updates and formulation - · pdf fileformulation by animal type from least to most difficult !...

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Implementing AMTS T.P. Tylutki PhD

President AMTS LLC Cortland NY USA

Formulation

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Level of Difficulty

S  Formulation by animal type from Least to Most difficult S  High Producing Cow

S  Low Producing Cow

S  Pre-Fresh Cow

S  Far-Off Dry Cow

S  Bred Heifer

S  Breeding Age Heifer

S  Young Heifer

Level of Complexity

S  Formulation by animal type from Least to Most Complex S  Far-Off Dry Cow

S  Bred Heifer

S  Breeding Age Heifer

S  Young Heifer

S  Pre-Fresh Cow

S  Low Producing Cow

S  High Producing Cow

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Why have I ranked them this way?

S  Basically, the difficulty ranking is based on what we have for data and observations S  We make a change to a diet for lactating cows, we see the milk

response quickly S  Yes, body condition and repro take longer to really see if there was

a change, but we can still see relatively quick

S  Pre-fresh cows, we’ll see the responses within a month

S  Far-off dry cows, two month response time

Heifers are difficult

S  Typically we do not have continuous production data S  How many farms have scales?

S  How many farms check heights on heifers?

S  Body condition score? S  And this is far from perfect for heifers given how quickly they can

build internal fat before we really see big shifts in condition score

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And the biggest reason is

S  Nutritionists tend to spend a lot more time with the lactating herd! S  Why?

S  They generate the cash whereas most nutritionists and farmers view heifers as a ‘cost’

What are we attempting?

S  Lactating cows

S  Achieve desired level of

S  Milk

S  Components

S  Health and Reproduction

S  Dry cows

S  Maintain condition

S  Ensure smooth transition

S  Heifers

S  Meet growth targets

S  Control condition score

S  Health

S  For all, to do this while ideally maximizing profit potential but typically to minimize cost

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Are there commonalities in formulation between animal types?

S  Dairy Cattle, Beef Cow/Calf, All Replacement Heifers S  Maintain condition scores

within acceptable ranges S  Maintain/improve

performance while maintaining/improving health and reproduction

S  Longevity of herd should be goal

S  No acidosis

S  Feedlot cattle S  Maximize growth S  Meet market requirements for

carcass size and degree of fattening

S  Keep them alive

Feed Efficiency and/or Residual Feed Intake

S  Typically, these numbers always look better the higher the level of production S  Is high feed intake with lower then expected performance an

genetic or a diet (digestibility) effect? S  We can not determine individual feed intakes (in many cases,

we do not know group intake) S  Most dairy farms can only give an estimate of feed disappearance

S  How much is related to health and growth in first two months of life? S  So is this a genetic or epigenetic issue?

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Requirement Inputs with greatest impact

S  For all classes of cattle S  Body weight S  Temperature S  Flat distance walked

S  If Replacement Heifer or any type of cow

S  Mature Body weight

S  If Feedlot animal S  Body weight at slaughter S  Final Body Fat %

S  Growing Cattle S  ADG

S  Pregnant S  Days preg if >191

S  Lactating S  Milk S  Fat S  Protein

ADG in Lactating and Dry Cows

S  I’ve stated in past that I use about 100 g/d ADG as safety factor when formulating cow diets.

S  Upon further evaluation, it is NOT a safety factor

Mature Weight 750 kg Calving interval 381 days 12.5 months

LactaCon % of herd % Mature Weight Weight Gain, kg ADG g/d 1 42 85% 638 53 138 2 28 92% 690 30 79 3 16 96% 720 30 79 4+ 14 100% 750 0 0

Overall Lacta+ng and Dry ADG 93

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Which feed analyses really matter?

S  The answer is that it depends on the type of feed S  While it is always good to get as much information as you can

regarding a feed, let’s think about the nutrients the feed supplies…

Forages

S  All forages S  DM S  NDF S  Lignin S  dNDF (I prefer 24 hr) S  Ash S  CP

S  Silages S  Ammonia S  Soluble Protein S  Fermentation acids

S  High Starch Forages S  Starch S  7 hr starch digestibility

S  Hays S  Sugar

S  Protein fractions are good to have but not very high sensitivity in model S  ADIP S  NDIP

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Special note

S  Van Amburgh’s group has developed a method to calculate the CHO B3 kd S  Based on long term in vitros going to 240 h

S  Estimate of the true indigestible NDF S  Then a proprietary dynamic non-linear model to calculate the rate

S  Actually 2 rates: slow and fast pools but that is version 7 CNCPS

S  CVAS currently offers this long term in vitro and other labs are evaluating. S  Right now, everyone is trying to figure out how to make this all

work S  Will replace need for lignin and traditional dNDF as there will

be fixed lignin x 2.4 relationship to represent C pool

Non-forages

S  Fibrous products S  DM S  NDF

S  Lignin? S  dNDF?

S  Ash

S  Fat S  CP S  Sugar

S  Feeds >20% protein S  DM S  CP

S  NDIP S  ADIP

S  Soluble Protein

S  Starch >15%

S  Starch S  7 hr starch digestibility

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In other words

S  We should look at each ingredient and think about S  The nutrients it supplies

S  The processing methods done to it

S  The consistency (or lack of) of the ingredient

S  Have there been changes in the production of the ingredient S  E.g. low fat vs normal DDG

S  Furthermore, we should develop sampling schedules and protocols both in mill and on farm to maximize the value of sampling while keeping costs controlled.

