Buckeye Dairy News: VOLUME 27: ISSUE 5

Dr. Kirby Krogstad, Assistant Professor, Department of Animal Sciences, The Ohio State University

Background

Enhancing milkfat production continues to be a vital and economically important goal for dairy farmers. Milkfat production is very responsive to a host of nutritional decisions, including carbohydrates, amino acids, and forage feeding. Other important nutritional decisions that affect milk fat production include managing the rumen environment through use of buffers or alkalizers and increasing the dietary cation anion difference (DCAD). The objective of this article is to review some scientific literature on buffers, alkalizers, and DCAD to help enhance milk fat production.

Feeding Buffers

The term ruminal buffer is used broadly in dairy nutrition, but it is not necessarily the most accurate term. Feed supplements that affect rumen pH can be categorized as true buffers or alkalizers. True buffers (i.e., sodium bicarbonate) resist changes in ruminal pH but they do not necessarily increase ruminal pH, while alkalizers may increase rumen pH (i.e., magnesium oxide). We’ll discuss these slightly different supplements as a broad category of feed additives to manage ruminal pH.

Sodium Bicarbonate

Sodium bicarbonate is one of the most prominent ruminal buffers fed to cattle, and there are a lot of data demonstrating its efficacy when feeding dairy cattle. One project supplemented 0.43 lb/day of sodium bicarbonate to lactating dairy cattle and increased the minimum rumen pH and reduced the time the rumen was less than 5.5 compared to a non-buffered diet. Another study demonstrated that feeding 0.40 lb/day of sodium bicarbonate increased rumen pH and dramatically reduced time with a pH <5.5; in the same experiment, sodium bicarbonate increased both milk fat concentration and yield. Reducing ruminal pH does not always occur though; Bach et al. (2018) subjected cows to increasing concentrations of barley while feeding sodium bicarbonate and did not observe differences in rumen pH or time spent with low rumen pH.

Providing the sodium bicarbonate increases milk fat concentration and milk yield (Cruywagen et al., 2015; Neville et al., 2019). A meta-analysis investigating sodium bicarbonate supplementation in dairy cattle suggests that providing approximately 0.45 lb/day of sodium bicarbonate per cow increases milk fat concentration and milk fat yield (Hu and Murphy, 2005).

Table 1.  Partial budget for feeding sodium bicarbonate at 0.45 lbs/cow/d in place of soybean hulls and assuming a 100 g/d increase in milk fat yield.

Item	$/cow/day
Cost of Sodium bicarbonate (A)	0.10
Increased revenue from milk fat (B)	0.63
Net change (B - A)	0.53

The question of sodium bicarbonate, like any other rumen buffer supplement, should come back to a return on investment. The increased milk fat in response to sodium bicarbonate ranged from 50 to 160 g/day, depending on the data cited. For our partial budget (Table 1), we’ll assume a100 g/day increase in milk fat when feeding 0.45 lb/day of sodium bicarbonate. The cost for the sodium bicarbonate was assumed at $610/ton, which results in $0.14/cow/day of ration cost. When including the soybean hulls that were removed ($175/ton) to make space for the sodium bicarbonate, the net cost of the sodium bicarbonate supplementation is $0.10/cow/day. The gain in milk fat equates to a $0.63/cow/day increase in milk fat revenue and the net return on investment of supplementing sodium bicarbonate would be $0.53/cow/day.

Calcareous Marine Algae

Another option for moderating rumen pH is through feeding calcareous marine algae (CMA). CMA has very strong buffering capacity and often increases rumen pH when fed to dairy cattle (Cruywagen et al., 2015; Neville et al., 2019). The feeding rate for CMA in these studies was approximately 0.20 lb/day. In both experiments, feeding CMA increased rumen pH and reduced time with a low pH more than feeding sodium bicarbonate. It also increased milk fat production by 90 and 270 g in these experiments, which shows that it can have commercial benefits when feeding it to dairy cattle. It is usually more expensive than sodium bicarbonate (~$1400/ton), but it does provide different nutrients in addition to its buffering effects.

