fV chives
5

gust, 1968 Station Bulletin 495

An Economic Analysis of

Hay Harvesting and Utilization

Using a Simulation Model

by
Clifton C. Cloud, George E. Frick, and R. A. Andrews

Department of Resource Economics

Agricultural Experiment Station

University of New Hampshire

Durham, New Hampshire

in cooperation with

Farm Production Economics Division

Economic Research Service

United States Department of Agriculture

ACKNOWLEDGEMENTS

The authors wish to thank C. W. Crickman of the Farm
Production Economics Division, Economic Research Service,
U. S. Department of Agricuhure for his contribution to the
editorial quality of this publication.

This work was done in part at the University of New Hamp-
shire Computation Center.

8/68/1 M/OP9 26

CONTENTS

SUMMARY AND CONCLUSIONS 3

INTRODUCTION 5

The Technological Features of Forage Growth 5

Weather Pattern 7

Equipment Complement 7

The Economic Problem of Forage Harvesting and Utilization 8

Analytical Procedure 8

THE FORAGE PRODUCTION-UTILIZATION SIMULATION MODEL 11

Computer Programs 11

Stage I 11

Stage II 11

Stage III 13

DATA AND ASSUMPTIONS FOR THE ANALYSIS 13

Acreage 13

Forage Crop Yield 13

Type of Forage 17

Total Losses of Forage 17

Weather Patterns and Date of Harvest 17

Labor and Machinery Requirements 18

Equipment Capacities 19

Labor Requirements for Cows 21

Harvesting Equipment Investment and Annual Costs 21

Milk Production 24

Cow Ability 25

Stage of Lactation 25

Other Factors 25

Forage Input 25

Grain Input 25

The Milk Production Surfaces 25

EVALUATION OF ALTERNATIVES WITH SIMULATION MODELS 32

Phase I Analysis 32

Most Profitable Grain Feeding Method 33

Most Profitable Forage Harvest Systems 33

Phase II Analysis 34

Influence of Date of Cut and Weather Pattern on Forage Balance 34

75 Cows and Selected Hay Harvest Systems 34

100 Cows and Selected Hay Harvest Systems 35

Number of Cows Varying and Selected Hay Harvesting Systems 35

Influence of Date of Cut and Weather Pattern on Gross Farm Income 35

75 Cows and Selected Hay Harvesting Systems 35

100 Cows and Selected Hay Harvesting Systems 38

Number of Cows Varying and Selected Hay Harvesting Systems 38

Influence of Date of Cut and Weather Pattern on Net Farm Income 38

75 Cows and the Crushed, Field Cured, and Baled Hay Harvest System 38
75 Cows and the Crushed, Baled, and Barn Dried Without Heat

Hay Harvest System 40

100 Cows and the Crushed, Field Cured, and Baled Hay

Harvest System 40

100 Cows and the Crushed, Baled, and Barn Dried Without

Heat Hay Harvesting System 40

Number of Cows Varying and the Crushed, Field Cured, and

Baled Hay Harvest System 40

Number of Cows Varying and the Crushed, Baled and Barn

Dried Without Heat Hay Harvesting System 42

Most Profitable Hay Harvest Date of Cut and System 42

SUMMARY AND CONCLUSIONS

This study assessed the effect of the forage harvesting system; the
date that harvest begins; the weather pattern; the method of grain feed-
ing; and the size of herd on farm income. Simulation techniques were
used to represent forage harvesting, feeding dairy cows, selling or buy-
ing forage, and output of milk. The simulation model as developed for
this study was comprised of 5 computer programs written in FORTRAN
with FORMAT. The model was built in 3 stages: Stage I simulated
mowing and harvesting of forage by date of cut and recorded the re-
sources used to perform these operations; Stage II simulated the pro-
duction of milk using the forage produced in Stage I and recorded the
resources used; and Stage III economically related Stage I and Stage II
and presented income and resource use statements.

A major advantage of simulation analysis with computers is the
speed of analysis completion after the simulation model has been de-
veloped. The methods used are similar to those of the conventional
budget techniques. The data used are the same, and the data problems
are comparable, but much less time is required for the analysis.

When coefficients for the simulation analysis model were developed
for forage production, quality and quantity relations as influenced by
starting date of cut, milk production according to quality of forage, and
labor and machinery productivity were considered. In performing the
analysis, particular emphasis was placed upon the development of
relation between systems of harvesting forage, quality-quantity forage
production, and dairy cow milk responses to quality of forage. The an-
alysis was carried out in 2 phases. Phase I analyzed the interactions of
6 hay harvesting systems, 3 grain feeding systems, 3 cow number options,
3 harvest starting dates, and 3 weather patterns representing "wet,"
"dry," and "average" conditions during the harvesting season. In Phase
II, only 2 hay harvesting systems, a single grain system, and 52 weather
patterns were vised. Phase I was designed to evaluate harvest systems and
select the most profitable combinations. Phase II was designed to study
more weather patterns to obtain variations in income for each starting
date of cut as influenced by weather.

Based on available data and the analysis made for this study, there
is no economic justification for beginning hay operations as early as
June 1. However, substantial declines in net farm income are encounter-
ed by postponing the beginning of the hay operation to as late as June
30. These observations held for all harvesting systems analyzed. The
results indicated that the optimal date, economically, to begin harvesting
operations appears to be around June 15.

There were 6 machinery systems analyzed; 4 were capable of har-
vesting hay in one day. Of these 4 systems, the crushed, baled, and barn
dried with heat, and the flail cut, flail harvest were about equal in net
income; the grass silage system was not quite as profitable; and the
system using heat drying was considerably less profitable. Of the two
remaining systems (that rely on natural field curing) the method using
a crusher was more profitable.

The crushed, field cured, and baled and the crushed, field cured,
and Ijarn dried without heat methods were chosen to represent the two
basic harvest systems for the analysis of date of cut and weather pat-
terns on farm income. The barn drying hay-in-a-day harvest system pro-
duced less variability in net income when lelated to the weather pat-
terns during the harvest seasons from 1910 through 1961. The average
net income under the variable weather patterns for each of the beginning
dates of cut was larger with this system than the system that included
field curing.

For a typical dairyman with a herd of approximately 75 cows and
100 acres of hay land, it would be profitable to aim toward a harvest
system that would mechanically cure hay in a day.

An Economic Analysis of Hay Harvesting
and Utilization Using a Simulation Model

by

CLIFTON C. CLOUD, GEORGE E. FRICK, AND R. A. ANDREWS 1

INTRODUCTION

The most important forage fed New Hampshire dairy cows is hay.
In 1964, of the 168,044 harvested cropland acres, 142,989 acres (85 per-
cent) were harvested as hay.^ Another 10,694 acres (6 percent) were
harvested as corn silage or grain. Almost all hay harvested is fed during
the 210-day winter feeding period. The importance of hay in feeding
programs suggests that the organization of forage harvest as it affects
the quality and quantity of hay harvested could substantially influence
the total income, total cost, and net income of a dairy operation.
Knowledge of the extent of this influence on net farm income would aid
farmers in planning their farm operation.

In this study, simulation techniques were vxsed to analyze the effect
of different harvesting systems on net farm income. Major emphasis
was given to dates of harvest of the first crop of hay with typical equip-
ment complements.

The Technological Features of Forage Growth

The major technological features of forage growth can be briefly
stated as follows:

First, the earlier the date of cut, the higher percentage of digestible
dry matter (DDM) in each unit of forage harvested. The percentage of
DDM reflects the quality of the forage; high quality forage has a high
percentage of DDM. The quantity (tons) of forage harvested per unit
of land increases with progressively later dates of harvest.

Second, cows will consume greater quantities of early-cut than later-
cut hay crops. During the barn feeding period about one-half ton more
early-cut (high DDM value) forage than late-cut (low DDM value)
forage will be consumed per cow.

Third, the rate of feeding grain in combination with the varying
quality of forage directly affects the amount of milk produced. When

1 Formerly Graduate Assistant, Department of Resource Economics; Agricultural
Economist, Farm Production Economics Division, ERS, USDA stationed at the Uni-
versity of New Hampshire; and Associate Professor, Department of Resource Eco-
nomics, respectively.

- U. S. Census of Agriculture, Bureau of Census, U. S. Department of Commerce,
Vol. I, Part 2, 1964.

the quality (DDM value) of the forage declines, increased amounts or
higher quality of grain must be fed to prevent reduced milk production.

