Acta IMEKO

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Dirk Röske — 2024-03-29

Introductory notes for the Acta IMEKO first issue in 2024

Francesco Lamonaca — 2024-03-29

Introductory notes to the Thematic issue on Measurements and Applications in Veterinary and Animal Sciences - part 2

Leopoldo Angrisani — 2024-03-29

Validation of a laboratory method for the traceability of a rainfall weighing gauge

Catarina Ferreira Simões — 2024-03-11

This study aims to develop a laboratory method for the traceability of a rainfall weighing gauge, including an evaluation of the measurement uncertainty. The adopted procedure is similar to the one used for the non-automatic weighing instruments. A static approach is followed to achieve the calibration deviation of the precipitation scale. The method used to evaluate the measurement uncertainty is based on a nonlinear mathematical model. The Monte Carlo method is used to calculate uncertainties and validate estimates following the conventional Guide to the Expression of Uncertainty in Measurement (GUM) approach. Measurement uncertainty contributions of input quantities to the mathematical model used to calculate rainfall also require specific calibration procedures. Results show the accuracy level achievable with rainfall weighing gauges commonly used as a reference for meteorological monitoring networks and data modelling.

A low-cost machine learning process for gait measurement using an electrostatic sensors network

Blandine Pichon — 2024-03-26

Continuous in-house measurement of gait of elderly People is relevant for health professionals. To be adopted by most, the system must be low-cost and non-intrusive. In this paper we present a solution for measuring the walking velocity based on a network of 4 electric potential sensors. In our experiments, we also add PIR sensors used in our previous work for comparative purposes. A temporary Depth camera is used for training the model on walking velocity. The first results presented are obtained without machine learning. Then a machine learning regression method is tested to reduce the uncertainty of the sensors. The results show that the electric potential sensors are suitable for the in-house measurement of walking speed of elderly people. The uncertainty is lower than the target of 0.15 m s^-1 known as the upper limit for detecting a reduction in speed due to illness. As for the PIR sensors, electric potential sensors consume very little energy, they are inexpensive, they can be embedded and hidden in the home which makes them less -intrusive and furthermore have better accuracy.

Photovoltaic cooling techniques’ effect on the average monthly performance

Antonino Rollo — 2024-03-04

Nowadays, mitigating climate-altering emissions resulting from air conditioning and mechanical ventilation of indoor spaces is of utmost importance. Encourage the adoption of renewable energy sources for power generation is a critical approach in this regard. Among the available technologies, photovoltaic technology stands as the most mature option. However, it does have limitations, such as reduced efficiency and performance degradation at elevated temperatures. To enhance the efficiency of photovoltaic systems, various solutions have been proposed over time, with significant research focusing on the exploration of new materials. One of the most promising solutions involves panel cooling through the utilization of external fluids, either in a forced or natural manner. Furthermore, the extracted heat from this cooling process can be effectively reused in other industrial processes, adding to its appeal. Nonetheless, despite its potential, the application of panel cooling technology is relatively recent, and assessing its suitability in specific scenarios at an early stage can be challenging. Currently, there is a lack of clear and straightforward methodologies to evaluate the performance gains achievable through the implementation of panel cooling. The primary objective of this research is to present an innovative methodology that can effectively assess panel cooling efficiency on an average daily-monthly basis. Specifically, we propose corrective parameters that modify the widely used Siegel method, which determines the monthly average daily efficiency of uncooled panels. Throughout the study, it has become evident that the input values derived from the UNI standard do not fully represent the real-world conditions. This finding may indicate the necessity for regulatory updates to accurately account for the practical operational environment.

Performance evaluation of a spark ignition engine using gasoline and essential oil fuel blend

Marthen Paloboran — 2024-03-04

Clove oil is an essential oil that has recently been used not only as a health or aromatherapy ingredient but is also widely used as an additive in fuel, especially for compression ignition engines. Essential oils are extracted through distillation from various parts of the clove tree, such as flowers, tree bark, leaves, and even fruit. This study aims to evaluate the combustion performance of a spark-ignition engine fueled by gasoline and essential oil at a concentration of 5-20 % as a blend. This type of research has not been conducted by many researchers, making it difficult to find scientific references related to this type of research. Experiments carried out on a research engine with engine speed variations of 1400–1800 rpm and a constant load of 3 kg. The results show that increasing the essential oil content increases the fuel energy and indicative power, thereby increasing the thermal efficiency. However, the brake power will decrease because most of it is lost owing to heat transfer and friction; therefore, the mechanical efficiency decreases if the percentage of essential oil in gasoline increases. Meanwhile, increased essential oils will reduce CO₂ emissions, but HC and CO emissions will increase, especially at high engine speeds.

