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71/-- ~ -
May 1985
Bulletin 850 (technical)
A Simulation Model for Backgrounding
Feeder Cattle in Florida
T. H. Spreen, J. A. Ross, J. W. Pheasant,
J. E. Moore, and W. E. Kunkle
Agricultural Experiment Stations
Institute of Food and Agricultural Sciences
University of Florida, Galnesvllle
- -- --- --
A SIMULATION MODEL FOR BACKGROUNDING
FEEDER CATTLE IN FLORIDA
by
Thomas H. Spreen
Joe A. Ross
Jim W. Pheasant
John Moore
William Kunkle
The authors are Associate Professor, former Assistant in,
and Assistant in Food and Resource Economics, and Professor and
Associate Professor, Department of Animal Science, University
of Florida, Gainesville, Florida.
Table of Contents
Page
Introduction............................................... 1
Backgrounding Feeder Cattle in Florida..................... 1
Backgrounding in North Florida......................... 2
Backgrounding in South Florida......................... 3
Model Overview............................................ 3
The Growth Simulation Model................................ 3
Model Input............................................ 3
Stocking Rate......................................... 6
Intake..................................... .......... 6
Net Energy Conversion ................................... 8
Gain and Updating..................................... 9
Adjustments for Breed, Frame Size, and Heat Stress..... 10
Growth Stimulants and Feed Additives................... 12
Cost Accounting Analysis.................................. 13
Allocation of Costs.................................... 13
Cash Costs............................................. 13
Other Costs........................................... 15
Output................................................ 16
Illustrative Example...................................... 16
Growth Stimulation.................................... 16
Cost Analysis.......................................... 25
Concluding Comments ......... ....................... ....... 30
Appendix A.............................................. 31
Forages in Florida.................................... 31
Forage Quality Index................................... 36
Bermudagrass ........................................ 36
Stargrasses..................... ................. 38
Digitgrass ................. ........... .... ................. 39
Page
Bahiagrass............................... ....... .. 40
Response of Bahia to Fertilizer..................... 41
Hemarthria.......................................... 41
Forage Legumes ......................................... 43
Winter Forage Legumes............................... 43
Warm Season Legumes.................................. 44
Temporary Winter Pastures in Florida................... 44
Cereal Grains as Forage.............................. 45
Ryegrass............................................. 46
Producing Combinations of Winter Forages............ 46
Summer Annual Forages in Florida....................... 46
Pearmillet.......................................... 47
Sorghum-Sudangrass................................. 47
Quantity and Quality of Forages Produced in Florida.... 47
Appendix B............................................. .... 49
Statistical Conversion Equations ....................... 49
TDN Requirements for Maintenance ....................... 49
Net Energy Concentration of Feeds ..................... 49
Appendix C....................................... .......... 55
User's Guide to the Bioeconomic Simulator.............. 55
Accessing the Program.................................. 56
Operating Instructions................................ 57
Data Entry ............................................ 58
Economic Budget Input.................................. 59
Menus for Output Tables................................ 60
Choices Relating to Output............................. 60
How to do Multiple Runs................................ 61
References................................................. 63
List of Tables
Table Page
1. Adjustment factors for heat stress................... 11
2. Echo of input to growth simulation model............. 20
3. Summary of output from growth simulation model........ 21
4. Forage utilization.................................. 23
5. Nutritional accounting in the growth
simulation model ..................................... 24
6. Example of budget from cost analysis model:
Estimated costs for pasturing a 400 pound calf
to 718 pounds, September through April................ 28
7. Per head breakeven prices from cost analysis model... 29
8. Estimated costs for 318 pounds of gain................ 29
9. Per head breakeven prices from cost analysis model... 30
A.1. Perennial warm season forages -- North Florida....... 32
A.2. Perennial warm season forages -- South and
Central Florida...................................... 33
A.3. Winter annual forages.................................. 34
A.4. Summer annual forages ................................ 35
A.5. Range in quality index, crude protein (CP), and
total digestible nutrients (TDN) in hay samples
from research plots.................... ......... 37
Page
A.6. Relationship between forage quality index and
expected performance by heifers and cows............. 37
B.I. TDN required for maintenance as a function of
weight.............................................. 50
B.2. Actual and predicted feed value for grain and
protein supplements... ............................. 52
B.3. Actual and predicted feed value for sugarcane
molasses and citrus pulp............................. 53
B.4. Actual and predicted feed values for forages......... 54
List of Figures
Figure Page
1. Integration of the growth simulation and cost
accounting components of the model................... 4
2. Schematic of the growth simulation model.............. 5
3. The format of the cost analysis budget: estimated
cost for pasturing a lb. calf to lb........ 14
4. Worksheet for growth simulation model................. 17
5. Worksheet for cost analysis.......................... 26
A SIMULATION MODEL FOR BACKGROUNDING
FEEDER CATTLE IN FLORIDA
Introduction
The production of beef in the United States has evolved
over the past 25 years to a system that involves three sub-
systems. The first step, production of weaned calves, is man-
aged by independent cattlemen. This takes place in nearly
every state, though the major supply regions are the Southeast
and Southwest. The next step is an intermediate phase called
"backgrounding." This usually involves weaned calves grazing
on a managed forage possibly supplemented with small amounts of
grain. The last step takes the backgrounded cattle into a
feedlot facility. The animals are confined to a fairly small
area and fed a ration of feed grains or other high energy feed-
stuffs. This step is called "finishing."
This bulletin focuses on the backgrounding stage of beef
production. The backgrounding stage, in this paper, is defined
as the growth of cattle after weaning and prior to placement in
a confined feedlot.
The need for a simulation model for backgrounding feeder
cattle specified for Florida arises from the wide array of
potential programs that can be utilized in the backgrounding
stage. The profitability of any particular backgrounding pro-
gram depends upon a long list of factors including breed, sex,
initial weight, and past performance of the feeder calf; the
type of forage and its level of fertilization; and environ-
mental factors such as time of year, soil types, and rainfall.
Environmental factors are sufficiently important that back-
grounding programs in Florida vary widely, depending upon sea-
son and location in the state.
The objective of this bulletin is twofold: development of
a computerized model that simulates the growth of feeder cattle
on a forage-based feeding program and integration of the growth
model into an economic model to allow evaluation of the pro-
jected costs and returns associated with a particular back-
grounding program.
Backgrounding Feeder Cattle in Florida
Florida is a large cattle-producing state with over 1.2
million beef cows in 1982 (Fla. Dept. of Agr., 1983). Nearly
all cattle producers market weaned calves. It has been esti-
mated that approximately 75% of available feeder cattle are
exported from the state (Simpson and Baker, 1979).
A SIMULATION MODEL FOR BACKGROUNDING
FEEDER CATTLE IN FLORIDA
Introduction
The production of beef in the United States has evolved
over the past 25 years to a system that involves three sub-
systems. The first step, production of weaned calves, is man-
aged by independent cattlemen. This takes place in nearly
every state, though the major supply regions are the Southeast
and Southwest. The next step is an intermediate phase called
"backgrounding." This usually involves weaned calves grazing
on a managed forage possibly supplemented with small amounts of
grain. The last step takes the backgrounded cattle into a
feedlot facility. The animals are confined to a fairly small
area and fed a ration of feed grains or other high energy feed-
stuffs. This step is called "finishing."
This bulletin focuses on the backgrounding stage of beef
production. The backgrounding stage, in this paper, is defined
as the growth of cattle after weaning and prior to placement in
a confined feedlot.
The need for a simulation model for backgrounding feeder
cattle specified for Florida arises from the wide array of
potential programs that can be utilized in the backgrounding
stage. The profitability of any particular backgrounding pro-
gram depends upon a long list of factors including breed, sex,
initial weight, and past performance of the feeder calf; the
type of forage and its level of fertilization; and environ-
mental factors such as time of year, soil types, and rainfall.
Environmental factors are sufficiently important that back-
grounding programs in Florida vary widely, depending upon sea-
son and location in the state.
The objective of this bulletin is twofold: development of
a computerized model that simulates the growth of feeder cattle
on a forage-based feeding program and integration of the growth
model into an economic model to allow evaluation of the pro-
jected costs and returns associated with a particular back-
grounding program.
Backgrounding Feeder Cattle in Florida
Florida is a large cattle-producing state with over 1.2
million beef cows in 1982 (Fla. Dept. of Agr., 1983). Nearly
all cattle producers market weaned calves. It has been esti-
mated that approximately 75% of available feeder cattle are
exported from the state (Simpson and Baker, 1979).
The types of backgrounding systems vary with location
within the state and time of the year. Fall is the dominant
season for marketing weaned calves, thus backgrounding programs
that encompass late fall, winter, and spring are most widely
observed. The term "overwintering" is used for cool season
backgrounding of feeder cattle.
Florida is a long state, extending over 400 miles in a
north-south direction. North Florida is quite different from
South Florida in terms of terrain and climate, and hence in
terms of technically viable beef cattle production systems. It
is instructive to partition Florida into subregions. In this
analysis the demarcation line is drawn along Interstate 4,
which runs in a south-southwesterly line from Daytona Beach on
the east coast to Tampa-St. Petersburg on the west coast.
Backgrounding in North Florida
Nearly all backgrounding of cattle in Florida takes place
in North Florida during the cool season. The primary back-
grounding program involves cultivation of a temporary cool
season forage, usually a small grain such as rye or wheat. In
years of adequate rainfall, the temporary pasture provides
sufficient nutrition to allow gains of up to 2 pounds per day
on weaned calves. Cool season pastures, however, are typically
productive only from around December 1 to mid-May. In North
Florida, permanent pastures become dormant in October. Cattle
grazing on permanent pastures during October and November will
usually require supplementation to maintain body weight.
A typical cool season backgrounding program in North Flor-
ida begins in the fall after weaning of feeder cattle. Calves
are placed on permanent pasture supplemented with shelled corn,
hay, or some other feed source. The cattle are moved to cool
season pastures around January 1. The animals are usually not
sold until April and possibly as late as June.
Although it is not done as widely, cattle are also back-
grounded over the summer in North Florida. While summer back-
grounding programs are forage-based, perennial forages are
typically of poorer quality than cool season annual forages,
and thus require supplementation to achieve rates of gain of
over one pound per day during the late summer and early fall
months. Supplements may be provided by a wide variety of feed-
stuffs including shelled corn, soybean or cottonseed meal, hay,
or molasses.
Summer backgrounding may also use a warm season annual
such as millet. Millet can provide ample high quality forage
that allows rates of gain up to 2 pounds a day.
Backgrounding in South Florida
The lack of dependable cool season rainfall combined with
sandy soils has discouraged overwintering of weaned feeder
cattle in South Florida on a forage-based program. In most
years, permanent pastures in South Florida are dormant during
the cool season, and temporary annual pastures are difficult to
cultivate without irrigation.
Any backgrounding program in South Florida is likely to
make greater use of supplements than its counterpart in North
Florida. Though not widely used, locally produced feeds such
as citrus pulp, cane and citrus molasses, bagasse, chopped
sugarcane, sugarcane tops, and brewer's grains can be incorpo-
rated into a backgrounding program.
Model Overview
The simulation model is a dynamic, deterministic, comput-
erized mathematical model. The model consists of two main
components: a growth simulation model and a cost accounting
model.
A schematic of the entire model is shown in Figure 1. The
input and output of the model are shown in rectangles, while
the computational phases are shown in ovals. As shown, the
growth simulation is performed first and its output serves as
input for the cost accounting analysis.
The Growth Simulation Model
The first component of the simulation model is a biologi-
cal simulation of animal growth. A schematic of the growth
model is presented in Figure 2. The model predicts weight gain
on a daily basis given a particular forage and fixed levels of
supplemental feeds. The model is recursive in that animal body
weight is updated each day.
Model Input
The beginning month and the length of the program in
months are specified by the user; for example, a program could
begin in October and last six months. Initial animal weight in
pounds and sex are indicated by the user.
The model can handle up to four different forages, but
requires that exactly one forage be used in any particular
month. For each month a particular forage is grazed, the
monthly dry matter yield in pounds per acre, total digestible
nutrients (TDN), and a quality index factor based on the scale
developed by Moore (1981) are specified by the user. Values
Backgrounding in South Florida
The lack of dependable cool season rainfall combined with
sandy soils has discouraged overwintering of weaned feeder
cattle in South Florida on a forage-based program. In most
years, permanent pastures in South Florida are dormant during
the cool season, and temporary annual pastures are difficult to
cultivate without irrigation.
Any backgrounding program in South Florida is likely to
make greater use of supplements than its counterpart in North
Florida. Though not widely used, locally produced feeds such
as citrus pulp, cane and citrus molasses, bagasse, chopped
sugarcane, sugarcane tops, and brewer's grains can be incorpo-
rated into a backgrounding program.
Model Overview
The simulation model is a dynamic, deterministic, comput-
erized mathematical model. The model consists of two main
components: a growth simulation model and a cost accounting
model.
A schematic of the entire model is shown in Figure 1. The
input and output of the model are shown in rectangles, while
the computational phases are shown in ovals. As shown, the
growth simulation is performed first and its output serves as
input for the cost accounting analysis.
The Growth Simulation Model
The first component of the simulation model is a biologi-
cal simulation of animal growth. A schematic of the growth
model is presented in Figure 2. The model predicts weight gain
on a daily basis given a particular forage and fixed levels of
supplemental feeds. The model is recursive in that animal body
weight is updated each day.
Model Input
The beginning month and the length of the program in
months are specified by the user; for example, a program could
begin in October and last six months. Initial animal weight in
pounds and sex are indicated by the user.