Fourth, the weather pattern (the combination of clear and rainy
days) during the forage harvesting period influences the length of the
harvesting season. Thus, the quality and quantity of forage harvested
varies in response to the length and frequency of rain-free day periods
during the harvesting season.

Recent research in dairy nutrition summarizes the effect that date
of cut has upon nonrow crop forage as follows:^

The date at which first growth forage is harvested is the
major known determinant of the intake and digestibility of
forage by ruminants. At least within a given climatic
region, the digestibile energy (or matter) value of first-
grown forage can be predicted quite accurately. Species of
plant, methods of preservation (provided that leaf loss
is not disproportionate) , and physiological growth stage
; have very little or no effect upon the relationship between

cutting time and the DDM value (digestible dry matter
value) of forage.

As growth approaches maturity, the DDM value of after-
math forage declines much more slowly that that of first-
growth forage. Regardless of its stage of growth, aftermath
forage has a lower DDM value than first growth forage
harvested prior to June 10 in the northeastern states.

Colovos reports that "The decrease in digestibilities of dry matter,
protein, energy, and total digestible nutrients averaged about one-half
of 1 percent per day of delay after the first of June.""* This study was
done using dairy steers rather than milk cows. Assuming that the same
relation holds, this research indicates that if the DDM value from
sources other than forage is held constant, the milk produced from a
unit of early-cut forage will be greater than from a unit of late-cut
forage. Also, milk obtained from a unit of aftermath forage will not be
as much as from a unit of "first-cut" harvested before June 10. ^ Milk
output per cow, on the other hand, will be greater from a unit of after-
math forage than from "first-cut" harvested after June 10. The decline
in milk production as the date of cut advances occurs because: (1) the
cows will not consume as much (unit weight) of the late-cut forage as
they will of the early-cut forage, and (2) the DDM value per unit of
forage consumed declines as the date of cut advances. These calendar
time periods as reported are applicable to conditions in the North-
eastern part of the United States.

3 Reid, J. T., et. al., "Our Industry Today: Effect of Growth Stage, Chemical
Composition and Physical Properties on Nutritive Value of Forage," Journal of Dairy
Science, 42:567, 1959.

^ Colovos, N. F., et. al.. The Effect of Nitrogen Fertilization and the Rate of
Harvest on Yield, Persistency, and Nutritive Value of Bromegrass Hay, U. Nutritive
Value, New Hampshire Agr. Expt. Sta. Bui. 472, p. 15, 1961.

"5 First cut is first growth forage.

In the Northeastern United States, when harvest is begun very
early in the season, 2 difficuhies are encountered: (1) Ahhough the
quality (DDM value) per unit of forage harvested is high, the quantity
(tons) of forage harvested per acre is very low, and (2) the number of
rainy days is usually high at this time of the year (May 30 to June 10) .^

Weather Pattern

The sequence of clear and rainy days that occur during the har-
vesting season is of great importance because of its influence on the
length of the harvest period and the quantity of rain-damaged hay. The
average number of clear days, occurring during the 2-month period of
June and July at Concord, New Hampshire, for example, varied from
a low of 31 to a high of 50 days over the last 50 years."

The weather pattern influences the quality and quantity of forage
harvested in the following manner: First, if rainy days are numerous
during the harvesting period, harvesting is prolonged, providing time
for the quantity (yield per acre) of the forage to increase while the
overall quality decreases. Second, if clear days are numerous during the
harvesting period, harvesting occurs relatively fast and the quality of
each unit harvested is relatively high while the quantity harvested tends
to be low. While the absolute number of clear days determines the
number of days of harvest operations, the sequence of clear days alters
the quality and quantity of hay harvested. The quantity of rain-
damaged hay is greater for systems requiring a longer field drying
period. For any particular weather sequence the date harvesting com-
mences also influences the quantity and quality of forage harvested. By
changing the date that harvesting begins, a different sequence of clear
and rainy days is encountered during forage harvest. For example, Fig-
ure 1 shows that: (1) if starting date 1 is used, only 2 out of 6 mowings
are harvested without getting wet; (2) if starting date 2 is used, 3 mow-
ings are harvested without getting wet; and, (3) if starting date 3 is
used, only 1 of the 6 mowings would get wet.

Equipment Complement

The working speed of the equipment complement influences the
quality and quantity of the forage harvested by affecting the time re-
quired to harvest a unit of forage. Each equipment complement has a
specific input-output relationship, and changing from one set of equip-
ment to another changes the resources used and products produced from
a given number of acres.

f' For the last 49 years, 4 out of the first 10 days of June were rainy on the
average with a range from zero to 8.

^ Local Cliniatological Data with Comparative Data, Concord, New Hampshire,
U. S. Department of Commerce, Weather Bureau, 1914 through 1962.

Figure 2 illustrates the effect on the quality and quantity of forage
harvested from a change in the equipment complement, everything else
held constant. An equipment complement that requires 2 days of field
curing time is compared with another that requires only 1 day of curing.
By this shift (1) the harvesting period is shortened from 28 to 13 days,
and (2) the numher of mowings that are rain damaged decrease from 4
out of 6 to zero out of 6.

The Economic Problem of Forage
Harvesting and Utilization

The interrelations hetween the weather pattern, the date that har-
vest hegins, and the harvesting system have an economic significance.
The optimum condition is the equipment complement and starting date
of harvest for a seasonal weather pattern or sequence of seasonal weather
patterns that will produce harvested forage of a quantity and quality
that, when fed in combination with grain to cows, will produce the
largest net farm income. Variations in labor requirements of various
harvesting systems is important.

Gross farm income will change as the organization of a harvesting
system changes because different quantities and qualities of harvested
forage will produce different amounts of milk. The organizational
changes may be in the harvesting equipment or in the beginning date of
harvest; both factors are associated with a particular weather pattern.

Differences in gross income may also result from changes in the
quantities of the resources used in the conversion of forage into milk,
such as: (1) changing grain feeding; (2) maintaining herd size while
buying or selling forage; or (3) allowing herd size to vary depending
on home-grown forage. Net income will also vary with resource organi-
zation l)ut not necessarily in proportion to gross income.

Analytical Procedure

A simulation model, consisting of a series of 5 FORTRAN computer
programs, was developed to simulate forage harvesting and utilization
systems for a dairy farm. Relevant input-output data used in the model
were obtained from many sources that are designated in the appropriate
sections. Milk response data and crop yield data were synthesized to
develop the relevant production relations incorporating forage quality
and quantity. The simulation model was designed to make particular
use of the quality-quantity production relations. The model also con-
sidered various systems of harvesting hay and feeding grain. The effect
of varying these conditions as well as the effect of weather patterns
and date of cut were measured in terms of physical product output and
income.

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THE FORAGE PRODUCTION-UTILIZATION
SIMULATION MODEL

The model simulated harvesting forage, feeding dairy cows, hiiying
and selling forage, and output of milk. The model treated each produc-
tion process separately (Figure 3), was designed so that most forage
harvesting systems (except for corn) could he simulated, and also pro-
vided for several grain feeding practices.

Computer Programs

The model was comprised of 5 computer programs written in FOR-
TRAN with FORMAT for a 1620 IBM digital computer. These pro-
grams may be used with any computer that can compile FORTRAN with
FORMAT programming language and has a memory unit of at least
40K.S The programs were written so that all the input-output coefficients
were controlled hy the operator. The model was built in 3 stages.

Stage I

Forage production, harvest, and storage were simulated in Stage I.
Because of the complexity of these operations, Stage I was written in
three programs. Program I simulated the production, mowing, and
harvesting of forage by quality and quantity using 2 different systems
that required more than 1 day to field-cure the forage. These systems
were: field-cured baled, and crushed field-cured baled. Each time a new
situation was processed, the program produced the following output:
(1) the name of the system and weather pattern used; (2) the day,
type, and amount (quality and quantity) of forage that was mowed and
harvested; and (3) the amount of resources used to mow and harvest all
the forage.

Program II used the output from Programs I and II as input and
further classified the forage harvested as to quality. For each situation
the program produced the following output: (1) the name and number
of the system and weather pattern used, (2) the day, type, and amount
(quality and quantity) of first-crop forage, (3) the day (assigned to
second crop to make its value, in terms of DDM, proportionate to same
day of first crop), type, and amount (quality and quantity) of second
crop, and (4) the amount of resources used to mow and harvest all the
forage.

