Bulletin 835 (technical)
Projecting the Impact of
Increasing Energy Costs on the
Florida Cattle Industry
Gwen S. Shonkwiler and Thomas H. Spreen
/b A s
Agricultural Experiment Stations
Institute of Food and Agricultural Sciences
University of Florida, Gainesville
F. A. Wood, Dean for Research
Projecting the Impact of
Increasing Energy Costs on the
Florida Cattle Industry
Gwen S. Shonkwiler and Thomas H. Spreen
Gwen Shonkwiler is a former research assistant and Thomas Spreen is
an associate professor in the Food and Resource Economics Department,
University of Florida.
The Florida cattle industry is primarily cow-calf with weaned calves the
primary output. Over the past decade, approximately three-fourths of
the available Florida-produced feeder calves were shipped out of the
state to be finished and slaughtered elsewhere. All exported feeder cattle
are transported by truck. Large quantities of beef are imported into the
state and nearly all of it moves by truck. Thus the current marketing
system for Florida feeder calves and Florida consumed beef is dependent
upon trucking services, and as such, is potentially vulnerable to increases
in energy costs.
In this study, the cost of finishing and slaughtering cattle in Florida is
compared with the cost of finishing and slaughtering cattle in Oklahoma
and shipping chilled beef from Oklahoma to Florida. Florida costs in-
clude the costs of transporting large quantities of corn from the Midwest.
The results indicate that in 1980, Florida costs exceeded Oklahoma
costs by less than three percent. Furthermore, as energy costs increase, it
is projected that Oklahoma costs rise relative to Florida costs and if real
energy costs were to increase by 100 percent, cost in the two regions
would be approximately equal.
The authors gratefully acknowledge data assistance provided by
Wayne Davis, James Simpson, James Trapp, and William Kunkle. Ear-
lier drafts of this manuscript were reviewed by Richard Beilock, Wayne
Davis, Robert Emerson, A. Zane Palmer, and James Simpson. The
authors are responsible for any errors or omissions in the paper.
Abstract ................... .... ......... ........ ... ...... ii
Acknowledgments .............................................. ii
Introduction ........................................... 1
Florida's Cattle-Feeding Industry ................... ............ 2
Florida's Livestock Slaughter Industry ............................. 3
The Present Situation ............................. ............. 4
Statement of the Problem .................. ..................... 6
Objectives ...................................................... 6
Organization of the Study ...................................... 7
Analysis of Feeder Cattle Spatial Price Differentials ................... 7
Spatial Price Differentials .................. ..................... 7
Price Differentials across Feeder Cattle Markets
in the United States ......................................... 8
Model Specification ........................................... 8
Model Estimation ............................................. 10
Cost Comparisons for the Production of High Quality
Slaughter Beef: Florida vs. Oklahoma ............................. 13
Stocker Program Costs in Florida ................................ 14
Stocker Program Costs in Oklahoma ............................. 16
Grain-Finishing Costs in Florida .................................. 17
Grain-Finishing Costs in Oklahoma ................................ 21
Regional Cost Differences ..................................... 22
The Impact of Changes in Energy Costs on
Regional Costs of Production ................................ 22
Concluding Remarks .............. .............................. 23
Sum m ary ................. .. ..................... ............. 23
Conclusions ............................................... 25
Implications for Future Research ................ ............... 27
Appendix A ............... .. ................... .............. 29
Appendix B ................ .................................. 31
Literature Cited .................... ........... .. ............. 41
Florida possesses a large, viable beef cattle industry which, according
to historians, dates as far back as the Sixteenth Century, when the
Spanish first brought cattle into the state. It has taken several centuries
for this industry to begin to realize its potential in Florida, and it has only
been within the past few decades that cattlemen have begun to adapt new
management techniques that have allowed the industry to develop this
far. During the past 50 years, advances in breeding practices, forage
production and disease prevention have allowed Florida cattle producers
to substantially increase the productivity of their land and livestock. In
1980 Florida led all other southeastern states in the number of beef cows,
and ranked ninth relative to all other states [Florida Crop and Livestock
Florida's growth in the beef cattle industry is a fairly recent phe-
nomenon. From Table 1 average inventory levels were calculated for the
periods 1955 to 1959 and 1976 to 1980 for both Florida and the United
States. Using these averages, a 62% increase in Florida beef cattle
numbers was observed, while the inventory levels for the United States as
a whole increased by only 25% during this same period.
Along with this rapid growth there have been major structural changes
within Florida's beef cattle industry, brought about in part by shifts
within the cattle industry as a whole. Prior to the early 1900s the type of
cattle that predominated Florida herds was not very different from the
animals first brought over by the Spanish. These animals, sometimes
referred to as scrub cattle, were well adapted to the harsh climate and
poor pastures, but did not produce high quality meat; most were slaugh-
tered and consumed within the state.
The development of the American Brahma, Santa Gertrudis, and
several other exotic European cross-breeds during the early part of the
Twentieth Century marked the beginning of a new era for the cattle
industry in Florida. Prior to World War II, cattlemen in all areas of the
United States marketed their beef animals after two or three years on
pasture [Mathis, 1976]. Florida's production practices were no different,
and the cattle in this state were slaughtered locally or shipped out to other
plants in the southeast.
Technological advances in agriculture during the 1940s and 1950s
combined with the development of hybrid sorghum that grew well in dry
sandy soils, to make possible the emergence of a commercial cattle
feeding industry in the High Plains and Western regions of the United
States. Cattle feeding had traditionally been done on farms in the Mid-
west, more as a marketing channel for the farmers' grain than as a
Table L.-January 1 beef cattle inventory, cattle and calves on farms, Florida and
United States; 1955-1980.
Year 1,000 head
SOURCE: U.S. Department of Agriculture [1955-1980]
separate activity. But during the 1950s large scale feedlots began to
appear and within ten years cattle feeding had become a full-fledged
industry, not simply a sideline operation.
Rapid U.S. population growth and increasing consumer incomes dur-
ing this same period of time caused a substantial increase in the demand
for high quality beef. Per capital beef consumption increased by almost
100% between 1959 and 1973, from 63 pounds per person to 111 pounds
[Reimund, et al., 1981].
Florida's Cattle-Feeding Industry
In Florida, there was also a shift toward large-scale fed cattle produc-
tion accompanied by a decline in the number of small-scale feedlots.
According to a survey conducted in 1979 and early 1980, only four out of
the 14 feedlots surveyed in Florida were established before 1970, and the
number of lots with a one-time capacity of less than 1,000 head has
declined by 98% since 1965 [Simpson and Baker, 1980]. The data from
Table 2 indicate how quickly these small-scale feedlots declined in im-
portance in Florida during the 1970s.
With the rise of the southwestern commercial feedlot industry, a new
production marketing system for Florida beef cattle evolved. A substan-
tial increase in the number of outshipped cattle occurred as shown in
Table 3 during the mid-1960s. The third column of Table 3 shows stocker
calf shipments as a percentage of the total number of calves marketed in
Florida over the 25-year period from 1955 to 1980.
Florida's Livestock Slaughter Industry
Livestock slaughter plants in Florida can be characterized by small,
State-inspected plants. Most plants are oriented towards custom slaugh-
ter or cull cow slaughter. As shown in Table 4, the number of facilities
and total kill has fluctuated over the past 25 years, with total kill declining
dramatically in 1980, due primarily to a low point in the cattle cycle. The
number of state inspected plants has turned up, but nearly all plants are
There are some exceptions, with fairly modern, assembly-line type
plants located at Bartow, Plant City, Center Hill, Hialeah, and Ocala.
These plants handle nearly all of the fed beef produced in Florida, but
also slaughter cull cows and bulls.
As shown in Table 4, the total number of cattle and calves slaughtered
in Florida during the period 1956 to 1980 changed little. But in 1956,
35.2% of the total inventory of cattle and calves was slaughtered, com-
pared to 17.5% in 1980. This indicates a 50% decrease in Florida cattle
slaughter relative to the rapid growth in the beef cattle herd during the
same 25-year period.
Table 2.-Number of Florida feedlots by size, 1965-1968, and 1973 and 1979.
Less than 1,000 head Total No. of Cattle
Year 1,000 head or more Lots on Feed
1965 461 16 477 73,000
1966 465 13 478 80,000
1967 415 15 430 79,000
1968 355 13 368 61,000
1973 250 12 262 58,000
1979 10 14 24 65,000
SOURCE: Simpson and Baker 
Table 3.-Calf marketing and outshipments south and east of the Suwannee
River, Florida, 1955-1980.
Calves Marketed Outshipments %
SOURCE: Florida Agricultural Statistics, Livestock Summary, 1969, 1972, 1980
The Present Situation
The Florida beef industry is currently composed primarily of cow-calf
operations. Most producers wean calves at 300 to 400 pounds, at which
time they are sold. A majority of those marketed calves are shipped out
of Florida. Fall is the heaviest marketing season for calves. Calves mar-
keted in the fall are usually backgrounded on winter pastures elsewhere
in the Southeast or Southern Great Plains. When they become yearlings,
they are placed in confinement feedlots. Florida crossbred cattle are
generally more resistant to heat and humidity and thus are among the
breeds preferred by feedlots in Texas, Arizona, and Oklahoma.
