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JUN 2 1553::

WI

 

 

 

ECONOMIC VALUATION OF DISTILLERS DRIED
GRAINS WITH SOLUBLES, CORN
GLUTEN FEED AND CORN
GLUTEN MEAL

By

Steven Lewis Longabaugh

A THESIS

Submitted to
Michigan State University
in partia] fulfiilment of the requirements
for the degree

MASTER OF SCIENCE

Department of Agricultura] Economics

I982

ABSTRACT

ECONOMIC VALUATION 0F DISTILLERS DRIED GRAINS WITH
SOLUBLES. CORN GLUTEN FEED AND CORN GLUTEN MEAL

BY

Steven Lewis Longabaugh

Economic valuation of the feedstuff by-products of fuel ethanol and
corn sweetner production was investigated. Two approaches were taken.
Statistical methods were used to quantify historical price relationships
between fuel ethanol by-products and substitute feedstuffs. Nutritional
models,incorporating linear programming techniques were used to formulate
livestock and poultry diets containing ethanol by-products and to deduce
the "break-even“ prices at which the by-products would be competitive
with traditional feedstuffs. The approaches yield comparable results
for 0065 and CGM. CGF has been priced 10 percent lower than would have
been predicted based upon its average nutritional value; this is consis-
tent with the fact that the nutrient variability of CGF is higher than
that of 0065 and CGM.

Comparing these approaches indicated that historical price rela-
tionships are not expected to continue. Based on growing implementation
of the bypass protein nutritional concept, prices of these feeds will be

significantly higher in the future.

TABLE OF CONTENTS

Page
LIST OF TABLES ......................... V
LIST OF FIGURES ........................ xi
CHAPTER
I. INTRODUCTION ....................... l
l.l Problem Setting ................... l
1.2 Objectives of the Study ............... 3
l.3 Research Approach .................. 4
II. METHODOLOGY ...................... 5
2.l Plan for the Chapter ................ 5
2.2 Econometric Model .................. 6
2.3 Nutritional Model .................. 7
2.4 Regional Model ................... l3
2. Characterization of Existing Markets ........ 16
III. HISTORICAL VALUATION .................. 20
3.1 Introduction ..................... 20
3.2 Information Required for Long-Run
Investment Analysis ................. 2l
3.3 Analysis of Annual Relationships .......... 26
3.4 Short-Run Price Movement Estimation ......... 3l
3. Summary ....................... 48
IV. NUTRITIONAL VALUATION .................. 50
4.l Introduction .................... 50
4.2 Ruminants: Beef and Dairy Dietary Needs ...... 51
4.3 Beef ........................ 56
4.4 Dairy ........................ 84
4.5 Monogastrics: Swine and Poultry Dietary Needs . . . 85
4.6 Swine ........................ 87
4.7 Poultry ....................... 9l
4.8 Pets ........................ ll4
4.9 Summary ....................... ll7

Page

CHAPTER
V. DISTILLERS DRIED GRAINS NITH SOLUBLES MARKETING

CASE STUDY ........................ 120
5.1 Introduction .................... l20
5.2 Potential Users of DDGS ............... 120
5.3 Estimation of 0065 Price .............. l23
5.4 Use of Tools by Potential Customers ......... l45

VI. CONCLUSIONS ....................... l5l
6.] Findings ...................... 151
6.2 Research Needs ................... 157

APPENDIX ............................ 159

BIBLIOGRAPHY .......................... 155

iv

Table
2.2.l

2.3.1

0.100000.)
h-h#-D
#00“)

00000000
h-fi-b-b
oouasm

LIST OF TABLES

Commercial Feeds: Disappearance for Feed

(1000 tons) ......................

Example of Diets for a Feedlot Steer Fed to Gain

an Equivalent Amount of lbs/day ...........

European Community Prices and Variable Levies for

the Week of September l5, l98l ($/MT) ........

Statistical Relationship of DDGS Prices to Corn and

Soybean Meal Prices .................

Statistical Relationship of CGF and CGM Prices to

Corn and SBM Prices .................
Seasonality of U.S. Average Corn Price (¢/bu) . . . .
Seasonality of Toledo Corn Price (¢/bu) .......
Seasonality of Chicago Corn Price (¢/bu) .......

Seasonality of Michigan Corn Price Received by

Farmers (¢/bu) ....................
Seasonality of Buffalo Soybean Meal Price (S/ton) . . .
Seasonality of Chicago Soybean Meal Price (S/ton) . . .

Seasonality of Decatur Soybean Meal Price ($/ton) . . .

Seasonality of Boston Distillers Dried Grains with

Solubles Price (S/ton) ................

Seasonality of Buffalo Distillers Dried Grains with

Solubles Price ($/ton) ................

Seasonality of Chicago Distillers Dried Grains with

Solubles Price ($/ton) ................

Seasonality of Cincinnatti Distillers Dried Grains

with Solubles Price (S/ton) .............

V

10

I7

27

29
32
33
34

35
36
37
38

39

4O

41

42

Table
3.4.12

4.3.11

Seasonality of Buffalo Corn Gluten Meal Price

($/ton) ........................

Seasonality of Chicago Corn Gluten Meal Price
($lton) ........................

Seasonality of Buffalo Corn Gluten Feed Price

($/ton) ........................

Seasonality of Chicago Corn Gluten Feed Price

($/ton) ........................

Energy Levels and Upper Bounds on Microbial Protein
Synthesis for Corn-~Corn Silage Combinations ......

Nutrient Characteristics of Feedstuffs Used in Diet

Formulations (Dry Matter Basis)

Beef Linear Programming Matrix .............

Values of L to Achieve Desired Level of Probability
of Meeting Dietary Requirements ............

Beef Diets Formulated for a 475 lb Steer,
Mcal/lb, Using Differing Prices of DDGS . . . . 9 . . .

Beef Diets Formulated for a 475 lb Steer,
Mcal/lb Using Differing Prices of DDGS ..... 9 . . .

Beef Diets Formulated for a 600
Mcal/lb, Using Differing Prices

Beef Diets Formulated for a 600
Mcal/lb, Using Differing Prices

Beef Diets Formulated for a 600
Mcal/lb, Using Differing Prices

Beef Diets Formulated for a 850
Mcal/lb, Using Differing Prices

Beef Diets Formulated for a 850
Mcal/lb, Using Differing Prices

Beef Diets Formulated for a 475
Mcal/lb, Using Differing Prices

Beef Diets Formulated for a 475
Mcal/lb, Using Differing Prices

vi

lb
of

lb
of

lb
of

lb
of

Steer,

.49

.53

.49

NE

NE

NE

DDGS . . . . 9 . . .

Steer, .

DDGS . . . . 9 . . .

45

46

56

57
59

64

65

65

66

66

67

67

68

68

69

Table Page
4.3.12 Beef Diets Formulated for a 600 lb Steer, .49 NE

Mcal/lb, Using Differing Prices of CGM ..... g. . . 69
4.3.13 Beef Diets Formulated for a 600 lb Steer, .53 NE

Mcal/lb, Using Differing Prices of CGM ..... 9. . . 70
4.3.14 Beef Diets Formulated for a 600 lb Steer, .63 NE

Mcal/lb, Using Differing Prices of CGM ..... g. . . 70
4.3.15 Beef Diets Formulated for a 850 lb Steer, .53 NE

Mcal/lb, Using Differing Prices of CGM ..... 9. . . 71
4.3.16 Beef Diets Fonmulated for a 850 lb Steer, .63 NE

Mcal/lb, Using Differing Prices of can ..... 9. . . 71
4.3.17 Beef Diets Formulated for a 475 lb Steer, .49 NE

Mcal/lb, Using Differing Prices of CGF ..... g. . . 72
4.3.18 Beef Diets Formulated for a 475 1b Steer, .53 NE

Mcal/lb, Using Differing Prices of CGF ..... g. . . 72
4.3.19 Beef Diets Formulated for a 600 lb Steer, .49 NE

Mcal/lb, Using Differing Prices of CGF ..... 9. . . 73
4.3.20 Beef Diets Formulated for a 600 1b Steer, .53 NE

Mcal/lb, Using Differing Prices of CGF ..... g. . . 73
4.3.21 Beef Diets Formulated for a 600 lb Steer, .63 NE

Mcal/lb, Using Differing Prices of CGF ..... 9. . . 74
4.3.22 Beef Diets Formulated for a 850 1b Steer, .53 NE

Mcal/lb, Using Differing Prices of CGF ..... 9. . . 74
4.3.23 Beef Diets Formulated for a 850 1b Steer, .63 NE

Mcal/lb, Using Differing Prices ......... g. . . 75
4.3.24 Relative Value of DDGS in Beef Diets ......... 75
4.3.25 Relative Value of CGM in Beef Diets .......... 77
4.3.26 Relative Value of CGF in Beef Diets .......... 78
4.3.27 Economic Value of 0063 in Beef Diets Given Prices

of Alternative Feedstuffs (All Values on Dry
Matter Basis) ..................... 31

4.3.28 Economic Value of CGM in Beef Diets Given Prices

of Alternative Feedstuffs (All Values on a
Dry Matter Basis) ................... 82

vii

Table

4.3.29

h-D-h-b
OSQO‘OI

.10

.11

.12

.13

.14
.15
.16
.17

Economic Value of CGF in Beef Diets Given Prices
of Alternative Feedstuffs (A11 Values on Dry

Matter Basis) .....................
Swine Linear Programming Matrix ............
Swine Diets for 22 to 44 1b Growing Swine Using
Differing Prices of DDGS ...............
Swine Diets for 44 to 77 lb Growing Swine Using
Differing Prices of DDGS ...............
Swine Diets for 77 to 132 1b Growing Swine Using
Differing Prices of DDGS ...............
Swine Diets for 132 to 220 1b Finishing Swine Using
Differing Prices of DDGS ...............
Swine Diets for 22 to 44 1b Growing Swine Using
Differing Prices of CGM ................
Swine Diets for 44 to 77 1b Growing Swine Using
Differing Prices of CGM ................
Swine Diets for 77 to 132 lb Growing Swine Using
Differing Prices of CGM ................
Swine Diets for 132 to 220 lb Finishing Swine Using
Differing Prices of CGM ................
Swine Diets for 22 to 44 lb Growing Swine Using
Differing Prices of CGF ................
Swine Diets for 44 to 77 lb Growing Swine Using
Differing Prices of CGF ................
Swine Diets for 77 to 132 lb Growing Swine Using
Differing Prices of CGF ................
Swine Diets for 132 to‘220 lb Finishing Swine Using
Differing Prices of CGF ................
Relative Value of DDGS in Swine Diets .........
Relative Value of CGM in Swine Diets .........
Relative Value of CGF in Swine Diets .........

Economic Value of DDGS in Swine Diets Given Prices
of Alternative Feedstuffs (A11 Values on Dry

Matter Basis) .....................

Page

83

89

9O

92

93

94

95

95

96

96

97

97

98

98
99
100
101

102

Table ' Page

4.6.18 Economic Value of CGM in Swine Diets Given Prices
of Alternative Feedstuffs (All Values on Dry
Matter Basis) ..................... 103

4.6.19 Economic Value of CGF in Swine Diets Given Prices
of Alternative Feedstuffs (All Values on Dry

Matter Basis) ..................... 104
4.7.1 Poultry Linear Programming Matrix ........... 106
4.7.2 Poultry Diets Formulated for a Starter Broiler

Using Differing Prices of DDGS ............ 107
4.7.3 Poultry Diets Formulated for a Finisher Broiler

Using Differing Prices of DDGS ............ 108
4.7.4 Poultry Diets Formulated for a Layer Using

Differing Prices of DDGS ............... 109
4.7.5 Poultry Diets Formulated for a Starter Broiler

Using Differing Prices of CGM ............. 110
4.7.6 Poultry Diets Formulated for a Finisher Broiler

Using Differing Prices of CGM ............ 111
4.7.7 Poultry Diets Formulated for a Layer

Using Differing Prices of CGM ............ 112
4.7.8 Relative Value of DDGS in Poultry Diets ....... 113
4.7.9 Relative Value of CGM in Poultry Diets ........ 115

4.7.10 Economic Value of DDGS and CGM in Poultry Diets
Given Prices of Alternative Feedstuffs (All
Values on Dry Matter Basis) ............. 115

4.9.1 Economic Value of Ethanol By-Products for
Ruminants and Monogastrics (All Values on
Dry Matter Basis) .................. 118

5.3.1 Transportaion Rate for Soybean Meal from Central
Michigan, Decatur, Illinois; Fort Wayne,
Indiana; and Fortoria, Ohio to Six Points in
New England and for DDGS from Central Michigan
to Six Points in New England ............. 125

5.3.2 Number of Animals in the Primary Market Area ..... 125

5.3.3 Expected Dry Matter Intake for Average Frame
Size Feedlot Beef Animals (lb/day) .......... 128

ix

Table

5.
5.

6.

A1

3.4
3.5

.3.6

.3.7

.3.8

.3.9

.3.10

.3.11

.3.12

.3.13

9.1

Projected Gains for Steers on Feed ..........

Computed Dry Matter Intakes, Projected Gains

and Days in Growing Phase for Fed Beef ........

Estimated Percentage of Feedlot Beef Cattle on .46,

.49, .53 or .63 NEg Mcal/lb Diets in Michigan

Feeding Programs Derived from Estimated Percentages

of Feedlot Beef Cattle on Different Diets ......

Number and Proportion of Days Spent in Each

Height Class .....................

Number of Fed Beef Animals in Michigan Primary
Market Divided into Feeding Program and Height

Group (1,000 head) ..................

Number of Fed Beef Animals in the Northeast U.S.
Market Divided into Feeding Program and Weight

Group (1,000 hd) ...................

Number of Fed Beef in the Northeast and Michigan

Primary Market Area (1,000 hd) ............

Percentage and Quantity of DDGS Used in Fed Beef
Depending on Weight Group, Energy Level of Diet

and DDGS:SBM Price Ratio ...............

Demand for DDGS (tons) in the Primary Market with

a DDGS:SBM Price Ratio of 1:1 ............

Economic Value of Ethanol By-Products for Ruminants

and Monogastrics (A11 Values on Dry Matter Basis). . .

Impact of Safety Factors for Net Protein Content
of DDGS and CGM on the Compostion of Diets for

a 475 lb .53 NEg Meal/1b Steer Diet .........

Page

 

129

130

133

133

134

135

137

138

139

142

153

163

Figure
3.2.1

3.2.2

3.2.3

4.2.1

LIST OF FIGURES

Relationship of the Price of DDGS to the Price of
Feedstock (Corn) and the Substitute (SBM) ......

Relationship of the Price of CGM to the Price of
Feedstock (Corn) and the Substitute (SBM) ......

Relationship of the Price of CGF to the Price of
Feedstock (Corn) and the Substitute (SBM) ......

Schematic Illustration of Nitrogen Utilization by
the Ruminant (Satter et a1. 1977) ..........

xi

Page

 

22

23

24

52

CHAPTER ONE
INTRODUCTION

1.1 Problem Setting

 

Rising real liquid fuel prices reduced supplies and the increasing
supply vulnerability have stimulated interest in alternative liquid
fuels. Ethanol (ethyl alcohol), used primarily in alcoholic beverages,
is an alternative receiving attention. The economic contribution of
the feedstuff by-products of ethanol will be an important determinant
of the economic viability of fuel ethanol production.

Fermentation and wet milling are the processes used to derive
ethanol from grain. Distillers dried grains with solubles (DDGS) is
the by-product of fermentation and corn gluten feed (CGF) and corn
gluten meal (CGM) are the by-products of wet milling. Corn starch is
converted to ethanol and carbon dioxide while the remaining nutrients
(protein, fat, minerals, and vitamins) undergo a three-fold concentra-
tion (P005 and Klopfenstein, 1979).

The steps followed in the production of by-products from grain
"fermentation" residues are as follows: the grain is ground, mixed
with water, cooked and cooled. Ground barley malt is added to convert
corn starch to sugar. Yeast is added, causing the mash to ferment.
After fermentation, the fermented mash is distilled to remove the alco-

hol.

The wet residue, containing yeast and the original grain's nutri-
ents minus starch, is then processed. Fibrous material is separated
from soluble nutrients in the liquid and dried, yielding distillers
dried grains (006).

The soluble residue portion is concentrated by evaporation to a
heavy syrup, which is either added to the fibrous grain material before
drying, thereby producing DDGS, or dried separately to produce distill-
ers dried solubles (DDS). When one bushel of corn is fermented, the
primary products are ethanol (2.6 gallons), distillers dried grains
(17 1b) and carbon dioxide (16.35 lb) (Reilly, 1979).

The parts of the corn kernel (hull, germ, soft starch and hard
starch) are separated before ethanol is produced under the "wet milling"
technique. The steps followed in the production of by-products are as
follows. Corn is steeped in tanks of warm water to soften the kernel
and loosen the hulls. The steepwater, containing soluble minerals and
proteins, is then removed leaving the softened kernels which are run
through a degerminating mill to free the germ from the kernel. Drying
the germ and applying heat and pressure removes the oil resulting in
two by-products, corn oil and corn oil meal (Corn Industries Research
Foundation, 1949).

The remaining portions of the kernel (starch, gluten and hulls) are
separated. The hulls go into CGF, while the starch is left as is or
converted to dextrin, syrup, sugar or ethanol. Gluten becomes CGM or
is combined with hulls to raise the crude protein concentration of CGF
to at least 21%. The steepwater removed during the first step is con-
centrated by evaporation, and is sometimes combined with CGF (Corn

Industries Research Foundation, 1949). The by-product yield of this

process is relatively constant at 3 pounds of CGM per bushel of corn
and approximately 10.5 pounds of CGF, depending on specific technique
used.

Product characteristics and competitive price relationships be-
tween DDGS, CGF and CGM and their substitutes need to be understood to
develop a marketing strategy for these by-products. The clientele
groups considered in the development of marketing strategies are farmers,
feed salesmen, feed manufacturers, brokers and fuel ethanol plant
managers. Good information contributes to good marketing. The informa-
tion contained in this study should improve the performance of the

ethanol feedstuff by-product markets.

1.2 Objectives of the Study

The objectives are:

1. To determine historical price relationships of ethanol feed-
stuff by-products relative to substitute feedstuffs, particu-
larly corn and soybean meal, and to evaluate these relation-
ships relative to those based upon nutritional values;

2. To characterize existing ethanol feedstuff by-products market-
ing channels;

3. To identify "new" markets for ethanol feedstuff by-products;

4. To estimate the percentage of beef, dairy, swine and poultry
diets which would be DDGS, CGF and CGM at alternative price
relationships between the by-products and competing feedstuffs;

5. To illustrate how each client group can use these findings.

1.3 Research Approach

 

The study of history is an important step in understanding the

workings of tomorrow. With this in mind, future price relationships

will be predicted based upon historical time series relationships bet-
ween DDGS, CGF CGM and relevant explanatory variables. Equations will
be statistically estimated to quantify these relationships.

History is an important source of information but may inadequately
reflect the future. Thus, nutritional models will be developed to pro-
vide another perspective from which to evaluate these by-products.
Linear programming techniques will be used to formulate diets for
specified biological performance criteria. Competitive price relation-

ships between the by-products and substitute feeds will be obtained.

A comparison of these approaches will indicate if DDGS, CGF and
CGM have been priced based on known nutritional characteristics. Agree-
ment in the parameters of the statistical and nutritional models implies
the by-products have been priced rationally from a nutritional point of
view. If this is the case, the market price would be expected to adjust
as new understanding of nutritionally properties is gained. If the
parameters of the models are not similar, then an understanding of the
reason for the discrepancy will be sought. This would indicate that

pricing is determined by criteria over the nutritional characteristics.

CHAPTER TWO
METHODOLOGY

2.1 Plan for the Chapter

 

The theory underlying the investigations conducted to understand
the determinants of the prices of DDGS, CGF and CGM is outlined in this
chapter. First, the econometric methodology used in estimating his-
torical price relationships will be discussed. Second, a nutritional
model will be examined. Third, a brief overview of transportation
costs will indicate how an individual plant can identify the geographic
market in which it can competitively trade. And, finally, existing

markets will be characterized.

2.2 Econometric Model

 

The pricing of DDGS, CGF and CGM must be examined in the context
of the medium and high protein feedstuff market. Historically, DDGS,
CGF and CGM made up 7-9% of the domestic market. Soybean meal accounted
for nearly 75%. Other feeds in the medium and high protein feedstuff
market include cottonseed meal, fishmeal, the by-products of the live-
stock and poultry slaughter industry and brewers grain (Table 2.2.1.).

The production of DDGS, CGF and CGM is exogeneous determined pri-
marily by the production of ethanol,corn starch and sweeteners. Their
price is determined by the prices of the substitute feedstuffs soybean
meal (SBM) and corn. Econometric models of corn and SBM supply and
demand are of interest, but are beyond the scope of this study.
Christensen (1979) and Nailes (1982) modeled the SBM and feed grain

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markets respectively. Researchers at Michigan State University have
integrated these two models into a single model which simulates the

interactions of the national and international agricultural sectors

(Mitchell and Ross, 1981).

Reduced forms of components of the Michigan State University
Agricultural model will be used to estimate by-product price relation-
ships. The reduced form of a model requires the dependent variable
(by-product price) to be expressed in terms of predetermined variables
and the disturbance of the system (HU, 1973). In this case, DDGS, CGF
and CGM prices are derived from corn and SBM price. Corn and SBM prices
are predetermined variables (in relation to these by-product feeds) so
that the use of reduced forms is acceptable in estimating DDGS, CGF and
CGM price. This procedure (reduced forms) will yield coefficients for
the prices of corn and SBM showing their influence on by-product price
which can be compared to results derived from the nutritional model.
This will allow the comparison of the historical and nutritional ap-

proaches.

2.3 Nutritional Model

 

A second approach to discovering the potential economic value of
DDGS, CFG and CGM is use of a nutritional model. The essence of this
approach is determining the economic value of the by-products by compar-
ing diets containing one of the by-products with "reference diets" which
contain feedstuffs typically fed (e.g. corn, SBM, dicalcium phosphate
and limestone). The economic value of the by-product is the price at
which the total cost per unit of performance resulting from feeding

the "alternative diet" equals that of the reference diet.

Total cost of reference diet = Total cost of alternative diet

To define the formula for economic value assume the following:

c]alti = competitive price of by-product in ith

th

alternative diet;

c. = price of the j

J
alti = ith alternative diet;

ingredient;

xj = fraction of jth ingredient in the diet;
x] = fraction of the by-product of interest (e.g. DDGS) in the
diet; and
N = number of feeds fed
The total cost of the reference diet is the sum of the cost of each
ingredient:

c. (x.FEf),

Total cost reference diet = J J
2

J'

IIMZ

Similarly, the total cost of the alternative diet is the sum of the

cost of each ingredient including the by-product:

N
Total cost alternative diet ? 2 c. (x.a1ti) + c1a1ti xlalti.

j=23 .1
Substituting into the first equation and solving, the competitive price

of the by-product in the ith alternative is found:

N

N
X c. (xref) = z c. (xq1ti) + c?1ti x?]ti
j=2 J J j=2 J J
N N
c?]ti xfi1ti = Z c.(xref) - 2 cj(x.a]ti)
j=2 J J j=2 J
calti xalti = g c.(xref _ xa]ti)
1 1 j=23j .1

 

N
2 cj(xgef - x31ti)
calti = i=2
1 alt.
x1 1

The economic value of the by-product feed in the ith alternative diet is
c1a1ti. This is a general approach with the relevant feedstuffs used
in the reference diet being available in the local market. In this
study, corn grain, soybean meal, dicalcium phosphate and limestone will
be the feedstuffs used in monogastric reference diets and corn grain,
corn silage, soybean meal, dicalcium phosphate and limestone will be
used in ruminant reference diets. Some of these feedstuffs could be
replaced in specific localities where different alternatives are avail-
able. For example, within a redius of a brewery, high moisture brewers
grain might be the prevailing protein supplement for ruminants instead
of SBM, in which case the reference diet for ruminants would contain
brewers grain.

As the by-products enter alternative diets, they are initially at
small percentages and at a relatively high economic value that exploits
their most favorable nutrient characteristics. As more and more of
the by-product is used, its value is based on characteristics that are
less favorable. There is an inverse relationship between economic

value and percent of by-product in the diet.

To illustrate the procedure, consider Table 2.3.1.

10

Table 2.3.1. Example of Diets for a Feed Lot Steer Fed to Gain an
Equivalent Amount of lbs/day

 

Feedstuff D‘et gflfii
Reference Alt.l Alt2

 

 

- - proportion of the diet - -

Corn silage .551 .548 .546 3.96
Corn .313 .322 .183 7.44
SBM .127 - - 14.15
DDGS - .111 .265 cla‘ti
Urea .001 .011 - 12.25
Limestone .002 .002 .003 4.00
Dicalcium phosphate .007 .005 .003 15.80

 

The price of DDGS for alternative diet one to be competitive with
the reference diet is:

3.96(.551-.548) + 7.44(.313-.322) + 14.15(.127-O) +

c alti =12.25(.001-.011) + 4.0(.002-.00§)+ 15.80(.007-.005)
1 .111

= $14.86/cwt

It is of interest to know how a price change of a feedstuff in the

reference diet changes the competitive price of DDGS (PDDGS). This is

 

given by:
- (xiref _ xjaIti)
A PDDGs ' x alti (A feedstuff price)

1
where A = change.

To illustrate, consider SBM in alternative 1:

_ .127-o =
A PDDGs ‘ ‘TTTT"(APSBM) 1:144 (APSBM)

For a $1.00 change in PSMB’ there will be a $1.144 change in PDDGS'

11

The impact of corn price on PDDGS is more complicated. We want
to know the effect a change in corn price has on PDDGS by silage and
grain. To do this, the price of corn silage needs to be known. Work
done by Woody and Black (1978) found that growers are indifferent bet-
ween harvesting corn as grain or silage when the relationship between
silage and grain price is:

$/ton corn silage (32% dry matter) = 6 ($/bu corn grain [85%

dry matter]).