Stage II

Program IV, the only program in Stage II, used the output from
Program III as input in the simulation of milk production under farm

8 For programs in FORTRAN, see A Simulation Model of Forage Production
and Utilization by C. C. Cloud, R. A. Andrews, and G. E. Frick, D.R.E., Agr. Expt.
Sta., University of New Hampshire, Special Report No. 5.

11

PROGRAM I

PROGRAM II

STAGE I:

Harvest system more than
one day drying time

Harvest system, one day
drying time

i i

Weather Patterns

i

Crop production and machinery coefficients

I
PROGRAM III

Assigns quality measure to harvested forage and
measures use of forage production resources

i

Forage quality coefficients

i
PROGRAM IV

STAGE II: ! Converts forage of various qualities into milk

STAGE III:

PROGRAM V

Converts all production output information into total
farm income, total cost, net income, and total invest-
ment

Figure 3. A Schematic Outline of the Forage Production
Utilization Simulation Model

12

conditions, ^ and also determined the amount of resources used in the
production of this milk. Milk production varied with quality of forage,
herd size, and grain feeding practices.

Stage III

Program V, the only program in Stage III, used the output from
Program IV (the total resources used and products produced for each
comhination of weather pattern, starting date, system, and method of
producing milk) as input. This program determined the total income,
total cost, net income, and total investment for the combinations ana-
lyzed.

DATA AND ASSUMPTIONS FOR THE ANALYSIS

The coefficients necessary to evaluate the harvesting systems and
the alternate methods of feeding grain are developed in this section.
These coefficients controlled the computer programs for simulating
farm conditions by specifying such things as: how many acres could
l)e mowed in a day; what losses occurred for each system; what date
mowing started; what weather patterns were assumed; how many labor
and machinery hours were needed to mow and harvest an acre of land;
and how many man-hours were required to milk, feed, and care for
cows.

Acreage

In this study, 100 acres of forage was the basis for analysis of har-
vesting and utilization. Each acre received the same application of
fertilizer, contained the same species of plant, and the yield per acre was
the same on any date for all systems.

Forage Crop Yield

The only source of variation in forage crop yields considered was
date of cut of the first crop harvested. Research in the Northeast indi-
cates vield increases (at a decreasing rate) as harvest date advances

'J Because there are many management methods possible in the production of
milk, no single method would be ideal for all circumstances. The program written
allowed the operator to combine the resources in 6 different ways. Variation in herd
size and method of feeding were permitted. Herd size could be held constant with
forage deficits and surpluses adjusted for by hay purchases and sales; or herd size
could be allowed to vary with size being determined by quantity of forage avail-
able. See Clifton C. Cloud, "The Effect of Alternative Harvesting Methods on the
Qualities and Quantities of Forage Harvested and on the Production of Milk and
Net Farm Income," unpublished M.S. thesis. University of New Hampshire.

13

from June 1 into the summer, i*^ Comparison of these dates revealed a
large degree of similiarity in yield behavior between areas as to calendar
date (Figure 4). A second degree equation was fitted to these yield

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.6265

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DATE OF CUT IN NUMBER OF DAYS AFTER MAY 31

Figure 4. Relation Between Date of Cut and Yield Per Acre

of First Crop Forage

If Kennedy, W. K., Nitrogen Fertilization of Meadoivs and Pastures, Cornell
Agr. Expt. Sta. Bui. 935, 1958.

Colovos, et. al.. op. cit.

Rakes, et. al., The Feeding Value for Milk Production of Hays Cut at Various
Dates. W. Va. Agr. Expt. Sta. Current Rpt. 35, 1962.

Rask, et. al.. Early Cut Hay and Silage: Cost and Returns. Cornell Ext. Bui.
1059, 1961.

Regional Forage Crops, Northeast Regional Project NE-21, University of Rhode
Island, Agr. Expt. Sta. Bui. 356, 1960.

Ross, V. E. and Fellows, I. F. An Economic Evaluation of the Barn-Finishing
Method of Harvesting Hay. Storrs Agr. Expt. Sta. Bui. 277, 1951.

Shepherd, J. B., et. al., "Conservation of Nutrients and Feeding Value of Wilted
Silage, Barn-Cured Hay and a Poor Quality Field Cured Hay," Journal of Dairy Sci-
ence, 31:688-689, 1948.

Shepherd, J. B., Experiments in Harvesting and Preserving Alfalfa for Dairy
Cattle Feed. U. S. Dept. Agr., Tech. Bui. 1079, 1954.

Slack, S. T., et. al. Effect of Curing Methods and Stage of Maturity upon Feeding
Value of Roughages. Cornell Agr. Expt. Sta. Bui. 957, 1960.

Slack, S. T., et. al.. Effects of Chopping on Feeding Value of Hays. Cornell Agr.
Expt. Sta. Bui. 950. 1960.

14

observations making yield on first erop a function of calendar datc^^
The correlation coefficient value "r" of 0.8189 and S value of ± 0.6265

was such that the function was considered acceptable for this analysis.
In other words, the function was considered an adequate representation
of yield response to time.

A similar analysis was performed for second-crop yields. The
scatter diagrams of yield are pictured in Figure 5. The fitted first degree
equation was not a good fit; a correlation coefficient of only 0.26 was
obtained. This means that a linear regression equation provides an
estimator of second crop yield that is not significantly more accurate
than the average of the observations. For this reason, the yield of sec-
ond crop was taken to be a constant 0.75 tons per acre.

The quality of first crop forage as related to time is described in
Figure 6. As the date of cut advances into the season, the quality per
unit decreases: for example, from 72.3 percent digestible dry matter
on .June 1 to 56.6 percent on July 1.^- Second crop forage does not have
as high a percentage of digestible dry matter as early cut first crop,
but neither does it decrease as fast. For the purposes of this study, sec-
ond crop digestibile dry matter is assumed to be the same as first crop
cut on June 20.

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DAYS BETWEEN FIRST AND SECOND CROP HARVEST

5. Yield of Second Crop Forage as it is Related to the Lapse of Time
Between First and Second Crop Harvest

11 The function is as follows: y = a + b + c x-. Where: y = yield of first
crop forage, x = date of cut. "

1- Reid, J. T., "Nutrition of High-Producing Cows," a paper given at the winter
meeting of the N. H.-Vt. Breeding Association, 1963.

15

Research by Wheeler indicates that the time required to field cure
forage depends upon the percentage of moisture of the forage at the
time of the cut.i^ After the initial moisture has dropped to about 65
percent, 1 day less is required to field cure forage than when the initial
moisture is above 65 percent.!^ Since the initial plant moisture of first
crop forage steadily declines and reaches 65 percent on approximately

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DATE OF CUT IN NUMBER OF DAYS AFTER MAY 31

Figure 6. Relation Between Date of Cut and the Digestible
Dry Matter of First Crop Forage

June 30, first crop forage can be field cured to 20 percent moisture in
1 day after June 30 if a crusher is used. Second crop forage is never
field cured in 1 day to 20 percent moisture because the initial moisture
never drops to 65 percent or below.

13 Wheeler, W. C, et. al.. Hay Conditioners in the Northeastern United States,
W. Va. Agr. Expt. Sta. Bui. 449, 1960.

14 Boyd, M. M., "Hay Conditioning Methods Compared," Agricultural Engineer-
ing Journal, 4:664-668, November 1959.

16

If the initial plant moisture is low when mowed: (1) the hay-in-a-
day systems (using hay conditioners) do not require the drying vinits
because the forage dries to 20 percent in 1 day, and (2) the hay-in-more-
than-a-day systems will reduce the drying time required by 1 day.

Type of Forage

There were 3 types of forage considered for harvest by the model:
(1) "Hay Good" — defined as forage that does not get rained on after
it is mowed. This forage is fed to cows or sold if the program options
allow surplus hay sales.

(2) "Hay Damaged" — defined as forage that is rained on at least

1 day but not more than 5 days after it is mowed. This forage is fed to
the cows or sold if there is more than enough for feeding and the pro-
gram permits selling.

(3) "Hay Salvaged" — defined as forage that is rained on more than
5 days after it is mowed. This forage is not fed to cows but is sold at a
lower price than good hay or damaged hay.

Total Losses of Forage

The losses of forage in harvesting, storing, and feeding make up the
total losses. Therefore, the remaining forage is available to produce
milk. The harvesting losses make up the largest percentage of the total
losses and depend upon the method of handling and the percentage of
moisture of the forage at harvest time.^^ To control as many variables
as possible, it was assumed that field-cured forage would be harvested
at 20 percent moisture, and artificially dried forage would be har-
vested at 40 percent moistvire. The total losses for first and second
crops, using the 6 harvesting systems, are given in Table 1.

