with biomass from Trifolium repens used as green manure. The key metrics of fresh and dry biomass, plant cover ratio, and chlorophyll content are measured. Results show significantly higher values of cover and biomass for these two species treated with green manure in comparison to chemical fertilization, with no significant differences in chlorophyll content, indicating that T. repens is a useful source of green manure in green roof management. Overall, the results are consistent with the research goals of suggesting sustainable solutions for green roof management, since low-maintenance vegetation and green manure contribute to the elimination of chemicals in urban green.

The control of environmental parameters in livestock farming is essential to achieve optimal ranges of temperature and humidity. HVAC systems for this purpose are characterized by high energy demands, causing significant GHG emissions when relying on fossil fuels. The aim of this study is the development and testing of a sustainable heating system for a nursery barn hosting 2500 weaners, as well as the assessment of the effectiveness and the performance of the new system. This work involved the implementation of a renewable energy source (RES) system incorporating a borehole thermal energy storage and photovoltaic thermal collectors, integrated with a Dual-Source Heat Pump. A smart control system was installed and the collected data were processed to define the optimal settings of the integrated plant for energy production and efficiency. The performance in terms of the control of the environmental conditions of the nursery barn was assessed on the basis of the environmental parameters analyzed, with particular reference to the animal-occupied zones. The results showed that a mix of RESs can be properly defined and integrated in an automated heating system to meet the specific requirements of a swine farm, thanks to a project specifically designed to exploit the renewable resources typically available in farming environments.

This research developed a predictive model using NeuralProphet to estimate energy consumption in the ventilation system of a dairy cattle farm. The necessity for energy management in livestock farming has increased due to the growing energy demands associated with climate control systems. Approximately two years of historical energy consumption data, collected through a smart monitoring system deployed on the farm, were utilized as the primary input for the NeuralProphet model to predict long-term trends and seasonal variations. The computational results demonstrated satisfactory performance, achieving a coefficient of determination (R²) of 0.85 and a mean absolute error (MAE) of 27.47 kWh. The model effectively captured general trends and seasonal patterns, providing valuable insights into energy usage under existing operational conditions. However, short-term fluctuations were less accurately predicted due to the exclusion of exogenous climatic variables, such as temperature and humidity. The proposed model demonstrated superiority over traditional approaches in its capacity to forecast long-term energy demand, providing critical support for energy management and strategic decision-making in dairy farm operations.

Livestock farming is an energy-intensive sector of agriculture and a significant contributor to greenhouse gas emissions. This study presents the first-year operational results of an integrated renewable energy system combining photovoltaic–thermal (PVT) collectors, borehole thermal energy storage (BTES), and a dual-source heat pump (DSHP) for space heating in a commercial swine nursery building.The BTES is based on a novel “double-circuit” configuration, allowing the exploitation of solar energy during mid-season and summer periods, while ensuring reliable heat supply during winter peak loads.A monitoring system, including dedicated piezometers aligned with groundwater flow, was implemented to continuously record environmental conditions inside the building, system operating parameters, energy flows, and subsurface temperature variations. During the first year, the system met the heating demand of the nursery barn, achieving an overall Seasonal Performance Factor (SPF) of 3.78. Approximately 54% of the heat pump operation occurred in hybrid ground–air mode, 38% in ground-only mode, and only 7% in air-only mode, demonstrating the effectiveness of the strategy in balancing performance and resource preservation. The installed system employed only 240 m of borehole heat exchangers, compared to an estimated 640 m required for conventional systems delivering comparable thermal performance. Subsurface monitoring confirmed that the thermal impact on the aquifer was limited and remained well below regulatory thresholds.The results demonstrate that solar-assisted BTES coupled with a DSHP represents a technically effective, energy-efficient, and environmentally compatible solution for decarbonising heating in livestock buildings, particularly in rural areas with available land and suitable hydrogeological conditions.