The Florida cattle industry does not produce enough slaughter beef for
Florida consumers, and large quantities of carcass and boxed beef are
imported into the state. Currently, all beef shipped into Florida and all
feeder cattle shipped out of Florida are transported by truck [Spreen and
Table 4.-Livestock slaughtering establishments and number of cattle and calves killed, 1955-1980, Florida.
Total Number Under Federal
of Plants Inspection Cattle Calves Total
1955 66 4 NA NA NA
1956 NA NA 323,000 162,200 485,200
1960 NA NA 327,000 196,400 523,400
^ 1961 49 8 411,000 259,000 670,000
1970 48 8 392,500 160,300 552,800
1975 92 8 448,600 166,600 615,200
1980 110 11 264,300 99,200 363,500
SOURCE: U. S. Department of Agriculture [1955-1980]
aNumbers in parentheses represent average liveweight of animals slaughtered.
Retention of feeder cattle to be grown and finished in Florida feedlots
has been suggested as a possible alternative to the current marketing
system [Baker, 1980]. A small Florida feedlot industry currently exists,
with 125,000 head marketed from those lots in 1979, or approximately 20
percent of all calves marketed in 1979 [Simpson and Baker, 1980]. A
recent set of figures by Ikerd, however, show that a substantial cost
advantage exists for a Midwestern-based feeding system over a Florida-
based system in the production of high quality slaughter beef. Feedlot
operators in Florida dispute these figures [Davis, 1981].
From all indications, the dependence on trucks to transport goods to
market is typical of most agricultural industries in Florida. The unique
geographic location of Florida and the lack of adequate railroad facilities
have furthered the dependence upon the trucking industry. Not only do
agricultural producers transport their products to out-of-state buyers by
truck, but to a greater degree Florida consumers depend upon trucks to
bring in goods consumed but not produced here.
Statement of the Problem
Only recently has concern been expressed by those within the beef
cattle industry about the dependence upon trucking services. This con-
cern has arisen from the fact that as the price of diesel fuel increases
rapidly, so do truck rates, which are sensitive to changes in fuel prices. As
the trucking rates go up, ceteris paribus, the farm price received for these
feeder calves goes down. The viability of Florida's present marketing
system is now being questioned by producers and researchers in Florida.
The topic to be addressed in this study is to project the impact of
increasing energy costs on the current marketing system.
The specific objectives of this study were as follows.
1) Calculate the price differential between f.o.b. prices paid for feeder
steers in Kansas City and Florida, and to determine the relationship
between diesel prices and the price differential.
2) Determine the cost of producing high quality carcass beef in the High
Plains at some fixed level of feed prices.
3) Determine the cost of producing high quality carcass beef in Florida
utilizing different combinations of imported and Florida purchased
4) Using the results of objectives 1 to 3, determine the cost relationships
between Florida and the Midwest and determine the sensitivity of
relative costs to changes in transportation costs for both cattle and
Organization of the Study
The analysis of the price differential between Midwestern feeder cattle
prices and Florida feeder cattle prices is required because price differ-
ences between the two regions can not be approximated by transport
costs. The next section presents a statistical model of feeder cattle price
differentials. The third section presents costs of backgrounding and
finishing cattle in Florida and Oklahoma. Oklahoma is a major destina-
tion of exported Florida cattle and it is representative of other major
destination points such as Texas and Kansas. The budgets and price
differential model are then utilized to project the changes in production
costs in each region under increasing energy costs. In the last section,
conclusions are drawn and suggestions for further research made.
ANALYSIS OF FEEDER CATTLE
SPATIAL PRICE DIFFERENTIALS
Spatial Price Differentials
The concept of a site price refers to the price of a product at a particular
location or site. Site-price can be defined as a base market price minus the
cost of transporting the good from the production point to the market
place. Following Bressler and King [1978, p. 127] the price, Pf, at any
farm location is therefore equal to the market price less the appropriate
transfer cost, T, which in turn, is a function of distance from marketf(D).
The resulting site-price function may be written as:
Pf = P,,, T = P,m -f(D).
where Pf is the price received by the farmer for his good, T, is per unit
transport cost to market, and P, is the price paid at the market for the
same good. This implies that producers closer to the market place re-
ceived a higher price for their good, relative to their competitors pro-
ducing the same good at a more distant production location.
Relating the site-price concept to a two-region trade model, when
transfer costs are assumed to be greater than zero, a positive difference
will always exist between the price received by producers of a
homogeneous good in the importing region and the price received by
those in the exporting region. Tomek and Robinson [1972, p. 144]
emphasize the fact that "price differences between any two regions or
markets that trade with each other will just equal transfer costs" in this
type of simple model. Furthermore, if price differences do exceed trans-
fer costs, then arbitrage will continue until it is no longer profitable to
transport the product between regions, or in other words, when the
difference between regional prices equals the cost of shipping the product
from one region to the other.
Price Differentials Across Feeder
Cattle Markets in the United States
In the United States, feeder cattle are produced in almost every region
of the country, but the major demand points are located almost exclu-
sively in the Midwest and High Plains region. Feeder cattle markets
approximately satisfy the assumptions of competitive market structure,
homogeneous product, and perfect knowledge. Therefore, by selecting
two different regional markets for feeder cattle, such as Florida and
Kansas City, an analysis of the effects of increasing transport costs on the
movement of this commodity between the two regions can be carried out.
As previously mentioned, Florida produces an excess supply of light-
weight feeder cattle and markets them primarily to buyers in Texas,
Arizona, and Oklahoma [Florida Department of Agriculture, 1980].
Producers marketing their lightweight animals at the Kansas City market
have a locational advantage over Florida producers relative to the de-
mand points, and therefore the spatial price differential should reflect this
advantage. Kansas City prices should always exceed Florida prices by an
amount equal to the transfer cost from Florida to a point close to both
Kansas City and the major feeding areas.
In Appendix A, plots of the monthly average prices for both weight
groups indicate that seasonal fluctuations occurred in both the Florida
series and the Kansas City series. Table A-1 and Figure A-3 reveal the
fact that the difference between Kansas City prices and Florida prices
does not remain constant by an amount equal to the transfer costs. These
results indicate that the simplified model previously described would not
explain the variability in prices during a 12-month period.
A statistical model is proposed to account for the apparent seasonality
of the price differential and discern the relationships, if any, between the
price differential and transport costs.
Feeder cattle are graded by the United States Department of Agricul-
ture; therefore, the only difference between a Choice 400- to 500-pound
feeder steer in the Midwest and in Florida should be the location. With
the qualitative factors held constant, the following model is suggested:
(1) PL1G1 PL2G1 = F(TC(d), Z)
PL1G1 is the price of a commodity grade G1 at location L1,
PL2G1 is the price of the commodity of the same grade at location Lz,
TC(d) is the transport cost expressed as a function of the distance, d,
between L1 and L2 and
Z is a composite of other factors.
Examples of these other factors are local supply and demand conditions
at both markets, institutional factors, feed prices, and slaughter cattle
prices [Judge and Hieronymus, 1962; Farris and King, 1961; Bressler and
Given the proximity of Kansas City to both the Corn Belt and High
Plains feeding areas, and that it is the most active market for feeder
cattle, it was considered to be an acceptable proxy for demand point
prices. Monthly prices for Choice Kansas City 400- to 500-pound and 600-
to 700-pound feeder steers and Choice Florida feeder steers in the same
weight classes were compiled over a ten-year period from 1971 to 1980.'
Spatial differentials were computed by subtracting the Florida price from
the corresponding Kansas City price, creating the variables D400 and
D600. These variables are expressed throughout in cents per hundred-
weight. Since all Florida feeder cattle move by truck, TC is the cost of
shipping cattle by truck. Several different variables were substituted into
the models as a proxy for the actual cost of shipping livestock from
Florida to the Midwest or High Plains regions. Livestock shipments are
not regulated by the Interstate Commerce Commission; therefore it was
not possible to obtain accurate monthly cost data on these types of
shipments. General agricultural transportation cost indices, such as the
one published by the USDA, were not satisfactory, because they include
rail and barge costs, which have not increased as rapidly as have truck
costs. The monthly price of No. 2 diesel fuel paid by commercial consum-
ers, expressed as an index number with January 1973 as the base, was
found to be the most reasonable proxy for transport costs, because
livestock trucking rates appear to be very sensitive to changes in the price
of diesel fuel. This price index was obtained from the United States
Department of Labor .
Preliminary analysis indicated that both series D400 and D600 con-
tained a strong seasonal component; thus binary variables were defined.
