v_ ”Vi—'-

 

THE RELATIONSHIP BETWEEN BUSINESS SUCCESS AND
SELECTED DAIRY BUSINESS ANALYSIS FACTORS 0N
LARGE SOUTHWESTERN U. S. DRYLOT DAIRIES

Thesis for the Degree 'of M. S.
MICHIGAN STATE UNIVERSITY '
TIMOTHY ROBERT LOGAN?
1976

IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII '

3 1293 10

 

 

C)

m

6/020

ABSTRACT
THE RELATIONSHIP BETWEEN BUSINESS SUCCESS
AND SELECTED DAIRY BUSINESS ANALYSIS

FACTORS ON LARGE SOUTHWESTERN
U.S. DRYLOT DAIRIES

By

Timothy Robert Logan

Records from Southwestern U.S. drylot dairies were
studied to determine the management factors most useful for
monitoring the dairy operation and to suggest a predictive
model for relating the financial, marketing, and production
facets of the business to business success.

All information was obtained anonymously through an
accounting agency. Raw data was converted to 25 specified
ratios measuring the various aspects of the dairy business.
The relationship of these ratios to business success was
then investigated by means of multiple regression analysis.

Ten factors resulted in a coefficient of determination,
R2, of .69. Milk revenue per cow contributed 98% of the
explained variation in percent return to net worth. The

relationship suggests that the genetic potential of cows to
produce milk, as well as those management practices which

contribute to high milk production, should be a top priority

item to dairymen.

THE RELATIONSHIP BETWEEN BUSINESS SUCCESS
AND SELECTED DAIRY BUSINESS ANALYSIS
FACTORS ON LARGE SOUTHWESTERN

U.S. DRYLOT DAIRIES

BY
Timothy Robert Logan

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Dairy Science

1976

DEDICATION

This study is dedicated to my wife, Donna,
as a thank you for the patience, encourage—
ment and love she has given me throughout
my graduate education.

ii

ACKNOWLEDGEMENTS

The author wishes to thank his parents, Mr. and Mrs.
Donald C. Logan, for their encouragement and sacrifices
which enabled him to pursue his college education.

Sincere thanks are expressed to his major professor,
Dr. J. A. Speicher, for his advice, patience, and energy
in preparation of this study. Appreciation is also
extended to committee members Dr. D. Hillman, Dr. S. B.
Nott, Dr. L. J. Connor, and D. Armstrong for their encour-
agement and advice.

Special appreciation is extended to the author's
brother, Patrick A. Logan, for his unselfish assistance
in all phases of the computer programming and statistical
analysis. The author wishes to thank Dr. J. Gill for his
advice on statistical matters.

Finally, appreciation is extended to Dr. Charles
Lassiter, former Department Chairman, for his help in

program planning.

iii

II.

III.

IV.

VI.

VII.

TABLE OF CONTENTS

INTRODUCTION.
REVIEW OF LITERATURE.

Changing Nature of the Dairy Business

Methods and Factors Used in Dairy
Business Analysis

Association Between Dairy Business
Analysis Factors and Measures of
Business Success.

CONCEPTUAL FRAMEWORK AND DATA SOURCE.
ANALYTICAL METHOD .

RESULTS AND DISCUSSION.

Financial Model

Income Generating Efficiency Model.
Input Marketing Efficiency Model.
Output Marketing Efficiency Model
Production Model. . . . . .
Complete Model. .

Analysis of Residuals

CONCLUSIONS

SUMMARY .

APPENDIX.

BIBLIOGRAPHY.

iv

Page

13
22
31
45

46
50

57
60

75
77
81
86
90

Table
11-1

III-l

IV-1
IV-2
IV-3
V-l

V-lO

V-ll

LIST OF TABLES

Explanation of variation in net income
with 14 farm management factors .

List of performance ratios to be used
for data base . . . . . .

A listing of functions analyzed .

A listing of variables used in functions.

A listing of models examined.

Stepwise regression summary: financial
model

Coefficients and confidence intervals:
financial model

Contribution of variables to explained
variation: financial model . . .

Stepwise regression summary: income
generating efficiency model

Coefficients and confidence intervals:
income generating efficiency model.

Contribution of variables to explained
variations: income generating effi-
ciency model. ' .

Stepwise regression summary: input
marketing efficiency model.

Coefficients and confidence intervals:
input marketing efficiency model.

Contribution of variables to explained
variation: input marketing efficiency.

Stepwise regression summary: output
marketing efficiency model.

Coefficients and confidence intervals:
output marketing efficiency model

V

Page

16

29
31
33
41

47

49

50

51

54

55

56

58

58

59

61

Table Page

V-lZ Stepwise regression summary: production
model . . . . . . . . . . . . . . . . . . . 62
V-lS Coefficients and confidence intervals:

production model. . . . . . . . . . . . . . 64

_V-l4 Contribution of variables to explained
variation: production model. . . . . . . . 65

V-lS Stepwise regression summary: complete
model . . . . . . . . . . . . . . . . . . . 66

V-l6 Coefficients and confidence intervals:
complete model. . . . . . . . . . . . . . . 72

V-l7 Contribution of variables to explained
variations: complete model . . . . . . . . 74

V-18 Contribution of specified variables to
explained variation when all variables
are fitted. . . . . . . . . . . . . . . . . 75
A1 Descriptive statistics: calculated ratios. 86

vi

LIST OF FIGURES

Figure Page
A1 Residuals plotted against T. . . . . . . . . 88
A2 1975 residuals plotted against 1974

residuals. . . . . . . . . . . . . 89

vii

I. INTRODUCTION

Modern dairy enterprises are characterized by increased
cows per farm, increased production per cow, increased use
of capital, technology, and managerial skills, and narrow
profit margins. These characteristics are most applicable
to large specialized drylot operations as found in the
southwestern states. Dairy managers intending to create,
maintain, or improve profitability under such circumstances
must efficiently monitor the financial, marketing, and pro-
duction facets of the business in order to quickly pinpoint
problem areas where improvements should be made. A tool
which can be used to monitor the business without requiring
the dairyman to have knowledge of the underlying economic
theory is the dairy business analysis. A dairy business
analysis consists of comparing a set of predetermined per-
formance factors developed from the dairyman's record
system to historical and industry norms. To date, few
such norms, or guidelines, are available for large special—
ized drylot dairies, nor are sufficient studies available
indicating the most suitable performance factors to use in
an analysis. This is particularly true when considering

measures dealing with acquisition and use of capital.

2

The objectives of this study are:

1) To determine which management factors are most
useful for monitoring:

a) the financial facet of the dairy enterprise
(acquisition and use of capital),

b) the marketing facet of the dairy enterprise
(procurement of inputs, pricing, and distri-
bution of outputs), and

c) the production facet of the dairy enterprise
(physical inputs and outputs, quantities,
and technologies).

2) To suggest a predictive model for relating the
financial, marketing, and production facets of
the business to business success.

3) To determine the relative value of financial,
marketing, and production factors when used
simultaneously in a dairy business analysis.

To accomplish these objectives literature was first
reviewed concerning 1) factors to be used in business
analysis, 2) currently available business analysis tools,
and 3) the relationship between specified factors and
business success. A set of factors to be studied was
then selected and data obtained from an established
southwestern U.S. accounting firm specializing in dairy
accounts. Descriptive statistics were then developed
from the data set and multiple regression analysis was

used to accomplish the remaining objectives. The

3

following pages detail this procedure and present the
results obtained from the study.

It is expected that results of this study will be
useful to dairy managers, extension dairy management
specialists, financial institutions, and related agencies

interested in dairy business analysis.

II. REVIEW OF LITERATURE

This review is concerned with the changing nature of
the dairy business; factors and methods used in dairy
business analysis; and the association between dairy

business analysis factors and measures of success.

Changing Nature of the Dairy Business

Changes in the dairy industry during the last few
decades help explain the increased need for use of
business analysis as a management tool. Farm numbers
have decreased with a corresponding increase in farm
size. In 1959 U.S. farms numbered over 4 million and
averaged 288 acres per farm. In 1975 these values were
under 3 million and over 385, respectively (11). Within
the dairy sector herds of 10 or more cows were reduced by

33

o\°

during the 1954-1964 era and 49% during the 1964-1974
era. During the entire 1954-1974 period total milk cow
numbers declined 30%; milk production per cow increased
30%; and average cows per farm increased by 26 cows (4).
Increased use of capital, technology, and managerial
skills is indicated by changes in labor and capital as
percents of agricultural input values. From 1940 to 1972
labor use decreased 35 percentage points while use of

capital increased 35 percentage points (34). Production

5
costs have risen at a faster rate than gross income.
Since 1972 feed and interest costs have increased by more
than 70%. Building and machinery costs have increased by
more than 50% while fertilizer and energy costs have gone
up 100%. The price received for milk since 1972 has gone
up by only 39% (3). The result of such cost-price changes
is narrower profit margins. Hoglund (4), during a lZ-month
study of the U.S. dairy industry today, reported that
modern dairies are larger, more highly mechanized, and
better managed than ever before.

Through Urhsveil of change has emerged the enigmatic
phrase large herd management. This study deals with busi-
.ness analySis of large dairies and it is therefore appro-
priate to expand upon the definition and nature of large
herd management. Albright (1) attempts to define large
herd in a manner applicable, within reason, to most of the
U.S. dairies, by stating that a dairy herd is large when
the owner-Operator: 1) manages resources such as feed,
labor, and capital; 2) handles and thinks of cows in terms
of groups rather than individuals; 3) no longer milks his
own cows; 4) raises most of the feed for his herd and owns
at least twice the average number of cows enrolled in his
state D.H.I.A. program; and S) has 100 or more cows in
milk at one location. Speicher (8) indicates that not
only herd size, but size of investment can be used in
classifying the size of a herd as large. Pelissier (6)
states that to define a herd as large is a relative matter

affected by location, environment, custom, market, and a

6

variety of economic factors. Consequently, discussion of
large herd management is facilitated by relating manage-
ment practices to specific geographical areas and situations.

The author concurs with Pellisier in that geographic
location facilitates understanding the nature of large
herd management. Although Longo (5) does not define large
herds by size, he describes successful large herd manage-

ment systems in New Englagd has having:

 

l) efficient records and accounting systems,
2) sufficient time for the manager to plan and
organize,
3) use of sophisticated investment analysis tools,
4) ration balancing and forage analysis programs,
5) group feeding methods,
6) planned breeding programs,
7) planned herd health programs,
8) free stall milking systems,
9) bunker silos.
Speicher (8) states that 50 or more cows is a reasonable
approximation of a large herd in the Midwest and he out-
lines those aspects which have a major effect on profits
from large dairy farms:
1) adequate herd size,
2) high level of output per cow,
3) controlled feed costs,
4) investment to accomplish desired goals at
least cost,

5) high output per man,

7
6) maintenance and use of adequate records,
7) sound youngstock management program,
8) milk price,
9) balance of operations.
From data supplied by Pelissier (6), it is reasonable to

define a Southwest herd as 400 or more cows. Such herds,

 

radical departures from more traditional dairying, are
characterized by small acreage drylots, purchasing of all
feed and replacements, high investments, a high degree of
mechanization, and narrow profit margins (2,7,9,10).