Converting to $/cwt dry matter,the equation is:

$/cwt corn silage = .446 ($/cwt corn grain) + .625

so that the effect of a change in PCORN on PDDGS is.

 

 

ref . alt. .
x s1lage - x 1 s1lage
x alti ('446 A PCORN) +
l
XFEf grain — xaAtigrain (A P )
alt. CORN
x1 1
From our previous example:
_ .551-.548 .313-.322
A PDDGS ‘ —Ti—’. 1 ('446 PCORN) + —_1T1—. (A PCORN)
= -.068 A PCORN

The price of DDGS and corn are inversely related in this case. The
same procedure can be applied to other feeds in the diet to find their
impact on DDGS price. An equation explaining PDDGS in the first alter-

native diet is:

.018 PDICAL
This procedure can be applied to other alternatives and diets.

Equations can be found deriving the competitive price of DDGS based on

12

proportion and prices of feedstuffs used in the reference and alterna-
tive. These equations can be used to determine PDDGS in any diet formu-
lated with appropriate prices for the competing feedstuffs.

This previous discussion assumed that daily gain and feed/gain are
comparable for the reference and the alternative diets. This may not
be the case. The objective is the same as in the previous case; namely,
to maintain equal total cost per unit of performance, where

TC

FC + NFC

_ feed rice/cwt daily nonfeed cost
’Gain) ( diet ) +( dafly ga1n )

If feed/gain differs, but not daily gain,

 

2

2 ref alt.
1ti = 5‘2 c3j (x. - Lx. 1)

 

a
C

h

where L is the ratio of the feed/gain of the it alternative diet to

that of the reference diet. If feed/gain and daily gain both differ,

N

2 ref alt.
lti . i=2 °I (xi 1t ij ‘) +
a .
in 1

 

a
C1

(nonfeed cost/dayFEf - nonfeed cost/dayaAti)

feed“.ef alt.
L (gain ‘) xi 1

The estimated economic value of DDGS, based on the nutritional model,
depends on other feedstuff prices and the proportion of DDGS used in

the diet.

13

2.4 Regional Model

 

By-Product Form

A question frequently asked about fuel ethanol by-products is the
form (dry or wet) in which they should be marketed. This decision in-
volves the trade off between lost and gained income. For our purposes,
we will examine this decicion with the objective of developing a
decision making framework, not definitive answers.

The ideal dry matter content of wet by-product appears to be
25-30 percent (Waller §t_gl, 1981a). Drier matieral is too light and
fluffy, enabling oxygen penetration and more spoilage. With wetter
material, seepage or runoff occurs.

Managers contemplating marketing wet by-products find themselves
at one of two decision points: 1) evaluating the initial decision of
what by-product marketing scheme to employ at a proposed plant or
2) determining if a switch should be made from a dry by-product market-
ing system to one that includes the wet by-product. The decision of
which by-product to market from a proposed plant will be illustrated
using a partial budgeting approach.

Income loss associated with adding the wet by-product alternative
is 1) equipment cost to handle the wet by-product, 2) increased trans-
portation cost per ton of dry matter and 3) loss of sales of the dry
by-product. Income gained results from 1) decreased drying expense;
2) savings of not buying drying and dry feed handling facitities. and
3) sales of the wet by-product. These income losses and gains form a
flow over the life of the investment. In order to correctly evaluate
the decision this stream of losses and gains needs to be converted to

net present value by discounting to what it is worth today. For example,

14

$1 received in the future is not worth the same value as $1 received
today. To find its value today a discount factor is used. The general

form of this factor is:

1
1+1)"

where: i discount rate

n number of years in the future
Assuming the discount rate (frequently the interest rate) is 14% and the

$1 is received 5 years in the future, the present value of the $1 is:

1

$1 X m5 = $.519

By finding the present value of all income gains and losses and summing
over the life of the investment, the net present value indicating the
net advantage (or disadvantage) of selling wet by-products, is foundl/.
A sublety that should be mentioned is the price received for the by-
product. One of the advantages of the dry by-product is that it can
be stored and sold at times of higher price so that the average price
received per ton of dry matter might be higher for the dry by-product due
to better marketing capabilities. Possibly a greater advantage in
Michigan is the flexibility the dry by-product has in going to a wider
variety of potential demanders. These are included in the budget by
adjusting the income resulting from sale of the byeproducts to reflect
price difference and increasing flexibility.

If management determines that the wet by-product should be marketed,

it is of interest to know its potential price range. The upper bound on

 

1/For further information on discount factors the reader is referred to
Nelson £5 £1. 1973.

15

price is based on the assumption that the price of the wet by-product
cannot exceed that of the dry by-products on a dry matter basis. At
the lower bound the savings of marketing the wet by-product is negated
either by low price or increased costs such as transportation.

The upper bound on the wet by-product price can be found by using

the following equation:

 

:=% dry matter in wet bysproduct *
Upper Bound % dry matter in dry by-product

$/ton dry by-product
This represents an equal pricing of the wet and dry by-product on a dry
matter basis. The lower bound represents the price at which there is
no advantage of marketing the by-product in dry or wet form. The
equation finding the lower bound is:

Lower = Upper _ Savings/ton of * % dry matter in wet by-product
Bound Bound dry by-product % dry matter in dry by-product

 

The savings/ton of dry by-product represents the net savings of market-
ing the wet by-product found by the partial budget.

A third question is how far the wet by-product can be shipped
before savings are negated by transportation costs. The general form

of this equation is:

 

 

Trangagrgggion = Savingslton dry bysproduct * % dry matter in wet by-product
Dgstance $/ton/mile %"ary matter in dry by-product

To illustrate the use of these three equations, assume the following:
25%
90%

% dry matter in wet by-product

% dry matter in dry by-product
$/ton dry by-product = $190

Savings/ton of dry by-product = $30
$/ton/mile = $.10 (trucking expense)

16

Substituting into the equations:

Upper Bound §%-* 5 190 = $52.78

Lower Bound $52.78 - $10 * gg-= 550.00

T Maximum. - $30 * 25 _ .
ransportat1on - $_TO' 90" 83.4 m11es
Distance '

This framework will not be expanded further, but is developed
to provide an approach for decision making which an individual can

adjust to their unique circumstances.

Geographic Markets

 

Areas deficient in high-protein feed are potential geographic markets.
Net imports in an area are determined by the difference between total
demand and supply of protein feed. Total demand is a function of the
number of milk cows, milk produced per cow, beef cows, cattle on feed,
pig crop, lamb crop, and egg, broiler, and turkey production. Total
high-protein supply is a function of alfalfa and soybean meal production.
The Northeastern U.S. has typically been a net demander of high-protein
feed while other potential demanders of Michigan production include
Ontario, the east-central and northeast section of Wisconsin and the

Western European market.

2/

2.5 Characterization of Existing Markets-

 

DDGS. CGF and CGM have traditionally been used in specialty markets

due to characteristics such as pellet-binding properties and unidentified

 

g/Discussions with Dr. John Waller, Michigan State University extension
feedlot beef specialist, contributed heavily to this market characteriza-
tion.

17

performance factors. Because of these and other reasons, swine and
poultry have been the largest consumers of DDGS and CGM, up to 5% of
their diet. Smaller quantities have been used in ruminant diets, but
usage is increasing especially in the New England dairy market because

of interest in bypass proteins. It is estimated that 30% of DDGS and
60-70% of CGF and CGM move into the international markets (U.S.D.A. 1981).
The effect of variable levies in the European community (EC) is especially
pronounced on the price of corn entering the EC and, as a result, on
exports of CGF from the U.S.}! For example, in September 1981 the
variable levy on corn increased price to European feed manufacturers

from the import price of $130/MT5/ to an EC price of $216/MT, a levy of
$86/MT. No variable levy is applied to DDGS, CGF, CGM or SBM (Mitchell
gt g1. 1981). This means that the corn:SBM price ratio changes from

130:234 or 1:1.8 in the U.S. to 2172234 or 1:1.08 in the EC (Table 2.5.1).

Table 2.5.1. European Community Prices and Variable Levies for the
week of September 15, 1981 ($/MT)§/

 

 

Commodity Import Price Variable Levy Total Price
Corn 130.00 ' 86.70 215.70
Soybean Meal 234.00 0 234.00

 

g/Cargo, Insurance and Freight to Rotterdam.

 

glFor a further discussion on variable levies the reader is referred
to Sorenson, 1975.

A/Metric ton.

18

Thus, the net effect is that CGF moves into the EC as a corn substitute
and is used in diets as an energy source. The relative value of CGF to
SBM will differ in the EC from in the U.S. The other by-products, DDGS
and CGM,compete with SBM as protein supplements. Their price relative
to SBM in the EC will be similar to that in the U.S.

Increased interest of the feed industry in bypass protein is
reflected in a recent advertisement by Farmland Industries using the
basic ideas that will be discussed in Chapter Four. The advertisement
reads: “Conventional all-natural protein supplements containing oilmeal
protein such as soybean meal and cottonseed meal contain protein which
is readily degradable in the rumen. The ammonia produced by the process
is utilized by the rumen bacteria. Degradable oilmeal protein is an
expensive source of ammonia for the rummen bacteria... Instead of
using expensive rapidly degraded oilmeal proteins, CoPass ®Beef Feeds
_use a combination of slowly degraded proteins and urea. More of the
slowly degraded proteins bypass the rumen and are digested in the
intestine. The urea then replaces the rapidly degraded natural protein.
The result is the same amount of bacterial and bypass protein in the
small intestine as with an all-natural oilmeat supplement, but at a
lower cost..." The market is aware of the bypass protein concept and is
adapting to it.

Production of by-product feeds using corn as a feedstock is centered
in and around the corn belt. Though New England has several family
distilleries that have been in operation for 200 years, they are considered
insignificant participants in the by-product market. Potential ethanol
plant openings include four in Michigan and one each at London, Ontario;

Waterloo, Iowa; and southern Ohio however, as a result of eroding govern-

l9

ment price support of gasohol, some of these openings are questionable.
Marketing schemes vary among companies. Some sell the product wet, one
through a subsidiary feed company, while most trade through brokers
dealing in several by-products.

Beverage plant operations intensify from fall to February. Dry by-
products are stored and marketed throughout the year (storage must be
monitored closely because of the by-products' ability to absorb moisture
and their high palatability to rats). Annual contracts with by-product
price tied to soybean meal or a combination of corn and soybean meal
prices are frequently used among industry participants. The pricing
structure is established by using least cost livestock diets to determine
the by-products' value based on a specific dietary useage, for example,
as a crude protein supplement. When used in specialty diets, a price
bonus is added. Price influences movement from storage but not production.
Because of cheaper feedstock cost and the sensitivity of fermentation to
high summer temperatures the bulk of production occurs during a four to
five month period from late fall to early spring while marketing continues
throughout the year.

.Michigan's position on the fringe of the corn belt gives it the
opportunity to penetrate unique market areas. Forty percent of the U.S.
dairy cow population is estimated to be within potential marketing dis-
tance. Potential market areas include Michigan, eastern Wisconsin,
Ontario and the northeast United States. Little movement of the by-
products has or will be made south of Michigan or west of Chicago due to

existing plant locations and rail bottlenecks in the Chicago area.

CHAPTER THREE
HISTORICAL VALUATION

3.1 Introduction

 

Historical pricing of DDGS, CGF and CGM is the focus of this Chap-
ter. The results of this analysis will be compared to the nutritional
analysis found in Chapter Four to determine if the nutritional valuation
is consistent with the historical. Because by-products from the bever-
age alcohol and wet milling industries have been used by feed manufac-
turers for a number of years, there is a relatively long time series
of price information.

The objective of this chapter is to define the historical price
relationships of DDGS, CGF and CGM with the major feedstock (corn) and
competitor (SBM). Three time frames will be examined: long-run,
annual and short-run (based upon information needs for decision making).
First, long-run price relationships of corn and SBM to the by-products
is important for investment analysis purposes, prior to plant construc-
tion. Ratios of these prices and their variability over time will indi-
cate how much of the feedstock cost can be expected to be covered by
sales of the by-product feeds. Second, analysis of average annual
price relationships will give valuable information for developing a
marketing strategy and estimation of probable cash flows. Crop year

average prices of the by-products will be forecasted as a function of

20

21

corn and SBM price. Third, forecasts of short-run price movements of
the by-products are important in pricing and inventory decision making.
These forecasts may be obtained by seasonal relationships, moving

averages, time series forecasting models and "feel for the market."

3.2 Information Required for LongeRun Investment Analysis

DDGS’ A Significant downtrend in DDGS price relative to SBM was re-
ported over the past twenty years while variability has sharply increased
during the 1970‘s reflecting the volatile commodity markets of the per-
iod (Figure 3.2.1). DDGS price relative to SBM declined from a ratio of
.90 in the early 1960's to recent values of .65 to .75. Also shown in
Figure 3.2.1 is a comparison of DDGS and corn price. Although not show-
ing a decline like the DDGS/SBM ratio, it did exhibit a similar increase
in variability. For investment purposes, a reasonable estimate of DDGS
price to its feedstock (corn) is .53.

§§M_to SBM and corn price ratios (Figure 3.2.2) indicate the higher
value of CGM relative to DDGS. CGM price relative to SBM declined from
a ratio of 1.60 to 1.70 in the mid to late 1960's to a recent value of
1.30 to 1.35. Although not showing as strong a decline as CGM/SBM
ratio the CGM/corn price ratio did exhibit a similar increase in vari-
ability. A reasonable figure to use for CGM/corn price for investment
purposes is .98.

C§§_to SBM and corn price ratios (Figure 3.2.3) indicate the lower
value of this by-product relative to the other by-products. Neither
CGF/SBM or CGF/corn price ratios had the steady downward trend, as did
the DDGS and CGM ratios. But the increased variability of the 1970's

22

 

 

 

 

 

 

 

 

 

 

 

 

 

1.00 41(
$/ton DDGS
.90- $7ton SBM
.80 -.
.70 -.
$/ton DDGS
.60~_- S/lOO bu Corn
.50
.40 L— U
.30 J I I
1960 1965 1970 1975 1980

Figure 3.2.1. Relationship of the Price of DDGS to
the Price of Feedstock (Corn) and the Substitute (SBM)

23

 

 

 

 

 

 

 

 

 

 

 

2.00
1.80 _.
$/ton CGM
S/ton SBM
1.50 - K1
1.40 p—.
. $/ton CGM
1.20 _ $/100 bu Corn
1.00 .—
.80 --
.50 I I I
1960 1965 1970 1975 1980

Figure 3.2.2. Relationship of the Price of CGM to the
Price of Feedstock (Corn) and the Substitute (SBM)

 

 

 

 

 

 

 

 

 

 

.80
$/ton CGF
.70 --. 1UH§§FT§§1
.60
$/ton CGF
.50 ~— S/TOO bu Corn
.40 h.-
.30 —
~20 L I l I I
1960 1965 1970 1975 1980

Figure 3.2.3. Relationship of the Price of CGF to
the Price of Feedstock (Corn) and the Substitute (SBM)

25

was observed. A reasonable estimate of CGF price to the feedstock
corn is .39.

In all three sets of price ratios, the 1972/73 crop year showed
substantial deviation from normal relationships because of a sharp in-
crease in SBM price. This was due to external factors occurring in
the high protein market. During that year there was a substantial de-
crease in anchovy catch and a shortfall in soybean production in other
countries. The net result was a nearly 400% increase in the price of
SBM due to increased international demand for high protein feeds. The
1972/73 crop year is therefore considered an outlier and is frequently
excluded from historical price analysis since it is considered a one-
time event.

A better understanding of DDGS and CGM's nutritional characteris-
tics is thought to be the reason for their downward trend in price rela-
tive to SBM. Their price value has been based on crude protein and un-
identified performance factors. Research has been progressively iden-
tifying these factors, enabling other feed sources to meet those needs
more cheaply, thereby reducing their value. A case illustrating this
is a feeding trial in which the enhanced performance response from feed-
ing DDGS was significantly larger than expected, based on known nutri-
tional characteristics. This unidentified performance factor was later
found to be due to a trace mineral deficiency which was being met, not
by DDGS, but by the metal tubing which sloughed off enough of this

mineral while DDGS passed through it to account for the performance

26

1/

increase-. Admittedly, this is an extreme example, but it illustrates
how identification of unidentified factors can decrease the value of
DDGS and CGM.

In recent years the steady decrease in the relative value of DDGS
and CGM to SBM seems to be ending, indicating that most unidentified
performance factors have been identified. Chapter Six will deal with

what the future relationships for DDGS, CGF and CGM are expected to be.

3.3 Analysis of Annual Relationships

 

DDGS, on a crude protein concentration basis, is equal to a 53:47
blend of corn and SBM.g/ The crude protein content of DDGS, corn and
SBM is 29%, 10% and 51% respectively (Table 4.2.2 ). One would expect
a similar price relationship to hold. A $1.00/ton increase in corn
price should lead to a $.53/ton increase in DDGS price, all others held
constant. Likewise, a $1.00/ton increase in SBM price would result in a

$.47/ton increase.

The coefficients estimated using multiple regression techniques are
consistent with a formulation on an equal crude protein content (Table
3.3.1). Equations describing the relationship between the price of DDGS.
corn and SBM with a variable included in the analysis to test for trends
(expected on the basis of plots shown in Firure 3.2.1) yielded sensible
results. The coefficient on corn is in the .448 to .503 range, while

the coefficient on SBM is in the .452 to .497 range.

 

A/Source of this illustration was an informal discusssion with Dr. John
Waller, feedlot beef extension specialist, Michigan State University.

yAP

.53 A P + .47 A P

DDGS
where A ‘ change

CORN SBM

.Laumumo .xee .xe:m\m

.mgoegm ugmocmum use memmzucmcmq cw mmzee>\m
.mex qogo meme\mmme aceuaeoxm eumo\m
.epmcceuceu .xeza .cop\» m_ woven mwoo\m

 

27

 

Amoo.v Amo.v Aeo.v Aem.v
_N. eo.~ em. moo.- eee. wee. me. eeeeee
Amo.v . Aeo.v Aeo.v AeN.v
em. m_.N em. eo.- Nme. owe. me. eeeeeeu
Amm.v Amo.v Aeo.v Aeo.ev
om. em.N me. me.- “me. mom. me.e emecese .m.=
cowmmmgmmm compo: a econommpv cop\a cou\m accumcoo poxgmz
eo Loesm newngzo N meek \mzmm cgoo :Lou

eceeeeem

 

\mxmmmumca Pam: :mmnxom use cgou op mmoega\mmwoo eo aesmcoeumemm Pmuwumwumum .~.m.m menee

28

The recent market value of DDGS, 68-73% the price of SBM, com-
pares to an economic value of 74% when DDGS price is based on the price
of a corn-SBM blend that is equal in crude protein. This implies that
the market has reflected the value of DDGS given what is known about its
nutrient characteristics.

CGF price forecasting performance is as good, or better, than the
DDGS equation, but the parameters make less economic sense (Table 3.3.2).
A 65:35 blend of corn and SBM is equivalent in crude protein to CGF
(24%).3/ However, in the statistical analysis a $1.00/ton increase in
corn price resulted in a $.34/ton increase in CGF price (holding SBM
price constant). That compares with an expected coefficient of $.65.
For SBM, an increase in price of $1.00/ton resulted in a CGF price
increase of $.41 compared with an expected coefficient of $.35. The
difference may be the result of the variable levy on corn import price
in the European Community (EC). Large quantities of CGF are exported
to that market where it is used as a corn substitute. The relative
value of CGF is higher in the EC than in the domestic market. This may
be the reason the annual model does not partition the impacts of changes
in corn and SBM prices on the PDDGS in a manner consistent with the

nutritional model.
CGF has maintained a price approximately 60% the price of SBM. Its

economic value based upon the price of a corn-SBM blend that is equal in
crude protein is 69%. An underevaluation of nearly 10% has been typical.
This underevaluation is a riddle, especially when it is known that it

has a higher relative value in the EC.

 

-3-/AP

=.65AP +‘.35AP

CGF CORN SBM

29

.meoeem ugmucmum men mommcucmgma :e mmaem>xm
.gmm» aoco mN\NNmP aceoaeoxm mpmo\m

.caumuma xeaa .Nee\m

.mUTLQ mmmgm>m .m.=\m

.emeeeeu xeee .eee\e\m

 

 

Amo.v Aeo.v A_N.V ANN.mV
_e. Ne.~ em. ee. Nee. Nee. e~.w- zeu
. ANo.V Amo.v
we._ ew._ mm. -- Fe. em. o.c ecu
cowmmwgmmm mo compo: m concome :oh\m coh\n “cmumcou two;
Loggm ugmccmum icengao N were 2mm :gou puzuogaiam

 

.I .I o L m.1: moo g cm.1 a m o m u . . . m
\o \c we e m\mzmm v: \n Lou ou e a\mzuo c \mmuu mo e; : ea Pom pm _umwumum N m m men e

3O

CGM has a crude protein value (68%) which is greater than both
corn and SBM. The ratio of the quantity of CGM to SBM that yields
equivalent amounts of crude protein is l:l.33.£/ From this it is
expected that a $l/ton increase in SBM price should result in a $1.33/
ton increase in CGM price.

This is different from the results of the statistical analysis
which indicated that a $l/ton increase in SBM price has lead to a
$.682/ton increase in CGM price. However, the recent market value of
130-135% the price of SBM compares to an economic value of 133% when

CGM price is based upon the amount of SBM it takes to equal CGM's crude

protein content. Thus, even though the annual relationships are

ambiguous, its market price has been based on a crude protein basis.

Although it is not clear why this ambiguity exists it may be a
result of CGM's use in poultry diets where its xanthophyll content is
valuable in adding the consumer preferred yellow coloration to egg yolks
and fat on broilers. A more complete analysis of CGM pricing would
require inclusion of the demand for CGM by the poultry industry.

Chicago, Toledo and U.S. average corn prices were examined to see if
these market corn prices more accurately reflect conditions in the
local market area than an aggregate price such as the U.S. average.
Chicago and Toledo market corn prices have similar coefficients to the
U.S. average. There is little preferance for the use of Chicago or
Toledo market corn prices, although either is preferred to the U.S.

average.

 

4/ _
—-A PCGM - 1.33 A PSBM

31

3.4 Short-Run Price Movement Estimation

A number of methods are available for short-run price forecasting
including seasonal relationships, moving averages, time series, fore-
casting models and feel for the market. Moving averages such as those
becoming widely used in industry (Nelson, 1973) were examined for fore-
casting monthly prices of DDGS. A preliminary analysis revealed a complex
pattern of DDGS price movement and, although some benefit could result,
from further study the extent of the investiagion required was perceived
to be beyond the scope of this study. Time series forecasting models
were evaluated for forecasting weekly and monthly prices of DDGS, CGF
and CGM. Monthly prices up to three months and weekly prices up to six
weeks were forecasted. Results from this indicated that although time
series models might be useful their ability to forecast DDGS, CGF and
CGM's price was deemed marginal.

The standard method of accounting for seasonal variation was
explored and found to be sufficient for short run pricing and inventory
control decisions. For a seasonal index, the average price of the
year is defined as 100% with each month's value indicating how that
month compares to the yearly average (Ferris, 1979). Tables 3.4.1
through 3.4.15 list monthly prices,§/ seasonal indices and variation of

indices for several corn, SBM, DDGS, CGF and CGM markets.

 

 

élSource Price Series
Feed Market News DDGS, CGF, CGM and SBM
Feed Situtation U.S. Average Corn
Ag. Prices Ann. Summary Michigan Average Corn

Grain Market News Chicago and Toledo Corn

Seasonality of U.S. Average Corn Price (¢/bu)

Table 3.4.1.

 

JAN FEB MAR AFF mar JUN JUL AUG 5”

n
U

DE

NOV

32

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flfir:HHHr6MHHrGC-1CJN‘J :vNo-‘HC‘JP’.C~J

a-mo‘Nees-rnc Q‘NU um finer—ncmh
cuff "HHmo-io-nmr-n-iwf-‘In-wu CURL-.1.
«MHHMHM HfiHNNNWNfiNNNN

m¢0~ Noah-«c mesosrmnwmccsoq-nc
cot-tHNNNGHNF)HCUW~QHDH\DI~H
HfiHHfiHr‘HHMo-ONNCVCUNF‘NNNH

Nlfi‘MD \Oulomhwwflrlzr-NKCVN-‘L CDJ‘N
O€‘ififiNNNoHsvchmulmh—de'cfl
fiHdev-OHHHHHFIHCJCVCUNN“INFO

Nnfihxoflm'huxflfmu?HIDQHLCU‘IDNQ
QC7HH¢JNNC>HVOHF1~DQ'fiQNNnd'CV
flrCfiHv-CHHHHHHHHNOJNNNNC‘IM

hfnammmsoommHnNHa.so.-.¢f~.o¢
UJHHNHRGHH¢H¢Q¢¢memm
flHHHHHfiHHo-QHNKYNWQCUOJIO

HP Qfi.Hf~ (Cmglqmul‘nlf‘c ‘L.’ Ul°~5— I.“
00‘ HHQHNO'JHd'v—POQOUIV‘. "01¢ n.
H "HdMHHH.~:HH.-4NNNWNNNPO

coo-Htoosmcmcimomxooxc 101110-01
OO‘cv-‘NNNoOv-iconhsccrITOv-dmm
H HHdflHdHHHflHNNNNNNNN)

~DU"I\O\G:U'Gw¢'1-CVCIO O 04“! QOHIDCB
J‘a‘ours—twofo-id-rnmovmawa'u—i
HHHHHHHHHdHNMCuNNNNr-l

Hmd’a‘ onmnmmmmmmhhvhm mm
mmc) QHHNC‘.C«OF’\LH¢NJNND(UU\ 5.40M
"HO'OHHHHHV‘QV‘H‘QK’\\NHNNN)

hemulh-d'xc‘zzchmhcmmnouxJth
QO-O‘QOfiNmOomva-inmm ’JNH
o-w-«Mn' vie—In "NMWI‘MHOINn

Jmnnnnocomcfimhmmmhhna
P C -CHHHNCZlO-HF‘P’T*HH~1(JPWDU‘Q‘U‘
NfiHHO-‘rio—o HVIHHNF’IC‘JCVHHCJ‘V

‘fii\fl)cf.uf\ l.3" .<:\.,."1¢u’4’1\_vd ,
mfiwwwmocwmfihhfifififihhhu
rrmn ourmo-n r; r'ho odixo-oxmmwo'r

" P'.v-4'1Hr.’ r-g -9H'—-'['—g94'—.f~.' a..‘P.'-Q' ,

INDEX OF SEASCNALITY

SLF
1C1.1

MAY JUN JUL AUG
102.6 102.7 102.6

101.2

APR

NOV DEC JAN FEB

OCT

97.2 94.0 99.5 99.7 “9.5 93.6 99.2

i'lr'll' k‘

STD

600 “01 207 400 503 602 600 407 9.6 605
‘02

6.6

DEV

CD 01

'01

’01

.0

.1

’01 02

Cl

1R”.

