Webinar

O Nutricionista

10 de junho 19:00

Toda segunda quarta-feira do mês

Noelia DaSilva – PhD – University of California –

Extension. Important points to measure at the farm to get

your diet close to the diet the cow is eating.

Focus on feed analysis

Noelia Silva-del-Río, DVM, PhD, CE Specialist

Yolanda Trillo, PhD Student

UC Davis Veterinary Medicine Teaching and

Research Center, Tulare, CA

Feeding Process:

Variation in TMR Preparation and

Delivery

DVM, 1998

Facultad de Veterinaria de Lugo

Technical Services, 1999-2001

COREBER

Ph.D. Dairy Science, 2007

UW-Madison “

Epidemiology, Physiology and

Nutritional Management of

Twinning in Holstein Cattle

University of California

Cooperative Extension

Aug 2008 – Feb 2012

Responsibilities

Vet Med Dairy Extension Specialist

Hypocalcemia Prevention

Judicious Use of Antibiotics

Lameness

Implement a research and education program for veterinarians,

producers and allied industry on:

Feeding Management

• Yolanda picture

Study 1 - Feeding Management Practices: On-farm

Assessments

Objective:

Develop a systematic approach to identify opportunities in

feeding management.

Approach:

Observe feeding management practices

Evaluate the mixing equipment

Analyze total mixed ration

Interview feeders

Analyze feeding management software data (1 month)

Study 2 - Feeding Process: Analysis of Variation of

TMR Preparation and Delivery

Objective:

Describe industry practices based on records from feeding

management software.

Materials and Methods:

Data from 12 consecutive months were extracted from the

feeding management software of 26 California dairies,

ranging in size from 600 to 6000 cows.

Central

Computer

Screen

NUTRICIONIST

Formulated Recipe

Feeder

Recipe Fed

Are Feeding

Management

Practices Important?

Inadequate feeding management practices on dairies can

result in an increased number of health events such as:

Displaced Abomasum Diarrhea

Hypocalcemia Pistachio Shell Impactions

If the ration fed differs from the

formulated one, cows will not

achieve their maximum production

potential and some nutrients (i.e.

nitrogen) will be wasted in manure

rather than converted to milk.

3.4%

- 1.9%

Dairy

1 2 3 4 5 6 7

Dif

fere

nce i

n C

P (

%)

-3

-2

-1

0

1

2

3

4

Difference in percentage units of crude protein (CP) between

the formulated and the analyzed CP in seven dairies in Merced

County (Silva-del-Rio and Castillo, 2012).

Cru

de

Pro

tein

of

the a

naly

zed

rati

on

(%

)

Crude protein of the formulated TMR (%)

Correlation between the crude protein of the formulated and

the analyzed ration in 15 Virginia dairies over a one year

period (James and Cox, 2008; r=0.45; P = 0.55).

Dairy 1

Dairy 1

Milking parlor

3&4

2

Hospital

12

7 & 8

9 &10

Feeding Center

Commodities

Green Chop Alfalfa

Alfalfa batches analyzed in July (n=93)

Alfa

lfa

DM

(%

)

40

50

60

70

80

Two to three trucks arrived everyday with green chop alfalfa. The feeder evaluated dry matter daily and updated the

feeding management software. He was equipped with a koster tester. He believed he was skilled to estimate dry

matter visually.

Green Chop Alfalfa (DM)

Green Chop Alfalfa (DM)

Sampling days

Aug 29 Sep 5 Oct 11

Alfalfa G

reen (

DM

%)

20

30

40

50

60

70

80Registered on FW

CV=8.9 % CV=22.2 %

*%CV=SD/mean

Mean (10 samples)

CV = 22.6 %

Green Chop Alfalfa (DM)

Wet Chemistry Formulated

% of dry matter

CP 16.5 17.8

ADF 24.7 20.0

NDF 33.3 25.0

Formulated vs Analyzed Ration

< 1wk

1x wk

2x mon

1x mon

6x yr

2x yr

1x yr

Da

irie

s (

%)

0

10

20

30

40

Corn silage dry matter was conducted at least once a month in 52.3% of

dairies. Only 8.3% of dairies determined DM weekly, or more often. Most

dairies delegated DM determination to an outside nutrition consultant (86.6%).

