J.M. Bewley

An Interdisciplinary Review of Body Condition Scoring for Dairy Cattle

Abstract In this review, methods for assessing energy reserves, the role of assigning BCS in dairy management, and the impact of varying BCS on animal productivity, health, and reproduction are explored from a whole-system viewpoint. The usefulness, validity, and precision of BCS for assessing body energy reserves are well documented. Generally, BCS decrease in early lactation as cows partition energy from body reserves to support milk production, and they then begin to increase throughout the remainder of lactation. Excessive loss of energy reserves during early lactation, generally associated with cows of higher BCS at calving, often results in impaired health and reproductive performance. Among diseases, the most consistent relationship has been an increased incidence of ketosis for cows with higher BCS at calving. Although published results have varied, either high or low BCS has also been related to greater incidences of metritis, retained placenta, milk fever, lameness, cystic ovaries, dystocia, displaced abomasum, and mastitis. Losses in BCS or the actual BCS are associated with various fertility measures including days to first ovulation, days to first estrus, days to first service, first service conception rate, number of services, calving interval, and embryonic losses. Patterns of BCS change within lactation are under genetic control indicating potential for inclusion of BCS in genetic evaluations. Concerns about subjectivity and the time required for scoring have limited the use of BCS in daily management. An automated BCS might provide a more objective, less time-consuming means of estimating energy reserves in dairy cattle.

Potential for estimation of body condition scores in dairy cattle from digital images.

Body condition scoring, an indirect measure of the level of subcutaneous fat in dairy cattle, has been widely adopted for research and field assessment or for management purposes on farms. The feasibility of utilizing digital images to determine body condition score (BCS) was assessed for lactating dairy cows at the Scottish Agricultural College Crichton Royal Farm. Two measures of BCS were obtained by using the primary systems utilized in the United Kingdom (UK-BCS) and the United States (USBCS). Means were 2.12 (+/-0.35) and 2.89 (+/-0.40), modes were 2.25 and 2.75, and ranges were 1.0 to 3.5 and 1.5 to 4.5 for the UKBCS (n = 2,346) and USBCS (n = 2,571), respectively. Up to 23 anatomical points were manually identified on images captured automatically as cows passed through a weigh station. Points around the hooks were easier to identify on images than points around pins and the tailhead. All identifiable points were used to define and formulate measures describing the cows contour. For both BCS systems, hook angle, posterior hook angle, and tailhead depression were significant predictors of BCS. When the full data set testing only the angles around the hooks was used, 100% of predicted BCS were within 0.50 points of actual USBCS and 92.79% were within 0.25 points; and 99.87% of predicted BCS were within 0.50 points of actual UKBCS and 89.95% were within 0.25 points. In a reduced data set considering only observations in which the tailhead depression angle was available, adding the tailhead depression to models did not improve model predictions. The relationships of the calculated angles with USBCS were stronger than those with UKBCS. This research demonstrates the potential for using digital images for assessing BCS. Future efforts should explore ways to automate this process by using a larger number of animals to predict scores accurately for cows across all levels of body condition.

Influence of milk yield, stage of lactation, and body condition on dairy cattle lying behaviour measured using an automated activity monitoring sensor

Time spent lying by lactating Holstein-Friesian cows of varying body condition scores (BCS) and milk yield was measured using an animal activity monitor. A 3-week average BCS was calculated for each cow; and in total, 84 cows were selected with 28 cows each among three BCS categories (Thin: BCS<2.75; Moderate: 2.75 > or = BCS<3.25; Heavy: BCS> or = 3.25) and two stage of lactation categories (<150 days in milk or >150 days in milk). Cows were kept in two management systems: parlour/freestall (n=60) or automated milking system/freestall (n=24). Behaviour was recorded for 5.3+/-0.1 d for each cow. Production levels were considered using a 28-d rolling average of daily milk production. Cows that exhibited clinical lameness before or during the observation period were excluded from analyses. For cows exhibiting oestrus, the day prior to, day of, and day following breeding were removed. The final analysis included 77 cows (408 d of observation). A mixed model was fitted to describe average daily hours spent lying. Results demonstrated that lying time increased as days in milk (DIM) increased (P=0.05). Variables that were tested but not significant (P>0.05) were BCS category, parity category (1 or 2) and 28-d rolling average daily milk production. Although a numerical trend for increasing hours spent lying with increasing BCS was observed, after accounting for other factors in the mixed model, BCS did not significantly impact lying time. Continued investigation of these management factors that impact lying time and bouts, using new technologies, more cows, and more herds will help dairy owners better manage facilities and cow movements to optimize this essential behaviour.