Mg Oxide

Mg oxide can aid in modulating rumen pH by acting as an alkalizer. Bach et al. (2018) investigated Mg oxide supplementation to maintain rumen pH when increasing barley, and they observed that providing Mg oxide resulted in greater rumen pH and less time spent with low rumen pH when feeding an additional 3 kg/day of barley. Mg oxide appeared more effective at maintaining rumen pH than sodium bicarbonate in this project. These investigators replicated these results for Mg oxide later on and also observed that Mg oxide supplementation increased NDF digestibility when supplied in diets with the greatest grain inclusion (Bach et al., 2023). In these experiments, Mg oxide was provided at 0.13 and 0.20 lb/day, respectively.

DCAD

Dietary cation-anion difference (DCAD) gets most of its attention for its importance in prefresh rations and controlling hypocalcemia, but DCAD is also critical for increasing milkfat production. Increasing DCAD is also related to feeding ruminal buffers, especially sodium or potassium-based buffers, because they can increase DCAD. So, the effects of some ruminal buffering products may be a result of both buffering ruminal pH and increasing dietary DCAD concentration.

DCAD is usually calculated as follows: DCAD = Na + K – Cl – S. In prefresh cows, we aim for a negative total diet DCAD, but in lactating cows, it must be positive. How positive you ask? Based on the data (Iwaniuk and Erdman, 2015), milk fat yield and concentration continue to increase as DCAD increases up to 500 mEq/kg of diet DM, while DMI and milk yield appear to plateau between 200 and 300 mEq/kg of diet DM. Based on their results, aiming for a lactating dairy cow DCAD ≥300 mEq/kg of DM will increase milk fat production.

How can you increase the DCAD of your ration? Feed more Na and K or feed less Cl and S while still meeting nutrient requirements for each. Some common supplement options are sodium bicarbonate, sodium sesquinate, potassium carbonate, and potassium bicarbonate. Remember, chloride has a negative DCAD so chloride-based salts like sodium or potassium chloride do not increase DCAD like the carbonate versions.

Certain feeds can also have substantial impacts on DCAD. For example, dry distillers grains with solubles (DDGS) has a negative DCAD, which contributes to reductions in milk fat when feeding DDGS in rations that are not properly formulated (Clark et al., 2024). In general, corn-based ingredients like DDGS and corn have lower DCAD than soy-based ingredients like soybean meal and soybean hulls. Formulate the rations accordingly!

Take Homes

Milk fat continues to be a critical part of the dairy producer’s milk check, and we need to use every tool in the toolbox to aid in maximizing milk fat production. Buffers and DCAD considerations when feeding diets can help increase milk fat production. When investigating buffers, you must consider the costs and the benefits. We did the math for sodium bicarbonate as an example, but the same approach can be used for any rumen buffer product. I highly encourage you to base your partial budgets on peer-reviewed literature. Based on the data for rumen buffering and milk fat production, feeding rates of some buffer products are listed below:

• Sodium bicarbonate: 0.4 lb/day
• Calcareous marine algae: 0.2 lb/day
• Mg Oxide: 0.15 lb/day

Also, ensure the DCAD of your ration is >300 mEq/kg to help increase milk fat production.

Properly buffered diets with adequate DCAD will help your herd reach your next milk fat milestone!

References

Bach, A., M. Baudon, G. Elcoso, J. Viejo, and A. Courillon. 2023. Effects on rumen pH and feed intake of a dietary concentrate challenge in cows fed rations containing pH modulators with different neutralizing capacity. J. Dairy Sci. 106(7):4580-4598. 10.3168/jds.2022-22734

Bach, A., I. Guasch, G. Elcoso, J. Duclos, and H. Khelil-Arfa. 2018. Modulation of rumen pH by sodium bicarbonate and a blend of different sources of magnesium oxide in lactating dairy cows submitted to a concentrate challenge. J. Dairy Sci. 101(11):9777-9788. https://doi.org/10.3168/jds.2017-14353

Clark, K. L., K. Park, and C. Lee. 2024. Exploring the cause of reduced production responses to feeding corn dried distillers grains in lactating dairy cows. J. Dairy Sci. 107(9):6717-6731. 10.3168/jds.2023-24356

Cruywagen, C. W., S. Taylor, M. M. Beya, and T. Calitz. 2015. The effect of buffering dairy cow diets with limestone, calcareous marine algae, or sodium bicarbonate on ruminal pH profiles, production responses, and rumen fermentation. J. Dairy Sci. 98(8):5506-5514. https://doi.org/10.3168/jds.2014-8875