Frequency of dry matter determination

(n=101/120)

Silva del Rio, et al 2010 – ADSA abstract

How Often Do You Evaluate

Silage Dry Matter?

Herd Size

50 100 200 400 800 1600

Time between samples (d) 30 16 11 7 5 4

Samples per month 1 2 3 4 6 7

http://www.uwex.edu/ces/crops/uwforage/F

orageSamplingFrequency-FOF.pdf

Basado en el número de vacas Based on the Number of Cows

Increase frequency when:

• Dry matter varies too much in between samplings.

• Extreme weather conditions.

• Changes in silage appearance.

• At the beginning and end of the silage.

Based on the Number of Cows

• Change by 3 units of %DM:

– Re-do the sample.

– Evaluate if the silage appearance match the results.

• Consistent change by one unit of %DM (3

consecutive days):

– Modify the recipe.

• Weighted average (St Pierre y Weiss, 2007):

– Most recent result, 50%,

– The second last result, 30%,

– The third last result, 20%.

Loading

Ingredients

Ingredient Front Middle Back Side Center

Enzabac-Canola I II II I

Corn gluten III I II

Mineral III III

Rolled corn II I II I

Wet destillers III I II

Whey I II III

Green Chop III I II

Premix III II I

Energy II III I II

Cotton seed III III

Corn silage III I II

Almond hulls III I II

Whey II I III

Times (loader position)

Front-end-Loader Position

The feeder empties a feed additive bag (50 lbs) in the front-end-loader.

Then, he loads canola and drops everything in the mixer wagon.

Feed Additive (50 lbs) Canola

Feed Additive as 1st

Ingredient

Physical properties of ingredients:

• Particle size

• Shape

• Density

• Hygroscopicity

• Statics charge

• Adhesivity

Orden de los ingredientes

Ingredient Order

Sugar Flour Yeast Oil

When we add ingredients with high adhesivity

(silages, molasses, fats)?

Orden de los ingredientes

Ingredient Order

When we add dense ingredients (minerals)?

Minerals

Orden de los ingredientes

Ingredient Order

When we add low density ingredients (hay)?

Hays

Orden de los ingredientes

Ingredient Order

What would you add first corn silage or alfalfa silage?

What we add first the grains or the minerals?

Corn silage is 33% more dense than alfalfa silage

Minerals are 2-3 times denser than grains

Ingredient Order

Hay Vertical Mixer Wagon Horizontal Mixer Wagon

1 2 3 4 5

Dairie

s (

n)

0

20

40

60

80

100

1 2 3 4 5

1

1 2 3 4 5

Dairie

s (

n)

0

20

40

60

1 2 3 4 5

Order of Ingredients

In which order are feeds

added to the TMR?

1 2 3 4 5

Dairie

s (

n)

0

20

40

60

80

100

Hay Silage

1 2 3 4 5

Dairie

s (

n)

0

20

40

60

Vertical Mixer Wagon Horizontal Mixer Wagon

1 2 3 4 5 1 2 3 4 5

Order of Ingredients

In which order are feeds

added to the TMR?

1 2 3 4 5

Dairie

s (

n)

0

20

40

60

80

100

Hay Silage Grains

1 2 3 4 5

Dairie

s (

n)

0

20

40

60

Vertical Mixer Wagon Horizontal Mixer Wagon

1 2 3 4 5 1 2 3 4 5

Order of Ingredients

In which order are feeds

added to the TMR?

1 2 3 4 5

Dairie

s (

n)

0

20

40

60

80

100

Hay Silage Grains Min Vit

1 2 3 4 5

Dairie

s (

n)

0

20

40

60

Vertical Mixer Wagon Horizontal Mixer Wagon

1 2 3 4 5 1 2 3 4 5

Order of Ingredients

In which order are feeds

added to the TMR?

