Buckeye Dairy News: Volume 18, 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: Better But Not Best

    In the last issue, the July Class III future price was projected to be $13.16/cwt and then jump to $15.07/cwt in August. The Class III price for July and August actually closed much higher than expected at $15.26/cwt and to $16.91/cwt, respectively. One year ago, the Class III milk price in Ohio was over $1/cwt higher at $16.33 for July and $0.64/ cwt lower at $16.27/cwt in August of 2015.  Currently, the Class III future price is set marginally lower at $16.63/cwt for September but higher than the $15.84/cwt of September 2015, when the market really started to take a turn for the worst.

    Typical of when the kids go back to school, the price of milk has temporarily spiked to cover the demand. But this spike is only partially compensatory for the low average Class III price we have had for 2016 thus far ($14.16/cwt). To have an equivalent year to that of 2015 (2015 Class III average $15.80/cwt), the Class III milk price would have to average at least $20.80/cwt for October, November, and December, which is probably unrealistic. Overall, the milk prices of July and August are good in comparison to the previous 12 months but most definitely fall short of the 5 year average of all milk sold in Ohio ($18.46/cwt). The future of 2017, however, looks slightly brighter than this year according to the USDA price forecasts that predict all milk classes should average around $15.75 ± 1.00/cwt. This number, however, should be taken with extreme caution and be simply used as a rough estimate.

    Nutrient Prices: Still The Good Side

    In the last issue, the potential for a very volatile market forth coming was discussed. Now at the brink of the harvest, the future looks extremely good for the animal production industry as nutrient prices are expected to continue to fall. The majority of the Midwest is expected to have yet another good year in terms of bushels per acre but not in terms of dollars per bushel. This will be especially true for corn prices which are already approaching $3.00/bu and will likely go lower; prices we have not seen since the ethanol boom. The price of soybeans has also dropped 80¢/bu since the last issue when it temporarily spiked in price to $10.87/bu. Needless to say, now would be a good time to start locking in good prices on commodities and reformulating rations to enable feeding bargain feedstuffs long term.

    In this issue, a new corn silage price for the year of $42.50/ton (35% dry matter) was calculated. This price is about $4/ton lower than last year’s and still a bargain compared to other ingredients. The calculated price is based upon the value of shelled corn rather than the nutritive value of the corn silage fed. The value, however, can vary considerably based on location (e.g. weather and growing conditions) and harvesting and storage conditions or practices, as well as the hybrid of corn planted. Thus, 75% confidence intervals are defined in Table 2 to reflect the real world variability in the nutritional value of corn silage and its range in net worth based on the price of other ingredients. This is especially true for the state of Ohio, as many areas did not experience the best growing conditions. Corn silage in these areas will likely differ in nutritive value (higher protein and lower energy) and also generate lower yields. Bottom line, regardless of variability, corn silage should be a no brainer for making up the majority of the forage component of rations for the upcoming year, but only if you have stored enough; running out of corn silage in July will be a huge financial burden. For more information about corn silage, I recommend readers look further into this issue of the Buckeye Dairy News for an article dedicated to the topic.

    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 all commodities traded in Ohio, and to identify feedstuffs that currently are significantly underpriced as of September 20, 2016. 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.  For MP, its current price ($0.52/lb) has increased slightly from July’s issue ($0.45/lb). The cost of NEL, e-NDF, and ne-NDF are nearly identical to last month at 10¢/lb, 8¢/lb, and -13¢/lb (i.e. feeds with a significant content of ne-NDF are priced at a discount), respectively.

    To estimate the cost of production at these nutrient levels, the Cow-Jones Index with a cow milking 70 lb/day at 3.7% fat and 3.1% protein eating 50 lb/day of DM was used. In this model, the average income over nutrient costs (IONC) in July’s issue were estimated at $6.61/cwt for this 70 lb/day of milk and $7.04/cwt for a cow milking 85 lb/day and eating 56 lb of DM. These IONC were calculated under the combination of low nutrient prices and poor milk prices and are likely unprofitable. However, milk price has increased since, and in this issue, our 70 lb/day and 85 lb/day cows are estimated to be making much more per cwt of milk at $9.49/cwt and $9.94/cwt, respectively. Even though the current price of all milk is $2.50/cwt below the five-year average ($18.46/cwt) and may not seem high, the current nutrient prices are staying low driving the cost of production down. Taken all together and using this index, milking cows should no doubt be profitable again using these market prices. 

