Report 2004-03: Crop Farmers' Use of Market Advisory Services
May 2004 (Updated July 2004)
Joost M.E. Pennings,
Scott H. Irwin, and
Darrel L. Good
2004 by Olga Isengildina, Joost M.E. Pennings, Scott H.
Irwin and Darrel L. Good. All rights reserved. Readers may
make verbatim copies of this document for non-commercial purposes
by any means, provided that this copyright notice appears on all
The advisory service marketing
recommendations used in this research represent the best efforts
of the AgMAS Project staff to accurately and fairly interpret the
information made available by each advisory service. In cases where
a recommendation is vague or unclear, some judgment is exercised
as to whether or not to include that particular recommendation or
how to implement the recommendation. Given that some recommendations
are subject to interpretation, the possibility is acknowledged that
the AgMAS track record of recommendations for a given program may
differ from that stated by the advisory service, or from that recorded
by another subscriber.
material is based upon work supported by the Cooperative State
Research, Education and Extension Service, U.S. Department of
Agriculture, under Project Nos. 98-EXCA-3-0606 and 00-52101-9626.
Any opinions, findings, conclusions, or recommendations expressed
in this publication are those of the authors and do not necessarily
reflect the view of the U.S. Department of Agriculture. Additional
funding for the AgMAS Project has been provided by the American
Farm Bureau Foundation for Agriculture and Illinois Council on
Food and Agricultural Research.
Farmers place a high value on
market advisory services (MAS) as a source of price risk management
information and advice. For example, in a rating of 17 risk management
information sources, Patrick and Ullerich report that MAS are outranked
only by farm records and computerized information services. Schroeder
et al. find that a sample of Kansas farmers rank MAS as the number
one source of information for developing price expectations. Davis
and Patrick report that marketing consultants have the largest impact
on the use of forward pricing by soybean producers. Norvell and
Lattz find that marketing consultants tie for first place (with
accountants), in a list of seven, as likely to be most important
to Illinois farmers in the future. The rating of importance of MAS
among participants at Purdue Top Farmer Workshops has steadily increased
from fifth in 1997 to fourth in 1999 to third in 2001 (Patrick).
Surveys also report that a growing number of farmers subscribe
to market advisory services. Among the participants at Purdue's
Top Farmer Workshop, the share of subscribers grew from 53 percent
in 1997 to 62 percent in 2001. Davis and Patrick report that 39
percent of farmers in Mississippi and 49 percent of farmers in Indiana
used marketing consultants or subscribed to market information services
in 1999. Along with the increased use of market advisory services
for management decisions, farmers are willing to spend increasing
amounts of money to receive this advice. Among Purdue's Top Farmer
Workshop participants, annual expenses on marketing advice moved
from the fourth highest expense for consultants to the second highest
from 1991 to 2001, growing in absolute terms from $755 to $3,455.
The majority of respondents that used marketing consultants in Coble
et al's survey indicated that they spent $1,000 or more on marketing
advice in 1998. It appears that the increasing importance of MAS
in the decision making process of farmers is part of an overall
trend towards increased firm reliance on external consultants in
operational capacities, as pointed out by some researchers (e.g.,
Previous studies have focused primarily on the pricing performance
of MAS in corn, soybeans and wheat (e.g., Martines-Filho, Good,
and Irwin; Irwin, Martines-Filho, and Good). A key assumption in
these evaluations is that a representative farmer follows the recommendations
exactly as provided by the advisory services. Limited evidence is
available on how farmers actually use the marketing recommendations
provided by advisory services. Pennings et al (2004, 2005) examine
factors that determine the impact of MAS on farmers' marketing decisions.
They argue that perceived MAS performance, the way in which MAS
recommendations are delivered, and the match between a particular
MAS and an individual farmer's marketing philosophy are important
factors explaining the impact of MAS recommendations. Other studies
have evaluated MAS as sources of consulting advice and information
(e.g., Ortmann, et al; Jones, Battle, and Schnitkey). These studies
have found that the use of consulting advice may be affected by
the operator's age, farm size, farm ownership, education and risk
aversion, among other factors. Ortmann, et al revealed that farmers
rate their marketing management skills lower than their other management
skills. They also found that marketing sources of information were
ranked lower than other sources of information, which may indicate
that the needs of farmers are not being met in this area. These
findings emphasize the need to investigate further the nature of
The purpose of this study is to provide new and more comprehensive
evidence about crop farmers' use of MAS. More specifically, in this
study we (1) identify the levels of MAS use by US commercial farmers,
(2) differentiate farmers who use MAS, (3) describe farmer valuation
of MAS relative to other sources of marketing information and their
selection of particular MAS, (4) demonstrate changes in MAS use
under different market conditions, (5) describe the nature of MAS
use, and (6) discuss the impact of MAS use on producer marketing
behavior. These issues are examined based on the results of a survey
of commercial agricultural producers conducted in January/February
2000. The study is concluded by providing practical implications
of the survey findings for advisory services, farmers, extension
programs and research. To introduce the subject of advisory services,
some background information is presented in the next section.