Hu, W., and M. R. Murphy. 2005. Statistical evaluation of early- and mid-lactation dairy cow responses to dietary sodium bicarbonate addition. Anim. Feed Sci. Technol. 119(1):43-54. https://doi.org/10.1016/j.anifeedsci.2004.12.005

Iwaniuk, M. E. and R. A. Erdman. 2015. Intake, milk production, ruminal, and feed efficiency responses to dietary cation-anion difference by lactating dairy cows. J. Dairy Sci. 98(12):8973-8985. 10.3168/jds.2015-9949

Neville, E. W., A. G. Fahey, V. P. Gath, B. P. Molloy, S. J. Taylor, and F. J. Mulligan. 2019. The effect of calcareous marine algae, with or without marine magnesium oxide, and sodium bicarbonate on rumen pH and milk production in mid-lactation dairy cows. J. Dairy Sci. 102(9):8027-8039. https://doi.org/10.3168/jds.2019-16244

 

Bradley Gotto, Undergraduate Student and Dr. Kirby Krogstad, Assistant Professor, Department of Animal Sciences, The Ohio State University

Introduction

Rumination is an important process in cattle and is associated with a high functioning rumen. As a cow ingests and chews its feed, the feed particles enter the rumen where microbial degradation begins. The feed will then be regurgitated for the cow to further chew and swallow again. The chewing of “the cud”, or the re-chewing of regurgitated feed, is what we call rumination. Rumination is impacted by numerous factors on the farm, including temperature-humidity index (THI), feed particle size, feed nutrient composition, and cow comfort.

Monitoring Rumination

Monitoring rumination is helpful for dairy farmers to detect illness, stress, or nutritional challenges in their herd. There are many methods used to monitor rumination. An “old school” approach to rumination monitoring is to conduct “cud counts”, where the number of cows ruminating in a pen are counted, with the goal being 60% of lying cows ruminating at any one time. With technological improvements in the dairy industry, activity collars, ear tags, and rumen boluses have become more common for monitoring rumination. These technologies track different data like head and jaw movements (collar or ear tags) to track time spent ruminating. Rumen boluses are administered orally and track data from inside the cow's reticulorumen.

An ongoing challenge for these technologies is to utilize the information generated by rumination monitoring technologies for economically impactful decisions. Our goal was to improve how to use and apply the data gathered from these rumination tracking technologies. We also wanted to understand whether rumination time and rumination variance were affected by different farm factors.

Our Study Objectives and Hypothesis

The objective was to determine associations of rumination time and rumination variance with environmental and dietary factors that were measured on the farm (Krauss Dairy Farm, Wooster, OH). Our initial hypothesis was that increasing THI would have a negative association with daily rumination time and would have a positive association with rumination variance. We expected that when performing the Penn State Particle Separator (PSPS) tests on each pen’s ration, an increased amount of the ration in the 19 mm sieve would be negatively associated with daily rumination time, and an increased amount of the ration in the 8 mm sieve would have a positive association on the daily rumination time. We also expected that an increased variance of particle size across the bunk would be positively associated with rumination variance measured for each pen.

Methodology

From 5/12/2025 to 7/31/2025, we gathered data from a collar-based rumination monitoring system (SCR, Allflex US, Madison, WI) at Krauss Dairy Farm. The animals were housed in a 4-row freestall barn with a 1:1 bed to cow stocking density, providing a non-competitive environment for the animals to access feed and lay down. The farm milks approximately 190 cows twice daily and feeds once daily. The Holstein and Jersey cows were housed in different barns.

Daily pen averages for rumination time, rumination variance, days in milk, milk yield, and activity data were collected from the herd management system. Daily pen dry matter intake (DMI), feed delivery time, and feed refusal rate were collected from the farm’s feeding program. Every Monday, footage from time lapse cameras in each barn were collected to analyze out of feed time and feed pushup occurrence. The THI data were collected from sensors placed in the center of each freestall barn. Twice each week, the PSPS test was performed on 3 locations across the feed bunk for each pen. The PSPS was also used to assess the particle size of each forage fed in the milk cow ration (corn silage, haylage, and baleage). Temperatures were taken from 5 locations across the feed bunk for each pen using an infrared temperature sensor. Temperatures were also taken from 5 locations on each silage face (corn silage and haylage).