The changing nature of the dairy industry accents
the increased need for accurate business monitoring and
analysis particularly in larger dairy herds.

Methods and Factors Used in
IDaIry—BuEIness AnaIysis

 

 

For a complete business analysis researchers in the
field of farm management science have developed selected
measures to evaluate the success and efficiency of the
whole farm, to describe organization of the farm, to
measure the level of farm inputs and outputs, and to
evaluate the efficiency of individual farm enterprises.
Black et a1. (13), Boss and Pond (14), Case et al. (18),
Chastain et al. (19), and Heady and Hopkins (25) outlined
a variety of such measures. A composite outline developed
from the above references is printed below to provide the

reader with adequate perspective:

8

Measurgs of whole-farm egrnings and efficiency

 

A. Aggregate measures

 

1. Net cash income is the difference between

 

cash input and cash output.

 

2. 93955 income is gross cash receipts adjusted
for inventory change, livestock purchases,
and feed purchases.

3. Net operating income is gross income less

 

operating expenses.

4. Net farm income is gross income less both

 

operating and fixed expenses.

5. Return to management is net farm income less

 

arbitrary charges for a) operator's labor,
b) family labor, c) interest on net worth,
and d) rental value of all land with
improvements.

6. Retgrngto_total investment is net farm

 

income less arbitrary charges for operator
and family labor plus interest on borrowed
capital.

7. Return to net worth is net farm income less

 

arbitrary charge for operator and family
labor.

B. Ratio megsures

 

1. Rate of return on investmgnt is return to

 

total investment divided by total investment.

2. Rate of_return to net worth is return to net

 

worth divided by net worth.

II.

III.

9

3. Operating ratio is operating expenses divided

 

by gross income.

4. Fixed ratio is fixed expenses divided by

 

gross income.

5. Gross ratio is total expenses divided by

 

gross income.

6. Cgpitalhturnover is gross income divided by

 

average capital investment.

Kind of_org§nizatipn

 

A.

B
C.
D

E.
F.

Percent of returns from livestock.
Percent of returns from crops.
Percent of land in different crops.

Diversity_igdex is a simple index which shows

 

the number of enterprises contributing signifi-
cantly to the whole farm income.
Feed fed per acre.

Animal units per acre.

Size of busingss

 

A.

B.

C.

Capital‘input

 

1. Total investment.
2. Size of important livestock enterprises.

Land input

 

1. Total acres.
2. Total acres tillable.

Labor input

 

1. Number of men.

IV.

VI.

VII.

10

2. Productivepman-work:unit§_is total labor

 

input required for whole farm at normal
regional labor efficiencies.

D. Combiggd inputs of land, labor, and capital

 

1. Total farm inputs.
2. Total nonfeed inputs.

Labor efficiengy

 

A. Work units_per m§n_is number of productive man-

 

work—units divided by number of men.
B. Enterprise units per man.
C. Gross income per man.

Power and maghinery efficiengy_

 

A. Power and machinery cost per acre.
B. Power and machinery investment per acre.

Capital ratios

 

 

A. Capigalpeggfiloo ggpss income is total invest-
ment divided by gross income X 100.
B. Capital per man.

Enterprise efficigngy;_gdairyl

 

Milk sold per cow.

Butterfat sold per cow.

Number cows per man.

A
B
C. Total dairy income per cow.
D
E Milk price per 100 pounds of milk.
F

Improvements per cow.

Only efficiency factors for the dairy enterprise

Were considered appropriate for this discussion. Non-
dairy enterprises were eliminated.

11
G. Replacements per cow.
H. Cattle income per cow.
I. Feed cost per cow.
Return per dollar of feed expense.
Percent cows in milk.
Grain fed per cow per day.

TDN fed per cow per day.

ZZL—‘WC-d

Hay equivalent per cow per day.
0. Pounds of milk per pound of grain fed.
Additional measures of financial soundness have been
suggested more recently by Nelson, Lee, and Murray (29)
and Hopkin, Barry, and Baker (26) as follows:

Current ratio is total current assets divided by total

 

current debt. The current ratio is a measure of the ability
of liquidated current assets to cover current outstanding
debts.

Intermediate ratio is total current and intermediate

 

assets divided by total current and intermediate debts.

Net capital_ratio is total assets divided by total

 

debt. The net capital ratio describes the long-run liquidity
or solvency position of the business in that it reflects

the ability of the sale of all assets to cover the entire
business debt.

Leverage or debt: equity ratig_is total debts divided

 

by owner's equity or net worth. This ratio indicates how
dependent the business entity is on nonequity capital.
Equity: value ratio is owner's equity divided by

value of assets.

12

In reviewing the above measures, two observations
should be made: 1) An almostinfinite number of measures
and ratios can be used to monitor a business. Only those
considered significant for field use are listed. 2) Several
measures can be used to measure the same characteristic of
the business. The type of analysis and the most readily
available information often determine which measure will
be used to describe a given characteristic.

Farm management researchers have historically sug-
gested two methods of business analysis (15,26,29). Iggnn
analysis is used to analyze changes in finance, production,
and marketing aspects of an individual farm business over

a period of time. Comparative analysis is used to compare

 

an individual farm with similar farms within a region
through the use of various ratio measures. Ratios facili—
tate analysis of all farm businesses on the same basis.
The usefulness of comparative analysis depends heavily on
the availability of reliable standards for comparison.
Extension personnel throughout the U.S. have promoted
state or regional comparative analysis and individual trend
analysis with varying degrees of intensity.2 The litera-

ture made available by these workers can be crudely

 

2In view of the pragmatic nature of this study and
the potential benefits to future researchers, the author
considers it appropriate to briefly describe such work.
'Fhe author requested information from 26 land-grant schools
(n1 dairy business analysis, categorized the material
ITHZBIVBd, and included the material in the bibliography.
'Theelist is not necessarily all-inclusive.

l3

classified into 3 categories: 1) Dairy Business Analysis

 

Guides (l7,24,33,34,37). These include specific instruc-
tion on the theory and use of business analysis. Some
contain a set of standards developed from field data or

a synthetic planning cost guide. Only two of the guides
examined by the author contained specific information on
financial management and standards which included short

and long term liquidity (34,37). 2) Dainy Business

 

Summaries (16,20,21,22,27,28,30,31,33,35,36,38). These

 

are the most common type of business analysis tools
available. The summaries contain cost standards et al.
developed from land-grant university based record keeping
systems. The data are accompanied by various explanations,
- but contain significantly less instructional material than
the business analysis guides. The summaries vary greatly

in thoroughness. 3) Synthetic Budget and Cost Guides

 

(12,32,39). These are cost estimates developed from
means other than those used for the business summaries.
The guides are primarily designed for budget and planning
purposes.

Association Between Dairy Business Analysis Factors
ann Measures of BESiness Success

 

In his Ph.D. dissertation Speicher (46) provides a
thorough review of literature prior to 1963 describing
the association between farm incomes and farm management
factors. The present review will therefore concern litera-

ture developed during and after 1963.

l4

Speicher (46,47) conducted an investigation to deter-
mine the amount of variation in net income that could be
explained by selected farm management factors and to
determine the relative importance of those factors in
explaining this variation. To do so the author used
Michigan D.H.I.A. and Michigan Mail-In Account Project
records from 340 dairies for the period 1958 through 1962.
All dairies received 70% or more of their gross income
from dairy product sales. Thirty-eight factors were
developed from the data source to reflect the effect of
size of operation, livestock program, cropping program,
and organization and intensity on net income. Prior to
the use of multiple regression analysis, simple correla-
tions between net income and each of the farm management
factors were computed to establish the degree of curvi-
linearity. Sets of factors from within the groups of
size, livestock and crop programs, and organization and
intensity were deve10ped since various combinations of
factors within a group could be of equal value in explain-
ing variation in net income. All sets were then combined
in all possible combinations to produce 12 prediction
equations. The largest coefficient of determination,

.7531, resulted from the equation: Y = F(X3,X32-

2. 2. 2. 3. 2. 7-.
Xe'xe ’ x9,x9 ’ X12’X12 ’ x12 ’ X13’X13 ’ XlS’Xls ’
x 2 2 2

X X ; X30,X30 ; X X ; X

X 31’ 31

2
16’ 18’ 329x32 )

Where:

21‘ Xze' 26

X3,X3 represents number of cows

15
X4 represents number of tillable acres
X6,X62 represents crOp value/tillable acres

X9,X92 represents soil value rating

12,X122,X123 represents percent cash crops
13,X132 represents machinery expense/tillable acre

X152 represents percent rented land

15’
16 represents tillable acres/cow

18 represents milk sold/man

X

X

X

X

X

X21 represents milk sold/cow

X26’X262 represents milk price/cwt

X30,X302‘represents base milk

X31,X312 represents dairy cattle income/cow

X32,X322 represents livestock income/$100 feed expense
Path coefficients were used to measure the relative

value of the farm management factors in the explanation of

variation in net income. The path coefficients and con-

version of the path coefficients to percents of explained

variation are shown in Table II-l. It should be noted

that the 3 groups, size of operation, livestock program,

and crapping program, explained 28%, 25%, and 29% of the

variation, respectively. The remaining 18% was accounted

for by machinery eXpense and organization and intensity.

Within the two groups, livestock and cropping program

factors, livestock income/$100 feed expense (X32) and crop

value/tillable acre (X6) accounted for 86% and 88% of

total variation attributed to the respective group.

Results of the entire investigation indicated size

of operation, cropping program, and livestock operation

16

Table II-l. Explanation of variation in net income with
14 farm management factors. Coefficient of

 

 

 

determination (R2) = .75
Path % of Explained variation
Coef. Ifidividual wArea
Farm management factors (P2) factors totals
X3 Number of cows .038 5.2 27.7
X4 Number of tillable .163 22.5
acres
X6 Crop value/tillable .163 22.5
acre
X9 Soil value rating .002 .3 25.4
X12 Percent cash crops .019 2.6
X13 Machinery expense/ .086 11.9 11.9
tillable acre
X15 Percent rented land .008 1.1 1.1
X16 Tillable acres/cow .032 4.4 4.8
X18 Milk sold/man .003 .4
X21 Milk sold/cow .006 .8
X26 Milk price/CWT .002 .3
X30 Milk production dis- .002 .3 29.1
tribution
X31 Dairy cattle sales/cow .019 2.6
X Livestock income/$100 .182 25.1
32 feed

 

to be of equal importance in determination of net income.
In addition it was shown that farm management factors
within a factor group exhibit a primacy of order in
explaining variation in net income.