Overview of the Market Advisory Service Industry
Market advisory services first
began to emerge in the mid-1970s (Doane Agricultural Services being
the one exception), following the huge run-up in commodity prices
due to several extreme and highly unusual developments that contributed
to historic market volatility.
Some of the first MAS included Farmers Grain and Livestock, in Des
Moines, Iowa; Top Farmers of America, in Milwaukee, Wisconsin; Doane
Agricultural Services, in St. Louis, Missouri; and Professional
Farmers of America (ProFarmer) in Cedar Falls, Iowa. Doane Agricultural
Services preceded all of the other companies by several decades,
as it was formed in the 1930s. However, the primary focus of Doane
in its early years was farm management, rather than marketing advice.
The first companies geared toward giving specific marketing advice
were Farmers Grain and Livestock and Top Farmers of America. ProFarmer
initially started with market and policy information and moved later
into the specific market advice area.
The early MAS were created in order to provide farmers with marketing
information in an environment of increased market volatility. During
the intervening years, these companies generally have gone through
four evolutionary stages: Stage I - providing fundamental and technical
market information, newsletters, and marketing tool seminars; Stage
II - providing specific marketing recommendations in addition to
stage I services; Stage III - providing electronic access via services
such as the Data Transmission Network (DTN); and Stage IV - providing
individual electronic access via e-mail and the Internet, as well
as offering "customized" marketing recommendations for
Overall, MAS may be described as firms whose primary business is
to provide marketing information to farmers in order to help them
decide how, when and where to market their crops and livestock.
As noted above, the central focus of advisory services is providing
market information, analysis and specific marketing recommendations
to subscribers. Related services often provided by such firms include
market and government policy information, seminars on marketing
tools and techniques, and in some cases, speculative futures and
options trading advice. Marketing recommendations range from the
relatively simple (e.g., sell 50% of 2003 soybean production today
in the cash market) to the highly complex (e.g., if futures reach
$3.25/bushel, sell 75% of expected 2004 corn production by purchasing
December 2004 corn put options with a strike price of $3.50/bushel;
to offset part of the cost of the put options write an equal amount
of call options on March 2005 corn futures with a strike price of
$3.75/bushel). Recommendations vary substantially across services
in a given crop year, and in many cases, within a crop year for
an individual MAS (Bertoli et al., 1999; Martines-Filho et al.,
2003a, 2003 b; Colino et al., 2004a, 2004b).
These services are delivered for a fee in the form of a newsletter,
hotline, website or e-mail. The fee structure typically differs
between "basic" and "customized" marketing programs.
A basic program provides market analysis, information, and what
is probably best described as "one-size fits all" or "generic"
marketing recommendations. A customized program generally provides
marketing recommendations tailored to individual client needs, direct
access to market analysts, as well as the information provided to
basic service subscribers. Statistics on the subscription fees for
the advisory services tracked by the AgMAS Project during the 1995
through 2001 crop years are shown in Figure
These fees represent the fixed annual cost for a basic program and
average about $300/year for this time period. The range of fees
is skewed with minimum fees around $140-$180/year and maximum fees
of about $550-$600/year. This data indicates the cost of basic programs
is relatively small compared to whole farm revenue for most commercial-size
farm operations. Irwin, Martines-Filho and Good report that subscription
costs in 2001 average less than one-tenth of one percent of total
advisory revenue for a 2,000 acre central Illinois corn and soybean
farm and about two-tenths of a percent for a 500 acre farm. Available
data on the cost of customized programs is sketchier. Information
from advisory service websites and other promotional material indicate
fees are charged based on anticipated production, either on per
acre or per bushel basis. A typical fee is in the range of three
to five cents per bushel. In contrast to the cost of a basic package,
costs for a customized package may be substantial. For example,
costs for a 2,000 acre corn/soybean farm could easily be as high
as $7,000/year (assuming production of 150,000 bushels of corn,
50,000 bushels of soybeans, $0.03/bushel fee for corn and $0.05/bushel
fee for soybeans)
Today, the market advisory service
industry is approaching maturity with dozens of firms offering services
to producers. There are serious challenges to would-be entrants,
because of the strongly-established customer positions of existing
firms. While evaluating their market shares is outside the scope
of this paper, informal evidence suggests that the industry leaders
include ProFarmer, followed by Doane and Brock Associates. In the
business of providing marketing information, MAS compete with each
other; traditional sources of information, such as university extension
services, magazines and newspapers (among others); and new sources,
such as E-Markets (http:www.e-markets.com).