Negative values for the variable D400 were observed eight times during
the 17-month period from January 1978 to May 1979. This type of
phenomenon is contrary to spatial economic theory, whereas Florida
prices actually exceeded Kansas City prices by as much as $6.63 per
hundredweight in April 1979. Analysis of cattle markets during this
period suggests that the demand for lightweight (300- to 500-pound)
feeder cattle was unusually strong relative to the demand for heavier
(600- to 700-pound) cattle. Florida is an active year-round market for
lightweight cattle and feedlots were willing to absorb all transport costs
'Prices reported for the Okeechobee auction market were used for the 400- to
500-pound weight class. Because of the scarcity of the heavier Choice feeder
steers in South Florida, a state average price was substituted for the 600- to
700-pound weight class.
plus a premium during this period. For a further discussion, see Shonk-
wiler (Chapter 3). A binary variable (ND) was defined to account for this
abnormal period in feeder cattle markets.
Furthermore, a lag structure was suggested by the data. The theory
assumes that the market structure is such that an immediate response to
increasing transport costs would be reflected in the price differential.
There were periods within the sample characterized by rapid increases in
diesel fuel prices. The ability of livestock haulers to immediately pass on
increased costs is unlikely.
Given the information presented in the preceding section, the follow-
ing models were suggested:
(2) D400 = f(gl(F), D2-D12, ND)
(3) D600 =f(g2(F), D2-D12)
D2 = 1 if month is February, 0 if otherwise;
D3 = 1 if month is March, 0 if otherwise;
D4 = 1 if month is April, 0 if otherwise;
D12 = 1 if month is December, 0 if otherwise;
ND = 1 if year is 1978 and 1979 if month is less than June, 0
gl(F), g2(F) = distributed lag functions in the variable F; and
F = monthly price index for No. 2 diesel fuel price paid by
commercial consumers (February 1973 = base).
The Haugh-Box technique [Haugh and Box, 1977] was used to identify
the appropriate lag structure, and it was determined that three and four
months lags on the diesel fuel price index were the most important ones
(see Appendix B for details). These lagged fuel price variables enter both
models significantly and, as will be demonstrated, have the expected
long-run effect upon the price differentials; i.e., as the price of diesel fuel
rises, the differential widens.
Ordinary least squares was used to estimate the parameter values for
both models after the dynamic regression results were calculated. The
results are presented in Table 5. The negative sign observed on the
parameter estimate for F4 in these models is explained in Appendix B.
The residuals from both (2) and (3) when estimated with ordinary least
squares exhibited significant autocorrelation. Lagged values of the de-
pendent variable were used to account for autocorrelated residuals. The
estimated model is shown in Table 5.
Table 5.-Results of regression model.
Intercept 88.967 (1.0084)a 58.139 (0.9557)a
F3 6.748 (2.9061) 3.935 (2.5840)
F4 -6.562 (-2.7654) -3.416 (-2.2114)
ND 2012.523 (0.5266) -
FD3 -35.522 (-1.4650) -
FD4 28.575 (0.8263) -
D400L 0.3516 (3.8146) -
D600L -0.3890 (4.3713)
D2 268.101 (2.7363) 49.020 (0.7463)
D3 286.013 (2.9646) 82.680 (1.2442)
D4 113.841 (1.1373) 133.887 (2.0051)
D5 311.429 (3.1605) 124.709 (1.9236)
D6 263.622 (2.7255) 95.993 (1.4877)
D7 96.368 (0.9998) 91.909 (1.4203)
D8 471.646 (4.8505) 229.892 (3.5365)
D9 402.829 (4.1057) 171.528 (2.6581)
D10 296.849 (3.0041) 172.544 (2.6854)
Dl1 241.789 (2.4881) 209.091 (3.2550)
D12 177.719 (1.8522) 166.104 (2.5801)
R2 = .6742 R2 = .6986
aThe ratio of the estimated coefficients to their estimated standard errors appear in
F3 = Diesel fuel index lagged three months,
F4 = Diesel fuel index lagged four months,
ND = 1 if observation is in 1978 or first half of 1979,
= 0 otherwise.
FD3 = F3 x ND
FD4 = F4 x ND
D400L = D400 lagged one month,
D600L = D600 lagged one month,
and D2 D12 monthly binary variables.
For both weight groups, the seasonal influences are greatest during the
late summer and early fall months. Florida 400- to 500-pound steers are
discounted more during the months of August through November than at
any other time during the year. Florida producers are competing with
cattle producers all over the United States because this is the peak
marketing season for weaned calves everywhere. Given the locational
disadvantage, Florida becomes a residual supply area for feeder calves,
thus depressing price.
In the 600- to 700-pound class, there also appears to be a seasonal effect
during April and May, which can also be explained by marketing patterns
for heavier feeder steers. Most stocker operations in the Midwest buy
lightweight calves in the fall and graze them on small grain pastures until
spring and then market them as yearlings to the feedlots. Therefore,
Florida cattlemen who carry weaned calves over the winter are competing
with producers closer to the feeding areas, and must accept lower prices
for their animals during the spring.
Because of the presence of lagged dependent variables in the models,
several computations must be made in order to determine the long-run
effects of increasing diesel fuel prices on the differential between Kansas
City and Florida feeder steer prices.2
The price differential during the peak marketing season is of special
interest, thus the monthly dummy variables for August through Novem-
ber were averaged for both models. The estimated long-run relationships
between the 400- to 500-pound price differential and diesel fuel prices is
(4) D400 = 682.477 + 0.2870 F
and the relationship between the 600- to 700-pound price differential and
diesel fuel prices
(5) D600 = 415.553 + 0.8494 F.
Because transport costs on a per head basis are greater for the 600- to
700-pound steers than for the 400- to 500-pound steers, the effects of
increasing fuel prices should be greater on the heavier weight class. The
models indicate that this is true, whereas the parameter estimate for
diesel fuel is almost three times as large in the D600 model as in the D400
To simulate the impact of real increases in diesel fuel prices, the
estimated equations (4) and (5) were used. The spatial price differential
was calculated assuming increases in the diesel fuel price index. These
differentials, presented in Table 6, reflect changes only in the price of
diesel fuel, all other costs associated with transporting feeder cattle and
other factors affecting price differentials were assumed constant.
As noted above, the 600- to 700-pound price differentials are less
meaningful as very few animals of this weight are shipped from Florida.
The projected 400- to 500-pound differentials, however, imply that fur-
ther increases in the price of energy translate to an ever-larger differential
between midwestern prices and Florida prices.
The implications of this analysis are that as the price of energy con-
tinues to increase, Florida cattle producers, as well as producers in other
southeastern states, will continue to receive discounted prices for feeder
cattle marketed to feedlots in the Midwest and High Plains region. If
these discounts continue to represent a large percentage of the value of
2Individual parameters of the estimated equations should be interpreted
cautiously. See Appendix B for a more complete discussion of the estimation
Table 6.-Projected price differentials.
Diesel Fuel Price D400b D600b
Basea 9.30 11.47
50% increase 10.53 15.13
100% increase 11.77 18.78
200% increase 14.24 26.10
500% increase 21.65 48.04
750% increase 27.83 66.33
aDecember 1980 prices
bPrice differential in dollars per hundredweight
the animal, it seems likely that Florida cattlemen will seek alternative
marketing channels for their feeder cattle. In the following section one
such alternative that may be viable for certain cattlemen in Florida is
COST COMPARISONS FOR THE PRODUCTION OF HIGH
QUALITY SLAUGHTER BEEF: FLORIDA VS. OKLAHOMA
Assuming that Florida consumers will continue to demand grain-
finished beef products and that the USDA's present grading system for
beef remains intact, the production of high quality slaughter cattle must
be considered by Florida cattle producers. The retention of feeder cattle
within the state and their use in the production of grain-finished beef is a
system which has received considerable attention [Prevatt et al., 1978;
Simpson and Baker, 1980]. Feedlot capacity in Florida presently stands
near 100,000 head, but these lots provide placements for fewer than 20%
of all the stocker calves marketed in Florida in 1980.3 According to recent
estimates only 11% of the USDA Prime, Choice and Good beef that is
consumed in Florida comes from feedlots in the state.4
3In 1980, approximately 611,000 calves were marketed and 98,600 calves were
slaughtered in Florida, which means that roughly 512,400 calves were marketed
as stocker or replacement animals.
4According to 1980 estimates, 90% of the cattle fed in Florida feedlots graded
USDA Good or better; the average slaughter weight for Florida cattle was 996
pounds with an average carcass dressing yield of 61%. The following formula was
used to approximate Florida production of fed beef:
(Number of cattle fed x 0.90) (996 x 0.61) = (128,400 x 0.90)
= 70,202,700 pounds (carcass weight basis).
Per capital consumption statistics for 1980 indicate that on a carcass weight
basis, 1.058 billion pounds of beef were consumed in Florida. For purposes of this
study, 60% was assumed to be USDA Good or better.
(1,058,000,000 x 0.60) = 634,800,000 pounds
Therefore, 70.2/634.8 = 0.1106 or 11%.