 

AUG

JUL

JUN

APR MAY

.39

FFB

JAN

F
V

OL

Seasonality of Toledo Corn Price (¢/bu)
MDV

CT

U

 

Tab1e 3.4.2.

I‘I‘ 145 U J‘.\Lf“flb .d" ‘r'l- "Vie; ”Luge?”
Liruh)(VNQH‘--CJQHNJF)¢C \OF‘U'-¢(\ \L
HHHHru-ir-imv-u-onv-A-NF’N<‘JF~*“-""N

mmH¢thwoowm¢c5hmcmnH
ousmicucva (JPN: mum: confirm: a r- .o
q—OHI-CH HHflr-irer-ir-iv-icernawv-‘N (\m‘r)

gown 940(3)me \DMHHFNPOC-ID Q (V0.1
HHNNnnNUNn¢NQNmmOHhcn
"HMO-4 fiHHv-‘Hfi .4HCJKJNNCNIN 01")!“

O‘Nsbxo GJ‘P1NO‘F)H(Y\F)¢CO¢‘11FODC-
OF‘NNFKVFIC. Nnm.mnm.uo-Nn~c~om
HHHHHHHHHHHF‘CJCVNN‘VNWN")

mmc.~ou1m~o.ocu,oc-N¢~o memento
ancamrDva-immq-mcwohbmc 0 cm
HHHHP‘Hflv-‘HHHHHNNNNC‘JNNFI

J\F)O’-\OF7$O~1‘H'.J Jinntr (us-runwmmm
.- H?tNF.NV}H\\N€fiU.\UW melatonin
HHHHMHHHMVIHHHNCVNCVNNC‘H‘.

K.r4001hl‘£u¢\f~)d FIGS" (V0 C‘J’)M\C\C
v-iv-INCV'F NFIflromqflmw-Cf.m¢m(\j¢rfi
wit-4eds-4mmHnr'piHHv-INNNNNOJN")

10.006— o‘c'oncmurr1mmo Otvmhlfl—c
nsTNmNNnHriNmH¢OQND¢CHLD¢
HHHflMHHHWtrCHHHnCdNNNCUN")

HCCFIOFCLFMFQ c mn—iu‘mmrn. 14"?
HC- fiNCUWMHMNmHGG'Hmd’O—Q Q”
HHHHNHHHHHHHHNF:C\CV(‘JCV (\H“)

c (Ionv-flwd'v-OQNGCJUIDIUHIOMWChe-0C)
QHHNNN¢Hnwammcmmuewc
c-i HHHv-ir-iHflr-Irinfiv-iwmwmm“NM

sodommcmnccir O‘IF-ommch-un meta“
U‘UOHHHF)OQNW:3n¢¢¢040'3¢n
«HrCHHv-ir-Oolf‘w-MH.‘Gflvst'VC‘JC‘i-CNF)

mu’HLIMHF-KSC‘JMC (I‘mcamoxonfimcv-i
t. .OC‘JNH:Hr"..-4Fr:.‘-FJ(\.1~LI.D¢G‘C \L'N
H'dHfiPIPI'aHP'HHflflC;n°v(.‘HNNF’

-"'4(\1F.¢UT~3F‘ LIP-.io-‘N'IQ'J‘xLF 1,71"
mowcommmoohhpppppppfiu
fT‘aIfl-U‘O‘flO‘ITWT-O‘ 3‘010‘0‘0‘0 "‘0‘” J\O:
0\MH90H"filH".u—§HP1HPQHV“‘4HNH“

(‘5';
-QQFJ
E.C

8.2

AUG
102.1

5.3

JUL
10300

JUN
103.0

HAY
101.6
6.0
-.1

APP
9°.7
602

SEASONALITY
MAR
.3 °°.4
5.1
-.1

PP:
1‘30

INDEX OF

JAN
99.4
3.5

99.7
.

CFC

NOV

2
.s1

UCT
6

1".

fTD

.1

(‘J

.2

‘01

'01

.1

.2

.1

 

Seasonality of Chicago Corn Price (¢/bu)

Table 3.4.3.

 

‘8

-

«1

nov 05: JAN FEB nae err MAY JUN JUL

JCT

34

Lcr‘ .13-T r-ZxL': Leetc‘JL-gahhuf F“ 5". C «70
Haun'ur‘wzrcoi'.“manna-«r$0 hue-11* ~tf~
Hmw'Hv-uufirir.pirirqbcmcdme-«NOIMLV

manflflia‘fl)0‘-O~OO‘T~HMNGINI‘MCC‘
menmumcox‘r: «(\mnwupuxifso-io- mu:
Hflv-iv4fio'4v-1PiHMriq—qCUFIN’TC‘Jw-{C‘J (grim

m¢m¢cnmn¢ 12:1: O-Nmtnsotnmom—o
nunmnd'nmmnc-mmmo‘momcoc
HHv-io-Ov-GHHHHHv-CHNIOCUNNNIOHH

arm—«mum :L'mHFFUDOJHO‘wufio-‘mQ—Q
«Hmmnnnwnmmmc-cr G'O‘derfic
HfiHv-OHHHHHHPIHNNCVNNOJNN'O

.n QWO~FH®0 NNNwHONG‘HNQON
HHNNPJmnHVJF’Ju‘} OJFJFCLQJQU‘OFC‘
HHHHHHHflHv-lr‘r-IINNNOINOJNNV.‘

Hd'rihufiuaawnc ate-«comm mmgdnflfl“.
«Hmmmmnamwmmcocwmmmwm
nHHHv-CHHHHHfiHHtJNNNNNNv-J

crime-c :1‘, «KING 112030104; TNONCQ;
HmNNF‘Nd' HHNmNutU-O ~0me VD?
HHflHfivfo—iptfirfd-1Hr-1Cu'OSCJN'JN‘JV)

Ln'mwoN—in—nmwsuhdmmwoe-«Irma
HONNF)F)¢—HNU‘3NLCHO‘I~ 1.06.116qu-
«MdfifirI—«MHHHHH'ONNNNNNM

(‘Jifls"¢c NthnOOU’ NJTOU‘NF'.U~U‘ ~T so
HHfi.’C\1(\AF‘)¢.-1Nmu'a NMCT‘FKD Ute-«(41:70
HHfiHHHHHHHfiHHNN‘JNNNNNn

Loo-numberm¢~om¢<u~wt~a~¢mr~m¢
eeflmNKucvcnv-Q. £10Nt£7fl1¢|n¢v~(u win
HHMHHHHMHHHFQHNMNNWNNUO

J‘Nmau-uomumtrlmfi-moai maria-(r mm
O\H€3HNHF)HHM¢OFMD¢mmflcflnq’
HHHrlflO-iv-iflhc—gfl' INK)C\0.\NNC\.N7

OHnNcmGhU‘w-scyrzmhc. qwerty-cm
HHHNCuc‘ud'H-CNQ H”V)1{‘I~¢'”l°-~¢
“Hair-4H...HMHo—ie-irfévmfflCva-‘(w‘w‘r

‘3Hi‘uf'utf; .05 Cd .1" \ae’J-‘rg’ Jr" h-‘TL-
flw¢mflcmmmLNNhththhfl
'I~U‘fl‘0‘3 G‘II‘UWMC‘O‘GD‘O‘G‘O’MT‘O 0‘“? 0‘~
Hririn'Hr-‘v-sv-hm‘q—dr’irir-ivsv—gr-lr-‘r-«rtc—q

SEASONALITV

INDEX OF

JAN

JUL
103.0

JUN

103

HAY
101.6

¢(

FF

C
Q3.9

HOV DL

‘(11

3.4

g)

1

102.3

.0

a9.4

39.4

E

99.

35.1

.4

'11‘

I..-

If

STD

99o2

6.F

6.6

In

In

6.1
“'01

3.2 4.2 5.1
*.0 “01

’01

06
.1

(\1

L0

5.4

'00 .1 01 ”01

‘01

.0 .2

LINN.)

 

Seasonality of Michigan Corn Price Received by Farmers (¢/bu)

Table 3.4.4.

 

MAY JUN JUL AUG EFF

A F' 5'

<1

F

JAN

3V

‘1

OCT

R. 4‘11 :.‘. 'l-'k‘ H«"\ur--J L’I,‘1 AJ\CI"L:'3\UV\\~‘
ch (\‘t-‘mFJqu-HK “ZHOCVQLDLDU‘U-x'i
H o—dwzv-Qv-ir1 r-fpiHvlwv-Xdflh-ov-‘QIC

¢O~OC‘~1:Y.0“ HQUIU. CPNCDVHMU‘QIO‘PONC:
a'ul Nv-we-imvour “Ne-swunm-LU‘MOO‘ wit“
,4 H'dHr-‘IH HHHPCCJPIQCJ'4HC-ICV

d’nthnNWWa'mU‘OmNIDOWDFFO
DQHfiNCOHC‘I-QNNODG‘ \DFFCOF
fir—OHo-Oo-QHH HHHwQNNNNo-«Nwm

w r.¢mmo:mso¢N.-«~Hmf~ma~cs no.0
cone-«wwwmu-«Nc-ccmmsoczmm <r
dfifiHHO-‘H HHF‘F‘NNNK‘JNOJNN

nwocmoomcocmimmomhmmmsosme
OCH-«40191010 HHmfiU3m¢¢an¢
HHHHHH'I HHHHHNNNNNCJ N

mnh-uhfim mmcocsxomc-nmricucin—ic:
0‘3 QHHHOIO‘ CiH-fl' o“-N‘J":L.’)"'o£»0.(.(\1
Hone-4.1.4 HHHHMCJNNNNNN

meimo¢msownmmccdm 'LHM";‘£H:
oi:- m¢->HHNC‘¢.)H¢GM\DI{.an-4fi€'
r4 HHHriHHHHflMHCUNO'CvNOiN

~tU-NLDHIT-NDF)K)C‘NFF’)CCFIH\DVJ$UL‘J
rt. WKJC‘HF‘CJC OHV‘CCVF‘FYONU HH
H HaHHMHAHo-«HMNNWNHNG-

Co~..a,:¢c.wna~c-Nwmu‘h'fiwa‘a‘0‘10er -.
arr-c czowcuocm-odfommo Nv-mcrcm
H manor-id HHHv-Qo-‘NNNNv-‘NN

(th-‘WDC'mlDFO‘hF': ctr mmwnHM—ic
UquO'CJL‘Ithx‘dT-U minor. nah-«untrue; «1.13
FIG-'1 H HHHv-imms'VNr-flmfvfi'b

~51; 01'3an NNFWWFWNU‘C’Q’ (‘20., \DLD
43‘: ISJO'O‘NoU‘UNWfiHNHC‘I‘o-NG
H H Hr: rlNVJNHvow-OCV‘F)

ho J.(‘vn'::mrfir~ .fiv..—.¢C.:.-n<;,~rr~u‘.r-..:
O‘C‘J‘HHI'N‘ -""'("')L: wit 4’ 70¢)? 01..
H PIHHHH HH P'PunkJmo-‘HNF'I

.. aren‘t-flxbh i‘-._;-.".-1(.1~‘,.rg“af~q “-.r'
@mmommwmwchfififihhhfikhm
T‘C‘ ‘10 3.047501") 1‘0 (71010» 'J\O~O-rT~-n\:n
.41—4. I01. ov-sHQ-‘vfifi.’ OHWCHrsvdflrcfio-flv\

SCASOMALITY

INDEX OF

JAN

1.1
,1)

'1)
~(

JUL
103.8

JUN
1030

MAY

APP

MAR

R
U

DL

NOV

1C1

102.1

104.1

‘11

99.0

9Co‘1

”7108 9606

.' ‘b45

IINF'L
.IL)

7.?

.5

U)

706- 6.13 603
'02

’03

5.9

CV]

3051

5.5

7.7

L'r'V

C

-.q

.0

C\

.1

.- I.
O -

.4

 

Seasonality of Buffalo Soybean Meal Price ($/ton)

Table 3.4.5.

 

0.

S

AIR MAY JUN JUL AUG

MA”

\4...‘

JAN

1)
at

36

J U r"“40 J‘w-WJv-u— .‘JL. L16 WNFUVHr-Cu‘,
we:«JILLLIDO-U-J;0-.LIHv-wmu‘.a..oa.rfl-cu
«NHMHHHmmm

QH\C «two Hmmr-cmwronmc.moo-C~
hiyafimnmui-t'n U~ fi-fi‘hu.a~u1uu-u‘d<r
H dNHrinHNO-Jtu

era quu‘IUJU-onmsond'h car-inflec—
hhmfimoonmmo‘omnm d'v-GDO‘NHQ'
.4 HPIflHNHHNNN

moo-«o “WONG“ sumo-soc NMNc'fONh—o
thrfi acmmwmcrov-dnnocq mm:
H HtrHHCJNv-ONHN

r010 auocrume-rd-mficshcmnc‘womm Nm
mhbhhoufOCC-UTCCO‘ oNNFhOhO‘oDUI
HnHv-iHNnNr-ON

)cum vie-1L5} .0: NII':~O\0~DO..D~.D¢H¢;:C~
3. NF .1. a..ec..u,.1-.t moo-«01nd c-c metal.
HNHHfiNHNo-OC‘J

WNHNONF¢HNWNHNR¢mCFC¢
hhcxfltf‘crnbfl {LIL‘OICJHQNQ¢ can-0C”?
H(‘\;HHv-4L\JNNH(\I

«Irma-chair)th hucmwann-«m
Nsc-nwmexq-coho‘aea nfimcr-h-ficsc
NfiviHNHNNN

unnmhfiupwuawoHLOHFIU‘I¢\D~~DU"N
(IN JImFCLO-ucfio from; acme-Nmocug
NfiMfiNc-‘CQNN

"HOFWHUNVONWDNG'HO‘ «numeric.
«1&4:th aali\u1¢.(hmrhu-osn¢ “NU-1.4m
HCJHHINHNNN

commmhoanO-mhmmcmoh (can
WW‘CCwFCCO‘QcSFCC-QNFU1nmmc-OO‘
u-iHHHw-«r-ec—icem

rsef—Ul HCVFN’TITIQ‘I‘CU‘U-F'HOWLDFLIICD
{AL}; 3 1'": 0 “.311 Qw’flfiwq5fl'KX-IIIO‘P’I‘
HH'NHHHF‘INN

)v-tatqd Lanl‘CJ D—.'JF;~:L;‘. 4.1“”,ch
cwmmwxoxflscxowa‘bhhhr‘hhh 1‘
4.71mi?!) mmo~ofonr~moloxaimmma mm

fi'fl' IH"H"'r"H"lHWIP.H—IHHHP1PC¢Q

INDEX OF SEASONALITV

LIA
(I)

AUG
101.6

DEC JAN FEB MAR APP MAY JUN JUL
100.5 103.7 103.1

NOV

OCT

". 1
\5.\'

10

q8.4

97.5

9.2
9.3

0.609 ~19

Q6.9

I H): .‘r‘.

9.6 no?

-.4

06

11.4 12.6 13.5 8

6.1

4.9
-.C

ANob

8.4

11.7

I’

.10 III-V

'01 01 .1 ’00

.1

01 .1 ‘0'.

.0

IRL'V

 

Seasonality of Chicago Soybean Meal Price ($/ton)

Table 3.4.6.

 

DE: JAN FEB MAR 41F MAY JUN JUL AUG SEF

NOV

OCT

37

3"”- arr-"'1: ‘ Nun ¢957®F1.e¢LFHt~--‘
soc. .EF 11:; u.U\f~~1--1..—n¢¢n.mho¢¢
v-iC‘Jo-Iv-sriHv-ONCCN

Hd’ 63-0.:- sr(\:v)U\.-4(ru1rsw<t nice-1.7.40.0
FBJIUIF‘DUHIW- J -1.umu.msu1r~owfi
"i HI'VHo-owie-immmw

mnsommmsmccozomsomcnfnccn
mhhhhccimm :oa'aficnohmncu
m HIGHHNHMNNN

61H¢fi0Fn¢NO i‘o-oU-Na‘fl-coc-odnq-
mhbhha-wmmmmoHONU-nd th-c
ncnfirimr-«NHN

DmNOMmrLC—JfioftHo-HOIINCJO‘OO‘Q'
hmfihhmfi 1051350. C‘NHNsDO'I OFF?
HinduiHNmNHN

5.094: at mm3:;..Cao-4C.¢ Shmeflfihu‘.
nghhmhflhmomnmmwumowfi
HNHF‘HNHC‘JHN

ummrwéwmhhmhscezmamrn
QQNFFFMthmmOmNmMNOFN
~VH¢OH£VHNNCU

MHhoeatmcanmhmfidcmfim
.065»th thth-am N001¢Noo¢‘m
NHHHNHNv-ON

03¢wawocmwmmmhmhnhmo
«nah... Ftt'mNNU @11va 10.0.4me15)
HHHHNHF‘fiN

mwmmmwmoaxooihho-im Handle-coon)
ulwhcnh-hulhhudhmmm munch 0.4.10
HHHHC‘JHNCUN

xmmmmcnommnomcohmmmm¢
cohhbmahhhmmmhcmohwmh
HflflHHrlHHLJ

“his“:u'fi'u’fl‘scf’i‘ cow-WHO \(Ncflxnflu'tu‘
rwohb NINL'CINW‘ exam—sor- V11: 1 0 LI;
aura—.HHHHHN

(in-«urn J‘ACNJK: f)v-(\,PIQJ.~UP‘ “3...? "‘
‘IZ~DNLWD~O~D'£(\J$NFFNNNFNNN(E-
J~"T‘~7 04013-0~Uim'7‘0-fl\0\17-U‘P‘O-O‘O T0:

adv-*v-iv-(riv-qo-iHmc—ic-uv—wp-eo CHP.r-1r‘£¢‘r-Q

SEASONALITY

INDEX OF

\N

«1

JUL
103.5

MAY JUN

1930‘.

PR
WH2

M68
12.2

FEB

J“
190.3

DEC
100.4

rmv

CCT

190.1

.5

'4

10

'309

16

97.5

5406

9605

36.6
'NV12.3

1M”

H3

”-1

I ._
L.‘4‘

9.2
'01

8.9

12.9

40“ 4.7 6.?
'01

’01

3.9

'03

-.4

’02 01 01

.1

.2 .1

 

Seasonality of Decatur Soybean Meal Price ($/ton)

 

Table 3.4.7.

1.!
v7

MAY JUN JUL AUG

re

A

FEB

JAN

n
U

NOV DE

OCT

38

"F-l-‘rti'friéc- b"~|w-1-‘)u‘1‘d'¢d‘¢€ru .I.
Shhhfi.ufi(..hu,fscg.fi’mh¢‘bu F70
fltVflHo-oHv-‘chfi

(Tu-cu'la'owtl‘ «1‘0 U‘Hlflflfil-FOl-iml‘m
NFNQI‘KT‘F uhcuh— 0:1 LnP)!~<t~oaU~-¢:-
HCJHt-dr-OHHHKNNV

maxonoa-hoLKm-Md-HmmccNNNotc-
owfiwfimhmhmmonnmmmhomo
Hnu-u-in—idcuv-im

thchlnmmc:‘memer-iczmflofip.
mor~~or~mr~<m~v~wo~Aonth-ioo
QHHrch-‘(MPM‘J

HQ 1‘ cmncmmmmmmmo‘ Nd‘F-CC-FM
FQVDQsofoFFFFFU‘HQHanF-JJQN
mr-cH—KUHHHN

IV‘H') £1100" QQKJWJI‘WanNtVI‘JJIOHfirO.
F \LGIV‘MBFFFI‘FF-U c; HNNFFU-LDN
NfiHHNHv-qflz'fl

.1 Fir-NCO NFLLIOWN "(37‘7“thch sfufic;
\owhhxbfihFhFNU'-O¢HNCUV~mule-i
"Von—(HHC‘JO‘GHHN

ruu'mmcabczmommmmohnunqen
omhhhhufihmhmnonnfimmhfi
N—umqmruuflm

Luge: ‘1. 1.-J-(\IL:,O«2}C‘F.'LLNO~ a.1\<unL‘-¢
u": \Dfitho-hhasbu 'XISNNCF~D tic? N
HHHHNHHHCJ

HCJHNQHWMNIONHGCNWIDLOQC\ (11¢
mmhh \DV‘AJhF-asuoaafifinfl'nlo«15.14.1103
HHHH14HHv-IN

mow-cinema Nccwcnhdommhmfi
crhhmhfifihfi-FNNOG'NJ. 01‘6ch
rduqHH'iF‘r-IHN

0.er van-swan Ulhuvo‘omsornuih -.\o
¢T¢FQFIFNFFFFQONNEF¢Q
HHfiHrIfiHv—(‘J

O‘fl-‘Jfi :01 0h117‘1‘C3vd(\F-l.fu. «Db g, 7 C)
somescuhowxoc-flhbhhhhhfihhu
0.0 franc-moJ~o~o\rr.a-Ir-ma~n~chm IT

0-0. .v 30"1'1HP'HHW HHF..-~v—.Hf “‘4'“. "H

AUG

JUL
1830‘

JUN
104.1

MAY
100.1

AFR

SEASONALITY
MAR

INDEX OF
FEB

JAN
100.0

P
U

DE
100.2

nov

0 C T
06.4

K

55.9

(\I
| J
r4

98.1

97.3

.7

‘36.5 98
4.

‘f

INFLI

13.5 19.6 9.7 10.6 3.1

12.8

6.6

0‘

4.9

02 -0] "I“ "

.1

o? .1 “.1 '01 .1

:1

 

TRF'D

th Solubles Price ($/ton)

1115 WI

Seasonality of Boston Distillers Dried Gra

Table 3.4.8.

 

SIP

AFR MAY JUN JUL AUG

MLR

.11

H

NOV DEC JAN

OCT

39

4 ‘. Inc-tun! (JP‘F.( U‘U-a'NDQv-tC‘J‘ L“ J'L"
Inf‘flwof‘flth-F..C€‘Lfl€'lflm¢7~0'~£u
HfirufiHfiHc-K‘J

I‘CJJVNF‘VPQQC‘; Q’flmo‘CNNGNDOC'U‘
u‘|!\\L‘\D\DF"\C\D\DF‘~JUu'I-ONO€1UPC'3F‘FLVO
HHHPIHHHMN

omwc (roundab- OJH¢~7N¢ U-c'C-rmwm
otoxommomsosohmq-a-Nncmmsoho
flHHHHHI—GFIN

stir. 10¢ chmwmmcnwocc‘o H¢HIC
~D~O~D~D hsososoxoborcucv-imnommsoc
HHHP'HHHHN

mmonfimnwmnoawmmhanONm
memomeoomhhwnammmemmw

HHHHHHHHI—d

0-HT.INCA)?“#nFQwOnO-(‘IU‘HOHNfl
$113150 (sOxLWVDWFFMHHFIGQ‘U‘UJJ.
fiHHHHHHv—irl

"k 00 (VJ. I‘C‘ (ah-’7‘ <3 O" U‘ICev-H‘ “VF-'7' \D 1'11}:
st: ~0~mextxoh¢bf~hmmfiwuocmmcf
nflofiréfivoc—Orh—G

«hmmhosxomnmr anU‘dwOu—tmo '7‘H
hQsflWsDOhh-hhcuhdmwnhvmflm
Fl—Ortr-‘He-Ur-qrim

0‘s i..-\.D\‘}C‘ cqu cmcunpoo (1chch
saxohxosbcc hhhwhuccnwmm Wm
HHr-Ie-IHde-sm

L100“? LDCZHOONNCUNC‘O‘P‘ d’fiHCCLDv-ON
.0.onwc-aimbhqume-mmscu-uicrm
F'Hv-OHHHV'FCQ

\Ds‘fh or. \DMOKWOC'WDIONG hoe—«4:001
~L-mhx0 wohohhooho‘.n~owm¢m mo
fiHHPiv-IHHN

u‘\‘~lF -m u’Lfl'; what-U" 01"qu L" ”10'0"“!
g'JTIKwaxL f-WkD'LJI‘F\’LeFJuJ~’3U3n¢3 C"
o-Qg—afl—h-qI—‘PIN

er—CJV)¢JMOY~J:I‘ «DP‘OIP’.¢J‘;~CI\ w: .1
JOWQQWQOxflxflNNNNNNFFY‘N '1.
m’th‘U-U‘mO-IT-U~m’7‘-H\U\O\O\n\r.m(«Tic

HP’f‘éHfiqu—iv ‘HMH'I «pH-(Hr? "Iv—c ir-i

SEASONALITV

INDEX OF

.
I

AUG
101.0

MAR AFR MAY JUN JUL
101.501

FEB
192.3

1

JA
10502

(3

NOV
101.9

JCT

(7:503)

{‘06
701

O\

94.8

03.7

9bo6

103.7

1.75.1.6

I'JUL)’
STD FFV

5.5 4.5 5.4 .1 5.8 4.6 7.0 7.“
’01 '01 “.1 .1 ’00 '02

707

7.3

.1

.1

.1

11'

IN

 

th Solubles Price ($/ton)

111$ W1

Seasonality of Buffalo Distillers Dried Gra

Table 3.4.9.