A comprehensive review of image super-resolution metrics: classical and AI-based approaches

Mukhriddin Arabboev — 2024-03-12

Image super-resolution is a process that aims to enhance the quality and resolution of images using various techniques and algorithms. The process aims to reconstruct a high-resolution image from a given low-resolution input. To determine the effectiveness of these algorithms, it's crucial to evaluate those using specific metrics. In this paper, we take a closer look at the most commonly used image super-resolution metrics, including classical approaches like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM). We also discuss advanced metrics like Learned Perceptual Image Patch Similarity (LPIPS), Fréchet Inception Distance (FID), Inception Score (IS), and Multi-Scale Structural Similarity Index (MS-SSIM). Furthermore, we provide an overview of classical and AI-based super-resolution techniques and methods. Finally, we discuss potential challenges and future research directions in the field and present our experimental results by applying image super-resolution metrics. In the result and discussion section, we have practiced some given metrics and proposed our image super-resolution results.

A parallel approach for ultra-fast state estimation in large power system using graph partitioning theory

Behnam Karim Sarmadi — 2024-03-14

This paper introduces a novel approach for multi-area state estimation in large transmission networks through the application of graph partitioning theory. By harnessing the eigenvalues and eigenvectors of the Laplacian matrix, a large-scale transmission network is partitioned into manageable sections. Within these partitions, state estimation processes run in parallel, markedly improving efficiency compared to conventional methods. Linear state estimation is employed within each area, expediting computations and making it adaptable to large-scale networks, which traditionally pose computational challenges. The method's efficacy is demonstrated through comprehensive validation, commencing with small networks and extending to real-world applications on the IEEE 118-bus test system and the 9241-bus European high-voltage transmission network. In comparison to the integrated network method, our approach has achieved state estimation answers with reduced computation time. The partitioning of the integrated network into multi areas has effectively mitigated computational loads, showcasing its potential for enhancing operational efficiency and reliability in complex power transmission systems. This approach not only offers a robust solution for state estimation but also represents a significant stride toward advancing the field of state estimation, promising to bolster the stability and performance of modern power grids.

An investigation into vibration analysis for detecting faults in vehicle steering outer tie-rod

Yousif Alaraji — 2024-03-18

This study presents a novel fault detection method in car gear steering systems, employing MSC Adams and MATLAB simulations to analyze angular acceleration from the outer tie rod. The approach closely mimics real accelerometer data to differentiate between normal and faulty conditions, including wear and obstacle navigation. Emphasis is on noise robustness, utilizing advanced noise injection and denoising techniques. The efficacy of wavelet scattering, discrete wavelet transform (DWT) methods, and classifiers like Support Vector Machines (SVM) and Neural Networks (NN) is extensively evaluated. Among fifteen fault detection methods, the combination of wavelet scattering with Long Short-Term Memory (LSTM) Neural Networks, optimized with Adam tuning, is notably stable across four scenarios. The research highlights the importance of precise feature selection, employing techniques like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Recursive Feature Elimination (RFE). This research significantly advances the reliability of autonomous driving systems and provides essential insights into fault detection in gear steering systems.

Realization of Martens hardness method in macro range with high accuracy force and indentation depth

Cihan Kuzu — 2024-03-21

TÜBİTAK UME Hardness Laboratory has been working on instrumentation in the field of hardness metrology since 2005 and three generations of hardness standardizing machines were developed since then to be used as reference (calibration/standardizing) machines in Türkiye. In former designs conventional hardness methods such as Rockwell, Brinell and Vickers scales were the main scope of the projects. In the final Project that was supported and funded by TÜBİTAK UME to develop three hardness standard machines to be used as national standards for the conventional hardness scales mentioned, the Instrumented Indentation Test (IIT) was also aimed at and some parameters like Martens hardness, creep, indentation hardness, (elastic and plastic) indentation work, etc. were also implemented onto the machines developed. It was a good occasion that the measurands in Rockwell hardness and IIT were the same, force and indentation depth besides time and this made it easier to realize the IIT on the same machine with a more suitable design to achieve the highest accuracy in terms of the measurands mentioned. In this paper the new design of the Rockwell-Brinell-Vickers hardness standard machine developed also for Martens hardness in macro range (3 kgf – 150 kgf) and preliminary Martens hardness measurements are explained.

