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Research Reports

 

Report 2003-06: The Pricing Performance of Market Advisory Services in Corn and Soybeans Over 1995-2001: A Non-Technical Summary

June, 2003                                                                                                               

Scott H. Irwin, Joao Martines-Filho and Darrel L. Good[1]

Copyright 2003 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 copies.


DISCLAIMER

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.


Introduction

Farmers in the US consistently identify price and income risk as one of the greatest management challenges they face. Surveys suggest that numerous farmers view professional market advisory services as an important tool in managing price and income risk. As a result, there is a need to develop an ongoing "track record" of the performance of market advisory services to assist farmers in identifying successful alternatives for marketing and price risk management. The Agricultural Market Advisory Service (AgMAS) Project was initiated in 1994 with the goal of providing such information.

The purpose of this research report is to summarize the pricing performance of professional market advisory services for the 1995-2001 corn and soybean crops. The results for 1995-2000 were released in earlier AgMAS research reports, while the results for the 2001 crop year are new. Complete details on data collection, computation of net advisory prices and benchmarks and pricing performance tests can be found in the full AgMAS research report by Irwin, Martines-Filho and Good (2003).

At least 23 advisory programs are included in the evaluations for each commodity and 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. Two indicators of pricing performance are presented. The first indicator is the proportion of advisory programs that beat benchmark prices. The second indicator is the average price of advisory programs relative to benchmarks. Both market and farmer benchmarks are considered in the evaluations.

At the outset, it is important to point out that only seven 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 seven 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 seven years of data currently available on advisory service pricing performance may be used to make some careful conclusions. Caution obviously is in order given the possibility of results being due to random chance in a relatively small sample of crop years.


Computing the Returns to Marketing Advice

In order to evaluate the returns to the marketing advice generated by advisory services, the AgMAS Project purchases a subscription to each of the programs offered by a service. [2]The information is received electronically via websites, e-mail or satellite service (DTN). Staff members of the AgMAS Project read the information provided by each advisory program on a daily basis. As a result, "real-time" recommendations are obtained.

After AgMAS staff collects the stream of recommendations for a particular crop year, 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. 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 facing a representative central Illinois corn and soybean farmer. 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) on-farm or commercial physical storage costs, as well as interest opportunity costs, are charged to post-harvest sales, iii) brokerage costs are subtracted for all futures and options transactions and iv) Commodity Credit Corporation (CCC) marketing loan recommendations made by advisory programs are followed wherever feasible. Based on these and other assumptions, the net price received by a subscriber to a market advisory program is calculated for the 1995-2001 corn and soybean crops. It should be interpreted as the harvest-equivalent net price received by a farmer because post-harvest sales are adjusted for physical storage and interest opportunity costs.

The next step in evaluating pricing performance is specification of objective standards of performance. These objective standards typically are referred to as "benchmarks." It is commonplace to compare performance to benchmarks in other economic contexts, such as financial investments. Some of the best-known stock investment benchmarks are the Dow-Jones Industrials Index, S&P 500 Index and the Wilshire 5000 Index.

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 pricing 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. The first market benchmark averages cash price over the entire 24-month marketing window, which begins on September 1 of the year prior to harvest and ends on August 31 of the year after harvest. The second market benchmark is computed by simply deleting the first four months of the 24-month pricing-window from the computations of the average market price. Behavioral market theory suggests that the average return actually achieved by market participants is 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. Note that the same simulation assumptions applied to advisory service track records (e.g., storage costs) are applied to the market and farmer benchmarks.


Net Advisory Prices and Benchmarks for 1995 - 2001

Net advisory prices and benchmarks for the 1995-2001 crop years are reported in Tables 1 and 2.  In order to obtain a consistent set of net advisory prices and benchmarks for the entire sample period, commercial storage costs are assumed. It is not possible to present parallel results assuming on-farm variable costs of storage, because the AgMAS Project first computed net advisory prices and benchmarks under this alternative storage cost assumption for the 2000 crop year. See the previously mentioned AgMAS research report by Irwin, Martines-Filho and Good for 2000 and 2001 crop year results that assume on-farm variable costs of storage. Also note that some of the market advisory services included in the s are not evaluated for all six years.