This study presents new evidence regarding MAS use. The evidence
was collected through a survey of US crop farmers from three major
production regions- the Midwest, the Great Plains, and the Southeast.
The next section describes the data-gathering procedures and the
characteristics of the sample generated as a result of the survey.
Data Collection and Sample Characteristics
The empirical evidence on farmers'
use of MAS, as presented in this study, was generated through a
survey of US crop farmers conducted in January/February 2000. The
survey instrument was sent to 3,990 farmers in the Midwest, Great
Plains and Southeast.
The sample of addresses was drawn from directories kept by a US
firm that delivers agricultural market information and MAS via satellite.
The questionnaires were sent on January 21, 2000, and the cut-off
date for returning questionnaires was March 10, 2000. A total of
1,399 usable questionnaires were sent back, yielding a response
rate of 35%, which is high compared to previous surveys among small-
and medium-sized enterprises (Jobber; Karimabay, and Brunn). The
details of survey development and execution are discussed in Pennings,
Irwin and Good. This study utilizes 1,285 complete responses.
The demographic characteristics
of survey respondents reported in Table 1
suggest that the survey respondents can be classified as relatively
large commercial farmers. The scale of the farm operation of the
survey respondents was about four times the national average (as
reported by the 1997 Census of Agriculture) if measured by total
acreage and about five times the national average if measured by
gross annual sales. On average, the respondents farmed nearly 2,000
acres and had gross annual sales exceeding $500,000. Most had annual
sales above $100,000. The survey respondents were, on average, somewhat
younger than the overall population of US farmers: 44 versus 54
years of age. Regionally, the highest concentration (52%) of survey
respondents was in the Midwest, followed by the Great Plains (30%),
and the Southeast (18%). As shown in Table
2, the principal crops for this group of farmers were corn,
soybeans and wheat. A total of 56 % of the respondents reported
that they also had livestock in their farm operation.
This group of farmers appears
similar to commercial farmers described in previous surveys in terms
of age (Shroeder et al.) and farm size (Patrick, Musser, and Eckman;
Goodwin and Schroeder; Coble, et al). However, the sample used in
this study is more general in geographic terms. The following sections
describe the use of MAS by this group of farmers.
Users of Market Advisory Services
Based on the findings from previous
studies (e.g., Ortmann, et al; Jones, Battle, and Schnitkey), the
sample of survey respondents was stratified between MAS users and
non-users across basic demographic characteristics shown to affect
farmers' use of consultants.
The data reported in Table 3 indicate that
about 82% of the survey respondents (1,053 respondents) used MAS,
while 18% (232 respondents) did not use MAS. The highest use of
MAS (85%) was reported in the Midwest, the lowest (78%) in the Great
Plains. Comparison of the sub-groups showed that MAS users and non-users
cannot be differentiated based on age and farm size. However, adoption
of MAS may be associated with geographical differences in MAS use.
Pennings et al (2004) argued
that heterogeneity in the likelihood of using MAS is determined
by crop producers' risk attitudes, among other factors. Because
producers' risk attitudes are unobserved, they were examined using
statements 2-5 listed in Table 4. Producers
were asked to indicate their agreement with these statements on
a nine-point semantic scale ranging from "strongly disagree"
(1) to "strongly agree" (9). The construct reliability
of this scale was analyzed using the method proposed by Hair et
al. Construct reliability refers to the extent to which an indicator
or set of items is consistent with what it is intended to measure
and hence relates to the consistency of the measures (Hair et al).
The construct reliability of this scale, which may range from 0
(not reliable) to 1 (perfectly reliable), was high, at 0.85 (Hair
et al). Other measures of reliability yielded the following results:
Chisquare /df = 1.0 ( p= 0.37); GFI = 0.99; RMSEA= 0.0.
Therefore this scale may be presumed to reflect accurately the attitudes
of producers toward risk. This analysis suggests that MAS users
reveal a significantly greater preference for risk than non-users.
This finding is intuitive, because if farmers are willing to take
more risk, they are more likely to be involved in sophisticated
marketing schemes and may be in greater need for marketing information
and advice. This conclusion is consistent with the findings of Goodwin
and Schroeder, who argued that farmers with more preference for
risk are more likely to adopt forward pricing.
Evaluation of Sources of Marketing Information and Selection of a Specific Service
As mentioned in the overview
section, MAS compete with other sources of marketing information.