    Table 1. Prices of nutrients for Ohio dairy farms, September 20, 2016.

    Economic Value of Feeds

    Results of the Sesame analysis for central Ohio on September 20, 2016 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 point in time, mid September in this case. 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 20, 2016. 

    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.

    Table 3. Partitioning of feedstuffs, Ohio, September 20, 2016.

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

    As coined by Dr. St-Pierre, readers must be reminded 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 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.


    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 Table 4.

    Table 4. Prices of nutrients using the 5-nutrient solution for
    Ohio dairy farms, September 20, 2016.


  2. Does TMR Sampling Provide Useful Nutrient Composition Data?

    Dr. Bill Weiss, Professor, Interim Department Chair, and Extension Dairy Specialist, Department of Animal Sciences, The Ohio State University1

    Laboratory data from total mixed ration (TMR) samples have potential value when evaluating consistency and accuracy of the diet that was delivered to a pen of cows including:

    1. Assessing within bunk variation in nutrient delivery. When evaluating consistency of TMR mixing and delivery, samples are taken at various locations across the bunk, analyzed for nutrients or particle size, and then some measure of variation, such as coefficient of variation (CV) or standard deviation (SD), is calculated and compared to a benchmark.
    2. Assessing day-to-day consistency of TMR delivery. The same basic approach as above except the TMR is sampled over multiple days and then variation is calculated among the daily samples.
    3. Determining whether the delivered ration matches the formulated one. Because of normal variation in ingredient composition and random or systematic errors associated with the individual doing the feeding and the scales on the mixer wagon, the delivered diet may differ markedly from the formulated diet. To evaluate accuracy (how close the delivered diet matches the formulated diet), samples are taken and results are compared to the specifications of the formulated diet.

    Although using TMR composition data to evaluate diets and troubleshoot nutritional problems has potential, to be useful TMR data must meet the following to criteria:

    • Sampling variation (e.g., variation among results from samples taken at the same location within a feedbunk on a given day) must be known. Without knowing sampling variation, you might conclude that mixing is poor because you have a high CV across the feedbunk (or across days), but in reality, the high CV might have been caused by poor sampling technique.
    • The nutrient composition of the sample must accurately reflect what was delivered to the pen (i.e., sample results must be accurate). If sample results do not match formulated expectations, you might assume ingredients have changed or blame the feeder for not following the recipe, when in reality, it might be the sample (or the sampler) that is to blame.

    Should sampling error be a concern for TMR data?

    Sampling error (or sampling variation) simply means that if you take multiple samples from the same population, you obtain different values. A TMR is comprised of particles that vary in density, size, shape, and nutrient composition. A stem of hay is light, long, and is generally high in fiber; whereas, a grain of salt is heavy, small, and has no fiber. The extreme heterogeneous nature of TMR makes them extremely difficult to sample accurately, thus sampling error is indeed a major issue with TMR data. In a field study (conducted by The Ohio State University) of commercial dairy farms across the U.S., sampling variation contributed 36 to 70% of the total within farm variation in TMR composition over a 12-month period.

    Bottom Line:

    • When assessing day-to-day variation, duplicate samples should be taken and then averaged.  Variation among the daily averages should be calculated.
    • When evaluating within bunk variation, the process should be replicated and results averaged.
    • For example, you could take 5 samples across the bunk, measure particle size on those samples, and calculate the CV; that entire process should be repeated and the 2 CV should be averaged.

    Comparing TMR sample results to the formulated diet

    An experiment was conducted at Ohio State (see 2016 Tristate Dairy Nutrition Conference Proceedings for full details) to determine whether TMR sample results accurately reflect the TMR that was delivered. The TMR was sampled immediately after it was delivered to the pen using the protocol outlined below. Three different TMR mixes were sampled over 6 days. One TMR mix contained only silages and concentrate; another contained dry hay, silages, and concentrates; and the third contained hay, silages, whole cottonseed, and concentrate.  Type of TMR did not have much effect so data from individual TMR will not be discussed.  Each day ingredients were sampled and analyzed and amounts of each ingredient put into the mixer was electronically recorded using commercial TMR software. Actual inclusion rates and ingredient composition data were used to calculate the ‘true’ composition of the TMR which was compared to composition determined on TMR samples. 

    Are TMR samples accurate?

    Accuracy has a flexible definition depending on how good is good enough. If you were constructing a nuclear submarine, tolerances might be expressed in nanometers, but if you are digging a hole for a fence post, tolerances may be several inches. For this project, if a sample result was within 5% of the true value, the result was considered accurate.  Using that definition, a single TMR sample can accurately reflect the TMR for some nutrients but for other nutrients, even averages of multiple samples are unlikely to be accurate.