The model can handle up to four different forages, but
requires that exactly one forage be used in any particular
month. For each month a particular forage is grazed, the
monthly dry matter yield in pounds per acre, total digestible
nutrients (TDN), and a quality index factor based on the scale
developed by Moore (1981) are specified by the user. Values
Figure 1. Integration of the growth simulation and the cost accounting components of the model.
supplemental
feeding
Figure 2. Schematic of the growth simulation model.
for these parameters are given in Appendix A for several peren-
nial grasses and annual cool season forages used in Florida.
Up to 10 supplemental feeds can be utilized. For each supple-
ment, the user specifies daily intake per month, TDN value,
percent moisture, and cost.
Stocking Rate
The user may elect one of four options to determine the
size and/or stocking rate of the feeding program. The first
option is to not specify either the number of animals to be fed
or the number of acres of forage available. In this case, 100
acres of each forage is assumed to be available and the number
of animals fed is determined by the carrying capacity of the
forage. The second option entails specification of the number
of animals to be fed. In this case, a sufficient number of
acres is assumed to be available to allow forage intake on an
ad libitum basis. The third option requires specification of
the number of acres of each forage available. The number of
animals fed is determined by the carrying capacity of the for-
age. The fourth option entails specification of both number of
animals and acres of each forage available, which together
determine stocking rates.
Intake
The model determines daily intake by the animal. To do
this, total digestible nutrients required to maintain body
weight (TDNR) is calculated by the equation:1
(1) TDNR = 0.927 + 0.008W 0.0000011W2
where W is the current weight of the animal in pounds. This
equation is the same for both steers and heifers. If no sup-
plements are fed, and forage is grazed ad libitum, daily forage
intake of TDN (FTDNIN) is given as a multiple of daily TDN
maintenance requirements; that is,
(2) FTDNIN = QUAL TDNR
where QUAL is the forage Quality Index. (See Appendix A for
further discussion of the forage Quality Index.) Dry matter
intake of forage is computed by dividing the TDN intake of
forage by its TDN percentage; the implied stocking rate is
calculated by totaling dry matter intake for a month and divid-
1For derivation of this equation, see Appendix B.
ing the result into monthly dry matter forage yield, giving
acres required per animal.
If supplements are included, forage intake is adjusted to
reflect the degree of substitution that takes place between
forage and supplements in the ration.
The work of Mott et al. (1968) and Golding et al. (1976)
indicates that there are different levels of substitution be-
tween forages and supplements depending upon the quality of the
forage. When a supplement is included with a high quality
forage (Quality Index of 2.20), nearly complete substitution
occurs. This means an increase of 1 pound of TDN of a supple-
mental feed decreases forage TDN intake by 1 pound. When sup-
plements are added to a low quality forage (Quality Index of
0.80), the effect is near additivity; that is, no substitution
occurs and a positive intake of supplement has no effect on
forage TDN intake. The animal will eat the added supplement,
while still consuming all of the forage that it normally would
without being supplemented.
In this model, an equation is derived to account for the
substitution of supplements for forages. A linear equation is
calculated between the two points, 0.80 and 2.20, which are the
lower and upper bounds for the forage Quality Index. The sub-
stitution factor (SUB) is equal to zero at 0.80 and equal to
one at 2.20. Thus, SUB is given by
(3) SUB = -0.5712 + 0.714 QUAL
and 0 < SUB < 1; that is, SUB is constrained to be between 0
and 1.
TDN intake of forage is adjusted to reflect TDN intake of
supplement (SUPIN) by
(4) FTDNIN = TDNIN (SUPIN SUB)
where
(5) TDNIN = QUAL TDNR.
Note that the model assumes supplements are consumed as
specified by the model user and the animal adjusts its intake
of forage. If more than one supplement is included, they are
treated as a ration and the average TDN of the ration is used.
If stocking rate is user specified, forage intake of TDN
may be less than the theoretical limit of forage TDN intake
given by Equation 2. Let FACTDN be the actual TDN intake of
forage and
(6) DIFF = FTDNIN FACTDN
where DIFF is the difference between theoretical and actual
forage TDN intake. If supplements are fed, the substitution
factor (SUB) is assumed to be zero for supplemental TDN intake
less than or equal to DIFF. If supplemental TDN intake exceeds
DIFF, the substitution factor is applied only to the increment-
al supplemental TDN intake that exceeds DIFF. In this second
case, forage TDN intake is given by
(7) FTDNIN = TDNIN (SUPIN DIFF) SUB.
Net Energy Conversion
Since the animal may have more than one feed source, the
net energy system is used to convert the various sources of
energy to a common unit, which in turn is used to check if
maintenance needs are met and, if so, the net energy available
for gain.
Animal utilization of feed varies with the quality of that
feed and differs according to whether the energy is for mainte-
nance or gain. To account for these differences, separate
conversion equations for net energy for maintenance and net
energy for gain are specified. Furthermore, separate equations
are used for forages and lower quality supplements with TDN
yields less than 65%. Other equations were calculated to ac-
count for the higher energy content of grain and protein sup-
plements, and for sugarcane molasses and citrus pulp. For the
derivation of these equations, see Appendix B. The net energy
for maintenance conversion (NEM/lb) for all forages, and for
supplements below 65% TDN is
(8) NEM/lb = (-0.10 + 2.33TDN )/2.2
where NEM is Mcal/lb. intake on a dry matter basis. The con-
version for cane molasses and citrus pulp is
(9) NEM/lb = (0.38 + 2.07TDN)/2.2
and for grain, protein, and other supplements
(10) NEM/lb = (-0.82 + 3.37TDN)/2.2
By taking an average of the net energy for maintenance
concentration weighted by the proportion of the total ration
for each feed source, the net energy for maintenance concentra-
tion per pound of ration is determined.
Net energy required for maintenance (NERM) is calculated
by the equation
(11) NERM = .0426W.75
where W is the current weight in pounds.2 Net energy required
for maintenance is assumed not to differ for steers and heif-
ers.
Using Equation 11, the pounds of feed required to meet net
energy for maintenance needs are determined. If feed require-
ments exceed total intake, the simulation terminates with a
message that maintenance requirements are not being met. If
total intake exceeds feed requirements, then the surplus is
assumed to be available for gain.
After determining total feed available for gain, the model
decomposes the ration back into its individual components.
Then separate conversion equations are used to calculate net
energy for gain. For derivation of these equations, see Ap-
pendix B. The net energy for gain conversion (NEG/lb) for
forages and supplements of TDN less than 65% is
(12) NEG/lb = (-1.51 + 3.58TDN)/2.2,
for citrus pulp and cane molasses
(13) NEG/lb = (0.08 + 1.59TDN)/2.2,
and for grain, protein and other supplements
(14) NEG/lb = (-0.74 + 2.47TDN)/2.2.
After the net energy for gain concentrations are deter-
mined, total net energy available for gain is the sum of the
net energy for gain contributions from each feed source.
Gain and Updating
Daily gain for a steer is
S(0.003112(NEG/W.75) + 0.0001748).5 0.01322
(15) Gain = 0.001556
and daily gain for a heifer is
(1) an (0.005756(NEG/W75) + 0.0001974).5 0.01405
(16) Gain = 0.002878
2This relationship is adapted from the National Research
Council.
where Gain is weight gain in pounds, NEG is total net energy
available for gain, and W is animal weight in pounds.
If the current day of the simulation is not the last day
of the specified backgrounding program, predicted gain is added
to the animal's weight and the model returns to the net energy
for maintenance calculation as indicated in Figure 2.
Adjustment for Breed, Frame Size, and Heat Stress
"Gainability" refers to the peculiar attributes of a feed-
er calf to gain weight faster or slower than the average rate
of gain of all animals of the same weight fed an identical
ration. In Florida, gainability varies widely and can play an
important role in determining a backgrounding program for a
particular group of calves. In this analysis, it is assumed
that the gainability of a particular feeder calf is character-
ized by two factors: (1) an evaluation of the overall poten-
tial of the animal for gain and (2) the heat tolerance of the
animal as indicated by its breed.
The current USDA feeder calf grading system uses frame
size (small, medium, or large) and muscling score (classes 1
through 3) to classify feeder cattle within weight classes.
The usefulness of the current system has been questioned by
researchers (e.g. Trapp, 1982). Instead of trying to directly
adapt the grading system, the user is asked to classify the
animal as superior, average, or inferior.
The classification is used to determine the value of the
animal quality factors, AQ. A superior animal yields AQ equal
to 1.1, average yields AQ equal to 1.0, and inferior implies AQ
equal to 0.9. AQ is used to adjust the net energy for the gain
utilization relationship. The modified relationship for steers
is
(17) Gain = (0.003112(NEG AQ/W'75) + 0.0001748).5 0.01322
Ga 0.001556
and the daily gain for heifers is
(18) Gain = (0.005756(NEG AQ/W.75) + 0.0001974).5 0.01405
0( .002878
3The gain equations are adapted from the National Research
Council (1976).
This method to account for differences among animals in their
propensity to gain has been previously used by Fox and Black
(1984) and Brorsen, et al. (1983).
In Florida, heat stress is a problem that will be encoun-
tered in any backgrounding program that encompasses months with
high temperatures. High temperatures combined with high humid-
ity reduce the efficiency of feeder cattle on a backgrounding
program.
The effect of heat stress on animal performance is not
well documented. The impact of heat stress can be mitigated to
some degree through the use of Zebu type cattle. In Florida,
Brahman and Brahman crossed cattle are found in considerable
numbers. The proportion of Brahman blood required to achieve a
heat tolerance is not precisely known. In the model it is
assumed that 3/8 Brahman or more reduces the effect of heat
stress on animal performance.
Although summers are equally hot in North and South Flor-
ida, higher temperatures persist for a longer period in South
Florida. It is assumed that heat stress occurs in May and
September as well as June, July, and August in South Florida
but is confined to the three summer months in North Florida.
Heat stress affects both the appetite and the efficiency
of feed conversion of feeder cattle. The precise effect is not
known. An ad hoc approach is used in which a parameter, HS,
calibrates the intake of the animal; that is,
(19) TDNIN = QUAL TDNR HS.
Equation 19 is a modification of Equation 5 and states that
intake of TDN is a multiple of TDN required for maintenance.
The multiplier is the product of the forage quality index
(QUAL) and the heat stress parameter (HS). HS is set to 1 for
all months except as noted in Table 1.
Table 1. Adjustment factors for heat stress.
May June July Aug. Sept.
North Florida
Brahman
(3/8 or more) 1.0 .90 .85 .90 1.0
Non Brahman 1.0 .85 .80 .85 1.0
South Florida
Brahman
(3/8 or more) .95 .90 .85 .90 .95
Non Brahman .90 .85 .80 .85 .90
Growth Stimulants and Feed Additives
Through the use of growth stimulants and feed additives, a
cattle producer can increase average daily weight gain and feed
efficiency of feeder cattle. The introduction of growth stimu-
lants is accomplished by placing pellets subcutaneous under the
skin of the ear and are commonly referred to as implants. Feed
additives can be introduced only if supplements are fed. The
producer can purchase a prepared feed in which the feed addi-
tive has been incorporated.
Growth stimulant implants have been shown to increase
daily weight gains with little or no increase in feed intake
(Fox and Black, 1984). The gain equations (Equations 15 and
16) were developed assuming the presence of the growth stimu-
lant DES (diethylstilbestrol) (Lofgreen and Garrett, 1968).
DES may no longer be used legally in cattle, and implants cur-
rently used include Compudose, Synovex-S, Synovex-H, Steroid
and Ralgro. Each of these implants are thought to have differ-
ent effects on animal growth, but these effects are not pre-
cisely known or understood.
In this study, it is assumed that a growth stimulant im-
plant is present and the effect of the growth stimulant implant
is adequately described by Equation 15 for steers and Equation
16 for heifers. This model is not valid if growth stimulant
implants are not used. Further work is required to understand
the modifications necessary when growth stimulant implants are
not used.
Feed additives have become an important component of feed-
lot rations. Feed additives apparently increase the animal's
ability to digest feedstuffs, which increases the net energy
for maintenance and net energy for gain of the ration (Fox and
Black, 1984). Feed additives can only be introduced through
supplemental feeds, thus a ration that consists entirely of
forage cannot include a feed additive.
In this study, the user is asked if Rumensin or Bovatec is
present in a supplemental feed. If so, the model assumes that
the feed additive is present at the proper level. The presence
of a feed additive multiplies both the net energy for mainte-
nance (NEM/lb) and the net energy for gain concentration (NEG/
lb) of the ration by 1.11. The multipliers are taken directly
from Fox and Black (1984).
Output
Upon simulation of the last day of the backgrounding pro-
gram, a set of tables summarizing the results of the simulation
is printed. Several of the endogenous variables of the model
are tabulated on a monthly basis. These include average daily
gain, intake (dry matter basis), ration composition, and pre-
dicted stocking rate (head/acre). If desired, the model will
also provide values for net energy required for maintenance,
feed required to meet maintenance needs, and net energy avail-
able for gain on an average daily basis for each month. The
growth simulation model sends, as input to the cost analysis,
predicted ending weight and stocking rate.
A sample of the output from the growth simulation program
is given in the illustrative example section of this paper.
Cost Accounting Analysis
Upon completion of the growth simulation, if desired, an
analysis of the costs associated with the simulated background-
ing program is performed. The user is asked to supply cost
information relating to the weaned feeder calf, forage, supple-
ments, and other operating costs including medication and min-
erals, growth stimulants, maintenance and repairs for equip-
ment, and interest costs. The user is also asked to indicate
marketing, labor, and land costs. These costs are allocated
between cash (out-of-pocket) and non-cash costs. The model
totals all cash costs and non-cash costs separately and prints
a "budget" detailing the cost information provided by the user.