LaDue and Bratton (44) continued an ongoing study of
the relationship of various dairy farm business factors to
labor income. Labor income was defined as return to
operator's labor and management. The data source used
was 731 farm business records of New York dairy farmers
cooperating in extension farm management projects. The

sample was considered slightly above average. Records

17
were summarized, put on electronic data processing cards,
and sorted in various ways to provide relationship data.
Beyond this sorting no statistical procedures were per-
formed. Factors were first considered one at a time and
the following observations were made: 1) Size was strongly
related to labor income. Larger farms made higher labor
incomes and had greater labor, livestock, crop, and
machinery efficiencies. No relationship appeared to
exist between size and capital efficiency.. 2) Higher
rates of production were associated with higher labor
incomes. 3) Labor incomes rose sharply with increased
labor efficiency. 4) Investments which increased labor
efficiency were associated with higher labor incomes.
5) Optimum cost levels for increased labor income fell
between maximum and minimum cost levels. 6) Sole pro-
prietorships made significantly higher incomes than equal
size partnerships. When combinations of size, rate of
production, labor efficiency, and cost control factors
were examined it was concluded that labor efficiency was
most consistently important.

Currin et a1. (42) used multiple discriminant analysis
to assign dairy farms to income groups on the basis of
selected business analysis factors. One hundred seventy-
eight records taken from the V.P.I. Farm Account System
during 1968 and 1969 served as the data base. The 8
business analysis factors studied were 1) number of dairy
cows, 2) pounds of milk sold per cow, 3) price received

per hundredweight of milk, 4) estimated feed cost per

l8
hundredweight, 5) investment per cow, 6) percent of total
investment in dairy cows, 7) percent cow turnover, and
8) pounds of milk sold per man. These factors were
related to 4 labor income groups ranging from less than
$1 to greater than $10,000. Feed cost per hundredweight
of milk sold proved to have the greatest power to dis-
criminate farms into the income groups. The analysis
indicated that farms earning a negative income would
have a .96 probability of receiving a positive income if
feed costs per hundredweight were reduced 30%. The
authors considered discriminant analysis a valuable tool
for farm business analysis in that the method can indi-
cate probability of increasing income if certain adjust-
ments are made.

The objectives of a study by Brown and White (41)
were to characterize relationships among selected herd
management variables and to determine the curvilinearity
of these variables when associated with average milk
yield and income over feed costs in Jersey, Guernsey,
and Holstein herds. The data source included average
yearly D.H.I.A. records completed from 1965 to 1970 from
2,269 Holstein, 230 Guernsey, and 134 Jersey herds in 8
southeastern states. Twelve independent variables were
related to income over feed costs: 1) milk yield, 2) milk
price, 3) milk fat percent, 4) percent days in milk,

5) feed costs per 45.4 kg milk, 6) other feed costs,
7) concentrates fed, 8) succulents fed, 9) dry forage

fed, 10) days on pasture, ll) grain price, and 12) herd

19
size. Variables were also related to milk production.
Correlations between herd size and income over feed cost
were significant and positive. Results of multiple regres-
sion analysis when income over feed cost was the dependent
variable indicated that the 12 variables included in the
model accounted for 99% of the variation in income over
feed costs. The full model included both linear and
quadratic effects. Milk price, milk production per cow,
concentrate feeding, grain price, and other feed costs
were relatively more important than other variables in
determining income over feed cost. This is in accordance
with earlier studies (46,47).

Literature concerning the relationship between
financial ratios and dairy business success measures is
rare. This is to be expected in light of the reluctance
of the average dairyman to divulge financial information
and in light of the limited and frequently unreliable
financial information available from land-grant university
record programs. Asset and liability valuation is per-
haps the most difficult farm record to incorporate into
such record programs since it is often based on the dairy—
man's judgment each year. Gordon (43) surmounted these
difficulties in an effort to determine which financial
ratios are associated with business success on Virginia
Grade A dairy farms as measured by net operating cash
income divided by net worth. The data source used was 95
anonymous financial statements furnished by various pro—

duction credit associations located in Virginia. Multiple

20
discriminant analysis was used to develop classification
functions to assign previously unclassified dairy farms
to profitability groups based on a selected set of finan-
cial ratios. The ratios were ranked according to their
ability to assign observations to the correct profitability
grouping. In order to do so the data were broken into
subsamples using one subsample to develop discriminant
functions and the remaining group to test the ability of
the functions to correctly assign dairies to income groups.
The five most useful ratios, in order of importance, were:
total operating expenses/total operating income, total
debt/total assets, total operating expenses/cow, current
liabilities/current assets, and total liabilities/number
of cows. These ratios assigned a significant number of
dairies to correct income groups.

Literature concerning the relationship between
financial ratios and non-dairy business success measures
is more plentiful than dairy-related literature. Two
studies merit comment. Altman (40) used financial records
of bankrupt and non-bankrupt firms to test the ability of
various financial ratios to discriminate between these
two groups. A discriminant ratio model using the ratios
working capital/total assets, retained earnings/total
assets, earnings before interest and taxes/total assets,
market value equity/book value of total debt, and sales/
total assets proved to be very accurate in predicting
bankruptcy with 95% of all firms in the bankrupt and non-

bankrupt groups assigned to their correct classifications.

21

O'Connor (45) analyzed various models relating
financial ratios to rate of return on investment in
common stocks and concluded that usefulness of ratios to
predict rates of return was doubtful.

It should be observed that none of the studies
reviewed by the author investigated the relationship
between all three economic facets of the business (finan-
cial, marketing, and production) and business success
simultaneously. It was felt that a study doing so would
be of benefit to future dairyman and associated accounting

agencies.

III. CONCEPTUAL FRAMEWORK AND DATA SOURCE

The economic facets of a dairy business may be clas-
sified as financial, marketing, and production activities.
A dairy manager is responsible for decisions in each of

these areas. Financial decisions concern the acquisition

 

and use of capital. Financial decisiOns have become

increasingly important to dairy managers. Marketing

 

decisions concern procurement of inputs, pricing, and

 

distribution of outputs. Some aspects of marketing are
little affected by a dairy manager's decisions. Milk

price is an example. Production decisions concern physical

 

inputs and outputs, quantities, and technologies. Tra-
ditionally, production decisions have been the dairyman's
chief concern.

Various tools are available to aid economic decision-
making, Economic theory states that maximum profits are
obtained when marginal costs equal marginal returns. It
is difficult for a dairy manager to put this theory into
practice via marginal analysis since in reality it is
difficult to accurately associate marginal costs and
returns associated with additional units of production.
Joint cost allocation is an example of one of the diffi-
culties encountered when applying economic theory. From

a practical standpoint a dairy business analysis presents

22

23

a more reasonable tool to aid decision-making in each
economic facet of the business. A business analysis is
a tool to aid the dairy manager in monitoring his present
status; relating this status to his objectives; and making
changes necessary to achieve these objectives. The analysis
consists of comparing performance measures of the economic
facets of the business to historical measures, or factors,
taken from past records of the individual dairy. This is
referred to as trend analysis. The current performance
measures can also be compared to norms established for
similar dairies operating within the region. This method
of analysis is referred to as comparative analysis. In
order to conduct a comparative analysis it is necessary
to have reliable industry norms. As outlined in the intro-
duction, this study attempts to determine such norms and
suggest which norms are most useful in analyzing the
economic facets of the business. Prior studies, as noted
earlier, have not dealt with financial, marketing, and
production factors simultaneously. Dairymen balk at
individually releasing financial information and land-
grant university record systems cannot generally obtain
reliable financial information. Production and marketing
information is more easily obtained.

To overcome these difficulties cooperation from an
old and well-established southwestern accounting firm was

sought and granted.3 The firm offers all accounting

 

3The firm has requested its name be kept anonymous.

24

services to its clients. This includes bookkeeping on

a cash basis and presentation of financial statements on
an accrual basis. Such matters as asset evaluation and
inventories are handled by the firm leaving little room
for input and recording error on the part of the dairyman.
This offers a marked advantage over land-grant university
record systems in that the data provided are consistent
and accurate. Data on dairies having 500 or more total
cows which purchase 85% or more of their feed were
requested. Data for 1974 and 1975 on each of 46 dairies
located in California and 4 located in Arizona were pro-
vided. The sample is considered to be slightly above
average by virtue of the fact that all dairies make use
of an accounting service. The information obtained for
each dairy is listed below with a brief explanation, as
provided by the firm, for each item. All valuations and
costs are historical and according to A.I.C.P.A. accepted
accounting principles.

1) Total assetn. Total assets includes current

 

assets, dairy herd, dairy fixed assets and other
assets such as partnership and stock investments,
long-term notes receivable, rental properties,
and others.

2) Total liabilitien. Total liabilities includes

 

current and long-term liabilities applicable to
both the dairy operation and other investments.

3) CUFEEEE assets. Current assets are primarily

 

cash, receivables and inventories easily liquidated

4)

5)

6)

7)

8)

9)

10)

25
within 12 months. Feed inventory is included
whether feed is located on the dairy or in the
hands of the seller.

Current liabilities. Current liabilities are all

 

debts due within 12 months including 100% of
bank feed loans, but excluding current portion
of long-term debt.

Milk sales. Milk sales are gross receipts from

 

sale of milk. Hauling and state and association
charges are classified as expenses.

Cattle sales. Cattle sales are gross receipts

 

from sale of cattle, primarily cull cows. Sale
of calves is excluded.

Other revennn. These revenues are primarily

 

calf sales and patronage dividends but can
include heifer feeding for other dairymen, sale
of feed, and sale of steers.

Total exnenses. Total expenses includes feed,
labor, herd replacement, and other operating
costs.

Feed costn. Feed costs include cost of consumed

 

hay, grain, and other feeds for milking and dry
animals and heifers less an average allowance of
$50 per heifer per quarter for expenses attribut-
able to raising.

Labor costs. Labor costs include salaries for

 

milkers and other farm workers and a provision

26
for partners' salaries where the business entity
is a partnership.
ll) Herd replacement cost. This includes a provision
for depreciation on mature cows plus loss or

- 4
less gain on mature cows sold.

 

4An explanation of accounting practice is required
here. Dairymen have the option to either purchase a bred
heifer as a replacement animal or raise their own replace-
ment animals or to do both. For statement purposes only
valuations for animals in these two situations are Handled
by the accounting firm in the following manners:

Purchased rnplacements: Purchased animals are
valued at cost when purEEased and a salvage value of 50%
of cost is established. Animals are depreciated at cull
rate per year. For example, a herd having an average
cull rate of 33.3% would depreciate cost less salvage
value by 33.3% for each of 3 years if kept the full 3
years. This depreciation is included in herd replacement
cost along with depreciation of self-raised replacements.
If the animal is sold for either a gain or loss on the
statement book value, this gain or loss is included in
herd replacement cost.

