2012 Penstate Nutrition Workshop Proceedings

Dairy CattleNutrition WorkshopContinuing education for feed industry professionals and nutritional consultants

2012 PROCEEDINGS

November 12-14, 2012Holiday Inn, Grantville, PA

Presented by the Penn State Extension Dairy Team

2012 Dairy Cattle Nutrition Workshop

Agenda

November 12, 2012

12:00 – 1:00 Registration for Feed Management session

1:00 – 5:00 Feed Management Certifi cation Workshop

November 13, 2012

7:00 – 8:00 Registration; coff ee and refreshments

8:00 – 12:15 Novus International Inc. Preconference Symposium

8:25 – 8:30 Introduction

8:30 – 9:15 Opportunities and Challenges to Achieving 50,000 Pounds of Milk with Components, Dr. Dale Bauman, Cornell University

9:15 – 10:00 Using Science to Improve Cow Comfort, Dr. Trevor DeVries, University of Guelph

10:00 – 10:30 Break

10:30 – 11:15 Identifying Bottlenecks and Collaborating on Solutions for Change, Mr. Jeff True, True Farms and Mr. Corwin Holtz, Holtz-Nelson Dairy Consultants

11:15 – 12:00 Unlocking the Bottlenecks to Performance, Dr. Gordie Jones, Central Sands Dairy LLC

12:00 – 12:15 Questions and Wrap Up

12:15 – 2:00 Lunch, Sponsored by Novus

12:15 – 1:30 ARPAS Northeast Chapter annual meeting

12:00 – 8:00 Exhibit hall open

2:00 – 3:15 Afternoon workshop - session 1

3:15 – 3:30 Break Sponsored by Novus

3:30 – 4:45 Afternoon workshop - session 2

5:00 ARPAS Exam off ered

5:00 – 7:00 Reception in exhibit area Sponsored by Alltech Inc.

7:15 – 9:30 Evening Session and Dinner Sponsored by Alltech Inc. (RSVP required)

Carbon Footprint Feeding Dairy Cows, Dr. Mike Hutjens, University of Illinois

Evidence Based Nutrition Meets the Alltech Dairy Advantage, Mr. Jay Johnston, Ritchie Feed and Seed

Mark your calendars!Future Dates for the Penn State Dairy Cattle Nutrition Workshop

November 12 - 13, 2013 November 12 - 13, 2014

Grantville, PA

November 14, 2012

6:30 – 7:45 Breakfast Sponsored by Multimin USA (RSVP required)

The Eff ect of Injectable Trace Mineral Supplementation on Postpartum Health, Reproduction, and Production Parameters in Holsteins, Dr. Rodrigo Bicalho, Cornell University

6:45 – 7:45 Registration; coff ee and refreshments

7:00 - 3:00 Exhibit hall open

8:00 – 8:45 Strategies for Management of Udder Health and Prevention of Clinical Mastitis, Dr. Ken Leslie, University of Guelph

8:45 – 9:30 Hot Topics in the World of Silages, Dr. Limin Kung, University of Delaware

9:30 – 10:15 Evaluating Starch Digestibility for Lactating Cows: An Integrated Approach, Mr. Pat Hoff man, University of Wisconsin-Madison

10:15 – 10:45 Break

10:45 – 11:45 Morning workshop - session 1

11:45 – 12:00 Break

12:00 - 1:00 Morning workshop - session 2

1:00 – 2:00 Lunch, Sponsored in part by RP Feed Components

2:00 – 3:00 Afternoon workshop session

2:00 ARPAS Exam off ered

Coff ee service and breaks sponsored by Prince Agri-Products, Varied Industries Corporation, and Virtus Nutrition

3:15 – 5:30 Post-conference Seminar Sponsored by RP Feed Components (RSVP required)

3:15 – 4:05 Managing Energy Metabolism in Transition Cows, Dr. Heather Dann, Miner Institute

4:05 - 4:55 Meeting the Amino Acid Needs of the Transition Cow, Dr. Tom Overton, Cornell University

4:55 - 5:30 Practical Ration Guidelines for Meeting the Needs of “Most” Transition Cows, Dr. Patrick French, RP Feed Components

2012 Dairy Cattle Nutrition Workshop

Contents of Proceedings

Opportunities and Challenges to Achieving 50,000 Pounds of Milk with Components

Dale E. Bauman, Cornell University .............................................................................................................................. page 1

Using Science to Improve Cow Comfort

Marina A. G. von Keyserlingk and Dan M. Weary, The University of British Columbia and Trevor J. DeVries, University of Guelph .............................................................................................................page 23

Identifying Bottlenecks and Collaborating on Solutions for Change

Jeff True, True Farms and Corwin Holtz, Holtz-Nelson Dairy Consultants ....................................................page 29

Unlocking the Bottlenecks to Performance

Gordie Jones, Central Sands Dairy LLC ......................................................................................................................page 35

Managing Udder Health and Mastitis with Lower SCC Limits

Ken Leslie, University of Guelph ..................................................................................................................................page 47

Hot Topics in the World of Silages

Limin Kung, Jr., Mateus C. Santos, Michelle C. Windle, and Jonathan M. Lim, University of Delaware ....................................................................................................................................................page 55

Evaluating Starch Digestibility for Lactating Dairy Cows: An Integrated Approach

P. C. Hoff man, R. D. Shaver, and D. R. Mertens, University of Wisconsin-Madison .....................................page 59

How Precision Dairy Technologies Can Change Your World

Jeff rey Bewley, University of Kentucky ......................................................................................................................page 65

Using Knowledge of Feeding Behavior to Maximize Ration Potential

Trevor J. DeVries, University of Guelph .....................................................................................................................page 75

Dairy Heifer Management is Changing Big Time

S. E. Nellis, K. A. Weigel, and P. C. Hoff man, University of Wisconsin-Madison ............................................page 81

Within Farm Variation in Nutrient Composition of Feeds

Bill Weiss, Dianne Shoemaker, Lucien McBeth, Peter Yoder, and Normand St-Pierre, The Ohio State University ..............................................................................................................................................page 89

Variation and Relationships of Nutrients within Feed Populations

P. S. Yoder, N. R. St-Pierre, and W. P. Weiss, The Ohio State University .............................................................page 99

Eff ect of Variation in Forage Quality on Milk Production and Intake

P. S. Yoder, N. R. St-Pierre, and W. P. Weiss, The Ohio State University .......................................................... page 103

Real World Recommendations for Minerals and Vitamins

W. P. Weiss, The Ohio State University ..................................................................................................................... page 107

Exhibitor Directory .................................................................................................................................................................... page 113

2012 Penn State Dairy Cattle Nutrition Workshop 1

Opportunities and Challenges to Achieving

50,000 Pounds of Milk with Components

Dale E. Bauman

Liberty Hyde Bailey Professor Emeritus

Cornell University

air

y

DDairy Products are included in Dietary Recommendations by Public Health Organizations

around the World

ori

es

& n

utr

ien

ts f

rom

da

% o

f c

alo

NHANES 2003-2006, ages 2 years and older

“meeting the needs of the present without compromising the ability of future

generationss too meett theirr ownn needs”

Sustainability

Sustainability

generations to meet their own needsUN World Commission on Environment and Development,

“Brundtland Report”, 1987.

EnvironmentalEconomic

Social

2 November 12-14 Grantville, PA

Productivity = Productive Efficiency

“milkk outputt perr resourcee inputs”

Productivity is Key

milk output per resource inputs

Productivity Continues to be theEngine of Growth in Agriculture



two thirds from selection and

Historic Gains in Milk Production

two-thirds from selection andgenetic improvement

one-third from nutrition and management-related aspects

Sound nutrition and management are essential for a dairy cow to fully express her genetic potential for milk production

Outline

• Basis for Animal Differences

Achieving High Production

• Limits to Productivity

• Benefits of Productivity

• Future Opportunities


Key Questions

Opportunities & Challenges

What has been the basis for the improvement in milk production?

What biological processes are related t th i t?to the improvement?

Cycle of Life


• CChemical and physical characteristics of

Digestion and Nutrient Absorption

p ythe diet have major effects

• WWhen type of diet and intake are the same, animal differences are minor

• GGenetic selection for milk yield has had very little effect on digestibility

Nutrient Use for Maintenance*Function % Basal Energy Expenditure

Service functionsKidney work 6 to 7yHeart work 9 to 11Respiration 6 to 7Nervous functions 10 to 15Liver functions 5 to 10

Total 36 to 50

Cell maintenanceProtein resynthesis 9 to 12Lipid resynthesis 2 to 4 Ion transport 30 to 40

Total 40 to 56

*Derivation of these estimates was discussed in detail by Baldwin (1968), Milligan (1971) and Baldwin and Smith (1974).


• Underr standardizedd conditionss

Efficiency of Nutrient Use/Maintenance

• Under standardized conditions, animal differences are minor.

• NNot affected by genetic selection.

• RRequirement essentially unchangedover time. Similar across mammals on metabolic body weight basis.

• Animalss differr veryy little inn efficiencyy off

Partial Efficiency of Nutrient Use/Productive Functions

• Animals differ very little in efficiency of nutrient use for milk synthesis

• BBiochemical pathways to make milk are essentially the same in all mammals

• NNo relationship between genetic merit and partial efficiency of nutrient use for milk synthesis


Amino acids ß OHBAcetateGlucose

Fatty acids

TGs ofchylomicrons

and VLDL

GlycerolGlucoseAmino acids ß OHBAcetate

LPL

Blood

Acetyl CoA

MalonylCoA

(Primer)

Glycerol 3Phosphate

Amino Acid Pool

Glucose 6Phosphate

Galactose

NEAA carbonprecursors (from TCA

cycle)ACC

MILK PROTEIN

MILK LACTOSE

MILK FAT

Fatty Acid Pool

TAGSynthesis FAS

9

Mammary Gland

Nutrient Partitioning

Major source of differences among dairy cows

• hhigh producing cows eat more and use nutrients for milk synthesis

• llow producing cows direct less nutrients for milk synthesis; if they do eat more it goes to body fat


Efficiency componentAmong-animal

variation

Sources of Variation

variation

Digestion and nutrient absorption Low

Nutrient use for maintenance and efficiency of use for productive functions

Low

Nutrient partitioning and intake High

Bauman et al., 1985

Outline



• Limits to Productivity




Key Questions


Is a higher production possible?

What are the limits to milk production?

A th b t hi iAre there concerns about achievinghigh production?

What are the limits to production?

al M

ilk/C

ow

An

nu

a

Year


1200

1400

ow)

Milk Protein and Fat Production

600

800

1000

1200

ProteinFat

rodu

ctio

n (lb

s/co

0

200

400

2010 US Average(milk/cow = 21,149 lbs)

2011 Koepke Herd(milk/cow = 31,563 lbs)

Ann

ual P

r

Ever-Green-View My 1326365d 72,170m 2,787F 2,142P

Thomas Kestell FamilyWaldo, WI


2500

3000

cow

)

Potential Yield for Milk Protein and Fat

1000

1500

2000

2500

ProteinFat

Prod

uctio

n (lb

s/c

0

500

2010 US Average(milk/cow = 21,149 lbs)

2011 Koepke Herd(milk/cow = 31,563 lbs)

Ever-Green-View 1326(milk/365d = 72,170 lbs)

Ann

ualP

Gillette E SmurfLifetime 478,167 lbs milk

(15 yr & 10 lactations)

Patenaude FamilyLa Ferme GilletteEmbrun, Ontario



SCC is Indication of Dairy Herd’s Mammary Health and Milk Quality

US data (2009-10; 16768 herds) compiled by H.D. Norman and J.R. Wright, USDA-ARS-AIPL (2010)

Relationship Between Stress, Production and Cow Health

Performance is the best indicator of a dairy cow’s welfare and well being

To achieve a sustained high production cows must be healthy animals under minimal stress

Increasing stress levels reduces production and increases health risks


Amish Dairy Farmer response to question “Does higher milk production cause your cows to be stressed?”

“The tightrope walker doesn’t fall off the rope because it’s too high; he

fallss becausee hee losess hiss

Daniel MillerDairy Extension Panelist

Shipshewana, IN

falls because he loses his balance.”

Outline



• Limits to Dairy Productivity




Key Questions


Why focus on productivity of dairy cows?

What is the value to dairy producers?What is the value to dairy producers?

Benefits in Productivity relate to Sustainability and the

“Dilution of Maintenance”


P d ti itt P d tii Effi i

Benefit: Resource Conservation

• Productivity = Productive Efficiency(milk output per resource input)

“Dilution of Maintenance” allows production of milk and dairy products

using fewer resources.

350

400

Lactation

Maintenanceent (

MJ

ME)

6.0 MJ ME/kgMilk

Dilution of Maintenance is Key“Reducing Nutrient Cost per Unit of Milk”

150

200

250

300

7.1 MJ ME/kg Milk

15.3 MJ ME/kg Milk's

Dai

ly E

nerg

y R

equi

rem

63%

80%

0

50

100

1944 2007 50,000 lbs

Milk

Year Goal

Aver

age

Cow

37% 20%69%

31%


Benefit: Environment

• EEnvironmental impact

The production of all food has an environmental impact

Ass productivityy increasess thee As productivity increases the environmental impact of milk

production is reduced

Carbon Footprint of a Gallon of Milk Has Been Reduced by 2/3 Since 1944

Source: Capper et al. (2009) J. Animal Sci. 87:2160


Environmental Impact of Milk Production Has Been Considerably Reduced Since 1944

2007 Production System• Average herd size: 155• TMR rations• 70% AI breeding• Antibiotics 1st available 1955• Hormones 1st available 1970s• Fertilizer 1st available 1946

1944 Production System• Average herd size: 6• Pasture based feed system• Natural service breeding• No antibiotics• No hormones• No mfg inorganic fertilizer

19441944

* Note: per unit of milk

Source: Capper et al. (2009) J. Animal Sci. 87:2160

Environmental Key: Reducing Carbon Footprint per Gallon of Milk

lent

Emis

sion

s (lb

/gal

)C

O2-

Equi

va

Year Goal


Outline



• Limits to Dairy Productivity



Opportunities and Challenges

The mammary gland is an

amazing biological factory


Challenge

Biology Is Not Magic!

Challenge is to understand the regulation of physiological processes and their role in animal productivity, well-being and disease prevention.

A ll thii U d t dii tt

Opportunity

Apply this Understanding to Optimize Nutrition and

Management so that Animal Well-beingg iss Preservedd Well being is Preserved

and the Cow Achieves her Genetic Potential.



Thank You!


INTRODUCTION

Poorly designed and managed facili es cause injuries and increase the risk of health problems including lameness and transi on cow disease, arguably two of the most seri-ous welfare challenges facing the dairy industry (see von Keyserlingk et al. 2009). Producers spend millions of dollars building housing systems for dairy ca le, with the aim of providing a comfortable environment for their animals—one that ensures adequate rest, protec on from clima c extremes, and free access to an appropriate, well-balanced diet. Despite these laudable aims, housing systems do not always func on well from the perspec ve of the cow. Poorly designed and maintained facili es can cause inju-ries, increase the risk of disease, and increase compe on among herd mates for access to feed and lying space.

This proceedings chapter will review empirical work fo-cused on the feeding, standing, and lying areas u lized by dairy ca le, and show how these can be be er designed and managed to improve cow comfort, prevent health problems, and improve produc vity. This work has gener-ally evaluated housing systems from the cow’s perspec ve by asking how the housing aff ects cow health, what hous-ing the cow prefers, and how the housing aff ects behav-ior. The aim of this research is to provide science-based solu ons to prevent problems and improve cow comfort.

BETTER LYING AREAS

The issue of cow comfort has received considerable interest within the dairy industry, with the bulk of research focused on the design of freestalls and the eff ect of stall design on stall occupancy and the me spent res ng. Objec ve and accurate records of lying me can be ascertained through use of inexpensive data loggers. To get a reasonable es- mate of lying me for a farm, we suggest monitoring a

minimum of 30 cows for at least 3 days (Ito et al., 2009). The research we describe below used data loggers and me-lapse video recordings to assess the eff ects of the

surface cows lie down upon and how the stall is confi gured.

Lying Surface

A growing body of research has demonstrated that the surface we provide for cows is one of the most important factors in designing a suitable lying area. Several research-ers have measured stall usage, when the animals have no choice between surfaces, to assess how diff erent bedding types aff ect behavior. For example, Haley et al. (2001) used a simple comparison between a space considered “high comfort” (a large box stall with ma resses) and a stall that represented “low comfort” (a e stall with concrete fl ooring). They measured many behaviors including lying, standing, and ea ng mes, the number of mes the cows stood up, and various leg posi ons during lying. Lying mes were 4 h longer and cows were more willing to stand up and change posi ons in the high-comfort housing. Cows also spent more me standing idle in the low-comfort stalls. Collec vely these studies tells us which behavioral measures are likely to change if a cow is uncomfortable, namely, me spent lying and standing, and the number of mes she is willing to stand up.

In some of our group’s fi rst work on cow comfort we found that cows on farms with ma resses (and li le bedding) have more severe hock lesions than do cows on farms that using deep-bedded stalls (Weary and Taszkun, 2000). Although similar results have now been found in other re-search (e.g. Wechsler et al., 2000), and most dairy profes-sionals are aware of the risks of poorly-bedded ma resses, too o en this surface con nues to be used.

Cows also clearly prefer lying surfaces with more bedding, and spend more me lying down in well-bedded stalls. In a more recent experiment we examined the eff ect of the amount of bedding on the me spent lying and standing by cows housed in free stalls (Tucker and Weary, 2004). Each stall was fi ed with a geotex le ma ress, and bedded with one of three levels of kiln-dried sawdust (0, 1, or 7.5 kg). Cows spent 1.5 h more me lying down in the heavily bedded stalls. In addi on, cows spent less me standing with only the front legs in the stall when the ma resses

Using Science to Improve Cow Comfort

Marina A. G. von Keyserlingk1, Dan M. Weary1, Trevor J. DeVries2

1Animal Welfare Program, The University of British Columbia,

2357 Main Mall, Vancouver, BC, V6T 1Z4, Canada2Department of Animal and Poultry Science, University of Guelph,

Kemptville Campus, 830 Prescott Street, Kemptville, ON, K0G 1J0, Canada


were heavily bedded. These changes in both standing and lying behavior indicate that cows are hesitant to lie down on poorly bedded ma resses.

These diff erences in stall comfort may also account for a second important health problem; cows housed on mat-tresses also have a higher incidence of severe lameness than those housed in deep-bedded sand stalls (Ito et al., 2010). The lying surface can also aff ect udder health, and many studies have now shown the advantages to cows of using sand or other inorganic bedding as a way of reduc-ing the growth of bacteria associated with environmental mas s (e.g. Zdanowicz et al., 2004).

Making the decision to provide a well-bedded surface is just the fi rst step in achieving a reasonable level of cow comfort—this surface must also be properly maintained. In a series of experiments we have documented how the sand level declines in stalls that are not maintained, and how this decline reduces stall use by cows (Drissler et al., 2005). Sand levels in deep-bedded stalls decrease over a 10-day period, with the deepest part at the center of the stall. Lying me by cows also declines as the stall emp es: every inch decline decreased lying me by about half an hour per day. Contact with concrete while lying down may explain lower lying mes in deep-bedded stalls with less sand, and this concrete also aff ects leg health. Lesions on the point of the hock are common in deep-bedded stalls (Mowbray et al., 2003), likely due to contact with the concrete curb when stalls are not well maintained. Cows also showed a strong preference for lying on dry bedding during the summer months and when forced to lie down on wet bedding showed a 5-h reduc on in lying me (Fregonesi et al., 2007).

Stall Confi guration

Most indoor housing provides more than just a lying surface for the cows. Typically the space is designed to encourage the cow to lie down in a specifi c loca on and to use the stall in such a way that feces and urine do not soil the stall. Unfortunately, most a empts to constrain how and where the cow lies down also reduce cow comfort as illustrated by the studies described below.

Although some excellent recommenda ons for stall di-mensions are now available, too o en new construc ons and renovated barns fail to provide appropriate space. We have conducted several experiments that show how stall size and confi gura on aff ect standing and lying mes. For example, in one study we tested the eff ect of stall width on cow behavior (Tucker et al., 2004), by providing cows

access to free stalls measuring 42, 46, or 50” between par ons. Cows spent an addi onal 42 min/day lying in the widest stalls, likely because they had less contact with the par ons in these larger stalls. Cows also spent more me standing with all four legs in the wider stalls, reducing

the me they spent standing par ally (i.e. perching) or fully on the concrete fl ooring available elsewhere in the barn.

STANDING AREA

According to popular thinking, when cows are not in the parlor they should be ea ng or lying down. Unfortunately, no one seems to have explained this to the cows. In a number of studies we have found that even when cows have access to well-designed stalls they spend only about half of the day lying down. Cows spend the other 12 h a day on their feet, and we need to take this into account in designing suitable housing.

In most barns the surface for standing outside of the stall is wet concrete—a known risk factor for hoof health (e.g. Borderas et al., 2004). Cows can use the stall as a refuge providing a dry, so er surface for standing. However, this increases the likelihood that cows will urinate and defecate in stall. The common response by barn designers has been to make the stalls more restric ve, forcing cows back into the concrete alley, and explaining in part why lameness is now the most prevalent and costly health problem for cows housed in freestall barns. With our current barn designs we are stuck with two bad choices: use restric ve stalls that keep the stall surface cleaner but force cows back onto the wet concrete or use more open designs and increase frequency of stall maintenance. Of these two op ons we favor the la er, but there may also be a third approach—improving the standing surface elsewhere in the barn. Both the height of the neck rail and its distance from the curb aff ect standing (Tucker et al., 2005); more restric ve neck-rail placements (lower and closer to the rear of the stall) prevent cows from standing fully in the stall, again increasing the me cows spend on concrete fl ooring elsewhere in the barn. Gait scores improve when neck rails are moved to a less restric ve posi on so that cows can stand with all four hooves in the stall and worsen when neck rails are more restric ve (Bernardi et al., 2009).

Keeping cows out of the stall obviously helps keep the stalls clean. We found that both the narrow free stalls and the more restric ve neck rail placements reduced the amount of fecal ma er that ended up in the stall (Tucker et al., 2005; Bernardi et al., 2009). Although dirty stalls are undesirable, readers should be aware that stall cleanliness alone is a poor measure of stall design. Free stalls that have


higher occupancy rates are most likely to contain feces. Thus well-used stalls require more stall maintenance, just like other equipment used on the farm.

Alterna ve standing surfaces may also help. A number of studies have shown that access to pasture improves hoof health, likely because under good grazing condi ons the pasture is a more comfortable and poten ally healthy surface to stand upon. For example, Hernandez-Mendo et al. (2007) showed that a rela vely brief period on pasture could help lame cows recover. Non-concrete surfaces can also provide be er trac on and be more comfortable for cows to walk upon. Cows will typically choose to walk upon a rubber surface and avoid concrete if the op on is available, and our research shows that cows slip less fre-quently and show improved gait when walking on rubber compared to concrete, a diff erence that is especially clear for lame cows (Flower et al., 2007).

Other work has shown that cows prefer to stand on so er surfaces, and moving the neck rail further from the curb reduces perching behavior and can reduce lameness cases. Bernardi et al. (2009) provided some of the fi rst experimental evidence that aspects of stall design can reduce the risk of lameness and hoof disease. This study assessed the eff ect of the posi on of the neck rail and found that over a 5 wk period, although we noted li le change in lying mes, gait scores improved for cows kept in pens without the neck rail compared to pens equipped with the neck barrier. However, these results also illustrate that some changes in design that result in improvements in hoof health come at the expense of cow hygiene and udder health. Although removing the neck rail comes at a hygiene cost (cows standing with all 4 feet in the stall will defecate and urinate more into the stall) there is no clear evidence that it increases the risk of mas s.

A high standing me could suggest a defi cit in the cow’s environment; for instance, cows housed in pens with insuf-fi cient number of lying stalls, low bedding, wet bedding, or restric ve neck rails spend more me standing than those with dry stalls and less restric ve neckrails (Tucker and Weary, 2004; Fregonesi et al., 2007, 2009). Cows that perch with their 2 front feet in the stall during transi on are also at increased risk for lameness (Proudfoot et al., 2010); as stated above, this behavior has been linked with restric ve stall design (Tucker et al., 2005; Fregonesi et al., 2009).

Providing cows the opportunity to avoid standing on wet concrete may be especially important during the transi on period. Increased standing me in the pre-partum period

is a key risk factor for hoof health problems later on in lacta on (Proudfoot et al., 2010).

BETTER FEEDING AREAS

There are several aspects of the feeding environment that aff ect the cow’s ability to access feed, including the amount of available feed bunk space per animal and the physical design of the feeding area. Reduced space availability increases compe on in ca le. For example, a recent study by DeVries et al. (2004) showed that dou-bling feed bunk space from 20” to 40” reduced by half the number of aggressive interac ons while feeding. This reduc on in aggressive behavior allowed cows to increase feeding ac vity by 24% at peak feeding mes, an eff ect that was strongest for subordinate animals.

In addi on to the amount of available feed bunk space, the physical design of the feeding area can also infl uence feeding behavior. One of the most obvious features of the feeding area is the physical barrier that separates the cow and the feed, and our research shows that some designs can reduce aggressive interac ons at the feed bunk. For example, Endres et al. (2005) compared the eff ects of a post-and-rail versus a headlock feed line barrier on the feeding and social behavior of dairy cows. Average daily feeding me (about 4.5 h day) did not diff er, but during periods of peak feeding ac vity (90 min a er fresh feed delivery) subordinate cows had lower feeding mes when using the post-and-rail barrier. This diff erence in feeding mes was likely due to posi ve eff ects of the headlock

barriers in reducing compe ve interac ons; there were also 21% fewer displacements at the feed bunk with the headlock barrier compared to the post-and-rail barrier. These eff ects are greatest for the subordinate cow, par cu-larly at high stocking densi es at the feedbunk. Figh ng for access to feed has also been shown to increase dra-ma cally when cows are temporally feed restricted (e.g. feeding to a slick bunk; Collings et al., 2011). Moreover, combining overstocking with a period of feed restric on may be especially problematic in terms of increasing compe on and reducing feeding behavior for lacta ng dairy cows. Adequate space and me to access feed is essen al to minimize feed bunk compe on in indoor group housing systems.

In further research on feed bunk compe on we retested the eff ects of diff erent feed bunk barriers, but did so over a range of stocking densi es (Huzzey et al., 2006). Cows were tested with the barriers described above but using stocking densi es of 0.81, 0.61, 0.41 and 0.21 m/cow (corresponding to 1.33, 1.00, 0.67 and 0.33 headlocks/cow). Daily feeding


mes were higher and the dura on of inac ve standing in the feeding area was lower when using a post-and-rail compared to a headlock feed barrier. As well, regardless of barrier type, feeding me decreased and inac ve standing increased as stocking density at the feed bunk increased. Providing adequate feed bunk space during the pre partum period is also essen al as work has shown that overstocking during this period reduces dry ma er intake (Proudfoot et al., 2009) and that cows that consume less are at higher risk for post partum disease (Huzzey et al., 2007).

Cows were displaced more o en from the feeding area when the stocking density was increased, and this eff ect was greater for cows using the post-and-rail feed barrier. Again we found that this eff ect was greatest for subordi-nate cows, par cularly at high stocking densi es. Clearly, overstocking the feed bunk decreases me spent at the feed bunk and increases compe on, resul ng in poor feed access. We have recently found very similar eff ects (less usage and more compe on) when lying stalls are overstocked (Fregonesi et al., 2007). Moreover, when cows are overstocked at the stalls we have observed that cows on average le the feed bunk 30 min earlier when stocked at 150% compared to when they were stocked at 100% (Fregonesi et al., 2007).

Other research has shown that providing addi onal par - ons (“feed stalls”) between adjacent cows provides ad-

di onal protec on while feeding and allows for improved access to feed (DeVries and von Keyserlingk, 2006). Pro-viding a feed stall resulted in less aggression and fewer compe ve displacements, eff ects that were again great-est for subordinate cows. This reduced aggression allowed cows to increase daily feeding me and reduced the me they spent standing in the feeding area while not feeding.

Thus, the provision of more bunk space, especially when combined with feed stalls, improves access to feed and reduces compe on at the feed bunk, and this eff ect is strongest for subordinate cows. These changes in feed bunk design and management could help reduce the between-cow varia on in the composi on of ra on con-sumed; under conven onal systems subordinate cows can only access the bunk a er dominant cows have sorted the feed (DeVries et al., 2005). The use of a barrier that provides some physical separa on between adjacent cows can reduce compe on at the feed bunk. A less aggressive environment at the feed bunk may also have longer-term health benefi ts; cows engaged in aggressive interac ons at the feed bunk are likely at higher risk for hoof health problems (Leonard et al., 1998).

BARN LAYOUT

Cow comfort may also be aff ected by overall layout of the barn. For example, some work has shown that cows rarely use certain stalls in a pen, while seemingly iden cal stalls are occupied more than 80% of the available me. One study (Gaworski et al., 2003) showed that stalls in the row closest to the feed alley were occupied 41% more frequently than were stalls in more distant rows. In addi- on, stalls located within the center of each row were used

12% more o en than those stalls located on the periphery of the row (i.e. either near a wall or fence). Natzke et al. (1982) also found that stalls on the periphery were used less than stalls in the interior of the row. These results suggest that certain stalls, par cularly those farther from the feed bunk and on the periphery, are less desirable to dairy ca le perhaps because cows need to walk farther, or because they have to navigate past certain physical (e.g. narrow alleys) or social (e.g. dominant cows) obstacles on their way to the more distant stalls. Indeed, earlier work has indicated that the movements of subordinate animals are prevented by the loca on of dominant cows (Miller and Wood-Gush, 1991). Such factors may partly explain reduced user sa sfac on and lower produc on in those barn designs consis ng of more rows (e.g. 6 and 4 row verses 2 and 3 row barns; Bewley et al., 2001).

We also strongly encourage producers to evaluate their facili es on an individual resource basis—the lying, feed-ing, and standing areas. For example, large diff erences in usage can occur even among iden cally confi gured stalls within the same barn. The fact that stalls within a pen vary in their popularity suggests that stall availability from the cows’ perspec ve is not the same as from the producer’s perspec ve. What looks to us as 1:1 cow-to-stall stocking density may seem considerably worse to the cows if some stalls are unacceptable. Another example is providing ad-equate feed bunk space on a per cow basis; for example, in a 6-row barn the amount of feed bunk space per cow is o en far less than that recommended. A number of lines of evidence now suggest that providing adequate feed bunk space is essen al to maintain dry ma er intake and reduced feed bunk space can have profound eff ects on rates of illness, par cularly during the transi on period (Huzzey et al., 2007; Goldhawk et al., 2009).

CONCLUSIONS

Scien fi c assessments have allowed us to determine which lying, standing, and feeding environments are preferred by dairy ca le, reduce the risk of health problems, and improve comfort. Cows like so er surfaces, for both lying down and for standing upon. Deep-bedded stalls work


well for cow comfort, but require maintenance. When it comes to the physical structures used to build freestalls, less is more—the hardware we place in the stall is for our benefi t and not the cows. The more restric ve we design stalls the less a rac ve they become for the cow. Use of restric ve stall designs can help keep stalls clean, but to avoid problems with hoof health these designs need to be accompanied by be er fl ooring op ons, such as so er and drier fl ooring. The design and management of the feeding area is also important. High stocking densi es at the feed bunk increase aggressive compe on and keep subordi-nate cows away from feed. Physical barriers between cows, including head locks and feed stalls, can help reduce this compe on and increase feed access.

ACKNOWLEDGMENTS

This paper is based upon an earlier proceedings paper en tled: von Keyserlingk, M. A. G. and D. M. Weary. 2008. Behaviour of Free Stall Housed Dairy Cows. Proceedings of the High Plains Nutri on Conference. pp. 7-16.

We gratefully acknowledge our students and our many collaborators in this research. The University of Bri sh Co-lumbia’s Animal Welfare Program is supported by Canada’s Natural Sciences and Engineering Research Council Indus-trial Research Chair Program with industry contribu ons from the Dairy Farmers of Canada, Westgen Endowment Fund, Pfi zer Animal Health, BC Ca le Industry Development Fund, BC Milk Producers, BC Dairy Founda on, BC Dairy Educa on and Research Associa on, and Alberta Milk.

REFERENCES

Bernardi, F., J. Fregonosi, D. M. Veira, C. Winkler, M. A. G. von Keyser-lingk, and D. M. Weary. 2009. The stall design paradox: neck rails increase lameness but improve udder and stall hygiene. J. Dairy Sci. 92:3074-3080.

Bewley, J., R. W. Palmer, and D. B. Jackson-Smith. 2001. A comparison of free-stall barns used by modernized Wisconsin dairies. J. Dairy Sci. 84:528-541.

Borderas T. F., B. Pawluczuk, A. M. de Passillé, and J. Rushen. 2004. Claw hardness of dairy cows: rela onship to water content and claw lesions. J. Dairy Sci. 87:2085-2093

Collings, L. M. K., D. M. Weary, N. Chapinal, and M. A. G. von Keyserlingk. 2011. Temporal feed restric on and overstocking increase compe - on for feed by dairy ca le. J. Dairy Sci. 94:5480–5486

DeVries, T. J., M. A. G. von Keyserlingk, and D. M. Weary. 2004. Eff ect of feeding space on the inter-cow distance, aggression, and feed-ing behavior of free-stall housed lacta ng dairy cows. J. Dairy Sci. 87:1432-1438.

DeVries, T. J., M. A. G. von Keyserlingk, and K. A. Beauchemin. 2005. Frequency of feed delivery aff ects the behavior of lacta ng dairy cows. J. Dairy Sci. 88:3553-3562.

DeVries, T. J., and M. A. G. von Keyserlingk. 2006. Feed stalls aff ect the social and feeding behavior of lacta ng dairy cows. J. Dairy Sci. 89:3522-3531.

Drissler M., M. Gaworski, C. B. Tucker, and D. M. Weary. 2005. Freestall maintenance: Eff ects on lying behavior of dairy ca le. J. Dairy Sci. 88:2381-2387.

Endres, M. I., T. J. DeVries, M. A. G. von Keyserlingk, and D. M. Weary. 2005. Eff ect of feed barrier design on the behavior of loose-housed lacta ng dairy cows. J. Dairy Sci. 88:2377-2380.

Flower, F. C., A. M. de Passillé, D. M. Weary, D. J. Sanderson, and J. Rushen. 2007. So er, higher-fric on fl ooring improves gait of cows with and without sole ulcers J. Dairy Sci. 90:1235-1242.

Fregonesi, J. A., D. M. Veira, M. A. G. von Keyserlingk, and D. M. Weary. 2007. Eff ects of bedding quality on lying behavior of dairy cows. J. Dairy Sci. 90:5468-5472.

Fregonesi, J. A., M. A. G von Keyserlingk, D. M. Veira, and D. M. Weary. 2009. Cow preference and usage of free stalls versus an open lying area. J. Dairy Sci. 92:5497-5502.

Gaworski, M. A., C. B. Tucker, and D. M. Weary. 2003. Eff ects of two free-stall designs on dairy ca le behavior. Pages 139-146 in Proc. of the Fi h Intl. Dairy Housing Conf., American Society of Agricultural Engineers, St. Joseph, MI.