Table 1 shows the average advisory price for corn ranges between $1.99 per bushel in 2001 and $3.03 per bushel in 1995 (based on commercial storage costs). 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 near a dollar per bushel. The three alternative benchmark prices for corn are shown at the bottom of Table 1.  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 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 2, the average advisory price for soybeans ranged from $5.44 per bushel in 2000 to $7.27 per bushel in 1996 (based on commercial storage costs). 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 2.  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.

Since many subscribers to market advisory services produce both corn and soybeans, it is relevant to examine a combined measure of corn and soybean pricing performance for each market advisory program. One way to aggregate the results is to calculate the per-acre revenues implied by the pricing performance results. The per-acre revenue for each commodity is found by multiplying the net advisory price for each market advisory service by the actual central Illinois corn or soybean yield for each year. A simple average of the two per acre revenues is then taken to reflect a farm that uses a 50/50 rotation of corn and soybeans.

Table 3 contains the combined corn and soybeans revenue results (based on commercial storage costs). The lowest average advisory revenue, $287 per acre, occurred in 2001, 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 seven crop years (1995, 1999 and 2000) the range in advisory revenue exceeds $100 per acre.


Advisory Service Pricing Performance Over 1995-2001

Before considering the pricing performance results, two important issues need to be discussed. First, the results presented in this section 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 et al. (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 marketing recommendations evaluated in this section, 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.


Directional Performance

The 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.

The proportion of advisory programs in corn, soybeans and 50/50 advisory revenue above the benchmarks over 1995-2001 is presented in Table 4.  Considering corn first (Panel A: Table 4), 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 seven crop years. The average proportion for 1995-2001 is 49% versus the 24-month benchmark and 60% versus the 20-month benchmark, indicating a zero to marginal chance of advisory prices in corn beating market benchmark prices. In contrast, the proportion of net advisory prices above the farmer benchmark exceeds 50% each crop year. The average proportion above the farmer benchmark over 1995-2001 is 73%. 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 farmer benchmark.

Moving to soybeans (Panel B: Table 4), 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. No clear trend is apparent for the proportions versus either market benchmark. Despite these differences for individual crop years, the average proportions for 1995-2001, 63% versus the 24-month benchmark and 74% versus the 20-month benchmark, both indicate a better than average chance of advisory prices beating market benchmark prices in soybeans. The proportions above the farmer benchmark are all above 50%, except the 2001 crop when only 27% of the programs were able to beat the farmer benchmark. The average proportion above the farmer benchmark over 1995-2001 is 67%. This indicates a reasonable chance of market advisory programs generating net prices in soybeans higher than the farmer benchmark.

Given the combined nature of 50/50 advisory revenue, it is not surprising that revenue proportions (Panel C:Table 4) typically are between those of corn and soybeans. The average proportion for 1995-2001 is 56% versus the 24-month benchmark and 70% 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 farmer benchmark exceeds 50% each crop year, except for 2001, and averages 71% over 1995-2001. This indicates a sizable chance of advisory revenue beating farmer benchmark revenue. It is interesting to note that 100% of the advisory programs in 1998 generated revenue that exceeded the 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 farmer benchmark in one commodity that more than offset the losses below the benchmark in the other commodity.

Overall, the directional performance results over 1995-2001 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 farmer benchmark.


Average Price Performance

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 5 and 6 basically tell the same story as those based on the proportion beating the benchmarks. Average differences from market benchmarks for corn over 1995-2001 (panel A: Table 5) are small, ranging from zero to three cents per bushel. [3] At 10¢ cents per bushel, the average difference from the farmer benchmark for corn is larger. Average differences for soybeans over 1995-2001 (panel B: Table 5) are even larger for both types of benchmarks, ranging from 11 to 18¢ per bushel versus market benchmarks and 17¢ 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 1995-2001 (Table 6).  The average revenue difference versus the farmer benchmark is $12 per acre [4]   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 5), where the average difference from the 24-month market benchmark is -4¢ per bushel, while the average difference from the 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 ($12 per acre) is nearly four percent of average farmer benchmark revenue. This represents a substantial increase in net farm income (defined as returns to farm operator management, labor and capital), typically about $50 per acre for grain farms in Illinois (Lattz, Cagley and Raab, 2002). The comparison does not account for yearly subscription costs, which is not a major problem because subscription costs are quite small relative to revenue. For example, subscription costs are less than one-tenth of one percent of average farmer benchmark revenue for a 2,000 acre farm and about two-tenths of one percent for a 500 acre farm. A more serious issue is fully accounting for the cost of implementing, monitoring and managing the marketing strategies recommended by advisory programs. Such costs are difficult to measure, but may well be substantial (Tomek and Peterson, 2001).

