8 Optimum farm organization for a representative irrigated farm in the Yellowstone Valley
by Gerald Melvin Schaefer
A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE
in APPLIED ECONOMICS
Montana State University
© Copyright by Gerald Melvin Schaefer (1978)
Abstract:
Agriculture is a dynamic industry which is undergoing continual adjustment. Impetus for adjustment
arises from changes in agricultural commodity prices, production technology, input prices, and
institutional changes. Farmers attempting to maximize profits make economic adjustments because of
these continual changes. The major purpose of this study was to determine what production
adjustments would maximize return over variable cost in the Billings area in the event the local sugar
beet processing plant were closed, thereby diminishing the profitability of sugar beets.
Nine crop alternatives, eighteen cattle alternatives, and five swine alternatives were considered. Fifteen
resource situations were defined for a model farm. These situations were generated by different
assumptions concerning labor availability, number of livestock enterprises allowed, and whether sugar
beets was a permissible crop enterprise.
Linear programming procedures were used to determine the optimal combination of crops and
livestock enterprises to maximize return over variable costs for the model farm. One hundred acres of
sugar beets, 100 acres of corn silage, 60 acres of alfalfa, 20 acres of malting barley, and 20 acres of
irrigated pasture was the cropping pattern that was most prevalent. Malting barley was usually
substituted for sugar beets when sugar beets were not allowed. Livestock alternatives were more
sensitive to input-output prices and labor avail-ability. A cattle fattening alternative was always
included in the optimum solution. If sufficient labor was available, a swine alternative also appeared in
the solution. When the number of livestock alternatives was not restricted, at least two livestock
alternatives appeared in the solution. If only one livestock alternative was allowed, return over variable
cost was reduced by about $5,000 at 1976-77 prices. Return over variable cost was reduced- by about
$3,000 when 1977-78 prices were used.
A restriction disallowing the hiring of seasonal labor reduced return over variable cost by about $7,000.
Return over variable cost was reduced about $4,500 in the model farm when 100 acres of sugar beets
were not in the solution. This translates into a net reduction in return over variable cost of
approximately $969,000 for the Yellowstone Valley sugar beet growers that presently sell their beets to
the Great Western Sugar Company. STATEMENT OF PERMISSION TO COPY
In presenting this thesis in partial fulfillment of the require
ments for an advanced degree at Montana State University, I agree
that the Library shall make it freely available for inspection.
I
further agree that permission for extensive copying of this thesis
for scholarly purposes may be granted by my major professor, or, in
his absence, by the Director of Libraries.
It is understood that
any copying or publication of this thesis for financial gain shall
not be allowed without my written permission.
OPTIMUM FARM ORGANIZATION FOR A REPRESENTATIVE
IRRIGATED FARM IN THE YELLOWSTONE VALLEY
by
GERALD MELVIN SCHAEFER
A thesis submitted in partial fulfillment
of. the requirements for the degree
of
MASTER OF SCIENCE
in
APPLIED ECONOMICS
Approved:
MONTANA STATE UNIVERSITY
Bozeman, Montana
March, 1978
TABLE OF CONTENTS
Chapter
Page
Vita............
Table of Contents................................
List of Tables....................................
Abstract..........................................
ii
ill
iv
v
INTRODUCTION......................................
Statement and Background of the Problem ........
Objectives of the S t u d y ........................
Background Information about the Upper
Yellowstone Valley ........................
Selection of the Analytical Model ..............
Analytical Technique Used ......................
I
I
5
6
8
12
2
METHODOLOGY ._...................
Method of Data Collection......................
Model Farm...................... ' ..............
Size of F a r m ..............................
Soil Productivity..........................
Management................................
Fixed Resources............................
Fixed Costs................................
Labor......................................
Capital.............................
Time Period................................
Technology................................
Activities......................................
Crop Activities.................
Cattle Activities. . . .. ..................
Hog Activities ............
Input and Output P r i c e s .......... -............
14
14
16
16
16
17
17
19
20
21
21
22
22
22
24
30
31
3
R E S U L T S ........ ......... ■ ......................
Linear Programming Model. .
'................
Optimal Farm Organization ......................
Effect of Major Assumptions ....................
34
34
35
.46
4
S U M M A R Y .............. .
1
. . ....................
BIBLIOGRAPHY. .............. ................. .. .
APPENDIX. . . . . . . . . . . . . .
..............
50
57
' . 60
iv
LIST OF TABLES
Table
1
.
Page,
An Inventory of Machinery Owned on an Irrigated
Farm in the Upper Yellowstone Valley with Their
Respective Fixed and Variable Costs................
18
. Crop Alternatives: Yields and Resource Require­
ments Per A c r e .....................................
23
Cattle Activities: General Information, and
Resource Requirements PerH e a d ....................
27
Swine Activities: General Information and
Resource Requirements PerHead . ...................
32
Input-Output Prices Used in the Crop and Live­
stock Budgets......................................
33
6
Resource Situations Considered . . . ..............
36
7
Income Measures, Optimal Combinations of Cropping
and Livestock Enterprises and Other Selected Items
for Specified Resource Situations. . . . . . . . . .
38
Linear Programming Matrix.............. ..........;
61
2
3
4
5
A-I
V
ABSTRACT
Agriculture' is a dynamic industry which is undergoing continual
adjustment. Impetus, for adjustment arises from changes in agricultural
commodity prices, production technology, input prices, and institution­
al changes. Farmers attempting to maximize profits make economic
adjustments because of these continual changes. The major purpose of
this study was to determine what production adjustments would maximize
return over variable cost in the Billings area in the event the local
sugar beet processing plant were closed, thereby diminishing the
profitability of sugar beets.
Nine crop alternatives, eighteen cattle alternatives, and five
swine alternatives were considered. Fifteen resource situations were
defined for a model farm. These situations were generated by differ­
ent assumptions concerning labor availability, number of livestock
enterprises allowed, and whether sugar beets was a permissible crop
enterprise.
Linear programming procedures were used to determine the optimal
combination of crops and livestock enterprises to maximize return
over variable costs for the model farm. One hundred acres of sugar
beets, 100 acres of corn silage, 60 acres of alfalfa, 20 acres of
malting barley, and 20 acres of irrigated pasture was the cropping
pattern that was most prevalent. Malting barley was usually substitut­
ed for sugar beets when sugar beets were not allowed. Livestock alter­
natives were more sensitive to input-output prices and labor avail­
ability. A cattle fattening alternative was always included in the
optimum solution. If sufficient labor was available, a swine alter­
native also appeared in the solution. When the number of livestock
alternatives was not restricted, at least two livestock alternatives
appeared in the solution. If only one livestock alternative was
allowed, return over variable cost was reduced by about $5,000 at
19.76-77 prices. Return over variable cost was reduced- by about $3,000
when 1977-78 prices were used.
A restriction disallowing the hiring of seasonal labor reduced
return over variable cost by about $7,000.
Return over variable cost was reduced about $4,500 in the model
farm when 100 acres of sugar beets were not in the solution. This
translates into a net reduction in return over variable cost of
approximately $969,000 for the Yellowstone Valley sugar beet growers
that presently sell their.beets to the Great Western Sugar Company.
Chapter I
INTRODUCTION
Agriculture is a dynamic industry which is undergoing continual
adjustment.
Impetus for adjustment arises from several sources.
Common sources are changes in agriculture commodity prices, input
prices, production technology, and institutions.
Farmers attempting
to maximize profits make adjustments to these continual changes.
At the present time, two important factors are raising questions
regarding the optimal organization of Yellowstone Valley farms.
First is the uncertain future of the Great Western Sugar Company's
processing plant in Billings.
Second is a rapidly changing structure
of input and output prices. Presently, at least nine different
irrigated crops are grown in the upper Yellowstone Valley.
Wintering
calves and cattle fattening enterprises are also common, and a few
farmers have swine enterprises.
The possible closure of the Great
Western plant coupled with rapidly fluctuating input and output prices
adds serious uncertainty to the Valley farmers' search for optimal
combinations of crop and livestock enterprises.
STATEMENT AND BACKGROUND OF THE PROBLEM
Recent uncertainty regarding sugar beet production surfaced during
the winter of 1975-1976 when the Great Western Sugar Company and the
sugar beet growers' association failed to reach agreement on a contract
2
for 1976.
Sugar beet price, purity, and transportation costs were
three of many issues on which the Company and growers differed.
Both
sides argued that increased operating costs necessitated the need for
more money to operate their businesses.■
As the probability of Great Western not processing sugar beets
in 1976 increased, growers began to consider alternatives for process­
ing their beets.
They also began to consider alternative enterprises
in the event processing should be discontinued.
Transportation costs play an important role in the location of
sugar beet growing.
Great Western paid up to $2.05 per ton plus
38 percent of the balance over $2.05 per ton for transportation of
raw beets.
As one would expect, $2.05 per ton covers the transporta­
tion charge for most of the sugar beets processed at the Billings
plant.
The exceptions are sugar beets grown in the Hardin and
Hathaway areas.^
If processing at the Billings plant were discon­
tinued, sugar beet growers.in the upper Yellowstone Valley would
probably have to pay the entire cost of transportation to another
factory or simply not grow beets.
The processing plants nearest
Billings are Holly Sugar in Sidney, Montana and Great Western in
Lovell, Wyoming.
I
Both are operating at near capacity and if additional
Statement by Merle Riggs, Agriculture Manager of the Great Western
Sugar refinery at Billings, Personal Interview, September 20, 1976.
3
production were desired, beet growers much nearer the plants stand
ready to increase their acreages.
Thus, at this time, shipping beets
from the upper Yellowstone Valley to alternative processing plants
does not appear feasible.
In September of 1971, 5000 growers contracting with the Great
Western Sugar Company began negotiations to purchase the sugar pro­
cessing subsidiary of Great Western United.
In October of 1972
plans were nearly finalized with sale of the subsidiary to the growers
anticipated by December, 1972.
2
A proxy fight among the stockholders
of Great Western United and the sudden rise in the price of sugar
prevented the sale.
Farmers continued to pursue purchase but failed
in their negotiations with insurance companies on ways to insure and
arrange for compensation against unforeseen losses.
In a Billings
Gazette article on August 3, 1975, Great Western announced that time
had expired on the purchase agreement under which the sugar beet
3
growers were to buy the Great Western subsidiary.
In 1971, the Holly Sugar plant in Hardin, Montana was closed
without advance warning.
On Wednesday morning, January 27, 1971,2
3
2
Presentation by Bob Owens, President of Great Western Cooperative
(Cooperative that was going to purchase Great Western Sugar) at the
Montana Farmers Union Convention, October, 1972.
3
The Billings Gazette.
August 3, 1975, sec. B, p. 3, col. 1-4.■
4
representatives notified local credit institutions and the County
Extension Office that the processing plant was being closed. That
evening a general meeting of the beetgrowers1 association was called
to officially announce the closure of the plant.
The principal
reason given for closure by the company was that they had been
operating at a loss for the past two or three years and the plant
was too old and inefficient to remodel.
4
When the Hardin factory closed, 10,000 acres were removed from
sugar beet production.
There was an annual payroll reduction of
nearly a million dollars and an estimated, two and a half million
■5
dollar reduction in gross income to beet growers in the area.
Some
of this reduction in gross income would be made up by growing other
crops.
When the Holly Sugar processing plant in Hardin closed in 1971,
the two questions most frequently asked were: . (I) What is the most
profitable alternative crop, and (2) What should- be done with the
investment in sugar beet equipment.
Individual farmers traveled
hundreds of miles to look at alternative crops.
Seeing an opportunity
^Statement by Harold Strobel, Big Horn County Extension Agent at the
time of the factory closing, personal interview, September 23, 1976.
^Based on personal correspondence between Thomas K. Cowden, Assistant
Secretary of Agriculture, USDA, and Torlief S. Aasheim, Director,
Montana Cooperative Extension Service.
5
to make a quick profit, seed salesmen flooded the area selling exotic
crops and miracle varieties of seed.
Large sums of money were spent
by sugar beet growers for seed for alternative crops.
One month after the announcement of the closing of the factory,
Montana State University, through the Cooperative Extension Service
and the Agriculture Experiment Station, was able to present some
feasible production alternatives to beetgrowers.
If the Billings plant is closed, 21,534 acres of cropland would
be planted to alternative crops with a reduction of over $11,000,000
in gross income from sugar beets.
6
decision is paramount.
Thus, the need to make the right
What are the crop alternatives?
or crops should be planted to maximize net income?
What crop
What role will
livestock enterprises play in the effort to maintain farm incomes?
These are some of the questions that this study is designed to
address.
OBJECTIVES OF THE STUDY
The specific objectives of this study are:
I) to develop enter­
prise costs for crop and livestock alternatives in the Billings area;
and 2) to define a representative farm firm in the Billings area of
^1975 average yield and payment per ton were used in making this
estimate.
6
the Yellowstone Valley; and 3) to determine the optimum organization
(enterprise mix) for the representative farm firm in order to maximize
return over variable cost.
BACKGROUND INFORMATION ABOUT THE UPPER YELLOWSTONE VALLEY
The study area includes those areas of Yellowstone, Stillwater,
Carbon, Rosebud, and Treasure Counties that grow sugar beets for
processing at the Great Western Sugar Refinery at Billings.
The
background information given pertains mostly to Yellowstone County,
the central county involved in this study.
During the late 1800's the sugar beet industry was established
as a successful manufacturing industry in the United States.
Con­
sumption of sugar in the United States in 1895 was 1,950,000 tons
with 6,260 tons consumed in Montana.
.The gross return from a good crop of sugar beets in 1895 was
$45 to $50 an acre compared to $8 an acre for corn.
The average price
paid per ton was between $4 and $5 for 12 percent sugar content.
The Montana Experiment Station established sugar beet research
plots in various parts of the State during the late 1890's, and a
feasibility study of the industry in Montana was conducted.
This
research concluded that sugar beets could be grown in many areas of
Montana and it was felt that seven conditions would determine where
sugar beet processing plants would be located.
7
They were:
1.
An abundance of beets of standard grade are required.
2.
Transportation to the factories must be cheap; the distance
cannot be great.
3.
An abundance of pure water must be available.
4.
Fuel must be cheap.
5.
Limestone of good quality must be near at hand.
6.
The factories should be located on a railroad, that the
product can easily reach the market.
7.
There should be a means' of disposing of the extracted pulp
to stockmen, for use in fattening cattle for market.
The
stock should be fed on the farms from which the beets are
received, in order that the plant food may be returned to
the land.
7
The Billings area was one of several areas where all conditions
were met.
In 1906 the Great Western Sugar refinery was opened.
In
1907 the Huntley Irrigation Project was completed and the sugar beet
industry flourished, aided by the establishment of a dependable water
supply.
Traphagen, F. W. The Sugar -Beet in Montana.. Bulletin 19, Montana
Experiment Station, Montana State University, Bozeman, Montana,
October 1898, p. 34.
(
8
SELECTION OF ANALYTICAL MODEL
Several techniques have been used by farm management educators to
determine production adjustments which maximize net farm income under
changing conditions.
Complete and partial budgeting are two ways that
production adjustments can be analyzed.
Complete budgets could be
prepared for several enterprise combinations.
These complete budgets
could be compared with each other to determine which one was the
"best".
Partial budgets could also be prepared for alternative
changes in the enterprise mix to see which enterprise would have the
greatest positive effect on net farm income.
The limitations of these simple budgeting processes is that they
only examine a few alternatives.
Resources and enterprises are not
allowed to interact with each other to insure limited resources are
used in enterprises that produce the greatest.net farm income.
Budgeting is not a method to thoroughly analyze problems. For
example, with budgeting it is seldom feasible to break labor into
subclasses by months or weeks, or soils into different classes.
calculations involved would soon become astronomical.
The
Linear pro­
gramming is a mathematical technique that allows many enterprise
alternatives to be examined, and insures that the profit maximizing
use of resources is determined.
There are three quantitative components to linear programming.
They are:
I) the objective function, 2) alternative methods or.
9
processes for obtaining the objective, and 3) resource or other
g
restrictions.
For this problem the objective function is a profit
equation that gives the return over variable cost per unit of output
for each activity.
The objective is to.find the maximum profit
attainable from the resource base.
The alternative activities or
processes are different ways of obtaining the objective function.
For example, a farmer may grow wheat, barley, and oats to maximize
return over variable cost and he might grow each of these crops in
several different combinations.
on the outcome of the problem.
Resource restrictions are constraints
They can take the form of limited
amounts of labor available during a certain period of time, a limited
amount of operating capital, or a limited amount of space available
to store harvested grain.
Another restriction is that the activities
must have non-negative values.
Budgeting and linear programming are normally considered as
complements rather than substitutes.
Budgeting is normally used when
a relatively small number of alternatives are to be considered, or
minor adjustments in farming'operations are to be made.
When several
alternatives are being considered or a large scale optimizing problem8
8
William J. Baumol. Economic Theory and Operation Analysis.
wood Cliffs: Prentice-Hall, Inc., 1972), pp. 75-76.
'
(Engle­
10
is being analyzed, linear programming offers a more efficient way to
obtain a solution.
Budgeting or linear programming could be used
for either situation but the time factor usually determines which
method to use.
If only a few alternatives are being considered, there
is no reason to set up matrices and perform lengthy calculations to
solve the problem.
Likewise, when many alternatives are being con­
sidered, budgeting would be too time consuming and may not give an
optimal solution.