In the following section a partial budgeting technique is employed to
compare the costs of producing high quality slaughter-weight cattle in
Florida versus the High Plains or Midwestern areas. Researchers in the
past have argued that because of a lack of locally available feed grains,
Florida producers cannot afford to grow out their feeder calves and
compete with the large commercial stocker and full-feeding facilities in
the aforementioned areas. Utilization of by-product feedstuffs that are
available in Florida has been considered (e.g. Prevatt, et al., 1978).
A two-stage program is considered for both Florida producers and
High Plains producers, which includes a stocker or backgrounding phase
and a finishing or feedlot phase. This was done because most of the calves
marketed in Florida weigh in the 300- to 500-pound range and usually
undergo a feeding program where they consume a ration high in
roughage before being placed on a feedlot. Backgrounding calves is also
a common practice in certain cattle production areas where lightweight
animals are weaned in the early fall and spend five to six months on small
grain pastures before going into a feedlot in the spring. It is also impor-
tant when comparing costs of production between regions to be sure that
the comparison being made is between similar production systems which
yield outputs that do not vary with respect to quality characteristics (i.e.,
the assumption of homogeneous products is not violated).
It is important to keep in mind that the prime objective of this report is
to determine the impact of increasing energy costs on the Florida cattle
industry. In the ensuing presentation of budgets of the cost of back-
grounding, finishing, and slaughtering cattle, those items deemed most
sensitive to increased energy costs are identified. In the following section,
the cost of energy in parametrically increased and its effect on production
costs is tabulated. Of particular interest is the question: will increased
energy costs have differential impacts on production costs in Florida
Stocker Program Costs in Florida
During the winter months, the quality of pastures in Florida is poor.
Therefore, to provide pastures that will produce weight gains competitive
with winter wheat pastures in the Midwest, fertilizer and grass seed such
as ryegrass must be used. The stocker phase for Florida producers con-
sists of a 120-day program which will take a 400-pound feeder calf up to
approximately 560 pounds. This assumes that calves are weaned at ap-
proximately 350 to 400 pounds and are then preconditioned for six to
eight weeks before being put on the ryegrass pastures in December.
The ration and production costs were calculated using 1980 prices. The
budgets presented in Tables 7 and 8 were obtained from the Food and
Resource Economics Department at the University of Florida [West-
berry, et al., 1980]. This type of program represents a typical north or
Table 7.-Costs of pasturing with grain a 400-pound feeder steer to 563 pounds in
Florida from December 1 to April 1.
Item Quantity/Head Cost/Unit Cost/Head ($)
(Choice or MF1)" 400 lbs $75.84/cwt 303.36
Improved pasture 0.6 acre $93.40/acre 56.04
Hayc 200 lbs $65.00/ton 6.50
Corn (shell corn #2
yellow)c 2 bu $3.02/bu 6.04
21-25% protein supplement 20 lbs $8.00/cwt 1.60
Growth stimulant 1 $1.10/unit 1.10
Medication and minerals 1 4.00
Maintenance and repairs 2.12
Interest on operating costs $380.76 14% 17.59
Death loss $303.36 1% 3.03
Labor 3 hrs $3.65/hr 10.95
Overhead costs 2.80
Marketing costs 2.90
Total All Costs $418.02
NOTE: Calculations assume 180 days on feed and an average daily gain of 2.78 pounds
per day from pay weight to pay weight, adjusted for a 3% shrink. All costs were computed
using 1980 prices.
"1980 Florida average price for August-October 400- to 500-pound Choice or medium
frame No. 1 steer (MF1).
bSee pasture budget, Table 8.
'Corn prices include $0.73 bushel transportation and handling charges compiled using
1977 ICC Rail Cost Scales updated to October 1981 [U.S. Department of Agriculture,
ESCS, December 31, 1980].
dIncludes depreciation, interest on investment, taxes, and insurance.
'$0.50/cwt order buy cost
central Florida backgrounding operation which, if used in the southern
area of the state, would sometimes require irrigation of the pastures
because of lack of rainfall during the cool season.
Because feeder cattle require more energy (feed) to maintain and gain
weight during periods of extremely hot or cold weather, the average daily
gain for Florida feeder steers was slightly higher than for Oklahoma
steers. During the confinement feeding stage the feed conversion advan-
tage shifts away from Florida because of the hot climate and high humid-
ity during the summer months.
Fertilizer prices are sensitive to increases in the price of petroleum and
natural gas. Because substantial quantities of nitrogen are required to
produce high quality forages in Florida, large increases in natural gas
prices would have serious consequences for Florida cattle producers. It
has been estimated that a 100% increase in the price of natural gas would
cause ammonium nitrate prices to increase by 50%. Likewise, mixed
Table 8.-Estimated cost of growing one
Florida, winter/spring 1980.
acre of rye-ryegrass-clover, North
Item Quantity/Head Cost/Unit ($) Cost/Acre ($)
Rye seeda 1 bu 9.00/bu 9.00
Ryegrass seed 15 lbs 20.00/cwt 3.00
Clover seed 6 lbs 101.00/cwt 6.06
Limea 670 lbs 20.40/ton 6.83
Fertilizer, 5-10-15a 700 lbs 132.00/ton 46.20
(33% N)b 80 lbs 184.00/ton 7.36
Labor 0.8 hr 3.65/hr 7.43
Total All Costs $93.40
SOURCE: Gunter et al. 
aPrices obtained from Farmer's Mutual Exchange, Gainesville, FL, for the months of
bAnnual average price for Florida [U.S. Department of Agriculture, Agricultural Prices,
fertilizers, such as the 5-10-15 type used frequently to improve pastures,
would increase by 25%, given a 100% increase in petroleum prices.5
Stocker Program Costs in Oklahoma
The backgrounding budget presented on Table 9 was obtained from
the Agricultural Economics Department at Oklahoma State University
. This type of program represents a typical stocker operation in
northwest Oklahoma, and the costs are those that would have been
incurred in 1980. Grazing lightweight feeder cattle on small grain pas-
tures during the winter months is a common practice in most of the grain
production areas of the High Plains and Southwestern regions of the
country. The cost of providing winter wheat pastures is not included in
this program because it is assumed to be a separate activity that will
generate income after the animals are taken off and the wheat is
Greater death loss percentages were assumed for the Oklahoma
budgets than for the Florida program, and a less efficient feed conversion
ratio was used. Protein supplement has been included in this program and
is normally introduced in the last few weeks to properly condition feeder
animals insuring steady weight gains when first placed in confinement
5Telephone conversation with Dr. Larry Jones, Chase Econometrics, Bala
Cywd, Pennsylvania, May 5, 1981.
Table 9.-Costs of pasturing with supplement a 400-pound feeder steer to 566
pounds in Northwest Oklahoma from October 15 to March 1.
Item Quantity/Head Cost/Unit Cost/Head ($)
(Choice or MF1)a 400 lbs $84.34/cwt 337.36
21-25% protein supplement 100 lbs $8.00/cwt 8.00
Salt and minerals 12.5 lbs $8.00/cwt 1.00
Vet medicine 4.00
and utilities 3.50
Interest on operating costs $353.86 14% 18.57
Death loss $337.36 2% 6.75
Labor 2 hrs $3.90/hr 7.80
Overhead costsb 3.47
Marketing costs 2.95
Total All Costs $393.40
SOURCE: Oklahoma State University 
NOTE: Calculations assume 180 days on feed and an average daily gain of 2.78 pounds
per day from pay weight to pay weight, adjusted for a 3% shrink. All costs were computed
using 1980 prices.
a1980 Kansas City average price for September-November, 400- to 500-pound Choice or
medium frame No. 1 steer (MF1) [U.S. Department of Agriculture, Livestock and Meat
bIncludes depreciation, taxes, interest on investment, and insurance.
'$0.50/cwt order buy cost.
Grain-Finishing Costs in Florida
The second stage of this alternative production-marketing system in-
volves confinement feeding of the backgrounded feeder steers on a high
energy ration for approximately six months. The formulation of the
ration and the length of time it is fed can both be varied depending upon
the desired quality of the carcass yields and costs of the inputs. For
example, during periods of depressed feed grain prices, feedlot operators
will typically place lightweight animals in their lots and feed them out to
slaughter weight, thereby obtaining a higher percentage of Choice grade
animals. On the other hand, when grain prices are high relative to feeder
and slaughter cattle prices, the animals are kept on pasture longer and
enter the feedlots at heavier weights.
The budget presented in Table 10 is a composite of two different partial
budgets compiled by members of the Food and Resource Economics
Department at the University of Florida. These costs are representative
of a feedlot with a 5,000 head one-time capacity, which by most standards
would be a medium size lot. Research done at the University of Florida
indicates that economies of size for feedlots over this size are small
[Simpson et al., 1981]. A flume-floor type waste disposal system was
Table 10.-Costs of grain-finishing a 563-pound feeder steer to 968 pounds in
Florida from April 1 to October 1.
