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Mastitis is one of the most costly diseases to dairy producers around the world with milk yield loss being the biggest contributor to economic losses. The objective of first study of this thesis was to determine the impacts of high somatic cell counts on milk yield loss. To accomplish this, over one million cow data records were collected from Southeastern US dairy herds. The objective of the second study was to determine optimum treatment cost of clinical mastitis by combining two economic modeling approaches used in animal health economics. The last objective of this thesis was to determine how much Southeastern US dairy producers are spending to control milk quality on farm and determine if they understand how milk quality affects them economically. This was accomplished through a collaborative project within the Southeast Quality Milk Initiative.

In experiment I), dry matter disappearance (DMD) and nitrogen disappearance (ND) of raw soybeans (RAW), solvent extracted soybean meal (SBM), heat treated whole soybeans (HT), mechanically extracted soybean meal (MEX), dry extruded soybeans (DEX), and wet extruded soybeans (TEX), were studied in situ for times of 3, 6, 12 and 24 h of rumen exposure. Five gram, air dry, samples were suspended in the rumen of a lactating Holstein cow fed a total mixed ration twice daily. The percent DMD for 24 h was as follows: RAW-85.9; SBM-56.6; HT-39.0; MEX-40.2; DEX-28.0; TEX-43.3. The greatest DMD was observed with RAW and was greater than all others (P<.01), followed by SBM which was significantly greater than all but Raw (P<.01). DEX presented the lowest DMD when compared to all other (P<.01). Percent ND values for 24 h for the soy products were: RAW-90.8; SBM-47.0; HT-32.7; MEX-23.7; DEX-16.5; TEX-23.0. The ND for RAW was significantly greater (P<.01) than all others, while the ND for SBM was similar P>.01) to HT but greater (P<.01) than MEX, DEX and TEX. ND for HT, MEX, DEX and TEX were similar (P>.01). Significant differences were observed in DMD and ND of various soybean products. As expected a high degree of degradation and ND was observed with raw soybeans. The application of heat decreased DMD and ND in SBM and application of greater heat (HT, MEX and DEX) and application of heat with moisture (TEX) resulted in products with lower DMD and lower ND. In experiment II) wheat straw acid detergent fiber (ADF) was subjected to in situ DMD and ND studies. Effects of time (612-24 h) and sample weight (1-2-3 grams) were evaluated. A lactating Holstein cow being fed a mixed ration was used. ADF dry matter (DM) weights (after incubation) expressed as a % of the original sample, were as follows: one gram: 101, 110 and 136; two grams: 99, 106 and 110; and three grams: 97, 110 and 114 for 6, 12 and 24 h respectively. The ADF DM weights of the one and two gram samples were significantly higher (P<.05) for 12 and 24 h than 6 h. When sample sizes were combined for each time, comparisons found 24 h to be significantly higher (P<.01) than 6 h. The DM changes were also reflected and magnified in the % N changes in the ADF residues. The amount of N of the one gram ADF samples increased 32% after 6 h, 122% after 12 h and 287% after 24 h (24>12>6-P<.01), and for two grams 29%-6 h, 97%-12 h and 117%-24 h (24>12>6-P<.05), and for three grams 34%-6 h, 140%-12 h and 142%-24 h (24 and 12>6-P<.01). Potential problems with DM and N contamination of ADF residue with in situ studies were demonstrated with small increases in DM weights and larger increases in N content.

Precision dairy monitoring is used to supplement or replace human observation of dairy cattle. This study examined the value dairy producers placed on disease alerts generated from a precision dairy monitoring technology. A secondary objective was calculating the accuracy of technology-generated disease alerts compared against observed disease events. A final objective was determining the economic viability of investing in a precision dairy monitoring technology for detecting estrus and diseases. A year-long observational study was conducted on four Kentucky dairy farms. All lactating dairy cows were equipped with a neck and leg tri-axial accelerometer. Technologies measured eating time, lying time, standing time, walking time, and activity (steps) in 15-min sections throughout the day. A decrease of ≥ 30% or more from a cow’s 10-d moving behavioral mean created an alert. Alerts were assessed by dairy producers for usefulness and by the author for accuracy. Finally, raw information was analyzed with three machine-learning techniques: random forest, least discriminate analyses, and principal component neural networks. Through generalized linear mixed modeling analyses, dairy producers were found to utilize the alert list when ≤ 20 alerts occurred, when alerts occurred in cows’ ≤ 60 d in lactation, and when alerts occurred during the week. The longer the system was in place, the less likely producers were to utilize alerts. This is likely because the alerts were not for a specific disease, but rather informed the dairy producer an issue might have occurred. The longer dairy producers were exposed to a technology, producers more easily decided which alerts were worth their attention. Sensitivity, specificity, accuracy, and balanced accuracy were calculated for disease alerts that occurred and disease events that were reported. Sensitivity ranged from 12 to 48%, specificity from 91 to 96%, accuracy from 90 to 96%, and balanced accuracy from 50 to 59%. The high number of false positives correspond with the lack of usefulness producers reported. Machine learning techniques improved sensitivity (66 to 86%) and balanced accuracy (48 to 85%). Specificity (24 to 89%) and accuracy (70 to 86%) decreased with the machine learning techniques, but overall detection performance was improved. Precision dairy monitoring technologies have potential to detect behavior changes linked to disease events. A partial budget was created based on the reproduction, production, and early lactation removal rate of an average cow in a herd. The cow results were expanded to a 1,000 cow herd for sensitivity analyses. Four analyses were run including increased milk production from early disease detection, increased estrus detection rate, decreased early lactation removal from early disease detection, and all changes in combination. Economic profitability was determined through net present value with a value ≥ $0 indicating a profitable investment. Each sensitivity analysis was conducted 10,000, with different numbers for key inputs randomly selected from a previously defined distribution. If either milk production or estrus detection were improved, net present value was ≥ 0 in 76 and 85% of the iterations. However, reduced early lactation removal never resulted in a value ≥ 0. Investing in precision dairy technology resulting in improved estrus detection rate and early disease detection was a positive economic decision in most iterations.

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.