We then calculated Pearson’s correlation coefficients for rumination time and rumination variance with each farm factor to identify potential factors that are related to rumination time and rumination variance.

Correlations

Correlations are a simple statistical method that we used to assess the strength of the relationships between different variables. The greater the correlation is between two factors, the stronger the relationship is. A positive correlation signifies that as one factor increases, the other will also increase. A negative correlation signifies that as one factor increases, the other will decrease. As the value approaches 1.0, the strength of the correlation is increasing. Table 1 lists the cutoffs we used for describing correlations.

Table 1. Strengths of correlation and adjectives used to describe the correlation.

Correlation	Description
1.00	Perfect
0.70-0.99	Very High
0.50-0.69	Substantial
0.30-0.49	Moderate
0.10-0.29	Low
0.01-0.09	Negligible

Results

Rumination is Higher in Holstein Cows

Through the summer, we observed that rumination time was greater in Holstein than Jersey cows. Pen 1, the early lactation Holstein cows, averaged 535 minutes/day of rumination. Pen 4, the late lactation Holstein cows, averaged 559 minutes/day of rumination. Pen 5, the peak lactation Holstein cows, averaged 546 minutes/day of rumination. Pen 8, the first lactation Holstein cows, averaged 526 minutes/day of rumination. Pen 21, the Jersey cows, averaged 418 minutes/day of rumination. Figure 1 shows the daily average rumination time for each group throughout the data collection period.

Figure 1. Average daily rumination time by pen at Krauss Dairy Farm. 

Rumination Variance Differs by Management Group

Rumination variance was greatest in pen 1, averaging 98 min/day. Pen 5 was the next highest, averaging 76 min/day, and pen 8 averaged 68 min/day. Pen 4 averaged only 52 min/day. Pen 21, the Jersey cows, averaged 52 min/day. Figure 2A and Figure 2B show the daily rumination variance on a min/day and as a percentage of total rumination for each pen.
Figure 2. Average daily rumination variance for each pen. Panel A is in variance on a min/day, while panel B displays variance as a proportion of the total rumination time.

Rumination Time is Associated with Greater Milk Production

When analyzing milk yield versus rumination time, we observed that a greater rumination time is associated with a greater milk yield. Our data suggest that for every 33 minutes/day of additional rumination time, we would expect a 1.0 lb/day increase in milk yield (Figure 3). The relationship between milk yield and rumination time was strongest in the early and peak lactation groups. Alternatively, when analyzing milk yield versus rumination variance, we observed that a greater rumination variance was associated with reduced milk yield. Our data suggest that for every 3 minute/day increase in rumination variance, we expect a 1.0 lb/day decrease in milk production (Figure 4).
Figure 3. Increasing rumination time is associated with increased milk yields.

Figure 4. Increasing rumination variance is associated with reduced milk yields.

Correlations of Rumination with Management Factors

When analyzing the correlations, a substantial positive correlation exists between rumination time and milk yield (r = 0.64, P = <0.001; Table 2). We also observed a very high positive correlation between dry matter intake (DMI) and rumination time (r = 0.78, P = <0.001). A very high negative correlation exists between activity and rumination time (r = -0.85, P = <0.001). Conversely, there was a moderate negative correlation between rumination variance and days in milk (r = -0.47, P = <0.001; Table 3).

Table 2. Correlations of daily pen-level production variables with rumination time (CI = confidence interval).

Variable	Correlation	95% CI	P-value
Days in milk	0.15	0.05-0.24	0.004
Activity	-0.85	-0.87- -0.82	<0.001
DMI	0.78	0.75-0.82	<0.001
Milk	0.64	0.58-0.69	<0.001
FE	0.26	0.17-0.35	<0.001
Adj. FE	0.40	0.32-0.48	<0.001
Refusal rate	0.12	0.03-0.22	0.01
Out of feed time	-0.13	-0.23- -0.01	0.02

Table 3. Correlations of daily pen-level production variables with rumination variance (CI = confidence interval).