Using these totals and predicted ending weight from the growth
simulation, a "breakeven" price is calculated, which is the
price at which the backgrounded animal must be sold to cover
costs. Breakeven prices are calculated for cash costs and all
costs.
Allocation of Costs
The "total cost" of backgrounding cattle differs from
producer to producer, depending upon factors such as the need
to rent land and labor costs. If feeder cattle are purchased,
costs also differ depending on the terms of the purchase. The
model is highly adaptable in that the user can specify a wide
array of purchase and/or selling arrangements, adjust labor
costs, and account for land rental costs.
The format of the cost accounting model is a modification
of the work of Gunter et al. (1979). Costs are allocated to
cash and other costs. Cash costs reflect expenses that cause a
cash outflow during the production period. Other costs may or
may not cause a cash outflow. A cash cost to one producer may
not be incurred by another producer or may represent a non-cash
cost.
Cash Costs
The format of the output of the cost analysis is shown is
Figure 3. The Stockerr calf" refers to the calf entering the
backgrounding program. If the calf is not purchased, then its
"cost" is not a cash cost. This analysis assumes that the cost
of the calf is a cash cost. The appropriate price is what the
calf would bring at the time the backgrounding program is ini-
tiated.
Figure 3. The format of the cost analysis budget: estimated
cost for pasturing a lb. calf to lb.
Quantity Price Cost/Head
Stocker calf
Forage
Supplement(s)
Medication,
minerals, etc.
Growth stimulant
Other operating
costs
Interest on
operating costs
Death loss
Total cash costs
Procurement costs
Marketing costs
Labor
Overhead costs
Land costs
Total all costs
The cost analysis can handle multiple forages. The cost
for each forage should reflect the cash costs incurred from
pasturing cattle on the forage during the months specified.
For a permanent pasture, the cost may be very small or zero.
For a temporary pasture, the cost is the cost of seed, fertil-
izer, lime, etc. used to cultivate the pasture. The quantity
of each pasture is the maximum of the acres required per head
per month as computed by the growth simulation program.
The quantity of each supplement used is total feed derived
from the supplementation specified in the growth analysis.
Medication and minerals are figured on a total cost basis
only as are other operating costs, such as maintenance and
repairs, that may be incurred.
An interest charge is calculated on the capital required
to conduct the backgrounding program. If all capital is bor-
rowed, the interest charge reflects the cost of capital. If
the backgrounding program is financed by the producer, interest
on operating capital is not a cash cost. The model assumes all
capital requirements are met through borrowing.
All death loss is assumed to occur at the beginning of the
program, so that it is calculated on the purchase cost of the
feeder animal only.
The sum of initial animal cost, forage cost, cost of sup-
plements, medication and minerals, growth stimulants, miscella-
neous operating expenses, interest charges, and death loss is
called "total cash costs."
Other Costs
Other costs include the cost of procurement, marketing,
labor, other overhead, and land. Procurement costs are in-
curred only if feeder calves are purchased. Depending upon the
method of purchase, they may include order buyer fee, transpor-
tation in, and death loss incurred in transport.
Marketing costs are those costs associated with selling
the backgrounded cattle. They include order buyer fee or auc-
tion charge, transportation out, and any miscellaneous fees.
Whenever cattle are transported, weight loss may occur.
This weight loss is called "shrink." A backgrounding operation
may incur both purchase and sales shrink. At the top of Figure
3, the initial and ending weight of the animal are indicated.
These values are adjusted to reflect the shrink, if any, in-
curred on purchase and sale. This method of accounting is
called "pay weight to pay weight."
"Labor" is the hours required per calf to cultivate pas-
ture (if not included in pasture cost) and manage the cattle.
Since labor may not be hired, labor cost is grouped with other
costs. Overhead costs are items such as depreciation, insur-
ance, taxes, and interest on investment. Land costs are costs
associated with land above any variable costs of the forage.
In particular, it may be land rent or a charge to reflect the
opportunity cost of land.
Output
The cost analysis program generates four tables. The
first table is a "budget" (Figure 3) itemizing the cost of
backgrounding a feeder calf. The second table shows the break-
even price required to cover cash costs and itemized for pro-
curement costs, marketing costs, and all other costs. The
third and fourth tables are "cost of gain" tables. The third
table uses a format similar to the first table, but the initial
cost of the feeder calf is excluded. The fourth table reports
the cost per hundredweight of weight gain realized from the
backgrounding program.
Illustrative Example
In order to better explain use of the computerized model,
an illustrative example is provided. Instructions related to
program access on the IFAS VAX computer are given in Appendix
C.
Upon reference to Appendix C, the reader will note that in
its current form, data are provided to the computer program
through an interactive input form; that is, upon entry to the
program the program asks that a particular piece of data be
entered and the user answers the query by entering the data.
In this form a handwritten input form need not be completed.
Such a form or worksheet is useful, however, to summarize spe-
cific items to be provided by the user.
Growth Simulation
In Figure 4, a worksheet for the growth simulation is
shown. The data included in the sample worksheet are used as
the illustrative example. The format of the worksheet shown is
somewhat specific to the example. Items in italics are user-
entered.
The illustrative example is a backgrounding program in
North Florida. The program begins in September with a 400-
pound steer and lasts 8 months, ending in April. The animal is
judged as average in its ability to gain weight, and heat
stress is not to be considered. Two pastures are used. The
number of animals and the acres of each pasture are unspecified
as indicated by zero in response to those questions. Pensacola
bahiagrass is used for the first three months of the program
and rye-ryegrass with clover is used for the last five months of
Figure 4. Worksheet for growth simulation model.
INITIAL WT IN POUNDS 400
SEX 1=STEER 2=HEIFER 1
LENGTH OF FEEDING PROGRAM IN MONTHS 8
NUMBER OF THE MONTH PROGRAM BEGINS IN (JAN=1) 9
NUMBER OF FORAGES OR FIELDS GRAZED ON (MAX= 4) 2
NUMBER OF SUPPLEMENTS USED (MAX=10) 2
NUMBER OF ANIMALS: 0
ANIMAL QUALITY EFFECT: RATE YOUR ANIMAL WITH RESPECT TO L'TS
POTENTIAL FOR GAIN
SUPERIOR = S AVERAGE = A INFERIOR = I A
DO YOU WANT TO ACCOUNT FOR HEAT STRESS? (YES/NO) [YES] No
BRAHMAM INFLUENCE FOR HANDLING HEAT STRESS
IS THIS ANIMAL (3/8) OR MORE BRAHMAN? (YES/NO) [YES] No
ARE YOUR CATTLE FOR BACKGROUNDING LOCATED SOUTH OF INTERSTATE-
4? No
NAME OF FORAGE 1: (20 CHARACTER LIMIT) Pensacola bahiagraes
VARIABLE COSTS FOR FORAGE 1 (DOLLARS PER ACRE) 20.00
NUMBER OF ACRES OF FORAGE 1 0
NAME OF FORAGE 2:
VARIABLE COSTS FOR FORAGE
NUMBER OF ACRES OF FORAGE
MONTHS FORAGE 1 IS GRAZED
MONTHS FORAGE 2 IS GRAZED
TDN BY MONTH FOR FORAGE 1
Sept. Oct. Nov
52 56 52
FORAGE QUALITY (VALUES RAN
Sept. Oct.
1.3 1.5
(20 CHARACTER LIMIT) Rye-ryegrass-clover
2 (DOLLARS PER ACRE) 100.00
5
(D.M. BASIS):
GE OF .8 2.2)
GE OF .8 2.2)
Nov.
1.1
Figure 4. Worksheet for growth simulation model, continued.
POUNDS PER ACRE (DRY MATTER) YIELD
Sept. Oct. Nov.
450 240 240
TDN (D.M. BASIS) FOR FORAGE 2
Dec. Jan. Feb. Mar. Apr.
68 66 64 62 62
FORAGE QUALITY (VALUES RANGE .8 2.2)
Dec. Jan. Feb. Mar. Apr.
2.1 2.0 1.9 1.8 1.7
POUNDS PER ACRE (DRY MATTER) YIELD
Dec. Jan. Feb. Mar. Apr.
650 1170 1820 1430 845
ENTER NAME OF SUPPLEMENT (20 CHARACTER LIMIT) Corn
DOES THE SUPPLEMENT INCLUDE RUMENSIN OR BOVATEC? No
ENTER LBS OF SUPPLEMENT FED PER DAY (AS FED) FOR EACH MONTH
1 2 3 4 5 6 7 8
2.0 2.0 3.0 0 0 0 0 0
ENTER TDN OF SUPPLEMENT (AS FED) 80
ENTER MOISTURE CONTENT OF SUPPLEMENT 15
ENTER DOLLAR COST ($) PER CWT OF SUPPLEMENT 8.00
ENTER NAME OF SUPPLEMENT (20 CHARACTER LIMIT) Cottonseed meal
DOES THE SUPPLEMENT INCLUDE RUMENSIN OR BOVATEC? No
ENTER LBS OF SUPPLEMENT FED PER DAY (AS FED) FOR EACH MONTH
1 2 3 4 5 6 7 8
1.0 1.0 1.0 0 5 0 0 0
ENTER TDN OF SUPPLEMENT (AS FED) 68
ENTER MOISTURE CONTENT OF SUPPLEMENT 8.5
ENTER DOLLAR COST ($) PER CWT OF SUPPLEMENT 14.00
the feeding program.4 These grazing times are indicated in
response to the question of how many months each forage is
grazed. TDN yield, forage Quality Index, and dry matter yield
by month for each forage are indicated. In Figure 4, Pensacola
bahiagrass is grazed in October (month 2 of the program) with
TDN yield of 56%, forage quality index of 1.5, and dry matter
yield of 240 pounds per acre. The user must ensure that the
correct values are entered for each month of the backgrounding
program. Variable costs per acre are $20.00 for the Pensacola
bahiagrass pasture and $100.00 for the rye-ryegrass-clover
pasture.
Corn and cottonseed meal are used as supplemental feeds.
Neither feed contains a feed additive such as Rumensin or Bova-
tec. Daily intake by month is specified for each. In the
example, both supplements are fed only during the first three
months while the animal is grazing on permanent pasture. At
15% moisture, corn is specified to have TDN yield of 80% and
cost $8.00 per hundredweight. Cottonseed meal is 8.5% mois-
ture, its TDN yield is 68%, and its cost is $14.00 per hundred-
weight. The model requires that supplemental feed costs be on
a per hundredweight basis. Prices in other units such as per
bushel or per ton must be converted.
The printed output of the growth simulation consists of
four tables. For the sample run, these four tables are shown
as Tables 2, 3, 4, and 5, respectively.
Table 2 is called an "echo" of the input data. It shows
the input data provided by the user in tabular form. The user
should examine this table to see if all data has been entered
correctly.
Table 3 shows the ration composition calculated on a
monthly basis. A breakdown of the ration on a per pound and
percentage basis is given. Since the supplements used are spec-
ified by the user, the model calculates the amount of forage
that is consumed. Forage and supplemental intake is summed and
the result is actual intake on a daily basis.
Values are given for intake and intake limit. When stock-
ing rate is unspecified, it is assumed the animal will consume
feed at its theoretical intake limit, thus actual intake and
intake limit will coincide. If stocking rate is user speci-
fied, there may not be sufficient forage available to allow the
animal to consume feed at its intake limit. In this case,
If rotational grazing is used, the user should calculate
the proportion of each month the animal grazes on each pasture,
and treat the weighted average of the pastures used in the
rotation as one pasture.
Table 2. Echo of input to growth simulation model.
Input Verification
----------------- Feeding Program-----------------
Sex
Steer
Length
in Months
8
Starting
Month
9
--------Number of--------
Supplements Forages
Our animal's physical condition
Forage: Pensacola bahiagrass
Forage: Rye-ryegrass-clover
Forage TDN (D. M. basis)
Forage quality index
Forage production (Ib/acre)
Supplement: corn
Pounds fed per day
Feed additives used
Supplement: cottonseed meal
and potential for gain has
Per Acre Variable Costs:
Per Acre Variable Costs:
SEP. OCT. NOV.
52 56 52
1.30 1.50 1.10
480 240 240
TDN (As fed)
Percent moisture
Cost per cwt., $
SEP. OCT. NOV.
been summarized:
$ 20.00
$100.00
DEC. JAN.
Average
FEB. MAR. APR.
68 66 64 62 62
2.10 2.00 1.90 1.80 1.70
650 1170 1820 1430 845
80
15.00
8.00
DEC. JAN.
FEB. MAR. APR.
2.00 2.00 3.00 0.00 0.00 0.00 0.00 0.00
NO NO NO NO NO NO NO NO
TDN (As fed)
Percent moisture
Cost per cwt., $
SEP. OCT. NOV.
68
8.5
14.00
DEC. JAN.
FEB. MAR. APR.
1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00
NO NO NO NO NO NO NO NO
Initial
Wt.
400
Pounds fed per day
Feed additives used
Table 3. Summary of output from growth simulation model.
Ration Composition (Dry Matter Basis)
Item SEP. OCT. NOV. DEC. JAN. FEB. MAR. APR.
