The objective of this study was to evaluate whether pre-weaning heifer calves divergent for residual feed intake ( RFI ) or residual feed intake and body weight gain ( RIG ) exhibit differences in thermography, blood, and ruminal parameters. Thirty-two Gyr heifer calves were enrolled in a 63-d trial and classified into 2 feed efficiency ( FE ) groups based on RFI and RIG (mean ± 0.5 SD). The groups were classified as high efficiency ( HE ) RFI (HE RFI, n = 9), HE RIG (HE RIG, n = 10), low efficiency ( LE ) RFI (LE RFI, n = 10), and LE RIG (LE RIG, n = 11). The amount of whole milk provided for each calf was calculated based on their metabolic weight at birth (42% x BW 0.75 ). The liquid diet was divided into two meals at 0700 and 1400 h. The total solid diet ( TSD ) was composed of 92% concentrate and 8% of Tifton 85 hay chopped in 5-cm lengths, as fed. Intake was measured daily. Blood concentrations of insulin, beta hydroxybutyrate, urea, and glucose, and ruminal pH, N-NH 3 , and volatile fatty acids ( VFA ) were evaluated at 14, 28, 42, 56, and 70 days of age. Thermal images of the calves were taken with an infrared camera (FLIR T420, FLIR Systems Inc., Wilsonville, OR) on d 56 (±3) at 0600 h, before the morning feeding. Total VFA concentration and propionate as % of total VFA were 24.2% and 22.2% lower in HE RFI compared to LE RFI calves, respectively. On the other hand, acetate as % of total VFA was 10.6% greater in HE RFI than LE RFI calves. Blood urea concentration tended to be greater in LE RFI than HE RFI calves. High efficiency HE RIG tended to have 6.8% greater acetate and 15.4% lower propionate as % of total VFA than LE RIG. Blood insulin concentration was greater and blood glucose tended to be greater for LE RIG than HE RIG group. Low efficiency RIG group had greater left rib, left flank, and anus surface temperature measured by infrared thermography than the HE RIG group. Differences in ruminal fermentation do not seem to be associated with pre-weaning calves efficiency, while differences in protein metabolism seem to affect RFI during this phase. Infrared thermography appears to be correlated to RIG in pre-weaning heifer calves.

The use of precision farming technologies, such as milking robots, automated calf feeders, wearable sensors, and others, has significantly increased in dairy operations over the last few years. The growing interest in farming technologies to reduce labor, maximize productivity, and increase profitability is becoming noticeable in several countries, including Brazil. Information regarding technology adoption, perception, and effectiveness in dairy farms could shed light on challenges that need to be addressed by scientific research and extension programs. The objective of this study was to characterize Brazilian dairy farms based on technology usage. Factors such as willingness to invest in precision technologies, adoption of sensor systems, farmer profile, farm characteristics, and production indexes were investigated in 378 dairy farms located in Brazil. A survey with 22 questions was developed and distributed via Google Forms from July 2018 to July 2020. The farms were then classified into seven clusters: (1) top yield farms; (2) medium–high yield, medium-tech; (3) medium yield and top high-tech; (4) medium yield and medium-tech; (5) young medium–low yield and low-tech; (6) elderly medium–low yield and low-tech; and (7) low-tech grazing. The most frequent technologies adopted by producers were milk meters systems (31.7%), milking parlor smart gate (14.5%), sensor systems to detect mastitis (8.4%), cow activity meter (7.1%), and body temperature (7.9%). Based on a scale containing numerical values (1–5), producers indicated “available technical support” (mean; σ2) (4.55; 0.80) as the most important decision criterion involved in adopting technology, followed by “return on investment—ROI” (4.48; 0.80), “user-friendliness” (4.39; 0.88), “upfront investment cost” (4.36; 0.81), and “compatibility with farm management software” (4.2; 1.02). The most important factors precluding investment in precision dairy technologies were the need for investment in other sectors of the farm (36%), the uncertainty of ROI (24%), and lack of integration with other farm systems and software (11%). Farmers indicated that the most useful technologies were automatic milk meters systems (mean; σ2) (4.05; 1.66), sensor systems for mastitis detection (4.00; 1.57), automatic feeding systems (3.50; 2.05), cow activity meter (3.45; 1.95), and in-line milk analyzers (3.45; 1.95). Overall, the concerns related to data integration, ROI, and user-friendliness of technologies are similar to those of dairy farms located in other countries. Increasing available technical support for sensing technology can have a positive impact on technology adoption.

