OFFICES IN PRINCIPAL CITIES WORLDWIDE

AN INDEPENDENT ACTUARIAL REVIEW OF 

QUALITY ADJUSTMENT 

5.6.3 SECTION 107 OF ARPA 

Prepared for: 

Risk Management Agency 

United States Department of Agriculture 

Prepared by: 

Milliman USA 

David Appel, Ph.D. 

Richard Lord, FCAS, MAAA 

Thomas Chan, Ph.D. 

June 4, 2002 
MILLIMAN USA 

AN INDEPENDENT ACTUARIAL REVIEW OF 

QUALITY ADJUSTMENT 

5.6.3 SECTION 107 OF ARPA 

TABLE OF CONTENTS 

INTRODUCTION ................................................................................................................................................1

SCOPE OF ANALYSIS........................................................................................................................................2

CONCLUSIONS AND RECOMMENDATIONS ..........................................................................................................4

LOCAL QUALITY DISCOUNT RATES.................................................................................................................................4

INSURABILITY OF COVERED PERILS................................................................................................................................8

CONSTRUCTION OF SCHEDULES ....................................................................................................................................9

RESULTS OF ANALYSIS ...............................................................................................................................................10

GRADING – MONITORING AND COMPLIANCE..................................................................................................................12

COTTON QUALITY ADJUSTMENT ON A BASIS SMALLER THAN A UNIT ................................................................................13

THE ECONOMIC SIGNIFICANCE OF QUALITY ADJUSTMENT AND CROP INSURANCE IN CROP PRODUCTION AND 

MARKETING...................................................................................................................................................14

CORN .......................................................................................................................................................................14

SOYBEANS ................................................................................................................................................................16

COTTON....................................................................................................................................................................19

GRAIN SORGHUM.......................................................................................................................................................20

WHEAT .....................................................................................................................................................................21

BARLEY.....................................................................................................................................................................23

OATS........................................................................................................................................................................25

RYE..........................................................................................................................................................................27

FLAXSEED .................................................................................................................................................................28

CANOLA ....................................................................................................................................................................28

SUNFLOWER..............................................................................................................................................................29

SAFFLOWER ..............................................................................................................................................................30

RICE.........................................................................................................................................................................31

BUCKWHEAT..............................................................................................................................................................32

CURRENT PROGRAM REVIEW.........................................................................................................................34

MARKET RESEARCH ......................................................................................................................................36

OBJECTIVE ................................................................................................................................................................36

DATA SOURCES .........................................................................................................................................................36

SURVEY OF ELEVATORS .............................................................................................................................................37

ANALYSIS PROCEDURE...............................................................................................................................................38

CORN ANALYSIS ........................................................................................................................................................41

SOYBEAN ANALYSIS ...................................................................................................................................................47

GRAIN SORGHUM ANALYSIS........................................................................................................................................51
MILLIMAN USA 

WHEAT ANALYSIS ......................................................................................................................................................53

BARLEY ANALYSIS......................................................................................................................................................60

OATS ANALYSIS.........................................................................................................................................................62

RYE ANALYSIS...........................................................................................................................................................64

FLAX ANALYSIS..........................................................................................................................................................65

CANOLA ANALYSIS .....................................................................................................................................................66

OIL SUNFLOWERS ANALYSIS .......................................................................................................................................67

CONFECTION SUNFLOWER ANALYSIS ...........................................................................................................................68

SAFFLOWER ANALYSIS ...............................................................................................................................................69

RICE ANALYSIS..........................................................................................................................................................70

DARK ROAST CONFECTION SUNFLOWER ANALYSIS .......................................................................................................71

BUCKWHEAT ANALYSIS...............................................................................................................................................72

RISK ANALYSIS..............................................................................................................................................73

CROPS IN GENERAL ...................................................................................................................................................73

COTTON....................................................................................................................................................................73

CLOSING.......................................................................................................................................................76

REFERENCE ..................................................................................................................................................77

APPENDIX A ..................................................................................................................................................78

CORN .......................................................................................................................................................................78

SOYBEAN ..................................................................................................................................................................81

WHEAT .....................................................................................................................................................................85

GRAIN SORGHUM.....................................................................................................................................................102

BARLEY...................................................................................................................................................................105

OATS......................................................................................................................................................................106

CANOLA ..................................................................................................................................................................106

OIL SUNFLOWER......................................................................................................................................................107

APPENDIX B ................................................................................................................................................109

COVERD QUALITY DEFICIENCIES ...............................................................................................................................109

FSA QUALITY DISCOUNTS ........................................................................................................................................111
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MILLIMAN USA 

AN INDEPENDENT ACTUARIAL REVIEW OF 

QUALITY ADJUSTMENT 

5.6.3 SECTION 107 OF ARPA 

INTRODUCTION 

Milliman USA, Inc. (Milliman) was engaged by the Risk Management Agency (RMA) of the United 

States Department of Agriculture (USDA) to perform a review of quality adjustment procedures as 

required by Section 107 of the Agricultural Risk Protection Act of 2000 (ARPA). This report presents the 

results of that review. 

The following sections present the (1) Scope of Analysis, (2) Conclusions and Recommendations, (3) The 

Economic Significance of Quality Adjustment and Crop Insurance in Crop Production and Marketing, (4) 

Current Program Review, (5) Market Research Analysis, (6) Risk Analysis and (7) Closing. 
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MILLIMAN USA 

SCOPE OF ANALYSIS 

Section 107 of ARPA requires a review of current quality loss adjustment procedures in order to develop 

procedures that more accurately reflect local quality discounts. Based on the results of this review, RMA 

may make adjustments to the current program, after consideration of actuarial soundness and prevention 

of fraud, waste and abuse1

. The Statement of Work issued by RMA further requires that the 

recommended procedures be easy to maintain, have a low maintenance cost, be national in scope, and be 

uniform for all crops. In the broadest sense, this report contains our review of current nationwide 

discounts and the development of recommendations regarding alternative indemnification methods 

consistent with the above considerations. 

Our analysis includes a review of current quality discount adjustment factors for the following 

agricultural commodities: corn, soybeans, grain sorghum, wheat, barley, oats, rye, flax, canola, sunflower 

and safflower. This review is based on a study of the procedures used to develop the discount factors for 

these crops and an econometric analysis based on discount schedules from a representative sample of 

major purchasers, warehouses and country and terminal elevators. 

Our analysis further develops new quality discount adjustment factors for rice, dark roast confectionary 

sunflowers and buckwheat, consistent with the recommended procedures developed in the analysis 

described above. 

A separate analysis is performed for cotton quality adjustment. The goal of this analysis is to simplify the 

current procedure and incorporate an option of selecting coverage on an individual bale basis rather than a 

farm unit. The proposed procedure must also promote equity amongst policyholders and minimize the 

potential for price manipulation. 

The following presents a description of the deliverables included in this report with respect to the above 

analyses. 

 

1

 Including the possibility of price manipulation. 
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MILLIMAN USA 

Current Program Review. This section presents our comprehensive review of the current procedures 

for quality discount adjustments. This portion of the analysis is based on our study of agricultural quality 

adjustment procedures, which includes interviews with grain inspectors and a review of the available 

literature. 

The current program review includes a study of the quality discount perils (quality deficiencies that affect 

grade and market price). Each peril is identified, its insurability is discussed, and recommendations are 

made as to which perils should be covered or excluded. 

Market Research. In this section we discuss the research approach, data sources and study on the 

uniformity of the variations in quality discount rates on the regional level. The data sources are identified 

and reviewed for effectiveness based on conversations with personnel from RMA, the Farm Services 

Agency (FSA), Economic Research Service (ERS), industry grain associations, commodity associations 

and data collection services. The analysis also addresses the potential economic impact of changes to 

quality adjustment procedures, and provides supporting discussion of the recommendations presented in 

this report. 

Risk Analysis. The risk analysis section will discuss hazard risk and compare the risk of insuring by unit 

versus insuring by bale for cotton. 

Implementation Support. After adoption of any new procedures based on the conclusions of this 

analysis, Milliman will assist RMA in developing handbooks and guidelines. We are further able to assist 

in systems implementation upon request; however, that task is outside the scope of this current 

assignment. 
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MILLIMAN USA 

CONCLUSIONS AND RECOMMENDATIONS 

During the course of this assignment, Milliman interviewed various crop science experts, staff of trade 

associations, staff of state crop commissions, approved insurance providers, major purchasers and crop 

elevators; conducted direct surveys of elevators and purchasers; obtained data from FSA and the 

Agricultural Marketing Service (AMS) of the USDA; and performed extensive analysis of both the 

survey data and data obtained from USDA agencies. The following are our main conclusions for crops in 

general and for cotton specifically. 

Local Quality Discount Rates 

Our research indicates that quality discounts applied at a local elevator can be heavily influenced by local 

supply and demand factors, particularly with respect to the average quality of the crop currently stored at 

the elevator. Therefore, actual discounts may deviate from the elevator’s published discount schedule; 

that is, in some instances, local elevator transactions may not reflect the published local area discount 

related to the covered quality deficiency, but instead may reflect discounts related to factors prevalent at a 

given local elevator on the day and time the transaction takes place. 

In addition, quality loss adjustment based on local elevator transactions can be problematic in that it may 

be: 

1. Not objective, 

2. Not applied uniformly, and 

3. Not auditable. 

As a consequence, the use of local elevator transactions would likely not meet the objective that the 

procedure should prevent fraud, waste and abuse2

. 

To accomplish the stated goal of developing quality loss adjustment procedures that more accurately 

reflect local quality discounts, we evaluated the following three options: 

 

2

 It may be for similar reasons that revenue based coverages avoid reliance upon local transactions as a basis for determining the 

harvest price used of “Calculated Revenue”. For example, a Crop Revenue Coverage (CRC) policy typically bases the harvest price 

on the average daily settlement rates for the commodity’s farmers contracts on the Chicago Board of Trade 15 to 45 days prior to 

contract expiration, rather than the price the producer receives at the local elevator. 
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MILLIMAN USA 

1. Develop regional quality adjustment factors based on an econometric analysis of quality discount 

schedules of local elevators grouped into regional markets. 