Table 5 reveals that MAS are the third
most important source of marketing information for this sample of
crop farmers. This finding is consistent with evidence presented
in previous studies (e.g. Patrick and Ullerich, Shroeder et al.,
Norvell and Lattz, Schnitkey et al). Satellite systems are considered
the most important source of marketing information, which is consistent
with Patrick and Ullerich's findings. Notable is the importance
of USDA reports. The impact of a local elevator was ranked very
high, while university extension services and marketing clubs received
relatively low ratings. It is important to keep in mind, however,
that these results reflect farmers' opinions dating to the spring
of 2000. With the emergence of new web-based information sources
and increasing access to them, the importance of associated Internet
sources may be rated much higher today.
As sources of marketing information,
MAS also compete with one another. The first column of Table
6 reports the percentage of farmers that have ever used a specific
MAS. The MAS listed in Table 6 represent
the ten most popular MAS in 2000 in terms of subscriptions by satellite
users. The largest proportion of survey respondents, nearly 70%,
had subscribed to ProFarmer at some point in time, followed by Brock
Associates, and AgLine by Doane. Nearly half of farmers used other
MAS not listed in this table. Only 18% of the farmers reported that
they did not use MAS at all. The distributional information found
in Figure 2 shows that only 43% of the
MAS users relied on a single MAS, while the other 57% subscribed
to multiple services. This observation implies that the majority
of MAS users rely on a portfolio of services and the impact of individual
MAS may be difficult to differentiate.
The use of any particular MAS
may be related to familiarity with it and farmer perceptions about
its marketing style. According to Table 6,
the survey respondents were most familiar with ProFarmer and least
familiar with CommStock Investments Inc. and Brent Harris Elliott
Wave. This finding is not surprising, given that ProFarmer is one
of the oldest MAS, while CommStock Investments Inc. and Brent Harris
Elliott Wave are newer MAS. In general, the trial rates reported
by farmers in the first column are closely correlated with familiarity
about specific MAS ( of the rankings is equal to 0.94).
also presents evidence regarding farmers' perception of the marketing
styles of various MAS. Brock Associates, AgResource Company, and
Allendale Inc. are considered the most aggressive MAS, while AgLine
by Doane, AgriVisor Services Inc., and Stewart Peterson are perceived
as the most conservative. Interactions with farmers during the pre-study
period revealed that farmers appear to associate MAS aggressiveness
with the intensity of use of futures and options markets rather
than with cash market instruments. Both Brock Associates and AgLine
by Doane are among the most commonly-used MAS, therefore both aggressive
and conservative features may be attractive to different farmers.
In fact, it may be the match between the MAS and the individual
farmer's marketing philosophy, rather than MAS marketing style alone,
that determines the choice of MAS. Survey respondents indicated
that they are likely to use a MAS if it matches their marketing
philosophy, with an average response of 6.23 on a one-to-nine scale
(1=certainly not use, 9=certainly use). On the other hand, they
indicated that they are not be likely to use a MAS if it does not
match their philosophy, with an average response of 3.07 on the
same scale. These findings are consistent with the results of Pennings
et al. (2004), who demonstrate that the likelihood of farmer's use
of MAS is driven, at least to a certain extent, by the match of
the farmer's marketing philosophy and the MAS' marketing style.
Another aspect of MAS use is
farmer satisfaction. Table 6 reports the
level of satisfaction with specific MAS of farmers that have subscribed
to a particular MAS. Interestingly, CommStock Investments, one of
the least-used MAS, received the highest satisfaction rating. Farmers
were also very satisfied with the use of AgResource, ProFarmer,
and Brock Associates, some of the most commonly-used MAS. Overall,
respondents appear to be moderately satisfied with the 10 advisory
services listed in Table 6. Satisfaction
with MAS use does not appear to be closely related to the other
categories of MAS use described in Table 6
except for marketing style.
According to these results producers appear to favor more aggressive
MAS. Previous studies (e.g., Ginzberg; Zeithaml, Parasuraman, and
Berry) suggest that farmers evaluate MAS based on the outcome of
the service (MAS performance) and the process of service delivery.
These aspects of MAS will be reviewed later in this paper.
3 describes the frequency of farmer switching between different
MAS. On average, the survey respondents switched MAS once every
3.3 years. This means that MAS must find a new pool of subscribers
approximately every three years. This finding is consistent with
the trial rates reported in the first two columns of Table
6. The percentage of farmers that have ever used a specific
MAS adds up to 331%, which implies that the average farmer in this
survey has tried about three different services. Only 28% of MAS
users reported that they had never switched MAS. Most of these "loyal"
users subscribed to the older MAS: ProFarmer, AgLine by Doane, and
Brock. The other 72% of MAS users seem to be chasing "the hot
advisor." This finding is consistent with similar evidence
presented in the finance literature (e.g., Chevalier and Ellison;
Sirri and Tufano) that describes how "hot" money flows
into and out of mutual funds.
Market Advisory Service Use in Different Market Conditions
This survey also investigated
how the use of MAS may change depending on different market conditions.