1 2 3 4 5

Dairie

s (

n)

0

20

40

60

1 2 3 4 5

Dairie

s (

n)

0

20

40

60

80

100

Hay Silage Grains Min Vit

Protein Mix

Vertical Mixer Wagon Horizontal Mixer Wagon

1 2 3 4 5 1 2 3 4 5

Order of Ingredients

In which order are feeds

added to the TMR?

Dropping leftovers of

the previous

ingredient as the new

ingredient

• Leftovers are left on the

front-end-loader and

next ingredient is

loaded

Commodities are far from the feeding center

Feeder signs the sheets for the

commodities delivered

Short Time

Long Time

Time between Ingredients

Time between ingredients (s)

< 15

15 - 30

30 - 45

45 - 60

60 - 75

75 - 90

90 - 105

=> 105

Fre

qu

en

cy (

%)

0

10

20

30

40

50

60

Observation: 60s

72.5%

80.4 %

Loading time between Premix

and Energy II

1. Alfalfa green chop

5. Corn silage

3. Energy II

2. Premix

4. Cotton seed

22.8%

6. Almond hulls

7. Whey

72.5%

16.7%

3.7%

34.9%

6.3%

4

3

2

1

5

6

7

Loading Time between

Ingredients <45 s

High Cow Ration

60 s

60 s

150 s

65 s

90 s

80 s

Feeders

Primary Relief Sporadic

Loads <

45 s

(%

)

0

10

20

30

40

50

n = 1009

n = 233

n = 42

Frequency of loading time

between ingredients <45 s

Dairies

20 3 16 26 1 25 8 5 17 23 24 9 6 19 22 15 11 4 18 13 2 14 12 21 10 7

Tim

es b

etw

ee

n in

gre

die

nts

(%

)

0

20

40

60

800-15s

>15-30s

>30-45s

Frequency of loading time

between ingredients

Mixer Wagon

Mixer Wagon Observations

Feed was building up under the augers and side walls.

Knives looked sharp. They were scheduled to be changed every 2 months, but no

records were available.

Mixer wagon scale was calibrated.

Bouncing scale between loads ranged from 15 to 80 Lbs

Mixer Wagon Scale

- There were 1000 lbs of premix left in the mixer wagon - estimated value $100

- After a premix, 35.1% of the times, a high cow ration (n=39) was prepared.

Mixer Wagon Clean Out

Recipes

Low High Premix

Fre

qu

en

cy o

f m

ixe

rs (

%)

0

20

40

60

<1x m

on4x

yr

2x yr

1x yr

Nev

er

Dair

ies (

%)

0

10

20

30

40

50

Frequency of checking mixer scale

Seventy-nine percent of producers checked the mixer scale at least once a

year. But, only 19 (%) checked it at least monthly. The mixer wagon was

calibrated by an outside service (60%) or an in house employee (40%)

(n=101/120)

79%

How often do you check if your

mixer box is calibrated?

Weight Deviations

from Target

Tolerance Level per Ingredient

Tolerance level is the pounds below the formulated target assigned to each

ingredient to avoid overloading.

During the loading process, the mixer wagon scale indicates the amount of feed

left to reach the formulated target but when the tolerance level is reached, the

software moves on to the next ingredient.

