Pricing Performance of Market Advisory Services In Corn and Soybeans Over
H. Irwin, Joao Martines-Filho,
and Darrel L. Good
Copyright 2002 by Scott H. Irwin,
Joao Martines-Filho 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 such
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. In addition, the net advisory prices
presented in this report may differ substantially from those computed
by an advisory service or another subscriber due to differences in simulation
assumptions, particularly with respect to the geographic location of production,
cash and forward contract prices, expected and actual yields, storage
charges and government programs.
This 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
Farmers in the US
consistently identify price and income risk as one of the greatest management
challenges they face. The roller coaster movement of corn and soybean
prices over the last decade is ample evidence of the uncertainty and risk
facing grain farmers. Surveys suggest that numerous farmers view market
advisory services as an important tool in managing price and income risk
(e.g., Patrick and Ullerich, 1996; Patrick, Musser, and Eckman; 1998;
Schroeder, Parcell, Kastens, and Dhuyvetter, 1998; Norvell and Lattz,
1999; Pennings, Irwin, Good and Gomez, 2001). As a result, farmers need
information on the performance “track record” of market advisory services
to help them identify successful alternatives for marketing and price
Given the high value
that farmers place upon market advisory services, it is somewhat surprising
that only two published studies investigate the pricing performance of
 Gehrt and Good (1993) analyze the performance of five
advisory services for corn and soybeans over the 1985 through 1989 crop
years.  Assuming a representative farmer follows
the hedging and cash market recommendations for each advisory service;
a net price received for each year is computed and compared to a benchmark
price. They generally find that corn and soybean farmers obtained a higher
price by following the marketing recommendations of advisory services.
Martines-Filho (1996) examines the pre-harvest corn and soybean marketing
recommendations of six market advisory services over 1991 through 1994.
He computes the harvest time revenue that results from a representative
farmer following the pre-harvest futures and options hedging recommendations
and selling 100% of production at harvest. Average advisory service revenue
over the four years is larger than benchmark revenue for both corn and
While a useful starting
point, the two published studies have important limitations. First, the
cross-section of advisory services tracked for each crop year is quite
small, with the largest sample including only six advisory services.
Second, the results may be subject to survivorship bias, a consequence
of tracking only advisory services that remain in business at the end
of a sample period. The literature on the performance of mutual funds
provides ample evidence of the upward bias in performance results that
can result from survivorship bias (e.g., Brown, Goetzmann, Ibbotson and
Ross, 1992; Carpenter and Lynch, 1999).
Third, the results may be subject to hindsight bias because advisory service
recommendations were not collected on a “real-time” basis (Jaffe and Mahoney,
1999). Hindsight bias is the tendency to collect or record profitable
recommendations and ignore or minimize unprofitable recommendations after
This discussion suggests
the academic literature provides farmers with little basis for evaluating
the performance of market advisory services. The Agricultural Market
Advisory Service (AgMAS) Project was initiated in 1994 with the goal of
providing unbiased and rigorous evaluation of market advisory services.
 ,  The AgMAS
Project has collected marketing recommendations for no fewer than 23 market
advisory programs each crop year since the project was initiated. While
the sample of advisory services is non-random, it is constructed to be
generally representative of the majority of advisory services offered
to farmers. Further, the sample of advisory services includes all programs
tracked by the AgMAS Project over the study period, so pricing performance
results should not be plagued by survivorship bias. Finally, the AgMAS
Project subscribes to all of the services that are followed and records
recommendations on a real-time basis. This should prevent the pricing
performance results from being subject to hindsight bias.
The purpose of this
research report is to evaluate the pricing performance of market advisory
services for the 1995-2000 corn and soybean crops. The results for 1995-1999
were released in earlier AgMAS research reports (e.g., Martines-Filho,
Irwin and Good, 2000), while results for the 2000 crop year are new.
Certain explicit assumptions are made to produce a consistent and comparable
set of results across the different advisory programs. These assumptions
are intended to accurately depict “real-world” marketing conditions.
Several key assumptions are: i) with a few exceptions, the marketing window
for a crop year runs from September before harvest through August after
harvest, ii) cash prices and yields refer to a central Illinois farm,
iii) storage is assumed to occur at on-farm or commercial sites, and iv)
marketing loan recommendations made by advisory programs are followed
wherever feasible. Based on these assumptions, the net price received
by a subscriber to a market advisory program is calculated for the 1995-2000
corn and soybean crops.
basic indicators of performance are applied to advisory program prices
and revenues over 1995-2000. The first indicator is the proportion of
advisory programs that beat benchmark prices. The second indicator is
the difference between the average price of advisory programs and benchmarks.
The third indicator is the average price and risk of advisory programs
relative to the average price and risk of benchmarks. The fourth indicator
is the predictability of advisory program performance from year-to-year.
Both market and farmer benchmarks are developed for the evaluations.
All benchmarks are computed using the same assumptions applied to advisory
service track records.
At the outset, it
is important to point out that only six crop years are available to analyze
market advisory service pricing performance. From a purely statistical
standpoint, samples with ten or fewer observations typically are considered
“sparse.” On the surface, this suggests the sample may not contain enough
information to draw conclusions about advisory service pricing performance.
There are several reasons why this may not be the case. First, Anderson
(1974) explored the reliability of agricultural return-risk estimates
based on sparse data sets and found the surprising result that even as
few as three or four observations can be very useful. Second, even though
the number of crop years is limited, at least 23 advisory programs are
tracked for each crop year. This has the potential to substantially increase
the information provided by the sample. Third, from a practical, decision-making
standpoint, samples with six observations often are considered adequate
to reach conclusions. The results of university crop yield trials represent
a well-known example. A typical presentation of the results includes
only current year yields and two-year or three-year averages. In many
cases, even the two-year and three-year averages cannot be presented because
of turnover in the varieties tested from year-to-year.  Despite the limitations, this type
of yield trial data is widely used by farmers in making variety selections.
On balance, then, it seems reasonable to argue that the six years of data
currently available on advisory service pricing performance may be used
to make some modest conclusions. Caution obviously is in order given
the possibility of results being due to random chance in a relatively
small sample of crop years.
This report has been
reviewed by the AgMAS Review Panel, which provides independent, peer-review
of AgMAS Project research. The members of this panel are: Frank Beurskens,
Director of Product Strategy for e-markets; Jeffrey A. Brunoehler, Market
President of the AMCORE Bank in Mendota, Illinois; Renny Ehler, farmer
in Champaign County, Illinois; Chris Hurt, Professor in the Department
of Agricultural Economics at Purdue University; Terry Kastens, Associate
Professor in the Department of Agricultural Economics at Kansas State
University and farmer in Rawlins County, Kansas; and Robert Wisner, University
Professor in the Department of Economics at Iowa State University.
The next section
of the report describes the procedures used to collect the data on market
advisory service recommendations. The second section describes the methods
and assumptions used to calculate the returns to advisory service marketing
advice. The third section presents the methods and assumptions used to
compute benchmark prices, which are used to evaluate advisory service
pricing performance. The fourth section of the report presents 2000 pricing
results for corn and soybeans. The fifth section presents a summary of
the combined results for the 1995-2000 crop years. The sixth section
discusses the performance evaluation results for 1995-2000. The final
section presents a summary and conclusions.
The market advisory
services included in this evaluation do not comprise the population of
market advisory services available to farmers. The included services
also are not a random sample of the population of market advisory services.
Neither approach is feasible because no public agency or trade group assembles
a list of advisory services that could be considered the "population."
Furthermore, there is not a generally agreed upon definition of an agricultural
market advisory service. To assemble the sample of services for the AgMAS
Project, criteria were developed to define an agricultural market advisory
service and a list of services was assembled.
To date, four criteria
have been used to determine which advisory services are included in the
AgMAS study. First, marketing recommendations from an advisory service
must be received electronically in real time. The recommendations may
come in the form of satellite-delivered pages, Internet web pages or e-mail
messages. Services delivered electronically generally ensure that recommendations
are made available to the AgMAS Project at the same time as farm subscribers.
This form of delivery also ensures that recommendations are received in
“real-time.” This avoids the problem of recommendations being delivered
after the date of implementation intended by an advisory service. Such
a problem could occur frequently with recommendations delivered via the
The second criterion
used to identify services is that a service has to provide marketing recommendations
to farmers rather than (or in addition to) speculators or “traders.”
Some of the services tracked by the AgMAS Project do provide speculative
trading advice, but that advice must be clearly differentiated from marketing
advice to farmers for the service to be included. The terms "speculative"
trading of futures and options versus the use of futures and options for
"hedging" purposes are used for identification purposes only.
A discussion of what types of futures and options trading activities constitute
hedging, as opposed to speculating, is not considered in the study.
The third criterion
is that marketing recommendations from an advisory service must be in
a form suitable for application to a representative farmer. That is,
the recommendations have to specify the percentage of the crop involved
in each transaction --cash, futures or options-- and the price or date
at which each transaction is to be implemented. It is also helpful if
advisory services make specific recommendations about implementation of
the marketing loan program, but that is not required. Note that some
advisory services evaluated by the AgMAS Project do not make any futures
and options recommendations, so it is not necessary to make such recommendation
to be included in the study. Services that make futures and options hedging
recommendations, but fail to clearly state when cash sales should be made,
or the amount to be sold, are not considered for inclusion.
The fourth criterion
is that advisory services must provide “blanket” or “one-size fits all”
marketing recommendations so there is no uncertainty about implementation.
While different programs for basic types of subscribers may be tracked
for an advisory service (e.g., a cash only program versus a futures and
options hedging and cash program), it is not feasible to track services
that provide “customized” recommendations for individual clients.
A fifth criterion
will be added in the future to address the issue of whether a candidate
service is a viable, commercial business. This issue has arisen due to
the extremely low cost and ease of distributing information over the Internet,
either via e-mail or a website. It is possible for an individual with
little actual experience and no paying subscribers to start a “market
advisory service” by using the Internet. Hence, there is a need to exclude
firms that are not viable commercial concerns. At the same time, any
filter in this regard should not be so restrictive that newer and smaller
advisory services are excluded from the AgMAS study for an unreasonably
long period of time. This same issue is prevalent when evaluating the
performance of other types of professional investment advisors, such as
commodity trading advisors. In these cases, it is not unusual to screen
firms by the length of track record and amount of funds under management.  An analogous screen for market advisory
services would be based on the length of time the service has provided
recommendations and the number of paying subscribers. The specific criterion
that will be used is that a candidate advisory service must have provided
recommendations to paying subscribers for a minimum of two marketing years
before the service can be included in the AgMAS study. This criterion
should exclude non-viable services, while at the same time providing a
relatively low hurdle for new and legitimate market advisory services.
The original sample
of market advisory services was drawn from the list of Premium Services
available from the two major agricultural satellite networks, Data Transmission
Network (DTN) and FarmDayta, in the summer of 1994.  While the list of advisory services
available from these networks was by no means exhaustive, it did have
the considerable merit of meeting a market test. Presumably, the services
offered by the networks were those most in demand by farm subscribers
to the networks. In addition, the list of available services was cross-checked
with other farm publications to confirm that widely followed advisory
firms were included in the sample. It seems reasonable to argue that
the resulting sample of services was generally representative of the majority
of advisory services available to farmers.
Additions and deletions
to the sample of advisory services have occurred over time. Additions
largely have been due to the increasing availability of market advisory
services via alternative means of electronic delivery, in particular,
websites and e-mail. Deletions have occurred for a variety of reasons.
A total of 36 and 35 advisory service programs for corn and soybeans,
respectively, have been included in the sample at some point in time.
Table 1 contains the complete list of advisory programs and includes a
brief explanation why each program that was not included for all crop
years was added or deleted from the sample. The term “advisory program”
is used because several advisory services have more than one distinct
marketing program. For example, AgLine by Doane, Brock, Pro Farmer, and
Stewart-Peterson Advisory Services each have two distinct marketing programs,
Risk Management Group has three distinct marketing programs and AgriVisor
has four distinct marketing programs. Allendale provides two distinct
programs for corn, but only one for soybeans.
The total number
of advisory programs evaluated for the 2000 crop year is 27 for corn and
26 for soybeans. Two new programs were added for the 2000 crop year:
Co-Mark and Grain Marketing Plus. One program, Ag Profit by Hjort, was
deleted from the sample for the 2000 crop year. This service went out
of business at the end of August 2000 without giving any specific recommendations
for 2000 corn and soybean crops. Two other programs, Cash Grain and Stewart-Peterson
Strictly Cash, were discontinued during the 2000 crop year. However,
these programs made specific recommendations for the 2000 corn and soybean
crops until being discontinued during Fall 2000 (last recommendations:
September 18, 2000 for Cash Grain and October 26, 2000 for Stewart-Peterson
Strictly Cash). As will be discussed below, excluding these two programs
from the 2000 sample could result in a form of selection bias, particularly
if discontinuation is related to poor performance. Including these programs
for the 2000 crop year does require an assumption about marketing the
cash positions remaining after the discontinuation date. A similar issue
has been treated extensively in the literature on the performance of commodity
funds and commodity trading advisors (e.g., Elton, Gruber and Rentzler,
1987). In this literature, if a commodity fund or trading advisor is
discontinued before the end of a calendar year, some form of benchmark
returns are substituted for the missing returns after the discontinuation
date. Following this logic, the cash positions for Cash Grain and Stewart-Peterson
Strictly Cash that remained after the date of discontinuation were sold
using the same strategy as the market benchmarks utilized for this study
(the details of the construction of these benchmarks are given in the
“Benchmark Prices” section). In effect, this simply means that cash bushels
after the date of discontinuation are sold in equal amounts over the remaining
days of the 2000 marketing window. Finally, note that any futures or options positions that remain
open on the date of discontinuation are closed on that date using settlement
futures prices or options premiums. 
Three forms of survivorship
bias may be potential problems when assembling an advisory program database.
Survival bias significantly biases measures of performance upwards since
"survivors" typically have higher performance than "non-survivors"
(e.g., Brown, Goetzmann, Ibbotson, and Ross, 1992; Carpenter and Lynch, 1999).
The first and most direct form of survivorship bias occurs if only advisory
programs that remain in business at the end of a given sample period are
included in the sample. This form of bias should not be present in the
AgMAS database of advisory programs because all programs that have been
tracked over the entire time period of the study are included in the sample.
The second form of survivorship bias occurs if discontinued advisory programs
are deleted from the sample for the year when they are discontinued.
This is a form of survivorship bias because only survivors for the full
crop year are tracked. The AgMAS database of advisory programs should
not be subject to this form of bias because programs discontinued during
a crop year remain in the sample for that crop year. As noted above,
cash positions remaining after the date of discontinuation are sold using
the same strategy as the market benchmarks utilized for this study. The
third and most subtle form of survivorship bias occurs if data from prior
periods are "back-filled" at the point in time when an advisory
program is added to the database. This is a form of survivorship bias
because data from surviving advisory programs are back-filled. The AgMAS
database should not be subject to this form of bias because recommendations
are not back-filled when an advisory program is added. Instead, recommendations
are collected only for the crop year after a decision has been made to
add an advisory program to the database.
consideration when assembling a database on advisory program recommendations
is hindsight bias (Jaffe and Mahoney, 1999). This is the tendency to
collect or record profitable recommendations and ignore or minimize unprofitable
recommendations after the fact. Since the AgMAS Project subscribes to
all of the services that are followed and records recommendations on a
real-time basis, the database of recommendations should not be subject
to hindsight bias. The information is received electronically, via DTN,
website or e-mail. For the programs that provide multiple daily updates,
typically in the morning and at noon, information is recorded for all
updates. In this way, the actions of a farmer-subscriber are simulated
When recording recommendations
of each advisory program, specific attention is paid to which year’s crop
is being sold, (e.g., 2000 crop year), the amount of the commodity to
be sold, which futures or options contract is to be used (where applicable),
and any price targets that are mentioned (e.g., sell cash corn when March
2001 futures reaches $2.40). If a price target is given and not immediately
filled, such as a stop order in the futures market, the recommendation
is noted until the order is either filled or canceled.
Some advisory programs
offer two or more distinct marketing programs. This typically takes the
form of one set of advice for marketers who are willing to use futures
and options (although futures and options are not always used), and a
separate set of advice for farmers who only wish to make cash sales.
 In this situation, both strategies are recorded and
treated as distinct strategies to be evaluated. In the past, when a service
clearly differentiated strategies based on the availability of on-farm
versus off-farm (commercial) storage, only the off-farm storage strategy
was tracked. Starting with the 2000 corn and soybean crops, if a service
clearly differentiates on-farm and off-farm storage strategies at harvest,
both strategies are recorded.
are used to check the recorded recommendations for accuracy and completeness.
Whenever possible, recorded recommendations are crosschecked against later
status reports provided by the relevant advisory program. Also, at the
completion of the crop year, it is confirmed whether cash sales total
exactly 100%, all futures positions are offset, and all options positions
are offset or expire.
final set of recommendations attributed to each advisory program represents
the best efforts of the AgMAS Project staff to accurately and fairly interpret
the information made available by each advisory program. In cases where
a recommendation is considered vague or unclear, some judgment is exercised
as to whether or not to include that particular recommendation. This
occurs most often when a program suggests that “a farmer might consider”
a position, or when minimal guidance is given as to the quantity to be
bought or sold. 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 program,
or from that recorded by another subscriber.
the Returns to Marketing Advice
the end of the marketing period, all of the (filled) recommendations are
aligned in chronological order. The advice for a given crop year is considered
to be complete for each advisory program when cumulative cash sales of
the commodity reach 100%, all futures positions covering the crop are
offset, all option positions covering the crop are either offset or expire,
and the advisory program discontinues giving advice for that crop year.