    Dry Matter (DM) Comparisons:

    For DM, a single sample of TMR (using the protocol outlined below) was almost always within 5% of the true value (Table 1) and no sample was more than 10% away from the true value. Although a TMR sample was accurate for DM, the value of knowing the DM concentration of a TMR is questionable because diets are usually not formulated to a specific DM percentage.

    Crude Protein (CP) Comparison:

    A single TMR sample was usually accurate for CP, but occasionally (approximately 1 out of every 15 samples) sample values were really wrong.

    NDF Comparison:

    A single TMR was not reliable to evaluate NDF concentration of TMR with approximately 1 out of every 6 samples being more than 10% wrong.

    Mineral Comparisons:

    A single TMR sample had no value in estimating the mineral concentrations of the delivered TMR. This finding may have practical implications for phosphorus-based nutrient management plans. If phosphorus intake is calculated by multiplying TMR delivery rates by phosphorus concentration of a TMR sample, estimated phosphorus intake could often be wrong by more than 20%.

    Single versus duplicate samples of TMR:

    For NDF and all minerals, single TMR samples were not accurate. But, what about means of duplicate samples? Taking 2 TMR samples and averaging them greatly improved the accuracy for NDF. The mean of duplicates were accurate 75% of the time and only about 1 out of 12 times was the mean more than 10% wrong.  Averaging TMR samples, however, did not greatly improve accuracy for minerals. Less than 20% of the averages of duplicate samples were accurate for any mineral and between 1 out of every 2 means to 1 out of every 5 means were wrong by more than 10%.

    Table 1. Sampling accuracy of TMR based on analytical data of various dietary components. Values represent the percentage of TMR samples (36 samples were taken) that were within 5% of the true values or more than 10% away from the true values1.

    Nutrient < 5% Deviation >10% Deviation
    Dry matter, % 95% 0
    CP, % 75%   6%
    NDF, % 58% 17%
    Phosphorus, %   3% 83%
    Sodium, %   8% 67%
    Copper, ppm 14% 86%
    1 See: http://tristatedairy.org/Proceedings 2016/Bill Weiss.pdf

    Recommended TMR sampling protocol

    Another objective of the projected outlined above was to test different sampling protocols. Details are available in the paper discussed above but based on results from that study the following sampling protocol is recommended.

    1. As you walk the feedbunk carrying a clean container such as a 5 gal bucket, take a handful of TMR approximately every 10 to 30 ft and place it into the bucket. For shorter bunks sample at 10 ft intervals but for very long bunks sample at 30 ft intervals. You want to have at least 10 handfuls by the time you reach the end of the bunk.
    2. Alternate samples so that the top, middle and bottom third of the TMR is sampled.
    3. When taking the handful, ensure that your palm is facing up to avoid dropping small particles.
    4. After you have walked the entire feedbunk, mix the contents of the bucket and then dump the contents onto a clean floor or large piece of plastic.
    5. Spread the contents out into a circle, divide the circle into quarters and then using a scoop to ensure you get all the particles, place one of the quarters into a sampling bag and send to the lab. The sample should be larger than a softball but smaller than a volley ball.


    Using a simple, yet good sampling technique for obtaining TMR samples was generally accurate for DM and CP; however, using results from a single sample had a high risk of being wrong (>10% different) with respect to NDF and minerals. Taking duplicate samples and averaging NDF values reduced the risk of being wrong to an acceptable level. Sampling TMR did not accurately assess mineral delivery and should not be used.

    1Published initially in the dairy nutrition series of DAIReNET, the dairy resource area of eXtension, at: http://articles.extension.org/pages/73922/does-tmr-sampling-provide-useful-nutrient-composition-data

  3. 2016 Corn Silage Crop in Ohio

    Dr. Maurice Eastridge and Dr. Bill Weiss, Extension Dairy Specialists, Department of Animal Sciences, The Ohio State University

    The weather conditions have been variable in Ohio this summer. Some areas have been extremely dry and other areas have been very wet during the past two to three months.  Thus, corn silage yields will likely be quite variable across Ohio this year. For those areas that have been very dry, yields will be adversely affected, but generally the concentrations of protein and energy will be better than average. Therefore, many dairy farmers in Ohio may need to purchase additional corn for silage or identify other ingredients to replace corn silage in the diet. Now is the time to make such decisions while some corn may still be standing in the field, other forages are readily available, and commodities will be less expensive near harvest time.