 

If

C

FEB Min era MAY JUN UL AUG

JAN

2

OCT

(3

4:

Far-(y; 'IWF. r0 ruu'artv-MLN :u'vOJK‘r‘T- \L.‘
LEIUJQGflI‘LDQIxDI‘I‘iUWHO¢¢0”5NU-I‘
MHHHv‘v-h-lmv-i

Uihu‘amvcm—iq-MU‘ia mowed”. sormm
u \rnou‘iohxosu@hhhuAm-‘Nrow. Ohm
HH'4HWMHHH

”\DK'HOQU‘INVCCF'CUWVCVNNV’:CMOO
\DQQOQQ‘DQQQFFHNnitdd’ \DOC‘
HP‘HHHHHHH

MFJU'H-Ofid' #Nfl'hf‘u’INI‘CCON'IdFLDC’PC
®O~D€QQQQOQFFQOHNQ¢¢QO
HHHHrIHHv-SN

NU 30.006115111006fo Lnd'mv-w-iChxo
uzmsoowsoiomsmoh-finauouaec :CX‘
HfiHHfiHflHv-Q

ohfi-x‘m1fic.a.L-n¢nm¢¢ Corr HHU‘MI
(.u’ruiuzsrnoxbu:wcuhbu‘ii—‘amou-erc a r~
HHHMHF’HH

Oh ~Cf~u‘.x0(\.¢¢1r-ln-I.Jjuw~?._'mhxcm
\DUILDII-DQQOQOhumfi—imiisveh
Hr¢.-i.-«-i.—i~h-4.«

(1‘ €K)HW\OU1OO\NOCU~DO-~BCKMNCHOU)
HHdHHHv-Iflfi

C1" L’ihstfi'nDNNd"P'OL'CCJv-O’CHNC‘U"\D
\U\0'.D~D\D\DUOI~~CI~COFH¢MNw¢Ifi~D~e
HHHHmHo-OHN

\Donr‘ficumv-hoand‘U\QIHNA¢JU‘.LDHQ)\DID
.omhccuimmcntumh dine-c Hunt-<1 .11 LI
Foo-1H v-IHHHHCV

¢N¢mncmrfa~DI~f~f~fiHNNJ¢HmHM
\Dmhw‘OQFQOQNQO‘nco-nd'mc'ho
HHO-fflHV-u—Om

mafiommmnm¢htaomc¢mhwh
we bwuwhxcwxbbho FIKJNH'VFUFUNJ‘
HHHHfiHHH

.n-v ru~3¢t’~.1!~1‘:C-="~N*.¢u‘ DIN ~11“ ~-
uccmiaoccwofihhbhbbvshhm
'rmrrimnunc .T n-c.mo\rra~o-a\n-mo-mm
"‘"H-‘H’4'4F1P‘F‘9I‘1'-H'“4H' 6"‘r-oc-{v-‘P-

H’ALITY

Map

(I:

A

U
U)

OF
FEB
10204

INDEX

JAN
105.5

SFP

3.

AUG
101.4

JUL
100.5

JUN
99.2

MAY
94.4

n "
LO‘

APP
6.9

95.7

r
La

DE
104.1

NOV
1010-“

')

OCT

(.4

0
.2

C
C

4.4

7.3

Cu

~O

603 5.3 407 505
'01 ‘02

7.o

STU [EV

04

'01

'01

‘01

’01

.0

IRCI'UL

 

Seasonality of Chicago Distillers Dried Grains with Solubles Price ($/ton)

Tab1e 3.4.10.

 

DE“ JAN FEB nan APP MAY JUN JUL AUG SEP

NOV

OCT

41

J'I‘I‘*.'.'}-‘.-u2~1“'.f§1' ‘u‘v-‘J LH"" ‘Y‘
Utu.u‘nxbntru.'>\(I-.Dt~(JH‘;HN‘.<-.('VU.\D~O
F‘NP.HHHOIO‘6H

CFC. VI'O/a NFWU-NDU' 51:130.“: q
\UU'U‘OUI'LDUF’UIQFU‘.¢':'“JP'CJU‘U‘I‘
fiflHF‘I‘IHsflq-l".

channKIOCJnhdmnm~0¢Onr~¢
smr mommomohnoonnmmeb
HHfiHNHv-u-ifi

UIF‘I‘I‘ XaNocfiflle-N‘CVIOL3FO (Vt?
mwamUImmocobna-oNc—cch
o-o HHo-IHHHH

0017' m'JQFICUmeVIQC-Dc'mߢm
LQLC'UDU‘OWWQQQHC‘C‘HWHGQO
H HHv-‘HHv-ifl

floJ..r-f-f~r~oxo-.Owa Inf. JU‘n—isffl’
lDU-Lfrl-I’IWEUIUHD\5\£'C3C-‘CH¢HQ Q 4.
flHfiHO-CHHHH

1,7": 1.4.7.46410 u1uivfimlnunriflr-4L?
trimmxoxoxosoxososoanoncnemxc
HMflPIr-CflHO—ifi

WN'K'U‘WDn'va—HDW :IFIDU'DQHQC.‘
anminxmosoxohoo-nnume enema"
HHHHHHHHH

MNQGV‘vflKfi-‘J ¢rma.o- cammz er
\Uu‘;nmhwm-LhmvfnoacmcLr‘ 0‘
fiflHHHHfir—ifi

mf‘mavnoflnw£UWM~DW~F‘\D¢.FC\I
QIU‘.IOUDN\D\I'1\D\I>JIJIF)H .JO’ (\JUIO .T\
Hv-IHV'IHv-fr-‘H

<ro'. \cmhc-Nmoinxa HnnFUWJIF‘
cmmmwmomomfifinmccmnh
F‘HHHHF‘H"

C‘vx'? NFP‘wU‘ 0' {Aid-IL. 3¢Lfl¢fl7~fi~fl
\sz rains;-U".L.‘v.£J-.:f~<x:~'1w'¢c :- Do
"H'dI-‘c-‘F H-‘t

‘JP;¢'C‘.DI\ :3?“ -'HC\2‘1~?IJ-‘.UI‘ C1”-
53“)“; DJIJ'HCQFBFI‘NFFFFN'“
5‘4?”G\IT~"{T~-’7‘(T"J-&T‘U‘C\3"G*UVT‘17'1h
f"'v—0HH"*‘HPHHO.‘4H..$H";PfH-“

INDEX OF SEASONALITV

NOV DEC FEB N39 APR MAY JUN JUL AUG SFP
101.7 111207 10001 101.7

103.2

101

‘ J

I:
.U

6.7

L": i.

05.4 9507 99-7

0

103.9

.1
7.1

(-1

N

IJDC

5.4 9.9

7.6
‘02

407

“.11

7’)

In

5.5

6.4

I”. 'I

(*0

‘01

[C

.1

'01

v.1

.2

.1

RFKL

V
J

 

Seasonality of Cincinnati Distillers Dried Grains with Solubles Price ($/ton)

Tab1e 3.4.11.

 

UDV DLC JAN FEB MAR APR MAY JUN JUL 505 SEF

OCT

.zcu‘ run—466.6 Fr’zmhu‘c'fficc c ¢’-~H..;'
:0m:w:~o~o¢wm».o.o~ocum01cco~¢ (“n
HHH.-¢HHH.—iow‘

'1;$¢~DN¢HQQ.£5U)WNOU\\DHC‘J\W\O
ifisfiuou- \DQ‘sCLDIDuT .0\D¢NH(\1HHW¢K‘0
HHHHHHHHH

nae-HOP- manwmhc c icmmnmmamm
01.0010 oommmmowwo‘omnmnno
H HfiHHfiHH

NQCFnQWJD¢U~H¢FMNC.fi\DmQQ
mmmmommmmmsmomfroacmmmw
H HHHHHHH

1171.0 P;mnmr~q)m~nHCJsor-.ON¢¢ 0101.0
ermimmmmncmcmmoo-.c.o¢<\:cuwin
drown-driven

N1Nu7Nv-‘0'FCVFCVHGQG’UHfiv-1P1NHID
lfoUJU.u')\DUVLn€'¢ID\lJWU‘O 04 -€(‘ “WV
Fido-‘Hr-lr-i

cmwchcmocma¢cmznczcm
.omimnoommmwemmcooemwwm
HHHHHHHriq—d

Hmwnmnw&mmm®@¢mmm¢crm
somxoomsoh-Inmo-ommN—onconcnsn
«HHHaHH-fl

'ncwmwmnrmwurmowuanmwmn:cm
mmmmomhmmmwrmnmocmmeh
HV'IHHHHHH

Inn-unpacomcrvmanhmmomfimmc-mm
manna)mmhuunu'.mmfsmmum wealth
.40-I Hfiflfifl

351.7(13'56'0'01555 d'car‘OflthrH
m¢mwmmh¢mmmmhfi¢HNHN¢h
H'CHHr-‘PCHH

7'.un '7 "Titlzbd'q Q'Q u'“"‘C"¢NI‘(U\UF}\b
Inc-Lemons: u...‘s\nwr~.-icxrmcw~~u so
HHHfiu-(v-ONH

Otd‘x‘JnQUIQF‘JDU‘QHV‘JV-‘3m'“h'l.L.‘ (3
wwwcommccohhhhhhhhhbt
(T (hr! (TIT-(7‘00” mmrfo S-C‘mmf‘hfl‘ WWI.”-
Ho1V1MHHFIHF4'5F—4P7F3F‘O‘4HfIHVSu-(H

SEASONALITY

OF
FEB
154.5

INDEX

JAN
109.6

SEP

AUG
102.7

JUL

MAY JUN
19C.0

APR

MAR

DEC
102.7

NOV

101.3

OCT
100.7

176.7

0-4

92.“ 31.9 96.8

93.7

17.11,.»

.‘QTII

6.9
-.5

7.8
-.0

309 603 4.5 406 607 7.7
'01

7.8

I} V V

.0 .1

01 '02

.2

.3

TREAT)

 

Seasonality of Buffalo Corn Gluten Meal Price ($lton)

Table 3.4 .12.

 

02‘

ADV DIC JAN FEB MAH APR MAY JUN JUL AUG

T

Fjfihc-idv-‘LJHOJI‘O Q": “3
ermma'umncnc VNZN‘IU:
v-(v-‘r-IFOI-QC’JNNFDHC‘QWIHDVJ

NifithPDHmNDHNflU'v-t
<T¢LF¢\OU\F‘F)~.UCVPI§WI u:
firdv-{HH‘VWLVNNNNJCUCJ

Nmmhflmnwmfl~flficl§
¢MID¢WNNNFWHNIDQ
HHvfiHv-"ONNNNNIONN

monomhnmeoo cmnn
mmmcmmo NNHNIDIOLT-
HHHHHFJHNCUHNNNN

«space-lemon crowned-co
«inmcmmc-‘Ncr win-cinch
HHHHHWCUNHV’INOINN

IDxflIDflJDCLILDOCumv-H‘LFN
«(new arm-sccuwc 0&7. Nb
HMHWBHCVOXVNH:UNNN

mccr (J ijvamc’Tc-L‘a
NN¢¢¢7~QngHQO¢Q
HMHrlHNWCJ.Un€\lCJNC\

€€NMQL7DC0C3CL F)COF"."J
CusV¢¢fiL'fQ"U‘\OO\DCl-\DOI
HfifiHHCJNv-‘Cunk‘dmccf‘.

WQFfiNNQuT-WmmOC‘ui
WNW) #7061060" \DFCDF)
«He-1.4HNNNNNCUNNF)

omeuowlnmm Non-"~01
mecrmmee-nmxnmmmn
HHHrIr-i. ONNNNNNNK)

LDHCJQWB‘JDCDOQU-ONV
“”070an HFMD da‘VsDQ'JN
F‘Mv‘ifiHHNCJNNNNCxF)

«Ha-mhc- .510". a a mail“
:C- 3!".1; HOG Her‘Hg‘ \Gt’LH
....eei..1H~NN.un—~N<\'m

r- (,3 ....-.c.1~)-n:‘. wh err o
_uosnfsfithfifofsre...
.‘T-c-maammo U tha-mmrrxox

-"‘.—.-—‘.r-4mri.—1';1~e‘rfr4q—1r‘.|—d

43

SEASONALITY

INDEX OF

SEF
lrno“

DEC JAN FEB MAR APR MAY JUN JUL AUG
101.9 101.5 100.9 103.7

NOV

(JCT

98.0 10209
12.9

99.5

96011 97.1 99.7

V 12.1

L. '
('1

4..

12.1

8.9
".5

8.7

17.8

7.9
'01

7.4 709 8.6
'02

.3

f.-

10.1

"-.l"

TD 2

"S

-105 ’09

-102

.7

.4

~L

G

*1

 

Seasonality of Chicago Corn Gluten Meal Price ($/ton)

Tab1e 3.4.13.

 

L- 4'
0‘}

JUL AUG

JUN

Y

“1A

tPP

‘4

DEC JAN ire

‘1') V

JCT

\‘JLT. .LHPIV‘C":U 4 NFUF“\V"-
H)(‘Jq~:’)¢CJ.-1mnt J.F)r~0L..O
u—lr'Oc—it". MRCUAJCVHWP‘JPVF‘J

HN\O\DH\OF1NQCL'\DLDIUO‘
If!” t": U.N\DC\H~Or‘CuJ. 3’
HOOHHHCJNLV .VCJNP‘ZAHV

mncwo ominfixowcnao
NF) ¢D¢HHHmt~czonn
HflHHHmNNCJNNF3NCV

HQN¢FLDCINC td Nev-1
rumen.?o-crxnaasmcwo
HrlHHr-OKMHNNOJNCVNQ.

O‘N‘3~1N¢u‘oM\D-OCJ¢NO
“(Verne-r 'rw-iccwmmmn
“NHHHnNNHchuvm

KT-LJNNNN-d' 011a; .OQQLD
HHF.~N)F1UIBJHC¢. Q’P‘LF,
Hv-so-irdr-M'JNNNNN‘JNN

HMSLDNHGF 030-40. U'CIP’H‘I‘
pic-4K. nr’.~8~£'1‘u:(f 3'0..er
r4v4PtvfiHmmv—QWNNNK\J(M

“Manhunt, Luv-NH? <— NsDI'O
HHF'inCJrzmavm fnth-q‘m
FHHF‘QHC-.NH(\N(‘JNS\IN

P3¢t~ HHHf‘U-CL Menuiu‘;
MnOJnNJ~¢O~¢IELPmJJCr
HHHfiHHNr-ONNLVNNF‘;

quaomcccsrnxoa'cnm
pfiOJFKHUIflQ¢Q¢¢GLfi
HHHHHv-ICJC‘JLJCJNNNN)

NO SONICHCXZ CI'QJLf'Ié'c—LO
HHNnNnOHnQv-‘Q‘QO‘
F‘HH. .‘HHNNCvO'WNNm

C c. ubinxflh).-1Nmanfi)mu>0\
v—(‘JOIQO-mdk‘érfin‘ u QNLC
«Hm wit-1'4NC‘x‘(\!(\.v-'C\.£\3W

N .13. ~e""u‘1¢d‘u"‘ . .I‘O
LQ QF‘F‘ F‘I‘F‘F‘I‘F‘F‘F‘VI
EDIT“. hff‘n‘f‘T-G-ITNFT\(F (I‘mm

rd” Ho-tr-{r-w.-.P.Hv -iv".H""—1

INDEX OF SEASONALITY

L’J

FEB MAR APR MAY JUN JUL AUG
10106 98 19102 1.04.0

1.12.2

JAN

(3
Lu

NOV

OCT

'Uo‘D

12

03.1

.0
.7
’103

99.5

96.4 99.6

95.5
12.6

9.4 12.7

'05

9.0
‘09

19.2

12

.3
'02

d.2 9.0

.0
1.3

a.

6.3

LFV 12.6

510

'105

N.

.4 1.0

5‘. r.

R

 

Seasonality of Buffalo Corn Gluten Feed Price ($lton)

Table 3.4.l4.

 

MAY JUN JUL AUG Sff

F

{AP

FEE MAR

JAN

LJ
Id

D

NOV

OCT

4r? HHLVC)C" LC fln‘.‘;u‘;fi)fi\1 rhyc‘tu‘,
camafinwwondmwc CT‘V‘NO‘flVLOn
HHv-h-i HP‘P‘H

4‘ xr -1..Nl\ cmotho—aevmmmmmn
Q a .r.¢mmufla- cu-mmumwncnomcm
"Ho-{H HHHH

Nma-hmwmsoc¢m~o<rw~ommnunmv~
{G ‘Q'WIDIDd'G'IDIDIDO‘O‘O‘v-OOOQ'ION
Hv-h-QHHH

a-a-omo-«ocoq r1a-mocno-4Hm0mo-l
q-cc¢mmmm¢mmm®moumocmc
HMHQ—‘lv-h-O

Ina-q: UCJU‘)€‘JF"ON3\1IOV’3H¢F1 :- ¢m~
<rc ¢¢mmLnlr1¢mrltniflr1)U-OK§QF'IF)M
HHfio-‘HM

J‘I¢f~mH€'LU.-4r‘lc~40‘ C3NQC»QJ'~DN¢ .000
Q «‘3 {lefiuiLCL’IUILDWNGU3U‘NQWINLD
u-c HHHH“

(I- 3nid'U-\L¢C\¢plcfiy\-J‘ [spymhhzu’
a": mac-.tmmmmu'uoaoaf- QU‘Ncsncvfi

HHHHN'

NC mucocbowmmnhmeehmfisf
ma-mmmxoommxoomcvmoononmer
”MHHfio-o

mpnmr:xof~ml~r~<r.mr-4f~¢l~o‘r~ cam-0
unruommunoomwhxmr Horne-unmh
flv-Cv-IHHHHIN

ua‘ mnmmcrofimcuwmmcwcaa HBO;
mvmcmmmmmm «Imam-commummm
HHHO4HHflv-1

1'0me 6 cmmmehmmmoommmc.
¢¢ mcmmommmomhaaomcmmc
HHMHv—‘PCHH

uur Hré'nd’r' .C' omwnnnwhnmo¢
«ers? Li". LIT-mm «mama! emu. 'JH 'HNU- Nun.)
HHHO“ “Ho:

"4N!" cu uN-C'J“. 0".MV‘JVU-NC" L‘J ’"
DCQ‘J‘JQ'QQCth-I‘FNNFV‘F—NFW.
"MTG O'O‘O'\R.C‘O"U‘O"C 0‘"th '3-L.“<C‘7‘(T‘U~
firifiv-nr-{OuP‘HHHr-flu-‘wqmv r1w-I--<Hv-?

SEASONALITY

INDEX OF

APR MAY JUN JUL AUG
9 93.7

MSR

EB

F
193.?

NOV DEC JAN
11005

133.9

JCT
1"0.6

5:7."-

97.5

LO

34.5

97.2
10.8

957.0.

37.8

1

1 4:31 ‘1‘

(‘4

“.3 9.7

5.3
.1

6.5

405’.

609

In

4.1
-.0

TD D‘V 5.9

C.
a

.1

.1 .D

'02

'01

.1

o. r
\L'

IN-

 

Seasonality of Chicago Corn Gluten Feed Price ($/ton)

Table 3.4.l5.

 

MfiY JUN JUL AUG

AH

HLFI

DE: JAN FLU

.1qu

JCT

’16

2136 (\J(\.' v0.6.3: C- 5.x; ."‘..'.(\’,. ':»¢ ( ul-l: UM-
"IQ¢q¢u7QQ€J‘-¢UWJC -(lv-OP‘O t‘qu‘.‘
H Hv-OI'".

mmv-nrntommohwmhnmwooed-F
turner- <r<r¢mc ¢¢ao N.3~.u;..r K-NC‘
H r-‘ Hr-iv-O

whoa-whwmo¢mmmn¢~am (Tic-Mom
WM¢0¢¢¢FW¢¢€ #hmmommnmo
H HHH

mm~OFNVBQNCHOt #‘YNWG‘O'TIVIQ'd
mnnn¢¢¢¢¢c¢¢l~t~mmommfln
.-. Hun-4

m mr~ c-chr ncacuahwaooccg coca-N
«mimcrccccd’cq'arhhcoc HU-HHN
H Hen-4

mumbcanm~0v¢fimmmgr OUI".0N‘DL.’)HHLDH
mntfld’ 4 3 st: <1 <r¢~1 \LI‘CLJw-flc “can
.4 HHH

‘4‘U‘IF'J0‘ that? 0: Lori-o F-C"\I~P’.:HC-" (Kr-«u?
¢”‘“¢m¢€¢¢m¢hhhmmwmmfi
.4 r—H'pa

nmunmawnc-o-~uc.'l~U-~u.-«~Nmmo‘cu
ernc¢¢mm¢¢mm¢t~ccmmmc~mnw
.-o «to-4H

¢F)Hf~u‘ad)U-r7:.-f~cr V)": LC. Kano. LU-J
aunt".¢¢mmmmmuiaummmmu—ur:
v-Q HHHHV4

«newshonc thHC‘-\CN)QW"IUJLDG)
€ :rma-cermuncmmmmwmmficunmm
H HHF‘IHH

crrscccmnmmcwnocc m¢¢h~xerMH
n¢c¢¢¢m¢¢mmcrmcmoo~mnn
.-1 H ”Ht-C

sawmmq-mr-rzhnovmw’f “75W “9
.“mmrcercu‘iccvu‘wrmcrt1.3-«.45:me
.-4 H H"'*

' 'Hfifl' #ulu)f~ 1' 1.. MCJOQJ‘on g-r . .
Dw$(0£¢@JONFPNFNFNNhr
Y‘U'TC m0.(‘.C-’.7 ”\mmn'fP‘MO‘U T-C’U‘C‘
PfiHv-lq—ar'vr Hv-‘iu-q 4v-yv—‘f' .—-'-‘1 “‘Hr'1’4'“r"

SCASONQLITY

lNflEX OF

PR MAY JUN JUL AUG SFP

A
q4.6

MAR

FEB
100.5

JAN
112.6

F
U

HOV DE

CT

q
IUJ

96.9

.2
11.7

92.9

93.7

97.3

9.?

Q
l
a

5.1
7.5

C.)

151th X

7.2

7.5 5.7 7.1 6.6 6.0 5.2
‘02 ’00 ’01

6.6

5.2

7.3

‘5.

D-)
12

ill

v-w'

.4

.2

.3

.1

OJ

.1

IR “'31)

 

47

To illustrate the use of these tables consider the index for U.S.
average corn price. In November it was 94.0 which means that, on the
average, the price of corn in November from 1960-80 was 94.0% of the
annual average (Table 3.4.1). This is the lowest of all the months.
December was 98.5% of the annual average and so on. Prices averaged
the highest in July at 102.7% of the annual average. The average price
increase from November to July was 8.7 percentage points (l02.7-94.0)
or 9.3% (8.7.94.0). If the price of corn is $2.40 in November, a reason-
able expectation is that the price would rise by 9 percent (22¢) by late
summer if the year is normal.

However, few years are normal and the pattern of price movement
through the season varies substantially from year to year. To indicate
something of this variation the standard deviation is computed for each
month. This measures the variability of the index. The larger the number,
the less reliable the index. For example, the 6.0 for November means
that prices are expected to average 94.0 plus or minus 6.0 two-thirds of
the time. Putting it in another way, corn prices in November are expected
to be between 88% and l00% of the annual average in two years out of
three.

The extent to which the seasonal price pattern has been shifting
over time is indicated by the trend. The number for each month indicates
the index's annual rate of change. The .2 for November indicates that

the November index rose about .2 each year over the l960-78 period.§/

 

é-/Further explanation of seasonals and their use is found in Ferris l979.

48

The price of corn increases in the post harvest period, from
November in response to storage and interest charges. Protein feed-
stuffs are more variable in their seasonal price patterns. There was
a rise in price for DDGS, CGM and SBM following fall harvest, but a
decrease appeared in late winter with price recovery in late spring and
a general weakening in price prior to harvest. CGF also exhibited the
price increase following harvest, but the late winter price decline
continued through spring and summer with a slight increase prior to
harvest.

While seasonal price patterns exist, year-to-year variations are
great. This means that there needs to be an understanding of the
underlying fundamentals of the supply and demand situation for a given
year in addition to the traditional seasonal price patterns when making
pricing and storage decisions. These indices of monthly prices are a
first approximation. Careful attention to current market conditions is

important.

3.5 Summary

This chapter identified three time frames of interest to fuel
ethanol plant managers requiring knowledge of CGF and CGM price. Long-
term feedstock to by-product relationships form a first approximation
for investment analysis purposes prior to plant construction. Annual
planning focuses on annual marketing strategy and estimation of probable
cash flows while short-run price movement forecasts can be used in pric-
ing and inventory decision making.

Reasonable estimates of the long-term by-product to feedstock

relationships are .53, .39 and .98 for DDGS, CGF and CGM respectively.

49

Analysis of annual and secular market relationships indicate that DDGS
has been priced based on its crude protein content. The secular price
relationship of CGM, relative to SBM, is consistent with their relative
crude protein concentrations. In the short-run, however, the price of
corn exerts a significant impact on the price of CGM. The analysis of
both secular and annual market relationships indicate that CGF has not
been priced based on crude protein characteristics; that is, it has been
consistently undervalued by nearly 10%.

Short-run price forecasts were studied. Indexes of seasonal varia-
tion "filter" most of the information relevant for short-run pricing and

inventory control decisions.