The returns to each recommendation are then calculated in order to arrive
at a weighted average net price that would be received by a farmer who
precisely follows the marketing advice (as recorded by the AgMAS Project).
In order to produce
a consistent and comparable set of results across the different advisory
programs, certain explicit assumptions are made. These assumptions are
intended to accurately depict “real-world” marketing conditions. Note
that discussion in the following sections center on the 2000 crop year.
Similar discussion and examples for the 1995-1999 crop years can be found
in earlier AgMAS pricing reports (e.g., Martines-Filho, Irwin and Good,
The simulation is
designed to reflect conditions facing a representative central Illinois
corn and soybean farmer. Whenever possible, data are collected for the
Central Crop Reporting District in Illinois as defined by the National
Agricultural Statistics Service (NASS) of the US Department of Agriculture
(USDA). The eleven counties (DeWitt, Logan, McLean, Marshall, Macon,
Mason, Menard, Peoria, Stark, Tazewell, and Woodford) that make up this
District are highlighted in Figure 1.
Caution should be
used when applying the results to other areas of the US, because yields
and basis patterns may be quite different from those of central Illinois.
The differences in yields and basis patterns could have a substantial
impact on prices computed for farmers or advisory services in another
area. The resulting change could be either up or down relative to AgMAS
advisory prices and benchmarks, depending on local conditions. Appendix
A to this report, entitled “A Cautionary Note on the Use of AgMAS Net
Advisory Prices and Benchmarks,” contains further discussion on this point.
In the present context,
the objective is to define the normal marketing window of a representative
farmer who subscribes to the advisory programs tracked by the AgMAS Project.
Good, Hieronymus and Hinton (1980) provide a useful starting point. They
define the marketing window for an Illinois grain farmer as the period
extending from the initial production planning time until the end of the
storage season. First production decisions in Illinois normally occur
in October through November of the year preceding planting (e.g., fall
tillage and application of fertilizer), while the storage season typically
extends through July or August of the year following harvest. This results
in a marketing window between 21 and 23 months in length.
The actual pricing
patterns of advisory programs included in the AgMAS study provide helpful
empirical evidence for defining the relevant marketing window. As noted
earlier, observed market positions cannot directly reveal the intended
pricing window of a representative farmer following advisory program recommendations.
However, averages over time and advisors should be suggestive as to the
typical starting and ending points used to make recommendations for a
crop. Figure 2 presents the average “marketing profile” of advisory programs
in corn and soybeans over the 1995-1999 crop years.  The marketing profiles show the average
amount of corn and soybean crops priced (sold) by advisory programs, on
a cumulative basis, each day over the two-year period beginning in September
of the year before harvest and ending August of the year after harvest.
The profiles suggest that a farmer following the recommendations of market
advisory programs included in the AgMAS study, on average, will begin
making significant marketing decisions (pricing more than one percent)
in September of the year before harvest and will not complete marketing
until August of the year after harvest.
Overall, this discussion
indicates it is reasonable to assume a 24-month marketing window for a
representative farmer subscribing to advisory programs. In the case of
the 2000 crop, the marketing window is then defined as the two-year period
beginning September 1, 1999 and ending on August 31, 2001. Such a specific
definition raises the issue of exceptions. For example, one program in
corn and two programs in soybeans started their first hedge program for
the 2000 crop year in the middle of July 1999. Two other advisory programs
had a relatively small amount (20-25%) of cash corn unsold as of August
31, 2001. These bushels were sold in the spot cash market on September
19, 2001. One program maintained relatively large (50% for corn) long
call “re-ownership” positions using November soybean options contracts.
This position expired worthless on October 19, 2001. Given that the marketing
window is defined as the “normal” window, it is argued that a representative
farmer would approach the marketing window with some flexibility, particularly
for recommendations that do not extend too far outside the limits of the
marketing window. Since the transactions in question for the 2000 crop
do not extend much outside the limits of the marketing window, they are
included in the relevant advisory program’s track record.  Finally, note that throughout the
remainder of this report, the term "crop year" is used to represent
the two-year marketing window.
The price assigned
to each cash sale recommendation is the central Illinois closing, or overnight,
bid. The North and South Central Illinois Price Reporting Districts are
highlighted in Figure 3. The data are collected and reported by the Illinois
Department of Ag Market News.
 The central Illinois price is the
mid-point of the range of bids by elevators in the North Central and South
Central Price Reporting Districts, as defined by the Illinois Department
of Ag Market News. Prices in this 35-county area best reflect prices
for the assumed geographic location of the representative central Illinois
farmer (Central Illinois Crop Reporting District).
forward contract prices for fall delivery are also needed. Pre-harvest
bids collected by the Illinois Department of Ag Market News are used when
available. The central Illinois pre-harvest price is the mid-point of
the daily range of pre-harvest bids by elevators in the North Central
and South Central Price Reporting Districts, again, as defined by the
Illinois Department of Ag Market News. Pre-harvest forward prices from
this source are available for corn and soybeans from February 1, 2000
to September 1, 2000.
Since the marketing
window for the 2000 corn and soybean crops begins in September 1999, and
the Illinois Department of Ag Market News did not begin to report actual
cash forward bids until February 1, 2000, pre-harvest prices need to be
estimated for the first few months of the marketing window. For a date
between September 1, 1999 and January 31, 2000, a two-step estimation
procedure is adopted. First, the forward basis for the period in question
is estimated by the average forward basis for the first five days the
Illinois Department of Ag Market News reports actual forward contract
bids (February 1-7, 2000).
 Second, the estimated forward basis is added to the
settlement price of the Chicago Board of Trade (CBOT) 2000 December corn
futures contract or 2000 November soybean futures contract between September
1, 1999 and January 31, 2000. This estimation procedure is expected to
be a reasonably accurate reflection of actual forward prices for the early
period of the marketing window, as the actual price of the harvest futures
contract is used and only the forward basis is estimated. In addition,
the estimation procedure is typically applied to a relatively small number
of transactions. The average net amount sold before February 1st
over 1995-1999 is only 12% for corn and 10% for soybeans, and many of
these transactions are in futures or options contracts rather than forward
market advisory programs recommended the use of post-harvest forward contracts
to sell part of the 2000 corn and soybean crops. The Illinois Department
of Ag Market News did report post-harvest bids for January 2001 delivery
from September 5, 2000 to December 1, 2000. They also report post-harvest
bids for March 2001 delivery from December 4, 2000 to January 31, 2001.
These bids for central Illinois are used wherever applicable. For the
2000 crop year, forward bids are available to match all advisory program
the future, if the positions recommended by advisory programs either do
not match the delivery periods or are made after the Illinois Department
of Ag Market News stops reporting post-harvest forward contract prices,
the following procedure will be used to estimate the post-harvest forward
contract prices needed in the analysis. First, three elevators in central
Illinois agreed to supply data on spot and forward contract prices on
the dates when advisors made such recommendations. Each of these elevators
is in a different county in the Central Illinois Crop Reporting District
(Logan, McClean, DeWitt). Second, the spread between each elevator’s
forward price and spot price will be calculated for the relevant date.
Third, the forward spread will be averaged across the three elevators
for the same date. Fourth, the average forward spread from the three
elevators will be added to the central Illinois cash price (discussed
at the beginning of the section) to arrive at an estimated post-harvest
forward contract price for central Illinois. This procedure was used
in a few cases for the 1998 and 1999 crop years.
The fill prices for
futures and options transactions generally are the prices reported by
the programs. In cases where a program did not report a specific fill
price, the settlement price for the day is used. This method does not
account for liquidity costs in executing futures and options transactions. 
Since most of the
advisory program recommendations are given in terms of the proportion
of total production (e.g., “sell 5% of 2000 crop today”), some assumption
must be made about the amount of production to be marketed. For the purposes
of this study, if the per-acre yield is assumed to be 100 bushels, then
a recommendation to sell 5% of the corn crop translates into selling 5
bushels. When all of the advice for the marketing period has been carried
out, the final per-bushel selling price is the average price for each
transaction weighted by the amount marketed in each transaction.
The above procedure
implicitly assumes that the “lumpiness” of futures and/or options contracts
is not an issue. Lumpiness is caused by the fact that futures contracts
are for specific amounts, such as 5,000 bushels per CBOT corn futures
contract. For large-scale farmers, it is unlikely that this assumption
adversely affects the accuracy of the results. This may not be the case
for small- to intermediate-scale farmers who are less able to sell in
5,000-bushel increments. 
Yields and Harvest
When making hedging
or forward contracting decisions prior to harvest, the actual yield is
unknown. Hence, an assumption regarding the amount of expected production
per acre is necessary to accurately reflect the returns to marketing advice.
Prior to harvest, the best estimate of the current year’s expected yield
is likely to be a function of yield in previous years. In this study,
the assumed yield prior to harvest is the calculated trend yield, while
the actual reported yield is used from the harvest period forward. The
expected yield for 2000 is based upon a log-linear regression trend model
of actual yields from 1972 through 1999 for the Central Illinois Crop
Reporting District. Previous research suggests this type of trend model
provides a reasonable fit to corn and soybean yield data (Fackler, Young
and Carlson, 1993; Zanini, 2001).
In central Illinois,
the expected 2000 yield for corn is calculated to be 149 bushels per acre.
Therefore, recommendations regarding the marketing quantity made prior
to harvest, are based on yields of 149 bushels per acre. For example,
a recommendation to forward contract 20% of expected 2000 production translates
into a recommendation to contract 29.8 bushels per acre (20% of 149).
The actual reported corn yield in central Illinois in 2000 is 159 bushels
per acre. The same approach is used for soybean evaluations. The calculated
2000 trend yield for soybeans in central Illinois is 48.5 bushels per
acre, and the actual yield in 2000 is 47 bushels per acre.
It is assumed that
after harvest begins, farmers have reasonable ideas of what their actual
realized yield will be. Since harvest occurs at different dates each
year, estimates of harvest progress as reported by NASS in central Illinois
are used. Harvest progress estimates typically are not made available
soon enough to identify precisely the beginning of harvest, so an estimate
is made based upon available data. Specifically, the date on which 50%
of the crop is harvested is defined as the mid-point of harvest. The
entire harvest period then is defined as a five-week window, beginning
two and one-half weeks before the harvest mid-point, and ending two and
one-half weeks after the harvest mid-point. In most years, a five-week
window will include at least 80% of the harvest.
For 2000, the harvest
period for corn is defined as September 8, 2000 through October 12, 2000.
For soybeans, the harvest period is September 20, 2000 through October
24, 2000. Therefore, for corn, recommendations made after September 8
are applied on the basis of the actual yield of 159 bushels per acre.
For soybeans, recommendations made after September 20 are applied on the
basis of the actual yield of 47 bushels per acre.
The issue of changing
yield expectations typically is not dealt with in the recommendations
of the advisory programs. For the purpose of this study, the actual harvest
yield must exactly equal total cash sales of the crop at the end of the
marketing time frame. Hence, an adjustment in yield assumptions from
expected to actual levels must be applied to cash transactions at some
point in time. In this analysis, an adjustment is made in the amount
of the first cash sale made after the beginning of the harvest period.
For example, if a program advises forward contracting 50% of the corn
crop prior to harvest, this translates into sales of 74.5 bushels per
acre (50% of 149). However, when the actual yield is applied to the analysis,
sales-to-date of 74.5 bushels per acre imply that only 46.85% of the actual
crop has been contracted. In order to compensate, the amount of the next
cash sale is adjusted to align the amount sold. In this example, if the
next cash sale recommendation is for a 10% increment of the 2000 crop,
making the total recommended sales 60% of the crop, the recommendation
is adjusted to 13.15% of the actual yield (20.91 bushels), so that the
total crop sold to date is 60% of 159 bushels per acre (74.5 + 20.9 =
95.4 = 0.6*159). After this initial adjustment, subsequent recommendations
are taken as percentages of the 159 bushels per acre actual yield, so
that sales of 100% of the crop equal sales of 159 bushels per acre.
While the amount
of cash sales is adjusted to reflect the change in yield information,
a similar adjustment is not made for futures or options positions that
are already in place. For example, assume that a short futures hedge
is placed in the December 2000 corn futures contract for 25% of the 2000
crop prior to harvest. Since the amount hedged is based on the trend
yield assumption of 149 bushels per acre, the futures position is 37.25
bushels per acre (25% of 149). After the yield assumption is changed,
this amount represents a short hedge of 23.4% (37.25/159). The amount
of the futures position is not adjusted to move the position to 25% of
the new yield figure. However, any futures (or options) positions recommended
after the beginning of harvest are implemented as a percentage of the
If actual yield
is substantially below trend, and forward pricing obligations are based
on trend yields, a farmer may have difficulty meeting such obligations.
This raises the issue of updating yield expectations in “short” crop years
to minimize the chance of defaulting on forward pricing obligations.
While not yet encountered in the AgMAS evaluations of corn and soybeans,
this situation has arisen in the evaluation of wheat (Jirik, Irwin, Good,
Jackson and Martines-Filho, 2000).
As in wheat, a relatively
simple procedure will be used to update yield expectations in any future
corn or soybean short crop years. First, trend yield will be used as
the expected yield until the August USDA Crop Production Report
is released, typically around August 10th. Second, if the
USDA corn or soybean yield estimate for the Central Illinois Crop Reporting
District is 20% (or more) lower than trend yield, a “reasonable” farmer
is assumed to change yield expectations to the lower USDA estimate. Third,
as with normal crop years, the adjustment to actual yield is assumed to
occur on the first day of harvest.
The 20% threshold
is intentionally relatively large for at least three reasons. First,
it is desirable to make adjustments to the trend yield expectation on
a limited number of occasions. Given the large variability in annual
yields, a small threshold could result in frequent adjustments. Second,
it is not uncommon for early yield estimates to deviate significantly
from the final estimate. A small threshold could result in unnecessary
adjustments prior to harvest. Third, yield shortfalls of less than 20%
are unlikely to create delivery problems for a farmer.
Brokerage costs are
incurred when farmers open or close positions in futures and options markets.
For the purposes of this study, it is assumed that brokerage costs are
$50 per contract for round-turn futures transactions, and $30 per contract
to enter or exit an options position. Further, it is assumed that CBOT
corn and soybean futures and options contracts are used, which have a
contract size of 5,000 bushels. Therefore, per-bushel brokerage costs
are one cent per bushel for a round-turn futures transaction and 0.6¢
per bushel for each options transaction.
While the 1996 “Freedom-to-Farm”
Act did away with government set-aside and target price programs, price
protection for farmers in program crops such as corn and soybeans was
not eliminated entirely. Minimum prices are established through a “loan”
program. Specifically, if market prices are below the Commodity Credit
Corporation (CCC) loan rate for corn or soybeans, farmers can receive
payments from the US government that make up the difference between the
loan rate and the lower market price.  There is considerable flexibility in the way the loan
program can be implemented by farmers. This flexibility presents the
opportunity for advisory programs to make specific recommendations for
the implementation of the loan program. Additionally, the prices of both
corn and soybeans were below the loan rate during significant periods
of time in the 2000-2001 marketing year, so that use of the loan program
was an important part of marketing strategies. As a result, net advisory
program prices may be substantially impacted by the way the provisions
of the loan program are implemented. Finally, all of the advisory programs
tracked by the AgMAS project for the 2000 crop year make specific recommendations
regarding the timing and method of implementing the loan program for the
entire corn and soybean crops.
the decision rules, it is useful to provide a brief overview of the loan
program mechanics. Then, the rules developed to implement the loan program
in the absence of specific recommendations can be described more effectively.
There are two mechanisms
for implementing the price protection benefits of the loan program. The
first mechanism is the loan deficiency payment (LDP) program. LDPs are
computed as the difference between the loan rate for a given county and
the posted county price (PCP) for a particular day. PCPs are computed
by the USDA and change each day in order to reflect the average market
price that exists in the county. For example, if the county loan rate
for corn is $2.00 per bushel and the PCP for a given day is $1.50 per
bushel, then the LDP is $0.50 per bushel. If the PCP increases to $1.60
per bushel, the LDP will decrease to $0.40 per bushel. Conversely, if
the PCP decreases to $1.40 per bushel, the LDP will increase to $0.60
LDPs are made available
to farmers over the period beginning with corn or soybean harvest and
ending May 31st of the calendar year following harvest. Farmers
have flexibility with regard to taking the LDP, because they may simply
elect to take the payment when the crop is sold in a spot market transaction
(before the end of May in the particular marketing year), or choose to
take the LDP before the crop is delivered and sold. Note that LDPs cannot
be taken after a crop has been delivered and title has changed hands.
second mechanism is the non-recourse marketing assistance loan program.
A loan cannot be taken on any portion of the crop for which an LDP has
been received. Under this program, farmers may store the crop (on the
farm or commercially), maintain beneficial interest, and receive a loan
from the CCC using the stored crop as collateral. The loan rate is the
established rate in the county where the crop is stored and the interest
rate is established at the time of loan entry. Corn and soybean crops
can be placed under loan anytime after the crop is stored through May
31st of the following calendar year. The loan matures on the
last day of the ninth month following the month in which the loan was
Farmers may settle
outstanding loans in two ways: i) repaying the loan during the 9-month
loan period, or ii) forfeiting the crop to the CCC at maturity of the
loan. Under the first alternative, the loan repayment rate is the lower
of the county loan rate plus accrued interest or the marketing loan repayment
rate, which is the PCP. If the PCP is below the county loan rate, the
economic incentive is to repay the loan at the posted county price. The
difference between the loan rate and the repayment rate is a marketing
loan gain (MLG). If the PCP is higher than the loan rate, but lower than
the loan rate plus accrued interest, the incentive is also to repay the
loan at the PCP. In this case only, interest is charged on the difference
between the PCP and the loan rate. If the PCP is higher than the loan
rate plus accrued interest, the incentive is to repay the loan at the
loan rate plus interest. In this latter case, interest is based on the
Under the second
alternative, the farmer stores the crop to loan maturity and then transfers
title to the CCC. The farmer retains the proceeds from the initial loan.