Pounds< -3

00< -2

50< -2

00< -1

50< -1

00< -5

0 < 0 > 0> 50

> 100

Devia

tio

n f

rom

targ

et

(%)

0

10

20

30

40

50

60

Lbs weighed: 8,729 (3,412 – 10,072)

Target

Tolerance

Level (TL)

Deviation from the Target

Weight – Corn silage

BOX PLOT

25%

BOX PLOT

75%

BOX PLOT

10%

90%

Ingredients

Alfalfa-Green

Premix

Energy2

Cottonseed

Corn-Silo

Almond-hullsWhey

Devia

tion

fro

m the

targ

et (L

bs)

-400

-200

0

200

400

Deviation from the Target Weight

by Ingredient – High Ration

300

100 100

150

300

200 150

Tolerance

level (TL)

Ingredients

Alfalfa-Green

Premix

Energy2

Cottonseed

Corn-Silo

Almond-hullsWhey

Devia

tion

fro

m the

targ

et (L

bs)

-400

-200

0

200

400

Deviation from the Target Weight

by Ingredient – High Ration

Tolerance

level (TL) 300

100 100

150

300

200 150

4.4%

2.6% 14.3%

62.5%

1.0%

8.1% Deviation

allowed by

TL (%) 7.5%

Feeders

Primary Relief Sporadic

De

via

tio

n f

rom

ta

rge

t (L

bs)

-400

-200

0

200

400

Deviation from the Target by

Feeder

2.7% 3.1%

2.6% Deviation

from

Target (%)

Deviation from Target (%)

Dairies

5 11 25 20 2 8 26 3 22 6 21 18 17 24 19 13 12 16 1 9 15 23 14 7 10 4

De

via

tio

n fro

m ta

rge

t b

y T

L (

%)

-15

-10

-5

0

5

10

15

Devia

tion fro

m targ

et by T

L (

kg

)

-100

-50

0

50

100

Dairies

5 11 25 20 2 8 26 3 22 6 21 18 17 24 19 13 12 16 1 9 15 23 14 7 10 4

Devia

tion fro

m targ

et (%

)

-15

-10

-5

0

5

10

15

De

via

tio

n fro

m ta

rge

t (k

g)

-100

-50

0

50

100

Deviation from Target

Dairies

5 11 25 20 2 8 26 3 22 6 21 18 17 24 19 13 12 16 1 9 15 23 14 7 10 4

De

via

tio

n fro

m ta

rge

t b

y T

L (

%)

-15

-10

-5

0

5

10

15

De

via

tio

n fro

m ta

rge

t b

y T

L (

kg

)

-100

-50

0

50

100

Dairies

5 11 25 20 2 8 26 3 22 6 21 18 17 24 19 13 12 16 1 9 15 23 14 7 10 4

Devia

tion fro

m targ

et (%

)

-15

-10

-5

0

5

10

15

Devia

tion fro

m targ

et (k

g)

-100

-50

0

50

100

Is Dairy 3

spending more

time to be precise

and accurate?

10 8 14 25 7 2 9 12 16 13 26 6 15 18 11 22 17 19 3 23 21 24 4 1 5 20

Tim

e (

min

)

0

10

20

30

Lo

ad

weig

ht (k

g)

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

10 8 14 25 7 2 9 12 16 13 26 6 15 18 11 22 17 19 3 23 21 24 4 1 5 20

Tim

e (

min

)

0

10

20

30

Lo

ad

weig

ht (k

g)

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

Dairies

10 8 14 25 7 2 9 12 16 13 26 6 15 18 11 22 17 19 3 23 21 24 4 1 5 20

Tim

e (

min

)

0

10

20

30

Lo

ad

we

igh

t (k

g)

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

High cow Ration:

Preparation Time

What it would be

an acceptable

deviation from

target?

Deviation from Target

Dairies

5 11 25 20 2 8 26 3 22 6 21 18 17 24 19 13 12 16 1 9 15 23 14 7 10 4

De

via

tio

n fro

m ta

rge

t b

y T

L (

%)

-15

-10

-5

0

5

10

15

De

via

tio

n fro

m ta

rge

t b

y T

L (

kg

)

-100

-50

0

50

100

Dairies

5 11 25 20 2 8 26 3 22 6 21 18 17 24 19 13 12 16 1 9 15 23 14 7 10 4

Devia

tion fro

m targ

et (%

)

-15

-10

-5

0

5

10

15

De

via

tio

n fro

m ta

rge

t (k

g)