Feed Variance Example: DDG

S  For every 25.4 kg maize (1 bushel)

S  9.62 l ethanol

S  7.72 kg CO2

S  7.72 kg dried distillers grains

S  30.4% of whole maize inputs end up as DDG

S  Assumptions

S  wet distillers grains at 35% DM

S  22 mt truck load size

S  Common plant size is

S  370,000,000 l per year

S  equals 1,013,699 l per day

S  equals 42,237 l per hour 24/7

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Production

S  42,237 l per hour ethanol production S  equals 111,626 kg whole maize per hour

S  This produces 33,887 kg DDG per hour S  or 85 mt wet DG per hour S  equals one truck WDG every 15.3 MINUTES 24/7 S  or, 34,249 loads per annum!

S  Objective: keep the distillers grains moving away from the plant!

DairyOne DDG Analysis Sept. ‘06

n Mean SD CV DM 863 89 2.2 2.4% CP 832 30.2 3.1 10.3% Soluble Pro. 472 15.8 7.2 45.6% NDIN 714 10.2 3.3 32.2% ADIN 262 4.6 2.1 45.3% NDF 708 34.2 3.7 10.8% Lignin 260 5.6 2.2 39.9% Fat 621 12.6 3.1 24.5% Ash 290 5.8 0.9 16.4% P 595 0.9 0.13 14.9% Sugar 313 4.4 1.6 36.0% Starch 484 5.6 2.3 40.5%

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So how many samples do we need?

S  Using the observed DairyOne standard deviations, to be within 5% of the 95% confidence interval of the mean, the following number of samples are required:

Number of samples required DM 72 CP 148 Sol. Protein 800 NDIN 167 ADIN 67 NDF 209 Lignin 76 Fat 146 Ash 14 Sugar 39 Starch 78

In other words

S  We as an industry (from producer level through feed mill level) do NOT sample enough. S  And sometimes we sample for the wrong items.

S  E.g. why do we analyze crude fibre? S  In the USA because it is required by law for feed tags.

S  Why measure NDF on maize?

S  Why do people formulate on an as fed basis? S  This combines variance just to meet a tag number

S  When in doubt, sample again. And maybe we should be sending duplicates in for analysis to help control sampling error!

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Formulation?

S  Wait, with all this variance, how can we use models to formulate, remove safety factors, and expect the cows to consistently perform?

What are models?

S  Models, in their complexity, are nothing more then accounting systems S  Same as an accountant

would split expenses into more specific categories to define, explain, and control costs; models allow us to identify more variance areas that we can then measure.

S  Think as in quality control:

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Most Important Input: Bodyweight

S  Ideally, we would measure bodyweight as follows: S  Dairy and Cow/Calf

S  Birth S  Weaning S  1st Breeding S  1st Calving S  Maturity

S  Feedlot S  Weight at beginning of feeding period S  Weight at end of each feeding period S  Final weight with data back from slaughter house regarding body fat

and carcass yield

Since most of us do not have that data, what can we do?

S  Dairy Cattle S  Lactating

S  Measure mature weight (even can estimate from cull dairy cows with a body condition score of 3-4) S  Scales or heart girth measurements

S  Evaluate Peak Milk Production of various lactating groups S  If age of first calving > 23 months, calculate peak milk of lactation of

interest as a percentage of peak milk of 3rd and greater cattle S  E.g. if 1st calf heifers peak at 30 kg milk and mature herd peaks at 45 kg

milk S  30/45 = 67%

S  This is an estimate of what calving weight of these 1st calf heifers was so S  Mature weight = 700 kg S  1st calf heifers in this example: 700 x 67% = 469 kg, significantly less

then the target of 595 kg (85% mature weight)

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Warning

S  If age of first calving is 21-23 months, the calculation must change. S  Calculate 1st lactation milk yield as percentage of mature full

lactation milk yield S  E.g. 1st lactation = 7,000 kg

S  Mature lactation = 9,000 kg

S  7,000/9,000 = 78%

S  Mature weight = 700

S  1st Calving weight = 700 x 78% = 546 kg

Further warning

S The method just described is based upon observations. It has not been

‘proven’, rather it appears this is the relationship.

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Estimating Growing Cattle Weights

S  I can not estimate growing cattle weights. S  So scales or heart girth measurements on 10-25% of cattle at

various stages of growth is required.

S  New born calves: there is a relationship between hoof circumference and weight.

Long et. al. JDS 95:7206

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Statistics from paper

Calf tape

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For Feedlot Cattle

S  Estimating final body fat S  Use Body Condition Score

S  0.037683 x BCS (1-9 scale) x 100 = Empty body fat

S  To convert BCS from 1-5 scale to 1-9 scale,

S  (BCS - 1) x 2+1

Do we think correctly about ingredients and nutrients?

S  How many of us think in terms of: S  Protein

S  Energy

S  Fiber

S  Fats

S  Minerals/Vitamins

S  Or should we think in terms of S  Amino Acids

S  Lactose precursors

S  Fatty acid precursors

S  Immune function

S  Enzymes and hormone activators…

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In other words

S  Functional metabolic definitions of nutrients versus general classifications.

S  Examples

Simple question: What is Milk?

S  ~87% water

S  3-4% fat

S  2.7-3.1% true protein

S  4.7-5.0% lactose

S  ~0.7% minerals

S  0.1-0.4% urea

8/25/14 Copyright 2013 AMTS LLC 49

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Where do these components come from?

S  Easy ones first S  Minerals: directly from the diet. Can not really alter mineral

composition of milk via dietary manipulations

S  Urea: excessive protein intake, poor amino acid balance, low energy, etc.

S  And water quality has to be good else she will not drink and milk volume will be lower


Milk Fat

S  Fat is not fat is not fat!