This was generally not an attractive alternative in the 2000 marketing
year since the PCP was often below the cash price of corn and soybeans.
Repaying the loan at the PCP and selling the crop at the higher cash price
was economically superior to forfeiture.
loan program establishes the county loan rate as a minimum price for the
farmer, as does the LDP program. For the 2000 crop, the sum of LDPs plus
marketing loan gains was subject to a payment limitation of $150,000 per
person. Forfeiture on the loans provided the mechanism for receiving
a minimum of the loan rate on bushels in excess of the payment limitation.
The average loan
rates for the 2000 corn and soybean crops across the eleven counties in
the Central Illinois Crop Reporting District are $1.95 and $5.41 per bushel,
respectively. Spot cash prices fell below these loan rates for almost
all of the 2000 post-harvest period for corn and the entire 2000 post-harvest
period for soybeans. This is reflected in Figure 4, which shows corn
and soybean LDP or MLG rates for central Illinois during the 2000 post-harvest
 ,  For corn, LDPs or MLGs
are relatively high during harvest, varying from $0.40 to $0.45 per bushel,
and then fall to zero or near zero by the end of calendar year 2000.
As cash corn prices fall during the winter and spring of 2001, corn LDP/MLGs
increase. Soybean LDPs or MLGs are high during the 2000 harvest time,
varying from $0.80 to $1.00 per bushel and decrease to $0.50 at the end
of December. During the winter and spring, they increase to almost $1.40
per bushel. As cash soybean prices increase during the summer of 2001,
soybean MLGs decrease to $0.20 per bushel at the beginning of July 2001.
Decision Rules for
Programs with a Complete Set of Loan Recommendations
an advisory program makes a complete set of loan recommendations, the
specific advice is implemented wherever feasible. However, specific decision
rules are still needed regarding pre-harvest forward contracts because
it is possible for an advisory program to recommend taking the LDP on
those sales before it is actually harvested and available for delivery
in central Illinois. To begin, it is assumed that amounts sold for harvest
delivery with pre-harvest forward contracts are delivered first during
harvest. Since LDPs must be taken when title to the grain changes hands,
LDPs are assigned as these “forward contract” quantities are harvested
and delivered. This necessitates assumptions regarding the timing and
speed of harvest. Earlier it was noted that a five-week harvest window
is used to define harvest. This window is centered on the day nearest
to the mid-point of harvest progress as reported by NASS. Various assumptions
could be implemented regarding harvest progress during this window. Lacking
more precise data, a reasonable assumption is that harvest progress for
an individual representative farm is a linear function of time.
2 and 3 summarize the information used to assign LDPs to pre-harvest forward
contracts. The second column shows the amount harvested assuming a linear
model. The third column shows the LDP available on each date of the harvest
window and the fourth column presents the average LDP through each harvest
date. An example will help illustrate use of the tables. Assume that
an advisory program recommends, at some point before harvest, that a farmer
forward contract 50% of expected soybean production. This translates
into 24.25 bushels per acre when the percentage is applied to expected
production (0.50*48.5 = 24.25). Next, convert the bushels per acre to
a percentage of actual production, which is 51.6% (24.25/47 = 0.516).
To determine the LDP payment on the 51.6% of actual production forward
contracted, simply read down Table 3 to October 6, 2000, which is the
date when 51.6% of harvest is assumed to be complete. The average LDP
up to that date (September 20, 2000- October 6, 2000) is $0.83 per bushel;
the last column of Table 3. This is the LDP amount assigned to the forward
that LDPs for any sales (spot, forward contracts, futures or options)
recommended during harvest are taken only after all forward contract obligations
are fulfilled. In addition, crops placed under loan by an advisory program
do not accumulate interest opportunity costs because proceeds from the
loan can be used to offset interest costs that otherwise would accumulate.
Decision Rules for
Programs with a Partial Set of Loan Recommendations Or No Loan Recommendations
an advisory program makes a partial set of loan recommendations, the available
advice is implemented wherever feasible. In the absence of specific recommendations,
it is assumed that crops priced before May 31, 2001 are not placed under
loan. Those crops receive program benefits through LDPs. After May 31,
2001, eligible crops (unpriced crops for which program benefits have not
yet been collected) are assumed to be under loan until priced.
the absence of specific recommendations, rules for assigning LDPs and
MLGs are developed under the assumption that loan benefits are established
when the crop is priced or as soon after pricing that is allowed under
the rules of the program. This principle is consistent with the intent
of the loan program to fix a minimum price when pricing decisions are
made. Two rules are most important in the implementation of this principle.
First, LDPs on pre-harvest sales (forward contracts, futures or options)
are established as the crop is harvested. Second, if the LDP or MLG is
zero on the pricing date, or the first date of eligibility to receive
a loan benefit, those values are assigned on the first date when a positive
value is observed, assuming a beneficial interest in that portion of the
crop has been maintained. Specific rules for particular marketing tools
and situations follow:
Pre-harvest forward contracts. The same decision rules are applied
as discussed in the previous section. Specifically, it is assumed that
amounts sold for harvest delivery with pre-harvest forward contracts are
delivered first during harvest. LDPs, if positive, are assigned as these
“forward contract” quantities are harvested and delivered. This necessitates
assumptions regarding the timing and speed of harvest. A linear model
of harvest progress is assumed in the five-week harvest window. The specific
information used to assign LDPs to pre-harvest forward contracts is again
found in Tables 2 and 3. As a final point, note that LDPs for any other
sales (spot, futures or options) recommended during harvest are taken
only after all pre-harvest forward pricing obligations are fulfilled.
Pre-harvest short futures. Pre-harvest pricing using futures contracts
is treated in the same manner as pre-harvest forward contracts. LDPs
are assigned on open futures positions as the crop is harvested, or as
soon as a positive LDP is available, if the futures position is still
in place and cash sales have not yet been made. These are assigned after
forward contracts have been satisfied. If the underlying crop is sold
before there is a positive LDP, then that portion of the crop receives
a zero LDP. During the harvest window, if the futures position is offset
before a positive LDP is available and the crop has not yet been sold
in the cash market, that portion of the crop is eligible for loan benefits
on the next pricing recommendation.
Pre-harvest put option purchases. Long put option positions, which
establish a minimum futures price, are treated in the same manner as pre-harvest
Post-harvest forward contracts. The main issue with respect to
post-harvest forward contracts is when to assign the LDPs or MLGs. Those
can be established on the date the contract is initiated, on the delivery
date of the contract, or anytime in between. Following the general principle
outlined earlier, LDPs and MLGs for post-harvest contracts are assigned
on the date the contract is initiated or the first day with positive benefits
prior to delivery on the contract.
Post-harvest short futures. As with post-harvest forward contracts,
the main issue with post-harvest short futures positions is when to assign
loan benefits. These are assigned when the short futures position is
initiated or as soon as a positive benefit is available if the futures
position is still in place and cash sales have not been made. If the
underlying crop is sold before a positive LDP is available, that portion
of the crop receives a zero LDP. If the short futures position is offset
before a positive LDP is available and the cash crop has not yet been
sold, that portion of the crop is eligible for loan benefits on the next
Post-harvest long put positions. Long put option positions established
after the crop is harvested are treated in the same manner as post-harvest
Spot sales before May 31, 2001. If a spot cash sale of corn or
soybeans is recommended before May 31, 2001, it is assumed that the LDP,
if positive, is established that same day.
Loan program after May 31, 2001. Since LDPs are not available
after May 31, 2001, it is assumed that any corn or soybeans in storage
and not priced as of this date, for which loan benefits have not been
established, are entered in the loan program on that date. This is a
reasonable assumption since spot prices are below the loan rate for both
soybeans and near the loan rate for corn in central Illinois on May 31,
2001 and a prudent farmer would take advantage of the price protection
offered by the loan program. When the crops are subsequently priced (cash
sale, forward contract, short futures, or long put option), the marketing
loan gain, if positive, is assigned on that day. Forfeiture is not an
issue for these bushels because all cash sales were made before the end
of the nine-month loan period. Note also that the $150,000 payment limitation
is not considered in the analysis, as production is based on one acre
of corn and/or soybeans.
An important element
in assessing returns to an advisory program is the economic cost associated
with storing grain instead of selling grain immediately at harvest. The
cost of storing grain after harvest consists of two components: physical
storage costs and the opportunity cost incurred by foregoing sales when
the crop is harvested. Physical storage costs depend on the type of storage
available and the horizon used by a farmer to make storage decisions.
From a representative farmer’s perspective, there are four relevant physical
storage scenarios: i) on-farm storage using a short-run decision-horizon,
ii) off-farm (commercial) storage using a short-run decision-horizon,
iii) on-farm storage using a long-run decision-horizon and iv) off-farm
(commercial) storage using a long-run decision-horizon. Short-run in
this context is defined to be one storage season, usually the ten-month
period after the harvest of a particular crop. Long-run is defined to
be any decision-horizon longer than one storage season. In each of the
previous scenarios, the physical storage charge should be the relevant
marginal cost of physical storage (Williams and Wright, 1991). In contrast,
opportunity cost should be the same regardless of the type of physical
storage used or whether a short- or long-run decision-horizon is considered.
Previous AgMAS pricing
reports have considered only one scenario: commercial storage using a
short-run decision-horizon. Starting with the 2000 crop year, net advisory
prices and benchmarks are computed using physical storage costs applicable
to each of the four storage scenarios. In all cases, storage and interest
charges are assigned beginning October 13, 2000 for corn and October 25,
2000 for soybeans, the first dates after the end of the respective 2000
harvest windows. It should be noted that the cost of drying corn to 15%
moisture and the cost of drying soybeans to storable moisture are not
included in the calculations. This cost is incurred whether the grain
is stored or sold at harvest, or whether the grain is stored on-farm or
off-farm. Therefore, this cost is irrelevant to the analysis and excluded.
first scenario considered is on-farm storage and a short-run decision-horizon.
Because pre-existing storage facilities are assumed to be available on-farm,
the marginal cost of physical storage equals the on-farm variable cost
of physical storage. Estimates of the on-farm variable cost of physical
storage are drawn from a recent study conducted at Kansas State University
(Dhuyvetter, Hamman and Harner, 2000). The estimates assume storage occurs
in a 25,000 bushel round metal bin, the “medium-sized” storage capacity
examined in the Kansas State study. The first component of on-farm physical
storage is a flat charge of 6.7¢ per bushel for conveyance, aeration,
insecticide and repairs. The flat charge is applied to both corn and
soybeans and reflects the fact that most physical costs of on-farm storage
are “one-time” in nature. That is, once the decision is made to store,
most costs are pre-determined and do not vary with the length of storage.
second component of on-farm physical storage is shrinkage. Corn shrinkage
is assumed in the Kansas State study to start at one-percent per bushel
for the first month of storage and increase at a rate of one-tenth of
one percent for each month stored thereafter. For example, if corn is
stored six months, the total shrinkage is assumed to be 1.5% per bushel.
Agricultural engineering specialists at the University of Illinois and
Purdue University indicated that the on-farm shrink schedule for corn
used in the Kansas State study is reasonable. In addition, the schedule
is consistent with published research about shrinkage of corn stored on-farm
(Hurburgh, Bern, Wilcke and Anderson, 1983). Given that the harvest-time
cash price of corn in central Illinois for 2000 is $1.64 per bushel, the
shrink charge assigned to corn stored on-farm for one-month is 1.64¢ per
bushel ($1.64*0.01*100). The shrink charge is increased 0.16¢ per bushel
($1.64*0.001*100) for each additional month of storage.
the Kansas State study did not estimate shrinkage costs for soybeans,
the same agricultural engineering specialists noted above were consulted
for a reasonable estimate. This turned out to be a constant 0.25% per
bushel shrink factor. Given that the harvest-time cash price of soybeans
in central Illinois for 2000 is $4.56 per bushel, the flat shrink charge
assigned to soybeans is 1.14¢ per bushel ($4.56*0.0025*100). 
The second scenario
considered is storage off-farm at commercial facilities and a short-run
decision-horizon. The marginal cost of physical storage in this case
is the sum of commercial storage, drying and shrinkage charges. As in
the past, storage costs at commercial elevators in 2000 are drawn from
an informal telephone survey of nine central Illinois elevators.  Based on this information, physical
commercial storage charges are assumed to be a flat 13¢ per bushel from
the end of harvest through December 31. After January 1, physical storage
charges are assumed to be 2¢ per month (per bushel), with this charge
pro-rated to the day when the cash sale is made. The drying charge to
reduce corn moisture from 15% to 14% is a flat 2¢ per bushel, while the
charge for shrinkage is 1.3% per bushel.
 The cost of commercial shrinkage is based on the harvest
price (no shrinkage is assumed for soybeans in commercial storage). Given
that the harvest-time cash price of corn in central Illinois for 2000
is $1.64 per bushel, the charge for volume reduction is 2.13¢ per bushel
($1.64*0.013*100). Therefore, the flat shrink charge assigned to all
stored corn is 4.13¢ per bushel.
 Interest opportunity cost is computed using the same
procedures and assumptions as outlined above for on-farm storage.
The third and fourth
scenarios shift to a long-run decision-horizon, where the on-farm scenario
is applicable to a farmer considering the construction of new on-farm
storage facilities and the commercial scenario is applicable to a farmer
that plans on using commercial storage facilities over the long-run.
Since all costs are variable in the long-run, the relevant marginal physical
storage cost in both of these scenarios is the total cost. Dhuyvetter,
Hamman and Harner (2000) estimate the on-farm fixed cost of physical storage
for a 25,000 bushel round, metal bin to be 14.6¢ per year. This fixed
cost can be added to the on-farm variable cost estimate discussed earlier
to compute the total physical cost of on-farm storage. Presumably, commercial
physical storage charges paid by farmers reflect total variable and fixed
costs of storage at commercial facilities. Consequently, the commercial
storage costs discussed earlier in the context of short-run decisions
also represent long-run commercial physical costs.
A comparison of the
estimated costs of storage for corn and soybeans in the 2000 crop year
is found in Tables 4 and 5, respectively. The first item of note is that
the on-farm variable cost of physical storage changes little for corn
as the storage length increases and is constant for soybeans as the storage
length increases. The reason is the previously mentioned “one-time” nature
of most physical costs of on-farm storage. As shown in Figure 5, this
results in a “non-linear” relationship between on-farm variable costs
of storage per month and the length of storage. For example, the on-farm
variable cost for corn stored two months after harvest is about 4.3¢ per
month. This can be compared to the on-farm variable cost of corn stored
six months after harvest of about 1.5¢ per month. The second item of
note is the much lower level of on-farm variable costs versus commercial
storage costs. Of course, this is not surprising given that variable
on-farm storage costs do not include fixed costs, while commercial storage
costs presumably reflect total variable and fixed storage costs at commercial
facilities. The third item of note is the similar level of total on-farm
costs (variable plus fixed) and total commercial costs for all but the
shortest and longest storage lengths. Figure 4 illustrates this finding
on a per month basis. This result is not surprising assuming reasonably
competitive conditions in the market for storage. If total on-farm storage
costs were substantially less than total commercial costs, this would
encourage a rapid expansion of on-farm storage and vice versa.
In fact, the proportion of on-farm versus off-farm storage capacity in
Illinois has been roughly equal for a number of years.  This is consistent with a basic equilibrium
in the storage market where total on-farm costs and commercial costs are
about the same.
Given the information
presented in Tables 4 and 5, it is possible to compute net advisory prices
and benchmarks under each of the four storage scenarios described at the
beginning of this section. It turns out that only two sets of storage
costs are necessary to represent all four scenarios. Most obviously,
on-farm storage costs in the short-run are estimated by on-farm variable
storage costs (fourth column in Tables 4 and 5). Commercial storage costs
in the short-run and long-run can be estimated by commercial storage costs
(last column in Tables 4 and 5). Based on the equilibrium argument made
above, on-farm storage costs in the long-run can also be estimated based
on commercial storage costs. Therefore, in the remainder of this report,
reference will be made only to on-farm variable storage costs and commercial
storage costs. It is left to the reader to interpret commercial storage
cost results in terms of one of the three applicable scenarios.
calculation of storage charges may be impacted by an advisory program’s
loan recommendations and/or the decision rules discussed in the previous
section. Specifically, during the period corn or soybeans are placed
under loan, interest costs are not accumulated, as the proceeds from the
loan can be used to offset interest opportunity costs that otherwise would
accumulate. This most commonly occurs after May 31, 2001, when it is
assumed that all unpriced grain, for which loan benefits have not been
collected, is placed under loan until priced. If a crop is priced (forward
contracts, futures or options) while under loan but stored beyond the
time of pricing, interest opportunity costs are accumulated from the day
of pricing until the time storage ceases (since it is assumed the loan
is repaid on the date of pricing).
could be argued that interest opportunity costs should be charged based
on the LDP available at harvest but not taken by an advisory program.
This adjustment is not made because it would not substantially impact
the results due to the small interest opportunity costs involved.
A final issue related
to storage costs is the use of different strategies based on the availability
of on-farm storage. Specifically, advisory programs may issue one set
of recommendations assuming on-farm storage is available and another set
of recommendations assuming only commercial storage is available. From
a practical standpoint, the alternative strategies must be differentiated
during harvest before grain is placed in on-farm or commercial facilities.
After harvest, when grain has already been placed in on-farm or commercial
storage facilities, such advice is of little practical value to most farmers.