Quantity/Head Cost/Unit Cost/Head ($)
Stocker calf (Choice or MF1)a 563 lbs $73.63/cwt 418.02
Ration I 212.93
Ration I See Table 11 2164.56
Ration I for details 164.56
Ration III J 176.52
Interest I 630.95 14% 44.16
Interest II 582.58 14% 40.78
Interest III 594.54 14% 41.62
Maintenance and repairs 3.03
Death loss 427.08 $0.75 3.13
Total I: 701.84
Total II: 650.90
Total III: 662.89
SOURCE: Simpson et al. ; Gunter et al. .
NOTE: Calculations assume 180 days on feed and an average daily gain of 2.41 pounds/
day from pay weight to pay weight, adjusted for a 3% shrink. All costs were computed using
1980 prices. Feedlot operates at 90% capacity with annual turnover of 2.03.
'Cost of producing a 563-pound Choice feeder steer from a 400-pound feeder steer (Table
bIncludes salaries, wages, FICA, insurance, etc.
'Includes utilities, gas, and oil.
dIncludes taxes, property insurance, telephone, market news and service, etc.
considered to be the most efficient for this type of feedlot from an
operating cost standpoint [Simpson et al., 1981]. Because of occasional
wet periods and high levels of humidity in Florida, it is extremely impor-
tant to consider all aspects of feedlot waste disposal. This type of opera-
tion requires less labor and equipment than would the traditional dirt
floor feedlot, which is common in the Midwest and High Plains area
where the dry climate facilitates waste disposal [Simpson, et al., 1981].
Costs for three different rations were calculated to demonstrate the
sensitivity of total feeding costs to changes in the ration formulation
[refer to Table 11]. The nutritive value, in terms of average daily gains
and carcass yield quality, is assumed to be equal across all rations. Ration
III contains quite a bit of citrus pulp and chopped sugarcane, and the
problems associated with these ingredients will be discussed presently.
An average feed conversion ratio of 9.5:1 was used to calculate total
quantities of feed ingredients for each ration. Rations I and II are
examples of rations fed most commonly in existing Florida feedlots.
Rations II and III are less expensive than Ration I, and incorporate the
use of more locally produced feedstuffs [refer to Table 12]. The shell corn
Table 11.-Florida beef cattle ration costs, 1980.
Quantity/Head ($) ($)
Ration I 49 bu shell corn 3.02/bua 147.98
320 lbs liquid protein
supplement 15.00/cwtb 48.00
160 lbs bagasse pellets 4.10/cwt 6.55
160 lbs blackstrap molasses 6.50/cwt 10.40
Ration II 38 bu shell corn 3.02/bua 114.76
80 lbs liquid protein
supplement 15.00/cwtb 12.00
480 lbs corn silage 25.00/ton 6.00
480 lbs grain dust pellets 5.25/cwtc 25.20
240 lbs dist. condensed
molasses 2.75/cwt 6.60
Ration III 28 bu shell corn 3.02/bua 84.56
2000 lbs chopped sugarcane 21.00/net tond 21.00
400 lbs dried citrus pulp 113.00/tone 22.60
256 lbs cottonseed meal 17.00/cwt 43.52
160 lbs sugarcane molasses 0.51/net tond .04
32 lbs mineral supplement 15.00/cwt 4.80
a$2.29/bushel plus $0.73/bushel transportation and handling charges.
b32-36% beef cattle concentrate supplement.
'20% less than shell corn.
dEstimated production cost for 1980 [Lopez et al., 1979].
"Annual average price [USDA, Agricultural Prices, 1980].
used in all three rations is assumed to be imported by rail from the
southeastern areas of the Corn Belt. Prices are based on terminal prices
at Evansville, Indiana plus appropriate handling and transport costs.
The cost of Ration III is higher than the cost of Ration II. Ration III
contains less imported feed than Ration II. The relative costs of the three
rations suggest that feeding costs can be reduced by substituting Florida
produced feedstuffs for imported corn, but only to some point.
Ration II includes corn silage as a source of roughage and minimal
protein and is priced at an estimated production cost of $25.00 per ton. If
the cattle feeding industry does expand in Florida, feedlot operators who
have the resources to produce grain silage to meet the roughage require-
ments of their animals will have a definite cost advantage over those who
must buy roughage. Distillers' molasses mixed with urea is another
Table 12.-Average daily rations, Florida feedlots, 1980.
(2.25 pounds per day average gain)
Ration I 83.0% shell corn 49 bu.
8.5% liquid protein supplement 320 lbs.
4.5% baggasse pellets 160 lbs.
4.0% blackstrap molasses 160 lbs.
Ration II 63% shell corn 38 bu.
3% liquid protein supplement 80 lbs.
15% corn silage 480 lbs.
13% grain dust pellets 480 lbs.
6% distilled condensed molasses 240 lbs.
(75% syrup 25% urea)
Ration III 53.1% shell corn 28 bu.
45.6% chopped sugarcane 2000 lbs.
9.1% dried citrus pulp 400 lbs.
5.8% cottonseed meal 256 lbs.
3.6% sugarcane molasses 160 lbs.
0.8% mineral mix 32 lbs.
SOURCES: Rations I and II, Dr. William Kunkle, Livestock Extension Specialist,
Animal Science Department, University of Florida; Ration III, Pate .
locally available feed ingredient that has been utilized somewhat by cattle
feeders located near the distilleries and breweries in central Florida. The
transportation requirements and perishability of most distillers' by-
products limit the potential use by cattle feeders not located close to the
breweries or distillation plants.
The utilization of all types of by-product feedstuffs by the cattle feeding
industry in Florida, and in other areas of the country, has received
considerable attention by the research community in recent years. The
citrus and sugarcane processing industries, together with the aforemen-
tioned alcoholic beverage industry, manufacture large quantities of by-
products that are presently being used as protein and energy supplements
in cattle feeding rations all over the United States. Citrus pulp is presently
being used in dairy and beef cattle rations by Florida producers, and
considerable amounts are exported for use in livestock feeds in other
areas of the world.
There are several problems associated with the use of by-product
feedstuffs, and the most serious one from the standpoint of the feedlot
operator is the variability of these ingredients with respect to their
nutritive value [Ammerman, 1973]. In order to consistently supply retail-
ers with high quality beef that consumers demand, cattle feeders must be
able to formulate rations that provide animals with the required nutrients
to produce a specified grade of beef. One of the major reasons cited by
Florida meatpackers for the demise of the slaughter industry in this state
is a lack of consistent, top quality (USDA Choice grade or better)
slaughter weight animals from Florida feedlots [Perry, 1981]. Continued
research into the potential for further utilization of these by-products will
be necessary to encourage cattle feeders in Florida to incorporate their
use into feedlot rations.
The total costs presented at the bottom of Table 10 are on a per head
basis. Interest charges are computed using only capital outlays for the calf
and the particular ration being fed. It is assumed that the fed steer being
produced in this operation would grade out in the Choice grade category.
Grain-Finishing Costs in Oklahoma
The costs of production presented in Table 13 were provided by the
Agricultural Economics Department at Oklahoma State University
. All costs are for 1980, and it is assumed that the feeder steer is fed
in a commercial lot in Northwest Oklahoma. The size of the lot and the
exact formulation of the item listed as Mixed Feed I are unknown.
It was assumed that the cow-calf producers also provided the winter
Table 13.-Costs of grain-finishing a 566-pound feeder steer to 1,034 pounds in
Northwest Oklahoma from March 1 to September 1.
Item Quantity/Head Cost/Unit Cost/Head ($)
(Choice or MF1)a 566 67.95 395.47
Mixed Feed Ib 3105 5.10/cwt 158.36
Interest 14% 547.41 38.32
Vet medicine 5.69
Labor 3 hr 3.84/hr 11.52
Maintenance and repairs 8.85
Sales commission 1.25
Death loss $395.47 1.5% 5.93
Total All Costs $638.10
SOURCE: Oklahoma State University ; U.S. Department of Agriculture, Live-
stock and Meat Situation [February 1981].
NOTE: Calculations assume 180 days on feed and an average daily gain of 2.78 pounds/
day from pay weight to pay weight, adjusted for a 3% shrink. All costs were computed using
aCost of producing a 566-pound Choice feeder steer from a 400-pound steer (Table 10).
bDry matter basis, specific content unknown.
'Transportation to feedlot, 600 miles.
wheat pasture and that the steer was custom fed on a feedlot in Okla-
homa. Therefore, no transfer of ownership occurs until after the finishing
The price used for the 566-pound medium frame No. 1 feeder steers is
the production cost of that animal from Table 9. As with the Florida
budget, interest charges were computed for six months on the price of the
calf and the cost of the ration. All other costs are self-explanatory. The
1,050-pound slaughter steer produced is assumed to grade Choice.
Regional Cost Differences
Since Florida would be a net importer of beef, even if all feeder calves
produced in the state were finished, slaughtered, and consumed in Flor-
ida, it is reasonable to assume that Florida finished beef will be consumed
in Florida. Thus to complete the cost comparison, slaughter costs and the
cost to transport chilled beef into Florida from the Midwest are included.