■A common concern of people using linear programming as a farm
planning tool is how much detail should be put in the model.
9
At
several points in the construction of a linear programming model, one .
must make choices between simplicity and complexity.
A trade-off
between realism of the model and expediency in getting the problem
solved exists.
A simplified model may be easy to construct, solve,
and interpret, but at the same time it may lack realism.
A complex
model which results from an attempt to allow interaction of a large
number of variables operating in an actual farm situation may be
impractical from a cost standpoint, i.e., in terms of time required
for construction, codification, solution and interpretation.
9
Larry I. Bitney. "Constructing the L.P. Model — How Much Detail?"
Research Report 10, Department of Agricultural Economics, University
of Nebraska, Lincoln, May 1970, p. I.
\
11
A study by Huffman and Stanton compared farm plans resulting
from a simplified programming matrix (20 x 20) with those of detailed
matrices (60 x 60).
The simplified matrix did not give acceptable
results when compared to the detailed matrix.
They concluded the
detailed matrix was the best estimate of the optimum organization.
One thing which may have biased the result against the simplified
matrix was that standard input-output coefficients were used for the
simplified matrix rather than those provided by the farmer for the
detailed matrix.
Brant also did a study comparing solutions from a simplified
programming process and a detailed matrix.
11
The simplified program­
ming process involved a linear interpolation of two or more optimum
plans for benchmark farms in a given area.
This simplified programming
process yielded solutions similar to those of the detailed matrices
when the user accurately categorized the land resource of the test
farms.
Donald C . Huffman and Lynn A. Stanton. "Application of Linear
Programming to Individual -Farm Planning." American Journal of
Agricultural Economics, Vol. 51 (1969), pp. 1168 - 1171.
^William Lewis Brant. "Analysis of the Representative Farm Concept
As A Tool in Area Supply Response Research and Farm Management
Education." (Unpublished Doctorate dissertation, Oklahoma State
University,. 1967), pp. 41-47.
12
The sequence of steps for linear programming, regardless of the
problem being considered is
I) build detailed enterprise budgets;
2) select potentially profitable processes; 3) select the limiting
factors of production, after a complete inventory of resources is
made; 4) specify the requirements of each process for the limiting
factors; 5) cost the inputs; and 6) solve the problem.
■'
12
'
'ANALYTICAL TECHNIQUE USED
Linear programming was selected as the technique because of the
size and complexity of the system being analyzed. Many alternatives
each with different resource requirements were considered in the
search for optimum production and adjustments.
The integer capability
of linear programming was also desirable in considering the livestock
alternatives.
The linear program used for this study was developed by R.
Shareshian of the IBM Corporation.
The program employs a branch
and bound algorithm based upon the Land and Doig, method
the mixed integer programming problem.
13
to solve
The program solves for an ■
optimal solution without regard to the integer constraints.
The
12
Chester 0. McCorkle, Jr. "Linear Programming as a Tool in Farm
Management Analysis." American Journal of Agricultural Economics,
Vol. 37 (1955), p. 1231.
•13
A. H. Land and A. G. Doig, "An Automatic Method of Solving Discrete
Programming Problems," Econometrics, July, 1960, Volume 28, Number
13
program then proceeds to solve the problem with the integer restric
tions and gives the optimal solution.3
13 continued
3, pp. 497-520,
Chapter 2
METHODOLOGY
This chapter discusses the procedure used to analyze the pro­
duction adjustment problem.
cribed.
The method of data collection is des­
The Oklahoma State University Crop Budget Generator was
used to process enterprise data and generate enterprise budgets for
the analysis.
are presented.
Assumptions relating to the representative farm firm
Summaries of the feasible production alternatives
(enterprise budgets) are presented along with the assumed input and
output prices.
METHOD OF DATA COLLECTION
Data used to construct the enterprise budgets were obtained from
farmer panels in the summer and fall of 1976.
These panels were
selected by the Yellowstone County Extension Agent.
Farmers selected
possessed an above average level of management, had a knowledge of
input costs, were from different parts of the study area, and were
familiar with farming practices in their area.
The farmer panel was selected to provide data representative of
a typical 320 acre irrigated farm in the Billings area.
base was developed for the farm.
A resource
Cultural practices and sequence of
operations were specified for each crop.
Tractor size, implement
size, and timing of the operations were specified by the panel.
15
Performance rates and amounts and costs of materials were also
included.
Machinery prices were obtained from local dealers. Prices for
similar models were averaged and then checked against actual sales
to determine if discounts were being given to farmers.
Similar methods were used to collect the livestock enterprise
data.
However, because some feasible livestock enterprises are not
common in the area, some of the necessary data were collected in other
areas of the State.
Enterprise budgets for wintering calves in the
feedlot and raising yearlings on irrigated pasture are based on
information obtained from the Billings area.
Data on feeding yearlings
to slaughter weight were obtained from the Billings and Forsyth area.
Data for ten sows farrowing once a year was obtained in the Plevna
area of southeastern Montana.
obtained from Whitehall.
Data for raising weaner pigs were
Data for a 60 sow confinement hog operation
was obtained in the Bozeman area and data for a 90 sow confinement
operation we,re obtained from enterprise cost studies done in Illinois
and adjusted to Montana conditions.
Cultural practices and costs for
a cow-calf enterprise were obtained in the Bozeman and Miles City
area.
Budgets for the livestock alternatives were hand calculated.
The format used was similar to the computations done by the computer
for the crop budgets.
Fixed and variable costs were separated and
\
16
itemized.
All enterprise budget data were then summarized to obtain the
technical coefficients for the linear programming model.
MODEL FARM
Size of Farm
The representative farm chosen for this study contains 320 acres.
Twenty acres are assumed to be in ditches, fences, roadways, and
farmstead.
The remaining 300 acres are irrigated.
A panel of
irrigated farmers and Yellowstone County Extension Agent, John Ranney9
described this as a "typical sized" farm in the Yellowstone Valley
irrigated area.
In this study no additional acres can be acquired
by the representative farm.
Soil Productivity
Soil productivity is an important factor influencing enterprise
selection, method of operation, and ultimate returns on a given farm.
Soil types and, consequently, variations in productivity do not
necessarily coincide with farm boundaries.
The soil resources of
a farm are typically a composite of several soil types.
Bureau of
Reclamation data show that 25 percent of the irrigated land in the
Huntley Project is not suitable for crop production.
Other parts of
the Yellowstone Valley are more suitable for crop production.
This
17
study assumes that 280 acres can grow any type of crop alternative and
the remaining 20 acres are limited to irrigated pasture.
Management
An above average level of management is assumed for this study.
The level of management is reflected in yields and input usage, such
as amount of fertilizer used, chemicals applied, seed varieties
planted, type of farm machinery owned and care and maintenance of
machinery.
No management fee is charged to any crop or livestock alternative
The returns from each enterprise are returns to the fixed resources.
For the purpose of this study,, land, management, machinery and
buildings that are presently being used in the farming operation and
operator labor are fixed resources.
Fixed Resources
Every farmer in the Yellowstone Valley has an inventory of
tractors and implements sufficient to produce the crops currently
being grown.
Since this analysis is concerned with production adjust­
ments necessary to maximize farm income if sugar beet production is
lost, the current inventory is considered a sunk cost, and thus not
included as a cost for each crop enterprise.
Table I lists an
inventory of machinery on the representative farm with the respective
fixed and variable costs per hour of use.
Grain storage for 4000
18
Table I
AN INVENTORY OF MACHINERY OWNED ON AN IRRIGATED FARM IN THE UPPER YELLOWSTONE
VALLEY WITH THEIR RESPECTIVE FIXED AND VARIABLE COSTS1
Machine
Size
Tractor
Tractor
Tractor
Tractor
Truck
Truck
Truck
Tandem Disk
Farmhand
Manure Spreader
Plow
Mulcher
Land Plane
Field Cultivator
Beet Planter
Incorporator
Ditch Burner
Ditcher
Beet Cultivator
Roller Packer
Beet Thinner
Ditch Closer
Top Saver
Beet Digger
Combine
Bean Cutter
Bean Windrower
Corn Planter
Band Sprayer
Sprayer
Corn Cultivator
Corn Chopper
Dozer Blade
Swather
Drill
Harrow
PTO Baler
Wagon
Harrow
125
90
70
60
18
16
14
12
Price
HP
HP
HP
HP
FT
FT
FT
FT
4-16
15 FT
12 FT
18 FT
6 ROW
12 FT
—
6 ROW
12 FT
6 ROW
6 ROW
3 ROW
12 FT
6 ROW
4 ROW
6 ROW
6 ROW
30 FT
6 ROW
2 ROW
10 FT
10 FT
12 FT
12 FT
30 FT
$24,100
8,500
13,700
4,500
11,000
5,000
1,000
2,700
2,200
4,500
3,300
3,800
4,200
2,200
2,800
1,100
675
850
2,200
2,850
10,600
550
10,300
17,000
5,000
2,000
3,200
2,800
1,850
885
1,800
6,850
1,500
10,250
3,400
220
4,750
600
550
Fixed Costs
Per Hour
$ 6.40
2.26
2.96
2.19
9.26
4.25
.85
13.60
2 37
3.33
4.42
3.53
6.65
5.28
2.08
8.42
2.57
1.11
8.61
32.02
1.66
17.22
25.68
13.83
10.07
16.11
12.09
7.99
6.68
3.88
13.67
2.83
14.32
25.68
1.66
10.06
.54
4.16
Variable Costs
Per Hour
$4.70
3.21
2.76
1.98
3.65
2.96
2.49
.19
I 24
3.03
1.35
1.93
.36
1.46
.32
36
.17
1.52
.47
1.40
.11
2.50
3.10
2.34
2.51
.24
.89
.79
.24
.39
1.31
.24
7.46
.61
.01
1.43
.08
.03
1Schaefer1 Jerry and LeRoy D. Luft1 Enterprise Costs of Irrigated Crops In South
Central Montana. Bulletin 1151, Cooperative Extension Service, Montana State
University, June, 1976.
19
bushels is located on the representative farm.
Machine storage and
shop area of 3200 square feet are also assumed to exist on the farm.
The farm has feedlot capacity for 170 head.
One feed storage tank
and feed mixer are owned for use with the feedlot.
If new machinery is purchased or buildings erected as a result
of production adjustments, the costs are treated as variable costs.
The costs which become annual fixed costs are assigned to the enter­
prise since the farmer decides whether or not it is to his benefit
to incur additional annual cost.
Prior to acquisition the ownership
costs are variable costs because their commitment to production is
at the farmer's discretion.
Fixed Costs
Every farm has some fixed costs that must be paid each year
regardless of whether or riot production takes place.
Taxes on real
estate and buildings of $6.28 per irrigated acre"*"^ must be paid.
A
water charge of $6.00 per acre must be paid or the right to use the
water passes to other producers..
15
s".
Schaefer, Jerry and LeRoy D. Luft, Eriterprise Costs of Irrigated
Crops in South Central Montana, Bulletin 1151, Cooperative Extension
Service, Montana State University, Bozeman, June, 1976.
20
Labor
The owner-operator provides labor for the farm business.
It is
assumed the operator will work ten hours a day for twenty five days
a month year around.
One full time man can be hired to work the
same number of hours as the owner.
Some alternatives will allow the
hiring of part time seasonal labor to supplement owner operator labor
Not all the labor available can be used for field work due to
weather and climatic conditions.
Critical labor periods for this study are defined as:
December
!-February 28, March I-May 31, June !-August 31, and September 1November 30.
Data concerning days available for field work in the
triangle area has been published by the Montana Agricultural
Experiment Station.
15
Soil types, climatic conditions, and weather
data were used to estimate days available for field work.
Although
the triangle area and the Billings area are not located near each
other, the data was applied to the Billings area because it is the
best data available.
The date for beginning spring field work and
average rainfall were similar for both areas:
74 percent of the days
(555 hours) in the March I-May 31 period were available for field
^ Y a g e r , William A. and R. Clyde Greer. Estimating Days Suitable for
Fieldwork, Research Report 67, Montana Agricultural Experiment
Station, Montana State University, Bozeman, December, 1974.
21
work:
83 percent of the days (622 hours) in the June !-August 31
period were available for field work;
and 93 percent of the days
(697 hours) in the September !-November 30 period were available for
field work.
It was assumed that weather would not hinder the labor operations
■associated with livestock.
Therefore, the labor available for live­
stock enterprises can be equal to the total labor supply for each
period.
Capital
Capital, requirements for an irrigated farm are high.
Most pro­
ducers have some long term debt as well as an operating capital loan.
A real estate debt of $100,000 was assumed.
The operating capital
loan was assumed not to exceed $80,000 at any one time during the
growing season.
Enterprise cash receipts and expenses occur at
different points in time, so total annual capital requirements can
be larger than the operating loan the farmer has with his lender.
Time.Period
The time period being considered is a short-run adjustment
period.
This would be from one to five years in length.
The time
period is long enough for new capital investment items such as
buildings and livestock equipment to be considered variable costs. ■
22
Technology
Technology is continually changing on an irrigated farm.
This
study assumes a level of technology for the livestock alternatives
that would exist on the best 10 to 15 percent of the farms in the
Upper Yellowstone Valley.
Crop alternatives assumed a level of
technology that was above average.
ACTIVITIES
The production year for all alternative activities was divided
into four periods.
labor periods:
These four periods were the same as the critical
December !-February 28, March I-May 31, June !-August
31, and September !-November 30.
Crop Activities
Nine different crop alternatives were considered in this analysis
The crop alternatives.considered were sugar beets, beans, corn silage,
corn grain, spring wheat, feed barley, malting barley, alfalfa hay,
and irrigated pasture.
Sugar beets were included in this analysis
to determine the change in net farm income resulting from the possible
loss of this enterprise.
.All crop alternatives include only the variable costs, since it
was assumed that the farmer already owns the machinery inventory
listed in Table I. ' Table 2 lists the yields and resource requirements
TABLE 2
CROP ALTERNATIVES: YIELDS AND RESOURCE REQUIREMENTS PER ACRE1
ITEM
Yield
Sugar
Beets
Pinto
Beans
Corn
Grain
Corn
Silage
Spring
Wheat
Feed
Barley
Malting
Barley
Alfalfa
Hav
Irrigated
Pasture
18 Tons
20 CWT
100 BU
20 Tons
65 BU
80 BU
80 BU
5 Tons
6 AUM'S
Operating Capital ($)
a. 3/1 - 5/31
$80.04
33.85
59.76
64.53
44.92
38.64
40.53
14.80
32.69
b. 6/1 - 8/31
38.78
13.43
6.17
10.94
8.87
8.87
8.90
27.50
25.84
c. 9/1 - 11/30
31.81
31.81
53.48
23.02
16.65
16.65
16.68
13.33
11.02
$150.63
79.09
119.41
98.49
70.44
64.16
66.11
55.63
69.55-
Total
Labor Requirements (hours)
a. 3/1 - 5/31
2.36
2.8
1.7
1.7
2.81
2.81
2.81
.16
.28
b . 6/1 - 8/31
2.25
2.25
1.0
1.0
1.72
1.72
1.72
4.56
.20
0
0
0
0
.11
4.53
4.53
4.53
4.72
.59
c. 9/1 - 11/30
6.37
6.37
3.56
4.27
Total
10.98
11.42
6.26
6.97
1Schaefer, Jerry and LeRoy D. Left, Enterprise Costs of Irrigated Crops in South Central Montana, Bulletin 1151,
Cooperative Extension Service, Montana State University, Bozeman, June, 1976.
24
per acre for the crop alternatives considered.
Costs for the crops
listed were also taken from Extension Bulletin 1151.
16
Fertilization
rates were changed to more closely coincide with recommendations of
soil scientists.
Soil tests are needed to further refine the
fertilizer requirements for each individual field.
One hundred
pounds of phosphorus were used for sugar beets rather than the 125
pounds listed in the bulletin.
Forty pounds of phosphorus were used
for pinto beans' rather than the ten pounds shown.
Spring wheat and
feed barley had 80 pounds of nitrogen applied rather than 50 pounds.
Cattle Activities
Four basic cattle alternatives were considered in this analysis.
The four alternatives were:
cow-calf, wintering calvessummer
yearlings on grass, and feeding out yearlings to market weight for
slaughter.
The cow-calf alternative raised 450 pound steers and 420 pound
heifers.
wintering.
The calves could be sold or placed in the feedlot for
A 90 percent calf crop was assumed.
Cows were culled at
the rate of 10 percent per year with.replacements coming from the
"^Schaefer, Jerry and LeRoy D . Luft. Enterprise Costs of Irrigated
Crops in South Central Montana, Bulletin 1151, Cooperative Extension
Service, Montana State University, Bozeman, June, 1976.
25
heifer calves.
The 450 pound steer calves could be wintered to gain 1.75 or
1.1 pounds per day while heifers were assumed to gain 1.5 or 1.1
pounds per day..
At the end of the wintering period the steers gaining
the 1.75 pounds per day could be placed directly into the feedlot for
fattening.
Steers gaining 1.1 pounds per day, heifers gaining 1.5
pounds per day, and heifers gaining 1.1 pounds per day could be sold
or placed on irrigated pasture for the summer.
Another set of calf wintering alternatives was to purchase 400
pound steers and feed them to gain 1.75 pounds per day, and 370
pound heifers to gain 1.5 pounds per day.
