Comparison of two water measurement systems for feedlot beef cattle.

The objective of this study was to compare cattle drinking water consumption collected electronically with that of direct human observation using water metres and to analyse whether an automated system compensates due to its greater precision. The study was conducted in the feedlot of Embrapa Pecuaria Sudeste. The reference unit had four pens: two with electronic drinkers and two with water metres. Experiment 1 utilised 52 Nelore steers and Experiment 2 utilised 44 Canchim steers. Nelore fed a conventional diet, the automated system median daily water intake (DWI) was higher than for animals drinking from the water metre, 17.9 L day-1 and 15.6 L day-1. The reverse was observed for animals fed the co-product diet, the automated system median DWI was 18.9 L day-1 and in the water metre pen was 23.0 L day-1. When the Canchim drank from water metres, the median DWI was lower than with the automated system group, 25.9 L day-1 and 27.8 L day-1, respectively. In Experiment 1, there was a statistical difference between the two sets of equipment for both diets. In Experiment 2, the animals were the same breed, had similar weights and were fed the same diet. There was no statistical difference between the equipment in these conditions. The results indicate that the water meter can have the same performance as high technology at a much lower cost. If a more simplified system for measuring water consumption has the same performance as an automated system, this will justify its use with environmental and economic advantages

Impact of Social Mixing on Feedlot Steer Behavior

Angus crossbred steers from two genetically similar sources (n = 48 BCS and n = 48 McG), were transported separately (833.64 ± 85.29 km) 39-d prior to commingling and housed at a feedlot without visual or tactile contact. Steers, blocked by source and d -34 body weight, were randomly assigned to 12 pens (n = 8 steers/pen). Pens housed either: NOMIX—100% from BCS (n=3 pens) or McG (n=3 pens) or MIX—50% from BCS and 50% from McG (n = 6 pens). Video recordings were decoded on d 0, 1, and 2 for the number of agonistic behaviors, allogrooming bouts, and drinking bouts initiated by each steer during the first four hours post-mixing. Rumination behavior was recorded on d 1, 2, and 3 post mixing. Mixed models evaluated the impact of treatment, day, and their interaction on cattle behavior. Orthogonal contrasts compared the impact of source on performance of each behavior and Pearson correlations were used to compare total performance of each behavior throughout the study. NOMIX steers performed more (P = 0.08) drinking bouts (10.54 ± 1.27 bouts/steer/pen) than MIX steers (7.68 ± 1.05 bouts/steer/pen). Steers ruminated less on d 3 (NOMIX: 7.97 ± 0.29 hours/steer/day MIX: 8.06 ± 0.29 h/steer/d) than on d 1 (NOMIX: 8.55 ± 0.29 h/steer/d MIX: 8.22 ± 0.29 h/steer/d) or d 2 (NOMIX: 8.77 ± 0.29 h/steer/d MIX: 8.46 ± 0.29 h/steer/d) across all treatments. Steers in MIX pens (7.81±0.30 h/steer/d) spent more time ruminating than steers in NOMIX pens from McG (7.46±0.47 h/steer/d) than steers in NOMIX pens from BCS (8.39±0.52 h/steer/d). NOMIX pens initiated more (P < 0.01) headbutts overall (1.98 0.13 count/steer/pen) and mounts on d 2 (1.07 0.11 count/steer/pen) than those in MIX pens (1.30 0.18 and 0.39 0.15, respectively). Social mixing reduced agonistic behavior and may cause cattle to take longer to establish social hierarchies. Social mixing also decreases drinking behavior and delays social hierarchy establishment

The application of systems thinking in cattle production

Doctor of PhilosophyDepartment of Diagnostic Medicine/PathobiologyRobert L. LarsonBradley J. WhiteApplying systems methods to cattle production requires investigators to think about whole systems when addressing study objectives. The research conducted for this dissertation emphasized studying whole systems using different methods. We studied cattle production systems through mathematical simulation and new indirect monitoring technologies. While the methods used for the research in this dissertation may be very different, all utilized systems methods to address the study objectives. Firstly, we applied systems thinking methods and developed a dynamic, deterministic systems simulation of cow-calf production over a 10-year horizon. This model was used to investigate the effects the duration of postpartum anestrus (dPPA) has on reproductive performance. A large range of dPPA have been reported, so various primiparous cow and multiparous cow dPPA were simulated. We found that increasing the dPPA for primiparous and multiparous cows had a negative impact on herd performance and that the dPPA is an important factor in determining cow-calf performance success. We then used the cow-calf simulation to explore the effects of breeding nulliparous cows prior to the rest of the herd, known as providing Heifer Lead Time (tHL). We found that increasing tHL improved herd performance, especially with longer dPPA for primiparous cows. Secondly, real-time location systems (RTLS) were used to indirectly monitor cattle behavior. These systems have been used to determine the amount of time cattle spend at eating and drinking locations. We modeled the probability of cattle participating in eating and drinking behavior when determined to be at these locations by RTLS and found that significant differences exist between individual calves and period of the day. Finally, we explored associations between bovine respiratory disease (BRD) and animal-to-animal contacts as determined by RTLS in beef cattle. We found that the probability of BRD diagnosis was associated with the amount of time 4 days’ ago that a calf was in calf-contact with calves assumed to be shedding BRD pathogens

Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle

Time constraints for dairy farmers are an important factor contributing to the under-detection of lameness, resulting in delayed or missed treatment of lame cows within many commercial dairy herds. Hence, a need exists for flexible and affordable cow-based sensor systems capable of monitoring behaviors such as time spent feeding, which may be affected by the onset of lameness. In this study a novel neck-mounted mobile sensor system that combines local positioning and activity (acceleration) was tested and validated on a commercial UK dairy farm. Position and activity data were collected over 5 consecutive days for 19 high-yield dairy cows (10 lame, 9 non-lame) that formed a subset of a larger (120 cow) management group housed in a freestall barn. A decision tree algorithm that included sensor-recorded position and accelerometer data was developed to classify a cow as doing 1 of 3 categories of behavior: (1) feeding, (2) not feeding, and (3) out of pen for milking. For each classified behavior the mean number of bouts, the mean bout duration, and the mean total duration across all bouts was determined on a daily basis, and also separately for the time periods in between milking (morning = 0630–1300 h; afternoon = 1430–2100 h; night = 2230–0500 h). A comparative analysis of the classified cow behaviors was undertaken using a Welch -test with Benjamini-t Hochberg post-hoc correction under the null hypothesis of no differences in the number or duration of behavioral bouts between the 2 test groups of lame and nonlame cows. Analysis showed that mean total daily feeding duration was significantly lower for lame cows compared with non-lame cows. Behavior was also affected by time of day with significantly lower mean total duration of feeding and higher total duration of nonfeeding in the afternoons for lame cows compared with nonlame cows. The results demonstrate how sensors that measure both position and acceleration are capable of detecting differences in feeding behavior that may be associated with lameness. Such behavioral differences could be used in the development of predictive algorithms for the prompt detection of lameness as part of a commercially viable automated behavioral monitoring system

Adaptive memory-based single distribution resampling for particle filter

The restrictions that are related to using single distribution resampling for some specific computing devices’ memory gives developers several difficulties as a result of the increased effort and time needed for the development of a particle filter. Thus, one needs a new sequential resampling algorithm that is flexible enough to allow it to be used with various computing devices. Therefore, this paper formulated a new single distribution resampling called the adaptive memory size-based single distribution resampling (AMSSDR). This resampling method integrates traditional variation resampling and traditional resampling in one architecture. The algorithm changes the resampling algorithm using the memory in a computing device. This helps the developer formulate a particle filter without over considering the computing devices’ memory utilisation during the development of different particle filters. At the start of the operational process, it uses the AMSSDR selector to choose an appropriate resampling algorithm (for example, rounding copy resampling or systematic resampling), based on the current computing devices’ physical memory. If one chooses systematic resampling, the resampling will sample every particle for every cycle. On the other hand, if it chooses the rounding copy resampling, the resampling will sample more than one of each cycle’s particle. This illustrates that the method (AMSSDR) being proposed is capable of switching resampling algorithms based on various physical memory requirements. The aim of the authors is to extend this research in the future by applying their proposed method in various emerging applications such as real-time locator systems or medical applications

Spatial behaviour of dairy cows is affected by lameness

Lameness is one of the major welfare problems on modern dairy farms, and additionally, it is difficult to control. Lameness is associated with changes in cow behaviour, and efforts have been made to automatically detect these behavioural changes. However, systems relying on a single behavioural variable are likely to fail. Indoor positioning could provide means to measure multiple behavioural variables with a single system. Our aim was to investigate how lameness affects the spatial behaviour of cows, measured with an indoor positioning system. In total, 71 lactating dairy cows were followed during a 7-month study period, with 48 cows in the study simultaneously. Cows were locomotion scored fortnightly with a 10-tier scale, and their daily time spent in the different functional areas of the barn, walking distance, and home range were calculated from the positioning data. Each locomotion score was merged with the 5-day average of the behaviour variables leading up to the scoring day, resulting in 376 observations in the final data. Linear mixed models were fitted with backwards stepwise elimination to test the associations between positioning-based daily behavioral variables and predictor variables comprising locomotion score, parity, lactation stage, breed and the proportion of missing positioning data. Increasing locomotion score was associated with increased time spent in the lying stalls (P = 0.0037) and decreased time spent in the alley (P < 0.0001). Positioning-based feeding time was confounded by parity (P = 0.011) as the model used to estimate the feeding time from the position data was less sensitive in classifying primiparous cows correctly as feeding or not feeding. Severe lameness was also associated with a shorter daily walking distance (P = 0.0447) and smaller core home range (P = 0.005). Proportion of missing positioning data affected only daily walking distance (P < 0.0001) and full home range (P = 0.0059), and distance-based variables seemed more sensitive to data quality compared to spatiotemporal variables. Our results show that indoor positioning of dairy cows has a potential to contribute to development of automatic lameness detection. However, reliability of positioning systems should be improved, and the amount of missing data should be minimised to improve the calculation of distance-based variables