2. Develop a system whereby producers are allowed to select from prescribed optional quality 

adjustment schedules when purchasing coverage. 

3. Develop quality adjustment schedules that follow the discount schedules of terminal elevators. 

We developed an econometric model to analyze these options, and implemented that model by 

conducting an extensive survey of local and terminal elevator discount schedules. During the course of 

our survey, we contacted by phone 360 individual grain terminals, elevators and warehouses, to request 

current discount schedules and discuss the uses and application of these schedules. In addition, at the 

recommendation of FSA, we also contacted six major national grain buyers, because the volumes of their 

transactions are extremely large and their discount schedules appear to be very influential in the market.3

After repeated follow-up, we ultimately received responses from 121 entities, for a response rate of 

approximately 33%. 

Based on our survey results, we conclude the following about the discount schedules issued for crops in 

general: 

1. Quality discount schedules are generally highly correlated across elevators in different regions. 

That is, the proportionate change in the discount for a given change in quality is comparable 

within and across regions. 

2. While the discount schedules are highly correlated, the absolute level of discounts can vary 

significantly, both within and across regions. 

3. The current quality adjustment rates in some cases may need to be updated to be consistent with 

current market discount rates. 

4. Most discount schedules are not updated frequently. Sampling schedules every one to three years 

should be sufficient to determine whether an update of the quality factors is required. 

 

3

 During our phone interviews, many local elevators indicated that they follow the schedules of these market leaders, such as ADM, 

Cargill, and the like. 
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MILLIMAN USA 

Recommendations. Of the three options noted above, it is our opinion that Option 1 – developing 

regional discount schedules – best satisfies the stated criteria4

. Options 2 and 3 are valid in terms of 

actuarial soundness, however, the administrative complexity of Option 2 lacks low maintenance costs, 

and the data available to implement Option 3 would likely not reflect local quality discounts applied to 

agricultural commodities as well as Option 1. 

For Option 1, we considered developing schedules based on the optimal clustering of published local 

elevator discount levels per crop. That is, we tested which discount schedules were statistically “closest” 

to each other, and developed regions which aggregated the discount schedules within those clusters. 

However, while this procedure is statistically sound, we concluded that it does not satisfy the criteria of 

low maintenance costs, in that different sets of regions would have to be developed and maintained for 

each crop. 

We therefore tested the option of developing a set of regional definitions that is consistent across all 

crops. Our analysis indicates that by defining regions as those serviced by the various RMA Regional 

Offices, the differences in average discount levels among these regions are frequently statistically 

significant.5

 Thus, using RMA regions satisfies the primary criteria that local discounts are more 

accurately reflected by the recommended quality adjustment procedures. Furthermore, the task of 

periodically sampling and updating the local regional schedules may be delegated to the Regional Offices, 

where personnel are likely to be familiar with the local markets and in a position to efficiently maintain 

the discount schedules. This would imply that the proposed procedures are likely to be relatively low in 

maintenance costs. Finally, because this option is intended to apply to all crops, it is both national in 

scope and uniform for all crops. 

We note that in some cases a crop may not have a high volume of production in a given region, in which 

case it may not make sense for that Regional Office to maintain separate discount schedules. In these 

 

4

 We recognize that regional discount schedules may result in differences in indemnities paid to producers on different sides of

regional boundaries. However, as with any regional or territorial classification system in insurance, there are, of necessity, territorial 

boundaries, across which circumstances (usually premium rates) will differ. It is often the case that the cost conditions that underlie 

premium rates change continuously over territories, so that on either side of the territorial boundaries, costs are roughly similar, and 

hence rates should be as well. One of the problems of classification ratemaking is striking a balance between precision in 

classification and the costs of administering the system. 

The same is true of different territorial or regional discount schedules. While it would be possible to construct a “tapered” discount 

schedule which varied continuously across regions, the overhead and administrative costs would be prohibitive, and the schedules 

would be much more complicated to compute. It would require a far more extensive survey of warehouses to provide all of the data 

points required to produce a reliable grid system of rates. It would also require a much larger database, much more costly database 

management, and much more elaborate software system for price determination. We believe that the recommended system, while 

imperfect, reasonably balances the theoretical ideal and the administrative cost burden. 
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MILLIMAN USA 

instances, we recommend one of two options. First, the Regional Office could adopt the schedules of a 

neighboring Regional Office, perhaps with a minor adjustment to reflect the local market’s differences if 

necessary. Alternatively, we also present a “National” discount schedule, which represents the average of 

all the regional schedules along with several schedules that are used nationally by specific purchasers. In 

cases where a Regional Office could not adopt the schedule of a neighboring region, the Office might 

choose to rely on the National schedule instead. 

In addition, even though we considered the regional and updated quality discount schedules to be one step 

forward in providing quality adjustments more reflective of local market conditions, it is inevitable that at 

times local transactions may deviate substantially from recommended discounts. We thus further suggest 

that quality discount could be determined based upon an Olympic average6

 price offered by five local 

buying points owned by at least five separate entities if: 

• the loss is an allowable quality deficiency that is due to insurable causes as defined in the 

applicable crop provisions, 

and if it satisfies any of the following conditions: 

• the grain has a market value of 50% or less of local market price as defined by the applicable crop 

provisions, as a result of an allowable quality deficiency that is due to insurable causes as defined 

in the applicable crop provisions; 

• the discount received is more than 150% of the chart listed discount; or 

• there are no data in the discount chart. 

In addition to the quality deficiencies enumerated in each crop policy, there are also quality factors, such 

as mycotoxins, that are identified by the Food and Drug Administration or other public health 

organizations of the U.S. as being injurious to human or animal health. In most instances, these quality 

characteristics were not in any of the discount schedules we received, but they are covered by the crop 

insurance policy. For these quality factors, we suggest one of two options. If there is current data from 

FSA on discounts for such conditions, then RMA should rely on those data. Otherwise, if the presence of 

these conditions causes the crop to be graded at or below the grade required to qualify for quality 

 

5

 For four of the major program crops – corn, wheat, soybeans and grain sorghum - the differences were statistically significant. 6

 An Olympic average is derived by discarding the high and low observations and averaging the remaining data. 
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MILLIMAN USA 

coverage (usually grade 5 or sample grade) then we suggest that RMA uses the Olympic average price of 

five local buying points as discussed above. 

Insurability of Covered Perils 

In the course of our research, we also considered the question of the insurability or uninsurability of 

covered quality perils for each crop. Our conclusions are based on the extent to which each peril is 

controllable by the producer. The purpose of this criterion of controllability is to encourage good farming 

practices, and to avoid the potential incentive problems created by offering compensation for perils that 

are entirely under the control of the insured producer.7

 

Regarding this issue, we emphasize that our findings throughout this report are based on extensive 

discussions and interviews with agricultural experts as to those dimensions of crop quality which are 

controllable by producers. Milliman does not take any position on the validity of those expert opinions. 

Rather, we simply note that it can be economically inefficient to provide insurance coverage against perils 

that are largely within the control of the insured. 

Our findings are that there are no currently covered quality perils that are entirely within the control of the 

producers. Furthermore, indemnification of a covered quality deficiency is contingent upon the 

deficiency resulting from a covered cause of loss, which in the case of quality deficiencies would 

primarily result from adverse weather conditions. Therefore, we conclude that even for perils which may 

be partially within the control of the insured, indemnification would result only in the event of loss 

outside of the producer’s control. 

We further note that there is a partial “experience rating” effect for covered quality deficiencies. Quality 

adjustment is applied to production-to-count, which in the event of an insured loss becomes a part of the 

producer’s actual production history (APH). Therefore, future coverage is in some part a function of past 

quality deficiencies, which helps to provide a more equitable distribution of rates. 

 

7

 If insurance is available for a particular peril that is controllable, a producer may take less care in preventing a loss due to that 

peril. This is familiarly known as the “moral hazard” problem. 
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MILLIMAN USA 

Construction of Schedules 

The regional (or national) discount schedules were constructed by calculating the arithmetic mean of the 

individual discount schedules of the relevant regions (or the whole nation), using the procedures 

described below. 

At the outset, we note that we were repeatedly told that while the published schedules were a guide, actual 

discount rates might deviate from the published schedules at times due to variations in market supply and 

demand. Nonetheless, we believe that the printed schedules are likely to be the most frequently used 

discount schedules, and we were given no reason to believe that the schedules are biased – meaning that 

on average, the deviations from the schedule will be zero. Thus, we believe that relying on the published 

schedules is reasonable.8 

Turning to the development of the discount schedules themselves, we adopted certain rules for applying 

discounts by grade and insured peril, which are described below. We note that there is no standardized 

method among elevators of structuring grade or specific deficiency discounts; to the contrary, different 

elevators may discount for certain deficiencies and not others, may have different ranges for specific 

deficiencies, and may reject crops entirely if they exceed a particular level of a given deficiency. 

In light of the variations in industry practice, we have developed the following rules based on extensive 

discussions with the Federal Grain Inspection Services (FGIS), individual elevators and industry trade 

associations as to typical industry practices and reasonable applications of discounts to market rates: 

1. Grade discounts are uniform regardless of the deficiency that causes a particular grade, and there 

is no "double discounting". For example, if a crop is grade 3 due to test weight and grade 4 due 

to total damage, the crop receives a single grade 4 discount (i.e., the lesser grade of the two). 

2. If a crop is sample grade, the crop receives the sample grade discount plus the incremental 

discount below the sample grade threshold. For example, wheat at test weight 49 lbs. receives the 

sample grade discount plus the incremental discounts from 51-50 and 50-49 (i.e., below the 

sample grade threshold of 51 lbs.). 

