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6348
HIGGS ET AL.
Journal of Dairy Science Vol. 98 No. 9, 2015
values in which equation [16] summed to 100% DM. The
optimization step was completed last in the calculation
process to fit the components within each feed together
within the described constraints. The process was dy-
namic in that the values calculated in the optimization
fed back into the matrix and regression calculations
described above. Typically, the optimizer had to be run
numerous times before it would converge and stabilize.
If insufficient data were available to perform any of
the calculation steps described above, current CNCPS
library values were retained. The approach was not ac-
ceptable for proprietary feeds due to a lack of robust
data of chemical components or the functional nature
of some ingredients beyond the nutrient content. For
example, products such as Met analogs are partially ab-
sorbed through the rumen wall and do not completely
flow to the small intestine, yet the supply of Met to
the animal or the conservation of the AA due to the
supplementation of the analog is documented (Chen
et al., 2011). Conventional chemical analysis does not
adequately estimate the true nutrient supply for these
types of feeds. Current library values were retained in
these circumstances. Approximately 75% of the feeds
in the feed library were updated and 25% remained
unchanged. Those remaining unchanged were primar-
ily commercial products, minerals, and vitamins, along
with unusual feeds with little information within the
databases.
AA
In addition to the chemical components described
above, each feed in the CNCPS feed library includes a
profile of the 10 essential AA. Amino acid profiles were
updated using data sets provided by Evonik Industries
AG (Hanau, Germany), Adisseo (Commentry, France),
and taken from the NRC (2001). Data provided were
mean values from analyses completed in the respec-
tive companies’ laboratories or published in the NRC
(2001). In all cases, AA analyses were completed on
the whole feed and are expressed in the CNCPS on a
percent CP basis. This differs from previous versions
of the CNCPS, where AA were expressed as a percent
of the buffer-insoluble residue (O’Connor et al., 1993).
The most appropriate profile was assigned based on
data availability and was used as received by the source
without alteration. If profiles for specific feeds were not
available in the data sets provided, current CNCPS val-
ues were retained. Proprietary feeds were not changed.
Model Sensitivity
The sensitivity of model outputs to variation in feed
library inputs was also evaluated. The analysis was
split into 2 parts. Part 1 looked at the likely range
in 6 major chemical components in the diet: (1) CP,
(2) starch, (3) NDF, (4) lignin, (5) ash, and (6) EE;
and 4 model outputs: (1) ME-allowable milk, (2) MP-
allowable milk, (3) MP from RUP, and (4) MP from
bacteria. To complete this part of the analysis, a refer-
ence diet was constructed in a spreadsheet version of
the CNCPS (Van Amburgh et al., 2013). The diet was
formulated using ingredients typically found in North
American dairy cattle rations and was balanced to
provide enough ME and MP for a mature, nonpreg-
nant, 600-kg cow in steady state (0 energy balance)
to produce 35 kg of milk containing 3.1% true protein
and 3.5% fat (Table 7). Probability density functions
were fit to chemical components within each feed in the
reference diet (Table 4) and correlated to each other
with Spearman rank order correlations (Table 5) using
@Risk version 5.7 (as previously described). Frequency
distributions for model outputs were then generated us-
ing a Monte Carlo simulation with 10,000 iterations to
describe the range of possible outcomes for each output
and the relative likelihood of occurrence.
Part 2 of the analysis investigated which feed library
inputs for the feeds in the reference diet had the most
influence on selected model outputs: (1) ME-allowable
milk, (2) MP-allowable milk, (3) MP from RUP, and
(4) MP from bacteria. The feed library inputs investi-
gated were those described in part 1 of the analysis, as
Table 7.
Diet ingredients, chemical composition, and model predicted
ME and MP for the reference diet used to analyze model sensitivity
Item
1
Unit
Diet ingredient (kg of DM)
Corn silage
4.76
Alfalfa silage
3.14
Grass hay
4.03
Corn grain ground fine
6.48
Soybean meal solvent extracted
2.58
Blood meal
0.20
Minerals and vitamins
0.50
Total DMI
21.69
Diet composition (% of DM unless stated)
CP
16.7
SP (% of CP)
35.3
ADICP (% of CP)
6.4
NDICP (% of CP)
15.6
WSC
3.5
Starch
29.0
NDF
31.8
Lignin (% of NDF)
11.5
EE
3.0
Ash
7.7
Model outputs
ME (Mcal/d)
53.7
MP (g/d)
2,385
1
WSC = water-soluble carbohydrates; SP = soluble protein; ADICP
= acid detergent-insoluble CP; NDICP = neutral detergent-insoluble
CP; EE = ether extract.