S  Milk fat comes from two sources S  De novo synthesis from acetic and butyric fatty acids

S  Primarily from fiber fermentation but also some from sugar and silage acids consumed

S  Dietary fatty acids


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Typical Milk Fatty Acid profile Fatty Acid % Total

4:0 4.4%

6:0 2.4

8:0 1.4

10:0 2.7

12:0 3.3

14:0 10.9

15:0 0.9

16:0 30.6

17:0 0.4

18:0 12.2

20:0 0.2

Total Saturated 69.4 8/25/14 Copyright 2013 AMTS LLC 52

Fatty Acid % Total

10:1 0.3

14:1 0.8

16:1 1.0

17:1 0.1

18:1 22.8

18:2 1.6

18:3 0.7

16:1t 0.4

18:1t 2.1

18:2t 0.2

Total Unsat 30.0

Others 0.6

Fatty Acid Sources


Fatty Acid % Total

4:0 4.4%

6:0 2.4

8:0 1.4

10:0 2.7

12:0 3.3

14:0 10.9

15:0 0.9

16:0 30.6

17:0 0.4

18:0 12.2

20:0 0.2

Total Saturated 69.4

All from de novo synthesis

~1/2 from de novo synthesis ~1/2 from diet

All from diet

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Unsaturated Fatty Acid Sources

S  All unsaturated are result of diet and rumen interactions. S  Rumen is very rich in Hydrogen

S  This, coupled with the bacteria, results in ‘bio-hydrogenation’, or converting saturated fatty acids to saturated

S  This is an incomplete process due to rumen residency time and microbial makeup and fatty acid profile

S  If 100% effective, there would be no unsaturated fatty acids in milk.

S  If bad fatty acid profile, coupled with low fiber, high NFC, we get into the whole CLA pathway and reduce milk fat quickly.


Manipulating Fatty Acids


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Continued


Impact

S  These changes in fatty acid profile are not the result of dilution because of more milk. S  Even in studies where milk volume increased, the increase

would not be enough to account for the shifts in profile.

S  Feeding fat will change the fatty acid profile of milk.


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What does this trial tell us?

S  Fatty acids S  Change milk fat composition

S  Change cows metabolism. S  De novo synthesis requires large amounts of glucose by mammary

gland (used in production of NADH—energy transfer)

S  Changing milk fatty acid composition appears to spare glucose for more lactose production!

S  Fats high in C16 tend to replenish body reserves more then fats with C18


Fat Supplementation

S  Old guideline: S  Dietary fat intake = milk fat output

S  E.g. 30 liters milk at 3.75% fat = 1.125 kg milk fat S  1.125 kg / 20 kg DMI = 5.6% dietary fat

S  Of which: S  1/3 from basal ingredients (alfalfa, maize, etc.)

S  1/3 from animal/vegetable fats (cottonseed, expellers soja, etc)

S  1/3 from products such as Megalac S  In this example, this would be 375 g fat from Megalac.

At 84% fat, would result in: 441 g Megalac fed.


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Impact

S  Very low-fat diets reduce milk and fat yields S  With almost 50% of fat yield being C16

S  Increasing the C18 content of low-fat diets resulted in a linear increase of C18 fatty acids in the milk fat: S  dietary C18 fatty acids were transferred to milk fat with 54%

efficiency

S  There seems to be an upper genetic limit on how much milk fat is excreted. Thus, by ‘feeding’ fatty acids to meet this, the mammary gland synthesis less thus ‘sparing’ glucose for lactose production. S  Same, or higher, fat YIELD (and probably concentration), but also

more milk volume.


Updated guideline

S  Total fat in diet <6.5%

S  C18:1 + C18:2 + C18:3 intake should be monitored. S  Greater then 500 g becomes a risk factor in potentially reducing milk fat

S  Other risk factors include

S  Quality and Quantity of forage NDF

S  NFC and overall starch fermentability

S  Monensin intake especially when above two items are high

S  Select fat supplements based upon desired outcome and follow 1/3, 1/3, 1/3 rule if possible. S  If anything, error on conservative side and feed more rumen stable fat vs.

highly unsaturated vegetable oils.


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Oh wait, what about Reproduction?

S  Do fats enhance reproduction? S  Data is somewhat mixed and can be very confusing

S  Mostly positive responses but in trials where milk volume increased, results inconsistent. Why? I believe it is due to the overall energy status of the cow, fatty acids going directly to milk fat, etc.


Repro and Omega Fatty Acids

S  Feeding fat sources rich in n-6 FA during late gestation and early lactation S  enhanced follicle growth, S  uterine PG secretion, S  embryo quality and S  pregnancy in cows.

S  Similarly, feeding n-3 FA during lactation S  suppressed uterine PG release, and S  improved embryo quality and S  maintenance of pregnancy.

S  These can be expensive though, and post-calving, should be fed <150 DIM to control costs.


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Adipose tissue

S  Lactic acid can (and is) converted to adipose tissue

Milk Protein

S  What makes milk protein?

S  Two basic requirements S  Amino Acids

S  Glucose


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76

Protein Flow

N Intake and Excretion from Rations Varying in CP Levels

S  Source: Olmos Colmenero & Broderick, 2006


0

100

200

300

400

500

600

700

800

g/day

13.5 15 16.5 17.9 19.4

Ration CP, %

N intakeMilk NManure N

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78

Fecal and Urinary N Excretion


79

Fecal and Urinary N Excretion


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80

Milk Nitrogen Efficiency (% of the N consumed in the milk)


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MUNs

S  650 cow dairy, 84 lbs milk

S  Goal for MUN: 7-10 mg/dl

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Milk performance when increasing MET and LYS (% of MP) were fed prior and after calving

S  + 2.3 kg/d milk

S  + 0.09% units milk protein

S  + 112 g milk protein

S  + 0.10% units milk fat

S  + 116 g milk fat

S  Summary of 5 trials – Garthwaite et al 1998

82

Courtesy of Luchini

NH 3 +

CH 3 S CH 2 CH 2 C COO -

H Met

NH 3 +

NH 3 + CH 2 CH 2 CH 2 CH 2 C COO -

H Lys

83

Impact of RPM (and RPC) on health during early lactation

Courtesy of Luchini

10 cows per treatment from wk -4 to wk 20 RPM = Co + 18 g RPM, RPC = Co + 60 g RPC, RPM + RPC = Co + 18 g RPM and 60 g RPC

Ardalan et al., 2010 J of Animal Phisiology and Anim. Nutr. 94:e259

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84

DMI was significantly greater for Met supplemented cows (14.7 kg/d vs. 12.2 kg/d)

Courtesy of Luchini

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Lactating Cows

S  Amino Acid formulation works S  most of the time!