3. If a crop is sample grade due to two perils, the crop receives a SINGLE sample grade discount 

plus the incremental discounts beyond sample grade for BOTH perils. For example, if the crop in 

Item 2, above, also has total damage 16%, the discount would be as above PLUS the total damage 

discount for 15-16% (i.e., above the sample grade threshold of 15%). 

 

8

 There is an analogous situation with compensation for a yield loss under the MPCI policy; the price election does not reflect daily 

variation in local supply and demand conditions which may affect the price the producer actually receives for his crop. 
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MILLIMAN USA 

4. Odors are treated differently. Smutty, garlicky and ergoty are "special" grades; i.e., they do not 

affect the numerical grade (a US no.1 could also carry the special grade "smutty"). These 

discounts apply independent of grade. 

5. Musty, sour and commercially objectionable foreign odor (COFO) are more serious and are 

automatically sample grade. They receive the sample grade discount in addition to the listed odor 

discount. (Also, for some crops musty may be easier for the elevator to blend than sour, which is 

easier to blend than COFO. For example, FSA wheat discounts for these perils are .10, .25, and 

.50, respectively, and all apply on top of the .70 sample grade discount.) 

6. Most elevators do not provide separate discounts for grade, and many elevators have different 

discounts for different perils that could produce a given grade. (For example, wheat grading #5 

due to total damage may have a different discount than wheat grading #5 due to test weight). 

Since quality deficiencies generally have to be sufficient to cause a crop to grade below a certain 

level – such as below grade 4 or 5 – it is important to have number and sample grade discounts 

available for the loss adjustment process. Therefore, for each elevator without identified grade 

discounts, we have constructed the average grade 5 and/or sample grade discounts (depending on 

the given crop's coverage), based on an analysis of the reported discounts for each quality 

deficiency that is covered under the crop’s policy (provided the elevator reports a discount for 

that peril). 

We note that these procedures are generally consistent with the “Pre-established DF’s from the Chart” 

section of the FCIC Loss Adjustment Manual Standards Handbook (Section 5, Part 85, Page 140-141), 

with respect to type/level of damage and special/specific allowable Sample Grade defect DF’s9

. The 

treatment of the DF’s for U.S. Sample Grade is the same as described in the Handbook

(5.85.G.(1).(b).1.b.(ii)), with the exception that only the portions of the type/level of damage DF’s that 

are in excess of the Sample Grade DF are applied to determine the total quality discount. 

Results of Analysis 

The results of our analysis by crop are reported in relevant subsections of the Market Research Section. In 

each case, we display a Table which shows the following information: 

• the quality deficiencies covered under the policy; 

• the current discounts for those perils based on the current FSA discount schedules; 

• the proposed regional discounts based on the data collected from individual elevators, warehouses 

and terminals; and 

• a national discount schedule, based on the average of all the regional schedules. 

 

9

 “Discount factors” 
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MILLIMAN USA 

As is evident from those Tables, it is only for the major program crops that there was sufficient data to 

construct regional schedules, and in no instances were discount schedules (for specific crop) provided by 

the elevators in all ten RMA regions. Indeed, in many cases there were only a handful of elevators with 

discount schedules for given crops. Thus, many of the proposed discounts are based on very small 

samples. This is to some extent due to the nature of the market for some crops; in some cases crops are 

produced in only a few areas, are traded in very small volume or may be grown predominantly under 

contract. As a result, many elevators will not have discount schedules applicable to those crops. We have 

reported the sample sizes used to construct the proposed schedules, and recommend that where the 

samples are small RMA may consider the option of applying the Olympic average. 

We have also reported a “National” discount schedule for each crop, based on an average of each of the 

individual elevator schedules along with several schedules that are used nationally (and are therefore not 

assigned to any specific region). If a specific region has no data for a particular crop, we have suggested 

that the RMA Regional office be authorized to utilize a neighboring region’s schedule, or to select the 

National schedule if desired.10 We note that for many crops the National schedule is the only one 

displayed, due to the small sample problems reported above, or the fact that the regional schedules appear 

to be statistically identical. 

Our suggested discount rates for each crop based on the survey results are displayed in the relevant 

subsection for the crop in the Market Research Section. The following briefly summarize the magnitude 

of the suggested discounts in comparison with the current discounts. 

• For corn, the suggested discount rates are somewhat higher for sample grade and test weight 

discounts, but somewhat lower for musty, sour and COFO discounts. 

• For soybeans, the suggested discounts are somewhat lower for sample grade, COFO and sour, but 

somewhat higher for musty. 

• For grain sorghum, the suggested discounts are significantly lower for sample grade, and 

somewhat lower for all other quality factors except kernel damage, which is higher. 

• For wheat, the suggested discounts are significantly lower for grade 5, sample grade, COFO and 

sour, but somewhat higher for test weight and kernel damage. 

 

10 As will be discussed in detail later in this report, the data collection process for this study was extensive, but we were still unable 

to obtain discount schedules for certain crops in certain regions. In such cases, the national discount schedule can be perceived as 

an update of the current national discount schedule. In addition, we have recommended an option for a producer to elect the 

Olympic average discount if there is no tabular discount available for a particular crop/deficiency. 
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MILLIMAN USA 

• For barley, the suggested discounts are lower for grade 5 and sample grade, but higher for test 

weight, kernel damage, thin, sour, musty and COFO. 

• For oats, the suggested discounts are lower for test weight and sound-cultivated barley, but 

significantly higher for sample grade. 

• For rye, the suggested discount is based on only one discount schedule and is slightly lower for 

sample grade. 

• For rice, we suggest using the FSA discount rates, but for areas outside of California only. 

• For buckwheat, we suggest a 1% discount for each pound of test weight below 45 and a 100% 

discount for soundness, musty, garlicky . 

• For the oilseed crops, the suggested discounts are much higher for flax and are based on a single 

major purchaser. 

• For canola, the suggested discounts are higher for sample grade, but slightly lower for kernel 

damage, and are also based on a single major purchaser. 

• For oil sunflower, the suggested discounts are lower for test weight, musty, sour and sample 

grade discounts, but higher for kernel damage discounts. 

• For confection sunflower, the suggested discounts are uniformly much higher than current 

discounts. We suggest a 50% discount rate for dark roast confection sunflower. 

• For safflower, the suggested discounts are based on only one purchaser and are substantially 

higher than the current discounts. 

Grading – Monitoring and Compliance 

For purposes of quality adjustment in crop insurance, RMA requires that graders be licensed under the 

United States Grain Standards Act (USGSA), or the United States Warehouse Act (USWA). In addition 

to demonstrating the requisite skills that are required to obtain a license, obtaining the credentials conveys 

an economic benefit to the holder that helps to ensure honesty and integrity when performing the task. 

Our interviews with USDA graders and approved insurance providers reveal that grading at local 

elevators is not always performed by an employee of the elevator who is a licensed grader. Graders 

licensed under the USGSA are generally state employees leased to the elevators or employed by private 

agencies; thus they lack the potential conflict of interest of being employed by the elevator. In addition to 

our recommendations listed above, we emphasize the importance of the RMA requirement that 

unofficially graded grain is not acceptable for quality loss adjustment, and that a USGSA license is the 

preferred standard for measuring quality deficiencies. 
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MILLIMAN USA 

Cotton Quality Adjustment on a Basis Smaller than a Unit 

Our risk analysis includes a measure of the relative frequency and severity differential between cotton 

quality loss adjustment on a unit basis and quality loss adjustment on a per-bale basis. The scope of the 

alternate coverage was confirmed through discussions with RMA and Kent Lanclos, an economist with 

the National Cotton Council (NCC). 

According to the NCC, cotton is generally marketed on a per-bale basis, and individual quality grades are 

attached to each bale, making this particular commodity unique among field crops. The primary 

difference between the current and alternative coverage is that currently, individual quality deficiencies 

on a per-bale basis are aggregated across a unit and applied to production to yield production-to-count. If 

the production-to-count does not fall below the yield guarantee, then no indemnity is paid. Under the 

alternate coverage, quality deficiencies are paid for each bale regardless of whether there is a yield loss. 

At the levels of coverage presented below, this results in approximately a 10% increase in loss costs. 

The above estimate is based on the quality adjustment factor estimation of all upland cotton classified in 

the crop year 2000. The cotton quality data comes from the USDA AMS Cotton Program's weekly cotton 

quality data file, which contains classification information for all bales classified in a given week as well 

as the classing office number. The cotton prices are from the AMS daily market price for the last day of 

the respective workweek. 
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MILLIMAN USA 

THE ECONOMIC SIGNIFICANCE OF QUALITY ADJUSTMENT AND 

CROP INSURANCE IN CROP PRODUCTION AND MARKETING 

Crop insurance is one of an integrated set of federal government programs aimed at protecting farmers 

against crop failure and crop market volatilities. Yield-based insurance protects farmers against the yield 

deficiencies in either quality or quantity. The role of insurance varies with the unique production and 

marketing process of each crop. This section reviews the production and marketing process and the 

importance of crop insurance for the various crops within the scope of this assignment. 

Corn 

Production and Marketing. Corn is the largest crop and the principal feed grain of the United States 

(U.S.). It had a farm value of $18.6 billion in 2000 and accounts for more than 90 percent of total feed 

grain production. Historically, U.S. corn acres declined from 113 million during the Great Depression to 

64 million in 1969. Since then, corn acres have climbed back and stabilized at around 80 million acres in 

recent years. 

The major corn production areas include Illinois, Iowa, Indiana, eastern portions of South Dakota and 

Nebraska, western Kentucky and Ohio, and the northern two-thirds of Missouri. Iowa and Illinois, the top 

corn producing states, produce around one-third of the total crop. According to the National Grain 

Farmers Association, there are three types of corn produced in the U.S. Dent corn accounts for 99% of 

total corn production, with the other types being sweet corn and popcorn. Dent corn is further divided into 

yellow, white and mixed corn, with the majority being yellow corn. 

