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•CFD analyses of natural ventilation in an horticultural greenhouse were performed.•Cooling achievable through various configurations of vents opening was assessed.•Closed windward ...roof vent and open wall vent entailed 64% of maximum heat removal.•The other possible scenarios showed a performance index of about 50%.•Results suggest to enhance vent control system considering wind direction as input.
Indoor microclimate control is fundamental in greenhouse design, and vent dimensions and positions play a crucial role in natural ventilation management. This research considers an Italian greenhouse for horticultural production and aims at identifying optimal vent configurations and opening management procedures for indoor environment control, focusing on summer cooling. Numerical modelling of airflows and temperature distributions was carried out through finite element CFD software, with streamline upwind discretization schemes for advection terms. Calibration of the numerical modelling was performed by comparing data collected in controlled environmental condition with simulations results. The automatic vent opening system of the greenhouse is programmed to fully open all the windows of each span when indoor air temperature overcomes a threshold value. Numerical simulations were performed to assess the efficacy of this solution in comparison with alternative strategies. Various configurations of roof vents were tested, with side wall vents always open. The best performances were obtained with windward roof vent closed, which entailed 64% of the maximum heat removal achievable through natural ventilation. The other possible scenarios considered showed a performance index of about 50%. The results suggest therefore to enhance the vent control system by considering also wind direction as input.
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.
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.
In dairy cattle farming, heat stress largely impairs production, health, and animal welfare. The goal of this study is to develop a workflow and a numerical analysis procedure to provide a real-time ...3D distribution of the THI in a generic cattle barn based on temperature and humidity monitored in sample points, besides characterizing the relationship between indoor THI and outside weather conditions. This research was carried out with reference to the study case of a cattle barn. A model has been developed to define the indoor three-dimensional spatial distribution of the Temperature-Humidity Index of a cattle barn, based on environmental measurements at different heights of the building. As a core of the model, the Discrete Sibson Interpolation method was used to render a point cloud that represents the THI values in the non-sampled areas. The area between 1-2 meters was emphasized as the region of greatest interest to quantify the heat waves perceived by dairy cows. The model represents an effective tool to distinguish different areas of the animal occupied zone characterized by different values of THI.
This paper presents an experimental evaluation of the performance of a solar photovoltaic-thermal (PVT) system in a swine farm at Mirandola in Italy. In this project named RES4LIVE, funded by the ...EU’s Horizon 2020 program, a PVT system is installed to replace fossil fuel consumption in one of the barns on the farm. The electrical energy from the collectors is utilized to operate the heat pump and provide electricity to the barn, whereas the thermal energy from the collector is stored in a borehole thermal energy storage (BTES) for further use by a 35 kW heat pump. The hybrid solar field consists of 24 covered PVT flat plate collectors (7.68 kWel and 25 kWth) with a total aperture area of 39.3 m2, which can increase the temperature of the heat transfer fluid (HTF) to up to 40 °C. The PVT system is connected to a modular solar central (SC) with a standardized design that can also be used for other similar applications. The hybrid solar system complemented by energy storage is expected to save approximately 20,850 kg CO2/year. The data collected from the PVT system, SC, and BTES are rigorously analyzed to evaluate its overall performance. A comprehensive performance assessment reveals the capability of the solar system to reduce carbon emissions and effectively replace fossil fuel consumption in the agricultural sector.
•Experimental assessment of a PVT system with BTES and HP for agricultural application.•Novel standardized solar central design for low-temperature PVT systems.•Pilot PVT system with further expansion potential due to BTES installed capacity.•Estimated emissions reduction of about 20,850 kg CO2/year.
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CFD has been increasingly applied to greenhouses to optimise indoor environmental conditions for cultivation and management. Numerical simulations have proved fundamental for the enhancement of ...energy-efficient design criteria and management procedures. The objective of the study is the comparison between different computational approaches for the study of airflow patterns in a representative study case of a glass greenhouse, also through the calibration of the models and the validation of simulation results against experimental data. A three-span greenhouse of about 300 m2 located in Emilia-Romagna (Italy) has been considered as study case. Several analyses with the same boundary and initial conditions were performed using two codes, broadly used for research and design purposes. With both programs, 2D or 3D models have been used and, for every case, the grid convergence was verified by performing multiple steady state analyses with increasingly finer meshes. The results led to define the most suitable solutions to set up computational models for the simulation of airflow patterns inside a greenhouse. The study provided a preliminary outline of the differences due to the adoption of various computational approaches characterised by different levels of accuracy and complexity. The results indicate the advisability of further developing the research by carrying out deeper experimental insights necessary to quantify more in detail the validity and the reliability of the adopted analytical methodologies.
•It is possible to identify individual cows based on their morphological appearance.•A real-time computer vision system for automatic recognition of cows is presented.•Indications about quantity and ...characteristics of the images were assessed.