-100

-50

0

50

100

3 26 22 16 18 8 6 11 21 17 2 24 12 13 20 9 1 23 25 19 7 15 14 5 10 4

Premix 2 11 4 12 15 12 14 14 14 16 12 28 15 19 14 24 29 36 23 24 42 41 45 24 42 49

Alfalfa hay 6 17 14 15 15 24 11 18 23 29 19 14 20 21 41 32 27 33 25 28 35 31 39 66 29 90

Corn silage 0 14 14 15 15 19 14 20 19 26 21 30 20 22 30 31 29 45 23 27 48 41 61 54 60 128

Rolled corn 3 6 4 13 13 5 14 ─ 11 9 14 14 18 14 12 16 20 23 21 19 29 40 36 41 63 175

Almond hulls 0 11 4 8 13 5 12 16 4 17 11 10 16 ─ 13 16 16 31 18 22 22 28 24 31 23 171

Liquids 2 6 17 9 9 ─ 29 7 1 13 57 8 ─ 12 24 4 31 10 24 43 9 14 15 26 73 34

Cottonseed 0 8 14 7 14 5 14 ─ 7 14 ─ 25 21 44 11 52 14 ─ 20 33 36 31 182 34 26 131

Canola 1 4 9 ─ ─ 6 ─ ─ 47 16 ─ 15 17 17 25 64 44 ─ 21 28 27 37 283 24 35 208

Premix 4 6 4 3 6 4 8 12 4 6 10 0 9 10 7 7 11 7 11 9 10 10 12 19 9 22

Alfalfa hay 7 9 8 6 9 14 11 15 5 9 14 7 9 12 0 13 11 10 14 13 12 10 11 44 8 28

Corn silage 5 6 7 6 7 9 8 14 7 8 11 9 10 10 11 10 12 13 9 9 12 12 13 41 19 29

Rolled corn 3 5 4 4 4 3 8 ─ 4 6 8 6 8 9 10 7 9 7 12 7 8 10 9 32 15 32

Almond hulls 3 5 4 4 6 3 0 13 4 6 10 5 9 ─ 9 6 8 9 15 9 9 9 8 28 8 31

Liquids 1 4 5 3 13 ─ 9 38 1 4 19 5 9 ─ 12 4 14 7 58 42 13 6 9 61 18 18

Cottonseed 4 4 8 4 6 6 8 ─ 4 5 ─ 7 9 13 10 14 8 ─ 13 12 11 9 22 25 7 29

Canola 3 5 4 ─ ─ 2 ─ ─ 7 6 ─ 6 9 8 15 17 11 ─ 12 10 8 8 33 18 10 32

Premix 6 18 9 15 22 16 22 27 18 23 22 29 24 29 22 31 41 43 34 33 52 52 57 43 51 71

Alfalfa hay 13 26 22 22 25 39 22 34 29 39 34 22 30 34 41 45 39 43 40 41 48 42 51 111 37 118

Corn silage 6 20 21 22 22 29 22 35 27 34 32 40 30 33 42 42 42 59 33 36 60 53 74 96 79 157

Rolled corn 7 12 9 18 18 9 22 ─ 15 16 22 20 26 23 22 24 30 30 33 27 38 51 46 73 78 208

Almond hulls 4 17 9 13 19 9 12 30 9 24 22 16 26 ─ 22 22 25 41 33 31 31 38 33 59 31 202

Liquids 4 11 23 13 22 ─ 38 45 3 18 76 13 22 ─ 36 9 46 18 83 85 22 21 24 87 92 52

Cottonseed 4 13 23 11 21 11 22 ─ 12 20 ─ 33 31 58 22 67 22 ─ 34 45 47 40 205 60 33 160

Canola 5 10 13 ─ ─ 9 ─ ─ 54 23 ─ 21 27 26 41 82 56 ─ 34 39 35 45 316 42 46 240

1 IQR: white (|<20| kg), grey ( |20| to |40| kg), dark (|>40| kg).

2 Q1: white (|<10| kg), grey ( |10| to |20| kg), dark (|>20| kg).

3 Q3: white (|<25| kg), grey ( |25| to |40| kg), dark (|>40| kg).

Ingredient typeDairies

IQR

(Q

3 -

Q1)

