Buckeye Dairy News : VOLUME 19, ISSUE 5

  1. Milk Prices, Costs of Nutrients, Margins and Comparison of Feedstuffs Prices

    Alex Tebbe, Graduate Research Associate, Department of Animal Sciences, The Ohio State University

    Milk Prices

    In the last issue, the Class III component price for May and June closed at $15.57 and $16.44/cwt, respectively. For the month of July, the Class III future was projected to stay stagnant at $16.58 and then decrease over a $1/cwt to $15.29/cwt in August. The Class III component price for the month of July actually closed at about $1/cwt lower than its future at $15.45/cwt, whereas the August Class III component price closed more than $1/cwt higher than its future price at $16.57/cwt. Class III futures for September and October are expected to remain relatively unchanged from the August Class III component price at $16.59 and $16.44/cwt, respectively.

    Nutrient Prices

    As in previous issues, these feed ingredients were appraised using the software program SESAME™ developed by Dr. St-Pierre at The Ohio State University to price the important nutrients in dairy rations, to estimate break-even prices of many commodities traded in Ohio, and to identify feedstuffs that currently are significantly underpriced as of September 25, 2017. Price estimates of net energy lactation (NEL, $/Mcal), metabolizable protein (MP, $/lb; MP is the sum of the digestible microbial protein and digestible rumen-undegradable protein of a feed), non-effective NDF (ne-NDF, $/lb), and effective NDF (e-NDF, $/lb) are reported in Table 1.

    In this issue, I have also calculated a new corn silage price for the 2017 growing year: $43.20/ton (35% dry matter). This price is nearly identical to the 2016 growing year ($42.50/ton) and is still a bargain compared to other common ingredients. The price I calculated is based on the crop value as if it was harvested for corn grain rather than silage. Because corn silage is dual purpose and provides marked amounts of both NEL and e-NDF for dairy cows, the true value of corn silage to the producer should actually be around $65.40/ton, about 40% higher than my calculation. However, corn silage quality varies considerably based on location (e.g., weather and growing conditions), harvesting and storage conditions, or management practices, as well as the corn hybrid planted. Using the 75% confidence intervals defined in Table 2 are better predictors of what corn silage may actual be worth to producers because of this real world variability. The intervals still do not contain the calculated value based off corn grain (i.e., the $43.20/ton estimate). Bottom line, corn silage should be a no brainer for making up the majority of the forage component for rations during the upcoming year, but only if you have stored enough – running out of corn silage in August will be a huge financial burden.

    Nutrient prices continue to remain relatively low as they have been for the past three years. For MP, its current value ($0.42/lb) has increased slightly from July’s issue ($0.37/lb) but is about 13% lower than the 5 year average ($0.48/lb). The cost of NEL is 7¢/Mcal, slightly lower than the July price at 9¢/Mcal and is lower than the 5-year average of 11¢/Mcal. The price of e-NDF at 7¢/lb is slightly lower than the July price (5¢/lb) and ne-NDF at -7¢/lb (i.e., feeds with a significant content of non-effective NDF are priced at a discount) is identical to the July price.

    To estimate the cost of production at these nutrient prices, I used the Cow-Jones Index for cows milking 70 lb/day or 85 lb/day at 3.7% fat and 3.1% protein. In the last issue, the average income over nutrient costs (IONC) was estimated to be $10.40/cwt for cows milking 70 lb/day and $10.78/cwt for cows milking 85 lb/day. For September, the IONC for our 70 lb/day and 85 lb/day cows are slightly higher than July at an estimated $10.68/cwt and $11.06/cwt, respectively. These IONC may be overestimated because they do not account for the cost of replacements or dry cows; however, they should be profitable when greater than about $9/cwt. Overall, farmers producing milk in Ohio should be making money.

    Table 1. Prices of dairy nutrients for Ohio dairy farms, September 25, 2017.

    Economic Value of Feeds

    Results of the Sesame analysis for central Ohio on September 25, 2017 are presented in Table 2. Detailed results for all 27 feed commodities are reported. The lower and upper limits mark the 75% confidence range for the predicted (break-even) prices. Feeds in the “Appraisal Set” were those for which we didn’t have a price. One must remember that SESAME™ compares all commodities at one specific point in time. Thus, the results do not imply that the bargain feeds are cheap on a historical basis.