Validity of iPhone health application step count in semi free-living conditions

Pedro Judice — 2024-03-21

The iPhone’s validity for measuring steps has been mainly investigated under laboratory conditions, while studies that include real-world conditions are still scarce. We examined the validity of iPhones in measuring steps in real-world walking conditions, while using direct observation with video as reference. A sample of 100 adults who owned an iPhone 5S or higher was included and participants were randomly allocated to one of two protocols. Limits of Agreement (LoA), Mean Absolute Percentage Error (MAPE), linear-regression and Bland-Altman analyses were carried out. In Protocol-1, which includes straight-line and zigzag conditions, we observed a low MAPE lower than 4%. Bland-Altman analyses assessed a high accordance of approximately 6 steps under both conditions. Differences between the iPhone and direct observation were only noticeable in straight-line condition (p = 0.002). Likewise, protocol-2 (three 50-step conditions: an upward 5 % slope, a flat surface, and a downward -5% slope) showed low MAPE values (3.78 %) for the upward slope, 2.41 % for the downward slope, and 2.37 % in absence of slope), and differences between the iPhone and direct observation were only observed for the downward slope (p = 0.017), with the iPhone overestimating. Our findings revealed that iPhone might be a reliable tool for monitoring walking in real-life conditions, however, downward slope seems to generate overestimation, which deserves future investigation.

Reference Intervals (RIs) in veterinary medicine

Martina Quagliardi — 2024-03-21

Reference Intervals (RIs) are necessary in veterinary clinical pathology to provide a data base in order to compare results obtained from healthy versus diseased animals. Data are obtained from laboratory tests and, depending on whether they have a Gaussian or non-Gaussian distribution, are processed through statistical tests to define the RIs. This process begins with a healthy reference population made up of individuals who have been initially chosen based on inclusion or exclusion criteria. It is frequently challenging to have a large number of healthy individuals on which to establish de novo RIs, especially in wild and exotic animals. However, the use of reference intervals in daily clinical practice remains a fundamental instrument for therapeutic and diagnostic decisions, but it must always be accompanied by clinical findings that can confirm the hypothesis.

Heat stress measuring methods in dairy cows

Alessandra Aloia — 2024-03-14

The most widely used predictor to assess the incidence of thermal stress in livestock is THI, the temperature humidity index. However, it is an indicator that disregards the individual animal and the specific farm conditions. This review aims to list and summarize other thermal stress predictor factors, by using non-invasive and cost-effective strategies, in particular with the aid of Precision Livestock Farming technologies. When it comes to dairy animals the metabolic load is already increased by milk production, so the effect of heat stress can exacerbate the overall welfare of the cow. Therefore, the animals enact copying mechanisms that may result in physiological, behavioral and productive alterations. Those animal-based parameters can be used as early predictors of heat stress, allowing the farmer to collect real time data and address the condition operating management strategies in order to prevent further detrimental effect on the performance and consequent economic losses.

Application of the ClassyFarm checklist as measurement tool to evaluate the welfare of cattle kept in tie-stalls in Aosta valley

Martina Moriconi — 2024-03-23

The growing sensitivity of consumers about animal welfare and the awareness of its impact on food security and safety recently influenced international policies and law scenario. To response to these needs, in 2018, the Italian Ministry of Health commissioned the development of a system named ClassyFarm able to categorizes the level of risk arising from farm animal welfare. The experts involved in the project provided specific checklists to analyze different animal species and housing system. The developed welfare measurement protocols consist of both resource-based and animal-based measures and are divided into three areas: (Area A) “Farm management and staff training”, Area B) “Housing and equipment”, (Area C) “Animal-based measures”. The aim of this study was to measure welfare conditions in cattle herds reared in the Aosta Valley through the ClassyFarm checklist for tie-stall dairy cattle. The scores (%) for each area of assessment were divided into three classes (insufficient, acceptable, and optimal) while the total Animal Welfare Score (AWS), obtained by summing the scores of the 3 areas, was divided into five classes. The total AWS showed the majority of the farm included in good to optimal result.