When viewing statistical test results, it is always important to assess whether the nature of the sample information or the comparisons bias 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 1 shows the average pattern of corn and soybean prices over the 24-month marketing window for the 1995-2001 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 60¢ and 80¢ 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.

Next, consider the average “marketing profiles” found in Figure 2 for corn and soybeans over the 1995-2000 crop years.[5]  . The marketing profiles show the average amount of corn and soybean crops priced (sold) by market benchmarks, advisory programs and farmers 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. Since USDA marketing weights represent grain purchases, which are not necessarily the same as pricing weights due to farmers' use of forward contracts, the marketing profile for farmers is only hypothetical. 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 advisor programs generated higher average prices than the farmer benchmark over the last seven crop years.

The key question is whether the price trends and marketing patterns of the last seven 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 seven 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 seven 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 1995-2001 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 and marketing behavior of services and farmers.

In sum, 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 seven crop years provides a reliable guide for the future. The persistence of downward price trends generally observed over 1995-2001 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 helpful.

Please note that the AgMAS research report by Irwin, Martines-Filho and Good (2003) contains additional pricing performance results. In particular, the additional results show that consideration of risk tends to weaken performance results based only upon average price and that it is difficult to predict the pricing performance of advisory programs from past performance.


Summary and Conclusions

The purpose of this research report is to summarize the pricing performance of professional market advisory services for the 1995-2001 corn and soybean crops. Two indicators of performance are presented. The first indicator is the proportion of advisory programs that beat benchmark prices. Between 49 and 60% of the programs in corn have net advisory prices above market benchmarks over 1995-2001, while 73% of the programs have prices above the farmer benchmark. Performance is stronger in soybeans. Between 63 and 74% of advisory programs in soybeans have advisory prices above the market benchmarks over 1995-2001 and 67% are above the farmer benchmarks. Between 56 and 70% of advisory programs have revenue above the market benchmarks over 1995-2001, while 71% 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 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 1995-2001 are small, ranging from zero to three cents per bushel. At 10¢ per bushel, the average difference from the farmer benchmark for corn is larger. Average differences for soybeans over 1995-2001 are even larger for both types of benchmarks, ranging from 11 to 18¢ per bushel versus market benchmarks and equaling 17¢ per bushel versus the farmer benchmark. Average differences for advisory revenue range from three to seven dollars per acre for market benchmarks over 1995-2001. The average revenue difference versus the farmer benchmark is $12 per acre.

The pricing performance results over 1995-2001 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 farmer benchmark. 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 1995-2001 sample period provides a reliable guide to future differences in pricing performance.

Overall, the results of this study provide an interesting picture of the performance of market advisory programs in corn and soybeans. There is mixed evidence that advisory programs as a group outperform market benchmarks. In contrast, there is more evidence that advisory programs as a group outperform the farmer benchmark. This raises the intriguing possibility that even though advisory services may not "beat the market," they nonetheless provide an opportunity for farmers to improve marketing performance because farmers under-perform the market. Mirroring debates about stock investing (e.g., Damato, 2001), the relevant issue is then whether farmers can most effectively improve marketing performance by pursuing "active" strategies, like those recommended by advisory services, or "passive" strategies, which involve routinely spreading sales across the marketing window. Recently, a number of grain companies began offering "averaging" or "indexing" contracts that allow farmers to easily implement a passive approach to marketing (Smith, 2001). The rising interest in these "new generation" marketing contracts suggests the potential for historic changes in farmers' approach to grain marketing. Future research that provides a better understanding of the costs and benefits of active versus passive approaches to marketing will be especially valuable.


References

Anderson, J.R.  “Sparse Data, Estimational Reliability, and Risk-Efficient Decisions.”  American Journal of Agricultural Economics, 55(1974): 564-572.

Damato, K. “Index Funds: 25 Years in Pursuit of the Average.” The Wall Street Journal, April 9, 2001, pp. R1, R6.