    Table 2. Actual, breakeven (predicted) and 75% confidence limits of 27 feed commodities used
    on Ohio dairy farms, September 25, 2017.


    For convenience, Table 3 summarizes the economic classification of feeds according to their outcome in the SESAME™ analysis. Feedstuffs that have gone up in price, or in other words moved a column to the right, since the last issue are red. Conversely, feedstuffs that have moved to the left (i.e., decreased in price) are green. These shifts (i.e., feeds moving columns to the left or right) in price are only temporary changes relative to other feedstuffs within the last two months and do not reflect historical prices.

    Table 3. Partitioning of feedstuffs, Ohio, September 25, 2017.

    Bargains At Breakeven Overpriced
    Corn,  ground, dry Alfalfa hay - 40% NDF Beet pulp
    Corn silage Bakery byproducts Blood meal
    Distillers dried grains Gluten meal Canola meal
    Feather meal Meat meal Citrus pulp
    Gluten feed Soybean meal - expeller 41% Cottonseed meal
    Hominy Soybean hulls Fish meal
    Solvent extracted canola meal   Whole cottonseed Molasses
    48% Soybean meal Wheat bran Tallow
    Wheat middlings   44% Soybean meal
        Whole, roasted soybeans

    As coined by Dr. St-Pierre, I must remind the readers that these results do not mean that you can formulate a balanced diet using only feeds in the “bargains” column. Feeds in the “bargains” column offer a savings opportunity, and their usage should be maximized within the limits of a properly balanced diet. In addition, prices within a commodity type can vary considerably because of quality differences as well as non-nutritional value added by some suppliers in the form of nutritional services, blending, terms of credit, etc. Also, there are reasons that a feed might be a very good fit in your feeding program while not appearing in the “bargains” column. For example, your nutritionist might be using some molasses in your rations for reasons other than its NEL and MP contents.

    Appendix

    For those of you who use the 5-nutrient group values (i.e., replace metabolizable protein by rumen degradable protein and digestible rumen undegradable protein), see the table below.

    Table 4. Prices of dairy nutrients using the 5-nutrient solution
    for Ohio dairy farms, September 25, 2017.

     

  2. Effect of Increased Somatic Cell Count on Herd Level Yield of Milk and Milk Components

    Hannah Walczyk, Undergraduate Student, and Maurice Eastridge, Professor, Department of Animal Sciences, The Ohio State University

    Introduction

    Dairy is an essential component to the human diet. With this being said, it is important that farmers continue to improve their productivity in order to supply the fast-growing population with dairy products (De Vliegher et al., 2012). Additionally, increasing the cow’s yield of milk, protein, and fat will benefit the farm economically. Unfortunately, there are many factors that can negatively affect the quantity of milk, as well as its quality. One of these factors is disease, especially mastitis. Mastitis is one of the most common and harmful diseases among dairy cows (De Vliegher et al., 2012). Mastitis is the inflammation of the mammary gland caused by an intra-mammary infection. Infection can be caused by multiple species of bacteria which enter the teat orifice and invade the mammary gland (De Vliegher et al., 2012). Diagnosis of mastitis can be achieved by evaluating the concentration of somatic cells in the milk. If the cow shows high concentrations of somatic cells, then mastitis is present in the gland.

    Mastitis is not only harmful to the cow’s biological functions but also to the profitability of the farm. As stated by Losinger (2005), there have been many past reports which have proven that an increase in somatic cell count (SCC) results in a decrease in milk production, thus resulting in financial loss to the farm. In fact, the economic loss in the US per cow per year for one clinical mastitis case has been estimated to be $80 (Sadeghi-Sefidmazgi et al., 2010). In our study, levels of SCC, somatic cell score (SCS), milk yield, milk fat, and milk protein on a herd basis were evaluated in order to determine the impact of increased SCC and SCS on yields of milk, fat, and protein.

