Amidst escalating production scales, sustainability imperatives, and heightened awareness of animal welfare with increasingly extreme weather conditions, heat stress in livestock has emerged as a paramount concern for the U.S. dairy industry. Conventional cross- and tunnel-ventilation systems, which have become the standard mechanical system for cow cooling and barn ventilation in large dairy barn facilities, might no longer meet the requirements per cow, given the expanding numbers of cows accommodated within single facilities. This dissertation proposed a series of elements that all pertain to one overarching goal of developing an intelligent animal-centric ventilation system design and control that could address some of the identified concerns with mechanical ventilation in large-scale dairy barns while meeting animal-specific cooling requirements. Solving this problem requires many different interdisciplinary efforts to perfect. However, the studies presented in this dissertation aim to serve as a foundation for future studies. Due to the nature of interdisciplinary studies, many of the presented solutions contain technical knowledge from various disciplines (e.g. Machine Learning, Computational Fluid Dynamics, and Internet of Things).The two major components required for developing an effective animal-centric ventilation system are a sound method to retrieve animal-based heat stress index and an appropriate targeted cooling ventilation design that can efficiently be controlled using that direct heat stress index. Chapter Two delves into the real-time monitoring of dairy cow heat stress by employing an Internet of Things-infused rechargeable ear tag. This wireless device tracks the subcutaneous ear temperature, offering a minimally invasive proxy to gauge core body temperatures. Chapter Four describes the design optimization and cooling performance evaluation of PPPV. Design parameter optimization and performance prediction using CFD are crucial, especially in a newly proposed system without a direct predecessor. Lastly, Chapter Three delineates the conception of a machine-learning-augmented computational fluid dynamics (CFD-ML) simulator. This CFD-ML model, which can "simulate" heat and mass transfer phenomena in a dairy barn with much less required computational cost, can be a blueprint for smart simulation tools that can eventually support and aid in ventilation design processes, as described in Chapter Three. In the future, these individual efforts should come together to attain the ultimate goal of developing a smarter, animal-centric dairy barn ventilation system.

In the United States, climate change is likely to increase average daily temperatures and the frequency of heat waves, which can reduce meat and milk production in animals. Methods that livestock producers use to mitigate thermal stress-including modifica-tions to animal management or housing-tend to increase production costs and capital expenditures. Dairy cows are particularly sensitive to heat stress, and the dairy sector has been estimated to bear over half of the costs of current heat stress to the livestock industry. In this report, we use operation-level economic data coupled with finely scaled climate data to estimate how the local thermal environment affects U.S. dairies' effec-tiveness at producing outputs with a given level of inputs. We use this information to estimate the potential decline in milk production in 2030 resulting from climate change-induced heat stress. For four climate model scenarios, the results indicate modest heat stress-related production declines over the next 20 years, with the largest declines occur-ring in the South.

In the United States, climate change is likely to increase average daily temperatures and the frequency of heat waves, which can reduce meat and milk production in animals. Methods that livestock producers use to mitigate thermal stress-including modifications to animal management or housing-tend to increase production costs and capital expenditures. Dairy cows are particularly sensitive to heat stress, and the dairy sector has been estimated to bear over half of the costs of current heat stress to the livestock industry. In this report, we use operation-level economic data coupled with finely scaled climate data to estimate how the local thermal environment affects U.S. dairies' effectiveness at producing outputs with a given level of inputs. We use this information to estimate the potential decline in milk production in 2030 resulting from climate change induced heat stress. For four climate model scenarios, the results indicate modest heat stress-related production declines over the next 20 years, with the largest declines occurring in the South.

The objective of this research was to design and test a novel conductive cooling system for controlling heat stress in lactating dairy cows by circulating chilled water through modified DCC waterbeds (Dual Chamber Cow Waterbeds). The system was tested to determine (1) the heat flux between the cooled waterbeds and the cows; (2) the production benefit to heat-stressed dairy cows; (3) the sensitivity of moisture accumulation and heat flux to type and thickness of bedding; and (4) the potential economic benefit. The calculated heat flux of the system was 439 W/m2 when the temperature of the circulating water in the waterbeds was 4.5oC and was 382 W/m2 when the circulating water temperature was 10.0oC. This was for live cows and about 1 cm of sawdust bedding. This amount of heat flux is significant compared to the amount of metabolic heat a lactating cow must lose. Conductively cooling the cows with 4.5°C water decreased core body temperature by 1.0°C (p