Self-raised replacements: For statement purposes
self-raisedireplacements are valued at $60 per quarter
from birth until entrance into the milking herd up to a
maximum of $480. This value is then depreciated down to
an arbitrary $250 in the same manner as purchased replace-
ments. For example, depreciation on a self-raised animal
in a milking herd having a 25% cull rate would be $57.50
per year (480-250)/4. For most dairymen a first-in -
first-out basis is used in valuing the herd. Deprecia-
tion on self-raised animals is also included in herd
replacement cost along with gain or loss on cow sold.

Tax implications: Statements used for this study
are accruaI based statements for use by the dairyman.

It should be understood that depreciation of a self-

raised animal is meaningless for tax purposes. Self-
raised animals are subject only to deductions for immediate
expenses while raising and a capital gain tax rate if the
animal is sold for gain. Depreciation of purchased animals
is tax deductible, but gains on sale are not normally
subject to special tax privileges unless the animal is

held for 24 months and sold for a gain.

 

 

12)

13)

14)

15)

16)

17)

18)

19)

20)

27

Other operating expenses. This includes all

 

operation costs except for feed, labor, and herd
replacement expenses.

Pnrtner's salaries. This is self-explanatory.

 

In a few instances, a provision may not be made
as profits are simply split equally.

Personal withnnawals. This is self-explanatory.

 

It does not include income taxes of proprietor-
ships but would for partners of partnerships.

Milk cows and dry cows. The cow numbers are

 

averages per year.
Heifers. Heifers being raised except for those
under 3 months of age.

Percentage replacements. Percentage replacements

 

equals the total mature head sold plus dead cows
divided by the average total mature herd size
for the year.

Grain and hay fed. This includes amount fed to

 

mature cows and heifers if they are raised on
the dairy.

Silage fed. Silage fed was estimated by dividing

 

total silage cost by the average cost per ton
for the region and converting to dry matter tons.
The item is only an approximation.

Greenfeed and other feeds. This item was con-

 

verted to dry matter tons in the same manner as

item 19.

28

21) Net income. Net income was calculated by the

 

author as milk revenues plus other revenues less
total expenses. Cattle sales were not included
as a revenue since herd replacement cost takes
cattle sales into account.

22) Net worth. Net worth is total assets less total

 

liabilities.
23) Age_of record. Age of record refers to the
number of years a dairy has utilized services
of the accounting firm. Since balance sheet
items are based on historical values it is
logical to assume that the date of asset and
liability valuation is an important factor.
After careful consideration of the literature cited
earlier, a list of ratios was compiled to facilitate
analyzing all sizes of the business on the same basis.
The raw data described above were used to calculate
values for these ratios on each dairy in the sample.
The resulting set of ratio-values served as the data base
for the analysis described in Section IV. Table 111-1
lists the ratios which were calculated and the abbreviated
ratio names which will be used throughout the remainder
of the text.
In reviewing the ratio list, the following observa-
tions should be made:
1) An attempt was made to include those ratios most
frequently recommended in the literature for use

in a dairy business analysis.

29

 

 

 

Table III-l. List of performance ratios to be used for
data base
Abbreviated

Ratio ratio name

1. (net income/net worth)100 NI/NW

2. current assets/current liabilities CA/CL

3. total assets/total liabilities TA/TL
4. total liabilities/net worth TL/NW

5. net worth/total assets NW/TA

6. gross income/total assets* GI/TA

7. feed expense/gross income** FE/GI

8. labor expense/gross income*** LE/GI

9. herd replacement expense/gross income HRE/GI
10. other Operating expense/gross inCome OOE/GI
ll. withdrawals/gross income W/GI
12. other revenue/gross income OR/GI
l3. feed expense/cow equivalent FE/CE
l4. labor expense/cow equivalent LE/CE
15. herd replacement expense/cow equivalent HRE/CE
16. other operating expenses/cow equivalent OOE/CE
l7. milk revenue/milking cow MR/MC
18. cattle revenue/milking cow CR/MC
19. other revenue/cow equivalent OR/CE
20. milk production/milking cow MP/MC
21. milk fat/milking cow MF/MC
22. raised heifers/total mature cows RH/TC
23. grain/cow equivalent G/CE
24. roughage/cow equivalent R/CE
25. replacements/total mature cows CSD/TC

*
Gross income = milk sales + cattle sales + other
revenue.
**

which do not,

in the raw data.

In order to evaluate those dairies which raise
their own heifers on an equal basis with those dairies

$50 allotment per quarter per raised heifer.

***

feed expense was adjusted by adding ($50 x 4)
(number of heifers raised) to the feed expense value given
The raw data value did not include the

In order to evaluate sole proprietorships and part-
nerships on an equal basis, labor expense was adjusted by
subtracting partners' salaries from the labor expense value
provided in the raw data. The raw data value included
partners' salaries.

+Cow equivalent = milking cows + dry cows + .5
(number of heifers raised). Cow equivalents were used in
order to evaluate those dairies which raise their own
heifers on equal basis with those that do not. Two heifers
were arbitrarily considered equivalent to a mature cow.

3O

2) An attempt was made to include ratios which
describe the financial, marketing, and production
facets of the dairy business.

3) The raw data set imposed some limitations on the
ratios which could be calculated. The primary
limitation was inability to calculate a ratio
describing labor efficiency. Data on actual
labor hours worked were unavailable.

Descriptive statistics for these ratios can be found in

Tables A-1 and A-2 in the Appendix.

IV. ANALYTICAL METHOD

It was decided that the six functions described in
Table IV-l were logical functions to analyze in order to
accomplish the objectives of the study. This decision
was made after reviewing the literature, consulting with

extension personnel, and considering the data source. The

Table IV-l. A listing of functions analyzed

 

 

Name of Function Function*
Financial Function Y = f(Xi,YR,AOR), where
i = l,...,5
Marketing Functions:
Income Generating Efficiency Y = g(Xi,YR,AOR), where
i = 6,...,ll
Input Marketing Efficiency Y = g'(Xi,YR,AOR), where
i = 12,...,15
Output Marketing Efficiency Y = g"(Xi,YR,AOR), where
i = 16,17,18
Production Function Y = h(Xi,YR,AOR), where
i = 19,...,24
Complete Function Y = k(Xi,YR,AOR), where
i = l,...,24

 

3':
For all functions: YR = year of record; AOR = age
of record (time since individual began keeping records).

31

32

variables used in these functions are described in Table
lV-2. Several observations concerning the selected func-
tions should be made, as follows: 1) The assumption was
made that most dairy managers desire to maximize profita-
bility. Therefore, the profitability measure, percent
return to net worth was used as a measure of business
success. 2) The financial function expected to adequately
cover the financial facet of the business included measures
of short-term liquidity (X1), solvency (X2), leverage
(X3), portion of ownership (X4) and capital turnover (X5).
3) The marketing functions covered 3 aspects of marketing:
use of monetary inputs and diversification to generate
income (X6 through X11), cost of inputs (X12 through X15),
and price of outputs (X16 through X18). 4) The production
function included measures of technical efficiency which
were measured by physical factors only (X19 through X24).
This function was expected to adequately cover the pro-
duction facet of the business with the exception of labor
efficiency. It will be recalled that a measure of labor
hours was not available. 5) The complete function
included all the variables used in the financial, market-
ing, and production functions. It was expected that the
complete function would adequately cover all measurable
facets of the business related to profitability.

Multiple regression analysis was used to approximate
the functional relationships under investigation. Use
Of multiple regression analysis involves the assumption

tflat the functional relationships are linear in unknown

33

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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......1 seam...” e..-1 ...mmme.§.. - ..
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Honexm oEmz Honezz IHonexm mamz ponesz

 

mcofiuucsm Ga com: meanmwsm> mo mcwumHH < .N->H ofinmb

34

 

 

 

 

 

 

 

 

 

mo< whoooh mo omm
m» whouoh mo amok UH\mm u mammowwwmeommwmu u me
.21. .21....1
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...1 .21....
Honexm eewz honesz Honsxm oEmz sonesz

 

Aeeeeeeeeev N->H eases

35

parameters. This assumption was justified in the litera-
ture reviewed prior to the analysis. Through this technique
values for the unknown parameters are estimated and a model
is developed which describes the functional relationship.
The validity of the model and underlying assumptions per-
taining to the data can be checked by statistical tests.
Various procedures can be used to select the ”best" regres-
sion equation for approximating a functional relationship.
In this study a stepwise selection procedure was used to
select the "best" regression equation for approximating
each of the 6 functions of interest. Four aspects of the
analysis merit explanations. These are l) the underlying
assumptions, 2) examination of the regression equation,

3) the stepwise procedure, and 4) examination of residuals.
Explanations are as follows:

1) Underlying Asnnnptions. Certain underlying

 

assumptions must be made before the regression equation

can be examined. Draper and Smith (49) list these

assumptions:
"...in the model Yi = Bo + 81X + 81’ i =
1,2, ..... ,n,
a) 8i is a random variable with mean zero and
variance 0 (unknown), that is, E(oi) = 0,
V(oi) = 02.

b) 61 and Ej are uncorrelated, i # j, so that
cov(Ei,e.% = 0. Thus E(Yi) 80 + lei’

J
V(Yi) = o and Y1 and Yj’ i # j are uncor-

related. A further assumption, which is
not immediately necessary...is that

36

C) Si is a normally distributed random vari-
able, with mean zero and variance 02 by
(a), that is 51 m N(0,oz)."

In brief, it is assumed that the errors have a zero mean,

a constant variance, are independent, and follow a normal

distribution. The assumptions were dealt with by examina-
tion of the residuals as outlined below.

2) Examination of_the Regression Equation. In

 

developing a regression equation to approximate a func-
tional relationship, the additional variation accounted
for by inclusion of more than one variable in the equation
is of prime importance. Four criteria are suggested by
Draper and Smith to assess what is accomplished by inclu-
sion of one or more additional variables in the equation.
These are as follows:

a) The sqpare of the multiple correlation

coefficient R2. R2 is defined as the sum of squares

 

 

due to regression after fitting bo divided by the
total corrected sum of squares. The larger the R2
value, the better the model explains variation. R2
values can be examined at each stage of the regres-
sion for substantial increases. Caution in using
the R2 criterion should be exerted when the number
of parameters in the model approaches the saturation
point and when repeat observations are used. It
should also be observed that addition of a new

variable will increase R2 but will not necessarily

decrease the residual mean square.