    Materials and Methods

    During this study, data collected from Dairy Herd Improvement (DHI Cooperative, Inc., Columbus, OH) were used to assess the impact of increased SCC and SCS at the herd level on yields of milk, fat, and protein. The data included the rolling herd averages (RHA) for SCC and yields of milk, fat, and protein from Ohio dairy farms for the years 2012 through 2015. The data were sorted to meet the following criteria: > 100 cows, and Holstein or mixed breed. The RHA SCC (1000 cells/mL) was used to calculate the RHA SCS using the following equation (Wiggans and Shook, 1987):

       

    Subsequently, data were analyzed using SAS (SAS Institute, Inc., Cary, NC). The simple statistics for number of cows, SCC, SCS, and yields of milk, fat, protein, and energy corrected milk (ECM) are shown in Table 1. The PROC CORR procedure of SAS was used for determination of the correlation coefficients for the variables of interest. The PROC MIXED procedure of SAS was used to estimate the impact of SCC and SCS on stated yields on a herd basis, with herd set as a random variable and year as a repeated variable. Data were considered to be significant if the P < 0.05 and a trend if P < 0.10.

    Table 1. Number of cows, somatic cell count (SCC), somatic cell score (SCS),
    and rolling herd average yields of milk, fat, protein, and energy corrected milk (ECM).

    Variable Mean Std Dev Minimum Maximum
    Cows/farm 265 266 100 2,675
    Milk, lb 23,720 3,387 12,102 33,109
    Fat, lb 888 132 480 1,375
    Protein, lb 735 97 390 1,058
    SCC, 1000 cells/mL 196 82 42 596
    SCS 3.84 0.61 1.70 5.60
    ECM, lb 24,890 3,415 13,137 34,720

    Results/Discussion

    As expected, milk yield and SCC were negatively correlated (Table 2). This means that as SCC increases, milk yield will decrease. Milk yield and SCS also were negatively correlated, with the coefficient only being slightly higher than for milk and SCC. In addition, it was observed that yields of fat and protein also were negatively correlated with SCC and SCS. Thus, an increase in SCS would be expected to decrease the quality of milk and the yields of milk, fat, and protein, which would all contribute to a loss of income to the dairy farmer. Each of the correlation coefficients provided in Table 2 were significant. 

    Finally, the impact of increased SCC on milk yield was determined.  From this analysis of milk yield and SCC, the y-intercept was 23,661 lb, and the slope was -1.56.  Thus, the relationship between milk yield and SCC is expressed as:

    Milk (lb) = 23,661 - 1.56x,

    where x is defined as SCC (1000 cells/mL).  The same analysis was performed in order to evaluate the relationship between milk yield and SCS.  In this case, the intercept was 24,151 lb and the slope was -207.  The following equation was used to express this relationship:

    Milk (lb) = 24,151 - 207x

    where x is defined as SCS (0 to 9 linear scale).

    When examining the relationship between milk yield and SCC, it was determined that for every 1000 cells/mL SCC increase, there would be a 1.56 lb RHA milk loss. Thus, if a herd had an SCC of 400,000 cells/ mL, then the RHA milk yield would be expected to decrease by 624 lb (400 x 1.56 = 624). Using this example, a 624 lb decrease in RHA milk yield for a 500 cow herd could result in about a $56,000 loss in income (assuming $18/cwt for milk). Likewise, for every 1 SCS increase, there would be a 207 lb loss in milk. Thus, a SCS of 5 (equivalent to 400,000 cells/ mL SCC) would result in about 1035 lb (5 x 207) decrease in RHA milk yield (Table 3). Using $18/cwt for milk with a 500 cow herd, this could result in about $93,000 of loss revenue. The slopes for both SCC and SCS tended to be significant (P < 0.10), but they differed in magnitude of milk loss (for the example above: 624 vs 1035 lb loss RHA milk). However, it has been determined in previous research that SCS has a better relationship than SCC with milk loss (Wiggans and Shook, 1987). The results of this study were somewhat different compared to the generally accepted milk losses used in the DHI system which are differentiated by parity (Table 3). For every linear score increase, or doubling of SCC, above SCS 2, there is estimated a loss of 200 lb/lactation for parity one cows and 400 lb/lactation of milk loss for those with 2+ lactations. The results of this study showed a loss of 207 lb of milk in RHA per SCS increase. Some of this difference between the estimated milk loss using the DHI system and our findings is because our study was on a RHA basis and not separated by parities of individual cows. If you assume that a typical herd consists of about one third parity one cows and two-thirds of cows with two or more parities, then the weighed estimated milk loss would be 334, 668, 1002, and 1336 lb/lactation for SCS of 3, 4, 5, and 6, respectively. These would compare to the 621, 828, 1035, and 1242 lb RHA milk, respectively, using the SCS coefficient from our study. Understanding the relationship between SCS and milk yield is important for improving mammary health within a herd and improving profitability for the farm.