Variable	Correlation	95% CI	P-value
Days in milk	-0.47	-0.55 - -0.39	<0.001
Activity	-0.09	-0.19-0.00	0.06
DMI	-0.12	-0.22- -0.02	0.01
Milk	0.18	0.09-0.28	<0.001
FE	0.33	0.24-0.42	<0.001
Adj. FE	0.14	0.04-0.23	0.006
Refusal rate	0.0	-0.10-0.10	0.93
Out of feed time	-0.21	-0.30- -0.10	<0.001

Correlations of Rumination with Dietary and Environmental Factors

Upon evaluating correlations between rumination time and dietary characteristics, a moderate negative correlation was found between the feed retained on the 8 mm (%), and rumination time (r = -0.43, P = <0.001; Table 4).  We also observed moderate positive correlation between 8 mm (%), and rumination variance (r = 0.37, P = <0.001; Table 5) and a moderate negative correlation between 19 mm (%), and rumination variance (r = -0.41, P = <0.001)

Table 4. Correlations of daily pen-level dietary variables with rumination time.

Variable	Correlation	95% CI	P-value
19 mm, %	0.14	-0.9-0.35	0.25
8 mm, %	-0.43	-0.60- -0.22	<0.001
4 mm, %	-0.28	-0.48- -0.06	0.01
Pan, %	0.40	0.19-0.58	<0.001
19 mm CV	0.07	-0.16-0.29	0.60
8 mm CV	0.07	0.00-0.43	0.05
4 mm CV	0.11	-0.12-0.33	0.33
Pan CV	0.13	-0.10-0.35	0.27
Feed bunk temperature	0.03	-0.20-0.26	0.79

Table 5. Correlations of daily pen-level dietary variables with rumination variance.

Variable	Correlation	95% CI	P-value
19 mm, %	-0.41	-0.59- -0.19	<0.001
8 mm, %	0.37	0.14-0.55	<0.001
4 mm, %	0.04	-0.19-0.27	0.76
Pan, %	0.23	0.00-0.44	0.06
19 mm CV	0.06	-0.18-0.28	0.65
8 mm CV	-0.08	-0.31-0.16	0.54
4 mm CV	0.10	-0.14-0.32	0.44
Pan CV	0.12	-0.11-0.35	0.31
Feed bunk temperature	0.30	0.06-0.51	0.01

We did not observe significant correlations between environmental variables with rumination time (Table 6). There was a low-to-moderate positive correlation between rumination variance and THI. As THI increased, rumination variance also increased (Table 7).

Table 6. Correlations of daily pen-level environmental variables with rumination time.

Variable	Correlation	95% CI	P-value
Average temperature	0.07	-0.02-0.17	0.17
Minimum temperature	0.06	-0.03-0.15	0.22
Maximum temperature	0.09	-0.01-0.19	0.07
THI	0.06	-0.03-0.16	0.21
Minimum THI	0.04	-0.06-0.14	0.44
Maximum THI	0.08	-0.01-0.17	0.10

Table 7. Correlations of daily pen-level environmental variables with rumination variance.

Variable	Correlation	95% CI	P-value
Average temperature	0.29	0.20-0.38	<0.001
Minimum temperature	0.27	0.18-0.36	<0.001
Maximum temperature	0.28	0.19-0.37	<0.001
THI	0.31	0.22-0.40	<0.001
Minimum THI	0.30	0.20-0.38	<0.001
Maximum THI	0.31	0.22-0.40	<0.001

Conclusions

We observed that diet particle size may be associated with rumination time, but it was not as we expected. The 8 mm screen of the PSPS was negatively associated with rumination time (r = -0.43, P = <0.001) and positively associated with rumination variance (r = 0.37, P = <0.001). We suspect that this may be related to how our cows were grouped and the diets they were fed. Pen 4 was fed a high amount of baleage in their ration, which reduced the proportion of feed retained on the 8 mm sieve and increased the proportion on the 19 mm sieve, while also being the pen with the highest rumination time and rumination variance.

Perhaps our most intriguing finding was that the environmental factors had negligible correlations with rumination time. However, we observed that as THI and temperature increased, rumination variance also increased. Perhaps, rumination variance may be used as an indicator of heat stress on dairy farms.