Weather Patterns and Date of Harvest

The weather patterns used in this study are from recorded weather
observation at the official weather station in Concord, New Hamp-
shire, i*^ These observations were for the 52-year period from 1910
through 1961.

The following assumptions were made about the weather patterns
and harvest operations:

(1) A rainy day was a 24-hour period starting at 5 p.m. in which
more than a trace of rain falls;

(2) The operator was unable to forecast the weather; and

(3) Mowing would occur every clear day except when there were

2 mowings already cut.

15 Slack, et. al.. Effect of Curing Methods and Stage of Maturity upon Feeding
Value of Roughages, Cornell Agr. Expt. Sta. Bui. 957. 1960.

16 Local Climatological Data with Comparative Data, Concord, New Hampshire,
U. S. Department of Commerce, Weather Bureau, 1910 through 1961.

17

The number of days that rain occurs during the first crop and the
quantity of rainfall affects the starting" date of the second crop. If there
are many days of rain, harvest of second crop will be delayed due to the
limitation of the harvesting system to handle the quantity of forage. On
the other hand, if rainfall is abundant during first crop harvest, the
aftermath grows faster than otherwise, and the second crop can be
mowed and harvested sooner. Conversely, less rainfall during first crop
means a longer growth period for aftermath, and the length of time
between the mowing of first and second crops is lengthened.

These were 3 starting dates used for each weather pattern: June 1,
15, and 30. These dates represent early, medium, and late-cut forage,
respectively.

Labor and Machinery Requirements

In determining the labor and machinery requirements and input-
output relations, labor was assumed to be used at all times in conjunc-
tion with equipment; i.e., when a man was working, he was using some

Table 1. Total percentage losses of first and second crop forages
by various harvesting methods*

Percentage of total crop lost

Method

Forage Rained on
1-5 days 5 plus days

Forage not
rained on

1st
cutting

2nd
cutting

1st
cutting

2nd
cutting

1st
cutting

2nd
cutting

Percent

Percent

Percent

Percent

Percent

Percent

Field cured

33.6

32.6

40.0

40.0

20.1

19.1

Crushed field cured

31.6

30.6

40.0

40.0

18.1

17.1

Crushed barn dried
without heat

14.8

12.8

Crushed wagon dried
with heat

14.8

12.8

Flail cut flail harvest

21.4

19.4

Wilted grass silage

19.5

19.4

* These losses were determined statistically from many experiments reported in
bulletins, 2 of which are: Ross, V. E., and Fellows, I. F., An Economic Evaluation
of the Barn-Finishing Method of Harvesting Hay, Storrs Agr. Expt. Sta. Bui. 227,
1951; and Trimberger, G. W., et al.. Effect of Curing Methods and Stage of Maturity
upon Feeding Values of Roughages, Cornell Agr. Expt. Sta. Bui. 910, 1955.

18

piece of equipment. Hence, labor and machinery hours were the same.
The requirements were based on an acre of land where the independent
variable was yield per acre. The base unit of account for input-output
coefficients was 1 acre. Coefficients varied with yield per acre.

Labor was available each day in fixed amounts for all harvesting.
No labor in addition to the regular work force was hired.

With the exception of the flail system, the data used to determine
the labor and machinery requirements on the farm, presented in Fig-
ures 7 and 8, were obtained from 1 source, i" The coefficients for the
flail system were developed from data from many sources, i*'

The hours of labor and machinery inputs per acre of cropland were
determined as yield varies for each of the 6 harvesting systems from
Figures 7 and 8. For example, solving the formula in Figure 7 indicates
that if the yield is 3 tons per acre, 3.4 hours of labor and machinery are
required to mow, harvest, and store the forage from 1 acre of cropland
using the system "field-cured baled", and 4.1 hours are required for the
systems "crushed barn dried without heat" and "crushed field cured".

Equipment Capacities

For each harvesting system a maximum number of acres and tons
of forage that could be mowed in a day were specified. These limits
were the capacities of the systems that could change as the limiting
factor was eliminated. For example, the limiting factor in the wagon-
dried system was the capacity of the drier, but after the initial moisture
fell below some percentage, the drier was not used and the ton limit
was consequently increased.

The number of acres that could be mowed and harvested per day
by each system depended upon the yield per acre. The assumption that a
total of 16 hours of labor were available per day limited the number of
acres that could be mowed and harvested per day. From this assumption
and the labor requirements developed previously, maximum acreages
that could be mowed and harvested per day were established. Since
yield varies, the relation was developed in fvinctional form for use in
the model. These functions are shown in Figures 9, 10, and 11. The
maximum number of acres that could be mowed, harvested, and stored
in 1 day for any system is 6 — the physical capacity of the resources
due to time required to travel over the land, independent of yield.

The maximum tons that a system could handle per day also depend-
ed upon the capacity of the slowest part of the operation. For example,
the capacity of the heat drying unit for the "crushed, wagon dried with
heat" system limited the system capacity to 8 tons per day when the
drier was used.^^ Therefore, the series of functions in Figures 9, 10,

I'i^ Agricultural Planning Data for the Northeastern United States, Pennsylvania
State University, A. E. and R. S. 51, July 1965.

1^ Two examples are: Wheeler, W. C, et. al., Hay Conditioners in the North-
eastern United States, W. Va. Agr. Expt. Sta. Bui. 449, 1960, and Phillips, Ross A.,
and Elliot, Kendall C, Using Flail Forage Harvesters, W. Va. Agr. Expt. Sta. Bui.
474, 1962.

19 Hay Drying, Farm Department of Central Vermont Public Service Corporation
and New Hampshire Farm Electric Utilization Council, Spring, 1963.

19

UJ

u

<

to

ID

o

X

8
6
4

0

Crushed Wagon Dried with Heat

Y = 2.1321 - 0.0583X + 0.28809X^

Crushed Field Cured and Crushec
Barn Dried without Heat

Y = 1.9712 0.1507X + 0.2888X-

Field Cured
Y- 1.4371

0.1259X + 0.1728X-

0

UJ

U
<

UJ
CL

CO

q::

o

X

YIELD PER ACRE (TONS)

Figure 7. Machinery and Labor Requirements Per Acre
as Yield Varies for Selected Systems of Harvesting

Flail Cut Flail Harvest

Y= 1.6541 1-0.3229X + 0.1283X'

6 -

Wilted Grass Silage
Y = 0.771 3 0.2003X

0

YIELD PER ACRE (TONS)

Figure 8. Machinery and Labor Requirements Per Acre
as Yield Varies for Selected Systems of Harvesting

and 11 represent the niaxiniuni number of acres and tons of forage that
could be mowed, harvested, and stored per day. The maximum 1 day
workload is to (a) mow and harvest a given number of acres, or (b)
to harvest and store 2 mowings. A yield of less than 0.5 tons per acre
was not harvested because it was assumed to be an uneconomical altern-
ative.

20

5

Q

IJU
CL

UJ
0£

U
<

12 -

10 -

8 -

0

Wilted Grass Silage'

= 30.7078 - 16.3310X + 2.5121X'

Maximum Acres Y

Field Cured''
Y = 11.9932 3.0985X
+ 0.2342X'

Crushed Field Cured '
Y 9.1353 1.6757X

0

1 2 3

YIELD PER ACRE (TONS)

Figure 9. Number of Acres That Can Be Mowed Per Day
as Yield Varies for Selected Systems of Harvesting

Labor Requirements for Cows

The labor requirements for cows on a dairy farm vary with the
method of milking and the type of forage fed. In this study the cows
were assumed to be milked in stanchions. There were 3 types of forage
harvested: baled hay, chopped hay, and haycrop silage. Therefore,
there are three different functions describing the labor requirements
for the cows (Table 2). Much of the labor required for a herd of cows
is fixed. Therefore, as the size of the herd increases, the average time
per cow decreases.

Harvesting Equipment Investment and Annual Costs

The equipment used by the base system included a 7-foot mower;
a 2-plow tractor; a 3-plow tractor; a side-delivery rake; a 32-foot ele-
vator; a baler with bale thrower; and 3 wagons. In addition to the
basic equipment, the "crushed barn-dried without heat" system had a

* These functions are obtained by dividing the yield into total crew hours per
day. In this case the crew hours per day are 16.

t The 6 acre maximum and this function are the limits. The acres per day 1st
crop for this system is limited by the 6 acre maximum and this function, while for
2nd crop the limits are the same as for crushed field cured baled.