Goldhawk, C., N. Chapinal, D. M. Veira, D. M. Weary, and M. A. G. von Keyserlingk. 2009. Prepartum feeding behavior is an early indicator of subclinical ketosis. J. Dairy Sci. 92:4971-4977

Haley, D. B., A. M. de Passille, and J. Rushen. 2001. Assessing cow comfort: Eff ects of two fl oor types and two e stall designs on the behaviour of lacta ng dairy cows. Appl. Anim. Behav. Sci. 71:105–117.

Hernandez-Mendo, O., M. A. G. von Keyserlingk, D. M. Veira, and D. M. Weary. 2007. Eff ects of pasture on lameness in dairy cows. J. Dairy Sci. 90:1209-1214.

Huzzey, J. M., T. J. DeVries, P. Valois, and M. A. G. von Keyserlingk. 2006. Stocking density and feed barrier design aff ect the feeding and social behavior of dairy ca le. J. Dairy Sci. 89:126-133.

Huzzey, J. M., D. M. Veira, D. M. Weary, and M. A. G. von Keyserlingk. 2007. Behavior and intake measures can iden fy cows at risk for metri s. J. Dairy Sci. 90:3320-3233.

Ito, K., D. M. Weary, and M. A. G. von Keyserlingk. 2009. Lying behavior: Assessing within- and between-herd varia on in free-stall housed dairy cows. J. Dairy Sci. 92:4412-4420.

Ito, K., M. A. G. von Keyserlingk, S. J. LeBlanc, and D. M. Weary. 2010. Lying behavior as an indicator of lameness in dairy cows. J. Dairy Sci. 93:3553-3560.

Leonard, F. C., I. S enezen, and K. J. O’Farrell. 1998. Overcrowding at the feeding area and eff ects on behavior and claw health in Friesian heifers. Pages 40-41 in Proc. 10th Intl. Symp. Lameness in Ruminants, Lucerne, Switzerland.

Miller K., and D. G. M. Wood-Gush. 1991. Some eff ects of housing on the social-behavior of dairy-cows. Anim. Prod. 53:271-278.

Mowbray, L., T. Vi e, and D. M. Weary. 2003. Hock lesions and free-stall design: Eff ects of stall surface. Pages 288-295 in Proc. of the Fi h Intl. Dairy Housing Conf. ASAE, St. Joseph, MI.

Natzke, R. P., D. R. Bray, and R. W. Evere . 1982. Cow preference for free stall surface material rubber mats, carpe ng, and a layered mat. J. Dairy Sci. 25:146-153.


Proudfoot, K. L., D. M. Veira, D. M. Weary, and M. A. G. von Keyserlingk. 2009. Compe on at the feed bunk during transi on changes the feeding, standing and social behavior of Holstein dairy cows. J. Dairy Sci. 92:3116-3123.

Proudfoot, K. L., D. M. Weary, and M. A. G. von Keyserlingk. 2010. Behavior during transi on diff ers for cows diagnosed with claw horn lesions in midlacta on. J. Dairy Sci. 93:3970-3978.

Tucker, C. B., and D. M. Weary. 2004. Bedding on geotex le ma resses: How much is needed to improve cow comfort? J. Dairy Sci. 87:2889-2895.

Tucker, C. B., D. M. Weary, and D. Fraser. 2004. Freestall dimensions: Eff ects of preferences and stall usage. J. Dairy Sci. 87:1208-1216.

Tucker, C. B., D. M. Weary, and D. Fraser. 2005. Neck-rail placement: Eff ect on freestall preference, usage, and cleanliness. J. Dairy Sci. 88:2730-2737.

von Keyserlingk, M. A. G., J. Rushen, A. M. B. de Passillé, and D.M. Weary. 2009. Invited review: The welfare of dairy ca le – Key concepts and the role of science. J. Dairy Sci. 92:4101-4111.

Weary, D. M., and I. Taszkun. 2000. Hock lesions and free-stall design. J. Dairy Sci. 83:697-702.

Wechsler, B., J. Schaub, K. Friedli, and R. Hauser. 2000. Behaviour and leg injuries in dairy cows kept in cubicle systems with straw bedding or so lying mats. Appl. Anim. Behav. Sci. 69:189-197.

Zdanowicz, M., J. A. Shelford, C. B. Tucker, D. M. Weary, and M. A. G. von Keyserlingk. 2004. Sand and sawdust bedding aff ect bacterial popula ons on teat ends of dairy cows housed in freestalls. J. Dairy Sci. 87:1694-1701.


Identifying Bottlenecks and Collaborating on Solutions for Change

Jeff True, True Farms

and Corwin Holtz, Holt-Nelson Dairy Consultants

Jeff, Glenn and Brian TruePerry, NY

Corwin Holtz – Nutrition and Management consultant

TRUE FARMS, INC.

September 2010 – scheduled to participate in NOVUS COWS assessment

Discussed with Jeff True that herd needed change in cow comfort after seeingmultiple sand herds over a 3 day period

Challenges – production level, foot and leg health, injured cows, involuntary culls and too many deaths from facility (flooring, stuck in stalls, etc.) issues.

Scheduled to be off of BST in another 6 months

Jeff, Glenn and Brian TruePerry, NY

Corwin Holtz – Nutrition and Management consultant

TRUE FARMS, INC.

History of “cow comfort” at True FarmsCell filled mattresses up to 2005 - 2006Waterbeds installed 2006 – 2007Rubber flooring at feed alleys

Rubber strips for travel lanes 2009Deep bedded sand at satellite farm - 2007Shallow bedded sand at home farm 2011-2012

10/18/10Test date 10/13/10

TRUE FARMS, INC.TRUE FARMS, INC.

SUM ... FOR MILK>0 BY PEN

By PEN Pct Count AvDIMTD Av LACT Av MILK AvPMILK AvMKDEV AvLS

------ ---- ------ ------- ------- ------- ------- ------- -------

Waterbeds (home farm)2-14 514 159 2.3 76 83 -2.2 3.4

Sand (satellite farm)

31-34 313 240 1.7 74 78 +3.4 2.2


By PEN Pct Count AvDIMTD Av LACT Av MILK AvPMILK AvMKDEV AvLS

------ ---- ------ ------- ------- ------- ------- ------- -------

Waterbeds2-14 514 159 2.3 76 83 -2.2 3.4

Sand

31-34 313 240 1.7 74 78 +3.4 2.2

SAME TMR BOTH HERDS!!!!!

TRUE FARMS, INC.Why do home farm cows “suffer”?Production and health expectations are at 2010 levels but cows are subjected to a 1990 facility that has additional stressors

Older population (not as mobile to begin with)Older population (not as mobile to begin with)Much longer walking distance to and from parlor 3x/dayInclines they deal with are another foot/leg stressorBasic observation shows lay down time is questionable (they aren’t getting off their feet!)


TRUE FARMS, INC.

The issues and challenge:“Stuck” at 250-350 SCCKnew that travel distance to and from p l (900 ’ 3x/d ) s l s in tparlor (900+ 3x/day) was always going to be an issueAlways fought the classic Sept/Oct lameness issues coming off of summer heat

TRUE FARMS, INC.

The issues and challenge:Bedding availability options difficult to obtain and continuing to increase in priceC ld mf t b imp d t minim lCould cow comfort be improved at minimal $ investment?Was sand bedding a viable option?Could the “manure” people accept sand?

TRUE FARMS, INC.

True Farms part of 40+ herd study in NY/PAPen 2

40 cows w/ankle bracelets over 72 hours to40 cows w/ankle bracelets over 72 hours to measure actual laydown timeAll cows in Pen 2 evaluated for Gait Score (i.e. lameness) on 9/7 as they returned from parlor. Scored while traveling on rubber in lower barn (between pens 5-4).

TRUE FARMS, INC.

Recent data collection – Univ. of British Columbia and Novus

UBC Gait Scoring Systemg y

Gait Score Description1 Sound2 Imperfect3 Lame4 Moderately Lame5 Severely Lame

TRUE FARMS, INC.

Pen 2All cows (109) in Pen 2 evaluated for Gait Score (i.e. lameness) on 9/7 as they returned from parlor. Scored while traveling on rubber p gin lower barn (between pens 5-4).


TRUE FARMS, INC.

Pen 2All cows (109) in Pen 2 evaluated for Gait Score (i.e. lameness) on 9/7 as they returned from parlor. Scored while traveling on rubber in lower barn (between pens 5-4).barn (between pens 5 4).

True Farms results 73% of Pen 2 scored 3 or greater8% of Pen 2 scored 4 or greater6% severe hock damage40% swollen front knees

TRUE FARMS, INC.

Knew that feet and legs were a problem but had begun to “accept” degree of lameness as “normal”NOVUS data opened our eyes when compared to other herds in NE and other parts of U.S.w/o NOVUS study to back-up internal herd data would probably still be talking versus taking action!Visited other herds that had moved to sand –solidified that it could be done


Perching was a constant observation So – what action did True Farm

take?

TRUE FARMS, INC.

Landscape Timber

7” x 5/8” Thunder bolts

4 – 6” of sand depth from rear to front of stallsfront of stalls



TRUE FARMS, INC.

What was the investmentShallow bedding stall changesPumps to handle daily sand removal from pits in barnin barn

~$20,000

TRUE FARMS, INC.

What has change meant to True FarmsStall changes in March 2011

Sept 2010 June 2011Laydown 10:18 10:52Laydown 10:18 10:52Lame 73% 32%Severe Lame 8% 2%Inj Hocks 90% 8%Sev Inj Hocks 5% 0%SCC 320K 240K


By PEN Count AvDIMTD AvLACT AvMILK AvMKDEV AvLGSCC

--------- ---- ------ ------- ------- ------- ------- ------- -------2 123 155 3.0 99 4.4 2.13 121 114 1.9 94 5.1 1.6 4 62 152 3.3 93 2.9 2.2 5 104 184 3 5 90 6 8 3 4

August 2012 test day

5 104 184 3.5 90 6.8 3.414 96 111 1.2 80 3.0 1.8

31 78 211 1.9 79 2.3 1.2 32 83 220 1.8 78 2.3 1.4 33 79 209 1.8 79 4.6 1.6 34 80 197 1.9 80 1.0 1.5

TRUE FARMS, INC.

What has change meant to True FarmsShipping more milk/cow than when on BST60 D cull rate from 9-12% to 5-7%Voluntary vs Non-voluntary cullingVoluntary vs. Non-voluntary culling

Past 12 mo. Sold 34 lactating cows for dairySold 188 calves and heifers for dairyInternal herd size has continued to grow!

Time away from pen – educating laborMilking and barn crew inputNeck rail placement

TRUE FARMS, INC.

What has change meant to True FarmsInjuries (slipping, down cows, etc.) has decreased dramatically!Foot health – as measured by hoof trimmingFoot health as measured by hoof trimming records and hoof trimmer observations and comments has improved dramatically!BOTTOM LINE – A DIFFERENT HERD OF COWS!

THANK YOU

Questions and Discussion?


One of the very fi rst things you must know about me is my reverence for the dairy cow. She is the reason we have civi-liza on; she truly is the foster-mother of the human race. Without her we do not have civiliza on, she gave us milk, cheese, other dairy foods, and her meat. She has protected us from smallpox, the most deadly disease known to the human race. Her fat in the form of CLA (conjugated linoleic acid) protects us from cancer. So all of us owe the dairy cow a great debt, and I have devoted my dairy veterinary career to taking care of her and making her comfortable.

LESSONS LEARNED FROM THE PAST 20+ YEARS

OF TROUBLE SHOOTING DAIRY FARMS

Dairy farms of all sizes have bo lenecks and these bo le-necks keep farms from achieving their goals. No ma er the size of the dairy farm, there are bo lenecks that keep it from performing at the dairy farm’s poten al. When you release a bo leneck, performance can increase un l the next bo leneck is reached. As dairy farms across the world increase in size from the 40 to 100 cow size to the 1,000+ cow size, they can become overwhelming to everyone. They become diffi cult for some people to understand. One simple thought to help dairymen, veterinarians, and nutri onists understand all dairy farms is to break them down into circles. If we understand the circles of dairying we can look at any size dairy farm and fi nd the bo leneck without being overwhelmed.

There are three circles on every dairy farm that we need to understand, and if you completely understand them, most bo lenecks become apparent. The fi rst circle is the 24-hour circle, or what does a cow do for 24 hours a day. Things like how o en is she milked, how long does she spend in the parlor and holding area, how long is she locked up to be found in heat? When does her feed arrive and how long is the manger empty? Does the dairy farm feed to a clean bunk or do they feed to an extra “weigh back” feed that is used somewhere else? All of these ques ons are easy to answer when we know the 24-hour circle of a herd or pen of cows. Just keep asking yourself what does 24 hours look

like in the life of a milking cow, and also what does 24 hours look like in the life of the dry cows and heifers.

The second circle starts at the maternity pen, and it is what does a year look like in the life of a cow? Another way to ask about the circle is how does the just freshening cow get back to the fresh pen a year later? The ques ons about the year circle might look like these: where does she freshen, where is she moved into the fresh pen, how long is she in the fresh pen, when is she moved into the breeding pens, when does breeding start, when does breeding quit, how many ra ons does she get fed? More ques ons would include when is she dried off , how long is she dry, how many dry cow ra ons is she fed, what are the ra ons? And how is labor detected, when is she moved to be by herself to calve?

The third circle also starts at the maternity pen, and the circle belongs to the calf. The circle is what does 2 years look like in a calf’s life? Ques ons include: when is she fed colostrum, how much colostrum is she fed, where is she housed and fed un l weaning, how many calves are together in the weaning pens? What is she fed, when is grain introduced, how many heifer ra ons is she fed, where is she housed un l breeding age? More ques ons include when is she bred, is she bred by size or age or both? When does she move into the close up pens, how is she handled at calving for the fi rst me?

Think about those three circles on all size dairy farms, and if you really understand your circles, or circles on the farms you work with, you can start to iden fy the bo lenecks to the performance you are trying to achieve and correct them. You can see that any size dairy farm is now much easer to understand and you will not be overwhelmed by large dairy farms.

Unlocking the Bottlenecks to Performance

Dr. Gordie Jones, DVM

Central Sands Dairy, LLC

N15927 Cty G

Nekoosa, WI 54457

[email protected]


Achieving Excellence in Dairying

Dr. Gordie JonesPartner

Central Sands Dairy, LLC.

Location: Wisconsin Golden Sands Area

Trends in the North American Dairy Industry over the last 25 years can be best described by the term:

CHANGECHANGE

PM Fricke, Ph.D.

“Pleistocene Mega fauna”

–Born during the last Ice Ageg g

The First Farmers• Were in Mesopotamia• Modern day Iraq • Large headed grains• Large headed grains• Wheat, Barley, Triticale• A stick in the sand• A little water and we were farmers!

The First Farmers• Our First fences• Were to keep the wild cows out!!• She opened the gate• She opened the gate• And we now had a cow!


Only 11 species were able to be domesticated.

• Our Cow is the star!• She Provided POWER, Protein, & Fertilizer• She truly is the foundation of civilization• She truly is the foundation of civilization.• The foster mother of the human race• All of the domesticated animals are “herd”

species - looking for a leader• Except the Cat!!

Covenant;

To care for, and keep

The Star of theof the show!

Guns, Germs & Steel

Jarred Diamond

Vaccine• First vaccine….• Small Pox• Cow Pox• Cow Pox• Latin Vacca = Cow• Vaccinate

Cow-inate!!!!


Dry CowsDry Cows NutritionNutrition Cow ComfortCow Comfort Y t kY t k

Milk Quality Milk Quality

ReproductionReproduction

Dry CowsDry Cows NutritionNutrition Cow ComfortCow Comfort YoungstockYoungstock

Sick CowsSick Cows

Your InterestYour Interest

Central Sands Dairy• 3,200 milk cows, 600 dry cows • 4 row freestall barns • 64 - 70 # /cow/day

72 C R• 72 Cow Rotary• Calve 300+ cows per month • 6 row dry cow barn• Methane digester• Sand bedding & 125-150,000 SCC

Rules that still apply• Cow Comfort is first • Forage is king • And Better Forage is betterF g• Preg rates means you keep cows• Dry Cow program stops early fresh cow losses• Milk Quality is EVERYTHING !!!

Rules that still apply• Nutrition • Dry Cow program • Cow Comfortmf• Reproduction• People get everything done above! BOTTLECKS


WATER IN A PIPE: Problems

IN OUT

Bottlenecks• Rate limiting problem

• Interferes with achieving Dairyman’s goal

• Any improvement pays off in better output

• Before it is completely “fixed”, something else will become the bottleneck

Improving the Dairy • Survey the status and performance trends of the

dairy farm’s performance

• Compare to benchmarks of industry performance and personal goals

• Identify bottlenecks!!

• Open them up

• Repeat the process

Books on “Dairy Management”

• “The Goal” by Goldratt

• “The E myth” Revisited by “Gerber”

Youngstock

Fresh Cow / Dry Cow

Milk Production

Cow Comfort

Ration

Feed Bunk ManagementReproduction

Milk Quality


3 things a Cows Should Do!

• Stand to EAT & DRINK

• Stand to MILK

• LAY DOWN

DrinkingDrinking

EatingEatingLayingLaying

“Cows Don’t Normally Just Stand Around”

Central Sands Dairy

“Concentric Consistency”

24 Hours

What does a day look like?• Times milked

• Time in parlor

h h f d• When is she fed

• How long is she locked up

• Etc.

Maternity

PenPen


1 Year

What does a year look like?• Group changes• Rations • When bred• When bred• Pen moves• How long is she dry• Etc.

2 Years2 Years2 Years

What does two years look like?• Maternity pen• Hutches• Weaning groupg g p• Rations• Group changes• When bred• Etc.

24 Hours24 Hours 1 Year1 Year

2 Years2 Years


Value of SOP’s

• Dairy personal are certified in their position!

• Every person knows and understands their job!

• Every person that is certified, has passed an exam (oral or written) about their job. of Cow Comfort

A B C’sAir quality & ventilationBunk management

* R ti f l t d i d & d* Ration formulated, mixed & consumed* Bunk design & space* Feed quality

Cow comfort

Management, Environment & Herd Performance

(20.5-34L)

(29 vs 27.5L)(29 vs 25L)

Relationship Between Resting and Milk YieldMiner Institute database

(1.7L)

Freestalls4 Reasons Freestalls Fail

Lack of CushionNeck Rail PlacementNeck Rail PlacementLunge Space LimitationsLack of Fresh Air / Vision


Neckrails• As critical for cows entering stall, as for cows leaving the

stalls• Low neck rails contact cows during entry at the withers• Optimal 44-46” (116cm) off of rear curbOptimal 44 46 (116cm) off of rear curb• Do not need to be moveable• 66” (168cm) from rear of useable curb

168cm

116cm

Bedding• Absorbs Moisture• Provides Cushion• Prevents Friction• Prevents Friction

Bedding Types

• Sand• MattressMattress• Dried manure• Combinations

Sand Bedding• Inorganic--reduces bacterial growth.• 40-70 lbs./Stall/day usage rates.• Slope 1-2 % to flush effectively.• Sand trap to catch sand 0 5% slope for 50ft• Sand trap to catch sand 0.5% slope for 50ft.• Types of sand.• Pitch to curb 1/4” per foot (.6cm/30cm).

Brisket locator properly bedded

8-10” 2“ max (50 mm)66” (168cm)

Rear curb

8 10(25cm)

45-60 degrees

Bedding must be maintained level with the curb for the curb width to be “useable”


Brisket locator poorly bedded

R b8-10”

58” (148cm)

45-60 degrees

Once bedding drops below the curb, effective bed length becomes 8-10 inches shorter which is unacceptable to the cow.

Rear curb

Wide stalls• Why are we making them wider?

• Cows lay diagonally in the stalls

• If a 28” loop is used with forward lunge width is• If a 28 loop is used with forward lunge, width is not so important.

• Next slide is a small Jersey springer in a 46” wide stall that is too wide for her, but because of the loop she is lying parallel to the dividers

68” (172cm) for Cows

2”

46-48”

68”

Nose to Nose• Why are we making them longer?• Some University recommendations are for16-18’ apart.• Just raises cost of building without increasing milk.• Feeding cows do NOT like to be closer than 3 meters• Feeding cows do NOT like to be closer than 3 meters• Lying cows not so much !!

Feed Alley Alley

1 2 3 4


Inches That Are Important

• Freestall loop design• Lunge space• Brisket boards• Crossovers• Water availability

Get it correct!!And it looks like this!

Get it

wrong!!And it looksAnd it looks

like this!

Alley’s & Cow FlowBarn & Alley WidthsOutside cow alleys minimum of 9’ Cowside feed alleys minimum 12’

Drive-through feed alley minimum of 18’. A 16’ alley won’t allow adequate feed to be offered without driving over the feed, contaminating the feed with dirt and manure borne pathogens.

“ Milk is the Absence of stress!!!”stress!!!


Notes to Consultants Consultants to my Dairy

• Do not care what you know!• Until I know that you care!• Until I know that you care!

Consultants

• Is today normal ?• Without the usual excuses

Consultants

• ID the problem• Is it “the” bottleneck?• What are the solutions?• What’s the return and how fast?• What are the expectations of success?

Consultants

• Take ownership of the solution• Do NOT make general suggestions

Consultants

• Be there for implementation


INTRODUCTION

Generally speaking, dairy producers across North America are proud of their ability to produce high quality milk for daily consump on and for manufacturing of a wide variety of dairy products. High quality milk standards are, in part, maintained through the implementa on of regula ons that focus on the monitoring of milk levels of bacteria, inhibitors, and somatic cells. However, considerable variability exists between various regions on the levels established and enforced through these regula ons. In addi on, many regions use incen ve programs established by local milk coopera ves to encourage par cipa on in udder health management and mas s control programs.

In Ontario, Canada, a 6 year reduc on program in bulk tank soma c cell counts (SCC) was implemented in 1989, resul ng in a regulatory framework with an upper limit of 500,000 cells/ml. This program became stable in August 1995, and has been maintained un l July 2012 (OMAFRA, 1990). Dairy markets worldwide have set various regula-tory upper limits for bulk tank soma c cell count (BTSCC). For example, the European Union, Australia, New Zealand, Norway and Switzerland are 400,000 cells/ml. The United States has an upper regulatory limit of 750,000 cells/ml, except California, which has a limit of 600,000 cells/ml. Ontario has recently followed the European Communi es, and on August 1 of 2012 the regulatory limit was lowered to 400,000 cells/ml.

HISTORICAL PERSPECTIVES OF SCC REDUCTION

IN DAIRY HERDS

Mastitis control has been an important dairy health management ini a ve for nearly 50 years. Early lessons learned in this area led to the development of a Na onal Mastitis Council (NMC) 5-point mastitis control plan. Subsequently, major extension eff orts have been carried out throughout the dairy industry (Rados ts et al., 1994). As a result, the prac ces of post-milking teat disinfec on and blanket applica on of long-ac ng an bio c therapy at drying-off have been widely implemented. In addi on,

milking machine func on, early detec on and treatment of clinical cases, as well as iden fi ca on and culling of chronically infected cows have been emphasized. Star ng about 35 years ago, the use of SCC, as an indirect indicator of intramammary infec on (IMI), became the standard method of monitoring bulk milk quality (Harmon, 1994). Furthermore, Dairy Herd Improvement (DHI) milk record-ing agencies began to off er the service of measuring SCC in individual cow composite milk samples on a monthly basis. Between these two sources of SCC data, dairy pro-ducers had the tools to accurately monitor their progress in the control of IMI, resul ng in many lessons learned and considerable progress being made.

The European Economic Community, as well as other countries such as Canada, developed and implemented SCC penalty programs to force a considerable economic incen ve for implemen ng the 5-point plan to bring herd level SCC under control. The industry dynamics, and the success associated with implementa on of these programs, are well both documented (Sargeant et al., 1998). Sev-eral other countries, including the United States, have not emphasized aggressive SCC regulatory limits. In contrast, incen ves for low SCC and other measures of milk quality have been off ered at the local milk marke ng level. The vast majority of dairy producers are clearly mo vated by such premium incen ves. Data from DHI centers would suggest that outstanding udder health and milk quality is being achieved by much of the U.S. dairy industry. However, the overall success of these programs appears to be regional, and formidable challenges to achieve excellent mas s con-trol s ll exist on many farms, par cularly in some regions.

As the NMC mas s control programs became widely adopted by the late 1980s, herds in many of the major dairying regions of the world experienced changes in the expression of IMI in their cows. New IMI throughout the dry and periparturient periods and clinical mas s in early lacta on became increasingly important (Smith et al., 1985). Valuable lessons have been learned about

Managing Udder Health and Mastitis with Lower SCC Limits

Ken Leslie

Population Medicine, University of Guelph

Guelph, ON, Canada, N1G 2W1

[email protected]


these changing dynamics of infec on, as well as about the preven on of both clinical and subclinical mas s. These lessons have required the development and use of new nomenclature. Mas s-causing bacteria have been classifi ed into contagious and environmental pathogens, based upon important reservoirs and modes of transmis-sion of each group. Changes in herd size, housing systems, and manure management have markedly increased the importance of environmental pathogens in the North American dairy industry. Also, lessons learned through epidemiological studies have had a major impact on our understanding of the risk factors and biological interac- ons that result in an increase of environmental IMI. In

addi on, studies conducted in several countries have elu-cidated the incidence and risk factors involved with clinical mas s on commercial dairy farms (Hogan et al., 1989). Nevertheless, signifi cant challenges for the preven on of new IMI, primarily by environmental pathogens, remain an important focus of the dairy industry.

In response to this situa on, NMC has developed, pub-lished, and extended a revised Recommended Mas s Control Program, which includes 10 major points of em-phasis (Appendix 1). The new aspects of this program are largely focussed on mee ng the challenges presented by environmental pathogens. In other words, considerable emphasis has been placed on diff erent aspects of mas s control. For example, the management of milking, housing environment, and the dry period have become extremely important. Pre-milking udder prepara on greatly infl uences the preven on of new IMI and maintenance of milk quality (Pankey, 1989). In some regions, the prac ce of premilking teat disinfec on has become a standard prac ce. Interest-ingly, in other regions of the world such as Scandinavia, in response to public pressure, pre-milking teat disinfec on is illegal. Methods to improve and monitor s mula on of milk letdown, milk-out, and machine removal have also been widely implemented. The design and func on of milking equipment and milking techniques to op mize cleanliness, teat health, and milk fl ow have all been greatly advanced in last three decades (Spencer, 1989). Appropriate instal-la on, use, and evalua on of func on of modern milking equipment con nue to be extremely important.

There have been important lessons learned in our under-standing of the role of the dry period on new IMI, and on subsequent clinical mas s, in early lacta on (Dingwell et al., 2003). Studies conducted in New Zealand, North America, and the United Kingdom have described the importance of management at drying-off , and the occur-rence of teat-end closure in the dry period, as important

factors for the preven on of infec on. Long-ac ng dry cow an bio c therapy has received renewed emphasis for the preven on of new IMI, in addi on to its tradi onal role for the elimina on of exis ng infec ons. However, methods to prevent new IMI in the dry period by protec on of the teat-end with external or internal teat sealant products have been developed, evaluated, and implemented (San-ford et al., 2006). The importance of the dry period for mas s control, and for general health, remains as the focus of intense research and development.

The design of free-stall housing systems has started to receive great emphasis. The selec on of stall surface and bedding material is known to have a major impact on the rate of new IMI by environmental pathogens (Smith et al., 1985). Considerable evidence from lessons learned points to sand as a preferred bedding material for both cow comfort and udder health. Thus, sand has been widely implemented, notwithstanding the major impact that it has on manure management. On-going challenges from environmental legisla on and public pressure have resulted in the implementa on of numerous sustainable technologies, such as anaerobic manure digesters. With these technologies comes the mo va on to use digested manure solids as bedding materials. However, there is the poten al for many challenges and issues to maintain ac-ceptable rates of clinical mas s and low BTSCC with the use of this bedding material.

Therapy of clinical mas s con nues to be a challenging topic of considerable study and debate. No consistent ap-proach to the early iden fi ca on and treatment of clinical mas s cases has been developed, even though numerous lacta ng intramammary and systemic an bio c prepara- ons are commercially available, and their effi cacies against

the common mas s pathogens have been well-document-ed (Guterbock et al., 1993). The risk and fear of an bio c residues in milk, combined with a reasonably high rate of relapse and recurrent clinical signs, are valid reasons for concern. Thus, it is highly recommended that the manage-ment and advisory team for each farm should develop a farm-specifi c clinical mas s treatment protocol that rec-ognizes the predominant pathogens involved and monitors the success of the protocol. The usefulness of an bio c treatment of subclinical IMI during lacta on remains as a subject of considerable debate. This subject merits further study. Streptococcus agalac ae has been greatly reduced. Conversely, Staphylococcus aureus remains a signifi cant challenge, with very poor an bio c treatment cure rates during lacta on, and only moderate rates of cure with dry period therapy. However, the use of advanced epidemiolog-


ical analy cal tools has elucidated well-defi ned risk factors for failure of cure, allowing prospec ve decision-making on cows with par cular chronic infec ons (Sol et al., 2000). In addi on, the importance of pre-partum mas s in heifers as an important aspect of mas s control, especially for Staph. aureus, has been well-documented (Oliver et al., 1992; Nickerson et al., 1995). Ra onal use of an bio cs has become a major focus, and appropriate targeted treatment strategies remain extremely important in mas s control programs. Recently, a empts to establish and validate on-farm clinical mas s milk bacteriological culture systems have been documented, and show considerable promise (Lago et al., 2011). On-going challenges to maintain and enhance these programs remain.

Throughout the last 30 years, a empts to develop meaning-ful vaccina on programs to prevent mas s in dairy ca le have been described. In general, the progress in this area is disappoin ng. However, the development and widespread implementa on of core-an gen coliform mas s vaccines has clearly reduced the incidence of severe clinical mas s, and has considerably reduced mortality due to coliform mas s (Gonzalez et al., 1989; Hogan et al., 1992).

The development of record systems and monitoring tools has been par cularly impressive, and certainly important for mas s control. Clinical mas s recording systems and automated incorpora on and analysis of individual cow SCC data have allowed dairy producers and their advisors to make decisions and evaluate their success on an on-going basis. There is a need for greater incorpora on of informa on on the microbiological iden fi ca on of the bacterial agents involved. The prospects for substan al improvements in this area are encouraging. Recently, new diagnos c techniques such as DNA fi ngerprin ng of mas s pathogens have helped to be er describe the epidemiology of mas s, factors associated with virulence, and profi les determining the source and chronicity of infec ons. These tools have greatly increased our ability to follow infec ons, and in turn have challenged exis ng paradigms about the behavior of environmental and contagious organisms. For example, situa ons where Streptococci and E. coli have become persistent infec ons have been documented. Fur-ther studies are needed to con nue to build our knowledge to combat the ever-diversifying nature of organisms that cause mas s. In addi on, we need to pursue a more com-plex and condi onal understanding of the interac ons of the cow, bacteria, and the environment for mas s control.

DEVELOPING A PROGRAM TO ACHIEVE UDDER

HEALTH WITH LOW SCC

It is clear that management programs aimed at the reduc- on and maintenance of low bulk tank soma c cell counts

(BTSCC) need to be a major focus for all dairy producers.

In this pursuit, it is useful learn from the experiences of other regions that have implemented lower regulatory limits. With the introduc on of the SCC Penalty Program in Ontario, Schukken et al. (1992a) found that udder health management prac ces associated with reducing BTSCC were also closely associated with the improvement of other milk quality indicators, such as microbial inhibitor scores and freezing point devia ons. In a further publica- on, Schukken et al. (1992b) suggest that dairy industries

must encourage producers with low BTSCC to maintain those low levels, which is as important as decreasing BTSCC on farms with high BTSCC.

Recent studies have shown that herds enrolled in DHI programs, with access to cow-level SCC records are bet-ter equipped to manage BTSCC, having lower SCC penalty rates than those herds not enrolled on DHI programs (Hand et al., 2012). In Ontario, par cipa on in DHI has steadily increased, with 75% of dairy herds par cipa ng in 2010 (CanWest DHI, 2010). To assess the benefi t of DHI par ci-pa on and thus having access to cow-level SCC, Hand et al. (2012) calculated the odds of a BTSCC (calculated as the weighted average of 4 observa ons per month) exceeding penalty thresholds, as well as the likelihood of incurring a BTSCC penalty (3 out of 4 consecu ve monthly BTSCC exceed penalty thresholds, as per the Ontario Milk Act; OMAFRA, 1990) were calculated for herds enrolled in a DHI program compared with those that were not enrolled. Hand et al. (2012) found that DHI herds were 30% less likely to exceed BTSCC penalty thresholds at a regulatory limit of 500,000 cells/ml and 28% less likely to exceed BTSCC penalty thresholds at a regulatory limit of 400,000 cells/ml.

As described in considerable detail earlier in this paper, there are well-established, recommended procedures for mas s control in dairy herds. Each herd should consider implemen ng all of the steps described in the “NMC Recommended Mas s Control Program” as presented in Appendix 1. Certainly, there are individual items under each of the steps that will not be necessary or useful in a par cular herd. It is suggested that Appendix 1 should be printed and reviewed, and each individual procedure that is not implemented should be recorded and checked for future reference, in order to determine if it should be implemented for a meaningful improvement in the future.


MAINTAINING A HIGHLY EFFECTIVE MASTITIS

CONTROL PROGRAM

A er the herd management team, in conjunc on with udder health and milk quality advisors, have established a meaningful mas s control program, it will be an ongo-ing challenge to fi ne-tune and maintain the program over me. As with most aspects of dairy herd management,

udder health is a very dynamic and responsive biological system. As environmental factors and management struc-tures change, so will mas s-causing organism popula- ons and other resistance factors. As such, it is important

that the herd management team implement systems to maintain a low bulk milk SCC. To do so, there are seven well-documented habits of a highly eff ec ve mas s con-trol program. These habits are as follows:

1. Set realis c herd goals - Each herd needs to establish goals for both bulk milk SCC and rates of clinical mas- s. These goals should include stated alarm points, a

list of priori es, investments that need to be made and a commitment to achieving the goals.

2. Iden fy cows with high soma c cell counts - Using DHI SCC data and other informa on sources, cows with (and generally at risk for) high SCC need to be iden fi ed. Generally speaking, these animals include cows a er calving, a er clinical mas s, a er repeated elevated monthly SCC and those with known chronic infec ons.

3. Analyze your herd data to iden fy the strong and weak points in your herd – On a regular basis, at least every 2 to 3 months, herd udder health data should be ana-lyzed to assess the situa on. These data need to include individual cow SCC pa erns, clinical mas s pa erns, and the on-going infec on profi le of the herd.

4. Perform a risk assessment of your current mas s con-trol management - It is very useful to use a validated “risk assessment protocol” for mas s control on a regular basis. This process will help to iden fy and rank known mas s risk factors at work in the herd.

5. Improve the weakest links in your mas s control man-agement - Using the analysis of the herd data and the results of the risk assessment, the herd management team should iden fy and establish a plan to a ack the weakest links in the mas s control program.

6. Develop adequate monitoring procedures – U liza on of individual cow SCC data, culture results from clinical mas s cases and regular bulk tank monitoring, as well as rou ne herd data like clinical mas s and reasons for culling, is absolutely paramount in maintaining a highly eff ec ve mas s control program.

7. Strive for con nuous improvement of milk quality and mas s - All of the above highly eff ec ve habits need to be implemented con nuously on a rou ne basis.

