Lameness is a painful disorder that decreases performance and is highly recognized as one of the most important health and welfare concerns for dairy cattle. Visual gait scoring is the most common way to detect gait change in dairy cattle. However, this is not only subjective, but is also time consuming and costly. A need to remove the subjective assessment of human observation exists. Therefore, automatic gait change detection for continuous monitoring by precision dairy monitoring technologies may be beneficial. The first objective of this research was to characterize behavior and production variables as cow gait changed to evaluate potential usefulness in gait change detection across two different studies. Weighted gait score was a significant (P < 0.05) predictor of rumination time for study 1. Rumination time decreased as weighted gait score increased. However, for study 2, numbers of steps and feeding time were significant predictors (P < 0.05). Number of steps increased as weighted gait score increased. Time at the feedbunk and feedbunk visits decreased as cows weighted gait score increased. The second objective was to compare behavior and production variables for each individual gait aspect in increasing gait scores to evaluate potential usefulness in gait change detection across two different studies. For study 1, milk yield, rumination, and neck activity decreased as cows as tracking score increased. For study 2, lying time decreased as cow’s general symmetry score increased. Feedbunk visits decreased as cows tracking score increased. Number of steps increased as cow’s spine curvature score increased. Time active increased as cows head bobbing score increased. Activity increased as cows speed score increased. Lying time decreased as cow’s abduction/adduction score increased. The third objective was to detect gait change utilizing multiple precision dairy monitoring technologies in two different studies. For study 1, 56% of predicted gait scores were within 0.25 points of the actual weighted gait score and for study 2, 41% of predicted gait scores were within 0.25 points of the actual weighted gait score. Pearson Correlation for study 1 and 2 was 0.43 and 0.46, respectively. For both studies, the Pearson Correlation yielded results in the low category, when evaluating goodness of fit.

Current feeding strategies for dairy cows focus on meeting the energy requirements for high levels of milk production. However a major concern is the effect that these feeding regimes might have on rumen pH, which can have harmful effects on the cow and rumen microbial population. Several interventions have been used to counteract the effects of low rumen pH such as the use of probiotics e.g. yeast (Saccharomyces cerevisiae). However benefits have been inconclusive due to large individual animal variation in responses to treatment observed. The use of novel monitoring technologies can help assess the effect that different dietary interventions have on performance, rumen pH and rumen health. Data from three on-farm dairy cow trials (Trial 1 standard diet plus yeast; Trial 2 standard diet plus acidotic challenge plus yeast; Trial 3 cows grazing grass plus yeast) was used to evaluate the use of rumination collars (RC), rumen pH boluses, a whole cow dynamic mechanistic simulation model (SM) and the effect that different feeding strategies have on performance rumen pH dynamics and rumination time. No statistically significant differences between Control (no yeast) and Treatment (addition of yeast) diets were observed on any of the parameters measured. The lack of animal response to yeast supplementation observed in the three feeding Trials could be attributed to the stage of lactation, as the cows were passed peak lactation. Comparison of rumination time obtained with the RC and visual observations (obtained directly and from video recordings) suggest that the RC can be used to determine rumination time in housed cows. However its poor performance in grazing environments makes its use not advisable in cows outside at grass. The rumen pH boluses provided detailed and accurate data on circadian rumen pH. Highly varied individual responses to the feeding strategies were observed. This resulted in a diverse degree of risk of individual cows which experienced sub-acute rumen acidosis. The SM was able to accurately predict circadian pH, compared against the data obtained from Trials 1 and 2. The model provided pH values that were in agreement with those obtained with the rumen boluses. The use of new technologies to monitor cows individually could aid in whole-herd management, for example by setting thresholds for rumen pH and rumination time related to individual cow status, and then trigger appropriate interventions.

Beneath North America's roads lie 1.6 million miles of pipeline that provides users with potable water and carry away wastewater. These buried infrastructure systems have been functioning for duration longer than their intended design life, often with little or no repair. Asset management of pipeline systems pose a major challenge for most municipalities due to budgetary constraints, demand for quality service, and need to preserve existing pipeline infrastructure. The first step in developing and implementing a comprehensive asset management plan is to perform a condition assessment. There is a gamut of inspection and monitoring technologies available to enable the condition assessment of pipelines. All of these have advantages and limitations, which determine the performance quality and effectiveness of an individual technology for particular utility assets. Unfortunately, utilities choose technologies not suitable for their specific assets and collect data that is not useful for understanding the condition of their system. The objective of this thesis is to develop a framework for the effective selection of condition assessment technologies for water and wastewater utilities. A Microsoft-Excel based framework is developed to help the utility managers in selecting condition assessment technologies for their water and wastewater pipeline assets. The recommended tool selection approach uses a multi-step exclusion protocol in which the tools are excluded on the basis of their applicability relating to technical feasibility and technical suitability for a particular situation. Usable tools are then compared against a performance and cost database to determine performance and cost in a given project/ utility condition. This thesis provides a brief description and review of 24 non-destructive commercialized condition assessment technologies, including the principal and implementation considerations. A framework for decision system tool was developed to facilitate utilities in selecting appropriate condition assessment technologies. This framework could facilitate the selection of usable technologies by excluding the options which are not technically feasible and suitable. The user can then further explore the usable tools and determine the most suitable technologies for their assets. The data considered in the research is provided by technology providers, thus it may lack complete understanding of the capabilities and limitations of technology. This thesis also presents a case study which highlights the existing gap between the understanding of capabilities and limitations of various technologies. A program is developed as a part of this thesis, Condition Assessment Selection Tool (CAST), which consists of performance and economic database, a graphical user interface to facilitate user input, and the results of the comparison of each usable technology in the database to the project information provided by the user for their assets. The results are presented as performance indices and economic indices indicating the performance and technology cost of usable technologies. A data reliability index was also developed to provide a scale for comparing the reliability of the existing data in the database. / Master of Science