Livestock Production/Industries, Productivity Analysis,

Jones, Rodney D.

Langemeier, Michael R.

English

Research Papers in Economics

Measuring the Productivity
of Cattle Finishing
Michael R. Langemeier
and
Rodney D. Jones
Paper Submitted for Presentation at the 1997 WAEA Meeting
Reno, Nevada
July 13-16, 1997
Michael R. Langemeier is an Associate Professor and Rodney D. Jones is an Assistant
Professor of Agricultural Economics at Kansas State University, Manhattan, Kansas.Measuring the Productivity
of Cattle Finishing
Abstract
This study examines the productivity of steer placements from 1980 to 1994 for
two feedlots in western Kansas.  Regression analysis is used to examine the trend in feed
conversions and to examine the growth in output.  Growth in output is decomposed into
changes in productivity and changes in input use.1
Measuring the Productivity
of Cattle Finishing
Introduction
The competitive position of the beef industry depends on the relative efficiency and
productivity of beef production.  Johnson et al. and Schroeder, Mintert, and Brester
indicate that beef producers need to reduce costs and increase productivity to compete
with the pork and poultry sectors.  Increases in productivity or technological change can
lead to a reduction in average production costs resulting in a more competitive industry.
An analysis of technological change needs to take into account output growth,
which is a function of technological change, changes in input use, and changes in technical
efficiency.  If changes in input use or technical efficiency are ignored, inaccurate
conclusions with respect to technological change or productivity growth will result.
The objective of this paper is to examine technological change for steers placed
from 1980 to 1994 in two western Kansas feedlots.  Trends in feed conversion are
examined using regression analysis.  A fixed effects production function is used to
decompose output growth into technological change, changes in technical efficiency, and
input use changes.
Empirical Models
Trends in feed conversion are examined using ordinary least squares regression. 
Feed conversion is the dependent variable.  Independent variables include a time trend,
dummy variables for February through December placements, and dummy variables for2
major weather shocks.  The dummy variables for February through December placements
capture the impact of seasonality on feed conversions (Albright et al.).  The standard
deviation of feed conversion is used to create dummy variables to account for adverse
weather conditions.  Any period that exhibits a feed conversion that is two standard
deviations from the mean for that period of time is identified as a weather shock.
Separate models are run for 600 to 700 pound placements, 700 to 800 pound
placements, and 800 to 900 pound placements.  These separate regressions are used to
capture differences in feed conversion trends that may exist between placement weight
categories.  For example, early in the study period, steers in the 600 to 700 pound were
primarily light yearlings.  Later in the period this placement group may have included
weaned calves or steers backgrounded for less than 60 days.   
Technological change is also examined.  Technological change can be examined
using nonparametric and parametric approaches (Grosskopf).  One of the primary
advantages associated with the nonparametric approach is that a functional form
assumption does not have to be made.  The parametric approach allows for the inclusion
of variables such as firm effects or other variables that may shift the frontier.
The parametric approach to productivity measurement is used in this paper.  This
approach allows for the inclusion of seasonality and weather dummy variables.  Firm
effects or the influence of different management between the feedlots are modeled using
the fixed effects production function (Fecher and Pestieau; Ahmad and Bravo-Ureta). 
Time invariant and time variant models are specified for each placement weight category.  3
The fixed effects model with time invariant technical efficiency can be written as
follows:
(1) ln Yit  = a + Sk bk ln Xkit + Sj Fj Zjit + dT + Si  gi Di + vit