CHAPTER FOUR
NUTRITIONAL VALUATION

4.l Introduction

 

The nutritional valuation of distillers dried grains with solubles
(DDGS), corn gluten feed (CGF). and corn gluten meal (CGM) will be de-
termined based on use in fed beef, swine, dairy and poultry diets.
Diets will be formulated to meet each species' nutrient requirements
for specified performance rates.

Fed beef diets will be formulated for three weight groups and
three rates of daily gain. Lactating dairy cow diets will not be for-
mulated but the approximate substitution rates of the by-products for
corn, SBM and urea can be inferred from fed beef diets. Swine diets
will be formulated for four weight categories and poultry diets will
be formulated for layers and broilers. All diets have one reference
formulation and at least one alternative for each by-product. The
techniques discussed in Chapter Two will be used to identify the most
valuable uses of these feeds and formulate equations describing the
fuel ethanol by-products' economic value.

The discussion will be divided into two broad categories: rumi-
nants (beef and dairy) and monogastrics (swine and poultry). A brief

description of nutrient requirements for each species will be given as

50

51

will the nutritional characteristics of DDGS, CGF and CGM. The linear
program model used to formulate the diets will be presented and the re-

sults summarized.

4.2 Ruminants: Beef and Dairy Dietary Needs

Use of distillers and corn gluten products in ruminant diets has
been as specialty feeds based on their fiber content (pellet-binding
ability), oil content (feed handling ease) or unidentified performance
factors (Black £3 11., l981a). With greater production of DDGS, CGF
and CGM, their use is expected to be determined by nutritional charac-
teristics such as protein, energy and minerals. The unique protein
characteristics of these by-products in ruminant diets are expected to
determine their best use and highest economic value.

Understanding recent developments in ruminant protein metabolism
is critical to optimize the use of ethanol by-products. Figure 4.2.l
presents an illustration of protein (nitrogen) utilization by ruminants.
Dietary protein consumed by the ruminant is either degraded by bacterial
proteolytic enzymes in the rumen to amino acids, peptides and ammonia
or escapes degradation in the rumen and flows to the lower digestive
tract for digestion. This latter protein is referred to as bypass pro-
tein. The extent of protein degradation depends on factors such as
structural characteristics of the protein, heat applied during proces-
sing and the diet in which the protein is fed. By-products produced
from corn have been shown to have protein resistant to ruminal degrada-
tion. Dietary nonprotein nitrogen (NPN) is readily degraded to ammonia.

If adequate energy from dietary carbohydrates in the rumen is pre-

sent, amino acids, peptides and ammonia from either dietary protein or

52

 

    
 
    
 
 
 
 
  
  

PROTEIN THAT ESCAPES “35°33”

BACTERIAL BREAKDOWN '

In")
L.-I-JJ

 

 

’2 DIGE'STED
TRUE NPN
PROTEIN 329% 338%?15“
F000 2
EXCRETED
'" ”RM RUMEN INTESTINE FECES

Figure 4.2.1. Schematic Illustration of Nitrogen Utilization by the
Ruminant (Sattergtgl. l977).

53

NPN can be incorporated into microbial protein. Any ammonia not con-
verted is lost and will require the expenditure of energy for excretion.
Both bypass and microbial protein are subject to digestion by the ani-
mal's proteolytic enzymes in the small intestine. Some dietary protein,
a fiber bound fraction, may be unavailable for digestion in the small
intestine. The unavailable protein, measured as acid detergent insol-
uble nitrogen (ADIN), is a function of the type of feedstuffs in the
diet and feed processing methods. It is the available bypass protein
that is nutritionally important. The amino acids and peptides absorbed
from the small intestine must meet the animal's amino acid requirements
for maintenance and production (gain, milk or fetal growth). Concep-
tually, the ruminant has a digestive system analogous to monogastrics
except that all nutrients must pass through a fermentation vat (the
rumen) prior to entering the small intestine.

The quantity of protein required by the high producing dairy cow
in early lactation and the rapidly growing feedlot calf usually cannot
be met from diets composed of feedstuffs grown on the farm (e.g. corn,
corn silage and alfalfa) and supplemental protein sources highly de-
graded in the rumen (e.g. urea). Alfalfa may be able to supply adequate
protein if its quality is excellent. Supplemental protein sources con-
taining a significant amount of bypass protein such as SBM, DDGS, CGF or
CGM are needed. In later stages of lactation for the dairy cow and in
the feedlot finishing stage when dietary protein requirements are lower,
sufficient energy is available from dietary carbohydrates to permit
meeting all or most of the supplemental protein requirements from high-

ly degraded protein sources.

54

SBM is the most commonly used protein supplement containing bypass
protein. DDGS, CGF and CGM have protein less degradable than SBM,
making them good sources of bypass protein.

The quality, as well as quantity, of the bypass protein entering
the small intestine is important. If lysine is one of the first limit-
ing amino acids for ruminants, as shown in some studies, feeding ethanol
by-products derived from corn will not necessarily supply more lysine
to the small intestine than SBM. Even though more by-product protein
escapes ruminal degradation the amount of lysine in the diet may be less
because lysine is a smaller percentage of the protein in these by-pro—
ducts than in SBM (Satter, 1977). Knowledge of this aspect of protein
metabolism is in its infancy.

Klopfenstein and co-workers (Rounds, 1975; Peterson, 1977; Waller,
1978; and Merchen, 1978) analyzed use of DDGS and CGM for their bypass
protein characteristics. For example, use of DDGS in combination with
urea to replace SBM in ruminant diets was examined. Because 70 to 80%
of the protein in SBM is degraded in the rumen, a less degradable source
of protein (e.g. DDGS is 45%) could be combined with a 100% degradable
source to yield a feedstuff equivalent to SBM in protein characteristics.

This concept was tested by feeding rapidly growing feedlot calves
diets equal in energy and crude protein across treatments (Klopfenstein
gt al., 1978). Calves fed a supplemental protein composed of a combi-
nation of CGM and urea (50% of the protein came from CGM, 50% from urea)
gained at the same rate as calves fed diets supplemented with SBM (1.58
versus 1.61 lb/day). Calves fed a control diet (100% degradable pro-
tein supplement) gained significantly less (1.26 1b/day). The average

initial weight of the calves was 500 lbs, a weight corresponding to the

55

period in the animal's growth when protein requirements are high and
responses to differences in protein characteristics can be demonstrated.
Results of these experiments are consistent with the bypass protein con-
cept and the lower degradability of these ethanol by-product feeds.

Klopfenstein and co-workers conducted experiments with finishing
cattle whose supplemental protein requirements were hypothesized to be
able to be met with a 100% degradable protein source such as urea, be-
cause their required dietary protein concentrations are lower. The
performance of cattle receiving the isocaloric and isonitrogenous urea-
supplemented and DDGS-supplemented diets were identical, as expected.
These results also are consistent with the protein metabolism concepts
outlined previously.

Formulating ruminant diets requires consideration of the animal's
protein requirement, the amount of microbial protein that can be synthe-
sized in the rumen and the quantity and quality of protein in each feed-
stuff. The limitation on the amount of ammonia convertible to micro-
bial protein in the rumen is defined as the ammonia utilization poten-
tial (AUP); it is a function of dry matter intake, digestible energy,
the extent ruminal organic matter digestion and efficiency of cell
yield (Bergen, Black and Fox, 1978). Table 4.2.1 depicts the upper
bound on microbial protein that can be synthesized from ammonia

(Waller gt_al, 1981a).

56

Table 4.2.1. Energy Levels and Upper Bounds on Microbial Protein Syn-
.thesis for Corn--Corn Silage Combinations

 

 

 

Diet Composition NE Upper Bound on Microbial
. . Protein Production
Corn grain Corn gilage Mcal/lb g/kg Diet Dry Matter
20 80 .49 20.7
40 60 .53 22.1
80 20 .63 26.5

 

Working with these concepts, linear programming techniques are used to
formulate diets for ruminants reflecting the importance of bypass pro-
tein.

Wide variation, caused by different feedstocks and distilling and
processing techniques, has typified the literature describing DDGS, CGF
and CGM's nutrient value. Values published by the National Research
Council provide a reference point (Table 4.2.2) and were used in the
ruminant analysis.

These feeds, as previously stated, are of special interest in
diets for rapidly growing calves and dairy cows in early lactation. In
these diets, optimal performance requires dietary protein concentrations
above the ruminants' ability to synthesize microbial protein; signifi-
. cant amounts of bypass protein are needed. DDGS, CGF and CGM are ex-
pected to be most valuable in this type of diet when, in combination

with a NPN feed, they replace SBM.

4.3 Beef
Linear programming techniques were used to formulate diets for
475 lb, 600 lb. and 850 lb steers of average frame size. Diets ranged

from high roughage to high concentrates reflecting alternative rates of

57’

 

 

 

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.~.~.¢ opauh

58

daily gain. Reference diets are formulated using commonly available
feedstuffs. Alternative formulations of each diet are found by: 1)
pricing DDGS, CGF or CGM such that they just enter the diet; and 2)
lowering their price to get alternatives with increasing proportions of
the by-products in the diet. Alternative methods of formulating diets
are available, but the Michigan Net Protein System (Bergen §t_al,, 1978)

was chosen because it incorporates the bypass protein concept.

Description of Linear Programming Model

Table 4.3.1 shows the linear programming matrix for fed beef.
Five components generally make up a linear programming matrix: 1)
requirements including type and value; 2) nutrient values; 3) upper
and lower bounds on the amount of feedstuff (or combinations of feed-
stuffs) that can enter the diet; 4) equations (or systems of equations)
defining biological and economic relationships; and 5) the objective
function (Black and Hlubik, 1980). These are presented in the matrix.

Requirements, by type and value, are the right hand side of the
horizontal rows in Table 4.3.1. In this study, there are seven sets of
right hand sides, one for each weight and energy level combination.
The type of restriction tells if the diet requires an amount "less than
or equal to," "equal to," or "greater than or equal to" the right hand
side. ‘

The nutrient values of the feeds make up most of the body of the

matrix and indicate the contribution each feed could make to a diet.

59

 

 

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60

Upper and lower bounds keep nutrients within a desired level as illus-
trated by the bound on the calcium-phosphorus ratio-l!

The interaction between performance and dietary nutrient concen-
trations determines the right hand side values. By selecting a per-
formance rate, the dietary nutrients necessary to obtain this rate
yields the nutrient specifications--the right hand side parameters.
Objective functions are either minimized or maximized. For this model,
as shown by the final equation in Table 4.3.l, the objective is feed
cost minimization.

The ammonia utilization potential (AUP) constraint enables diet
formulation based on bypass protein characteristics of the by-products.
Feeds that are net suppliers of ammonia from crude protein degradation
in the rumen must be balanced by ones that are net users. For an in-
dividual feed, total digestable nutrients and microbial protein upper
bound influence the amount of ammonia used in the rumen while protein
quantities and degradability determine the amount of ammonia supplied.
The difference is the AUP of the feed. Requiring the sum of all AUP to
be greater than or equal to zero restricts the use of feeds that are

highly degraded (large net suppliers).

 

l/Let Ca Za1.x. .93 = Zaij’fj > A - Aazj)xj 3_0
J J P zaszj

P

2a..
23x3

jth

feedstuff

Ca content in j feedstuff
= P content in jth feedstuff
required ratio

where x
th

III
II

>2
ll

61

The AUP of an individual feed is:
AUP(g/kg of feedstuff) = NH3 Used - NH3 Supplied

NH3 Used = [(Microbial protein upper bound)(Total
digestible nutrients)(2)]

NH3 Supplied = [(Protein concentration)(454)(Degradation
rate of crude protein in the rumen)(NH3
fraction retained in the rumen)]

AUP(g/kg of feedstuff) = [(Microbial protein upper bound)(To-
tal digestible nutrient)(2)] -
[(Protein concentration)(degradation

rate of crude protein in the rumen)
(NH3 fraction retained in rumen)]_/

The values for total digestible nutrients, total protein and de-
gradation of crude protein for each feedstuff are presented in Table
4.2.2. The upper bound on microbial protein is a function of the energy
level of the diet (Table 4.2.l); the NH3 fraction retained in the rumen
is treated as a constant 0.85. AUP for a particular feed is the
amount of ammonia that can be utilized from another source (in addition
to its own). The only feeds capable of utilizing all of their ammonia
as well as some of that of other feeds are corn grain, corn silage and
CGF (Table 4.3.1). Even though they are net suppliers of ammonia, DDGS
and CGM do not supply as much as SBM or urea. This is the advantage
of the by-product feeds. For a further understanding of the AUP, the

reader is referred to Bergen gt_gl, (l978) and Waller et 31, (1979).

Special Problems

 

The by-products DDGS and CGF are characterized as having large

variation of feedstuff nutrient density which may be handled by

 

g-/Appendix A includes an example of how this is applied to an indivi-
dual feedstuff.

62

implementing a safety factor. Two general approaches are available.

One increases the nutrient specification of the diet so that a require-
ment of bi becomes bi + safety factor. A weakness of this approach is
that no distinction is made between feedstuffs of equal average nutrient
density but different coefficients of variation. This approach masks
the fact that feedstuffs having higher variation of nutrient densities
are less valuable. The other method accounts for an individual feed's
nutrient variability. By subtracting a fraction of the feed's standard
deviation from the average nutrient density an adjusted nutrient density
is obtained which can be used in place of the average nutrient density
in the linear programming matrix. How large an adjustment to make on
the average nutrient density depends on the desired probability of
success which is a trade-off between increasing feed costs (to meet

the requirement) and losing performance (when the requirement is not
met) (Black and Hlubik, l980).

Black, Peterson. and Fox (l978) dealt with the question of safety
factor size and found that as the feed beef animal or pig becomes more
mature the cost of being wrong increases; thus the safety factor for
heavier animals should be higher than for lighter ones to minimize (in
a probabilistic sense) expected average cost. Once the desired rate of
success is chosen, the safety factor can be found. The general equa-

tion for this approach is:

Aij = U.” " L Vii.

Where: Aij is the adjusted nutrient density

uij is the average nutrient density

L is a proportion of the standard deviation

Vij is the variance of the feedstuff nutrient value.

63

If the variation of nutrient density follows the normal distribution,
values of L can be found from a standard normal probabilities statisti-
cal table for varying levels of probability. Table 4.3.2 depicts the
values of L necessary to bring about different probability levels of
success in meeting dietary requirements. For example, if a manager has
a feed with an average nutrient density of 10% and a standard deviation
of 2% and has a desired probability of success of 90% (L = 1.29), the
adjusted nutrient density would be:
Aij = lO - (l.29)(2) = 7.42

Risk is a concern of managers with most being risk adverse; knowing
how to best utilize these feeds in the face of variable nutrient den-
sities is important. Appendix A goes into further detail on the imple-

mentation of safety factors and impact on ration composition. This

study will use the average nutrient density unadjusted for variability.

Results

Diets were formulated for each combination of weight category and
energy level (Tables 4.3.3 through 4.3.23) by implementing the linear
programming techniques previously discussed. By-products are incorpor-
ated in the diet at the price which makes the new diet equal in cost to
the reference diet. The by-product price is then reduced to examine
the impact of the price of the by-product, relative to SBM, on the pro-
portion of the by-product in the diet. This information is used to find
the economic value of the by-products as a function of the prices of the
competing products.

Ranking the different uses of the by-product, the most valuable
uses of DDGS (Table 4.3.24), CGM (Table 4.3.25) and CGF (Table 4.3.26)

64

Table 4.3.2. Values of L To Achieve Desired Level of
Probability of Meeting Dietary Requirements

 

Probability of

 

Meeting Value of Adjustment Factor L
Requirement Standard Deviations

.50 .OO

.60 .26

.70 .53

.75 .68

.80 .85

.85 1.04

.90 1.29

.95 l.65

.99 2.33

 

65

Table 4.3.3. Beef Diets Formulated for a 475 lb Steer, .49 NE Mcal/lb,
Using Differing Prices of DDGS 9

 

Price of DDGS/Price of SBM

 

 

Ingredient Reference 1.066 1.016 .595 .358
------- Proportion of Dry Matter- - - - - -
Corn Grain Nfé/ .072 .073 -- --
SBM 44 -- .094 -- -- --
Urea -- -- .008 .004 --
Corn Silage —- .759 .759 .767 .731
DDGS -- .071 .156 .227 .266
Limestone -- .001 .001 .002 .023
Dicalcium Phosphate -- .003 .003 .001 --

 

2{Not feasible.

Table 4.3.4. Beef Diets Formulated for a 475 lb Steer, .53 NE Mcal/lb,
Using Differing Prices of DDGS 9

 

Price of DDGS/Price of SBM

 

Ingredient Reference. 1.016 .595 .519 .487 .279

 

------ Proportion of Dry Matter - - - - - -

Corn Grain .303 .304 .130 .119 -- --
SBM 44 .126 -- -- -- -- --
Urea —- .012 -- —- -- --
Corn Silage .564 .564 .584 .582 .559 .431
0065 -- .114 .281 .295 .435 .561
Limestone .002 .003 .004 .005 .006 .008

Dicalcium Phosphate .004 .004 -- -- -— --

 

66

Table 4.3.5. Beef Diets Formulated for a 600 1b Steer, .49 NE Mcal/lb,
Using Differing Prices of DDGS 9

 

Price of DDGS/Price of SBM

 

Ingredient Reference .600 .595 .519 .487

 

------ Proportion of Dry Matter- - - - - -

Corn Grain .217 .183 .034 .018 --
SBM 44 -- -- -- -- --
Urea .012 .010 -- -- --
Corn Silage .767 .771 .788 .786 .783
DDGS -- .031 .176 .194 .215
Limestone -- -- .002 .002 .002
Dicalcium Phosphate .005 .004 .001 -- --

 

Table 4.3.6. Beef Diets Formulated for a 600 1b Steer, .53 NE Mcal/lb,
Using Differing Prices of DDGS 9

 

Price of DDGS/Price of SBM

 

Ingredient Reference .594 .519 .487 .279

 

------ Proportion of Dry Matter- - - - - -

Corn Grain .401 .228 .196 -- --
SBM 44 -- -- -- -- --
Urea ' .012 -- -- -- ~-
Corn Silage .581 .601 .597 .559 .431
DDGS -- .167 .203 .435 .561
Limestone .001 .003 .004 .006 .008

Dicalcium Phosphate .005 .001 -- -- --

 

67

Table 4.3.7. Beef Diets Formulated for a 600 lb Steer, .63 NE Meal/1b,
Using Differing Prices of DDGS 9

 

Price of DDGS/Price of SBM

 

Ingredient Reference .618 .577 .487

 

----- Proportion of Dry Matter- - - - -

Corn Grain .778 .650 .298 .120
SBM 44 .103 .073 -- --
Urea -- -- -- --
Corn Silage .109 .104 .058 .023
DDGS -- .165 .632 .843
Limestone .007 .009 .012 .014
Dicalcium Phosphate .003 -- -- --

 

Table 4.3.8. Beef Diets Formulated for a 850 1b Steer, .53 NE Hcal/lb,
Using Differing Prices of DDGS 9

 

Price of DDGS/Price of SBM

 

Reference .595 .519 . .487

 

- - - - Proportion of Dry Matter - - - -

Corn Grain .375 .349 .287 --
SBM 44 -- -- -- --
Urea .002 -- -- --
Corn Silage .619 .622 .615 .559
DDGS -- .026 .095 .435
Limestone .002 .002 .003 .006

Dicalcium Phosphate .002 .002 -- --

 

68

Table 4.3.9. Beef Diets Formulated for a 850 1b Steer, .63 NE Mcal/lb,
Using Differing Prices of DDGS 9

 

Price of DDGS/Price of SBM

 

Ingredient Reference .618 .577 .487

 

- - - - Proportion of Dry Matter - - - -

Corn Grain .775 .770 .298 .120
SBM 44 .099 .098 -- ~ —-
Urea -- -- -- --
Corn Silage .119 .119 .058 .023
DDGS -- .006 .632 .843
Limestone .001 .007 .012 .014
Dicalcium Phosphate .0001 -- -- --

 

Table 4.3.10. Beef Diets Formulated for a 475 1b Steer, .49 NE Meal/1b,
Using Differing Prices of CGM 9

 

Price of CGM/Price of SBM

 

 

 

Ingredient Reference. 2.197 2.036 2.030 » .695
------ Proportion’of Dry Matter - - - - - -
Corn Grain NFE/ .119 .147 .179 .131
SBM 44 -- .097 .051 -- --
Urea -- -- .004 .004 --
Corn Silage -- .758 .758 .757 .773
CGM -- .022 .035 .049 .091
Limestone -- .001 -- -- --
Dicalcium Phosphate -- .004 .005 .006 .006
271-. feasible.

69

Table 4.3.11. Beef Diets Formulated for a 475 1b Steer, .53 NE Mcal/lb,
Using Differing Prices of CGM 9

 

Price of CGM/Price of SBM

 

Ingredient Reference 2.035 .695 .463

 

----- Proportion of Dry Matter - - - - -

Corn Grain .303 .381 .316 .242
SBM 44 .126 -- -- --

Urea -- .012 -- --

Corn Silage .564 .564 .584 .553
CGM -- .036 .092 .199
Limestone .002 .002 .002 .002
Dicalcium Phosphate .004 .007 .006 .004

 

Tab1e 4.3.12. Beef Diets Formulated for a 600 lb Steer, .49 NE Mcal/lb,
Using Differing Prices of CGM 9

 

Price of CGM/Price of SBM

 

Ingredient Reference .695 . . .466 .463

 

----- Proportion of Dry Matter - - - - -

Corn Grain .217 .151 .130 .079
SBM 44 -- -- -- --
Urea .012 -- -- --
Corn Silage .767 .788 .780 .758
CGM -- .058 .086 .159
Limestone -- -- -- .001

Dicalcium Phosphate .005 .004 .004 .003

 

70

Table 4.3.13. Beef Diets Formulated for a 600 lb Steer, .53 NE
Mcal/lb, Using Differing Prices of CGM g

 

Price of CGM/Price of SBM

 

Ingredient Reference .699 .463

 

--Proportion of Dry Matter--

Corn Grain .401 .338 .238
SBM 44 -- -- --

Urea .012 -- --

Corn Silage .581 .601 .559
CGM -- .055 .199
Limestone .001 .002 .003
Dicalcium Phosphate .005 .004 .002

 

Tab1e 4.3.14. Beef Diets Formulated for a 600 lb Steer, .63 NE
Mcal/lb, Using Differing Prices of CGM g

 

Price of CGM/Price of SBM

 

Ingredient Reference .422

 

--Proportion of Dry Matter--

Corn Grain .778 .682
SBM 44 .103 .112
Urea -- --

Corn Silage .109 .067
CGM -- .129
Limestone .007 .008

Dicalcium Phosphate .003 .001

 

71

Table 4.3.15. Beef Diets Formulated for a 850 lb Steer, .53 NE Mcal/lb,
Using Differing Prices of CGM 9

 

Price of CGM/Price of SGM

 

Ingredient Reference .695 .463 .445

 

----- Proportion of Dry Matter- - - - -

Corn Grain .375 .367 .260 .234
SBM 44 -- -- -- --
Urea .002 -- -- --
Corn Silage .619 .622 .577 .564
CGM -- .009 .160 .199
Limestone .002 .002 .003 .003
Dicalcium Phosphate .002 .002 -- --

 

Tab1e 4.3.16. Beef Diets Formulated for a 850 lb Steer, .63 NE Mcal/lb,
Using Differing Prices of CGM . 9

 

Price of CGM/Price of SBM

 

Ingredient . . .Reference . .422. .» .390

 

- - - Proportion of Dry Matter - - -

Corn Grain .775 .769 .679
SBM 44 .099 .099 .111
Urea -- -- --

CGM -- .008 .132
Limestone .007 .007 .008

Dicalcium Phosphate .0001 -- --

 

72

Table 4.3.l7. Beef Diets Formulated for a 475 1b Steer, .49 NE Mcal/lb,
Using Differing Prices of CGF 9

 

Price of CGF/Price of SBM

 

 

Ingredient Reference .942 .893 .517 .476
------- Proportion of Dry Matter- - - - - -

Corn Grain NFE/ .072 .105 .081 --

SBM 44 -- .093 -- -- --
Urea -- -- .010 .009 .003
Corn Silage -- .731 .707 .701 .668
CGF -- .102 .176 .208 .327
Limestone -- .001 .002 .002 .003

Dicalcium Phosphate -- .001 .001 -- --

 

éjNot feasible.