21

<

Q
(^

UJ
Qu

to
UJ
Q£
U
<

10

8
6

4
2

0

0

10 Ton Capacity Line''

Crushed Barn Dried without Heat''
Y 9.1353 1.6757X

Maximum Acres Y 6

Crushed Wagon Dried with Heat"'
Y 8.5799 - 1.601 6X

8 Ton Capacity Line^
Y = 8/X

2 3

YIELD PER ACRE (TONS)

Figure 10. Number of Acres That Can Be Mowed Per Day
as Yield Varies for Selected Systems of Harvesting

>-
<

UU
Q-

CO
UJ

U
<

10

8

0

\ Flail Cut Flail Harvest-

Y = 9.9684 - 2.5893X

Maximum Acres Y =

0.2249X'

10 Ton Capacity L
Y= 10. X

0 12 3

YIELD PER ACRE (TONS)

Figure 11. Number of Acres That Can Be Mowed Per Day
as Yield Varies for Selected Systems of Harvesting

* These functions are obtained by dividing the yield into total crew hours per
day. In this case the crew hours per day is 16.

t This is the non-heat drier capacity per day. The line is determined by dividing
yield per acre into 10.

± This is the heat drier capacity per day. The line is determined by dividing
yield per acre into 8.

22

Table 2. Daily milk cow labor requirements with three types of forage
when milking is done in stanchions

Operation

Haycrop silage

Baled hay

Chopped hay

Fixed

Variable

Fixed

Variable

Fixed

Variable

Hours

Hours

Hours

Hours

Hours

Hours

2 Single

milking units

0.093

0.103

0.093

0.103

0.093

0.103

Cleaning

0.733

0.733

0.733

Hay feeding

0.210

0.005

0.210

0.005

0.146

0.005

Silage feeding

0.200

0.009

Grain feeding

0.142

0.005

0.142

0.005

0.142

0.005

Manure handling

-.073

-.073

-.073

Bedding

0.154

0.005

0.154

0.005

0.154

0.005

Other

0.078

0.009

0.078

0.009

0.078

0.009

Miscellaneous

0.137

0.137

0.137

Total

1.674

0.136

1.474

0.127

1.410

0.127

crusher, 5-HP motor and 36-incli fan, and duct work in the harn: and
the "crushed field-cured" system had a crusher.

There are 2 different types of harvesting operations represented by
these 6 harvesting systems. Type I reHed entirely upon field curing and
required more than one day for the forage to dry to a moisture content
in order to safely store it. The "crushed field-cured" system required 2
days, including the day of mowing to harvest. The field cured system
required 3 days, including the day of mowing. The Type II system,
"crushed, harn dried without heat," is a hay-in-a-day system, i.e., the
forage was harvested the day it is mowed.

Table 3. Investment and costs for six forage harvesting systems

System

Total
Invest-

Fixed cost

Variable
cost

Variable cost

per hour to

artificallv

ment

per year

per hour

dry forage

Dollars

Dollars

Dollars

Dollars

Field cured

11,010

2,077.55

4.70

0.0

Crushed field cured

11,860

2,253.00

5.13

0.0

Crushed barn dried
without heat

13,410

2,574.10

5.13

0.125

Crushed wagon dried
with heat

23.910

4,940.95

5.13

0.778

Flail cut flail
harvest

13.220

2,145.50

4.13

0.125

Wilted grass silage

16,545

3,039.46

4.75

0.0

23

The cost, investment, and price data used were those prepared for
use by the Northeast Dairy Adjustment Study Group.- "^ Tables 3 and
4 summarize the cost, price, and investment requirements for each
system.

Milk Production

Because the development of the milk production surface for an
individual cow or herd of cows is a complex operation, the variables used
are described in some detail.

The general form of the milk production function was defined as:
MP = f(F, G, L, A, M, Xi . . .X ) (1)

n
where MP refers to milk production in a specific period; F refers to
forage (quality and quantity considered) ; G refers to grain; L refers to
the stage of lactation; A refers to the ability of the cow to produce milk;
M refers to the dairy herd management; and Xj . . .X refers to other

n
unspecified factors.

To assure that the major objectives of this study would be accom-
plished, i.e., to evaluate the organization of forage harvesting systems
and grain feeding alternatives, the only variables that were allowed to
vary were forage (F) and grain (G) . Therefore, equation (1) becomes:
MP = f(F, G/L, A, M, Xi . . .X ) (2)

n
The / notation indicates that forage (F) and grain (G) are allowed
to vary while lactation (L) , ability (A), management (M) and other

factors fXi ... X ) are held constant.

n
Equation 2 states that milk production is a function of feed in-
puts and that everything else is held constant at some level. Therefore.

Table 4. Cost or price per unit of resources used and products produced

Resource or product Dollars

Annual fixed cost per cow 100.00

Investment per cow 350.00

Price per ton of hay to sell 26.10

Building investment per cow 585.00

Price of labor per hour 1.13

Price of grain per ton 79.80

Price of milk per cwt. 5.46

Price of hay salvaged to sell per ton 20.00

Price of hay to buy per ton 35.30

Building fixed cost per thousand 159.83

20 Agricultural Planning Data for the Northeastern United States, op. cit.

24

any change in the quality or quantity of the feed will result in a change
in the milk produced per unit of time. The milk production function for
the purposes of this study is a herd function. It was assumed that the
herd composition is homogeneous and that changes in feed inputs can
be reflected in output for the herd much as they would be for the
individual cow. The assumptions used in this study to control the non-
feed and feed factors are outlined in the following sections.

Cow Al>ility

The ability of a cow or a herd of cows to convert the combinations
of forage and grain into milk is determined by 2 closely related factors:
(1) the inherent milk producing ability of the cow and (2) the ability
of the cow to consume large quantities of forage. To overcome varia-
tions that occur between cows, it was assumed that all cows had a high
inherent ability and could consume large amovints of forage.

Stage of Lactation

The freshing pattern of a herd of dairy cows has a great influence
upon the seasonal milk produced; therefore, the assumption was made
that the freshing pattern was randomly distributed throughout the
year and that any cows added to the herd would not disturb this
distribution.

Other Factors

There are many other factors that affect the quantity of milk pro-
duced by a cow or a herd of cows such as cow age, body weight, and
temperature. In this study it was assumed that body weight did not
change. This implied that (1) the total amounts of nutrients consumed
by the cow would be used in the production of milk, and (2) that the
age of the cows was randomly distributed. The same number of cows,
therefore, would be replaced every year; thus, the milk production sur-
face was not affected. Further, it was assumed that all other variables,
such as cow temperatures, were constant.

Forage Input

Forage is the major input in the production of milk; therefore, a
complete understanding of the meaning of forage as used in this study
is necessary. It should be understood that quality and quantity of forage
produced per acre by date of cut are inversely related.

In addition to the quality and quantity relation, nutrients are
lost through rain damage; thus, there are 2 types of forage available
to produce milk: (1) forage undamaged by rain and (2) forage damaged
by rain. The difference between the 2 types of forage is that for each
and every date of cut, the percentage of digestible dry matter of rain-
damaged forage will be less than if no rain damage occurred. When
rain-damaged forage is fed, nutrient intake will drop unless more forage
is consumed or grain consumption is increased.

25

Other factors associated with both types of forage are : ( 1 ) the
feed acceptance level of the cow by date of cut of forage and (2) the
minimum amount of forage that will be consumed per cow per day.

The feed acceptance level of the cow is the combination of forage
and grain that she will consume per day, and depends upon the date of
cut and whether the forage was rain damaged or not. The acceptance
level reflects the quality of the forage by date of cut and weather
damage.

There is an acceptance level for each type of forage (rain damage or
no rain damage) by date of cut. Further, a minimum amount of forage
will be consumed per cow per day, independent of the type or date of
cut.

Grain Input

Grain is the second major input used in the production of milk.
Purchased grain is not as variable in quality as forage. Therefore, the
quality of the grain fed was held constant, while the quantity was allow-
ed to vary. These methods Avere considered : ( 1 ) grain constant : I 2 )
milk constant; and (3) constant milk-grain ratio. This means that 3
comparisons were made, 1 for each type of grain feeding management.
These 3 methods outline the extreme ways that milk can be produced
using forage and grain as variables.