Hence, if a program clearly differentiates on-farm and commercial storage
strategies during harvest of the 2000 crop, the on-farm recommendations
are used in computing the net advisory price under on-farm variable costs
and the commercial recommendations are used in computing the net advisory
price under commercial costs.  In this case, the net advisory price
for a program under the two alternative storage cost assumptions will
vary due to the difference in costs and underlying strategies. If a service
does not clearly differentiate on-farm and commercial storage strategies
during harvest of the 2000 crop, the same recommendations are used in
computing net advisory prices under on-farm variable and commercial storage
costs. In this case, the net advisory price for a program under the two
alternative storage cost assumptions will vary only due to the difference
in costs, as the underlying strategies are the same.
The essential concept
underlying performance evaluation of market advisory programs is fairly
simple: the comparison of the net prices generated by advisory programs
with prices that could have been obtained by a farmer through one or more
appropriate alternative strategies (Sharpe, Alexander and Bailey, 1999,
p. 829). The comparison strategies are commonly referred to as benchmarks
because they serve as objective standards of performance, much like a
yardstick provides an objective measurement of distance. Within this
broad framework, two basic types of performance evaluation can be applied
to market advisory programs. The first type is based on comparison to
“peer-group” benchmarks, whereby net advisory prices are compared to each
other or the average price across all advisory programs. The second type
is based on comparison to “external” benchmarks, whereby net advisory
prices are compared to prices from strategies that do not depend upon
market advisory program behavior. In financial markets, it is commonplace
to compare investment performance to external benchmarks, such as the
Dow-Jones Industrials Index, S&P 500 Index and Wilshire 5000 Index.
The AgMAS study focuses
on performance evaluation using external benchmarks. While peer-group
evaluation provides useful information about the rank of advisory programs,
it cannot answer the question of whether performance of advisory programs
as a group or an individual advisory program is “superior” or “inferior”
in an absolute economic sense. To answer this question, external benchmarks
must be specified based on theories of market pricing.
The first class of
external benchmarks is based on the theory of efficient markets. This
theory assumes that market participants are rational and that competition
instantaneously eliminates all profitable arbitrage opportunities. In
its strongest form, efficient market theory predicts that market prices
always fully reflect available public and private information (Fama, 1970).
The practical implication is that no trading strategy can consistently
beat the return offered by the market. Hence, the return offered by the
market becomes the relevant benchmark. In the context of the AgMAS study,
a market benchmark should measure the average price offered by the market
over the marketing window of a representative farmer who follows advisory
program recommendations. The average price is computed in order to reflect
the returns to a naïve, “no-information” strategy of marketing equal amounts
of grain each day during the marketing window. The difference between
advisory prices and the market benchmark measures the value of advisory
service information. The theory of efficient markets predicts this difference,
on average, will equal zero.
If all market participants
are rational in the way efficient market theory assumes, then the only
interesting external benchmarks are market benchmarks. However, there
is growing evidence that many market participants may not be fully rational
in the efficient market sense. Hirshleifer (2001) provides a comprehensive
review of the judgment and decision biases that appear to affect securities
market investors, such as framing effects, mental accounting, anchoring
and overconfidence. He also provides an exhaustive review of empirical
studies that attempt to measure the potential impact of such biases on
securities prices and investment returns. As an example, Barber and Odean
(2000) finds that individual stock investors under-perform the market
by an average of one-and-a-half percentage points per year, an economically
significant amount, particularly when viewed over long investment horizons.
He argues that a combination of overconfidence and excessive trading explains
this finding. New “behavioral” theories of market pricing have been developed
based on the assumption that market participants are subject to judgment
and decision biases (e.g., Daniel, Hirshleifer and Subrahmanyam, 1998).
theory suggests that the average return actually achieved by many market
participants may be less than that predicted by efficient market theory,
due to the judgment and decision biases that plague most participants.
As a result, the average return actually received by market participants
becomes an appropriate external benchmark. In the context of the AgMAS
study, a behavioral benchmark should measure the average price actually
received by farmers for a crop. The difference between net advisory prices
and a farmer benchmark measures the value of market advisory service information
relative to the information used by farmers. Behavioral market theory
does not predict a specific value for this difference. It may be positive,
negative or zero, depending on the impact of judgment and decision biases
on advisory programs versus farmers.
It is important to
point out that the value of advisory program information provided by comparison
to a farmer benchmark is not as precise as that offered by a market benchmark.
The reason is that many farmers are known to follow market advisory program
recommendations, at least to some extent. Hence, a farmer benchmark already
reflects the impact of market advisory program information to some degree.
While it is not possible to know the true dimensions of this problem,
it should be kept it in mind when making the comparison.  This helps to explain
the importance usually placed on market benchmarks as a standard in performance
comparisons. Simply put, market benchmarks allow a clear and straightforward
interpretation of performance results, which may not be the case for a
Finally, it is important
to re-iterate that market and farmer benchmarks convey quite different
information about the performance of market advisory programs, even though
both are forms of a relative benchmark. This should be carefully considered
when making performance comparisons based on the two types of benchmarks.
In addition, there are some desirable properties from a practical perspective
that both types of benchmarks should possess: i) they should be relatively
simple to understand and to calculate; ii) they should represent the returns
to a marketing strategy that can be implemented by farmers; and iii) they
should be directly comparable to net advisory prices (Jackson, Irwin and
For most of the life
of the AgMAS Project, net advisory prices were compared only to market
benchmarks. Starting with performance evaluation for the 2000 crop year,
net advisory prices are compared to both market and farmer benchmarks.
As noted above, a
market benchmark is designed to measure the average price offered by the
market to farmers. The appropriate time period for computing the average
price is the marketing window of a farmer who follows the recommendations
of the advisory programs included in the AgMAS study. This window was
defined earlier (see the “Market Window” section) as the 24-month period
that begins on September 1st of the year before harvest and
ends on August 31st of the year after harvest. A 24-month
market benchmark is simply computed as the average price over the two-year
Figure 6 presents
average marketing profiles for market benchmarks and advisory programs
in corn and soybeans over the 1995-1999 crop years. For comparison purposes,
average marketing profiles for 24- and 20-month market benchmarks are
included. The 20-month benchmark simply deletes the first four months
of the 24-month marketing window from the computations of the average
market price. As a result, this benchmark is based on the average price
over the period that begins on January 1 of the year of harvest and ends
on August 31 of the year after harvest. For both corn and soybeans, the
market benchmarks appear to provide a surprisingly good “fit” to the average
profile of the advisory programs. More specifically, if a simple linear
trend regression is fit to the average profile of the advisory programs
(not shown), the estimated trend line is remarkably close to the 24-month
benchmark for corn and the 20-month benchmark for soybeans. The profiles
of the two market benchmarks also bracket the average starting date of
significant pricing by advisory programs (one percent or more).
The results discussed
in the previous paragraph suggest there is some uncertainty about specification
of the most appropriate market benchmark for corn and soybean performance
evaluations. Leamer (1983) argues persuasively (and famously) that in
this type of situation it is crucial to understand the “fragility” of
results when key assumptions are changed. Consequently, both a 24-month
and a 20-month market benchmark will be used in comparisons to net advisory
prices. Only the 24-month market benchmark has been used for the last
several years in AgMAS pricing reports. Cash forward prices for central
Illinois are used during the pre-harvest period, while daily spot prices
for central Illinois are used for the post-harvest period. The same forward
and spot price series applied to advisory program recommendations are
used to construct both market benchmarks. Details on the forward and
cash price series can be found in the earlier “Prices” section of this
are made to the daily cash prices to make the 24-month and 20-month average
cash price benchmarks consistent with the calculated net advisory prices
for each marketing program. The first is to take a weighted-average price,
to account for changing yield expectations, instead of taking the simple
average of daily prices. This adjustment is consistent with the procedure
described previously in the "Yields and Harvest Definition"
section. The daily weighting factors for pre-harvest prices are based
on the calculated trend yield, while the weighting of the post-harvest
prices is based on the actual reported yield for central Illinois. The
second adjustment is to compute post-harvest cash prices on a harvest
equivalent basis, which is done by subtracting on-farm variable or commercial
storage costs (physical storage, shrinkage and interest) from post-harvest
spot cash prices. The daily storage charges are calculated in the same
manner as those for net advisory prices. The third adjustment is made
with respect to the loan program. In the context of evaluating advisory
program recommendations, it was argued earlier that a “prudent” or “rational”
farmer would take advantage of the price protection offered by the loan
program, even in the absence of specific advice from an advisory program.
This same logic suggests that a “prudent” or “rational” farmer will take
advantage of the price protection offered by the loan program when following
the benchmark average price strategy. Based on this argument, the 24-month
and 20-month average cash price benchmarks are adjusted by the addition
of LDPs and MLGs. Bushels marketed in the pre-harvest period according
to the benchmark strategy are treated as forward contracts, with the LDPs
assigned at harvest. Bushels marketed each day in the post-harvest period
are awarded the LDP or MLG in existence for that particular day. Finally,
just as in the case with comparable advisory program recommendations,
interest opportunity costs are not charged to the benchmark after May
31, 2001 to reflect the assumption that stored grain is placed under loan.
While the 24- and
20-month market benchmark prices can obviously differ for a given crop
year, averages of the two benchmark prices across crop years are not expected
to differ substantially. First, the difference in the marketing windows
for the two benchmarks is relatively small, as the 20-month benchmark
reduces the 24-month marketing window by only about 17%. Second, given
a sufficiently large sample of crop years and efficient corn and soybean
markets (cash, futures and options), annual averages of different average
price benchmarks should be similar when stated on a harvest equivalent
basis. Of course, if corn and soybean markets were inefficient the equivalence
would not hold. In particular, if pre-harvest prices contain a “drought
premium” as some argue (e.g., Wisner, Baldwin and Blue, 1998), then the
24-month benchmark price may be consistently higher than the 20-month
In contrast to averages,
the variation of 24- and 20-month market benchmark prices across crop
years is expected to differ. The first reason for the difference is the
well-known result in statistics that the sampling variation of the mean
(average) is inversely related to the sample size used to compute the
average (e.g., Griffiths, Hill and Judge, 1993, p.82). Since the sample
of daily prices used in computing the 24-month benchmark is larger than
the sample for the 20-month benchmark, the variation of the 24-month benchmark
should be smaller than variation of the 20-month benchmark. The second
reason for the difference is a “time diversification” effect. Specifically,
the 20-month benchmark is not generated by randomly sampling prices over
the entire 24-month marketing window, but instead all prices are sampled
after deleting the first four months from the 24-month window. This non-random
effect shortens the time interval over which prices are sampled. All
else constant, the shorter time interval for the 20-month market benchmark
will lead to larger variation across crop years than the 24-month benchmark.
Since both the sample size and time diversification effects work in the
same direction, the 20-month market benchmark is expected to vary more
than the 24-month benchmark.
A practical concern
with the market benchmarks is that a farmer may not be able to implement
the benchmark strategies since they involve marketing a small portion
of the crop every day. There are two reasons to believe this concern
is not overly serious. First, a number of companies have developed and
offer grain “index” contracts that allow farmers to receive the average
market price over a pre-specified time interval. Representative examples
can be found at, http://www.cargillaghorizons.com/aghorizons/performancemarketing/index.htm,
http://www.cgb.com and http://www.e-markets.com. Second, a strategy
of routinely selling at less frequent intervals closely approximates the
market benchmark prices. For example, a farmer might consider alternative
“tracking” strategies of marketing only once a month or once every other
month over the 24-month window. Using mid-month prices, a tracking strategy
of marketing only once a month (24 times) generates average prices over
1995-2000 that are quite close to 24-month market benchmark prices. The
average difference is only two cents per bushel for corn and one cent
per bushel for soybeans, and the maximum difference for any particular
crop year is eight cents per bushel in corn and five cents per bushel
in soybeans. A tracking strategy of marketing once every other month
(12 times) also generates average prices over 1995-2000 that are quite
close to 24-month market benchmark prices. The average difference is
only three cents per bushel for corn and five cents per bushel for soybeans.
The average difference
results for the benchmark tracking strategies should not be a surprise
given the previous argument about averages of different benchmark prices
in efficient markets. More surprising is the result that the tracking
strategies vary only two to four cents per bushel more than the 24-month
benchmark over 1995-2000. This is evidence that the time diversification
effect can swamp the sample size effect, as the tracking strategies are
based on dramatically smaller samples (12 or 24 observations compared
to about 500 observations) but have only a marginally higher variation.
Time diversification is approximately the same for the tracking strategies
and the 24-month benchmark because sample observations for the tracking
strategies, like the benchmark, are spaced equally across the 24-month
As noted earlier,
a farmer benchmark is designed to measure the average price received by
farmers for a crop. This type of benchmark should reflect the actual
behavior of farmers in marketing grain, and include all of the transactions
(e.g., cash, forward, futures and options) that farmers employ in this
regard. In theory, a farmer benchmark should not be difficult to calculate.
First, a representative sample of grain farmers in the relevant geographic
area would be drawn (randomly). Next, the average price received by each
farmer would be computed (using the same assumptions as in the computation
of net advisory prices and market benchmarks). Last, the farmer benchmark
would be computed as the weighted-average price received by all farmers
in the sample, with the weights equal to the sample proportion of the
crop produced by each farmer.
In practice, the
detailed type of data needed to construct a valid farmer benchmark is
not available, so an approximation must be used. The only known approximation
is the USDA average price received series. This series has some good
features and bad features with respect to measuring the average price
received by farmers. On the plus side, the USDA series reflects the actual
pattern of cash grain marketing transactions by farmers, and thus, incorporates
the marketing windows and timing strategies actually used by farmers;
includes forward contract transactions for both the pre-harvest and post-harvest
periods, with the transactions recorded at the forward price, not the
spot price at time of delivery; and grain sales are adjusted to industry
standards for moisture. On the negative side, the USDA series is only
available on a statewide basis; includes cash transactions for different
grades and quality of grain sold by farmers; does not include futures
and options trading profits/losses of farmers; and reflects a mix of old
and new crop sales by farmers.
of the problems mentioned above are prohibitive with respect to the use
of the USDA series as a measure of the average price received by farmers.
Since spatial basis patterns are relatively stable, it is straightforward
to adjust the USDA series to an alternative geographic location. It turns
out that this type of adjustment is not necessary for AgMAS performance
evaluations because central Illinois prices closely mirror the average
price for the entire state of Illinois. For example, the average cash
price of corn and soybeans for central Illinois over January 1995-December
2001 differs from the state average price by about one cent (state average
higher for corn, lower for soybeans). The correlation of daily prices
for central Illinois and the state is 0.96 for corn and 0.99 for soybeans.
Hence, from a statistical standpoint, central Illinois and state average
prices are equivalent.
It is not possible
to adjust the USDA series to a constant grade and quality, to reflect
futures and options trading profits/losses of farmers or to only reflect
new crop sales because the data simply are not available. However, the
resulting biases probably are small and some may work in opposite directions.
Consider the grade and quality issue first. It is well known that some
fraction of the corn crop is discounted relative to the standard number
two yellow corn grade. This is also true for the soybean crop relative
to the standard number one yellow soybean grade, but likely to a smaller
extent than corn. As a result, the USDA average price received reflects
a weighted-average of both undiscounted and discounted grain sales. The
weights are unknown, but the direction of the bias relative to average
prices for the standard grade is clearly downward. In other words, when
compared to the average price at the standard grade, the USDA average
price received should be adjusted upwards to reflect the impact of discounts.
A key question, of
course, is the magnitude of the grade and quality bias discussed above.
An extensive search of the literature was conducted and no previous study
was uncovered that directly measured the proportion of corn and soybeans
sold at a discount or the average magnitude of price discounts in central
Illinois (or other Midwestern US areas). The Federal Grain Inspection
Service of the US Department of Agriculture (FGIS) was contacted and staff
indicated that FGIS does not have an historical series of this type.
One older study was located that contained some information on the issue.
Hill, Kunda and Rehtmeyer (1983) reported the results of a 1982 survey
of grain elevator operators in Illinois. One question in this survey
asked elevator operators to estimate the percentage of corn and soybean
receipts at country elevators that typically exceed grade factors. Unfortunately,
the results were not netted across grade factors, so it is not possible
to estimate the typical proportion of the crop sold at a discount (if
a lot is over one grade limit it will have a higher than average chance
of being over the grade limit for other factors). In addition, the average
magnitude that grade factors were exceeded is not reported, so it is impossible
to estimate the dollar value of the average discount. Nonetheless, the
results provide some perspective on the quality issue. For corn delivered
in the fall, the percentage typically above a grade factor ranged from
0.2 to 7.5% of deliveries. For soybeans delivered in the fall, the percentages
were about the same, except for foreign material, where over 30% of the
bushels delivered typically exceeded the grade factor. When winter and
summer delivery was considered, the percentages increased somewhat for
corn and decreased for soybeans. Other than foreign material for soybeans,
this evidence suggests that less than 10% of the corn and soybean crops
in the early 1980s were sold at a discount to the standard grade.
To provide more recent
evidence on quality, the nine central Illinois elevators surveyed annually
for commercial storage costs were queried in December 2001 about the average
quality of corn and soybean crops. The most frequent response from the
elevator managers in this informal survey was that less than one percent
of corn and soybeans is sold at a discount (for non-moisture factors)
relative to the standard grade. The range was from zero to less than
five percent. The largest estimate of the average dollar value of discounts
was two to three cents per bushel. These figures provide enough information
to make a very rough estimate of maximum quality bias in the USDA average
price received series. Using the maximum proportion of five percent and
the maximum average discount value of three cents from the informal survey,
the downward bias relative to the standard grade would be only 0.15¢ per
bushel (0.05*3). Furthermore, if the average discount is three cents,
then one-third of the crop would have to be sold at a discount to induce
a downward bias even as large as one cent (0.33*3 = 1). In sum, while
the evidence is limited and sketchy, it does suggest that any downward
quality bias in the USDA average price received series, at least for corn
and soybeans in central Illinois, is quite small.