CortiCow project: development of a rapid and non-invasive lateral flow immunoassay for the evaluation of cortisol levels in bovine saliva

Elena Diaz Vicuna — 2024-03-23

Being able to obtain an objective and repeatable measurement of stress levels from a single subject represents a primary issue in animal welfare science, especially in relation to cattle farming. A potential solution has been recently identified in the determination of cortisol’s salivary levels: a non-invasive method strictly related to acute stress. The development of an on-field, easy-to-use method to perform the analysis is yet to be validated. In this study, we developed and tested the reliability of salivary cortisol as a marker for the evaluation of animal welfare. We aimed to develop an on-field use Lateral Flow Immunoassay (LFIA) for the evaluation of acute stress in bovines.

The availability of this non-invasive diagnostic tool will facilitate monitoring of animal welfare. In the short term, this innovation is expected to assist farmers and veterinarians in performing a more objective evaluation of the animal’s acute stress levels.

In the long term, the device could become a key-instrument for the EU’s growing necessity to better monitor and identify the potential stressor factors in animal farming.

Could colour and volatile compounds be measurements of oxidation in horse meat?

Lucrezia Forte — 2024-03-21

Predicting and, consequently, controlling oxidative phenomena is a big challenge for meat industry. Since color modifications and volatile compounds synthesis are processes closely linked to the oxidative reactions occurring in meat, oxidation could be measured through colorimetric parameters or some volatile compounds. A Spearman’s correlation analysis of oxidative parameters and enzymatic activity with colorimetric parameters and volatile profile was performed on horse steaks subjected to short (14 days) and long aging time (56 days). In long aging, TBARS were negatively correlated with redness (rs = -0.76, p < 0.001). Some volatile compound families, such as thiols, carboxylic acids, lactones, hydrocarbons and sulphur compounds, were well correlated with enzymatic activity (p < 0.001), contrary to aldehydes, alcohols and ketones. Higher correlation indexes were recorded in short aging, if compared to those found in long aging.

An innovative approach for analysing and evaluating enteric diseases in poultry farm

Federica Borgonovo — 2024-03-21

Volatile organic compounds (VOCs) produced by pathogens, host-pathogen interactions and biochemical pathways are present everywhere such as in blood, breath, faeces, sweat, skin, urine, and vaginal fluids. Their qualitative and quantitative composition is influenced by pathophysiological responses to infections, toxins, or endogenous metabolic pathway perturbations. In poultry, VOCs analysis has been explored to evaluate air quality in sheds, but they have never been monitored to determine if birds were affected by enteric diseases. The enteric disorders represent one of the most important groups of diseases that affect poultry and cause illness, mortality, and economic losses. For this reason, monitoring the health status of broilers and the early detection of any health problem is of great importance in intensive farming, especially nowadays that antibiotics are banned. Precision Livestock Farming, through the combination of cheap technologies and specific algorithms, can provide valuable information for farmers starting from the huge amount of data collected in real time at farm level.

This study was aimed to the application of a PLF diagnostic tool, sensible to the variation of volatile organic compounds, to promptly recognize enteric problems recognize enteric problems (as coccidiosis and necrotic enteritis) in intensive farming, supporting veterinarians and enabling specific treatments in case of disease.

Determination of sulfonamides in muscle: a metrological tool for food safety

Irene Diamanti — 2024-03-27

Sulfonamides represent a wide class of synthetic drugs commonly used in veterinary therapy for the treatment of several bacterial and protozoan infections in cattle, swine and poultry. The use of these drugs in farming can lead to the possibility of having their residues in animal products intended for human consumption. Consequently, to ensure high consumer protection, for sulfonamides European Union (EU) set a Maximum Residue Limit (MRL) equal to 100 µg/kg, either as a single molecule or as a sum of all detected compounds within the class). Official laboratories are directly involved in the execution or residue plans by developing, validating and then applying analytical methods for the measurement of drug residues. Accordingly, official laboratories should update their procedures following the evolution of required drugs and MRLs. A multiclass method previously developed and validated for the determination in animal muscle of ten classes of antibiotics was adjusted to comply with the current European requirements which establish the minimum set of sulfonamides to be determined. Therefore, eight new sulfonamides were added assessing method performance characteristics according to European Regulation (EU) 808/2021.