Forage consumed (lb.) 8.58 9.49 8.36 15.05 16.00 16.66 17.10 16.82
(%) 76.63 78.38 70.69 100.00 100.00 100.00 100.00 100.00
Corn (lb.) 1.70 1.70 2.55 0.00 0.00 0.00 0.00 0.00
(%) 15.19 14.05 21.57 0.00 0.00 0.00 0.00 0.00
Cottonseed meal (lb.) 0.92 0.92 0.92 0.00 0.00 0.00 0.00 0.00
(%) 8.18 7.56 7.74 0.00 0.00 0.00 0.00 0.00
Intake (lb.) 11.19 12.10 11.82 15.05 16.00 16.66 17.10 16.82
Intake limit (lb.) 11.19 12.10 11.82 15.05 16.00 16.66 17.10 16.82
Results for Steer on the Above Feeding Program on a Per Head Basis
Purchase Weight (lb): 400. Purchase Shrink (%): 0.0 Beginning Month: SEP
Initial Weight (lb): 400. Ending Month: APR.
Name SEP. OCT. NOV. DEC. JAN. FEB. MAR. APR.
Days on feed 30 31 30 31 31 28 31 30
Average daily gain (lb.) 1.02 1.27 1.06 2.06 1.80 1.56 1.31 1.16
Initial weight (lb.) 400 430 470 502 565 621 665 706
Ending weight (lb.) 430 470 502 565 621 665 706 741
Substitution factor 0.36 0.50 0.21 0.00 0.00 0.00 0.00 0.00
intake is less than intake limit, and the difference is the
pounds (dry matter) of additional forage per day that the ani-
nal would consume if available.
Next, the purchase weight of the animal is given and the
purchase shrink, if any, is applied before the simulation be-
gins. In Table 3 no purchase shrink is deducted from the ani-
mal's weight. The beginning and ending months of the simula-
tion are also shown.
A monthly summary of the backgrounding program is shown
including days on feed, average daily gain, initial weight,
ending weight, and substitution factor. In the example shown
in Table 3, the substitution factor is relevant only during the
first three months of the simulation when supplements are fed.
The substitution factor in September is 0.36, which means that
for each pound of supplement fed daily in September (dry matter
basis) reduced daily forage consumption by 0.36 pounds. Thus
forages and supplement are nearly additive.
Table 4 summarizes the utilization of forage in the simu-
lation. Since neither the number of animals nor the number of
acres of each pasture is specified, the model assigns 100 acres
of each type of forage. In the example, 100 acres of Pensacola
bahiagrass can carry 82 animals over the first three months.
Available forage is completely utilized in October, thus the
estimated carrying capacity of 0.82 animals per acre is deter-
mined by forage availability in October. If stocked at 0.82
animals per acre, there is approximately 270 pounds per acre of
unharvested forage in September and approximately 35 pounds per
acre unharvested in November. Increased feeding of supplements
in October can increase the carrying capacity of the bahiagrass
pasture.
Similar information is given on the utilization of the
rye-ryegrass-clover pasture over its five-month grazing period.
Available forage is completely utilized in December, which
determines the estimated carrying capacity of 1.39 animals per
acre. Supplements would have to be fed in December to increase
the carrying capacity of the pasture.
In current form of the model, unharvested forage in one
month is not carried over to the next month. Future work is
required to account for forage carryover, adjusted to a lower
forage Quality Index.
Table 5 is output which provides, on an average daily
basis, the specifics of the net energy accounting. The model
uses both the TDN and net energy systems. The pounds of TDN
supplied by the ration or forage (if no supplements are fed) is
shown along with the pounds of TDN required for maintenance.
The net energy system is used to break the ration into separate
components for maintenance, and for gain. The maintenance
requirements must be met first, and any extra available feed is
Table 4. Forage utilization.
Forage 1: Pensacola Bahiagrass
Assuming 100 acres available, then 82 animals can be pastured at a
carrying capacity of 0.82 head/acre
----------Unharvested Forage----------
Total Pounds Quality
Month Dry Matter Factor TDN
Sep.* 27004 1.3 52
Oct. 0 1.5 56
Nov. 3540 1.1 52
Indicates month with lowest carrying capacity
Forage 2: Rye-Ryegrass-Clover
Assuming 100 acres available, then 139 animals can be pastured at a
carrying capacity of 1.39 head/acre
----------Unharvested Forage---------
Total Pounds Quality
Month Dry Matter Factor TDN
Dec. 0 2.1 68
Jan. 47904 2.0 66
Feb. 117021 1.9 64
Mar. 69180 1.8 62
Apr. 14226 1.7 62
Indicates month with lowest carrying capacity
Table 5. Nutritional accounting
Item SEP.
TDN supplied (lb.) 6.74
TDN required for
maintenance (lb.) 4.06
Net energy required
for maintenance (Mcal) 3.92
Net energy
available gain (Mcal) 1.31
Ration net energy
maintenance (Mcal/lb) 0.61
Ration net energy (Mcal/lb) 0.272
Feed for
maintenance lb DM basis 6.40
Feed for gain lb DM basis 4.79
Actual intake lb DM basis 11.19
in the growth simulation model.
OCT. NOV. DEC.
7.59 7.43 10.24 1
4.30 4.55 4.87
4.16 4.40 4.72
1.76 1.54 3.39
0.64 0.65 0.67
6 0.3152 0.3069 0.4202
6.53 6.81 7.00
5.58 5.01 8.06
12.10 11.82 15.05 1
FEB.
10.66
5.61
5.44
2.86
0.63
0.3551
8.60
8.07
16.66
MAR.
10.60
5.89
5.70
2.51
0.61
0.3225
9.33
7.77
17.10
APR.
10.43
6.13
5.94
2.29
0.61
0.3225
9.71
7.10
16.82
=
JAN.
0.56
5.28
5.12
3.17
0.65
3.3876
7.83
8.17
6.00
considered available for gain. Net energy required for mainte-
nance is shown along with the net energy available for gain.
Also shown are net energy for maintenance concentration and the
net energy for gain concentration. The pounds of feed required
for maintenance are calculated and are labeled "FEED FOR
MAINT." This quantity is determined by dividing the net energy
required for maintenance by the net energy concentration For
maintenance (MCAL/LB for MAINT); for example, 3.92/.61 = 6.40
pounds of feed for maintenance in Septeimber. The feed avail-
able for gain is the difference between the toial intake and
the feed required for maintenance and is labeled "FEED FOR
GAIN." When this quantity is multiplied by the net energy
concentration For gain (MCAL/LB for GAIN) the result is the net
energy available for gain; for example, 4.79 x .27 = 1.31 for
September. This value is used to determine average daily gain.
Cost Analysis
The growth simulation can be executed alone or in conjunc-
tion with the cost analysis model. Output from the growth
simulation model in the form of predicted ending weight, re-
quired stocking rate, and total intake of supplements are input
to the cost analysis. Variable costs per acre for forages and
per pound price of supplements are also passed to the cost
analysis model.
The USER may also find it helpful to use a worksheet as a
means of assembling data for the cost analysis program. A
worksheet for the illustrative example is shown as Figure 5.
The items in italics are user-supplied. In this example, the
400-pound calf cost $0.55 per pound; medication and minerals
cost $2.00; one growth stimulant at $1.00 is used; other oper-
ating expense is $2.50; interest rate is 13% per annum, and
pasturing death loss is set at 1%.
Death loss associated with procurement, order buyer
charge, purchase shrink, and transportation in cost are zero.
Auction charge is $5.00 per head, sale shrink is 3%, transpor-
tation out is $4.00 per head, and other marketing costs are
$1.50 per head.
Estimated labor requirements per calf are 0.67 hours and
the wage rate is $3.50 per hour. Other overhead costs are
$5.00 per head.
The output of the cost analysis consists of four tables.
For the Illustrative example, the "budget" is given in Table 6
and breakeven prices are given in Table 7. The "cost of gain"
analysis is provided in Tables 8 and 9.
Figure 5. Worksheet for cost analysis.
FIXED COSTS) PER ACRE OF LAND, FORAGE-1
20.00
FIXED COSTS) PER ACRE OF LAND, FORAGE-2
30.00
CALF PRICE (COST PER CWT.)
55
ORDER BUYING COST PER CWT.
0.
TRANSPORTATION IN (COST PER CALF)
0.
PERCENT DEATH LOSS ASSOCIATED
WITH PROCUREMENT
0.
PERCENT PURCHASE SHRINK
0.
MEDICATION, MINERALS, ETC.: COST/CALF
2.
QUANTITY OF GROWTH STIMULANT/CALF
2.
PRICE PER GROWTH STIMULANT
1.
OTHER OPERATING COSTS PER CALF
2.5
INTEREST RATE
13
PERCENT DEATH LOSS DURING PASTURING OR FEEDING
1.
OVERHEAD COSTS PER CALF
5.
LABOR COST PER HOUR
3.50
HOURS OF LABOR PER CALF
.67
PERCENT SALES SHRINK
3
AUCTION CHARGE PER CALF
5.
TRANSPORTATION OUT (COST PER CALF)
4.
OTHER MARKETING COSTS PER CALF
1.50
Table 6 shows the estimated costs for pasturing a 400-
pound calf to 718 pounds with the program beginning in Septem-
ber and running through April. Notice that a 3.0% sales shrink
is deducted from the 741 pound ending weight giving a sale
weight of 718 pounds. The stocking rate or acres required per
animal is multiplied by the variable cost per acre for each
forage, giving forage cost per head. The costs for the supple-
ments are the amount consumed multiplied by the price per
pound. Costs for medication, minerals, growth stimulant, and
other operating costs are presented in Table 6. The user spec-
ified interest rate is used to calculate the interest on the
operating capital. Death loss is calculated as a percentage of
the total herd, reflected back to a cost per head basis.
Other costs include procurement, marketing, labor, other
overhead, and land. Procurement costs include order buying
fee, charge for transportation, and death loss associated with
transport. Marketing costs are auction charge, transportation
out, and miscellaneous marketing costs. Labor costs and man-
agement costs are calculated directly from user supplied infor-
mation. Overhead costs are the other overhead indicated by the
user. Land costs are the rental rates (fixed land costs) ad-
justed for stocking rate and the fraction of the year the land
is used.
Breakeven prices per hundredweight are shown in Table 7.
For the illustrative example, the breakeven price to cover cash
costs is $53.45 per hundredweight and to cover all costs is
$58.02 per hundredweight.
Table 8 shows the estimated costs of the weight gain for
one animal. Table 8 would be applicable for a "custom back-
grounding" operation in which a producer cultivates pasture and
contracts with another individual to place cattle on the pas-
ture. The second individual maintains ownership of the cattle
and pays the first individual for the pounds of weight gain
realized while grazing on the pasture. In this case, since
animals are not purchased, the purchase cost of a feeder calf
and interest charges on the calf purchase cost are not included
as costs. It is assumed that costs associated with death loss-
es are not borne by the pasture producer.
In the illustrative example, after adjusting for sales
shrink, each feeder steer gained 318 pounds. As reported in
Table 8, the estimated cash cost of 318 pounds of weight gain
is $142.84 and the estimated total cost is $165.21.
Cost of gain on a per hundredweight basis is reported in
Table 9. In the illustrative example, the pasture producer
should charge $44.92 per hundredweight of gain to cover cash
costs and $51.95 per hundredweight of gain to cover all costs.
Table 6. Example of budget from cost analysis model: Estimated costs
for pasturing a 400-pound calf to 718 pounds, September
through April.a
Item Quantity Price Cost/Head
Stocker calf 400.00 lb. $ 55./cwt. $220.00
Forage(s):
Pensacola bahiagrass 1.23 ac. $ 20.00/ac. $ 24.51
rye-ryegrass-clover 0.72 ac. $100.00/ac. $ 71.80
Supplementss:
corn 212.0 lb. $ 8.00/cwt $ 16.94
cottonseed meal 91.0 lb. $ 9.00/cwt $ 12.74
Medication, minerals, etc. $ 2.00
Growth stimulant 1.0 $ 1.00 1.00
Other operating costs $ 2.50
Interest on operating capital $351.51 13.0% 30.30
Death loss 1% $220.00 $ 2.20
Total cash costs $384.00
Procurement costs $ 0.00
Marketing costs $ 10.50
Labor 0.67 hr. $ 3.50/hr. $ 2.35
Overhead costs $ 5.00
Land costs, pasturef $ 15.02
Total all costs $416.87
apay weight to pay weight, sales shrink used: 3.0%
bMaintenance and repairs
c$0.00/hd order buying = $0.00/cwt. 400. cwt.
$0.00/hd transportation-in
$0.00/hd death loss = 0.00 percent $220.00
d$5.00/hd auction charge
$4.00/hd transportation-out
$1.50/hd other marketing costs
eDepreciation, interest on investment, taxes, insurance, etc.
f0.91 acres/calf at $26/acre per year for 242 days
Table 7. Per head breakeven prices from cost analysis
model.
Cost Included Total Amount Price/Cwt.
----------Dollars---------
Cash costs 384.00 53.45
Procurement costs 0.00 0.00
Marketing costs 10.50 1.46
Labor, overhead and land 22.37 3.11
Total all costs 416.87 58.02
Table 8. Estimated costs for 318 pounds of gain.a
Item Quantity Price Cost/Head
Forage(s):
pensacola bahiagrass 1.23 ac. $ 20.00/ac. $ 24.51
rye-ryegrass-clover 0.72 ac. $100.00/ac. $ 71.80
Supplement(s):
corn 212.0 lbs. $ 8.00/cwt $ 16.96
cottonseed meal 91.0 Ibs. $ 14.00/cwt $ 12.74
Medication, minerals, etc. $ 2.00
Growth stimulant 1.0 $ 1.00 1.00
Other operating costs $ 2.50
Interest on operating capital $131.51 13.0% $ 11.33
Total cash costs $142.84
Labor costs 0.67/hr. $3.50 $ 2.35
Overhead costs $ 5.00
Land costs, pasture $ 15.02
Total all costs $165.21
aEnding weight: 718 (lb.)