To explore this issue, farmers were asked to indicate the probability
of subscribing to MAS under the following scenarios: (1) low crop
prices, (2) normal crop prices, (3) and high crop prices. Figure
4 indicates that, on average, farmers revealed a downward-sloping
use of MAS relative to crop prices, with the average probability
of subscribing to MAS ranging from 56 to 64 percent, depending on
market conditions. Interestingly, this "demand indicator"
for MAS in different market conditions was not homogeneous. Farmers
may be divided into three groups, based on their use of MAS in different
market conditions. As shown in Figure 3,
Group A represents about 39 percent of the survey respondents, who
follow the general tendency of downward-sloping MAS use relative
to crop prices. Thus, the probability of MAS use by this group increases
as the crop prices fall. This relationship suggests that this group
may be most interested in the risk-reducing characteristics of MAS.
Group B, composed of about 15 percent of the survey respondents,
revealed an upward-sloping use of MAS relative to crop prices. This
group is most likely to subscribe to MAS when the crop prices are
high. This group may be sensitive to the cost of MAS, as MAS become
relatively less expensive with high crop prices. Finally, Group
C, which represents about 40 percent of survey respondents, revealed
a flat 70 percent likelihood to subscribe to MAS regardless of market
conditions. This group clearly represents the most stable and loyal
Several characteristics of these
groups, similar to the ones reviewed before for all MAS users, are
presented in Table 7. This information
reveals that these three groups exhibit regional differences, with
the largest proportion of Midwest farmers belonging to group C (flat
use), and the largest proportion of farmers in the Great Plains
and the Southeast representing group A (downward-sloping use). Farmers
can also be differentiated based on their farm size, measured by
gross sales, with the largest producers following Group C-type behavior,
followed by Group A and B. Producers representing Group A appear
most risk averse, and have the highest belief in the risk-reducing
properties of MAS of all three groups. Group A also highly values
the price-enhancing characteristics of MAS. Group B had the lowest
scores in all categories, except risk attitude and the cost of MAS.
This group appears to be the most sensitive to the cost of MAS.
This suggests that this group may be the least interested in using
MAS of the three groups. Group C appears the most likely to use
MAS, as it has the highest scores in all categories.
Nature of Market Advisory Service Use
After the choice of MAS has been
made and a farmer has selected to a particular service or combination
of services, the subscriber receives information and pricing recommendations
from the MAS. At this point it becomes interesting to know how farmers
use this information. Table 8 describes
the extent to which farmers use various types of MAS advice. These
data suggest that MAS are used to the greatest extent for marketing
information, market analysis, and to keep up with markets. Advisory
services are more often used in an attempt to receive an above-average
price than to reduce price and income risk and reduce price fluctuations.
Somewhat contrarily, however, farmers do not believe that the use
of MAS will give them much chance to beat the market. General guidelines
(e.g., market strategies and price information) are utilized more
than specific advice (e.g., specific pricing decisions, price forecasts).
Farmers appear to be cautious about using specific MAS recommendations
to make pricing decisions, as they indicated that they generally
use MAS recommendations as background information, compare it with
other information sources and do not follow MAS advice precisely.
Only 11 percent of the farmers follow the specific pricing recommendations
of MAS closely.
Thus, only a relatively small segment of MAS users follow the type
of behavior assumed in previous studies of MAS performance (e.g.,
Irwin, Martines-Filho, and Good).
Since the behavior of the average
MAS user may be different from that of close followers of MAS, Table
9 compares all MAS users and close followers in terms of the
impact of MAS and the implementation of MAS pricing recommendations.
The impact of MAS on farmer pricing decisions is substantial for
the entire group of users (6 on the scale from 1 to 9) and very
strong (8 on the scale from 1 to 9) for close followers. Both groups
are very likely to implement recommendations associated with the
use of cash-market strategies, both before (in the form of cash
forward contracts) and after harvest. The next most popular pricing
recommendations are buying call options and selling futures after
harvest, for all users, and selling futures before and after harvest,
for close followers. These are followed by buying put and call options
prior to harvest for both groups. The use of both instruments may
be indicative of sophisticated options positions, such as fences
or window strategies. The least favored recommendations for both
groups were buying futures before and after harvest, and buying
put options after harvest, which, interestingly enough, is a conventional
hedging strategy. Overall, there is little difference between the
two subgroups in terms of which types of recommendations they implement,
only that close followers are significantly more likely to implement
Finally, the nature of MAS use
may be affected by the process of service delivery (e.g., Ginzberg).
