AOAC-RI ERP Book - FAOM METHOD.pdf

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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.

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

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)

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

3.0

Ash

7.7

Model outputs

ME (Mcal/d)

53.7

MP (g/d)

2,385

WSC = water-soluble carbohydrates; SP = soluble protein; ADICP

= acid detergent-insoluble CP; NDICP = neutral detergent-insoluble

CP; EE = ether extract.