CONCLUSION

In conclusion, the establishment and maintenance of a program to achieve superior udder health with an on-going low bulk milk SCC is a rela vely complex and con nuous task. That said, there is ample historical evidence and sup-port that it can be achieved and maintained. In addi on, there are excellent resources readily available from NMC and other sources, as well as well-established highly ef-fec ve habits to maintain success.

REFERENCES

CanWest DHI. 2010. DHI par cipa on hits 75% milestone in Ontario [News release]. Guelph, Ontario.

Dingwell, R. T., D. F. Kelton, and K. E. Leslie. 2003. Management of the dry cow in control of peripartum disease and mas s. Vet. Clin. North Am. Food Anim. Pract. 19:235–265.

Dingwell, R. T., K. E. Leslie, Y. H. Schukken, J. M. Sargeant, L. L. Timms, T. F. Duffi eld, G. P. Keefe, D. F. Kelton, K. D. Lissemore, and J. Conklin. 2004. Associa on of cow and quarter-level factors at drying-off with new intramammary infec ons during the dry period. Prev. Vet. Med. 63:75–89.

Dohoo, I. R., and K. E. Leslie. 1991. Evalua on of changes in soma c cell counts as indicators of new intramammary infec ons. J. Prev. Vet. Med. 10:225–238.

Gonzalez, R. N., J. S. Cullor, and D. E. Jasper. 1989. Preven on of clinical coliform mas s in dairy cows by a mutant Escherichia coli vaccine. Can. J. Vet. Res. 53:301–305.

Guterbock, W. M., A. L. Van Eenennaam, and R. J. Anderson. 1993. Effi -cacy of intramammary an bio c therapy for treatment of clinical mas- s caused by environmental pathogens. J. Dairy Sci. 76:3437–3444.

Hand, K. J., A .M. Godkin, and D. F. Kelton. 2012. Bulk milk soma c cell penal es in herds enrolled in Dairy Herd Improvement programs. J. Dairy Sci. 95:240-242.

Harmon, R. J. 1994. Physiology of mas s and factors aff ec ng soma c cell counts. J. Dairy Sci. 77:2103–2112.

Hogan, J. S., K. L. Smith, and K. H. Hoblet. 1989. Field survey of clinical mas s in low soma c cell count herds. J. Dairy Sci. 72:1547–1556.

Hogan, J.S., K. L. Smith, and D. A. Todhunter. 1992. Field trial to deter-mine effi cacy of an Escherichia coli J5 mas s vaccine. J. Dairy Sci. 72:1547–1553.

Lago, A., S. M. Godden, R. Bey, P. L. Ruegg, and K. Leslie. 2011. The selec- ve treatment of clinical mas s based on on-farm culture results: I.

Eff ects on an bio c use, milk withholding me, and short-term clinical and bacteriological outcomes. J. Dairy Sci. 94:4441–4456.

Nickerson, S. C., W. E. Owens, and R. L. Boddie. 1995. Mas s in dairy heifers: Ini al studies on prevalence and control. J. Dairy Sci. 78:1607–1618.

Oliver, S. P., M. J. Lewis, and B. E. Gillespie. 1992. Infl uence of prepartum an bio c therapy on intramammary infec ons in primigravid heifers during early lacta on. J. Dairy Sci. 75:406–414.


OMAFRA. 1990. The Milk Act and Regula ons of Ontario. Ontario Min-istry of Agriculture, Food and Rural Aff airs, Toronto, Ontario.

Pankey, J. W. 1989. Premilking udder hygiene. J. Dairy Sci. 72:1308–1312.

Rados ts, O. M., K. E. Leslie, and J. Fetrow. 1994. Herd Health –Food Animal Produc on Medicine. 2nd ed. W. B. Saunders Co., Philadel-phia, PA.

Sanford, C. J., G. P. Keefe, I. R. Dohoo, K. E. Leslie, R. T. Dingwell, L. DesCôteaux, and H. W. Barkema. 2006. Effi cacy of using an internal teat sealer to prevent new intramammary infec ons in nonlacta ng dairy ca le. J. Am. Vet. Med. Assoc. 228:1565-1573.

Sargeant, J. M., Y. H. Schukken, and K. E. Leslie. 1998. Ontario bulk milk soma c cell count reduc on program: Progress and outlook. J. Dairy Sci. 81:1545–1554.

Schukken, Y. H., K. E. Leslie, A. J. Weersink, and S. W. Mar n. 1992a. Ontario bulk milk soma c cell count reduc on program. 1. Impact on soma c cell counts and milk quality. J. Dairy Sci. 75:3352-3358.

Schukken, Y. H., K. E. Leslie, A. J. Weersink, and S. W. Mar n. 1992b. Ontario bulk milk soma c cell count reduc on program. 2. Dynamics of bulk milk soma c cell counts. J. Dairy Sci. 75:3359-3366.

Smith, K. L., D. A. Todhunter, and P. S. Schoenberger. 1985. Environ-mental mas s: Cause, prevalence, and preven on. J. Dairy Sci. 68:1531–1553.

Sol, J., O. C. Sampimon, H. W. Barkema, and Y. H. Schukken. 2000. Fac-tors associated with cure a er therapy of clinical mas s caused by Staphylococcus aureus. J. Dairy Sci. 83:278–284.

Spencer, S. B. 1989. Recent research and development in machine milk-ing: A review. J. Dairy Sci. 72:1907–1917.


Notes


RECOMMENDED MASTITIS CONTROL PROGRAM

1. Establishment of Goals for Udder Health

Set realistic targets for average herd somatic cell count (SCC) or linear score and clinical mastitis rate.

Review goals on a timely basis, with input from the Herd Udder Health Advisory Team (veterinarian, producer, herd manager, milking personnel and advisors).

Prioritize management changes to achieve stated goals.

Other:

2. Maintenance of a Clean, Dry, Comfortable Environment

Ensure proper stall usage by ensuring adequacy of stall size and design.

Maintain clean, dry, and comfortable stalls through appropriate bedding management.

Keep cow lots or housing and traffic areas clean and dry.

Ensure ventilation system is functioning properly.

Ensure proper stocking density in facilities.

Control detrimental environmental influences (heat stress, frostbite, stray voltage, insects etc.).

Ensure that cows remain standing after milking (provide fresh feed and water).

Other :

3. Proper Milking Procedures

Examine foremilk to facilitate early detection of clinical mastitis and proper milk letdown.

Apply pre-milking teat disinfectant that completely covers the teat skin and allow it to remain on teats for at least 30 seconds.

Dry teats using a properly washed and disinfected cloth towel for use on one cow, or a single service paper towel.

Wear clean gloves during the milking process to limit spread of contagious pathogens.

Attach teat cups squarely and level with the udder within 90 seconds of udder preparation.

Adjust cluster during milking to prevent liner slips and squawks.

With manual removal, avoid machine stripping and shut off vacuum to the claw before removing cluster.

Apply teat disinfectant immediately following teat cup removal, and assure complete coverage of teats.

Pre- and post-milking teat disinfectants should be selected based on documented efficacy data which can be found on the NMC website (www.nmconline.org)

To optimize mastitis control and reduce costs, teat dipping is preferred to spraying as the method of disinfectant application.

Milk cows with confirmed contagious intramammary infections last.

Other :

4. Proper Maintenance and Use of Milking Equipment

Install or update equipment to ISO 5707 (International Organization for Standardization, “Milking machine installations–Construction and performance”).

Service, maintain, and regularly evaluate equipment function according to manufacturer’s guidelines, using dynamic evaluation methods and an appropriate record form.

Replace inflations and other rubber and plastic parts regularly, according to manufacturer’s guidelines.

Replace broken or cracked inflations and short milk tubes immediately.

Sanitize equipment prior to each milking and thoroughly wash and sanitize equipment after each milking.

Other:

5. Good Record Keeping

For each case of clinical mastitis, record cow identification, date detected, days in milk, quarter(s) affected, number and type of treatments, outcome of treatments (i.e. return to normal milk, time to discard milk) and the causative bacterial pathogen if a sample was cultured on-farm or in a laboratory.

Use a computerized or manual record system to manage information, such as individual cow SCC data, on the prevalence and incidence of subclinical mastitis.

Other :

6. Appropriate Management of Clinical Mastitis During Lactation

Develop and implement a herd clinical mastitis treatment protocol with the Herd Udder Health Advisory team.

Carefully consider the economic ramifications of therapy decisions.

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Name /Farm: Date:


Collect a pre-treatment milk sample aseptically for microbiological culture so that antimicrobial susceptibility tests can be used when appropriate.

Use an appropriate therapeutic regimen; use drugs according to the protocol, or as recommended by the health advisors.

Prior to infusion, disinfect the teat with a germicide and scrub the teat-end with an alcohol swab.

For infusion of intramammary antibiotics, use a single-dose, regulatory approved product by the partial insertion method.

Do not treat chronic non-responsive infections.

Observe the correct withdrawal period for the antibiotic used, as stated on the label. If extra-label drug use is necessary, follow regulatory guidelines under the supervision of a veterinarian (i.e. in the systemic treatment of coliform mastitis).

Always follow recommended drug storage guidelines and observe expiration dates.

Clearly identify all treated cows, and record all treatments in a permanent record.

Other :

7. Effective Dry Cow Management

Decrease the energy density of the ration during late lactation to reduce milk production before dry-off.

Dry cows off abruptly and dry treat each quarter immediately following the last milking of lactation.

Disinfect teats and scrub the teat-end with an alcohol swab before infusing.

Treat all quarters of all cows with a commercially available approved [long-acting] dry-cow antibiotic and/or an approved internal teat sealant.

Use the partial insertion method of dry treatment infusion.

Disinfect teats immediately following infusion using any approved post milking disinfectant teat dip.

Provide adequate dry cow nutrition to enhance immune system function.

Maintain a clean, dry, comfortable environment for dry cows. Dry cow environmental management is important to minimize exposure to pathogens.

In situations of high environmental pathogen exposure, use an internal or external teat sealant for dry cows in addition to any antimicrobial product.

In herds with coliform mastitis problems, vaccinate with a core antigen endotoxin vaccine following manufacturer’s directions.

Clip flanks and udders to remove excess body hair. Udder singeing may be useful to ensure hair removal.

Other :

8. Maintenance of Biosecurity for Contagious Pathogens and Marketing of Chronically Infected Cows

Request bulk tank and individual cow SCC data. For suspect animals, further diagnostic efforts may be indicated to identify cases of subclinical mastitis prior to purchasing cows.

If possible, obtain aseptically collected milk samples for bacteriological culture from cows prior to purchase.

Isolate recently purchased cows, and milk separately, until there is assurance of the absence of intramammary infection.

Segregate cows with a persistently high SCC or linear score (i.e. SCC greater than 200,000 or linear score greater than or equal to 4.0 for several months) and observe response to dry treatment or other recommended therapy.

Market or permanently segregate cows persistently infected with Staphylococcus aureus or other non-responsive microbial agents (Mycoplasma, Nocardia, Pseudomonas, or Arcanobacterium pyogenes).

Consider udder health status of first-calf heifers as this can impinge on herd biosecurity.

Other :

9. Regular Monitoring of Udder Health Status

Enroll in an individual cow SCC program or use some other monitor of subclinical infections.

Use a sensitive cow-side monitor of inflammation in cows suspected of infection and in high risk periods (i.e. early lactation).

Monitor distributions of high SCC cows, and rates of change to elevated SCC.

Conduct milk bacteriological culture of clinical cases and high SCC cows regularly.

Monitor udder health for the herd using reports from the regional regulatory agency or milk marketing organization and DHI.

Calculate clinical mastitis rates and distributions regularly, paying particular attention to infections in heifers.

Use SCC and clinical mastitis records to evaluate protocols, and to make treatment and marketing decisions.

Other :

10. Periodic Review of Mastitis Control Program

Obtain objective evaluations from veterinarian, industry field person or extension representative.

A step-by-step approach to the review, and a standard evaluation form are useful.

Make use of the entire Herd Udder Health Advisory Team: veterinarian, producer, herd manager, milking personnel, and advisors.

Other :

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Hot Topics in the World of Silages

Limin Kung, Jr., Mateus C. Santos, Michelle C. Windle, and Jonathan M. Lim

Department of Animal & Food Sciences

Dairy Nutrition and Silage Fermentation Laboratory

University of Delaware

GOT SHREDLAGE?

There is much interest in a new way of chopping and processing corn silage that results in a fi nal product called “shredlage” (Ferrare o and Shaver, 2012). Converted choppers have the ability to shred, rather than chop, the corn plant at harvest. Interest in this process is due to the fact that the shredded material has more eff ec ve fi ber and has the poten al to be more diges ble. About 25 modifi ed machines were in place this fall. Research on animal performance, packing density, processing scores, and sor ng is underway.

ARE YOUR “BUGS” OKAY?

In order for microbial inoculants to have a chance of being eff ec ve, they must supply the correct number of viable microorganisms when applied to the forage mass. Inoculants are commonly mixed in water for applica on to forage. Mulrooney and Kung (2008) found that some inoculants died rapidly if the water they were in reached high temperatures (>100°F). In a recent fi eld study (Win-dle et al., 2012) we collected 29 samples of inoculants from inoculant tanks in the fi eld. Nine of 29 samples had numbers of lac c acid bacteria (LAB) that were lower than expected based on theore cal mixing rates. Of six samples that had water temperatures above 89°F, four samples had lower numbers of LAB than expected. Two tanks had water temperatures exceeding 100°F (111 and 116°F). In those tanks, if applied at the suggested rate, the numbers of fi nal applied LAB would have been less than 3,000 and 200 cfu/g of fresh forage, respec vely (compared to a theore cal fi nal applica on rate of 100,000 cfu/g). On a posi ve note, a high percentage of samples had concentra ons of LAB that were higher than expected. Thus, small errors resul ng in under applica on of cor-rect amounts of water probably s ll resulted in adequate numbers of LAB added to the forage. When applying a microbial inoculant, the temperature of the water must be monitored to insure it does not reach levels that would result in death of the bacteria.

EFFECT OF ENSILING TIME ON NUTRITIVE VALUE

A growing body of evidence has shown that as corn si-lage ensiles, the poten al starch diges bility (starch-D) in the rumen increases rela ve to values in fresh forage (Stock et al., 1991; Newbold et al., 2006). This process is probably a result of proteolysis that aff ects the prolamin matrix interac ng with starch granules (Hoff man et al., 2011). Because the process is linked to proteolysis, the concentra on of soluble N also increases with me of stor-age (Der Bedrosian et al., 2012). In order to deal with the poten ally low starch-D in fresh corn plants, preliminary studies from our lab (Young et al., 2012) have shown that the addi on of exogenous proteases to fresh corn plants at ensiling increases in vitro ruminal starch-D faster than that naturally occurring in untreated silage. Research is on-going to determine if this approach is prac cal and cost eff ec ve. Although starch-D increases with me of ensil-ing, it does not appear that NDF-D improves with me in the silo, although more corrobora ng evidence is needed.

GOT VOLATILES?

Vola le organic compounds (VOC) add to global warming. Research has iden fi ed silages as the primary source of VOC from dairy farms with ethanol being a major contribu-tor to the problem (Malkina et al., 2011). More emphasis is being placed on determining how to control the emis-sions of vola le organic compounds (VOC) from silages. Rule 4570 is currently in place, and California dairy farms are now required to implement mi ga ons that will lower VOC emissions from silage piles (h p://www.valleyair.org/farmpermits/applica ons/4570/dairy_caf_phase_2.pdf). Most of the suggested mi ga ons can be characterized as “good management” prac ces. For example, minimizing the amount of loose silage on the bunker fl oor between feedings, packing silos ghtly, using a homolac c acid inoculant, and keeping a clean, fl at silo face are prac ces that will tend to reduce VOC emissions. Could legisla on like this come our way in the future?


EFFECTS OF HURRICANE IRENE ON

CORN SILAGE FROM 2011

Hurricane Irene caused severe damage to the corn crop of 2011 in the Northeastern U.S. due to fl ooding and (or) lodging. Samples of damaged and undamaged corn silage were collected from farms and analyzed for microbial and chemical content. The most signifi cant fi ndings were higher numbers of yeasts (7.3 log10 cfu/g in damaged vs. 4.9 log10 cfu in undamaged samples) and higher concentra ons of ash in damaged samples (average of 8.93%, with a high of 28.2%) than in undamaged samples (average of 3.85%). In par cular, damaged samples had higher concentra- ons of iron (average of 2,477 ppm and as high as 12,534

ppm) and aluminum (average of 1,334 ppm and as high as 4,410 ppm). Concentra ons of heavy metals in silages were in general not aff ected by the hurricane. Overall, indices of silage fermenta on appeared normal, although damaged corn silage had slightly lower concentra ons of fermenta on acids but higher pH than undamaged silage. Deoxynivalenol was detected in 75% (6 out of 8) and 90% (27 out of 30) of undamaged and damaged corn samples, respec vely. Zearalenone was detected in 1 out of 8 (12.5%) control samples, while 7 out of 30 (23.3%) fl ooded corn samples contained this mycotoxin. Although some complaints about poor animal performance were recorded, it does not appear that there were wide spread nega ve eff ects from feeding the damaged crop.

IN SEARCH OF MARKERS IN SILAGE THAT ARE

NEGATIVELY CORRELATED WITH INTAKE

AND/OR PRODUCTION

Silage researchers have measured the same end products of fermenta on for years. For example, the fi nal pH, con-centra ons of lac c acid, ace c acid, butyric acid, ethanol, and ammonia are the most common chemical components that have been measured. However, it is clear that the ac-cumula on of these compounds do not fully explain the variances observed in intake and animal performance. Several recent publica ons have documented that in fact, silages contain hundreds of vola le compounds (Figueire-do et al., 2009; Malkina et al., 2011), and some may have detrimental eff ects on rumen microbes or the animal. Some work from Germany (Gerlach et al., 2012) suggests that ethyl lactate may be nega vely associated with DM intake. More work on the poten al of vola le compounds in silages to aff ect animal performance is on-going.

WILD YEASTS IN SILAGES

In the U.S. it is generally accepted that lactate-assimila ng yeasts ini ate aerobic spoilage in silage. Measurements

from commercial labs and from research studies have shown that the numbers of total yeasts in spoiling silages can be as high as 109 cfu/g of silage. It is unknown whether the consump on of high numbers of these spoilage yeasts nega vely impacts rumen microbes or the animal directly. Recently, Santos et al. (2011) reported that adding high concentra ons of a spoilage yeast isolated from silage to in vitro ruminal fermenta ons reduced the NDF-D of a TMR in a dose-dependent fashion. The fact that yeasts may have nega ve impacts on rumen fermenta on has also been reported by Chung et al. (2011), who found that the addi on of a yeast from an experimental direct fed mi-crobial formula on resulted in a more acidic rumen when fed to dry cows. More research is needed to determine if consump on of high numbers of yeasts from spoiled silage have nega ve eff ects on animal performance.

ACKNOWLEDGEMENTS

We acknowledge the following companies and agencies that provided funding for the analyses on the hurricane-damaged corn: University of Vermont, University of Delaware, Prince Agri Products, BASF, Mycogen Seeds, Lal-lemand Animal Nutri on, Novus Interna onal, and Renais-sance Nutri on. We also thank Ralph Ward and Cumberland Valley Analy cal for their assistance in sample analyses.

REFERENCES

Chung, Y. H., N. D. Walker, S. M. McGinn, and K. A. Beauchemin. 2011. Diff ering eff ects of 2 ac ve dried yeast (Saccharomyces cerevisiae) strains on ruminal acidosis and methane produc on in nonlacta ng dairy cows. J. Dairy Sci. 94:2431–2439.

Der Bedrosian, M. C., K. E. Nestor, Jr., and L. Kung, Jr. 2012. The eff ects of hybrid, maturity and length of storage on the composi on and nutri ve value of corn silage. J. Dairy Sci. 95:5115-5126.

Ferrare o, L. F., and R. D. Shaver. 2012. Eff ect of corn shredlage™ on lacta on performance and total tract starch diges bility by dairy cows. Prof. Anim. Sci. In press.

Figueiredo, R., A. I. Rodrigues, and M. do Ceu Costa. 2007. Vola le com-posi on of red clover (Trifolium pratense L.) forages in Portugal: The infl uence of ripening stage and ensilage. Food Chem. 104:1445–1453.

Gerlach, K., K. Weiss, F. Ross, W. Bü scher, and K.-H. Sü dekum. 2012. Changes in maize silage fermenta on products during aerobic dete-riora on and its impact on feed intake by goats. Page 38. Proc. XVI Int. Silage Conf. University of Helsinki.

Hoff man, P. C., N. M. Esser, R. D. Shaver, W. Coblentz, M. P. Sco , A. L. Bodnar, R. Schmidt, and B. Charley. 2011. Infl uence of ensiling me and inocula on on altera on of the starch-protein matrix in high-moisture corn. J. Dairy Sci. 94:2465-2474.

Malkina, I., A Kumar, P. G. Green, and F. M. Mitloehner. 2011. Iden fi -ca on and quan ta on of vola le organic compounds emi ed from dairy silages and other feedstuff s. J. Environ. Qual. 40:28–36.

Mulrooney, C. N., and L. Kung, Jr. 2008. The eff ect of water temperature on the viability of silage inoculants. J. Dairy Sci. 91:236-240.


Newbold, J. R., E. A. Lewis, L. Lavrijssen, H. J. Brand, H. Vedder, and J. Bakker. 2006. Eff ect of storage me on ruminal starch degradability in corn silage. J. Dairy Sci. 89 (Suppl. 1):190. (Abstr.)

Santos, M. C., A. L. Lock, G. D. Mechor, and L. Kung, Jr. 2011. Spoilage yeasts in silage have the poten al to directly impact rumen fermenta- on. J. Dairy Sci. 94(Suppl. 1):207. (Abstr.)

Stock, R. A., M. H. Sindt, R. M. Cleale, 4th, and R. A. Bri on. 1991. High-moisture corn u liza on in fi nishing ca le. J. Anim. Sci. 69:1645-1656.

Windle, M. C., C. Wacek-Driver, and L. Kung, Jr. 2012. A survey on the concentra ons of lac c acid bacteria, the pH and temperature of water in tanks for used for microbial inoculants. Study in progress.

Young, K. M., J. M. Lim, M. C. Der Bedrosian, and L. Kung, Jr. 2012. Eff ect of exogenous protease enzymes on the fermenta on and nutri ve value of corn silage. J. Dairy Sci. In Press.


Notes


nor Baker and Herrman (2002) defi ne whether HMC is to be dried prior to sieving. The MPS of dry or HMC is o en determined on farm by dairy producers or nutri on con-sultants by sieving un-dried samples through a short stack of sieves (Baker and Herrman, 2002). Typically corns are sieved through 6 screens (4000, 2000, 1000, 500 and 250 um) and pan. Recent data (Hoff man et al., 2012) suggest on-farm determina on of MPS for dry corn is accurate, but on-farm determina on of MPS on un-dried, un-ground HMC causes a large bias, overes ma ng the MPS of HMC by approximately 500 um.

Mean Particle Size—Corn Silage

Quan fying the MPS of starch in corn silage with or with-out kernel processing in commercial forage tes ng systems has been challenging. As a commercially viable surrogate, Ferreira and Mertens (2005) recommended determining the starch content of the total sample and the starch content of the DM remaining on screens > 4.75 mm for corn silage. This yields a kernel processing score (KPS) that serves as a starch par cle size index for corn silage. Corn silage is defi ned as op mally processed if > 70% of starch passes through the 4.75-mm screen. Corn silage can also be defi ned as adequately (50 to 70%) or inadequately (< 50%) processed by the KPS scoring system. The MPS of corn par cles in corn silage can be es mated (Zwald et al., 2008) from KPS using the equa on (MPS, um = 7,491 + (−93.81KPS + 0.37KPS2).

Particle Bonding Strength—Unfermented (Dry) Corn

Hoff man et al. (2012) observed that the rela onship be-tween MPS of dry corn and starch degrada on rate could be improved when the MPS of dry corn was adjusted for par cle bonding strength. The best marker of par cle bonding strength in unfermented corns was determined to be prolamin proteins, which encapsulate starch in corn endosperm. Endosperm proteins (prolamin-zein) are not soluble in water (hydrophobic) nor in the innate rumen environment (Lawton, 2002). The nega ve rela onship between prolamin-zein and starch diges on in ruminants

INTRODUCTION

Grain management prac ces, such as grinding, (Theurer, 1986; Remond et al., 2004), steam fl aking (Callison et al., 2001), ensiling (Oba and Allen, 2003), or endosperm type selec on (Allen et al., 2008; Lopes et al., 2009), alter physi-cal or chemical characteris cs of feed grains which in turn alter starch diges on and lacta on performance of dairy cows. Despite knowledge (Firkins et al., 2001) of physical and chemical factors that infl uence feed grain u liza on by dairy cows, scien sts, nutri on consultants, and feed and forage tes ng laboratories have been challenged to integrate physical and chemical composi on of feed grains into feed grain evalua on systems. Recently, Hoff man et al. (2012) integrated mean par cle size (MPS) with chemical diff erences associated with fermenta on and endosperm type into a common unit, en tled eff ec ve MPS (eMPS). Eff ec ve MPS of feed grains, which is conceptually similar to physically eff ec ve NDF (peNDF), has the poten al to integrate physical and chemical variances within dry and high moisture corn (HMC) into a single unit. Simply, eMPS considers the size of a feed grain par cle plus the degree of bonding (eff ec veness; strong or weak) within the par cle. The principal components required for determina on of eMPS and the adapta on of eMPS into an integrated feed grain evalua on system are described in this paper.

INTEGRATED FEED GRAIN EVALUATION:

PRINCIPAL COMPONENTS

Mean Particle Size

The eff ect of MPS on starch diges on in ruminants is well known (Firkins et al., 2001). Procedures to determine MPS of dry ground corn are also well defi ned (Baker and Her-rman, 2002; ASAE, 2008). Approximately 150 g of dried, un-ground corn is placed in a series of 14 screens with nominal apertures of 4750, 3350, 2360, 1700, 1180, 850, 600, 425, 300, 212, 150, 106, 75, and 53 um and a pan. The series of screens and pan are placed on an oscilla ng sieve shaker for 15 minutes. Procedures to determine MPS of HMC are nebulous because neither ASAE (2008)

Evaluating Starch Digestibility for Lactating Dairy Cows:

An Integrated Approach

P. C. Hoff man, R. D. Shaver, and D. R. Mertens

Department of Dairy Science

University of Wisconsin-Madison


has been previously defi ned (Lopes et al., 2009). The prolamin content of dry corns ranges from 2.5 to 5.5% of dry ma er. Corns > 4.5% prolamin as a % of DM are likely more vitreous, may contain more fl int genes, are short rela ve maturity hybrids (more fl int genes) and had adequate N fer lity. Corns with prolamin protein < 3.0% maybe unique opaque-fl oury hybrids or be grain from N-defi cient corn. A prolamin protein assay (Larson and Hoff man, 2008) is available at a number of commercial feed and forage tes ng laboratories.

Particle Bonding Weakness—

Fermented (High Moisture) Corn

Similar to dry corn, Hoffman et al. (2012) observed that the rela onship between MPS of HMC and starch degrada on rate was improved when the MPS of HMC was adjusted for par cle bonding weakness. Ammonia-nitrogen (NH3-N) was observed to be the best marker of par cle weakness in HMC.

Ammonia-nitrogen

Ammonia-nitrogen is an eff ec ve marker of par cle weak-ness in HMC because it is a chemical marker of proteolysis. Ensiling (Hoff man et al., 2011) has been shown to reduce all α, β and δ prolamin-zein subunits of the starch-protein matrix from 10 to 40%. The degrada on of the γ prolamin-zein subunits of the starch-protein matrix of HMC can be more extensive with 60% reduc ons observed. Because γ prolamin-zeins are primarily responsible for cross-linking starch granules together, the degrada on of γ zeins in HMC results in starch granule clusters falling apart (weaker) as a result of fermenta on since the cross links holding starch granules together have been degraded. In extensively fermented HMC, NH3-N may represent > 6% of the total nitrogen (or protein). High-moisture corns with < 1% of the total nitrogen (or protein) as NH3-N indicate the degrada- on of starch-matrix proteins is minimal. Commercial tests

for NH3-N are widely available.

Soluble Protein

Soluble protein is also a marker of the degrada on of starch matrix proteins in HMC and corn silage. Der Bedrosian et al. (2012) observed weakening of kernel par cles within corn silage to be concomitant with increasing soluble protein, re-sul ng in greater in vitro starch diges on. Likewise Hoff man et al. (2011) observed increasing soluble protein content in HMC with advancing ensiling me with concomitant in-creases in vitro starch degrada on (Hoff man et al., 2010). Similar results have been previously reported by Benton et al. (2005). Prior to ensiling, about 20% of the protein

in corn is soluble in a buff er solu on, but in extensively fermented HMC up to 70% of the protein may be soluble.

AN INTEGRATED APPROACH—FEEDGRAINV2.0

Concept

As a result of current research (Oba and Allen, 2003; Ben-ton et al., 2005; Taylor and Allen, 2005; Allen et al., 2008; Larson and Hoff man, 2008; Lopes et al., 2009; Hoff man et al., 2010, 2011, 2012; Der Bedrosian et al., 2012) on grain physico-chemistry and its eff ects on starch diges bility, a simple model was developed to predict rate of starch diges on, ruminal starch diges on, and total tract starch diges on. The model, FeedGrainV2.0, is publically available at h p://www.uwex.edu/ces/dairynutri on/. FeedGra-inV2.0 was developed to provide a simple educa onal pla orm for dairy educators, consultants, and producers to evaluate feed grains using principal components which in-fl uence feed grain diges on and dairy ca le performance. The following text and tables in this proceedings paper defi ne the general concepts employed in FeedGrainV2.0.

Corn Types Redefi ned

One of the major obstacles in developing an integrated feed grain evalua on system is removing empirical-user defi ned classifi ca ons of corn as either dry or high moisture. There is confusion in the industry whether corns containing low moisture (18.0 to 24.0%) feed like dry corn or HMC. Likewise, there is confusion of whether HMC at harvest or fermented a few days feeds like dry corn or HMC. Data from Hoff man et al. (2010) and Benton et al. (2005) suggest fresh, unfermented HMC have similar starch degrada on characteris cs as a dry corn contemporary. As a result, dry corn and HMC are redefi ned as unfermented and fer-mented corns using the concentra on of NH3-N in corn as an unbiased classifi catory nutrient. Using NH3-N to defi ne corn as unfermented or fermented is simple, cost eff ec ve, unbiased, and accurate for the following reasons. First, dry corn or freshly harvested corn > 15% moisture does not contain any appreciable amount of NH3-N. Therefore, when corn is devoid of NH3-N the corn is not fermented. Second, recent research (Hoff man et al., 2011; Der Bedrosian et al., 2012) has demonstrated that NH3-N defi nes both the intensity and dura on of the ensiling process. Because NH3-N concentra ons in HMC increase due to intensity and length of fermenta on, the eff ects of me and intensity on starch diges bility can be accounted for. Finally, because NH3-N concentra on in HMC is rela vely easy to determine with ammonia probes or via near infrared refl ectance spec-troscopy (NIRS), defi ning corn types, intensity, and length of fermenta on is rapid and economical.


FeedGrainV2.0 Calculations

Calcula ons within FeedGrainV2.0 employ a mechanis c model with both physical and nutri onal chemistries of corns infl uencing starch diges on parameters. FeedGra-inV2.0 does not require measures of in vitro starch di-ges bility or in vitro gas produc on of feed grains. The following basic steps are used in FeedGrainV2.0 to cal-culate the outputs described below. A complete view of all calcula ons of FeedGrainV2.0 are detailed within the opera onal program which is available at h p://www.uwex.edu/ces/dairynutri on/.● Feed grains are dried and MPS is determined using

ASAE (2008) methods.● The NH3-N concentra on of feed grains is determined.

Because dry and fresh corns are devoid of NH3-N, corns with < 0.50% of NH3-N, % of total N are classifi ed as unfermented. Corns with NH3-N concentra ons > 0.50 are classifi ed as fermented.

● The prolamin concentra on of unfermented feed grains is determined.

● Prolamin concentra on in unfermented feed grains is then used to adjust MPS to eMPS. The MPS of unfer-mented feed grains with greater prolamin concentra- ons are considered to be more eff ec ve at resis ng

bacterial diges on of starch, and as a result eMPS may be > MPS. Conversely, unfermented feed grains with lesser prolamin concentra ons are considered to be less eff ec ve at resis ng bacterial diges on of starch, and as a result eMPS may be < MPS (Figure 1).

● The NH3-N concentra on in fermented feed grains is used to adjust MPS to eMPS. The MPS of fermented feed grains with lesser NH3-N concentra ons are considered to be equally or marginally less eff ec ve at resis ng bacterial diges on of starch and as a result eMPS may be ≤ MPS. Conversely, fermented feed grains with greater NH3-N concentra ons are considered to be considerably less eff ec ve at resis ng bacterial diges on of starch and as a result eMPS will be < MPS (Figure 2).

● A frac onal rate of starch diges on is es mated from eMPS.

● Post-ruminal fl ow of starch is es mated assuming feed grain passage rates of 16.0 and 12.0% per hour for unfermented and fermented corns, respec vely.

● Ruminal starch diges bility is calculated by diff erence (100 – post-ruminal starch fl ow).

● Post-ruminal starch diges bility is es mated assuming surface area of par cles presented post-ruminally is posi vely related to starch hydrolysis poten al.

Required Laboratory Measurements

Item (Abbrevia on) Units MethodDry Ma er (DM) % as fed Chemistry

Mean Par cle Size (MPS) microns ASAE, 2008

Starch % of DM NIRS or ChemistryCrude Protein (CP) % of DM NIRS or ChemistryNH3-N % of CP1 NIRS or ChemistryProlamin Protein2 % of DM Larson and Hoff man, 2008

Neutral Detergent Fiber (aNDF) % of DM NIRS or Chemistry

Ash % of DM NIRS or ChemistryFat % of DM NIRS or Chemistry1 Or as % of total N.2 Unfermented.

Common Range of Feed Grain1 Inputs

Item (Abbrevia on) Units Low Avg HighDry Ma er (DM) % as fed < 65 75 > 85

Mean Par cle Size (MPS) microns < 750 1500 > 2250

Starch % of DM < 65 69 > 73Crude Protein (CP) % of DM < 8.0 9.0 > 10.0NH3-N % of CP2 0.0 3.0 > 6.0Prolamin Protein % of DM < 3.4 3.9 > 4.4

Neutral Detergent Fiber (aNDF) % of DM < 7.0 9.0 > 11

Ash % of DM < 1.3 1.7 > 2.1Fat % of DM < 3.5 4.0 > 4.51 Values for dry and high moisture corns.2 Or as % of total N.

Figure 1. Rela onship between actual and eff ec ve mean par cle size (MPS) for a range of prolamin concentra ons. In FeedGrainV2.0 prolamin concentra on of unfermented feed grains is used to adjust MPS to eff ec ve MPS.


● Total tract starch diges bility (TTSD) is es mated as the sum of ruminal starch diges bility plus net post-ruminal diges bility.

● Es mated TTSD is used as the diges on coeffi cient for starch in a summa ve equa on.

● Energy values for feed grains are calculated using a summa ve equa on (NRC, 2001) with independent

diges on coeffi cients for starch and non-starch, non-fi ber carbohydrates.

