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operations with more than 300 animal units (one

animal unit equals 1,000 pounds of animal) and the

potential to impact water quality are now required

to have nonpoint-source pollution permits. Animal

feeding enterprises of this size are classified as con-

centrated animal feeding operations. The permits

are similar in scope to those required by many

industries for point-source pollution prevention.

The permits require additional management, record

keeping, and short- and long-term planning for

capital expenditures to ensure compliance.

Research conducted at Cornell University shows

most of the excess nutrients on

dairy farms are the result of pur-

chased feeds.

3, 4, 5

These excessnutrients are prone to environ-

mental loss, potentially harming

the environment as they either

leach into groundwater or run off 

into surface water. Decreasing

feed purchases can have a pro-

found impact on nutrient excre-

tion.6 To decrease feed purchases,

a farm must improve the quality

and increase the quantity of 

homegrown feeds.The challenge is that most diets

have been formulated with safety

factors to limit short- and long-

term animal production varia-

tion. Short-term variation (one to

10 days) is easily identifiable and

tracked as milk output per cow.

Longer-term variation (six to 12

months) ranges from decreased

reproductive efficiency to post-

calving metabolic disorders andis not addressed until problems

arise.

Variation in milk output per

cow arises from several sources

including feed dry matter varia-

tion, feed chemical composition

variation, feeder accuracy, weigh-

ing and blending, cow genetics,

stage of lactation, individual cow

feed intake fluctuations, weather, cow production

variance, milker consistency, milking parlor perfor-

mance and milk weighing accuracy. An article in

the  Journal of Dairy Science said the mean variance

for Holsteins was 1.23 kilograms per day through-

out lactation, with the variance for months one

through four averaging 1.75 kilograms per day.7

Several of these variance sources reside in the feed-

ing system and are potentially controllable.

This supports the hypothesis that variation in

ingredients, feeder accuracy and the resulting mix

must be controlled to lower costs, decrease excretion

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  F E B R U A R Y 2 0 0 2I  35

Dairy Farm SystemsFIGURE 1

Feedstorage

Croppingsystem

Feedingsystem

Replacement

system

Milkingsystem

Manure/nutrientmanagement

system

Manurestorage

Cropinputs

Purchased

feed

Replacementsales

Milksales

Feed sold

Farmindirectmaterial

Milkstorage

Sellon or off

farm

Cull whichanimals

Feed soldoff farm

Allocation

 Productiveanimalsales 

    O   n  -    f   a   r   m

Off-farm

On-farm Off-farm

Feedneed

Demand vs. supply

    W   a   s   t   e    d    /   s   p   o    i    l   e    d    f   e   e    d

Purchase what feed

Feedstorage

Sellon or off

farm

 Non-productive

animals

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and improve safety factors while maintaining consis-tent production. This idea, along with the fact that allanimal and crop information flows through thefeeding system (see Figure 1, p. 35), has led to

the feeding system’s being identified as a criticalcontrol point in quality management on dairy farms.The feeding system has become the quality manage-ment development area for farms.

EZ-Acres case study

We set out to design a quality management pro-gram on a commercial dairy farm to decrease varia-tion in the diets offered to groups (potentiallyminimizing milk production variation), reduce feedcosts and improve the safety factor levels used inration formulation. Achieving these goals will causethe cattle to excrete less nutrients and, we believe,

increase long-term milk production.We decided to study McMahon’s EZ-Acres near

Homer, NY. We planned to document the feedingsystem in a quality framework, identify sources of variation and determine those that are potentiallycontrollable, determine the amount of variation pre-sent in feeds and begin developing a quality man-agement program.

EZ-Acres is a 500-cow dairy farm currentlyowned by two brothers. One brother is general man-ager and crop manager; the other is herd manager.

The farm is located over the Homer-Cortland aquiferthat provides drinking water for approximately 50,000people. The farm is currently developing its concentrat-ed animal feeding operation plan. In 1995, a facility withroom for 500 lactating cows and a milking center wasconstructed. Facilities were also constructed to housereplacement heifers, dry cows and feed storage. Figures1 (p. 35), 3, 4 and 5 (p. 38) represent this particular farm.

The farm’s cropping system grows feedstuffs (cornand alfalfa) that are sold to and stored by the feedingsystem. The cropping system is seasonal with approxi-mately 550 hectares and 4.5 full-time equivalent (FTE)

employees. It has an investment (land and machinery)of approximately $2.2 million.

The feeding system consists of all stored feed, storagefacilities, equipment and one FTE. The system is respon-sible for mixing and delivering diets to all cattle and hasan investment (buildings and equipment) of approxi-mately $450,000.