Detection of common adulterants in bulk bovine milk using fourier transformed mid-infrared spectroscopy

Alberto Guerra — 2024-03-22

Fourier transformed mid-infrared spectroscopy is a widespread method for routine analysis in milk. This method can also be used for the detection of adulterants in bovine milk which represent a current risk for dairy industries and public health. This work focuses on the detection of seven adulterants present in three known concentrations in bulk milk sample. The adulterants were: sodium bicarbonate, sodium chloride, hydroxyproline, glucose, sodium citrate, water, and urea. Partial least squares – discriminant analysis was used to develop statistical models to predict the presence of adulterants in milk samples. The obtained models could provide an easy, efficient, and rapid tool for the dairy industry to detect specific adulterants in milk.

Performance evaluation of a prototype for the defense against wolf attacks on livestock animals

Riccardo Primi — 2024-03-26

This study seeks to evaluate a prototype's effectiveness in safeguarding livestock against wolf attacks. With an increasing imperative to protect livestock from predation, the prototype's performance was systematically examined under diverse conditions. The study primarily aimed at assessing the prototype's ability to detect wolf attacks by analyzing noise variations inherent to predator assaults. Simultaneously, the prototype aimed to mitigate livestock casualties and foster coexistence between wolves and livestock. A series of controlled experiments were meticulously carried out, replicating real-world wolf encounter scenarios. The findings yield valuable insights into the prototype's practical utility and its potential to mitigate conflicts between predator populations and livestock farming. Overall, this research contributes to advancing innovative strategies for sustainable cohabitation between wildlife and agriculture.

Measurement of rheological properties in raw and cooked meat aged with a controlled dry-aging system

Marika Di Paolo — 2024-03-27

Texture of meat is a critical parameter of consumer’s acceptability. In this regard, aging technology has become essential to enhance meat tenderness and flavour. The present study evaluated the effect of dry-aging on the rheological properties of cooked and raw meat (Longissiumus dorsi) using objective instrumental measurements: colorimeter (CIEL*a*b*), texture profile analysis (TPA-test) and Warner Bratzler shear force test (WBSF-test). In this study, a monitored refrigeration device was used for 60 days of dry aging of meat. Physical-chemical analyses were also evaluated on raw meat. The analyses were carried out at 2 (T₀), 15 (T₁), 30 (T₂) and 60 (T₃) days post-slaughter. During dry aging, a_w values tended to decrease while the pH values increased. The mayor color changes occurred in the first 15 days of dry aging. As regard the texture traits, significant differences were found between the cooked and raw meat mainly due to the effect of temperatures on collagen. The correlation data showed significant positive correlations between WBSF and hardness values. Results showed that aging time and cooking tended to decrease the hardness and shear force implying more tender meat for the consumer.

Portable milkmeters for the rapid in-field collection of milkability phenotypes in dairy goats

Silvia Magro — 2024-03-25

The present study aimed to investigate the phenotypic factors affecting the variability of the milkability traits measured in Italian dairy goats through a portable milkmeter in commercial herds. Milkability descriptors combine either milk production, milking time, or milk flow and are important to consider for reasons related to udder health, e.g., they can be exploited for large- or small-scale monitoring of mechanical milking stress. The milkability traits recorded in this study were analyzed through a linear model. Almost all the traits were influenced by lactation stage, but only part of them were affected by parity and season of sampling. With exception of blind phase (overmilking indicator), estimates decreased along the lactation, suggesting that fresh animals – especially pluriparous - are those with the greatest productivity, milk emission flow, and milking time. As regards the effect of parity, for the majority of traits the estimates of primiparous were lower compared to those of pluriparous. Monitoring milking ability of goats through milkmeters in the field can be a strategy to evaluate milking procedures of farmers and could thereby boost the implementation and effectiveness of udder health improvement schemes with positive implications on milk quality and farm profitability.

Mixed hay produced in Southern Italy: nutritive value and environmental impact

Alessandro Vastolo — 2024-03-27

The aim of this study was to characterize eight mixed forages preserved as hays produced in four agricultural and livestock farms located in four Provinces (Avellino, Benevento, Campobasso, and Potenza) of Southern Italy, different for environmental condition. Nutritive value, in vitro fermentation characteristics and kinetics, including methane production were determined. The little differences observed between sampling areas highlighted that the forage produced in Avellino area is the most interesting in terms of chemical composition, nutritive value, in vitro characteristics, and environmental impact. Data obtained allow having more information about hays produced in the study area, useful for farmers to make balanced rations, to maintain animal health and guarantee high quality of production.