Now, consider the
potential bias from omission of futures and options profits/losses. If
a farmer uses futures and options exclusively for “pure” hedging purposes,
they will consistently take short positions at about the same points in
the marketing window each year. Unless futures prices are biased upwards
or downwards, this type of hedging will not result in large profits or
losses, as the price changes from upward and downward price trends should
roughly offset over time.
 If a farmer uses futures and options to
engage in “selective” hedging, they may have large profits or losses related
to the timing of trading. While no direct evidence on the profits or
losses of farmers is available in this context, there is convincing evidence
that small traders in general consistently lose money in futures and options
markets.  It seems reasonable
to assume that farmers engaged in selective hedging are similar to other
small traders, and hence, selective farmer hedging in futures and options
markets results in aggregate trading losses.  Given that pure hedging
is expected to yield zero profits on average and selective hedging is
expected to yield losses on average, the net effect of the two types of
futures and options trading by farmers should be negative. In this case,
when compared to average prices at the standard grade, the USDA average
price received should be adjusted downward to reflect the impact of net
Next, consider the
potential bias from mixing old crop and new crop sales during the 12-month
marketing year used to compute the USDA average price received. The first
step is to determine the potential magnitude of the problem. Fortunately,
bounds for the “shifting” of old crop sales into the next marketing year
can be computed by dividing ending stocks for a marketing year by crop
production for the same marketing year (e.g., September 1, 2000 soybean
stocks divided by 1999 soybean production). Over the 1995/1996 through
2000/2001 marketing years, on-farm ending stocks in Illinois averaged
four percent of statewide corn production and three percent of statewide
soybean production. These percentages are the lower bounds on shifting
because farmers presumably own on-farm stocks and sales of these stocks
will be shifted to the next marketing year. Over the 1995/1996 through
2000/2001 marketing years, total ending stocks (on-farm and off-farm)
in Illinois averaged 12% of statewide corn production and 8% of statewide
soybean production. These percentages are the upper bounds on shifting
assuming farmers own all of the stocks in off-farm storage facilities.
Clearly, this assumption is unrealistic, as commercials own some, if not
most, of the stocks in off-farm facilities at the end of a marketing year.
The bottom-line is that shifting of old crop sales into the next marketing
year, on average, is somewhere between 4 and 12% of corn production and
3 and 8% of soybean production. This suggests the magnitude of shifting
from one crop year to the next probably is not large.
The second step is
to determine the impact shifting old crop sales will have on the USDA
average price received. Consider the simplest case where old crop sales
in the next marketing year are made at spot prices for the new crop and
the same proportion is shifted every year. The same price received would
result as in the no shifting case. Only to the degree that the proportion
shifted varies from year-to-year will the average price received differ
from the no-shifting case. The proportion does vary from year-to-year,
but not by a substantial amount. For example, on-farm ending stocks in
Illinois varied from only two to six percent of corn production over the
1995/1996 through 2000/2001 marketing years. The impact of this variability
on average price received will depend on farmers’ ability to time shifts
to take advantage of favorable spreads between old crop and new crop prices.
If farmers as a group have timing ability in this context, then the USDA
average price received will be biased upwards relative to the average
price at the standard grade. However, given the difficulty of predicting
old crop-new crop price spreads (Lence and Hayenga, 2001) and the small
absolute magnitude of actual shifting of sales, it seems reasonable to
argue that the bias in average price received from shifting old crop sales
across marketing years is quite small.
In total, the evidence
and arguments discussed above suggests that the net systematic bias in
the USDA average price received relative to average prices for the standard
grade is small, at least for corn and soybeans in central Illinois. It
is difficult to construct a scenario where the level of bias would materially
effect performance evaluation of market advisory programs. However, direct
evidence on the bias issue is limited and sketchy, so it is difficult
to reach a firm conclusion. The USDA average price received is best viewed
as an approximation of the “true” average price received by farmers.
are made to the USDA average price received data for the state of Illinois
in order to make the computed farmer benchmark consistent with net advisory
prices. To begin, mid-month on-farm or commercial storage charges are
applied to the monthly average price received in the 12-month marketing
year (September through August). Next, the annual weighted-average price
received is computed using the percentage of the crop marketed in each
calendar month as the weights. Finally, actual state average LDPs and
MLG’s are added for the 1998, 1999 and 2000 crops.
Given the uncertainties
involved in measuring the average price received by farmers, it would
be useful to specify alternative farmer benchmarks. Unfortunately, as
the discussion in this section has detailed, there simply is no alternative
measure that reflects the actual marketing behavior of farmers. The inability
to provide information on the sensitivity of performance comparisons to
alternative farmer benchmarks is a limitation of the analysis and should
be kept in mind when viewing the results.
Finally, it is interesting
the consider arguments about the expected difference in averages and variation
between the farmer benchmark and the market benchmarks. If corn and soybean
markets are efficient and farmers are rational, then the average price
across crop years for the farmer and market benchmarks should be similar.
Under these assumptions, the variation in farmer benchmark prices across
crop years could be smaller or larger than the variation in market benchmark
prices, depending on the length of the marketing window used by farmers
and the exact nature of the marketing strategies implemented by farmers.
is not possible to determine the average marketing window or the pricing
pattern of farmers using USDA monthly marketing weights. For perspective,
average monthly USDA marketing weights for corn and soybeans in Illinois
over 1995-2000 are presented in Figure 7. These weights reflect the pattern
of grain deliveries by farmers to commercial facilities over the 12-month
marketing year. Grain deliveries do not necessarily reflect the pricing
pattern of farmers due to the use of forward pricing instruments. There
is ample survey evidence that significant numbers of farmers use pre-harvest
forward contracts to price a portion of their crops, and that post-harvest
forward contracts are widely used, particularly for January delivery (e.g.,
Patrick, Musser and Eckman, 1998; Pennings, Good, Irwin and Gomez, 2001).
The difficulty is that almost no concrete evidence exists on the exact
length of the typical marketing window of farmers or the precise pattern
of forward pricing. Anecdotal evidence suggests that farmers may use
a marketing window not unlike the 24-month and 20-month windows assumed
for the market benchmarks, but the amount of pre-harvest forward pricing
is far less than assumed for the market benchmarks. All else equal, this
would lead to the expectation that the variation of farmer benchmark prices
would exceed that for the market benchmarks.
it is still possible for the variation of farmer benchmark prices to be
smaller than for market benchmarks if farmers employ market-timing strategies
that successfully reduce price variation. Alternatively, if farmers are
subject to the same judgment and decision biases as appears to be the
case for participants in other markets, then it would be reasonable to
expect the farmer benchmark to have a lower average price and higher variation
than the market benchmarks. Which of the above scenarios is correct can
only be determined empirically.
Net advisory prices
and benchmarks for the 2000 corn and soybean crops are presented in Tables
6 through 11. These results are new and add to the sample of net advisory
prices and benchmarks previously available for analysis. For a specific
example of how marketing recommendations are translated into a final net
advisory price that incorporates the simulation assumptions, see Jackson,
Irwin and Good (1996). It is important to emphasize that all of the net
advisory prices and benchmarks presented in Tables 6 through 11 are stated
on a harvest equivalent basis using either on-farm variable or commercial
Net advisory prices
and benchmarks for corn in 2000 assuming on-farm variable storage costs
are presented in Table 6. In addition, this table shows the components
of the advisory prices and benchmarks. The 2000 average net advisory
price for all 27 corn programs is $2.21 per bushel under the assumption
of on-farm variable costs. It is computed as the unadjusted cash sales
price ($1.97 per bushel) minus storage charges ($0.11 per bushel) plus
futures and options gain ($0.04 per bushel) minus brokerage costs ($0.02
per bushel) plus LDP/MLG gain ($0.32 per bushel). The range of net advisory
prices for corn in 2000 assuming on-farm variable storage costs is $1.90
to $2.90 per bushel. Corresponding benchmark prices range from $2.06
per bushel (USDA farmer benchmark) to $2.15 per bushel (24-month average
Net advisory prices
and benchmarks for soybeans in 2000 assuming on-farm variable storage
costs are presented in Table 7. The 2000 average net advisory price for
all 26 soybean programs is $5.51 per bushel under the assumption of on-farm
variable costs. It is computed as the unadjusted cash sales price ($4.70
per bushel) minus storage charges ($0.15 per bushel) plus futures and
options gain ($0.04 per bushel) minus brokerage costs ($0.02 per bushel)
plus LDP/MLG gain ($0.94 per bushel). The range of net advisory prices
for soybeans in 2000 assuming on-farm variable storage costs is $5.05
to $6.88 per bushel. Corresponding benchmark prices range from $5.38
per bushel (USDA farmer benchmark) to $5.47 per bushel (24-month average
many Corn Belt farmers grow both corn and soybeans, it also is useful
to examine a combination of the results for the corn and soybean marketing
programs. In order to do this, gross revenue is calculated for a central
Illinois farmer who follows both the corn and soybean marketing advice
of a given program. It is assumed that the representative farmer splits
acreage equally (50/50) between corn and soybeans and achieves corn and
soybean yields equal to the actual yield for the area in 2000. The 50/50
advisory revenues are computed on a per acre basis and compared with the
revenue a central Illinois farmer could have received based on benchmark
prices for both corn and soybeans. Advisory revenue per acre is calculated
only for those programs that offer both corn and soybean marketing advice.
revenues and benchmarks in 2000 assuming on-farm variable storage costs
are presented in Table 8. The average revenue achieved by following both
the corn and soybean programs offered by an advisory program is $306 per
acre. The range of 50/50 advisory revenue in 2000 assuming on-farm variable
storage costs is $276 to $393 per acre. Corresponding benchmark revenues
range from $291 per acre (USDA farmer benchmark) to $300 per acre (24-month
average market benchmark).
comparison purposes, the annual subscription cost of each advisory program
also is listed in the last column of Table 8. Subscription costs average
$333 per program, a level that does not appear to be large relative to
total farm revenue, whether a large or small farm is considered. For a
1,000 acre farm, subscription costs average about one-tenth of one percent
of total advisory revenue. For a 250 acre farm, subscription costs average
about four-tenths of one percent of total advisory revenue. While subscription
costs do not appear to be large relative to revenue, it is important to
point out that the cost of implementing, monitoring and evaluating the
strategies recommended by advisory programs is not incorporated in the
comparisons. Such costs are difficulty to quantify, but informal feedback
from farmers suggests they are not trivial, especially in terms of the
opportunity cost of management time.
Net advisory prices
and benchmarks for corn in 2000 assuming commercial storage costs are
presented in Table 9. The 2000 average net advisory price for all 27
corn programs is $2.13 per bushel under the assumption of commercial storage
costs. It is computed as the unadjusted cash sales price ($1.97 per bushel)
minus storage charges ($0.20 per bushel) plus futures and options gain
($0.04 per bushel) minus brokerage costs ($0.02 per bushel) plus LDP/MLG
gain ($0.32 per bushel). The range of net advisory prices for corn in
2000 assuming commercial storage costs is $1.79 to $2.78 per bushel.
Corresponding benchmark prices range from $1.95 per bushel (USDA farmer
benchmark) to $2.09 per bushel (24-month average market benchmark).
Net advisory prices
and benchmarks for soybeans in 2000 assuming commercial storage costs
are presented in Table 10. The 2000 average net advisory price for all
26 soybean programs is $5.45 per bushel under the assumption of commercial
storage costs. It is computed as the unadjusted cash sales price ($4.70
per bushel) minus storage charges ($0.21 per bushel) plus futures and
options gain ($0.04 per bushel) minus brokerage costs ($0.02 per bushel)
plus LDP/MLG gain ($0.94 per bushel). The range of net advisory prices
for soybeans in 2000 assuming commercial storage costs is $5.00 per to
$6.83 per bushel. Corresponding benchmark prices range from $5.29 per
bushel (USDA farmer benchmark) to $5.42 per bushel (24-month average market
revenues and benchmarks in 2000 assuming commercial storage costs are
presented in Table 11. The average revenue achieved by following both
the corn and soybean programs offered by an advisory program is $298 per
acre when commercial storage costs are assumed. The range of 50/50 advisory
revenue in 2000 assuming commercial storage costs is $265 per to $381
per acre. Corresponding benchmark revenues range from $279 per acre (USDA
farmer benchmark) to $293 per acre (24-month average market benchmark).
8 and 9 show the pattern of corn prices for the 2000 crop year based on
on-farm variable and commercial storage costs, respectively. The top
chart shows daily cash prices from September 1, 1999 through August 31,
2001. The pre-harvest prices are the cash forward contract prices for
harvest delivery. The middle chart is a repeat of the top chart with
daily LDP or MLG added to the daily price. For the pre-harvest period,
the LDP is the average LDP available at harvest time. The third chart
offers a different perspective, in that post-harvest daily cash prices
are adjusted for cumulative storage costs (interest, physical storage,
and shrinkage charges). The chart illustrates the pattern of harvest
equivalent prices plus LDP or MLG.
Corn prices for the
2000 crop year are highest in the pre-harvest period, with the cash forward
contract price remaining well above $2.00 and moving near $2.40 in late
April. Prices decline into harvest as average yields and total production
exceed expectations, but make a significant post-harvest recovery in November
and December as basis levels strengthen. Prices decline in the spring
of 2001 in the face of good planting weather, but recover somewhat in
July and August due to some areas of dry weather in the US. The price
pattern for the 2000 crop year is typical of a large crop year.
Figures 10 and 11
show the pattern of soybean prices for the 2000 crop year based on on-farm
variable and commercial storage costs, respectively. The three charts
are the same as for corn, depicting daily cash prices, cash prices plus
LDP/MLG, and cash prices plus LDP/MLG minus storage charges. Soybean
prices for the 2000 crop follow a similar pattern to that for corn. Cash
prices are more volatile in the pre-harvest period, peaking just above
the CCC loan rate in May. That is the only time the cash price is above
the loan rate in the entire crop year. Prices decline sharply during
the growing season, but make a small rally into harvest. Prices fall
in the early spring of 2001 under the weight of another large South American
crop, but rally modestly in the summer months due to some concern about
dry weather in parts of the US. The price pattern for the 2000 crop year
reflects dual harvest periods, the US in the fall months and South America
in the spring months. The largest LDPs/MLGs occur in the spring of 2001.
Advisory Prices and Benchmarks for 1995-2000
As shown in Table
12, the average advisory price for corn ranges between $2.02 per bushel
in 1999 and $3.03 per bushel in 1995. Range statistics reveal that net
advisory prices for corn vary substantially within individual crop years.
The most dramatic example is 1995, where the minimum is $2.29 per bushel
and the maximum is $3.90 per bushel. Even in years with less market price
volatility, it is not unusual for the range of prices across advisory
programs to be nearly a dollar per bushel. The three alternative benchmark
prices for corn are shown at the bottom of Table 12. The variation in
benchmark prices from year-to-year is similar to that of average net advisory
prices. However, there can be substantial differences in benchmark prices
for a particular crop year. For example, the 24-month market benchmark
in 1998 is $2.24 per bushel, while the USDA farmer benchmark is only $1.97
per bushel. These data suggest performance results for corn may be sensitive
to the selected benchmark.
As reported in Table
13, the average advisory price for soybeans ranged from $5.45 per bushel
in 2000 to $7.27 per bushel in 1996. Similar to corn, the range of individual
net advisory prices within a crop year is substantial. The most dramatic
example is 1999, where the range in advisory prices approaches $2.50 per
bushel. The three alternative benchmark prices for soybeans are shown
at the bottom of Table 13. The variation in soybean benchmark prices
from year-to-year is similar to that of average net advisory prices.
Once again, there can be substantial differences in benchmark prices for
a particular crop year.
Table 14 contains
the combined corn and soybeans revenue results. The lowest average advisory
revenue, $298 per acre, occurred in 2000, while the highest average advisory
revenue, $369 per acre, occurred in 1996. Given the results for corn
and soybeans, the large range of individual advisory revenues within a
crop year is not surprising. Nonetheless, it is startling to see the
possible economic impact of following the best versus the worst performer
in a given crop year. For example, in three of the six crop years (1995,
1999 and 2000), the range in advisory revenue exceeds $100 per acre.
For the reader’s
convenience, Tables 15 through 17 report the most recent two-year averages
(1999-2000), three-year averages (1998-2000), four-year averages (1997-2000),
five-year averages (1996-2000) and six-year averages (1995-2000) of net
advisory prices, revenues and benchmarks.
 The averages are computed in these tables only for the
advisory programs active in each of the indicated crop years. The reported
averages may reflect survivorship bias as a result of this assumption,
which should be considering when viewing the averages.
 Finally, note that the average, minimum and maximum
reported for each column in the Tables 15 through 17 are computed across
the advisory program averages in each column.
Information on the
sources of the differences between net advisory prices and benchmarks
in corn and soybeans is found in Table 18. Panel A shows average net
advisory prices and benchmarks broken out by component. Panel B presents
the average difference in the components between advisory programs and
the benchmarks. All of the averages in the table assume commercial storage
costs. In cases where the average net advisory price is above the average
benchmark price (e.g., net advisory price in corn vs. the USDA farmer
benchmark) the difference is largely explained by the higher net cash
sales price of advisory programs. The average net futures and options
gain of advisory programs is relatively small, as is the difference in
LDP/MLGs between advisors and the benchmarks.
