Current feeding strategies aim to maximize efficiency at the pen level. However, feed intake varies across animals and in response to diet composition, making it difficult to capture these variations and control feeding effectively. A precision feeding system is required to feed animals individually, continuously monitor responses, and make timely adjustments to feed tailoring. Such a system would efficiently integrate dairy operations to enhance profitability and reduce their environmental footprint. Thus, the objectives of this dissertation were to build, test, and apply a precision feeding system able to tailor feeding strategies to animals more precisely and closely match their individual requirements. In Chapter 3, we describe the precision feeding system framework using directional data streams. The system integrates real-time farm data, segmented into data-analytic modules for independent testing and troubleshooting. It provides feeding instructions to automatic feeders and generates animal and financial monitoring reports. In Chapter 4, we describe the "Animal Performance" system module. This study developed a predictive model to estimate individual dry matter intake (DMI) by integrating markers, animal characteristics, dietary nutrient concentrations, and chewing sensor data. The performance of the developed model was then assessed and contrasted with the NASEM (2021) DMI equations. By incorporating covariates derived from short-term use of external and internal markers we demonstrated a greater accuracy of DMI predictions when using a fixed effects model, supporting its predictive capabilities for further application. In Chapter 5, we describe the "Diet Optimization" systems module, used to maximize profit by optimizing rations using a developed compact-vectorized version of NASEM (2021). The study aimed to simulate optimized diets, evaluate the economic impact of feeding individual diet, compare feed costs and income over feed cost (IOFC) for optimized group diet, and compare optimized diets against pen-averages (PEN). The results showed that IND diets had lower costs, higher milk production, and increased IOFC compared to CLU diets. Additionally, both IND and CLU diets outperformed PEN solutions. This work established methods for deriving efficient diet solutions for individual animals and using clustering techniques for more precise pen-level feeding. In Chapter 6, we describe the application of "Animal Performance", "Diet Optimization", and "Nutrient Titration" system modules. The former DMI model described in Chapter 4 was applied to the experimental data. The middle utilized optimized diets generated by the optimizer developed in Chapter 5, with additional algorithm updates. The latter aimed to investigate individual milk true protein production responses of dairy cows to varying levels of metabolizable protein (MP) and rumen-protected amino acids (RPAA) using automatic feeding systems and rank animals based on their individual gross milk protein efficiencies. Results demonstrated heterogeneous animal responses across MP and RPAA levels, ranging from linear, and quadratic to no response, emphasizing the necessity of addressing individual variability within a common pen. High-efficiency animals behaved consistently across MP treatments with lower variability, while low-efficiency animals showed high variability but consistently remained in the bottom efficiency rank. In conclusion, the precision feeding system underscores true capabilities to tailor nutrient delivery to individual cows, maximizing economic and environmental benefits, and sets the stage for future research focused on further refinement and automation of these technologies / Doctor of Philosophy / Feeding practices for dairy cattle have evolved significantly from manual grain mix offering to group feeding. While pen-level feeding has its benefits, it overlooks opportunities to maximize efficiency and minimize feed waste and nutrient excretion by not using individual animal variation to apply control feeding. With modern farms and increased technology adoption, feeding animals while being individually milked, even when group-housed, is now possible, leveraging this variability to apply precision feeding. In Chapter 3, we described the development of a precision feeding system that leverages technological advancements on dairy farms to gather and analyze data, supporting informed decision-making. This system includes various modules for testing and adjusting feeding strategies according to animal needs, providing feeding instructions to automatic feeders, and generating reports to help farmers monitor their animals and manage costs. Recognizing that precision feeding relies on quality data and accurate predictions of crucial metrics such as dry matter intake (DMI), Chapter 4 focused on developing a mathematical equation to predict DMI on an individual animal basis. This model demonstrated potential for commercial dairy operations due to its use of readily available farm-level predictors and its adequate performance compared to gold-standard field equations. Given the lack of efficient optimizers that incorporate individual animal data, in Chapter 5, we described the development of a new optimizer incorporated into the system to maximize profits. We simulated different feeding strategies, including optimized individual and group diets, and demonstrated that these tailored diets were more cost-effective and led to higher milk production compared to pen-average diets. To complete the development, testing, and application cycle, in Chapter 6, we applied the precision feeding system to determine the metabolizable protein (MP) requirements of dairy cows and assess milk protein production responses to different levels of MP and rumen-protected amino acids (RPAA). The results indicated varied responses among cows, highlighting the importance of individualized feeding to account for animal-to-animal differences within the same pen. Top-efficient animals were consistent in their responses across treatments, whereas bottom-efficient animals exhibited greater variability and consistently underperformed. In conclusion, the precision feeding system demonstrated significant potential to improve the efficiency of dairy farming by more accurately meeting the specific needs of dairy cattle. Future research will focus on refining this system and further automating the process for broader farm applications.