The milking system consists of 500 lactating and 100dry cows, cow housing, a milking parlor and five FTEs.This system would be analogous to an assembly linewith 500 machines creating one product. It has an

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M O O O O V I N G T O W A R D S I X S I G M A

Characteristics of Large New YorkCommercial Dairy Farms*

FIGURE 2

Average Lower andvalue upper deciles

Number of cows 594 320 to 1,432 Farm capital per cow $5,872 $3,549 to $8,088

Equity percentage 53% 25 to 81%

Rate of return on all capital 10.4% 2.4 to 21%(without appreciation)

Debt per cow $2,834 $986 to $4,529

Purchased grain percentage 25% 20 to 32%of milk sales

 Worker equivalent (53 hours 13.18of labor per week)

Cows per worker 45 33 to 61

Hired labor cost $31,081 $18,503 to $39,853

Years of education 14(primary operator)

* Jason Karszes, W.A. Knoblauch and L.D. Putnam, New York Large Herd Farms,

300 Cows or Larger, 1999 (Ithaca, NY: Cornell University, Department of

Agricultural, Resource and Managerial Economics, 2000).

EZ-Acres’ Feeding SystemFIGURE 3

Bags

Commodity

Whichforage

Adjust

amountsoffered

Add andweigh

Inspection

Deliver togroup

Hand add

Unload

Forage

Whichcommodity

Whichmix

Mix   Return tofeed area

Howmuch

Whichbags

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investment (buildings, equipment and cattle) of approximately $2 million.

The replacement system consists of all replacementheifers and housing. When a calf is born, she enters

the replacement system and remains there until shecalves (22 months later). This system has approxi-mately 1.5 FTEs and an investment of $725,000 (build-ings and cattle).

The manure/nutrient management system collectsall the waste (manure, water and spoiled feed), stores itand disposes of it according to its nutrient content andthe nutrient requirements of crops grown by the crop-ping system. It consists of approximately two FTEs anda $225,000 investment (equipment). Current environ-mental regulations directly impact this system becausea waste disposal plan must be followed. The planincludes record keeping and equipment calibration,

which are new requirements of and costs to the system.A large dairy farm’s feeding system is a complex

weighing, blending and delivery operation (seeFigures 3, 4 and 5). The feeder at the case study farmdaily manufactures 10 loads ranging in weight from700 to 7,000 kilograms per load. Each batch containsfrom two to 10 ingredients with all but two loadedwith an industrial loader tractor. The truck used forfeeding has a four-auger horizontal mixer with fourload cells (each with 0.1% accuracy), a scale head andinterface with a desktop computer via wirelessmodems. The software instructs the feeder what kindsand amount of feed to add to each batch. It alsorecords how much was actually loaded. The datashow feeder accuracy varies from 0.05 to 10%.

Six Sigma dairy farm quality management program

The current paradigm for many dairy farms andsupporting professionals is problem solving, step fourof the Six Sigma roadmap.8 Six Sigma is the desiredquality management model because it represents ahighly integrated system with aggressive goals.

A sweeping cultural change is needed in produc-tion agriculture to improve its long-term competitive-ness in global markets, while maintaining

environmental quality.9

Upper management’s supportis critical on dairy farms as they are small businesseswhere owners are typically upper management, butstill perform many of the day-to-day tasks alongsideemployees. True Six Sigma quality (3.4 defects permillion opportunities) is an aggressive long-term goalrequiring long-term commitment in an industry cur-rently operating between one and two sigma.

The define, measure, analyze, improve and controlmodel is being used to develop a Six Sigma quality man-agement program for the feeding system on EZ-Acres.The feeding system was chosen to initiate this process

 because it contributes the highest proportion to variablecosts in producing milk, is quantifiable, has an easilydemonstrated impact and is the most separable enter-prise (in terms of manufacturing costs) on the farm.

We developed flowcharts (see Figures 3, 4 and 5)with the assistance of one of the feeders to determinepotential sources of variation. The starting point iscompleted at the beginning of the feeders’ shift for allgroups, and then the loop is repeated until feeding iscompleted.

The feeding system primarily deals with internalcustomers (as purchaser from the cropping systemand seller to the replacement and milking systems).External transactions include purchasing commoditiesand special mixes and occasionally selling forages.

Communication between the feeding and croppingsystem is limited, and a several month lag time exists before changes recommended by the feeding systemcan be implemented in the cropping system. The for-age component of the feeding system represents stor-age and inventory. The time in inventory makes itdifficult to implement a change as proposed changesare not documented, and current weather conditionsdictate the cropping system schedule.