● A rela ve grain quality index is calculated from TTSD.

Validation

FeedGrainV2.0 was validated using published research trials involving lacta ng dairy cows as a guide. FeedGra-inV2.0 was not evaluated for and is not intended for use for growing beef ca le, cereal grains, or steam fl aked corns. The valida on of FeedGrainV2.0 required some fl exibility in research literature interpreta on because of a lack or absence of physical and chemical measurement con nuity in the literature. Placing more rigid trial criteria into the valida on process would have been desirable but would have resulted in a very limited data base to conduct a valida on. In general, trials used to validate FeedGrainV2.0 fed > 80% of starch from grain, reported MPS, made direct comparisons of grain type and reported in vivo TTSD. Most trials did not report prolamin values and a value of 3.9% of DM was used for all trials involving unfermented corn unless otherwise defi ned. An average NH3-N concentra- on of 4.0% was used for all trials involving fermented

corn. In vivo TTSD for unfermented corns were adjusted for random study eff ects (St-Pierre, 2001) but insuffi cient data was available for fermented feeds and TTSD were adjusted for an average MPS slope eff ect. Finally, in some

Figure 2. Rela onship between actual and eff ec ve mean par cle size (MPS) for a range of ammonia-nitrogen concentra- ons. In FeedGrainV2.0 ammonia-nitrogen concentra on of

fermented feed grains is used to adjust MPS to eff ec ve MPS.

Feed GrainV2.0 Outputs

Item Abbrevia on Units Low Average High

Moisture % as fed < 15 25 > 35

Eff ec ve Mean Par cle Size1 eMPS microns < 600 1200 > 2400

Starch Fermenta on Rate (As Fed)2 kd % per hour < 13 18 > 23

Ruminal Starch Diges bility RSD % of starch < 50 60 > 70.0

Starch Diges bility (Total Tract) TTSD % of starch < 89 92 > 95

Non Fiber Carbohydrate NFC % of DM < 77 80 > 83

Non-starch NFC % of DM < 8.0 9.0 > 10

Total Diges ble Nutrients, 1X TDN % of DM < 86.5 87.5 > 89.0

Net Energy Lacta on, 3X NEL Mcals/lb < 0.86 0.88 > 0.92

Net Energy Maintenance NEM Mcals/lb < 0.93 0.95 > 0.97

Net Energy Gain NEG Mcals/lb < 0.63 0.65 > 0.67

Metabolizable Energy, 3X ME Mcals/lb < 1.35 1.37 > 1.40

Rela ve Grain Quality RGQ < 130 150 > 1701 Starch within par cles is es mated to eff ec vely ferment at this compara ve mean par cle size.2 Es mated ruminal starch fermenta on rate of the grain in its original form as fed to dairy ca le. Translated from in vitro gas produc on rates of un-dried, un-ground dry and high moisture corns to ruminal passage rates of 16.0 and 12.0% per hour for unfermented and fermented corns, respec vely.


Capstone literature used to establish relationships between observed and predicted in vivo ruminal and total tract

starch digestibility for FeedGrainV2.01,2

Author(s) Cita on Grain Type Processing Moisture MPS,

umProlamin,

% DM3NH3-N, % of N4

In vivo TT SD

Ruminal SD

In vivo TT SD (Study adj)5

FeedGrainV2.0 Predicted

TT SD Ruminal SD

Eastridge et al.* 2011 J. Dairy Sci. 94:3045-3053 Dry Ground 15 800 3.9 . 96.3 . 94.6 93.8 .Dry Ground 15 1900 3.9 . 94.1 . 91.4 90.0 .

Reis et al. 2001 J. Dairy Sci. 84:429–441 HMC Ground 24.7 2220 . 4.0 92.4 . 93.0 92.0 .HMC Rolled 24.7 3140 . 4.0 87.2 . 91.1 91.6 .

San Emeterio et al. 2000 J. Dairy Sci. 83:2839–2848 HMC Rolled 30.0 4430 . 4.0 85.5 . 88.4 89.2 .HMC Ground 30.0 1320 . 4.0 90.2 . 94.9 95.3 .HMC Rolled 30.9 3780 . 4.0 84.1 . 89.7 90.4 .HMC Ground 30.9 1020 . 4.0 91.8 . 95.5 96.0 .Dry Ground 15 3280 3.9 . 80.4 . 84.3 85.3 .Dry Ground 15 1110 3.9 . 88.1 . 90.0 92.6 .

Callison et al6 2001 J. Dairy Sci. 84:1458–1467 Dry Fine Grind 15 1200 3.9 . 98.0 . 93.6 92.3 .Dry Medium Grind 15 2600 3.9 . 92.2 . 85.8 87.7 .Dry Coarse Grind 15 4800 3.9 . 91.3 . 81.9 80.1 .

Dhiman et al. 2002 J. Dairy Sci. 85:217–226 Dry Fine Grind 15 1130 3.9 . 96.1 . 94.3 92.6 .Dry Coarse Grind 15 1650 3.9 . 93.6 . 91.4 90.8 .

Knowlton et al. 1996 J. Dairy Sci. 79:557-564 Dry Ground 15 827 3.9 . 92.2 . 92.3 93.7 .Dry Cracked 15 3265 3.9 . 85.6 . 87.2 85.3 .Dry Ground 15 1250 3.9 . 87.3 . 87.7 92.2 .

Yu et al. 1998 J. Dairy Sci. 81:777–783 Dry Rolled 15 1180 3.9 . 95.8 . 95.3 92.4 .Dry Rolled 15 2450 3.9 . 87.4 . 86.4 88.2 .

Lopes et al. 2009 J. Dairy Sci. 92:4541-4548 Dry Rolled 15 1792 7.5 . 89.6 . 87.5 87.1 .Dry Rolled 15 1394 2.8 . 95.1 . 93.3 92.5 .Dry Rolled 15 1456 1.7 . 96.6 . 94.8 93.3 .

Krause and Combs* 2003 J. Dairy Sci. 86:1382-1397 Dry Rolled 15 3200 3.9 . 88.2 . 86.0 85.4 .HMC Rolled 26.9 3900 . 4.0 93.4 . 89.5 92.3 .

Krause et al.7 2003 J. Dairy Sci. 86:1341-1353 Dry Ground 15 682 3.9 . 92.4 . 94.7 94.2 .Dry Ground 15 1292 3.9 . 86.4 . 89.6 92.0 .Dry Ground 15 1017 3.9 . 90.2 . 93.0 92.9 .Dry Ground 15 1540 3.9 . 85.1 . 88.6 91.1 .

Ekinci and Broderick 1997 J. Dairy Sci. 80:3298–3307 HMC Rolled 32.0 4330 . 4.0 94.2 . 88.6 89.2 .HMC Ground 32.0 1660 . 4.0 98.8 . 94.2 94.5 .

Krause et al.* 2002 J. Dairy Sci. 85:1936-1946 Dry Ground 15 1550 3.9 . 93.1 . 92.1 91.1 .HMC Ground 25.8 1600 . 4.0 97.4 . 94.3 94.6 .

Knowlton et al. 1998 J. Dairy Sci. 81:1972–1984 HMC Ground 30.0 489 . 4.0 98.2 86.8 96.6 97.5 80.0HMC Rolled 30.0 1789 . 4.0 95.7 81.2 93.9 94.2 62.5Dry Ground 15 618 3.9 . 88.9 60.9 93.3 94.4 59.8Dry Rolled 15 1725 3.9 . 76.4 69.2 83.6 90.6 47.4

Remond et al.8 2004 J. Dairy Sci. 87:1389–1399 Dry Ground 15 730 5.0 . 91.4 58.6 93.7 93.6 56.4Dry Ground 15 1800 5.0 . 86.0 49.8 88.6 89.3 44.7

Oba and Allen* 2003 J. Dairy Sci. 86:184-194 Dry Ground 15 880 3.9 . 93.6 46.4 93.5 93.5 56.0HMC Ground 36.7 1860 . 4.0 94.6 64.8 93.7 94.1 62.0

Taylor and Allen9* 2005 J. Dairy Sci. 88:1413-1424 Dry Ground 15 1594 5.0 . 91.7 34.9 89.9 90.1 46.4Dry Ground 15 1377 2.0 . 96.3 57.0 94.7 93.1 54.7

1 HMC = high moisture corn, MPS = mean par cle size, TT = total tract, SD = starch diges bility, DM = dry ma er, CP = crude protein.2 Trial Criteria 1) > 80 % of starch from grain, 2) MPS reported, 3) direct comparsion grain type, 4) in vivo TT SD measured.3 An average prolamin value of 3.9% of DM was used for all trials involving unfermented corn unless otherwise defi ned.4 An average NH3-N value of 4.0% was used for all trials involving fermented corn.5 In vivo total tract starch diges bili es were adjusted for random study eff ects (St-Pierre, N.R., JDS:84-741-755).6 Non structural carbohydrate diges bility was used as a surrogate for TT SD.7 Pure starch was added to reduce MPS.8 Inclusion of all treatments in experiments resulted in failure of mixed sta s cal models to converge, sugges ng outlier in vivo data. Prolamin values of 5.0% of DM u lized as the trial indicated semi-fl int corns were fed.9 Prolamin values of 5.0 and 2.0 were u lized to represent vitreous (66%) and fl oury corns (0%) fed in the trial.* Treatments converged.


trials non-structural carbohydrate diges bility had to be used as a surrogate for TTSD.

CONCLUSIONS

The objec ve in developing an integrated feed grain evalu-a on system (FeedGrainV2.0) is to provide an educa onal pla orm for dairy educators, consultants, and producers to evaluate feed grains using the principal components which infl uence feed grain diges on and dairy ca le per-formance. FeedGrainV2.0 is a public educa onal program and may be used for applica on or advanced study to improve feed grain u liza on in dairy ca le.

REFERENCES

Allen, M. S., R. A. Longuski, and Y. Ying. 2008. Endosperm type of dry ground corn grain aff ects ruminal and total tract diges on of starch in lacta ng dairy cows. J. Dairy Sci. 91(Suppl.1):529. (Abstr.)

American Society of Agricultural and Biological Engineers. 2008. Methods of determining and expressing fi neness of feed materials by sieving. ASAE S319.4. St Joseph, MO.

Baker, S., and T. Herrman. 2002. Evalua ng par cle size: MF-2051. Kan-sas State University Agricultural Experiment Sta on and Coopera ve Extension Service. Manha an, KS.

Benton, J. R., T. Klopfenstein, and G. E. Erickson. 2005. Eff ects of corn moisture and length of ensiling on dry ma er diges bility and rumen degradable protein. Nebraska Beef Ca le Reports: 31-33.

Callison, S. L., J. L. Firkins, M. L. Eastridge, and B. L. Hull. 2001. Site of nutrient diges on by dairy cows fed corn of diff erent par cle sizes or steam-rolled. J. Dairy Sci. 84:1458-1467.

Der Bedrosian, M. C., K. E. Nestor Jr., and L. Kung Jr. 2012. The eff ects of hybrid, maturity, and length of storage on the compos on and nutri ve value of corn silage. J. Dairy Sci. 95:5115-5126.

Dhiman, T. R., M. S. Zamain, I. S. MacQueen, and R. L. Boman. 2002. Infl uence of corn processing and frequency of feeding on cow per-formance. J. Dairy Sci. 85:217–226.

Eastridge, M. L., A. H. Lefeld, A. M. Eilenfeld, P. N. Go , W. S. Bowen, and J. L. Firkins. 2011. Corn grain and liquid feed as nonfi ber carbohydrate sources in diets for lacta ng dairy cows. J. Dairy Sci. 94: 3045-3053.

Ekinci, C., and G. A. Broderick. 1997. Eff ect of processing high mois-ture ear corn on ruminal fermenta on and milk yield. J. Dairy Sci. 80:3298–3307.

Ferreira, G., and D. R. Mertens. 2005. Chemical and physical charac-teris cs of corn silages and their eff ects on in vitro disappearance. J. Dairy Sci. 88:4414-4425.

Firkins, J. L., M. L. Eastridge, N. R. St-Pierre, and S. M. No sger. 2001. Eff ects of grain variability and processing on starch u liza on by lacta ng dairy ca le. J. Anim. Sci. 79:E218-E238.

Hoff man, P. C., D. R. Mertens, J. Larson, W. K. Coblentz, and R. D. Shaver. 2012. A query for eff ec ve mean par cle size of dry and high moisture corns. J. Dairy Sci. 95:3467-3477.

Hoff man, P. C., N. M. Esser, R. D. Shaver, W. Coblentz, M. P. Sco , A. L. Bodnar, R. Schmidt, and B. Charley. 2010. Infl uence of inocula on and storage me on in vitro gas produc on of high moisture corn. J. Dairy Sci. 93(Suppl. 1):725. (Abstr.)

Hoff man, P. C., N. M. Esser, R. D. Shaver, W. Coblentz, M. P. Sco , A. L. Bodnar, R. Schmidt, and B. Charley. 2011. Infl uence of inocula on and storage me on altera on of the starch-protein matrix in high moisture corn. J. Dairy Sci. 94:2465-2474.

Knowlton, K. F., B. P. Glenn, and R. A. Erdman. 1998. Performance, ruminal fermenta on, and site of starch diges on in early lacta on cows fed corn grain harvested and processed diff erently. J. Dairy Sci. 81:1972-1984.

Knowlton, K. F., M. S. Allen, and P. S. Erickson. 1996. Lasalocid and par- cle size of corn grain for dairy cows in early lacta on. 1. Eff ect on

performance, serum metabolites and nutrient diges bility. J. Dairy Sci. 79:557-564.

Krause, K. M., and D. K. Combs. 2003. Eff ects of forage par cle size, for-age source, and grain fermentability on performance and ruminal pH in midlacta on cows. J. Dairy Sci. 86:1382-1397.

Krause, K. M., D. K. Combs, and K. A. Beauchemin. 2002. Eff ects of for-age par cle size and grain fermentability in midlacta on cows. I. Milk produc on and diet diges bility. J. Dairy Sci. 85:1936-1946.

Krause, K. M., D. K. Combs, and K. A. Beauchemin. 2003. Eff ects of increasing levels of refi ned cornstarch in the diet of lacta ng dairy cows on performance and ruminal pH. J. Dairy Sci. 86:1341-1353.

Larson, J., and P. C. Hoff man. 2008. Technical Note: A method to quan- fy prolamin proteins in corn which are nega vely related to starch

diges bility in ruminants. J. Dairy Sci. 91: 4834-4839.

Lawton, J. W. 2002. Zein: A history of processing and use. Cereal Chem. 79:1-18.

Lopes, J. C., R. D. Shaver, P. C. Hoff man, M. S. Akins, and S. J. Ber cs. 2009. Type of corn endosperm infl uences nutrient diges bility in lacta ng dairy cows. J. Dairy Sci. 92: 4541-4548.

Na onal Research Council. 2001. Nutrient Requirements of Dairy Ca le. 7th rev ed. Natl. Acad. Sci., Washington, DC.

Oba, M., and M. S. Allen. 2003. Eff ects of corn grain conserva on method on ruminal diges on kine cs for lacta ng dairy cows at two dietary starch concentra ons. J. Dairy Sci. 86:184-194.

Reis, R. B., F. San Emeterio, D. K. Combs, L. D. Sa er, and H. N. Costa. 2001. Eff ects of corn par cle size and source on performance of lacta ng dairy cows fed direct cut grass legume forage. J. Dairy Sci. 84:429–441.

Remond, D., J. I. Cabrer-Estrada, M. Chapion, B. Chauveau, R. Coudure, and C. Poncet. 2004. Eff ect of corn par cle size on site and extent of starch diges on in lacta ng dairy cows. J. Dairy Sci. 87:1389-1399.

San Emeterio, F., R. B. Reis, W. F. Campos, and L. D. Sa er. 2000. Eff ect of coarse or fi ne grinding on u liza on of dry or ensiled corn by lacta ng dairy cows. J. Dairy Sci. 83:2839–2848.

St-Pierre, N. R. 2001. Integra ng quan ta ve fi ndings from mul ple studies using mixed model methodology. J. Dairy Sci. 84:741-755.

Taylor, C. C., and M. S. Allen. 2005. Corn grain endosperm type and brown midrib 3 corn silage: Site of diges on and ruminal diges on kine cs in lacta ng cows. J. Dairy Sci. 88:1413-1424.

Theurer, C. B. 1986. Grain processing eff ects on starch u liza on by ruminants. J. Anim. Sci. 63:1649-1662.

Yu, P., J. T. Huber, F. A. P. Santos, J. M. Simas, and C. B. Theurer. 1998. Eff ects of ground, steam fl aked, and steam rolled corn grains on per-formance of lacta ng cows. J. Dairy Sci. 81:777-783.

Zwald A., A. E. Dorshorst, P. C. Hoff man, L. M. Bauman, M. G. Bertram. 2008. Technical Note: A near infrared refl ectance spectroscopy tech-nique to predict par cle size of starch within corn silage. J. Dairy Sci. 91: 2071-2076.


INTRODUCTION

Across the globe, the trend toward fewer, larger dairy opera ons con nues. Dairy opera ons today are char-acterized by narrower profi t margins than in the past, largely because of reduced governmental involvement in regula ng agricultural commodity prices. Consequently, small changes in production or efficiency can have a major impact on profi tability. The resul ng compe on growth has intensifi ed the drive for effi ciency resul ng in increased emphasis on business and fi nancial manage-ment. Furthermore, the decision making landscape for a dairy manager has changed drama cally with increased emphasis on consumer protec on, con nuous quality assurance, natural foods, pathogen-free food, zoono c disease transmission, reduc on of the use of medical treatments, and increased concern for the care of animals. These changing demographics refl ect a con nuing change in the way in which dairy opera ons are managed. In large part, many of these changes can be a ributed to tremendous technological progress in all facets of dairy farming, including gene cs, nutri on, reproduc on, dis-ease control, and management. W. Nelson Philpot (2003) captured this change eff ec vely in describing modern dairy farms as “technological marvels.” Conceivably, the next “technological marvel” in the dairy industry may be in Precision Dairy Farming.

WHAT IS PRECISION DAIRY FARMING?

Precision Dairy Farming is the use of technologies to measure physiological, behavioral, and produc on in-dicators on individual animals to improve management strategies and farm performance. Many Precision Dairy Farming technologies, including daily milk yield recording, milk component monitoring (e.g. fat, protein, and SCC), pedometers, automa c temperature recording devices, milk conduc vity indicators, automa c estrus detec on monitors, and daily body weight measurements, are already being u lized by dairy producers. Eastwood et al. (2004) defi ned Precision Dairy Farming as “the use of informa on technologies for assessment of fi ne-scale

ABSTRACT

Precision Dairy Farming is the use of technologies to mea-sure physiological, behavioral, and produc on indicators on individual animals to improve management strategies and farm performance. Many Precision Dairy Farming technologies, including daily milk yield recording, milk com-ponent monitoring, pedometers, automa c temperature recording devices, milk conduc vity indicators, automa c estrus detec on monitors, and daily body weight mea-surements, are already being u lized by dairy producers. Other theore cal Precision Dairy Farming technologies have been proposed to measure jaw movements, ruminal pH, re cular contrac ons, heart rate, animal posi oning and ac vity, vaginal mucus electrical resistance, feeding behavior, lying behavior, odor, glucose, acous cs, proges-terone, individual milk components, color (as an indicator of cleanliness), infrared udder surface temperatures, and respira on rates. The main objec ves of Precision Dairy Farming are maximizing individual animal poten al, early detec on of disease, and minimizing the use of medica on through preven ve health measures. Perceived benefi ts of Precision Dairy Farming technologies include increased efficiency, reduced costs, improved product quality, minimized adverse environmental impacts, and improved animal health and well-being. Real me data used for monitoring animals may be incorporated into decision support systems designed to facilitate decision making for issues that require compila on of mul ple sources of data. Technologies for physiological monitoring of dairy cows have great poten al to supplement the observa onal ac vi es of skilled herdspersons, which is especially cri -cal as more cows are managed by fewer skilled workers. Moreover, data provided by these technologies may be incorporated into gene c evalua ons for non-produc on traits aimed at improving animal health, well-being, and longevity. The economic implica ons of technology adop- on must be explored further to increase adop on rates

of Precision Dairy Farming technologies. Precision Dairy Farming may prove to be the next important technological breakthrough for the dairy industry.

How Precision Dairy Technologies Can Change Your World

Jeff rey Bewley

University of Kentucky


animal and physical resource variability aimed at improved management strategies for op mizing economic, social, and environmental farm performance.” Spilke and Fahr (2003) stated that Precision Dairy Farming, with specifi c emphasis on technologies for individual animal monitor-ing, “aims for an ecologically and economically sustainable produc on of milk with secured quality, as well as a high degree of consumer and animal protec on.” With Preci-sion Dairy Farming, the trend toward group management may be reversed with focus returning to individual cows through the use of technologies (Schulze et al., 2007). Technologies included within Precision Dairy Farming range in complexity from daily milk yield recording to mea-surement of specifi c a ributes (e.g. fat content or proges-terone) within milk at each milking. The main objec ves of Precision Dairy Farming are maximizing individual animal poten al, early detec on of disease, and minimizing the use of medica on through preven ve health measures. Precision Dairy Farming is inherently an interdisciplinary fi eld incorpora ng concepts of informa cs, biosta s cs, ethology, economics, animal breeding, animal husbandry, animal nutri on, and engineering (Spilke and Fahr, 2003).

POTENTIAL BENEFITS OF PRECISION DAIRY

FARMING

Perceived benefi ts of Precision Dairy Farming technologies include increased effi ciency, reduced costs, improved prod-uct quality, minimized adverse environmental impacts, and improved animal health and well-being. These technologies are likely to have the greatest impact in the areas of health, reproduc on, and quality control (de Mol, 2000). Realized benefi ts from data summariza on and excep on report-ing are an cipated to be higher for larger herds, where individual animal observa on is more challenging and less likely to occur (Lazarus et al., 1990). As dairy opera- ons con nue to increase in size, Precision Dairy Farming

technologies become more feasible because of increased reliance on less skilled labor and the ability to take advan-tage of economies of size related to technology adop on.

A Precision Dairy Farming technology allows dairy produc-ers to make more mely and informed decisions, resul ng in be er produc vity and profi tability (van Asseldonk et al., 1999b). Real me data can be used for monitoring ani-mals and crea ng excep on reports to iden fy meaningful devia ons. In many cases, dairy management and con-trol ac vi es can be automated (Delorenzo and Thomas, 1996). Alterna vely, output from the system may provide a recommenda on for the manager to interpret (Pietersma et al., 1998). Informa on obtained from Precision Dairy Farming technologies is only useful if it is interpreted and

u lized eff ec vely in decision making. Integrated, comput-erized informa on systems are essen al for interpre ng the mass quan es of data obtained from Precision Dairy Farming technologies. This informa on may be incorpo-rated into decision support systems designed to facilitate decision making for issues that require compila on of mul ple sources of data.

Historically, dairy producers have used experience and judgment to iden fy outlying animals. While this skill is invaluable and can never be fully replaced with automated technologies, it is inherently fl awed by limita ons of hu-man percep on of a cow’s condi on. O en, by the me an animal exhibits clinical signs of stress or illness, it is too late to intervene. These easily observable clinical symp-toms are typically preceded by physiological responses evasive to the human eye (e.g. changes in temperature or heart rate). Thus, by iden fying changes in physiological parameters, a dairy manager may be able to intervene sooner. Technologies for physiological monitoring of dairy cows have great poten al to supplement the observa onal ac vi es of skilled herdspersons, which is especially cri -cal as more cows are managed by fewer skilled workers (Hamrita et al., 1997).

PRECISION DAIRY FARMING EXAMPLES

The list of Precision Dairy Farming technologies used for animal status monitoring and management con nues to grow. Because of rapid development of new technolo-gies and suppor ng applica ons, Precision Dairy Farming technologies are becoming more feasible. Many Preci-sion Dairy Farming technologies including daily milk yield recording, milk component monitoring (e.g. fat, protein, and SCC), pedometers, automa c temperature recording devices, milk conduc vity indicators, automa c estrus detec on monitors, and daily body weight measurements are already being u lized by dairy producers. Despite its seemingly simplis c nature, the power of accurate milk weights should not be discounted in monitoring cows, as it is typically the fi rst factor that changes when a problem develops (Philpot, 2003). Other theore cal Precision Dairy Farming technologies have been proposed to measure jaw movements, ruminal pH, re cular contrac ons, heart rate, animal posi oning and ac vity, vaginal mucus electrical resistance, feeding behavior, lying behavior, odor, glucose, acous cs, progesterone, individual milk components, color (as an indicator of cleanliness), infrared udder surface temperatures, and respira on rates. Unfortunately, the development of technologies tends to be driven by avail-ability of a technology, transferred from other industries in market expansion eff orts, rather than by need. Rela ve


to some industries, the dairy industry is rela vely small, limi ng corporate willingness to invest extensively in de-velopment of technologies exclusive to dairy farms. Many Precision Dairy Farming technologies measure variables that could be measured manually, while others measure variables that could not have been obtained previously.

ADOPTION OF PRECISION DAIRY FARMING

TECHNOLOGIES

Despite widespread availability, adop on of these tech-nologies in the dairy industry has been rela vely slow thus far (Eleveld et al., 1992; Huirne et al., 1997; Gelb et al., 2001). In fact, agricultural adop on of on-farm so ware packages, as a whole, has been much lower than predicted (Rosskopf and Wagner, 2003). The majority of informa on management systems available and used by many dairy producers are underu lized. In prac cality, their use is o en limited to crea ng produc on tables, a en on lists, and working schedules (van Asseldonk, 1999). Perceived economic returns from inves ng in a new technology are likely the main factor infl uencing Precision Dairy Farming technology adop on. Addi onal factors impac ng tech-nology adop on include degree of impact on resources used in the produc on process, level of management needed to implement the technology, risk associated with the technology, producer goals and mo va ons, and hav-ing an interest in a specifi c technology (Dijkhuizen et al., 1997, van Asseldonk, 1999). Characteris cs of the primary decision maker that infl uence technology adop on include age, level of formal educa on, learning style, goals, farm size, business complexity, percep ons of risk, type of pro-duc on, ownership of a non-farm business, innova veness in produc on, overall expenditures on informa on, and use of the technology by peers and other family mem-bers. Eleveld et al. (1992) demonstrated that technology adop on is improved when the technology fi ts within the normal daily work pa erns of the personnel who will be using it. Farm opera ons with more specializa on of labor are more likely to successfully adopt informa on technol-ogy (Eleveld et al., 1992). The most progressive produc-ers will adopt those new technologies that appear to be profi table. When a proven technology is not adopted, the opera on observes a lost opportunity cost that may lead to a compe ve disadvantage (Galligan, 1999).

INVESTMENT ANALYSIS OF PRECISION DAIRY

FARMING TECHNOLOGIES

Today’s dairy manager is presented with a constant stream of new technologies to consider including new Precision Dairy Farming technologies. Galligan and Groenendaal (2001) suggested that “the modern dairy producer can be

viewed as a manager of an investment por olio, where various investment opportuni es (products, management interven ons) must be selected and combined in a man-ner to provide a profi t at a compe ve risk to alterna ve opportuni es.” Further, dairy managers must consider both biological and economic considera ons simultane-ously in their decisions. Tradi onally, investment decisions have been made using standard recommenda ons, rules of thumb, consultant advice, or intui on. Thus, more objec ve methods of investment analysis are needed (Verstegen et al., 1995).

Adop on of sophis cated on-farm decision-making tools has been scant in the dairy industry to this point. Yet, the dairy industry remains a perfect applica on of decision science because: (1) it is characterized by considerable price, weather, and biological varia on and uncertainty, (2) technologies, such as those characteris c of Precision Dairy Farming, designed to collect data for decision making abound, and (3) the primary output, fl uid milk, is diffi cult to diff eren ate, increasing the need for alterna ve means of business diff eren a on. In “Compe ng on Analy cs: The New Science of Winning,” Davenport and Harris (2007) pose that in industries with similar technologies and prod-ucts, “high performance business processes” are one of the only ways that businesses can diff eren ate themselves.

Investment analyses of informa on systems and technolo-gies are common within the general business literature (Streeter and Hornbaker, 1993; Bannister and Remenyi, 2000; Ryan and Harrison, 2000; Lee and Bose, 2002). How-ever, dairy-specifi c tools examining investment of Precision Dairy Farming technologies are limited (Carmi, 1992; Gelb, 1996; van Asseldonk, 1999), though investment analyses of other dairy technologies abound (Hyde and Engel, 2002). Empirical comparisons of technology before or a er adop- on or between herds that have adopted a technology and

control herds that have not adopted are expensive and biased by other, possibly herd-related diff erences. As a re-sult, the norma ve approach, using simula on modeling, predominates in decision support models in animal agricul-ture (Dijkhuizen et al., 1991). Inves ng in new agricultural technologies is all too o en a daun ng and complex task. First, the standard approach using the Net Present Value is o en misleading because it does not adequately account for the underlying uncertain es. Second, the incremental costs and benefi ts of new technologies require complex interac ons of mul ple variables that are o en non-linear and not intui ve. The complexi es surrounding investment in Precision Dairy Farming technologies is one example of this type of complex decision.


Ward (1990) listed three benefi ts to investment in technol-ogy: 1) subs tu ve, replacing human power with machine power, 2) complementary, improving produc vity and employee eff ec veness through new ways of accomplish-ing tasks, and 3) innova ve, obtaining a compe ve edge. In addi on to impacts on produc on, many technologies may also change milk composi on, reproduc ve effi ciency, and disease incidences (Galligan and Groenendaal, 2001). In an analysis of an investment opportunity at the dairy level, cash fl ows are generally uncertain because of bio-logical variability or incomplete knowledge of the system (Galligan and Groenendaal, 2001). The impact that a Precision Dairy Farming technology has on produc ve and economic performance is diffi cult to examine because of the changing nature of the decision environment where investments are o en one- me investments but returns accrue over a longer period of me (Ward, 1990; Ver-stegen et al., 1995; van Asseldonk, 1999; van Asseldonk et al., 1999a,b). Further, benefi t streams resul ng from investment in a Precision Dairy Farming technology are highly dependent upon the user’s ability to understand and u lize the informa on provided by the new technology (Bannister and Remenyi, 2000). An economic analysis of the value of Precision Dairy Farming technologies requires considera on of the eff ect of adop on on both quality and meliness of decisions (Verstegen et al., 1995). Improve-

ments associated with adop on of new Precision Dairy Farming technologies may increase profi ts directly through improved u liza on of data provided by the technology or indirectly through recommenda ons of consultants u lizing the new informa on (Tomaszewski et al., 1997). It is diffi cult, if not impossible to quan fy the economic value of personal welfare associated with a proposed change (e.g. free me or pres ge; O e and Chilonda, 2000). For example, it is nearly impossible to quan fy the sa sfac on of having a healthy herd, reduc on of animal suff ering, reduced human health risks, and environmental improvements (Huirne et al., 2003). Despite eff orts to for-malize the ra onal decision making analysis of investment in informa on technologies, many business execu ves ul mately make their investment decision based on “gut feel” or “acts of faith” (Silk, 1990; Bannister and Remenyi, 2000; Passam et al., 2003). Ul mately, decision making is and should be dependent upon both ra onal analysis and ins nct (Bannister and Remenyi, 2000).

SIMULATION OF DAIRY FARMS

Mayer et al. (1998) proposed that with the variety of man-agement issues a dairy manager faces in an ever-changing environment (e.g. environmental, fi nancial, and biologi-cal), best management strategies cannot be verifi ed and

validated with fi eld experiments. As a result, simula on is the only method of “integra ng and es ma ng” these eff ects (Mayer et al., 1998). Simula ons are mathema cal models designed to represent a system, such as a dairy farm, for use in decision-making. Simula on models are useful and cost-eff ec ve in research that requires complex scenarios involving a large number of variables with large groups of animals over a long period of me under a large range of condi ons (Bethard, 1997; Shalloo et al., 2004). The primary advantages of using mathema cal computer simula on models in evalua ng dairy produc on issues are the ability to control more variables within the model than with a fi eld trial and the reduced costs associated with this kind of eff ort (Skidmore, 1990; Shalloo et al., 2004). These economic models can also be useful in evalua ng alterna- ves where very li le real data is available yet (Dijkhuizen

et al., 1995). Simula ng a system is par cularly useful when uncertain, complex feedback loops exist (e.g. disease aff ects produc on which then impacts other variables fur-ther back in the system) (Dijkhuizen et al., 1995). Models that represent system uncertainty, while eff ec vely using available informa on, provide more realis c insight than models that do not consider a range of responses (Benne , 1992; Passam et al., 2003).

Simula on or other systemic methods are preferred to capture the complexity of a dairy system as they can evaluate mul ple biological and economic factors aff ec ng performance, including management, feeding, breeding, culling, and disease (Skidmore, 1990; Sorensen et al., 1992). Because the dairy system includes environmen-tal, economic, and physical components, accoun ng for interac ons among components and tracing the eff ects of an interven on through the en re system are essen- al (Cabrera et al., 2005). Simula on models are ideal

for analyzing investment strategies because they can ef-fec vely examine improvement in biological parameters based on farm-specifi c data rather than simple industry averages (Jalvingh, 1992; Dijkhuizen et al., 1995; Delorenzo and Thomas, 1996; van Asseldonk et al., 1999b; Gabler et al., 2000). Simula on of a farm can be accomplished by conduc ng two simula ons, one with and one without a proposed change or interven on and then comparing these simula ons to examine the impact on biological or economic parameters of interest (van Asseldonk, 1999). The output of a series of simula ons provides a range of results, more realis cally depic ng biological variability than simple models (Marsh et al., 1987).

Risk and uncertainty are major considera ons within a dairy produc on system because of the random nature


of milk produc on, biology, disease, weather, input costs, and milk prices (Delorenzo and Thomas, 1996). This risk and uncertainty represents a major por on of the diffi culty and complexity of managing a dairy opera on (Huirne, 1990). Uncertainty must be considered in decision-making to avoid biased es mates and erroneous decisions (Kris-tensen and Jorgensen, 1998). Future costs and returns are always uncertain (Lien, 2003). Within precision ag-riculture, accurate representa on of risk associated with technology adop on is cri cal in the decision making process (Marra et al., 2003).

When managers do not have suffi cient informa on to assess the risk outcomes of decisions, they use subjec- ve probabili es based on past experiences and their

own judgment (Huirne, 1990). In most situa ons, deci-sion makers are primarily concerned with the chances of the realized returns from an investment being less than predicted (Galligan et al., 1987). The ability of a model to refl ect real world condi ons increases with consider-a on of more variables (Jalvingh, 1992). Nevertheless, to ensure that the model remains prac cal and reasonable, only variables with the most infl uence on the fi nal desired outcome should be entered into the model as random (Jalvingh, 1992; Lien, 2003).

PURDUE/KENTUCKY RESEARCH MODEL

Bewley et al. (2010b) developed a simula on model of a dairy farm to evaluate investments in precision dairy farm-ing technologies by examining a series of random processes over a ten-year period. The model was designed to charac-terize the biological and economical complexi es of a dairy system within a par al budge ng framework by examining the cost and benefi t streams coinciding with investment in a Precision Dairy Farming technology. Although the model currently exists only in a research form, a secondary aim was to develop the model in a manner conducive to future u lity as a fl exible, farm-specifi c decision making tool. The basic model was constructed in Microso Excel 2007 (Mi-croso , Sea le, WA). The @Risk 5.0 (Palisade Corpora on, Ithaca, NY) add-in for Excel was u lized to account for the random nature of key variables in a Monte Carlo simula- on. In Monte Carlo simula on, random drawings are

extracted from distribu ons of mul ple random variables over repeated itera ons of a model to represent the impact of diff erent combina ons of these variables on fi nancial or produc on metrics (Kristensen and Jorgensen, 1998).

