Evaluating Extension Integrated Pest
Management Programs with Factor
Analysis Indices
G. Keith Douce, David K. Linder, Michael E. Wetzstein, and Wesley N. Musser
I
ntegratedpest management (IPM) is
the intelligent use of pest control actions that will ensure favorable
economic, ecological, and sociological
consequences (Metcalf and Luckmann
1975). IPM is not an entity by itself;
rather, it is part of an overall crop production system. With this in mind, we set
out to design an evaluation system (process) that put IPM in this crop production perspective.
Although Extension IPM programs
have expanded into 50 states and 2 protectorates on over 45 commodities (Blair
and Edwards 1980), relatively few comprehensive program evaluations have
been completed (see Gholson 1981). As
pointed out by previous authors (Blair
and Edwards 1980, Gholson 1981,
Boutwell and Smith 1981), traditional
evaluations of Extension programs have
tended, due to the extreme complexities
involved and the lack of significant data
sets, to focus on either economic or
biological (principally entomological)
aspects of IPM. These inherent evaluation difficulties are further compounded
by differences in environmental factors
that impinge upon individual fields, and
by management
practices
and
capabilities of individual growers which
are beyond the scope of the IPM programs themselves.
Previous studies have attempted to
quantify the benefits of IPM usage by
comparing yields and production costs
incurred by users and non-users of IPM
on commercial farms (Carlson 1981, Hall
1977, Masud et al. 1981, Reichelderfer
FALL 1983
and Bender 1979). However, this approach introduces several opportunities
for uncontrolled bias, for example, differences in soil type, rainfall, or other
natural phenomena, as well as differences
in pest populations and management
abilities of producers. Also, the nonparticipant often received IPM information
as a result of the frequent contact with the
evaluator that was necessary to collect the
detailed data needed for the evaluations.
Additionally, the simple fact that growers
have been exposed to IPM technology
and employ IPM field scouts does not
mean that they have assimilated the IPM
technology into their crop production
system in an efficient manner. As pointed
out by Boutwell and Smith (1981), after a
certain point, participants or cooperators
are no longer characterized by the use or
non-use of IPM, but instead by how
much of the available IPM practices and
information are used. Consequently,
most of the federally mandated evaluations of ongoing IPM programs rely upon
the benefits of IPM usage as inferred
from indirect evidence. Indirect evidence
frequently used includes the number of
growers and acreages enrolled (in the
program) and the growth that has been
observed in these programs through
time. Also, in programs that have evolved and grown in an area, the nonparticipant or control group has become small
or nonexistent. Such is the case in a
number of crops in the Southeast, such as
cotton, as observed by Boutwell and
Smith (1981). With ongoing, intensive
IPM educational programs, a point is
reached in a given geographical area
where all growers of a particular commodity have been exposed to and have
been influenced by IPM techniques and
methods.
An Alternative
As IPM programs increasingly influence growers, alternative methods for
evaluation are required. Instead of evaluating a dichotomous group of growers, it
becomes necessary to evaluate a spectrum of growers participating in IPM
programs. At this point, program evaluators need to establish criteria for categorizing growers on the basis of degree of
participation. Then, of course, it is necessary to collect this information from producers and their fields.
After collection of this information, a
problem arises as how to systematically
reduce these characteristics down to a
manageable size for evaluation, a problem that most researchers have not addressed. Boutwell and Smith (1981), a
notable exception, established five major
characteristics to measure insect management practices. They reduced these characteristics to an index by subjectively
weighting each of the characteristics for
their relative importance in measuring
the degree of utilization of IPM practices,
and then totaling the weighted characteristics for individual growers.
A disadvantage of this approach is that
the degree of influence a characteristic exhibits on an index is not known. Likewise, a question arises as to which of the
various characteristics actually exerts the
most influence by the evaluator intro43
duces bias. The influence that each characteristic exerts on an index is of major
importance in the evaluation process.
Knowledge of this influence would provide an indication of which IPM techniques resulted in significant increases in
yield (or profit).
