Detection of Methane Eructation Peaks in Dairy Cows at a Robotic Milking Station Using Signal Processing

The aim of this study was to investigate the use of signal processing to detect eructation peaks in CH4 released by cows during robotic milking, and to compare recordings from three gas analysers (Guardian SP and NG, and IRMAX) differing in volume of air sampled and response time. To allow comparison of gas analysers using the signal processing approach, CH4 in air (parts per million) was measured by each analyser at the same time and continuously every second from the feed bin of a robotic milking station. Peak analysis software was used to extract maximum CH4 amplitude (ppm) from the concentration signal during each milking. A total of 5512 CH4 spot measurements were recorded from 65 cows during three consecutive sampling periods. Data were analysed with a linear mixed model including analyser × period, parity, and days in milk as fixed effects, and cow ID as a random effect. In period one, air sampling volume and recorded CH4 concentration were the same for all analysers. In periods two and three, air sampling volume was increased for IRMAX, resulting in higher CH4 concentrations recorded by IRMAX and lower concentrations recorded by Guardian SP (p < 0.001), particularly in period three, but no change in average concentrations measured by Guardian NG across periods. Measurements by Guardian SP and IRMAX had the highest correlation; Guardian SP and NG produced similar repeatability and detected more variation among cows compared with IRMAX. The findings show that signal processing can provide a reliable and accurate means to detect CH4 eructations from animals when using different gas analysers.

Impact of Heat Stress on Selected Parameters of Robotic Milking

The values of the temperature-humidity index and its influence on the performance parameters of dairy cows were monitored on four farms located in the southern part of the central Slovakia during a period of three years. The observed parameters included: the milk yield per cow per day, average milk speed and maximum milk speed. The thermal-humidity index was calculated based on a formula. The individual periods were divided according to the achieved THI. The results of dairy cows with a milk yield of 29 kg to 31 kg show that there is not a decrease in the milk yield per milking if the THI value is lower than 68. It was also found that there was a decrease in the milk yield per dairy cow in the robotic milking parlor for a THI value greater than 72. The influence of a THI value higher than 68 in these dairy cows results in a higher average milk speed, as well as a higher maximum milk speed. These two parameters are not yet in the main area of research interest. This study enriches the area with new knowledge, according to which dairy cows can show thermal stress by increasing the milk speed as well as the maximum milk speed.

Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence

New and emerging technologies, especially those based on non-invasive video and thermal infrared cameras, can be readily tested on robotic milking facilities. In this research, implemented non-invasive computer vision methods to estimate cow’s heart rate, respiration rate, and abrupt movements captured using RGB cameras and machine learning modelling to predict eye temperature, milk production and quality are presented. RGB and infrared thermal videos (IRTV) were acquired from cows using a robotic milking facility. Results from 102 different cows with replicates (n = 150) showed that an artificial neural network (ANN) model using only inputs from RGB cameras presented high accuracy (R = 0.96) in predicting eye temperature (°C), using IRTV as ground truth, daily milk productivity (kg-milk-day−1), cow milk productivity (kg-milk-cow−1), milk fat (%) and milk protein (%) with no signs of overfitting. The ANN model developed was deployed using an independent 132 cow samples obtained on different days, which also rendered high accuracy and was similar to the model development (R = 0.93). This model can be easily applied using affordable RGB camera systems to obtain all the proposed targets, including eye temperature, which can also be used to model animal welfare and biotic/abiotic stress. Furthermore, these models can be readily deployed in conventional dairy farms.

PSX-B-7 Effect of supplementation of a robotic milker pellet on mixed diet intake, milk production, and blood metabolites of lactating cows

The objective of this experiment was to determine the effect of concentrate pellets, typically supplied by a voluntary robotic milking system (VMS), on milk production and composition, nutrient metabolism, and diet dry matter intake (DMI) of lactating dairy cows. Holstein cows (n = 24) were assigned to receive either no supplemental pellets (CON), 2 kg/d (LP), or 6 kg/d (HP) in addition to ad libitum diet access, with treatments balanced by lactation number and days in milk. The LP group was allotted 1 kg of pellets every 12 hours and the HP group was allotted 2 kg every 8 hours, to simulate the amount consumed while visiting a VMS unit two or three times daily. Pellet and diet consumption were measured using the Insentec feeding system. Milk production was measured daily, utilizing a milk meter in a conventional parlor. Milk samples were collected twice weekly for 35 days and analyzed for components. Blood samples, body weight, and body condition scores were obtained weekly after the morning milking. Data were analyzed using a mixed model (SAS 9.4) with fixed effects of treatment, day, and their interaction, with day as a repeated measure. No treatment × day interaction was present for diet DMI (P = 0.72); however, addition of pellets to the LP and HP treatment groups decreased diet DMI when compared to CON (P &lt; 0.006). Plasma lactate tended to be greater for CON (P = 0.09) than LP and HP treatment groups with no treatment effect on other blood metabolites (P ≥ 0.30). There was no effect of treatment on milk yield, milk components, or body weight and condition (P ≥ 0.23). These data suggest that feeding a pellet similar to conditions in a robotic milking system could alter DMI without negatively impacting metabolism or milk production.