The basic structure of the model is depicted in Figure 1. The underlying behavior of the dairy system was represented using current knowledge of herd and cow management with

rela onships defi ned from exis ng literature. Historical prices for cri cal sources of revenues and expenses within the system were also incorporated as model inputs. The fl exibility of this model lies in the ability to change inputs describing the ini al herd characteris cs and the poten al impact of the technology. Individual users may change these inputs to match the condi ons observed on a specifi c farm.

A er inputs are entered into the model, an extensive se-ries of intermediate calcula ons are computed within 13 modules, each exis ng as a separate worksheet within the main Excel spreadsheet. Each module tracks changes over a 10-year period for its respec ve variables. Within these inter-connected modules (Figure 2), the impact of inputs, random variables, and technology-induced improvements are es mated over me using the underlying system be-havior within the model. Results of calcula ons within 1

Figure 1. Diagram depic ng general fl ow of informa on within the model.

Figure 2. Diagram of model modules.


module o en aff ect calcula ons in other modules with mul ple feed-forward and feed-backward interdepen-dencies. Each of these modules eventually results in a calcula on that will infl uence the cost and revenue fl ows necessary for the par al budget analysis. Finally, the costs and revenues are u lized for the project analysis examining the net present value (NPV) and fi nancial feasibility of the project along with associated sensi vity analyses.

Agricultural commodity markets are characterized by tre-mendous vola lity and, in many countries, this vola lity is increasing with reduced governmental price regula on. As a result, economic condi ons and the profi tability of investments can vary considerably depending on the prices paid for inputs and the prices received for outputs. Producers are o en cri cal of economic analyses that fail to account for this vola lity, by using a single value for cri cal prices, recognizing that the results of the analysis may be diff erent with higher or lower milk prices, for example. In a simula on model, variability in prices can be accounted for by considering the random varia on of these variables. In this model, historical U.S. prices from 1971 to 2006 for milk, replacement heifers, alfalfa, corn, and soybeans were collected from the “Understanding Dairy Markets” website (Gould, 2007). Historical cull cow prices were defi ned using the USDA-Na onal Agricultural Sta s cs Service values for “beef cows and cull dairy cows sold for slaughter” (USDA-NASS, 2007). Base values for future prices (2007 to 2016) of milk, corn, soybeans, alfalfa, and cull cows were set using es mates from the Food and Agricultural Policy Research Ins tute’s (FAPRI) U.S. and World Agricultural Outlook Report (FAPRI, 2007). Varia- on in prices was considered within the simula on based

on historical varia on. In this manner, the vola lity in key prices can be considered within a profi tability analysis.

Although there is probably no direct way to account for the many decisions that ul mately impact the actual profi tability of an investment in a Precision Dairy Farm-ing technology, this model includes a Best Management Prac ce Adherence Factor (BMPAF) to represent the po-ten al for observing the maximum benefi ts from adop ng a technology. The BMPAF is a crude scale from 1 to 100% designed to represent the level of the farm management. At a value of 100%, the assump on is that the farm man-agement is capable and likely to u lize the technology to its full poten al. Consequently, they would observe the maximum benefi t from the technology. On the other end of the spectrum, a value of 0% represents a scenario where farm management installs a technology without changing management to integrate the newly available data in ef-

forts to improve herd performance. In this case, the farm would not recognize any of the benefi ts of the technology. Perhaps most importantly, sensi vity analyses allow the end user to evaluate the decision with knowledge of the role they play in its success.

INVESTMENT ANALYSIS OF AUTOMATED

BODY CONDITION SCORING

To show how it can be used prac cally, this model was used for an investment analysis of automa c body condi on scores on dairy farms (Bewley et al., 2010a). Automated body condi on scoring (BCS) through extrac on of infor-ma on from digital images has been demonstrated to be feasible, and commercial technologies are being developed (Bewley et al., 2008). The primary objec ve of this research was to iden fy the factors that infl uence the poten al profi tability of inves ng in an automated BCS system. An expert opinion survey was conducted to provide es mates for poten al improvements associated with technology adop on. Benefi ts of technology adop on were es mated through assessment of the impact of BCS on the incidence of ketosis, milk fever, and metri s, concep on rate at fi rst service, and energy effi ciency. For this research example, industry averages for produc on and fi nancial parameters, selected to represent condi ons for a U.S. dairy farm milk-ing 1000 cows in 2007 were used. Further details of model inputs and assump ons may be obtained from the author.

Net present value (NPV) was the metric used to assess the profi tability of the investment. The default discount rate of 8% was adjusted to 10% because this technology has not been marketed commercially; thus, the risk for early adopters of the technology is higher. The discount rate par ally accounts for this increased risk by requiring higher returns from the investment. The general rule of thumb is that a decision with a NPV greater than 0 is a “go” decision and a worthwhile investment for the business. The investment at the beginning of the project includes the purchase costs of the equipment needed to run the system in addi on to purchasing any other setup costs or purchases required to start the system. Recognizing that a simpler model ignores the uncertainty inherent in a dairy system, Monte Carlo simula on was conducted using the @Risk add-in. This type of simula on provides infi nite opportuni es for sensi vity analyses. Simula ons were run using 1000 itera ons in each simula on. Simula- ons were run, using es mates provided by experts, for

scenarios with li le to no improvement in the distribu on of BCS and with defi nite improvement.


PROFITABILITY ANALYSIS

For the small likelihood of improvement simula on, 13.1% of simula on itera ons resulted in a posi ve NPV whereas this same number was 87.8% for the scenario with a defi nite improvement. In other words, using the model assump ons for an average 1000 cow U.S. dairy in 2007, in-ves ng in an automated BCS system was the right decision 13.1% or 87.8% of the me depending on the assump on of what would happen with BCS distribu on a er tech-nology adop on. The individual decision maker’s level of risk aversion would then determine whether they should make the investment. Although this serves as an example of how this model could be used for an individual decision maker, this profi tability analysis should not be taken liter-ally. In reality, an individual dairy producer would need to look at this decision using herd-specifi c variables to assess the investment poten al of the technology. The main take home message was that because results from the investment analysis were highly variable, this technology is certainly not a “one size fi ts all” technology that would prove benefi cial for all dairy producers.

SENSITIVITY ANALYSES

The primary objec ve of this research was to gain a bet-ter understanding of the factors that would infl uence the profi tability of inves ng in an automated BCS system through sensi vity analysis. Sensi vity analysis, designed to evaluate the range of poten al responses, provides further insight into an investment analysis (van Asseldonk et al., 1999b). In sensi vity analyses, tornado diagrams visually portray the eff ect of either inputs or random vari-ables on an output of interest. In a tornado diagram, the lengths of the bars are representa ve of the sensi vity of the output to each input. The tornado diagram is arranged with the most sensi ve input at the top progressing toward the least sensi ve input at the bo om. In this manner, it is easy to visualize and compare the rela ve importance of inputs to the fi nal results of the model.

Improvements in reproduc ve performance had the largest infl uence on revenues followed by energy effi ciency and then by disease reduc on. Random variables that had the most infl uence on NPV were as follows: variable cost increases a er technology adop on; the odds ra os for ke-tosis and milk fever incidence and concep on rates at fi rst service associated with varying BCS ranges; uncertainty of the impact of ketosis, milk fever, and metri s on days open, unrealized milk, veterinary costs, labor, and discarded milk; and the change in the percent of cows with BCS at calving ≤ 3.25 before and a er technology adop on. Sca er plots of the most sensi ve random variables plo ed against NPV

along with correla on coeffi cients demonstrate how ran-dom variables impact profi tability. In both simula ons, the random variable that had the strongest rela onship with NPV was the variable cost increase. Not surprisingly, as the variable costs per cow increased the NPV decreased in both simula ons (Figure 3). Thus, the value of an automated BCS system was highly dependent on the costs incurred to u lize the informa on provided by the system to alter nutri onal management for improved BCS profi les.

Finally, the results of any simula on model are highly dependent on the assumptions within the model. A one-way sensi vity analysis tornado diagram compares mul ple variables on the same graph. Essen ally, each input is varied (1 at a me) between feasible high and low values and the model is evaluated for the output at those levels holding all other inputs at their default levels. On the tornado diagram, for each input, the lower value is plo ed at the le end of the bar and the higher value at the right end of the bar (Clemen, 1996). Simula ons were run for high and low feasible values for 6 key inputs that may aff ect NPV. The tornado diagram for the 95th percen le NPV from the simula on with a small likelihood of improvement in BCS distribu on is presented in Figure 4. Herd size had the most infl uence on NPV. The NPV was higher for the larger herd because the investment costs and benefi ts were spread among more cows.

The next most important variable was the BMPAF. Again, this result was not surprising and reiterates that one of the most important determinants of project success was what the producer actually does to manage the informa-

Figure 3. Sca er plot of Net Present Value versus annual percentage increase in variable costs (for simula on using all expert opinions provided).


on provided by the technology. There are many nutri- onal, health, reproduc ve, and environmental decisions

made by the dairy producer that have a major impact on changes in body reserves for both individual cows and groups of cows. Management level plays a cri cal role in determining returns from inves ng in a Precision Dairy Farming technology. The level of management in day-to-day handling of individual cows may also infl uence the impact of Precision Dairy Farming technologies. Van As-seldonk (1999) defi ned management capacity as “having the appropriate personal characteris cs and skills to deal with the right problems and opportuni es in the right moment and in the right way.” Eff ec ve use of an infor-ma on system requires an investment in human capital in addi on to investment in the technology (Streeter and Hornbaker, 1993). Then, the level of milk produc on was the next most sensi ve input. As the level of milk produc- on increased, the benefi ts of reducing disease incidence

and calving intervals increased. As would be expected, the NPV increased with an increased base incidence of ketosis because the eff ects of BCS on ketosis would be exagger-ated. The purchase price of the technology had a rela vely small impact on the NPV as did the base culling rate.

CONCLUSIONS AND OUTLOOK

Though Precision Dairy Farming is in its infancy, new Pre-cision Dairy Farming technologies are introduced to the market each year. As new technologies are developed in other industries, engineers and animal scien sts fi nd ap-plica ons within the dairy industry. More importantly, as these technologies are widely adopted in larger industries, such as the automobile or personal compu ng industries,

the costs of the base technologies decrease making them more economically feasible for dairy farms. Because the bulk of research focused on Precision Dairy Farming technologies is conducted in research environments, care must be taken in trying to transfer these results directly to commercial se ngs. Field experiments or simula ons may need to be conducted to alleviate this issue. Because of the gap between the impact of Precision Dairy Farming technologies in research versus commercial se ngs, ad-di onal eff ort needs to be directed toward implementa on of management prac ces needed to fully u lize informa- on provided by these technologies. To gain a be er

understanding of technology adoption shortcomings, addi onal research needs to be undertaken to examine the adop on process for not only successful adop on of technology but also technology adop on failures.

Before inves ng in a new technology, a formal investment analysis should be conducted to make sure that the tech-nology is right for your farm’s needs. Examining decisions with a simula on model accounts for more of the risk and uncertainty characteris c of the dairy system. Given this risk and uncertainty, a stochas c simula on investment analysis will represent that there is uncertainty in the profi t-ability of some projects. Ul mately, the dairy manager’s level of risk aversion will determine whether or not he or she invests in a technology using the results from this type of analysis. Perhaps the most interes ng conclusion from our model case study was that the factors that had the most infl uence on the profi tability investment in an automated BCS system were those related to what happens with the technology a er it has been purchased as indicated by the increase in variable costs needed for management changes and the management capacity of the farm. Deci-sion support tools, such as this one, that are designed to inves gate dairy herd decisions at a systems level may help dairy producers make be er decisions. Precision dairy farming technologies provide tremendous opportuni es for improvements in individual animal management on dairy farms. In the future, Precision Dairy Farming technologies may change the way dairy herds are managed.

REFERENCES

Bannister, F. and D. Remenyi. 2000. Acts of faith: ins nct, value, and IT investment decisions. J. Inf. Technol. 15:231-241.

Benne , R. M. 1992. The use of ‘economic’ quan ta ve modeling techniques in livestock health and disease-control decision making: a review. Prev. Vet. Med. 13:63-76.

Bethard, G. L. 1997. A microcomputer simula on to evaluate strate-gies for rearing dairy replacements. Page 161. Vol. PhD Diss. Virginia Polytechnic Ins tute and State University, Blacksburg, VA.

Figure 4. Tornado diagrams for inputs aff ec ng 95th percent-ile of Net Present Value for simula ons using the es mates of all survey respondents (BMPAF is the Best Management Prac ce Adherence Factor, RHA milk produc on is rolling herd average milk produc on in lbs).


Bewley, J. M., M. D. Boehlje, A. W. Gray, H. Hogeveen, S. J. Kenyon, S. D. Eicher, and M. A. S. Russell, M.M. 2010a. Assessing the poten al value for an automated dairy ca le body condi on scoring system through stochas c simula on. Agric. Finance Rev. 70:126-150.

Bewley, J. M., M. D. Boehlje, A. W. Gray, H. Hogeveen, S. J. Kenyon, S. D. Eicher, and M. A. S. Russell, M.M. 2010b. Stochas c simula on using @Risk for dairy business investment decisions. Agric. Finance Rev. 70:97-125.

Bewley, J. M., A. M. Peacock, O. Lewis, R. E. Boyce, D. J. Roberts, M. P. Coff ey, S. J. Kenyon, and M. M. Schutz. 2008. Poten al for es ma on of body condi on scores in dairy ca le using digital images. J. Dairy Sci. 91:3439-3453.

Cabrera, V. E., N. E. Breuer, P. E. Hildebrand, and D. Letson. 2005. The dynamic North Florida dairy farm model: A user-friendly computerized tool for increasing profi ts while minimizing N leaching under varying clima c condi ons. Comput. Electron. Agric. 49:286-308.

Carmi, S. 1992. The performance of an automated dairy management data-gathering system. Pages 346-352 in Proc. Intl. Symp. Prospects for Automa c Milking. European Associa on for Animal Produc on, Wageningen, The Netherlands.

Clemen, R. T. 1996. Making hard decisions: an introduc on to decision analysis. 2nd ed. Duxbury Press, Belmont, CA.

Davenport, T. H., and J. G. Harris. 2007. Compe ng on analy cs: the new science of winning. Harvard Business School Press, Boston, MA.

de Mol, R. M. 2000. Automated detec on of oestrus and mas s in dairy cows. Page 177. PhD Thesis. Wageningen University, Wageningen, The Netherlands.

Delorenzo, M. A., and C. V. Thomas. 1996. Dairy records and models for economic and fi nancial planning. J. Dairy Sci. 79:337-345.

Dijkhuizen, A. A., R. B. M. Huirne, S. B. Harsh, and R. W. Gardner. 1997. Economics of robot applica on. Comput. Electron. Agric. 17:111-121.

Dijkhuizen, A. A., R. B. M. Huirne, and A. W. Jalvingh. 1995. Economic analysis of animal diseases and their control. Prev. Vet. Med. 25:135-149.

Dijkhuizen, A. A., J. A. Renkema, and J. Stelwagen. 1991. Modelling to support animal health control. Agric. Econ. 5:263-277.

Eastwood, C., D. Chapman, and M. Paine. 2004. Precision dairy farming-taking the microscope to dairy farm management.

Eleveld, B., R. B. M. Huirne, A. A. Dijkhuizen, and G. Overbeek. 1992. Users in search of farm computer informa on technology: what do farmers want or need? Pages 27-32 in Farm Computer Technology in Search of Users: 4th Intl. Congress for Computer Technology in Agriculture, Paris-Versailles, France.

FAPRI. 2007. Food and Agricultural Policy Research Ins tute 2007 U.S. and World Agricultural Outlook. Iowa State University and University of Missouri-Columbia. Ames, IA.

Gabler, M. T., P. R. Tozer, and A. J. Heinrichs. 2000. Development of a cost analysis spreadsheet for calcula ng the costs to raise a replacement dairy heifer. J. Dairy Sci. 83:1104-1109.

Galligan, D. T. 1999. The economics of op mal health and produc vity in the commercial dairy. Rev. Sci. Tech. Off . Int. Epiz. 18:512-519.

Galligan, D. T., and H. Groenendaal. 2001. Economic concepts in the valua on of “products” used in dairy produc on including a real op on’s approach. Pages 233-245 in 36th Annual Pacifi c Northwest Anim. Nutr. Conf., Boise, ID.

Galligan, D. T., W. E. Marsh, and J. Madison. 1987. Economic decision making in veterinary prac ce: Expected value and risk as dual u lity scales. Prev. Vet. Med. 5:79-86.

Gelb, E., C. Parker, P. Wagner, and K. Rosskopf. 2001. Why is the ICT adop on rate by farmers s ll so slow? Pages 40-48 in Proc. ICAST, Vol. VI, 2001, Beijing, China.

Gelb, E. M. 1996. The economic value of informa on in an informa on system. Pages 142-145 in 6th Intl. Congress for Computer Technology in Agriculture Wageningen, The Netherlands.

Gould, B. W. 2007. University of Wisconsin-Madison: Understanding Dairy Markets.

Hamrita, T. K., S. K. Hamrita, G. Van Wicklen, M. Czarick, and M. P. Lacy. 1997. Use of biotelemetry in measurement of animal responses to environmental stressors.

Huirne, R. 1990. Basic concepts of computerised support for farm man-agement decisions. Euro. R. Agr. Eco. 17:69-84.

Huirne, R. B. M., S. B. Harsh, and A. A. Dijkhuizen. 1997. Cri cal success factors and informa on needs on dairy farms: the farmer’s opinion. Livest. Prod. Sci. 48:229-238.

Huirne, R. B. M., H. W. Saatkamp, and R. H. M. Bergevoet. 2003. Eco-nomic analysis of farm-level health problems in dairy ca le. Ca le Prac ce 11:227-236.

Hyde, J., and P. Engel. 2002. Inves ng in a robo c milking system: a Monte Carlo simula on analysis. J. Dairy Sci. 85:2207-2214.

Jalvingh, A. W. 1992. The possible role of exis ng models in on-farm decision support in dairy ca le and swine produc on. Livest. Prod. Sci. 31:351-365.

Kristensen, A. R., and E. Jorgensen. 1998. Decision Support Models. Pages 145-163 in Proc. 25th Intl. Dairy Congress, Aarhus, Denmark.

Lazarus, W. F., D. Streeter, and E. Jofre-Giraudo. 1990. Management informa on systems: impact on dairy farm profi tability. North Cent. J. Agric. Econ. 12:267-277.

Lee, J., and U. Bose. 2002. Opera onal linkage between diverse dimen-sions of informa on technology investments and mul faceted aspects of a fi rm’s economic performance. J. Inf. Technol. 17:119-131.

Lien, G. 2003. Assis ng whole-farm decision-making through stochas c budge ng. Agric. Sys. 76:399-413.

Marra, M., D. J. Pannell, and A. Abadi Ghadim. 2003. The economics of risk, uncertainty and learning in the adop on of new agricultural tech-nologies: where are we on the learning curve? Agric. Sys. 75:215-234.

Marsh, W. E., A. A. Dijkhuizen, and R. S. Morris. 1987. An economic com-parison of four culling decision rules for reproduc ve failure in United States dairy herds using DairyORACLE. J. Dairy Sci. 70:1274-1280.

Mayer, D. G., J. A. Belward, and K. Burrage. 1998. Op mizing simula on models of agricultural systems. Ann. Oper. Res. 82:219-231.

O e, M. J. and P. Chilonda. 2000. Animal health economics:an introduc- on. in Fron ers in Bioscience. FAO.

Passam, H. C., A. Tocatlidou, B. D. Mahaman, and A. B. Sideridis. 2003. Methods for decision making with insuffi cient knowledge in agricul-ture. Pages 727-731 in EFITA 2003 Conference, Debrecen, Hungary.

Philpot, W. N. 2003. Role of technology in an evolving dairy industry. Pag-es 6-14 in 2003 Southeast Dairy Herd Management Conf., Macon, GA.

Pietersma, D., R. Lacroix, and K. M. Wade. 1998. A framework for the development of computerized management and control systems for


use in dairy farming. J. Dairy Sci. 81:2962-2972.

Rosskopf, K., and P. Wagner. 2003. Requirements for agricultural so ware and reasons for adop on constraints-results of empirical studies. Pages 651-657 in EFITA 2003 Conf., Debrecen, Hungary.

Ryan, S. D., and D. Harrison. 2000. Considering social subsystem costs and benefi ts in informa on technology investment decisions: A view from the fi eld on an cipated payoff s. J. Manage. Inf. Sys. 16:11-40.

Schulze, C., J. Spilke, and W. Lehner. 2007. Data modeling for Precision Dairy Farming within the compe ve fi eld of opera onal and analy -cal tasks. Comput. Electron. Agric. 59:39-55.

Shalloo, L., P. Dillon, M. Rath, and M. Wallace. 2004. Description and valida on of the Moorepark Dairy System Model. J. Dairy Sci. 87:1945-1959.

Silk, D. J. 1990. Managing IS benefi ts for the 1990’s. J. Inf. Technol. 5:185-193.

Skidmore, A. L. 1990. Development of a simula on model to evaluate eff ec veness of dairy herd management. Page 236. PhD Diss. Cornell University, Ithaca, NY.

Sorensen, J. T., E. S. Kristensen, and I. Thysen. 1992. A stochas c model simula ng the dairy herd on a PC. Agric. Sys. 39:177-200.

Spilke, J., and R. Fahr. 2003. Decision support under the condi ons of automa c milking systems using mixed linear models as part of a precision dairy farming concept. Pages 780-785 in EFITA 2003 Conf., Debrecen, Hungary.

Streeter, D. H., and R. H. Hornbaker. 1993. Value of informa on systems: Alterna ve viewpoints and illustra ons. Pages 283-293 in Farm level informa on systems, Zeist, The Netherlands.

Tomaszewski, M. A., A. A. Dijkhuizen, A. G. Hengeveld, and H. Wilmink. 1997. A method to quan fy eff ects a ributable to management infor-ma on systems in livestock farming. Pages 183-188 in First European Conf. for Informa on Technology in Agriculture, Copenhagen.

USDA-NASS. 2007. Agricultural Prices Summary.

van Asseldonk, M. A. P. M. 1999. Economic evalua on of informa on technology applica ons on dairy farms. Page 123. PhD Diss. Wagenin-gen Agricultural Univ.

van Asseldonk, M. A. P. M., R. B. M. Huirne, A. A. Dijkhuizen, and A. J. M. Beulens. 1999a. Dynamic programming to determine op mum invest-ments in informa on technology on dairy farms. Agric. Sys. 62:17-28.

van Asseldonk, M. A. P. M., A. W. Jalvingh, R. B. M. Huirne, and A. A. Dijkhuizen. 1999b. Poten al economic benefi ts from changes in management via informa on technology applica ons on Dutch dairy farms: a simula on study. Livest. Prod. Sci. 60:33-44.

Verstegen, J. A. A. M., R. B. M. Huirne, A. A. Dijkhuizen, and J. P. C. Klei-jnen. 1995. Economic value of management informa on systems in agriculture: a review of evalua on approaches. Comput. Electron. Agric. 13:273-288.

Ward, J. M. 1990. A por olio approach to evalua ng informa on systems investments and se ng priori es. J. Inf. Technol. 5:222-231.


Using Knowledge of Feeding Behavior to Maximize Ration Potential

Trevor J. DeVries

Department of Animal and Poultry Science, University of Guelph,

Kemptville Campus, 830 Prescott Street, Kemptville, ON, K0G 1J0, Canada

Email: [email protected]

INTRODUCTION

Promo ng feed intake by lacta ng dairy cows, par cularly those in early lacta on, is cri cal for the improvement and maintenance of milk produc on, health, and body condi on (Grant and Albright, 2001). Gene c selec on prac ces have given rise to dairy ca le that are capable of producing quan es of milk in greater amounts than can be maintained by nutrient intake, par cularly during early lacta on. Past research in dairy ca le nutri on has focused almost exclusively on the nutri ve aspects of the diet, resul ng in many discoveries and improvements in dairy cow health and produc on. Despite many advances this fi eld we are s ll faced with the challenge of ensuring adequate dry ma er intake (DMI) to maximize produc on and prevent disease, par cularly in dairy cows during the transi on period. This paper will describe the importance of understanding behavior and how knowledge in this area of science can be used to evaluate nutri onal management and housing strategies. It is an cipated that with an im-proved understanding of feeding behavior, combined with the con nued eff orts of nutri onists, dairy producers can manage and design their dairy produc on systems in ways that will allow their cows to fully maximize the poten al of the ra on provided, thereby improving the health, produc on, and welfare of their animals.

IMPORTANCE OF FEEDING BEHAVIOR

Over the years many researchers have evaluated the many physical, diges ve, and metabolic factors that contribute to the physiological regula on of DMI (see reviews by Allen, 2000; Ingvartsen and Andersen, 2000). The iden fi ca on of these factors is necessary for the formula on of ra ons that help maximize DMI, par cularly for those early lacta- on cows whose energy demands o en cannot be met

(Ingvartsen and Andersen, 2000). Since changes in DMI must ul mately be mediated by changes in feeding behavior (Nielsen, 1999), it is important to understand the factors that infl uence cow feeding behavior pa erns. To date, the majority of research on dairy nutri on has largely ignored how the diet is consumed. Formula ng diets has tradi on-

ally required li le knowledge about how the diet is con-sumed; it was enough to simply es mate daily DMI without considering how and what feed was actually consumed.

Modern, intensively-housed dairy ca le fed a conserved ra on typically consume their daily DMI in up to 6 hours per day, spread between 7 or more meals per day (DeVries et al., 2003). Management prac ces that cause adult dairy ca le to eat fewer and larger meals more quickly have been associated with an increased incidence of sub-acute ruminal acidosis (Krause and Oetzel, 2006). The reason for this risk is that ruminal pH declines following meals, and the rate of pH decline increases as meal size increases and as dietary ef-fec ve fi ber concentra on decreases (Allen, 1997). Further, as cows spend less overall me feeding and increase their rate of feed consump on, daily salivary secre on is reduced (Beauchemin et al., 2008), decreasing the buff ering capacity of the rumen and reducing rumen pH. Alterna vely, when cows slow down their rate of DM consump on and have more frequent, smaller meals throughout the day, rumen buff ering is maximized, large within-day depressions in pH are avoided, and the risk of sub-acute ruminal acidosis is decreased. Thus, to maximize rumen health, effi ciency and produc vity, it is important to u lize feeding management strategies that promote the frequent consump on of feed in small meals throughout the day.

Total mixed ra ons (TMR) are designed as a homogenous mixture with the goal to help minimize the selec ve con-sump on of individual feed components by dairy ca le, promote a steady-state condi on conducive to con nuous rumen func on and ingesta fl ow, and ensure adequate intakes of fi ber (Coppock et al., 1981). It is not surprising, therefore, that providing feed as a TMR has become the standard on most commercial dairies, par cularly for the lacta ng animals. Unfortunately, even when providing feed as a TMR cows have been shown to preferen ally select (sort) for the grain component of a TMR and dis-criminate against the longer forage components (Leonardi and Armentano, 2003). Sor ng of the diet can lead to the


cows consuming an inconsistent ra on, whereby the ra- on actually consumed by cows is greater in fermentable

carbohydrates than intended and lesser in eff ec ve fi ber, and thus can increase the risk of sub-acute ruminal acidosis (DeVries et al., 2008). Likely related to this, in two recent studies it has been observed that such sor ng of a TMR is associated with producing milk with lower fat percentage (milk fat decreases by 0.15% for every 10% refusal of long forage par cles in the ra on; DeVries et al., 2011; Fish et al., 2012). Sor ng of the TMR can also reduce the nutri ve value of the TMR remaining in the feed bunk, par cularly in the later hours past the me of feed delivery (DeVries et al., 2005; Hosseinkhani et al., 2008). For group-fed cows, this may be detrimental for those cows that do not have access to feed at the me when it is delivered, for example when there is high compe on at the feed bunk. In such cases, these cows may not be able to maintain adequate nutrient intake to maintain high levels of milk produc on (Krause and Oetzel, 2006). Thus, feeding management strategies are required to ensure that cows consume the feed as it is formulated for them.

It is clear that in addi on to proper formula ng of dairy ra- ons we need to also consider how the ra on is consumed

to ensure that the poten al of that ra on is maximized. There is an increasingly growing body of literature in which the knowledge of feeding behavior can be used to iden fy nutri onal management and housing strategies to maximize ra on poten al; including ensuring cows have access to fresh feed throughout the day and minimizing feed bunk compe on.

IMPACT OF NUTRITIONAL MANAGEMENT ON

FEEDING BEHAVIOR

When grazing, ca le o en synchronize their behavior such that many animals in the group feed, ruminate, and rest at the same mes (Miller and Wood-Gush, 1991). Cur s and Houpt (1983) reported that group-housed dairy cows housed indoors also synchronized their behavior, par cu-larly at feeding. They reported that when cows are fed in groups, the act of one cow moving to the feed bunk s mulates others to feed. It has typically been accepted that dairy ca le exhibit a diurnal feeding pa ern where the majority of feeding ac vity occurs during the day, par cularly around sunrise and sunset (Albright, 1993). However, this observa on is almost exclusively based on the feeding pa erns exhibited by grazing ca le. To gain a be er understanding of how management factors infl u-ence dairy ca le behavior, we have previously examined the normal feeding pa ern of group-housed lacta ng cows fed a TMR ad libitum (DeVries et al., 2003). We found that

the diurnal feeding pa ern was mostly infl uenced by the me of feed delivery, feed push-up, and milking. Further,

it was clear that the most drama c peaks in feeding ac v-ity occur around the me of feed delivery and the return from the milking parlor. To follow up on this, we set out in an experiment to determine which of these management prac ces is the primary factor s mula ng dairy ca le to go to the feed bunk (DeVries and von Keyserlingk, 2005). We tested this objec ve by separa ng feed delivery and milking mes by 6 h. When animals were fed 6 h post milking, they increased their total daily feeding me by 12.5%. This change was predominantly driven by a small decrease in feeding me during the fi rst hour post-milking and a very large increase in feeding me during the fi rst hour immediately following the delivery of fresh feed. These results indicate that the management prac ce of feed delivery acts as the primary infl uence on the daily feeding pa ern of lacta ng dairy cows and not, as previ-ously thought, the me of day.

The delivery of fresh feed is clearly an important factor in s mula ng cows to eat. Thus, the frequency of feed deliv-ery should infl uence the feeding pa erns of lacta ng dairy cows. To test this predic on, we conducted an experiment to determine whether increasing frequency of feed delivery aff ects the behavior of group-housed dairy cows (DeVries et al., 2005). This objec ve was tested in two experiments. In the fi rst experiment, the treatments were: 1) delivery of feed once per day (1x), and 2) delivery of feed twice per day (2x). The treatments for the second experiment were: 1) delivery of feed 2x, and 2) delivery of feed four mes per day (4x). In both experiments, increased frequency of feed provision increased total daily feeding me by 10 and 14 minutes, respec vely, as well as increased the distribu- on of feeding me throughout the day. The distribu on

of feeding me in both experiments indicated that cows had more equal access to feed throughout the day when provided feed more frequently. Frequency of feed delivery had no eff ect on the daily lying me of the cows or the total number of aggressive interac ons at the feed bunk. How-ever, we did fi nd that subordinate cows were not displaced as frequently when fed more o en, indica ng that these cows would have greater access to feed, par cularly fresh feed, when the frequency of feed delivery is high. In addi- on to these behavioral measures, we also looked at the

eff ects of frequency of feed delivery on feed composi on throughout the day. In both experiments we noted that the neutral detergent fi ber (NDF) content of the TMR present in the feed bunk increased throughout the day, indica ng that sor ng of the feed had occurred, par cularly on the 1x treatment. Corrobora ng this, it was recently shown in


a mul -herd study (Endres and Espejo, 2010) that a greater change in ra on NDF content over the course of the day was associated with lower frequency of feed delivery (i.e. 1x vs. 2x or 3x per day).

Increased sor ng of a TMR when fed 1x per day may be par cularly troublesome under some management situa ons. In two recent studies we have inves gated the eff ects of water addi on to a TMR, fed 1x per day, contain-ing solely haylage and corn silage forage sources (Miller-Cushon and DeVries, 2009; Felton and DeVries, 2010). In a fi rst study we reduced TMR dry ma er concentra on from 58 to 48% (Miller-Cushon and DeVries, 2009). In a second study we reduced TMR dry mater concentra on from 56 to 50 to 44% (Felton and DeVries, 2010). In both studies we found that the addi on of water to these higher moisture TMR, containing no dry forage, actually resulted in greater amounts of feed sor ng. In addi on to this, increased amounts of water added to the ra ons also resulted in lower DMI. Reduced DMI with lower dietary DM is likely due, in part, to the increased fi lling eff ect of higher moisture ra ons. Interes ngly, in the second study we found that greater amounts of water added to the TMR resulted in greater increases in feed temperature in the hours a er feed was placed in the feed bunk, par cularly with higher ambient temperatures (Felton and DeVries, 2010). This increased feed temperature may be indica ve of feed spoilage and, thus, may be contribu ng to these eff ects. Thus, when feeding higher moisture TMR during periods of high ambient temperatures, delivering feed 1x per day may not only increase the risk of greater amounts of sor ng, but may also limit DMI.

An alterna ve to adding water to TMR may be to add a liquid feed. Liquid feeds, par cularly those molasses-based ones, have good poten al to help bind ra on par cles to-gether and make sor ng more diffi cult. Oelker et al. (2009) found that using a molasses-based liquid feed reduced sor ng of a corn silage based diet. Similarly, DeVries and Gill (2012), recently found that adding 4.1% of a molasses-based liquid feed to a TMR resulted in less sor ng against the longest ra on par cles. Further, in that study DMI was 1.4 kg/d higher (5.1% increase) and cows produced 3.1 kg/d more 4% fat-corrected milk (7.8% increase) on the liquid feed diet. Interes ngly, Eastridge et al. (2011) recently reported no eff ect of adding liquid feed on sor ng behavior; however, those researchers used a ra on that was very dry (> 64% DM).

One of the most common feeding management prac ces believed to s mulate feeding ac vity is feed push-up.

When fed a TMR, dairy cows have a natural tendency to con nually sort through the feed and toss it forward where it is no longer within reach. This is par cularly problema c when feed is delivered via a feed alley and, thus, producers commonly push the feed closer to the cows in between feedings to ensure that cows have con nuous feed access. In an observa onal study Menzi and Chase (1994) noted that the number of cows feeding increased a er feed push up, however they concluded that feed push ups had “minor and brief eff ects” in comparison to milking on the feed bunk a endance. In a more recent study, we tested the s mulatory eff ect of feed push-up by increasing the number of push ups during the late evening and early morning (DeVries et al., 2003). In that study we found that the addi on of extra feed push ups in the early morning hours did li le to increase feeding ac vity. However, push up does play a vital role in ensuring that feed is accessible when cows want to eat.