    Conclusions

    Mastitis is a costly disease on dairy farms. Its impact on animal health and farm profitability are well recognized. The impact on the farm economics results from the costs associated with the disease on the farm (e.g. loss of milk, cost of treatment, labor, etc.) and the premiums for milk quality from the processor. Understanding the relationship of SCS on milk loss is important since over 50% of the cost of mastitis is caused by loss of milk sales. Monitoring this relationship can assist in culling decisions and making management changes and facility modifications to improve the mammary health status of the herd. Whether using the estimates of milk loss from this study or those referenced in the DHI system, it is very apparent that mastitis can be very costly on a dairy farm.

    Table 2. Correlation coefficients and P-values for milk, fat, protein, somatic cell
    count (SCC), somatic cell score (SCS), and energy- corrected milk (ECM).

      Milk Fat Protein SCC SCS ECM
    Milk 1.00 0.8421
    <0.0001
    0.9712
    <0.0001
    -0.2867
    <0.0001
    -0.3003
    <0.0001
    0.9568
    <0.0001
    Fat   1.00 0.8583
    <0.0001
    -0.1867
    <0.0001
    -0.2018
    <0.0001
    0.9603
    <0.0001
    Protein     1.00 -0.2618
    <0.0001
    -0.2704
    <0.0001
    0.9619
    <0.0001
    SCC       1.00 0.9590
    <0.0001
    -0.2422
    <0.0001
    SCS         1.00 -0.2562
    <0.0001
    ECM           1.00

    Table 3. Comparison of estimated milk loss for the DHI adopted system 
    versus the equation derived in this study.1

      Milk Loss, Per cow
    (lb/lactation)2
     

    SCS
    SCC
    (1000 cells/mL)

    Parity 1

    Parity 2+
    Milk Loss, RHA milk3
    (lb/lactation)
    0 12.5 --- --- ---
    1 25 --- --- ---
    2 50 --- --- ---
    3 100 200 400 621
    4 200 400 800 828
    5 400 600 1,200 1,035
    6 800 800 1,600 1,242
    7 1,600 1,000 2,000 ---
    8 3,200 1,200 2,400 ---
    9 6,400 1,400 2,800 ----

    1SCS = Somatic cell score, SCC = somatic cell count, and RHA = rolling herd average.
    2Based on system adopted by DHI (NMC, 2017)
    3Based on the SCS equation in this paper; estimated RHA loss only provided for
    SCS 3 to 6 because of range of data used for deriving the equation.

    Works Cited

    De Vliegher, S., L.K. Fox, S. Peipers, S. McDougall, and H.W. Barkema. 2012. Invited review: Mastitis in dairy heifers: Nature of the disease, potential impact, prevention, and control. Journal of Dairy Science, 95:1025-1040. http://dx.doi.org/ 10.3168/jds.2010-4074

    Losinger, W. C. 2005. Economic impacts of reduced milk production associated with an increase in bulk-tank somatic cell count on US dairies. Journal of the American Veterinary Medical Association, 226(10), 1652-1658. doi:10.2460/javma.2005.226.1652

    NMC. 2017. The value and use of Dairy Herd Improvement somatic cell count. National Mastitis Council Fact Sheet, http://www.nmconline.org/wp-content/uploads/2016/09/The-Value-and-Use-of-Dairy-Herd-Improvement-Somatic-Cell-Count.pdf (accessed August 3, 2017)