The comparison is shown in Table 14. An estimate of $40.00 per head in
Florida versus $30.00 per head in the Midwest slaughter charge was
assumed to reflect the size economies of the large Midwestern slaughter
plants. Transport costs for chilled beef were obtained using a cost simula-
tion model [Beilock, 1981].
The sensitivity of the cost of finishing cattle in Florida to the proportion
of the ration that is imported is displayed in Table 14. Production cost per
pound carcass weight ranges from $1.28 for Ration I to $1.19 for Ration
II. All three rations give per pound production costs which exceed per
pound production costs for the Oklahoma based system.
The Impact of Changes in Energy Costs
on Regional Costs of Production
Total system cost for each region was simulated as the cost of energy is
increased by 50%, 100%, and 500%. In Table 15, those components of
the system deemed to be most sensitive to changes in energy costs are
listed separately. Fertilizer prices were varied using factor price elasticity
estimates of 0.25 for 5-10-15 and 0.50 for ammonium nitrate. Rail costs
were adjusted using a budget which breaks out diesel fuel as a separate
component [Interstate Commerce Commission, 1977]. Feeder cattle
prices were computed by holding Kansas City 400- to 500-pound Choice
steer prices constant and using the estimated price differential equation
to project Florida prices. Only the costs associated with Ration II are
As energy prices increase, Florida production costs change little as
increased fertilizer and imported feed costs are offset by lower projected
feeder cattle prices. Northwest Oklahoma costs steadily increase, but
even with a 500% increase in energy costs, projected Oklahoma costs are
less than Florida costs.
Table 14.-Cost of carcass beef f.o.b. Florida.
Ration Ration Ration N. W.
Item I II III Oklahoma
Finished steer 701.84 650.09 662.89 639.95
Slaughter costs 40.00 40.00 40.00 30.00
Chilled beef transport 27.76
Total cost per head 741.84 690.09 702.89 697.71
Cost per poundb 1.28 1.19 1.21 1.12
Difference (FL OK) 0.16 0.07 0.09
'Source: Beilock . Assumes a 621-pound carcass is hauled 1500 miles with a
backhaul rate of 90%.
bCarcass weight. Assumes 60% yield. Florida finished carcass weight 581 pounds, Okla-
homa finished carcass weight 620 pounds.
Two points should be made. First is the relative insensitivity of total,
system costs to increases in energy costs. For example, a 100% increase in
energy costs is projected to increase Oklahoma system costs by approx-
imately 2%. The use of budgets to project costs which implies a fixed
proportion production relationship will tend to overstate the effect of
increased input costs because producers do substitute less costly inputs
for more costly inputs as relative costs change, so far as is technically
feasible. On the other hand, increases in real energy costs of the magni-
tude presented in Table 15 will affect the cost of nearly all inputs.
Accounting for both of these factors is beyond the scope of this study.
The second point is that as energy costs increase, Florida system costs
remain nearly unchanged as Oklahoma system costs increase. In Florida,
however, the increased costs of nitrogen fertilizer and transport for
imported corn are offset by projected decreasing prices for lightweight
feeder cattle. Assuming Kansas City prices for a MF1 400-pound feeder
steer hold at $84.34/cwt, it is projected that a 100% increase in the cost of
energy gives a Florida price of $72.57/cwt and a 500% increase drops the
Florida price to $62.69/cwt. At these projected prices, it is unclear how
many Florida producers could survive.
The Florida beef cattle industry can be characterized by predominantly
cow-calf production units which are dependent upon year-round forage
supplies, with limited stocker and feedlot production capacity. Because
of the nature of this industry, cattlemen in Florida are dependent upon
out-of-state buyers to market a large quantity of lightweight feeder calves
produced each year. This type of production-marketing system is also
Table 15.-Projected changes in selected costs due to increasing energy prices for production of a USDA Choice beef carcass from a
400-pound calf in Florida and Oklahoma.
Cost Base = 1980 50% increase 100% increase 500% increase
Backgrounding OKLA- OKLA- OKLA- OKLA-
Costs FLORIDA HOMA FLORIDA HOMA FLORIDA HOMA FLORIDA HOMA
400 lb. MF1 Steer 303.36 337.36 295.24 337.36 290.28 337.36 250.76 337.36
(5-10-15 and Ammonium 56.04 0 60.61 0 65.18 0 101.73 0
Other Costs 58.62 56.04 58.62 56.04 58.62 56.04 58.62 56.04
Grain Finishing Costs:
Feed Costs (rail costs 164.56 161.77 165.51 161.77 166.46 161.77 174.06 161.77
for shell corn)
Other Costs 67.51 84.78 67.51 84.78 67.51 84.78 67.51 84.78
Slaughter and Process- 40.00 30.00 40.00 30.00 40.00 30.00 40.00 30.00
ing Costs per Head
Costs for Chilled 0 27.76 0 30.79 0 34.08 0 53.16
Beef by Truck
Total Cost 690.09 697.71 687.49 700.31 688.05 704.03 692.68 723.11
Cost per 1.19 1.12 1.18 1.13 1.18 1.13 1.19 1.16
Difference 0.07 0.05 0.05 0.03
contingent upon the livestock trucking industry to move feeder cattle out
of Florida to stocker and feedlot operations in other areas of the United
States. There has been concern expressed by those within the industry
that because of increasing transportation rates producers in Florida have
been receiving considerably lower prices for feeder calves than do those
producing and marketing calves closer to the major demand points. The
purpose of this study has been to determine if Florida prices for feeder
calves have been affected by increasing transport costs and to examine
one marketing alternative that may be feasible for certain cattlemen in
The alternative production-marketing system involved backgrounding
and finishing feeder cattle to approximately 1,000 pounds in Florida for
consumption within the state. The sensitivity of costs incurred to increas-
ing energy prices for this type of system and for a similar system in
Oklahoma was analyzed.
Increasing transportation costs for livestock, expressed as the price of
No. 2 diesel fuel, have affected the spatial price differential between
Kansas City prices and Florida prices for Choice feeder steers. As trans-
port rates continue to grow, so will this differential, which means that
Florida producers will continue to accept lower prices for feeder calves
relative to producers closer to the major feeding areas.
The price differential contains a seasonal component characterized by
larger than average differentials in the heavy marketing season of August
through November and smaller differentials January, April, and July.
Producers who market calves to be outshipped can reduce the price
differential by marketing during those months when the differential is
smaller. This is easier said than done, however, as period of marketing is
determined by several other factors including availability of pasture.
Partial cost analysis indicates that cattlemen with access to small grain
pastures for winter grazing do have a definite cost advantage over Florida
producers who must improve native pastures for winter grazing. This
leverage is compounded for Oklahoma producers when the lower pro-
duction cost for 600-pound feeder cattle is used in the confinement
feeding program, together with lower grain prices. If locally produced
feedstuffs can be utilized by Florida feedlot operators, then production
costs for confinement feeding of cattle in Florida compare more favorably
to Oklahoma feeding costs. The total system cost analysis, which includes
the slaughter/processing charge and transport costs for carcass beef from
Oklahoma to Florida, demonstrates that if a major proportion of the
Florida ration is imported, Oklahoma system cost is lower.
The winter wheat pastures provided for lightweight calves in the Okla-
homa budget were assumed to provide steady weight gains at zero cost
with very little supplemental feeding. This may be an unrealistic assump-
tion in many circumstances, such as winter snow storms that temporarily
stop the crop from growing until spring; therefore, calves require more
supplemental feed at a greater cost to the producer. An early fall drought
would be another example of climatic conditions that prevent small grain
pastures from providing the proper forage requirements for steady
Whereas many of the production and marketing costs involved in both
Florida and Oklahoma programs are sensitive to increasing energy
prices, further study was necessary. Table 15 summarizes the impact of
certain energy-sensitive cost changes on the production advantages held
by Oklahoma cattle producers. The results indicate that as energy prices
continue to increase, the total cost advantage between Oklahoma and
Florida remains in Oklahoma favor. Total costs do not move very rapidly
though, which leads to the final conclusion that under the assumptions
made in the preceding analysis, increased energy costs alone will not
provide the impetus to spur an expansion of the stocker and feedlot
production capacity in Florida.
There are numerous other production-marketing systems that have not
been discussed thus far that may provide Florida cattle producers with
more feasible alternatives for their feeder cattle. Shortening the length of
time that cattle spend in a confinement feeding program or feeding more
roughage and less grain during the final few months are examples of other
alternatives that may lower costs.
Forage-finished cattle, or those placed on high energy (grain) rations
for fewer than 90 days before slaughtering, will produce leaner carcasses.
Preliminary results from research conducted at Louisiana State Univer-
sity indicates that consumers will accept beef that has been finished on
forages alone if certain techniques such as postmortem electrical stimu-
lation are used to improve tenderness and overall palatability. Fur-
thermore, beef finished on forage with small amounts of grain and
carcasses from animals fed for only 70 days on a feedlot were rated very
highly by consumers when these products were marketed through a
major grocery chain [Carpenter, 1980].