Beginning weight: 400 (lb.)
bMaintenance and repairs
c0.91 acres/calf at $26/acre per
Sales shrink used: 3.0%
Purchase shrink used: 0.0%
year for 242 days
Table 9. Per head breakeven prices from cost analysis model.
Cost Included Total Amount Price/Cwt.
----------Dollars---------
Cash costs 142.84 44.92
Labor, overhead and land 22.37 7.03
Total all costs 165.21 51.95
Concluding Comments
The growth simulation model is viewed strictly as experi-
mental. The model has not been widely tested, although the
illustrative example is based on a grazing trial by Bertrand
and Dunavin (1983) in North Florida. Thirty-two steers weigh-
ing 447 pounds were placed on a rye-ryegrass-clover pasture in
December. On May 2 the trial ended, and the animals showed an
average 1.61 pounds per day gain. In the illustrative example,
the animals weighing 451 pounds are placed on a similar pasture
in December and show an average gain of 1.58 pounds per day
through April. Other test runs simulating backgrounding trials
at Ona show comparable results in terms of the model's valid-
ity.
Appendix A
Forages in Florida
There are approximately 12 million acres of "grassland" in
Florida, consisting of approximately 4.7 million acres of
grazed forestlands, 4 million acres of native range lands, iand
3.2 million acres of improved permanent grass pastures (Ruelke
and Killinger, 1976). High quality forages play a central role
in backgrounding feeder calves in Florida. The purpose of this
appendix is to describe in more detail the types of forages
that may be used in various backgrounding programs. They in-
clude improved permanent grass and legume pastures, summer
annual grasses and legumes, and temporary cool-season crops
such as ryegrass and small grains. Improved permanent grass
pastures provide and abundant feed from May to September, but
forage production is low from October to April. Temporary
annual forages can partially fill this void and provide a high
quality forage, but production is highly dependent upon receiv-
ing adequate moisture and these forages require large amounts
of fertilizer.
Tables A.1 (perennial summer grasses for North Florida),
A.2 (perennial summer grasses for South Florida), A.3 (winter
annual small grains and grasses), and A.4 (summer annual grains
and legumes) show estimates of monthly dry matter yields,
monthly total digestible nutrients (TDN) percentages, and an
estimate of the forage Quality Index for the selected forages.
These tables were constructed to typify expected yields and
forage quality of a normal production year for an average Flor-
ida producer. They are designed to be used as a guideline for
input data by a user of the growth simulation model who can
modify the required input to meet his own particular situation.
One should note that for some months, with the perennial
summer grasses, no forage production is estimated. A producer
may choose not to graze a forage during its growth period. In
this case, estimates of TDN and forage Quality Index should be
revised downward if it has been reserved for grazing during the
cool season months. There is also the possibility that when
dormant pastures first start growing in the spring, there may
be both old forage and small amounts of new forage available.
This creates a problem in calculation of the TDN percentage and
forage Quality Index for the spring months. Only estimated new
growth is considered for the TDN percentages and forage Quality
Index. If there is a combination of both, the user could ad-
just these numbers presuming that the animal will eat some of
the residual forage.
Table A.1. Perennial warm season forages North Florida.
Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec Total
Coastal Bermudagrass
TDN, %
Forage quality index
Dry matter yielda
% monthly production
Alicia Bermudagrass
TDN, %
Forage quality index
Dry matter yield
% monthly production
Tifton 44 Bermodagrass
TDN, %
Forage quality index
Dry matter yield
% monthly production
Bahlagrass
TDN, %
Forage quality index
Dry matter yield
% monthly production
White Clover-Bahiagras
TDN, %
Forage quality index
Dry matter yield
% monthly production
64 63 62 58 55 53 55 55
1.9 1.8 1.8 1.6 1.4 1.2 1.4 1.4
220 660 1650 2310 2970 1540 1210 440
2 6 15 21 27 14 11 4
64 63 60 55 52 50 52 52
1.9 1.8 1.7 1.5 1.3 1.0 1.3 1.3
150 550 1600 2100 2700 1400 1100 400
1 5 16 21 28 14 11 4
64 63 61 57 54 52 54 54
1.9 1.8 1.8 1.6 1.4 1.2 1.4 1.4
120 600 2400 2640 3240 1680 1200 120
1 5 20 22 27 14 10 1
65 65 64 60 54 50 52 54
1.9 1.9 1.9 1.7 1.4 1.2 1.3 1.5
266 798 1164 1596 1330 798 399 190
4 12 19 24 20 12 6 3
70 70 66 65 60 54 50 52 54
2.2 2.2 2.0 1.9 1.8 1.5 1.2 1.3 1.5
380 760 1425 1805 1900 1615 1045 380 190
4 8 15 19 20 17 11 4 2
11,000b
100
10,o000
100
12,000c
6,650d
100
aAll dry matter yields are expressed as pounds per acre.
Expected North Florida fertilized with 100 lbs. of nitrogen annually in split application (Burton, 1967).
cExpected North Florida yields fertilized with 100 Ibs. of nitrogen annually in split application.
dAverage 10 year D.M. yield of Pensacola bahiagrass on Leon fine sand fertilized with 100 lbs. of nitrogen yearly (Blue, 1974).
Table A.2. Perennial warm season Forages South and Central Florida.
Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec Total
Bahiagrass
TDN, %
Forage quality index
Dry matter yielda
% monthly production
White Clover-Bahiagrass
TDN, %
Forage quality index
Dry matter yield
% monthly production
Pangola Digitgrass
TDN, %
Forage quality index
Dry matter yield
% monthly production
White Clover-Pagola Digitgrass
TDN, %
Forage quality index
Dry matter yield
% monthly production
Stargrass and Bermudagrass
TDN, %
Forage quality index
Dry matter yield
% monthly production
Hemarthria
TDN, %
Forage quality index
Dry matter yield
% monthly production
65 65 64 60 54 50 52 56
1.9 1.9 1.9 1.7 1.4 1.2 1.3 1.5
160 800 1600 2000 1680 1040 480 240
2 10 20 25 21 13 6 3
8,000b
100
70 70 68 68 65 60 54 50 52 54
2.2 2.2 2.0 2.0 1.9 1.8 1.4 1.2 1.3 1.5
110 440 990 1650 2090 2310 1650 990 550 220
1 4 9 15 19 21 15 9 5 2
66 65 63 58 55 55 58 58
2.0 2.0 1.8 1.5 1.4 1.4 1.6 1.6
300 1200 3000 2000 1600 1000 600 300
3 12 30 20 16 10 6 3
10,000c
100
13,000c
100
70 70 68 68 67 63 58 55 55 58 58
2.2 2.2 2.0 2.0 2.0 1.9 1.5 1.4 1.4 1.6 1.6
130 390 910 1560 2080 2340 2080 1560 1040 650 260
1 3 7 12 16 18 16 12 8 5 2
65 65 62 60 53 52 55 57 57 57
1.9 1.9 1.8 1.6 1.3 1.2 1.4 1.6 1.6 1.6
120 600 1200 3000 1800 1680 1440 960 840 360 12,000c
1 5 10 25 15 14 12 8 7 3 100
65 65 65 65 63 60 58 58 62 63 63 63
1.8 1.8 1.8 1.8 1.6 1.4 1.4 1.3 1.5 1.6 1.6 1.6
100 100 200 500 1100 2200 2000 1600 1200 700 200 100 10,000bd
1 1 2 5 11 22 20 16 12 7 2 1 100
aAll dry matter yields are expressed as pounds per acre.
bEpected Central and South Florida yields fertilized with 100 Ibs. of 12- 6-6 annually in split application.
CExpected South Florida yields fertilized with 100 lbs. of 20-10-20 annually in split application.
dRotationally grazed with 8 week regruoth periods.
Table A.3. Winter annual forages.
Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec Total
Ryegrass
TDN, %
Forage quality index
Dry matter yield
% monthly production
Rye Pasture
TDN, %
Forage quality index
Dry matter yield
% monthly production
Oats Pasture
TDN, %
Forage quality index
Dry matter yield
% monthly production
Wheat Pasture
TDN, %
Forage quality index
Dry matter yield
% monthly production
Rye-Ryegrass and Clover
TDN, %
Forage quality index
Dry matter yield
% monthly production
67 67 63 60 58
2.1 2.1 1.9 1.7 1.5
1008 1288 1456 1008 560
18 23 26 18 10
66 66 60 57 54
2.0 2.0 1.7 1.5 1.4
1200 1650 1300 300 50
24 33 26 6 1
68 64 62 59
2.2 2.1 1.8 1.6
1125 1350 1125 225
25 30 25 5
68 65 61 58 55
2.1 2.0 1.8 1.6 1.3
880 1200 1000 400 120
22 30 25 10 3
66 64 62 62 59
2.0 1.9 1.8 1.7 1.6
1170 1820 1430 845 585
18 28 22 13 9
aAll dry matter yields are pounds/acre.
280 5,600
5 100
69
2.1
700 5,000
10 100
70
2.2
675 4,500
L5 100
70
2.2
400 400
10 100
68
2.1
650 6,500
10 100
Table A.4. Summer annual forages.
Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec Total
Tifleaf Millet
TDN, %
Forage quality index
Dry matter yield
% monthly production
Pearl Millet
TDN, %
Forage quality index
Dry matter yield
% monthly production
Sorghum Sudangrass
TDN, %
Forage quality index
Dry matter yield
% monthly production
Aeschynomene
TDN, %
Forage quality index
Dry matter yield
% monthly production
68 68 66 64 -
2.0 2.0 1.8 1.7
1440 2040 1440 1080 -
24 34 24 18 -
66 64 60 60
1.8 1.8 1.6 1.6
2800 3200 1600 400
35 40 20 5
64 62 60 56 54
1.7 1.7 1.6 1.4 1.4
2200 3300 3300 1870 330
20 30 30 17 3 -
64 64 62 60
2.0 2.0 1.9 1.8
300 1050 1050 600
10 35 35 20
aAll dry matter yields are pounds/acre.
expected yields harvested by grazing when 120 lbs, nitrogen applied in split application.
8,000b
100
11,000b
100
Forage Quality Index
Forage Quality Index is an overall quality estimate based
on the voluntary intake of TDN expressed as a multiple of main-
tenance requirement for TDN (Moore et al., 1984). Intake of
TDN is an overall measure of forage quality in that it combines
both forage intake and digestibility. When forages are fed
"free choice" and alone, animal performance is closely related
to the intake of TDN.
A study was conducted to determine the forage Quality
Index for tropical summer grass hays grown in Florida (Moore et
al., 1984). Sheep were fed tropical grass hays and voluntary
intake of TDN was determined. Quality Index was calculated by
dividing the actual TDN intake by the TDN maintenance require-
meats of sheep as stated by the National Academy of Science
(Nutrient Requirements of Sheep, 1975). The result was ex-
pressed as a multiple of maintenance requirement; a value of
less than 1.0 is considered a low quality forage, while a for-
age with greater than 1.8 is considered an excellent forage.
Table A.5 summarizes the results of this research. It shows a
range in Quality Index, crude protein, and TDN for various
samples. The results were used in compilation of the forage
Quality Index values shown in Tables A.1, 4.2, A.3, and A.4.
The forage Quality Index usually ranges between 0.80 and
2.2 although some clovers and alfalfa may exceed the 2.2 level
for short periods of time depending on the stage of maturity,
and it may also be possible to have forages of such low quality
to go below 0.80. An example of the relationship between for-
age Quality Index and expected performance by heifers and cows
is given in Table A.6.
Bermudagrass
Bermudagrass (Cyodo dactylon) is an improved pasture
grass grown throughout Florida and the Southern states. It is
one of the most common and important pasture plants in the
South. Bermudagrass probably originated in India and can be
found throughout the tropical and subtropical parts of the
world. African selections have shown more diversity than those
from India. Introductions from Africa have been released as
cultivated varieties and are being used in breeding new varie-
ties.
Bermudagrass is more drought-resistant than bahiagrass,
carpetgrass, or dallisgrass, but it does not produce much
growth under arid conditions. Bermudagrass grows best on heavy
soils provided it has adequate moisture and nutrients, espe-
cially nitrogen.
Bermudagrass makes its best growth when mean temperatures
are above 750 F. Little growth is made when these temperatures
Table A.5. Range in quality index, crude protein (CP), and total
digestible nutrients (TDN) in hay samples from research
plots.
Quality No. of Composition, % of Dry Matter
Index Samples CP TDN
All grassesa
0.7 4 38.8 8.2 44.0 52.0
0.8 9 4.3 8.8 43.6 53.1
0.9 6 5.6 10.4 41.7 59.0
1.0 7 5.0 16.7 46.0 54.5
1.1 29 5.1 15.3 48.0 58.0
1.2 18 5.2 14.2 44.3 59.4
1.3 17 5.2 16.8 52.7 64.1
1.4 16 7.3 16.5 50.3 62.4
1.5 11 8.4 18.1 57.8 64.7
1.6 5 12.9 19.5 57.3 65.0
1.7 5 10.8 17.8 57.3 65.2
1.8 3 10.7 14.4 57.9 65.8
1.9 1 13.0 61.1
aBahiagrass, bermudagrass, digitgrass, and limpograss.
Table A.6. Relationship between forage quality index and expected performance
by heifers and cows.
Quality Index
Expected Performance <1.0 1.0 1.2 1.4 1.6 1.8 2.0 2.2
Heifer Weight Change lb/day loss 0 0.3 0.6 1.0 1.3 1.6 1.9
Cow Milk Production Ib/daya 0 0 5 10 15 20 25 30
aLactating cows producing more milk than expected will suffer excessive weight
loss unless supplemented.
drop 10 to 15 degrees. Temperatures of 26 to 280 F. usually
kill the stems and leaves back to the ground (Burton, 1967).