The flowcharts, discussions with employees anddata collection showed us several areas where wecould control variation and improve overall quality.

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  F E B R U A R Y 2 0 0 2I  37

Forage Component of EZ-Acres’Feeding System

FIGURE 4

New cornsilage

Old cornsilage

Grass bag

Grass bunkfront

Grassbunkback

Alfalfabunkfront

Alfalfabunkback

Whichforage

Forage

Croppingsystem

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The ranges encompass the variationobserved from the sampling project and pro-vide different levels of precision to achieve a95% confidence interval. For example, corn

silage sampled at feed out has a dry matterstandard deviation of 3.3 units. Ideally, a farmwould control dry matter to within 0.5 units.This would require 178 samples (many farmssample weekly giving a precision of one to 2.5units). Rapid moisture determination method-ology is a limitation to improving precision because current methods require 30 minutesto 36 hours for each sample. Improvements infeed quality require ongoing discussions withthe cropping system and could take up to fouryears to fully implement because the croppingsystem has a multiyear plan.

Control charts have been implemented in thefeeding system. Several parameters consideredcritical in the feeding system include forage drymatter content, dry matter intake by lactatingcow group and feed cost per 45.4 kilograms of milk produced. The first two parameters trackinputs; the third tracks output and consistencyof ration costs over time. The feeder maintainsthe control charts (see Figure 7 for an exampleof a corn silage percentage dry matter chart).

The dry matter standard operating proce-dure (SOP) contains guidelines related tointerpretation. For example, if the dry matterof the sample collected today (sample 1) dif-fers by more than five units from the previoussample (sample 0), a second dry matter sam-ple (sample 2) is collected. If sample 2 agreeswith sample 1, the mean of the two samples isused (the assumption is that a large shift indry matter occurred). If sample 2 agrees withsample 0, the dry matter value from sample 2

is used (the assumption is that the original differencewas sampling error). A feed such as corn silage typi-cally varies between 25 and 40% dry matter so a five-unit change represents a 12.5 to 20% change. While a

five-unit shift appears high, most farms determine drymatter either weekly or monthly. Thus, decreasing thesampling interval to two to three days increases accu-racy.

Improving the feeders’ technical skills was an areawe needed to look at to improve the quality manage-ment of the feeding system. Feeders are now beingtrained in simple statistics, equipment maintenanceand basic dairy nutrition. Initial results suggest feed-ers have thus developed a higher degree of commit-ment to batch consistency, sampling protocols and the business as a whole. These employees have been

Purchased feeds were identified as one controllablesource of variation. The nutritionist interviewed rep-resentatives from current and potential suppliersregarding their quality programs and discussed the

quality management project, the farm’s expectationsand the spot sampling protocols the farm wouldimplement. This process resulted in the creation of apreferred supplier list.

Another potential source of variation was feed drymatter and chemical composition. Chemical composi-tion between supplier farms was quite high, accordingto the results from a commercial feed testing laborato-ry.10 Two years of intensive sampling have shown vari-ation within a farm is slightly less than variation between farms (see Figure 6). The feed variation datawere used to generate a sample size table.

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M O O O O V I N G T O W A R D S I X S I G M A

Commodity Component of EZ-Acres’Feeding System

FIGURE 5

Bay 1 bulk

mineralmix

Bay 6 cornmeal

Bay 2chopped

hay

Bay 5soybean

meal

Bay 4homermeal

Bay 3cottonseed

Whichcommodity?

Commodity

Commoditybroker

Commercialfeed

manufacturer

What feed is needed?

    W    h    i   c    h   c   o   m   m

   o    d    i   t   y   t   o    b   u   y    ?

Croppingsystem

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working with managementon the development of SOPs for each area they areinvolved in, and they are

learning about normal dis-tribution, mean, variance,confidence intervals, linearregression, t-tests and rootcause analysis.

Future work

As the project progresses,the Six Sigma staff delin-eations (Champions, BlackBelts and Green Belts)are slowly taking form.Currently, members of 

upper management areChampions. Additionaltraining in statistics willslowly result in middlemanagement (head feeder)rising to a Green Belt leveland off-farm consultantsrising to Black Belts. Thisloose adoption of Six Sigmavocabulary will take approx-imately 18 to 24 months tofully implement on the casestudy farm. We’re currentlydeveloping a quality manu-al containing SOPs andgood quality practices forall staff, continuing to trainmanagement and staff instatistics and humanresource management, andincorporating new technol-ogy into daily workflows.