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86

Milk protein and yield

Milk Protein Yield, g

Milk

Yie

ld, l

/d

Milk Protein %

Also dependent upon Energy Availability

87

Reanalysis of NRC data set-lys

Protein Response = -0.58 + 0.09 x LYS (%MP) - 0.026 x (LYS %MP - 6.41)2

R-sq = 0.88 RMSE = 0.025

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88

Met

Protein Response = -0.39 + 0.19 x MET (%MP) - 0.13 x (MET %MP - 2.16)2

R-sq = 0.90 RMSE = 0.017

89

Impact of RP MET

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90

Impact 2

91

And then there is energy

S  The industry has been focused on manipulating milk protein with amino acids but there is a very important concept missing in this: S  Lactose production

S  Enzymes involved are heavily dependent upon LYS and MET (primarily via CYS)

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92

Lactose Synthesis

93

MET and LYS Formulation Guidelines

S  Currently

S  Most diets are low in grams of MET

S  Cows are being overfed Crude Protein (>17%) as an insurance policy to meet requirement of the most limiting AA

S  The LYS/MET ratio is high S  (~ 3.3 – 3.6 to 1)

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94

Expectations

S  For each gram of MET added, S  get 7 gram of milk protein

S  If add >5 gram of MET, also expect an increase in BF of 0.1%

S  For each gram of LYS, S  expect 5 gram of Milk Protein

S  ½ in increased concentration ½ in increased yield

95

A Dose Response Study to Supplying Incremental Amounts of LYS Through Smartamine® ML.

S  10 multiparous Holstein cows (63 to 126 DIM) were assigned to a balanced split-plot 5x5 Latin square design involving one replicate of two squares

S  All treatments were formulated to contain 2.60% MET as a % of MP

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96

Improvements to LYS Resulted from Both Milk Yield and Milk Protein % Increases

Moral of the story

S  Fatty acids and amino acids are tightly linked with glucose utilization S  So, energy is not energy and protein is not protein

S  We must think in terms of what nutrients do in terms of metabolism to better understand how our diets ‘work’

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Steps

S  Adequate peNDF

S  Maximize fermentable CHO S  Sources vs. end products?

S  Adequate rumen ammonia

S  Supplement Amino Acid sources

S  Use fatty acids

S  Minerals and Vitamins and other Additives

99

peNDF

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100

Relationship

Inflection point: 21.6% peNDF

101

What model is using

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102

ph impact on CHO B3

103

ph on Fiber Microbial yield

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What happens when pH drops?

S  We will just focus on the bacteria

S  As pH drops (becomes more acidic), S  fiber digestion slows

S  FC bacteria very pH sensitive and will all die by a pH of 5 S  one NFC bacteria changes their end-product from propionic to

lactic acid S  lactic is 10x stronger acid S  pH starts to drop fast

S  lactic bacteria take over and pH continues to drop S  end point: can be death of cow

104

105

Rumen wall

S  Normal rumen wall S  looks like a shag carpet

S  short-term insult S  papillae appear burnt and shrink to

about 1/4 normal size S  May recover over time (1+ lactation)

S  long-term insult

S  papillae fall off and rumen has texture of a brick

S  Will never recover

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106

107

pendf History

S  As originally defined by Mertens S  pef = physically effective factor

S  peNDF = pef x NDF

S  pef varies based on S  particle size

S  hydration rate

S  degree of lignification

S  density

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108

Forage peNDF

S  Majority of peNDF comes from forages and can be altered by

S  chop TLC S  processing level of corn silage

S  storage method

S  hay vs bunks vs uprights vs bags

S  unloading method

S  especially frozen silage being ground out of silo by auger unloaders

S  mixers S  vertical vs auger vs reel vs other types

S  too wet at harvest

109

Methods

S  Penn State Shaker Box (3 pan vs 4 pan) vs. Z Box vs. Ball Mill vs. 1.18 mm screen and vertical shaker S  None of them adequately relate to all of original intentions

S  What I am going to show results in conservative values. Having dealt with herds in acidosis, I prefer having healthy rumens and cows even if it means giving up a couple pounds of milk.

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110

New equation

inputs parameter Intercept 6.05 Diet peNDF % DM 0.044 peNDF^2 -0.0006 Rumen degraded starch from grains (RDSG) % DM -0.017 DMI kg/d -0.016

Example peNDF 21.00 RDSG 15.00 DMI 24

AMTS pH 6.07

NEW OUTPUT AMTS pH<5.8 hrs/d 4.7

Requires higher peNDF as RDSG increases. Will not impact model predictions but will give index.

111

Toms recommended method

S  Grains: 1.18 mm sieve, dried sample S  Roughages

S  Silages S  top two screens of Penn State shaker box as base

S  if DM < 30, subtract 10 units S  if stored in a bag, subtract 10-15 units

S  Hays S  take a handful of hay, make it a ball, unwrap, do it again. Simulate

a mixer. S  Screen with Penn State shaker box S  In other words, mimic the particle size being offered the cow

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112

How about TMR screening?