There are several cultivars of bermudagrass grown in Flor-
ida. Coastal bermudagrass is a high yielding pasture grass
that is well suited for hay production on fertile, well-drained
soils.
Coastcross-1 bermudagrass is more digestible than Coastal
but is less cold tolerant and may be subject to winter-killing
with severe winters. Yields are slightly lower than with Coast-
al and fertilizer requirements are similar. It may be more
adaptable to wetter areas than Coastal.
Tifton 44 bermudagrass is well suited for North Florida
and is winter hardy. Yields and digestibility are lower than
Coastal but it has finer stems that cure faster when cut for
hay and makes a denser sod.
Suwannee bermudagrass is another hybrid developed at Tif-
ton, Georgia and is similar to Coastal in many respects. It is
well adapted to light, sandy, drought soils and is more pro-
ductive than Coastal on such soils, and does not require much
fertilizer. It may be inferior to Coastal on heavily grazed
pastures because of its more open sod. It must be established
by planting stolons and is slow to sod over, and planting mate-
rial is limited.
Callie bermudagrass is well suited to south-central Flor-
ida for both hay and grazing. It is also very productive and
is more digestible than Coastal.
Bermudagrass may be propagated by planting either seed or
vegetative sprigs. The seed does not germinate well when the
mean daily temperatures are less than 650 F. Most bermudagrass
is propagated by planting sprigs. Spring and summer seem to be
the best seasons for planting, but it can be done successfully
in any month.
When legumes are not grown with bermudagrass, 50 to 400
pounds of nitrogen are usually applied annually per acre along
with phosphorus and potash. It is recommended that these ap-
plications be split in two, with half applied in March-April
and the other half applied in the fall to prevent leaching
losses. Smaller, but more frequent applications will also be
beneficial, especially on grazed pastures.
Stargrasses
Stargrasses are a long-lived perennial grass forage origi-
nating in Africa and belong to the genus Cynodon, the bermuda-
grass family. Stargrasses spread rapidly from vegetative
plantings made in moist soil and compete well with common ber-
mudagrass. Among the more common varieties grown in Florida
are Ona, McCaleb, and Sarasota. Although stargrasses will grow
in North Florida, yields are low and the plants are easily
frost damaged. Stargrasses grow best south of a line from
Brooksville to Orlando. Stargrasses require good soils and
heavy fertilization of 300 pounds per acre of 20-10-20 applied
twice a year.
Ona stargrass is slightly more drought tolerant than
McCaleb and forms a denser sod cover. In a forage production
trial at the Agricultural Research Center at Ona, forage pro-
duction over a three-year period averaged 7.3, 6.4, 6.4, and
5.1 tons per acre respectively, for Ona stargrass, McCaleb
stargrass, Pangola digitgrass, and Pensacola bahiagrass (Hodges
and Martin, 1975). Stargrasses perfocia best when a four to
five week growth period is allowed between grazing. This al-
lows high yields of good quality and maintains a stand which
resists invasion of other grasses. Cattle make excellent use
of this young, leafy forage, but rapid maturity results in a
mature grass which is low in quality and palatability. When
stargrasses are fertilized, and cattle are kept off for approx-
imately six weeks in the fall, stargrasses can produce a good
hay crop.
Digitgrass
There are three principal cultivars of digitgrass in Flor-
ida: Pangola, Transvala, and Slenderstem.
Pangola (Digitaria decumbens Stent) is a widely utilized
creeping perennial grass. It is a high quality, highly palat-
able, and productive pasture and hay grass for Florida south of
Gainesville. Since it was released to Florida livestock pro-
ducers in 1945, it has been estimated that over half a million
acres have been established in South Florida (Hodges et al.,
1975). The palatability of Pangola is so high that cattle
often prefer it to other vegetation, thus weeds and less ac-
ceptable grasses can infest a good stand if it is not properly
managed. Allowing the grass to reach a height of 18 inches at
one date during the year greatly increases the the competitive
position of Pangola, and thus prevents crowding out by less
desirable plants. Pangola should be grazed rotationally when-
ever possible, allowing one week between grazing periods in
mid-summer and two to three weeks during the rest of the grow-
ing season (Hodges et al., 1975).
Pangola has a high fertilizer requirement and responds
well to nitrogen. Annual fertilization is required to maintain
a good stand. Application of 400 pounds of 12-6-6 per acre
with copper added as a trace mineral once or several times a
year produces excellent results for pastures. Trials, in which
all forage was harvested and removed, have shown forage yields
from a 2-1-3 fertilizer ratio to be superior to those from a 2-
1-1 fertilizer (McCaleb et al., 1966; McCaleb et al., 1965).
When Pangola is grown in combination with white clover, nitro-
gen fixation occurs and little or no nitrogen is needed.
Transvala dlgitgrass was a new cultivar released to Flor-
ida producers in 1969. It is adapted to the same areas as
Pangola and has the same characteristics. Transvala is resis-
tant to certain nematodes and to the Pangola stunt virus, and
therefore, usually has higher yields than Pangola.
Slenderstem is another digitgrass that is adapted to the
same geographical areas as Pangola and has similar growing
conditions. Its chief characteristic is that it produces more
forage in the cooler months than Pangola, but total year-round
yields are usually lower.
Survenola digitgrass is a new cultivar and the first hy-
brid digitgrass to be released from Florida's tropical grass
breeding program. Survenola has a much different appearance
than the other digitgrasses. The leaf blades of Survenola are
usually 10 to 13 mm wide, while those of Pangola grass are
usually less than 8 mm in width. Survenola is not adapted to
state-wide conditions, but can be successfully grown on sandy
upland soils between Gainesville and Brooksville (Schank et
al.).
Bahiagrass
Bahiagrass is a deep rooted perennial primarily used for
pasture on sandy soils in Florida. It is estimated that there
are over 2 million acres of bahiagrass in Florida (Ruelke and
Killinger, 1976). It forms a dense sod even on sandy soils,
and once the sod is formed, few other plants are able to en-
croach. Bahiagrass is native to the West Indies and much of
South America, and was introduced into the United States around
1913 (Scott, 1920). There are three main cultivars grown in
Florida: Pensacola, Argentine, and Paraguay.
Pensacola is the most popular variety and is a widely
adapted grass that will tolerate heavy grazing. It is frost
resistant and adapted to the sandy soils of the coast area from
east Texas to eastern North Carolina. It grows very little in
cold weather and is often slow starting because of lack of soil
nutrients or lack of seed coverage. Its ability to grow on
poor soils, excellent seed habits and high nutritive value and
productivity have caused it to be recommended over the other
varieties.
Argentine bahiagrass is similar to Pensacola in productiv-
ity but is easily frost damaged. It has slightly wider leaves
than Pensacola and is a less palatable. Like Pensacola, its
quality decreases rapidly with maturity.
Parag;iay 22 bahiagrass is similar to Argentine in growth,
appearance, and cold tolerance. It is reported to be slightly
nore productive than Argentine under some conditions and is of
comparable palatability (Whittey et al., 1982).
Response of Bahia to FPrtilizer
Because of sandy soils, rapid water percolation, and rapid
nitrification, nitrogen leaching is a major problem facing
Florida farmers and ranchers. Blue (1974) reported that the
high recovery percentage in forage and the large amounts of
nitrogen in stolen-root material and soil strongly suggest
that nitrogen leaching is not a major loss route when nitrogen
is applied to perennial pastures at biologically usable rates
during the growing season. Table A.7 shows the response to
different nitrogen rates on Leon fine sand for Pensacola bahia-
grass.
Although Pensacola bahiagrass has been the principle grass
used in most fertilizer experiments in Florida, the responses
of other grasses to nitrogen have not been substantially dif-
ferent than that of bahiagrass (Blue, 1983).
Hemarthria
Commonly called "limpograss", Hemarthria was first intro-
duced into Florida in 1964. Individual varieties available in
Florida are Greenalta, Redalta, Bigalta, and Floralta. Limpo-
grass produces few seed and is vegetatively propagated. They
are best adapted to the poorly drained sandy soils of peninsu-
lar Florida, but good production has been obtained in west
Florida. Lime should be added to soils to attain a soil pH of
5.5-6.5 and an application of 500 lbs per acre of a complete
fertilizer such as 10-10-10 is recommended one to four weeks
after planting (Quesenberry, et al., 1978).
Bigalta is the most widely used limpograss in Florida. It
has less cold tolerance than Redalta and Greenalta, but in
years with milder winters, Bigalta produces more dry matter
with higher nutritional value (Quesenberry, et al., 1978).
Floralta has recently been released for commerical use.
Floralta appears to be superior to other limpograsses in total
dry matter yield. Experiments have shown that Floralta is
superior to Redalta and Greenalta in in vitro organic matter
digestion (IVOMD). Floralta withstands grazing and is markedly
superior to Bigalta in grazing persistence (Quesenberry, et
al., 1984).
Hemarthria has been shown to be a high quality forage
ideal for sandy, poorly drained soils. Rotational grazing may
be essential for continued production. Unless carefully man-
Table A.7. Pensacola bahiagrass response to alternative nitrogen rates as
averages of five nitrogen sources on leon fine sand tested over.
N Oven-Dry Forage, Ib/Acrea
Rate High Low Average
Ib/Acreb
0 3,810 2,810 2,123
100 7,580 5,380 6,651
200 11,210 6,560 10,108
aHarvest dates were approximately May 15, July 1, Aug. 10, and Oct. 1 for each
year.
bSplit application in late March and July 1 each year. 4 lb. P205 and 42 lb.
K20 were applied per acre at the 0 and 100 lb. N rates, and twice this amount
was applied to the 200 lb. N rate.
aged, limpograss may be overgrazed and less desirable plants
may invade the stand. When used in a well-planned rotational
system, limpograss has an extended growing season and can be
grazed over much of the year in Central Florida with relatively
high animal performance.
Forage Legumes
The use of forage legumes has grown rapidly in Florida
with an estimated 400,000 acres of production in 1983 (Balten-
sperger and Chambliss, 1983). When properly inoculated with
bacteria, legumes have the ability to take nitrogen from the
air and transfer it to the soil. Thus when grown in combina-
tion with other grasses, legumes reduce nitrogen fertilizer
requirements. Appropriate combinations of forage legumes and
other grasses can also extend the productivity of pastures
across seasons and maintain forage availability during wet and
dry periods.
Winter Forage Legumes
Clovers are grown alone or in combination with warm season
perennial grasses or in combination with cool season annual
forages in Florida. Several types of clover can be used in
Florida. These include white clover, crimson clover, arrowleaf
clover, and subterranean clover. White clover is the most
widely adapted clover in Florida. It prefers wet soils and
will tolerate flooding for short periods. Osceola, Regal, and
Arcadia are recommended ladino type varieties and Louisiana S-1
and Nolin's Improved are recommended intermediate types. All
varieties may live from year to year, but Osceola is the most
persistent.
Crimson clover is grown on the well-drained heavier soils
of North Florida. It does poorly on dry sandy soils and will
not tolerate flooding. It has an earlier grazing season than
white clover. It is well suited to be grown in combination
with annual winter forages.
Sweet clover is considered an annual as it usually does
not produce enough seed to sustain a stand the next year.
Sweet clover will tolerate drier soils than white clover but
will not tolerate flooding. It is less palatable than other
clovers, but cattle will learn to eat it.
Red clover has soil requirements similar to white clover,
but is more sensitive to excessive moisture. Under Florida
conditions, red clover produces few seed and is generally con-
sidered as an annual. Varieties suggested for Florida are
Nolin's, Pennscott, Kenland, Florie, Redland, and Kenstare.
Arrowleaf clover is adapted to most of the well-drained
soils in North Florida and will grow on soils that are too wet
for crimson clover. Recommended varieties are Yuchi and Amelo.
The growth subterranean clover is prostrate, and thus is
primarily a grazing crop. It has been successfully grown on
well-drained soils in the northern half of Florida. Mixtures
of Mt. Barker, Woogenellup, and Tallarook are suggested.
Warm Season Legumes
Aeschynomene, also known as "jointvetch", is an upright,
warm season annual legume which can be grown throughout Florida
as a forage crop. It is best adapted on fertilized and limed
flatwood and moist upland oils and is more tolerant of wet
conditions than drought.
After liming, if it is necessary to adjust ph to 5.5 to
6.5, apply up to 500 pounds per acre of 0-14-14 fertilizer or
similar mixture. Aeschynomene may then be broadcast seeded or
precision planted into a prepared seed bed. Planting is
usually done as rainy season begins, usually around June 1 in
Central Florida. The pasture should not be grazed until the
plants attain a height of 24 inches which usually is 60 to 70
days after planting. Aeschynomene should be rotationally
grazed as undergrazing results in insect damage and overgrazing
causes reduced forage productivity.
Under moderate grazing pressure, aeschynomene can produce
adequate seed to provide a stand next year. It is suggested
that stocking rates be reduced in late September and early
October to permit reseeding. After seeds appear, the forage
should be grazed intensively until frost. After frost, forage
quality diminishes rapidly.
Aeschynomene is a warm season annual legume which has been
grown successfully in Central and South Florida. If properly
inoculated, it can fix nitrogen to the soil. Aeschynomene can
be produced alone or grown in combination with perennial
grasses, increasing the productive grazing time of the forage
and reduce the need for application of nitrogen fertilizer.
Other warm season legumes, produced on a limited commercial
basis in Florida, are Hairy Indigo and Alyceclover.
Temporary Winter Pastures in Florida
The cultivation of small grains like rye, oats, and wheat
for temporary winter pastures is a widely used practice in
North and Central Florida. Additional forage production and
extending the grazing period is possible by seeding in rye-
grass. Clover may also be grown in combination with cereal
grains, provides a source of nitrogen, and produces good yields
in the spring when the grains are maturing.