where Y is output, Xk is the k
th input, Zj is the j
th seasonality or weather variable, and Di is
a dummy variable for the i
th firm.  Technical efficiency for each firm is calculated using the
following equation:
(2) TEi = {exp(gi)}/max{exp(gi)}
The fixed effects model with time variant technical efficiency can be written as
follows:
(3) ln Yit  = a + Sk bk ln Xkit + Sj Fj Zjit + dT + Si {gi + ri T}Di + vit
The ri coefficient is a firm-specific slope parameter with respect to time.  This coefficient
allows for time variant technical efficiency.
Time variant efficiency can be estimated in two steps (Cornwell, Schmidt, and
Sickles; Fecher and Pestieau; Ahmad and Bravo-Ureta).  The first step is to estimate
equation (3) without the gi and ri parameters.  The second step regresses the residuals
from this regression on Di and DiT.  This regression can be specified as follows:
(4) _it  =  h + gi Di + ri Di T
The predicted values from equation (4), r, are then used to compute technical efficiency
for each firm and time period.  Specifically, time variant technical efficiency is computed
as follows:4
(5) TEit = {exp(rit)}/max{exp(rit)}
Equation (4) can be used to test whether technical efficiency is time variant or time
invariant.  An insignificant F value for the regression in equation (4) would indicate that
technical efficiency is time invariant.  If this was the case, equations (1) and (2) would be
used.  A significant F value, on the other hand, would indicate that technical efficiency is
time variant and thus equations (3), (4), and (5) would be used to measure productivity
growth.
Output growth for the fixed effects model is decomposed into technological
change, technical efficiency change, and input change.  The coefficient on the time trend is
used as an estimate of technological change.  The change in technical efficiency is
computed by subtracting the natural log of technical change in time period t-1 from the
natural log of technical efficiency in time period t.  Change in inputs are computed using
input information for successive periods and the regression coefficient for each input.  If
technical efficiency is time invariant, technical efficiency does not vary over time and
output growth is decomposed into technological change and input change.
Data
Feedlot closeout data were collected from two large feedlots in western Kansas. 
Data from over 10,000 individual pens of steers were used to compute monthly output
and input information.  Gain per head was used as the output measure.  Inputs included
feed, interest, and other expenses.  Pounds of feed fed (on an as-fed basis) was used as the
feed input.  Implicit quantities for interest and other expenses were computed using costs5
per head and price indices.  An interest rate index was used for interest and a labor price
index was used for other expenses.  Output and input data were separated into three
placement weight categories: 600 to 700 pounds, 700 to 800 pounds, and 800 to 900
pounds. 
Table 1 presents summary statistics for output, inputs, and feed conversion for
each placement weight category.  Due to seasonal placement patterns, data for some
placement weight, month, and feedlot combinations was not available.  The number of
observations available by placement weight category was 336 for the 600 to 700 pound
placements, 348 for the 700 to 800 pound placements, and 328 for the 800 to 900 pound
placements.  A complete data set would have contained 360 observations or data for 180
months for each feedlot.
Two adverse weather periods were identified.  The first period was the January
1980 to March 1980 placement period.  The second period was the September 1992 to
December 1992 placement period.  Monthly average feed conversions during these two
periods were more than two standard deviations above the average for these months
across the 15 year period.  Thus, dummy variables for these two periods were used in the
empirical models.
 Results
Table 2 presents the results of the feed conversion regression for each steer
placement weight.  Confirming previous findings, feed conversions follow a pronounced