Corn is planted in early April through late May and is harvested in early October through mid November, 

depending on the region. About one-third of the crop typically remains on the farm to be used as animal 

feed. The rest is sold by the farmer, usually to a country elevator located near the farm. The country 

elevator dries, stores and conditions the corn. It then sells the corn to possibly a livestock feeder, feed 

mill, dry corn miller, wet corn miller, terminal elevator or export elevator. 

Economic Significance of Quality Adjustment and Crop Insurance. In 2000, corn producers paid 

$739.4 million in insurance premiums for protection against yield failures, far exceeding any other crop. 

Based on conversations with country elevators, over the last ten years around 10% of the total crop was 
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discounted for quality deficiency. This seems to reflect the consistency of recent harvests. Part of the 

reason for the increase in quality consistency could be innovations in agricultural technology, which 

should continue into the future. Nonetheless, quality requirements by the market may also increase over 

time, keeping quality adjustment a potentially important factor for crop insurance in the future. In 

addition, there appears to be a trade-off between quantity and quality in crop production. Larger farmers 

may not be able to play a hands-on role and thus may target quantity more than quality, while small 

farmers may be able to target quality more effectively. 

Based on conversations with elevator operators and agronomists, there are essentially two factors that 

affect corn quality - weather and the farmer’s machinery and decision-making. Farmers can affect the 

quality of the crop to some extent by managing the timing of the harvest and the care and effort they 

invest into the storage, handling and transportation processes. In recent years, the most important 

discount factors for corn have been, in the order of importance: damage, foreign material and others. In 

southern areas such as Georgia, where the temperature is relatively high, bug damage seems to be more 

important; it is however, less important in the major corn production areas such as the Midwest. 

Based on a conversation with Dr. Robert Caldwell at the University of Nebraska, there are several quality 

measures which can be affected by farming procedures and equipment. Specifically, farmers have good 

control over heat damage (often caused by overheating with propane heaters during the drying process), 

and reasonable control over foreign material (the farmer can adjust the combine to exclude foreign matter 

during harvest). 

In contrast, farmers have less control over other quality deficiencies. Specifically, we were told that 

farmers have very little control (perhaps 10%) over test weight; the other 90% is caused primarily by dry 

weather. Dry weather causes the plant’s growth to cease and leads to low test weights. Also, natural 

conditions are estimated to cause about half of all aflatoxin11 discounts, while the other half is potentially 

farmer controlled. Discounts listed in the “other” category include sour, musty, waxy, buggy, infested, 

etc. Farmers may have some control over these discounts some of the time, while in other instances they 

are uncontrollable due to varying weather conditions. 

We have also interviewed trade organization and country elevator staff on the issue of controllability of 

quality deficiencies. The general response from these groups is that for most quality factors, even if there 
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is an opportunity for control, it can be costly for producers to spend the resources necessary to minimize 

damage. This is further aggravated by the uncertainty regarding the effectiveness of some of the measures 

available to mitigate damage. Therefore, it is reasonable to assume that most of the quality problems that 

occurred were not really within the control of the producers. This has been the predominant view of the 

country elevator and trade organization staff that we interviewed for almost all crops, except those 

otherwise discussed. We note that the reason why lengthy passages were used to describe the opinions of 

agronomists is simply because of the diversity of their opinions in comparison to the relatively uniform 

opinions we received from other parties on all quality factors and all crops. This applies to all crops 

discussed in this report. 

We emphasize that our discussion of discount factors is based on the results of interviews with 

agricultural experts as to those dimensions of quality that are controllable by producers. Milliman does 

not take any position on the validity of those expert opinions. We simply note that it can be economically 

inefficient to provide insurance coverage against perils that are well within the control of the insured. 

Thus, if it is widely agreed upon that a particular quality dimension is outside of the farmer’s control, then 

it should be subject to discount under the insurance program. 

Soybeans 

Production and Marketing. Soybeans are the second largest crop produced in the U.S. The total farm 

value of U.S. soybean production in 2000 was $13.1 billion. As the world's leading producer of soybeans, 

the U.S. produces more than 50% of the world crop and exports about 40 percent of its output. More than 

80 percent of U.S. soybean acreage is concentrated in the upper Midwest. 

Of the more than 150 varieties of soybeans produced in the U.S., the yellow soybean is the dominant class 

used in commercial markets. It accounts for around 99% of U.S. soybean production. Other minor classes 

include green, brown and black soybeans. 

 

11 Aflatoxin is a naturally occurring byproduct of a fungus that grows on corn and is toxic to animals, especially young animals. 

Aflatoxin targets corn grown in the southern region of the U.S. due to this region’s unique humid weather conditions. 
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Soybeans are usually planted in May and harvested in late August or early September. Most of the 

soybeans grown by farmers are sold to a nearby country elevator. The country elevator dries, stores and 

conditions the soybeans. Some farmers may sell directly to a soybean processor located in the growing 

area. 

Soybean acres have increased nearly 30% since 1990, to 74.5 million acres in 2000. The main reasons for 

this rapid expansion of soybean acreage include12: 

• Increased planting flexibility under the 1996 Farm Act; 

• Rising yields from narrow-rowed seeding practices; 

• More half-corn, half-soybean rotations; and 

• Lower production costs (partly due to widespread adoption of herbicide-tolerant varieties). 

Economic Significance of Quality Adjustment and Crop Insurance. For crop year 2000, producers 

paid $455.3 million in premiums for soybean crop insurance. Although the quality of soybean crops has 

been extremely consistent, there is a general consensus among country elevators that quality adjustment 

remains very important. The consistent soybean yield could be related to the increased use of herbicides 

and new farming technology. Most of the elevators we interviewed estimate that only 5% to 10% of their 

crop is discounted in any given year. There is some concern, however, that small country elevators are 

not enforcing the same strict standards for quality as the larger terminals. This has made making money 

at the elevator level more difficult and has lowered the overall quality of soybeans at the terminal level. 

This trend could force country elevators in the future to levy higher discounts for below-average quality 

soybeans and therefore make quality adjustment an even more important factor for crop insurance. 

Soybean production has had a very stable decade, partly because soybeans tend to be a relatively durable 

crop. There are approximately 11 different basic quality measures for soybeans, but test weight discounts 

make up the majority of soybean quality adjustments. Test weight is unpredictable and mainly due to 

genetic variation and weather conditions. Even with effective management, however, producers can only 

control this discount to a limited extent. 

As for other quality deficiencies, according to James Beuerlein at the Ohio State University, farmers may 

have some control over many of the important quality measures. For instance, farmers can eliminate some 
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soybean discounts through the use of herbicides. Also, splits are a handling problem that farmers can 

control because it is usually during the harvest process. Further, there is minimal discounting for heat 

damage, the result of farmers providing excess heat during the drying process. Heat damage also can be 

caused by poor aeration in storage bins, and therefore may be the result of poor management. Discounts 

covered by the “other” category include sour, musty, cofo, dirt, stones, sticks, etc. These quality 

deficiencies are typically rare for soybeans, but when they do occur, they are frequently due to human 

error. 

In addition to these factors, there was serious doubt about the justification for a frost discount. Frost 

damage usually occurs only in locations where there is double cropping and very low temperatures. In 

regions of generally milder weather such as Iowa or Illinois, this is a preventable quality deficiency. In 

states such as Minnesota, according to Dr. Seth Naeve at the University of Minnesota, frost is a seasonal 

concern with little or no farmer control. Green discounts are fairly rare as well, and are closely related to 

weather. 

Damage is a “catch all” category that includes a variety of defects that may be caused mainly by 

negligence in the harvesting, handling and transporting processes. Damage probably can never be 

eliminated entirely, but, according to crop scientists, there is certainly some ability on the farmer’s part to 

mitigate damage. Discounts for other colors being present on the soybeans have nothing to do with 

quality. These discounts are taken for cosmetic reasons rather than for a loss in quality. Producers can 

mitigate this problem by keeping their fields clean. 

Other quality measures are harder for farmers to control. Foreign material discounts come in two varieties 

for soybeans. One is trash and other debris, and the other is chipping of soybeans caused by low moisture 

content. Around 50% of the time, foreign material can be controlled through proper combine adjustment. 

As with corn, careful monitoring of the soybeans while they are stored in bins can lessen the degree to 

which a farmer is discounted at the elevator. There appears to be a slight tradeoff in soybean production 

between quantity loss and quality loss, although the extent of the tradeoff is still questionable. 

Conversations with country elevator operators revealed the belief that farmers have a strong desire to 

push for higher output volume. 

 

12 Source: ERS, USDA 
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Cotton 

Production and Marketing. Cotton farming had a crop year 2000 production value of $4.78 billion. The 

harvest acres in the U.S. have fluctuated in the last four decades, declining from around 5.9 million acres 

to around 3 million acres in 1983 and then climbing back to around 5.3 million acres currently. 

Primary cotton production areas in the U.S. include: 

• The Texas High and Rolling Plains; 

• The Mississippi, Arkansas, and Louisiana Delta; 

• California's San Joaquin Valley; 

• Central Arizona; and 

• Southern Georgia. 

Texas, which produces the most cotton of any state, accounted for about 21% of the total value of U.S. 

production. Five other major production states, - California, Georgia, North Carolina, Mississippi and 

Arkansas - accounted for 54% of total U.S. production. 

While all 17 southern cotton-producing states harvest upland cotton, pima cotton is the predominant 

choice in California, Southern Texas, Arizona and New Mexico. 

Upland cotton constitutes about 96% of the cotton produced in the U.S. Pima cotton, which has relatively 

low yield but also relatively long, strong and fine fibers, accounts for the remainder. Technological 

developments have helped improve the quality of cotton over time. For example, between 1985 and 1995, 

the average fiber strength for upland cotton tested by the USDA increased by 17%, while the average 

length has increased by 8.7%. 