    Sadeghi-Sefidmazgi, A., M. Moradi-Shahrbabak, A. Nejati-Javaremi, S.R. Miraei-Ashtiani, and Amer. 2010. Estimation of economic values and financial losses associated with clinical mastitis and somatic cell score in Holstein dairy cattle. Animal, 5(01), 33-42. doi:10.1017/s1751731110001655

    Wiggans, G., and G. Shook, G. 1987. A lactation measure of somatic cell count. Journal of Dairy Science, 70(12), 2666-2672. doi:10.3168/jds.s0022-0302(87)80337-5

     

     

  3. Dairy Feed Bunk Management

    Rory Lewandowski, Extension Educator Wayne County, Ohio State University Extension

                The current state of the dairy economy has dairy farm managers looking for ways to improve cow productivity and reduce expenses. One management area that may offer some of these returns is the feed bunk. It is important to work with the herd nutritionist to provide a ration that will allow the dairy cow to produce a high level of milk, but beyond the nutrient composition of the ration, the manager must understand and work with cow feeding behavior to promote maximum dry matter intake (DMI). The following comments are based upon an eXtension article entitled “The Feeding Behavior of Dairy Cows: Considerations to Improve Cow Welfare and Productivity.”

                Dairy cows managed in an indoor production system typically spend 4 to 6 hours per day eating, ideally divided into 9 to 14 separate meals or feeding sessions. The delivery of fresh feed is a major stimulus to cow feeding and research demonstrates that the 60 minutes following fresh fed delivery produces a peak feeding pattern. Research has also shown that there is benefit to coordinating the delivery of fresh feed with a return from the milking parlor. Cows that had access to feed after milking stood longer (48 versus 21 minutes) than cows that did not have access to feed after returning from milking. The additional standing time is beneficial from the standpoint of providing adequate time for the teat sphincter muscle to fully close, thus reducing the risk of intramammary infection from exposure to environmental bacteria when cows lie down too soon after milking. Based on this research, adding an additional fresh fed delivery could help to improve DMI intake or, more likely, result in a more even feeding time distribution.  Increased feed delivery can reduce diurnal fluctuations in rumen pH and possibly reduce the risk of subacute ruminal acidosis in some situations.

                If an additional fresh fed delivery is out of the question, more frequent feed push-up is another management practice that can offer a number of benefits, including higher DMI, greater fat-corrected milk yields, less feed refusal, and an increase in standing time after milking. Typically, sorting occurs by the first cows to eat the freshly delivered feed, which create holes in the feed pile. Cows that eat later do not have the same ration consistency as those first cows. Pushing feed up remixes the feed pile, which provides a better ration to those cows that follow the first eaters. When feed is pushed up, it can also stimulate another feeding session for the cows, creating another meal opportunity. The goal is to get cows to eat more frequent, smaller meals throughout the day. This creates a better pH balance within the rumen as compared to a situation where cows slug feed with fewer, larger meals.  Slug feeding can disrupt rumen pH balance and lead to milk fat depression. After the initial feeding period, the feed bunk piles are often scattered, providing a large surface area for oxygen to degrade the forage portion of the ration, in particular ensiled forages. Pushing feed up puts feed back into piles with less surface area, which can help to prevent or reduce heating and reduce feed waste by refusal.  If feed is not delivered after milking, then pushing up feed after milking can stimulate cows to eat and increase standing time after milking, allowing more time for the teat canal to close.

                A final factor to look at to help improve the DMI and distribution of feeding times and meals for cows is stocking density. The eXtension article says, “recent research suggests that overcrowding at the feed bunk may have deleterious effects on feeding behavior.”  In 2000, Batchelder (Proceedings from Dairy Housing and Equipment Systems: Managing and Planning for Profitability, Camp Hill, Pennsylvania) reported that using 30% overcrowding (1.3 cows/headlock) reduced daily DMI and resulted in substantially fewer cows eating during both the hour following milking and following delivery of fresh feed. Other research has shown that in overcrowding situations, cows will stand and wait for a feeding spot.  Increased standing times are associated with a higher risk of developing hoof and leg injuries. In addition, some researchers have noted increased aggression in feeding areas when cows are overcrowded and this behavior can lead to higher incidences of hoof lesion development and lameness.