S  Too many variables go into making the TMR to determine the peNDF from a TMR sample S  loading accuracy

S  DM of silages

S  Mixing

S  Quality of mixer

113

TMR screening

S  Use TMR screening as a component of mix quality S  good sampling technique required

S  place trash bags at four even spots along bunk S  feed S  pull bags out and divide pile into 4 S  screen one quarter from each pile S  look at CV of particle sizes (ideal less then 10%) S  also submit sample (or do on-farm) for Cl analysis. S  check CVs on Cl (<10%)

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114

pendf goals

S  Farm dependent S  Lactating herd

S  High management, >21% S  may go to 20% for short periods of time

S  Low or Average Management, >22 or 23% S  Dry and heifers

S  not really an issue given diets but will typically be >30%. S  I watch forage NDF %BW in these animals and try to keep it at

least 0.85% (but below 1.1% unless pasture fed (then around 1.3 to 1.5%))

S  Feedlot cattle: 8-10% peNDF will maximize growth and keep acidosis low

115

Most important feeding tool

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Lactating Cows

Sugar vs Starch

S  Is this the right question? S  I don’t think so. I think the question should be how do get the

precursors we want.

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Example

Molasses Corn Starch kp 11% 6% 6% 6% kd 40% 10% 15% 20% Degraded 78% 63% 71% 77% VFAs Escape 22% 38% 29% 22% ID 100% 75% 75% 75% Absorbed 22% 28% 21% 17% Glucose Fecal 0% 9% 7% 6%

Dietary Sugar Addition—Trial II Broderick & Radloff, 2004

Liquid Molasses Total Sugar

0 2.6%

3% 4.9%

6% 7.4%

9% 10%

Milk, lb/d

96ab 100a 97ab 93b

Fat, %

lb/d

3.67

3.6

3.74

3.7

3.54

3.4

3.72

3.3

Protein, %

lb/d

2.96c

2.9b

3.21a

3.2a

3.12b

3.0ab

3.13ab

2.8b

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So how do we explain this?

S  Again, it comes back to glucose S  Increase rumen fermentability of carbohydrates can give us

more propionate and lactate S  Propionate goes to liver to glucose

S  Lactate will go to glucose and/or fat synthesis

S  Post-ruminal starch goes to glucose BUT

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From Reynolds…

S  In general, increasing glucose absorption will increase glucose supply to the lactating dairy cow, but even in early lactation the primary metabolic response appears to be an increase in body tissue energy balance, rather than an increase in milk energy yield. This effect on insulin status and body energy balance may have important implications for the reproductive success of the cow (Gong et al., 2002)

S  In summary, it appears that there is a reasonable capacity for small intestinal digestion of starch in lactating dairy cows, and increased glucose absorption, although increased small intestinal starch digestion is invariably accompanied by increased starch fementation in the hindgut. However, increases in glucose absorption into the portal vein are accompanied by increased utilization of arterial blood glucose by tissues drained by the portal vein, such as mesenteric and omental fat deposits.

In other words

S  Shifting starch site of digestion from rumen to small intestine, while both can provide glucose to the cow, result in shifts in glucose metabolism

S  There is debate as to if this glucose being used by tissues spares glucose from gluconeogensis or if the animal simply down regulates total glucose production. S  Probably an increase total supply but also see higher irreversible

losses with higher post-ruminal glucose absorption S  Could be good to help modulate body condition in early lactation

S  Do high starch diets in late lactation make ‘fat’ cows? S  Do high starch diets in early lactation reduce BCS loss?

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Conclusion…

S  Maximize rumen fermentation!

S  And if going to try to manipulate post-ruminal starch, pay careful attention to digestibility of starch.

Rumen Nitrogen

S  Fact S  NFC bacteria grow better in presence of peptides

S  Fact

S  Normal RDP levels from soy, alfalfa, canola supply adequate amounts to ensure this improvement

S  From Russell:

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Iso-acids. FC bacteria

Can NOT make these!

Rumen N

S  So, a good mix of feeds will supply adequate peptides and in most cases ammonia. S  In all cases, rumen ammonia balances need to be >100%

S  How close to 100% you get is dependent upon a couple factors

S  What the diet is (with a lot of soy, canola, alfalfa, typically >150%) S  How accurate you are with having dNDF and starch digestibility

on as many feeds as possible

S  The more data you have, the lower you can run ammonia. S  Regardless, I still maintain >110%. Typically >140%

because of ingredients.

S  A little extra ammonia will not harm the diet but if you are >200%, there is opportunity to either reduce protein or increase fermentable carbohydrates.

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Amino Acids and Fats

S  We’ve really already talked about…

Minerals/vitamins

S  Is there any reason to feed more then NRC 2001 recommends? S  With exception of Cobalt, which should be 0.2 ppm vs 0.1 (per

Weiss personal communication), NO S  Other exception would be Vit. E based on literature

S  Other reasons to consider differences S  Water quality resulting in antagonists

S  Molybdenum

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How do I formulate?

S  Evaluate current diets based upon available population data S  If all I have is average of entire herd for milk, components, and

DMI, I make the model predict that average first. S  If I have group data, I do the above with each group

S  I will then 2-4 times per year double check to see how predictions are matching actual performance to ‘recalibrate’ the model and my mind.

S  Determine inventory, contracts, etc. to ensure new formulas will match these constraints and work within allowable allocation management constraints.