At the end of the wintering
period the calves could be sold or placed on grass for the summer.
The length of the wintering period was 180 days for all calf wintering
alternatives.
A detailed summary of costs of wintering calves can
be found in Extension Circular 1194.
17
• Five summer yearlings-on-grass alternatives were considered.
The
costs and input requirements were the same for all alternatives con­
sidered.
The only difference between the alternatives was the
starting weights of the steers and heifers and the corresponding
ending weights.
17
Yearlings were summered on grass for 135 days and
Cornelius, James.C . and Jerry Schaefer. Enterprise Costs for Winter­
ing Feeder Calves in Yellowstone County, Circular 1194, Cooperative
Extension Service, Montana State University, Bozeman, November, 1976.
26
had an average daily gain of 1.2 pounds.
Table 3 presents the back­
ground information and resource requirements per head for all cattle
alternatives.
Three feeding yearling steers alternatives were analyzed.
Steers
were fed H O days and had an average daily gain of 2.75 pounds.
Costs
and input requirements were the same for the three feeding yearling
steers alternatives.
The only difference between the alternatives
was starting weights and the time of year the steers were in the
feedlot.
Three feeding yearling heifers alternatives were analyzed.
Heifers were fed 80 days and had an average daily gain of 2.5 pounds.
Costs and input requirements were the same for the three feeding
yearling heifer alternatives.
The only difference between the alter­
natives was initial weights of the heifers when placed in.the feedlot.
The six wintering calves alternatives and five fattening year­
lings alternatives, compete for the same feedlot space.
Therefore,
only one of these alternatives could be allowed at a given time.
One
feeding alternative was. not competitive for the winter feedlot capacity.
Wintering 450 pound steers to gain 1.75 pounds per day yields a 765
pound steer by May 15.
These steers could be placed directly into
the feedlot for fattening to slaughter weight over the summer.
This
alternative does not compete for winter feedlot space with the other
feedlot feeding alternatives.
Table 3
CATTLE ACTIVITIES:
Item
Enterprise Number
Starr of Production
Period
End of Production Period
Days in Production Period
Beginning Weight
Final Weight
Cow Calf
I
April I1
2
Nov. 15
225
NA
Steers 450
Heifers 420
Total Production in Lbs. Steers
202.5
Heifers
147
Cull Cows 100
Average Daily Gain (lbs.) NA
Disposition of Output
Sale or
Transfer to
#3 and #5
Operating Capital (S)
a. 12/1 - 2/28
Sll.13
b. 3/1 - 5/31
11.13
c . 6/1 - 8/31
9.83
d. 9/1 - 11/30
9.83
41.92
Total
Labor Requirements (hrs.)
.37
a. 12/1 - 2/28
.37
b. 3/1 - 5/31
c . 6/1 - 8/31
.20
d. 9/1 - 11/30
.20
1.14
Total
Feed Requirements
a. Barley (bu.)
b. Corn Silage (ton)
c . Alfalfa Hay (ton) 1.65
6.5
d. Pasture (AVM)
GENERAL INFORMATION, AND RESOURCE REQUIREMENTS PER HEAD1
Wintering
3
4
5
Nov. 15
Mav 15
180
400
Nov. 15
May 15
180
450
Nov. 15
May 15
180
450
Nov. 15
May 15
180
420
Nov. 15
May 15
180
370
Nov. 15
May 15
180
420
715
765
650
690
640
620
315
1.75
Sale or
Transfer to
#8
315
1.75
Sale or
Transfer to
#15
200
1.1
Sale or
Transfer to
#9
270
1.5
Sale or
Transfer to
#10
270
1.5
Sale or
Transfer to
#11
200
1.1
Sale of
Transfer to
#12
18.58 (22.47)
9.16 (11.07)
16.34 (20.23) 18.58 (22.47)16.19 (20.07) 18.43 (22.33)
8.05 ( 9.96) 9.16 (11.07) 7.97 ( 9.89) 9.08 (11.00)
18.43 (22.33)
9.08 (11.00)
27.74 (33.54)
24.39 (30.19) 27.74 (33.54)24.16 (29.96) 27.51 (33.33)
27.51 (33.33)
1.42 (.53)
.70 (.17)
1.42 (.53)
.70 (.17)
1.42 (.53)
.70 (.1 7 )
1.42 ( .53)
.70 (.17)
1.42 (.53)
. 70 (.1 7 )
1.42 (.53)
.70 (.17)
2.12 (.70)
2.12 (.70)
2.12 (.70)
2.12 (.70)
2.12 (.7 0 )
2.12 (.70)
11.25
2.25
.27
11.25
2.25
11.25
1.8
.18
—
11.25
1.8
.27
----
11.25
1.8
.27
—
7.5
1.98
.18
----
—
—
.27
1Nunfcerin parenthesis refers to requirements for activity levels greater than 170 head.
Average calving date.
Wintering Heifers
6
2
7
Table 3 (Continued)
CATTLE ACTIVITIES:
Summer Yearlings
10
9
Item
Enterprise Number
8
Start of Production
Period
End of Production Period
Davs in Productio Period
Beginning Weight
Final Weight
May 15
Oct. I
135
715
875
Mav 15
Oct. I
135
650
810
Total Production in Lbs.
160
Average Daily Gain (lbs.) 1.2
Disposition of Output
Sale or
Transfer to
#13
Operating Capital ($)
a. 12/1 - 2/28
2.37
b. 3/1 - 5/31
4.74
c. 6/1 - 8/31
d. 9/1 - 11/30
2.37
9.48
Total
Labor Requirement (hrs.)
a. 12/1 - 2/28
.04
b. 3/1 - 5/31
.10
c. 6/1 - 8/31
.046
d. 9/1 - 11/30
.186
Total
Feed Requirements
a. Barley (Bu.)
b . Corn Silage (ton)
c. Alfalfa Hav (ton)
d. Pasture (AUM)
3.15
INumber
GENERAL INFORMATION, AND RESOURCE REQUIREMENTS PER HEAD1
11
12
May 15
Oct. I
135
690
850
Mav 15
Oct. I
135
640
800
Mav 15
Oct. I
135
620
780
160
160
160
160
1.2
Sale or
Transfer to
#14
1.2
Sale or
Tranfer to
#16
1.2
Transfer to
-17
1.2
Sale or
Transfer to
#18
2.37
4.74
2.37
9.48
2.37
4.74
2.37
9.48
2.37
4.74
2.37
9.48
2.37
4.74
2.37
9.48
.04
.10
.046
.186
.04
.10
.046
.186
.04
.10
.046
.186
.04
.10
.046
.186
—
3.15
3.15
3.15
3.15
in parenthesis refers to requirements for activity levels greater than 170 head.
Table 3 (Continued)
CATTLE ACTIVITIES:
Fattening Steers
14
Item
Enterprise Number
13
Start of Production
Period
End of Production Period
Davs in Production Period
Beginning Weight
Final Weight
Oct. I
Jan. 20
HO
875
1,175
Total Production in Lbs.
300
Average Daily Gain (Lbs.) 2.75
Sale
Disposition of Output
Operating Capital ($)
a. 12/1 - 2/28
b. 3/1 -5/31
c. 6/1 - 8/31
d. 9/1 - 11/30
Total
Labor Requirement (Hrs.)
a. 12/1 - 2/28
b. 3/1 - 5/31
c. 6/1 - 8/31
d. 9/1 - 11/30
Total
Feed Requirements
a. Barley (bu.)
b. Corn Silage (Ton)
c. Alfalfa Hav (Ton)
d . Pasture (AUM)
GENERAL INFORMATION, AND RESOURCE REQUIREMENTS PER HEAD
14.57 (17.47 )
Fattening Heifers
18
17
15
16
May 15
Sept.
5
HO
765
1.065
Oct. I
Dec. 20
80
850
1,050
300
300
200
200
200
2.75
Sale
2.75
Sale
2.50
Sale
2.50
Sale
2.50
Sale
6.91 (8.82)
6.91 (8.82)
6.91 (8.32)
Oct. I
Jan. 20
HO
810
1.110
14.57 (17.47 )
Oct. I
Dec. 20
80
800
1,000
Oct. I
Dec. 20
SC
780
980
6.00 (7.00)
23.14 (27.94)
14 =57 (17.47)
29.14 (34.94)
11.57 (17.47)
29.14 (34.94)
.65 (.16)
—
14.03 (17.92)14.03 (17.92) 14.03 (6.82)
29.14 (34.94) 20.94 (26.74)20.94 (26.74) 20.94 (26.74)
.65 (.16)
.14 (.04)
.14 (.04)
.14 (.04)
.80 (.20)
.94 (.24)
.80 (.20)
.94 (.24)
.80 (.20)
.94 (.24)
.10 (.02)
1.2 (.30)
..■I?. (.16)
1.3 (.32)
.65 (.16)
1.3 (.32)
—
39
39
39
.275
.11
—
1.3
.275
.11
—
(.32)
.275
.11
—
28.3
.20
.04
28.3
.20
.04
28.3
.20
.04
-----
-----
—
Hlumber in parenthesis refers to requirements for activity levels greater than 170 head.
30
For all feeding alternatives, it was assumed that the feedlot
had a capacity of 170 head.
Livestock feeding costs included
variable costs of production for only the 170 head feedlot.
If it
were profitable, a second and a third lot for 170 head each could be
built.
If feedlot facilities were added, the costs of construction
were treated as variable costs.
Hog Activities
• Five hog alternatives were analyzed.
They were:
I) farrow to
finish, 60 sow capacity; 2) farrow to finish, 90 sow capacity; 3)
farrow to finish, 10 sow capacity; 4) weaner pigs, 30 sow capacity;
and 5) 90 weaner pigs, purchased and fed out.
The farrow to finish 60 and 90 sow capacity assumed farrowing
every two months with each sow farrowing twice a year.
Sows in the
farrow to finish, 10 sow capacity were farrowed in April in a building
previously used for other purposes.
After the pigs were weaned, they ■
were placed outside for feeding to market weight.
The weaner pigsj 30 sow capacity alternative had each sow far­
rowing in March and September.
of age.
Weaner pigs were sold at nine weeks
A complete description of the alternative can be found in
.18
Extension Circular 1196.
18
The 90 weaner pigs, purchased and fed out
Schaefer, Jerry and-LeRoy D. Luft, Enterprise Costs for Raising Feed-r
er Pigs in Madison and Jefferson Counties, Circular 1196, Cooperative
Extension Service, Montana.State University, Bzoeman, February, 1977.
31
consisted of weaner pigs purchased in May at nine weeks of age.
Table
4 presents the general information and resource requirements for the
five hog alternatives considered.
INPUT AND OUTPUT PRICES
Two different sets of input-output prices were used in this
analysis.
The actual prices that existed in the fall and winter of
1976 were one set of input-output prices.
area furnished crop price information.
Businesses in the Billings
Livestock prices for choice
livestock from October to December at the Billings Livestock Auction
Market were used.
Projected crop and livestock prices for the fall and winter of
1977 were the second set of prices used.
LeRoy Luft and James
Cornelius, Extension Economists, furnished the projected prices.
These projections were made during the summer of 1977, based on
outlook information that was available at the time.
Table 5 summarizes
the input-output price information that was used in this study.
Table 4
SWINE ACTIVITIES:
GENERAL INFORMATION AND RESOURCE REQUIREMENTS PER UNIT
Item
90-Sow1
60-Sow^
10-Sow
30-Sow
90 Feeder Pigs
Description of
Production
System
Confinement with
sows farrowing
twice a year.
Thirty sows far­
row every 2 months
Confinement with
sows farrowing
twice a year.
Twenty sows far­
row every 2 months
Farrow inside;
feed pigs
outside. Ten
sows farrowing
in April
Farrow inside;
sell weaner pigs.
Thirty sows farrow
every 6 months
Feeder pigs
purchased in
May and fed to
market
weight
Unit used
Pigs per litter
Total output (pigs)
Beginning Weight
Ending Weight
litter
7.5
1350
litter
7.5
900
litter
7.5
75
litter
7.5
225
litter
7.5
90
40
220
—
220
220
220
Operating Capital ($)
$90.34
a. 12/1-2/28
90.34
b. 3/1-5/31
90.34
c . 6/1-8/31
90.34
d. 9/1-11/30
361.36
Total
$69.15
69.15
69.15
69.15
276.60
$193.24
65.07
50.67
86.40
395.38
$21.98
79.75
21.98
79.75
203.46
Labor Requirements (hr)
a. 12/1-2/28
4.5
4.5
b. 3/1-5/31
4.5
c . 6/1-8/31
4.5
d. 9/1-11/30
18.0
Total
6.88
6.88
6.88
6.88
27.52
2.0
13.3
2.3
2.3
19.9
2.0
10.2
2.0
10.2
24.4
.30
1.80
1.80
3.90
302
.164
360
.332
16.72
.288
152
.332
Feed Requirements
Barley (Bu.)
Alfalfa (tons)
254
—
^$270 per litter per year fixed building cost is required for this activity.
2
$130 per litter per year fixed building cost is required for this activity.
$---32.65
42.78
78.52
153.95
N>
33
Table 5
INPUT-OUTPUT PRICES USED IN THE CROP AND LIVESTOCK BUDGETS
Crop
Beets per ton
Beans per cwt.
Corn grain per bu.
Corn silage per ton
Spring wheat per bu.
Feed barley per bu.
Malting barley
Alfalfa per ton
Irrigated pasture
per AUM
Price Fall-Winter 1976-77
Sold
Purchased
Price Fall -Winter 1977-78
Sold
Purchased
$20.00
10.00
2.52
18.00
2.40
1.80
2.75
50.00
$20.00
10.00
2.52
18.00
2.40
1.80
2.75
50.00
$22.00
1.92
65.00
8.00
$22.00
1.92
65.00
8.00
Livestock
40 lbs. weaner pig
per lb.
$ .525
220 lbs. market pig
.35
per lb.
,40
450 lb. steer per lb.
420 lb. heifer per lb. .35
.38
765 lb. steer per lb.
690 lb. heifer per lb. .37
810 lb. steer per lb. .33
850 lb. heifer per lb. .29
1065 lb. steer per lb. .36
1110 lb. steer per lb. .36
1050 lb. heifer per lb. .34
1000 lb. cull cow per
lb. .20
$.525
.40
.35
.38
.37
.33
.29
.36
.36
.34
$.57
.38
.45
.40
.40
.36
.39
.35
.43
.43
.41
.23
$.57
—
.45
.40
•40
.36
.39
.35
.43
.43
.41
Chapter 3
RESULTS
This chapter presents the results of linear programming solutions
indicating the optimal combination of enterprises under different
situations.
A comparison between optimal enterprise combinations and typical
enterprise combinations in the study area is made.
Operator prefer­
ences and other factors which might preclude adoption of the optimal
combination.of enterprises are discussed.
LINEAR PROGRAMMING MODEL
Preliminary analysis indicated some alternatives would not be
included in optimal solutions for the resource situations to be '
19
examined.
The alternatives were eliminated from the feasible set.
The alternatives eliminated were:
wintering 400 pound steers and
370 pound heifers; wintering 450 pound steers and 420 pound heifers,
to gain 1.1 pounds per day;
all summer yearling grazing alternatives;
and fattening 780 pound heifers, and 800 pound heifers.
These
deletions reduced the matrix to a more manageable size.
The matrix
was reduced from 99 rows and 131 columns to its present size of 64
rows by 77 columns.
19
The matrix is presented in Table A of the
These alternatives were dominated by other alternatives.
35
Appendix.
OPTIMAL FARM ORGANIZATION
Fifteen different resource situations were programmed.
The
fifteen resource situations were generated by different assumptions
concerning.the availability of hired labor, the number of livestock
alternatives permitted and whether sugar beets were permitted or not.
Table 6 summarizes the different resource situations.
No restrictions on maximum or minimum acres of different crops
were used except the upper bound of 300 acres available for crop
production for resource situation one.
With
the exception of
situation.one, restrictions were based on minimum acres that farmers
would require to justify ownership of the machinery required for the
crop.
Sugar beets had a minimum restriction of 60 acres.
Irrigated
pasture had a minimum of 20 acres because this amount of land was not
suitable for other crops.
Maximum restrictions were placed on some
•crops because of machinery limitations, crop rotations, contract
acreage maximums, and local markets for some crops.
Sugar beets,
corn for grain, and corn silage had maximum restrictions of 100 acres
Alfalfa had a maximum restriction of 60 acres.
Situation one had.no
maximum or minimum restrictions for crop enterprises.
Part-time seasonal labor could be purchased in some of the
situations (see Table 6).
Other situations excluded seasonal hired
36
Table 6
RESOURCE SITUATIONS CONSIDERED
Resource
Situation
Seasonal
Labor
Permitted
Number of Live­
stock Enterprises
Permitted
Sugar
Beets
Permitted
I
YES
2
YES
2
YES
2
YES
3
NO
2
YES
4
YES
I
YES
5
NO
I
YES
6
YES
2
NO
7
NO
2
NO
8
YES
I
NO
9
. YES
2
YES .
10
NO
2
iss
11
YES'
I
YES
I
' YES
12 ...
' NO
13
YES
2
14
NO •
2
NO
I
NO
15
'YES
.
NO
37
labor but allowed hiring a full-time man.
One of the options was to choose between the most profitable
beef and swine alternatives.