Economic Significance of Quality Adjustment and Crop Insurance. Cotton crop insurance plays an 

important role in this industry. Cotton producers face numerous production hazards during the season, 

such as hail, drought, insects and diseases. Production hazards lead to yield deficiencies in both quantity 

and quality. Many producers purchase crop insurance to protect themselves against production hazards. 

For the crop year 2000, producers paid a total of $371.6 million in insurance premiums. 

Cotton is classified by the following quality factors: color; extraneous matter; fiber diameter; fiber length; 

fiber length uniformity; fiber strength; and elongation before breakage. According to Professor Wayne 
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Smith of Texas A&M University, extraneous matter is the only quality aspect that farmers can partially 

control through proper plant preparation, defoliation and other measures. All other quality aspects are 

mostly genetically and environmentally determined. Producers have limited control over the color of 

cotton through timely harvesting. However, harvesting time is also frequently determined by weather. 

Grain Sorghum 

Production and Marketing. The value of grain sorghum farming was $823 million in crop year 2000. 

Both harvested area and production levels have been declining in the last two decades. 

Economic Significance of Quality Adjustment and Crop Insurance. Grain sorghum crop insurance 

premiums totaled $49.9 million in 2000. The consensus among country elevators with a focus on grain 

sorghum production is that quality adjustment does not play a very large role in determining prices. This 

is mainly due to the fact that grain sorghum is used predominantly for cattle feed and ethanol 

manufacturing. However, in Kansas the 2001 crop suffered from excess dry weather and upwards of 50% 

was discounted for low test weight. Outside Kansas, in Texas (for example) only 10% of the grain 

sorghum was deemed below quality and subject to test weight discounts. Thus, there is some regional 

importance to quality adjustment, especially when the quality of farmer-stored grain sorghum is 

examined. In addition, although U.S.-produced grain sorghum is essentially only used for feed and 

ethanol manufacturing, there is an increasing trend toward growing food-grade grain sorghum 

domestically, according to Dr. Dale Fjell of Kansas State University. This would place a higher 

importance on grain sorghum quality and therefore, quality adjustment. 

Almost all grain sorghum quality variation comes from weather cycles and patterns. Dr. Fjell explained 

that grain sorghum behaves similar to other crops in that there are certain quality measures that are 

completely weather related. These include test weight, heat damage and, to a certain degree, moisture. At 

country elevators the only measures that are consistently graded are moisture and test weight. The only 

way a farmer can influence test weight, and this is to a very limited extent, is to avoid planting late in the 

season and thus having a shorter growth span and more cold weather. Grain sorghum is grown in warm 

weather climates. The plant develops well in dry, hot climates. Heat damage can occur in the field during 

exceptionally hot seasons. More often than not, the hot weather lowers the test weight. Conversations 

with country elevators revealed that quantity loss is much more important than quality loss because of 

grain sorghum’s intended usage. For every farmer the production of the maximum amount of bushels is 

an economically superior strategy to producing only high-quality grain sorghum. 
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Farmers have a high level of control over tannin and damage discounts. Dr. Fjell explained that in the 

U.S., grain sorghum with tannin in the seed coating of the kernel has been bred out and is not typically 

grown. Farmers can easily avoid growing grain sorghum with tannin by using the correct seed variety. 

Damage discounts are reduced through the proper adjustment of combines. With grain sorghum, the 

cleaner a farmer gets the crop, the higher the test weight will be because the foreign material is generally 

lighter than the grain sorghum itself. 

Wheat 

Production and Marketing. Wheat is the largest volume food grain produced in the U.S. It had a farm 

value of $6 billion in crop year 2000. Among U.S. varieties, 70% to 80% of wheat production is “winter 

wheat,” while the remainder is “spring wheat.” Winter wheat is sown in the fall and usually becomes 

established before entering dormancy when cold weather arrives. In the spring, the plants resume growth, 

which is rapid until summertime harvest. In the Northern Plains, where winters are harsh, spring wheat 

and durum wheat are planted in the spring and harvested in the late summer or fall of the same year. 

According to ERS, the following are the major wheat classes in the U.S.: 

• Hard red winter (HRW) wheat accounts for about 40% of total production and is grown primarily 

in the Great Plains (Texas and north to Montana). HRW wheat is principally used to make bread 

flour. 

• Hard red spring (HRS) wheat accounts for about 25% of production and is grown primarily in the 

Northern Plains (North Dakota, Montana, Minnesota and South Dakota). HRS wheat is valued for 

its high protein levels, which make it suitable for specialty breads and blending with lowerprotein wheat. 

• Soft red winter (SRW) wheat, which accounts for 15% to 20% of total production, is grown 

primarily in states along the Mississippi River and in the eastern states. Flour produced from 

milling SRW wheat is used for cakes, cookies and crackers. 

• White wheat accounts for 10% to 15% of total production, and is grown in Washington, Oregon, 

Idaho, Michigan and New York. Its flour is used for noodle products, crackers, cereals and 

white-crusted breads. 

• Durum wheat accounts for 3% to 5% of total production, and is grown primarily in North Dakota 

and Montana. It is used in the production of pasta. 
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Due mostly to declining returns (relative to other crops) and alternative options under government 

programs, wheat acres have been declining slowly in the last two decades. 

Economic Significance of Quality Adjustment and Crop Insurance. For the crop year 2000, 

producers paid $331.8 million in premiums for wheat crop insurance. From information gathered through 

conversations with country elevators and terminals, there seems to be a genuine concern for quality 

adjustment. Although consistent harvests have been achieved in some locations, there are areas that have 

suffered from excess dockage, or easily removable foreign material. This stems from country elevators 

not strictly enforcing discounts for objectionable amounts of foreign material. Larger elevators and 

export terminals stated that this issue is being addressed and there could be an increased rate of 

discounting going forward. In the future, a lack of tolerance toward average or below-average wheat 

could make quality adjustment potentially a more important component of crop insurance. Among larger 

grain terminals there is the expectation that discounts will rise in the export channel, signaling to suppliers 

that low-quality wheat will not be tolerated. 

Wheat yields and quality do not vary greatly unless the weather fluctuates from season to season. Rainfall 

seems to have the greatest effect on quality: too much or too little rainfall can have equally detrimental 

effects on quality measures such as protein and test weight. However, there are many categories of 

quality deficiency for wheat, some of which may be controllable to some extent by the producer. 

As one example, in some areas of the country a downward trend has occurred in wheat quality, at least in 

part as a result of a lack of attention toward dockage. We were told that farmers have some ability to 

control dockage by adjusting their combines more precisely, but this requires time and patience, which 

can be hard to come by during harvest time. As another example, it appears that fertilizers and chemicals 

can have a positive effect on protein content and cleanliness; however, the application of these products 

can be a significant expense to a farmer. 

According to Dr. Brent Bean of Texas A&M University, farmers can have a modest impact (around 20%) 

on protein levels, based on their control of nitrogen levels in the soil. Test weight is highly weather 

related, but to a certain degree can be influenced in wheat by the last irrigation date. Procedures and 

techniques also tend to impact each variety of wheat differently in terms of test weight. HRW wheat, for 

example, is typically grown on dry land and its test weight is solely affected by late weather patterns. 

Sprout damage depends on harvest timing, but more importantly is affected by weather patterns late in the 

growth cycle. 
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Some quality factors are easier to control. As mentioned earlier, dockage is a farmer-controlled 

deficiency. Frost typically is a rare deficiency, but in locations in which it does occur it usually is a 

byproduct of double cropping and is closely related to this procedure. Dr. Bean also suggested that wheat 

of other classes, contrasting classes and rye should be considered controllable. Most fungi that affect 

wheat can be deterred somewhat through the use of herbicides, but this deficiency is also largely affected 

by weather. Fungi can be controlled around half the time depending on the severity of the condition. 

Based on a conversation with Dr. Emerson Nafziger of the University of Illinois, fungi, germs, insects and 

other adverse conditions are more prevalent in humid areas, and some of these problems may be mitigated 

by technology and investment in new equipment. 

Barley 

Production and Marketing. The U.S. produced $632 million of barley in crop year 2000. The varieties 

are either malting or non-malting. While the total crop value is still sizable, both production and harvested 

acres have been declining for the majority of the last four decades, in both the U.S. and globally. The total 

planted acres in the U.S. were 15.6 million in 1961, but are only around 5.8 million currently. In recent 

years, Idaho, North Dakota, Montana and Washington have produced over 70% of the total U.S. yield. 

Barley can be planted either as a winter or summer crop. In the northern states, where winters are severe, 

barley is planted in April or May and harvested in the fall. In the warmer states, barley is planted 

sometime from mid-September through May and harvested in June. 

The majority of barley grown in the U.S. is used as feed for livestock. The farmer sells the barley crop to 

the country elevator located near the farm, or a terminal elevator in some cases. The elevator then 

determines the quality of the barley before cleaning, drying, storing and conditioning it. The elevator 

eventually sells the barley to a buyer. If the barley is feed quality, it may be sold to a feed manufacturer. 

If it is malting quality, it may be sold to a brew master or food processor. Malting barley has rigid quality 

requirements, including uniform high quality, plumpness, relatively free of broken and skinned kernels, 

and brightness of color. 

 

Economic Significance of Quality Adjustment and Crop Insurance. In crop year 2000 producers paid 

$18.2 million for barley crop insurance. Estimates made by country elevators put the average discount on 

quality deficiencies over the past year at around $0.40 to $0.50. It was also estimated that around 80% of 

the entire barley crop suffered from DON, a toxin, which led to the high discounts. As a result, only a 
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small portion, perhaps 5%, of the barley crop made it into the malting channels. This means that up to 

95% of barley supplies went solely to the feed channels due to imperfections. Quality deficiencies 

therefore make quality adjustment very important to crop insurance within the barley growing industry. 