Cereal Grains as Forage
Cereal grains provide an important source of nutritious
green forage during the winter and early spring months when
perennial grasses are dormant. In fact, greater weight gains
are possible on forage produced from small grains during the
winter and spring months than on perennial warm season grasses
in Florida such as bermudagrass or bahiagrass pastures during
their productive summer months. Florida's relatively mild
winters offer excellent growing conditions for forage produc-
tion from grains. Yet, weather conditions can be sufficiently
adverse to cause extreme variation in production due to lack of
adequate rainfall at required intervals on sandy soils.
Cereal grains are often planted on tilled soil but can be
successfully "sod seeded" into dormant permanent pastures. The
choice of a cereal grain for grazing purposes is based on its
adaptation to the local environment, productivity, and palata-
bility.
Rye is the most widely used small grain for grazing pur-
poses in Florida. When compared to wheat, oats, or triticale
(across between durham wheat and rye), rye consistently main-
tains better production under such adverse conditions as low
temperature, poor soil fertility, and low soil moisture. Rye
will grow at lower temperatures than oats, as the growth of
oats is sometimes stunted by a sudden drop in the temperature.
Research data on the forage production and quality of
wheat pastures in Florida is limited. Wheat is grown mainly
with the intention of harvesting it in the spring as a cereal
grain. It is possible to graze wheat for limited time periods
in January and February, but proper management is needed to
insure that overgrazing does not occur. A grain crop of about
30 bushels per acre is possible under normal conditions when
overgrazing is avoided. Wheat is similar in yield and palata-
bility to oats and is less subject to frost damage.
Oats are also a valuable temporary winter forage crop in
Florida. Oats may be planted and grazed earlier than rye, but
are susceptible to frost damage. Oats are also an excellent
crop from which to make hay or silage. They are more digesti-
ble than rye, but all of the cereal grains are highly digesti-
ble.
There are major differences in the growth habits, winter
hardiness, and maturity of the small grains among species and
cultivars. Oats can provide early grazing in the Fall. Among
the varieties of oats recommended for Florida are Florida 501
which produces much higher early season yields than Coker 227.
The reverse is true in the spring as Coker produces more late
season forage. Elan may out produce both, as was the case in a
forage production and quality comparison experiment (Barnett
and Stanley, 1976).
Ryegrass
Ryegrass is a valuable winter and spring grazing crop that
is usually considered an annual, although it has the ability to
reseed itself if properly managed. It can be seeded alone or
in combination with cereal grains or clovers. It is adapted to
the flatland soils or the heavier, more fertile sandy loam
soils in north. Heavy pasturing of ryegrass is desirable, as
it keeps the grass in a succulent condition, thus utilizing a
higher percentage of the forage during its short productive
period. When planted alone or with cereal grain crops and
clover, ryegrass is often seeded at a rate of 10 to 25 pounds
per acre. When ryegrass is overseeded into dormant permanent
pastures like bermuda and bahiagrass pastures in the fall to
provide winter grazing, rates up to 100 pounds per acre are
required for seeding.
Producing Combinations of Winter Forages
A recommended practice for production of winter forage in
Florida is grow combinations of two or more cereal grains,
cereal grains with ryegrass, or a three-way combination such as
rye, ryegrass, and crimson clover. This practice possesses two
valuable attributes. First cereal grains, ryegrass, and clo-
vers mature at different rates. Oats and rye tend to be more
productive early in the cool season while ryegrass shows more
growth later on. Production of combinations extends the period
over which a particular field can be grazed. Second, certain
forages are more productive in wet years while others prefer
drier conditions. By wisely selecting combinations wet prefer-
enced and dry preference annuals, the producer reduces the
risk of crop failure and the need to buy supplemental feed.
Clovers are suggested because they reduce the need to
apply nitrogen fertilizer and possess wet-dry preference and
maturity characteristics that can compliment cereal grains and
ryegrass.
Summer Annual Forages in Florida
Permanent pastures and native ranges are the primary
source of nutrition for beef cattle in Florida in the warm
season. Annual warm season forages such as pearlmillet and
sorghum-sudangrass, however, grow rapidly and can provide large
quantities of supplemental forage. As such, summer annual
forages can play a role in stocker cattle production.
Pearlmillet
Pearlmillet is a highly palatable warm season annual that
can be grown throughout Florida. Pearlmillet prefers fertile
and moist soils, but is damaged from flooding. Recommended
varieties for Florida include Common Gahi 1, Gahi 3, Starr, and
Tifleaf 1 Hybrid. Common pearlmillet has been used for many
years, but studies have shown that Gahi or Starr are preferred
to Common (Jones). Gahi 1 produces more forage than Starr and
is equal to Starr in quality of forage. In grazing trials,
heifers grazing on Tifleaf 1 Hybrid and Gahi 3 exhibited higher
rates of gain than Gahi 1 (Wright).
Pearlmillet is propogated by broadcasting seed into a well
prepared seedbed. Application of 500 pounds of 4-12-12 per
acre or similar mixture is recommended at planting and refer-
tilization after each grazing is suggested to enhance regrowth.
Growth of pearlmillet is rapid. Newly planted fields
should not be grazed until the plants reach a height of 24
inches. Pearlmillet pastures can carry three animals per acre
for periods of 3 to 6 weeks with adequate rainfall. When the
plants are grazed down to a height of 4 to 5 inches, the cattle
should be moved to another field to allow regrowth.
Pearlmillet may be planted anytime after the danger of
frost is past. Earlier planting dates give a longer season.
Little forage is produced after early September, regardless of
planting date.
Sorghum-Sudangrass
Sorghum-Sudangrass is a warm season annual which possesses
many of the attributes of pearlmillet. When compared to pearl-
millet, Sorghum-Sudangrass produces more dry matter whose qual-
ity is generally lower.
Quantity and Quality of Forages Production in Florida
The biological simulation program requires that data on
forage dry matter production, its TDN on a dry matter basis,
and an estimate of the forage Quality Index be provided on a
monthly basis. Tables A.1, A.2, A.3, and A.4 have been devel-
oped to assist users in generating their forage data. Table A.1
provides estimates of annual production on a monthly basis for
Coastal bermudagrass, Alicia bermudagrass, Tifton 44 bermuda-
grass, bahiagrass, and bahiagrass and white clover grown in
combination produced in North Florida. Table A.2 provides
similar information for six perennial forages grown in Central
and South Florida: bahiagrass, bahiagrass-white clover, pango-
la digitgrass, white clover-pangola digitgrass, stargrass and
bermudagrass, and hemarthia.
Table A.3 shows monthly production and quality estimates
for five widely used cool season annual forages. These values
are most valid for North Florida. The forages shown are rye-
grass, rye, oats, wheat, and rye-ryegrass-clover. Table A.4
shows estimated production for four warm season annuals:
Tifleaf pearlmillet, Gahi pearlmillet, sorghum-sudangrass, and
aeschynomene.
The estimated production levels shown in all four tables
assume typical soils for that forage, adequate moisture, and
adequate fertilization. Observed production levels for any
particular field will probably deviate from the estimates shown
in the tables. As such, the tables serve only as a guideline
to prospective users of the simulation model.
Appendix B
Statistical Conversion Equations
The Nutrient Requirements of Beef Cattle, 5th edition,
published in 1976 by the National Research Council (NRC) is a
valuable source of information required to construct a simula-
tion model of stocker cattle. The publication contains numer-
ous tables but does not always provide the mathematical rela-
tionships that facilitate construction of a computerized simu-
lation model.
To discern the mathematical relationship between two vari-
ables of interest whose apparent relationship has been provided
by a table contained in the NRC, the tabulated values of the
two variables are plotted. A mathematical function is hypothe-
sized based on the shape of the plot. Then a regression is
performed to estimate the parameters of the mathematical func-
tion.
TON Requirements for Maintenance
Table 1 in the NRC gives TDN required for maintenance for
animal weights ranging from 100 kilograms (kg) to 500 kg in 50
kg increments. Steers and heifers are assumed not to differ.
Using weight (W) as the independent variable and TDN required
for maintenance (TDNR) as the dependent variable, a plot indi-
cates that there is an approximate quadratic relationship be-
tween W and TDNR.
The values are converted to pounds. Using the transformed
data, least squares estimates are:
(B.1) TDNR = .92738 + .00804W .0000011W2 R2 = .99
(.174) (.0006) (.0000004)
where the numbers in parentheses are the estimated standard
error of the estimated parameters and R2 is the coefficient of
determination. Actual and fitted values for TDNR versus W are
shown in Table B.1.
Net Energy Concentration of Feeds
The NRC provides tables for net energy concentration for
maintenance and net energy for gain for several forages and
feeds. In this bulletin, the TDN value specified by the user
is assumed to allow determination of net energy concentration.
Thus a relationship between TDN and net energy is required.
Appendix Table B.1. TDN required for maintenance as a function of
weight.
TDN Required for TDN Required for
Weight Maintenance (Ib) Maintenance (Ib)
(Ib) Actual NRC Fitted
220 2.64 2.64
331 3.53 3.47
441
551
661
772
882
992
1102
4.19
5.07
5.73
6.39
7.28
7.94
8.38
4.26
5.02
5.76
6.48
7.17
7.82
8.45
Forages and concentrate feeds differ in terms of their
utilization by the animal. Futhermore, in Florida, citrus pulp
and molasses are widely used low quality supplemental feeds.
It is recognized that these two feeds differ from higher energy
feeds such as corn and lower energy feeds such as hay. Animal
utilization also differs whether net energy is used for mainte-
nance or gain. Thus six equations are required: three For
maintenance and three for gain.
Using NRC TDN and net energy values for several grain and
protein supplements, the following equations are estimated:
(B.2) NEM/kg = -0.82 + 3.37TDN,
(B.3) NEG/kg = -0.74 + 2.47TDN,
where NEM/kg is net energy concentration for maintenance (Mcal/
kg), NEG/kg is net energy concentration for gain (Mcal/kg), and
TDN is TDN yield expressed as a decimal. NRC values and fitted
values for selected grains and protein supplements are shown in
Table B.2.
Citrus pulp, citrus molasses, cane molasses, and other
related by-products may be used as animal feeds. Using NRC
values, estimated net energy concentrations as a function of
TDN for these feeds are
(B.4) NEM/kg = 0.38 + 2.07TDN,
(B.5) NEG/kg = -0.08 + 1.59TDN.
NRC values and fitted values for three feeds are shown in Table
B.3. Net energy concentrations for forages are estimated to be
(B.6) NEM/kg = -0.10 + 2.33TDN,
(B.7) NEG/kg = -0.1.51 + 3.58TDN.
These conversion equations are also used for supplemental feeds
other than citrus pulp and molasses with TDN value less than 65
percent. NRC values and fitted values for selected forages are
shown in Table B.4. Equations B.2 through B.7 are given in
Mcal/kg. Dividing by 2.2 yields the relationships used in
equations 6, 7, 8, 10, 11, and 12.
Appendix Table B.2. Actual and predicted feed value for grain and protein
supplements.
NRC Feed NEM NEG NEM NEG
Reference TDN (Mcal/kg) (Mcal/kg) (Mcal/kg) (Mcal/kg)
Feed Name Number (%) Actual Actual Predicted Predicted
On a Dry Basis
s (Moisture Free)
Corn grain
US No. 2
Oats, grain
Oats, white
gr. US No. 2
gr. US No. 3
gr. US No. 4
Rye grain
Sorghum grain
(9-12% protein)
Wheat grain
Cottonseed Meal
(Solv. extd.)
Peanut Meal
(Solv. extd.)
Soybean Meal
4-02-931
4-03-309
4-03-388
4-03-389
4-03-390
4-04-047
4-08-139
4-05-211
5-01-621
91.0 2.28
76.0 1.73
75.0
71,0
66.0
85.0
80.0
1.70
1.57
1.45
2.04
1.86
88.0 2.15
75.0 1.69
5-03-650 77.0 1.76
5-04-604 81.0 1.93
1.48
1.14
1.11
1.00
0.87
1.36
1.24
1.42
1.11
1.16
1.29
2.25
1.74
1.71
1.57
1.40
2.04
1.88
2.15
1.71
1.77
1.91
Appendix Table
B.3. Actual and predicted feed value for sugarcane molasses and
citrus pulp.
NRC Feed NEM NEG NEM NEG
Reference TDN (Mcal/kg) (Mcal/kg) (Mcal/kg) (Mcal/kg)
Feed Name Number (%) Actual Actual Predicted Predicted
On a Dry Basis (Moisture Free)
Sugarcane
Molasses,
dehydrated 4-04-695 68.0 1.78 1.18 1.79 1.16
Citrus 4-01-241 77.0 1.97 1.32 1.97 1.30
molasses
Citrus Pulp 4-01-237 77.0 1.97 1.32 1.97 1.30
dried
Molasses 4-04-696 72.0 1.91 1.20 1.87 1.30
Appendix Table B.4. Actual and predicted feed value for forages.