Farmers' valuation of some of these aspects is described in Table
10. These aspects are grouped in three general categories that
reflect the delivery process of MAS, namely, the process itself,
methods used to arrive at recommendations, and particular tools
recommended for application. This data suggests that the most valued
features of the delivery process are daily updates of analyses and
consistency of recommendations. The most important methods used
to arrive at recommendations are fundamental analysis, specialist
opinions regarding particular crops, and technical analysis. Farmers
appear to value recommendations that include futures and options
more than recommendations that use only cash instruments. However,
as discussed in the previous paragraph, they seem more likely to
follow cash-oriented recommendations. This discrepancy may be explained
by the fact that all farmers have to sell their crops in the cash
market, but not all of them use futures and options. Farmers do
not seem to care too much whether the analysis is based on the knowledge
of one person or a group, nor do they care about the way the information
is presented (text versus charts). The frequency of futures and
options use is not important to them either. Overall, this evidence
demonstrates that farmers do not evaluate service quality solely
on the marketing performance of the service, but also on the process
of service delivery.
Market Advisory Service Use and Marketing Behavior
The impact of MAS use on farmer marketing behavior is examined next
in terms of the use of forward pricing tools and marketing frequency.
The use of forward pricing tools by MAS users, non-users, and close
followers is reported in Table 11. These
data reveal that MAS users are generally more active marketers,
as they use all of the selected forward-pricing techniques more
than non-users. The smallest difference in use is for the simplest
instruments, such as cash forward contracts. This difference increases
for hedge-to-arrive contracts and almost doubles for the use of
futures and options. The use of forward-pricing techniques is even
greater among close followers of MAS, particularly futures and options
both before and after harvest and hedge-to-arrive contracts before
harvest. These results indicate that MAS users (especially close
followers) are much more likely to use forward-pricing techniques,
particularly futures and options, than non-users. This finding is
consistent with the finding in previous studies (e.g., Davis and
Patrick) that market advisory service use is an important determinant
of the forward pricing behavior of farmers.
Current respondents, on average, use forward-pricing techniques
much less than the participants in the Purdue Top Farmer Workshop
(Patrick) and the respondents to the 1996 Kansas survey (Schroeder
et al). The use of forward pricing techniques by the participants
of these previous surveys appears similar to the responses of the
close followers of MAS. The results of the current survey are very
similar to the results of Coble et al.'s study into the use of futures
and options, but differ dramatically in the use of minimum-price
contracts. The participants of the Coble et al. study are likely
to have used MAS as well, but to a much smaller degree, since only
about 20% of the participants in this study reported non-zero spending
on marketing consultants. This comparison suggests that the use
of forward-pricing techniques in the current survey falls between
the more general sample of farmers used in Coble et al.'s study
and the more restricted sample used in Purdue studies.
Another interesting aspect of
farmer marketing behavior is marketing frequency. Table
12 shows the number of times that producers of corn, soybeans,
wheat and cotton make pricing decisions, based on the current survey
and some previous studies (Coble et al; Goodwin and Kastens). Consistent
with the evidence presented in previous studies, the current survey
reveals that most farmers make 2-5 pricing decisions a year. In
both the current and the Coble et al. survey, the lowest pricing
frequency was reported for cotton farmers (2-3 times a year) and
the highest for corn farmers (6 times a year). Wheat farmers make
about four pricing decisions a year, on average, which is consistent
with Goodwin and Kastens' findings. In general, the pricing frequency
found in the current survey was consistent with Coble et al.'s findings
and slightly higher than Goodwin and Kastens' results for corn and
soybeans (6 versus 4 times, respectively, for corn, and 3 versus
4.5 times for soybeans).
Only among producers of soybeans and cotton did MAS users show a
greater pricing frequency than non-users. The pricing frequency
among corn and wheat producers revealed no significant differences
between users and non-users of MAS. This evidence suggests that
the use of MAS does not always result in a higher pricing frequency
Summary and Conclusions
Farmers in the US continue to
identify price and income risk as one of their greatest management
challenges. Numerous surveys show that farmers place a high value
on market advisory services (MAS) as a source of price risk management
information and advice. While the pricing performance of MAS has
been examined in detail, there is limited evidence about how farmers
actually use these services. This study sought to examine the nature
of farmers' use of advisory services based on the results of a survey
of US crop producers. The survey questioned 3,990 farmers in the
Midwest, Great Plains, and Southeast and provided 1,285 complete
responses for the purposes of this study. The sample of survey respondents
appears representative of large-scale commercial farmers in the
The survey revealed that about 82% of the respondents used MAS.
Users of MAS cannot be differentiated from non-users based on demographic
characteristics, such as age and farm size. However, MAS users tend
to be significantly more risk seeking than non-users. The use of
a specific MAS appears to be closely correlated with farmer familiarity
with the MAS. Farmers value both aggressive and conservative MAS,
which suggests the match between the marketing philosophy of a farmer
and MAS may play a key role in MAS choice. These findings imply
that a MAS may be able to expand its customer base if it makes more
producers aware of its services, and its marketing style, in particular.