Producers interested in only one live­
stock enterprise could then pick the most profitable one.
This
restriction was not applied to the summer fattening of 765 pound
steers.
The summer fattening alternative
is more competitive with
crop alternatives than it is with livestock alternatives, so it was
not included when restricting the number of livestock alternatives.
The main objective of this study was to determine the optimal
combination .of crop and livestock enterprises when sugar beets were
not permitted.
Optimal solutions indicating return over variable cost
and the optimal crop and livestock enterprises with and without sugar
beets were, obtained.
Resource situations one to eight were made using crop and live­
stock prices that existed in the fall and winter of 1976-77, Table
5.
Resource situations nine to fifteen were made using projected
prices for the fall and winter of 1977-78, Table 5.
Table 7 presents
the results for these resource situations.
Resource situation one had no minimum or maximum restrictions on
the type of crops grown.
restrictions.
All other resource situations had crop
Resource situations two through five are similar with
■respect to resource base, input and output prices, and that sugar .
beets were permitted in the solution.
The availability of seasonal
)
TABLE 7
Income Measures, Optimal Combinations of Cropping and Livestock Enterprises and
Other Selected Items for Specified Resource Situations
Item
Seasonal Labor Permitted
Winter Livestock Activities
Sugar Beet Acreage Allowed
Units
I
(No.)
Yes
2
Yes
Resource Situation
2
3
Yes
2
Yes
No
2
Yes
82.508
8,356
69,843
- 4,309
62,590
-11,562
(Acres)
(Acres)
(Acres)
(Acres)
(Acres)
(Acres)
(Tons)
(Bu)
(Tons)
(AUMs)
60
227
0
13
0
0
4.396
38,010
0
0
100
100
20
60
20
0
1,860
38,010
234
120
77
100
43
60
20
0
1,860
38,010
234
120
765 lb. Steers Fed
850 lb. Heifers Fed
810 lb. Steers Fed
Sows Farrowed to Finish
in 60 Sow Capacity
Sows Farrowed to Wean
in 30 Sow Capacity
(No.)
(No.)
(No.)
0
0
0
0
510
0
510
0
510
(No.)
60
60
60
(No.)
0
0
0
Full Time Hired Man
Owner Labor Crops Mar.-MayA^
Owner Labor Crops June-AugF'
Owner Labor Crops Sept-Novl/
Part Time Labor Mar.-May
Part Time Labor June-Aug.
Part Time Labor Sept-Nov.
(No.)
(Hrs.)
(Hrs.)
(Hrs.)
(Hrs.)
(Hrs.)
(Hrs.)
0
0
433
380
697
96
42
654
407
487
619
70
150
447
I
1,000
625
922
Operating Capital Dec.-Feb.
Operating Capital June-Aug.
(S)
(S)
0
0
0
0
Return over Variable Cost
(S)
Returns to Labor & Management (S)
Sugar Beets
Corn Silage
Malting Barley
Alfalfa
C o m Grain
Silage Sold
Feed Barley Purchased
Hay Sold
Pasture Sold
0
0
0
0
0
4
5
(Assumptions)
Yes
No
I
I
Yes
Yes
(Income Measures)
64,902
57,725
-16,427
- 9,250
(Cropping System)
100
60
100
73
92
20
60
55
20
20
0
0
1.860
1,452
19,890
1,783
244
274
120
120
(Livestock System)
0
0
0
63
510
0
0
0
0
0
(Labor Requirements)
0
0
6
7
8
Yes
2
No
No
2
No
Yes
I
No
65,260
- 8,892
61,622
-14,076
60,504
-13,648
0
100
120
60
20
0
1,860
38,010
234
120
0
100
120
60
20
0
1,860
20,146
239
120
0
100
120
60
20
0
1,860
19.890
244
120
0
0
0
0
510
510
0
0
510
60
0
0
0
15
0
0
0
522
584
429
0
555
584
429
0
0
0
0
0
0
537
555
430
622
622
461
697
697
301
0
0
93
15
0
123
369
0
128
(Operating Capital Requirements)
8,417
435
0
0
0
0
-^When a full time hired man is hired. this also includes the full time hired man's hours of labor
6,345
8,417
0
0
on crop activities.
Table 7 Continued
Item
Units
9
10
Seasonal Labor Permitted
Winter Livestock Activities
Sugar Beet Acreage Allowed
—
(No.)
Yes
2
Yes
No
2
Yes
Return over Variable Cost
Returns to Labor & Management
(S)
(S)
100,341
24,643
93,437
17,739
Sugar Beets
C o m Silage
Malting Barley
Alfalfa
(Acres)
(Acres)
(Acres)
(Acres)
(Acres)
(Acres)
(Tons)
(Bu)
(Tons)
(AUMs)
100
100
20
60
20
0
1,760
34,825
215
120
88
100
32
60
20
0
1,758
34,825
215
120
765 lb. Steers Fed
850 lb. Heifers Fed
810 lb. Steers Fed
Sows Farrowed to Finish
in 60 Sow Capacity
S<*#s Farrowed to Wean
in 30 Sow Capacity
(No.)
(No.)
(No.)
510
510
0
510
510
0
(No.)
0
0
(No.)
30
30
Full Time Hired Man
Owner Labor Crops Mar.-May=/
Owner Labor Crops June-Augiz
Owner Labor Crops Sept-Novl/
Part Time Labor Mar.-May
Part Time Labor June-Aug.
Part Time Labor Sept-Nov.
(No.)
(Hrs.)
(Hrs.)
(Hrs.)
(Hrs.)
(Hrs.)
(Hrs.)
0
449
511
653
29
127
413
I
483
631
990
0
0
0
Operating Capital Dec.-Feb.
Operating Capital June-Aug.
(S)
(S)
0
2,998
0
2,185
C o m Grain
Silage Sold
Feed Barley Purchased
Hay Sold
Pasture Sold
i
Resource Situation
11
12
(Assumptions)
Yes
No
I
I
Yes
Yes
(Income Measures)
97,395
90.264
23,243
16,112
(Cropping System)
100
90
100
100
20
30
60
60
20
20
0
0
1,758
1,758
34.323
34,323
224
284
120
120
(Livestock Svstem)
510
510
510
510
0
0
0
0
13
14
15
Yes
2
No
No
2
No
Yes
I
No
96,028
20,330
89.612
15,460
93,095
18,943
0
100
120
60
20
0
1,758
34.825
215
120
0
100
113
24
20
33
1,758
34,323
46
120
0
100
120
60
20
0
1,758
34,323
224
120
510
510
0
510
510
0
510
510
0
0
0
0
0
0
0
555
444
546
0
0
0
0
555
514
429
0
70
0
4.174
13,681
4,174
5,628
0
0
30
(Labor Requirements)
0
I
0
555
482
471
541
484
1,000
697
1,000
335
0
0
51
97
0
100
369
0
95
(Operating Capital Requirements)
4,174
4,174
0
4,671
3,965
0
-^When a full tine hired man is hired, this also includes the full time hired man's hours of labor on crop activities.
UJ
40
labor and' number of livestock enterprises permitted were varied for
these situations.
Resource situations six through eight are similar .
to resource situations two through four except sugar beets were not
permitted in the solution.
Resource situations nine through twelve are suitable for com­
parison because they have a similar resource base, a similar set
of input and output prices and sugar beets were permitted in the
solution.
Resource situations nine through twelve are similar to
resource situations two through five except projected 1977-78
prices were used for the crops and livestock sold rather than 1976-77
prices.
Resource situations thirteen through fifteen are similar to
resource situations nine through twelve except sugar beets were not
allowed in the solution.
The return over variable cost figures in Table 7 must be inter­
preted carefully.
These figures are a return to the fixed resources
that were present on the model farm.
The annual ownership cost of
any new piece of machinery or new building that had to be purchased
for a new enterprise on the farm is considered a variable cost.
The return to labor and management was calculated by subtracting
annual costs for fixed resources that were originally present on the
farm.
Resource situation
one had $27,456 of fixed machinery costs,
$1,432 of fixed costs in the 170 head feedlot, $2,064 of building
depreciation and interest, and $43,200 of land costs at nine percent.
\
41
These costs were deducted from return over variable cost to derive
the return to labor and management of $8,356.
Situations seven, nine,
ten and thirteen had an additional fixed cost of $1,546, associated
with facilities for raising weaner pigs, deducted from their return
over variable costs.
Resource situation one had a return over variable cost of
$83,508 and a return to labor and management of $8,356.
Sixty acres
of sugar beets, 227 acres of corn silage, and 13 acres of alfalfa were
grown.
Seasonal labor was hired in the March-May, June-August, and
September-November time periods with the largest amount, 654 hours,
required in the September-November harvesting period.
There were
510 head of 810 pound steers fattened over the winter and 60 sows were
farrowed in the 60 sow confinement facility.
Imposing acreage restrictions (situation two) reduced return over
variable cost by over $13,000 to $69,842.
ment were -$4,309.
Returns to labor and manage­
Sugar beets, corn silage, and alfalfa were in the
solution at their maximum levels of 100 acres, 100 acres and 60 acres
respectively.
Irrigated pasture had the required minimum of 20 acres.
Twenty acres of malting barley was also in the solution.
Seasonal
labor was hired in the March-May, June-August, and September-November
time periods with the largest requirement being 447 hours hired in
the September-November.period.
There were 510 head of 810 pound
steers fattened over the winter and 60 sows were farrowed in the
.42
60 sow confinement facility.
Situation three produced a return over variable cost of $62,589.
The reduction in return over variable cost resulted from the exclusion
of seasonal labor.
Returns to labor and management fell to -$11,562.
A full time man was hired to supplement the owner-operator's labor.
Lack of seasonal labor caused sugar beet acreage to be reduced to
77 acres and malting barley to be increased to 43 acres when compared
to situation two.
Situation four, compared to situation two, showed a reduction
in return over variable cost from $69,843 to $64,902.
management return was reduced to -$9,250.
Labor and
These reductions were due
to elimination of pigs because of the restriction of only one live­
stock activity.
No pigs in the solution required $8,417 of operating
capital to be borrowed in the December-February period.
Situation five had the lowest return over variable cost of any
\
of the runs, $57,725.
Returns to labor and management were -$16,427.
Lack of the opportunity to hire seasonal labor and the restriction
of only one livestock activity caused many changes in the optimal
solution.
The more labor intensive crops of sugar beets, corn silage,
and alfalfa were reduced to acreages of 60, 73, and 55 respectively.
Malting barley acreage increased to 92 acres.
Only 63 head of 850
pound heifers were fattened for slaughter.
Malting barley increased by 100 acres in situation six when sugar
43
beets.were not allowed in the solution.
Return over variable cost
decreased from $69,843 in situation two to $65,260.
and management was -$8,892.
Return to labor
Less seasonal labor was hired in the
three time periods with the- largest amount of 128 hours hired in the
September-November period.
Other crops and livestock alternatives
did not change.
The unavailability of seasonal labor caused.a reduction in re­
turn over variable cost from $65,620 in situation six to $61,622 in
situation seven.
Return to labor and management was -$14,706.
optimal combination of activities did not change.
Five hundred ten
head of 810 pound steers were fattened over the winter.
of labor caused a
The
A shortage
switch in the swine alternative to farrowing out
fifteen sows and selling the pigs at weaning time.
There was $6,345
of operating capital borrowed in the December-February period.
When only one livestock alternative is allowed, net farm income
decreased from $61,622 to $60,504 (comparison of situation seven to
situation eight).
Return to labor and management was -$13,648.
The
optimal crop and livestock alternatives did not change except the
swine enterprise was eliminated.
There was $8,417 of operating capi­
tal borrowed in the December-February period.
Projected 1977-78 prices were higher than 1976-77.
Prices
were not scaled up proportionately, but were based on projections of
what the prices would be.
In some instances, prices moved up
44
proportionately but in others they moved in opposite directions.
Under 1977-78 prices, return over variable cost increased from
$69,843 to $100,341 (situation two compared to situation nine).
Returns to labor and management increased to $24,643.
The optimal
solution consisted of the following crops and their acreages:
beets, 100; corn silage, 100;
irrigated pasture, 20.
sugar
malting barley, 20; alfalfa , 60; and
Seasonal hired labor was hired in the March-
May, June-August, and September-November period with the largest
requirement being 413 hours hired in the September-November period.
There were 510 head of 765 pound steers fattened during the summer
and 510 head of 850 pound heifers fattened over the winter.
Thirty
sows were farrowed and the pigs sold at weaning time.
The lack of seasonal hired labor resulted in a reduction in
return over variable cost to $93,437 in situation ten.
labor and management was $17,739.
Return to
A full time hired man was employed.
Sugar beet acreage was 88 acres and malting barley was 32 acres.
Other
optimal crop acreages and livestock alternatives remained the same.
The only borrowed operating capital required was in the June-August
period was was $2,185.
Situation eleven compared to situation nine shows a reduction in ■
return over variable cost to $97,395.
was reduced to $23,243.
Return to labor and management
This reduction was due to allowing only
one livestock alternative in the solution.
Optimal crop and livestock
Z
45
alternatives remained the same except there were no pigs in the
solution.
This also reduced the amount of seasonal labor required.
There were 97 hours of labor hired in the June-August period and 369
hours hired in the September-November period.
There was $4,174 of
borrowed capital in the December-February period and $4,671 in the
June-August period.
A restriction of only one livestock alternative and no seasonal
hired labor available reduced net farm income in situation twelve to
$90,264.
Return to labor and management was reduced to $16,112.
full time hired man was employed.
A
Sugar beet acreage was reduced to
90 acres and malting barley was increased to 30 acres.
and livestock alternatives remained the same.
Other crop
There was $4,174 of
borrowed capital in the December-February period and $3,965 in the
June-August period.
Malting barley increased to 120 acres when sugar beets were not
allowed in the solution in situation thirteen.
Return over variable
cost decreased from $100,341 in situation nine to $96,028 in situation
thirteen.
Returns to labor and management decreased to $20,330.
Less
seasonal labor was hired with the largest quarterly amount being 100
hours hired in the June-August period.
Other optimal crop and live­
stock alternatives remained,the same.
The lack of seasonal labor caused a reduction in net farm income
from $96,028 in situation thirteen to $89,612 in situation fourteen.
46
Return to labor and management decreased to $15,460.
Malting barley
and alfalfa acreages were reduced to 113 and 24 acres respectively.
This reduction was offset by 33 acres of corn for grain.
There was
$4,174 of borrowed operating capital in the December-February period
and $13,681 in the June-August period.
The summer fattening of 510
head of 765 pound steers and winter fattening of 510 head of 850
pound heifers remained the same.
No swine alternatives were in the
optimal solution.
When only one livestock alternative was allowed, net farm income
decreased from $96,028 in situation thirteen to $93,095 in situation
fifteen.
Return to labor and management decreased to $18,943.
The
optimal crop and livestock alternatives did not change except the
swine enterprise was eliminated.
There was $4,174 of borrowed
operating capital in the December-February period and $5,628 in
the June-August period.
EFFECT OF MAJOR ASSUMPTIONS
The exclusion of seasonal labor reduced return over variable
cost by about $7,000 except in the case of resource situation seven,
where the reduction was only about $4,000.
This restriction caused .
a shift toward less labor intensive enterprises.
The restriction to only one livestock alternative in the solution
reduced return over variable cost by about $5,000 when 1976-77
47
prices were used.
The restriction reduced return over variable cost
by about $3,000 when projected 1977-78 prices were used.
This
smaller reduction in return over variable cost was because the net
income derived from the weaner pig enterprise that was eliminated
when using 1977-78 prices was less than the net income derived from
the 60 sow enterprise that was eliminated using 1976-77 prices.
The restriction of no sugar beet acreage reduced return over
variable cost about $4,500.
for sugar beets.
Malting barley was usually substituted
The lack of seasonal labor in resource situations
seven and fourteen caused other shifts.
In resource situation seven
the 60 sow enterprise was replaced by the weaner pig enterprise.
■Return over variable cost was reduced by only $1,000.
In resource
situation fourteen, return over variable cost was reduced by $3,800.
A full time hired man was not hired, so there was a shift to less
labor intensive crops during the critical labor periods.
A typical sugar beet farm in the Yellowstone Valley of southcentral Montana similar to the model farm would grow 100 acres of sugar
beets, 100 acres of corn silage, 60 acres of spring wheat, and 40
acres of alfalfa.
silage.
Some farms would substitute corn grain for corn
Feed barley, malting barley, or pinto beans might be
substituted for spring wheat. A few farms would have 100 acres of
irrigated pasture.
The typical sugar beet farm does not differ very much in crop
48
activities from the optimal solution for the model farm.
solutions for the model farm
The optimal
suggests that alfalfa and malting
barley would be superior to the spring wheat typically grown.
The
low projected price received for spring wheat would explain this
difference and if the projected price relationships materialize, the
indicated shifts will probably occur.
The low livestock prices for the last few years have reduced the
number of farmers wintering livestock in the Yellowstone Valley.
The
optimal solution showed that a livestock fattening enterprise offers
a valid alternative for producers to increase net farm income.
Operator preference in the past has favored wintering calves; however,
the optimal solution favors a fattening enterprise.
Lack of avail­
ability of yearling cattle may have influenced operator preferences
toward wintering calves.
A fattening enterprise during the summer would also increase net
farm income.