NRC Feed NEM NEG NEM NEG
Reference TDN (Mcal/kg) (Mcal/kg) (Mcal/kg) (Mcal/kg)
Feed Name Number (%) Actual Actual Predicted Predicted
On a Dry Basis (Moisture Free)
Native plants,
hay, post ripe
Bermudagras,
Coastal hay
Brome hay
Alfalfa aerial
part, ensiled
Lespedeza hay
mid bloom
Clover, Crimson
hay
Ryegrass, Italian
aerial part
grazed
Clover, Red
aerial part
grazed, full
bloom
Wheatgrass,
crested aerial
part grazed,
early bloom
Clover, Red
aerial part
grazed, early
bloom
1-03-188 45.0 0.97
1-00-716 49.0 1.05
1-00-890 52.0 1.10
3-00-212 54.0 1.15
1-02-511 57.0 1.22
1-01-328 60.0 1.29
2-04-073 62.0 1.33
2-01-429 64.0 1.39
2-05-420 67.0 1.46
2-01-428 70.0 1.56
0.11
0.23
0.35
0.43
0.55
0.66
0.72
0.80
0.88
0.99
0.11
0.25
0.36
0.43
1.23
1.30
1.35
1.39
1.46
1.53
Appendix C
User's Guide to the Bioeconomic Simulator
It is expected that users of this program will vary widely
in their knowledge of and exposure to computer related terms
and equipment. This appendix briefly explains some of the
computer terminology used in the text. Further help may be
obtained from county agents or IFAS personnel located through-
out the state. The following conventions are used throughout
this appendix:
terminal local input/output device connected via phone
line to a computer, either a paper or a
screen device
modem data communication device used for converting
digital signals into analog signals for
transmission over public telephone
system system of commands to operate the computer
and its various software packages
prompt a character or symbol used by the operating
system to inform the USER it is ready for the
next command
brackets () to indicate a possible USER response to pro-
gram questions or system prompts; the USER
should type only what is inside the brackets,
not the brackets themselves
editor subset of the operating system allowing the
USER to "edit" or change character represen-
tation in a data set
return means carriage return key
program bioeconomic simulator program
tone two- or three-part pitched tone indicating
transmission heard via the phone receiver
indicating transmission of computer signal
a capitalized word in the following text that
is underlined is a response to the terminal
from the system program
Accessing the Program
The program is available via the IFAS VAX computer net-
work. A data terminal and modem is all that is required for
accessing. Computer services are normally available 24 hours
per day, seven days a week.
Terminal switches should be set before dialing the compu-
ter. Terminal will vary as to the number of switch settings
required. The more common switches and settings for using the
IFAS VAX 11/750 are as follows:
SWITCHES SETTINGS
Line feed Single or double
Speed 300 or 1200 baud
Duplex Full
Parity Odd
On line key Depressed
Power On
Dial IFAS VAX at Gainesville, FL. (904) 392-5760, and
after hearing the "tone", attach the telephone to the acoustic
coupler on the modem as diagrammed or turn the appropriate
switch on the modem and place the phone on its cradle. The
indicator (carrier) light is visible once the circuit is com-
plete. If the indicator light does not come on, set switches
to the "off" position and dial the number again. The following
steps are necessary to sign on and engage the program:
step 1. Push return key
step 2. Computer responds with USERNAME: (enter VAX user
name), push return
step 3. Computer responds with PASSWORD: (enter appropriate
passwords) push return. (NOTE: The password is not
echoed back to your terminal for security reasons.)
If login has been successful, the computer responds
with WELCOME TO VAX... and then the dollar sign ($)
prompt.
1Type in your own special password. User accounts can be
obtained through the IFAS Computer Network, Bldg. 810, Room 10,
campus.
step 4. Type (BEEF), push return. The computer will respond
with a message giving the program name and the IFAS
departments responsible for the program's develop-
ment. A second screen gives a phone number and the
persons to contact if problems should develop while
using the beef simulator.
Data is requested from the USER, via questions sent to the
terminal from the program. After typing each answer, the USER
pushes the return key, and the next question is asked. This
pattern continues until all data for calculations and questions
about program options have been answered. The program com-
pletes execution, according to the chosen options. Upon com-
pletion of the program, the program returns control to the VAX
system and gives a dollar sign ($) prompt to the USER. The
USER may then end the session or continue with a new set of
data. To end this terminal session, the USER responds to the
($) prompt with (LOGOFF). To enter a new set of data, the USER
goes to step 4 to re-enter the program.
Operating Instructions
Upon entering the computer program, the USER must remember
that it is the program which is controlling the terminal and
flow of information. Normal VAX commands or editing procedures
should not be attempted; they will cause errors from the compu-
ter program. Answers typed by the USER may be in either upper
or lower case characters but must be followed by a carriage
return. For questions which can be answered in a yes/no fash-
ion, the desired word is typed in response. Alternatively, the
response (YES) can also be indicated by pushing return.
After the USER has entered (BEEF) and pushed return, an
introductory message is returned, giving a general description
of what the computer program is designed to do. The next
screen of information lists the data that the USER must be
prepared to provide to the biological simulation model. If at
this point, the USER is not prepared to answer the data ques-
tions, he or she types (NO). (The USER should then reread the
example given in the main document and prepare a worksheet as
suggested there.) After the data screens, the program asks if
instructions are desired.
General instructions about using this program have been
recorded for review. Three main points are noted here. First,
a push of the return key can be substituted for typing (YES)
for yes/no questions. Second, for nearly all numeric re-
sponses, an appropriate range of values is indicated in paren-
theses. Most numeric data for the growth section of the simu-
lator are checked against a predetermined range of values, in
an effort to keep responses within a "normal" range. Data
outside the range cause a message indicating out-of-range val-
ues and an option of changing the entry is given. All yes/no
and A, B, C answered questions respond only to those exact
answers. Any other type of response produces an error message
and a repeat of the question just asked. This cycling contin-
ues until a "correct" response has been given or a (CTRL-Y)
combination has been pushed. Third, if the USER wishes to exit
(stop) the program, the (CTRL) and (Y) buttons should be pushed
simultaneously. A dollar sign prompt ($) should then appear on
the left side of the screen. If not, push both buttons again.
This signal causes release from the program's control and re-
turns control to the VAX system.
Data Entry
The first major decision for the USER is the method of
data entry. The USER is queried if data is to be entered via a
stored data file. A (NO) response means that the USER does not
have a stored data file available for use. This is the usual
situation for first-time users or those entering a very differ-
ent feeding program than what was used in earlier runs. In-
stead the USER is branched to a section which goes through a
question-USER response sequence until all data related to the
growth simulation is entered. Alternatively, a (YES) or "re-
turn" prompts the USER to enter the name of the data file con-
taining the feeding data. This choice implies that the USER
has already successfully run the program at least one time and
stored (saved) the input typed during that session or that he
or she has access to a file created by someone else.
Once the production (feeding) data has been entered, the
USER is given the option of reviewing the feeding data. Re-
gardless of how the data was entered, it is suggested that the
data be reviewed. Review of the data provides one last chance
for any desired alterations.
During review, information is displayed on the terminal in
small blocks. Each item of data has a corresponding numeric
identifier. A question asking if any of the above items are
to be changed is shown following each block of data. If a
(YES) or "return" is given by the USER, then the USER is asked
to type the corresponding identifier, a comma, and the new
value. All data items in that block are redisplayed, including
the newly-changed item. Again the USER is asked about making
changes. This cycle can be repeated as many times as necessary
until all needed changes are made. When (NO) is the response,
the program continues on and displays another block of data for
review.
Presentation of blocks of data for review continues until
all of the feeding data has been shown to the USER. Similar
to the initial data entry process, each numeric data change is
checked against the appropriate predetermined ranged of values;
an error message occurs for any abnormal entries. The expected
range for the value is presented, along with an opportunity to
adjust the entry.
Following the review section, the USER may choose to store
the data set. Storing (saving) the data prevents the USER from
having to re-enter all of the feeding values again, if this
particular data set is to be reused. This allows for setting
up a "base case" that could be used for many runs with or with-
out alterations. Given a (YES) or "return" prompt by the USER,
the program asks for a name by which the input data is to be
identified while in storage. Filenames must be nine characters
or less in length with no embedded blanks or special characters
used. By default, the file type is .DAT2 if nothing else is
specified. All names should be made meaningful to aid in re-
membering what is saved. A (NO) response causes the program to
skip over asking about a filename.
Economic Budget Input
This section refers to economic data. The computer pro-
gram has a separate section for calculating breakeven values,
based on implied or USER given stocking rates and rates of gain
as calculated in the growth simulation. However, the USER may
choose to run the growth simulation only. A (NO) response
causes the program to skip over the economic input and calcula-
tion sections.
Alternatively, a (YES) response indicates to continue on
into the budget section. Three ways for entering data are
presented to the USER. Choice (A) means that budget informa-
tion is to be entered from the terminal, via a question/
response sequence. Questions are displayed for the USER to
answer, with appropriate examples indicating the type of numer-
ic response expected. Examples show whether whole numbers or
decimals are executed and what type of units each value repre-
sents. Over 20 questions follow. After each question is show
on the terminal, the program waits for a response from the
USER. This style of data entry can be used for the budget
section, regardless of whether the feeding data was entered via
the terminal or from a stored file. Options two and three,
indicated by (B) and (C), respectively imply that the budget
data has already been entered and saved at some prior time. If
(B) is answered, the budget data is read from the same stored
data file that was used earlier for the feeding data. This
choice is not valid if the production data was entered via the
terminal. An error message is sent to the USER, indicating a
different choice is necessary. Option (C) implies that there
2See Vax User's Guide for more information on file naming.
is some other stored file containing only budget data. A
prompt for the filename is sent by the program. This choice
may also be used regardless of how feeding data was entered.
Once the budget data have been entered, the USER has the
option of reviewing the entries. It is suggested that the data
be reviewed. This provides a chance for making any desired
changes. Unlike the feeding data, there does not exist inter-
nal checking of the numeric values. Because of the wide range
of prices and costs possible, the USER is asked to check his
own entries for possible errors, especially in regards to mag-
nitude. The budget data is echoed in two screens to the USER's
terminal. As before, the USER is queried if changes are de-
sired. The procedure is the same as described before. The
USER enters the appropriate numeric indicator, comma, and new
value, and pushes "return". The block of data is echoed,
showing the change. This cycle can be repeated as needed for
both sets of data.
Once the budget data has entered, the USER has three
choices regarding saving the data. Answer (A) saves none of
the budget data. Answer (B) places the economic and production
data together under the same filename and filetype identifier.
Answer (C) saves the budget data under a completely different
label than that given to the production data. If (C) is
chosen, a prompt is given for a filename. Option three might
be chosen if the production data had not been stored earlier of
if the cost data entered is a set that could be used with a
number of different feeding data files.
Menus For Output Tables
The USER is given the option of selecting the output
tables to be viewed for a particular run of the computer pro-
gram. Each table is identified by a title and a numeric code,
shown on the screen in a menu format. Any combination of ta-
bles for which data has been entered can be chosen as output.
One of the numeric codes representing a table is expected as an
answer to the question/prompt at the bottom of the screen.
Following an entry, the screen is then reset, indicating the
previous selections) and awaits another choice. This cycle is
repeated until all desired tables are chosen. Options are
given, which facilitate the selection of all tables as output
and for exiting the menu. Note that once a selection is made,
it cannot be changed.
Choices Relating to Output
After choosing the output tables, the USER is queried on
the options relating to the disposition of the output tables.
Again, three options are presented. The first choice (A) is to
display all output back to the terminal and store (save) noth-
ing. If a printing terminal is being used, a hard (paper) copy
of the output is automatically made. A video terminal would
only allow for viewing, with no hard copy listing available for
later reference. However, a printer attachment to the video
terminal would rectify this problem. The second choice (B)
stores the output information on the USER's account for later
viewing and/or printing. None of the output tables are dis-
played. The third choice (C) combines the actions of the
aforementioned options. Not only is the information sent to
the terminal, but it is stored as well. Under these last two
options, the USER is asked to specify a filename. A good rule
to follow is to use meaningful names as aids in remembering the
contents of the data file. Any time the output tables are sent
to the terminal, they are divided into blocks of 20 lines or
less for ease in viewing. A prompt is printed with each block,
indicating that the return must be pushed when the USER is
ready to continue.
How to Do Multiple Runs
After the output of the program has been viewed (or
stored), the USER is given the choice of ending the session or
running the program again. The option of stopping (leaving)
the program is indicated by (A). This branch takes the USER
back to the dollar sign ($) prompt and the VAX system. The
terminal is no longer controlled by the program. The USER
types (DIRECTORY) to check names and spellings of files stored
by the program. A further check of a file's contents could be
done by typing (TY "filename.filetype"). The indicated file
would then be displayed on the terminal. For a more complete
discussion of VAX operating instructions see the Vax User's
Guide.
Alternatively, the answer (B) keeps the USER within the
program. First, an option of reviewing the feeding data is
given. As described earlier, a block of data is displayed with
the choice of changing any item. For example, assume a basic
feeding program was entered originally. This set of data could
be altered as often as needed to allow study of alternative
ways of backgrounding the animal. Different permanent pastures
could be tested. A variation of TDN and pounds available by
month per forage could be done to explore the risk of bad
weather or desirability or irrigation. Different combinations
of supplements could be tested. Upon completing the review of
the feeding data, the same question is asked about budget data.
This set of information can also be reviewed and/or changed.
If a person has not entered any budget data prior to this time,
it could be done by making the appropriate changes to the zero
values shown. At the end of each review, a prompt is given
about storing the data. A (YES) brings forth the question of
filenames.
Following the data reviews, the same set of questions are
encountered. "Which tables are wanted as output?" "Where
should the output be sent?" "Is another run of the model to be
made?" This pattern of cycling continues until the USER de-
cides to stop.
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This public document was promulgated at a cost of $1,691.50,
or 68 cents per copy, to inform professionals and others about
a user-oriented, computerized model for evaluating feeder cattle
programs.
All programs and related activities sponsored or assisted by the Florida
Agricultural Experiment Stations are open to all persons regardless of race,
color, national origin, age, sex, or handicap.
ISSN 0096-607X
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