Table 4.3.18. Beef Diets Formulated for a 475 lb Steer, .53 NE Mcal/lb,
Using Differing Prices of CGF 9

 

Price of CGF/Price of SBM

 

Ingredient Reference .893 .517 .476 .420

 

------ Proportion of Dry Matter - - - - - -

Corn Grain .303 .327 .257 .146 --
SBM 44 .126 -- -- -- --
Urea .0004 .013 .008 -- --
Corn Silage .564 .526 .508 .463 .353
CGF -- .218 .223 .386 .640
Limestone .002 .003 .004 .005 .007

Dicalcium Phosphate .004 .003 -- -- --

 

73

Table 4.3.19. Beef Diets Formulated for a 600 lb Steer, .49 NE Mcal/lb,
Using Differing Prices of CGF 9

 

Price of CGF/Price of SBM

 

Ingredient Reference .523 .517 .476 .420

 

------ Proportion of Dry Matter - - - - -

Corn Grain .217 .193 .107 .043 --
SBM 44 -- -- -- -- --
Urea .012 .011 .005 -- --
Corn Silage .767 .762 .740 .714 .681
CGF -- .031 .147 .241 .317
Limestone -- -- .001 .002 .003
Dicalcium Phosphate .005 .004 -- -- --

 

Table 4.3.20. Beef Diets Formulated for a 600 1b Steer, .53 NE Mcal/lb,
Using Differing Prices of CGF 9

 

Price of CGF/Price of SBM

 

Ingredient Reference .517 . .476 .420

 

- - - - Proportion of Dry Matter - - - -

Corn Grain .401 .287 .236 --
SBM 44 -- -- -- --
Urea .012 .004 -- --
Corn Silage .581 .552 .531 .353
CGF -- .154 .230 .640
Limestone .001 .003 .004 .007

Dicalcium Phosphate .004 -- -- --

 

74

Table 4.3.21. Beef Diets Formulated for a 600 1b Steer, .63 NE Mcal/lb,
Using Differing Prices of CGF 9

 

Price of CGF/Price of SBM

 

Ingredient Reference .490 .429

 

- - - Proportion of Dry Matter - - -

Corn Grain .778 .714 .616
SBM 44 .103 .097 .095
Urea -- -- --
Corn Silage .109 .074 --
CGF -- .107 .279
Limestone .007 .008 .009
Dicalcium Phosphate .003 -- --

 

Table 4.3.22. Beef Diets Formulated for a 850 1b Steer, .53 NE Mcal/lb,
Using Differing Prices of CGF 9

 

Price of CGF/Price of SBM

 

Ingredient Reference .517 .462 . .420

 

- - - - Proportion of Dry Matter - - - -

Corn Grain .375 .349 .326 --
SBM 44 -- -- -- --
Urea .002 -- -- --
Corn Silage .619 .612 .600 .353
CGF -- .036 .072 .640
Limestone .002 .002 .002 .007

Dicalcium Phosphate .002 .001 -- _-

 

75

Table 4.3.23. Beef Diets Formulated for a 850 lb, .63 NE Mcal/lb,
Using Different Prices of CGF 9

 

Price of CGM/Price of SMB

 

Ingredient Reference .490 .429

 

--Proportion of Dry Matter--

Corn Grain .775 .772 .616
SBM 44 .099 .099 .095
Urea -- -- --
Corn Silage .119 .118 --
CGF -- .004 .279
Limestone .007 .007 .009

Dicalcium Phosphate .0001 -- --

 

 

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77

Table 4.3.25. Relative Value of CGM in Beef Diets

 

 

CGM PRICE Fraction CGM Diet
SBM Price In Diet Weight NEgMcal/lb Comment
2.197 .022 475 .49 Combine with SBM to give a
feasible diet
2.036 .035 475 .49 Replace limestone
2.035 .036 475 .53 Combine with urea to replace
SBM
2.030 .049 475 .49 Combine with urea to replace
SBM
.695 .055 600 .53 Replaces urea
.695 .09l 475 .49 Replaces urea
.695 .092 475 .53 Replaces urea
.695 .058 600 .49 Replaces urea
.695 .009 850 . .53 Replaces urea
.466 .086 600 .49 Replaces some corn
.463 .199 475 .53 Replaces some corn
.463 .l59 600 .49 Combines with linmstone
to replace dicalcium phosphate
.463 .199 600 .53 Replaces some corn
.463 . l60 850 . 53 Replaced dicalcium phosphate
.445 .199 850 .53 Replaces some corn

.422 .129 600 .63 Used as an energy source

 

78

 

 

 

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79

can be identified. Table 4.3.24 lists the relative value of DDGS in

fed beef diets. Its highest price relative to SBM (1.066:1) is obtained
when it makes up 7.1% of the 475 lb .49 NEg Mcal/lb diet. The lower pro-
tein degradation rate of 0065 is used to offset the higher rate of SBM;
thus, a combination of SBM and DDGS gives a feasible diet where SBM alone
will not. At a relative price of 1.016, DDGS is 15.6% and 11.4% of the
475 lb .49 NEg Mcal/lb and the 475 lb .53 NEg Mcal/lb diets, respectively.
A combination of DDGS and urea replaces SBM. The tables for CGM and
CGF's most valuable uses can be interpreted similarly. Nine tenths lb

of DDGS and .10 lb or urea equals one pound of SBM (Table 4.3.3).31
Relative values typically cluster in groupings which enable a market
clearing price to be estimated. For instance, the highest valued

cluster for DDGS is DDGS/SBM price 1.016, the next .618, then .600 and
.595. If DDGS will be used in lactating dairy cow and rapidly growing
feedlot calf diets, based upon its bypass protein characteristics, then
the DDGS/SBM price ratio should be 1.016 (Table 4.3.24). The next most
valuable use is as a replacement for dicalcium phosphate in high energy
diets in which the relative value of DDGS decreases to .618.

The results of the formulations confirm what was expected. The high-
est value of these by-products is in diets where the animal is unable to
synthesize sufficient microbial protein for optimal performance. The high-
er relative value of CGM (2.030) compared to DDGS (1.016) and CGF (.893)

is due to the fact the CGM's crude protein concentration is much greater.

 

3lThis ratio is determined by dividing the quantities of DDGS and urea
that replace SBM in the 475 lb .53 NE Mcal/lb fed beef diet by the
quantity of SBM replaced. 9

80

Use of the by-products for other than bypass protein characteristics
results in a significant reduction in value. Other uses include re-
placing urea (an inexpensive protein source), dicalcium phosphate and
use as an energy source.

This analysis is helpful in identifying the most valuable uses of
DDGS, CGF and CGM. But, to find the economic value, it is necessary to
use the methodology discussed in Chapter Two to formulate equations
which value the by-product feeds based on the prices and proportions
of all feedstuffs used in the reference and alternative diets for DDGS
(Table 4.3.27), CGM (Table 4.3.28) and CGF (Table 4.3.29). For the
three feedstuffs their highest economic value is obtained when they

are used in the 475 lb .53 NEg/lb diet. The three equations are:

PDDGS = -.009 PC + 1.105 PSBM - .105 PU - .009 PL
PCGM = 2.167 PC + 3.500 PSBM - .333 PU - .083 PDc
PCGF = -.055 PC + .984 PSBM - .098 PU - .008 PL + .008 PDc

These equations change as the proportion of the by-products in the diet
changes. This price more accurately reflects the by-products' competi-
tive price than the price relative to SBM price. A basic assumption in
this economic evaluation is that a reference diet exists to which a1-
ternatives can be compared.

This assumption is violated in the 475 lb .49 NE Mcal/lb diet. For

9

the .49 NE Mcal/lb diet it was impossible to formulate a diet that

9
simultaneously meets the net protein requirement without exceeding the
ammonia utilization potential when using corn silage and SBM. A diet
could not be formulated to achieve the daily gain potential of the
energy available. The framework developed in Chapter Two which takes

into account differing feed efficiency and daily gains is the

81

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84

appropriate analytical framework. However, this was determined to be
beyond the scope of this study. Because no .49 NEg Mcal/lb diet,
reference diet was formulated for the 475 lb feedlot animal an equation
describing the by-products' economic value in that diet was not derived.
However, the by-products economic value will be at least as large as
the highest value of the by-products in the 475 lb .49 NEg diet is in-
cluded in the grouping of other rapidly growing calf diets (Tables

4.3.24 to 4.3.26).

4.4 Dairy

The bypass protein concept originated in the dairy industry, but
its application is better understood in fed beef diets. Diets were
not formulated for lactating dairy cows because of lack of definitive
knowledge of the parameters of the "protein utilization" component of
the lactating dairy cow diet formulation model. Also, the impact of
feeding a diet containing a combination of ethanol by-products and urea
for a number of years is important. Risk management is also a greater
concern in lactating dairy cow diets. The cost of being wrong, partic-
ularly in early lactation, is higher in dairy than beef. Once milk
production is lost as a result of dietary deficiencies, the previous
milk production level cannot be regained by improving the diet. In con-
trast, the rapidly growing beef calf exhibits compensatory weight gain
with an improved diet.

Even though there are uncertainties, the similarities between pro-
tein requirements for the rapidly growing calf and the dairy cow in

early lactation makes a generalization from the two diets possible.

85

Both the dairy cow in early lactation and the rapidly growing beef calf
are unable to synthesize sufficient microbial protein for optimal per-
formance; a source of bypass protein is needed.

The results developed for growing and finishing feedlot steers
can be extrapolated to lactating dairy cows. For example, a blend of
90% DDGS and 10% urea has the same crude protein content and similar
protein degradation characteristics as SBM. Thus, the blend can be sub-
stituted for SBM according to recommended SBM feeding schedules.5/ The
procedure can also be extended to other ethanol by-products (Black gt
31,, l98lc). As research answers questions about feeding these by-
products to dairy these estimates may change but in the interim they
should be useful in estimating the amount of by-products useable in

dairy diets.

4.5 Monogastrics: Swine and Poultry Dietary Needs

Monogastrics are unable to synthesize protein from non-protein
nitrogen sources; a proper balance of amino acids must be present in
the diet for optimal performance. 0f the ten essential amino acids,
lysine and tryptophan are typically most limiting in swine and poultry
diets (Black gt 31.,198la).

A measure of the protein quality required for the growing and
finishing pig is given by the ratio of lysine required to the crude
protein concentration of the corn-SBM reference diet; the ratio is

approximately 0.044. In contrast, the ratio for corn is 0.027 compared

 

fl/In practice, the blend would be substituted for SBM at ever increas-
ing rates and resultant performance observed. The computer formulated
diets serve as guidelines, but must be field tested.

86

to 0.064 for SBM. Since DDGS from corn is a concentration of the pro-
tein fraction in corn (modified slightly by the yeast added during fer-
mentation), its lysinezcrude protein ratio is also 0.027. In contrast,
a blend of 53.4% corn and 46.6% SBM, a 29.3% crude protein product like
DDGS, has a ratio of 0.057. Similarly, the ratio of tryptophan to crude
protein for DDGS is 0.68 vs. 1.23 for the corn-SBM blend. Thus, DDGS

is a relatively poor protein source for swine because of its amino acid
composition, as is CGF and CGM (Black gt_gl,, l98la).

Phosphorus is an important mineral in the nutrition of livestock
and poultry. Unfortunately, plants are poor sources of available phos-
phorus for monogastrics. A large proportion of the phosphorus in
plants is in the phytate form which is not readily utilized by monogas-
trics. Approximately two-thirds to three-fourths of the phosphorus
present in common feed grains and protein sources is in the phytate form
(Cromwell, l979). Distillers feeds have a lower percentage of phytate
phosphorus than other plant sources, presumably due to the fermentation
process (Singsen, l948). In contrast, most inorganic phosphorus sources
have high concentrations of available phosphorus (Hays, 1976). Research
has shown that the availability of phytate phosphorus in feed grains
and oilseed meals is low compared to inorganic sources (Cromwell, l979).

DDGS, CGF and CGM have been used in small proportions as specialty
feeds in monogastric diets based on their pellet binding characteristics

and unidentified performance factors. Additionally, CGM is used in

broiler and layer diets because of its xanthophylll content. Use of DDGS,
CGF and CGM will depend more on nutritional characteristics such as amino

acid balance and mineral content as specialty uses are exhausted.

87

Swine diets were formulated for four weight groups and compared to
reference diets using SBM as the protein supplement. Poultry diets for
laying hens and starter and finisher broilers were developed. CGF was
not expected to be used in poultry diets because of its high crude

fiber content; it was excluded from the poultry analysis.

4.6 Swing

DDGS is relatively high in phosphorus (.77%) and low in phytate
(P005 and Klopfenstein, 1979). The phosphorus requirement was handled
by requiring 30% of the phosphorus supplied to the diet come from
non-plant sources. An improvement on this procedure would be to ex-
plicitly enter the available phosphorus of feedstuffs and that diet
formulation would be on an available phosphorus basis. 0065' amino
acid balance is poor reflecting the feedstock corn's, so that it does
not replace SBM as sole supplemental protein supplement; additionally,
its high crude fiber content along with the low amount allowed in swine
diets limits its use. CGM has a slightly better amino acid profile en-
abling it to replace SBM but only after large quantities of it enter the
diet. Linear programming techniques were used to formulate reference
diets for four weight classes of swine (22-44, 44-77, 77-l32 and 132-220

lbs), and alternative diets using the by-products for each class.

Description of Linear Programming Model and Results

 

Nutrient requirements are stated in relation to a corn-SBM refer-
ence diet as a percent of dry matter, except for energy which is stated
in Kcal/lb diet dry matter. Protein requirements are defined for con-
centrations of specific amino acids, not for minimum crude protein.

Also, calcium, phosphorus, the calciumzphosphorus ratio and the

88

percentage of phosphorus coming from inorganic and animal sources (to
balance for available phosphorus) are taken into account in diet formu-
lation. As discussed, this is the strategy used to ensure that suffi-
cient available phosphorus is supplied to the diet.

Nutrient requirements in the least-cost diet formulation model
(Table 4.6.1) are dependent on the energy concentration of a corn-SBM
diet. Our working hypothesis is that swine eat to a constant energy
intake per day, unless dietary fiber concentration is excessive (Black
33 21-, 1981a). All alternative diets are compared to the corn-SBM re-
ference diet. If the energy concentration of an alternative diet is
less than that of the reference diet, more feed will be eaten of the
alternative diet to meet the pig's 'energy goal.’ Or, if the energy
concentration of the alternative is greater than the reference, less
dry matter intake is required per day.

The relative dry matter intake reflects how much of a particular
alternative would be fed to equal the energy of the corn-soy reference
diet. For example, a 22-44 lb pig fed a diet of 8.6% DDGS must eat
1.0076 lbs of that diet to equal one pound of the reference diet (Table
4.6.2). For both quantities the pig consumes 1633 kcal per day. Re-
ference and alternative diets are formulated so that amino acid and
mineral intake per day is constant, irrespective of the level of dry
matter intake.

The only equation that is structurally different from those in the
beef matrix is equation 9. This requires that at least 30% of the
phosphorus come from nonplant sources such as limestone and meat and bone
meal which are high in available phosphorus. This is a rule of thumb

used to apply the concept of available phosphorus. Upper and lower

89

 

 

 

 

 

 

 

 

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o o o o u 3.8 2.3. 8.8 8.8 :8 8.8 - -- 3.5 8.... a
c—muogm auagu
.3. to. 2.... .8. M 2o. 8.. - 8... 8.8.. 88. - - .5. 8.... 2.23. 3:5
«38:339.
msam. msoa. momm. mnom. - — p . p p . . . p . acm.u3
cmw-Nm. ~m.-~ nn-ve ev-NN co.» poo: .oo: oc.m»4 mun: uwu tau 4<u.o ocean cc :.ucu co.uazcm
6.33.. ..8... 25o 3.2: -3: son 88
can chm
:3:
usu.oz an muoua mos—u> acovcuaz
x.guo: m:.esacm¢gm Loos—4 0:.3m ...o.v u.no.

90

Table 4.6.2. Swine Diets for 22 to 44 lb Growing Swine Using
Differing Prices of DDGS

 

Price of DDGS/Price of SBM

 

 

 

Ingredient Reference .583 .554 .550
----- Proportion of Dry Matter----—

Corn Grain .773 .705 .561 .538
SBM 44 .203 .186 .148 .142
DDGS -- .086 .268 .296
Limestone .010 .011 .009 .010
Dicalcium Phosphate .012 .010 .011 .011
Salt .003 .003 .003 .003
Relative Dry

Matter Intake 1.000 1.0076 1.0262 1.0297

 

91

bounds on calcium as well as requiring calcium to be at least as plenti-
ful as phosphorus in the diet are the same techniques used in the beef
analysis (see section 4.3). With the prices used, meat and bone meal
and blood meal did not enter the diets.

Diets were formed for the four weight categories considering DDGS,
CGF and CGM as alternatives (Tables 4.6.2 through 4.6.13). The by-pro-
ducts are not as valuable in swine diets when use is based on nutrition-
al and not specialty characteristics. The low relative value of .583
for DDGS (Table 4.6.14) shows that it is not highly valued in swine
diets compared to beef where its value was 1.016. Because of its poor
amino acid balance, it only replaces a small amount of SBM and then
only at a relatively low DDGS:SBM price ratio. CGM (highest relative
value of .724) is a more valuable feed in swine diets than DDGS (Tab1e
4.6.15) but still substantially below its value found in the fed beef
analysis (2.030). Due to better amino acid characteristics, CGM will .
replace SBM, but only at a price ratio of .718. CGF is not very valu-
able (highest relative value of .535) in these diets with its most
valuable use based upon its calcium and phosphorus characteristics
(Tab1e 4.6.16). Based on these results, DDGS, CGF and CGM are not ex-
pected to see increased use in swine diets unless their supply becomes
so enlarged that they enter significantly lower valued uses. In the
event this happens, Tables 4.6.17 through 4.6.19 list equations that

can be used to determine the by-products potential economic value.

4.7 Poultry

Poultry diets can be formulated using a linear program model simi-

lar to the one used for swine. Again the amino acid levels are

92

Table 4.6.3. Swine Diets for 44 to 77 1b Growing Swine Using
Differing Prices of DDGS

 

Price of DDGS/Price of SBM

 

 

 

Ingredient Reference .583 .554 .550
----- Proportion of Dry Matter-----

Corn Grain .808 .788 .642 .486
SBM 44 .170 .165 .128 .088
DDGS -- .026 .209 .402
Limestone .010 .010 .009. .010
Dicalcium Phosphate .010 .009 .010 .012
Salt .003 .003 .003 .003
Relative Dry

Matter Intake 1.00 1.0022 1.0209 1.0445

 

93

Table 4.6.4. Swine Diets for 77 to 132 1b Growing Swine Using
Differing Prices of DDGS

 

Price of DDGS/Price of SMB

 

Ingredient Reference .554 .550

 

----- Proportion of Dry Matter-----

Corn Grain .843 .724 .467
SBM 44 .137 .107 .043
DDGS -- .149 .466
Limestone .010 .008 .010
Dicalcium Phosphate .008 .010 .012
Salt .003 .003 .003

 

Relative Dry
Matter Intake 1.000 1.0149 1.0540

 

94

Table 4.6.5. Swine Diets for 132 to 220 1b Finishing Swine Using
Differing Prices of DDGS

 

Price of DDGS/Price of SMB

 

r

Ingredient Reference .554 .550

----- Proportion of Dry Matter-----

Corn Grain .859 .802 .441
SMB 44 .122 .108 .019
DDGS -- .071 .515
Limestone .008 .008 .010
Dicalcium Phosphate .008 .009 .013
Salt .003 .003 .003

 

Relative Dry
Matter Intake 1.000 1.0071 ' 1.0617

 

Table 4.6.6.
Prices of CGM

95

Swine Diets for 22 to 44 lb Growing Swine Using Differing

 

Price of CGM/Price of SBM

 

 

 

Ingredient Reference .724 .718 .579
- - - - Proportion of Dry Matter - - - -
Corn Grain .773 .454 .131 --
SBM 44 .203 .103 -- --
CGM -- .419 .845 .976
Limestone .010 .011 .010 .009
Dicalcium Phosphate .012 .010 .012 .012
Salt .003 .003 .003 .003
Relative Dry Matter Intake 1.0000 .94802 .90132 8896

 

Table 4.6.7.
Prices of CGM

Swine Diets for 44 to 77 1b Growing Swine Using Differing

 

Price of CGM/Price of SBM

 

 

 

Ingredient Reference .724 .718 .579 .577
----- Proportion of Dry Matter- - - - -

Corn Grain .808 .719 .283 .106 ~-
SBM 44 .170 .141 -- -- --
CGM -- .119 .694 .872 .977
Limestone .010 .010 .009 .008 .008
Dicalcium PhOSphate .009 .009 .011 .012 .012
Salt .003 .003 .003 .003 .003
Relative Dry Matter Intake 1.0000 .9847 .9180 .9016 8727

 

96

Table 4.6.8. Swine Diets for 77 to 132 1b Growing Swine Using Differing
Prices of CGM

 

Price of CGM/Price of SBM

 

Ingredient Reference .718 .579 .577

 

- - - -Proportion of Dry Matter- - - -

 

Corn Grain .843 .430 .341 --

SBM 44 .137 -- -- --

CGM -- .549 .638 .977
Limestone .010 .008 .008 .008
Dicalcium Phosphate .008 .010 .011 .012
Salt .003 .003 .003 .003
Relative Dry Matter Intake 1.0000 .9343 .9257 .8964

 

Table 4.6.9. Swine Diets for 132 to 220 1b Finishing Swine Using
Differing Prices of CGM

 

Price of CGM/Price of SBM

 

Ingredient Reference .718 .579 .577

 

- - - -Proportion of Dry Matter- - - -

Corn Grain .859 .603 .496 --
SBM 44 .122 .036 -- --
CGM -- .343 .484 .977
Limestone .008 .007 .008 .008
Dicalcium Phosphate .008 .009 .010 .012
Salt .003 .003 .003 . .003

 

Relative Dry Matter Intake 1.0000 .9579 .9419 .8987

 

97

Table 4.6.10. Swine Diets for 22 to 44 lb Growing Swine Using Differing
Prices of CGF

 

Price of CGF/Price of SBM

 

Ingredient Reference .535 .487

 

- - - Proportion of Dry Matter - - -

 

Corn Grain .776 .741 .674
SBM 44 .203 .188 .159
CGF -- .052 .147
Limestone .007 .007 .008
Dicalcium Phosphate .012 .010 .010
Salt .003 .003 .002
Relative Dry Matter Intake , 1.013 1.041

 

Tab1e 4.6.11. Swine Diets for 44 to 77 1b Growing Swine Using Differing
Prices of CGF

 

Price of CGF/Price of SBM

 

.Ingredient . Reference .531 .483

 

- - - Proportion of Dry Matter - - -

Corn Grain .811 .798 .693

SBM 44 .170 .165 .123
CGF -- .020 .164
Limestone .007 .007 .007
Dicalcium Phosphate .010 .009 .010
Salt .003 .003 .003

 

Relative Dry Matter Intake 1.005 1.048

 

98

Table 4.6.12. Swine Diets for 77 to 132 lb Growing Swine Using Differing
Prices of CGF

 

Price of CGF/Price of SBM

 

Ingredient Reference .527 .478

 

- - - - Proportion of Dry Matter - - - -

 

Corn Grain .844 .823 .612
SBM 44 .137 .129 .053
CGF -- .030 .314
Limestone .007 .007 .008
Dicalcium Phosphate .010 .009 .012
Salt .003 .003 .002
Relative Dry Matter Intake 1.007 1.097

 

Table 4.6.13. Swine Diets for 132 to 220 1b Finishing Swine Using
Differing Prices of CGF

 

Price of CGF/Price of SBM

 

Ingredient Reference .476

 

- -Proportion of Dry Matter- -

Corn Grain .861 .616
SBM .122 .036
CGF -- .327
Limestone .007 .008
Dicalcium Phosphate .008 .012
Salt .003 .002

 

Relative Dry Matter Intake 1.103

 

99

 

 

 

 

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mogoom zmgmcu mm.-~ woe. omm.
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mugoom zmgmcu ee-NN emu. omm.
wugoom .ocmc.z cum-mm. .mo. omm.
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mugoom .ococ.z “Knee mom. «mm.
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muczom :vmuogo ce-NN owe. mmm.
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um.a mwoa co.uuogm mu.co moon
mum.o 0:.3m c. mung we m=.o> m>.uo.w¢ .e..m.c m.oo.

100

 

 

 

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cgou «moo.omm mm.-n~ mum. 88m.

:gou mmuo.oma ounce mum. mum.

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mucoseoo ooomo ooo.o2 .m.o o. mo.co zoo
om.o mooo oo.ouoeo mo.eo mooo

 

mum's m:.3m c. zwu $0 mapm> m>wumpmm .m..o.¢ opamh

Table 4.6.16.

101

Relative Value of CGF in Swine Diets

 

 

CGF Price Fraction CGF Diet

SBM Price in Diets WeighE)Range Comments
.535 .052 22-44 CalciumzPhosphorus limiting
.531 .020 44-77 CalciumzPhosphorus limiting
.527 .030 77-132 CalciumzPhosphorus limiting
.487 .147 22-44 Crude fiber limiting
.483 .164 44-77 Crude fiber limiting
.478 .314 77-132 Crude fiber limiting
.476 .327 132-220 Crude fiber limiting

 

102

 

 

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103

no

 

 

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m 2mm

104

 

 

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\8
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105

important as contrasted to crude protein. Among them lysine, methionine,
cystine and tryptophan are normally first limiting. Xanthophyll con-
tent is a unique poultry requirement because of its contribution to con-
sumer-preferred yellow coloration of egg yolks and broiler meat. CGM is
an excellent source of xanthophyll. CGF is excluded from poultry use

because of its high fiber content.

Description of Linear Program

 

Table 4.7.1 presents the linear program matrix used to balance
poultry diets. It closely resembles the swine matrix with additional
equations and feedstuffs used. No structurally different techniques
were used in these equations, but a different method was used to calcu-
late dietary requirements.

Chickens, like swine eat to a constant energy intake so that nu-
trient requirements are quoted as a percent of metabolizable energy
(ME). These percentages hold oVer a range of ME. For example, the
requirements for a starter broiler is the same for ME levels ranging
from 1425 to 1500 kcal/lb. The requirement used in the poultry matrix
is the midpoint 1487. Multiplying this by the percent ME requirements
gives requirements per pound of feedstuff when a pound has 1487 kcal/lb
ME. The resulting diets are shown in Tables 4.7.2 through 4.7.7.

DDGS highest value is in layer diets where it reaches .606 the
price of SBM while replacing synthetic methionine (Table 4.7.8). It
does not replace SBM as the primary protein source. Because of its
amino acid make-up and crude fiber content there does not seem to be

much likelihood of DDGS being used extensively in poultry diets.