    Harvesting Corn Silage

    Chop at the correct dry matter (DM) concentration.  The factor primarily responsible for obtaining a good fermentation is the DM concentration of the plant when chopped.  This is the same whether it is a beautiful, record breaking corn crop or a severely drought stressed field with short plants containing no ears.  Chopping corn silage at the wrong DM concentration will increase fermentation losses and reduce the nutrient value of the silage.  The recommended ranges for silage DM are:

    Bunker: 30 to 35%                                           Upright: 32 to 38%
    Bag: 32 to 38%                                                Sealed upright: 35 to 38%

    Drought-stressed corn plants are often much wetter than they appear, even if the lower plant leaves are brown.  Before starting to chop, sample some plants (cut at the same height as they will be with the harvester) and either analyze DM using a Koster tester or microwave or send to a commercial lab (turn-around time may be a few days if you send it to a lab).  If the plants are too wet, delay chopping until the desired plant DM is reached.  By delaying harvest, the plant may continue to accumulate DM (increase yield), and you will not suffer increased fermentation losses caused by ensiling corn that is too wet.

    Use a proven inoculant.  When silage is worth upwards of $80/ton (35% DM), reducing shrink by 2 percentage units has a value of about $2/ton. Homolactic inoculants (these are the ‘standard silage inoculants’) produce lactic acid which reduces fermentation losses but sometimes can increase spoilage during feedout. The buchneri inoculants increase acetic acid which slightly increases fermentation losses but greatly reduce spoilage during feedout.  Severely drought-stressed corn can have a high concentration of sugars because the plant is not depositing starch into the kernels.  High sugar concentrations can increase spoilage at feed out because it is a food source for yeasts and molds.  Use of a good (from a reputable company with research showing efficacy) buchneri inoculant may be especially cost-effective with drought-stressed corn. 

    Check for nitrates.  Because of the growing season this year, the risk of nitrate accumulation is not extremely high, but you should still test silage from drought-stressed corn plants. Ideally, corn plants should be sampled and assayed for nitrates prior to chopping (most labs offer very rapid turn-around times for a nitrate assay).  If values are high, raising the cutting height will reduce nitrate concentrations in the silage because the bottom of the stalk usually has the highest nitrate concentrations.  However, do not raise the cutting height unless necessary to reduce nitrate concentrations because this will reduce yield.  Nitrate concentrations are often reduced during silage fermentation so that high nitrates in fresh corn plants may end up as acceptable concentrations in the fermented corn silage. Silage with more than 1.5% nitrate (0.35% nitrate-N) has a high risk of causing nitrate toxicity in cattle.   The yellow or brown gas you might see coming from a silo a day or two after filling is a result of the conversion of nitrates to other compounds. CAUTION - this gas is very toxic to humans and animals.

    Chop at correct particle length.  Do not chop the corn too finely such that the effective fiber concentration of corn silage is reduced.  If the corn plants have limited ear development, fine chopping is not needed for good starch digestibility.  Generally a theoretical length of cut (TLC) of about ½ inch is acceptable (longer with kernel processing and BMR silage), but this varies greatly between choppers and crop moisture concentration.  If using a Penn State particle size sieve, aim for 5 to 10% on the top screen at the time of chopping.

    Reduce Shrink. Fill quickly, pack adequately, cover, and seal the silo as soon as you are done chopping.  Practicing good silage-making techniques can reduce shrink by more than 5 percentage units, which can be worth more than $4/ton of corn silage (35% DM). 

    Additional recommendations on harvesting corn silage are available on the eXtension web site in the dairy cattle section where feature articles have been posted on forages and other topics: http://articles.extension.org/pages/71253/dairexnet-feature-article-series. Delaying the feeding of the silage for about 60 days will increase the digestibility of the silage, and thus optimize animal performance from consuming the silage. If the harvest of the corn is delayed and frost occurs, frosted corn can still be a valuable feed, but you have to be careful with the rapid dry-down to harvest the silage at the proper DM.