Analysis of different options of use of milking robots in dairy livestock

It has been established that currently there are about 700 milking robots in dairy farming in Russia, with a predominance of mono-box models. In flowconveyor robotic milking, it is possible to significantly reduce the number of automatic handlers and reduce the cost of a set of equipment due to the separate performing process steps for connecting the teatcups and the milking process itself compared to mono-boxes (one robot – one cow). It is noted that the latter are more expedient to use on small farms counting for up to 200-250 heads. For those farms that have 400 cows and more, it is more rational to build Yolochka milking parlors equipped with quarter-milking handlers and serviced by operators, which can then be robotized while keeping the milking trench for training animals in robotic milking. For those farms that have 800 heads and more, it is advisable to use automated rotating milking parlors, also along with their gradual transfer to robotic systems.

METHOD FOR SELECTING TECHNOLOGICAL TYPE COWS FOR ROBOTIC MILKING

The technological properties of the udder of cows have been studied under the conditions of using a robotic milking system. The research material was Holstein cows in the peasant (farm) economy of the Republic of Tatarstan, serviced by "Astronaut A4" robotic milking from "Lely Industries N.V.". A method for selecting cows for robotic milking has been developed. At the same time, in the herd, first, cows of the 1st lactation at 2-4 months of lactation are assessed according to the duration of milking and animals with duration of milking from 3 to 6 minutes are selected. Then, cows are selected from this group, whose lactation intensity indicator at 2-4 months of lactation exceeds the average value of this group by 0.5 sigma (M + 0.5σ), where M – the arithmetic mean of the indicator; σ – the standard deviation of the indicator. The proposed selection method makes it possible to form a breeding core and increase the milk yield in 305 days of lactation in the group of first-calf cows by 9.5 % (P˂0.01), daily milk yield – 14.4 % (P˂0.001), the milk flow rate – 33 % (P ˂ 0.001) and reduce the duration of cows' stay in the boxing by 17.5 %, the duration of milking cows – 21.7 %. When using this method, the efficiency of using robotic technology for milk production is increased.

LACTIVE ACTIVITIES OF FIRST-CALF COWS USING ROBOTIC MILKING

Studies have been conducted to assess the lactation productivity of Kholmogory breed Tatarstan type first-calf cows with robotic milking systems. The frequency of milking distribution during the day was observed: in the interval of 4-6 hours, the minimum number of milking occurs (6.4 %), in the interval of 12-14 hours, the maximum number (10.7 %). Most milking (34.7 %) is carried out in 6-8 hours after the previous one. More frequent milking (after 4-6 hours) is rarely observed is in 6 % of cases. 48.6 % of milking occurs after the 8-12 hour interval. For an interval of more than 12 hours occurs for 10.7 % of milking. With an increase in the milking interval, the milk yield also increases. At 4-6 hour interval, the milk yield is 5.2 kg, at 6-8 hour is 6.0 kg. An increase in the milking interval by 2 hours is accompanied by an increase in the single milk yield by 0.8-1.3 kg. During the first month, the cows were milked the least number of times, which was 75, or 2.4 times per day; during the second month, they were milked 86 times (2.8 per day). By the 4th month, milkings reached a peak of 93 times (3 per day). By the 5th month, a decline was observed (by 11 % compared to the 4th month). The peak milk production takes place during the second month of lactation, 681.3 kg. By the third month, a decline was observed by 4.8 %, to 648.1 kg. The maximum decrease in milk production, by 12.4 %, occurred between the 5th and 6th month of the tested lactation period.

Cultura microbiológica na fazenda - revisão

Mastitis can be considered the main obstacle to Brazilian dairy productivity, resulting in further risks to public health, especially due to the indiscriminate use of antimicrobials. Therefore, technologies that aim to contribute to the dynamization of the diagnosis and the consequent adoption of control, prevention and treatment measures are of high importance for the sector. In this sense, the present review addresses the concept of on-farm culturing (microbiological), arguing about its implementation, available methodologies, execution and results interpretation. In addition, the performance of selective dry cow therapy, and health monitoring of animals submitted to robotic milking systems is argued in view of on-farm culture routine. In conclusion, it is an efficient alternative for on-farm microbiological monitoring of mastitis.

Cultura microbiologica na fazenda - revisao

Mastitis can be considered the main obstacle to Brazilian dairy productivity, resulting in further risks to public health, especially due to the indiscriminate use of antimicrobials. Therefore, technologies that aim to contribute to the dynamization of the diagnosis and the consequent adoption of control, prevention and treatment measures are of high importance for the sector. In this sense, the present review addresses the concept of on-farm culturing (microbiological), arguing about its implementation, available methodologies, execution, results interpretation, as well as its contribution for the performance of selective dry cow therapy, and health monitoring of animals submitted to robotic milking systems. In conclusion, it is an affordable and efficient alternative for on-farm microbiological monitoring of mastitis.