                Dairy managers have opportunities to increase productivity and reduce costs by improving feed bunk management to take advantage of cow feeding behaviors. The entire eXtension article is available online at http://tiny.cc/cowfeedingbehavior.

  4. Veal Calf Care – Starts at the Dairy Farm: Best Practices for Dairy Bull Calves

    Dr. Marissa Hake, Midwest Veal, LLC @CALFVET on FB and Instagram

    **At a Glance -- Calf care on the dairy farm is critical to early success of calves entering the veal market **

    Veal calves are a small, but successful portion of the U.S. dairy industry. As a veterinarian who works exclusively with veal calves, I’ve found there is a lot of misunderstanding about the veal industry, even among dairy farmers. The success of calves entering the veal market is highly dependent on early care at the dairy farm. All of the same principles of calf care used for heifers should be applied to care of bulls, regardless if they are entering the beef or veal market.

    What is a veal calf? The American veal calf industry is split into two major markets, the formula-fed veal calf and the bob veal calf. USDA reports 479,900 calves were harvested in 2016.

    Formula-fed Veal (also known as milk-fed or special-fed)

    • Approximately 85% of the veal consumed in the U.S. is formula-fed veal. These calves are marketed around 6 months old (approximately 450 to 500 lb) and are consuming milk and grain, which makes them very different than their bob-veal counterparts.
    • Drug residues originating from the dairy farm are less likely in this class of veal because of age at processing.

    Bob Veal

    • Less than 10% of total volume of all veal marketed today is bob veal. Bob veal calves are usually sold directly from the dairy farm to a meat processor or through a sale barn to a meat processor for harvesting. Calves typically weigh less than 150 lb.
    • Because of bob veal’s proximity to slaughter, the potential for residue violations originating at the dairy is higher.
    • Dairy producers should be very careful not to use medications that can cause residues.

    Best practices for on-farm care of dairy bull calves entering the veal market include:

    • Most importantly - Adequate, clean and timely colostrum is given even if immediately transported off of the farm – 10% of calf’s body weight should be fed within 2 hours of birth 1,2
    • Navels are disinfected with 7% tincture iodine or 1:1 Chlorhexidine/70% alcohol within 30 minutes of
      birth 2
    • Adequate food and water is provided to maintain health, growth, and vitality 2
    • Vaccines, if needed, given for enteric and respiratory diseases are approved by a veterinarian
    • Clean, dry and sanitary housing is provided with proper ventilation and biosecurity
    • All calves have identification 3
    • Veal calves do not need to be castrated or dehorned
    • Care and oversight is provided from trained calf care takers
    • Do not sell or transport sick or injured calves

    Communication and transparency between dairy and calf buyer/veal grower

    • Have a good relationship and open communication between both parties
    • Ensure that all medication, treatments and vaccines are documented and provided as needed
    • Know which market the calves will be entering - special-fed veal or bob veal

    Medications and Treatments

    • Veal calves should not be denied treatment for disease on the dairy farm if warranted – Follow all withdrawal times
    • Keep records of all treatments and identify calves
    • Medicated milk replacer and milk from treated cows should be avoided in calves intended for veal
    • Avoid treating calves with medications that are not labeled for use in veal calves
    • Work with veterinarian to develop appropriate treatment protocols for dairy bulls entering the veal market

    Calf Handling

    • Calves are handled in a calm, controlled and gentle manner
    • Calves are moved from the dairy onto the truck or in the auction market by walking or lifting them, or using clean, properly designed mechanical transport devices.
    • Animal caretakers are trained to handle and restrain calves with minimum stress to the animal
    • The consequences of inhumane handling are known and enforced. Calves can be injured if they are dragged, pulled or caught by the neck, ears, limbs, tail or any other extremities, or if they are thrown. The Veal Quality Assurance program does not tolerate abusive behavior of animals.4

    Calves, regardless of gender and future use, should have proper care to ensure they can thrive and prosper. Young calf treatment should not be based on financial motivations, but rather considered a welfare standard for all calves.