Using the grain-constant method of producing milk, grain was held
constant at a fixed number of pounds per cow per day. Forage was fed
free choice. Milk production per cow per day varied according to the
amount of nutrients supplied by the fixed quantity of grain and the
free-choice quantity of forage consumed.

Using the milk constant method, milk was held constant at a fixed
number of pounds per cow per day. The nutrients required to produce
this milk had to be supplied by the forage and grain consumed.

The milk-grain ratio type of management is very commonly used
by farmers, and implies that cows will be fed 1 pound of grain for fixed
amounts of milk produced. Forage is fed free choice. Less forage is
consumed as the date of cut advances; therefore, less nutrients from
forage are consumed by the cow. As a result, milk production declines,
and the amount of grain fed declines. This becomes a cycle in that:
(1) late cut forage is not as palatable as early cut forage and has less
digestible dry matter per unit; thus (2) milk production declines be-
cause only nutrients consumed are used to produce milk; therefore,
(3) less grain is fed, because it is fed according to the amount of milk
produced.

The Milk Production Surfaces

It was necessary to use 2 production surfaces to describe the x'ela-
tions developed from the available data because of the 2 types of forage,

26

i.e., no rain damage and rain damage. ^^ The difference in the nutrient
vahie of these forages is reflected in research hy Rakes, et. al.-^ They
found that the digestihle dry matter was reduced about 5.3 percent and
the vohintary intake about 17.4 percent when forage was rained on as
compared to no rain damaged forage. The production surfaces are
shown in Figures 12A and 13A, where Figiire 12A represents the daily
milk production surface using no rain damaged forage and grain and
Figure ISA represents the daily milk production surface using rain
damaged forage and grain. Figure 12A needs to be explained since the
only difference is the type of forage fed; Figure 13 is self-evident.

The X and Y axes in Figure 12A represent the pounds of grain and
forage available for consumption per cow per day, respectively. The
isoquants represent the expected daily milk output per cow resulting
from the various combinations of inputs (forage and grain).

There are 2 sets of isoquants. The solid and broken isoquants repre-
sent the milk output per cow per day that can be expected using the
combinations of early cut and late cut forage and grain, June 1 and
July 10, respectively. The difference in the isoquants reflects the re-
sults expressed by Slack, et. ah, "approximately 20 percent more late-
cut forage must be eaten to provide the same amount of digestible dry
matter."-^

The acceptance level lines represent the combinations of forage and
grain that the cow will consume per day, if the forage is fed free choice
and grain is regulated by date of cut of the forage. The highest feed
acceptance level is for June 1 forage and the lowest for July 10. The
2 lines converge at 13.6 pounds of forage and 22 pounds of grain. This
convergence occurs because the cow will eat a mininnim quantity ( 13.6
pounds ) of any type and quality of forage and a maximum quantity
(22 pounds) of grain.

There are 2 oliservations inherent in this analysis. First, the 2 sets
of isoquants become identical if time is considered a variable, i.e., June 1
isoquants become July 10 isoquants. This should be obvious, since there
is a milk production surface for each date of cut represented only by
June 1 and July 10. Second, the feed acceptance level lines become
identical if time is considered a variable. As date of cut advances, the
amount of forage that the cow will consume will decline such that when
date of cut is July 10, the coml)inations of forage and grain consumed
is expressed by the July 10 acceptance level line. The lowest isoquant in
Figure 12A represents those combinations of early cut forage and grain
that will produce 25 pounds of milk per cow per day. A possible feed
combination may be 3 pounds of grain and 26.8 pounds of forage. If 3

21 Data were obtained from many sources. For a representative sample, see:
Slack, E. T., et. ah. Effect of Chopping on Feeding Value of Hays, Cornell Agr. Expt.
Sta. Bui. 950, 1960, and Loosli, J. K., et. al., "The Comparative Value of Ladino Clover.
Birdsfoot Trefoil, Timothy and Alfalfa Hays for Yield and Quality of Milk," Journal
of Dairy Science, 33:228-236.

-- Rakes, A. H., et. al.. The Feeding Value for Milk Production of Hays Cut at
Various Dates, W. Va. Agr. Expt. Current Rpt. 35.

23 Slack, S. T., et. al.. Effect of Curing Methods and Stage of Maturity upon Feed-
ing Value of Roughages, Cornell Agr. Expt. Bui. 957, 1960, P. 24.

27

See Figure 12B for
enlargement

June 1 Forage Cut
July 10 Forage Cut

Forage Acceptance Level
June 1

Forage Acceptance Level
July 10

0 2

Figure 12A.

10 12 14 16 18 20 22 24

CONCENTRATES, POUNDS

Estimated Daily Milk Isoquants, No Rain Damaged Forage,
and Two Dates of Cut

additional pounds of grain are used, 25 pounds of milk can be produced
with the 6 pounds of grain and only 12.3 pounds of forage.

To increase milk production, using only the June I isoquants, in-
creasing quantities of forage and grain may he fed, or 1 input may he
held constant and the other increased. For example, shifting from the
25- to the 30-pound isoquant can he accomplished hy holding forage
constant at 26.8 pound level and increasing grain from 3 to 5.3
pounds. Shifting to still higher levels of milk production can he ac-
complished hy increasing grain feeding and holding forage constant,
increasing forage with grain held constant, or hy increasing both hay
and grain feeding. In all instances, the acceptance level for the date of
cut and the minimum forage line describes the outer limits of hay and

28

36

34

32

O

z

^ 30

O

a.

<
>

o

LU

>-
<

X

28

26

24

A^

June 1 Forage Cut
July 10 Forage Cut

Forage Acceptance Level
June 1

Forage Acceptance Level
July 10

Grain Feeding Management
A - Grain Constant
B = Milk Constant
C = Milk-Grain Ratio

A5

i

0 •— \^

35 40

POUNDS OF MILK

8 10 12

CONCENTRATES, POUNDS

14

Figure 12B. Estimated Daily Milk Isoquants, No Rain Damaged Forage^
Two Dates of Cut and Three Methods of Grain Feeding

grain consumption. Thus, the quantity of milk produced per cow per
day can be determined for the different qualities and quantities of forage

and gram

Figure 12B is an enlargement of the outlined rectangular section of
Figure 12A. This is the section of the isoquants and acceptance lines
used to determine the functions that represent the 3 management meth-
ods of feeding grain in the production of milk, i.e., grain constant, milk
constant, and constant in milk-grain ratio. The X and Y axes, forage ac-
ceptance lines, and isoquants in Figure 12B are represented in the same
manner as in Figure 12A, while lines A, B, and C are the functions that
represent the management methods of feeding grain, grain constant,
milk constant, and milk-grain ratio, respectively. Implied in this Figure
12B, as in Figure 12 A, is that forage acceptance line June 1 moves down
and becomes forage acceptance line July 10, and the Jime 1 isoquants

29

move up and to the right and hecome July 10 isoquants. Therefore, the
intersection of the isoquants and acceptance lines for the same date of
cut represents the combinations of forage and grain to produce the
different quantities of milk. For example, if milk output is held constant
at 35 pounds per cow per day, forage and grain consumption by date of
cut will range from 33.7 and 6.3 pounds per day to 24.9 and 9.6 pounds
per day, respectively.

The following functions represent lines A, B, and C in Figures 12B
and 13B, i.e., these functions represent the forage and grain consumed
and milk produced by date of cut for the 3 grain feeding management
methods with undamaged forage and rain damaged forage. In these
functions F refers to forage, G refers to grain, T refers to date of cut,
and M refers to milk produced. All of the inputs and outputs are in
pounds per cow per day.

