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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
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
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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