    Pricing Corn Silage

    The price for corn silage depends on its nutrient composition and the price of other feed ingredients in the market. In each issue of the Buckeye Dairy News (BDN) (https://dairy.osu.edu/newsletter/buckeye-dairy-news), an article is provided that provides the predicted value of feeds based on chemical composition and current prices of commodities, including the predicted price for corn silage. For example, in this issue (September 2016) of BDN, corn silage is reported at an actual price of $42.50/ton but having a predicted price of $76.70/ton (95% confidence interval of $69 to 85/ton). Some articles are available on the OSU dairy web site for pricing standing corn for silage and for pricing drought-stressed corn for silage: https://dairy.osu.edu/resources/feeding-and-nutrition.

    However the ultimate determinant of price is still supply and demand in a local market (corn silage cannot be transported long distances). If a local area has a lot of corn that is not worth harvesting for grain, the price of the standing corn may be substantially less than its nutrient value.

    Dietary Replacement of Corn Silage

    Corn silage is certainly a valuable ingredient in diets for dairy cattle. It is a very efficient crop to grow in Ohio, and it provides valuable energy and fiber usually at bargain prices in diets of high-producing dairy cows. Typical chemical composition of corn silage is provided in Table 1. Some strategies for stretching the supply of corn silage or replacing it in diets are as follows:

    1. Reduce the amount of corn silage in the diet to stretch the supply by increasing the inclusion level of high-quality legume or grass hay or silage.
    2. You can stretch the supply of corn silage by removing it from rations for the growing heifers and dry cows and feeding them all hay or haycrop forages.
    3. All of the corn silage in the diet of the lactating cows can be removed and effective fiber and nutrients balanced using other high-quality forages and concentrates. Some University of Georgia researchers several years ago advocated the feeding of up to 10 to 15 lb/day of an artificial corn silage consisting of 40% soybean hulls, 30% cottonseed hulls, 25% ground corn, and 5% cottonseed meal. This results in a mixture with the composition of 11.9% CP, 53.5% NDF, 19% starch, and about 0.63 NEL/lb. Given that soybean hulls have been overpriced for quite some time (see latest issue of BDN mentioned above), wheat middlings was used to formulate a different mixture consisting of 40% wheat middlings, 34% cottonseed hulls, and 26% ground corn (11.9% CP, 45.9% NDF, 19.7% starch, and 0.64 NEL/lb). Caution is expressed in using cottonseed hulls in OH due to their cost; presently, they are only valued at $23/ton using the nutrient values published in the July BDN. However, the bottom line on this approach is to work with your dairy nutritionist so a diet can be formulated without corn silage that can provide the nutrient supply needed, keep the rumen healthy, and not limit intake of the animal. Then you will want to monitor animal performance with the new feeding strategy so adjustments can be made if necessary.

    With variable weather conditions throughout the State, composition of corn silage will likely be quite variable in Ohio this year. Thus, as usual, multiple samples will need to be analyzed so diets can be adequately formulated. Hopefully, yield of corn and soybeans will be good which will help to keep feed prices moderated. At the moment, milk prices are low and on the bubble, hopefully tipping upward. So carefully monitoring income over feed costs will be pivotal in the upcoming months. You and the nutritionist will need to be on each other’s speed dial.

    Table 1. Typical composition of corn silage based on samples submitted from May 1, 2000 to April 30, 2016
    to the Dairy One Forage Lab in Ithaca, NY (http://dairyone.com ).1

    Item Average SD CV Typical Range
    DM, % 33.7 9.3 27.6 24.4 - 43.0
    CP, % of DM     8.27   1.06 12.8 7.21 - 9.32
    Starch, % of DM 31.8 7.5 23.6 24.3 - 39.3
    ADF, % of DM 25.8 4.1 15.9 21.7 - 29.9
    NDF, % of DM 43.6 5.9 13.5 37.7 - 49.6
    NDFD 30 hr, % of NDF 52.5 6.1 11.6 46.4 - 58.6
    1DM = dry matter, CP = crude protein, ADF = acid detergent fiber, NDF = neutral detergent fiber,
    NDFD = NDF digestibility, SD = standard deviation, and CV = coefficient of variation ((SD/average) * 100).


  4. Dairy Farm Tour to Argentina and Uruguay

    Dr. Alejandro Relling, Assistant Professor, Department of Animal Sciences, The Ohio State University

    We are trying to organize a dairy farm tour to Argentina and Uruguay for dairy farmers.  The trip will be in mid-March and will last 10 days. We will visit some different dairy farming systems and visit the cities of Buenos Aires and Montevideo as little side cultural visits. To be able to do the trip, we need a minimum of 18 people and a maximum of 21. If you are interested in going or you want more information, please contact Dr. Alejandro Relling (relling.1@osu.edu, or 330-263-3900).