Acceptable ranges, Lactating

Item Range Item Range

DMI Within CNCPS/NRC range

Sugar % DM 2-10

ME % rqd 98-110 Starch % DM 20-30

MP % rqd 100-110 Sugar + Starch <35

Ammonia % rqd 120-200? Fat % DM 3-6.5

NFC % DM <43 Days to change 1 BCS >100

Forage NDF % BW 0.8-1.0 RUFAL, g/d <500

peNDF % DM >21 LYS %MP >6.4

Hrs pH<5.8 <5 LYS:MET 2.8-2.9:1

C18:2 intake <400g LYS:MET 2.65:1

C18:3 intake <85 g MET:ME Mcal 1.1:1

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Special Note

S  ME allowable milk S  For example,

S  inputted milk = 40 kg S  ME allowable = 38 kg (but MP = 40)

S  Is this a big deal? S  This can be around 2 Mcal of ME difference so

S  Days to Change 1 BCS can be around 200 days S  Anything over 100 days to change is basically Energy Balance

S  Remember also, we are looking at one cow, one day so small changes in intake, weather, etc. can quickly change this 2 Mcal

S  Bottom line: watch Days to Change 1 BCS. Goal should be >100 days

Special Note 2

S  RUFAL S  Many times I am 700-900 g

S  Remember, the 500 g point is a trigger to begin evaluating other risk factors S  NFC S  Starch S  peNDF S  Rumensin S  Fatty Acid Profiles

S  If a lot of C18:2 and 18:3, highly probably of milk fat depression

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New recommendations

S  Milk protein yield and milk volume are tightly regulated and highly correlated S  To maximize: MET MP g : Mcal ME supply

S  1.0 – 1.15 : 1 S  E.g. 32 kg milk

S  57 Mcal ME supply (54 required) S  Thus 57 to 65.5 g MP MET

S  LYS: 2.9 – 3.0 g per Mcal ME S  Same example: 165 – 171 g

S  Equal to LYS:MET of 2.65:1

S  I would drive LYS as high as possible without RP LYS available and drive MET to 1-1.15 g / Mcal ME

Findings

S  Higher levels of feed intake DECREASED C18:3 flows (ie greater biohydrogenation) S  Suggesting ‘younger’ bugs are more bioactive

S  Similar finding by Higgs while developing v7 that younger bugs tend to grow faster and escape the rumen faster.

S  Diets <50% forage reduce biohydrogenation of 18:2 and 18:3

S  Calcium salts, whole oil seeds, and formalin treatment REDUCE biohydrogenation of 18:3 significantly

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Fatty Acids of Common Feeds


Feed Name Maize Oil Canola Oil Soya Oil Sunflower Oil Megalac Grass Pasture Italian RyegrassTFA (%EE) 88.00 88.00 88.00 88.00 100.00 60.60 64.30Glycerol (%DM) 9.70 9.70 9.70 9.70 0 0.3 0.3C12:0 0 0.1 0.1 0 0.2 1.20 0.8C14:0 0 0.1 0.1 0 1.60 0.4 0.2C16:0 11.10 4.40 10.80 7.30 50.80 16.40 13.50C16:1 0 0.3 0.1 0.1 0 0.5 0C18:0 1.60 2.10 3.90 10.60 4.10 1.30 1.10C18:1 Trans 0 3.50 0 0.6 0 0.1 0C18:1 Cis 27.00 57.30 22.80 43.40 35.70 2.50 2.10C18:2 59.00 19.00 53.70 35.50 7.00 23.40 13.30C18:3 1.10 7.60 8.20 0.8 0.2 49.90 66.50Other Lipid 0.4 5.70 0.1 1.70 0.4 4.30 2.50

Summarizing

S  Biohydrogenation mostly done by fibre fermenting microbes

S  While it may seem odd that higher forage and higher levels of intake decrease duodenal flows of 18:3, it is primarily the incomplete biohydrogenation that causes milk fat depression via CLA pathways. S  The Glasser paper looks at duodenal flows.

S  There is an error in the CPM fat model where it assumes all 18:3 biohydrogenation goes through same 18:2 pathways as 18:2 does. IT DOES NOT! It follows different isomer pathways.

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Overall fatty acids

S  More and more data confirming that different fatty acid sources change milk fatty acids

S  Milk fat depression is the result of a combination of risk factors S  Fatty acid profiles

S  Forage level

S  Level of intake

S  Etc.

Fatty acid recommendations

S  Pay careful attention to sources of fatty acids S  Especially 18:2 and 18:3

S  I try to keep 18:3 intake < 85 g

S  But right now I’m feeding >100 due to grass silage. 70% coming from grass silage!)

S  Watch forage and starch levels

S  Use calcium salts for supplementation

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Special Note 3

S  How about Fermentable Carbohydrates? S  Can try for fermentable starch >20%

S  I’ve had higher sugar diets where fermentable starch was around 17%. Good performance

S  Thus, I do not have a good number for this.

S  Maybe a goal for total fermentable carbohydrate is better S  >50% total carbohydrates

Special Note 4

S  Lysine S  I’ve seen many diets where milk and milk protein have been

very good when lysine was low (as low as 5.4%) but the LYS:MET ratio was within the 2.8-2.9:1 S  Thus I am starting to lean more towards if the LYS:MET ratio is

off, at a minimum correct that.

S  In an ideal world, correct both LYS and LYS:MET but if can only correct one, I would select LYS:MET first.

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Special Note 5

S  MP allowable production and Rumen Ammonia S  Remember, depending upon the quantity and quality of feed

data you have, the tighter you can formulate these to be closer to requirements.

Lead Feeding?

S  Again, dependent upon the quality and quantity of the data available S  The more data you have, the lower the level of ‘safety’ factor

required S  I also consider this when including management level of the farm

S  At most, I formulate for 5-7 pounds higher then actual production on lower management level farms. S  Good management, I formulate to current production and let feed

intake differences take care of production S  Good idea: on a recipe, increase DMI 10% to see how ME and MP

milk look.