seasonal pattern for each placement weight category (Albright et al.).  In addition, feed6
conversions during the two adverse weather periods were substantially above average for
each placement weight.  Feed conversions for feedlot 2 were significantly lower than those
for feedlot 1.  This result could have been due to the type of cattle fed in each feedlot. 
The time trend variable was significant at the 1% level in each of the regressions.  On an
annual basis, feed conversion decreased (improved) by 0.42%, 0.48%, and 0.44% per year
for the 600 to 700, 700 to 800, and 800 to 900 pound placement categories, respectively.
The results of efforts to test for technological change are presented in Table 3. 
The time invariant F statistic was insignificant for each of the placement weight categories
suggesting that technical efficiency is time invariant or does not vary over time.  Equations
(1) and (2) were thus used to estimate the fixed effects production function and to
compute technical efficiency for each feedlot.  Table 3 presents the results of estimating
equation (1).  Using equation (2), feedlot 1 was technically efficient (index of 1.000) in the
production of 600 to 700 pound and 700 to 800 pound placements.  Feedlot 2 was
technically efficient (index of 1.000) in the production of 800 to 900 pound placements. 
Inefficiency was a  minor problem and ranged from 0.9817 for 800 to 900 pound
placements produced in feedlot 1 to 0.9971 for 600 to 700 pound placements produced in
feedlot 2.
Output for each placement weight category exhibited a pronounced seasonal trend.
 Output during the January 1980 to March 1982 period was significantly lower for the 700
to 800 and 800 to 900 pound placements.  For the September 1992 to December 1992
period, output was significantly lower for each of the placement weight categories.  The7
parameter on the time variable is significant at the 1% level for each placement weight
category.  Technological change on an annual basis averaged 0.58% for 600 to 700 pound
placements, 0.45% for 700 to 800 pound placements, and 0.30% for 800 to 900 pound
placements.  Thus, the rate of technological change for the lighter placement weight
category was almost double that of the heaviest placement weight category.
Information on output growth and technological change can be used to examine
changes in input use over the study period.  Output growth ranged from 0.14% per year
for 600 to 700 pound placements to 0.43% per year for 800 to 900 pound placements. 
Output growth for the 700 to 800 pound placement category averaged 0.18%.  These
output growth rates were consistent with increases in sale weights that occurred over
time.  The output growth rates for the 600 to 700 and 700 to 800 pound placement weight
categories were lower than the rate of technological change.  Thus, input use declined
over time for these two placement weight categories.  To achieve the output growth rate
of 0.43%, input use for 800 to 900 pound placements had to increase over time.
Summary and Implications
This paper examined trends in feed conversions and technological change for cattle
finished from 1980 to 1994 in two western Kansas feedlots.  Annual average declines in
feed conversions for the three placement weight categories ranged from 0.42% to 0.48%.
 Technological change on an annual basis was 0.58% for 600 to 700 pound steer
placements, 0.45% for 700 to 800 pound steer placements, and 0.30% for 800 to 900
pound steer placements. 8
Technological change in cattle finishing was considerably lower than that
experienced by all of U.S. agriculture over a similar time period.  Technological change
for U.S. agriculture averaged 2.37% over the 1980 to 1993 period (USDA).  Slow
technological change in cattle finishing may have contributed to the deterioration in the
competitive position of the beef industry during the 1980's and early 1990's.  To address
this issue, future research could compare the rates of technological change found in the
cattle industry to those experienced in the pork and/or poultry industries over the same
time period.     9
Table 1.  Summary Statistics for Output, Inputs, and Feed Conversion.
Variable
Unit of