37

b) The standard error of the estimate, 5. s is

 

 

the square root of the residual mean square. The
residual mean square is an estimate of the variance
about the regression. The smaller the standard
error of the estimate the better the model explains
variation. Caution should be exerted when the model
approaches the saturation point and when repeated
observations are made.

c) The sequential F-tnsp_priterion. This cri-

 

terion assesses the value of adding a new variable
after one or more other variables have been included
in the model by examining the calculated F values
after addition of each new variable. The F value is
calculated by dividing the mean square due to addi-
tion of the new variable above and beyond that due
to variables already included in the model by the
residual mean square. The calculated F-value is

compared to a tabulated F-value, F where a

a,r1,v2’
is a preset desired level for the probability of a
Type I error (rejection of a hypothesis which is in
fact true).

d) The partial F-test criterion. The partial

 

F-test criterion is used when the order of variable
entry into the equation is of interest. For example,
X1 may be entered into an equation before or after
X2. If entered before, it may be of interest to
know what contribution X2 would have made if it had

been entered first. Also of interest might be the

38

contribution of X1 above and beyond the contribution

of a first-entry X2.

In this study the R2 value criterion, the standard
error of the estimate, and partial F-tests were used when
examining regression equations produced by the stepwise
procedure. The SPSS (Version 6.0) regression program (50)
used in this study provided overall F-values and F-values

to enter or remove a variable. These F-values are calcu-

SS .
lated as F = reg/K and F = 1ncremental SS due to X111

SSres/(N-k-l) SSres(N-k-l)
respectively, where k is the number of independent variables

in the equation and N is the sample size.

Users of the SPSS regression program have the option
of specifying three statistical criteria for inclusion of
a variable in a model. These are n, the maximum number
of independent variables to be entered into the equation,
F, the minimum F-value to be accepted, and T, a tolerance
index ranging from 0 to 1 indicating the degree of corre—
lation between the variable to be entered and those already
included. If the user does not specify n, F, and T the
SPSS program has minimum default values of 80, .01, and
.001, respectively. The author wished to examine the
addition of variables which would not have been included
in the regression equation if stringent default levels had
been set and therefore set no specific levels for n, F,
and T.

In addition to the criteria for excepting or reject-
ing additional X's, the standard error of the B's and

associated confidence intervals are of interest in

39
describing models. These statistics were available from
the SPSS Regression program.

3) The stepwise procedure. To build a useful pre-
dictive model the researcher should attempt to include as
many X's as possible in order to insure a reliable pre-
diction model (without saturating the model) yet the
cost of information and problems in monitoring variables
often induce the modeler to reduce the equation to
include as few X's as possible. In selecting the "best"
regression equation a compromise between these opposing
criteria must be made. Procedures used for selecting
the ”best" regression equation include a) all possible
regressions, b) backward elimination, c) forward selec-
tion, and d) stepwise regression. The stepwise procedure
was selected for use in this study for two reasons: a) the
procedure eliminates wasteful computer time and laborious
examination of cumbersome computer printouts, and b) the
procedure reexamines all variables included in the model
at each stage of the regression. A CDC 6500 was used to
run the SPSS stepwise regression program. The procedure
is as follows:

a) The variable most correlated with the
response is entered into the regression equation.

b) Again, using partial correlation coefficients,
the next most highly correlated variable is entered
into the regression equation.

c) The contribution of the first variable if

it had been entered second in the equation is

40

examined. If the contribution is insignificant, the

variable is rejected.

d) The process is continued until all variables

have been examined.

The stepwise procedure was used in developing models of
the six functions of interest. The models examined are
shown in Table IV-3. Inclusion of first and second order
terms for each independent variable included in the model
was decided upon as means of incorporating the effects
of diminishing returns into the model. Economic theory
holds that as the quantity of one factor of production is
held constant, the additional output derived from unit
increases of a variable factor declines after a certain
level of that factor has been obtained. The principle
of diminishing returns can be applied to both physical
and monetary data. The inclusion of second order terms
served as a means of investigating possible curvilinear
relationships which the principle of diminishing returns
suggests.

It should be observed that discrete classification
variables accounting for farm effects were not included
in any model. It was felt that to include such variables
in the models would sacrifice too many degrees of freedom.
In an effort to make an objective statement about varia-
tion due to a repeated observation on each dairy, correla-
tion of the two residuals produced from each repeated

observation was studied.

41

Table IV-3. A listing of models examined

 

Name of Model

Model

 

Financial Model Y = 80 +
+YR+
Marketing Models:
Income Generating Y = B +
. . o
Eff1c1ency
+YR+
Input Marketing Y = Bo +
Efficiency
+YR+
Output Marketing Y = 80 +
Efficiency
+YR+
Production Model Y = Bo +
+YR+
Complete Model Y = Bo +
+YR+

4) Examination of residuals. A

 

ference between the observed response

2
BiXi + 81.xi + AOR
e, where i = l,...,5

i' = 6,...,10

2
eixi + 81.xi + AOR

e, where i = 6,...,11
i' = 12,...,17

2
eixi + 81.xi + AOR

e, where i = 12,13,

14,15
1' = 16,17,
18,19
2
BiXi + Bi'xi + AOR
e, where i = 16,17,18
i' = 19,20,21
2
Bixi + Bi'Xi + AOR
e, where i = 19,...,24
i' = 25,...,30
2
Bixi + Bi'xi + AOR
e, where i = l,...,24

1' = 25,...,48

residual is the dif-

and the predicted

response. If the fitted model is correct, the residuals

should exhibit tendencies which indicate whether or not the

underlying assumptions described above appear to be violated.

42

Residuals may be examined by one or more of the five sug-
gested graphical means: a) overall plotting, b) plotting
in time sequence, c) plotting against the predicted
responses, d) plotting against the independent variables,
and e) plotting in any way that is appropos for the problem
under examination. In this study residuals were first
plotted against the predicted Y's. According to Draper

and Smith (48), the shape of this plot can indicate whether
or not the regression analysis appears to be valid. Typi-
cally, one of four plots may appear as follows:

a) A horizontal band. This would indicate no

 

abnormality and the underlying assumptions would
not appear to be violated.

b) A horizontal band which expands in width

 

as the magnitude of_predicted Y's increases. This
would indicate that the variance of the errors is
not constant. Perhaps weighted least squares or
transformations on the Yi observations should be
used.

c) A band which slopes negatively or positively.

 

This would indicate that there is a systematic
departure from the fitted equation. The analysis
should be reconsidered.

d) A band in the shnpe of a parabola. This

 

would indicate an inadequate model. Additional terms
on transformations of the Yi observations should be

considered.

43

Residuals from 1974 observations were then plotted against
residuals from 1975 observations in an effort to determine
the degree of correlation between observations taken from
the same farm.

Residuals were also standardized and plotted casewise
in an effort to spot outliers. Suspected outliers were
those falling outside of the range of standard deviation
suggested by use of the Mean Ranges of Samples from a
Normal Distribution table (Table 2) found in Sokal and
Rohlf (51). Acceptance or rejection of outliers was by
use of the method suggested by David, Hartley, and
Pearson (48). Borderline cases were also investigated
by rerunning the stepwise regression without the suspected
outlier. If significant changes in the regression occurred,
the borderline case was deleted from the study and the
second-run models were examined.

Finally, in an effort to determine how much of the
explained variation in each model was accounted for by
the variables in the model, path coefficients were used
in the manner suggested by Wright (52). According to this
method, the direct contribution of a single variable
(uncorrelated with remaining variables) is the square of
the standardized partial regression coefficient, or "path"
coefficient, denoted as (13)..)2 = [bj(::i)]2. (bj,)2 is
called the coefficient of determination. The indirect
contribution of two correlated variables, Xi and Xj, is
2(b'i)(b'j)rij. This term is referred to as the joint

coefficient of determination. The total sum of these two

44
types of contribution should approach unity and may be
used as the denominator in determining the percent of

explained variation accounted for by each variable.

V. RESULTS AND DISCUSSION

Results of the statistical analysis are described in
this section. Any conclusions drawn from the analysis
are withheld until Section VI.

The results are divided into six parts. Each part
describes one of the six models of interest. Included in
each part are three tables used to present results and
facilitate discussion. The types of tables and appro-
priate descriptions are as follows:

Stepwise Reggeeeion Summary, The summary table

 

includes the following statistics for each new variable
entered into the equation: F to enter or remove the
variable, R2, overall F, and standard error of the
estimate. Significance levels are also provided.
Coefficients and Confidence Intervals. The table
of coefficients includes the regression coefficients,
standard errors of the coefficients, and a 95% confidence
interval for each coefficient.

Contribution of Variables to Explained Variation.

 

The explanation of variation table includes path coef-
ficients and a percent figure indicating the percent of

explained variation attributable to each variable.

45

46

General discussion of each model will include selec-
tion of the "best" regression equation to approximate the
function of interest utilizing the R2, partial F test,
and change in standard error of the estimate criteria.
Discussion of the complete model includes examination of

the residuals for that model.

Financial Model

 

The summary table for the financial model is Table
V-l. The overall model in which all variables were fitted
accounted for 24.6% of the variation in Y. This suggests
little potential as a predictive model. However, examina-
tion of the regression to find the ”best” model is of use.
In doing so the following practical question can be
answered: If a dairyman wishes to monitor his financial
status (i.e., his acquisition and use of capital), which
financial ratios are of most use?

The R2 criterion for the "best" regression equation
suggests inclusion of the first 4 variables. These
variables account for 19.2% of the variation. Addition
of the remaining variables increases the explained varia-
tion by less than 2% for each new variable. The author
considers the less than 2% increase to be negligible.

The partial F-test criterion substantiates the R2
criterion. Probability of a type 1 error is less than or
equal to 9.1% each for entrance of the first 4 variables.
For additional variables this probability is equal to or

greater than 14.1%.