25 30 35 40 45 50

— June 1 Forage Cut
July 10 Forage Cut

See Figure 13B for
enlargement

Forage Acceptance Level
June 1

>^^ Forage Acceptance Level
July 10

8 10 12 14 16 18 20
CONCENTRATES, POUNDS

22 24

Figure 13 A. Estimated Daily Milk Isoquants, Rain Damaged Forage,

Two Dates of Cut

30

No Rain Damaged Forage Functions (Figure 12B)

A. Grain Constant (6.3)

F = 33.7743 - 0.1743 (T)
M= 9.5716 + 0.7568 (F)

B. Milk Constant (35.0)

F = 33.7743 -0.2217 (T)
G = 6.2154 + 0.0846 (T)

C. Milk-Grain Ratio (4:1)
Frr 28.6205 - 0.0705 (T)

M = — 86.9157 + 4.6178 (F)
G = M/4

30

28

t/> 26

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<
>

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18 -

Forage Acceptance Level
June 1

• U

POUNDS OF MILK

Forage Acceptance Level
July 10

Grain Feeding Management
A =^ Grain Constant
B =Milk Constant
C = Milk-Grain Ratio

^\ ^

0 6

8 10 12

CONCENTRATES, POUNDS

14

Figure 13B. Estimated Daily Milk Isoquants, Rain Damaged Forage,
Two Dates of Cut and Three Methods of Grain Feeding

31

Rain Damaged Forage Functions (Figure 13B)

A. Grain Constant (6.3)

F =30.3358 - 0.1858 (T)
M = 1.0890 + 0.7769 (F)

B. Milk Constant (35.0)

F =29.9294-0.2794 (T)
G = 6.6449 + 0.1551 (T)

C. Milk-Grain Ratio (4:1)

F = 27.4294 - 0.1294 (T)
M = — 7.4357 + 1.6938 (F)
G = M/4

EVALUATION OF ALTERNATIVES WITH SIMULATION MODELS

The situations that can be analyzed using simulation analysis are
numerous. Different research questions can be answered by: (1)
changing production functions within the model, such as a forage yield
response; or (2) adding an alternative machinery system or a weather
pattern sequence. The number of obtainable solutions of the simulation
model is the product of the number of alternatives included. The initial
design for this analysis was to appraise: 6 forage harvesting sys-
tems, 52 weather patterns, 3 forage harvest starting dates, 3 grain feeding
systems, and 3 sizes of herd.

All combinations of these options would have required 8,424 solu-
tions. This would have created a data summarization problem, which
raised a question as to the necessity for computation of all the possible
problem combinations. The machine computation, therefore, was
divided into 2 phases. Phase I analysis was designed to evaluate grain
feeding methods and machine harvest systems to select the most profit-
able combinations. Phase II analyisis was designed to study variations
in forage balance (purchases or sales) and in income as influenced by
52 weather patterns for 2 harvest systems, 3 dates of cut, and 3 herd
sizes.

Phase I Analysis

Phase I included combinations of 6 forage harvesting systems, 3
dates of cut, 3 grain feeding systems, 3 herd sizes, and 3 weather pat-
terns. A total of only 486 solutions were possible. The 3 weather patterns
were selected from 52 years of records from 1910 through 1961 to repre-
sent "average," "wet," and "dry" rainfall-clear day weather relations.
Acreage of hayland was set at 100 acres. Herd size was set at 3 levels —
75 cows, 100 cows, and allowed to vary so as to consume all the eatable
forage produced.

32

Most Profitable Grain Feeding Method

&

The 3 grain feeding methods inchided feeding grain to each cow at
a ratio of 1 pound of grain to 4 pounds of milk, feeding a fixed quantity
of grain to each cow, and feeding grain and forage in the proportions
needed to obtain a fixed quantity of milk.

The Phase I analysis indicated that feeding each cow the same
quantity of grain is an uneconomic method. Although as many cows
could be fed as with the other grain feeding methods, net farm income
was lower than with the other 2 grain feeding methods. This result is
compatible with the long standing recommendations of many nutrition
specialists for feeding cows according to milk production.

Difficulties, mainly stemming from inadequacy of data, were en-
countered in the analysis with feeding grain to maintain constant milk
output. Net income became progressively larger as the date of cut ad-
vanced. The feeding trials that the milk production functions were based
upon were not designed to measure the substitution rate of grain for
quality of hay. Due to inadequate data further analysis of this feeding
method was considered beyond the scope of this study.

The Phase I analysis, therefore, indicated that feeding according to
a milk-grain ratio would be of most value for further study in Phase II.

Most Profitable Forage Harvest Systems

The five forage harvesting systems analyzed were: field cured and
baled: crushed, field cured and baled; crushed, baled and barn dried
without heat; crushed, baled and wagon dried with heat; and flail cut,
flail harvested and bam dried without heat.

Crushed, baled, and wagon dried with heat was economically in-
ferior to the other systems, and this result is compatible with farmers'
experience with this system of forage harvesting.

Although the flail cut, flail harvest system is particularly well
suited for small farms, it is not commonly used by farmers. The speed of
harvest may not be as rapid as many large farmers desire. However, Phase
I of the analysis showed the flail cut, flail harvest system to be 1 of the
more profitable systems. Its performance in terms of income was about
equal to the systems of harvest in which the hay was crushed, baled,
barn dried without heat. Preference of one of these systems over the
other would be based upon considerations or features not considered in
the simulation model.

Crushing hay after mowing for quick drying generally cuts 1 day
off curing time. The addition of the crushing operation appreciably in-
creased income for June 1 and June 15 cut hay in all cases. For June
30 cut hay the advantage of the crusher in the system generally was
not as great, and under several situations was of no advantage in the
harvesting systems. From the analysis using three weather patterns, it
can be concluded that the crusher offers advantages in the early cut hay
operations.-*

-^ For more detailed information on the 5 forage systems and the 3 grain feeding
systems, see Cloud, Op. Cit.

33

Based on the Phase I analysis of the 6 machinery systems, the
number of systems was reduced from 6 to 2 for study in Phase II. The
2 systems selected were: crushed, field cured, and haled; and crushed,
baled, barn dried without heat. These are the more common systems
found on farms.

Phase II Analysis

Phase II of the analysis was designed to include combinations of
the 2 most profitable harvest systems, the most profitable grain feeding
system, 3 harvest starting dates, 3 cow number options, and 52 weather
patterns.

The 2 forage harvesting systems were: crushed, field cured, and
baled; and, crushed, baled, and barn dried without heat. The grain
feeding method was the milk-grain ratio of 1 pound of grain to 4 pounds
of milk. The 3 starting dates of harvest were June 1. 15, and 30. The 52
weather patterns were those of the period from 1910 through 1961.
Variations in intensity of land use and consequent sales and purchases
of hay were represented by 3 herd sizes on the 100 acres of cropland. ^ 5
The 3 herd sizes studied were 75 cows, 100 cows (])oth with hay purchase
or sales allowed), and cows varying according to the number needed to
consume the harvested forage. None of these situations reflect an actual
course of action taken by farmers with respect to ratio of cows to crop-
land because farmers tend to cull their herd a little heavier when feed
supplies are short. It is doubtful if a dairyman could buy and sell
enough animals of the desired quality to obtain the degree of variability
in herd size assumed when cow numbers are permitted to vary in re-
sponse to forage supply. These 3 situations tend to bracket the alterna-
tives within which dairymen operate.

Influence of Date of Cut and Weather
Pattern on Forage Balance

75 Cows and Selected Hay Harvesting Systems

The influence of hay purchases and sales on farm income is im-
portant in the analysis because of the many and varied problems in-
volved under actual operating conditions over time. Usually when one
farm is short on hay, other farms are also short on hay, and relatively
higher prices for hay purchases prevail. When hay supplies are plentiful
on the individual farm, this usually means that all farms have plenty
of hay and relatively lower prices prevail. Many farmers alleviate the

-5 Harrington, D. H., Andrews, R. A., "Net Incomes and Resources Valuations
of Optimum Organizations for Dairy Farms in Northern New England," Agr. Expt.
Sta. Bui. 490» 1967.

34

problem by establishment of a "hay bank." Essentially, this is carrying
inventories from years of abundant supply over to years of short supply.
The intensity of cropland use with 75 cows was at a level of 1.5 acres
providing the hay for 1 cow. At this level of intensity little hay was
bought or sold when date of cut was June 1.2 c As date of cut advanced,
greater quantities of hay sales were made. With date of cut advanced to
June 30, sizable quantities of hay were sold. The extent of and variation
in hay purchases or hay sales in 52 weather patterns for harvesting hay
are shown in Table 5.

100 Cows and Selected Hay Harvesting Systems

This ratio of cows to cropland represented a rather intensive use of
cropland and the operations were essentially based upon some hay pur-
chases occuring as early as the June 15 date of cut. With a June 1 date of
cut, sizable quantities of hay were purchased at assumed prices. Hay
purchases or sales for this size option are shown in Table 5.

Number of Cows Varying and Selected Hay Harvesting Systems

The herd size under this situation was allowed to vary to the ex-
tent that all eatable forage produced on the 100 acres was fed to dairy
cows. This assumed that the farmer is able to buy and sell dairy cows in
the market each year to vary his herd size. It also assumed that the
complementary farm production resources are available. The particular
advantage of this situation lies in eliminating problems with the pricing
of hay, and hence, in concentrating the full influence of date of cut on
net farm income in terms of feeding quality.