Bovine anaplasmosis causes considerable economic losses in dairy cattle production systems worldwide, ranging from $300 million to $900 million annually. It is commonly detected through rectal temperature, blood smear microscopy, and packed cell volume (PCV). Such methodologies are laborious, costly, and difficult to systematically implement in large-scale operations. The objectives of this study were to evaluate (1) rumination and activity data collected by Hr-Tag sensors (SCR Engineers Ltd.) in heifer calves exposed to anaplasmosis; and (2) the predictive ability of recurrent neural networks in early identification of anaplasmosis. Additionally, we aimed to investigate the effect of time series length before disease diagnosis (5, 7, 10, or 12 consecutive days) on the predictive performance of recurrent neural networks, and how early anaplasmosis disease can be detected in dairy calves (5, 3, and 1 d in advance). Twenty-three heifer calves aged 119 ± 15 (mean ± SD) d and weighing 148 ± 20 kg of body weight were challenged with 2 × 10 7 erythrocytes infected with UFMG1 strain (GenBank no. EU676176) isolated from Anaplasma marginale. After inoculation, animals were monitored daily by assessing PCV. The lowest PCV value (14 ± 1.8%) and the finding of rickettsia on blood smears were used as a criterion to classify an animal as sick (d 0). Rumination and activity data were collected continuously and automatically at 2-h intervals, using SCR Heatime Hr-Tag collars. Two time series were built including last sequence of −5, −7, −10, or −12 d preceding d 0 or a sequence of 5, 7, 10, or 12 d randomly selected in a window from −50 to −15 d before d 0 to ensure a sequence of days in which PCV was considered normal (32 ± 2.4%). Long shortterm memory was used as a predictive approach, and a leave-one-animal-out cross-validation (LOAOCV) was used to assess prediction quality. Anaplasmosis disease reduced 34 and 11% of rumination and activity, respectively. The accuracy, sensitivity, and specificity of long short-term memory in detecting anaplasmosis ranged from 87 to 98%, 83 to 100%, and 83 to 100%, respectively, using rumination data. For activity data, the accuracy, sensitivity, and specificity varied from 70 to 98%, 61 to 100%, and 74 to 100%, respectively. Predictive performance did not improve when combining rumination and activity. The use of longer time-series did not improve the performance of models to predict anaplasmosis. The accuracy and sensitivity in predicting anaplasmosis up to 3 d before clinical diagnosis (d 0) were greater than 80%, confirming the possibility for early identification of anaplasmosis disease. These findings indicate the great potential of wearable sensors in early identification of anaplasmosis diseases. This could positively affect the profitability of dairy farmers and animal welfare.

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Cattle are animals that stay in the herd for a large period of the day in the open air pasture. This daily routine leaves them exposed and susceptible to infectious diseases. One of them is the Bovine Parasitic Sadness which is considered one of the greatest cattle health problems due its high rate of mortality and morbidity. In this work preliminary results are presented from calf inoculated with anaplasmosis. Blood samples were collected and analyzed through Fourier Transform Near Infrared spectroscopy (FT-NIR). The animal was monitored for 60 days until its recovery. This exploratory work shows the potentiality of the FT-NIR in the detection, follow-up and recovery of the anaplasmosis. The possibility of prevention of this disease in the herd via a fast optical analysis may bring a great breakthrough in disease control in dairy and beef cattle.