Increased Cattle Feeding Precision from Automatic Feeding Systems: Considerations on Technology Spread and Farm Level Perceived Advantages in Italy.

Automation reduces the impact of farming on climate change and helps farmers adapt to its financial impact. Automatic feeding systems (AFSs) increase the ruminant's feeding precision and ease operators' workload. Such systems exist on a spectrum, requiring varied levels of operator support and installation complexity. A recent survey on farmers pointed out that those already running an AFS and those willing to buy one appreciate its ease of use, the resulting animal welfare, and the resulting overall benefits (increased production, farm profitability, and reduced feed waste). Whether technologically complex or not, studies have confirmed the benefits that farmers perceive to be underlying the remarkable reduction in the environmental impact of feeding operations (AFSs are electrically powered), the increase in animal welfare resulting from reduced conflicts around accessing the feed bunk, and the constant availability of fresh feed. However, their introduction should follow accurate and holistic structural and economic planning for existing and newly built facilities. The availability of public subsidies plays an essential role in pushing farmers to adopt the most modern digital technologies, whose benefits may even increase when farmers couple them with interconnected sensors to monitor animals' physiological states.

Changes in the lipidome of water buffalo milk during intramammary infection by non-aureus Staphylococci.

This study aimed to determine the lipidome of water buffalo milk with intramammary infection (IMI) by non-aureus staphylococci (NAS), also defined as coagulase-negative staphylococci, using an untargeted lipidomic approach. Non-aureus Staphylococci are the most frequently isolated pathogens from dairy water buffalo milk during mastitis. A total of 17 milk samples from quarters affected by NAS-IMI were collected, and the lipidome was determined by liquid chromatography-quadrupole time-of-flight mass spectrometry. The results were compared with the lipidome determined on samples collected from 16 healthy quarters. The study identified 1934 different lipids, which were classified into 15 classes. The abundance of 72 lipids changed in NAS-IMI milk compared to healthy quarters. Significant changes occurred primarily in the class of free fatty acids. The results of this study provided first-time insight into the lipidome of dairy water buffalo milk. Moreover, the present findings provide evidence that NAS-IMI induces changes in water buffalo milk's lipidome.

Peptidomic changes in the milk of water buffaloes (Bubalus bubalis) with intramammary infection by non-aureus staphylococci.

Mastitis by non-aureus staphylococci (NAS) is a significant issue in dairy buffalo farming. In a herd with subclinical NAS mastitis, we identified Staphylococcus microti as the predominant species. To assess milk protein integrity and investigate potential disease markers, we characterized 12 NAS-positive and 12 healthy quarter milk samples by shotgun peptidomics combining peptide enrichment and high-performance liquid chromatography/tandem mass spectrometry (LC-MS/MS). We observed significant changes in the milk peptidome. Out of 789 total peptides identified in each group, 49 and 44 were unique or increased in NAS-positive and healthy milk, respectively. In NAS-positive milk, the differential peptides belonged mainly to caseins, followed by milk fat globule membrane proteins (MFGMP) and by the immune defense/antimicrobial proteins osteopontin, lactoperoxidase, and serum amyloid A. In healthy milk, these belonged mainly to MFGMP, followed by caseins. In terms of abundance, peptides from MFGMP and immune defense protein were higher in NAS-positive milk, while peptides from caseins were higher in healthy milk. These findings highlight the impact of NAS on buffalo milk quality and mammary gland health, even when clinical signs are not evident, and underscore the need for clarifying the epidemiology and relevance of the different NAS species in this dairy ruminant.

Influence of subclinical mastitis and intramammary infection by coagulase-negative staphylococci on the cow milk peptidome.