Evaluation Results for 1995-2000
Four basic indicators
of performance are applied to advisory program prices and revenues over
1995-2000. The first indicator is the proportion of advisory programs
that beat benchmark prices. A valuable feature of this directional indicator
is that it is not influenced by extremely high or low advisory prices.
The second indicator is the difference between the average price of advisory
programs and benchmarks. This indicator is useful because it takes into
account both the direction and magnitude of differences from benchmark
prices. The third indicator is the average price and risk of advisory
programs relative to the average price and risk of the benchmarks. Evaluations
based on this indicator are important because risk is incorporated into
the performance comparisons. The fourth indicator is the predictability
of advisory program performance from year-to-year. This indicator provides
information on the value of past pricing performance in predicting future
the performance evaluation results, a couple of important issues need
to be discussed. First, the results presented in this section of the
report address the performance of market advisory programs as a group.
In other words, average pricing performance across all programs is considered.
This is a different issue than the pricing performance of a particular
advisory program.  Simply put, it is inappropriate
to make performance inferences for an individual advisory program based
on aggregate results. Second, farmers subscribe to market advisory programs
for a variety of reasons. For example, Pennings, Good, Irwin and Gomez
(2001) survey farmer-subscribers and find that the two highest rated uses
of market advisory programs are marketing information and market analysis.
While the quality of marketing information and market analysis is likely
to be positively correlated with the returns to marketing recommendations,
this does not necessarily have to be the case. It is possible that advisory
programs provide valuable information and analysis to farmer-subscribers,
yet fail to exhibit superior pricing performance.
first, and simplest, indicator of pricing performance is the proportion
of advisory programs that beat the market or farmer benchmarks. Positive
performance is indicated if the proportion of advisory programs beating
a benchmark exceeds 50%, the proportion one would observe if advisory
performance is random, like flipping a fair coin. A noteworthy feature
of this “directional” indicator is that it is not influenced by extremely
high or low advisory prices or revenue.
proportion of advisory programs in corn, soybeans and 50/50 advisory revenue
above the benchmarks over 1995-2000 is presented in Table 19. Note that
average proportions for 1995-2000 are computed over the full set of advisory
programs, and therefore, do not necessarily equal the average of the individual
crop year proportions. This “grand” average equally weights each of the
net advisory prices or revenues in the sample, whereas an average of the
individual crop year averages would equally weight the crop years. The
first average is preferred for the present purpose as it implies an equal
probability of selecting an individual advisory program across the entire
corn first (Panel A: Table 19), there is some variation in the proportion
of net advisory prices above the two market benchmarks for individual
crop years, particularly 1998, but the patterns are similar overall.
There also does not appear to be any discernable trend in the proportions
for either benchmark over the six crop years. The average proportion
for 1995-2000 is 51% versus the 24-month benchmark and 59% versus the
20-month benchmark, indicating a slight to marginal chance of advisory
prices in corn beating market benchmark prices. In contrast, the proportion
of net advisory prices above the USDA farmer benchmark exceeds 50% each
crop year and appears to increase somewhat over time. The average proportion
above the USDA farmer benchmark over 1995-2000 is 74%. This is substantially
higher than the average proportions versus the market benchmarks and indicates
a sizeable chance of market advisory programs generating net prices higher
than the USDA farmer benchmark.
to soybeans (Panel B: Table 19), there is more variation in the proportion
of net advisory prices above the two market benchmarks for individual
crop years. Particularly sharp differences are observed in 1998 and 1999,
where the spread between the proportions is between 26 and 45 percentage
points. There also appears to be a noticeable downward trend in the proportions
versus the 24-month benchmark. No clear trend is apparent for the proportions
versus the 20-month benchmark. Despite these differences for individual
crop years, the average proportions for 1995-2000, 61% versus the 24-month
benchmark and 70% versus the 20-month benchmark, both indicate a better
than average chance of advisory prices beating market benchmark prices
in soybeans. Once again, the proportions above the USDA farmer benchmark
are all above 50% and appear to increase somewhat over time. The average
proportion above the USDA farmer benchmark over 1995-2000 is 74%, the
same as found for corn. This indicates a sizeable chance of market advisory
programs generating net prices in soybeans higher than the USDA farmer
the combined nature of 50/50 advisory revenue, it is not surprising that
revenue proportions (Panel C: Table 19) typically are between those of
corn and soybeans. The average proportion for 1995-2000 is 57% versus
the 24-month benchmark and 66% versus the 20-month benchmark, indicating
a marginal to better than average chance of advisory revenue beating market
benchmark revenue. The proportion of advisory revenues above the USDA
farmer benchmark exceeds 50% each crop year and averages 77% over 1995-2000.
This indicates a sizable chance of advisory revenue beating USDA farmer
benchmark revenue. It is interesting to note that 100% of the advisory
programs in 1998 generated revenue that exceeded the USDA farmer benchmark,
despite the fact that less than 100% did so in corn and soybeans. This
simply reflects a situation where some programs had gains above the farm
benchmark in one commodity that more than offset the losses below the
benchmark in the other commodity.
the directional performance results over 1995-2000 suggest several key
findings. First, advisory programs in corn do not consistently beat market
benchmarks, but they do consistently beat the farmer benchmark. Second,
advisory programs in soybeans tend to beat both market and farmer benchmarks.
Third, in terms of 50/50 revenue, advisory programs only marginally beat
market benchmarks, but consistently beat the farmer benchmark. So, the
results provide mixed performance evidence with respect to market benchmarks
and consistently positive evidence with respect to the USDA farmer benchmark.
Finally, it is interesting
to compare the directional pricing performance results for market advisory
programs to that of other investment professionals. Malkiel (1999) reports
a typical estimate of the proportion of active mutual funds managers that
beat the stock market. Specifically, he shows that only 33% of active
mutual fund managers generate returns higher than the S&P 500 stock
index over 1974-1998. By comparison, market advisory programs perform
better, with more than half of the programs beating the market in corn
and about two-thirds beating the market in soybeans. This divergence
may simply reflect a unique time period in corn and soybean markets, relatively
less efficient commodity markets, the skillfulness of advisory programs,
or a return to risk.
The second indicator
of pricing performance is the difference between the average price of
advisory programs and the market or farmer benchmarks. This indicator
takes into account both the direction and magnitude of differences from
the benchmarks. The results found in Tables 20 and 21 basically tell
the same story as those based on the proportion beating the benchmarks.
Average differences from market benchmarks for corn over 1995-2000 (panel
A: Table 20) are small, ranging from zero to three cents per bushel.
 At 11¢ cents per bushel, the average difference from
the farmer benchmark for corn is larger. Average differences for soybeans
over 1995-2000 (panel B: Table 20) are even larger for both types of benchmarks,
ranging from 13 to 17¢ per bushel versus market benchmarks and equaling
22¢ per bushel versus the farmer benchmark. Average differences for 50/50
advisory revenue range from three to seven dollars per acre for market
benchmarks over 1995-2000 (Table 21). The average revenue difference
versus the USDA farmer benchmark is $14 per acre. Note that the average
differences can mask considerable variability across the benchmarks within
a crop year and across crop years. A dramatic example of this occurred
in 1998 for soybeans (Panel B: Table 20), where the average difference
from the 24-month market benchmark is –4¢ per bushel, while the average
difference from the USDA farmer benchmark is +64¢ per bushel.
It should be pointed
out that average differences versus the farmer benchmark appear to be
non-trivial from an economic decision-making perspective. For example,
the average advisory return relative to the farmer benchmark ($14 per
acre) is over four percent of average farmer benchmark revenue. This
represents a substantial increase in the average returns to farm operator
management, labor and capital in Illinois (e.g., Lattz, Cagley and Raab,
this juncture, the findings should be considered only suggestive. The
reason is that the statistical significance of the results has not been
investigated. In other words, are the returns to marketing advice simply
the result of random chance or do they reflect truly positive pricing
performance? Standard statistical tests are available to help answer
this question. In the present case, the appropriate test is the matched
sample t-test of zero difference in the mean of net advisory prices
and a particular benchmark. The samples are matched because the same
crop year receives different “treatments” from advisory programs and benchmarks.
The treatments correspond to the differing marketing strategies applied
by advisory programs and the benchmarks.
of the t-test to average pricing performance is complicated by
the fact that net prices across programs are positively related. This
type of statistical test assumes that sample differences are generated
independently (e.g., Griffiths, Hill and Judge, 1993, p. 152).  It should come as no surprise that
this assumption is violated for market advisory programs. Many of the
programs appear to use similar methods of analysis, and all make heavy
use of similar supply and demand information (primarily from the USDA).
Furthermore, alternative programs offered by the same advisory service
are likely to generate similar pricing results. Statisticians call this
an “implicit factor” problem.
coefficients estimated across net advisory prices provide evidence on
the magnitude of the dependence problem. The sample is limited to the
17 programs active in all six crop years of the AgMAS study in order to
maximize the number of time-series observations available for each pair
of programs. The possible range in correlation coefficients is –1 and
+1, with –1 indicating perfect negative correlation in advisory prices
and +1 indicating a perfect positive correlation in advisory prices.
A correlation of zero indicates no (linear) relationship. A few of the
estimated correlations are negative, but, as expected, the vast majority
of the correlations are positive.  The average correlation coefficient
across all possible pairs of advisory programs (136) is 0.73 for corn,
0.78 for soybeans and 0.65 for revenue. These estimates are quite high
and confirm that dependence across advisory programs is a serious problem
in testing the statistical significance of average price performance. 
high level of correlation across net advisory prices and revenue basically
creates an information problem in the sample. Take the case of corn.
There are 152 computed net advisory prices across all programs and crop
years. However, the 152 net advisory prices are not independent, due
to the strong positive correlation across programs. The key question
is the amount of independent information contained in the sample of 152
net advisory prices. It is not possible to come up with a precise estimate,
but it is certainly far less than 152 observations. Similar logic holds
for soybeans and 50/50 advisory revenue.
bottom-line from this discussion is that an assumption of independence
for advisory prices and revenue will overstate the reliability of sample
estimates. This in turn will bias statistical tests towards a conclusion
that pricing performance is significantly positive. The approach taken
here to deal with the problem is “conservative.” Specifically, statistical
tests assume the minimum possible number of independent observations in
the sample. This minimum is six observations, one for each crop year.
The tests are conservative since conclusions are based on the minimal
possible assumption about the amount of information in the sample. If
test results based on this conservative assumption indicate statistical
significance, then a high degree of confidence can be placed on conclusions.
The cost of this approach is an increased probability that positive pricing
performance is mistakenly attributed to chance.
conservative testing approach is straightforward. First, the average
net advisory price or revenue is computed across all programs active in
a crop year, and it is considered the return for an “average” advisory
program. Second, the averaging process is repeated for each of the crop
years to form a sample of six observations for the average advisory program.
These averages can be found in Tables 12 through 14 under the “Descriptive
Statistics” heading. Third, benchmark prices are subtracted from each
of the average advisory prices or revenues. Fourth, a matched sample
t-test is applied to the six difference observations to determine
if average price performance is statistically significant.
from the benchmarks each crop year and statistical test results for an
average advisory program are presented in Table 22. Note that the average
differences reported in Table 22 are nearly identical to those reported
in Tables 20 and 21. This outcome is not surprising. The average differences
in Table 22 assume an equal weighting of the six crop years, while the
average differences in Tables 20 and 21 assume an equal weighting of each
net advisory price or revenue in the sample. The two types of averages
differ only because the number of advisory programs changes across crop
years. Since this change is quite small across crop years, the difference
in the two types of averages is negligible.
The impact of the
conservative approach to testing the significance of average differences
is reflected in the standard error estimates. This statistic measures
the “typical” error, without regard to sign, in estimating the average
difference between advisory programs and a particular benchmark (Mirer,
1995, p. 238).  For example, the standard error estimate
for the average difference in soybeans versus the 24-month market benchmark
indicates that the typical error in estimating the true difference, without
regard to sign, is five cents per bushel. A measure of reliability is
needed because a sample is being used to make an inference about the “true”
population difference, and the sample will not perfectly reflect the characteristics
of the population. This is the essence of the role of random chance in
estimation. The key point in this regard is that standard error estimates
vary inversely with sample size.
 As a result, standard error estimates (typical estimation
errors) will be much larger if it is assumed that six independent observations
are available as opposed to, say, 152 independent observations.
With this background,
the statistical test results in Table 22 can be considered. The relevant
information in the sample for testing statistical significance is summarized
by the t-statistic, which is just the ratio of the average difference
estimate to the standard error estimate. The two-tail p-value
indicates the probability of observing a value of the t-statistic
(or higher in absolute value) across many random samples. It is usually
argued that p-values must be equal to or smaller than 0.05 to confidently
conclude that average differences do not equal zero (Griffiths, Hill and
Judge, 1993, p. 134). Stated differently, there should be less than a
1 out of 20 chance that the wrong conclusion is reached. In corn, the
p-values for average differences versus both market benchmarks
are substantially larger than 0.05, so it can be concluded that average
differences are insignificantly different from zero. Just the opposite
conclusion is reached versus the USDA farmer benchmark. The p-value
of 0.02 indicates the average difference of 11¢ per bushel in corn is
highly significant. Soybean results versus the market benchmarks are
mixed, with statistical significance indicated for the average difference
from the 20-month benchmark, but not the 24-month benchmark. With a p-value
of 0.07, the 24-month average difference just misses the cutoff for significance.
Like corn, the average difference of 23¢ per bushel in soybeans versus
the USDA farmer benchmark is significantly different from zero. Test
results for 50/50 advisory revenue follow a similar pattern as in soybeans.
Overall, the test results indicate no evidence of statistically significant
average price performance in corn versus market benchmarks, mixed evidence
of significant performance in soybeans and 50/50 advisory revenue versus
market benchmarks and consistent evidence of significant performance versus
the farmer benchmark in corn, soybeans and 50/50 advisory revenue.
When viewing statistical
test results, it is always important to assess whether the nature of the
sample information or the comparisons biases the results in one direction
or the other. There is in fact
a systematic trend in corn and soybean price movements during the sample
period that has an important impact on the tests results. Figure 12 shows
the average pattern of corn and soybean prices over the 24-month marketing
window for the 1995-2000 crop years. These charts are based on the same
harvest equivalent forward and spot cash prices (including LDP/MLGs) used
to compute net advisory prices and the market benchmarks. The downward
trend in corn and soybean prices over the 24-month window is substantial,
with pre-harvest highs in corn and soybean prices about 70¢ and 90¢ per
bushel, respectively, higher than post-harvest lows. A marketing strategy
that systematically priced more heavily in the pre-harvest period relative
to the post-harvest period would have generated much higher returns than
a strategy that did not.
Now consider the
average marketing profiles for corn and soybeans shown in Figure 13.
The market benchmark and advisory program profiles were presented earlier
in Figure 6 and the USDA marketing weights were presented in Figure 7.
Since the USDA marketing weights represent grain deliveries rather than
pricing, a hypothetical marketing profile for farmers also is presented
(labeled “Farmers ?”). It is based on a similar marketing window as the
market benchmarks and advisory programs, but reflects substantially less
pricing in the pre-harvest period.  In light of the downward price trends,
the marketing profiles make it is easy to understand why market benchmarks
and advisory programs generated higher prices than the farmer benchmark
over the last six crop years.
The key question
is whether the price trends and marketing patterns of the last six years
provide a reliable picture of the future. Scenario analysis is helpful
in illustrating the range of possible outcomes. Consider first a scenario
where future upward price trends offset the downward price movements of
the last six crop years and advisors and farmers do not significantly
change their marketing behavior. Future performance results under this
scenario will be just the opposite of those for the last six crop years
because farmers will benefit relatively more than advisors from the upward
price trends. Of course, it is possible for advisory programs to outperform
farmers in an environment of rising prices if they time strategy changes
better than farmers. Consider an alternative scenario where downward
price trends continue to be the norm and advisors and farmers do not significantly
change their marketing behavior. Future performance results basically
will be the same as those observed over the 1995-2000 sample period.
Farmers could equal the performance of advisors under a downward price
trend scenario if they systematically increase pre-harvest pricing. These
scenarios show that future performance differences could range from complete
reversal to no change, depending on future price trends.
sum, it is difficult to know whether a high degree of confidence should
be placed on the average price results for 1995-2000. Pricing performance
depends on a complex set of variables that include corn and soybean price
behavior, advisory program strategies and the marketing behavior of farmers.
It is on open question whether the behavior of these variables in the
last six crop years provides a reliable guide for the future. The persistence
of downward price trends generally observed over 1995-2000 is an especially
hotly debated issue. While the results clearly provide some evidence
on the pricing performance of advisory programs, there is simply no replacement
for a larger sample of crop years when attempting to reach firm conclusions.
In particular, more observations are needed on crop years with rising
prices. Longer-term evidence on the performance of farmers versus the
market would also be especially helpful.
if average price results for 1995-2000 persist into the future, the results
will be open to differing interpretations. The reason is that the definition
of “skill” and “luck” in pricing performance depends on the market theory
considered. Based on efficient market theory, marketing skill is defined
only as the component of average advisory price that exceeds a market
benchmark. The component of average advisory price represented by the
difference between the market benchmark price and the farmer benchmark
price is considered luck. If this difference is positive, it should not
be attributed to the marketing skill of advisory programs under efficient
market theory because a simple no-information strategy of marketing equal
amounts each time period could have achieved the same results. Based
on behavioral market theory, marketing skill is defined as the entire
difference between the average advisory price and the farmer benchmark,
assuming the difference is positive. A luck component is not defined
in this framework. Regardless of the source of performance improvement
over the farmer benchmark, it is regarded as marketing skill.