The biggest potential for the new customer base is among more risk-seeking
producers that may be in greater need of marketing advice.
Stability of customer base may be an important issue for MAS. Respondents
to this survey reported that they switched MAS on average about
once every three years. This finding implies that MAS must find
a new pool of subscribers about every three years, and therefore,
their marketing efforts are extremely important. Additionally, farmers
reported moderate levels of satisfaction with MAS use overall. These
findings are consistent with conclusions by Ortmann et al. that
producers' needs for marketing information are not being fully met.
Therefore, MAS (as well as other sources of marketing information)
may need to invest in further research to identify these specific
Stability of MAS use also may be affected by market conditions.
A simple experiment included in the survey indicated that farmers
differed in their likelihood of subscribing to MAS in different
market conditions. Three groups of farmers were identified that
revealed (A) decreasing, (B) increasing, and (C) constant probability
of subscribing to MAS relative to crop price levels. Thus, in order
to increase stability of use in different market conditions, MAS
should concentrate their efforts on the first two groups. It appears
that the first group (A) may be most interested in risk-reducing
characteristics of MAS, and therefore this group should be presented
with recommendations targeted at reducing producers' risk exposure.
The second group (B) seems to be very sensitive to the cost of MAS.
Hence, some price-discriminating strategies may make MAS more attractive
for these farmers.
This survey showed that farmers use MAS for various reasons. Most
often MAS are used for marketing information, market analysis, and
to keep up with markets. Advisory services are more often used in
an attempt to receive an above-average price than to reduce price
and income risk. Most farmers use MAS recommendations as background
information, compare it with other sources and do not follow MAS
advice precisely. Only 11 percent of farmers follow the specific
pricing recommendations of MAS closely. Based on this information,
it appears that MAS may benefit from providing more differentiated
products, some concentrated on general marketing information, some
focused on specific pricing recommendations. Such product differentiation
may allow MAS to better meet the needs of farmers. In view of our
findings regarding the importance of the match between MAS and farmers'
marketing philosophies, it is critical that these new products be
The results of this study may be used by producers to compare their
use of MAS with that of other users and to form expectations for
MAS use. The survey revealed the importance of a good match between
the marketing philosophy of a farmer and a MAS in farmers' selection
of MAS. Therefore, farmers should carefully consider the marketing
style of a particular service while making their choice of MAS.
A better "fit" between farmers and MAS may result in higher
satisfaction levels and lower switching rates. Only 28% of MAS users
reported that they have never switched MAS. The other 72% of MAS
users may be chasing "the hot advisor." Such behavior
may result in substantial switching costs. Similar behavior on part
of mutual fund investors has been shown to be quite costly in terms
of realized performance (e.g., McDonald). Additionally, previous
studies of MAS performance (e.g., Irwin, Martines-Filho, and Good)
show that past performance is not indicative of future performance.
This emphasizes the importance of selecting a MAS based on its marketing
style rather than past performance.
The insights about the nature of MAS use by U.S. crop farmers presented
in this study also have interesting implications for extension program
development. University extension services received a very moderate
ranking as a source of marketing information by survey respondents,
which suggests that information the extension service provides to
large commercial farmers is not, in general, highly-valued. Two
findings of this study are particularly curious in this context:
(1) MAS users are more risk-seeking than non-users and may have
a greater need for marketing advice because they are involved in
more sophisticated marketing strategies; and (2) farmers appear
more interested in the price-enhancing characteristics of MAS rather
than in their risk-reducing features. These findings contribute
evidence to the ongoing debate in the agricultural economics literature
about the relevance of risk-management education and research. Numerous
arguments have been made that risk reduction is not of primary interest
to farmers (Christensen and Wimberley), that risk only matters when
a producer is in a tight financial situation or is contemplating
a major change in farm operations (Patrick and De Vuyst), or that
producers' primary concern is how to use the information in order
to make money (Anderson and Mapp). On one hand, these arguments
emphasize the need for educational programs that incorporate information
on price-enhancement opportunities available from various marketing
strategies and help producers better understand marketing information.
This can be accomplished in part by incorporating more outlook information
into extension programs. On the other hand, these findings indicate
the importance of educating farmers about market efficiency concepts,
which challenge their focus on price enhancement (e.g., Zulauf and
Results of this study clearly show that advisory services are highly
influential with marketing decisions of large commercial farmers.
If this group of farmers is deemed an important target of extension
programs, than advisory services may provide an effective way to
reach this audience. One approach would be to involve MAS in the
design, and potentially, even delivery of extension programs. Another
approach would be to create "train-the-trainer" type programs
focused on MAS staff directly. This approach has proven quite successful
with other groups, such as agricultural lenders.
The results of this study also have important research implications.