Comparison of Reticular and Rectal Core Body Temperatures in Lactating Dairy Cows

The Phase IV Cattle Temperature Monitoring System (CTMS; Phase IV Engineering Inc., Boulder, CO) marketed by MaGiiX (MaGiiX Inc., Post Falls, ID) uses a passive bolus equipped with a temperature sensor, a panel reader placed at a parlor entrance or exit to query the bolus, and a software package to collect, analyze, and view data. The biologically inert bolus resides in the cows reticulum and is queried each time the cow passes the reader. Reticular temperature (RETT) and rectal temperature (RECT) were recorded simultaneously in the milking parlor exit lane in 4 consecutive milkings in each of 4 seasons, totaling 16 measurements per cow. The RETT were obtained by using the phase IV CTMS, whereas the RECT were obtained manually with a GLA M750 thermometer (GLA Agricultural Electronics, San Luis Obispo, CA). Data were edited to remove RETT likely to have been affected by a recent drinking bout. For the 2,042 observations used in analyses, means (+/-SD) were 39.28 (+/-0.41), 38.83 (+/-0.36), and 0.45 (+/-0.33) for RETT, RECT, and the difference between RETT and RECT, respectively. The RETT and RECT were strongly correlated (r = 0.645). The relationship between RETT and RECT varied by season, milking, housing system, and parity. Because dairy producers and veterinarians are accustomed to viewing rectal temperatures, equations to adjust reticular temperatures to a rectal-based scale may increase the utility of the phase IV CTMS. The resulting conversion equations were RECT = 19.23 + 0.496(RETT) for the a.m. milking and RECT = 15.88 + 0.587(RETT) for the p.m. milking.

Impact of Intake Water Temperatures on Reticular Temperatures of Lactating Dairy Cows

Automatic temperature recording may allow early detection of disease, estrus, heat stress, and the onset of calving. The phase IV Cattle Temperature Monitoring System (MaGiiX Inc., Post Falls, ID) utilizes a passive bolus equipped with a temperature sensor, a stationary panel reader to query the bolus, and software to collect, analyze, and display data. One potential limitation to collection of reticular temperatures is the effect of water temperature and consumption on recorded temperatures. Two replicated 3 x 3 Latin square experiments were conducted at the Purdue Dairy Research and Education Center to assess the impact of water intake on reticular temperatures using the Cattle Temperature Monitoring System. Nine high-producing, mid-lactation, second-parity cows with low somatic cell counts were selected. Before administering a water treatment, access to feed and water was restricted for at least 2 h. Baseline reticular temperatures were established from measurements before water intake. In experiment 1, treatments were 25.2 kg of hot water (34.3 degrees C +/- 1.0), warm water (18.2 degrees C +/- 0.4), or cold water (7.6 degrees C +/- 0.4). In experiment 2, treatments were 18.9 kg of body-temperature water (38.9 degrees C +/- 0.2), cold water (5.1 degrees C +/- 0.4), or control (no water). Following water intake, reticular temperatures were collected for 3 h. In experiment 1, an initial dramatic decrease in reticular temperature was observed followed by a gradual increase toward baseline. Least squares means for maximum drop in temperature were 8.5 +/- 0.5, 6.9 +/- 0.5, and 2.2 +/- 0.5 degrees C for cold, warm, and hot water treatments, respectively. Yet at 3 h, reticular temperatures did not return to the baseline. In experiment 2, control cows remained within the baseline confidence interval through the observation period, and cows receiving body temperature water experienced an initial decrease in temperature (0.4 +/- 0.2 degrees C) with a return to within the baseline confidence interval within 15 min. Cows receiving cold water did not return to within the baseline confidence interval after a large decrease of 9.2 +/- 0.2 degrees C during the 3-h observational period. Moreover, a regression analysis of continued ascent in temperatures predicted that temperatures would return to baseline within 3.5 h. These results demonstrate that, when cows consume large quantities of cold water, the effect of water intake is sizable and sustained. The value of reticular temperatures for daily monitoring in a production setting hinges largely on the implications of this impact.