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Acceptable ranges, Far-Off Dry


DMI +/- 5% Predicted Sugar % DM 2-10

ME % rqd 98-105 Starch % DM <15


Ammonia % rqd 120-200? Fat % DM <4


Forage NDF % BW 0.8-1.0 RUFAL, g/d N/A

peNDF % DM >25 LYS %Rqd >100

Hrs pH<5.8 <5 MET %Rqd >100

Acceptable ranges, Close-Up


DMI (@265 days preg) +/- 5% Predicted

Sugar % DM 2-10







Hrs pH<5.8 <5 MET g 27-30

Milk Fever Risk <1%

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Dry Cow Notes

S  I prescribe the low energy/high fill dry cow programs S  I use low quality grass silage (~65 NDF), straw, alfalfa hay, oat

hay, sorghum silage, hay grazer, etc. S  Must be palatable

S  For far-offs, I make sure minerals/vitamins are balanced to NRC

My typical Close-up mineral

In th

is ex

ampl

e, th

e m

ilk fe

ver r

isk

was

cal

cula

ted

as 0

.7%

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Dry Cow Note 2

S  Should we watch lactic acid in dry cows? S  If lactic acid results in fat deposition, then the answer should

be yes. S  Goal should probably be <3% but given short period of time, <4%

probably acceptable

S  There is no data on this!

Acceptable ranges, Heifers


DMI with Bias Adjustment On

+/- 5% Predicted

Sugar % DM 2-10




NFC % DM <37 Days to change 1 BCS N/A



Hrs pH<5.8 <5 MET %Rqd >100

Lactic Acid % DM <3

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Heifer Notes

S  Always have MP allowable gain > ME allowable gain S  <12 months old, 100 – 300 g difference

S  >12 months old (especially bred heifers) this difference can be as high as 750 g. I really don’t worry about this big of a spread.

S  Inputted body weight is to be the AVERAGE of the growth period being modeled. S  If cattle enter at 300 kg and leave at 400 kg during this period,

inputted body weight is 350 kg

S  Gains must be related to age of first calving

Heifer Notes 2

S  Bred Heifers

Mature Weight Breeding Weight Calving Weight Required ADG kg kg kg g/d 450 248 369 434 500 275 410 482 550 303 451 530 600 330 492 579 650 358 533 627 700 385 574 675 750 413 615 723 800 440 656 771 850 468 697 820

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Heifer Notes 2

S  Bred Heifers

Mature Weight Breeding Weight Calving Weight Required ADG lbs lbs lbs lb/d 1000 550 820 0.96 1100 605 902 1.06 1200 660 984 1.16 1300 715 1066 1.25 1400 770 1148 1.35 1500 825 1230 1.45 1600 880 1312 1.54 1700 935 1394 1.64 1800 990 1476 1.74

Heifer Notes 3

S  Pre-breeding (assume double birth weight at 60 days age)

Mature Weight Breeding Weight Weaning Wt Age of First Calving (mo) ADG lbs/d lbs lbs lbs 22 23 24 25 26 27 1000 550 114 1.30 1.20 1.10 1.02 0.96 0.90 1100 605 125 1.43 1.31 1.21 1.13 1.05 0.99 1200 660 137 1.56 1.43 1.32 1.23 1.15 1.08 1300 715 148 1.69 1.55 1.43 1.33 1.24 1.17 1400 770 160 1.83 1.67 1.54 1.43 1.34 1.25 1500 825 171 1.96 1.79 1.65 1.54 1.43 1.34 1600 880 182 2.09 1.91 1.77 1.64 1.53 1.43 1700 935 194 2.22 2.03 1.88 1.74 1.63 1.52 1800 990 205 2.35 2.15 1.99 1.84 1.72 1.61

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Heifer Notes 3

S  Pre-breeding (assume double birth weight at 60 days age)

Mature Weight Breeding Weight Weaning Wt Age of First Calving (mo) ADG g/d kg kg kg 22 23 24 25 26 27 450 248 51 587 538 496 461 430 403 500 275 57 652 598 552 512 478 448 550 303 63 717 657 607 563 526 493 600 330 68 782 717 662 615 574 538 650 358 74 847 777 717 666 621 583 700 385 80 913 837 772 717 669 627 750 413 86 978 896 827 768 717 672 800 440 91 1043 956 883 820 765 717 850 468 97 1108 1016 938 871 813 762

Heifer Notes 4

S  Minerals/Vitamins to NRC

S  If possible, exercise! S  Big difference between free-stall heifers and dry-lot heifers

S  Dry-lot heifers have much better muscle development

S  Limit feeding: to me option of last resort S  Are we training heifers to slug feed for life?

S  Must have adequate bunk space for all to eat on once and groupings must be of uniform size/age

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Where can Refusals be fed?

S  Depends upon the quality of the refusals S  If good quality (as defined by me on-farm: cool, smell fresh,

not just sticks and stones, etc.) I will use them as an ingredient in bred heifers and far-off dry cows (up to 4.5 kg DM)

S  Never use with any animal <6 months old S  Regardless of how well the farm is managed, they are

contaminated with fecal material. Heifers <6 months old are most susceptible to picking up things like Johnes, BVD, etc. from fecal/oral transmission.

Feedlot cattle


DMI (@265 days preg) +/- 5% Predicted

Sugar % DM 2-10




NFC % DM Depends Days to change 1 BCS >100

Forage % Diet 5-50 RUFAL, g/d N/A

peNDF % DM 8-25 LYS %Rqd >100

Hrs pH<5.8 N/A MET %Rqd >100

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Feedlot Notes

S  Forage level, NFC level, etc. depend upon feedlot period S  Finishing diet typically 8-10% peNDF

S  Special note: S  If using a beta agonist, during that feeding period, REDUCE

the Final Body Fat 2 units (e.g. 28 to 26%) S  This changes where the cattle are on the growth curve to represent

greater protein requirements

Feedlot notes 2

S  Final Body Weight is the weight at the desired body fat level.

S  Typically, 28% empty body fat represents physiological maturity. S  But this may not be true maturity

S  Can increase final body weight by manipulating grower period and fat precursors.

