The objective of this study was to validate an electronic system for monitoring individual feeding behavior and feed intake (Intergado Ltd., Contagem, Minas Gerais, Brazil) in freestall-housed dairy cattle. No data have been published that validate either the behavioral measurement or the feed intake of this system. Feeding behavior data were recorded for 12 Holstein cows over 5d using an Intergado system and time-lapse video. The cows were fitted with an ear tag containing a unique passive transponder and provided free access to 12 feed bins. The system documented the visit duration and feed intake by recording the animal identification number, bin number, initial and final times, and the difference between feed weight at start and end of each feed bin visit. These data were exported to Intergado web software and reports were generated. Electronic data on animal behavior were compared with video data collected during the same evaluation period. An external scale was used to manually measure and validate the electronic system's ability to monitor dairy cow feed intake for each feed bin visit. The feed intake was manually measured for 4-h time periods and compared with the sum of the feed intake recorded by the monitoring system for each cow visit during the same time period. Video and manual weight data were regressed on the electronic feeding behavior and feeding intake data to evaluate the precision of the monitoring system. The Intergado system presented high values for specificity (99.9%) and sensitivity (99.6%) for cow detection. The visit duration and feed intake per visit collected using the electronic monitoring system were similar to the video and manual weighing data, respectively. The difference between the feed intake measured manually and the sum of the electronically recorded feed intake was less than 250g (25,635±2,428 and 25,391±2,428g estimated using manual weighing and the electronic system, respectively). In conclusion, the Intergado system is a reasonable tool to monitor feeding behavior and feed intake for freestall-housed dairy cows.

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.

Performance, rumen development, and metabolism of male Holstein calves (n=54) were evaluated according to 1 of 3 liquid feeding strategies: 4 L of milk replacer (MR)/d until 60 d old (4 L-60 d), 6L of MR/d until 29 d old and 4 L/d from 30 to 60 d (6L-29 d/4 L-60 d), or 6L of MR/day until 60 d old (60 d-6L). Water and starter were provided ad libitum. Intakes of MR and starter were monitored daily and body weight (BW) weekly. Blood samples for glucose and insulin concentrations and ruminal content samples for volatile fatty acids and ruminal ammonia concentrations were collected at 15, 30, 45, 60, 75, and 90 d of age. Six calves on each treatment were euthanized at 30, 60, and 90 d of age. Empty weights of forestomach and abomasum, papillae length, and mitotic index were measured. Average MR intake/d (expressed as a percent of BW) was greater for calves receiving 6L/d than for those receiving 4 L/d until 30 d of age. Calves on the 6 L-29 d/4 L-60 d had the smallest MR intake from 30 to 60 d old, followed by the 4 L-60 d and 6L-60 d treatments. Starter intake (kg of dry matter/day) did not differ between groups. It increased from 0.065 kg/d in the first month to 0.386 kg/d in the second month, and to 2.065 kg/d after weaning. Weight gain was greater for calves fed more MR in the first month, but no difference was observed during the second month. After weaning, 6L-60 d calves had greater rate of weight gain than others and were heavier at 30, 60, and 90 d of age. Weight of empty forestomachs, ruminal pH, and ammoniac nitrogen concentration were not different among groups. Propionate concentration was lower for 6 L-60 d calves, but acetate and butyrate concentrations were not influenced by MR feeding strategy. Calves fed more MR until d 30 had greater ruminal epithelium mitotic index. The different MR feeding strategies did not influence papillae length or ruminal epithelium thickness. Lesions such as ruminal parakeratosis or hyperkeratosis were not observed. The MR feeding strategy did not affect glucose concentration, but insulin was higher in 6L-60 d calves than in the other groups. Glucose concentration increased with age, whereas insulin decreased until 45 d old, and then started to increase until 90 d. In conclusion, MR feeding strategy did not influence ruminal development. Feeding calves 6 L of MR/d over 60 d resulted in greater rate of weight gain without negative effects on starter intake or forestomach development. The weight advantage that 6L-60 d calves obtained preweaning was maintained until 90 d of age.

The objective of this study was to validate an electronic system for monitoring individual feeding and drinking behaviour and intake developed for young cattle housed in group. A total of 35 Holstein-Gyr crossbred heifers (BW: 180±52 kg; age: 121.5±32.5 days), fitted with an ear tag containing a unique passive transponder, were distributed in three groups of 12, 12 and 11 animals per period and had free access to 12 electronic feed bins and two electronic water bins (Intergado® Ltd). The dimensions of feed and water bins, as well as the sensors position were appropriate for young cattle. The system documented the visit frequency and duration, as well as the feed and water intakes, by recording the animal's identification tag, bin number, initial and final times of visits and the difference of feed/water weight at the start and end of each bin visit. Feed bins were monitored using time-lapse video recording over 4 days and the water bins were monitored over 6 days. For each feed bin, two feeding events were monitored using manual weighings with an external scale immediately before and after the animal's visit and the difference between them was assumed as feed intake (n=24 observations). For the water bins, 60 manual weighings were made. Video and manual weighing data were regressed on the electronic feeding and drinking behaviour and intake data to evaluate the system's precision and accuracy. The system showed high specificity (98.98% and 98.56% for the feed and water bins, respectively) and sensitivity (99.25% and 98.74%, respectively) for identifying an animal's presence or absence. Duration of feed and water bin visits as well as feed and water consumption per visit estimated by the system were highly correlated and precise compared with the observed video and manual weighing data (r 2=0.917, 0.963, 0.973 and 0.986, respectively). It was concluded that Intergado® system is a useful tool for monitoring feeding and drinking behaviour as well as water and feed intakes in young cattle housed in groups.

Caracterizaram-se as respostas fisiológicas de termorregulação em conforto e estresse calórico de bovinos cruzados F2 (½ Gir x ½ Holandês), machos e fêmeas, com idade entre 14 e 20 meses. Os animais foram alocados, em número de cinco, e submetidos ao conforto térmico (22°C a 70% de umidade relativa) por 12 horas e ao estresse calórico (42ºC e 60% de umidade relativa) durante seis horas, logo após o período de conforto, nas estações de verão e inverno, em câmara bioclimática. Nas duas etapas, foram registradas a temperatura retal (TR), a freqüência respiratória (FR) e a temperatura de superfície corporal (TSC) às 6 e 16 horas. Os animais apresentaram aumento da TR, da FR e da TSC no período de estresse calórico (P<0,05). O aumento da TR e FR foi mais acentuado no verão (P<0,05). A TSC não apresentou diferença entre as estações (P<0,05).