Stochastic simulation using @Risk for dairy business investment decisions

Purpose - The purpose of this paper is to develop a dynamic, stochastic, mechanistic simulation model of a dairy business to evaluate the cost and benefit streams coinciding with technology investments. The model was constructed to embody the biological and economical complexities of a dairy farm system within a partial budgeting framework. A primary objective was to establish a flexible, user-friendly, farm-specific, decision-making tool for dairy producers or their advisers and technology manufacturers. Design/methodology/approach - The basic deterministic model was created in Microsoft Excel (Microsoft, Seattle, Washington). The @Risk add-in (Palisade Corporation, Ithaca, New York) for Excel was employed to account for the stochastic nature of key variables within a Monte Carlo simulation. Net present value was the primary metric used to assess the economic profitability of investments. The model was composed of a series of modules, which synergistically provide the necessary inputs for profitability analysis. Estimates of biological relationships within the model were obtained from the literature in an attempt to represent an average or typical US dairy. Technology benefits were appraised from the resulting impact on disease incidence, disease impact, and reproductive performance. In this paper, the model structure and methodology were described in detail. Findings - Examples of the utility of examining the influence of stochastic input and output prices on the costs of culling, days open, and disease were examined. Each of these parameters was highly sensitive to stochastic prices and deterministic inputs. Originality/value - Decision support tools, such as this one, that are designed to investigate dairy business decisions may benefit dairy producers.

Assessing the potential value for an automated dairy cattle body condition scoring system through stochastic simulation

Purpose - Automated body condition scoring (BCS) through extraction of information from digital images has been demonstrated to be feasible; and commercial technologies are being developed. The primary objective of this research was to identify the factors that influence the potential profitability of investing in an automated BCS system. Design/methodology/approach - An expert opinion survey was conducted to provide estimates for potential improvements associated with technology adoption. A stochastic simulation model of a dairy system, designed to assist dairy producers with investment decisions for precision dairy farming technologies was utilized to perform a net present value (NPV) analysis. Benefits of technology adoption were estimated through assessment of the impact of BCS on the incidence of ketosis, milk fever, and metritis, conception rate at first service, and energy efficiency. Findings - Improvements in reproductive performance had the largest influence on revenues followed by energy efficiency and then by disease reduction. The impact of disease reduction was less than anticipated because the ideal BCS indicated by experts resulted in a simulated increase in the proportion of cows with BCS at calving 3.50. The estimates for disease risks and conception rates, obtained from literature, however, suggested that this increase would result in increased disease incidence. Stochastic variables that had the most influence on NPV were: variable cost increases after technology adoption; the odds ratios for ketosis and milk fever incidence and conception rates at first service associated with varying BCS ranges; uncertainty of the impact of ketosis, milk fever, and metritis on days open, unrealized milk, veterinary costs, labor, and discarded milk; and the change in the percentage of cows with BCS at calving 3.25 before and after technology adoption. The deterministic inputs impacting NPV were herd size, management level, and level of milk production. Investment in this technology may be profitable but results were very herd-specific. A simulation modeling a deterministic 25 percent decrease in the percentage of cows with BCS at calving =3.25 demonstrated a positive NPV in 86.6 percent of 1,000 iterations. Originality/value - This investment decision can be analyzed with input of herd-specific values using this model.

Producer assessment of dairy extension programming in Kentucky.