47

 

 

 

s.km mac. mN.N eem. eos. Ho. ae\<e . x NH
e.em moo. mm.m eeN. eee. mm. mo< as
e.am eoo. NA.N oeN. New. mm. NAAU\<UV Hxx OH
e.am eoo. Ho.m emN. mam. em. Nflae\<ev Nxx a
m.am Noe. wm.m mmN. mom. mm. qo\<o fix w
H.em Hoe. ew.m NMN. Hme. em. Nfl<e\HuV mxx A
H.am Hoe. He.e ANN. Has. Hm.H Nfi<e\zzv exx e
N.em Hoe. ew.e NHN. HeH. NN.N a» m
m.em Hoe. me.m Hes. moo. ee.a <B\Hu mx e
m.wm eoo. ae.e mNH. Hmo. Na.N <e\zz ex m
N.mm moo. eH.m woo. Nmo. ew.m Nazz\sev mxx N
e.sm eHo. NN.e Hes. eHo. NN.e zz\ae mx H
opmsfiumm oocmofimwcmflm m m oocmoflmflcMMm hopcm Honexm vopopcm doom
mo Hospm Hawso>o N cu m oanmfihm> oanmfihm>
wumwcmum
Howoe Hmfiocmcfim ”xpmeesm cowmmosmop omwzmoum .H-> oHan

48
The change in the standard error of the estimate was
considered negligible for variables entered after the
first 4 steps of the stepwise analysis.
Consideration of the 3 criteria suggest the following
model as the model which best approximates the relation-

ship between the financial ratios and return to net worth:

Y = 39.67 - 12.42x + .38X 2 - 80.93X

3 3 + 35.81X

4 5

This model has an overall significance level of .001. It
suggests that when measures of financial status alone are
considered in relation to return to net worth, the amount
of leverage (X3, total liabilities/net worth), the equity
to value ratio (X4, net worth/total assets), and the
asset turnover (X5, gross income/total assets) are the
most important factors. Furthermore, it suggests that
the amount of leverage employed has a curvilinear rela—
tionship to the percent return on net worth. Statistics
on the regression coefficients are found in Table V-2.
Use of path coefficients for determining direct and
indirect contribution of variables included in the "best”
model to Y is shown in Table V-3. Table V-3 suggests
that almost all of the explained variation in Y for this
model is attributable to the direct and indirect effects
of leverage (X3, total liabilities/net worth) if this
model was considered as a closed system. Examination of
the correlation matrix for the entire set of variables
suggests that net worth/total assets (X4) is correlated

to a number of other variables as is X3. Thus, considering

49

Table V-2. Coefficients and confidence intervals:
financial model

 

 

 

 

Standard
Variable B Error B 95% Confidence Interval
Overall Model
X3 -21.91 6.79 -35.4 , -8.4
XX3 .63 .19 .2 , 1.0
X4 -392.93 197.64 -786.0 , 1.0
X5 107.40 84.96 -6l.6 , 276.3
YR 9.94 8.06 -6.1 , 26.0
XX4 245.91 167.22 -86.6 , 578.5
XX5 -36.50 42.47 -121.1 , 47.9
X1 3.72 4.45 —4.3 , 11.8
XX2 -.27 .64 -l.5 , 1.1
XX1 -.ll .14 .4 , .2
AOR .75 .90 -1.1 , 2.4
X2 1.70 13.44 -25.0 , 28.4
Constant 84.82 58.91 -32.3 , 202.0
"Best" Model
X3 -12.42 3.18
XX3 .38 .11
X4 -80.93 29.58
X5 35.81 13.11

Constant 39.67 18.01

50

Table V-3. Contribution of variables to explained varia-
tion: financial model

 

0

e Explained Variation

 

 

Path Coef— Correlation Direct Indirect
Variable(s) ficient Coefficient Contrib. Contrib.
X3 -1.51 29.6
XX3 1.05 14.3
X4 -.49 3.1
X5 .28 1.0
X3,XX3 .92 37.3
X3,X4 -.67 8 9
XX3,X4 -.43 5.8

the financial model as a closed system may mask the overall
contribution of these variables. This means that when
financial variables are considered alone the leverage

ratio is the most important variable, but examination of
the complete model may or may not suggest that leverage

is important when non-financial variables from other

models are considered.

Incone Generating Efficiency Model

 

The summary table for the income generating efficiency
model is Table V-4. The overall model in which all variables
are fitted accounted for 47.4% of the variation in Y. This
too suggests little potential as a predictive model. How-
ever, examination of the regression to find the "best"

model will help answer the question: If a dairyman wishes

51

 

OH

 

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mo hoppm Homho>o N cu m oHnmflhm> oanmfihm>
Ohmucmwm

 

HoOoE xocowoflmwo mcfiumpocom osoocw ”xmeESm coammopmoh omwzmoum. .v-> oanwh

52
to use ratios measuring income generating efficiency,
which ratios are most useful? It is to be remembered
that the author uses the phrase "income generating
efficiency ratios" to mean ratios involving menetary
inputs or monetary diversifications as numerators and
gross income as a denominator.

The R2 criterion for the "best" regression equation
suggests inclusion of the first 2 variables. These
variables account for 40.3% of the total variation in Y.
Addition of the remaining variables increases the explained
variation by less than 3% for each new variable entered.
The partial F-test criterion substantiates the R2
criterion in that the probability of a type 1 error is
less than .1% each for entrance of the first 2 variables
and greater than 4% each for entrance of the remaining
variables.

Change in the standard error of the estimate appears
negligible for variables beyond the first 2 accepted in
the equation.

Consideration of these criteria suggests the follow-
ing model as the model which best approximates the rela—
tionship between income generating efficiency ratios and

return to net worth:

Y = 101.97 - 210.16X62 - 3578.15X82

The overall significance level of this model is less than
.001. The model suggests that when income generating

efficiency ratios are related to return to net worth,

53

herd replacement expense/gross income (X8) and feed
expense/gross income (X6) are the most important factors.
Curvilinear relationships are suggested between X8 and Y
and X9 and Y. It should be observed that labor expense/
gross income (X7) explained an insignificant amount of
variation in Y. This is perhaps attributable in part to
the fact that most California dairies hire unionized
milkers and generally run high through-put parlors sug-
gesting the po-sibility of little labor efficiency varia-
tion between dairies. Labor expense/gross income had a
relatively low correlation with Y (rX = -.l7622).

7,Y
Statistics on regression coefficients are found in Table

V-5.

The contribution of each variable accepted in the
"best" model is shown in Table V-6. Table V-6 indicates
that feed expense/gross income (X6) and herd replacement
-expense/gross income (X8) directly contribute an almost
equal amount to the explained variation if the model is
considered as a closed system. However, high correlations
between X6, X8, and variables outside of the model again
suggest these effects may be masked by other variables

when the complete model is examined.

Input Marketing Efficiency Model

 

The summary table for the input marketing efficiency
model is Table V-7. The overall model accounts for only
20.9% of the variation in Y. It too is of little use as

a predictive model, but it can be examined to help answer

54

Table V-S. Coefficients and confidence

 

intervals: income
generating efficiency model
Standard
Variable B Error B 95% Confidence Interval

 

Overall Model

 

XX8

XX6

YR

Constant

Best Model

 

XX8

XX6

Constant

-2202.
-249.

15.
2125.

761.
-306.

2581

-210.
1297.
-158.
-484.
3170.

76.

35.

-3578.
~210.
101.

68
66
54
24

.46

72

17

.46

72
32
20
30
08
56
72

15
16
97

2023.
S94.

1737

605.
39.

15.

74
79

.14
.98
.84
.73
.56
.29
.30
.26
.67
.13
.ll
.51
.42

48
40
13

-6228.5 , 1823.

-1432.9 , 933.
1.3 , 29.

-5582.6 , 1332.
-1.7 ,

-707.8 , 2231.
--947.8 , 335.
-9100.3 , 14263.
-1277.6 , 856.

-404.5 , 6635.

-933.3 , 616.
—3034.8 , 2066.

-l4169.4 , 21509.
-1372.6 , 1525.
-418.6 , 490.

55

Table V-6. Contribution of variables to explained varia-
tions: income generating efficiency model

 

O

6 of Explained Variation

 

 

 

 

Variable Path Coefficient Direct

XX8 -.47 55.1

XX6 -.43 44.9
100.0%

the question: If a dairyman wishes to monitor cost of

inputs, which cost ratios are most useful?

The R2 criterion for the "best" regression equation
suggests inclusion of the first 2 variables. These variables
account for 18.6% of the variation in Y. Addition of the
remaining variables increases the explained variation by
less than 3% for each new variable entered.

The partial F-test criterion substantiates the R2
criterion in that the probability of a type 1 error is
less than 1% each for entrance of the first 2 variables
and greater than 15% each for entrance of the remaining
variables.

Change in the standard error of the estimate is
negligible after entrance of the first 2 variables.

Consideration of these criteria suggests the follow-
ing model as the model which best approximates the rela-
tionship between cost ratios and percent return to net

worth:

Y = 12.77 - .0025x142 + 22.27YR

56

 

 

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Osmwcmum

 

Houoe socowofimmo mefiuoxswe usmcfi ”seweesm :owmmopwou omfizmoum .A-> oHan

57

The overall significance level of this model is less
than .001. The model suggests that herd replacement
expense/cow equivalent (X14) and the effect of time on
the market are the most important factors. It is of

interest to note that feed expense/cow equivalent (X12)

was not included in the model, nor was labor expense/cow

equivalent (X13).5 Correlation between X and Y was low

12

(r = -.068) as was true for X and Y (r
X12,Y 13 X13,Y

perhaps indicating that consideration of input costs with-

= -.03l),

out considering prices received is of little benefit.
Statistics on regression coefficients are shown in Table
V-8.

The contribution of each variable accepted in the

"best" model is shown in Table V-9.

Output Marketing Efficiency Model

 

The summary table for the output marketing efficiency
model is Table V-lO. The overall model accounted for 36.8%
of the variation in Y. Little predictive value for the
model is indicated here. However, as in the case of the
previously reviewed models, a practical question can be
dealt with: If a dairyman wishes to monitor price-control
efficiency, which price ratios are most valuable?

The R2

criterion for selecting the ”best" model sug-
gests inclusion of the first 2 variables. These variables

account for 34.4% of the variation in Y. Addition of new

 

SX13 was not included in the summary table due to the
automatic default criteria (n,F,T) built into the SPSS
program.

58

Table V-8. Coefficients and confidence intervals: input
marketing efficiency model

 

 

 

 

Standard
Variable B Error B 95% Confidence Interval
Overall Model
XX14 -.30E-02 .19E-02 -.68E-02 , .84E-03
YR 24.45 8.38 7.78 , 41.12
X12 -.64 .46 -1.57 , .27
XX12 .44E-03 .33E-O3 -2.99E-03 , .llE-OZ
XX15 -.48E-03 .17E-02 -.38E-02 , .29E-02
X15 .21 .83 -l.45 , 1.87
X14 .56E-01 .33 -.65 , .72
AOR -.76E—01 .68 -1.44 , 1.29
Con~ 218.49 172.94 -125.21 , 562.19
stant
"Best" Model
XX14 -.25E-02 .59E-03
YR 22.27 7.85
Constant 12.77 5.95

 

Table V-9. Contribution of variables to explained varia-
tion: input marketing efficiency

- -__. __-__ _‘ _ -jfl '— I I 1...“- .—_-——_—‘.:’-...__._ -._ :- :: ;::.—— :;:Tf"'..1.; 1:1'1—"' :7 .-_ o w———..

% of Explained Variation

 

 

Variable Path Coefficient Direct
XX14 -.42 69.6
YR .27 30.4

59

 

OH

 

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”xxmeezm cofimmowmos omfizmoum

.OH-> oHan

60
variables would increase the explained variation by less
than 2% for each new variable entered. The author con-
siders such an increase to be negligible.

The partial F-test criterion confirms the choice
made above. The probability of making a type 1 error is
less than . % each for entrance of the first 2 variables
into the equation. This probability is greater than 15%
each for addition of remaining variables. Changes in the
standard error of the estimate appeared negligible for
variables entered beyond the first 2 variables.

After consideration of the above criteria the follow-
ing model was selected as the "best" model to approximate
the relationship between price ratios and the percent

return to net worth:

2

Y = —928.73 + 1.09X - .00031X16

16

This model has an overall significance level of less
than .001. The model suggests that milk revenue/milk cow
(X16) is the most important factor. It also suggests a
curvilinear relationship between X16 and Y. Coefficients

and confidence intervals are shown in Table V-ll.