25

th p

erce

nti

le (

Q1)

75

th p

erce

nti

le (

Q3)

Dairies

11 19 16 25 13 22 24 2 12 7 21 9 6 17 23 8 10 3 14 20 18 26 15

Devia

tion fro

m targ

et cost ($

/ton)

-10

-5

0

5

10

Media

n targ

et cost ($

/ton)

100

200

300

400

Variation of recipe $/ton

Deviation from Target

Dairies

5 11 25 20 2 8 26 3 22 6 21 18 17 24 19 13 12 16 1 9 15 23 14 7 10 4

De

via

tio

n fro

m ta

rge

t b

y T

L (

%)

-15

-10

-5

0

5

10

15

De

via

tio

n fro

m ta

rge

t b

y T

L (

kg

)

-100

-50

0

50

100

Dairies

5 11 25 20 2 8 26 3 22 6 21 18 17 24 19 13 12 16 1 9 15 23 14 7 10 4

Devia

tion fro

m targ

et (%

)

-15

-10

-5

0

5

10

15

Devia

tion fro

m targ

et (k

g)

-100

-50

0

50

100

W E

S

N

Lactating Cow Pens Feedbunk Observations

W E

S

N

Feedbunk Observations

Dairy 2

Dairy Lay-Out

Milking parlor

7

5, 15, 13, 14

Feeding Center

Commodities

Cottonseed & Almond hulls

Rolled corn

Canola & Alfalfa

Poultry waste

Mineral mix

Cottonseed meal

Megalac

Premix

Hay Processing Issues

2.75”

Hay Processing Issues

Mixer Wagon

- Kicker amplification

- Change the angle of the blades

- Door blocked with feed

After

Before

Mixer Wagon Issues

High cow ration:

preparation and delivery times

Dairies

25 14 8 10 2 26 16 18 15 7 22 13 19 23 24 3 12 4 17 9 6 11 21 5 1 20

Tim

e (

min

)

0

10

20

30

40

50

60

Lo

ad

we

igh

t (k

g)

4000

6000

8000

10000

12000

14000

16000

18000

Dairies

8 14 25 10 2 18 22 16 15 17 19 4 3 12 7 13 23 6 24 26 9 21 5 1 11 20

Tim

e (

min

)

0

10

20

30

40

Preparation

Driving/mixing

Delivery

Saturday

pen5-7 pen13-5-14 pen14-15

0

5

10

15

20

25

Wednesday

pen5-7 pen14-5-13 pen14-15

0

5

10

15

20

25

29,600 28,200 30,700

25800

16000 16800

28,000

30,300 30,700

Thrusday

pen13-5-14 pen14-15 pen5-7

Loading

Driving

Dropping

Friday

pen14-15 pen13-5-14 pen7-5

30,000

Sunday

pen15 pen5-7 pen13-5 pen14

Loading

Driving

Dropping

18,200

22,500

28,500

21,200

28,000

31,000

28,600 30,000

30,000

Main feeder

Preparation

Driving/Mixing

Feeding

10 8 14 25 7 2 9 12 16 13 26 6 15 18 11 22 17 19 3 23 21 24 4 1 5 20

Tim

e (

min

)

0

10

20

30

Lo

ad

weig

ht (k

g)

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

10 8 14 25 7 2 9 12 16 13 26 6 15 18 11 22 17 19 3 23 21 24 4 1 5 20

Tim

e (

min

)

0

10

20

30

Lo

ad

weig

ht (k

g)

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

Dairies

10 8 14 25 7 2 9 12 16 13 26 6 15 18 11 22 17 19 3 23 21 24 4 1 5 20

Tim

e (

min

)

0

10

20

30

Lo

ad

we

igh

t (k

g)