Under this situation there were no hay purchases but only sales of
small quantities of hay that was beyond use as feed due to rain damage.

Influence of Date of Cut and Weather
Pattern on Gross Farm Income

75 Cows and Selected Hay Harvesting Systems

Gross farm income declined as date of cut advanced for both har-
vest systems. The distribution of gross income by date of cut for the 52
weather patterns with the crushed, field cured, and baled system is
shown in Table 6. The distribution for the crushed, baled, barn dried

-'' The option of purchasing and selling hay presents at least 2 analytical prob-
lems. The first involves estimating prices paid and prices received. The second in-
volves assumptions about variations in quality of purchased forage. To reflect market-
ing services performed such as transportation and commission costs on sales, the
purchased price for hay was established at a higher level than the sales price. These
prices reflect what the farmer would receive or pay for hay at the farm storage
point of sale. Purchased hay was assumed to be of the quality obtained by cutting on
June 30. This seemed to be a realistic assumption in light of actual quality of hay
exchanged in the hay market.

35

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36

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37

without heat system is also shown in Tahle 6. Variation in gross farm in-
come was greater with the crushed field cured, baled system. A hay-in-a-
day type system reduced the variability in income due to year-to-year
weather differences. Moving the date of cut from June 1 to June 15 and
from June 15 to June 30 reduced average gross income by $636 and
$1,161, respectively, with the crushed field cured, baled system. Results
were similar as date of cut was advanced from June 1 to June 30 with the
other forage harvest system.

100 Cows and Selected Hay Harvesting Systems

The gross income distribution with 100 cows was quite similar to
that with 75 cows (Table 6). The system with the shorter cut-to-storage
time reduced the income variability over time. For both harvest systems,
gross income declined as date of cut was moved later than June 1.
Moving from June 1 to June 15 with the crushed, field cured, Ijaled sys-
tem lowered income by $2,937. Going from June 15 to June 30 lowered
income by $3,535. The hay-in-a-day system showed changes which lower-
ed gross income by $3,155 and $2,420, respectively.

Number of Cows Varying and Selected Hay Harvesting Systems

When the size of the herd was allowed to adjust in the model to the
production of eatable forage, the average gross income increased as
date of harvest moved from early to late June. (Table 6) . For the crush-
ed, field cured system the June 1 to 15 increase was $4,772; and the
June 15 to June 30 increase was $2,264. The income increases for the
barn dried system was $5,692 and $2,158 respectively for these time
periods.

Influence of Date of Cut and Weather
Pattern on Net Farm Income

The level of net farm income and the change in net farm income
associated with the advancing date of cut were the most relevant con-
siderations in assessing alternative management systems. The an-
alysis showed average net farm income and variations in net farm income
associated with 52 weather patterns, by date of harvest, for each of 3
herd sizes, and for each of 2 hay harvesting systems. No effort to analyze
income differences between the 3 different herd size situations was made
because this study was not designed to investigate scale economies.
Therefore, the net income analysis will be presented by herd size
options.

75 Cows and the Crushed, Field Cured, and Baled Hay Harvest
System

Table 7 illustrates the variations in net farm income associated
with the 52 weather patterns for the 3 dates of cut. Considerable varia-

38

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Hay Harvest System,
Starting Date of Cut

75 Cows; Hay Crushed,
Field Cured and Baled:

June 1

June 15

June 30

75 Cows; Hay Crushed,
Baled and Barn Dried:

June 1

June 15

June 30

100 Cows; Hay Crushed,
Field Cured and Baled:

June 1

June 15

June 30

100 Cows; Hay Crushed,
Baled and Barn Dried:

June 1

June 15

June 30

Cows Vary; Hay Crushed,
Field Cured and Baled:

June 1

June 15

June 30

Cows Vary; Hay Crushed,
Baled and Barn Dried:

June 1

June 15

June 30

39

tion in net income exists within each of the 3 harvest dates. The greatest
variation in net income exists when using the June 1 starting date, and
the range in incomes covers a spand of $3,500. The average net income
for all 52 weather patterns by date of cut is shown in Table 8. The
averages ranged from $10,557 for June 30 to a high of $11,524 for June
15. The differences between these mean incomes are also present in
Table 8. Moving from June 1 to June 15, income increased $477. Com-
paring June 15 and June 30, net income decreased by $967. These differ-
ences are significantly different statistically and probably economically
over time. However, the distribution of incomes as shown in Table 7 is
so great that farmers may not be able to associate the differences with
date of cut.

75 Cows and the Crushed, Baled, and Barn Dried Without Heat
Hay Harvest System

Income variations associated with 52 weather patterns by date of
harvest are shown in Table 7. Compared with the above system, there is
far less income variation for each date of cut. The average differences
by date of cut ranged from $11,992 to $12,780 (Table 8) . The differences
among the net incomes of the 3 dates of cut were statistically different.
The June 1 and 15 dates of harvest result in incomes quite similar and
considerably higher than June 30. Farmers probably would recognize
this difference in their income.

100 Cows and the Crushed, Field Cured, and Baled Hay Harvest
System

An outstanding observation about the analysis of this harvesting
system is the variability of net farm income (Table 7). Just as with the
smaller herd size, farmers would experience large variations from year
to year in net income using this hay system. Average income (Table 8)
is about the same for the 2 early dates of cut and declines about $2,000
when cutting begins on June 30. The difference in income was statis-
tically different and would be expected to be economically important
to farmers.

100 Cows and the Crushed, Baled, and Barn Dried Without Heat
Hay Harvesting System

This hay-in-a-day system reduces the variability in net income
(Table 7). Again, the first 2 harvest dates are almost identical in
average net farm income (Table 8). As the date of cut is advanced to
June 30, income decreases by about $1,600. Farmers would recognize
this difference due to the limited variability in net income within each
date of cut.

Numl)er of Cows Varying and the Crushed, Field Cured, and Baled
Hay Harvest System

The range of variation in net incomes for this harvest system with

40

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41

cows varying was not different than when cow numbers were stipulated
(Table 7). Income increased considerably when date of cut was moved
from June 1 to June 15. Moving from June 15 to June 30, while more
profitable than June 1, was less profitable than June 15 by almost
$1,200.

Number of Cows Varying and the Crushed, Baled and Barn Dried
Without Heat Hay Harvesting System

Table 7 illustrates the variation of net incomes by date of harvest.
Relative to the other machinery systems there is very little difference in
income distribution. June 15 is again the optimum economic date of
harvest and is much more profitable than either June 1 or June 30.

Most Profitable Hay Harvest Date of Cut and System

Of the 6 possible comparisons of date of cut shown in Table 8,
June 15 was superior to June 30 for each harvest system and each herd
size option. Since only 3 dates of starting were tested, it was impossible to
select the precise optimum date of cut. June 30 is definitely inferior.
However, based on the results, the optimum economic harvest beginning
date falls after June 1 and before June 30. Selecting the harvest starting
date to maximize net income seems to be an adjustment which farmers
should easily be able to make. The only conflicting goal which might
arise is one of competition for the use of productive resources during
the harvest season. Labor may be needed to plant corn, and its return
may be considerably greater in this use than in adhering to a strict time
sequence for forage harvesting.

Differences associated with shifting through the 3 dates of cut
were not found to be large. Year-to-year differences in the weather
pattern had a marked influence, particularly with the system using
natural field curing. However, farmers committed to a machinery har-
vest system can select the approximate starting date to obtain the
greatest net income.

The second problem tested in this study was one of appraising
several machinery systems for harvesting forage. These were reduced
to the 2 representative systems in Table 9; 1 system represented field
drying; the other represented the more intensive single-day artificial
drying system. The use of the hay-in-a-day system resulted in a marked
drop in year-to-year variation in income variability (Tables 6, 7). As
shown in Table 9, the artificial (without heat) drying method resulted
in higher net farm income when all other factors were held constant.
This indicated that some farm income advantage can be obtained using
a harvest system that shortens the time between cut and storage. The
system tested in this study involved considerable additional investment
compared with field drying, and the problem of capital accumulation
was not studied. For the same beginning date of harvest, net farm in-
come could be increased by amounts varying from over $1,200 to under
$3,000 (Table 9) . These are magnitudes that warrant the consideration
of dairy farmers in economic planning.

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