Irwin, S.H., J. Martines-Filho, and D.L. Good. “The Pricing Performance of Market Advisory Services In Corn and Soybeans Over 1995-2001.”  AgMAS Project Research Report 2003-05, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, June 2003. (http://www.farmdoc.uiuc.edu/agmas/reports/index.html)

Lattz, D.H., C.E. Cagley and D.D. Raab.  Summary of Illinois Farm Business Records for 2001, Circular 1384, University of Illinois Extension, 2002.

Martines-Filho, J., S.H. Irwin, D.L. Good, S.M. Cabrini, B.G. Stark, W. Shi, R.L. Webber, L.A. Hagedorn and S.L. Williams.  “Advisory Service Marketing Profiles for Corn Over 1995-2000,AgMAS Project Research Report 2003-03, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, June 2003a. (http://www.farmdoc.uiuc.edu/agmas/reports/index.html)

Martines-Filho, J., S.H. Irwin, D.L. Good, S.M. Cabrini, B.G. Stark, W. Shi, R.L. Webber, L.A. Hagedorn and S.L. Williams.  “Advisory Service Marketing Profiles for Soybeans Over 1995-1999,AgMAS Project Research Report 2003-04, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, June 2003b. (http://www.farmdoc.uiuc.edu/agmas/reports/index.html)

Pennings, J.M.E., D.L. Good, S.H. Irwin and J.K. Gomez.  “The Role of Market Advisory Services in Crop Marketing and Risk Management: A Preliminary Report of Survey Results,”  AgMAS Project Research Report 2001-02, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, March 2001. (http://www.farmdoc.uiuc.edu/agmas/reports/index.html)

Smith, L.H. “Can Robots Replace a Marketing Mastermind?”  Top Producer, November 2001, pp. 12-13.

Tomek, W.G. and H.H. Peterson.  “Risk Management in Agricultural Markets: A Review.”  Journal of Futures Markets, 21(2001):853-985.


Appendix: 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 benchmarks.

First, 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 A1 for corn and the bottom panel for soybeans. The top line in each chart shows the 2001 harvest cash price for each crop (corn: $1.87 per bushel; soybeans: $4.33 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 $1.87 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 $1.58 per bushel after adjusting for commercial storage costs. Thus, the difference between unadjusted and adjusted post-harvest prices in this example is 29¢ 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.

In 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.


Endnotes


[1] Scott H. Irwin is a Professor in the Department of Agricultural and Consumer Economics at the University of Illinois at Urbana-Champaign.  Joao Martines-Filho is the former Manager of the AgMAS and farmdoc Projects in the Department of Agricultural and Consumer Economics at the University of Illinois at Urbana-Champaign.  Darrel L. Good is a Professor in the Department of Agricultural and Consumer Economics at the University of Illinois at Urbana-Champaign.  The authors gratefully acknowledge the research assistance of Lewis Hagedorn, Wei Shi, Rick Webber and Silvina Cabrini, AgMAS graduate research assistants in the Department of Agricultural and Consumer Economics at the University of Illinois at Urbana-Champaign.  Helpful comments on this research report were received from members of the AgMAS Project Review Panel.  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.  Correspondence with the AgMAS Project should be directed to: 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: agmas@uiuc.edu.  The AgMAS Project also has a website that can be found at the following address: http://www.farmdoc.uiuc.edu/agmas/

[2]The term "advisory program" is used because several advisory services have more than one distinct marketing program.

[3] Differences are calculated as advisory price minus benchmark price. So, a positive difference indicates an advisory price above the benchmark price and vice versa.

[4] To facilitate direct comparisons across corn, soybeans and 50/50 revenue, average differences for 1995-2001 also are computed on a percentage basis:

 

Average Difference Between Advisory Programs and Benchmark

 

24-Month Market

20-Month Market

Farmer

Corn

-0.1%

+1.7%

+4.8%

Soybeans

+2.0%

+3.2%

+3.3%

50/50 Revenue

+0.9%

+2.4%

+4.1%

It is interesting to note that the percentage difference versus the farmer benchmark is larger for corn than soybeans, just the reverse of the results on a cents per bushel basis.

[5] A detailed explanation of the construction of the marketing profiles and results for individual advisory programs and crop years can be found in Martines-Filho et al. (2003a, 2003b). Note that these reports do not contain marketing profiles for the 2001 crop year. The AgMAS Project will compute the 2001 profiles at a later date.

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