Daily dry matter deter-mination of stored feeds isa controllable source of 

variation; however, we need a rapid and accurate on-farm method. The coefficients of variation for drymatter determination of four methods ranged from 2.4to 3.4% for corn silage, and the method commonlyused on-farm averaged three to four units higher thanthe one used in the laboratory.11 One new method,portable near infrared spectrophotometry, wouldallow a farm to determine dry matter once a day ormore frequently and require only two to five minutesper sample vs. the 45 to 60 minutes the currentmethod requires.

Although farms are using more technology and it is

 becoming more complex, most producers are unawareof the technology’s level of accuracy and stability.Information regarding accuracy and testing is beingcollected from the manufacturers, and their input is being used to develop gage rel iabil ity and repro-ducibility (R&R) studies. Accomplishing this requires better record keeping for equipment service, activity based costing analysis and R&R results.

Reinforcing the farm’s commitment to continuousimprovement is the foundation of all future work. Asin manufacturing and service sectors, the issue of theday often overrides long-term goals and strategy set

Q U A L I T Y P R O G R E S SI

  F E B R U A R Y 2 0 0 2I  39

Comparing Composition of Several Feeds*FIGURE 6

* A.F. Kertz, “Variability in Delivery of Nutrients to Lactating Dairy Cows,” Journal of Dairy Science , 1998, Vol. 81.

 Within one farm Between two farms

1998 corn 1999 corn Grass Alfalfa Corn Grass Alfalfasilage silage silage silage silage silage silage

Mean DM 27.72 31.02 30.75 35.10 34.1 37.3 41.4

DM CV percentage 6.4 9.1 28.4 23.2 19.9 30 26.1

Mean NDF  50.46 44.76 59.75 46.67 46 59.4 46(DM percentage)

NDF CV percentage 8.7 9.8 11.9 12.5 13.5 13.3 13.5

DM – dry matter content, a percentageCV – coefficient of variationNDF – neutral detergent fiber, a laboratory analysis that measures cellulose, hemicellulose and lignin

Feeding System Control Chart for Percentage of Corn Silage Dry MatterFIGURE 7

0%

5%

10%

15%

20%

25%

30%

35%

10/2 10/12 10/22 11/1 11/11 11/21 12/1 12/11 12/21 12/31 1/10

Sample date

    D   r   y   m   a   t   t   e   r   p   e   r   c   e   n   t   a   g   e

Percentage of corn silage dry matterMoving range

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M O O O O V I N G T O W A R D S I X S I G M A

Bag: Feed storage method for silages.Bunk: Flat feed storage method for silages.Chopped hay: Dry hay chopped so it can be

 blended with silages in the feeding system.Commodity: Feeds purchased as individual

ingredients. They are high protein or high ener-gy (soybean meal, cottonseed and corn grain).

Concentrated animal feeding operations:Facilities defined by the Environmental

Protection Agency as consisting of a specificnumber of animals and other conditions requir-ing them to obtain wastewater discharge per-mits pursuant to the Clean Water Act.

Corn silage: Whole plant corn (includes ear,grain, stalks and leaves) harvested annually andstored as silage.

Cow feed intake (dry matter intake): Mass of feed consumed (typically on a moisture-free basis) per cow per day.

Cow genetics: The majority of dairy cattlereproduction is accomplished via artificialinsemination.

Cropping system: A series of activities rang-ing from land preparation to harvest.

Dry cows: Following a lactation period, cattlemust be given a 40- to 60-day rest period duringlate pregnancy to allow the mammary glands torejuvenate. These cattle are dry.

Feed dry matter: 100 minus the water contentof feeds.Feeder: Employee responsible for the feeding

system.Feeding system: A weighing/blending/

delivery series of activities combining inputsfrom the cropping system with purchasedinputs to feed the herd.

Forage: Feeds higher in fiber (alfalfa, grassesand whole-plant grain crops).

Group: Cattle on large farms housed accord-ing to production class/level (dry cow group orcows less than 30 days post-parturition) for

feeding/management purposes.Homer meal: A specialty soy product

processed via screw extrusion. It contains high-er fat than soybean meal, and the heating in theextrusion process alters the protein structure soless is fermented in the cow’s rumen (fore-stomach).

Lactating cows: Cattle currently producingmilk. Typically, cows will be milked for 330 to360 days before being given a dry period.

Manure/nutrient management system: Wastehandling and treatment focusing on manuredisposal in an environmentally responsible

manner.Milk weighing: A general term used to

describe methodology to determine the mass of milk harvest. All milk is sold on a hundred-weight basis and weight is determined on-farmvia volumetric methods.