Coagulase-negative staphylococci (CNS) are the most prevalent microorganisms isolated from cow milk and are associated with subclinical mastitis and persistent increases in the bulk milk somatic cell count (BMSCC) of low BMSCC herds. By combining peptide enrichment, LC-ESI-MS/MS, and statistical analysis, we investigated the influence of subclinical mastitis and CNS infection on the milk peptidome. Quarter milk samples from clinically healthy Holstein cows were subjected to bacteriological culture (BC) and somatic cell counting (SCC) for two consecutive samplings and 28 (including 11 negatives and 17 positives) were selected for peptidomic analysis. The study identified 1363 different endogenous peptides and highlighted a significant increase of peptides in CNS-positive milk, mainly represented by casein fragments. Milk peptidome changes increased with the SCC, as also demonstrated by protein electrophoresis and densitometry. Peptides significantly different in CNS or CONTROL samples were identified and characterized. Our results indicate that subclinical mastitis by CNS can induce significant changes in the milk peptidome, opening the way to future studies for the identification of a biomarker panel as well as for the understanding of their consequences for the technological and sensorial characteristics of cow milk and dairy products. SIGNIFICANCE: This is the first investigation on the impact of subclinical CNS mastitis on the bovine milk peptidome. The peptide enrichment strategy combined with a highly sensitive MS/MS analysis enabled the compilation of a very large peptide dataset for healthy and mastitic milk. The comparison of CNS and Control samples, also considering SCC classes, highlighted several peptides with potential for understanding milk protein and peptide dynamics in subclinical mastitis, with possible implications for its detection.

The Use of Infrared Thermography for the Monitoring of Udder Teat Stress Caused by Milking Machines.

The aim of this study was to test infrared thermography (IRT) as a possible tool for scoring teat color changes after cluster removal; thus, indirectly, to classify the short-term stress of teats caused by milking machines. Thermographic images (n = 137) from three farms were collected and evaluated to calculate the average and maximum skin surface temperatures (SSTs) at the base, center, and tip of each teat (Tavg,B, Tavg,C, Tavg,T, Tmax,B, Tmax,C, and Tmax,T). Obtained results confirmed a significant relationship between the indicators Tavg, Tmax and the levels of teat color change (level one: pink-colored teat; level two: red-colored teat; level three: blue or purple-colored teat). Nevertheless, when a teat was considered to be stressed because its scoring fell in level 3 of the color-change scale used, sensitivity and specificity in the classification of the teat status ranged respectively between 45.6% and 54.3%, and 54.4% and 59.2%, for the indicators Tavg; and 56.5% and 60.9%, and 59.7% and 61.8%, for the indicators Tmax. When a teat was considered stressed because its scoring fell between the levels 2 and 3 of the scale adopted, sensitivity and specificity were between 49.0% and 55.8%, and 58.3% and 61.8%, for the indicators Tavg; and 55.8% and 59.9%, and 60.6% and 61.4%, for the indicators Tmax. As a consequence, the low values of sensitivity and specificity do not seem to justify the development of an ad hoc infrared device for the monitoring of udder teat stress. Nonetheless, this technology can be a viable solution for a preliminary evaluation of the mechanical stress of teats if a milking system would be equipped with an infrared sensor already in place for other purposes (e.g., the monitoring of udder health status).

Improved Fuzzy Logic System to Evaluate Milk Electrical Conductivity Signals from On-Line Sensors to Monitor Dairy Goat Mastitis.

The aim of this study was to develop and test a new fuzzy logic model for monitoring the udder health status (HS) of goats. The model evaluated, as input variables, the milk electrical conductivity (EC) signal, acquired on-line for each gland by a dedicated sensor, the bandwidth length and the frequency and amplitude of the first main peak of the Fourier frequency spectrum of the recorded milk EC signal. Two foremilk gland samples were collected from eight Saanen goats for six months at morning milking (lactation stages (LS): 0-60 Days In Milking (DIM); 61-120 DIM; 121-180 DIM), for a total of 5592 samples. Bacteriological analyses and somatic cell counts (SCC) were used to define the HS of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as not healthy (NH). For each EC signal, an estimated EC value was calculated and a relative deviation was obtained. Furthermore, the Fourier frequency spectrum was evaluated and bandwidth length, frequency and amplitude of the first main peak were identified. Before using these indexes as input variables of the fuzzy logic model a linear mixed-effects model was developed to evaluate the acquired data considering the HS, LS and LS × HS as explanatory variables. Results showed that performance of a fuzzy logic model, in the monitoring of mammary gland HS, could be improved by the use of EC indexes derived from the Fourier frequency spectra of gland milk EC signals recorded by on-line EC sensors.