This study demonstrated that MAS have a substantial impact on producer
pricing decisions. Therefore, MAS use should be included in future
studies of producer marketing behavior. In fact, some recent studies
(e.g., Katchova and Miranda) already consider MAS use as a part
of farmers' decision process regarding the use of marketing contracts.
Additionally, previous studies of MAS performance (Irwin, Martines-Filho,
and Good) are based on the assumption that farmers exactly follow
the marketing recommendations provided by services. The conclusions
strictly refer to the 11 percent of producers that follow MAS recommendations
closely. Research is needed that examines the relationship between
the degree of implementation of MAS advice and subsequent pricing
performance. This study also emphasized the importance of the match
between farmers' and MAS marketing styles in farmers' use of MAS.
However, objective information about advisory service marketing
style is quite difficult for farmers to obtain. Thus, there is a
need to investigate marketing styles of various MAS in order to
determine style categories based on objective quantitative factors.
Such information may be used by farmers to improve their choice
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Olga Isengildina is a Visiting Scholar in the Department
of Agricultural and Consumer Economics at the University
of Illinois at Urbana-Champaign. Joost M.E. Pennings is
an Associate Professor in the Department of Agricultural
and Consumer Economics at the University of Illinois at
Urbana-Champaign and the AST Distinguished Professor in
Futures Markets in the Department of Social Sciences at
Wageningen University, The Netherlands. Scott H. Irwin
is the Laurence J. Norton Professor of Agricultural Marketing
in the Department of Agricultural and Consumer Economics
at the University of Illinois at Urbana-Champaign, and
Darrel L. Good is a Professor in the Department of Agricultural
and Consumer Economics at the University of Illinois at
Urbana-Champaign. The co-operation and assistance of the
Data Transmission Network in the research is gratefully
acknowledged. The authors appreciate the input of Robert
Wisner who provided valuable information about the history
of the market advisory service industry. Funding for this
research was provided by the following organizations:
Illinois Council on Food and Agricultural Research; Cooperative
State Research, Education, and Extension Service, U.S.
Department of Agriculture; Economic Research Service,
U.S. Department of Agriculture; the Risk Management Agency,
U.S. Department of Agriculture, and the Initiative for
Future Agriculture and Food Systems, U.S. Department of
Agriculture. Any opinions, findings, conclusions, or recommendations
expressed in this publication are those of the authors
and do not necessarily reflect the view of the U.S. Department
Material in this section is heavily based on private
e-mail communication with Robert Wisner of Iowa State
 The data are found in the annual AgMAS corn and
soybean pricing reports published for the 1995-2001 crop
years. The latest example is Irwin, Martines-Filho, and
Good, 2003. Earlier reports can be accessed at the AgMAS
Given the level of market expenditures reported by
attendees at Purdue Top Farmer Workshops ($3,455 in 2001),
the cost comparisons presented here suggest that commercial
farms make substantial use of customized programs.
 The Midwest is represented by Illinois, Iowa, Minnesota,
Missouri, Nebraska, Ohio, and Wisconsin. Great Planes
include Colorado, Kansas, Montana, North Dakota, Oklahoma,
South Dakota, and Texas. South East includes Alabama,
Arkansas, Georgia, Kentucky, Mississippi, North Carolina,
Tennessee, South Carolina and Virginia.
Details on producers' livestock operations
are available upon request.
Non-users of MAS are producers who answered
"Yes" to the statement "Do not use market
advisory services at all." Producers who used one
of the market advisory services listed in the survey or
another market advisory service are considered MAS users.
The likelihood-ratio Chi-square statistic
tests whether the matrices observed and those estimated
differ. Statistical significance levels indicate the probability
that these differences are due solely to sampling variations.
The Goodness-of-Fit Index (GFI), which represents the
overall degree of fit, that is, the squared residuals
from prediction compared with the actual data, ranges
from 0 (poor fit) to 1.0 (perfect fit). The Root Mean
Squared Error of Approximation (RMSEA) estimates how well
the fitted model approximates the population covariance
matrix per degree of freedom. Browne and Cudeck (1986)
suggested that a value below 0.08 indicates a close fit.
The rank correlation between satisfaction
and trial rate is 0.17, between satisfaction and familiarity
is 0.17, and between satisfaction and marketing style
The sub-segment of close followers, relative
to all MAS users, is concentrated more in the Midwest
and less in the Southeast, with shares of 61 and 10 percent,
respectively. They are slightly younger (43 years) than
the average user and operate larger farms with gross sales
Close followers are producers who indicated
that they follow MAS recommendations very closely (Table
7). All MAS users include producers who used one of
the market advisory services listed in the survey or another
market advisory service.
This difference may be caused by the fact
that Goodwin and Kastens' survey was based in Kansas,
where corn and soybeans are secondary crops.
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