There is evidence to suggest that the ming of feed deliv-ery is also important for dairy cows. Availability of fresh feed following the return from milking has typically been used to encourage cows to remain standing (while feed-ing) rather than to lie down. Researchers have shown that the presence of fresh feed in the bunk encourages longer post-milking standing mes (DeVries and von Keyserlingk, 2005). DeVries et al. (2010) recently found that the provi-sion of feed around milking me resulted in the longest post-milking standing mes. Further, this was the fi rst study to document how post-milking standing me relates to the risk of subclinical udder infec on; cows that lay down, on average, for the fi rst me 40 to 60 min a er milking tended to have lower odds of a new subclinical udder infec on caused by environmental bacteria compared to cows that lay down within 40 min a er milking. These results suggest that management prac ces that discourage cows from lying down immediately a er milking, such as providing fresh feed frequently through the day (near the me of milking) may help decrease the risk of subclinical mas s.

In addi on to the frequency and ming of feed delivery, the amount of feed delivered can also impact the feeding behavior of lacta ng dairy cows. Much of the research in this area has focused on inves ga ng the impact of feed access me. A substan al limita on (8 h) of feed access me has been shown to limit intake and produc on (Mar- nsson and Burstedt, 1990). Alterna vely, more moderate

limita ons (2.5% refusal at 18 h vs. 5% refusal at 23 h) have been shown to have adverse eff ects on feeding behavior, causing cows to have shorter meals, less feeding me, and faster ea ng rates (French et al., 2005). In a more recent


study, Collings et al. (2011) found that temporal restric- on of feed access (14 vs 23 h/d access) resulted in lower

feeding mes and greater feed bunk compe on. Those researchers also found that at increased stocking densi es this temporal restric on increased feeding rates across the day. Interes ngly, in another recent study, Miller-Cushon and DeVries (2010) found that at lower feeding amounts, feed sor ng was reduced, while DMI was also reduced.

IMPACT OF FEEDING ENVIRONMENT ON

FEEDING BEHAVIOR

One of the specifi c objec ves of ca le housing is to provide a comfortable environment that will allow cows to meet their behavioral and physiological needs (Phillips, 2001). There are several aspects of the feeding environment that have the poten al to infl uence the ability of cows to access feed, including the amount of available feed bunk space per animal and the physical design of the feeding area.

Recent observa ons have suggested that at the current industry standard of 0.61 m of feeding space per cow not all animals can access feed at the same me (DeVries et al., 2003). As social animals, ca le tend to synchronize their behavior, including a strong desire to access the feed bunk as a group. Reduced space availability has been shown to result in increased aggressive behavior in ca le (Kondo et al., 1989). When feed bunk space is limited, increases in aggressive behavior are thought to limit the ability of some cows to access feed at mes when feeding mo va- on is high, par cularly a er the delivery of fresh feed

(DeVries et al., 2004; Huzzey et al., 2006). Hosseinkhani et al. (2008) recently demonstrated that compe on at the feed bunk drama cally increased the feeding rate at which cows feed throughout the day. These researchers also found that compe vely-fed cows have fewer meals per day, which tend to be larger and longer. In the study by Hosseinkhani et al. (2008) it was also found that compe - on changed the distribu on of DMI over the course of the

day, resul ng in higher intakes during the later hours a er feed delivery a er much of the feed sor ng had already occurred. Thus, increased compe on promotes feeding behavior that forces subordinate cows to consume more of their feed a er the dominant cows have sorted the TMR. These results suggest that increased compe on at the feed bunk promotes feeding behavior pa erns that will likely increase the between-cow varia on in composi- on of TMR consumed and the risk of sub-acute ruminal

acidosis. Providing more space than the current industry norm has been shown to improve feed bunk access; this increases feeding mes and decreases compe on, with subordinate cows showing the greatest responses (DeVr-

ies et al., 2004; Huzzey et al., 2006). This change will help reduce the varia on in the composi on of feed cows con-sume as subordinate cows will be able to access the feed prior to it being sorted through by those dominant cows.

In addi on to increasing the amount of available feed bunk space, compe on for feed can also be reduced through design of the feeding area. Researchers have shown that a headlock system greatly reduces compe on at the feed bunk compared with a post-and-rail system (Endres et al., 2005; Huzzey et al., 2006). Another op on to reduce com-pe on is the use of par ons (feed stalls) between the bodies of adjacent cows at the feed bunk. DeVries and von Keyserlingk (2006) demonstrated that feed stalls resulted in increased feeding me and decreased compe on, par- cularly for subordinate cows. Their results suggest feed

stalls provide addi onal protec on for feeding cows, and improved access to feed beyond that provided by simply increasing the amount of space per animal.

It has become increasingly evident from this research that the provision of more feed bunk space (than tradi onally provided), par cularly when combined with a physical bar-rier separa ng adjacent cows (e.g. feed stalls), will improve access to feed and reduce compe on at the feed bunk, par cularly for subordinate cows. This could help reduce the between-cow varia on in the composi on of ra on consumed by preven ng subordinate cows from being forced to access the bunk only a er dominant cows have sorted the feed.

CONCLUSIONS

This proceedings chapter summarizes a number of studies that have been undertaken that collec vely provide us with a basic understanding of how knowledge of feeding behavior, par cularly how, when, and what cows eat of the feed provided to them, can be used to maximize the poten al of the ra ons provided to them. In par cular, these can be accomplished through ensuring cows have access to fresh feed throughout the day and minimizing feed bunk compe on.

ACKNOWLEDGEMENTS

This paper is an updated version of a proceedings paper wri en for, and presented at, the 2011 California Animal Nutri on Conference held in Fresno, CA in May 2011.

The paper reviews concepts and data largely generated in collabora on with colleagues, par cularly Marina von Keyserlingk, at The University of Bri sh Columbia Animal


Welfare Program. This research was funded in part by the Natural Sciences and Engineering Research Council of Canada, Dairy Farmers of Canada, Westgen Endowment Fund, Investment Agriculture Founda on of Bri sh Co-lumbia, Agriculture and Agri-Food Canada, the Canadian Bovine Mas s Research Network, the Ontario Ministry of Agriculture, Food, and Rural Aff airs, the Canadian Founda- on for Innova on, and the Ontario Research Fund, the

University of Guelph, and the University of Bri sh Colum-bia Animal Welfare Program.

REFERENCES

Albright, J. L. 1993. Feeding behavior in dairy ca le. J. Dairy Sci. 76:485-498.

Allen, M. S. 1997. Rela onship between fermenta on acid produc on in the rumen and the requirement for physically eff ec ve fi ber. J. Dairy Sci. 80:1447-1462.

Allen, M. S. 2000. Eff ects of diet on short-term regula on of feed intake by lacta ng dairy ca le. J. Dairy Sci. 83:1598-1624.

Beauchemin, K. A., L. Eriksen, P. Nørgaard, and L. M. Rode. 2008. Salivary secre on during meals in lacta ng dairy ca le. J. Dairy Sci. 91:2077-2081.

Collings, L.K.M., D.M. Weary, N. Chapinal and M.A.G. von Keyserlingk. 2011. Temporal feed restric on and overstocking increase compe - on for feed by dairy ca le. J. Dairy Sci. 94:5480-5486.

Coppock, C. E., D. L. Bath, and B. Harris, Jr. 1981. From feeding to feeding systems. J. Dairy Sci. 64:1230-1249.

Cur s, S. E., and K. A. Houpt. 1983. Animal ethology: its emergence in animal science. J. Anim. Sci. 57:234-247.

DeVries, T. J., F. Dohme, and K. A. Beauchemin. 2008. Repeated ruminal acidosis challenges in lacta ng dairy cows at high and low risk for developing acidosis: Feed sor ng. J. Dairy Sci. 91:3958-3967.

DeVries, T. J., L. Holtshausen, M. Oba, and K. A. Beauchemin. 2011. Eff ect of parity and stage of lacta on on feed sor ng behavior of lacta ng dairy cows. J. Dairy Sci. 94:4039–4045.

DeVries, T. J., M. A. G. von Keyserlingk, and K. A. Beauchemin. 2003. Diur-nal feeding pa ern of lacta ng dairy cows. J. Dairy Sci. 86:4079-4082.

DeVries, T. J., M. A. G. von Keyserlingk, and D. M. Weary. 2004. Eff ect of feeding space on the inter-cow distance, aggression, and feed-ing behavior of free-stall housed lacta ng dairy cows. J. Dairy Sci. 87:1432-1438.

DeVries, T. J. and M. A. G. von Keyserlingk. 2005. Time of fresh feed delivery aff ects the feeding and lying pa erns of dairy cows. J. Dairy Sci.88:625-631.

DeVries, T. J., M. A. G. von Keyserlingk, and K. A. Beauchemin. 2005. Frequency of feed delivery aff ects the behavior of lacta ng dairy cows. J. Dairy Sci. 88:3553-3562.

DeVries, T. J. and M. A. G. von Keyserlingk. 2006. Feed stalls aff ect the social and feeding behavior of lacta ng dairy cows. J. Dairy Sci. 89:3522-3531.

DeVries, T. J., S. Dufour, and D. T. Scholl. 2010. Rela onship between feeding strategy, lying behavior pa erns, and incidence of intramam-mary infec on in dairy cows. J. Dairy Sci. 93:1987-1997.

DeVries, T. J., and R. M. Gill. 2012. Adding liquid feed to a total mixed ra on reduces feed sor ng behavior and improves produc vity of lacta ng dairy cows. J. Dairy Sci. 95:2648-2655.

Eastridge, M. L., A. H. Lefeld , A. M. Eilenfeld , P. N. Go , W. S. Bowen , and J. L. Firkins. 2011. Corn grain and liquid feed as nonfi ber carbohydrate sources in diets for lacta ng dairy cows. J. Dairy. Sc. 94:3045-3053.

Endres, M. I., T. J. DeVries, M. A. G. von Keyserlingk, and D. M. Weary. 2005. Eff ect of feed barrier design on the behavior of loose-housed lacta ng dairy cows. J. Dairy Sci. 88:2377-2380.

Endres, M. I., and L. A. Espejo. 2010. Feeding management and charac-teris cs of ra ons for high-producing dairy cows in freestall herds. J. Dairy Sci. 93:822-829.

Fish, J. A., and T. J. DeVries. 2012. Varying dietary dry ma er concentra- on through water addi on: Eff ect on nutrient intake and sor ng of

dairy cows in late lacta on. J. Dairy Sci. 95:850-855.

Felton, C., and T. J. DeVries. 2010. Eff ect of water addi on to a total mixed ra on on feed temperature, feed intake, sor ng behavior, and milk produc on of dairy cows. J. Dairy Sci. 93:2651-2660.

French. P., J. Chamberlain, and J. Warntjes. 2005. Eff ect of feed refusal amount on feeding behavior and produc on in Holstein cows. J. Dairy Sci. 88(Suppl. 1):175.

Grant, R. J., and J. L. Albright. 2001. Eff ect of animal grouping on feeding behavior and intake of dairy ca le. J. Dairy Sci. 84:E156-E163.

Hosseinkhani, A., T. J. DeVries, K. L. Proudfoot, R. Valizadeh, D. M. Veira, and M. A. G. von Keyserlingk. 2008. The eff ects of feed bunk com-pe on on the feed sor ng behavior of close-up dry cows. J. Dairy Sci. 91:1115-1121.

Huzzey, J. M., T. J. DeVries, P. Valois, and M. A. G. von Keyserlingk. 2006. Stocking density and feed barrier design aff ect the feeding and social behavior of dairy ca le. J. Dairy Sci. 89:126-133.

Ingvartsen. K. L. and J. B. Andersen. 2000. Integra on of metabolism and intake regula on: A review focusing on periparturient animals. J. Dairy Sci. 83:1573-1597.

Kondo, S., J. Sekine, M. Okubo, and Y. Asahida. 1989. The eff ect of group size and space allowance on the agonis c spacing behaviour of ca le. Appl. Anim. Behav. Sci. 24:127-135.

Krause, K. M., and G. Oetzel. 2006. Understanding and preven ng subacute ruminal acidosis in dairy herds: a review. Anim. Feed Sci. Technol. 126:215-236.

Leonardi, C., and L. E. Armentano. 2003. Eff ect of quan ty, quality, and length of alfalfa hay on selec ve consump on by dairy cows. J. Dairy Sci. 86:557-564.

Mar nsson, K., and Burstedt, E. 1990. Eff ect of length of access me to feed and allotment of hay on grass silage intake and produc on in lacta ng dairy cows. Swed. J. Agric. Res. 20:169-176.

Menzi, W., Jr., and L. E. Chase. 1994. Feeding behavior of cows housed in free stall barns. Page 829-831 in Dairy Systems for the 21st Century. American Society of Agricultural Engineers, St. Joseph, MI.

Miller, K., and D. G. M. Wood-Gush. 1991. Some eff ects of housing on the social behaviour of dairy cows. Anim. Prod. 53:271-278.

Miller-Cushon, E. K., and T. J. DeVries. 2009. Eff ect of dietary dry ma er concentra on on the sor ng behavior of lacta ng dairy cows fed a total mixed ra on. J. Dairy Sci. 92:3292-3298.

Miller-Cushon, E. K., and T. J. DeVries. 2010. Feeding amount aff ects the sor ng behaviour of lacta ng dairy cows. Can J. Anim. Sci. 90:1-7.


Nielsen, B. L. 1999. On the interpreta on of feeding behaviour mea-sures and the use of feeding rate as an indicator of social restraint. Appl. Anim. Behav. Sci. 63:79-91.

Oelker, E. R., C. Reveneau, and J. L. Firkins. 2009. Interac on of mo-lasses and monensin in alfalfa hay- or corn silage-based diets on rumen fermenta on, total tract diges bility, and milk produc on by Holstein cows. J. Dairy Sci. 92:270-285.

Phillips, C. J. C. 2001. Principles of ca le produc on. CABI Publish-ing, CAB


Dairy Heifer Management is Changing Big Time

S. E. Nellis, K. A. Weigel, and P. C. Hoff man

University of Wisconsin-Madison

DAIRY REPLACEMENT HEIFER MANAGEMENT: IS CHANGING BIG TIME

S.E. S.E. NellisNellis, , K.A.WeigelK.A.Weigel and and P.C.HoffmanP.C.HoffmanDepartment of Dairy ScienceDepartment of Dairy ScienceUniversity of WisconsinUniversity of Wisconsin--MadisonMadison

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Onfarm ID 933 941Net merit (NM$) 436 -96Breed Performance Index (BPI) 1791 1186Milk Yield (Milk) 1562 -677Fat % -0.05 -0.03Protein % -0.04 0.02Genomic Individual Inbreeding 11.4 12.3HH1 F FHH2 F FHH3 F C

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SampleCollectorBarcode

Official ID (Registration#, USDA AIN, Calfhood

Vaccination #)

Breed(HO/JE/

BS)Dam Registration # or

USDA AIN

Fill out the Form- Tedious Detail- Improper Parent/Sire/Dam/Breed ID = 10-15 % Normal

6759 982000133517332 x US4071533 F HO 11/30/2010 7H10489 35SHE7938 6764 982000133517337 x US4071521 F HO 12/6/2010 14H4929 35SHE7999 6766 982000133517339 x US4071517 F HO 12/8/2010 11H9703 35SLS7488 6768 982000133517341 x US4071525 F HO 12/9/2010 7H9030 35SHE5165 6770 982000133517343 x US4071550 F HO 12/12/2010 29H13245 35SHE5981 6771 982000133517344 x US4071513 F HO 12/12/2010 14H4929 8400030039323316775 982000133517348 x US4071554 F HO 12/15/2010 29H13245 35TDN0336 6857 98200013351430 x US4071536 F HO 4/12/2011 29H13245 35SHE5128

Hand EntryDairy Comp 305 (Cut and Paste)

Genomic Lab Results* Samples Due 1st of Month* Lab Turnaround = 30-40 days

• Genomic Results GPTA Milk (4 Heifer Example)

GPTA Milk +1114 +141 -1041 +490Average = 176 GPTA Milk


University of Wisconsin Heifers – GPTA Milk

Superior Genetics

Normal Genetics

Potential Culls ?

Potential Problem : Selecting for Milk Alone IgnoresOther Important Traits

Some Math: Culling the bottom 15-25 % of heifers has a big effect on genetic improvement

GPTA milk +1114 +141 -1041 +4903 Heifer Average = 581 GPTA milk

4 Heifer Average = 176 GPTA milk (Common AI Improvement)

StringingStringing

Old Heifer Management Paradigms Cause Stringing

- Average Daily Gain- Average Age at First Calving- Breed when she’s 875 by eyeballametrics- Breed when she’s 50 “ tall

All of these general historic concepts increase the variance of days on feedAll of these general historic concepts increase the variance of days on feed

Total Rearing Cost = $$/day * days on feed

Heifers shouldn’t tell dairy producers when their ready to breed.Dairy producers tell heifers…….

Normal AI Efficiency Causes Stringing +- Sexed Semen ?- ET ? - Recipients ?

Service Rate = 90Conception Rate = 70

Service Rate = 50Conception Rate = 50

Minimum breeding age 13 moMinimum breeding BW 875 lbsAverage daily gain 1.8 lbs/dGrowth variance 0.2 lbs/dPregnancy rate 50 %

Herd Average Calving Age

= 25.5 mo

Breed Target Varianceand Reproductive Efficiency

Influence Age of Calving

A Normal Distribution is Not Normal - Stringing

Minimum breeding age 13 moMinimum breeding BW 875 lbsAverage daily gain 1.8 lbs/dGrowth variance 0.1 lbs/dPregnancy rate 60 %Herd Average

Calving Age = 23.8 mo Steep Right Tail Distribution

are Desired – No Stringing


200

250

300

utio

n

AFC = 23.3 monthsNormal AI StringingVariance (Days on Feed) = 125 daysNon Genetic Weight Variance = 225 lbs

Target (1st Straws)

Straws 2-4

0

50

100

150

20.9 21.6 22.3 23 23.7 24.3 25 25.7 26.4 27.1 27.9

Dis

trib

Calving Age, mo

Loss Pregnancies -Rebreeding

Carryover Effect of Age at First Calvingon First Lactation Milk Yield

Curran et al., 2012 (unpublished)

Carryover Effect of Age at First Calvingon Lifetime Days in Milk


Carryover Effect of Age at First Calvingon Lifetime Milk


Onfarm ID HH1 HH2 HH3

6658 F F F6660 F F F6661 F F F6662 F F F6664 F F F6666 F C F6667 F F F6669 F F F6670 F F F

Supplemental Information

Haplotype Carriers (HH1 HH2 HH3)are Indentified

6671 F F F6673 F F F6676 F F F6677 F F F6678 F F F6679 F F F6680 F F F6681 F C F6682 F F F6683 F F F6684 F C F6686 F F F

- Do not breed to HH Carrier Bulls

- > Chance of Failed Conception,Early Embryonic Loss, or Abortion

Supplemental Information

AI Mating Services are Adapted for Genomic Data

Onfarm ID Cheese Merit (CM$)

Milk Yield (Milk) Fat % Protein % Final Score

Type (Type FS)Stature

(ST)Strength

(SG)

Genomic Individual Inbreeding (Ind

Inbrd)

924 22 215 -0.01 0.02 -0.26 0.36 0.02 7.6

- Production Mating (NM, Cheese Yield, Milk, Fat, or Protein etc)

- Inbreeding Defined by Genomics is Considered

- Many Type and Traits for Consideration (Limited Example Above)


Heifer Management is Changing

Year 1 Technologies =

Advanced Breeding Pen Management

Breeding AgeHeifers

Advanced Breeding Pen Management

Precision Feeding of Gravid Holstein Heifers: Effect on Growth,Manure Nutrient Excretion, and Subsequent Early Lactation Performance

JDS 90 P.C. Hoffman, C.R. Simson, M. Wattiaux

Effect of precision feeding on body size and growth of replacement heifers.Effect of precision feeding on body size and growth of replacement heifers.

Item C-100 L-90 L-80Intial

Weight, lbs 1036 1021 1011Hip height, in 54.2 54.6 54.9Body condition score 3.1 3.0 2.9

Final

Treatment1

Hoffman et al. 2005

Weight, lbs 1220 1234 1217Hip height, in 56.0 56.3 56.4Body condition score 3.2 3.2 3.2

GrowthAverage daily gain, lbs/d 1.66 1.92 1.84Feed efficiency, lbs DM/lb gain 13.2 10.7 11.1

ExcretionDM, lbs/d 7.7 6.9 5.8

Fecal Excretion – 1100 lb Precision Fed Holstein Heifers

30

40

50P < 0.01

0

10

20

C-100 L-90 L-80

Lbs/d Manure DMManure Wet

Milk Production: 150 DIM (3.5 % FCM):

10000

11000

12000

n, lb

s/15

0 d

NS (P > 0.10)

6000

7000

8000

9000

C100 L90 L80

Milk

Pro

duct

io

Penn State – Lactation Trial

HighForage

PrecisionFed SE P <

Milk, lbs 20761 23041 1045 0.081

Fat, % 3.74 3.98 0.13 0.138

Protein, % 3.05 2.95 0.05 0.118


Production: Milk Yield Kruse et al., 2010

C100 L85 L80 + I SEM

Heifers, no. 26 29 26

45 DIM45 DIM

3.5% FCM yield, kg/d 80.9 83.0 78.4 1.1

90 DIM

3.5% FCM yield, kg/d 75.0 77.5 75.9 1.1

Nutrient ManagementProgress (?)

Bone development in dairy heifers fed diets with and without supplemental phosphorus.

N.M. Esser*1, P.C. Ho man*, W.K. Coblentz†, M. W. Orth†† and K.A. Weigel*.

•Department of Dairy Science, University of Wisconsin, Madison,WI.•† USDS ARS Dairy Forage Research Center Marshfield and Madison, WI.

•†† Michigan State University , East Lansing, MI.

Effect (P<Item Holstein Crossbred Holstein Crossbred Diet

Body weight, lbs 1370.0 1263.1 1406.3 1293.1 nsHip height in 57 4 54 5 57 3 54 8 ns

0.28 % P 0.38 % P

Holstein and Crossbred Heifers 3 22 months of age.Experimental Diets

Unsupplemented = 0.28 % PSupplemented = 0.38 % P

Hip height, in 57.4 54.5 57.3 54.8 nsHip width,in 22.0 20.8 22.0 21.3 nsBody length, in 65.1 64.9 65.1 64.8 nsHeart girth,in 81.1 78.9 81.0 79.8 nsCannon bone,in 11.1 10.6 11.4 10.6 nsPelvic height,cm 16.9 16.5 16.6 16.8 nsPelvic width,cm 15.8 15.8 16.4 15.9 nsPelvic area,cm2 210.8 205.4 214.3 210.4 nsPelvic length,in 22.1 21.4 22.3 21.5 ns

Effect (P<Item Holstein Crossbred Holstein Crossbred Diet

Bone density

0.28 % P 0.38 % P

Holstein and Crossbred Heifers 3 22 months of age.Experimental Diets

Unsupplemented = 0.28 % PSupplemented = 0.38 % P

Bone densityTrabecular bone density,mg/cm3 466.5 439.3 407.9 456.5 nsCortical bone density, mg/cm3 573.2 588.6 628.1 562.5 nsTotal bone density, mg/cm3 525.4 521.6 529.2 514.7 ns

Chemical compositionP, % 10.3 10.4 10.6 10.4 0.08Ca, % 20.2 20.5 21.0 20.4 nsAsh, % 58.2 58.3 58.4 58.0 ns

1st Lactation ProductionPhosphorous Treatment

High(N=165)

Low(N=168)

305 Day Milk 18,982 18,808

ME 305 Milk 23,846 23,768

Peak Milk 75 6 75 7Peak Milk 75.6 75.7

Total Milk 20,598 19,978

Total Fat 810 800

Total Protein 661 645

Fat Corrected Milk 66.5 66.3

Average Log SCC 2.82 2.95

Average MUN 14.5 14.4

Average Milking Speed 5.59 5.43


Notes


Within Farm Variation in Nutrient Composition of Feeds

Bill Weiss, Dianne Shoemaker, Lucien McBeth, Peter Yoder and Normand St-Pierre

Department of Animal Sciences and Ohio State University Extension

The Ohio State University

SUMMARY

Es mates of the amount of varia on (i.e., standard devia- ons and ranges) in nutrient composi on of common feeds

are readily available from some na onal feed tes ng labs; however, es mates of varia on on feeds within a farm are needed to determine appropriate feed sampling and ra on re-formula on schedules. We conducted a large na onal survey to determine within farm varia on for some com-mon feeds. On average within farm varia on for crude protein (CP) and neutral detergent fi ber (NDF) was 2 or 3 mes less than varia on in the na onal popula on. Within

a farm, substan al day to day varia on in dry ma er, NDF, CP and starch was observed for corn silage and haycrop silage, indica ng that samples should be taken over sev-eral days and those results averaged for diet formula on purposes. In some cases, day to day varia on was as great as month to month varia on. The concentra ons of NDF, but not CP, in high moisture corn, dis llers grains, and brewers grains varied almost as much as NDF concentra- ons in some forages, indica ng that these feeds should

be sampled on a regular basis.

INTRODUCTION

Unques onably the nutrient composi on of all feeds var-ies, which means that the composi on of the ra ons fed to cows also varies. Although varia on in nutrient composi- on is a well-accepted fact of life, this varia on has not

been extensively quan fi ed and the eff ects of varia on on cows have not been studied. A group of researchers and extension specialists at The Ohio State University with the help of collabora ng nutri onists and dairy farmers are working on a large project designed to: 1) Determine the varia on in nutrient composi on of common feeds on commercial dairy farms; 2) Determine how varia on in ingredients aff ects varia on in nutrient composi on of TMR on commercial dairy farms; 3) Par on sources of varia on (e.g., lab, sampling, and ‘real’); 4) Determine co-variances among nutrients in common feeds; 5) Determine how varia on in the nutrient composi on of diets aff ect lacta ng dairy cows; and 6) A empt to put a dollar value

on varia on. This project is ongoing (as of 2012) and it is beyond the scope of this paper to discuss all those objec- ves. This paper will concentrate on the degree of varia on

observed in nutrient composi on of common feeds on dairy farms, some factors infl uencing that varia on, and some implica ons of that varia on on ra on formula on.

THE IMPORTANCE OF KNOWING NUTRIENT

VARIATION

The amount of varia on in the nutrient composi on of a feed can aff ect diet formula on strategies, the economic value of the feed, the sampling and analysis schedule for the feed, and possibly the produc vity and health of cows. An important component of a good ra on formula- on strategy is risk management. Diets are formulated,

in part, to minimize the risk of a nutrient defi ciency that would reduce produc on or impair cow health. The safety factors included in ra on specifi ca ons are dependent on factors such as variability in cow factors within a pen (stage of lacta on, milk yield, parity, etc.), overall quality of nutri onal management on a given farm (monitoring feed bunks, consistency of the total mixed ra on, etc.), and the variability in the nutrient composi on of the ingredients in the diet. A total mixed ra on (TMR) based on ingredients that are highly variable in protein concentra ons will likely be formulated to a greater protein concentra on than a diet based on very consistent ingredients. This over-formula on reduces the risk that the diet will be defi cient in protein because of changes in the composi on of the ingredients. The need for greater safety factors for variable feeds should aff ect the economic value of the ingredients. For example, a diet might be formulated to contain 16% protein when based on consistent ingredients, but when based on a highly variable ingredient the diet might be formulated to contain 17%. The price of the highly vari-able ingredient must be discounted enough to cover the cost of feeding the higher protein diet. This also has an environmental cost (i.e., greater N excre on), and in many loca ons that translates directly into an economic cost. The goal of an op mal sampling (and analysis) schedule is


to minimize cost. Sampling too frequently increases feed analysis cost, but sampling too infrequently may result in lost produc on or health problems because a change in diet composi on was not iden fi ed in a mely manner. Feeds that are consistent will have a very diff erent op mal sampling schedule than that of highly variable feed. At this me, very li le is known about how varia on in nutrient

composi on of the ra on aff ects produc vity of dairy cows, but if ra on varia on aff ects the cow then the cost of lost milk (or health problems) will have to be factored into the valua on of the ingredients.

VARIATION IN NUTRIENT COMPOSITION OF FEEDS

Two excellent sources for nutrient composition data (including varia on) of common feeds are the websites maintained by:

DairyOne Coopera ve (Ithaca, NY): www.dairyone.com/Forage/services/default.asp

Dairyland Laboratories Inc. (Arcadia WI): www.dairylandlabs.com/

(Note: Other qualifi ed labs are available)For many feeds, these data bases include thousands of samples from a wide geographic area, mul ple years, mul ple hybrids and varie es, diverse growing condi ons, diff erent manufacturing systems (e.g., dis lleries or fl our mills), etc. so that the data from those sources represent a na onal popula on. In other words, if you take a ran-dom sample of corn silage or dis llers grain from the U.S. it would likely fi t the popula on of samples in those two data bases. Although na onal data are valuable, they most likely do not accurately refl ect varia on in nutrient composi on in feedstuff s within a farm. As a popula on becomes more specifi c (e.g., corn silage from across the U.S. vs. corn silage grown on Farm X in 2012), we expect the standard devia on to become smaller because many fewer sources of varia on occur on Farm X than what oc-curs across the U.S. Knowing varia on in feed composi on at the farm level, rather than at a na onal or global level, will allow us to fi ne tune ra on safety factors, compare economic value of feeds more accurately, and set up op- mal sampling schedules for specifi c farms.

Methodology

To determine variability in feed composi on at the farm level, 50 well-managed dairy farms from across the U.S. (20 within Ohio, 30 outside of Ohio) were enrolled in this project. The nutri onists for the farms were given a detailed sampling protocol so that sampling procedures would be consistent across farms, and then all major ingredients

added at the farm to the TMR mixer for the ‘high group’ were sampled once monthly. Nutri onists were free to add new ingredients or stop feeding ingredients at their discre- on. All samples from all farms were sent to a common

lab (Cumberland Valley Analy cal Services, Hagerstown, MD) and analyzed via wet chemistry for dry ma er (DM), crude protein (CP), neutral detergent fi ber (NDF), ash, and major minerals (ash and minerals will not be discussed in this paper). Sta s cs on nutrient composi on were then calculated within each farm and feed. If a farm iden fi ed diff erent popula ons of a feed, sta s cs were calculated for each designated popula on. For example, if a farm sent in samples iden fi ed as “bunker corn silage” and “bag corn silage,” those two feeds were kept separate. If samples were designated simply as corn silage, all samples were consid-ered to be from the same popula on. The fi nal data set contained 4,700 samples from 49 farms (one farm dropped out of the project) from 10 states (CA, IA, MI, NM, NY, OH, SD, UT, TX, and WI). Data from 49 general ingredients (i.e., all corn silages were considered one ingredient even though farms may have used two diff erent corn silages) plus samples of commercial mixes, preblends, and TMR were obtained. For this analysis, ingredients had to be sampled at least 4 mes on a specifi c farm during the 12-month period and had to be fed by at least 5 farms. Many feeds met these criteria, but for this paper discussion will be limited to corn silage, legume hay, legume silage, small grain silage, straw, dry shelled corn, high moisture corn, dried dis llers grains, soybean meal, and wet brewers grains.

Short term (day to day rather than over a year) varia on in haycrop silage and corn silage was evaluated by sam-pling those silages on 14 consecu ve days on 8 farms near Wooster, OH. On 4 of those farms, independent duplicate samples (mul ple handfuls were taken, mixed, and a sub-sample was placed in a bag; then that process was repeated) were taken each day. Corn silage was assayed for DM, NDF, and starch, and haycrop silage was assayed for DM, NDF, and CP. All assays were conducted in duplicate at the OARDC Dairy Nutri on Lab using standard wet chemistry methods.

Day to Day Variation in Nutrient Composition of

Silages

Within a farm and over a rela vely short period, day to day varia on in nutrient composi on of silages on many farms was substan al (Figure 1 and Table 1). For corn silage, the day to day varia on in starch was greater than NDF which was greater than DM (coeffi cients of varia on (CV) were 9.5, 6.2 and 5.3, respec vely). The day to day varia on in those nutrients did not follow any discernable pa ern (Figure 1). For the 8 farms, the average range in


starch concentra on in corn silage was 12.2 percentage units over a 14 day period. The most consistent corn silage (within a farm) had a range in starch concentra on of 6.3 percentage units and the most variable had a range of 27.7 units (Table 1). If that varia on is real (i.e., not caused by sampling or laboratory error) a devia on of about 14 units from the mean (i.e., half the range) would alter the starch concentra on of the diet by about 3.5 percentage units if silage comprised 25% of the TMR. That degree of change could be enough to cause rumen upset. Day to day varia on in corn silage NDF was also substan al but the varia on was more consistent from farm to farm than the farm to farm varia on in starch. The most consistent farm had a range of 7.3 percentage units in NDF whereas the most variable farm had a range of 11.2 percentage units in corn silage NDF. For the most variable farm, a devia- on of 5.6 units from the mean would change TMR NDF

by about 1.4 percentage units (assuming corn silage had a 25% inclusion rate). Although DM was more consistent than the carbohydrates, it s ll ranged within a farm from 5.1 units up to 10.4 units. Because diets are formulated on a DM basis but delivered on an as-fed basis, a devia- on of 5 percentage units in DM could substan ally alter

diet composi on.

For hay crop silage, DM was most variable, followed by CP and then NDF (CV = 8.5, 5.8, and 5.0, respec vely). The range in varia on between farms was large (Table 1). The range in DM concentra ons within a farm was more than 5 mes greater for the least consistent hay crop silage com-

pared with the most consistent. The range in NDF and CP varied about 4-fold between the most and least consistent haycrop silages. Contrary to conven onal wisdom, within a farm over a short period of me, haycrop silage was a more consistent source of NDF than was corn silage (5.0 vs. 6.2 for haycrop and corn silage, respec vely).

Even though day to day varia on in corn and haycrop silage was substan al, it was much less than what was observed in the na onal popula on. The average standard devia- on within a farm was 2 to 3 mes less than SD for the

na onal popula on (Table 1). When developing op mal sampling schedules, if actual variance on a given farm is not known, the average within farm SD shown in Table 1 would be more accurate than using the na onal SD.

An obvious ques on is: Why are corn and haycrop silages so variable over a short period of me? One possible rea-son is laboratory error; however, all assays were conducted in duplicate, and averaging the duplicate values had es-

Figure 1. Day to day changes in DM (%) and NDF, CP, and starch (% of DM) of corn silage and haycrop silage on two farms. The farms were chosen because the day to day range in nutrient composi on was approximately the average for the data set (see Table 1).

10

15

20

25

30

35

40

45

50

0 2 4 6 8 10 12 14Day

DM NDF Starch10

15

20

25

30

35

40

45

50

0 2 4 6 8 10 12 14Day

DM NDF CP

Corn Silage Alfalfa Silage


sen ally no eff ect on SD, CV, or ranges when compared to using a single laboratory value for each day (data not shown). Another potential source of variation is sampling error. Sampling er-ror can be defi ned as varia on among samples from a defi ned popula on (assuming no labora-tory error). For example, if you had a pile of 2,000 lbs. of corn silage that was going to be fed to a single pen of cows today and you took 10 samples from the pile, the variation among those samples is sampling error (again assuming no analy cal er-ror). To evaluate sampling error, duplicate samples were taken each day on 4 farms. Averaging the results from the duplicate samples reduced varia on by 13 to 25% depending on the nutri-ent and type of silage (Figure 2). Sampling error is clearly an important source of varia on, and it is probably greater than our es mates because with the sampling protocol we followed, two duplicate samples may not have been ad-equate to represent all the sampling error. Indeed a sub-stan al amount of the day to day varia on we observed over the 14 day period is most likely sampling error. Even though the cow does not experience sampling error, it can have a signifi cant impact on diet formula on and the cows. High sampling error means that you should not have great confi dence in the results from a single sample. Rather, mul- ple samples should be taken over a short period of me

(days) and the average of those samples should be used for diet formula on. We used Monte Carlo techniques to randomly select samples from the day to day study and determined that the mean of 3 samples consistently matched the overall mean (over 14 days) for NDF within each farm. A single randomly selected sample was within ± 5% of the mean only 50% of the me.

