Dairy farm decision support systems (DSS) are tools which help dairy farmers to solve complex problems by improving the decision-making processes. In this paper, we are interested in newer generation, integrated DSS (IDSS), which additionally and concurrently: (1) receive continuous data feed from on-farm and off-farm data collection systems and (2) integrate more than one data stream to produce insightful outcomes. The scientific community and the allied dairy community have not been successful in developing, disseminating, and promoting a sustained adoption of IDSS. Thus, this paper identifies barriers to adoption as well as factors that would promote the sustained adoption of IDSS. The main barriers to adoption discussed include perceived lack of a good value proposition, complexities of practical application, and ease of use; and IDSS challenges related to data collection, data standards, data integration, and data shareability. Success in the sustainable adoption of IDSS depends on solving these problems and also addressing intrinsic issues related to the development, maintenance, and functioning of IDSS. There is a need for coordinated action by all the main stakeholders in the dairy sector to realize the potential benefits of IDSS, including all important players in the dairy industry production and distribution chain.

The objective of this study was to examine the effects of a Zn-methionine complex in diet on milk yield, milk component yields, SCC, and milk Zn concentration of Holstein cows around peak lactation. After matching for parity and days in milk (DIM), 12 lactating Holstein cows (67 ± 2.5 DIM; 1385 ± 43 kg BW) were assigned to one of two dietary treatments: 1) control (CTL, n = 6), a TMR diet with 74 mg/kg added Zn in the form of zinc sulfate, n = 6) or 2) CTL supplemented with Zn-methionine complex (Zn-Met, n = 6) providing additional 20 mg of Zn/kg (512 mg/head/d). Dry matter intake (DMI) was lower by 0.8 kg/d for Zn-Met than CTL throughout the study (P = 0.05). Milk yield of Zn-Met decreased compared to CTL (40 vs 42 kg/d, P = 0.01) during the first 35 d but had similar milk yield during the last 35 d of the study. Milk protein and fat percentages, and fat yield were not different between treatments. Milk protein yield was similar between treatments during the first 35 d but tended to increase for Zn-Met (1.41 vs. 1.33 kg/d, P = 0.10) during rest of the study. Cows receiving Zn-methionine complex tended to have lower SCC (126 vs 328 ×10 3 cells/mL, P = 0.07) and greater concentration of Zn (4.48 vs 4.06 ppm, P = 0.05) in milk throughout the study. Overall, the present Znmethionine complex tended to improve milk protein yield and SCC more prominently as feeding progressed. However, it decreased DMI suggesting a negative impact on palatability of the diet.

Feed is the largest expense for dairy farms, thus feed efficiency is essential to the sustainability and future of the industry. Our objective was to evaluate the association of milking collar activity with feed intake and health status in lactating cows. Health status was classified for impact of three durations of time (overall, current, or post diagnosis) and as: healthy, mastitis, lame, multiple, or other. Activity data for 155 lactating cows with feed intake records were averaged across two-hour windows to obtain a daily two-hour average. A larger population (n > 1,600) was used to filter out sensor failures and normalize data. Sensor data were adjusted for parity and contemporary group creating adjusted sensor measure (ASM). Dry matter intake (DMI) was adjusted (aDMI) for metabolic body weight, days in milk, and energy sinks used to calculate residual feed intake. Associations between ASM and aDMI, DMI, or health were conducted in SAS9.4. An association of ASM with aDMI was identified (estimate = 0.1635 kg/log count of average activity in a 2-hour period; P < 0.0029). ASM was also associated with DMI (0.2329 kg/log count of average activity, P < 0.0007). ASM was associated with current and overall health timeframes (P < 0.0008 and P < 0.0001, respectively). When health, ASM, and their interaction were included in a model with the response variable aDMI, significant associations were found in the models, including current and overall health (current health: ASM and health: P < 0.0001, interaction: P < 0.0009; overall health: ASM, health, and interaction: P < 0.0001). These results indicate that milking collar data may be useful as a predictor of feed intake either directly or indirectly through detection of health events. Additional studies are needed to determine the predictive ability of collar activity data and the relationship between collar data and health, and to assess if collar activity is an environmental proxy or heritable trait.

Feed costs represent the greatest expense on a dairy farm, making feed efficiency an important trait to consider among production traits. Current tools to measure feed intake have limited application in commercial settings, due to affordability and lack of portability of technologies. Therefore, development of automated sensor-based indicator traits for feed intake could prove to be valuable. The objective of the current study was to determine if automated eartag data was associated with feed intake. Activity and inner ear temperature were collected every 19 minutes utilizing Quantified Ag eartags (n = 48 lactating cows). Ear tags were placed 5 days prior to the start of the trial, with cows ranging from 67-192 days in milk (DIM). Daily feed intake, milk weights, milk components and body weight (BW) were also recorded. Data were analyzed using PROX GLIMMIX in SAS. Dry matter intake (DMI) was modeled including fixed effects for DIM, milk weight, component composition, metabolic body weight (BW0.75), eartag activity or temperature, as well as the random effects of parity and group. To identify informative timeframes with reduced influence of environmental noise, data were analyzed over 3-day rolling windows of time. Six windows were significantly associated with dry matter intake (P ≤ 0.05) when utilizing ear tag activity. Three windows of time of ear tag temperature were found to be significantly associated with DMI (P ≤ 0.05). These findings indicate that eartag sensor data may be useful indicators of feed intake; however, days in milk and season may impact the informativeness of sensor data. Additional studies are warranted to validate the efficacy of activity and ear temperature as indicators of feed intake and determine the impact of other variables on these potential sensor indicator traits over time.

Transfer students make up 16.5% of the undergraduate population in the Animal Science (ANS) major, and transfer enrollment is on the rise. Transfers often face challenges that are different from their direct-from-high-school peers. The objective of this study was to determine the factors that affected the transfer transition from a previous institution to the ANS department. Based on focus group (n=6) feedback, a survey instrument was developed and piloted. Using pilot data, a factor analysis was performed and the instrument was tested for reliability. Once validated, the instrument was used to collect data from first semester ANS transfer students (n=47). No incentives were offered, and participation was voluntary. Between-factor Pearson correlations were estimated, and responses to scale questions were tallied. Significant negative correlations were observed between social integration and risk of attrition (r=-0.53, p< 0.01), and between social integration and academic struggle (r=-0.48, p< 0.01). Social integration was positively correlated with overall satisfaction within the ANS department (r=0.638, p< 0.01). Students who felt socially integrated were more confident academically, more comfortable at ISU, and were less likely to leave. Students had a positive view of the ANS department if they were involved in clubs and student organizations (r=0.42, p< 0.05). Transfers who were satisfied with instructor interactions tended to be more content with the overall transfer process (r=0.44, p< 0.01). Approximately 72% of respondents felt that paying for school created a lot stress in their life, but 52% felt satisfied with the financial assistance they received. Only half of the respondents reported that the pre-registration orientation helped prepare them for transfer to ISU, but 95.9% of ANS transfers reported that they understood the requirements for graduation. These data will inform policy and procedures related to transfer student onboarding and the promotion of social interactions with peers.