1(16

 

 

 

 

 

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88.888
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107

Tab1e 4.7.2. Poultry Diets Formuiated for a Starter Broiier
Using Differing Prices of DDGS

 

Price of DDGS/Price of SBM

 

 

Ingredient Reference .501
Corn Grain .500 .523
SBM 44 .385 .202
Synthetic Lysine .001 .004
Meat and Bone Meal .072 .073
Synthetic Methionine .001 .002
Limestone .001 .002
Fat .041 .042
DDGS -- .153

Relative Dry
Matter Intake .977 .965

 

108

Table 4.7.3. Poultry Diets Formulated for a Finisher Broiler
Using Differing Prices of DDGS

 

Price of DDGS/Price of SBM

 

 

Ingredient Reference .512
Corn Grain .693 .699
SBM 44 .194 .147
Synthetic Lysine .002 .003
Meat and Bone Meal .065 .066
Synthetic Methionine .002 .002
Salt .002 .002
Alfalfa Meal .042 .042
DDGS -- .040

Relative Dry
Matter Intake .975 .974

 

109

Table 4.7.4. Poultry Diets Formulated for a Layer Using
Differing Prices of DDGS

 

Price of DDGS/Price of SBM

 

 

Ingredient Reference .606 .594
Corn Grain .619 .646 .637
SBM 44 .106 .137 .140
Meat and

Bone Meal .073 .076 .077
Synthetic

Methionine .0002 -- --
Limestone .060 .068 .068
Salt .002 .002 .002
Fat -- -- .005
Alfalfa Meal .140 .022 .017
DDGS -- .049 .053

Relative Dry
Matter Intake .960 .917 .911

 

110

Table 4.7.5. Poultry Diets Formulated for a Starter Broiler
Using Differing Prices of CGM

 

Price of CGM/Price of SBM

 

 

Ingredient Reference 1.214 .925 .919
Corn Grain .500 .473 .475 .539
SBM 44 .385 .390 .385 .217
Synthetic

Lysine .001 .0005 .0006 .004
Meat and

Bone Meal .072 .072 .072 .077
Synthetic

Methionine .001 -- -- --
Limestone .001 .001 .001 .002
Salt -- -- -- .002
Fat .041 .041 .041 .044
CGM -- .023 .026 .115

Relative Dry
Matter Intake .977 .976 .974 .913

 

111

Table 4.7.6. Poultry Diets Formulated for a Finisher Broiler
Using Differing Prices of CGM

 

Price of CGM/Price of SBM

 

 

Ingredient Reference 1.700 1.182 .578
Corn Grain .693 .652 .616 .500
SBM 44 .194 .239 .278 .143
Synthetic

Lysine .002 .001 -- .002
Meat and

Bone Meal .065 .065 .065 .078
Synthetic

Methionine .002 .001 .0002 --
Salt .002 .001 .001 .003
Fat -- -- -- .046
Alfalfa Meal .042 f .031 .020 --
CGM 2- .011 .020 .228

Relative Dry
Matter Intake .975 .983 .988 .876

 

112

Table 4.7.7. Poultry Diets Formulated for a Layer Using Differing
Prices of CGM

 

Price of CGM/Price of SBM

 

 

Ingredient Reference 1.038
Corn Grain .619 .612
SBM 44 .106 .103
Meat and Bone Meal .073 .073
Synthetic Methionine .0002

Limestone .060 .060
Salt .002 .002
Alfalfa Meal .120 .l37
CGM -- .012

Relative Dry
Matter Intake- .960 .957

 

113

 

 

mogaom :Pmpoga gougmum mmp. Pom.

mugzom cvmuoga gmgmpcpm owe. Npm.

mugaom xmgmcm some; mmo. «mm.

mcpcorgums ovumguczm mumpaom gmxmd meo. coo.
acmsEou pawn “or: cw muwgm 2mm

 

m¢no cowuumgu woven mwoo

 

mumwo zgppzoa cw mane mo mzpm> m>wumpmm .m.m.e mpnmh

114

CGM has an advantage over DDGS in poultry diets. First, its
xanthophyll content is significant. Second, the amino acid balance is
favorable and third, crude fiber is not limiting. When used primarily
as a xanthophyll source, it attains a price 1.700 times that of SBM in
finisher broiler diets (Table 4.7.9). When used as a protein source,
its value drops to 1.214 the price of SBM. In most diets CGM made up
only 1.1% to 2.4% of the diet, so unless CGM becomes substantially
cheaper, it will not be used beyond current levels.

The poultry analysis is completed by summarizing the determination
of economic value of DDGS and CGM (Table 4.7.10) based on percentages

and prices of feeds used in the reference and alternative diets.

4.8 Pets

Pet food sales have increased in dollar volume and tonnage during
the past 25 years. Approximately 4,246,000 tons or 3.288 billion
dollars worth of pet food is estimated to have been sold in 1979 (McCook,
1979). Of this total, 2,222,500 tons of dog food represent a sizeable
market for use of protein supplement feeds.

As a result of digestion trials, it was concluded that DDGS can be
successfully incorporated into dog diets without significantly altering
the utilization of other nutrients (Corbin gt_al,, 1980). Diets were
fed in these trials with up to 15.7% DDGS. Other work indicates a pa-
latability limit of 30% of the diet (McCay, gt_gl,, 1957). This area
is in need of further research and is mentioned here to acknowledge

the potential use of these by-products in pet foods.

115

 

 

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2mm meow mumpamm gaugmbm mpp. mpm.
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116

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117

4.9 Summary

Evaluating these by-products from a nutritional perspective it is
evident that their growth area is in diets for rapidly growing feedlot
calves and lactating dairy cows. Use in other diets, both ruminant and
monogastric, results in a substantial reduction in economic value.

The least valuable diet the by-products are used in determines
their price. As shown, the value of these feeds tend to cluster within
a range according to use based upon bypass protein characteristics, as
energy sources, or as mineral source. Tables 4.3.27 through 4.3.29
list equations estimating the economic value of the by-products in
fed beef diets. The equations summarizing economic value for their
best use based on the prices and proportions of feedstuffs in the re-
ference and alternative diets is derived from the 475 lb .53 NEg Mcal/lb
diet:

DDGS price = -.009 (Price of corn) + 1.105 (Price of SBM) -

.105 (Price of urea) - .009 (Price of limestone)

CGM price = 2.167 (Price of corn + 3.500 (Price of SBM) -
.333 (Price of urea) - .083 (Price of dicalcium phos-
phate)

CGF price = -.055 (Price of corn) + .984 (Price of SBM) - .098

(Price of urea) - .008 (Price of limestone) + .008
(Price of dicalcium phosphate)
The value of DDGS, CGF and CGM is highest in diets or rapidly
growing beef calves and dairy cows in early lactation. The relative
value of these by-products in different uses clearly shows this (Table

4.9.1). Use of these by-products in ruminant diets on a crude protein

118

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119

basis or as sources of energy in monogastric diets would result in a
substantial reduction in value of DDGS, CGF and CGM. As the bypass
protein concept is better understood and integrated into feed formula—
tions and nutrient specifications the relative value of DDGS, CGF and

CGM should increase.

CHAPTER FIVE
DISTILLERS DRIED GRAIN WITH SOLUBLES MARKETING CASE STUDY

5.1 Introduction

 

The analysis conducted in Chapters Three and Four contain tools
which can help potential clients market DDGS, CGF and CGM. To illus-
trate how these tools can be used, a "case study" will be conducted
to develop a marketing program for a dry milling ethanol plant located
in central Michigan. Basic concepts and approaches should be applicable
to other locations, although details will differ.

The perspective will be that of a person responsible for marketing
the by-product, DDGS. The plant is assumed to be constructed and
operating, and the question is: "How should we market the by-product?"
To begin, we consider potential users of DDGS and how to approach market-
ing DDGS to them. A procedure to price DDGS will be illustrated,
followed by a discussion of how the client groups can use the tools
presented in this study.

SBM is the dominant feedstuff in the medium and high protein feed
market (Table 2.1.1). Thus, the standard method of characterizing

supply and demand in this market is on a SBM "equivalent" basis.

5.2 Potential Users of DDGS

 

Three potential user groups exist for DDGS from a plant in

central Michigan. There are two groups within the Michigan market:
120

121

farmers and elevators with feed blending capability and feed manu-
facturers. The third is potential out-of-state users.

Direct marketing of DDGS to elevators and farmers with feed blend-
ing capability is a likely prospect. DDGS may have to be more vigorously
marketed to this group then to others because most farmers and elevator
operators are not familiar with DDGS and the significance of its nutri-
tional characterisitcs, especially bypass protein. Special effort will
be needed to nurture this group into a significant demander. Steps to
develop this market include: initial prices lower than those based on
nutritional value, guidance in feeding practices and sharing technical
know how.

Lower prices may be needed to induce them to try an unfamiliar feed.
Guidance on diet formulation, along with feeding trail results, will
demonstrate how to effectively use DDGS. Providing a portfolio of diets,
such as those listed in Chapter Four, would enable them to formulate
least—cost diets using DDGS. Another service that could be offered is
running computerized diet formulation programs, adjusted for specific
feedstuffs on hand, to fine tune diets for a specific situation.

Depending on the technological sophistication of the farmer,

and elevator operator, it may be possible to offer further assistance.

With the adoption of micro computers, it will become increasingly common
to find individuals, especially those who have heavily invested in feed
handling and storage facilities, who use computers to formulate diets.
It will be possible to instruct them on the use of "user friendly"
computer software that employs the linear programming techniques dis-

cussed in Chapter Four and Appendix A as solution algorithms for diet

122

formulation. The software would be designed to account for the bypass
protein characteristics of each feedstuff and, therefore, properly
evaluate the role of DDGS in diets. This may be facilitated by provid-

ing them with the matrices developed (as listed in Chapter Four).

Vigorously marketing DDGS is a time consuming process. Whether or
not it is feasible for the plant manager to do this in addition to other
responsibilities is a decision that must be carefully assessed. Addi-
tional staff, relative to a plant producing a widely used product like
soybean meal, may be required to effectively market the by-product--at
least in the initial phases of business operation.

Feed manufacturers are the second local market for DDGS. It would
not take as much effort to "sell" DDGS to these demanders who should be
more aware of the latest findings about the use of DDGS. Part of the
marketing program could be to show how the by-product should be used;
this has been the role of the Distillers Feed Research Council of the
beverage ethanol production industry. It is important that the DDGS
marketer insure that feed manufacturers are kept up to date on research
information involving DDGS use so that diet formulations reflect current
understanding.

The third market is potential users, domestic and international,
outside the central Michigan market. Marketing techniques for these
users will be similar to feed manufacturers. This market will differ
from the Michigan market in that the focus would be on feed manufacturers
and brokers, not elevators and farmers. Transportation cost will be
more significant, possibly precluding DDGS from having any significant
market in areas where other protein feeds are relatively cheaper. Addi-

tionally, the price of DDGS relative to SBM in out-of-state markets will

123

differ from the central Michigan relative price so that DDGS may not
be used in the same diets in different geographic markets because of

transportation cost differences (example to follow).

5.3 Estimation of DDGS Price

 

The following procedure will be used to estimate DDGS price. First,
potential geographic market areas will be identified. Second, size of
livestock and poultry industries in those areas will be estimated.

Third, the size of the livestock and poultry industries will be divided
into categories for which diets are formulated. Fourth, the potential
quantity of DDGS demanded at a specified price will be estimated by

summing the amounts demanded from each category.

Potential Geographic Markets

 

The first step is to define the relevant market area. An area
must meet two requirements: 1) it must be within transportation dis-
tance that is economically feasible and 2) it must be a net user of
supplemental medium and high protein feedstuffs.

The potential market area is also influenced by the location of
existing plants producing medium and high protein feedstuffs. Most
plants producing DDGS are located in central Illinois, Indiana and Ohio
so that little 0065 will move south or west of Michigan. Jackson and
Black (1982) identified primary and secondary market areas for a poten-
tial SBM processing plant located in central Michigan. The primary
market affords advantages because of transportation links. Secondary
markets are less favorable due to less certain transportation links
(e.g., uncertainty about which rail lines will remain) and/or competition

from existing plants.

124

The primary markets (adapted from Jackson and Black, 1982) for a
fuel ethanol plant in central Michigan consist of Michigan's lower
peninsula excluding the southern tier of counties in Michigan, Counties
in extreme southern Michigan are not included in the primary market be-
cause of their proximity to out-of-state plants. The second market is the
Northeastern U.S.l/ Transportation rates (Table 5.3.1) to different
locations in that market indicate that a Michigan plant can be competi-
tive with competitors in Ohio, Indiana, and Illinois.

Secondary markets include Michigan's upper peninsula, the southern
tier of Michigan counties, eastern Wisconsin and Ontario. Movement of
products into these markets is possible, but not likely. For example,
even though east central Wisconsin has a high number of dairy cows
(421,000 in 1978 compared to 306,000 in the Michigan market area) which
could use DDGS, it is not likely because of uncertainties in transporta-
tion linkages across Lake Michigan.

For purposes of this case study, only the primary market will be
considered. If one was interested in including the secondary markets

this analysis could be expanded.

Size of Animal Industries

The second step is to estimate the potential number of livestock
and poultry demanding SBM equivalent feeds. These numbers (Table 5.3.2)
can be found in statistical bulletins published by the U.S.D.A. and

individual state crop reporting boards.

 

l/This includes the states of: Connecticut, Maine, Massachusetts, New
Hampshire, New York, Rhode Island and Vermont.

125

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126

Table 5.3.2. Number of Animals in the Primary Market Area

 

Michigan's Lower
Peninsula Excluding Northeastern /
the Southern Tier United StatesD-
of Countiesi

 

Dairy, milk cows (1000 head) 306 1,279
Swine, production (cwt) 1,915,1295/ 78,0009/
Poultry d/ f/
Chicken (100 dozen eggs) l,027,783—- 4,491,667?7
Chicken (cwt broilers) NA 3,691,590?7
Turkey (cwt) NA 80,940—
Fed beef (1000 head marketed 148 14
Beef cow (1000 head) 97 / ll7
Sheep (1000 head marketed) 319- 80

 

E/SOUrce: Michigan Agricultural Statistics, 1980.
2-/Source: Agricultural Statistics, 1979-

E-/Calculated by multiplying the number of swine by the state average
weight of 239 pounds.

g/Calculated by subtracting the percentage of eggs equal to the
percentage of hens of laying age from the state total.

E-/Calculated based on a 220 lb market weight.
171978 Figures. '

9/Calculated as percentage of total sheep and lambs on hand on
December l, 1979.

127

Determining_the Number of Animals in Each Class

 

To determine the quantity of DDGS demanded, it is necessary to
know the number of animals in the different classes for which diets
can be formulated in order to determine the quantity of DDGS demand.
This requires the division of livestock and poultry numbers (Table
5.3.2) into classes of interest in DDGS use. Dairy and beef numbers
will be categorized by using the aggregate number of animals and the
proportion of days an animal spends in each yield or weight group dur-
ing its production phase. For fed beef, the weight classes are
475-600, 600-850 and 850-1050 lb, paralleling the classes used in
Chapter Four for diet formulation.

To illustrate this approach, estimation of the number of days an
average frame size steer is in the 475-600 lb growing phase (.49 NEg
Mcal/lb dry matter diet) will be demonstrated. Mean values for dry
matter intake (DMI) (Table 5.3.3) and projected gains (Table 5.3.4)
over the weight range will be used. The projected DMI is 13.6 lbs/day,
resulting in a projected gain of 2.20 lbs/day. Approximately 57 days
are required for the animal to grow from 475 to 600 lbs. Daily gains,
DMI and days in growing phase for each weight group and dietary energy
level combination are summarized in Table 5.3.5. Using this information,
the number of days a steer takes to gain from 475 to 1050 lbs can be
estimated.

The number of animals in each weight group during the year can be
estimated from the proportion of animals in each dietary energy level
and weight class combination. It is assumed that the marketer has
reliable estimates of the distribution of animals among the different

energy levels, or can contact someone who does. To illustrate the

128

Table 5.3.3. Expected Dry Matter Intake for Average Frame Size
Feedlot Beef Animals (lb/day)

 

Steers Started on Heifers Started on Steers Started on

 

Weight Feed as Calves Feed as Calves Feed as Yearlings
400 10.91 10.91 --
450 11.91 11.91 --
500 12.84 12.84 --
550 13.85 13.85 --
600 14.78 14.78 16.26
650 15.70 15.63 17.27
700 16.59 16.18 18.25
750 17.48 16.67 19.22
800' 18.34 16:97 20.18
850 18.87 17.12 20.99
900 19.37 17.12 21.57
950 19.82 -- 22.12

1000 20.06 -- 22.83

1050 20.24 -- 22.95

1100 20.24 -- 22.95

 

Source: "Expected Daily Dry Matter Intakes of Growing and Finishing
Cattle," Danny G. Fox and J. Roy Black, MSU Cooperative
Extension Factsheet #1212.

129

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130

Table 5.3.5. Computed Dry Matter In akes, Projected Gains and
Days in Growing Phase for Feed Beefi

 

 

 

 

Weight Energy Level DRY Matter Projected
Group NEG Intake Galn Growing Phase
(1b) (MC8171b) (Ib/daY) (Ib/daY) (Days)

475-600 .46 13.6 2.0 63

.49 13.6 2.2 57

.53 13.6 2.4 52
600-850 .53 16.8 2.4 104

.63 16.8 3.1 81
850-1050 .53 19.6 2.3 87

.63 19.6 2.8 71

 

é-/Average frame size, implanted steers.

131

general procedure, assume all fed beef animals go through the 475-600
lb .49 NEg Mcal/lb, 600-850 lb .53 NEg Mcal/lb and the 850-1050 .63
NEg Mcal/lb phases.§/ This would take 232 days (Table 5.3.5).§/

The proportion of time the animal spends in each class is found
by dividing the number of days an animal spends in each weight class
by the total needed to finish. The proportions are .246, .448 and
.306 of the tine spent in the 475-600 lb, 600-850 lb and 850-1050 1b
classes respectively. Multiplying these proportions by the number of
animals marketed during the year, the number of animals in each phase

during the year is obtained. To illustrate this procedure numerically:

 

 

Weight (1b) NEH- Biff. Proportion
475-600 .49 57 .246
600-850 .53 104 .448
850-1050 .63 _]fl_ .306

232

Proportion of Time Spent in * Number of Animals in

 

 

 

Each Weight Class Marketings/Year Each Weight Class/Year
.246 (475-600 lb) * 148,000 = 36,408 (475-600 1b)
.448 (600-850 lb) * 148,000 = 66,304 (600-850 lb)
.306 (850-1050 lb) * 148,000 = 45,288 (850-1050 lb)

 

g-/Beginning and ending weights are those representing the range over
which supplemental feeds are consumed.

é/Days spent for the animal to regain weight lost during transportation
was assumed to be zero.

132

For this simplified illustration it was found that 36,408, 66,304
and 45,288 head were in the 475-600 lb, 600-850 lb and 850-1050 lb
weight classes, respectively.

Application of the "size of market" technique has been illustrated;
it will now be applied to the estimation of the quantity of DDGS demand-
ed in the primary market area. Estimates of the proportion of fed beef
cattle in each weight class and dietary energy level combination in
Michigan were obtained from John Waller, Michigan State University Co-
operative Extension Service feedlot beef specialist (Table 5.3.6). Feed-
ing programs can be derived from these percentages for various weight
class and dietary energy level combinations (Table 5.3.7).

The assumption used to derive the feeding programs is that an ani-
mal consuming a particular concentration of energy in its diet will not
be fed diets containing a lower energy concentration when it moves to the
next highest weight class. For example, feedlot cattle in the 600-850
lb weight class receiving a .63 NEg Mcal/lb diet will be fed a .63 NEg
Mcal/lb diet when they weigh 850-1050 lbs. Feeding programs I, IV and V
were estimated to contribute 10%, II 20% and III 40% of the fed beef
marketings. The remaining 10% represents beef raised on feeding programs
which would not include DDGS.

Multiplication of the proportion of days in each weight class by
the proportion of all fed beef in each feeding program gives the propor-
tion of all beef in each feeding program and weight class (Table 5.3.8).
The number of animals in each feeding program and weight class is found
by multiplication of the final proportions by the total number of fed

beef cattle fed in the market area (Table 5.3.9).

133

Table 5.3.6. EstimaUaiPercentage of Feedlot Beef Cattle on .46,

.49, .53 or .63 NEg Mcal/lb Diets in Michigan

 

Energy Level of Diet NE Mcal/lb

 

 

Weight 9

Group Total
.46 .49 .53 .63

475-600 20 20 60 -- 100

600-850 5 5 80 10 100

850-1000 5 5 70 20 100

 

Table 5.3.7. Feeding Programs Derived From Estimated Percentages
of Feedlot Beef Cattle on Different Diets

 

 

 

NEg Mcal/lb

Feeding

Program

475-600 600-850 850-1050

I .46 .53 .53
II .49 .53 .53
III .53 .53 .53
IV .53 .53 .63

V .53 .63 .63

 

134

 

 

 

 

 

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135

Table 5.3.9. Number of Fed Beef Animals in
Michigan Primary Market Divided Into Feed-
ing Program and Weight Group (1,000 head)

 

Weight Group

 

 

Feeding

Program 475-600 600-850 850-1050
I 3.7 6.1 5.0
II 6.8 12.4 10.4
III 12.7 25.3 21.2
IV 3.4 6.8 4.6

V 3.8 5.4 5.2

 

136

The same procedure was used to apportion the 14,000 marketings of
fed beef in the Northeastern U.S. market. Tab1e 5.3.10 depicts the

number of fed beef, divided into feeding program and weight group.

DDGS Demandfl/

 

DDGS demanded by fed beef can be estimated from the number of fed
beef in the Michigan and Northeastern U.S. primary market area (Table
5.3.11) and the quantity of DDGS demanded per head. Quantity demanded
per head is a function of weight class, energy level of diet and DDGS
price (Table 5.3.12). For a given price, the total quantity of DDGS
demanded can be estimated by multiplying the quantity demanded per head
by the number of animals in the appropriate weight group-feeding system
combination.

The estimated quantity of DDGS demanded by dairy will be based on
the assumption that the relative substitution rates of DDGS and urea
for SBM in lactating dairy cow diets will be equivalent to those in the
.49 NEg Mcal/lb fed beef diet for the 475 1b animal. A mixture of 90%
DDGS and 10% urea is estimated to have protein degradation characteris-
tics similar to SBM. Black gt_al, (1981c) estimated the quantity of
SBM "equivalent" demanded per lactating dairy cow at .43 ton/year. The
number of dairy cows in the primary market areas of central Michigan
and the Northeastern U.S. is estimated to be 306,000 and 1,279,000 head,
respectively. Thus, the SBM "equivalent" demanded is 681,550 tons/year

in the primary market area. This results in a potential of 613,335

 

E/Swine and poultry are excluded from the case study because DDGS is
not expected to be used in significant quantities in these diets.

137

Table 5.3.10. Number of Fed Beef Animals in
Northeast U.S. Market Divided into Feeding
Program and Weight Group (1000 hd)

 

Weight Group

 

Feeding
Program 475-600 600-850 850-1050

 

I .35 .57 .48
II .64 1.18 .98
III 1.20 2.39 2.00
IV .32 .64 .43

 

138

Table 5.3.11. Number of Fed Beef in the Northeast and Michigan
Primary Market Area (1,000 hd)

 

Weight Group

 

 

Feeding

Program 475-600 600-850 850-1050
I 4 1 6.7 5 5

II 7.4 13.6 11.4
III 13.9 27.7 23.3

IV 3.7 7.4 5.0

V 4.2 6.5 5.7

 

 

139

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140

tons of DDGS demanded based upon the assumption SBM "equivalent" de-
mand can be met by a 90% DDGS and 10% urea combination. These projec-
tions are based upon the assumption of a 1:1 SBM:DDGS price relation-
ship.

The analysis now turns to transportation rates and their impact on
relative prices. Transportation cost must be considered because of its
effect on the price of DDGS relative to competing feedstuffs. The by-
product price is determined by the last unit sold. For illustrative
purposes, assume that the Northeastern U.S. market is where the last
unit of by-product is sold and that the by-product must compete with
SBM coming from Decatur, Illinois. The hopper car rail rates for SBM
shipped from Decatur to central Michigan and to a Northeastern U.S.
market location (Binghanton, NY) are $.80/cwt and $1.73/cwt, respective-
ly (Table 5.3.1). The transportation rate for DDGS from central Michi-
gan to Binghanton is $1.36/cwt (Table 5.3.1). The following relation-
ships result from a 1:1 SBM:DDGS price relationship in the Northeastern

U-$~ market, soybean meal at $14/cwt at Decatur, and prevailing rail

 

rates:

Location SBM Price DDGS Price SBM:DDGS
Northeastern $(14 + l.73)/cwt $(l4 + l.73)/cwt 1:1
Decatur $14/cwt . Mcé/ NC
Michigan $(14 + .80)/th $(14 + 1.73 - 1.36)/th 1:.97

 

é/Nc = Not Considered.

141

A Decatur SBM price of $14/th yields a SBM price in the North-
eastern U.S. of $(l4 + 1.73) or $15.73/cwt after transportation cost is
considered. One:one pricing of SBM:DDGS in the Northeastern U.S. gives
a DDGS price of $15.73/cwt. The price of DDGS in central Michigan is
$(15.73 - 1.36) or $14.37/cwt, and SBM price is $(l4 + .80) or $14.80/
cwt. A 1:1 pricing of SBM:DDGS in the Northeastern market results in
1:.97 in central Michigan. DDGS will be slightly cheaper in Central
Michigan then in the Northeastern U.S.

DDGS use in diets will be in the same proportion in both markets.
Thus, DDGS demand can be estimated by the number of animals (Tab1e
5.3.11) multiplied by the per head quantities (Tab1e 5.3.12).

Quantities demanded are summarized with DDGS use based on its
bypass protein characteristics in rapidly feedlot calf diets and lac-
tating dairy cows (Table 5.3.13). DDGS demand of 622,367 tons corre-
sponds to 73 million bu of the feedstock, corn. This level of feed-
stock produces 190 million gallons of ethanol (2.6 gallons per bushel
of corn).§/ Experience will dictate if the marketer can realistically
expect DDGS to reach a 100% market share of the soybean meal "equiva-
lent" market. With a 40% market share, a 76 million gallon ethanol
plant in central Michigan should be able to market its DDGS by-product
at the 1:1 SBM:DDGS price relationship that is implied by DDGS's bypass

protein characteristics.