Production Model

 

The summary table for the production model is Table
V-lZ. The overall model in which all variables were
fitted accounted for 39.2% of the variation in Y. The
model is insufficient for predictive purposes, but examina-

tion of the regression equation can help answer the

61

Table V-ll. Coefficients and confidence intervals: output
marketing efficiency model

 

 

 

Standard
Variable B Error B 95% Confidence Interval
Overall Model
X16 1.10 .21 .67 , 1.53
XX16 -.3lE-03 .67E-04 -.45E-03 , -.18E-03
XX17 -.90E-O3 .71E-03 —.23E-02 , .50E-03
Xl7 .23 .25 -.26 , .73
YR 5.49 7.68 —9.78 , 20.77
AOR -.16 .60 -1.36 , 1.03
X18 .16 .57 -.95 , 1.30
XX18 -.16E-02 .62E-02 —.14E-01 , .lOE-Ol
Con- -953.11 172.82 -1293.57 ,-606.65
stant
"Best" Model
X16 1.09 .21
XXl6 -.3lE-03 .65E-04
Con- -928.73 167.50
stant

 

question: If a dairyman wishes to monitor technical effi-
ciency, which production ratios are most useful?

The R2 criterion suggests inclusion of the first 6
variables. These variables account for 37.3% of the varia-
tion in Y. Addition of the remaining variables increases
the explained variation by less than 1% for each new variable

added. Such an increase is considered negligible.

62

HN

 

 

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wpmucmum
HoOoE cofluoswoxm "xpwEESm cowmmopwoh omNZQopm .NH-> oHan

63

The partial F-test criterion suggests the same model.
The probability of a type 1 error for entrance of the first
6 variables is less than 3% each. This probability is
greater than 36% each for entrance of the remaining
variables. Changes in the standard error of the estimate
appear negligible beyond entrance of the first 6 variables.

Use of these criteria suggests the following model
as that model which best approximates the relationship
between production ratios and the percent return to net

worth:

__ _ -5 2__
Y - 640.65 + .08x19 .22 x 10 x19 29 47x
2 2

- 2.95X22 - 1.67X23

21
+ 22.11YR

The overall significance level of this model is less
than .001. The model suggests that when production ratios
are considered in relation to net worth, milk production/
milk cow (X19), grain/cow equivalent (X22), roughage/
cow equivalent (X23), raised heifers/cow equivalent (X21),
and the effect of time on the market are the most impor-
tant factors. The model also suggests curvilinear rela-
tionships between X19 and Y, X22 and Y, and X23 and Y.
Regression coefficients are shown in Table V-l3.

The contribution of each variable accepted in the
"best" model is shown in Table V-l4. Table V—l4 suggests
that milk production/milk cow (X19) directly and indirectly
contributes nearly all of the explained variation in Y if
this model is considered as a closed system. High correla-

tions between X21, X22, X23, and non-product1on var1ables

64
Table V-l3.

 

 

 

 

Coefficients and confidence intervals: pro-
duction model
Standard

Variable B Error B 95% Confidence Interval
Overall Model
X19 .88E-01 .44E-01 .51E-03 , .17
XX19 -.24E-05 .14E-05 -.53B-05 , .51E-05
XX22 -.54 4.07 -8.65 , 7.57
XX23 ~1.76 .48 ~2.73 , -.79
YR 23.05 7.78 7.56 , 38.54
X21 -36.15 33.98 ~103.74 , 31.44
XX24 -240.50 227.73 -693.45 , 212.44
X24 16.08 179.93 -197.88 , 517.97
X22 -l9.22 27.76 -74.45 , 36.01
X20 -.33 .58 -1.50 , .83
XX20 .24E-03 .48E-O3 -.73E—03 , .lZE-OZ
AOR .22 .64 -l.06 , 1.50
XX21 4.67 32.84 -60.65 , 70.00
Constant -593.48 325.92 -1241.65 , 54.83
"Best" Model
X19 .80E-01 .38E-01
XX19 -.22E-05 .12E-05
XXZZ -2.95 .78
XX23 -l.67 .45
YR 22.11 7.46
X21 -29.47 12.20
Constant -640.65 295.45

 

65

Table V-14. Contributioncifvariables to explained varia-
tion: production model

 

o
’0

Path Coef— Correlation Explaining Variation

 

 

Variable(s) ficient Coefficient Direct Indirect
X19 -2.88 29.5

XX19 -2.39 20.3

XX22 -.36 .5

XX23 .35 .4

YR .27 . .2

X21 .24 .2

X19,XX19 .998 48.9

is masked by examining the production model as a closed

system.

Complete Model

 

The summary table for the complete model is Table
V-15. The overall model accounts for 81.7% of the varia—
tion in Y. This suggests predictive potential over the
range and type of dairies dealt with in this study. Exami-
nation of the regression equation will help determine the
”best" regression equation to use for predictive purposes
and will help answer the question: Which management
factors, as measured by selected ratios, are most useful
in a business analysis? The information used in this
study is not considered difficult for the individual

dairyman who uses an accrual accounting system to obtain.

66

 

 

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67

 

 

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68

 

 

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69

 

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70
Therefore the author felt justified in using a less strin-
gent R2 criterion in order to improve the predictive poten-
tial of the "best" model. This was not done with the first
5 models since their predictive potential was poor and the
only interest was in finding the most important factors

contributing to the variation. The R2

criterion suggests
inclusion of the first 11 variables. These variables con-
tribute 1.2% or more to the total variation in Y. Addition
of remaining variables would increase the explained varia-
tion by less than 1% for each addition.

The partial F-test criterion suggests that addition
of variables in steps 7 through 11 is with a probability
of a type 1 error of 2.2% or more up to 8.4%. The author
felt that the additional 7% increase in R2 justified this
situation. Change in the standard error of the estimate
was felt to be negligible after entrance of'the first 5
variables.

These criteria suggest the following model as one

possible choice for the "best" model:

Y = -681.75 - 30.94x4 - 300.31x6Z + 1199.36X82
2 -3 2
- 630.17x9 - .90 x 10 x13 - .34x14 + 1.02x16
-3 2 -3 2 2
- .29 x 10 x16 - .66 x 10 x17 + 31.35x21
- 8.43x22

This model accounts for 68.8% of the variation in Y.
It is felt that this model could serve as a limited pre-
dictor for making generalizations. It would not serve

for precise predictions. The author believed that inclusion

71

of more variables in the model would begin to saturate the
model. The model suggests that when ratios measuring the
financial, marketing, and production facets of the business
are related to return on net worth, the following ratios
are most important:

herd replacement expense/gross income (X8 )

feed expense/gross income (X6)

cattle revenue/milk cow (X17)

milk revenue/milk cow (X16)

grain/cow equivalent (X22)

labor expense/cow equivalent (X13)

other operating expenses/gross income (X9)

net worth/total assets (X4)

raised heifers/total cows (X21)

Table V-l6 provides coefficients for the entire
model.

Table V-l7 shows the direct contribution of these
variables to explained variation in Y (the contribution

' 2 . . . .
of X16 and X16 15 con51dered jOintly).

The high contribution of X16 to the explained varia-
tion is indicative of the manner in which the regression
procedure is run (i.e., those variables highly correlated
with X16 are not entered into the regression equation
until the final steps of the procedure). The above model
therefore serves as a predictive model, but does not help
indicate which variables are most useful in a dairy business
analysis. To see which variables are most useful, the
entire model was examined and variables with relatively
high path coefficients were arbitrarily selected as those

variables providing the most direct contributions to Y.

These variables were:

72

 

 

 

Table V-16. Coefficients and confidence intervals:

complete model

Standard

Variable B Error B 95% Confidence Interval
Overall Model
XX8 2199.84 2577.24 ~2969.45 , 7369.13
XX6 -547.20 193.21 -934.73 , -159.67
XX17 .111E-02 .llE-OZ -.12E-02, .35E-02
X16 1.20 .29 .52 , 1.79
X22 31.76 32.59 —33.62 , 97.15
XX16 -.34E-03 .91E-04 -.52E-03, -.16E—03
X14 -.22 .29 -.82 , .38
XX13 .35E-02 .60E—02 -.86E-02, .15E-04
X4 -223.01 192.67 —609.47 , 163.44
XX21 32.15 32.56 ~33.15 , 97.47
X17 -.77 .54 -l.87 , .32
XX5 22.74 37.84 -53.15 , 98.64
X3 -4.42 7.29 -18.67 , -l8.59
XX12 .96E-O3 .33E—03 .29E—03, .16E-02
X12 -l.23 .53 -2.30 , -.l6
XX22 -4.96 4.51 -14.92 , 4.18
X20 —.65 .53 -l.73 , .41
XX20 .44E-03. .43E-03 -.42E-03, .13E-02
XX15 .92E—04 .1OE-02 -.19E-02, .21E-02
X13 -1.89 1.14 -4.18 , .39
XX7 9549.01 5789.46 -2023.06 , 21121.10
X21 50.31 41.45 -32.83 , 133.47
X9 -352.83 598.52 -1553.32 , 847.65
XX1 .22 .16 -.10 , .558450
XX24 234.36 332.84 -433.23 , 901.96
XX11 -23226.15 11096.99 -45483.90 , -968.39
XX18 .28E-Ol .14E-01 -.15E-02, 57E-01
XX4 80.45 166.53 ~253.56 , 414.48
X -.70 .60 -1.91 .50

18

73
Table V-l6 (continued)

 

 

 

Standard
Variable B Error B 95% Confidence Interval
XX10 1325.23 2723.47 -4137.37 6787.83
XX14 -.l9E-02 .ZOE-OZ -.6lE-02 .22E-02
XX3 .27E-01 .20 -.38 .43
X24 -150.31 320.03 —792.22 491.58
XS -25.36 76.52 -l78.85 128.11
XX2 -.72 .55 -l.83 .38
X2 15.39 11.78 -8.23 39.02
AOR -.12 .77 —l.69 1.43
X10 -118.97 392.63 -906.50 668.54
X19 -.21E-02 .91E-02 -.20E-01 .165-01
YR -2.56 13.38 -29.47 24.27
XX23 1.91 2.36 -2.83 6.66
X23 -l6.16 20.96 -58.69 25.76
Con- 109.04 345.32 -583.59 801.67
stant
”Best" Model
XX8 1199.36 1307.56
XX6 -300.31 41.55
XX17 -.66E-03 .16E-03
XX16 -.29E-03 .62E-04
X16 1.02 .21
X22 -8.43 3.98
Xl4 -.34 .16
XX13 -.90E-O3 .67E-03
XX9 -630.17 190.90
X4 —30.94 13.09
XX21 31.35 13.39
Con- -681.75

stant

74

Table V-17. Contribution of variables to explained varia-
tions: complete model

 