Beneficial fungal microbes as novel ecosustainable tools for forage crops

Daria Lotito — 2024-03-28

Traditional approaches to protect agricultural crops are based on the use of chemical pesticides. Nevertheless, these products are costly, contaminate the environment and are harmful to animals and humans. Reduction or elimination of chemical applications in agriculture is extremely needed, and the application of biostimulants and biological control agents is an efficient alternative for an eco-sustainable agriculture. This study demonstrated that formulations based on fungi belonging to Trichoderma genus had beneficial effects on forage crops and significantly affected plant metabolome.

Assessing accelerometer thresholds for cow behaviour detection in free stall barns: a statistical analysissis

Simona Maria Carmela Porto — 2024-03-14

Monitoring daily cow behavioural activities of cows in livestock farms is strategic for improving the herd management. For this reason, IoT techniques and smart sensors are become the most common technological support in barns.

The aim of this paper is to validate the use of predefined accelerometer thresholds in timely detecting of cow behavioural activities through the statistical analysis of the data acquired from accelerometers housed in collars. Applying ANOVA and TUKEY tests to the median of the accelerations measured with 4 Hz sampling, the behavioural activities analysed in this study, i.e., feeding, lying, rumination, were found to be discriminable along one or more axes. This could allow the implementation of threshold-based algorithms in the firmware of devices housed in the cow collars.

Remote post-mortem veterinary meat inspections in bovine and analysis of post-mortem inspection outcomes: preliminary results

Maria Francesca Peruzy — 2024-03-26

The work aimed to collect and analyse data obtained from the routine post-mortem veterinary inspection of bovine viscera and to evaluate the reliability of remote veterinary meat inspections. A comparative study was conducted on viscera belonging to 30 bovines. For each bovine, findings were recorded simultaneously by two veterinarians, one conducting an on-site post-mortem inspection and another performing the remote inspection through a tablet. A total of 10 (33.33 %) bovines were healthy. In a total of 18 (60.00 %) and 11 animals (33.00 %) pulmonary and hepatic lesions were respectively, observed. The most reported finding was pleuritis recorded in 17 animals (56.67 %), followed by pneumonia (16.67 %). The agreement between the two methods used for the post-mortem inspection of bovines was generally high. In an overall 73.33 % of bovines the findings recorded by the veterinarian who conducted the on-site post-mortem inspection and those recorded by the veterinarian performing the remote inspection were overlapping. In conclusion, a move to a remote post-mortem inspection has a negligible negative impact on public health since the most frequently detected lesions recorded were of scarce significance and in most cases, they were effectively detected by the veterinarian through the tablet.

GIS-based analysis to assess biogas energy potential as support for manure management in Southern Italy

Ester Scotto di Perta — 2024-03-26

Anaerobic digestion can provide a valuable aid to manure management while producing renewable energy. Biogas production is highly dependent on the size and composition of livestock farms, and the availability of fresh manure can vary throughout the year, limiting reliable assessment of potential production. The aim of this study is to develop an affordable GIS-based analysis to support manure management, based on a highly detailed livestock farm database. Databases refer to the years 2013 and 2019 and report the herd consistency and the age of each class head. Kernel density (KD) was used to identify emerging hotspot areas with potentially high concentrations of nitrogen applied to the field. Three KD classification methods were compared: defined interval (DI) into 3 classes (0-170, 170-340, > 340 kg N/ha), quantile (Q) and natural break (NB). The results show that the DI and NB areas correspond to 40 % and 84 % of the total N of buffalo origin in the Campania region, with a N surplus in the hotspot areas localized in nitrate vulnerable zones of 55 and 6 % respectively. The biogas produced from 50 % of the buffalo manure in these areas generates sufficient energy to allow the removal of the N surplus.