This alternative would allow the fixed costs of feeding
livestock to be spread over more animals, thereby reducing total
fattening costs per animal.
The addition of a 60 sow confinement facility increased net .
farm income almost $5,000 and the 30 sow weaning enterprise increased
net farm income almost $3,000.
The swine enterprise either eliminated
or greatly reduced the amount of operating capital that was borrowed.
The availability of full time or seasonal labor would be an
49
important, factor to consider before the summer fattening or swine
enterprises would be considered viable alternatives.
Operator
preference may also reduce the desirability of these enterprises.
Certainly, the additions of these livestock enterprises would
require more management input as measured by both quantity and
quality.
Earlier in this paper it was estimated that the closure of the
Billings plant would cause a reduction of $11,000,000 in gross
income for sugar beet growers.
This reduction would be offset by
growing substitute crops on the acres that were formerly used for
growing sugar beets.
The returns over variable cost for sugar beet
growers in the Yellowstone Valley will be reduced by approximately
$969,000.
20
This is much less than the reduction in gross income
that was stated earlier.
This figure was derived by multiplying the number of acres of
sugar beets grown in the Yellowstone Valley in 1976 (21,534 acres)
times the amount returns over variable cost was reduced when 100
acres of sugar beets were not grown ($4,500).
Chapter 4
SUMMARY
The primary objective of this study was to determine production
adjustments which would maximize return over variable cost for
irrigated farmers in the Billings area in the event the production
of sugar beets is discontinued.
- The basic data was obtained from a panel of Billings area
farmers selected by Yellowstone County Extension Agent, John Ranney.
The farmers were owners of typical irrigated farms and were located
in different parts of the study area.
The farmers were judged to
be above average managers, and provided the general characteristics
of a representative farm and enterprise input-output information.
The farm modeled in this study consisted of 320 acres.
acres of the farm were in fences, ditches, and farmstead.
Twenty
Soil types
and drainage problems limited the use of 20 acres to irrigated
pasture.
The remaining 280 acres were suitable for any of the crop
alternatives grown in the study area.
• A limit of $80,000 of operating capital could be borrowed in any
quarter.
The year was divided into quarters as follows:
February, March-May, June-August, and September-November.
DecemberTotal
capital requirements might be greater than $80,000 per quarter.
Since crops are marketed at different times of the year, not all
51
the capital required must be borrowed, A long term real estate debt
of $100,000 was assumed for the model farm.
The majority of the labor for the farm was furnished by the
owner-operator.
Research done at Montana State University was used
to arrive at days available for field work.
Days that were unsuitable
for the crop enterprises were not excluded from use by the livestock
enterprises.
If livestock alternatives were more profitable than
crop alternatives, labor available on days fit for field work could
be used for the livestock enterprises.
situations were considered.
Different labor resource
In addition to owner-operator labor,
some situations allowed hiring no additional labor, some allowed
hiring seasonal labor, and some situations allowed hiring a. full time
man only.
The model farm had a full complement of machinery available for
the crop alternatives;
it was assumed that no additional crop
equipment would be purchased.
This assumption was made to allow
uniform comparison between all crops.
Fixed costs for crop alternatives were not included in the
budgets.
Fixed costs are costs that are committed and continue
whether production takes place or not.
Since this study is con­
cerned with production adjustments a farmer might make to maximize
return over variable cost, only those costs that were not committed
are included in the budgets.
52
The case farm -had 3200 square feet of shop and machine storage,
and 4000 bushels of grain storage.
There was machinery and feedlot
capacity for 170 head of cattle on the farm.
Additional feedlot
capacity could be acquired; the costs of construction were included
in the budgets.
No facilities were available for pig enterprises
on the farm, thus annual ownership costs of buildings and equipment
were included in the swine budgets.
Nine crop alternatives, eighteen cattle alternatives, and five
swine alternatives were considered.
Data for the alternatives considered were obtained from various
producers across Montana.
area.
Crop data was obtained from the study
Budget data for wintering calves in the feedlot and raising
yearlings on irrigated pasture were also obtained in the Billings
area.
Data on feeding yearlings to slaughter weight were obtained
in the Billings and Forsyth area.
Data for raising ten sows and
farrowing once a year were obtained in the Plevna area of south­
eastern Montana.
Data on weaner pigs were obtained from the Whitehall
area.. Information on a 60 sow confinement operation were obtained
from Illinois enterprise cost studies and adjusted to Montana
conditions.
Cultural practices and costs for a cow-calf enterprise
were obtained from the Bozeman and Miles City areas.
The Oklahoma State University Budget Generator was used to
generate enterprise budgets for the crops.
Livestock alternatives
53
were hand calculated following a similar format as used for the
crops.
Enterprise costs and input-output information were taken
from these enterprise budgets for use in the linear programming
model.
Fifteen resources situations were defined for the model farm.
These situations were generated by different assumptions concerning
labor availability, number of livestock enterprises allowed, and
whether sugar beets were a permissible crop enterprise.
The optimum combination of enterprises derived from the different
resource situations are summarized in Tables 6 and 7.
One hundred
acres of sugar beets, 100 acres of corn silage, 60 acres of alfalfa,
20 acres of malting barley, and 20 acres of irrigated pasture was
the cropping pattern which was most prevalent.
Sugar beets, corn
silage, and alfalfa always appear in the solutions at the maximum
acreage allowed.
Maximum restrictions were placed on these crops
because of machinery limitations, crop rotations, contract acreage
minimums, and local market for some crops.
When these maximum
restrictions were removed, 227 acres of corn silage, 60 acres of
sugar beets, and 13 acres of alfalfa were the optimum enterprise
combination (resource situation one).
The restriction of zero sugar beet acreage reduced return over
variable cost about $4,500..
for sugar beets.
Malting barley was usually substituted
The lack of seasonal labor in resource situations
54
seven and fourteen also caused shifts in the optimum enterprise mix.
In resource situation seven, the 60 sow enterprise was replaced by
the weaner pig enterprise.
by only $1,000.
Return over variable cost was reduced
In resource situation .fourteen, return over variable
cost was reduced by $3,800.
It was not profitable to hire a full time
man, so there was a shift to less labor intensive crops during the
critical labor periods.
At least one livestock alternative appeared in all solutions.
Fattening 810 pound steers entered all solutions except resource
situation five when 1976-77 livestock prices were used.
Fattening
765 pound steers and 850 pound heifers were included in the optimal
solution when 1977-78 prices for livestock were used.
If more than one livestock alternative was allowed, a swine
enterprise usually was in the solution.
The exception to this was
resource situation fourteen when the swine enterprise could not
increase return over variable cost enough to justify a full time
hired man.
With the exception of resource situation seven, a farrow to
finish, 60 sow swine enterprise was included in. the optimal solution
using 1976-77 livestock prices.
Weaner pigs, 30 sow capacity, was
part of the optimal solution using 1977-78 prices.
The restriction of only one livestock alternative in the
solution reduced return over variable cost by about $5,000 when
55
1976-77 prices were used.
The restriction reduced return over
variable cost by $3,000 when 1977-78 projected prices were used.
This
smaller reduction in return over variable cost was because the return
over variable cost derived from the weaner pig enterprise that was
eliminated when using 1977-78 prices was less than the return over
variable cost derived from the 60 sow enterprise that was eliminated
using 1976-77 prices.
The restriction which prohibited seasonal labor reduced return
over variable cost by about $7,000 except in the case of resource
situation seven, where the reduction was only about $4,000.
The return over variable cost and return to labor and management
derived from the different resource situations should be used for
purposes of comparison only. No allowance for machinery replacement
for the crop enterprises has been subtracted from return over variable
cost.
New machinery and investments must continually be made in order
to keep the farm an efficient operating unit.
management have all costs deducted.
Returns to labor and
At the lower level of price
assumptions, these returns were negative.
A high level of technology was used on the model farm.
level of efficiency would not be achieved on many farms.
This
Labor
efficiency probably would not be as high day-in day-out as was used
in the model farm.
Weather and diseases would lower average crop
yields and average daily gains for the livestock alternatives.
56
However, it is felt that a good manager could achieve these levels
of productive efficiency.
Further research could be done using the same enterprise alter- .
natives.
Level of management, efficiency of inputs., 'technology,
yields, and daily gains could be varied to reflect long run averages.
The return over variable cost, figures derived would be lower.
The.
enterprise combinations would probably be the same as those obtained
in this study.
Livestock alternatives would probably be the same,
but fewer numbers would be fed.
The outcome of the optimal livestock
alternatives would be harder to predict.
Efficiency of feed conver­
sion would influende which livestock enterprise would be most
profitable.
BIBLIOGRAPHY
58
BIBLIOGRAPHY
Baumol, William J. , Economic Theory and Operations Analysis,. Engle­
wood Cliffs: Prentice-Hall, Inc., 1972).
Bitney, Harry I., "Constructing the L.P.'Model - How Much Detail?"
Research Report 10, Department of Agricultural Economics,
University of Nebraska, Lincoln, May 1970.
Brant, William L:., "Analysis of the Representative Farm Concept.as a
Tool in Area Supply Response Research and Farm Management
Education." (Unpublished Doctorate Dissertation, Oklahoma
State University, 1967). ■
Cornelius, James C . and Schaefer,Jerry, Enterprise Costs for Wintering
Feeder Calves in Yellowstone County, Circular 1194, Cooperative ;
Extension Service, Montana State University, Bozeman, November
1976.
Edwards', Clark, "Using Discrete Programming", Agricultural Economic
Research, Volume XV, No. 2, April, 1963.
Huffman, Donald C . and Stanton, Lynn A., "Application of Linear
Programming to Individual Farm Planning." American Journal of
Agricultural Economics, Vol. 51 (1969).
Land, A. H.: and Doig,. A. G., "An Automatic Method of Solving Discrete •
Programming Problems," Econometrics, July 1960, Volume 28, Number
.3. •
,
McCorkle,. Chester 0., "Linear Programming as a. Tool in FarmManagement
Analysis." American Journal of Agricultural Economics, 'Vol. 37
(1955). .
Naylor, Thomas H. and Vernon, JOhn M . » .Macroeconomics and Decision
Models of the Firm. (New York: Harcourt; Brace and World, Inc.,
' 1969).
Schaefer, Jerry and Luft, LeRoy D., Enterprise Costs of Irrigated Crops .
in South Central Montana, Bulletin 1151, Cooperative Extension
Service, Montana State University, Bozeman, June 1976. ■
Schaefer, Jerry and Luft, .LeRoy D., Enterprise Costs for Raising Feeder
Pigs in Madison and Jefferson Counties, Circular 1196, Cooperative
59
•Extension Service, Montana State University, Bozeman, February
1977.
Traphagen, F. W., The Sugar Beet in Montana. Bulletin 19, Montana
Experiment Station, Montana State University, Bozeman, Montana,
October 1898.
.Yager, William A. and Greer, R. Clyde. Estimating Days Suitable for
Fieldwork, Research Report 67, Montana Agricultural Experiment
Station, Montana State University, Bozeman, December 1974.
APPENDIX
61
APPENDIX
Linear Programming Matrix
Description of Matrix Activities and Constraints
i
I.
Column
Activities
Description
1
Right hand side constraints
2
Full time hired man
3
Zero, one variable to insure high cost 450 pound steers
gaining 1.75 pounds per day enter before lower cost steers
4
Zero, one variable to insure high cost 420 pound heifers
gaining 1.50 pounds per day enter before lower cost heifers
5
Zero, one varialbe to insure high^cost 810 pound steers
gaining 2.75 pounds per day enter before lower cost steers
6
Zero, one variable to insure high cost 765 pound steers
gaining 2.75 pounds per day enter before lower cost steers
7
Zero, one variable to insure high cost 850 pound heifers
gaining 2.50 pounds per day enter before lower cost heifers
8
Zero, one variable to set upper limit on 60 sow farrowfinish activity
9
Zero, one variable to set upper limit on 90 sow farrowfinish activity
10
Zero, one variable to set upper limit on 30 sow, sell weaner
pigs activity
11
Zero, one variable to set upper limit to feed out weaner pigs
12
Zero, one variable to set upper limit on 10 sow farrowfinish activity
62
Column
Description
13
Zero, one variable to represent 450 pound steers gaining
1.75 pounds per day
14
Zero, one variable to represent 420 pound heifers gaining
1.50 pounds per day
15
Zero, one variable to represent 810 pound steers gaining
2.75 pounds per day
16
Zero, one variable to represent 850 pound steers gaining
a.50 pounds per day
17
Sugar Beets, acres
18
Beans, acres
19
Corn for grain, acres
20
Corn silage, acres
21
Spring wheat, acres
22
Barley for feed, acres
23
Malting Barley, acres
24
Alfalfa Hay, acres
25
Pasture, acres
26
'
Sell corn silage, tons
27
Buy corn silage, tons
28
Sell feed barley, bushels
29
Buy feed barley, bushels
30
Sell alfalfa hay, tons
31
Buy alfalfa hay, tons
32
Sell pasture, AUM's
63
.Column
Description
33
Buy pasture, AUM's
34
Transfer labor available for field work, March-May, hours
35
Transfer labor available for field work, June-August, hours
36
Transfer labor available for field work, September-November,
hours
37
Transfer labor available for field work, December-Tebruary,
hours
38
Hire labor, March-May, hours
39
Hire labor, June-August, hours
40
Hire labor, September-November, hours
41
Transfer operating capital, December-February, dollars
42
Transfer operating capital, March-May, dollars
43
Transfer operating capital, June-August, dollars
44
Transfer operating capital, September-November, dollars
45
Feed steers, 450 pounds, 1.75 pound daily gain, high cost
46
Feed steers, 450 pounds, 1.75 pound daily gain, lower cost
47
Buy 450 pound steers
48
Sell 765 pound steers
49
Feed heifers, 420 pounds, 1.5 pound daily gain, high cost
50
Feed heifers,, 420 pounds, 1.5 pound daily gain, lower cost
51
Buy 420 pound heifers
52
Sell 690 pound heifers'
53
Feed steers, 810 pounds, 2.75 pounds daily gain, high cost
64
Column
Description
54
■Feed steers, 810 pounds, 2.75 pounds daily gain, lower cost
55
Buy 810 pound steers
56
Sell 1110 pound steers
57
Feed steers, 765 pounds, 2.75 pounds daily gain, high cost
58
Feed steers, 765 pounds, 2.75 pounds daily gain, lower cost
59
Buy 765 pound steers
60
Sell 1065 pound steers
61
cost
Feed heifers, 850 pounds, 2.5 pounds daily gain. high ■
62
Feed heifers, 850 pounds, 2.5 pounds daily gain. lower cost
63
Buy 850 pound heifers, head
64
Sell 1050 pound heifers, head
65
Cow-Calf, head
66
Sell 450 pound steers, head
67
Sell 420 pound heifers, head
68
Sell cull cows, head
69
Farrow - Finish (60 sows), head
70
Sell 220 pound market hogs, head
71
Farrow - Finish (90 sows), head
72
Farrow -,Wean (30 sow), 2-litter system
73
Sell weaner pigs, head
74
75
.
Feed out weaners, head
Buy weaners, head
-
■
65
Column
Description
76
Farrow - Finish (10 sows), head
77
Sell 220 pound markeg hogs, head
II.
Row
Constraints
Description
1
Objective function
2
Low cost 450 pound steers gaining 1.75 pounds positive,
equals I
3
equals I, high cost 450 pound steers gaining 1.75 pounds
equals maximum (170)
4
Low cost 420 pound heifers gaining 1.5 positive, X^ equals I
5
X^ equals I, high cost 420 pound heifers gaining 1.5 pounds
equals maximum (170)
6
Low cost 810 pound steers gaining 1.75 pounds positive,
Xg equals I '
7
' Xg equals I, high cost 810 pound steers gaining 1.75 pounds
equals maximum (170)
8
Low cost 765 pound steers gaining 2.75 pounds positive, X^
equals I
9
Xg equals I, high cost 765 pound steers gaining 2.75 pounds
equals maximum (170)
10
Low cost 850 pound heifers gaining 2.50 pounds positive, X^
equals I
11
Xy equals I, high cost 850 pound, heifers gaining 2.50 pounds
equals maximum (170)
12
Maximum number of sows, 60 sow farrow-finish head
66
Row
Description
13
Maximum number of sows, 90 swos farrow-finish, head
14
Maximum number of sows, 30 sows, sell weaner pigs, head
15
Maximum number of feeder pigs, head
16
Maximum number of sows, 10 sow, 1-litter system
17
Total land, acres
18
Allow I winter cattle feeding activity
19
Allow I hog activity
20
Total December-February, labor balance, hours
21
Total March-May, labor balance, hours
22
Total June-August, labor balance, hours
23
Total September-November, labor balance, hours
24
Labor available for field work, March-May, hours
25
Maximum labor transfer, March-May, hours
26
Labor available for field work, June-August, hours
27
Maximum labor transfer, June-August, hours
28
Labor available for field work, September-November, hours
29
Labor available for field work, September-November, hours
30
Operating capital transfer, December-February, dollars
31
Operating capital transfer, March-May, dollars
32
Operating capital transfer, June-August, dollars
33
Operating capital transfer, September-November, dollars
67
Row
.34
Description
Operating capital balance, December-February, dollars
35
Operating capital balance, March-May, dollars
36
Operating capital balance, June-August, dollars
37
Operating capital balance, September-November, dollars
38
Maximum sugar beets, acres
39
Minimum sugar beets, acres
40
Corn silage balance, tons
41
Feed barley balance, bushels
42
Alfalfa hay balance, tons
43
Maximum alfalfa, acres
44
Irrigated pasture balance, AUM1s
45
Minimum irrigated pasture, acres
46
450 pound steer balance, head
47
420 pound heifer balance, head
48
765 pound steer balance, head
49
690 .pound heifer balance, head
50
810 pound steer balance, head
51
1110 pound steer balance, head
52
1065 pound steer balance, head
53
850 pound heifer balance, head
54
1050 pound heifer balance, head
. 55
Cull cow balance, head.