Measure Mean
Standard
Deviation
600-700 Pound Placements
Gain Lb./Head 455.75 49.69
Feed Lb./Head 3737.80 385.96
Interest $/Head 31.42 5.69
Other Expense $/Head 57.48 16.51
Feed Conversion Index 8.23 0.70
700-800 Pound Placements
Gain Lb./Head 413.17 43.94
Feed Lb./Head 3490.90 373.49
Interest $/Head 29.54 5.46
Other Expense $/Head 52.72 15.26
Feed Conversion Index 8.48 0.75
800-900 Pound Placements
Gain Lb./Head 382.07 49.76
Feed Lb./Head 3326.90 435.83
Interest $/Head 28.67 5.61
Other Expense $/Head 49.75 14.72
Feed Conversion Index 8.75 0.8210
Table 2.  Regression Analysis Examining the Relationship Between Feed Conversion,  Seasonality, and Technological 
Change.
Placement Weight
Independent
Variable 600 - 700 lb. 700 - 800 lb. 800 - 900 lb.
Intercept  8.3445***
(0.1152)
 8.9192***
(0.1027)
 9.3658***
(0.1255)
Time -0.0029***
(0.0006)
-0.0034***
(0.0005)
-0.0032***
(0.0006)
February -0.1485
 0.1389
-0.3789***
(0.1248)
-0.2860*
(0.1523)
March -0.2398*
(0.1389)
-0.4621***
(0.1248)
-0.5833***
(0.1554)
April -0.0727
(0.1394)
-0.4520***
(0.1252)
-0.5926***
(0.1502)
May -0.0417
(0.1381)
-0.3636***
(0.1252)
-0.5407***
(0.1503)
June -0.0482
(0.1394)
-0.3605***
(0.1252)
-0.5395***
(0.1503)
July  0.2574*
(0.1369)
-0.2917**
(0.1241)
-0.5153***
(0.1515)
August  0.4841***
(0.1369)
-0.0027
(0.1231)
-0.2781*
(0.1503)
September  0.7130***
(0.1375)
 0.3579***
(0.1236)
-0.0427
(0.1485)
October  0.8132***
(0.1364)
 0.6033***
(0.1236)
 0.3195**
(0.1485)
November  0.6646***
(0.1387)
 0.6103***
(0.1236)
 0.5552***
(0.1485)
December  0.3836***
(0.1388)
 0.3760***
(0.1236)
 0.3679***
(0.1561)
1/80 to 3/80  0.9690***
(0.3059)
 1.1239***
(0.2820)
 1.2617***
(0.3265)
9/92 to 12/92  1.5766***
(0.1909)
 2.0317***
(0.1755)
 2.3396***
(0.2159)
Feedlot 2 -0.2680***
(0.0560)
-0.3408***
(0.0508)
-0.4251***
(0.0607)
Adjusted R
2  0.4730  0.6032  0.5609
Notes: Numbers in parentheses are standard errors.  Single, double, and triple asterisks (*) denote significance at the 10%,
5%, and 1% level, respectively.11
Table 3.  Regression Analysis Examining the Relationship Between Output, Inputs,  Seasonality, and 
Technological Change.
Placement Weight
Independent
Variable 600 - 700 lb. 700 - 800 lb. 800 - 900 lb.
Intercept  0.39791
(0.37140)
 1.02290***
(0.31000)
-0.69702**
(0.30620)
Feed  0.66385***
(0.05059)
 0.54944***
(0.04375)
 0.74986***
(0.04662)
Interest  0.00999
(0.02970)
 0.08503***
(0.02657)
 0.07046**
(0.03324)
Other Expense  0.05177***
(0.01486)
 0.05331***
(0.01150)
 0.07416***
(0.01350)
Time  0.00048***
(0.00010)
 0.00037***
(0.00007)
 0.00025***
(0.00009)
February  0.01872
(0.01468)
 0.04272***
(0.01177)
 0.02516
(0.01582)
March  0.02963**
(0.01470)
 0.05839***
(0.01178)
 0.05900***
(0.01609)
April  0.02122
(0.01490)
 0.06294***
(0.01188)
 0.06276***
(0.01554)
May  0.00184
(0.01470)
 0.04519***
(0.01192)
 0.04996***
(0.01561)
June -0.00005
(0.01484)
 0.04156***
(0.01204)
 0.04923***
(0.01573)
July -0.04078***
(0.01458)
 0.02807**
(0.01189)
 0.04120***
(0.01590)
August -0.06260***
(0.01459)
 0.00286
(0.01182)
 0.01836
(0.01579)
September -0.08527***
(0.01481)
-0.03737***
(0.01184)
-0.00627
(0.01549)
October -0.09330***
(0.01461)
-0.05595***
(0.01191)
-0.04004***
(0.01550)
November -0.07357***
(0.01480)
-0.05733***
(0.01179)
-0.05890***
(0.01549)
December -0.04519***
(0.01469)
-0.04159***
(0.01166)
-0.03978**
(0.01613)12
Table 3.  (Continued)
Placement Weight
Independent
Variable 600 - 700 lb. 700 - 800 lb. 800 - 900 lb.
1/80 to 3/80 -0.04116
(0.03384)
-0.07131***
(0.02751)
-0.06886*
(0.03510)
9/92 to 12/92 -0.16106***
(0.02021)
-0.20982***
(0.01657)
-0.22732***
(0.02233)
Feedlot 2 -0.00288
(0.00695)
-0.00617
(0.00603)
0.01852**
(0.00758)
Adjusted R
2  0.7449  0.8246  0.8198
Time Invariant F  0.531  0.640  0.237
Notes: Numbers in parentheses are standard errors.  Single, double, and triple asterisks (*) denote significance
at the 10%, 5%, and 1% level, respectively.  The critical F for time invariant technical efficiency is 2.60.13
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Measuring the Productivity of Cattle Finishing

Measuring the Productivity of Cattle Finishing

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