14 shows the division of average price performance over 1995-2000 into
skill and luck components based on efficient market theory and behavioral
market theory. The number at the top of each bar is the average difference
between advisory price or revenue and the USDA farmer benchmark over 1995-2000
(see Tables 20 and 21). The skill and luck components are computed as
a proportion of this average difference to facilitate comparison across
prices and revenue. Based on efficient market theory and the 24-month
market benchmark (Panel A), only 5% of the 11¢ per bushel average difference
between advisory prices and the farmer benchmark is attributed to skill.
The comparison is more favorable for soybeans, with about 50% of the 22¢
per bushel average difference between advisory prices and the farmer benchmark
in soybeans attributed to skill. About 25% of the $14 per acre average
difference between advisory revenue and farmer benchmark revenue is attributed
to skill. The components attributed to skill versus luck are higher for
the 20-month market benchmark (Panel B), but do not change conclusions
markedly. In contrast, behavioral market theory (Panel C) attributes
all of the average differences between advisory programs and the farmer
benchmark to skill. The differing interpretations cannot be reconciled,
as they reflect profoundly different views about market behavior
Price and Risk Performance
of average advisory prices or revenues to benchmarks is an important indicator
of performance. However, average price or revenue comparisons may not
provide a complete picture of performance. For example, two advisory
programs can generate the same average advisory price, but the risk of
the programs may differ substantially. The difference in risk may be
the result of using different pricing tools (cash, forward, futures or
options), different timing of sales and variation in the implementation
of marketing strategies.
A number of theoretical
frameworks have been developed to analyze decision-making under risk.
One of the simplest and most popular is the mean-variance (EV) model,
which uses variance as a measure of risk. The basic idea in this case
is to look at risk as the chance farmers will fail to achieve the net
price they expect based on following an advisory program. This approach
to quantifying risk does not measure the possibility of loss alone. Risk
is seen as uncertainty: the likelihood that what is expected will fail
to happen, whether the outcome is better or worse than expected. So an
unexpected return on the upside or the downside – a net price of $2.50
or $1.50 per bushel when a net price of $2.00 per bushel is expected –
counts in determining the risk of an advisory program. Thus, an advisory
program whose net price does not depart much from its expected (mean)
price is said to carry little risk. In contrast, an advisory program
whose net price is quite volatile from year-to-year, often departing from
expected net price, is said to be quite risky.
To apply the EV model
to a particular decision, either distributions of outcomes must be normal
or decision-makers must have quadratic utility functions (Hardaker, Huirne
and Anderson, 1997, p.141). If either or both of these conditions hold,
then risky choices can be divided into efficient and inefficient sets
based on the famous EV efficiency rule: if the mean of choice A is greater
than or equal to the mean of choice B, and the variance of A is less than
or equal to the variance of B, with at least one strict inequality holding,
then A is preferred to B by all risk-averse decision makers. Since quadratic
utility has the unlikely characteristic that absolute risk aversion increases
with the level of the outcome, application of the EV model usually is
based upon an assumption of normally distributed outcomes. This presents
a potential problem in the case of market advisory programs that employ
options strategies. Such strategies are designed to create non-normal
price distributions by truncating undesirable prices, either on the downside
or the upside, or both. Fortunately, simulation analysis suggests that
the EV model produces reasonably accurate results even in cases where
options strategies are employed (Hanson and Ladd, 1991; Ladd and Hanson,
1991; Garcia, Adam and Hauser, 1994).
The basic data needed
for assessing market advisory pricing performance in an EV framework are
presented in Table 23. For each advisory program tracked in all six crop
years of the AgMAS study, the six-year average net advisory price or revenue
and standard deviation of net advisory price or revenue is reported.
The average price and standard deviation of the three benchmarks also
are reported. Standard deviation is substituted for variance as the
measure of risk because it easier to understand.
results are the same whether standard deviation or variance is used to
measure risk (Hardaker, Huirne and Anderson, 1997, p.143), hence the use
of the simpler measure. Standard deviation estimates can be thought of
as the “typical” variation in net advisory prices from year-to-year.
The larger the standard deviation for an advisory program, the less likely
a farmer is to get exactly the net price expected, though it is possible
by chance to get a higher price instead of a lower one for any particular
The sample of advisory
programs for the EV analysis is limited to those tracked all six crop
years in order to maximize the number of observations available to estimate
risk (standard deviation).
 Even with this restriction, six observations would appear
to be a relatively small sample for estimating the risks of market advisory
programs. However, as noted in the introduction, Anderson (1974) explored
the reliability of agricultural return-risk estimates based on limited
data and found the surprising result that even as few as three or four
observations can be very useful. Nonetheless, the standard deviations
reported in Table 23 may be somewhat inaccurate estimators of the true
risks of advisory programs. With that in mind, the standard deviations
suggest that the risk of advisory programs varies substantially. In corn,
the standard deviations range from a low of $0.18 per bushel to a high
of $0.67 per bushel. In soybeans, the standard deviations range from
a low of $0.35 per bushel to a high of $1.03 per bushel. Finally, revenue
standard deviations for the 17 programs range from a low of $18 per acre
to a high of $44 per acre. Standard deviations of the benchmark prices
tend to be near the average standard deviation of the 17 advisory programs
for corn, soybeans and 50/50 advisory revenue.
The average price
and risk (standard deviation) for individual advisory programs and the
benchmarks are plotted in Figures 15 through 17. Panel A in each of the
figures is divided into four quadrants based on the average price (or
revenue) and standard deviation of the 24-month market benchmark, while
panel B is divided into four quadrants based on the average price (or
revenue) and standard deviation of the USDA farmer benchmark. Advisory
programs in the upper left quadrant of each chart have a higher average
price (or revenue) and less risk than the benchmark, which is the most
desirable outcome from a farmer’s perspective. According to the EV efficiency
rule introduced earlier, advisory programs in this quadrant are said to
“dominate” the 24-month market benchmark or the USDA farmer benchmark.
Advisory programs in the lower right quadrant have a lower price and more
risk than the benchmark, which is the least desirable outcome from a farmer’s
perspective. The 24-month market benchmark or the USDA farmer benchmark
dominates an advisory program located in this quadrant. The two remaining
quadrants reflect a higher price and more risk than the benchmarks or
a lower price and less risk than the benchmarks. A farmer may prefer
an advisory program to the benchmark in either of these two quadrants,
but this depends on personal preference for risk relative to average price.
The data plotted
in panel A of Figure 15 show that only 1 of the 17 advisory programs in
corn dominates the 24-month market benchmark (upper left quadrant). Six
advisory programs are dominated by the 24-month market benchmark (lower
right quadrant). In contrast, panel B in Figure 15 indicates stronger
performance, with 10 of the 17 advisory programs in corn dominating the
USDA farmer benchmark (upper left quadrant). No program in corn is dominated
by the USDA farmer benchmark (lower right quadrant).
The data plotted
in panel A of Figure 16 indicate that 6 of the 17 advisory programs in
soybeans dominate the 24-month market benchmark (upper left quadrant),
while only four advisory programs are dominated by this market benchmark
(lower right quadrant). Panel B in Figure 16 again suggests stronger
performance, with 13 of the 17 advisory programs dominating the USDA farmer
benchmark (upper left quadrant). Only one program in soybeans is dominated
by the USDA farmer benchmark (lower right quadrant).
are evident for 50/50 advisory revenue. Panel A of Figure 17 shows that
in terms of revenue only 1 of the 17 advisory programs dominates the 24-month
market benchmark (upper left quadrant), while 9 of the 17 are dominated
by this market benchmark (lower right quadrant). Panel B in Figure 17
shows 8 of the 17 programs dominates the USDA farmer benchmark (upper
left quadrant), and no program is dominated by this farmer benchmark (lower
A key motivation
for this analysis is to determine whether consideration of risk alters
performance conclusions based only upon average price. This is most easily
assessed by comparing the proportion of advisory programs that beat the
benchmarks in terms of price in Table 19 with the proportion of programs
that dominate the benchmarks in terms of average price and risk (upper
left quadrant proportions in Figures 15-17). For corn, 51% of the advisory
programs beat the 24-month market benchmark based on price alone over
1995-2000. This drops to 6% when risk is considered. The same proportions
for the USDA benchmark in corn drop from 73 to 59%. For soybeans, 60%
of the advisory programs beat the 24-month market benchmark based on price
alone over 1995-2000, while only 35% do so when risk is considered. The
proportions for the USDA benchmark in soybeans actually increase from
74 to 76%. For 50/50 advisory revenue the declines are the steepest,
with 56% of the advisory programs beating the 24-month market benchmark
based on price alone over 1995-2000 and only 6% doing so when risk is
considered. The proportions for the USDA benchmark in terms of advisory
revenue decrease sharply from 77 to 47%. Overall, the results indicate
that consideration of risk tends to weaken conclusions about the performance
of advisory programs.
The comparisons also
imply that at least part of the skill components discussed in the previous
section (Figure 14) can be attributed to risk. From an efficient market
theory perspective, the limited size of the skill premium in corn and
50/50 advisory revenue appears to be largely explained by risk. The more
substantial size of the skill premium in soybeans is only partly explained
by risk. From a behavioral market theory perspective, the substantial
skill premium in corn is only partly explained by risk. The skill premium
based on this theoretical perspective in soybeans appears to be unaffected
by the consideration of risk, while the skill premium in 50/50 advisory
revenue appears to be substantially explained by risk. Overall, the comparisons
reveal the importance of considering risk in performance evaluations of
market advisory programs.
Two other issues
with respect to risk need to be considered. The first is the sensitivity
of EV comparisons to the alternative market benchmarks. Comparing the
results for the 24-month and 20-month market benchmarks, the number of
programs in the upper-left quadrant increases from one to six for corn
(panel A: Figure 15), from six to ten for soybeans (panel A: Figure 16),
and from one to six for 50/50 advisory revenue (panel A: Figure 17).
These comparisons suggest EV performance results are somewhat sensitive
to changing the market benchmark specification. Nonetheless, the qualitative
implications of the EV comparisons are not substantially different for
the two market benchmarks. The second issue is the statistical significance
of EV performance differences. Paralleling the argument in the previous
section, it is possible that positive performance of advisory programs
in an EV context is due to random chance. Collender (1989) developed
a statistical test that can be applied to EV comparisons. The joint confidence
regions for this test are exceptionally large and indicate advisory program
performance is not significantly different from any of the benchmarks.  With only six observations to estimate
both the mean and standard deviation, the power of this particular test
to detect positive performance is low.
Finally, the mean-variance
evaluation presented in this section can be extended to portfolios of
advisory programs. For example, a soybean portfolio might consist of
50% marketed by advisory program #1, 25% marketed by advisory program
#2 and 25% marketed by advisory program #3. Within a mean-variance framework,
modern portfolio theory (MPT) can be used to form portfolios that have
the highest return for a given level of risk. MPT produces optimal portfolios
by taking advantage of the diversification opportunities available through
combinations of advisory programs. In fact, it is possible for a portfolio
of advisory programs to generate higher prices and less risk than a benchmark,
even if individual advisory programs that make up the portfolio do not.
The potential improvement in performance depends on the degree that net
advisory prices or revenues are uncorrelated. Application of MPT to market
advisory programs represents an interesting area of future research.
Even if, as a group,
advisory programs generate positive marketing returns, there is a wide
range in performance for any given year. For example, soybean net advisory
prices for 1995 vary from $5.71 per bushel to $7.94 per bushel (see Table
13). While this example is one of the most dramatic, the variation across
advisors in other cases is substantial. This raises the important question
of the predictability of advisory program performance from year-to-year.
In other words, is past performance indicative of future performance?
Three types of predictability tests are used to answer this question:
i) the predictability of “winner” and “loser” categories across crop years,
ii) the correlation of advisory program ranks across crop years and iii)
the differences between prices for “top” and “bottom” performing advisory
programs across crop years. The testing procedures have been widely applied
in studies of financial investment performance (e.g., Elton, Gruber, and
Rentzler, 1987; Irwin, Zulauf and Ward, 1994; Lakonishok, Shleifer and
Vishny, 1992; Malkiel, 1995).
The first test of
predictability is based on placing advisory programs into “winner” and
“loser” categories across adjacent crop years. This non-parametric test
is robust to outliers, which is important when analyzing predictability
across all advisory programs. For a given commodity, the first step in
this testing procedure is to form the sample of all advisory programs
that are active in adjacent crop years. The second step is to rank each
advisory program in the first year of the pair (e.g., t = 1997)
based on net advisory price. For example, the program with the highest
net advisory price is ranked number one, and the program with the lowest
net advisory price is assigned a rank equal to the total number of programs
for that commodity in the given crop year. Then the programs are sorted
in descending rank order. The third step is to form two groups of programs
in the first year of the pair: winners are those programs in the top half
of the rankings and losers are programs in the bottom half. The third
step is to rank each advisory program in the second year of the pair (e.g.,
t +1 = 1998) based on net advisory price and once again form winner
and loser groups of programs. The fourth step is to compute the following
counts for the advisory programs in the pair of crop years: winner t-winner
t+1, winner t-loser t+1, loser t-winner t+1,
loser t-loser t+1. If advisory program performance is unpredictable,
approximately the same counts will be found in each of the four combinations.
The appropriate statistical test in this case is known as Fisher’s Exact
Test (Conover, 1999, pp.188-189).
of the winner and loser predictability test are shown in Table 24. Winner
and loser counts for individual crop years indicate a modest difference,
at best, in the chance of a winner or loser in one period being a winner
or loser in the subsequent period. As an example, consider the results
for corn in 1997 and 1998. Of the eleven winners (top half) in 1997,
six are winners in 1998 and five are losers (bottom half). Of the twelve
losers in 1997, five are winners in 1998 and seven are losers. In other
words, the conditional probability of a winner from 1997 repeating in
1998 is 55% (6/11) and the conditional probability of a loser from 1997
repeating in 1998 is 56% (7/12). These probabilities are only slighter
higher than what would result from flipping a coin (randomness). There
is only one case (50/50 revenue, 1999 vs. 2000) where individual year
counts are significantly different from the equal distribution expected
under an assumption of no predictability. These results imply that the
performance of winning and losing advisory programs is not predictable
Pooled counts for
1995-2000 also are shown in Table 24. Pooling provides an overall test
of predictability and should improve the power of the tests by increasing
sample size. Results after pooling are strongest for soybeans, where
the conditional probability of a repeat winner is 61% and the conditional
probability of a repeat loser is 62%. Conditional probabilities are less
than or equal to 60% for corn and 50/50 advisory revenue. The p-value
for pooled test results in corn (0.09) suggests marginally significant
predictability, while the p-value for soybeans (0.02) indicates
significant predictability. No predictability is indicated for 50/50 advisory
revenue. The pooled corn and soybean test results need to be viewed cautiously
given that Fisher’s Exact Test assumes sample observations are independent.
As discussed in the section on average price performance, this clearly
is not the case, and therefore, the p-values reported in Table
24 overstate the true significance of the results. In this light, the
pooled results provide only limited evidence of predictability in the
performance of winning and losing advisory programs through time.
predictability may be limited or non-existent across all advisory programs,
it is possible for sub-groups of advisory programs to exhibit predictability.
In particular, predictability may only be found at the extremes of performance.
That is, only top-performing programs in one year may tend to perform
well in the next year, or only poor-performing programs may perform poorly
in the next year, or both. This is the motivation for the second test
of predictability, which is based on the correlation between ranks of
all advisory programs active in adjacent pairs of crop years. For a given
commodity, the first step in this testing procedure is to once again form
the sample of all advisory programs that are active in both adjacent crop
years. The second step is to rank each advisory program in the first
year of the pair (e.g., t = 1997) based on net advisory price.
Then the programs are sorted in descending rank order. The third step
is to sort and rank the sample of programs in the second year of the pair
(e.g., t + 1 = 1998). The fourth step is to compute the correlation
coefficient between ranks for the two adjacent crop years. If advisory
program performance is unpredictable, the estimated correlation will be
near zero. Assuming the standard error of the correlation coefficient
is approximately equal to
, the appropriate statistical test is a Z-test.
Results of the rank
correlation predictability test are presented in Table 25. Rank correlation
coefficients for corn range from of 0.01 to 0.53. Statistically significant
correlations are found for three of the five comparisons in corn. The
range of rank correlation coefficients for soybeans, 0.10 to 0.65, is
similar to the range for corn. However, statistically significant correlations
are found for only one of the five comparisons in soybeans. Rank correlation
coefficients for 50/50 revenue have the widest range, from 0.00 to 0.72.
Statistically significant correlations are found for two of the five revenue
comparisons. Once again, caution should be used when considering the
reported p-values, as they overstate the significance of the rank
correlation estimates due to the dependence across advisory programs.
Average rank correlation coefficients across the five comparisons are
nearly identical for corn, soybeans and 50/50 advisory revenue. With
values of either 0.34 or 0.35, the average rank correlations suggest some
predictability in the pricing performance of top- and bottom-performing
market advisory programs. It is interesting to observe that the strongest
evidence of predictability is concentrated in the last three crop years
of the sample.
Given the rank correlation
tests results, it is important to determine the magnitude of predictability
in top- and bottom-performing advisory programs. Hence, the third test
of predictability is based on the difference between net advisory prices
for top- and bottom-performing advisory programs across adjacent crop
years. For a given commodity, the first step in this testing procedure
is to sort programs by net advisory price in the first year of the pair
and group programs by quantiles (thirds and fourths). The second step
is to compute the average net advisory price for the quantiles in the
second year of the pair. Note that the same programs make up the quantiles
in the first and second year of the pair. For example, the average price
of the top fourth quantile formed in 1995 is computed for 1996. The third
step is to compute the difference in average price for the top- and bottom-performing
quantiles. If performance for the top- and bottom-performing quantiles
is the same, the difference will equal zero. The appropriate statistical
test in this case is a matched sample t-test of the difference
in the means of the top- and bottom-performing quantiles. There are a
total of five comparisons (1995 vs. 1996, 1996 vs. 1997, 1997 vs. 1998,
1998 vs. 1999 and 1999 vs. 2000), so there are four degrees of freedom
for the t-test. Since differences are computed for an “average”
advisory program in top- and bottom-performing quantiles, dependence across
individual advisory programs is not an issue, and p-values for
the t-test are unbiased. Carpenter and Lynch (1999) recommend
this test because it is well-specified and among the most powerful in
their comparison of several predictability tests for mutual funds.
