Measurement of gas concentrations constitutes basic knowledge for the computation of emissions from livestock buildings. Although it is well known that hot climate conditions increase gas emissions, in the literature the relation between gas concentrations from open barns and animal-related parameters has not been investigated yet. This study aimed at filling this gap by evaluating daily gas concentrations within an open-sided barn in hot Mediterranean climate. The influence of microclimatic parameters (MC) and cow behavior and barn management (CBBM) were evaluated for ammonia (NH3), methane (CH4), and carbon dioxide (CO2) concentrations. Results showed that both MC and CBBM affected concentrations of NH3 (p < 0.02), CH4 (p < 0.001), and CO2 (p < 0.001). Higher values of NH3 concentration were detected during the cleaning of the floor by a tractor with scraper, whereas the lowest NH3 concentrations were recorded during animal lying behavior. Measured values of CO2 and CH4 were highly correlated (C = 0.87–0.89) due to the same sources of production (i.e., digestion and respiration). The different management of the cooling systems during the two observation periods reduced significantly CH4 concentrations in the barn when the cooling system in the feeding area was switched off. Based on methodological choices due to the specific barn typology, parameters related to animals can provide information on the variation of gas concentrations in the barn environment in hot climate conditions.

South Italy is characterised by a semi-arid climate with scarce rain and high evaporative demand. Since climate change could worsen this condition, the need to optimise water resources in this area is crucial. In citrus cultivation, which involves one of the most important crops bred in Southern Italy, and more generally in Mediterranean regions, deficit irrigation strategies are implemented in order to cope with limited resource availability. On this basis, knowledge on how the territorial distribution of citrus would change in relation to these strategies represents valuable information for stakeholders. Therefore, the objective of this study was to determine the probability of the presence of citrus in Sicily based on changes in the percentage of water deficit in order to identify and analyse change in the surface area as well as the location of the crop. The methodology was based on the application of species distribution models (SDM) and Geographic Information Systems (GIS) to the case study of the province of Syracuse in Sicily. Different geostatistical and machine learning models were applied based on bioclimatic variables measured over three decades, a Digital Terrain Model and irrigation. Assessment of the outcomes was carried out using classification evaluation metrics. The analysis of the outcomes showed that uncorrelated predictor layers mainly included water input that most affected the probability of the presence of citrus fruits. Moreover, GIS analyses showed that deficit irrigation strategies would generate an overall reduction of cultivation surfaces in the territory (e.g., for the Random Forest model the surface reduction was equal to 41.15%) and a decrease of citrus presence in southern areas of the considered territory. In this area, climate conditions are less favourable in terms of temperature and precipitation; thus, these analyses provide useful information for decision support tools in agriculture and land use policy.

The need to investigate suitable alternatives to conventional fossil fuels has increased interest in several renewable energy sources, especially in widely available sources of biomass. This has made environmental and socio‐economic improvements possible. In recent years, biogas from biomass has increasingly been considered the most feasible alternative to energy from fossil fuel, as it allows both the possibility to reduce waste disposal treatment and the opportunity to produce green energy. Opuntia ficus‐indica (OFI) has been suggested as a possible biomass as it could represent a suitable resource for producing biomethane as a new frontier within the context of a circular economy. This study aims to define a methodology for evaluating the distribution of OFI biomass that is potentially available for biogas production by applying maximum entropy modeling in a case study of a province located in southern Italy. The geographic information system (GIS)‐based model that was developed made it possible to estimate the suitability of the species in the territory and to define specific indices at the municipal level by enabling the localization of the highest productive territorial areas. Municipalities were grouped into three different classes ranging from low to high potential ones. In future years, new municipalities could be part of the high‐potential group, due to global warming resulting from climate change. The results could be relevant to the intervention priorities established by the European Union related to the planning activities supported by the European Structural and Investment Funds within the Smart Specialization Strategy. In this regard, knowledge of the potential production and distribution of a species in a territory is highly valuable information for the quantification of the biomass potentially available for biogas production, prior to strategic planning for the improvement of the biogas sector. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd

Increased knowledge on the factors that affect emissions from open-sided dairy buildings may lead to an improvement of the mitigation strategies. In this study, ammonia (NH3) and methane (CH4) emissions were assessed in an open dairy barn in a hot Mediterranean climate at different managements of the cooling system, as well as the influence of environmental and animal-related parameters on daily emissions. Measurements of gas concentrations and micro-climatic parameters were carried out in a cubicle free-stall dairy barn located in the province of Ragusa (Italy) in two weeks of 2016 characterised by similar climatic conditions in the warm period. Emissions of NH3 and CH4 were estimated through the application of the carbon-dioxide (CO2) mass balance method. Data collected were organised in specific datasets to carry out different statistical analyses on gas emissions depending on selected parameters for the two weeks with a different management of the cooling system. The results showed higher NH3 emissions and lower CH4 emissions in W1 than those in W2. The variability in gas emissions was related to the effect of temperature humidity index (THI) (p < 0.001) and cow behaviour (p < 0.01). The highest emissions were recorded during the cleaning procedures for both NH3 (p < 0.001) and CH4 (p < 0.001), whereas the lowest emissions were recorded during the central hours of the day.

Knowledge of how different management strategies affect gas production from livestock buildings can be helpful for emission predicting purposes and for defining mitigation strategies. The objective of this study was to statistically assess whether and how measured concentrations of ammonia (NH3), methane (CH4) and carbon dioxide (CO2) were influenced by milking frequency. Concentrations of gases were measured continuously by using infrared photoacoustic spectroscopy in the breeding environment of an open dairy barn located in Sicily in hot climate conditions. Data were acquired by specific in-field experiments carried out in 2016 and 2018, when milking sessions occurred twice a day (2MSs) and three times a day (3MSs), respectively. The number of the milking cows was 64 in both 2MSs and 3MSs. The results showed that concentrations of NH3, CH4 and CO2 were statistically influenced by the number of milking sessions. From 2MSs to 3MSs, NH3 concentrations were enhanced (P < 0.001) due to the higher cow’s activity. Conversely, gas concentrations of CH4 and CO2 were lower for 3MSs compared to those for 2MSs due to the effect of the different feeding frequency. Overall, the milking frequency influenced barn management and cow behaviour by modifying the level of gas concentrations in the barn environment.

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In the field of precision livestock farming, many systems have been developed to identify the position of each cow of the herd individually in a specific environment. Challenges still exist in assessing the adequacy of the available systems to monitor individual animals in specific environments, and in the design of new systems. The main purpose of this research was to evaluate the performance of the SEWIO ultrawide-band (UWB) real time location system for the identification and localisation of cows during their activity in the barn through preliminary analyses in laboratory conditions. The objectives included the quantification of the errors performed by the system in laboratory conditions, and the assessment of the suitability of the system for real time monitoring of cows in dairy barns. The position of static and dynamic points was monitored in different experimental set-ups in the laboratory by the use of six anchors. Then, the errors related to a specific movement of the points were computed and statistical analyses were carried out. In detail, the one-way analysis of variance (ANOVA) was applied in order to assess the equality of the errors for each group of points in relation to their positions or typology, i.e., static or dynamic. In the post-hoc analysis, the errors were separated by Tukey’s honestly significant difference at p > 0.05. The results of the research quantify the errors related to a specific movement (i.e., static and dynamic points) and the position of the points (i.e., central area, perimeter of the investigated area). Based on the results, specific information is provided for the installation of the SEWIO in dairy barns as well as the monitoring of the animal behaviour in the resting area and the feeding area of the breeding environment. The SEWIO system could be a valuable support for farmers in herd management and for researchers in the analysis of animal behavioural activities.

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