Rumination time and variance are associated with milk production, and this can be helpful to look at while managing your herd. Increased rumination variance may suggest that something is wrong with the cows and that you may be leaving milk on the table. While rumination time and variance are useful data points, they should not be used or viewed in isolation. They should be used within the constellation of other data points at your disposal to determine what may be going on with a group of cows.

Works Cited

Antanaitis, R., et al. The impacts of heat stress on rumination, drinking, and locomotory behavior, as registered by innovative technologies, and acid-base balance in fresh multiparous dairy cows. Animals: An Open Access Journal from MDPI, U.S. National Library of Medicine, 13 Apr. 2024, pmc.ncbi.nlm.nih.gov/articles/PMC11047379/.

Grant, R.J. Chewing behavior of dairy cows: Practical perspectives on ..., www.appliedanimalscience.org/article/S2590-2865(23)00027-7/pdf. Accessed 11 Aug. 2025.

MacPherson, L. The benefits of rumination monitoring: Helping farmers in Scotland. FAS, 19 Dec. 2023, www.fas.scot/article/the-benefits-of-rumination-monitoring/.

 

Dr. Gustavo M. Schuenemann, Department of Veterinary Preventive Medicine, The Ohio State University

The NWS has been largely eradicated in the United States for over 50 years, but a current outbreak in Central America and Mexico is moving closer to the U.S. On August 18, 2025, the Secretary of Health and Human Services (HHS) issued the emergency declaration after determining that NWS “has a significant potential to affect national security or the health and security of United States citizens living abroad and that involves New World Screwworm”. This emergency declaration allows veterinarians to use certain treatments under extra-label use provisions to help control potential infestations.  

What is NWS?

It is a parasitic fly (Cochliomyia hominivorax) that lays eggs in and on open wounds and mucous membranes of warm-blooded animals. When larvae hatch (maggot), they burrow into the flesh of these animals and eat healthy living tissue, causing the myiasis. This infestation is seasonal in livestock, with outbreaks linked to warm conditions that favor the fly's life cycle and host availability, often peaking in the warmer spring and summer months and being suppressed by cold winters. NWS can infest all warm-blooded animals, including livestock, pets, wildlife, humans, and even birds.

What to Look for in my Animals?

Be alert for the following clinical signs that may indicate NWS infestation:

• Unusual or irritated behavior (restlessness, excessive movement)
• Isolation from the herd or flock (reduced interaction)
• Frequent head shaking or rubbing against objects
• Foul odor with strong smell of tissue decay
• Visible fly larvae (maggots) in wounds or natural body openings
• Bloody or pus-like discharge from wounds

Even small wounds (e.g., castration, dehorning, navel in newborns) can become infested. EARLY DETECTION AND TREATMENT ARE CRITICAL.

Source: New World Screwworm by USDA-APHIS

Here is the Photo Gallery. Warning: Graphic images!

NWS Life Cycle, click here.

NWS Information in English and Spanish.

What are the Treatment Options?

Treatment for NWS involves killing and removing larvae from wounds using topical parasiticides, wound dressings, and insecticides, with potential for systemic treatments. Currently, there are no animal drugs approved by the FDA to treat or prevent of NWS myiasis in animals. However, the emergency declaration by the Secretary of HHS allowed FDA to authorize the emergency use of certain animal drugs to treat NWS infestation. This requires a prescription from a licensed veterinarian due to the emergency use authorization and the need for extra-label use of these drugs under a valid Veterinary-Client-Patient Relationship (VCPR). Farmers and livestock owners should work closely with their herd veterinarian to access and use these emergency treatments. 

How to Prevent NWS?

The most effective way to protect the United States from NWS is to prevent its introduction. To help prevent NWS infestation in your animals, follow these key steps:

• Monitor animals closely for early signs of infestation.
• Promptly treat all wounds, including umbilical cords of newborn animals, using an approved insecticide or would dressing to prevent egg-laying by adult flies.
• Ensure returning pets from international travel are properly inspected for NWS before entering the U.S.
• Requirements for pet dogs returning from Foot and Mouth Disease (FMD) or Screwworm-affected countries.

When it comes to protecting your animals from preventable diseases like NWS, early detection and timely treatment are critical to protect your animals. After all, an ounce of prevention is worth a pound of cure!