Results for the t-test
of predictability are shown in Table 26. The first column under each
commodity heading shows the average price of the different quantiles in
the first year of the comparisons (five in total). The average price
for the first year is “in-sample” because this is the formation year for
the quantiles. The second column under each heading reports the average
price of the same quantiles in the second year of the comparisons. The
average price for the second year is “out-of-sample” because this is the
year after formation of the quantiles. In all cases, the average price
or revenue of the top quantile relative to the bottom quantile declines
substantially from the first to the second year of the comparisons. Nonetheless,
the average difference between top- and bottom-performing quantiles for
the second year of the pair is consistently positive. For example, programs
in the top third beat the bottom third in the second year by an average
of 14¢ per bushel in corn, 29¢ per bushel in soybeans and $14 per acre
for revenue. Average differences are significantly different from zero
for both cases in corn and 50/50 revenue and marginally significant in
soybeans. Average prices for the top quantile out-of-sample also exceed
benchmark prices for the same period (1996-2000). Top third returns beat
the 24-month market benchmark by an average of 5¢ per bushel in corn,
26¢ per bushel in soybeans and $9 per acre for 50/50 revenue. Top fourth
returns beat the 24-month market benchmark by an average of 9¢ per bushel
in corn, 31¢ per bushel in soybeans and $12 per acre for 50/50 revenue.
The quantile results
provide evidence that the performance of top- and bottom-performing market
advisory programs can be predicted across adjacent crop years. However,
the evidence is not sufficient to conclude that performance predictability
is useful from an economic standpoint, due to the overlapping nature of
the marketing windows for each crop year. To see the point, consider
the case of a farmer who uses 1995 performance results to select a top-performing
advisory program. Since the 1995 marketing window ends on August 31,
1996, halfway through the 1996 marketing window and one day before the
beginning of the 1997 marketing window, the farmer could not implement
their selection of an advisory program until the 1997 crop year. Performance
would have to persist across three crop years, 1995, 1996 and 1997, for
a farmer to benefit from the predictability.
for non-overlapping crop years are shown in Table 27. The testing procedure
is the same as before, except there are only four comparisons (1995 vs.
1997, 1996 vs. 1998, 1997 vs. 1999, and 1998 vs. 2000) and three degrees
of freedom for the t-test. The results for non-overlapping crop
years continue to show a positive difference between top- and bottom-performing
quantiles in the second year of the pair. However, the magnitude of the
differences is substantially smaller than in the case of adjacent crop
years. For example, programs in the top fourth beat the bottom fourth
in the second year only by an average of 1¢ per bushel in corn, 14¢ per
bushel in soybeans and $1 per acre for revenue. None of the average differences
are significantly different from zero. These results indicate predictability
of pricing performance for top and bottom advisory programs is short-lived,
in the sense that performance does not persist long enough to be taken
advantage of by farmers.
results presented so far are all based on individual crop year comparisons.
It is possible for performance to be predictable over long time horizons,
but unpredictable over short horizons due to a large amount of “noise”
in performance from year-to-year (e.g., Summers, 1986). This is consistent
with the argument that over the long-term “cream rises to the top” in
terms of performance. To assess long-term predictability, the sample
is limited to the 17 programs active in all six crop years of the study.
Next, net advisory prices are averaged for each of the 17 programs for
the first three crop years of the sample (1995-1997) and the second three
years (1998-2000). The three tests of predictability are then applied
to the two sets of averages. The results are striking, in that virtually
no evidence of predictability is found for any of the tests. Winner-loser
counts are quite close to what is expected under randomness, rank correlations
are all insignificantly different from zero (corn and soybean correlations
are actually negative), and the average difference between top- and bottom-performing
programs is very small (zero difference for 50/50 advisory revenue).  These results occur despite the fact
that the same program is ranked first in both sub-periods for corn and
50/50 advisory revenue.
The test results
presented in this section provide little evidence that the pricing performance
of advisory programs can be usefully predicted from past performance.
This conclusion does not mean it is impossible to predict advisory program
performance. There may be other variables that are useful for predicting
performance. Chevalier and Ellison (1999) study whether mutual fund performance
is related to characteristics of fund managers that indicate ability,
knowledge or effort, and find that managers who attended higher-SAT undergraduate
institutions generate systematically higher returns. Barber and Odean
(2000) examine the trading records of individual stock investors and report
that frequent trading substantially depresses investment returns. Similar
factors, such as education of advisors, cash only programs versus futures
and options programs, frequency of futures and options trading, or storage
costs, may be useful in predicting the performance of market advisory
Surveys suggest that
numerous farmers view market advisory services as an important tool in
managing price and income risk. As a result, farmers need information
on the performance “track record” of market advisory services to help
them identify successful alternatives for marketing and price risk management.
The Agricultural Market Advisory Service (AgMAS) Project was initiated
in 1994 with the goal of providing unbiased and rigorous evaluation of
market advisory services.
The purpose of this
research report is to evaluate the pricing performance of market advisory
services for the 1995-2000 corn and soybean crops. No fewer than 23 market
advisory programs are available for each crop year. While the sample
of advisory services is non-random, it is constructed to be generally
representative of the majority of advisory services offered to farmers.
Further, the sample of advisory services includes all programs tracked
by the AgMAS Project over the study period, so pricing performance results
should not be plagued by survivorship bias. The AgMAS Project subscribes
to all of the services that are followed and records recommendations on
a real-time basis, which should prevent pricing performance results from
being subject to hindsight bias.
assumptions are made to produce a consistent and comparable set of results
across the different advisory programs. These assumptions are intended
to accurately depict “real-world” marketing conditions. Several key assumptions
are: i) with a few exceptions, the marketing window for a crop year runs
from September before harvest through August after harvest, ii) cash prices
and yields refer to a central Illinois farm, iii) storage is assumed to
occur at on-farm or commercial sites, and iv) marketing loan recommendations
made by advisory programs are followed wherever feasible. Based on these
assumptions, the net price received by a subscriber to market advisory
programs is calculated for the 1995-2000 corn and soybean crops.
Two different types
of benchmarks are developed for the performance evaluations. Efficient
market theory implies that the return offered by the market is the relevant
benchmark. In the context of this study, a market benchmark should measure
the average price offered by the market over the marketing window of a
representative farmer who follows advisory program recommendations. Both
a 24-month and a 20-month market benchmark are specified in order to test
the fragility of performance results to different market benchmark assumptions.
Behavioral market theory suggests that the average return actually achieved
by market participants as an appropriate benchmark. In the context of
the present study, a behavioral benchmark should measure the average price
actually received by farmers for a crop. A farmer benchmark is specified
based upon the USDA average price received series for corn and soybeans
in Illinois. All benchmarks are computed using the same assumptions applied
to advisory program track records.
Four basic indicators
of performance are applied to advisory program prices and revenues over
1995-2000. The first indicator is the proportion of advisory programs
that beat benchmark prices. Between 51 and 59% of the programs in corn
have net advisory prices above market benchmarks over 1995-2000, while
74% of the programs have prices above farmer benchmarks. Performance
is stronger in soybeans. Between 61 and 70% of advisory programs in soybeans
have advisory prices above the market benchmarks over 1995-2000 and 74%
are above the farmer benchmarks. Between 57 and 66% of advisory programs
have revenue above the market benchmarks over 1995-2000, while 77% have
revenue above the farmer benchmark. The results provide mixed performance
evidence with respect to market benchmarks and consistently positive evidence
with respect to the USDA farmer benchmark.
The second indicator
is the difference between the average price of advisory programs and the
market or farmer benchmarks. The results basically tell the same story
as those based on the proportion beating the benchmarks. Average differences
from market benchmarks for corn over 1995-2000 are small, ranging from
zero to three cents per bushel. At 11¢ per bushel, the average difference
from the farmer benchmark for corn is larger. Average differences for
soybeans over 1995-2000 are even larger for both types of benchmarks,
ranging from 13 to 17¢ per bushel versus market benchmarks and equaling
22¢ per bushel versus the farmer benchmark. Average differences for advisory
revenue range from three to seven dollars per acre for market benchmarks
over 1995-2000. The average revenue difference versus the USDA farmer
benchmark is $14 per acre.
tests indicate no evidence of significant average pricing performance
in corn versus market benchmarks, mixed evidence of significant performance
in soybeans and 50/50 advisory revenue versus market benchmarks and consistent
evidence of significant performance versus the farmer benchmark in corn,
soybeans and 50/50 advisory revenue. Caution should be used when considering
the results, due to the relatively small sample of crop years available
for analysis. In particular, the presence of sharp downward price trends
in most crop years makes it difficult to determine whether the 1995-2000
sample provides a reliable guide to future differences in pricing performance.
The third indicator
is the average price and risk of advisory programs relative to benchmarks.
A small number of advisory programs in corn generate a combination of
average price and risk superior to market benchmarks over 1995-2000.
In sharp contrast, a majority of programs in corn generate a combination
of average price and risk superior to the USDA farmer benchmark. A moderate
number of programs in soybeans generate a combination of average price
and risk superior to market benchmarks, while most programs generate a
combination superior to the USDA farmer benchmark. Relatively few advisory
programs generate a combination of revenue and risk superior to market
benchmarks. A moderate number of programs produce a revenue combination
superior to the USDA farmer benchmark. The results indicate that consideration
of risk tends to weaken performance results based only upon average price.
The fourth indicator
is the predictability of advisory program performance from year-to-year.
“Winner” and “loser” predictability results are similar for corn, soybeans
and advisory revenue. The conditional probability of a winner (top half
of programs) repeating averages 57% and the conditional probability of
a loser (bottom half of programs) repeating averages 60%. These probabilities
are only slighter higher than what would result from flipping a coin (randomness),
and provide scant evidence that pricing performance for all advisory programs
can be predicted from past performance. The performance of top- and bottom-performing
programs does not appear to be predictable in a useful sense either.
For example, comparisons of non-overlapping crop years show that programs
in the top fourth beat the bottom fourth only by an average of 1¢ per
bushel in corn, 14¢ per bushel in soybeans and $1 per acre for 50/50 advisory
the results provide an interesting picture of the performance of market
advisory programs in corn and soybeans. There is limited evidence that
advisory programs as a group outperform market benchmarks, particularly
after considering risk. In contrast, substantial evidence exists that
advisory programs as a group outperform the farmer benchmark, even after
taking risk into account. Whether the superior performance of advisory
programs versus the farmer benchmark is attributed to skill or luck depends
on the theoretical perspective. Efficient market theory favors a luck
interpretation, while behavioral market theory favors a skill interpretation.
Regardless of the theoretical perspective, there is little evidence that
advisory programs with superior performance can be usefully selected based
on past performance.
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A: A Cautionary Note on the Use of AgMAS Net Advisory Prices and Benchmarks
The net advisory
prices and benchmarks computed by the AgMAS Project are designed to reflect
“real-world” marketing conditions and assure that net advisory service
prices and benchmarks are computed on a rigorously comparable basis.
This latter point is especially important, as performance evaluations
must compare “apples to apples” and not “apples to oranges.” Comparison
problems may arise if prices computed by an individual farmer or another
market advisory service are compared to AgMAS net advisory prices and
and foremost, AgMAS net advisory prices and benchmarks are stated on a
harvest equivalent basis. This means that spot cash prices for post-harvest
sales are adjusted for storage costs, which include physical storage charges,
shrinkage charges and interest opportunity costs. The impact of this
assumption is illustrated in the top panel of Figure 18 for corn and the
bottom panel for soybeans. The top line in each chart shows the 2000
harvest cash price for each crop (corn: $1.64 per bushel; soybeans: $4.56
per bushel). The bottom line reflects a cash sale at the same harvest
price one to eleven months after harvest, with the cash price adjusted
for commercial costs of storage. As a specific example, consider a six-month
storage horizon for corn. In this case, the cash price of the sale six-months
after harvest is assumed to be $1.64 per bushel, the same as the harvest
cash price (equivalent to saying cash prices do not change over the six-month
storage period). However, the harvest equivalent price for the sale six
months after harvest is only $1.34 per bushel after adjusting for commercial
storage costs. Thus, the difference between unadjusted and adjusted post-harvest
prices in this example is 30¢ per bushel, a substantial difference by
any standard. The magnitude of the difference is larger for longer storage
horizons and for soybeans relative to corn. Note also that the difference
will not be as large if on-farm variable costs of storage are assumed
instead of commercial costs.
This discussion should
make clear the potential pitfalls in comparing the unadjusted average
cash price for an individual farmer or another market advisory service
to the harvest equivalent advisory prices and benchmarks computed by the
AgMAS Project. If such a comparison is made, it is not difficult to imagine
a scenario where it is mistakenly concluded that the performance of the
farmer or market advisory service is superior to the advisory services,
market benchmarks and farmer benchmarks included in the AgMAS Project.
Second, AgMAS evaluations
assume a particular geographic location. Specifically, the evaluation
is designed to reflect conditions facing a representative central Illinois
corn and soybean farmer. This means comparisons made by farmers or advisory
services in other areas of the US may not be valid, because yields and
basis patterns may be quite different. The differences in yields and
basis patterns could have a substantial impact on prices computed for
farmers or advisory services in another area. The resulting bias could
be either up or down relative to AgMAS advisory prices and benchmarks,
depending on local conditions.
Third, wherever feasible,
marketing loan recommendations from advisory programs are followed by
the AgMAS Project. Consequently, marketing loan payments or benefits
are incorporated into net advisory prices. Market and farmer benchmark
prices also include marketing loan payments or benefits. Hence, it would
not be appropriate to compare prices for individual farmers or another
market advisory service if marketing loan payments or benefits are not
included in the prices or included in some other way.
sum, it is inappropriate to directly compare prices for individual farmers
or another market advisory service to AgMAS net advisory prices or benchmarks
unless the same assumptions are used. To make valid comparisons, AgMAS
assumptions regarding storage costs, yield, basis, and marketing loans
have to be applied.
For a given commodity
and benchmark the statistical model underlying the average price performance
tests can be stated as,
is the net price for the ith advisory program in the tth crop year,
is the benchmark price in the tth crop year,
is the expected value (mean) of the difference between the
net price for the ith advisory program and the benchmark price
is the error term for the ith advisory program in the tth crop
year. Note that the model assumes the expected value of the difference
between net advisory prices and the benchmark is the same for all programs
and crop years. The statistical assumptions about the error term are,
The first assumption,
that errors are normally distributed with an expected value of zero and constant
variance equal to
that errors for the same advisory program are not correlated through time. The
that errors for the same crop year are not correlated across advisory programs.
The discussion in the section on average price performance focuses on correlation
across advisory programs because this is considered the most serious problem.
As shown in the section on predictability of performance, there is some evidence
that net prices for advisory programs are positively correlated through time.
However, this correlation is substantially smaller in magnitude and does not appear
to be as serious of a problem.
 King, Lev and Nefstad (1995) examine the corn and soybean
recommendations of two market advisory services for a single year.
The focus of their study is not pricing performance, but a demonstration
of the market accounting program Market Tools. Several analyses
have appeared in the popular farm press. Marten (1984) examines the
performance of six advisory services for corn and soybeans over 1981
through 1983. Otte (1986) investigates the performance of three services
for corn over the period 1980 through 1984. Each of these studies indicates
the average price generated by the services exceeds a benchmark price.
Top Producer magazine has provided evaluations of some advisory
services in corn, soybeans and wheat for a number of years (e.g., Powers,
1993). Kastens and Schroeder (1996) examine futures trading profits
based on the information reported in Top Producer for the 1998-1996
crop years. They find negative trading profits for wheat and positive
trading profits for corn and soybeans.
 Dr. Darrel L. Good and Dr. Scott H. Irwin of the University
of Illinois at Urbana-Champaign jointly direct the project. Correspondence
with the AgMAS Project should be directed to: Dr. Joao Martines-Filho,
AgMAS Project Manager, 434a Mumford Hall, 1301 West Gregory Drive, University
of Illinois at Urbana-Champaign, Urbana, IL 61801; voice: (217)333-2792;
fax: (217)333-5538; e-mail: email@example.com. The AgMAS Project also
has a website that can be found at the following address: /agmas/.
Funding for the AgMAS project is 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.
 A recent national survey of advisory service subscribers
by the AgMAS Project provides some perspective on the dimensions of
this problem. While only 11 percent of the survey respondents said
they followed market advisory service recommendations closely, two-thirds
indicated they followed the recommendations loosely. Further, when
asked to rate the impact of advisory service recommendations on their
marketing, subscribers gave an average rating of six on a nine-point
scale, with a one indicating no impact at all and a nine indicating
great impact. To the extent that farmers subscribe to market advisory
services, these results suggest that the average price received by farmers
for a crop is influenced by the marketing advice of advisory services.
For more detail on the survey results see Pennings, Good, Irwin and
is the number of crop years in the sample, yt is the
advisory program’s net price for the tth crop year
is the average net advisory price over the T crop years.
 Fisher’s Exact Test is the appropriate statistical test
because both row and column totals are pre-determined in the 2 x 2 contingency
table formed on the basis of winner and loser counts.
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