UTILIZING PRECISION TECHNOLOGIES TO VALIDATE A REAL-TIME LOCATION SYSTEM FOR DAIRY CATTLE AND MONITOR CALF BEHAVIORS DURING HEAT STRESS

With the increase in on-farm precision dairy technologies (PDT) utilization, large quantities of information are readily available to producers. A more recently available technology for use in livestock species is the real-time location system. These technologies offer dairy producers the opportunity to monitor and track real-time locations of cows, track locomotion patterns, and summarize specific area usage. However, the usefulness of these insights is heavily dependent on the performance of the technology. Therefore, the first objective of this dissertation was to assess the positioning recording performance and the usefulness of the data recorded of a real-time location system (Smartbow GmbH; Zoetis Services LLC., Parsippany, NJ, USA) for use in freestall-housed dairy cattle on a commercial farm. The first objective evaluated a technology’s positioning abilities under static and dynamic conditions. The system was able to accurately determine locations while under both static and dynamic conditions. Furthermore, PDT are also utilized to monitor the behaviors and activity of dairy calves. The second objective of this dissertation was to investigate the effects of heat stress on the behaviors of dairy calves using information gathered by PDT. Information recorded from automated milk feeders and pedometers were used to investigate the effects of an elevated temperature-humidity index on dairy calf behaviors. The changes in behavior recorded suggest that PDT can detect behavioral patterns changes of calves during heat stress

Monitoring and mitigating heat stress in cattle

Heat stress related production loss, compromised welfare and cattle mortality are global concerns which are increasing in the context of climate change. Cattle response to heat stress varies based on individuality and thermal environment. However, current cattle heat stress monitoring and mitigation is directed at the herd level, and primarily based on climatic indices that do not monitor the individual animal. The objectives of this work were to validate a sensor-based method to monitor individual cattle heat stress responses through behavioural and physiological indicators (panting score and core body temperature), and climatic indices (temperature humidity index, THI and heat load index, HLI); and to determine the ability to detect heat-susceptible animals for isolated mitigation strategy through an advanced sensor system. The feasibility of this automated monitoring system and the management of cattle heat stress was reviewed in Chapter 2 and the sensor system validated in Chapter 3. The behavioural association with different levels of panting severity (Chapter 4) and cattle heat response diversity was revealed in Chapter 5 in relation to THI/HLI and core body temperature. These results also revealed the panting duration for Individual cattle within the same group to vary significantly with diverse levels of panting being associated with the timing of resting and eating (Chapter 4 and 6). This research highlights the potential for genetic selection for heat resilience and reveals the opportunity for strategic amelioration of heat for susceptible cattle to benefit animal welfare and productivity

Mathematical modeling and social network analysis applications in foot-and-mouth disease transmission and livestock movements in U.S. production types

Doctor of PhilosophyDepartment of Diagnostic Medicine/PathobiologyMichael W. SandersonThe U.S. has been FMD-free since 1929. The U.S. has a large beef industry with over 45% of cattle on-feed concentrated in feedlots with a one-time head capacity ≥32,000 cattle. The country has a complex production system in which there is a continuous flow of livestock. An incursion of FMD could be devastating, so an understanding of the dynamics of a hypothetical outbreak in these large operations is needed. Also, an understanding of movement patterns to identify areas at-risk that can be targeted during disease response is needed. Mathematical modeling is the only tool available to study epidemics of infectious diseases such as FMD while Social Network Analysis (SNA) is an approach that helps to understand movement patterns. Parameterization of mathematical models is challenging due to the variability of the FMDv and the lack of specific data to U.S. beef populations. We developed an FMD expert survey to collect key parameter values of FMD natural history and transmissibility in beef U.S. feedlots. Data synthetized, used in combination with experimental and outbreak investigation data, will help to parameterize FMD-transmission models to evaluate implications of epidemics in U.S. beef feedlots. We developed a meta-population model to study FMDv transmission and evaluate interventions strategies within U.S. beef feedlots. We found that the projected outbreak duration was shorter for those feedlots with over 12,000 cattle population that operated with one hospital-pen compared to those feedlots that operated with two hospital pens. Restriction of movements of cattle from home pens to hospital pens within the feedlots was found to prolong the projected outbreak duration but did not interrupt FMDv transmission in feedlots modeled. Partial depopulation interventions were not found to be highly efficient in controlling FMDv transmission or required depopulation of a large proportion of cattle in feedlots modeled. We used social network analysis to describe inter-state movements of beef cattle, dairy cattle, swine, and small ruminants, and identify trade-communities within the contiguous U.S. for each livestock type network. We found that outputs generated resemble the nature of the beef feedlot industry (cow-calf to feedlot) while areas with large animal counts in the swine and dairy cattle networks were found to have high degree centrality. We also found between 1 to 2 largest communities in the beef cattle, dairy cattle, and swine networks which accounted for up to 65% of arcs in each network. The outputs of these networks could be useful to parameterize network models to assess disease transmission such as FMD at a national scale and evaluate the application of intervention strategies