According to Dr. Rich Horsley of North Dakota State University, fluctuating weather patterns have 

caused the quality variation in recent barley crops. Poor barley growth has been a direct result of high 

moisture and humidity. The sustained poor weather and adverse effect on quality and quantity have 

caused some farmers to switch their acreage to more profitable crops. Farmers are extremely concerned 

with the recent quality loss. The cost of the discounts and the amount of barley being discounted is 

forcing farmers to focus solely on the quality of their yield. The most consistent cause of barley 

imperfection has been toxin. Large discounts have caused vomitoxin to take center stage over previous 

measures of quality such as plumpness, sprout damage, and protein due to the increase in humid weather 

and the lack of resistant varieties and fungicides that prevent DON. 

There are a few discounts that farmers can affect to a limited degree. Dockage and foreign material levels 

can be reduced, but not entirely eliminated, through the precise use of combines and by controlling 

weeds. Farmers can alter the protein content of their crop by varying the nitrogen levels in the soil. For 

malting barley this means not allowing the protein content to become very high by adding too much 

nitrogen to the soil. Equipment, facilities, and timely harvesting also have some effect over moisture. 

Most farmers in the prime barley-growing regions of the upper Midwest, however, do not have drying 

facilities at all. The skinned and broken kernels discounts, like the dockage and foreign material 

discounts, can be reduced by setting the combine properly. This is especially important when the crop is 

particularly dry. 

Quality deficiencies that are strictly weather related include test weight, thinness, plumpness, DON and 

sprouting. Dr. Horsley stated that all of these discounts are uncontrollable and tend to cause the least 

amount of quality adjustment when there is timely rain, moderate temperatures and drier conditions 

during the grain fill. 

The current quality issues affecting barley farmers are beyond farming techniques and procedures. The 

single cause of DON is weather. Farmers are suffering the same problems across all levels and regions, 

meaning that different farming techniques, procedures and equipment cannot reverse the present causes of 

quality adjustment. 
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Oats 

Production and Marketing. The total value of oats produced in the U.S. was less than $165 million in 

2000. Both the acres planted and the value of production have been declining almost uniformly since 

1946. The number of acres planted was around 46.5 million in 1946, compared to only 4.5 million acres 

in 2000. This could be the result of the sharp decline in nominal prices relative to the increase in yield. 

From 1980 to 2000, yield increased from 53 to 64.2 bushels per acre (21% increase), while price per 

bushel declined from $1.79 to $1.05 (41% decline). 

Economic Significance of Quality Adjustment and Crop Insurance. Oats insurance premiums totaled 

$3.35 million for the crop year 2000. Based on conversations conducted with country elevators, the 

likelihood of an elevator discounting for various quality measures is slim. In addition, it was discovered 

that most farmers in the regions contacted, including Wisconsin and Minnesota, preferred to chop down 

their oats for hay rather than bring them to market. However, this is probably because the majority of the 

elevators we contacted dealt primarily with oats at the lower end of the scale or crops that are destined for 

feedlots. 
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Weather conditions in recent years have not been conducive to successful oat yields and thus oats that do 

make it to the food channels are experiencing heavy quality adjustment. Although oats are a small crop 

with declining acreage, quality adjustment may play an important role in crop insurance for oats food 

production. 

Oats have experienced consistent deficiencies for several years now. According to the elevators, although 

oats normally have good quality, there have been 3 to 4 successive years of low-quality harvests. Test 

weight discounts are being seen across the board for all farmers, due to extreme temperatures and 

moisture in the prime growing regions of North Dakota, South Dakota, Minnesota and Wisconsin. Oats 

are a relatively small crop for which yield increases cannot offset defects in quality. The incentive to 

produce a high-quality crop is always present, and it is quite possible that some farmers sacrifice a bit of 

quantity to generate higher quality. 

According to Dr. Mike McMullin of North Dakota State University, oats mainly suffer quality 

deficiencies as a result of changes in weather conditions during the late stages of growth. Therefore, all of 

the discounts for oats, to a high degree, are unexpected and uncontrollable. The same discounts are seen 

across the board for farmers in any one region regardless of the procedures, equipment or technique. 

Specifically, foreign material is affected mainly by weather conditions, as certain conditions may lead to 

unexpected germination or high weed content. It is possible, however, for farmers to reduce the 

probability of these events by selecting a high-quality seed or spraying the proper types and amounts of 

herbicides. 

Damage discounts are also almost entirely the result of adverse weather conditions. Most damage to oats 

comes in the form of sprouting or heating. Dr. McMullin mentioned that this is due more to luck than 

anything else. Test weight discounts are primarily weather related as well, although early planting seems 

to reduce the probability of a poor test weight yield. Garlic discounts are very rare in North and South 

Dakota, and Dr. McMullin believes that they are mainly seen in the corn belt region of the U.S. These 

discounts are certainly controllable as they reflect some inadvertent mixing of crop varieties. Dockage is 

graded in oats on the basis of foreign material and therefore is influenced by the prevailing weather 

conditions. Discounts listed in the “other” category, such as sour, musty, infested, etc., are usually the 

result of high moisture content. Therefore it is difficult to predict their arrival or to control them once they 

have affected the oat crop. 
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Agronomists have succeeded in finding the best oats seed for a particular type of soil and this has helped 

improve the standard for oats quality. Weather patterns like those that have occurred over the past few 

growing seasons, however, ensure that there will be unpredictable behavior in oat yields and qualities. 

Rye 

Production and Marketing. The U.S. produced about $22 million from rye farming in 2000, down from 

$30.4 million in 1998 and $25.1 million in 1999. Acres harvested declined steadily from 1.7 million in 

1960 to around 302 thousand in 2000. Based on conversations with extension agronomists, rye is now 

mainly grown as cover crop or for forage. Georgia and Oklahoma are the two biggest production states, 

combining for about half of the total U.S. production. 

Economic Significance of Quality Adjustment and Crop Insurance. The economic significance of rye 

appears to have declined to a point where it is now a relatively less significant crop than most other crops 

considered in this study. Producers paid $98,638 in insurance premiums in crop year 2000. As harvested 

acres and production continue to decline, which appears likely considering the persistent historic trend, 

total crop insurance premiums will likely decline as well. Hence, the economic significance of crop 

insurance for rye appears modest at this point. Based on conversations with country elevators, rye has 

become a fairly insignificant cash crop. Among those we contacted in the Midwest and Georgia, rye had 

little financial value to farmers. It was deemed a “rotation” or “cover” crop, or one that is planted the 

season before another cash crop is to be introduced on the same tract of land. Therefore, quality 

adjustment had little effect on farmers in those areas. 

According to Dr. Robert Hall of South Dakota State University, for those farmers who do produce rye, 

there are a few discounts that are controllable. Foreign material discounts are taken typically for a wide 

range of items such as trash, weeds and other objectionable debris. Combine adjustment can eliminate 

much of these types of debris. Discounts listed under the “other” category, including sour, musty and 

infested, can be curtailed by limiting the moisture content of the rye and by initiating programs of 

herbicides, fungicides and insecticides. On the other hand, like all other cash crops, test weight discounts 

are 99% weather related. Under unique circumstances, according to Dr. Hall, farmers may be able to 

affect their crop’s test weight, but this is highly unusual. 
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Flaxseed 

Production and Marketing. The U.S. produced about $35 million of flaxseed in crop year 2000. The 

two major production states, North and South Dakota, contributed about 93% and 3% of total U.S. 

production, respectively. Minnesota and Montana are two other significant flaxseed production states. 

Flax is typically planted in the spring and harvested in the fall. After harvesting, the producer usually 

hauls the flaxseed directly to a processing plant. About 88% of the flaxseed is crushed for oil and meal. 

The rest is mainly exported (about 8%) or used for seed. The stalk is used to produce flax linen. The straw 

or stubble from the flax plant is used by the paper industry. 

Economic Significance of Quality Adjustment and Crop Insurance. Producers paid $2.5 million in 

flaxseed insurance premiums in crop year 2000. 

According to agronomist Dr. Duane Berglund, test weight, damage problems and stones are difficult for 

farmers to control. Farmers have more control over other quality problems such as foreign material, 

dockage, heat damage, heating and moisture content. 

Canola 

Production and Marketing. The total value of U.S. canola production in crop year 2000 was $135 

million, 80% of which is attributable to North Dakota. Planted acres in the U.S. have increased from 140 

thousand in 1992 to over 1.5 million in 2000. Most canola is grown under contract, which provides a 

guarantee of price. 

The canola plant produces yellow flowers that in turn produce pods. These pods contain tiny round seeds 

that are crushed to obtain canola oil. Each seed contains approximately 40% oil. The remainder of the 

seed is processed to be used as livestock feed. Because it is perceived as a "healthy" oil, canola use is 

rising steadily both as a cooking oil and in processed foods. The consumption of canola oil is expected to 

surpass corn and cottonseed oils, becoming second only to soybean oil.

Economic Significance of Quality Adjustment and Crop Insurance. Canola producers paid $12.4 

million in crop insurance premiums during crop year 2000. Conversations with trade association staff 

suggest that quality adjustment has not been an important issue for the industry as crops have been free 
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of quality problems in the last ten years. However, green damage, which is mainly caused by unexpected 

weather conditions, has been a quality problem for producers in recent years. Quantity has been a more 

important problem overall. 

According to extension agronomist Dr. Duane Berglund, of the many kinds of quality problems, farmers 

have limited control over green damage and almost no control over other damages such as mold and 

sprouting. These damages are primarily caused by adverse weather conditions. The presence of stones is 

another problem that is extremely costly for farmers to control. Many other problems, including 

admixtures and heat damage, can be largely controlled by farmers. 