Analysis of electric energy consumption of automatic milking systems in different configurations and operative conditions.

Automatic milking systems (AMS) have been a revolutionary innovation in dairy cow farming. Currently, more than 10,000 dairy cow farms worldwide use AMS to milk their cows. Electric consumption is one of the most relevant and uncontrollable operational cost of AMS, ranging between 35 and 40% of their total annual operational costs. The aim of the present study was to measure and analyze the electric energy consumption of 4 AMS with different configurations: single box, central unit featuring a central vacuum system for 1 cow unit and for 2 cow units. The electrical consumption (daily consumption, daily consumption per cow milked, consumption per milking, and consumption per 100L of milk) of each AMS (milking unit + air compressor) was measured using 2 energy analyzers. The measurement period lasted 24h with a sampling frequency of 0.2Hz. The daily total energy consumption (milking unit + air compressor) ranged between 45.4 and 81.3 kWh; the consumption per cow milked ranged between 0.59 and 0.99 kWh; the consumption per milking ranged between 0.21 and 0.33 kWh; and the consumption per 100L of milk ranged between 1.80 to 2.44 kWh according to the different configurations and operational contexts considered. Results showed that AMS electric consumption was mainly conditioned by farm management rather than machine characteristics/architectures.

Evaluation of the Fourier Frequency Spectrum Peaks of Milk Electrical Conductivity Signals as Indexes to Monitor the Dairy Goats' Health Status by On-Line Sensors.

The aim of this study is a further characterization of the electrical conductivity (EC) signal of goat milk, acquired on-line by EC sensors, to identify new indexes representative of the EC variations that can be observed during milking, when considering not healthy (NH) glands. Two foremilk gland samples from 42 Saanen goats, were collected for three consecutive weeks and for three different lactation stages (LS: 0-60 Days In Milking (DIM); 61-120 DIM; 121-180 DIM), for a total amount of 1512 samples. Bacteriological analyses and somatic cells counts (SCC) were used to define the health status of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as NH. For each milk EC signal, acquired on-line and for each gland considered, the Fourier frequency spectrum of the signal was calculated and three representative frequency peaks were identified. To evaluate data acquired a MIXED procedure was used considering the HS, LS and LS × HS as explanatory variables in the statistical model.Results showed that the studied frequency peaks had a significant relationship with the gland's health status. Results also explained how the milk EC signals' pattern change in case of NH glands. In fact, it is characterized by slower fluctuations (due to the lower frequencies of the peaks) and by an irregular trend (due to the higher amplitudes of all the main frequency peaks). Therefore, these frequency peaks could be used as new indexes to improve the performances of algorithms based on multivariate models which evaluate the health status of dairy goats through the use of gland milk EC sensors.

Preliminary evaluation of a nest usage sensor to detect double nest occupations of laying hens.

Conventional cage systems will be replaced by housing systems that allow hens to move freely. These systems may improve hens' welfare, but they lead to some disadvantages: disease, bone fractures, cannibalism, piling and lower egg production. New selection criteria for existing commercial strains should be identified considering individual data about laying performance and the behavior of hens. Many recording systems have been developed to collect these data. However, the management of double nest occupations remains critical for the correct egg-to-hen assignment. To limit such events, most systems adopt specific trap devices and additional mechanical components. Others, instead, only prevent these occurrences by narrowing the nest, without any detection and management. The aim of this study was to develop and test a nest usage "sensor", based on imaging analysis, that is able to automatically detect a double nest occupation. Results showed that the developed sensor correctly identified the double nest occupation occurrences. Therefore, the imaging analysis resulted in being a useful solution that could simplify the nest construction for this type of recording system, allowing the collection of more precise and accurate data, since double nest occupations would be managed and the normal laying behavior of hens would not be discouraged by the presence of the trap devices.