In this study. IPM utilization by a
grower was measured by systematically
obtaining a value for each of several IPM
characteristics. These IPM characteristics were then reduced to a manageable
size by factor analysis, to obtain relative
weights for each of the IPM characteristics, providing a measure of the influence
that each characteristic exerted on an index. The resultant indices were then used
as contributing variables in evaluation of
Table 1. IPM characteristics used to evaluate insecticide use vs. insect population levels for evaluation of !PM utilization by Georgia cotton producers. 1981" (see text for details)
the economic impact of IPM utilization
4. Proportion of applications
not identical across all
fields relative to the total
number of applications
made
on producers.
Methods
As a rigorous test of our evaluation
method. data were collected from 30 cotton producers in eight geographically dispersed Georgia counties during 1981.
Cotton IPM (before 1972, insect scouting
only) programs had been operational
through the local county Extension office
in seven of these counties for more than 4
years before the initiation of this study
(range. 4 to 10 years). Although no cotton had been grown in the eighth county
for several years, the growers who produced cotton during 1981 were extremely
interested in IPM and worked closely
with the local Extension agent. Thus, we
feel that nearly all of the cotton producers
in these counties have been influenced by
Extension IPM educational programs.
All of the producers from which data
were collected were participants (i.e.,
were having their fields scouted) in Extension-sponsored IPM programs. Detailed records were collected by frequent
contact with each grower throughout the
growing season by a pest management
assistant working in the local Extension
office and by field personnel employed by
the Farm Economics Information Center.1 Field level information collected from
each grower consisted of the following:
Insect reports (generated twice per week
by the field scout), pesticide use records,
land preparation. planting information,
as well as the other production inputs
needed to generate enterprise budgets.
1Farm Economics
Information
Departmellt of Agn'wltllral Economics,
of Georgia, Athens, GA 30602.
44
Center,
University
Characteristic
I. Proportion of "proper" to
total insecticide applications
No. of sprays made after
thresholds
-
No. of improper sprays (improper materials and made
after 48 h)
Total sprays
2. Proportion of economic
thresholds treated to total
no. of economic thresholds reached
No. of times thresholds
were reached
-
No. of times thresholds were
reached and no proper
spray was made
No. of times thresholds were reached
3. Proportion of properly
timed insecticide sprays
to total insecticide sprays
made with respect to
thresholds only
No. of properly timed
sprays (but not necessariIy proper rates or materials) made after thresholds
-
No. of sprays made before
thresholds
Total no. of sprays
Total no. of applications
made
-
No. of times identical applications were made with
respect to date. material.
and rate
Total no. of applications made
aControl recommendations used were those published by the Georgia Cooperative Extension Service (Lambert and Herzog 1981) for controlling major cotton insect pests.
Great care was taken in collecting these
data to include the date as well as the
technical aspects of the activity (e.g .. actual chemical used, rate, application
method, etc.). Actual grower costs were
used whenever possible; when these were
not available, average costs were substituted. (See Douce [1982] and Linder et
al. [1983] for details of the locations. procedures, and methods used in this study.)
Factor Analysis Indices
The concept of factor analysis essentially amounts to transforming a set of k
variables with n observations of each into
a new set of variables which are based on
an initial correlation matrix of the k variables (Muliak 1972). The variables are
normalized so that their mean is 0 and
their variance is 1, causing the total
amount of variance in the data to equal
the total number of variables.
The first factor computed in a factor
analysis represents the linear combination of the variables in the data which accounts for more variance among the variables than any other combination. The
second factor is considered the linear
combination of variables that accounts
for the most residual variance not accounted for by the first factor. As a result,
the second factor is orthogonal (uncorrelated) to the fir~t. Any other extracted
factors may be similarly defined until all
of the variance within the data has been
removed. For a given set of data, 100% of
the variance will be explained by the extracted factors. and the number of extracted factors will be equal to the number of variables within the data set.
Therefore, unless the first factor represents a perfectly linear combination of
variables which explains 100% of the
variance, the number of extracted factors
will always equal the number of variables
(Kim 1975).
By using the FACTOR program of
the Statistical Analysis Systems Institute
(1979), the only factors retained for further analysis were those factors having an
eigenvalue of one or greater. The eigenvalue is the total amount of variance
within the data set which is explained by
a given factor (Kim 1975). Therefore, the
factors which are retained are known to
explain at least the amount of variance
within a single variable.