Precision Livestock Farming relies on several technological approaches to acquire in the most efficient way precise and up-to-date data concerning individual animals. In dairy farming, particular attention is paid to the automatic cow detection and tracking, as such information is closely related to animal welfare and thus to possible health issues. Computer vision represents a suitable and promising method for this purpose.
This paper describes the first step for the development of a computer vision system, based on deep learning, aiming to recognize in real-time the individual cows, detect their positions, actions and movements and record the time history outputs for each animal.
Specifically, a neural network based on deep learning techniques has been trained and validated on a case study farm, for the automatic recognition of individual cows in videos recorded in the barn. Four cows were selected to train and validate a YOLO neural network able to recognize a cow starting from the coat pattern. Then, precision-recall curves of the identification of individual cows were elaborated for both the specific target classes and the whole dataset in order to assess the performances of the network.
By means of data augmentation techniques, an enlarged dataset has been created and considered in order to improve the performance of the network and to provide indications to increase detection efficiency in those cases where data acquisition is not easy to be carried out for long periods. The mean average precision of the detection, ranging from 0.64 to 0.66, showed that it is possible to properly identify individual cows based on their morphological appearance and that the piebald spotting pattern of a cow’s coat represents a clearly distinguishable object for a computer vision network. The results also led to obtain indications about the quantity and the characteristics of the images to be used for the network training in order to achieve efficient detections when facing with applications involving animals.
The analysis of data recorded by Automatic Milking System (AMS) in dairy livestock barns has a great potential for herd management and farm building design. A big amount of data about milk production ...and cow welfare is available from milking robot and many researches are focussing on them in order to find relationships and correlations among the various parameters.
The goal of the study is to develop and test an innovative procedure for the comprehensive analysis of AMS-generated multi-variable time-series, with a focus on herd segmentation, aiming to support dairy livestock farm management. In particular, the study purpose is to develop and test a cluster-graph model using AMS-generated data, designed to provide an automatic grouping of the cows based on production and behavioural features. First, a k-means cluster analysis has been implemented to the average of the time series of the main parameters recorded for each cow by AMS in a barn in Italy over a summer period. Then, all the resulting subgroups have been converted in a network and a cluster-graph analysis has been applied in order to find herd-descriptive subgraphs.
The results of the study have the potential impact of improving herd characterisation and lending support to cow monitoring and management. Furthermore, this method could represent a feasible procedure to convert alphanumeric data in a simple graphic visualisation of the herd without losing the quantitative information about every single animal.
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•A cluster-graph method has been developed to analyse datasets from AMS.•The method identified groups of animals with different conditions.•Clusters of cows with clearly different average productivity were obtained.•The method is fit to support cow monitoring and herd management.
The study represents the first step of a broader research aimed at outlining specific building and landscape design criteria for small to medium-sized farm wineries. With reference to a study area of ...the Emilia- Romagna region (Italy) representative of the regional wine-growing and producing sector, the specific aims of the study are the identification and quantification of the main production parameters, and the formulation of a preliminary framework of dimensional and functional requirements of wineries. We acquired, georeferenced, and analysed the available databases about wine farm production and sizes. We analysed a representative sample of such farms and the national and local codes about building design for that sector. The study has led to the definition of the main characters of the production process and a layout of the main parameters influencing the design process.
•A CFD study of wind-driven ventilation of a greenhouse is proposed.•The standard k-e turbulence is used to investigate the air-flow dynamics.•Shading screens permeability and air resistivity are ...experimentally defined.•Shading screens cause reduction of air velocity picks and air speed in crop area.
This paper is devoted to the CFD study of wind-driven ventilation in a greenhouse, with particular focus to the effect of screens on the inner airflow distribution. Although the use of shading screens to cover agricultural crops has been constantly increased to reduce high radiation loads, their effect on airflow distribution within the greenhouse is still not fully understood. In this paper, CFD simulations of the ventilation in a greenhouse with and without screens are performed, by means of a finite volume CFD code (Ansys-Fluent 17.2), with a standard k-ε turbulence model, together with proper user defined functions (UDF) for the inlet velocity and turbulent profiles. The screens have been modeled as porous surfaces and the porosity and the permeability have been obtained experimentally and set into the model. The code has been validated by a comparison with velocity measurements performed in a greenhouse owned by the University of Bologna. Comparisons between the airflow velocity patterns obtained within the greenhouse with screens and without screens have been obtained for different external airflow velocities. The cases with screens show a more uniform distribution of velocity field inside the greenhouse than the cases without screens, especially near the crops. All the cases show that screens strongly affect the airflow velocity distribution inside the greenhouse and the distribution of volume flow rates through the vents. This work shows how the characteristics of the screens and their positioning near the vents are critical for the ventilation within a greenhouse.
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