Precision dairy farming technologies provide a variety of functions to dairy farmers. Little is known about dairy producer perception of these technologies. A study was performed to understand dairy producer perception of parameters monitored by precision dairy farming technologies. Calving has potential to be predicted using these same parameters and technologies. A second study was performed using two commercially marketed technologies in calving prediction. In order for these technologies to generate accurate and useful information for dairy farm use, they must accurately quantify these parameters. The final study evaluated the accuracy of five commercially marketed technologies in monitoring feeding, rumination, and lying behaviors.

Body condition scoring is a technique used to noninvasively assess fat reserves. It provides an objective estimate to describe the current and past nutritional status of the dairy cow and has been associated with increased disease risk and breeding success. Traditionally body condition scores are taken manually by visual appraisal on a 1 to 5 scale, in one-quarter increments. However, recent studies have shown the potential of automating the body condition scoring of cows using images. The first objective was to estimate the likelihood of disease development and breeding success, using odds ratios, associated with body condition score scored automatically at various points in lactation. The second objective of our research was to use a commercially available automated body condition scoring camera system to monitor body condition across the lactation period to evaluate differences between stratified parameters and to develop an equation to predict the dynamics of the body condition score. We found that poor body condition score at different times during the transition period are associated with increased disease occurrence and lower reproductive success. Automated body condition scoring (ABCS) curve during lactation was influenced by many factors, such as parity, ABCS at time of calving, disease occurrence, and milk production.

The inclusion of precision dairy farming technologies in dairy operations is an area of increasing research and industry direction. Machine vision based systems are suitable for the dairy environment as they do not inhibit workflow, are capable of continuous operation, and can be fully automated. The research of this dissertation developed and tested 3 machine vision based precision dairy farming technologies tailored to the latest generation of RGB+D cameras. The first system focused on testing various imaging approaches for the potential use of machine vision for automated dairy cow feed intake monitoring. The second system focused on monitoring the gradual change in body condition score (BCS) for 116 cows over a nearly 7 month period. Several proposed automated BCS systems have been previously developed by researchers, but none have monitored the gradual change in BCS for a duration of this magnitude. These gradual changes infer a great deal of beneficial and immediate information on the health condition of every individual cow being monitored. The third system focused on automated dairy cow feature detection using Haar cascade classifiers to detect anatomical features. These features included the tailhead, hips, and rear regions of the cow body. The features chosen were done so in order to aid machine vision applications in determining if and where a cow is present in an image or video frame. Once the cow has been detected, it must then be automatically identified in order to keep the system fully automated, which was also studied in a machine vision based approach in this research as a complimentary aspect to incorporate along with cow detection. Such systems have the potential to catch poor health conditions developing early on, aid in balancing the diet of the individual cow, and help farm management to better facilitate resources, monetary and otherwise, in an appropriate and efficient manner. Several different applications of this research are also discussed along with future directions for research, including the potential for additional automated precision dairy farming technologies, integrating many of these technologies into a unified system, and the use of alternative, potentially more robust machine vision cameras.

Poor estrus detection can limit the reproductive performance of a dairy herd. One objective of this research was to evaluate an alternative method to traditional estrus detection in the form of automated monitoring technologies. To accomplish this, the first study considered the ability of automatically monitored parameters (activity, number of steps, lying bouts, lying time, feeding time, rumination time, and temperature) to detect estrus. A second study compared automated activity monitoring to timed artificial insemination as reproductive management strategies on commercial herds. The other objective of this research was to evaluate the economic potential of automated estrus detection technologies. This was accomplished by creating and evaluating a farm specific decision support tool to determine the net present value of adopting an automated estrus detection technology.