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.

The aim of this study was to evaluate how the inclusion of a blend of essential oils in milk replacer (MR) affects different outcomes of dairy heifers. The outcomes evaluated: feed intake, performance, body development, blood cells and metabolites, insulin-like growth factor-1 (IGF-1), rumen fermentation, fecal scores, and respiratory scores. All outcomes were evaluated during pre-weaning (4–60 d of age), and carry-over effects during post-weaning (61–90 d of age) periods. The experimental units utilized were 29 newborn Holstein × Gyr crossbred dairy heifers, with genetic composition of 5/8 or more Holstein and 3/8 or less Gyr and body weight (BW) at birth of 32.2 ± 5.2 kg. Experimental units were assigned to either a control (CON, n = 15) or a blend of essential oil supplementation (BEO, n = 14) treatment, maintaining a balance of genetic composition. The BEO was supplemented in the MR with 1 g/d/calf of a blend of essential oils (Apex Calf, Adisseo, China) composed by plant extracts derived from anise, cinnamon, garlic, rosemary, and thyme. During the pre-weaning phase, all heifers were fed 5 L of MR/d reconstituted to 15% (dry matter basis), divided into two equal meals. Water and starter were providedad libitum. During the post-weaning, animals received a maximum of 3 kg of starter/d, andad libitumcorn silage, divided into two meals. Feed intake, fecal and respiratory scores were evaluated daily. The BW was measured every three days, while body development was recorded weekly. Blood samples were collected on 0, 30, and 60 d of age for total blood cell count, weekly and on the weaning day to determinate ß-hydroxybutyrate, urea and glucose, and biweekly for IGF-1. Ruminal parameters (pH, volatile fatty acids, ammonia-N, and acetate:propionate proportion—C2:C3) were measured on days 14, 28, 42, 60, 74 and 90. A randomized complete block design with an interaction between treatment and week was the experimental method of choice to test the hypothesis of the BEO’s effect on all outcomes. An ANOVA procedure was used for continuous outcomes, and a non-parametric test was used for the ordered categorical outcomes, both adopting a CI = 95%. Results indicated that there was not enough evidence to accept the alternative hypothesis of the effect of BEO in MR on feed intake, performance, body development, and blood metabolites during both pre-weaning and post-weaning periods. However, results indicated that the inclusion of BEO in MR significantly affects the proportion of C2:C3 during pre- and post-weaning (P= 0.05). Similarly, the effect was significant for basophil (P≤ 0.001), and platelet (P= 0.04) counts pre-weaning. The interaction between week and treatment was also significant for lymphocytes (P≤ 0.001), revealing a cumulative effect. Lastly, fecal scores were also significant (P= 0.04) during pre-weaning, with lower values for BEO. The BEO contributed to ruminal manipulation in pre-weaning and carry-over effects in post-weaning, immunity improvement, and decreased morbidity of neonatal diarrhea in the pre-weaning phase.

This study aimed to estimate energy requirements of pregnant Holstein × Gyr cows. Different planes of nutrition were established by two feeding regimens: ad libitum or maintenance. Sixty-two nonlactating cows with average body weight of 480 ± 10.1 kg and an age of 5 ± 0.5 years were used. Cows were divided into three groups: pregnant (n = 44), non-pregnant (n = 12), and baseline reference (n = 6). The 56 pregnant and non-pregnant cows were randomly allocated into a feeding regimen: ad libitum or maintenance. To evaluate the effects of days of pregnancy, pregnant and non-pregnant animals were slaughtered at 140, 200, 240, and 270 days of pregnancy. Energy requirements for maintenance differed between pregnant and non-pregnant cows, thus two equations were developed. Net energy and metabolizable energy requirements for maintenance of non-pregnant cows were 82 kcal/kg empty body weight^0.75/day and 132 kcal/kg empty body weight^0.75/day, respectively. The efficiency of use of metabolizable energy for maintenance of non-pregnant cows was 62.4%. Net energy and metabolizable energy for maintenance of pregnant cows were 86 kcal/kg empty body weight^0.75/day and 137 kcal/kg empty body weight^0.75/day, respectively. Efficiency of use of metabolizable energy for maintenance of pregnant cows was 62.5%. The efficiency of use of metabolizable energy for gain was 41.9%. The efficiency of use of metabolizable energy for pregnancy was 14.1%. Furthermore, net energy requirement for pregnancy was different from zero from day 70 of pregnancy onwards. In conclusion, net energy and metabolizable energy requirements for maintenance of non-pregnant cows are different from pregnant cows. Furthermore, we believe that the proposed non-linear equations to estimate net energy requirements for pregnancy are more adequate than current NRC equation, and should be recommended for Holstein × Gyr cows.

The dairy Nutrients Requirements of Cattle (NRC) was developed using data from purebred Holsteins and it might not accurately predict the performance of crossbred cattle. Our objectives were to evaluate the effects of two feeding levels (FLs) and three breed compositions (BCs) on nutrient intake, digestibility, performance, and methane (CH₄) emissions of prepubertal dairy heifers. We used thirty-six heifers from three BCs: purebred Holstein (H), purebred Gyr (G), and F1 Holstein × Gyr (HG). Each BC had 12 animals and the experiment was designed as twelve incomplete three by three Latin squares, in a factorial arrangement three by two, with three BCs and two FLs (400 and 800 g/day). Total tract nutrient digestibility was determined using total fecal collection and DMI was individually measured. The data were analyzed using the PROC MIXED in SAS. Dry matter intake of all nutrients increased from the medium to high feeding level and the nutrients digestibility coefficients did differ among BCs. Achieved body weight gain in the medium FL treatment was greater than those predicted using the NRC, suggesting that crossbred and Gyr heifers have similar performance to Holsteins. Breed composition does not influence body weight gain of confined dairy heifers, but Holstein heifers fed a medium FL had higher feed efficiency and reduced CH₄ emissions intensity.

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 × 10⁷ 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 short-term 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.