68
Row
Description
56
Market hog balance, head
57
Meaner pig balance, head
58
Meaner pig balance, head
59
Market hog balance, head
60
Low cost 450 pound steers gaining 1.75 pounds positive,
equals I
61 '
Low cost 420 pound heifers gaining 1.5 pounds positive,
equals I
62 .
Low cost 810 pound steers gaining 1.75 pounds positive,
X^ equals I
63
Low cost 850 pound heifers gaining 2.50 pounds positive, X^
equals I
64
Maximum Minter livestock feeding activities
V
Table A.I
Linear Programming Matrix
C01U»N
»**t************t**»****s******t**t***********e*****»********»**************************
QPU *
i
^
3
4
'
6
7
P
Q
tt**tttt*******tt***************************************************** ******************
I*
?*
3*
4
*
«- t
6 *
7*
8 *
9 ♦
IC *
11 *
I2 *
I3 *
14 *
15 *
16 *
17 *
18 ♦
19 ♦
ZC *
21 ♦
22 ♦
23 *
*
*
*
♦
#
$
$
*
. 0 0
555.CC
. on
622.00
. 00
69 I .Cu
80000.CU
aOOOO.GO
ROOO 0 . OU
.00
.00
.00
.00
.00
.00
. 0 0
. 0 0
. 0 0
. 0 0
. 0 0
O
O
*
25
26
27
ZR
29
30
31
32
.00
.OC
.00
.CO I2390.CO 24300.00
5000.00
.00
.00
.00
.00
.00
.03
.00 lire.00
.00
.00
.00
.00
.00
.00 -340.00
.00
170.00
. 00
.00
.00
.00
.03
.00
.CO
.CO
.00
.00
.00
.00
.00
•OC -340.00
.00
170.00
.00
.03
.00
.00
.39
.uo
.00
.CO -340.00
.GO
.00
.00
.00
.00
.00
.00
.00 170.00
.00
.00
.00
.00
.00
. OO
.00 -340.90
. OO
.00
.00
.00
170.00
.03
.00
.00
.00
.00
.00
.OC
.00
.00
.00
.00 -340.00
.00
.00
.00
.CO
.00
.00 —60.00
.00
.00
.00
.00
.00
.00
.00 -90. 00
.00
.00
.00
.00
.00
.JO
.00
.00
.OO
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.03
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
1.00
1.00
.00
.00
. 00
.00
.00
.00
.00
-750.00
.00
.00
.00
.00
. 0 0
-750.00
. 00
. 0 0
. 0 0
.0 0
.00
.00
. 0 0
. 0 0
. 0 0
-750.00
.00
.00
. 0 0
.00
.03
-750.00
.00
.ou
.CO
. 0 0
.00
.00
.00
. OO
.00
.OU
.00
.00
.00
.0 0
-555.00
.00
.00
.00
.00
.00
.00
. 0 0
.CO
.00
.00
. 0 0
.00
. 0 0
.00
. 0 0
. 0 0
.00
-622.03
.00
. 0 0
.00
. 00
. 0 0
.00
.00
.00
.00
. 0 0
.00
.00
.00
-697.00
.00
.00
.00
. 0 0
.00
.00
. 0 0
. 0 0
.00
.00
O
O
24
.CO
. OO
.CO
• 0u
.OU
.OC
.CO
.00
.00
•Ou
.00
.00
.00
.CO
. CO
.CO
300.CO
1.00
I.OC
750.00
750.00
750.00
75 0. OU
. 0 0
. 0 0
. 0 0
. 0 0
****************************************************************************************
O'
VO
Table A.I (Continued)
Linear Programming Matrix
COLUMN
****************** t t t * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
)
d
7
6
4
5
3
2
OOVi *
t * t* * * t ****** ***************************************************************************
i
3*
34 *
35 *
3A *
J7 *
3B *
39 »
40 *
41 *
42 «
43 *
44 *
45 *
46 *
47 «
4b *
49 *
SC *
SI *
40000.00
.00
.CO
. 00
.CC
320.00
60.00
.00
.00
.00
320.00
.00
.03
. 00
.00
.00
.CO
.00
.03
.00
.03
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.CO
.30
.00
.00
.00
. 36
.00
.00
.00
.00
.00
.00
.CO
.00
.00
.00
.CO
.CO
.CO
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.CO
.00
.OO
.00
. 00
. uO
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.on
.00
.00
.00
.CC
.00
. 30
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.03
.00
.00
.00
. 03
.00
.00
.00
.00
.00
.00
.00
.00
.00
. 00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.CO
.00
.00
.CO
.00
.OC
.00
.00
.00
.00
.00
.00
.00
.CO
.CO
.CO
.00
.00
.CO
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.CL
.00
. 0O
.00
.00
.00
.00
.CO
.00
.00
.00
.00
.00
.00
.00
.Cu
52 *
.00
.00
.00
.00
.00
.0 0
.00
.00
S3 *
.00
.00
.00
.00
.00
.00
.OC
.00
. OC
54 *
.00
.00
.00
.00
.00
.CO
.03
SA *
.00
.00
.00
.00
.00
.00
. 30
.00
.CJ
.60
.PO
56 *
.00
.00
.00
.00
.00
.00
.00
.CO
. Of
57 *
.00
.00
.00
.00
.00
.00
.00
.CO
. CO
5P «
.CO
.00
.00
.00
.00
.00
.00
.00
.00
59 *
.00
.00
.00
.00
.00
.CO
.03
.00
.OC
60 *
.00
.00
.00
.CO
.00
.CO
.00
.00
.PO
61 ♦
.00
.OO
.00
.00
.CO
.00
.00
.03
.CO
62 *
.OO
.00
.00
.00
.00
.00
.00
.00
.CO
63 *
1.00
1.00
.00
.00
.00
.00
.00
.00
I. CO
64 *
*»*»***$*****»**»*♦#**********************************•*♦**********♦***********♦********
. CO
.CO
Table A.I (Continued)
Linear Programming Matrix
CCLUVN
$****$$$<.«:****$*$*$*$*$$**$$**$*$ *************$******** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
Ctju *
Il
11
I?
13
14
11
16
17
16
♦♦,t««*»tt»«*t**«t**********<»«»************«*******************************************
.00
-209.39 -133.19
.00
.CO
.00
1
.00
.00
. Ou
.00
.CO
.00
.00
.00
.CO
.00
.00
.Cu
2
.00
.CO
.00
.00
.00
.OC
.00
.00
. 00
3
.00
.00
.CO
.CO
.00
.00
.00
.GO
. OC
4
.00
.00
.CO
.CO
.00
.00
.Ou
. 30
• 00
5
.00
.CO
.OC
.00
.CO
.00
.Ju
.00
.Cl
.30
.CO
.CO
.CO
.39
.03
.p-:
.CC
7
. CC
.00
.00
.00
.CO
.00
.CO
.CO
.00
.CO
f
.CO
.CO
.00
.00
. 00
.CO
.00
.03
. 00
.00
.00
.00
.00
.00
.03
.CO
.00
.00
IU
.CO
.00
.00
.CO
.00
.00
.30
.00
.L:
II
.00
.00
.00
.OC
. OO
.00
.00
.00
. CO
12
.00
.00
. OO
.CO
.00
.00
.33
.00
. 00
13
.00
.00
.00
.00
.00
.00
.00
.00
-30.OU
I*
.00
. CO
.00
.00
.CO
.OC
. 00
.4.00
.Ur
ItJ
.09
.CO
.CO
.00
.CO
.CO
.00
— IC 00
. CU
16
1.00
.CO
!.CO
.00
. OO
.CO
.JJ
.00
. UC
17
1.00
.CO
1.00
1.00
.00
.00
1.00
.00
. CO
Iti
.CO
.00
.00
. OO
.CO
.00
I.CO
1.00
I. CO
11
.00
.00
.00
.CO
.00
.OC
.OC
.00
.CO
2C
.00
.00
.00
.00
.00
.00
.00
.00
. 00
21
.00
.00
.CO
.00
.00
.00
.g o
.CO
.03
22
.00
.00
.00
.00
.CO
.Cu
. Oi
. 00
. CO
23
.CO
.00
2.80
.00
2.36
.CC
.CC
.00
24
.CO
.00
.00
.00
.CO
.00
.CC
.0/
.30
.Cu
26
2.06
.00
.00
2.25
.00
.CO
.Ou
24
.00
.00
.00
.00
.CO
.00
.00
.OG
.00
.OC
.Cu
27
2.77
6.37
.00
.CO
.00
.00
.CO
.00
.00
21
.00
.00
.00
.00
. 00
.CO
.03
.00
. 90
29
.00
.00
.00
.CO
.00
.CO
.00
.00
. 00
30
.CO
.CO
.00
.00
.00
.00
.00
.00
. 00
U
.CO
.00
.00
.00
.00
.00
.00
.CO
.00
32
****************************************************************************************
H
Table A.I (Continued)
Linear Programming Matrix
COLUPN
ttttt*ti*t***i*************»***************************************** **************
ci) W e
I)
11
11
I3
14
15
16
17
11
tttttf^it^******************************************************************************
.00
.00
.00
.00
.00
31
34
. CO
.CO
.00
Bi
.00
.00
36
37
3 '■
JS
-tf'
4I
42
43
44
45
46
47
4"»
.CO
.CC
.so
.CO
. 00
. Oo
. 0O
.00
.CO
. OO
.OC
.0 '
.OC
.CO
.OC
.03
.00
.00
.Cl
.00
.00
.00
.00
.CO
.CO
.00
.00
.Cu
.00
.03
.00
.CO
.00
.00
. 1)11
.00
.00
.00
.00
.UC
.00
.00
.OC
.Cu
.00
.CO
SO
Jl
52
53
54
55
«6
07
SP
59
60
61
62
63
64
.00
.OC
.CO
.00
.00
.00
.00
.00
.c:
. PO
. Co
. Co
. OC
. 00
.03
.00
.00
.03
.00
.00
.00
.OC
.CO
.00
. 00
.00
.00
.00
.CJ
. or
.CO
.CO
.Go
.CO
. Cu
.CO
. CO
• CO
.CO
I. Ov
.00
.00
.00
.00
.00
.00
. OC
.CO
. CO
.00
.00
.00
.00
.00
.00
.00
.00
. OO
.CO
.00
.00
.OC
.00
.00
.OC
.CO
.00
.00
.00
.00
.00
.03
.00
.00
.CO
.00
.00
. 00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.Ou
.00
.00
.00
.CO
.CO
.00
.00
.00
.00
-170.00
.00
.00
.00
.00
.00
-170.00
.00
.30
.00
.00
-170.00
.00
.00
1.00
.00
.00
.00
.00
.03
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
. 00
-170.00
1.00
1.00
1.00
1.00
.00
.03
.00
1.00
.00
.OP
.CO
.OC
.00
.03
.00
.00
.09
.00
.00
.00
.00
.00
.CO
.00
.00
.CO
.00
.00
.00
.00
80.04
3d.78
31.81
!.CO
33.85
13.43
19.54
.03
1.00
.00
.00
.00
.00
.00
.00
.00
. OO
. 00
.00
.00
.00
.00
.03
.00
.00
.OU
.00
.00
.00
.00
.CO
.00
.03
.00
.CO
.00
.CC
.00
.00
.00
.00
.CO
.CO
.00
.00
.00
.00
.00
.Oo
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.no
.00
.00
****************************************************************************************
M
Table A.I (Continued)
Linear Programming Matrix
COLURN
*«»*»* * * t - s * * * * * * * * » * * * » * * * * * * * * * * * » » * * * » * * * * * * * * # * * * « * * * * * * * * * * * * * * * * * * * » * * * * * * * » * * * * » * »
11
20
21
22
23
24
25
26
27
or<v *
$***$$ « * * i * * t * * * * « * * # * » * * * * * * * * » * * » * » » » * * * * * » * » * * * * * * * * * * * * * * * * * * * » * » R * * * * * * * » * * * * * * » « » *
-13.00
22.00
61.55
64.lt -153.81
55.63
98.41 -85.56
-132.51
1 *
.00
. CO
.CO
.00
.00
.CO
.00
.00
.OC
2
.00
.00
.00
.00
.CO
.00
.00
.00
. Gt
3
.00
.00
.00
.00
.00
.CO
.00
.00
. CO
4
.00
.00
.00
.00
. 00
.CO
.00
.01
. Cu
5
.00
.00
.00
.00
.00
.CO
.CO
.CO
.CO
b
.00
.00
.CO
.00
.00
.CO
.O"
.00
7
. CO
.00
.00
.00
.00
.09
F
.CO
.00
. 00
.00
.CO
.00
.00
.00
.00
.CO
.00
.00
.Cu
y
.CO
.00
.00
.CO
.00
.00
in
.CO
.00
.CO
.00
.00
.00
.00
.00
.CO
.OC
.00
.CC
11
.00
.00
.00
.00
.00
.00
.CO
.00
.OC
12
.00
.00
.00
.00
.00
.00
.UU
.00
. CC
13
.00
.00
.00
.00
.00
.CO
.u0
.00
. 00
14
.00
.00
.00
.00
.00
.CO
.CO
.00
. CC
10
.00
.00
.00
.00
. 00
.On
.CO
.00
. 00
16
.00
1.00
.00
1.00
1.00
1.00
1.00
I. 10
I OU
17
.00
.00
.00
.00
.00
.00
.00
.00
.00
IR
.00
.00
.00
.00
.00
.00
.00
.00
. 01
11
.00
.00
.00
.00
.00
.00
.CO
.00
.CO
20
. OO
.00
.00
.00
.00
.00
.00
.00
.Cu
21
.00
.00
.00
.00
.00
.00
.00
. 00
.00
22
.00
.00
.00
.00
.OC
.00
.00
.00
. CO
23
.28
.00
2.81
.16
.00
2.61
1.70
2.31
24
I. 7C
.00
.00
.00
.00
.00
.30
.00
.00
. PO
25
26
27
28
29
30
31
32
I. 00
.00
1.00
.00
I.72
1.72
1.72
4.56
.00
.00
. OO
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.CO
.on
.GO
.CO
.00
.00
.00
.00
.00
3.56
.00
4.27
.CO
.00
.00
.20
.00
.11
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
**********«****»«*♦***»****♦***♦»*****«#******»»•*•*«»************»*********************
OJ
Table A.I (Continued)
Linear Programming Matrix
CQLUvN
****************************************************************************************
PCU *
Vl
20
11
22
23
24
25
26
27
**»»*»»«*«$»*«*♦***********♦*<■*******»»****»***»*******♦***♦****************************
.00
.00
.00
OO
.CO
CO
.00
.00
.Cv
33
.00
.00
.00
.00
.00
.00
.01
.00
34
.00
32.69
14.30
.00
.CO
36.64
40.53
44.12
64.53
59. 76
J125.84
27.50
8.90
4.67
.00
h.b7
.CO
10.94
S. 17
36
11.02
22.00
-13.00
16.65
13.33
16.63
Io.65
23.02
5 3.4c
37
.On
.00
.00
.00
.00
.00
.00
.CO
.OC
3.00
.CO
.CO
.CO
.uC
•UQ
.00
.00
.Cj
i r1.00
.CO
.00
-1.00
.00
.00
.00
.50 -20.00
tO
-8C.00
.00
.00
.00
.00
.00
.00
.Ov
.Cl
4I
-5.00
.00
.00
.00
.00
.00
.00
.03
.03
42
.00
.OO
.00
.00
1.00
.00
.00
.00
.00
t'j
.00
-6.00
.00
.00
.00
.00
.00
.OC
.Co
44
.00
.00
1.00
.00
.00
.01
.OC
44
.OC
■ .CO
.00
.00
.00
.00
.OO
.00
.00
.00
.Cu
46
.00
.00
.00
.00
.00
.00
.3u
.00
.00
47
.00
.00
.00
.00
.00
.00
.CU
.00
.CO
4P
.00
.00
.CO
.ou
.00
.00
.CO
.00
.CO
49
.00
.00
.00
.CO
.00
.00
.00
.00
.CO
50
.00
.OC
.00
.00
.CO
.00
.00
.00
.Cu
6I
.00
.00
.00
.00
.00
.00
.CO
.00
.CC
52
.00
.CO
.00
.00
.00
.00
.00
.00
.00
53
.00
.00
.CO
.CO
.OO
.00
.OC
.00
.CO
54
.00
.00
.00
.00
.00
.UO
.JO
.00
.30
5 1.00
.00
.00
.00
.OO
.00
.10
.00
.Ou
34
.00
.00
.00
.CO
.60
.00
.00
.00
.OC
57
.00
.00
.00
.00
.00
.00
.IC
.00
.Cu
56
.00
.00
.00
.00
.00
.00
.00
.00
40
.00
.OO
.00
.00
.00
.00
.00
.CC
.00
.00
60
.00
.00
.00
.00
.00
.00
.00
.30
.CO
61
.00
.00
.00
.00
.CO
.CO
.00
.00
.00
62
.00
.00
.00
.00
.00
.00
.00
.00
.Ou
63
.W
OO
00
OO
.00
.00
O
** ♦
•.V
U
•
VU
•
VV
•.CO
VIv
•
v
•
VI
•.W
v
WWW
•V
IV
.00
.00
00
64
****** S*** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * • * * * * • • * • * • * * • * • * * * * * * * * * *
Table A.I (Continued)
Linear Programming Matrix
CULUlN
****************************************************************************************
O
O
O
O
3.