Innovative sensors for the assessment of exercise stress in athlete horse

Elisabetta Porzio — 2024-03-26

Exercise tests are indicated for the evaluation of a horse’s physical condition and for the analysis of poor athletic performance, often associated with discomfort during training or competition that creates excessive stress in the animal. In order to understand the different biological mechanisms of adaptation to exercise-induced stress, a large amount of data need to be collected in real time, to obtain what is called “deep phenotyping” (DEPH) that opens the way to the full exploitation of omic techniques. The aim of the study is the configuration of innovative low-cost sensors for real time detection of crucial stress parameters that will allow early identification of metabolic dysfunctions preserving the horse’s welfare.

Prediction of sheep bulk milk coagulation properties from mid-infrared spectral data

Carlo Boselli — 2024-03-04

The technological features of milk are essential for cheese manufacturing. This is particularly true for Italy, where most of the milk produced by sheep is intended for cheese production. The possibility to evaluate technological characteristics and coagulation aptitude of milk in advance, before any treatment, is crucial for decision-making at industry level. In the present study, we tested the ability of mid-infrared spectroscopy for prediction of coagulation traits (rennet coagulation time and curd firmness) by using more than 4000 bulk milk samples of 344 sheep herds. The models developed with a partial least square regression showed that spectral data points can be successfully used to predict the two traits. The coefficient of determination in external validation was 0.42 for rennet coagulation time and 0.28 for curd firmness, indicating that sheep milk delivered to dairies can undergo a preliminary screening only to assess the expected coagulation time. This finding will allow manufacturers to evaluate the milk received from farmers. Further investigation will be need to improve the prediction of rennet coagulation time that can be coupled with composition traits to define premiums or penalties in the payment system.

Short review of current limits and challenges of application of machine learning algorithms in the dairy sector

Lucia Trapanese — 2024-03-15

In the last years, the livestock sector is moving towards a more sustainable animal-based industry, mitigating the environmental impact of livestock while meeting the demand for high-quality food. To achieve these goals, farms are using a more technological approach, adopting algorithms to manipulate the vast amount of data from sensors and routine operations. The results will be useful for making more objective decisions. In this context, machine learning - a branch of Artificial Intelligence applied to the study of prediction, inference, and clustering algorithms - can be successfully employed. Nowadays, machine learning algorithms are successfully used to solve many issues in the livestock sector, such as early disease detection, and they are expected to be employed in the future for welfare monitoring. This brief review gives an overview of the current state of the art of the most popular applications for dairy science and the most widely used and best-performing algorithms, highlighting the challenges and obstacles for broad acceptance of these techniques in the dairy sector.

Bio-logging reveals heritable patterns of natural behaviours in sheep

Joss Langford — 2024-03-25

The measurement of behaviour in extensively managed livestock for the assessment of welfare remains a challenge. Bio-logging devices offer the opportunity to collect continuous behavioural data over long periods while animals are in their normal physical environment. Using collar-measured acceleration from 84 ewes over 28 days in a commercial flock, we built two-component daily phenotypic profiles with data-driven analysis techniques not reliant on human observations. Our analysis demonstrates the moderate repeatability of both components (0.58 ± 0.06 and 0.53 ± 0.05), the heritability of one component (0.53 ± 0.15), and environmental associations with wind chill index (−0.63 ± 0.13), and day length (−0.66 ± 0.19). We discuss these results in relation to previous work, showing that our methods of measurement and analysis are capable of revealing the daily patterns of behaviour in sheep and how these are influenced by the physical environment and genetics. This approach can be generalised to assess behavioural phenotypes and the determinants of behaviour in other breeds or species.

Microbiome studies in veterinary field: communities’ diversity measurements pitfalls

Ugo Ala — 2024-03-28

In recent years, the role of the microbiota has proved to be extremely important in medicine as one of the most important aspects for the characterization of living beings in both healthy and pathological conditions. Moreover, the development of shotgun technology, and in particular the cheaper 16S ribosomal RNA (rRNA) gene sequencing, made possible its wide diffusion. In veterinary sciences, microbiome studies have seen applications not only in medicine in the strict sense (e.g diagnosis) but also, for example, in food inspection (quality, fraud, etc.) and in animal feed preparation itself. However, focusing on microbial profiling by 16S rRNA sequencing, there are several crucial aspects to be considered: from the experimental design definition and the sample size problem to the data analysis steps. This latter involves several layers, e.g. which 16S rRNA databases to use, which metrics for alpha and beta diversity, etc. In this work, we want to present, as a case study, a critical discussion about the large number of alpha and beta diversity metrics and their impact in the statistical comparisons among groups.