 

é-/Conversion factors can be found in Reilly (1979).

142

Table 5.3.13. Demand for DDGS (tons) in the Primary Market with a
DDGS:SMB Price Ratio of 1:1

 

Weight Group

 

 

 

Feeding

Program 475-600 600-850 850-1050 Total
1 -- -_ -- --

II 2 9865 '- " 2,865
III 39933 " "’ 39933
IV 1,046 -- -- 1,046
V 1,188 -- -- 1,188
Dairy Cow 613,335

 

622,367

 

143

Upper and Lower Bounds on DDGS Price

The upper and lower bounds on the expected price of DDGS can be
found. The system of equations depicting the economic value of DDGS as
a function of the alternative feedstuff prices (Table 4.3.27) were not
used in the case study. These equations can be used to fine tune DDGS
price by taking into account the prices of competing feedstuffs. The
highest valued use of DDGS was in a cluster of diets which has a
DDGS:SBM relative value of 1.016. The potential price, considering
the prices and proportions of all feedstuffs in the reference and alter-
native diets, can be found using the economic value equation. For the
475 lb feedlot animal fed a .53 NEg Mcal/lb diet containing 11.4% DDGS,
the potential price of DDGS is a function of corn, SBM, urea and lime-
stone prices ($/cwt dry matter basis):
= -.009 P + 1.105 P

M -.105 P -.009 P

PDoes CORN SB UREA LIMESTONE

The following prices ($/cwt dry matter basis) represent historical

average relative prices:

.Fe_ee £1.98:
Corn 8.00
SBM 15.00
Urea 12.00
Limestone 4.00

The upper bound on potential price of DDGS is:

P = -.009 (8) + 1.105 (15) - .105 (12) -.009 (4) = $15.21/cwt

0065

where PDDGS is on a $/cwt, 100% dry matter baSis.

144

The lower bound on DDGS is set by historical trading relationships.
The average annual price of DDGS, using the Toledo market corn price and
the Decatur soybean meal price (Table 3.3.1), can be estimated by:

P .43 + .448 P

+ .466 P -.003 TIME

DDGS = CORN SBM
where, prices on 2,000 lbs 90% dry matter basis for SBM and DDGS and
85% for corn. This equation depicts the lower bound on the DDGS price
as:

.43 + .448 (136) + .466 (270) -.003 (82)

$187/ton

DDGS ‘

= $9.35/cwt 90% dry matter basis

= $10.38/cwt 100% dry matter basis.
This reflects how DDGS has been valued in the past based upon crude
protein characteristics.

Short-run price relationships (Tables 3.4.1 through 3.4.15 pro-
vide the fuel ethanol plant marketing agent with a "feel" for seasonal
price movement, which is helpful in cash flow and inventory planning.
The lower bound on annual average price determined by historical annual
relationships is $10.38/cwt. The season high and low price for the

Cincinnati DDGS price is 104.6% in January and 91.9% in May, respective-

ly (Table 3.4.11). Thus, over a 10 year period, the average DDGS range

in prices can be expected to be from $9.54 in May to $10.86 in January.

The pattern of price movement through the season varies from year
to year. A measure of variability is the standard deviation; the larger
its value the less reliable the index. For example, the 7.7% standard
deviation for May Cincinnati DDGS price (Tab1e 3.4.11) means that

prices are expected to average 91.9% plus or minus 7.7 two-thirds of

145

the time. The standard deviation of the price of DDGS at Cincinnati is
7.2%, which compares to 5.7% of the price of corn at Toledo and 9.8%

of the price of SBM at Decatur.

5.4 Use of Tools by Potential Clients

The purpose of this section is to explain how the tools presented
in this analysis can be used by farmers, feed salesmen, feed manufactur-
ers, brokers and fuel ethanol plant managers. The tools discussed are
those relevant for long-run investment decisions, annual decisions and

short-run decisions.

Long-Run Investment Decisions

Ethanol plant managers need the long-run relationship between the
price of the by-products and the feedstock to estimate the proportion
of feedstock cost that will be covered by by-product sales. This in-
formation will be valuable in the planning stages as an input in the
"to build” or "not to build" decisions. For example, in section 3.2 the
DDGS:Corn relative price was estimated to be:

$/ton DDGS
S/lOO bu corn

 

= .53

Three tons of corn feedstock, results in one ton of DDGS by-product.
The cost of the feedstock is 3 * $/ton corn while the revenue generated
from by-product sales is 1.5 * $/ton corn. Thus, one-half the feedstock

cost is covered by by-product sales revenues.

Decisions Based Upon Knowledgeiof Annual Price Relationships
Knowledge of the average annual prices of the by-product feeds

should be of interest to feed manufacturers, brokers and ethanol plant

146

managers. This can be used to develop a marketing strategy and to es-
timate probable cash flows.

The forecasting of the annual DDGS price depends on the users
ability to forecast corn and soybean meal prices since both are deter-
minants of by-product price. Use of an econometric model such as the
Michigan State University Agriculture Model (Mitchell and Ross, 1981)
could be used as the source of these forecasts. Also, forecasts of
corn and SBM prices are available from a large number of private sources.

The estimated price of DDGS at Cincinnati, using Toledo corn mar-

ket and Decatur SBM price, is estimated by (Table 3.3.1):

$/ton DDGS .49 + .448 ($/ton corn) + .466 ($/ton SBM) -.003 TIME

.49 + .448 (136) + .466 (270) - .003 (82)

$187/ton (as is basis)
= $207/ton (100% dry matter basis)

(using the same prices for corn and SBM as in section 5.3). This value
of $207/ton DDGS can be used in annual planning of cash flows. The
price can be expected to vary within the year, but this value will give
an estimate of the average. This method will reflect the lower bound
on price of DDGS because it is based on the pricing of DDGS on its
crude protein characteristics.

The net cost of the feedstock, corn, can be determined. Three
tons of corn undergoing fermentation yields one ton of DDGS. The net

cost Of corn is ($/ton 100% dry matter basis):

Cost of Corn - Revenues from DDGS Sales

Net Cost of Corn = Tons of Corn

= (3 Tons of Corn)($l60/ton) - ($207/ton DDGS)
3 Tons of Corn

$91/ton

147

This indicates that for cash flow planning the net price of corn is ex-
pected to average $9l/ton on a 100% dry matter basis. The DDGS price
used was the lower bound on expected DDGS price. Thus,this corn price

is a conservative estimate.

Short-Run Price Movement Estimation

 

Short-run price movement estimation will be of interest to feed
manufacturers, brokers and ethanol plant managers. Understanding how
prices of these feeds have varied seasonally within a year is of in-
terest in inventory decision making and pricing. These seasonal esti-
mates can be used as a bench mark and, in combination with market funda-
mentals, estimate short-run prices.

The preceding discussion estimated the annual average of Cincinnati
DDGS price to be $207/ton. Seasonality indices for Cincinnati price
indicate how price has historically varied within a year (Tab1e
3.4.11).Z/ Assume the marketer is interested in the profitability of
storing DDGS from November production for sale in January. The average
November price is 101.3% of the annual average price, or $210/ton while
January is expected to be 104.6% or $217/ton. If storage and interest
charges are less than ($217 - $210), or $7/ton then it will, on the'
average, be profitable to store in November and sell in January. This

decision is not to be made in a vacuum. If a fundamental analysis of

 

Z/Plants in southern areas typically decrease production during summer
months because of the deleterious effects of summer temperatures on
fermentation. The opening of plants in northern areas where summer
temperatures are not as high may mean that the seasonal pattern of
variation will change.

148

the market suggests that historical patterns will be overridden because
of atypical current market conditions, the decision should be adjusted

appropriately.

Linear Programming of Diets

 

Clients who could benefit from the application of linear program- .
ming techniques for diet formulation described in Chapter Four include
farmers, feed salesmen, feed manufacturers and ethanol plant managers.
Application of these techniques is based upon the assumption that an
understanding of the nutritional relationship being modeled exists.

Farmers with the technological sophistication to formulate least-
cost diets would find the linear programming models useful. This would
allow the flexibility to enter nutrient coefficients that more accurate-
ly represent the characteristics of their on farm feeds and potential
protein and mineral supplements.

This flexibility is also of interest to feed salesmen and manu-
facturers who are interested in deriving least-cost feeds for sale to
their customers. They could formulate a series of diets for their cus-
tomers similar to those found in Chapter Four. Use of the ammonia
utilization potential (AUP) concept in the beef diet formulation should
be of particular interest. An understanding of the theory behind its
use will enable the formulation as diets maximizing DDGS, CGF and CGM's

economic value based on their bypass protein characteristics.

Relative Value
Relative value will be of interest to those who are concerned that

the by-products are marketed in uses in which they have their highest

149

value. For example, as has been previously discussed, the fuel ethanol
by-products have their highest value when used in diets for rapidly
growing calves and dairy cows in early lactation. This was concluded
by examining the relative value of these feeds in all the species and
weight class categories for which diets were formulated. Thus, the
marketers will not sell the by-products to lesser valued uses while
there remains untapped demand in higher valued uses.

Relative value tables also indicate what diets should be given
priority in further research. For example, the relative value table
for DDGS in beef diets (Tab1e 4.3.24) and swine diets (Table 4.6.14)
reveal where funds should be spent. If choice has to be made between
funding research dealing with DDGS in beef diets or when it is used to
replace a portion of SBM in growing swine diets, the choice would be

the beef, ceteriS paribus, because of its higher relative value.

Economic Value

 

Economic value is of interest to all clients, but especially to
those that are involved with the pricing of the by-products. The po-
tential economic value fine tunes the relative value of the by-products
by taking into account the prices of other feeds involved in the diet
formulation and is considered the upper bound on price of the by-pro-
ducts.

Once it has been established which diet determines the market
clearing price, the potential economic value for use in that diet can

be found (in the case of DDGS) by using one of the equations found in

150

Table 4.3.27. The market clearing price is determined by a 475 lb
.53 NEg Mcal/lb diet, so then, the potential economic value of DDGS is:

P .009 P + 1.105 P

DDGS = c SBM ' '105 P

U -.009 P

L

DDGS price is $15/cwt or $300/ton when using the same prices as in sec-
tion 5.3. This compares with the annual estimation, based on histori-
cal relationships, which yielded a price of $207/ton. The range of
$300 to $207 is the upper and lower bound on DDGS price given the as-

sumed prices for competing feeds.

CHAPTER SIX
CONCLUSIONS

The objective of this study was to determine the historical price
relationships of distillers dried grains with solubles (DDGS), corn gluten
feed (CGF) and corn gluten meal (CGM) with substitute commodities and
to determine whether it is probable these relationships will continue.
Knowledge of by-product characteristics and these relationships is an
important part of the development of marketing strategies by fuel
ethanol producers, feed manufacturers and farmers. The economic con-
tributions of these feedstuffs, which are by-products of ethanol pro-
duction, will be an important determinant of the economic viability of

ethanol use as a fuel.

6.1 Findings
Historical pricing relationships of DDGS, CGF and CGM have been

based on speciality characteristics and crude protein content. Nutri-
tional research, by steadily identifying previously unidentified per-
formance factors associated with DDGS and providing cheaper alterna-
tives, has resulted in the steady deterioration of the price of DDGS
relative to the price of soybean meal (SBM). The relative price of
DDGS has declined from nearly 90% of SBM price in the early 60's to

a pleateau of 70% since the mid 1970's. The relative price of CGM has
declined from nearly 170% of SBM price in the mid 60's to 130% Since

the mid 70's. In contrast, CGF has maintained its value relative to

151

152

SBM, and experienced little of the secular trend of DDGS and CGM price.
The relative price of CGF to SBM has averaged 60% of SBM price.

Price relationships based on nutritional value of the by-products
are highest when their bypass protein characteristics are matched with
the bypass protein needs in diets for rapidly growing feedlot calves
and dairy cows in early lactation. The by-products' economic value is
less when used in monogastric diets or in ruminant diets on other than
bypass protein characteristics (Table 6.1.1). When use is based upon
bypass protein characteristics, the relative values are 101%, 89% and
203% the price of SBM for DDGS, CGF and CGM respectively. This compares
to 74%, 69% and 135% when based on an equal crude protein basis, and
59%, 47% and 46% when used as energy sources in ruminant diets.

DDGS and CGF are not expected to be used in significant proportions of
monogastric diets. Use in monogastric diets requires pricing below
DDGS and CGF's value on a crude protein basis in ruminant diets which
is significantly below their value based upon bypass protein character-
istics. If they are fed to monogastrics, it will be because of uniden-
tified performance factors (Black gt_al, 1981a). There is evidence
to suggest that DDGS has been used in certain poultry diets because
of unidentified performance factors. No such evidence exists for CGF.
CGM will continue to be used in poultry diets because of its xanthophyll
content which is valuable in adding the consumer preferred yellow colora-
tion to egg yolks and broiler fat. The best use of these by-products in
monogastric diets, based upon amino acid properties, yield relative
values of 60%, 53% and 170% the price of SBM for DDGS, CGF and CGM,
respectively. DDGS and CGF use in these diets is restricted by poor

amino acid balance and high crude fiber content. These restrictions are

153

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154

less constraining for CGM use. However, in order for CGM to be used
in monogastric diets for other then its xanthoplyll content its price
will have to be reduced well below its value based upon bypass protein
characteristics in ruminant diets.

The ability of the market place to value these by-products based
on known nutritional concepts can be evaluated. The analysis of annual
price relationships and recent market value indicates that DDGS has
been traded based on its crude protein characteristics. The annual
equation (using Toledo corn price)l/on a ton per ton basis.

+ .466 A P

A P .448 A P

DDGS = CORN SBM.
This is similar to the equation

= .530 A P + .470 A P

A POOCS CORN SBM
which was derived from use of DDGS determined by its crude protein con-
tent. The recent market value of DDGS, 68-73% the price of SBM, com-
pares to an economic value of 74% when DDGS price is based on the price
of a corn-SBM blend that is equal in crude protein. The annual pricing
relationships also indicated a statistically discernible downward trend
in DDGS price relative to SBM, which is consistent with historical data.
This implies that the market has reflected the value of DDGS given what
is known about its nutrient characteristics. Thus, it is reasonable to
assume that the future DDGS price will adjust to recent developments in
understanding the bypass protein concept.

The analysis of annual price relationships and recent market value

yield mixed results for CGM. The annual statistical equation,

 

l/

— Toledo corn price was used because it was felt that it more accurately
reflects DDGS market conditions.

155

A P = .682 A P

CGM + .682 A P

CORN SBM’
does not agree with the value of CGM based on crude protein character-
istics

A P = 1.33 A P .

CGM SBM

However, the recent market value of 130-135% the price of SBM compares
to an economic value of 133% when CGM price is based upon the amount
of SBM it takes to equal CGM's crude protein content. Thus, even
though the annual relationships are ambiguous, its market value has
traded on a crude protein basis. Therefore, it is assumed that the
price of CGM will adjust to recent developments in understanding the
bypass protein concept.

The by-products DDGS and CGM have been priced based on their
crude protein content in recent years. This is not the case for CGF
which has maintained a price of approximately 60% the price of SBM.
Its economic value based upon the price of a corn-SBM blend that is
equal in crude proteint is 69%. An underevaluation of nearly 10% has
been typical. Also, the statistical analysis of annual relationships
yielded coefficients

(A P

= .34 A P + .41 A P

CGF CORN SBM)
which differed from pricing relationships based on crude protein content

= .65 A P + .35 A P

(A PCGF CORN SBM)'
This underevaluation may be the result of the wet-milling process from

which CGF is produced. CGF is an 'Odds and ends' by-product feed mean-
ing that the quality and consistency of its nutrient content is not as

closely scrutinized as for DDGS or CGM. This results in increased

variability of its nutrient content. Feedstuffs with high nutrient

156

variability are less valuable, particularly when feed manufacturer

risk aversion is taken into consideration. It is hypothesized that

this is the reason for the decreased price of CGF. The underevaluation
is even more of a riddle because significant amounts of CGF is sold

in the European Community as a substitute for corn. Because the variable
levy is applied to corn, and not to SBM or CGF, CGF would be expected

to be priced at a higher value relative to SBM. It is believed that

CGF price will increase as understanding of the bypass protein concept
grows, but less than what it could if its nutrient concentration were
less variable.

Future price relationships of the by-products are expected to vary
significantly from historical ones. This is based on the projection that
by-products use will be based on their bypass protein Characteristics
as the new nutritional concept is being implemented into dietary
requirements, feedstuff nutrient values and diet formulation. As the
market adjusts to this new concept, by-product prices will increase
relative to SBM. The nutritionally based price relationships set an
upper bound on the price of these by-products with recent historical
relationships acting as a lower bound.

A note of caution needs to be injected. Operational understanding
of the role of bypass protein is a recent development since the mid
and late 1970's. One quandry is that the protein quality (amino acid
balance) of distillers by-products is relatively poor. The fact that
performance response to their inclusion in ruminant diets has been so
good is a riddle. This is one reason that nutritionally based price

relationships are considered an upper bound and not a definitive price.

157

Market demand and the price of a by-product feed can be estimated
based upon knowledge of how much can be fed in specific livestock and
poultry diets, the number of animals in the market area and familiarity
with the production phases of those animals. This was illustrated by
a case study. First, potential geographic market areas were identified
based upon transportation distance and demand for protein supplements.
Second, size of animal industries in those areas were estimated. Third,
the size of the animal industries were divided into classes for which
least cost diets were formulated. Fourth, the total quantity of by-
product demanded at a certain price was found by summing the quantity
demanded from each class. From this, it is estimated that the by-product
DDGS from a 76 million gallon/year ethanol plant located in central
Michigan could be priced at its optimal value based upon its bypass
protein characteristics. This is dependent On capturing 40% of the
market potential for use in diets for rapidly growing beef calves and

dairy cows in early lactation.

6.2 Research Needs

One of the major assumptions is that a by-product feed, in combina-
tion with urea, is equivalent to SBM based upon its bypass protein
characteristics for the lactating dairy Cow. The nutritional model used
this concept in formulating feed-lot beef diets. Research is needed to
determine for the lactating dairy cow: 1) if this concept is valid and,
if so; 2) the limits on the proportions that can be fed; and 3) the best
uses of these by-products in dairy diets. How do these feeds or, more
appropriately, how do diets utilizing these feeds impact performance of

the dairy animal? Research emphasis needs to be placed on proportion

158

limits and best uses of these by-products. Little doubt remains that
the concept is valid. These findings will have a major influence on

the most valuable use of these feeds if they vary significantly from our
assumptions.

Pet diets are a potential market. Work considering by-product feeds
and their contribution to pet requirements may open up a substantial
market.

Thirty percent of DDGS and 60-70% of CGM and CGF are exported.

This market option was not taken into account in the case study. When
international market areas are included, this could influence by-product
pricing because of the impact of variable levies on relative prices.

Risk, by using average nutrient values in this analysis, was ig-
nored. This problem occurs in the pricing of CGF. It has been under-
valued based upon known crude protein characteristics in the domestic
market while in the European community it has a higher value relative to
SBM. As risk is accounted for, diet composition will change, not only
for CGF but also for DDGS and to a lesser extent CGM. Refining the use
of safety factors to account for risk and delineating the impact on

competitive price is another area in need of future work.

APPENDIX A
IMPLEMENTATION AND IMPACTS OF SAFETY FACTORS

159

Risk control is a concern of managers, and most are risk adverse;
that is, they are willing to give up some profits if risks are reduced,
at least to a point. Knowledge of how to best utilize DDGS, CGF and
CGM requires taking into account the variability of their nutrient
densities. This appendix expands on the introductory discussion on the
use and safety factors for risk control. The implementation of safety

factors will be discussed followed, by their impact on diet formulation.

Implementation

 

Computer software has been developed which transforms the informa-
tion contained in Table 4.2.2 into the fed beef linear programming
martix (Table 4.3.1). The simplex solution algorithm can then be used
to solve for a least-cost diet (Black and Hlubik, 1980). For net pro-
tein and ammonia utilization potential (AUP), additional computation is
needed to adjust for variations in net protein and rumen ammonia values
of each feed. Variation also exists in the content of other nutrients
but their lower cost makes them of less interest. To illustrate how

this adjustment is conducted, the net protein concentration of corn

160

grain will be calculated adjusted for a safety factor L. The calcula-
tions arezl/
Adjusted net protein = Average net protein -
(L) (Standard deviation of protein concentration)
where:

Average net protein =

(Average crude protein) (Average protein fraction);

Standard deviation of protein concentration

 

«Net protein fraction)2 * VARCP;

L = Value chosen from Table 4.3.2 to assure a specified prob-
ability of meeting the net protein requirement;

AVCP = Average crude protein

VARCP = Variance of crude protein
This yields the adjusted net protein value used in the diet formulation
linear programming matrix. For corn:

AVCP = 10% of dry matter

Ratio of net to crude protein = .35

Average net protein = 10% * .35 = 3.5% of dry matter

Standard deviation of net protein concentration =

 

V(.35)2 * .25 = .175
L = 1.29 (representing a 90% probability of success)
Adjusted net protein = 3.5 -(1.29) (.175) = 3.27 of dry matter

 

1-/The relevant theorems from probability theory are:
(1) Expected value of aZ = (a) (Expected value of Z) and
(2) Variance of al = a2 Variance Z

where a is a constant and Z is a random variable.

161

An adjustment is now made to the AUP of corn.

Ammonia utilization potential (1b) =
[(Microbial protein upper bound) (Total digestible nutrient) (2)]
-[(Protein concentration) (Degradation rate of crude protein

in the rumen) (NH3 fraction retained in the rumen)].

 

-LVVariance of degradation rates.
Microbial protein upper bound = Table 4.2.1 (dependent on energy level)
NH3 fraction retained in rumen = assumed to be .85
Variance of degradation rates = (Degradation of crude protein)

(NH3 fraction retained in rumen)]2 VARCP

Applying this formula to corn:

[(.60) (85)]2 * (10 * 5/TOO)]2
= .065

Variance of degradation rates

Ammonia utilization potential, grams/lb corn dry matter =
(20.7 * 91/100 * 2)
(10/100 * 454 * .60 * .85)

1.29 .065

14.19 (for animal on .49 NEg Mcal/lb diet)
. Research has shown that corn supplies ruminally available energy such
that rumen microogranisms utilize all of corn's ammonia with energy
left to utilize ammonia from other feedstuffs, resulting in a positive
value for the ammonia utilization potential of corn.

The size of the optimal safety factor is left to the discretion

of the manager. Black, Peterson and Fox (1978) dealt with this question

and found that the cost of incorrectly formulation beef diets increases

162

with the weight of the animal. Optimalg/ safety factors for 475 1b,

600 1b and 850 1b fed beef animals were found to be .68, 1.04 and 1.20
corresponding to 75%, 85% and 90% probabilities of success. Values for
L are assigned depending on the animal's weight and the manager's aver-

sion to risk.

Impact
Table A1 demonstrates the impact on the composition of the 475 lb

.53 NEg Mcal/lb growing steer diet when safety factors are used to adjust
the net protein content of DDGS and CGM. Reference diets supplemented
with SBM were not feasible when the safety factor was above zero because
requirements could not be met without exceeding the specified energy
level. As the safety factor increases from zero to 1.65, the DDGS pro-
portion in the diet increases from 11.4 to 25.8% while the urea decreased
from 1.2 to .7%. The pattern is similar for CGM as the safety factor in-
creased from zero to 1.65, CGM increases from 3.6 to 6.2% and urea de-
creased from 1.2 to .9%.

As the safety factor increases, a larger proporation of the stand-
ard deviation is subtracted from the mean nutrient value, decreasing the
adjusted nutrient mean for diet formulation purposes. Increasingly
greater amounts of DDGS and CGM are required to supply the same quantity
of net protein. More DDGS and CGM must be fed with increasing coeffi-

cients of variation. The relative substitution rates of DDGS and CGM

 

g-/0ptimal in the sense that they minimized the total expected cost per
1b gain where "expected" is used in the weighted average sense.

.eewe eesesewes eeeweesew ee ewswmmes ee2\m

 

163

 

 

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Nee. mee. Nee. eee. Nee. eee. Nee. mee. Nee. nee. Nee. eeeeaeese
ese. -- Nee. -- see. -- eee. -- ewe. r- -- see
-- eNm. -- eme. -- New. -- es_. -- e__. --, meee
mew. mew. mew. eem. mem. eem. mem. eem. eem. eem. eem. eeessm esee
eee. mee. ese. see. ese. eee. see. e_e. use. Nee. -- eese
-- -- -- -- -- -- -- -- -- -- ewe. sew
emm. mes. smm. ee_. Nee. eeN. meme New. Fem. eem. mom. esese esee
see meee see meee see meee see wees see wees sew eeseem eseeese
\eme._ \me~.s \eee._ \mee. e MMWNMM
e

eewo seeem s_\~ee: mz mm. sp mme e
sew meewo we sewuwmeeeeu ese se :wu ese mean we “seesem sweeess eez sew msepeem :Jewem we ueeesm ._< ewseh

164

for SBM depends on the coefficient of variation of SBM as well as

that of the by-products' coefficient of variation, relatively large
amounts of DDGS or CGM are needed to replace SBM. However, if SBM has
a high coefficient of variation, then relatively less DDGS or CGM iS
needed to replace SBM.

Decrease in urea content is also due to the safety factor and
limitations placed on excess ammonia in the rumen. An upper bound on
the amount of ammonia convertible into microbial protein exists;
ammonia supplied in excess of this is wasted. The amount of ammonia
supplied by a feedstuff is determined by its protein degradation and
crude protein content. Safety factors are not used to adjust degrada-
tion of feedstuff protein, but only mean protein content. To ensure
against ammonia excess, a proportion of the standard deviation (deter-
mined by the safety factor) is added to the mean protein value when
calculating ammonia production. As the safety factor increases, greater
amounts of ammonia are assumed to be produced. As more ammonia comes
from feedstuff protein, less is needed from non-protein nitrogen

sources so urea level decreases.

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