36
34
35
32
33
31
29
2H
=0 W *
*****»************$»*»»>*****$**»»****»******»*»**»*****************♦***********♦*******
.00
.00
.00
-e.oo
8.30
65.C-O
I.32 -rC.OC
;»
-I. PC
.00
.00
.00
.03
.CO
.CO
.oo
.00
.CO
’ *
.00
.00
.00
.00
.00
.00
.VU
.03
.V
.v
3 *
.00
.00
.00
.00
.CO
.00
.00
.OC
4 *
.LU
.00
.00
.00
.00
.CO
.00
.CC
.OC
.CO
5 *
.00
.00
.00
.CO
.00
.CC
.CO
.00
. 05
fr 4
.00
.00
.00
.00
.00
.00
.Jv
.OC
7 t
.00
.00
.00
.00
.00
.00
.00
.00
P »
.00
.00
.00
.00
.00
.CC
.00
.00
G »
. Cu
.00
.00
.03
.00
.00
.00
.00
.00
.UO
IC *
.OC
.00
.00
. 30
.00
.00
.Ou
.3 0
• Cu
I I 4
.00
.00
.00
.00
.00
.00
. 50
.00
. Ou
LI »
.00
.00
.00
.00
. OC
.CC
.00
.Cu
.Ou
13 *
.00
.00
.00
.00
.00
.00
. 0 0
.00
. CC
I4
»
.00
. OO
.00
.00
.00
.00
.00'
.00
. C
O
Ic *
.00
.00
.00
. OO
.00
.UU
.uo
. n
16 *
.03
.00
.00
. OO
.CC
.CO
.00
1 7
»
.00
. UU
.00
.00
.00
.00
.00
.00
.OC
.uo
.CO
Ifi *
.OC
. 0 0
.CO
. 0 0
.00
.CO
. OC
.CO
. 00
19 4
. 0 0
.00
. 0 0
. 0 0
.30
.CO
.CU
.CO
.03
20 *
. 0 0
1 . 0 0
. 0 0
. 0 0
. 0 0
. 0 0
.Ov
. 0 0
.00
2I 4
.00
1 . 0 0
. 0 0
. 0 0
.00
.OC
.CO
.00
. CO
2? 4
1 . 0 0
. 0 0
.0 0
.00
.00
23 4
. uC
.00
.00
.c :
. 0 0
.CO
- 1.00
.00
.00
.05
.• j
.03
24 4
1 . 0 0
. 0 0
. 0 0
. 0 0
. 0 0
.CO
24 4
. 0 0
.C C
.C. 00
- 1 . 0 0
. 00
. 0 0
. 0 0
.00
.CO
.00
.Ou
26 4
. 0 0
1 . 0 0
. 0 0
. 0 0
.00
. 0 0
.00
.00
27 4
.CO
.00
.00
.00
- 1.00
.00
.00
.00
.00
. 0 0
28 4
I.OC
.00
.00
.00
.00
.00
.00
.00
.00
29 *
.00
.00
.00
.00
.00
.00
.00
.CO
.CO
3C *
.00
.00
. 0 0
.00
.00
.00
.00
.00
31 4
.OC
.00
.00
.00
.00
.00
.00
.00
.OU
32 ♦
^t************************************************************************* *************
Table A.I (Continued)
Linear Programming Matrix
COLUMN
*t*t«$$$****»************$$$****$**t*******t*$*$$**$*$*********$***********$*$*****$$*$*
OQW *
31
32
29
30
34
35
33
36
******#<=t^*<:t:<:**********):»*t*»*t**t**********^***1t***»**t:*t**************4»***********»*
. CO
.UC
.CO
.00
si
.00
.CO
.00
.00
.00
)4 *
. c:
. 00
. 'C
.OC
.CO
.00
.CO
.00
.00
eUL
3b ♦
.00
. JO
.00
.00
.00
b. 30
.00
.CO
.CC
.00
-51.00
36 *
.UC
-o . JO
.00
.00
.00
.00
-I. PC
.37 *
.33
1.92
6 5.00
.00
.00
.03
.00
.00
.Cr
. 30
3P *
.00
.CO
.CO
.CO
.00
.CO
.00
34 $
.CC
.00
. OP
. 00
.00
.00
.00
.OC
.00
.00
4 I.' *
.CO
.30
.OC
.00
.00
.CO
.00
.00
4I *
I. Cj
- 1 .UO
.00
.CO
.00
.00
.00
.00
-CU
42 *
. CO
.00
1.03
- I . OC
.CO
.00
.00
.00
.00
43 ♦
. CO
.00
.00
.CC
. OC
.00
.00
.00
.00
44 *
.00
. Ct
.00
.00
I. OC
-1. 0 0
.00
.00
.00
• UU
.00
4b $
.00
.30
.00
.00
.00
.00
.CO
.on
46 $
. CO
.09
.00
.03
. 00
. CO
.00
.00
47 $
. CG
.00
.00
.00
.00
.00
.00
.00
.00
4S *
. Cu
.09
. 00
.00
.00
.CO
. 03
.00
.00
.CO
44 t
.00
.00
.00
.00
.00
.00
.CO
.00
. PO
50 $
.OC
.03
.00
.00
.CO
.00
.00
.00
.00
. 00
.00
.00
51 *
.00
.00
.00
.00
.00
42 *
. OC
.00
.30
.03
.00
.CO
.00
.CO
.00
.00
.00
. CO
.00
.00
.00
53 *
.00
.00
.00
54 $
. CO
.CO
.00
.CO
. OO
.00
.00
.00
.CO
.00
55 *
. OS
.CO
.00
.00
.OC
.00
.00
.00
• U»Li
.CO
.30
c6 $
.00
. OC
.00
.CO
.00
.03
57 ♦
. OG
.00
.00
.OU
.00
.00
.00
.00
.00
*
5"
. CC
.00
.OP
.00
.00
.00
.00
.00
.00
4<J *
.00
.00
.00
.00
.00
.00
.00
.00
.00
oC $
. 00
.00
.00
.00
.03
.00
.00
.00
.00
61 *
. GO
.00
.00
.00
.00
.00
.00
.00
.00
62 *
. CO
.00
.00
.00
. OO
.00
.00
.00
.00
63 ♦
.CO
.00
.00
.OC
.00
.00
.00
.00
.00
64 ♦
. 00
.00
.00
.00
.00
.00
.00
.00
.00
*******$*$****$***$***$*$*$$******$******$***********#$*$***$******$**$*****$***$*******
Table A.I (Continued)
Linear Programming Matrix
CuLUfN
t**t>4ts*;:*t«»*****t*********t»**t*t*t*************»************************************
O
O
49
37
<tP
4I
42
43
44
17
3?
'JW *
*****C$3**** »***t«r»******#**»»»»***»****»*«»»»***»*************»»********«***«**********
.09
3.uu
3.GO
.OS
.OS
.09
24.39
3. I.J
3.CO
I ft
.U.1
.01
.00
.00
-1.00
.CO
.00
2 ft
.00
.00
.00
.Cs
.GC
.00
.CO
.CO
.30
J ft
.09
.00
.00
.00
.00
.00
.CO
.00
•Gu
S ft
r, ft
.00
.CO
.00
.00
.00
.03
.Ov
.00
.00
.00
.00
.CO
.CO
.CO
.OC
.03
.Cu
.OS
tr ft
7 ft
.00
.CO
.00
.CS
.CO
.00
.00
.UU
.r:
.03
P ft
.OO
.CG
.00
.00
.CC
.CO
.00
.CS
.00
.00
.00
.00
.00
.GP
ft
.30
.Ou
.os
.30
.00
.00
.OC
.00
.00
.OC
.00
.OG
11' ft
.00
.00
.0'
.00
.00
.00
.00
.00
.Cu
JI ft
.00
.CO
.Cl
.00
.00
.00
.00
.03
.00
I? ft
.Cu
.00
.00
.00
.OO
.CO
.00
.00
.OS
13 ft
.00
.00
.UO
.00
.00
.00
.00
.00
.CS'
I4 ft
.00
.00
.CO
.00
.09
.00
.00
.30
.CO
Ib ft
.00
.03
.30
.CC
.00
.Co
.00
.CO
.Ou
Ifc ft
.CO
.CO
.00
.03
.00
.CO
.03
.OC
.Cu
I7 ft
.00
.00
.00
.CO
.00
.00
.00
.CO
.00
IS ft
.00
.CO
.CO
.00
.OC
.OC
.00
.OC
.OP
Id ft
.CO
.00
.00
-!.CO
.CO
.OC
.00
.00
1.42
PO ft
.OC
.GC
.00
.00
.00
.00
.OC
-1.00
.70
Pl ft
.on
-1.00
.OO
.00
.00
.00
.03
.(if.
.00
PP ft
.Os
.00
.OC
.00
.00
-I.OC
.00
.00
.OC
U j ft
.00
.PO
.GO
.CO
-1.00
.GO
.CO
.00
P4 ft
.JU
.00
.00
.00
.00
.00
.00
.00
.OC
PS ft
-1.30
.03
.00
.00
.00
.0.)
.CO
.00
.CO
Pfc ft
.00
.00
.GC
.00
.00
.00
.OC
.00
PT ft
.OC
.OG
.00
-1.00
.00
.00
.OC
.CO
.00
PP ft
.00
.00
.30
.00
.00
.00
.00
.00
.00
PO ft
.CO
.00
.CP
1.00
.00
.00
.03
.CO
.00
.00
30 ft
1.00
.30
.CO
.00
.00
.00
.00
.OG
.OO
31 ft
.00
.CO
.00
.00
.00
1.00
.00
.00
.00
3P ft
*********************************************************************************•**•**•
Table A.I (Continued)
Linear Programming Matrix
COLU-N
C**sf****s$t**t****$#*###$$*t**$*****t#t*#c*$#***ft*************************************
;n w
23 *
34 *
ic »
36 *
37 *
3b t
39 »
40 *
41 *
42 *
4i *
44 *
45 t
46 fr
47 t
43 $
*.9 *
SC *
5I *
52 *
S3 *
54 $
56 *
56 ♦
>7 S
5? *
59 *
60 $
61 *
62 *
63 ♦
64 ♦
Jf
. OG
. OS
.CO
.00
.00
. GO
. Cu
. CS
. CO
. 1)0
. Cu
.CS
.0)
.CO
.OS
. 00
.CO
. CO
. OC
.00
. CO
.CO
. OS
.00
.CO
.00
. 00
• Ou
. CO
. OO
. CO
.00
3=
.00
.OS
.00
.00
.0 0
.00
.00
.CO
.00
.00
.00
.OS
.00
.OS
.OS
.00
.00
.00
.OS
.00
.00
.00
.00
.00
.OC
.OC
.00
.00
.00
.00
.30
.00
3I
.CO
.90
.OS
.OC
.SC
.00
.UG
.GC
.00
.00
.00
.00
.OC
.30
.CO
.00
•U C
.CO
.00
.30
.OC
.00
. Of
.00
.03
.00
.00
.00
.30
.00
.00
.00
40
.CO
.uO
.00
.CO
.CO
.CO
.00
.CO
.00
.00
.00
.CO
.OC
.00
.CO
.CO
.CO
.CO
.00
.00
.00
.on
.00
.CO
. PO
.CC
.CO
.00
.CC
.00
.00
.CO
4I
.30
-1.03
.OS
.00
.on
. 00
.00
. UU
.00
.00
.00
.30
.00
.00
.00
.00
. OO
.00
.00
.00
.00
.00
.00
.00
.00
. CO
. OO
.00
. 00
.00
.00
.00
42
.00
. OO
-1.00
.CO
.00
.00
.CO
.PO
.00
.00
.00
.CO
.00
.00
.00
.OC
.00
.CO
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
43
.00
. OO
.CO
-1.03
.00
.OS
.00
.00
.09
.00
.00
. OO
.00
.00
.00
.00
.00
.00
.00
.00
.00
. 00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
44
1.00
.CO
.00
.00
-1.00
.CO
.00
.00
.00
.CO
.00
.00
.00
.CO
.CO
. 00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.CO
.00
.00
.00
45
.CO
16.34
P.05
.00
.00
.00
.00
2.25
11.25
.27
.00
.00
.00
1.00
.00
-1.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
1.00
.00
.00
.00
.00
****************************************************************************************
Table A.I (Continued)
Linear Programming Matrix
COLUMN
****************************************************************************************
9QW*
46
47
4d
43
50
51
52
53
54
*t*irt:tt4it»»****t»t«;*«#*«:**»t*et**»tt4**************»*****#»**#*******************»*****
I
2
3
4
30. iq
*
t
«
*
5 *
6*
7 t
d
q
10
11
12
13
14
I5
16
II
IF
iq
2C
*
*
*
»
*
*
*
*
*
»
*
*
»
21 »
22 *
23 «
24 *
25
26
27
2B
25
30
31
32
*
*
*
*
*
*
*
*
1.UC
. 00
.CO
.OC
. Cv
.O
'.
.OV
.C
f,
.00
.Cu
.Cl/
.CO
.CO
.CC
. PC’
.Ou
.00
.53
. 17
. CO
. CO
.CO
. OU
. CO
.CO
. CO
. 00
. OP
.OC
.CC
133.35 -2'0.1O
.CO
.CO
.30
.00
.OC
.00
.OG
.CO
.00
.0 )
.CO
.50
.00
.03
.00
.00
.CC
.00
.00
.30
•U'O
.30
.00
.30
.30
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.03
.00
.CO
.30
.00
.00
.00
.03
.00
.00
.00
.00
.00
.CO
.OC
.00
. OO
.00
.00
.00
.00
.00
.00
.OC
24.16
.OC
.00
-I.on
.CO
.0'
.cr
.00
.00
.CC
.00
.00
.CO
.00
.00
.CO
.00
.CO
.00
I .42
.73
.00
.00
.CO
.00
.00
.00
.00
.00
.00
.00
.00
20.36
.00
.00
.00
1.00
.CO
. 00
. 00
.00
.00
• uO
.03
.CO
.00
.00
.00
.00
.00
.00
.53
.17
.00
.00
.OO
.00
. 00
.00
. OO
.00
.00
.00
.00
150.35
.00
.CO
.00
.00
.30
.CO
.00
.OC
.00
.CO
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.03
.00
.00
.00
.00
.00
.00
.00
.00
-253.75
.00
. OO
.00
.00
.00
.00
.00
.00
.00
. OO
.03
.00
.00
.CO
.00
.00
.00
.00
.00
.00
. CO
.00
.03
.00
.CO
.00
.00
.00
.00
.00
.00
29.14
.00
.CO
.00
.CO
- I . CC
.00
.00
.00
.00
.00
.00
.00
.CC
.00
.OC
.00
.00
.00
.65
.00
.CO
.65
.CO
.00
.00
.00
.00
.00
.00
.00
.CO
34.94
.CO
.00
.00
.00
.OC
I.OC
.00
.00
.00
.00
.00
.00
.00
. 00
. 00
.00
. OO
.00
.16
.00
.Iti
.00
. 00
.00
.00
.00
.00
.00
.00
.00
***********************************************************************************
'■J
<£>
Table A.I (Continued)
Linear Programming Matrix
COLUMN
**************************************** **************************** **********
f.ou *
oO
4 i,
tmtztttttt****************************************************************************
.OC
.00
150.35
.00
.00
.CO
.00
.GO
.00
.00
.00
.OC
.00
.00
.00
-1.00
.00
.00
.00
-253.70
.00
.00
.00
.00
. 11
.11
.00
.00
.OC
.00
.00
.00
.00
.OO
.OC
.00
1.00
1.00
.0 0
.00
.Cu
.00
.00
I. VO
.CO
- I. CO
1.00
.Jo
.00
.00
.00
.00
I.GG
I.00
.00
I .00
.00
.00
.00
00
.00
.00
.00
.00
.00
.00
.00
.00
-!.CO
-1.00
.CO
1.00
.00
.
OO
.00
.GO
.CC
.OC
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.03
.00
.00
2 0. 23
9. 56
.CO
6C
. CO
5I
52
53
54
55
56
57
5j
59
60
61
b2
63
64
. 0 0
. CO
• V x.
2. 25
I 1.25
.27
.CO
.CO
. 0 0
. 00
. OC
.CO
.CO
. 00
. CO
. CO
OC
I a 3 35
.00
.0 3
.OC
.00
.00
-269.10
.00
.00
.00
.OU
.GC
.00
.00
.00
.00
.00
.CO
.GO
.CO
.00
. OC
.00
.GO
.00
.00
.00
.PO
.CG
.CO
.00
.00
.00
.CO
16. 19
7.97
.OC
.CO
.00
• GO
1.60
I 1.25
.27
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
. OC
.00
.OC
.00
.00
.00
. 00
.00
.00
PC
.00
.00
.00
.00
. 00
1.00
.00
. OO
.00
.00
.00
.00
.00
.00
.00
.