The advantage of using factor analysis
is that the composition of a given index
may be readily determined by the load ins
of each factor among the variables used.
Rather than using subjective weights to
build an IPM index, as done by Boutwell
and Smith (1981). the interrelationships
between the components of IPM are considered. Each factor is composed of regression weights derived in the scoring
coefficient matrix which in turn is calcuBULLETIN
OF THE
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lated exactly from the factor pattern matrix. The ability to separate each index
into components and to note the variables
or characteristics which were most influential is particularly useful in index evaluation. Factor analysis was therefore used
to construct participation indices based
on a derived set of IPM characteristics
listed in Table 1
Individual grower values for each of
the IPM characteristics were obtained
through detailed analysis of individual
field level insect (= scouting) reports and
pesticide use records. Only those insect
reports and insecticide reports that occurred after insect scouting began (ca. 15
June) a nd before the cotton field reached
the open boll stage were included in these
analyses. To retain an objective analysis,
strict adherence
to the treatment
(economic) thresholds published in the
Georgia Cotton Insect Control circular
(Lambert and Herzog 1981) was maintained, although we realize that this
results in a somewhat artificial analysis,
due to the number of mitigating factors
that go into a recommendation; but the
alternative was to introduce unknown
subjective bias.
An insecticide application was considered "proper" if it was administered within 48 h after an economic threshold had
been reached
and the selection of materi-
als was consistent with the insect pest(s)
reported and with Extension Service control recommendations. The first and
third characteristics measure the proportion of proper chemical applications
made to the total number of applications
made. Total number of applications is
the sum of applications before and after
an economic threshold.
The third
characteristic was designed to capture the
chemical applications made before an
economic threshold was reached. By
spraying after a threshold was attained,
the producer not only maximized the
benefit of the beneficial insects in his field
for pest control, but also improved the effectiveness of his insecticide program.
Presumably, a producer participating in
IPM would tend to vary not only the timing of applications but also the material
and the rate of applications based on insect populations in the field. Thus, the
fourth characteristic is designed to capture this tendency by measuring the
amount producers vary their treatments
across fields.
FALL 1983
Table 2. Weights associated with the two indices
attributable by each of the four IPM characteristics
Characteristic
I
2
3
4
Index 1
0.510
-0.124
0.512
-0.089
Index 2
-0.076
0.688
0.033
0.618
Results
By using factor analysis, the four IPM
characteristics were reduced to two uncorrelated indices which accounted for
75% of the total variance associated with
the four characteristics. The values of the
derived indices ranged from 0 to 3.7986
for index I, and from 0 to 4.209 for index
2.
Table 2 presents the influences (or
weights) associated with the two indices
that were attributable to each of the four
characteristics. The major contributing
variables to index 1 were IPM characteristics 1 and 3, whereas IPM characteristics 2 and 4 contributed most heavily to
index 2.
The IPM participation indices were
then included as variables in multiple regression analysis performed to account
for the influence of the major production
variables on yield. The variables included
in the regression analysis were fertilizer,
lime, pesticides,
machinery,
irrigation,
la-
bor, county location, and the IPM utilization indices (see Linder et al. 1983). Results of the multiple regressional analysis
revealed that only index I of the IPM
utilization variables had a significant influence on yield. Those growers who
tended to apply pesticides based on the
actual insect pressure rather than on predetermined (or convenience) schedules
had a significantly higher yield. Our results indicate that rPM characteristics 2
and 4 tend not to significantly increase
yield. Without the ability to differentiate
the influence characteristics have on the
indices, an evaluator may have concluded that all the rPM characteristics
result in a significant increase in yield.
Our results indicate that utilization of
rPM practices did not significantly influence the pesticide cost to growers. Although growers were applying approximately the same amount of pesticides regardless of their rPM usage, growers who
tended to have higher rPM utilization
levels appeared to time their applications
better, which resulted in an increase in
yield. This finding is consistent with rPM
philosophy and the relatively sophisticated cotton production systems that exist in Georgia. As pointed out by Linder
(1982), Georgia cotton producers are, in
general, approaching efficient usage of insecticides.