     

    About The Beef Checkoff:

    The Beef Checkoff Program (www.MyBeefCheckoff.com) was established as part of the 1985 Farm Bill. The checkoff assesses $1 per head on the sale of live domestic and imported cattle, in addition to a comparable assessment on imported beef and beef products. In states with qualified beef councils, states may retain up to 50 cents of the dollar and forward the other 50 cents per head to the Cattlemen’s Beef Promotion and Research Board, which administers the national checkoff program, subject to USDA approval.

    Best Practices for Dairy Calf Care are provided as part of the Veal Quality Assurance Program funded by the beef checkoff.   

    _____________________________________________________________________________

    1Dairy Calf and Heifer Association – Gold Standard – Colostrum Harvest and delivery
    2Dairy Calf and Heifer Association – Gold Standard – New Born Care
    3http://www.nationaldairyfarm.com/sites/default/files/Version-3-Manual.pdf
    4http://www.vealfarm.com/certification-resources/

     

    More information and resources are available on www.VealFarm.com

     

     

     

     

     

     

     

     

     

     

     

     

  5. Launch of the Dairy Farm Manager Academy Program

    Dr. Maurice Eastridge, Dairy Extension Specialist, Department of Animal Sciences, The Ohio State University

    Finding employees for dairy farm labor and the management of the labor force has become a struggle for many dairy farmers. Many times in recent years, dairy farmers have contacted university personnel looking for a dairy farm manager. Given the need to fill such positions and the expertise needed by these individuals, we at Ohio State felt compelled to develop the Dairy Farm Manager Academy program. As we looked around the country, similar programs with this focus are quite limited. The Academy is a four-module program with the expectation that an individual would participate in all four modules. Each module will occur over 2 days (Friday and Saturday), with a new module being offered every other month. Two of the modules will be taught in Wooster and two taught in Columbus near the campus of The Ohio State University. During the month between modules, a webinar with participants will be held to reinforce the content in the previous module and encourage completion of the homework. Participants will receive a certificate of completion at the end of the program. The program will be held for the first time February through August, 2018. A brochure describing the program and providing registration information is available at https://dairy.osu.edu/sites/dairy/files/imce/PDF/Dairy Manager Academy Brochure.pdf . Although the first module does not begin until February 2018, the registration deadline has been set for December 1 so we can have about two months to prepare for the program, including the inviting of guest speakers.

                The purpose of the Dairy Farm Manager Academy is to provide training for dairy farm managers to increase their skills for managing dairy cattle, personnel, and the aspects affecting the financial success of the operation. Using science-based practices, the goal is to train dairy farm managers to meet the current demands for the dairy industry and successfully manage modern dairy operations. Improving the skills and job satisfaction of managers and reducing turnover are expected outcomes of the program.

  6. Ohio State Wins at the Pennsylvania All-American

    Ms. Bonnie Ayars, Dairy Program Specialist, Department of Animal Sciences, The Ohio State University

    The 2017 All-American Invitational Youth Dairy Cattle Judging Contest was held on September 18 at the Farm Show Complex and Expo Center in Harrisburg, PA. Contestants judged 10 classes of cattle and then gave oral reasons on four or five of the classes depending on their division. By a margin of over 20 points, the Ohio State Dairy Judging Team won the Pennsylvania All American Contest in the collegiate division. Members of the team included Alex Houck, Ella Jackson, Lexie Nunes, and Tanner Topp. Ella also captured the High Individual Honors and was 5th in Reasons overall. It is noteworthy to mention that Lexie presented a set of reasons that scored a perfect 50 and another set earned a 49. In the breed divisions, the placings by the Ohio State team were as follows: 1st in Ayrshire and Ella was High Individual; 5th in Brown Swiss; 3rd in Guernsey; 3rd in Holstein; and 4th in Jersey. The team is coached by Bonnie Ayars. Congratulations to the Buckeyes!

     Pictured: Ella Jackson, Tanner Topp, Lexie Nunes, Alex Houck,
      and Bonnie Ayars (coach)                      

     Pictured: Ella Jackson and Bonnie Ayars (coach)