S  Inputted body weight is to be the AVERAGE of the growth period being modeled.

S  If cattle enter at 300 kg and leave at 400 kg during this period, inputted body weight is 350 kg

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Feedlot notes 3

S  Again, think precursors S  Lactic acid production goes to fat

S  Intestinal glucose absorption goes to fat

S  So, to produce heavier weight at same level of fat, reduce these precursors to grow the animal frame and muscle

Body comp related to weight

100

200

300

400

500

600

700

0 200 400 600 800 1000 1200 1400 1600 1800Body Weight

com

pone

nt w

eigh

t

Water

Fat

ProteinWe c

an change th

e shape o

f this c

urve.

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Optimization

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Basics

S  Curve peeling S  behind the scenes, it adds 0.1 kg of each feed one at a time to

generate

S  ME, MP, AAs, Ammonia and other concentrations of feeds

S  optimizes

S  if number of iterations set to >1,

S  takes results of optimization, inputs those amounts, rederives feed values, reoptimizes. Does this loop for number of iterations inputted

S  Most feed values converge (ie stabilize) between 2 and 3 iterations but sometimes takes up to 7 with odd feeds.

Constraint Theory

S  We do not use % in the calculations. S  all values are done as mass in the optimization

S  e.g. S  Min DMI = 50 lbs

S  Min NFC = 30% S  Max DMI = 52 lbs S  Max NFC = 40%

S  Gives contraints of NFC min = 15 lbs, max = 21.84 lbs S  If solution found is 21.84 lbs NFC but at 50 lbs, then NFC

concentration is: 21.84 / 50 * 100 = 43.68%

S  Mathematically, this is correct.

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Constraints 2

S  Set DMI +/- 0.25 lbs (100 g) S  e.g. 49.5 to 50.5 lbs S  I actually set my MAX equal to DMI of original diet

S  if diet is 50 lbs, I’ll do DMI as 49.5 to 50.0 S  why? I can’t be certain the cow will eat an extra 0.5 or more lbs DMI!

S  ME and MP: must use % rqd

S  MIN can be anything you want S  NOTICE: by default, MIN is 99%

S  MAX I usually will set as 105% for ME and 107% for MP S  why higher MP? MP is easy to meet and some extra MP may be cheaper.

ME is the tricky one to meet in 6.1 biology!

Constraints 3

S  Amino Acids S  Can constrain ratios, % Rqd, or grams

S  Behind scenes, all are converted to grams

S  All other constraints S  peNDF > 22% lactating cows, >30% other classes, 8-10% feedlot S  NFC, starch, sugar, minerals, forage, etc: follow normal guidelines

S  You can also do Ferm Starch, Sugar, Soluble Fiber. Default MIN values on ration outputs screen adapted from Sniffen recommendations

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Reinforcements

S  Constrain DMI tightly!

S  Make sure Feed Constraints are Realistic too!

S  Great comment from a nutritionist: S  “The optimizer does what I tell it to do”

S  So if your constraints are set poorly, the optimization will be poor!

Example of Crazy Results…

S  Several times I have observed the following: S  Optimized diet significantly more expensive, S  Lysine way in excess of Max constraint S  Why?

S  Max constraint on fat supplement (or no fat supplement in available feeds)

S  Unable to meet ME requirement

S  Most LYS products use a FAT coating S  So the optimizer was using the LYS product as a FAT supplement for ME!

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Results Interpretation

S  We are using ‘shadow’ variables. This allows us to return a result >99% of the time. S  IF the optimizer returns a list of constraints outside min/max

(e.g. NFC max = 40%, optimizer returns 40.1%), the resulting diet is NOT always least cost! S  It represents as close as it could get given the constraints.

S  If you change some constraints, you will get a different answer.

S  IF it comes back saying “This meets all constraints,” it is the true least cost diet available.

Setting Constraints

S  The best advice: S  Set the constraints realistically.

S  Use ration output templates to customize default constraint sets for yourself S  I commonly see people forgetting to set Min/Max on ME and MP

leaving them as the defaults of 99-101%. It is nearly impossible to solve.

S  If you can NOT manually solve a formula using the constraints you set, neither can the optimizer!

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Now we go to Examples of Optimization in

AMTS

Implementation Steps

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Steps-New Farm

S  Start AMTS

S  Exit Open Farm Screen

S  Create New Farm S  Name Farm

S  Create Locations

S  Create Cattle Groups within Locations

S  Add Feeds from Feed library to Farm

S  Edit feed composition where appropriate

S  Create Recipe

S  Add feeds and formulate

S  Optimize if you desire

S  Create Composites

S  Create and print/save required reports

Steps-New Farm From Template File

S  Start AMTS

S  Open Farm Template

S  File/Save As: Input new farm name

S  Modify Locations

S  Modify Cattle Groups

S  Modify/Add Feeds from Feed library to Farm

S  Edit feed composition where appropriate

S  Modify Recipe/Input and Evaluate Current Recipe

S  Optimize if you desire

S  Create Composites

S  Create and print/save required reports

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Welcome

S  AMTS LLC

S  Owners have over 50 years combined experience with CNCPS

S  Tylutki is one of the original inventors of the CNCPS

S  This is my 24th year working with the model

S  Objective of AMTS is to provide nutritionists the tools and technical support (nutritional) to improve the profitability of the farm and the company.

S  Agreement with Adifo to distribute and add value to Adifo products (mill level formulation software)

S  We work globally with over 600 users

S  About Tylutki

S  Raised on a 60 cow dairy in eastern NY state

S  Cornell University:

S  BS, MS, PhD

S  Worked for Extension for 5 years between MS and PhD

S  Minority partner in 700 cow dairy

S  Wife is a small animal veterinarian

S  Two children (16 and 9)

S  Own old animals…

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