This report constitutes the first attempt (to the authors' knowledge) to
examine the entire post weaning through slaughter system. The impor-
tance of analysis of production-marketing alternatives in the context of
the entire system cannot be understated. For example, consider a post
weaning management program in which fall calves are carried to the
spring. Given the existing marketing system for Florida feeder cattle,
there is a high probability that those cattle will be exported from the state.
The implication of the statistical analysis is that heavier feeder cattle
incur larger percentage discounts than lighter feeder cattle.
The key to the future of the beef cattle industry in Florida lies in the
ability of those within the industry to work together. Cooperation be-
tween cow-calf producers and cattle feeders would facilitate the type of
animals required by feedlot operators when they need them, not only
when producers want to market them. Cooperation between cattle feed-
ers and the livestock slaughter industry should provide a more stable
supply of slaughterweight animals and a more consistent quality. Finally,
markets need to be developed for all types of beef products that are being
produced in Florida through cooperation between the retail meat indus-
try and the slaughter industry in the state. Proper communication chan-
nels and marketing strategies are vital to the continuation of a viable beef
industry in Florida.
Implications for Future Research
There are several extensions of this research that would encompass
studies in many disciplines. Further research by animal scientists, econ-
omists, and agronomists in the area of efficient forage production for
both north and south Florida is probably the most important topic sug-
gested by this study. Increased by-product feed utilization by cattle
feeders in all regions of the United States is another logical extension that
is currently receiving a considerable amount of attention by animal
scientists at the University of Florida.
Cooperative research efforts between economists and members of the
livestock slaughter industry are desperately needed, especially in Florida,
where the industry faces an unstable supply situation. The lack of accu-
rate cost data on the beef cattle slaughtering and processing industry as a
whole has hindered this study in providing a more complete analysis of
projected changes in total system costs due to increasing energy prices.
Finally, more research is needed in the field of transportation efficiency
within the agricultural sector. Because of the degree of regional spe-
cialization that has developed in agricultural production all over the
world, energy efficient modes of transport will become increasingly more
important in helping keep food prices at a reasonable level for consumers
Table A-i.-Comparison of spatial value differences and transport costs for
400-pound Choice feeder steers.
Year-Quarter Costsa Differenceb (2) (1)
1971-1 5.75 12.10 6.35
1971-2 5.75 11.57 5.82
1971-3 5.75 16.45 10.70
1971-4 5.75 19.28 13.53
1972-1 5.75 13.99 8.24
1972-2 5.75 15.52 9.77
1972-3 5.75 21.40 15.65
1972-4 5.75 23.43 17.68
1973-1 5.75 21.56 15.81
1973-2 5.75 15.97 10.22
1973-3 5.75 33.05 27.30
1973-4 6.75 25.36 18.61
1974-1 7.97 18.08 10.11
1974-2 8.55 33.92 25.37
1974-3 11.50 32.36 20.86
1974-4 12.30 25.85 13.55
1975-1 11.50 19.99 8.48
1975-2 11.50 32.40 20.90
1975-3 11.50 29.93 18.43
1975-4 12.30 31.72 19.42
1976-1 12.55 34.17 21.62
1976-2 12.55 32.38 19.83
1976-3 12.55 27.66 15.11
1976-4 13.00 31.38 18.38
1977-1 13.46 26.92 13.46
1977-2 13.46 26.45 12.99
1977-3 13.46 27.22 13.76
1977-4 14.03 20.17 6.14
1978-1 14.03 5.86 -8.17
1978-2 14.73 15.89 1.16
1978-3 14.73 20.98 6.25
1978-4 16.34 16.01 -0.33
1979-1 16.34 -10.09 -26.43
1979-2 16.34 -2.96 -19.30
1979-3 19.34 43.46 24.12
1979-4 19.34 36.60 17.26
1980-1 20.18 27.93 7.75
1980-2 20.18 32.05 11.87
1980-3 23.71 28.80 5.09
1980-4 24.59 32.38 7.79
'Source: Simpson and Stegelin . Per head costs based on 1500-mile trip.
bCalculated as Kansas City price minus Florida price times four. Prices are 400- to
500-pound Choice feeder steer price in each market in dollars per hundredweight.
,OsO 20 ,, 0.
,, '00 O...' O.
0 0 Florida
I I I I I I I I I I I I
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure A-1. Monthly feeder steer prices, 1971-1980 (weight, 400 to 500 pounds;
grade, Choice). Source: Florida Department of Agriculture.
0---0 Kansas City
0-- 0 Florida
I I I I I I I I I I I I
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure A-2. Monthly feeder steer prices, 1971-1980 (weight, 600 to 700 pounds;
grade, Choice). Source: Florida Department of Agriculture.
300- 0---0 600-700 Ib feeder steers
0--0 400-500 Ib feeder steers
I I I I I I I I I I I I
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure A-3. Monthly price differentials, 1971-1980 (Kansas City prices niinus
The hypothesized spatial price differential model is
PLG1 PL2G1 = f(TC(d), Z)
where PL1G1, and PL2G1 are the prices of a commodity of a particular
quality at locations L1 and L2, respectively, TC is a measure of transport
costs between L1 and L2, and Z is a composite of other factors.
Two general observations were made. First, the period of study was
characterized by rapidly increasing transport costs. Since monthly prices
were to be analyzed, it is reasonable to hypothesize that the spatial price
differential is a distributed lag function of transport costs. Second, the
spatial price differential series for both weight categories contained a
seasonal component. Analysis revealed that even after the seasonal
component was removed, both series, (D400 and D600) were autocorre-
lated. Furthermore, a measure of transport costs, the diesel fuel price
index, was also found to be autocorrelated. Thus, care must be taken in
specifying a distributed lag relationship between a spatial differential
series (D400 or D600) and the diesel fuel price index series to avoid the
"spurious regression" problem noted by Granger and Newbold .
The procedure adopted was developed by Haugh  and Haugh
and Box  and extended by Pierce . It has been utilized in
agricultural applications by Spreen and Shonkwiler  and Shonk-
wiler and Spreen .
Consider two stationary time series, X, and Y,. Stationarity implies that
the series possesses a finite, time invariant variance. While theory and/or
observation can suggest a distributed lag relationship between the two
series, the exact nature of the relationship is rarely known. The Haugh-
Box procedure adopts the view that instead of imposing a priori restric-
tions on the lag structure, a data-oriented approach should be adopted to
allow the data itself reveal to the exact nature of the lag relationship.
The Haugh-Box procedure (the literature also refers to this method as
a transfer function model or a dynamic regression model) utilizes the
Granger notion of causality to discern if, in fact, a distributed lag rela-
tionship between the two series does exist. Specifically, Granger [1969,
p. 428] says that Y is causing X, if we are better able to predict X, using all
available information than if the information apart from Y, had been
used." This prediction-oriented definition of causality is intuitively
appealing but is subject to the drawback that all information relating to Xt
is rarely known. One approach to sidestep this stringent requirement says
that Y, causes X, when it can improve the prediction of X, compared to the
prediction of X, taking into account the past history of X, alone.
Implementation of the procedure requires four steps. First, identifica-
tion and estimation of a univariate time series model (sometimes called
the filter) for each series of interest is completed. This step accounts for
the ability of a series, say X,, to predict itself utilizing its past history
alone. The Box-Jenkins [Box and Jenkins, 1976] method is utilized in this
report. Second, the residuals of the filtered series (sometimes called the
innovations) are used to determine the lead-lag patterns, if any, between
series. Third, the impulse response weights are calculated from the
residual cross-correlations, and a dynamic shock model which relates the
residual series is identified. The calculations are performed below.
Fourth, a dynamic regression or distributed lag model is identified from
knowledge of the original univariate models for each series and the
dynamic shock model.
The Box-Jenkins method is utilized to account for the portion of each
series of interest which can be explained by its own past history. For a
discussion of the Box-Jenkins technique see Nelson  or Box and
Jenkins . The data D400, D600, and DIESEL are defined in
equations (2) and (3). Both D400 and D600 contained seasonal compo-
nents, and D400 acted in an atypical manner over the 18-month period
from January 1978 to June 1979. D600 was regressed on monthly binary
variables D2 to D12 as defined in equations (2) and (3) to remove its
seasonal component, and D400 was regressed on D2 to D12 and ND as
defined in equations (2) and (3). The residuals from these regressions are
used in the analysis described below. The notation D400 and D600 is
Analysis of the autocorrelations of the residuals indicated that first
differencing is required to make D600 and DIESEL stationary, while
undifferenced D400 was stationary [Box and Jenkins, 1976, p. 174]. The
estimated autocorrelation and partial autocorrelation functions for each
of the series (after necessary differencing) are shown in Table B.1. The
patterns of the autocorrelations and partial autocorrelations suggests the
(B.1) (1 a, B) D400t = ut,
(B.2) (1- blB b2B2)(1 B)D600t = v
(B.3) (1 cB c2B2 c4B4)(1 B)DIESEL, = z,
'B denotes the backshift operator. (1 B)X, = X, X,_1 and (1 B)2X, = X
- x,-1 (X,_-1 x,-2).