Sunflower 

Production and Marketing. The total value of all U.S. sunflower crops produced in 2000 was $241 

million. Of this total, $169 million belongs to oil-type sunflower, while $72 million belongs to 

confectionary sunflower. The major production areas are North and South Dakota, which constitute about 

78% of the total U.S. production. The other production areas are Colorado, Kansas, Minnesota, Nebraska 

and Texas. 

Economic Significance of Quality Adjustment and Crop Insurance. Producers paid about $21.5 

million in insurance premiums for crop year 2000. Information gathered from elevators, terminals, and 

other sunflower purchasers reveals that sunflowers, both confectionery and oil, have suffered from a 

variety of defects over the past few years. There has been some variation in the entire crop yield in terms 

of quality. A rough estimate had the share of discounted sunflower at 33% to 40% of the entire yield. 

This estimate includes sunflowers that were discounted for any one of a number of reasons, but the 

majority of discounts in the 2001 season were the result of insect damage. With such a large share of the 

sunflower yield suffering from one defect or another, quality adjustment is an important factor for crop 

insurance now and in the future. 

Most of the variation in quality appears to be the result of weather conditions. Although there appears to 

have been some volatility in quality from one pocket of farmers to another, hot and humid weather 

conditions have attracted a large number of insects. Insects were responsible for almost the entire damage 

caused to the sunflower crop in 2001. In 2000, for comparison, the entire damage was caused by 
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sclerotinia. With both types of sunflower, but particularly with confectionery sunflower, there is a need 

for farmers to protect quality in order to maximize yields; this is the consensus of both the elevator and 

terminal operators. 

For confectionary sunflower, agronomist Duane Berglund suggested that most quality problems are not 

controllable by the producer. As for oil-type sunflower, producers appear to have reasonable control over 

heat or heating damage. 

Safflower 

Production and Marketing. The total value of U.S. safflower production was $30 million in crop year 

2000. Most safflower is grown and sold under contract. Major production areas include North and South 

Dakota, Montana, California, Washington, Idaho and Utah. 

Economic Significance of Quality Adjustment and Crop Insurance. Producers paid $666,969 in 

safflower insurance premiums for the crop year 2000. Safflower production over the past few years has 

been fairly stable in terms of quantity and quality. However, the Dakotas tend to have more problems 

with toxins and diseases than Montana and the other western states. According to Dr. Gerald Bergman, 

the acreage should continue to yield high-quality safflower if weather conditions remain as they have over 

the recent years. 

Many of the discounts levied on safflower for quality deficiencies can be reduced or eliminated through 

better crop management. According to Dr. Bergman, dockage, foreign material and moisture, for 

example, should be eliminated. Dockage is determined by a dockage machine and usually includes empty 

seeds or hulls that are the result of planting late in the season. Discounts for foreign material are taken for 

excess weeds. Farmers have control over the weed presence in safflower fields, although there is a small 

amount of unpredictable variation in weed levels. 

Two safflower discounts that are more difficult to control by the farmer are oil and heat damage. Oil 

discounts are taken when the crop has been harvested and there are seeds that are not completely filled or 

mature. Disease and fungi play a big part in determining a yield’s oil content. However, timely 

management and frequent use of fungicides can mitigate this effect. We note that the available fungicides 

are not 100% effective, and Dr. Bergman stated that the industry is currently testing new fungicides with 

the potential for increased success against diseases. 
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Rice

Production and Marketing. U.S. rice farming produced $1.1 billion in income in crop year 2000. Rice 

acres have followed a dramatic upward trend for most of the last century, until the 1990s. The number of 

acres appears to have leveled at 3 million in recent years. In the last few years, while the total acres 

planted has not been increasing, there seems to be a shifting of locations, from relatively low-yield areas 

outside of California to higher-yield areas within California. Domestically, per capita rice consumption 

has been increasing for the past two decades, currently standing at about 26.5 pounds per year. 

The following six regions produce more than 99% of U.S. rice: 

• The Arkansas Grand Prairie; 

• Northeastern Arkansas and the Bootheel of Missouri; 

• The Mississippi River Delta (in Arkansas, Mississippi and northeast Louisiana); 

• Southwest Louisiana; 

• The Coastal Prairie of Texas; and 

• California's Sacramento Valley. 

Almost all of the remainder is produced in Florida. Arkansas, with more than 45% of U.S. rice acreage, is 

the largest producing state. California is the second largest, producing more than 18% of the U.S. crop 

and boasting the highest yields. 

Rice is referred to by length of grain: long, medium and short. Long grain is typically indica rice, while 

the medium and short grains are typically japonica. Long grain rice, grown almost exclusively in the 

South, accounts for more than 70% of U.S. production. Medium grain rice, grown both in California and 

the South, accounts for more than a fourth of total U.S. production and forms the bulk of California's rice 

crop. California grows more than two-thirds of the U.S. medium grain crop. Arkansas and Louisiana 

account for almost all of the southern medium grain production. Short grain rice is grown mostly in 

California and accounts for 1% to 2% of total U.S. production. 

Economic Significance of Quality Adjustment and Crop Insurance. Rice has a much higher farm 

value than many other crops such as grain sorghum, but has a relatively modest crop insurance premium. 

For crop year 2000, producers paid $20.3 million in insurance premiums for protection against yield 
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failures, which is smaller than grain sorghum or sunflower. Several of the industry people we spoke with 

concurred that crop insurance is not as important for rice as for other crops. Accordingly, the reasons are: 

• Rice is mostly grown in the south, which has relatively mild weather and therefore is less 

susceptible to yield failures. 

• Rice fields are irrigated and thus are insulated from drought or water deficiency, which frequently 

affects other crops such as soybeans. The only potential problem is persistent flooding, which 

may affect a farmer’s ability to drain the fields; but even that would unlikely cause a catastrophic 

loss of the crop. 

• Perhaps more importantly, rice yield has been increasing every year for the last few years, 

therefore the value of insurance is perceived to be low by farmers. (Some farmers seem to infer 

further that the insurance is thus very expensive.) 

As for quality adjustment, we were told that it is very infrequent (less than 10% of the time) that farmers 

would bring deficient-grade rice to the cooperatives or mills. One industry contact suggested that most of 

the quality problems could be controlled by farmers through proper application of pesticides, drying and 

other means to safeguard the crops, although these efforts do come at a cost to the farmers. Also, a staff 

person in a farmers’ cooperative in California told us that a substantial proportion of quality problems are 

caused by the storage process, such as warehouse roof leakage. Furthermore, an extension crop scientist 

suggested that about half of the time quality problems are caused during storage and processing, while the 

remaining time the problems are caused during harvesting. Of the problems during harvesting, he 

estimated that the producer has 50% to 75% control of the quality through timely harvest. Weather and 

other abnormal situations have 25% to 50% control. 

Buckwheat 

Production and Marketing. According to Zepp, Harwood, Hamond and Somaru (1996), the farm value 

of buckwheat output likely ranges from $4 million to $6 million annually, making it the smallest crop in 

terms of farm value in this study. The leading buckwheat production areas are North Dakota, South 

Dakota, Minnesota, Washington, New York, Montana and Pennsylvania. About three quarters of the U.S. 

buckwheat acreage is located in North and South Dakota and Minnesota. The factors affecting buckwheat 

yield include local growing conditions, variety, soil type and management practices. Yield varies 

dramatically among states and across time. Yield can range from 184 to 1,840 pounds per acre in New 

York, or from 500 to 2,000 pounds per acre in Wisconsin and Minnesota. From 1980 through 1995, yield 
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varied from 170 pounds per acre in 1980 to 807 pounds per acre in 1987 in North Dakota. Buckwheat 

should be sold within a few months of harvest because quality deteriorates continually during storage. 

Buckwheat has many uses, including human food, feed, honey crop, etc., but its major use today is for 

human food, such as an ingredient for pancake mix or oriental noodles. It is almost always grown under 

contract, with the seed furnished by the contracting company. The main contracting company for the 

northern Midwest area is Minn-Dak Growers, Ltd. The main contracting company for the New 

York/Pennsylvania area is Birkett Mills in Penn Yan, New York. 

Economic Significance of Quality Adjustment and Crop Insurance. Production perils for buckwheat 

include excessive heat, drought, wind damage, heavy rainfall, pesticide residues, hail and fall frosts. 

According to agronomist Duane Berglund, most of the production perils lead to quantity loss instead of 

quality problems, so even though yield is highly volatile, quality adjustment should be relatively 

unimportant for buckwheat crop insurance. Agronomist Dr. Thomas Bjorkman also concurred that only 

10% of the variation in income from buckwheat production is caused by quality problems. 

Buckwheat is primarily examined for three different criteria in the U.S. These criteria (or quality 

measures) are dockage, moisture content and test weight. Dr. Bjorkman mentioned that farmers have 

control over dockage and have a minimal level of control over test weight. Farmers typically, however, do 

not attempt to clean the buckwheat thoroughly with proper combine adjustment because they often have a 

higher paid weight even after the dockage discount than they would if they had cleaned their crop 

entirely. Other minor discounts that are rarely assessed are odor discounts from poor storage combined 

with high moisture content and damage discounts, which are usually part of the dockage discount. These 

discounts are both controllable, yet highly infrequent. 
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CURRENT PROGRAM REVIEW 

Prior to 1995, quality deficiency adjustments were created using the actual price received for a given 

producer’s commodity (excluding quality discounts for non-covered causes of loss) compared with the 

U.S. No. 2 price. This method had the advantage of making a direct measurement of a producer’s loss 

due to quality deficiencies, rather than approximating the loss using national average discounts. However, 

RMA determined that this advantage was outweighed by the following drawbacks: 

• Low prices due solely to supply and demand effects; 

• The potential for price manipulation by producers or warehouses; and 

• Daily price variations. 