To assess the dairy production issues extension programming should be addressing, a survey was distributed to all licensed milk producers in Kentucky (n=1,074). A total of 236 surveys were returned; 7 were omitted due to incompletion, leaving 229 for subsequent analyses (21% response rate). Mean herd size was 83.0 ± 101.8 cows with a projected increase to 102.1 ± 114.4 cows by 2013. Mean producer age was 50.9 ± 12.9 with a range of 22 to 82. Mean milk production (kg/cow per day) was 23.9 ± 5.4 with a range of 6.8 to 38.6 kg. Mean somatic cell counts (SCC) were 304,824 ± 123,580 with a range of 75,000 to 750,000 cells/mL. When asked about meeting attendance frequency, 25% of producers indicated they attended meetings annually, whereas 29% attended twice yearly, 13% quarterly, 3% monthly, 2% at least twice monthly, and 28% indicated they never attended meetings. Surveyed producers were asked to assess what level of importance should be placed on a predetermined list of management topics. Mean response to each topic was calculated after assigning the following numeric values to producer response categories: not important: 1, important: 3, and very important: 5. Producers indicated mastitis and milk quality was the most important management topic with a response of 4.35 ± 1.05, followed by animal well-being (4.05 ± 1.14), disease prevention and vaccinations (4.01 ± 1.06), cow comfort (3.97±1.09), disease treatment (3.95 ± 1.10), and lameness and hoof health (3.95 ± 1.16). Producers were asked to identify their preferred information delivery method. The most effective delivery methods were printed farm magazines (81.0%), agricultural newspapers (77.4%), printed newsletters from county agricultural agents (75.7%), printed newsletters from university extension (65.0%), and local or regional meetings (55.8%). The least effective delivery methods were university website (11.9%), indirect access through allied industry consultants (11.5%), webinars (2.7%), podcasts (0.4%), and blogs (0.4%). These results provide invaluable insight for future dairy-related Cooperative Extension Service programming efforts.

Comparison of two methods of assessing dairy cow body condition score.

Two body condition scoring systems were compared for assessing body condition of cows at the Scottish Agricultural Colleges Crichton Royal Farm. The weekly body condition scores (BCS) were collected for a period of 12 weeks (5 September-21 November). Scores were obtained using the primary systems utilized within the UK and USA. The USBCS were obtained by the same evaluator each week, while the UKBCS were obtained by two different evaluators alternating between weeks. Paired scores (n=2088) between the two systems within week were moderately correlated (r=0.75, P<0.0001). Regression equations to convert scores between the two systems were created using the GLM procedure of SAS (SAS Institute Inc., Cary NC, USA). The simple GLM models to convert from UK to US scores and US to UK scores were USBCS=1.182+0.816 * UKBCS (R2=0.56) and UKBCS=0.131+0.681 (R2=0.56), respectively. These equations may be used to interpret scores within the literature obtained using these two BCS systems, although they must be used with caution.

EXTENSION EDUCATION SYMPOSIUM: Reinventing extension as a resource—What does the future hold?

The mission of the Cooperative Extension Service, as a component of the land-grant university system, is to disseminate new knowledge and to foster its application and use. Opportunities and challenges facing animal agriculture in the United States have changed dramatically over the past few decades and require the use of new approaches and emerging technologies that are available to extension professionals. Increased federal competitive grant funding for extension, the creation of eXtension, the development of smartphone and related electronic technologies, and the rapidly increasing popularity of social media created new opportunities for extension educators to disseminate knowledge to a variety of audiences and engage these audiences in electronic discussions. Competitive grant funding opportunities for extension efforts to advance animal agriculture became available from the USDA National Institute of Food and Agriculture (NIFA) and have increased dramatically in recent years. The majority of NIFA funding opportunities require extension efforts to be integrated with research, and NIFA encourages the use of eXtension and other cutting-edge approaches to extend research to traditional clientele and nontraditional audiences. A case study is presented to illustrate how research and extension were integrated to improve the adoption of AI by beef producers. Those in agriculture are increasingly resorting to the use of social media venues such as Facebook, YouTube, LinkedIn, and Twitter to access information required to support their enterprises. Use of these various approaches by extension educators requires appreciation of the technology and an understanding of how the target audiences access information available on social media. Technology to deliver information is changing rapidly, and Cooperative Extension Service professionals will need to continuously evaluate digital technology and social media tools to appropriately integrate them into learning and educational opportunities.