. 0 0
. CO
• OU
.OC
.CO
.00
.00
.00
.CO
.00
.CO
.00
.00
.00
.00
.00
.00
.00
.CO
14.57
.00
.CO
14.57
.00
.CO
.27
39.00
.00
20.07
9.39
.00
.00
•OO
.CO
I ,,80
11 <,25
.27
.00
.00
.00
.00
33
34
35
36
37
3a
35
40
4I
4?
43
44
4r
46
47
4S
45
.CO
.00
.
.00
.00
.00
.00
.00
.00
.00
.00
.00
-
17.47
.00
.00
17.47
.Cu
.OC
.27
39.03
.00
.00
.00
.00
1.00
-I.GO
.CO
.00
.00
.00
.00
. 0 0
.GO
. 0 0
. 0 0
.CO
. 0 0
.00
.00
.00
1 . 0 0
.00
.00
.00
.00
.00
.00
.00
.00
.CO
.00
.00
****** ****************************************************************************
CO
O
Table A.I (Continued)
Linear Programming Matrix
CCLUWN
**»*»*♦*»***»***».*»**»*«***»»**♦*«****»♦***»*****»*#♦******»****»**»**#******#*****#****
OOW
****************************************************************************************
I t
20.94
26.74
34.94
294.05
-333.40
249.85
2°. It
270.63
-390.60
.CO
.00
.00
.00
.00
.00
.00
.00
.Cl
.CO
.00
.00
.Uu
.00
.00
.00
.00
.10
3 »
.00
.00
.00
.00
.CO
.00
.OC
.00
4 *
. 00
.00
.00
.00
.00
.00
.00
.CO
.CC
.OC
5 *
.00
.00
.00
.00
.00
.CO
.00
. 0°
.03
6 *
.OC
.00
.00
.00
.00
. C„
.Ov
.92
. OO
7 *
.00
.00
.00
.CO
R *
.00
- 1.0 C
.OC
.00
.00
.00
.00
.00
.00
I .00
.00
.CO
.00
9 *
. OC
-1.00
.00
.CO
.CO
.00
.00
.30
.CO
.00
IC *
.00
.00
.00
.00
1.00
.00
.00
.O 0
.00
iI »
.00
.00
.00
.CO
.00
.33
.CO
.00
.00
12 *
.00
.00
.00
.00
.00
.00
.CO
.00
.00
13 *
.00
.00
.00
.00
.01
.CO
.00
. CO
.00
14 *
.00
.CO
.00
.00
.00
.00
.00
.93
. CO
15 *
.00
. 00
. 00
.00
.00
.00
.00
.00
. OC
16 *
.00
.00
.CO
.00
.00
.00
.00
.00
17 *
. r:
.00
.00
. 00
.00
.00
.CO
.OC
.00
.CO
IP ♦
.00
.00
.00
.00
.00
.03
.00
.00
. CO
19 *
.14
.00
.00
. 04
.39
.CO
.00
.00
.00
20 *
.00
.00
.00
.00
.02
.IC
.00
.00
.00
21 »
.00
.00
.00
.00
1.29
.30
.00
.00
• Cu
22 *
.09
.80
. 00
.20
.CO
.00
.00
.CO
. nU
23 *
.00
.CO
. 00
.u C
.00
.00
.00
.03
24 *
.Cf
.90
.00
.00
.00
.00
.00
.00
.00
. CC
25 *
.00
.00
.00
.00
.Cr
.00
.00
.00
26 *
.00
.00
.00
.00
.00
.00
. 00
.00
. 00
27 *
.00
.00
.00
.00
.OC
.00
.00
. 00
.00
2P *
.00
.00
.00
.00
.00
.00
.30
.00
29 *
.CO
.00
.00
.OC
.00
.00
.00
.00
.00
.00
30 *
.30
.00
.00
. 00
.00
.00
.00
. Ov
.O-J
31 *
.00
.00
.00
.00
.00
.00
.00
.00
.00
32 *
***** ************* *********** * * * * * * * * • * * • * * * * * * * • * * • • * • • * * * * * • • * • * * • * * * * * • • • * • * * * * • * * * • *
I
*
O
O
Table A.I (Continued)
Linear Programming Matrix
COLUVi
****************************************************************************************
tS
ij
**#*t*»*»**«*«****»«*»*******»****vt»**»»»*»»**i**»******************##*********i*******
33
3*
35
35
37
3*
39
40
41
42
43
*
*
»
*
*
«
*
t
*
»
»
51 *
52 *
53 *
54 *
55 *
J6 »
57 *
5° *
59 *
69 *
61 »
62 *
*
o4
*
.or
.00
.CO
.CO
.CO
.00
-I.CO
.Cu
.00
.00
.00
. CO
.OC
.00
. CO
. 00
. 00
.CO
.CO
.OC
. CO
.00
.00
- 3 5 4 •6 0
.00
.CO
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.OC
I . OC
.00
.00
.00
.00
.00
.00
.CO
.00
.00
.00
.00
.00
.00
.0 r
.OC
6.0 J
23.14
.00
.00
.00
.27
39.00
.11
.30
.00
.00
.0 0
.00
1.00
.00
.00
.00
-1.00
.CO
.00
.00
.'JO
.00
.00
.00
.00
.00
.00
.00
.00
.CC
.CO
7.00
27.94
.CC
.00
.OC
.27
39.CO
.11
.CC
.00
.CO
.00
.00
1.00
.00
.OC
.OC
-!.CO
.CC
.OC
.00
.00
.00
.00
.00
.00
.CO
.CO
.CO
.CO
.00
.00
2 i 4 •05
.00
.00
.00
.00
.00
.00
.00
.00
.00
.OO
.00
.00
-1.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
. 00
.00
.OC
.OC
.00
.00
-393 . 4 0
.00
.00
.00
.CO
.00
.CO
.00
.00
.00
.CO
.00
.00
.00
.00
1.00
.00
.00
.00
.00
.OC
.00
.00
.00
.00
.00
.00
.00
.00
6.91
.00
.00
14.03
.00
.00
.20
26.30
.04
.00
.00
.00
.00
.00
.00
.00
.03
.00
1.00
-1.00
.00
.00
.00
.00
.00
.00
.00
.00
1.00
O
O
63
.OC
. GO
. Ou
-265. 7 C
.CC
.O j
.CO
.00
.00
.CO
O
O
*
45 *
46 *
47 *
4o »
4o *
iC *
44
.C C
.OC
8.62
.00
.00
17.92
.CO
.00
.20
28.30
. 04
.00
.00
.00
.00
.00
.00
.00
.00
.00
.OC
1.00
-1.00
.00
.CO
.CO
.00
.00
.00
.CO
.00
.00
.00
.OC
.00
.00
.00
249.85
.00
.00
.00
.00
.00
.CO
.OO
.00
.00
.00
.00
.00
.00
.03
.00
-1.00
.00
.00
. 00
.00
.00
.00
.00
.00
.00
.00
.00
**s***«***»*»****«***»*«»•»*»***»*******»******»*»*•*•»****•*♦•**»•****•»**»»******♦*»**
Oo
NJ
Table A.I (Continued)
Linear Programming Matrix
C CLMmN
$ * $ $ * * 3 $ $ * * * * * * * * * $ * * * * * * $ $ $ « : $ $ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
SuW
*
64
I
?
'.CO
. C .
3
4
5
. Cr
. Ov
. 00
4
7
9
*
IC
I I
I 2
13
14
15
16
17
IR
19
20
21
bfi
So
67
6c
69
70
71
72
» * « * * * * * » * * * * * » » * * » * » » * * * * ♦ » * * * » * » * * # ♦ * * * * * ♦ * » * * » * * * * * * * * * * » * * * * * * * * * * »
3 61.36
2 07 .5 6
-77.00
•1 9 P • 4 0
2 7 6 . 6 0
- 1 4 5 . 4 0
- 1 7 0 . 4 0
4 I .92
.CO
.CO
.00
.CO
.00
.90
.CC
.00
.09
.OC
.CO
.OG
• cr
.00
.OG
. OO
.00
.00
.OC
.00
.00
.C 0
• Ci*
.00
. OC
.CO
.03
. J 0
.00
.00
.Cl
.00
.CO
.OU
.03
.00
.Uv
.CO
.00
.00
.OC
. JO
•OO
.CO
.00
.00
.30
.00
.CO
.00
.CO
. 0 0
.03
.00
.00
. CO
. 00
.00
•0 0
.OC
.OC
.CO
.CO
.CO
. UO
. CC
.00
.00
.00
.OU
.00
.00
.00
.Cu
.00
.OC
.00
.00
.CO
.00
.00
.00
. u C
.30
.00
.00
.37
.37
.Cu
.00
.00
.00
.00
.CC
.00
.uo
.Lu
.00
.Cu
.CO
• Cu
.00
.00
.Cu
.Ou
.00
22
.Cu
.C"
.01
.09
.00
2 3
.20
.20
.00
.00
.CO
. uO
24
• vu
.CO
.CO
.Ci
. 00
.30
.CO
.CO
.CO
.00
25
26
27
.CO
.Ou
.OU
.CC
.CO
.00
.00
.00
.00
.00
.30
.00
.00
.CO
. OU
.00
.00
.0 0
.CO
.00
.00
.00
. CO
. OC
.00
.00
.00
2 P
2 9
3 C
J I
.00
.00
.00
1.00
.00
.OC
.00
.00
.00
.00
.03
.03
.CO
.OC
.CO
.00
.00
.00
.00
.00
. 00
.00
.03
.00
.00
.00
.00
.CC
.00
.00
.00
.00
.00
.00
.00
.CO
.CC
.00
.00
1.00
.00
.00
.00
6.88
6.88
.CO
.00
.00
.03
.03
.00
.00
.00
.00
.CO
.00
1.00
-CO
.00
.CO
.00
.00
.00
.00
.00
.00
.00
.OC
.00
.00
6 . 8P
.03
.CO
. 00
.00
.00
6.89
.CU
4.50
4.50
4.50
4.50
.00
.00
.CO
.00
.00
2.00
10.20
2.00
10.20
.00
.00
.00
.00
.00
.00
.00
.CO
.CO
.00
.00
.00
.00
.00
.00
.00
. 00
.00
.00
.00
.CO
. 00
.00
.00
.CO
.00
.00
.00
.00
.00
32
****************************************************************************************
OO
IO
Table A.I (Continued)
Linear Programming Matrix
CCLU*N
*$*tt*$$$***t*$$t*t$$****$****$$$*$$$**$$$$$****$**$*****$**************$**$$***********
7?
Lk
*******»****»*»**«****»*«»**»»»»»****♦*****«*»***********»*♦*»♦*#♦**»*****##*****»****♦*
33
34
35
35
37
33
3y
40
41
42
43
44
45
46
47
4 fl
49
SC
SI
52
S3
S4
SS
56
57
Sf
59
60
61
62
63
64
*
♦
*
*
*
*
*
*
*
*
*
*
*
$
*
*
*
*
*
$
$
$
*
*
$
*
♦
*
*
*
$
*
. 00
. 00
- 3 5 7 . LU
. 00
.GC
.CO
. 00
.GC
.CO
.CO
. 00
. CO
. OC
. CC
. 00
. OC
.00
. 00
.00
.00
.CO
I. 00
. oO
.00
11.13
11.13
11.13
11.13
.00
.00
.00
.00
I .65
.00
6.50
.00
-.4 5
-.30
.00
.00
.00
.00
.00
.00
.00
-.10
.no
.oo
.CO
. 00
.UO
. 00
.CU
.00
.00
.00
.00
.00
.00
.00
.CO
.CO
.CO
. OO
.00
-173 . 4 0
.00
.00
.00
. Ou
.OC
.00
.OC
.00
.00
.Ov
.00
I.OC
.00
.00
.00
.00
.OJ
.00
.00
.OC
.00
.00
.00
. 00
.00
.00
.00
.00
.00
.00
.CC
- IH 5 •4 C
.CO
.00
.00
.00
.00
.CO
.CO
.CO
.00
-CO
.00
.00
1.00
.00
.00
.CO
.CO
.00
.00
.00
.00
.CO
.00
.00
.00
.00
.00
.CO
.CO
.00
. 00
-196.40
.00
.00
.00
.00
.00
.CO
.00
.00
. OO
. OO
.00
.00
.00
.00
.90
.00
.00
. OO
.00
. 00
1.00
.00
.00
.00
.00
.00
.00
.00
. 00
.00
.00
69 . IS
69.15
69.15
69.15
.00
.00
.00
302.00
.16
.00
.CO
.00
.CO
.00
.00
.00
.00
.00
.00
.00
.c :
.00
-15.00
.00
.00
.00
.00
.00
.00
.UO
.00
.00
-19.25
-19.25
-19.25
19.25
.00
.09
.CO
.00
. Ou
.00
.00
.CO
.CO
.00
. 00
.00
.00
.00
.00
.00
.00
.00
1.00
.00
.00
.00
.00
.00
.00
.00
.00
.CO
90.34
90.34
90.34
90.34
.OC
.00
.OC
2 5 4 . CO
.CO
.CO
.00
.00
.00
.00
.00
.00
.CO
.00
.00
.00
.00
.00
-15.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
21.93
61.80
21.96
51.60
.00
.00
.CO
16.72
.29
.00
. CO
.00
.00
.CO
.00
.00
.00
.00
. OO
.00
.OO
.00
.CO
-15.00
.CO
.00
.00
.00
.CO
.00
.00
****************************************************************************************
Oo
Table A.I (Continued)
Linear Programming Matrix
*******
P0 W *
7*
Th
7b
76
77
************** ***** * * * * * * * * * * * * * * + * $ * $ $ $ $ * $ * * * * * * * * $ * $ * * * * * * + * * * * * * * * * * * * * * * * * * * * * * * *
*
-2 I. CC
—
153.96
*
*
.CO
.OC
.00
.CO
.CO
» Uu
.00
.00
*
*
*
*
*
*
iC
11
*
*
12 *
13 *
I4
13
IL
17
;f
:9
20
*
$
*
*
*
*
*
21 *
22 *
.ca
.cc
.CO
. OO
.00
.00
.ac
.00
.00
.00
.00
21.01
.00
.00
.00
.00
.00
.CO
.CO
.00
.CC
.00
.CC
.00
.00
.OC
.00
. 3C
.00
.00
.CO
1.00
.CO
.00
.10
.00
.00
.O U
.30
.CC
.Cu
.30
.00
.CO
.00
-77.00
.00
.00
.00
.00
. OC'
. Cu
.Ou
. ca
.CO
. CO
.CO
.00
.00
. CO
396.38
.00
.00
.00
.CO
.00
.00
.00
.00
.00
.00
.00
.00
. OO
.uO
.00
.00
.OC
.OC
.00
.00
I. OC
.CO
.OC
.00
.00
.00
.00
.00
.30
2.00
.Ou
13.30
2.30
2.30
.OC
.00
.00
.00
.00
.00
I.mo
.00
.OC
I .CO
.CO
.00
.00
.00
.Cu
24
.00
.00
.10
. 00
.Cu
Sb
.00
.00
.00
.00
.CO
26
.00
.OO
.00
. Cu
27
.00
.CO
.OC
.00
.CO
26
.00
.CO
.00
.00
. CC
29
.OC
.OC
.00
.00
30 *
.CO
.00
.00
.00
. OC
31 *
OO
.00
.CO
.OG
.00
32
***»**»»*<:***« t*************************************************************************
*
*
*
*
♦
*
♦
.oc
.cr
«
OO
Ln
Table A.I (Continued)
Linear Programming Matrix
CGLUwN
ttt^************************************************************************** **********
Rf1W *
Ti
33
34
35
Jfc
37
38
3°
40
41
42
43
44
45
46
47
48
40
50
5I
52
53
54
44
56
57
58
59
60
61
62
63
. 00
-10.50
. UO
-I 0.50
.CO
.CO
. 00
. 00
7A
75
76
77
ttmtttt***+***************************************************************** **********
.00
.00
.00
.CO
.00
.00
.00
32.65
42.78
78.52
.UO
.00
.00
21.01
133.24
65.07
S C . 67
86.40
•77.03
.UO
.00
.00
.00
.00
152.00
.24
. OU
.00
.00
.00
.00
.00
. 00
. 00
.00
. 00
.00
.00
.00
.00
.00
. CO
. 00
1.00
. 00
.00
. 00
.00
. CO
.00
.Ou
.00
.0 0
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.01
.00
.00
7.50
-7.50
.00
.00
.00
.00
.CO
.00
.00
360.00
.24
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.03
.00
.00
.00
.00
.00
. 00
.00
.00
.00
.00
.00
.00
.00
.30
. 00
.00
. 00
.00
.00
.00
1.00
.00
.00
.00
.00
-
.00
.00
.00
.00
.00
.00
.00
.00
.03
.00
.00
.00
.00
.00
.00
1.00
.00
.03
-7.50
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
. 00
64
tt.t*************************************************************************** **********
.00
.00
OO
O'
N378
Schl25
cop, 2
DATE
Schaefer, Gerald M
Optimum farm organiza­
tion for a representative
irrigated farm in the
Yellowstone Valley
ISSUED TO
Sth