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

High cow ration:

preparation times

Samples

B

U

C

K

E

T

R

E

S

T

O

Kg

(56 x 61cm)

1 13.1

2 26.9

3 21.9

4 29.7

5 13.5

6 33.0

7 57.1

8 13.3

9 28.4

10 17.2

Dropping Uniformity

SamplesBUCKETRESTOTOTAL

1 29.1

2 59.7

3 48.7

4 66.1

5 30.0

6 73.4

7 126.9

8 29.6

9 63.0

10 38.3

SamplesBUCKETRESTOLbs

(22x24")

1 22.7

2 48.5

3 48.6

4 1.4

5 47.5

6 9.0

7 30.6

8 14.7

9 34.3

10 52.6

CV=59.5%

Dropping Uniformity

CV=59.5%

Loading

Ingredients

Dairies

20 3 16 26 1 25 8 5 17 23 24 9 6 19 22 15 11 4 18 13 2 14 12 21 10 7

Tim

es b

etw

ee

n in

gre

die

nts

(%

)

0

20

40

60

800-15s

>15-30s

>30-45s

Frequency of loading time

between ingredients

Deviation from Target

Dairies

5 11 25 20 2 8 26 3 22 6 21 18 17 24 19 13 12 16 1 9 15 23 14 7 10 4

De

via

tio

n fro

m ta

rge

t b

y T

L (

%)

-15

-10

-5

0

5

10

15

De

via

tio

n fro

m ta

rge

t b

y T

L (

kg

)

-100

-50

0

50

100

Dairies

5 11 25 20 2 8 26 3 22 6 21 18 17 24 19 13 12 16 1 9 15 23 14 7 10 4

Devia

tion fro

m targ

et (%

)

-15

-10

-5

0

5

10

15

Devia

tion fro

m targ

et (k

g)

-100

-50

0

50

100

Dairies

11 19 16 25 13 22 24 2 12 7 21 9 6 17 23 8 10 3 14 20 18 26 15

Devia

tion fro

m targ

et cost ($

/ton)

-10

-5

0

5

10

Media

n targ

et cost ($

/ton)

100

200

300

400

Variation of recipe $/ton

TMR Delivery

Pen #

5 7 13 14 15

Dro

ps (

%)

0

20

40

60

80

1001 drop

2 drops

3 drops

Drops per pen/day

Dairies

21 4 23 12 10 26 8 7 17 15 22 18 9 25 16 3 19 2 13 5 11 6 24 20 14 1

Fre

qu

en

cy o

f fe

ed

de

live

ry p

er

da

y (

%)

0

20

40

60

80

100 once

twice

third

fourth

Frequency of drops/pen/day

Pen #

5 7 13 14 15

Se

qu

en

ce

(%

)

0

20

40

60

80

100Seq 1

Seq 2

Seq 3

Seq 4

Sequence of Delivery

Days

Wed Thr Fri Sat Sun Mon

Sta

rt tim

e (

h:m

in:s

)

04:00:00

05:00:00

06:00:00

07:00:00Pen 15

Pen 14

Pen 13

Pen 7

Pen 5

First drop: delivery time

Feeding sequence

Seq Wed Thr Fri Sat Sun Mon

1 5 - 7 13 - 5 - 14 14 - 15 5 -7 15 5 - 7

2 14 - 5 - 13 14 - 15 13 - 5 - 14 13 - 5 - 14 5 - 7 13 - 5

3 14 - 15 5 -7 7 -5 14 - 15 13 - 5 14

4 _ _ _ _ 14 15

Dropping order by pen - variation along the week

Seq Wed Thr Fri Sat Sun Mon

1 5 - 7 13 - 5 - 14 14 - 15 5 -7 15 5 - 7

2 14 - 5 - 13 14 - 15 13 - 5 - 14 13 - 5 - 14 5 - 7 13 - 5

3 14 - 15 5 -7 7 -5 14 - 15 13 - 5 14

4 _ _ _ _ 14 15

Dropping order by pen - variation along the week

Feeding sequence

Dairies

20 17 24 8 18 5 15 7 26 9 1 13 23 4 22 12 3 11 16 21 25 2 14 19 10 6

Tim

e (

min

)