Milker consistency: Employees (milkers) inthe milking system are expected to harvestwithin throughput guidelines (cows per hour orpounds of milk harvested per hour) to maintain

Dairy Terms and Definitions

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Q U A L I T Y P R O G R E S SI

  F E B R U A R Y 2 0 0 2I  41

forth by the strategic leadership team. Regular meet-ings are being held with the strategic leadership teamand various staff members to discuss problems and toreinforce long-term continuous improvement goals.

REFERENCES

1. Jason Karszes, W.A. Knoblauch and L.D. Putnam, New

York Large Herd Farms, 300 Cows or Larger, 1999 (Ithaca, NY:

Cornell University, Department of Agricultural, Resource and

Managerial Economics, 2000).

2. Ibid.

3. J.L. Hutson, R.E. Pitt, R.K. Koelsch, J.B. Houser and R.J.

Wagenet, “Improving Dairy Farm Sustainabili ty II:

Environmental Losses and Nutrient Flows,”  Jo ur na l of 

Production Agriculture, 1998, Vol. 11, No. 2.

4. S.D. Klausner, D.G. Fox, C.N. Rasmussen, R.E. Pitt, T.P.

Tylutki, P.E. Wright, L.E. Chase and W.C. Stone, “Improving

Dairy Farm Sustainability I: An Approach to Animal and CropNutrient Management Planning,”  Jo ur na l of Prod uc ti on

 Agriculture, 1998, Vol. 11, No. 2.

5. S.J. Wang, D.G. Fox, D.J.R. Cherney, S.D. Klausner and

D.R. Bouldin, “Impact of Dairy Farming on Well Water Nitrate

Level and Soil Content of Phosphorus and Potassium,”  Journal

of Dairy Science, 1999, Vol. 82, No. 10.

6. T.P. Tylutki and D.G. Fox,  Mana gi ng Nu tr ie nt s an d

Pathogens From Animal Agriculture (Camp Hill, PA: Natural

Resource, Agriculture and Engineering Service, 2000).

7. R.W. Everett, H.W. Carter and J.D. Burke, “Evaluation of 

the Dairy Herd Improvement Association Record System,”

 Journal of Dairy Science, 1968, Vol. 51, No. 1.

8. P.S. Pande, R.P. Neuman and R.R. Cavanagh, The Six

Sigma Way: How GE, Motorola and Other Top Companies Are

 Honing Their Performance (New York: McGraw-Hill, 2000).

9. Ibid.

10. A.F. Kertz, “Variability in Delivery of Nutrients to

Lactating Dairy Cows,” Journal of Dairy Science, 1998, Vol. 81.

11. G.R. Oetzel, F.P. Villalba, W.J. Goodger and K.V.

Nordlund, “A Comparison of On-farm Methods for Estimating

the Dry Matter Content of Feed Ingredients,”  Journal of Dairy

Science, 1993, Vol. 76.

THOMAS P. TYLUTKI is a research and support specialist in the

Department of Animal Science at Cornell University. He earned

a master’s degree in animal nutrition from Cornell and is a mem-

ber of ASQ.

DANNY G. FOX is a professor at Cornell University and earned a

doctorate in animal nutrition from Ohio State University.

If you would like to comment on this article, please post your

remarks on the Quality Progress Discussion Board on

www.asqnet.org, or e-mail them to editor@asq.org.

approximately equal time intervals betweenmilking sessions (8 to 12 hour intervals)while ensuring maximum milk harvest percow.

Milking parlor: General term used todescribe the structure and equipment used toharvest milk.

Milking system: Large farms have a struc-ture and staff specifically for harvesting milk.

Parturition: The act or process of giving birth.Replacement heifers: Cattle that have yet

to produce milk (birth through first parturi-tion). They replenish current cattle (turnover)or expand herd size.

Replacement system: Cattle, like equip-ment, must be replaced. Many dairy opera-tions raise all females from birth to providereplacement production units or expansion.

Reproductive efficiency: To continue milkproduction, cattle must be bred on an annual basis, so dairy producers strive for one calf 

per cow annually and track breedings perconception with a goal of less than 2.0.

Silage: An anaerobically fermented feed.This method of feed preservation allows fortimely harvest of feeds by stabilizing wetterfeeds (20 to 45% water content) so they can be stored for up to 18 months.

Soybean meal: By-product of the soy oilindustry. Guaranteed to contain at least47.5% total protein on a wet basis (12%water).

Stage of lactation: Cows’ daily milk pro-duction over time is comparable to the three

stages of production in production econom-ics with a rapidly increasing phase (up to 90days post-calving—early lactation), an apexwith a slight decline (90 to 150 days post-calving—peak or midlactation) and a grad-ual decline until milk production ceases(about 360 days post-calving—late lactation).

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