The eff ect of an ingredient on the varia on observed in the TMR is dependent on varia on in the ingredient and on the inclusion rate of the ingredient. The eff ect an ingredient has on the varia on of the total diet changes with the square of the inclusion rate. This means that when the inclusion rate of an ingredient is doubled, its eff ect on total diet variance

does not increase by a factor of 2, but by a factor of 4 (i.e., 2 squared). Relying on mul ple ingredients, each with a limited inclusion rate, can greatly reduce varia on in the TMR. This is illustrated in Figure 3. The NDF concentra ons of corn silage and hay crop silage from two farms are highly variable day to day; however, if the TMR contained 25% corn silage and 25% haycrop silage, the concentra on of forage NDF in the diets is less variable. In Farm A (Figure

Table 1. Day to day varia on in nutrient composi on of corn silage and haycrop silage on 8 dairy farms in Northeastern Ohio1

Corn Silage Hay Crop SilageDM, % NDF, % Starch, % DM, % NDF, % CP, %

Averages2

Mean 38.8 40.4 31.7 43.6 47.6 17.3 SD 2.07 2.52 3.02 3.7 2.38 1.01 CV 5.3 6.2 9.5 8.5 5.0 5.8 Range 7.3 8.8 12.2 11.8 8.5 3.4Ranges3

Mean 31.5 - 45.7 35.4 - 45.0 27.0 - 39.2 32.5 - 55.7 36.1 - 58.2 15.1 - 21.9 SD 1.50 - 3.04 2.16 - 3.27 2.05 - 5.26 1.00 - 6.66 0.92 - 3.64 0.37 - 1.61 Range 5.1 - 10.4 7.3 - 11.2 6.3 - 27.7 3.4 - 19.1 3.2 - 13.6 1.2 - 4.9Na onal Popula on sta s cs4

Mean 33.4 41.7 34.0 40.1 47.0 20.5SD 6.1 5.4 7.3 10.3 5.7 3.0CV 18.3 13.0 21.5 25.7 12.1 14.61 Samples of corn silage and haycrop silage were taken for 14 consecu ve days during a me when the silages did not knowingly change (i.e., same storage structure, same growing season, same cu ng).2 The mean, standard devia on (SD), coeffi cient of varia on (CV) and range (maximum daily value - minimum daily value) were calculated for each farm and then averaged for the 8 farms.3 These ranges are calculated between farms (e.g., on the most consistent farm corn silage DM ranged by 5.1 units but on the most inconsistent farm, it ranged by 10.4 units).4 Data are from DairyOne Forage Summary (mixed mostly legume silage was used for haycrop silage) on samples analyzed from May, 2010 to May 2011.

0

0.5

1

1.5

2

2.5

3

3.5

CS DM CS NDF CS Starch HCS DM HCS NDF HCS CP

Stan

dard

Dev

iatio

n U

nits

No DupsDuplicates

Figure 2. The eff ect of taking independent duplicate samples each day on varia on in nutrient composi on of corn silage (CS) and haycrop silage (HCS) on SD within a farm over 14 days. Each bar is the average of the standard devia ons calculated for each farm (4 farms).


Farm A

20253035404550556065

0 5 10 15Day

CS Haylage Mix

Farm B

15202530354045505560

0 5 10 15Day

CS Haylage Mix

Figure 3. The eff ect of blending forages on reducing varia on in the TMR. Forages were sampled daily and assayed for NDF. The line designated as Mix is the concentra on of forage NDF in the TMR assuming corn silage and haycrop silage each comprised 25% of the TMR and no other forages were fed (data are % of DM of silage or total TMR).

10

15

20

25

30

35

40

45

50

0 2 4 6 8 10 12Month

DM NDF

2025303540455055606570

0 2 4 6 8 10 12Month

DM NDF

Corn Silage Legume Silage

Figure 4. Month to month changes in concentra on of DM (%) and NDF (% of DM) in corn silage and legume silage. The farms were chosen because the across months range is approximately equal to the average for the survey (Table 2).


3), the CV for corn silage and haycrop silage are 5.3 and 4.3 but the CV for the mix is only 3.7. For farm B, the CV for corn silage is 7.3 and 2.9 for haycrop and for the mix it is 4.3. For both farms, the CV of the mix is much less than the average of the CV for the two forages.

Month to Month Variation in Forages

Measures of the month to month varia on in nutrient composi on of forages within farms are shown in Tables 2 and 3 and varia on in concentrate feeds are shown in Tables 4 and 5. For comparison purposes, means and SD of the same feeds but for the na onal popula on are shown in Table 6. General observa ons include:1. As expected, DM concentra ons of silages were much

more variable than DM of dry forages, and corn silage DM was less variable the haycrop silage DM.

2. Rela ve varia on in CP concentra on (CV) were similar between the forages except for straw, but the high CV for straw is misleading because straw has such a low concentra on of CP.

3. The variation in NDF concentration varied among forages (legume hay was most variable, then legume silage, then small grain and corn silages, and straw was most consistent).

4. Unexpectedly, the rela ve varia on in corn silage NDF over 12 months was essen ally the same as observed over a period of 14 days (Tables 1 and 2 and Figures 1 and 4). However, as would be expected, the average varia on in NDF of haycrop silage over a 12 month pe-riod was substan ally greater than what was observed over a 14-day period (CV = 8.7 vs. 5.0).

5. The average SD (within farm) for DM, CP, and NDF were usually one-third to one-half as great as the SD for the

Table 2. Varia on in nutrient composi on of forages within farms over a 12 month period1

DM, % CP, % DM NDF, % DMCorn silage (data from 48 farms) Mean 34.1 7.9 40.8 SD 2.67 0.55 2.60 CV 7.8 7.0 6.4 Range 9.1 2.0 8.8Legume hay (data from 21 farms) Mean 87.8 21.2 37.5 SD 2.46 2.01 4.20 CV 2.8 9.4 11.2 Range 8.2 6.3 13.2Legume silage (data from 38 farms) Mean 42.8 21.5 39.9 SD 6.29 1.64 3.47 CV 14.7 7.6 8.7 Range 20.7 5.2 10.9Small grain silage (data from 9 farms) Mean 37.3 13.0 53.6 SD 3.27 1.46 3.29 CV 8.8 11.2 6.1 Range 10.6 4.6 9.5Straw (data from 15 farms) Mean 87.8 4.7 79.8 SD 3.78 1.16 2.41 CV 4.3 24.7 3.0 Range 11.7 3.4 7.31 Samples were taken once monthly if the forage was fed on the farm. Samples are not necessarily from the same popula on (i.e., corn silage could come from diff erent structures, fi elds, etc; hay crops could be diff erent cu ngs, fi elds, etc.). The sta s cs are calculated within each farm and then averaged across farms.

Table 3. Within farm ranges (over a maximum 12 month per-iod) in varia on in nutrient composi on of forages1

DM, % CP, % NDF, %Corn silage (data from 48 farms) Mean 29.9 - 43.1 6.8 - 11.8 35.1 - 51.2 SD 0.80 - 5.00 0.24 - 1.27 1.16 - 6.52 Range 2.5 - 17.9 0.8 - 4.7 4.1 - 22.1Legume hay (data from 21 farms) Mean 72.3 - 88.5 18.3 - 24.6 30.9 - 43.9 SD 1.17 - 9.79 1.18 - 3.62 0.68 - 7.32 Range 2.1 - 20.7 3.7 - 11.1 1.6 - 16.6Legume silage (data from from 38 farms) Mean 27.2 - 49.9 17.8 - 23.8 32.1 - 47.3 SD 2.93 - 11.1 0.33 - 2.64 1.21 - 6.01 Range 8.6 - 41.3 0.9 - 9.1 2.6 - 19.0Small grain silage (data from 9 farms) Mean 31.4 - 61.7 10.3 - 17.1 42.2 - 64.0 SD 1.67 - 4.62 0.59 - 3.76 1.36 - 6.88 Range 4.4 - 17.1 1.6 - 13.6 4.0 - 17.2Straw (data from 15 farms) Mean 82.3 - 90.8 3.7 - 5.8 72.4 - 82.6 SD 0.76 - 13.40 0.27 - 2.73 1.21 - 4.59 Range 1.4 - 42.5 0.8 - 8.9 2.8 - 15.51 Samples were taken once monthly if the forage was fed (not all forages were fed for 12 months on all farms). Samples are not ne-cessarily from the same popula on (i.e., corn silage could come from diff erent structures, fi elds, etc; haycrops could be diff erent cu ngs, fi elds, etc. The sta s cs are calculated within each farm and then the minimum and maximum among farms were determined.


na onal popula on except for legume hay. The SD for legume hay in the limited popula on (Table 3) may have been similar to the na onal popula on (Table 6) be-cause farms o en purchase hay from na onal markets.

More important than average within farm varia on in forage composi on is the range in within farm varia on that was observed (Table 3). Some of the farm to farm diff erences in variability could be caused by sampling varia on (diff erent people sampled at diff erent farms, storage structures diff ered, etc.) but some is likely real varia on. For the forages on the most consistent farms, the range in CP and NDF concentra ons were so small as to have li le biological (and economic) importance. On the other hand, for the most variable farms, the range in NDF was 16 to 22 percentage units depending on the

forage (8 to 11 percentage unit devia on from the mean). At a 25% inclusion rate, this amount of varia on could change forage NDF concentra ons of the TMR by 2 to 3 percentage units. The range in CP concentra ons for the least consistent farms is great enough to also cause sub-stan al change in CP concentra on of the TMR. For the most consistent farms, repeated sampling of the forages would be unnecessary; however, for the more variable farms, frequent sampling would be needed to quickly iden fy when forage composi on changed.

Month to Month Variation in Concentrates

As expected, the average varia on in DM, CP, and NDF for the concentrates was less than for the forages (Tables 2 and 4). However, varia on in NDF in many of the con-centrates was substan al. In most situa ons, the average within farm varia on in CP or NDF concentra ons would have minor eff ects on TMR nutrient composi on. The average devia on (range divided by 2) in CP concentra- on for most of the concentrates would change the CP

Table 4. Varia on in composi on of concentrates within farms over a 12 month period1

DM, % CP, % NDF, %Corn grain (data from 27 farms) Mean 85.3 8.4 11.3 SD 1.67 0.43 1.30 CV 2.0 5.1 11.5 Range 4.6 1.2 3.7High moisture corn grain (data from 23 farms) Mean 68.8 8.1 11.6 SD 3.04 0.58 1.87 CV 4.4 7.2 16.1 Range 9.5 1.8 5.9Dried dis llers grains (data from 11 farms) Mean 89.9 30.4 32.6 SD 1.31 1.02 2.25 CV 1.5 3.4 6.9 Range 3.6 2.9 6.4Soybean meal (data from 18 farms) Mean 88.3 52.4 8.7 SD 0.91 1.05 0.95 CV 1.0 2.0 10.9 Range 2.5 2.3 3.1Wet brewers grains (data from 11 farms) Mean 22.9 31.3 49.1 SD 1.70 2.03 2.60 CV 7.4 6.5 5.3 Range 5.5 6.3 8.11 Samples were taken monthly if the concentrate was fed (not all feeds were fed for 12 months on all farms). Samples are not necessarily from the same popula on (i.e., diff erent lots, mul ple sources, etc.). The sta- s cs are calculated within each farm and then averaged across farms.

Table 5. Within farm ranges (over a maximum 12 month per-iod) in varia on in nutrient composi on of concentrates1

DM, % CP, % NDF, %Corn grain (data from 27 farms) Mean 83.0 - 86.5 7.8 - 9.2 9.9 - 13.3 SD 0.39 - 4.50 0.18 - 1.14 0.40 - 2.70 Range 1.0 - 10.0 0.5 - 2.7 1.2 - 7.7High moisture corn grain (data from 23 farms) Mean 57.3 - 77.6 6.9 - 9.1 7.9 - 23.6 SD 0.69 - 5.76 0.24 - 0.89 0.64 - 5.85 Range 1.5 - 19.4 0.8 - 3.2 1.9 - 18.9Dried dis llers grains (data from 12 farms) Mean 86.9 - 91.8 28.1 - 39.6 31.2 - 34.5 SD 0.58 - 2.22 0.64 - 1.97 1.12 - 5.05 Range 1.7 - 5.4 1.5 - 6.2 3.1 - 11.9Soybean meal (data from 18 farms) Mean 87.2 - 91.2 49.6 - 53.6 8.0 - 11.0 SD 0.29 - 3.25 0.29 - 3.69 0.22 - 3.16 Range 0.7 - 8.6 0.6 - 4.4 0.5 - 14.9Wet brewers grains (data from 11 farms) Mean 21.0 - 24.8 25.0 - 34.3 45.8 - 54.4 SD 1.15 - 2.58 1.47 - 3.15 1.41 - 3.66 Range 3.7 - 9.2 4.7 - 10.0 3.0 - 12.01 Samples were taken monthly if the concentrate was fed (not all feeds were fed for 12 months on all farms). Samples are not neces-sarily from the same popula on (i.e., diff erent lots, mul ple sources, etc.). The sta s cs are calculated within each farm and then the minimum and maximum among farms were determined.


concentra on of the TMR by about 0.3 percentage units with an inclusion rate of 20% (a change of 0.6 units is possible with wet brewers grains). Assuming a 20% inclusion rate and average within farm varia on for concentrate NDF, diet NDF could change by 0.3 to 0.8 percentage units (a change that likely would have li le eff ect on the cow). The average within farm SD for the concentrates was much smaller than the SD for the na onal popula on (Tables 4 and 6) even though many of those concentrates are marketed na on-ally. For the na onal popula on dry corn was more variable in CP and NDF than high moisture corn, but the opposite was observed for average within farm varia on. This discrepancy may be caused by sample mis-iden fi ca on in the na onal database (e.g., ear corn iden fi ed as shell corn). In the na onal data base, dried dis llers and wet brewers grains had similar varia on in CP concentra on but the dis llers grains were much more variable in NDF concentra on (CV = 16.1 vs 9.1). For the average within farm varia on, dis llers grains were less variable in CP than were wet brewers and the two feeds had similar variability in NDF.

The range in varia on among farms for the concentrates (Table 5) is more important than the average varia on (Ta-ble 4). For example, the DM concentra on for high moisture corn from one farm varied by 19 percentage units over the year, which is enough to substan ally aff ect the amount of corn DM fed in a diet. The largest month to month range in DM concentra on of wet brewers could also aff ect TMR. For most of the concentrates (the excep on is wet brew-ers), even for the most inconsistent farm, the varia on in CP probably would not cause major issues. The most inconsis-tent wet brewers with respect to CP could cause important changes in CP concentra ons of the TMR. On farms that experienced the greatest range in NDF concentra ons for high moisture corn, dis llers, and wet brewers grains, the NDF concentra on of the TMR could be altered enough to aff ect cows. The NDF concentra on of soybean meal could vary tremendously; however, inclusion rate for soybean meal is usually <10% and some of that varia on is likely a result of not iden fying low and high protein soybean meal as separate feeds. Assuming that concentrate feeds within a farm do not vary enough to jus fy sampling and nutrient analysis is clearly wrong at least for NDF. The NDF concentra on of high moisture corn, brewers grains, and dis llers grains should be monitored. An o en stated com-plaint about dis llers grains is that it is too variable. Based on data in Table 4 and 5, dis llers and brewers grains have similar varia on, and for NDF dis llers grains were actually more consistent than high moisture corn.

IMPLICATIONS

1. The amount of within farm varia on for specifi c feeds differs widely among farms. This means sampling schedules should diff er widely among farms (increased sampling for farms with greater variability).

2. Day to day variability in corn silage and haycrop silage is very large and o en as great as month to month varia on. Single samples should not be relied upon to provide an accurate descrip on of the feed, and sub-stan al changes in diet formula on should not be done based on results from a single sample. Results from 2 or 3 samples taken within a short period of me (1 or 2 weeks) should be averaged and the average used in diet formula on. Duplicate samples taken on a single day reduced day to day varia on but probably not enough to jus fy the added costs at least on smaller farms.

3. Mul ple sources of nutrients, even highly variable sources, greatly reduce the varia on in the nutrient composi on of the TMR.

4. For many concentrates, the CP and NDF concentra on (and DM for wet feeds) vary substan ally. The varia on in NDF was large enough that eff ects on cows might be observed if the changes in composi on were not used in diet formula on.

5. A month to month (day to day) change in nutrient composi on of a feed could substan ally alter its in-clusion rates when linear programming is used for diet formula on. If the change in nutrient composi on was not ‘real’ (e.g., sampling varia on), mul ple sampling of ingredients could actually increase varia on in TMR. Averaging sample results should reduce this eff ect.

Table 6. Na onal popula on sta s cs for feedstuff s evaluated in the varia on project1

DM, % CP, % DM NDF, % DMMean SD Mean SD Mean SD

Corn silage 33.4 6.16 8.4 1.02 41.7 5.40Legume hay 91.0 1.13 21.3 2.70 38.8 5.23Legume silage 41.3 11.00 22.1 2.92 43.7 5.55Small grain silage 31.8 11.52 13.8 3.68 56.7 7.83Straw 93.4 1.50 5.4 2.31 73.1 7.75

Corn grain 86.6 2.96 8.3 1.36 10.2 3.14High moisture corn 71.2 6.14 8.6 0.81 10.0 2.53Dried dis llers grain 88.0 1.79 32.0 4.88 34.9 5.78Soybean meal 90.9 2.47 51.4 4.79 13.4 6.74Wet brewers grains 24.5 6.34 29.0 4.43 50.6 4.611 Data are from DairyOne (Ithaca, NY) for samples analyzed from May, 2010 through May 2011.


ACKNOWLEDGEMENTS

This project was supported by Na onal Research Ini a- ve Compe ve Grant no. 2009-55206-05242 from the

USDA Na onal Ins tute of Food and Agriculture. We also thank the coopera ng dairy farms and their nutri onal consultants.

This paper was originally published in the 2012 Proceedings of the Tri-State Dairy Nutri on Conference, Ft. Wayne, IN.


Notes


INTRODUCTION

Varia on in composi on can be large and diff erent across feeds and nutrients. Dietary nutrient varia on can nega- vely aff ect feed costs, animal health, and milk produc on

(Weiss and St-Pierre, 2009). Inadequate energy supply can reduce milk produc on, impair reproduc on, decrease resistance to disease, and may cause severe weight loss (Weiss, 1997). Strategies such as dietary safety factors (Kohn, 2006), using more ingredients in TMR with lower inclusion rates (Weiss and St-Pierre, 2009), and avoiding feeds that are highly variable in nutrient composi on are all strongly recommended. Safety factors are added to nutrient requirements to reduce the likelihood that a diet will be defi cient in certain nutrients; however, these safety factors usually increase feed cost but may increase milk produc on and income over feed costs (IOFC; St-Pierre and Thraen, 1999). Determina on of diet safety factors is somewhat guesswork as common nutri on models are incapable of predic ng the varia on in dietary net energy (NEL) or metabolizable protein (MP; Kohn, 2006).

Forages and some byproducts are the most variable feeds in nutrient concentra ons; however, these feeds o en are considered “bargain feeds” based on their mean nutrient concentra ons and cost. Nutrient varia on (expressed as standard devia on, SD) within feed popula ons is provided by the NRC (2001) and several commercial laboratory websites. However, Weiss and St-Pierre (2009) suspect that many of the NRC (2001) es mates may be in error because of small sample size, old data, and presence of outliers. We aim to improve the es mates of varia on in the nutrient composi on of feeds, including NEL and MP, which should allow us to develop be er strategies for managing nutrient varia on and maximizing IOFC.

FEED SUMMARIES

Composi on data were collected from two major commer-cial laboratories on a wide range of feeds. Feed classifi ca- on (i.e., feed name or code) provided by the labs was not

changed. Feed sta s cal summaries were kept separate

by lab so we would avoid lab eff ects, diff erences in clas-sifi ca on procedures between labs, and other unknown sources of varia on. We developed a stepwise univariate and mul variate sta s cal procedure to iden fy outlier samples and sub-popula ons (Yoder et al., 2012). Outliers in feed popula ons occur because of erroneous labeling of samples on farm (e.g., iden fying a mixed grass-alfalfa hay as alfalfa), taking a poor sample (i.e., the sample does not refl ect the popula on), improper classifi ca on of sample by the lab, laboratory error, and other unknown factors. Large sets of composi on data for a given feed likely con-tain mul ple subpopula ons because of manufacturing techniques (e.g. brewery) or geographic loca on. Other subpopula ons could simply be the result of a misclassifi -ca on (e.g., labeling corn gluten feed as corn gluten meal). In our procedures we set a limit that if a subpopula on comprised less than 10% of the total number of samples, it was removed because it did not represent the primary popula on but it was not large enough to be classifi ed as a separate feed.

We applied this sta s cal procedure to the data provided by the labs and generated several sta s cs of interest (mean, SD, 90% confi dence intervals, and also the corre-la ons between major nutrients; e.g., DM, NDF, CP, and ash). The lack of homogeneity in some feed popula ons greatly aff ected varia on and correla on es mates but had minimal eff ects on es mates of the mean in most feeds. We discovered that across many feeds, es mates of SD and correla on changed greatly a er outlier samples or subpopula ons were removed (Table 1). For example, the SD es mates of CP concentra on were reduced by 52% for soybean meal and 76% for corn gluten meal a er outliers were removed. Contamina on by roasted soybeans and 44% soybean meal and corn gluten feed, respec vely, likely greatly infl ated the SD es mates.

Across most feeds, the mean nutrient concentra ons were similar to the NRC (2001) and this is shown for six feeds in Table 2. However, some means diff ered greatly from NRC.

Variation and Relationships of Nutrients within Feed Populations

P. S. Yoder, N. R. St-Pierre, W. P. Weiss


Wooster


Wet brewers grains had a mean fat concentra on 4.6 units higher than NRC (2001) and this is likely because of the number of samples, 260 versus 1. Dried dis ller grains was 2.5 units higher in mean fat concentra on than NRC (2001), and this may be because of diff erences in current manufacturing versus approximately 15 years ago. Based on the revised popula ons, SD es mates were much lower for dried dis llers grains and corn gluten feed, but corn silage, legume silage, grass hay, and wet brewers grains were similar or slightly reduced compared to the NRC (2001). Dried dis llers grains contained a small subpopula- on that had approximately a mean fat concentra on of

4.5% (Figure 1) and a er removal of that subpopula on, the distribu on of fat concentra on (Figure 2) in the main popula on was more normal and SD was greatly reduced (Table 2). Three large subpopula ons for wet brewers grains were found based on mul ple nutrient concentra- ons, DM, CP, NDF, and ash. Figure 3 visually shows at

least two popula ons for CP concentra ons. The lack of homogeneity infl ated the SD of wet brewers grains since these large subpopula ons were combined. Varia on in nutrient composi on (i.e., SD) likely aff ects the percep on of nutri onal risk when u lizing some feeds. As shown in Table 1, the SD in the NRC (2001) for some feeds may not be appropriate for that feed today. If a feed is suspected to have large subpopula ons, SD es mates should be evaluated with more scru ny, and iden fi ca on of the actual subpopula on you are using would likely reduce your SD es mate. The SD of a feed also should be consid-ered when evalua ng the results of a single sample and when determining the frequency of sampling. If the SD

Table 1. Feed popula on mean and SD nutrient concentra- ons before and a er sta s cal procedure was applied

Before Procedure1

Post Procedure2 Before

Procedure Post

Procedure CP, % of DM Ash, % of DM

Soybean meal n3 405 346 405 346 Mean 49.4 51.1 7.0 6.9 SD4 7.0 3.3 4.8 0.7Rye grass silage n 7,849 6,719 7,849 6,719 Mean 15.2 15.3 10.3 10.3 SD 3.6 3.0 2.9 2.5High moisture corn n 17,788 16,772 17,788 16,772 Mean 8.5 8.5 1.5 1.5 SD 0.9 0.9 0.4 0.3Corn gluten meal n 127 63 127 63 Mean 46.2 68.1 5.0 3.4 SD 22.9 5.4 2.5 1.9Canola meal n 642 515 642 515 Mean 40.5 41.1 8.0 7.9 SD 4.6 2.4 1.2 0.71Before Procedure = Dataset sta s cs prior to removal of outliers and subpopula ons.2Post Procedure = Dataset sta s cs a er removal of outliers and subpopula ons.3n = Number of samples in dataset. 4SD = Standard devia on.

Subpopulation of low fat samples

Figure 1. Sample frequency distribu on of fat concentra- on in the dried dis llers grains data set prior to removal of

outliers; n = 3,323, mean = 11.8, SD = 2.5.

Figure 2. Sample frequency distribu on of fat concentra on in the dried dis llers grains dataset a er removal of outliers; n = 2,740, mean = 12.5, SD = 1.3. A er removal of outliers and subpopula ons, the distribu on of fat is more normal.

Subpopulation of low fat samples

removed??


in a feed popula on is high, using a single sample result should be avoided and more frequent sampling may be needed. If a nutri onist desires to have a consistent diet,

SD of feeds should be considered when determining their inclusion rates, purchase price, and confi dence in nutrient formula on inputs for each feed.

The correla on matrix of nutrients within a feed popula on refers to how the nutrients are jointly expected to vary from sample to sample. For corn silage, the correla on be-tween starch and NDF is -0.88. Therefore, one should expect corn silage with a low starch concentra on to usually have a high concen-tra on of NDF and vice versa. Observing the correla on matrices provides some under-standing of what mul ple nutrient concentra- ons are expected to be within a single feed

sample. For example, if a corn silage sample contains 45% NDF, 39% starch, and 25% dry ma er, based on the corn silage popula on correla on matrix, one should realize that the sample is highly improbable, although if you examined each nutrient independently, you would conclude that the data looks reasonable. Observing multiple nutrients within a feed sample will provide much be er confi dence that the sample is representa ve or similar to the general popula on. Knowl-edge of nutrients and their rela onship also provide support for determining how NEL will vary from sample to sample. Weiss (1997) showed that alfalfa silage with a constant 35% ADF does not result in a constant NEL value, but ranges from 1.28 to 1.44 Mcal/kg due to diff erent concentra ons of NDF, lignin, and ash. The range in NEL es mates is because other nutrients (e.g. lignin) are not perfectly correlated to ADF. Observing the correla on matrices of feeds (Table 3) shows that using one nutrient to determine economic or en-ergy value is diffi cult because some correla- ons maybe weaker than expected (e.g. CP

and NDF) in some feeds.

Current ra on so ware uses the mean con-centra on of nutrients in feeds to formulate diets. The variability in nutrient composi on of the formulated diet caused by varia on in feed ingredients is not calculated. Therefore, the confi dence that the supply of dietary nu-trients will be similar day to day is unknown and is not considered when formulating diets. If you set the minimum constraint for

Table 2. Mean and standard devia on es mates for several feed popula ons

OSU (2012)1

NRC (2001) OSU

(2012)NRC

(2001) OSU (2012)

NRC (2001)

CP, % of DM NDF, % of DM Fat, % of DMCorn silage n2 103,119 1033 103,119 1033 102,172 75 Mean 7.9 8.8 41.9 45 3.1 3.2 SD3 0.9 1.2 4.7 5.3 0.3 0.5Legume silage n 12,597 8,576 12,597 8,567 12,533 1,325 Mean 20.7 20.0 42.0 45.7 3.4 3.1 SD 2.3 3.0 5.3 6.5 0.5 0.7Grass hay n 15,146 4,702 15,146 4,695 14,711 542 Mean 10.7 10.6 66.3 64.4 2.8 2.6 SD 3.3 3.1 5.2 6.2 0.6 0.7Dried dis llers grains n 2,740 879 2,740 493 2,740 464 Mean 29.6 29.7 35.3 38.8 12.5 10 SD 1.6 3.3 4.4 7.8 1.3 3.4Corn gluten feed n 347 186 347 122 139 68 Mean 23.2 23.8 36.5 35.5 4.1 3.5 SD 2.2 5.7 4.6 6.8 1.1 1.1Wet brewers grains n 924 1,127 924 685 260 N. A.4

Mean 29.4 28.4 50.0 47.1 9.8 5.2 SD 4.8 4 5.2 6.8 1.2 N. A.1Datasets obtained from two commercial laboratories and outliers were removed. Corn silage data is from the years 2007 to 2011 and the other feed datasets are from the years 2003 to 2011 (majority 2007 to 2011). 2n = number of samples in dataset. 3SD = Standard devia on.4N.A. = NRC (2001) does not list n or SD for wet brewers grain.

Table 3. Correla on es mates of several feed datasets

n1 CP/NDF Lignin/NDF Starch/NDFCorn silage 103,1191 0.20 0.76 -0.88Alfalfa silage 12,597 -0.72 0.53 0.02Grass hay 15,146 -0.64 0.71 -0.02Dried dis llers grains 2,740 0.05 0.27 0.23Corn gluten feed dry 347 0.13 -0.08 -0.35Wet brewers grains -0.40 0.22 -0.261Number of samples in dataset.


CP at 16% and a least cost algorithm is used, the diet will be formulated to provide 16% CP using the op mal inclu-sion of each ingredient that results in the lowest cost. If ingredient “X” is the cheapest source of CP, the inclusion rate will be the maximum within the user’s specifi ed con-straint. However, if ingredient “X” is highly variable in CP, it would greatly decrease your confi dence that CP supply of the diet will be similar day to day. If the SD of dietary nutrients is considered, confi dence intervals can be con-structed, and the formulator can have more informa on on how successful the diet will be at supplying 16% CP. If including ingredient “X” in the diet results in a confi dence interval of 14.5 to 17.5% CP, but a diet with ingredient “Y” results in a confi dence interval of 15.5% to 16.5% CP, you could feed a lower CP diet with ingredient “Y.” This might reduce feed costs, and you would have a lower risk of supplying a diet less than 15% CP.

So ware (PingPongTM) that calculates the SD of nutrients in diets is being developed at Ohio State. The so ware re-quires accurate es mates of SD for nutrients and accurate es mates of correla ons among nutrients. Correla on matrices are important for es ma ng NEL and MP since they are calculated from mul ple nutrients (NRC, 2001). Predic ng the varia on of these nutrients via PingPongTM allows for es mates of SD and confi dence intervals of NEL, MP, and metabolizable amino acids such as methionine and lysine (although es mates of varia on for amino acids are based on very li le data). Observing the SD and confi dence intervals of nutrients will allow nutri onists to evaluate diff erent formula on strategies for minimizing varia on risk. Sensi vity of a model’s op mal solu on to changes in constraints (e.g. nutrient requirements) or inputs (e.g. feed nutrient concentra ons) should also be evaluated since the SD will be known for nutrient supply. Using PingPongTM, we can also es mate the SD for NEL of individual feeds, which can be used to compare and select individual feeds based on their ability to consistently supply energy.

In summary, the preliminary results of this study indicate previous SD es mates of some feeds are highly infl ated, which may change the percep on of nutri onal risk for a par cular feed. The correla on matrices of some feeds show that some important nutrients (e.g. CP, NDF, and lignin) may have a weaker rela onship than expected and illustrate the importance of using a mul component net energy equa on (Weiss, 1997). The correla on matrices also facilitate the evalua on of mul ple nutrient concen-tra ons and their rela onships within a feed sample to determine the probability of the sample result occurring, which is more robust than observing individual nutrient

concentra ons to determine the sample result probability. Accurate SD and correla on matrices enable us to evalu-ate diet formula on stochas cally, which may possibly improve our ability to formulate an op mal diet.

REFERENCES

Kohn, R. A. 2006. How can dairy nutri on models deal with uncertainty? J. Dairy Sci. 89(Suppl. 1):382. (Abstr.).

NRC. 2001. Nutrient Requirements of Dairy Ca le. 7th rev. ed. Na onal Academy of Science, Washington D. C.

St-Pierre, N. R., and C. S. Thraen. 1999. Animal grouping strategies, sources of varia on, and economic factors aff ec ng nutrient balance on dairy farms. J. Anim. Sci. 77 (Suppl 2):72-83.

Weiss, W. P. 1997. Es ma ng the available energy content of feeds for dairy ca le. J. Dairy Sci. 81:830-839.

Weiss, W. P., and N. R. St-Pierre. 2009. Impact and management of vari-ability in feed and diet composi on. Pages 83-96 in Proc. Tri-State Dairy Nutri on Conf. Ft. Wayne, IN.

Yoder, P. S., N. R. St-Pierre, and W. P. Weiss. 2012. Iden fying improbable feed samples using a mul variate procedure. J. Dairy Sci. 95(Suppl. 2):737. (Abstr.).

Figure 3. Wet brewers grains, n = 924, mean = 29.4, SD = 4.8. Sample frequency distribu on of CP concentra on in the dataset a er removal of outliers. Three large subpopula ons were indicated by the mul variate sta s cal procedure that included DM, CP, NDF, and ash. The sample frequency distribu- on visually shows at least two subpopula ons of CP. Deter-

mina on of what the three popula ons precisely represent is unknown, but we speculate that the popula ons may repre-sent diff erences in brewery manufacturing.


mul ple samples if needed. The control diet (CON) was 24.8% FNDF, and we a empted to maintain 24.8% FNDF concentra on for the en re period (Figure 1). The variable diet (VAR) averaged 24.6% FNDF but varied randomly day to day (Figure 1). Random varia on of FNDF was done by Monte Carlo simula on, and the SD was 2.0% FNDF. The varia on of FNDF in the VAR diet was achieved by chang-ing the ra o of legume silage and grass silage fed but not changing forage to concentrate ra o.

The over-reac ng diet (ORR) averaged 24.9% FNDF, and we changed FNDF concentra on four mes during the 21-d period on day 5, day 10, day 15, and day 20 of the period (Figure 1). The varia on of FNDF of the ORR diet was achieved by changing the forage to concentrate ra o but not changing the legume to grass silage ra o. The four changes mimicked extreme sampling error causing diets to be reformulated assuming forage NDF changed when in reality the true concentra on of NDF did not change. To compensate for the false change in NDF, the forage to concentrate ra o was adjusted to maintain the dietary target FNDF. The result was an unnecessary change in forage to concentrate ra o and FNDF with no change in the forage composi on.

RESULTS AND DISCUSSION

Period DMI was aff ected by treatment (Table 1), and the ORR treatment cows consumed 1.3 lbs more DM than the control cows (P < 0.05). Average intake of cows on the VAR treatment did not diff er from the control cows. The ORR cows appeared to consume similar amounts as the CON cows when the FNDF was more than the CON diet. But when the FNDF was less than the CON diet, the ORR cows consumed more than the CON cows.