-100

-50

0

50

100

Dairies

20 17 24 8 18 5 15 7 26 9 1 13 23 4 22 12 3 11 16 21 25 2 14 19 10 6

Fre

qu

en

cy (

%)

0

5

10

15

20

25>60-90 min

>90-120 min

>120 min

Day-to-day variation on first

recipe delivery

Dairies

22 4 3 26 8 21 23 7 18 10 12 6 5 13 1 11 17 15 9 2 20 16 19 25 14 24

Tim

e (

h)

10

12

14

16

18

20

22

24

Time elapsed from the last

delivery

days

Lbs / c

ow

20

40

60

80

100

120

Pen 5

pen 7

pen 13

pen 14

pen 15

As fed (lbs) – cows /pen /day

Through our feeding management assessment, based on farm

observations and feeding management software records, we were able

to identify opportunities to improve the feeding process:

Dairy 1

- Green chop alfalfa management

- Re-evaluating the tolerance level assigned per ingredient

Dairy 2

- Training feeder

- Understand the implications of his work

- Arrive on-time every day

- Be more careful with the equipment

Take Home Message

1. Develop a feeding management assessment and

monitoring program that can be implemented on dairies.

Future work:

Conduct more feeding management assessments – “each dairy is a

new learning experience”

2. Establish benchmarks for the dairy industry based on

feeding management software data.

Future work:

We are finalizing the report from the initial 26 dairies.

Goals and Future Work

3. Evaluate the implications of feeding management

practices on milk yield.

Future work:

Examine if there is any association between the various

management practices described and milk yield. Pending funding.

Goals and Future Work

Alfonso Lago

Doug Degroff

Miguel Morales

Phil Jardon

Chel Moore

Tyler Colburn

Chris Dei

Aaron Highstreet

Enrique Schcolnik

Matt Budine

Alejandro Castillo

Diamond V

Valley Agriculture

Software

Thanks

UC Davis Center for Food Animal Health

Fundación Barrie de la Maza

Questions?

nsilvadelrio@ucdavis.edu

8 de julho 19:00

Toda segunda quarta-feira do mês

Randy Shaver – PhD – University of Wisconsin – Madison.

What we are able to apply at our farms to improve starch

utilization.

Focus on forage quality

Sua empresa pode ser parceira no próximo Webinar.

Ajude-nos a trazer aos nutricionistas Brasileiros o que

existe de mais novo em nutrição de vacas leiteiras no

mundo.

Marianna.correa@terra.com.br

11 - 999756429

Cadastre-se nos nossos meios de comunicação para

receber os slides em português e o Webinar

gravado:

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Excelente material para treinamento de equipes/grupos de estudos

0

20

40

60

80

100

120

16% Irá variarum pouco,

entre15.5% a16.5%

Dependeda

fazenda,variaçãopode ser

grande oupequena

Não sei,não

analisamosTMR

Nãoimporta, é

o quepodemos

fazer

Brasil

EUA

Argentina

Precisão TMR

0

20

40

60

80

100

120

Nãofazemos

MS

3 vezesna

semana

Tododia

Quandoabrimos

silo

PossodetectarMS comas mãos

Brasil

EUA

Argentina

MS e frequência

0

10

20

30

40

50

60

70

80

Forragemdepois

concentrado

Concentradodepois

forragem

Depende dotipo de vagão

Não temosordem

específica

Não temosvagão

Brasil

EUA

Argentina

Ordem ingredientes

0

10

20

30

40

50

60

70

Uma vez aoano

Duas vezes noano

Nãorealizamos

manutenção

Substituimospartes do

vagão quandonecessário

Nãopossuimosum vagão

Brasil

EUA

Argentina

Manutenção vagão