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 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.

Background: Methane (CH₄) emissions from rumen fermentation are a significant contributor to global warming. Cattle with high CH₄ emissions tend to exhibit lower efficiency in milk and meat production, as CH₄ production represents a loss of the gross energy ingested by the animal. The objective of this study was to investigate the taxonomic and functional composition of the rumen microbiome associated with methane yield phenotype in dairy cattle raised in tropical areas. Methods and results: Twenty-two Girolando (F1 Holstein x Gyr) heifers were classified based on their methane yield (g CH₄ / kg dry matter intake (DMI)) as High CH₄ yield and Low CH₄ yield. Rumen contents were collected and analyzed using amplicon sequencing targeting the 16 and 18S rRNA genes. The diversity indexes showed no differences for the rumen microbiota associated with the high and low methane yield groups. However, the sparse partial least squares discriminant analysis (sPLS-DA) revealed different taxonomic profiles of prokaryotes related to High and Low CH₄, but no difference was found for protozoa. The predicted functional profile of both prokaryotes and protozoa differed between High- and Low CH₄ groups. Conclusions: Our results suggest differences in rumen microbial composition between CH₄ yield groups, with specific microorganisms being strongly associated with the Low (e.g. Veillonellaceae_UCG − 001) and High (e.g., Entodinium) CH₄ groups. Additionally, specific microbial functions were found to be differentially more abundant in the Low CH₄ group, such as K19341, as opposed to the High CH₄ group, where K05352 was more prevalent. This study reinforces that identifying the key functional niches within the rumen is vital to understanding the ecological interplay that drives methane production.

The objective of this work was to evaluate the effects of nutritional plans and breed composition on nutrient intake and digestibility, nitrogen balance, and performance of dairy heifers in tropical conditions. Thirty-six heifers - 12 Holstein, 12 Gyr, and 12 Gyrolando F1 (½ Holstein × ½ Gyr) - were housed in a tie-stall system and randomly distributed, adopting a completely randomized design in a 3×3 factorial arrangement (nutritional plans and breed composition). The diets were formulated to enable weight gains of 200, 400, and 800 g per day, corresponding to the nutritional plans 11, 14, and 19 g kg^-1 body weight (BW), respectively. The intake of dry matter, fibrous fractions (g kg^-1 BW^-0.75), and metabolizable energy (kcal^-1 BW^-0.75) in the 11-g kg^-1 plan were greater for Gyr heifers than for Gyrolando and Holstein heifers. Ether extract digestibility was 13.85% higher for Gyr heifers compared with Gyrolando F1 in the 19-g kg^-1 plan. The increase in the nutritional plans shows a quadratic response to nutrient intake and a linear response to average daily gain, but does not affect the digestibility coefficients. Gyr and Gyrolando F1 heifers present similar nutritional efficiency to that of Holstein heifers.