The lack of cumula ve aff ect for the VAR treatment across 21 days infers the cows were not nega vely aff ected by the increased forage quality varia on. Daily DMI tended to be more variable (P < 0.10) with a higher coeffi cient of varia on (CV) for the VAR cows, indica ng that the cows

Eff ect of Variation in Forage Quality on Milk Production and Intake

P. S. Yoder, N. R. St-Pierre, W. P. Weiss


Wooster

INTRODUCTION

Forages are the most variable in nutrient composi on of all feeds (Mertens, 2006), usually have the highest dietary inclusion, and o en are an economical source of nutrients. High forage diets that rely on one or two sources of for-age can have high varia on in the nutrients provided by the forages. Forage quality, (e.g. NDF and CP), has been shown to be highly variable on farms from day to day and month to month (Stone, 2004; Weiss et al., 2012). Sam-pling error and highly variable feeds can reduce accuracy of ra on formula on, especially if single samples are re-lied upon. If sampling error is high (possibly 13 to 25% of varia on; Weiss et al., 2012), reliance on single samples for ra on formula on may result in more frequent and larger changes of diet composi on and therefore increase varia on. Our objec ve was to determine whether forage quality varia on (high sampling error or forage varia on) has an eff ect on milk produc on. We hypothesized that feeding a high forage diet (25% forage NDF; FNDF) that was highly variable day to day to high producing dairy cows would nega vely aff ect produc on and intake over me and decrease feed effi ciency.

METHODS

Six primiparous (DIM = 71) and 18 mul parous (DIM = 74) were the subjects in a replicated La n square experiment balanced for carryover eff ects with 21-d periods. Within parity, cows were randomly assigned to groups of 3 and then each cow was randomly assigned to treatment se-quence within group. Cows were housed in estalls, fed once daily for ad libitum intake with refusals collected and measured daily, and milked twice daily.

One average diet was formulated for the three treatments. Two legume silages and one grass silage were fed and on average had approximately the same dietary inclusion across all three treatments. Dry ma er was tested daily and diets were adjusted immediately if needed. Silage NDF was tested (wet chemistry) two to three mes a week, and diets were adjusted immediately based on


responded to higher or lower FNDF diets as would be expected. However, the VAR cows appeared to compen-sate for reduced DMI on days of high FNDF by ea ng more on the days of low FNDF diets. For example, on day 16, the VAR cows consumed 60.5 lbs of DM and on day 19 consumed 52.9 lbs of DM. We expected cows to respond to diet FNDF concentra on, and this was observed, but no nega ve cumula ve eff ects were observed. Therefore, we were unable to accept our hypothesis that fl uctua ng FNDF varia on nega vely aff ects cows over me.

Milk produc on was not aff ected by treatment and averaged 100.2, 100.3, and 101.2 lbs/day for the CON,

VAR, and ORR treatment diets, respec vely (Table 1). Daily milk yield appeared to respond to daily changes in dietary FNDF concentra ons as we expected. Milk produc on responded to VAR diet FNDF changes (e.g., 106.5 lbs on day 17 and 93.9 lbs on day 21). The CV was increased and aff ected by the VAR treatment (P < 0.05) but not aff ected by the ORR treatment. The change in grass to legume silage ra o (VAR treatment) appeared to have more of an eff ect on DMI varia on than changes in forage to concentrate ra o (ORR treatment).

Based on these preliminary results, intake and milk produc on did not respond to daily fl uctua ons in for-age quality. However, the cows were able to produce more milk during transient increases in forage quality to compensate for losses in milk produc on during mes of decreased forage quality. The cows also appeared to compensate for losses in milk produc on due to the fi ve day changes in forage to concentrate ra o. This suggests that given a high forage diet with adequate eff ec ve fi ber, forage quality varia on does not appear to have a cumula ve nega ve eff ect on intake or milk produc on over 21 days during daily or fi ve day varia on periods.

REFERENCES

Mertens, D. R. 2006. Quan fying assay varia on in nutrient analysis of feedstuff s. J. Dairy Sci. 89(Suppl. 1):383. (Abstr.).

Stone, W. C. 2004. Nutri onal approaches to minimize subacute ru-minal acidosis and lamini s in dairy ca le. J. Dairy Sci. 87:E13-E26.

Weiss, W. P., D. Shoemaker, L. R. McBeth, P. S. Yoder and N. R. St-Pierre. 2012. Within farm varia on in nutrient composi on of feeds. Pages 103-117 in Proc. Tri-State Dairy Nutri on Conf. Ft. Wayne, IN.

Figure 1. Treatment FNDF across 21-d period; control (CON) in top panel, variable (VAR) in middle, and over-reac ng (ORR) at bo om. FNDF concentra ons were determined using daily fed data, daily DM of the silages and fi ve-day com-posite NDF samples of the silages.


Table 1. Milk Produc on, milk composi on, and intake results

CON VAR ORR SE

CON vs. VAR CON vs. ORR P <1 P <Milk, lbs/d 100.2 100.3 101.2 2.6 0.92 0.30Milk CV, %2 5.0 6.0 5.4 0.4 0.02 0.36DMI, lbs/d 57.0 56.7 58.3 1.5 0.66 0.04DMI CV, % 6.1 7.1 6.6 0.6 0.08 0.35FNDF intake, lbs/d 14.1 13.9 14.4 0.4 0.29 0.02FNDF intake CV, % 8.1 9.8 10.2 1.2 0.02 < 0.01Milk Fat, % 3.54 3.60 3.58 0.18 0.37 0.48Milk Fat, lbs/d 3.59 3.66 3.64 0.20 0.23 0.39Milk Prot, % 2.78 2.76 2.78 0.05 0.50 0.79Milk Prot, lbs/d 2.81 2.80 2.82 0.09 0.82 0.811Eff ect of fi xed eff ect of treatment, Period and Cow as random eff ect.2CV: Coeffi cient of varia on was calculated by period average SD divided period mean.


Notes


INTRODUCTION

A more accurate tle for this paper would be “Under or Over Supplementa on of Minerals and Vitamins Can be Bad for Cows and Dairy Farm Profi tability,” but that is a bit verbose. Although it is becoming dated, the Dairy NRC (2001) is s ll the best source for es mates of requirements of minerals and vitamins. For good health and high milk yields, cows need to be fed the proper amounts of available minerals and vitamins; however, numerous factors (many of which are unknown) infl uence the requirements of min-erals and vitamins and their availability. Concentra ons of minerals and vitamins in feeds can be extremely variable and o en they are not assayed. Newer data plus the sub-stan al uncertainty regarding the quan ty of minerals and vitamins consumed, the quan ty absorbed (or available), and the quan ty needed by cows under diff erent situa ons mandates appropriate adjustments to NRC requirements.

CALCIUM

The cow can remove Ca from bone so that a short term defi ciency of Ca has li le eff ect on lacta ng cows. A long term defi ciency can result in weak bones and other skeletal problems. However, because most common feedstuff s contain some Ca and Ca supplements are inexpensive, Ca defi ciency is not a real world problem in the U.S. A Ca defi -ciency during the dry period and early lacta on period can cause clinical (i.e., milk fever) and subclinical hypocalcemia; however, Ca defi ciency is rarely the cause of hypocalcemia. Excess Ca is much more common than Ca defi ciency, and just modest over feeding of Ca (e.g., 115% of requirement) to dry cows increases the risk of hypocalcemia. With lactat-ing cows, substan al overfeeding (>150% of requirement is needed before problems might be seen. Diets with >1% Ca substan ally reduced selenium absorp on by dry cows. This has not been shown in lacta ng cows but because Se status is o en sub-op mal, feeding lacta ng cows diets with more than 1% Ca should be avoided. Diets with >1.5% Ca may reduce feed intake and milk yield (this is more than twice the requirement). Because Ca is inorganic and con-tains no energy, high Ca diets tend to be lower in energy.

Bottom Line:

For lacta ng cows, no data suggest that modest overfeed-ing (i.e., 120% of requirement) causes any problem and will ensure diets are not defi cient. Dietary Ca for dry cows should be fed precisely to requirements.

PHOSPHORUS

For a lacta ng cow, a diet with approximately 0.35 to 0.4% P is usually adequate, and diets based on typical ingre-dients without any supplemental P usually will contain between 0.3 and 0.4% P. Diets that provide inadequate P to dry cows (an extremely rare event) can increase the risk of hypocalcemia. Milk yields were reduced when cows were fed diets that provided approximately 85% of P requirement (Wu et al., 2000; Wu, 2005), but no study has reported any benefi ts (produc on, reproduc on, or health) when diets provided more P than NRC require-ments. Modestly overfeeding P (~120% of requirement) to dry cows signifi cantly increases the risk of hypocalcemia. Diets with up to ~0.7% P generally have not adversely af-fected lacta ng dairy cows, but reduc ons in availability of Ca and Mg are possible. There is no reason to feed diets with 0.7% P, but because many byproducts (e.g., dis llers grains, corn gluten feed, wheat midds) are rich in P and are o en economical, diets for lacta ng cows with 0.5 to 0.55% P are not uncommon. This concentra on of P should not have adverse eff ects on cows, but you should consider increasing dietary Ca and Mg slightly. Manure excre on of P can be an environmental issue, and it will be substan ally greater when cows are fed diets with 0.5% P compared with diets at requirement (~0.38%).

Bottom Line:

For lacta ng cows, low P diets have reduced milk yields, indica ng that modest overfeeding should be prac ced. Because of environmental concerns, a safety factor of 105 to 110% of requirement is probably adequate when inorganic P is needed. When byproducts provide extra P, diets with 0.5 to 0.55% P are safe but be aware of en-

Real World Recommendations for Minerals and Vitamins

W. P. Weiss

Department of Animal Sciences

Ohio Agricultural Research and Development Center

The Ohio State University, Wooster 44691


vironmental implica ons. For dry cows, P should be fed precisely to requirements.

POTASSIUM

Diets for lacta ng or dry cows rarely require K supple-menta on to meet requirements (~0.55% for dry cows and 1.1% for lacta ng cows) because haycrop forages are extremely rich in K. Diets based heavily on corn silage, especially corn silage with a high starch concentra on, may be marginal in K. Erdman et al. (2011) reported increased milk yield and improved feed effi ciency when a corn silage-based diet was supplemented with K (increased diet K from about 1.1% to 1.4%). The K requirement increases under heat stress, but if diets have some haycrop forage supplemental K probably will not be needed. Dry cow diets will almost never need supplemental K, however, excess K is a substan al problem in prac cal diets. For dry cows, increasing K linearly increases the risk of milk fever (Lean et al., 2006). For lacta ng cows, diets with up to 3% K probably will not aff ect intake or milk but will increase urine output (more manure) and reduce Mg absorp on substan ally. Dietary Mg should be increased whenever dietary K is greater than about 1%:[1] Increase dietary Mg by 0.08 percentage units above

NRC (2001) for every 1 percentage unit dietary K ex-ceeds 1% (Weiss, 2004).

[2] Increase dietary Mg by 0.02 percentage units above NRC (2001) for every 1 percentage unit dietary K ex-ceeds 1% (Schonewille et al., 2008).

Those two equa ons are markedly diff erent, possibly be-cause the data bases used were very diff erent. In Schone-wille et al. (2008) dietary K ranged from 0.7 to 7.5% (most from grasses), and in Weiss (2004) dietary K ranged from 1 to 2.65% (most from alfalfa). A diet with approximately 0.16% Mg usually meets NRC requirement. If the diet contained 1.7% K, dietary Mg should be 0.22% (equa on 1) or 0.17% Mg (equa on 2). Because Mg defi ciency is more costly than excess Mg, I would err on the side of poten ally overfeeding and use Equa on 1.

Bottom Line:

In most situa ons, K defi ciency will not occur but supple-mental K may be benefi cial for high corn silage diets. High K for dry cows (>0.7 to 1%) increases risk of milk fever and high K (>1%) in lacta ng diets reduces Mg absorp on. Extra Mg is needed when high K diets are fed.

MAGNESIUM

Most typical diets without supplemental Mg will contain

0.15 to 0.2% Mg which is approximately the NRC require-ment; however, typical diets tend to be high in Mg absorp- on antagonists (e.g., K and soluble N). Inadequate Mg

is a clear risk factor for milk fever. A meta-analysis found that increasing Mg up to 0.4% (NRC requirement is ap-proximately 0.13%) linearly (and substan ally) reduced the risk of milk fever, indica ng that dry cow diets should contain up to 3X NRC recommended concentra ons of Mg. Feeding diets with 0.4% Mg should not cause any problem but will increase supplementa on costs. Benefi ts of high dietary Mg for lacta ng cows are much less clear. Some (but not all) older studies (milk yields averaging about 60 lbs) reported increased milk yields and/or milk fat when diets contained 0.3% Mg compared with control diets (ap-proximately 0.2%). Although diets with up to 0.4% Mg will not cause problems to lacta ng cows, current data do not jus fy that rate of supplementa on. Because of poten al milk yield response and poten al antagonism from high K, diets with 0.25 to 0.3% Mg can be jus fi ed.

Bottom Line:

Dry cows must be fed diets with 0.3 to 0.4% Mg to reduce the risk of milk fever. Benefi ts of feeding high concentra- ons of Mg to lacta ng cows is less clear but balancing the

cost of overfeeding (only higher supplementa on costs) to poten al increases in milk and milk fat, a safety factor of 1.4 to 1.6 mes NRC is jus fi ed.

SODIUM AND CHLORIDE

All diets will need supplemental Na (i.e., salt), but because salt is inexpensive NaCl defi ciencies are extremely rare. Over feeding Na via excess salt or in combina on with Na bicarbonate is common. Feeding Na at approximately 2X NRC requirements from a variety of sources (salt + sodium sulfate + sodium sesquicarbonate) over an en re 308-d lacta on had no eff ect on milk yield (lacta on average milk yield = 70 lbs, 3.5% fat), composi on, or intake (Clark et al., 2009). Some mes feeding excess Na (from buff ers) can in-crease milk fat, but this is dependent on starch, fi ber, forage, etc. Cows can tolerate high Na diets (up to about 1% of diet) as long as non-saline water is readily available. Diets with high Na will increase urine excre on and manure output.

Bottom Line:

Diets with excess Na (approximately 0.5 to 0.6%, which is about 2X NRC) from buff ers will not cause any problem for cows if clean water is readily available, but milk yield responses are not consistent.


SULFUR

Sulfur is essen ally a nutrient for rumen bacteria, not the cow. Diets with inadequate S can reduce fi ber diges bility, microbial protein synthesis, and feed intake. Diets with approximately 0.2% S are usually adequate to prevent these problems, and in most situa ons dairy cow diets are about 0.2% S without any supplemental S. Because most S in diets is found in amino acids, diets with lower protein also tend to have lower S concentra ons and may need some supplemental S. Diets with supplemental S from inorganic sources during the prefresh period reduce the risk of hypocalcemia. The amount needed depends on concentra ons of Na, K, and Cl. Diets with more than 0.25 to 0.3% S can cause problems when fed for long periods of me (months, not weeks), and diets with more than about 0.5% S can cause problems even when fed for short periods of me. With the excep on of prefresh diets (i.e., low DCAD), there is no reason to increase dietary S above 0.25% with supplemental S; however, because of the in-creased use of dis llers grain and because some water can be very high in sulfate, cows o en consume diets (or diets equivalent) with more than 0.25% S (water with 350 mg/L sulfate-S is equivalent to increasing dietary S by 0.2 percent-age units). A diet with 35% dis llers may have more than 0.4% S. Diets with >0.25% S reduce Cu and Se absorp on, and supplementa on rates for those two minerals should be increased or more bioavailable forms should be fed. Although very rare for dairy ca le, diets with 0.35 to 0.4% S have caused increased mortality in feedlot ca le (mostly via polioencephalomalacia). High starch diets increase the risk of S toxicity; dairy ca le fed moderate starch diets (rela- ve to feedlot diets) probably can handle diets with 0.5% S.

Bottom Line:

Supplementa on of S for lacta ng cows is usually not required with the possible excep on of lower protein diets. Feeding diets with S concentra ons of 0.3 to 0.4% during prefresh period can reduce hypocalcemia. Excess S is a much greater risk than S defi ciency. A empt to keep diets (including water) to 0.25% S or less. Increase Cu and Se by at least 1.2X NRC if dietary S is >0.25%.

TRACE MINERALS

Cobalt

Cobalt is required by ruminal bacteria, and the current NRC requirement is 0.11 mg/kg of diet DM. Data published since NRC suggest that increased concentra ons of Co may be benefi cial (Stangl et al., 2000; Kincaid et al., 2003). Depending on the response criteria, cows, including dry cows, should be fed diets with between 0.2 and 0.9 mg/

kg Co. Older cows may benefi t more from extra Co than younger cows. The Co eff ect is likely via vitamin B-12, and factors aff ec ng responses to B-12 are numerous including supply of methionine, choline, and folic acid. Because of the uncertainty of response, supplementa on rates of Co be-tween 0.2 to 0.5 mg/kg are a good compromise. Ca le can tolerate high concentra ons of Co (at least 20 mg/kg diet).

Copper

The current NRC requirement for Cu is about 14 mg/kg. Diets that are marginally low in Cu are a risk factor for increased infec ous disease, and feeding diets that are excess in Cu increases the risk for sudden death. Most typical diets contain about 10 mg/kg Cu from basal ingre-dients, which means about 4 or 5 mg/kg supplemental Cu will be required. Numerous real-world antagonists to Cu absorp on can be present including S (>0.25% of diet), reduced (as opposed to oxidized) Fe, Mo, excess Zn, and pasture consump on (most likely caused by soil inges on). Because of poten al antagonists and because of high vari-ability in Cu concentra on in feeds a safety factor of about 20% can be easily jus fi ed (i.e., feed diets with about 17 mg/kg Cu). With high S water and substan al inclusion of dis llers grains you should increase another 20 to 40% (20 to 25 mg/kg) and consider using organic source of Cu. If water is also high in Fe (>3 mg/L) addi onal Cu may be needed. Diets with more than 25 mg/kg total Cu are rarely jus fi ed and long term feeding of excess Cu will cause it to accumulate in the liver. Long term feeding (many months) of diets with 37 mg/kg total Cu resulted in clinical Cu toxic-ity in Holstein cows (Bradley, 1993). Jersey cows appear to accumulate Cu more effi ciently than Holstein which puts them at increased risk of Cu toxicity.

Iron

The NRC iron requirement is approximately 20 to 25 mg/kg, and most unsupplemented diets are well in excess of that concentra on. However, much of the iron in feeds, especially forages, is poorly absorbed because the Fe is actually from soil contamina on. When evalua ng dietary Fe, the concentra on of Fe in forages should be set at 0. If the remaining basal ingredients provide about 25 mg/kg Fe, the diet is probably adequate. If supplemental Fe is needed, use reduced sources of Fe (e.g., iron sulfate). Because of an-tagonism of Fe to Cu and Zn and because high Fe increases the requirements for Se and vitamin E, supplemental Fe should be limited to 50 mg/kg. A study with early lacta- on cows reported no milk yield response when 30 mg/kg

supplemental Fe was fed (Weiss et al., 2010). Iron in water at levels as low as 0.3 mg/L can reduce water consump on but is not adequate to cause other problems.


Manganese

The NRC requirement for Mn is approximately 18 mg/kg, and data published a er NRC clearly show that the NRC requirement is much too low. In beef heifers, feeding di-ets with 18 mg/kg Mn resulted in clinical Mn defi ciency (Hansen et al., 2006). Using diges bility trials to es mate Mn maintenance requirements, we es mated total Mn requirements were between 30 and 50 mg/kg (Weiss and Socha, 2005). Because cows can be fed diets with 1,000 mg/kg of Mn with no eff ect, feeding diets with 40 or 50 mg/kg of Mn poses no risk and will essen ally eliminate any poten al of defi ciency.

Selenium

The benefi ts of feeding diets adequate in Se to the health of dairy cows are unequivocal. All diets fed to dairy animals (calves, heifers, dry cows, lacta ng cows) in the Eastern U.S. should contain 0.3 mg/kg of supplemental Se (this is the maximum allowed by FDA regula ons). Basal ingredients typically contain about 0.1 mg/kg Se so that total diet is about 0.4 mg/kg. Cows have been fed diets with as much as 12 mg/kg of Se (from selenite) for 4 months without any nega ve eff ects. Because of legal constraints, a safety factor for Se cannot be recommended. If feeding 0.3 mg/kg of supplemental Se from selenite is not adequate, replacing some or all of the supplemental Se with Se-yeast may help. On average true absorp on of Se from inorganic sources is about 50% and about 60% for Se from Se-yeast (calculated from Walker et al., 2010). Based on blood, enzyme, and true absorp on, Se from Se-yeast is about 20% more available than Se from selenite when antagonists are not present. The diff erence may be greater for diets with high S.

Zinc

The NRC requirement for Zn is between about 40 and 55 mg/kg (depending on milk yield), and there is no data showing substan al benefi ts from feeding more than this. Ca le can be fed very high concentra ons of Zn (>500 mg/kg) without nega ve eff ects. High Zn (approximately 100 mg/kg), however can reduce Cu absorp on and therefore should be avoided. Diets with a reasonable safety factor (1.2X NRC) to account for uncertainty in Zn concentra ons of basal ingredients are jus fi able and pose no risk.

Bottom Line:

The NRC requirements for Co and Mn are too low based on recent studies. Dietary Co should be 0.2 to 0.9 mg/kg (0.4 mg/kg seems a good compromise). Dietary Mn should be between 30 and 50 mg/kg. The Zn and Fe requirements appear adequate. For Zn a small safety factor of 20% is

jus fi ed based on varia on in Zn of basal ingredients. The Fe contribu on from forages should be ignored when com-pu ng supply of Fe, but otherwise the NRC Fe requirement is adequate. Because high Fe can be a problem, a safety factor is not recommended. The maximum legal amount of Se should be supplemented. Ini ally this should be from inorganic or a blend of inorganic and Se-yeast (this is based solely on economics). If Se status is s ll not adequate, in-crease the amount of Se-yeast and reduce or eliminate the inorganic Se. The NRC requirement for Cu is adequate for many situa ons but because of uncertainty a safety factor of 20% is jus fi ed. With high S diets (e.g., dis llers grains) or water, Cu should be increased another 20 or 40%. Diets with more than 25 ppm total Cu are rarely jus fi ed and long term feeding (months) may cause problems.

FAT SOLUBLE VITAMINS

Vitamins were discussed in detail at the 2005 mee ngs. Requirements for vitamins are diffi cult to establish because measuring vitamins in feeds is some mes very diffi cult, vitamins can be destroyed or synthesized in the rumen, and responses to changes in vitamin supply is o en very subtle and may take months to observe.

Vitamin A

The NRC (2001) requirement for vitamin A is about 82,000 IU/day for a dry cow and 72,000 IU/day for a lacta ng cow. Cows fed adequate vitamin A have reduced mas s, metri s, retained fetal membranes, and abor ons com-pared to cows fed no supplemental vitamin A, but there are no data sugges ng any health benefi t to feeding more than the current NRC recommenda on. However, cows fed 170,000 IU/day (about 2X NRC) during the dry period and fi rst 6 wk of lacta on produced more milk (88 vs. 77 lbs/day) than cows fed 50,000 IU/day (0.7X NRC) during that period (Oldham et al., 1991). Conversely, feeding very high vitamin A during the dry period (550,000 vs. 80,000 IU/day) reduced total yield of energy-corrected milk during the fi rst 100 days of lacta on (Puvogel et al., 2005). Vitamin A is among the least stable vitamins and loss of ac vity can be ~10%/month depending on storage condi ons (Shurson et al., 2011).

Vitamin D

Because of the interest in vitamin D for human health, vi-tamin D for dairy cows is also being re-evaluated. Although adequate data are not available to quan ta vely adjust the current NRC vitamin D requirement (approximately 20,000 IU/day), data are available sugges ng poten al benefi ts from increasing vitamin D supplementa on. Stud-


ies with humans and limited research with bovine cells have shown that vitamin D has important roles in immune func on and that blood concentra ons of 25-OH vitamin D required for maximal immune response was greater than concentra ons required for op mal Ca metabolism in humans (Lippolis, 2011). Whether this is true for dairy cows will require new studies. Dairy cows housed inside without exposure to sun and fed vitamin D at NRC rates had signifi cantly lower (about 30%) plasma concentra ons of 25-OH vitamin D than cows fed no supplemental vitamin D but housed outside in the summer with extensive sun exposure (Hymøller et al., 2009). The op mal concentra- on of plasma 25-OH vitamin D is not known but current

supplementa on rates do not provide for maximal concen-tra ons. Some older studies (see page 165 in NRC) found improved reproduc on measures and increased milk yield when vitamin D was supplemented at about 40,000 IU/day (i.e., 2X NRC). Excess (~2,000 IU/day for months) dietary vitamin D can cause calcifi ca on of so ssues.

Vitamin E

The NRC requirement for vitamin E is about 500 IU/day for lacta ng cows and 1,000 IU/day for dry cows. Essen ally no new research has been conducted evalua ng vitamin E requirements for lacta ng cows, but several experiments have been conducted with dry cows. Increasing vitamin E supplementa on during the transi on period (2 or 3 wk prepartum un l 1 or 2 wk post partum) has improved measures of immune func on or improved mammary gland health in several, but not all, studies. Supplementa- on rates during the transi on period ranged from 2,000

to 4,000 IU/day. No study has shown any nega ve eff ects of high supplementa on rates in the prefresh period. Therefore the only known cost is the cost of the addi onal vitamin E, but because the supplementa on period is short (a few weeks) and the poten al payoff is high (re-duced mas s and retained placenta) increasing vitamin E supplementa on during the prefresh period is jus fi ed. Increasing vitamin E supplementa on during the en re dry period, however is not jus fi ed. A recent study (Bouwstra et al., 2010) evaluated the eff ects of feeding 3,000 IU of vitamin E/day (controls were fed approximately 130 IU/day) during the dry period. On 3 of 5 farms, more cases of mas s occurred when cows were fed high vitamin E, and on 2 farms li le diff erence in mas s was observed between treatments. Overall, they reported that feed-ing high vitamin E increased the risk of mas s by 1.7X compared with the control. Although I have some techni-cal concerns regarding the paper (e.g., methods used to diagnose mas s), the paper clearly shows no benefi t of increasing amounts of vitamin E and poten ally it might

have nega ve eff ects. Vitamin E supplementa on during the dry period should be limited to 1,000 IU/day.

Bottom Line:

Poor stability of vitamin A and poten al for increased milk yield with extra vitamin A jus fy supplemen ng vitamin A at 1.1 to 2X NRC (e.g., 80,000 to 150,000 IU/day). Supple-menta on in excess of 150,000 IU/day cannot be jus fi ed based on available data. The current vitamin E levels are adequate except during the prefresh period when 2,000 to 4,000 IU/day can be jus ied. Much more research is needed with vitamin D, but circumstan al evidence sug-gests benefi ts from increasing supplementa on up to 2X NRC or ~40,000 IU/day. Because of poten al eff ects on milk fever and complete lack of data, dry cows should be fed at approximately NRC levels for vitamin D.

WATER SOLUBLE VITAMINS

Bio n, choline, and niacin are the only water soluble vi-tamins commonly supplemented to dairy cows. The data suppor ng rou ne bio n supplementa on (approximately 20 mg/day) is strong. A meta-analysis (Lean and Rabiee, 2011) determined that a 2.9 lb/day increase in milk yield is expected with bio n supplementa on, and most studies evalua ng hoof health have reported benefi ts with supple-mental bio n. Based on current prices, these responses would be profi table. Data suppor ng the response to supplemental rumen-protected choline (RP-Choline) is also good. A meta analysis found an expected increase in milk yield of about 4.4 lbs; however, based on their sta s cal model, a supplementa on rate of about 20 g of RP-choline (actual choline, not product) was required to obtain this response (Sales et al., 2010). Feed intake is o en not sta- s cally altered, but there is no reason to think choline

improves effi ciency, so an increase in DM intake of 2 to 2.5 lbs/day should be considered in any economic analysis. The milk yield response to RP-choline is substan al but RP-choline is expensive so that its use may not always be profi table (especially at the 50 g/day rate). Most experi-ments with RP-choline use early lacta on cows because they are most likely to be limited in choline. We do not know whether these responses will occur in later lacta on. If pens contain cows at several stages of lacta on, rather than just early lacta on, overall response may be less and this should be considered when deciding on poten al profi tability. Supplemental RP-choline (only studied at the 50 g/day rate) during the prefresh and fresh period may have some value in reducing fa y liver and type 2 ketosis (Cooke et al., 2007). Ketosis/fa y liver is a very expensive disease, and if RP-choline reduces its incidence it would almost defi nitely be profi table. Longer term approaches


to reducing incidence of these diseases are available (e.g., proper management of body condi on and DM intake dur-ing the dry period) and should be pursued, but if ketosis/fa y liver is a problem supplementa on of RP choline at 50 g/day during the prefresh period should be considered. A meta-analysis (Schwab et al., 2005) found li le benefi t of supplemental niacin at 6 g/day, but at 12 g/day milk pro-tein yields were increased and, depending on milk price, it may be profi table at mes. Rumen protected niacin is now available and some data show that it may help cows under heat stress condi ons, but it is expensive and responses in milk yield have generally been quite small.

Bottom Line:

Cows should be supplemented with bio n (20 mg/day); RP-choline increases produc on, but because of cost the decision to supplement depends on milk price. Current data do not jus fy rou ne supplementa on of niacin.

REFERENCES

Bouwstra, R. J., M. Nielen, J. A. Stegeman, P. Dobbelaar, J. R. Newbold, E. H. J. M. Jansen, and T. van Werven. 2010. Vitamin E supplementa- on during the dry period in dairy ca le. Part I: Adverse eff ect on

incidence of mas s postpartum in a double-blind randomized fi eld trial. J. Dairy Sci. 93:5684-5695.

Bradley, C. H. 1993. Copper poisoning in dairy herd fed a mineral supple-ment. Can. Vet. J. 34:287-292.

Clark, J. H., R. A. Christensen, H. G. Bateman, and K. R. Cummings. 2009. Eff ects of sodium sesquicarbonate on dry ma er intake and produc on of milk and milk components by Holstein cows. J. Dairy Sci. 92:3354-3363.

Cooke, R. F., N. S. Del Rio, D. Z. Caraviello, S. J. Ber cs, M. H. Ramos, and R. R. Grummer. 2007. Supplemental choline for preven on and allevia on of fa y liver in dairy ca le. J. Dairy Sci. 90:2413-2418.

Erdman, R. A., L. S. Piperova, and R. A. Kohn. 2011. Corn silage versus corn silage:Alfalfa hay mixtures for dairy cows: Eff ects of dietary potas-sium, calcium, and ca on-anion diff erence. J. Dairy Sci. 94:5105-5110.

Hansen, S. L., J. W. Spears, K. E. Lloyd, and C. S. Whisnant. 2006. Feeding a low manganese diet to heifers during gesta on impairs fetal growth and development. J. Dairy Sci.89:4305-4311.

Hymøller, L., S. K. Jensen, H. Lindqvist, B. Johansson, M. O. Nielsen, and E. M. G. Nadeau. 2009. Supplemen ng dairy steers and organically managed dairy cows with synthe c vitamin D3 is unnecessary at pasture during exposure to summer sunlight. J. Dairy Res. 76:372-378.

Kincaid, R. L., L. E. Lefebvre, J. D. Cronrath, M. T. Socha, and A. B. Johnson. 2003. Eff ect of dietary cobalt supplementa on on cobalt metabolism and performance of dairy ca le. J. Dairy Sci. 86:1405-1414.

Lean, I. J., P. J. DeGaris, D. M. McNeil, and E. Block. 2006. Hypocalcemia in dairy cows: Meta-analysis and dietary ca on anion diff erence theory revisited. J. Dairy Sci. 89:669-684.

Lean, I. J., and A. R. Rabiee. 2011. Eff ect of feeding bio n on milk produc on and hoof health in lacta ng dairy cows: A quan ta ve assessment. J. Dairy Sci. 94:1465-1476.

Lippolis, J. D. 2011. The impact of calcium and vitamin d on the immune systems. Mid-South Ruminant Nutri on Conf., Grapevine, TX:29-34.

Na onal Research Council. 2001. Nutrient requirements of dairy ca le. 7th rev. ed. Natl. Acad. Press, Washington DC.

Oldham, E. R., R. J. Eberhart, and L. D. Muller. 1991. Eff ects of supple-mental vitamin A and B-carotene during the dry period and early lacta on on udder health. J. Dairy Sci. 74:3775-3781.

Puvogel, G., C. R. Baumrucker, H. Sauerwein, R. Ruhl, E. Ontsouka, H. M. Hammon, and J. W. Blum. 2005. Eff ects of an enhanced vitamin A intake during the dry period on re noids, lactoferrin, IGF system, mammary gland epithelial cell apoptosis, and subsequent lacta on in dairy cows. J. Dairy Sci. 88:1785-1800.

Sales, J., P. Homolka, and V. Koukolova. 2010. Eff ect of dietary rumen-protected choline on milk produc on of dairy cows: A meta-analysis. J. Dairy Sci. 93:3746-3754.

Schonewille, J. T., H. Everts, S. Ji akhot, and A. C. Beynen. 2008. Quan- ta ve predic on of magnesium absorp on in dairy cows. J. Dairy

Sci. 91:271-278.

Schwab, E. C., D. Z. Caraveillo, and R. D. Shaver. 2005. Review: A meta-analysis of lacta on responses to supplemental dietary niacin in dairy cows. Prof. Anim. Sci. 21:239-247.

Shurson, G. C., T. M. Salzer, D. D. Koehler, and M. H. Whitney. 2011. Eff ect of metal specifi c amino acid complexes and inorganic trace minerals on vitamin stability in premixes. Anim. Feed Sci. Tech.163:200-206.

Stangl, G. I., F. J. Schwarz, H. Muller, and M. Kirchgessner. 2000. Evalua- on of the cobalt requirement of beef ca le based on vitamin B-12,

folate, homocysteine and methylmalonic acid. Brit J Nutr. 84:645-653.

Walker, G. P., F. R. Dunshea, J. W. Heard, C. R. Stockdale, and P. T. Doyle. 2010. Output of selenium in milk, urine, and feces is propor onal to selenium intake in dairy cows fed a total mixed ra on supplemented with selenium yeast. J. Dairy Sci. 93:4644-4650.

Weiss, W. P. 2004. Macromineral diges on by lacta ng dairy cows: Fac-tors aff ec ng diges bility of magnesium. J. Dairy Sci. 87:2167-2171.

Weiss, W. P., J. M. Pinos-Rodríguez, and M. T. Socha. 2010. Eff ects of feeding supplemental organic iron to late gesta on and early lacta on dairy cows. J. Dairy Sci. 93:2153-2160.

Weiss, W. P., and M. T. Socha. 2005. Dietary manganese for dry and lacta ng holstein cows. J. Dairy Sci. 88:2517-2523.

Wu, Z. 2005. U liza on of phosphorus in lacta ng cows fed varying amounts of phosphorus and sources of fi ber. J. Dairy Sci. 88:2850-2859.

Wu, Z., L. D. Sa er, and R. Sojo. 2000. Milk produc on, reproduc ve performance, and fecal excre on of phosphorus by dairy cows fed three amounts of phosphorus. J. Dairy Sci. 83:1028-1041.

Originally published as “Minerals and Vitamins for Dairy Cows: Magic Bullets or Just Bullets?” in Proc. 2012 Herd Health and Nutri on Conferences, Syracuse, NY and West Lebanon, NH, pages 27-36.

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2012 Penstate Nutrition Workshop Proceedings