Variable Path Coefficient % Explained Variation
XX8 .16 .062
XX6 .61 .922
XX17 -.29 .203
Xl6 4.77 98.030
XX16 -4.33
X22 -.16 .062
Xl4 -.33 .271
XX13 -.09 .018
XX9 -.27 .182
X4 -.19 .086
XX21 .25 .164

100.00 %

 

feed expense/gross income (X62 %)

milk revenue/milk cow (X16, X16)

net worth/total assets (X4)

feed expense/cow equivalent (X12,X122 )
milk fat/milk cow (X20)

labor expense/cow equivalent (X}3)
other revenue/gross income (X11)

other revenue/cow equivalent (X132 )

The direct contribution of these variables to varia-
tion explained by the entire model (R2 = 81.7) is shown in
Table V-l8. Table V-l8 suggests that 91.7% of the explained
variation is directly contributed by these variables. In

order of importance, these variables are:

75

Table V-18. Contribution of specified variables to
explained variation when all variables

 

 

are fitted
Variable Path Coefficients % Explained Variation

XX6 -l.ll 1.3
Xl6 5.62 33.8
XX16 5.11 28.1
X4 1.35 1.9
X12 3.24 11.3
XX12 2.94 9.5
X20 1.15 1.4
X13 1.00 1.1
XX11 1.19 1.5
XX18 1.32 1.8

91.7%

 

milk revenue/milk cow (X16)

feed expense/cow equivalent (X12)
net worth/total assets (X4)

other revenue/cow equivalent (X18)
other revenue/gross income (X11)
milk fat/milk cow (X20)

feed expense/gross income (X6)
labor expense/milk cow (X13)

Both Tables V—17 and V—18 suggest that an analysis
using milk revenue/milk cow as a dependent variable is an

appropriate investigation for future research.

Analysis of Residuals

 

Residuals were examined after fitting the entire

model. Casewise plotting of residuals suggested the

possibility of an outlier. The outlier was tested by

76
the David, Hartley, and Pearson method (48) and retained
in the study. The plot of residuals against predicted
Y's suggested no abnormality (see Figure A1) according
to the standards set by Draper and Smith, thus supporting
underlying assumptions. Finally, 1975 residuals were
plotted against 1974 residuals. Figure A2 in the
Appendix suggests little correlation of residuals, thus

supporting underlying assumptions.

VI. CONCLUSIONS

Results of this study suggest a variety of practical
applications for dairy managers, extension personnel,
accountants, financial institutions, and others interested
in drylot dairy business analysis. It is to be understood
that these applications do not necessarily extend to
dairies other than the type studied. Practical implica-
tions are as follows:

1) Historical valuation of assets and liabilities
does not prohibit the use of financial ratios developed
from accrual based accounting data in a drylot dairy
business analysis. In all models studied, the age of the
accounting record did not have any significant bearing on
the variation of return to net worth. This suggests that
when ratios are used balance sheets may be compared
between farms. It does not suggest that land apprecia-
tion and other inflationary effects should be ignored.
Rather, if they are ignored, balance sheets can still be
compared on a similar basis.

2) Leverage, as measured by total liabilities/net
worth or in effect by net worth/total assets, is an impor-
tant financial ratio for use in business analysis of
highly capitalized dairies. Leverage directly contributed

over 80% of the explained variation in the financial model

77

78
and almost 2% in the entire model. The difficulty of
measuring indirect contributions of leverage may have
masked its importance and the author believes that alter-
native statistical means might unravel this contribution.
The function of debt capital in such highly specialized
operations deserves further investigation.

3) Milk revenue/milk cow appears to be the most
important of all ratios studied. In examining path coef-
ficients from the entire model, it was discovered that
milk revenue/milk cow directly contributed over 60% of
the explained variation in return to net worth (R2 = .817).
One would expect this to be true when it is realized that
milk revenue/milk cow is highly correlated with a number
of other ratios. This does not necessarily imply that the
price of milk is the important factor. Rather, it sug-
gests that the quantity of milk produced and sold is the
important factor.

4) Feed expense/cow equivalent is an important
"marketing" ratio for use in a drylot dairy business
analysis. When path coefficients for the entire model
were examined, feed expense/cow equivalent directly con-
tributed over 20% of the explained variation in return to
net worth. It should also be realized that feed expense/

grOss income directly contributed over 44% of the explained

 

variation when income generating efficiency ratios alone
were considered and less than 2% when the entire model
was considered. However, the correlation between milk

revenue/milk cow and feed expense/cow equivalent is much

79
higher than between milk revenue/milk cow and feed
expense/gross income. This suggests to the author that
efficient feed conversion by livestock and feed cost con-
trol are important factors easily monitored by using the
ratio feed expense/cow equivalent.

5) Two ratios measuring minor enterprise diversifi-
cation, other revenue/cow equivalent and other revenue/gross
income, accounted for a small amount of variation in return
to net worth. This suggests to the author that such enter-
prises as heifer feeding for other dairymen or sale of
feed contribute some to the variation. Whether such
auxiliary activities are profitable or not has not been
determined. The author believes these ratios to be of
minor importance.

6) Labor expense/milk cow and milk fat/milk cow also
appeared to be important when path coefficients for the
entire model were considered although neither ratio con-
tributed more than 1.4% of the explained variation.

7) Finally, it is believed that business analysis
ratios can be used in forming a business success-predictive
equation when all facets of the business are studied simul-
taneously. The predictive equation deve10ped considered
return to net worth as a function of:

herd replacement expense/gross income
feed expense/gross income

cattle revenue/milk cow

milk revenue/milk cow

grain/cow equivalent

labor expense/cow equivalent

other operating expenses/gross income

net worth/total assets
raised heifers/total cows

80

In retrospect it is the author's opinion that the
analytical method used for this study has strong limita-
tions. Although the study seems to have satisfactorily
put the use of financial ratios in preper perspective,
the true direct and indirect contributions of marketing
and production ratios have remained somewhat elusive. It
is the author's opinion that a keener insight into the
use of ratios in dairy business analysis will be obtained
when statistical methods beyond the author's present

abilities are applied.

VII. SUMMARY

Financial, marketing, and production data were obtained
from 50 southwestern U.S. drylot dairies owning 500 or
more mature cows for the purpose of discovering which busi-
ness measures are most useful for conducting a simple dairy
business analysis of a drylot dairy. In addition, the use
of business measures for deve10ping a dairy business
success-predictive model was investigated. All information
was obtained through an accounting agency and was based on
the accrual accounting method. Raw data were converted to
specified ratios measuring the financial, marketing, and
production facets of the dairy business. These ratios were
then used in investigation of six functions by means of
multiple regression analysis and the Wright method of find-
ing direct and indirect contributions of independent
variables to explained variation in the response variable.
These functions were:

1) percent return to net worth as a function of
financial ratios (ratios measuring the acquisition
and use of capital),

2) percent return to net worth as a function of
income generating efficiency ratios (monetary
inputs or diversifications per unit of gross

income),
81

82

3) percent return to net worth as a function of input
marketing efficiency ratios (cost control ratios),

4) percent return to net worth as a function of out-
put marketing efficiency ratios (price ratios),

5) percent return to net worth as a function of
production ratios (ratios measuring physical
inputs, outputs, and efficiencies), and

6) percent return to net worth as a function of all
the ratios described above (the complete function).

When financial ratios were considered in relation to

 

the return to net worth, total liabilities/net worth con-
tributed over 80% of the explained variation. This suggests
that total liabilities/net worth is the most useful measure
of financial status. A high correlation between total
liabilities/net worth and net worth/total assets suggests
that this second measure may serve as well. Financial
ratios alone did not produce a satisfactory predictive
model.

When lnoome generating efficiency ratios were con-

 

sidered in relation to the return to net worth, herd
replacement expense/gross income directly contributed
over 55% of the explained variation. Feed expense/gross
income directly contributed over 44% of the explained
variation. This suggests that these two ratios are the
most useful measures of income generating efficiency.
Income generating efficiency ratios alone did not produce

a satisfactory predictive model.

83

When input marketlngnefficiency ratios were con-

 

sidered in relation to return to net worth, herd replace-
ment expense/cow equivalent accounted for 70% of the
explained variation. The analysis suggests that such
variables are of little use for predictive purposes when
considered independently of other types of variables.

When output marketing efficiency ratios are considered

 

in relation to the return to net worth, milk revenue/milk
cow accounted for 100% of the explained variation. This
suggests that milk revenue/milk cow is the most useful
measure of output marketing efficiency. Output marketing
ratios alone did not produce a satisfactory prediction
model.

When ppoduction patios alone were considered in rela-

 

tion to return to net worth, milk production/milk cow
contributed directly and indirectly over 98% of the
explained variation. This suggests that milk production/
milk cow is the most useful measure of production effi—
ciency. Production ratios alone did not produce a satis-
factory prediction model.

The above observations indicate that isolated groups
of performance ratios designed to monitor the financial,
marketing, and production facets of the dairy enterprise
cannot serve as predictive models. In addition, it is
realized that the high correlation between these facets
limits the researcher's ability to analyze contributions
of variables to explained variation in return to net worth

when the facets are studied independently. Ratios such as

84
milk revenue/milk cow are so highly correlated with
numerous other ratios that contributions of these other
ratios are effectively masked in the regression procedure.
When financial, marketing, and production ratios

were considered simultaneously in a complete model, 11
variables were selected for the prediction model. The
prediction model accounted for 68.8% of the variation in
return to net worth. Such a model might serve as a pre-
dictive model depending on the needs of the user. Over
98% of the explained variation in this model was contributed
by milk revenue/milk cow. In examining the path coeffi-
cients of all 50 variables included in the complete model
(before a predictor model was selected), it was observed
that 8 variables and 2 associated second order terms con-
tributed 91% of the explained variation (R2 = 81.7)
directly. These variables, then, are suggested as those
most useful to dairymen, extension personnel, and related
institutions attempting to quickly and efficiently analyze
large drylot dairy operations of the type studied:

milk revenue/milk cow

feed expense/cow equivalent

net worth/total assets

other revenue/cow equivalent

other revenue/gross income

milk fat/milk cow

feed expense/gross income

labor expense/milk cow
In addition, it was observed that the age of the balance
sheet record (i.e., the length of time the dairy had been

a client of the accounting firm) did not significantly

account for variation of return to net worth in any model

85

studied. The practical implication of this is that the
use of ratios appears to nullify the effect of historical
valuation when using accrual accounting records.

Finally, it is the author's belief that other statis-
tical procedures, beyond the author's present ability,
could provide keener insight on the objectives of this
study. It is believed that this attempt can well serve

as initial groundwork for further studies.

 

APPENDIX

 

86

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BIBLIOGRAPHY

 

10.

ll.

BIBLIOGRAPHY

Changlng_Nature of the Daipy Business

 

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91

Methods and Factors Used in
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