Discussion
Of course, additional years of information will be required to conclusively document the benefits to producers for using
IPM technology. Certainly, this would
reduce the observed variations in production practices and production results. As
pointed out by Boutwell and Smith
(1981) and by Gholson (1981), shortterm or I-year evaluations exhibit extreme limitations on the evaluation process, due to factors beyond the scope of
the rPM program that have an overriding influence on measurements such as
yield and insect control. This was certainly the case with the current study, because cotton production was hampered in
several of the counties involved in this
study by localized droughts. Nonetheless, we feel that we were successful in developing and implementing a flexible
evaluation methodology for evaluating
ongoing IPM programs.
In conclusion, a technique that distinguishes between participants and nonparticipants
in IPM
is necessary
for fu-
ture evaluation, especially in those areas
where rPM programs have become established. Collecting grower IPM characteristics as done by Boutwell and
Smith (1981) and in the current study is
one method for distinguishing degree of
participation. However, a problem exists
in systematically reducing these characteristics to a manageable size for evaluation. rt is suggested that factor analysis
may be employed to alleviate this problem. An application of this analysis to an
rPM program implemented in Georgia
by the Cooperative Extension Service illustrates how the analysis creates a participation index. Advantages of using factor analysis for index construction include
the following: (I) the task of assigning
weighting factors for each variable by the
evaluator is avoided; (2) the relative contribution made by each variable used in
index construction becomes known; and
(3) the subjective influence of the
evaluator is reduced and is limited only to
the selection of the rPM characteristics.
Based upon this analysis, growers with a
higher degree of rPM utilization were
45
shown to have significantly higher yields
than growers who had lesser IPM utilization levels.
The methodology developed in 1981
and presented here is currently being used
to evaluate other Extension-sponsored
IPM programs in Georgia. As our methods and techniques are refined, and as
additional years of data are collected, we
should be better able to quantify the impact of IPM programs on growers 111
Georgia.
Acknowledgment
We acknowledge W. R. Lambert and
David B. Adams for their part in the
selection of IPM characteristics used in
this project. W. R. Lambert is Cotton
Entomologist/IPM
Coordinator,
and
David B. Adams is Pest Management
Specialist,
Extension
Entomology
Department, University of Georgia.
We also acknowledge the assistance of
the following University of Georgia personnel for their part in the collection,
coding, and processing of the data used
by this project: Barbara Hall and Chad
Sivasailam,
Extension
Entomology
Department; John Meier, Bill Fox, and
John Mackert, Farm Economics Information Center, Agricultural Economics
Department, and the County Extension
directors and IPM assistants who worked
directly with each of the farmers.
Georgia
Cooperative
Extell.<ion Service 111tegrated Pe.<t MaT/agemellt Programs for CottOIl.
Univ. Ga. Agric. Exp. Sin. Res. Bull. No. 293.
Masud.S. MooR. D.l...1gcwell.
C. R.Taylor.J. G. Benedict.and
L A Lippke. 1981. &onomic impact of iT/tegrated
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evaluatillg
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Agric. Econ. \3: 47-52.
Metcalf.R. L. andW. H. wekmann.1975. IT/troductiOT/ to
illsect pe.<t maT/agement. John Wiley and Sons,
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Muliak.S. A 1972. The foulldation of factor analysis.
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Reichelderfer.
K. H.. andF. E. Bender.1979. ApplicatioT/ of a
simulative
approach
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method.< for the cOT/trol of agncultural
altemative
pests. Am.
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Statistic.alAnalysisSystems Institute.Statistical Analysis
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Raleigh.
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Kim.J. 1975. Factor analysi.<, pp. 458-489. In Statistical package for the social sciences. McGraw-Hill
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L1mbert.W. R..andG. A Herzog.1981. Cottoll iI/sect control. Circ. SOl. Ga. Coop. Ext. Serv., University
of Georgia, Athens.
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M.S. thesis, University of Georgia, Athens. 97
pp.
Note: 'This research was partially funded
by USDA Agreement 58-319V-l-0242X
and by USDA Smith Lever 3(d) funds
for IPM. Received for publication 2 June
1983; accepted 14 June 1983.
Address: (Dol/ce) Dept. of Entomology, University of Georgia, 71fton,
GA 31793. (Linder, Wetzstein, and
l\1uJSer) Dept.
of Agricultural
Economics,
University of Georgia,
Athens, GA 30602.
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