Table B.1.-Estimated autocorrelation and partial autocorrelations for D400, D600, and DIESEL.
g Autocorrelations Partial Autocorrelations
Lag Variable Variable
D400 D600a DIESELa D400 D600a DIESELa
aD600 and DIESEL are first difference.
where u,, v,, and z, are the white noise processes associated with D400,
D600, and DIESEL, respectively. Estimation of these models indicated
that B.1 and B.2 were adequate representations for D400 and D600, but a
fourth order moving average term was required for DIESEL. The esti-
mated univariate models are shown in Table B.2. The calculated chi-
square statistics for testing for white noise indicate that the estimated
models are adequate [Box and Pierce, 1970].
Table B.2.-Estimated univariate models for D400, D600, and DIESEL.
(1 -0.57B) D400 = 2.74 + u, cr = 209.2b
(0.076) (44.7) 2(18) = 25.33c
(1 +.41B + 43B2) (1 -B)D600 = v, r, = 139.4
(0.084) (0.084) X2(18) = 14.6
(1 -1.09B + .34B2 -.21B4)(1- B)DIESEL = (1 -.40B4)z,
(0.095) (0.108) (0.067)
-z = 5.69
X2(16) = 11.6
aEstimated standard errors of the estimated coefficients appear in parentheses.
bResidual standard error.
'Box-Pierce statistic (Box and Pierce, 1970). Number in parentheses indicates the degrees
Analysis of Cross Correlations
Following the procedure outlined in the preceding section, the esti-
mated residuals from the two filter models were analyzed in order to
make inferences concerning causality. Since each noise process (u,, v, and
z, in Table B.2) is not autocorrelated and represents that part of the time
series which cannot be explained by its own past, Haugh  suggests
examining the cross correlation between the two residual series as a
means of assessing Granger causality in a systematic manner. The sample
cross correlation between the residuals u, and z, at lag k is denoted by
(B.4) ruz(k) rutz
where ut- k z, is the sample variance between ut-k and z,, and 6, and 6z
are the sample standard deviations of the time series u, and z,, respec-
tively. The calculated cross correlations between u, and z,, and v, and zt
are presented in Table B.3. Under the null hypothesis that the two series
are independent, Haugh  has shown that the r(k) are asymptotically
normal and independently distributed with mean zero and standard
deviation T-1/2, where T is the sample size. Thus, the individual cross
correlations can be divided by their estimated standard error, and an
approximate t test can be performed to identify non-zero cross correla-
Table B.3.-Estimated cross correlations between the residuals of the univariate
Lags on Variable Variable
DIESEL D400 D600
0 -.078 -.004
1 -.106 -.151
2 -.022 .040
3 .196 .231
4 .039 .027
5 .034 .132
6 .011 -.009
tions. This procedure provides a systematic means for permitting the data
themselves to suggest patterns of interrelationships and generates the
major results necessary for specifying the transfer function. It should be
noted that several difficulties with this approach have been noted. It has
been shown by Sims  that the chi-square tests for unidirectional
causality are biased toward acceptance of the null hypothesis. Addi-
tionally, Feige and Pearce  have pointed out that the causality tests
may be highly conditioned by the filters used to obtain the white noise
processes u, v, and z.
The estimated cross correlations are shown in Table B.3. The esti-
mated standard errors are approximately 0.09. Thus, the cross correla-
tion at the third lag for both cases can be judged significantly different
Identification of the Distributed Lag Model
Given the nature of the cross-correlations, the following models were
(B.5) ut = W3B3z, + u(B)E,
(B.6) vt = V3B3z, + -t(B)'it
where W3 and V3 are the impulse response weights [Box and Jenkins,
1976, p. 338], 4,(B) and 4v(B) are polynominals in the lag operator B,
and Et and qI, are white noise processes.
Since the u, are orthogonal to each other (independent by construc-
tion) then an estimate of W3 is given by the bivariate regression coefficient
of zt on u,, i.e.
(B.7) W3 = (3) = 209.2 (.196) =7.21
Similarly, for V3
(B.8) 1V3 = r(3) = 1394 (.231) = 5.66
Thus, the tentatively identified dynamic shock models are
(B.9) u, = 7.21z,_3 + Qu(B) E,
(B.10) vt = 5.66z,_3 + M,(B) q,
Using the univariate models, B.9 and B.10 can be written in terms of
D400, D600, and DIESEL. Writing the univariate models in random
shock form gives
(B.11) u,= -2.74 + (1-0.57B) D400,
(B.12) v, = (1 + .41B + 0.43B2) (1 -B) D600,
(B.13) z, = (1- 1.09B + 0.34B2 0.21B4)
(1- 0.40B4)-1(1 B) DIESEL,
Substituting B.11 and B.13 into B.9 gives
(B.14) -2.74 + (1 -0.57B)D400, = 7.21B3(1 1.09B + 0.34B2
0.21B4)(1 40B4) -1(1 B)DIESEL, + (,(B)e,
(B.15) D400, = 2.74(1 0.57B)- + (1 0.57B) -17.21B3(1 1.09B
+ 0.34B2 -0.21B4) (1 0.40B4) -(1 B) DIESELt
Simplifying B.15 yields
(B.16) D400 = 6.37 + (7.21B3 3.89B4) DIESEL, + ,u(B)E,
after small terms (less than one in absolute value) are dropped. Perform-
ing a similar set of steps for D600 yields
(B.17) D600, = (5.66B3 8.49B4) DIESEL, + 4,(B)v,.
The lag structures B.16 and B.17 are estimated using ordinary least
squares (i.e., ,(B) = Qu(B) = 1). The residuals from both estimated
equations exhibited significant first order autocorrelation. Several
alternatives are available to handle this problem. Inclusion of a lagged
dependent was chosen. The estimated equations are shown on Table 5.
Note that this approach explains the "wrong" sign on the diesel fuel
variable lagged four months (DIESELt-4). The important result is that
the sum of the estimated parameters on lags of the diesel fuel variable is
positive, which implies a positive long run relationship between the
spatial price differential and diesel fuel prices.
Calculation of Long-Run Coefficients
The estimated models for D400 and D600 are distributed lags in the
variable DIESEL and contain lagged dependent variables. An attempt to
directly use either of these equations to simulate impacts of alternative
diesel fuel prices on the spatial price differential will be frustrated be-
cause of the lags that appear.
A more fruitful approach is to mathematically manipulate the esti-
mated equations into what is called the final form [Chow, 1975, p. 159].
The final form relates D400 to DIESEL (or D600 to DIESEL) without
reference to time as all lags in the system have been eliminated. Thus, the
final form is said to represent the long-run relationship between depen-
dent endogenouss) variables and independent (exogenous) variables.
The estimated equations may be written as
(B.18) Y = aY_ + blXt-3 + b2Xt-4 + cH
where Yt is the dependent variable (D400 or D600), X, is DIESEL, and H
is shorthand for the intercept and binary variables. In the long run, a
steady-state equilibrium is assumed, thus, X 3= Xt-4= Xt. Let
d = bl + b2; then rewriting (B.18) yields
(B.19) Yt= aYt + dX + cH.
It follows that
(B.20) YtE_ = at-2 +dXt-1+ cH.
Substituting (B.20) into (B.19) gives
(B.21) Y = a(aYt_2 + DX,-1 + cH) + dX,+ cH.
= a2Y-_2 + adX_1 + dXt + (ac + c)H.
(B.22) Y_, = aY-,- + dX,_, + cH.
for any n.
By successively substituting (B.22) into (B.21) for n= 2, n = 3, etc.,
(B.22) can be written as
(B.23) Y = a"Yt- ,+ I a'dX,_n + a'cH.
In steady-state equilibrium X-i = Xt_j for any i andj; thus (B.23) can be
(B.24) Y, = a"_, + (dX, +cH) a'.
For al <1, then
(B.25) lim a = 0
and lim I ai 1
n-- i=0 1-a
Substituting (B.24) and (B.25) into (B.23) and dropping the time sub-
(B.26) Y 1 (dX + cH).
Equation (B.26) expresses the long-run relationship between Y and X.
Next substitute the appropriate values from Table 5.
(B.27) D400 = [0.182 DIESEL + H(D2 D12, ND)]
(B.28) D400 = 0.287 DIESEL + (1 0.352)-1H(D2- D12, ND)
(B.29) D600 = [0.519 DIESEL + H2(D2 D12)]
(B.30) D600 = 0.849 DIESEL + (1 0.389)-1H2(D2-D12)
where Hi(D2 D12, ND) and H2(D2 D12) are the intercept and binary
variable components of each equation.
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This public document was promulgated at an annual cost of $2575
or a cost of 640 a copy to provide information on the impact of
increasing transportation costs on the Florida cattle industry.
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