The current procedure indemnifies producers for covered quality deficiencies using predetermined quality 

discount factors (DF). The discount factors are based on FSA loan discounts for crop years 1997 through 

1999. These discount factors thus represent a national average of quality discounts which may vary from 

local or regional discounts. 

A grader licensed by the United States Grain Study Act or United States Warehouse Act is required to 

determine many of the factors that affect quality. The quality adjustment factor (QAF) is unity less the 

sum of the pre-determined DFs. (The QAF is limited to a minimum of zero.) The production is 

multiplied by the QAF in determining the production-to-count. 

Following is a partial list of crop quality perils13. 

• Harmful Substances: Crops found to be contaminated with harmful substances are graded as 

“U.S. Sample grade.” Examples of harmful substances include animal filth, glass and stones. 

• Objectionable Odors: Commodities with musty, sour or commercially objectionable foreign 

odors are labeled U.S. Sample grade. 

• Heating: Heating refers to commodities that are spoiling due to insect infestation or the 

action of microorganisms in grain with high moisture content. These commodities are graded 

U.S. Sample grade. 

• Insect Infestation: The presence of live insects results in a grade of “infected.” 

 

13 Source: USDA; Inspecting Grain, Practical Procedures for Grain Handlers.
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• Test Weight: Test weight refers to the weight of a commodity of a standard volume (for 

example, one Winchester bushel). “Dockage” (extraneous material that may be removed with 

sieves or other cleaning devices) is removed before weighing. Lower test weights reduce the 

commodity’s grade. 

• Foreign Material: A higher amount of extraneous material, other than dockage, reduces a 

commodity’s grade. 

• Damaged Kernels: Kernels may be damaged due to heat, frost, rot, mold or other causes that 

generally result in a change in color or kernel texture. A higher percentage of damaged 

kernels reduces a commodity’s grade. 

• Aflatoxin: Harmful levels of aflatoxin may be produced by certain types of mold that may be 

found in corn or grain sorghum grown under stressful conditions such as drought. 

• Vomitoxin: Scab or head blight may result from vomitoxin, a naturally occurring mycotoxin 

(toxin produced by fungus). 

The perils currently covered for each crop are listed in Exhibit 1. 
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MARKET RESEARCH 

Objective 

We conducted a substantial amount of market research to determine how discount schedules vary among 

regions and over time. More specifically, we attempted to identify the patterns by which discount rates 

varied across regions in the U.S. The answers to these questions provide the basis for our 

recommendations on the optimal quality adjustment program which balances costs and accuracy. 

Data Sources 

We spoke with various trade organizations, different agencies of the USDA and USDA extension service 

agronomists, and extensively reviewed the relevant literature on agricultural economics and crop 

insurance. Based on these efforts, we identified the following potential data sources: 

AMS daily market price and weekly cotton quality data: The AMS daily market prices are market 

prices for the standard grades of most crops in major terminal areas, based on daily surveys of elevators. 

Among them, AMS cotton market prices include complete daily prices for all classes of cotton traded in 

each of the seven spot markets. The AMS Cotton Program's weekly cotton quality data file contains 

classification information for all bales classed in a given week, as well as the state or number of the 

relevant classing office. 

FSA Loan Program: FSA maintains a significant amount of data containing transaction information and 

market prices and discount rates used in the marketing assistance program. The FSA data include: 

• The quantity and quality of the crops used to take out non-recourse loans for each customer 

for the last three years. Limited payment information by customer is also available up to the 

last ten years. Also, data by entities obtaining loan deficiency payments is maintained, but 

with far more limited information on the quality of the crop used to obtain the payments. 

• Posted daily county prices for various crops based on daily surveys of market prices from the 

AMS and price differentials across counties; the price differentials are based on surveys 

conducted in the 1980s and timely updating based on changes in market conditions. 

• Annual countrywide quality deficiency discount rates for various crops: the discount rates are 

based on surveys of producers. 
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ERS surveys of producers: ERS, in conjunction with other USDA agencies, conducted various surveys 

of the marketplace for different research projects, both in the past and on an ongoing basis. These data 

elements revealed many issues including the extent of information problems in the agricultural production 

process, which provided background information for this analysis. In general, however, these data do not 

contain enough quality adjustment information for them to be directly useful for this analysis. 

Milliman Survey. Other than the cotton market data, we are unaware of any other source of data that 

would provide U.S. regional quality discount rates for the crops within the scope of this study. We thus 

concluded that to obtain local discount rates, a direct survey of local elevators and oilseed purchasers was 

essential. 

Survey of Elevators 

Milliman contacted by phone 360 different grain terminals, elevators and warehouses. These terminals or 

elevators were chosen from the FSA list of the uniform grain and rice storage agreement approved 

warehouse list. We first chose all of the states that produce at least ten percent of the crops within the 

scope of this study. We then contacted one elevator from each county until we had obtained at least ten 

responses. 

In addition to the above procedure, we also directly contacted six major grain purchasers or terminal 

elevators based on recommendations from Mr. Tim Murray of the FSA. We received limited response 

from these companies. The reasons for separately contacting these corporations include: 

• They may have a better record of discount schedules; 

• They have large amounts of information because they purchase many crops through their 

elevators in various locations; and 

• They are particularly representative or influential of the actual market condition because their 

volume of transactions is large. 

Ideally, we wanted to have a target number of responses for each crop. However, a number of factors 

hindered our goal. We did not know which elevator was selling which crop and some elevators did not 

send us discount schedules for all of the crops they purchase. Thus, the response rate was unpredictable. 

In the end, we received responses from over 200 sources. We obtained faxed discount schedules from 113 

elevators, 8 were relayed over the phone, and eighty two replied that they followed other elevators. The 
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schedules included corn, soybeans, HRW wheat, SRW wheat, soft white wheat, spring wheat, dark 

northern spring wheat, white club wheat, durum wheat, barley, oats, rye, flax, canola, oil sunflower and 

confectionary sunflower. The elevators that imitated a terminal’s schedules tended to conduct business 

with that particular terminal and would transfer the exact same discounts down to the farmer. 

Of the terminals and elevators that responded to us, 117 purchase corn, 85 purchase soybeans, 72 

purchase wheat,14 30 purchase oats, 33 purchase barley, 17 purchase sunflower, and one purchases rye. 

We received 45 discount schedules for soybeans, 58 discount schedules for corn, 11 discount schedules 

for oats, 8 discount schedules for barley, and 7 discount schedules for sunflowers (confectionary and oil). 

The elevators and terminals that make up the difference between the number of purchasers and the 

number of suppliers of discount schedules either did not provide any information or simply followed the 

schedules of a larger terminal. 

Analysis Procedure 

The quantitative analyses comprise the following steps: 

1. data transformation; 

2. analysis of the distribution of average discount levels and correlation coefficients among discount 

schedules; 

3. cluster analysis of discount schedules; 

4. nonparametric tests of the equality of discount levels for the average discount rates across RMA 

regional office regions; 

5. computation of the average discount schedules for regional office regions. 

Step 1 transforms discount schedule data to common units and common quality ranges so that 

comparisons and groupings of different discount rates are feasible. Based on step 2, we found that in 

general, while discount schedules tend to be highly correlated (indicating that they have similar pattern of 

 

14 Originally, we also analyzed the discount schedules for different classes of wheat separately. For the 7 different types of wheat listed, 32 

discount schedules were received via fax or phone out of 64 contacted elevators and terminals that purchase HRW wheat; twelve schedules were 

received out of 23 contacted elevators and terminals that purchase SRW wheat; nine schedules were received out of 29 contacted terminals and 

elevators that purchase spring wheat; six schedules were received out of 16 contacted terminals and elevators that purchase soft white wheat; 

three schedules were received out of 9 contacted terminals and elevators that purchase durum wheat; four schedules were received out of four 

contacted elevators and terminals that purchase white club wheat; and six discount schedules were received via fax or phone out of 10 contacted 

terminals and elevators that purchase dark northern spring wheat.
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variations), discount levels vary by large magnitudes. Step 3 determines how elevators group together if 

we group them based on the similarity in discount rates.15 In general, cluster analysis suggested that 

similar discount schedules are frequently widely scattered in groups that are not geographically 

contiguous. Having geographically discontinuous regions does not pass the tests of reasonableness or 

administrative efficiency. We further applied judgment and cluster analysis results to draw regions with 

relatively similar discount schedules. As it turned out, it appeared that there was significant correlation 

between the regions drawn by cluster analysis and the regions under the geographic domains of the RMA 

regional offices. Considering that using the domains of RMA regional offices should significantly reduce 

the administrative costs of regional discount schedules, we turned to that as a preferred approach. 

Step 4 explores whether RMA regions improve the accuracy of quality discount rates by testing if there is 

statistical evidence that average discount schedules vary by RMA regions. The statistical tests we relied 

upon were repeated for each peril. Based on the results of step 4, it appears that RMA regions have 

different discount schedules for corn, soybean, wheat, and grain sorghum. However, it was frequently the 

case that the discount rates did not differ across regions for all of the perils covered. We recommended 

regional discount schedules for all perils as long as there were enough discount rate differences for some 

important perils for the crop. Based on our experience with the survey, as long as country elevators 

provided the discount schedules, they usually included multiple perils. Step 5 computes the regional 

discount rates for these four crops, as well as the countrywide discount rates for all other crops. 

Two common problems emerged in step 5. First, for each crop we did not receive any discount schedules 

for some RMA regions. (Typically, this was the case in regions that are not the major producers of the 

crop in question.) We considered two options for choosing the discount schedules for these regions. One 

was to use discount schedules applied to all regions in the country that we received from major terminal 

elevators, and the other was to use the average of all discount schedules. Upon review of the discount 

schedules computed, it appears that using the average of all discount schedules might be a better 

approach. The problem with using the countrywide discount schedules is that there were only a few of 

them; as a result, the average of those discount schedules sometimes has erratic patterns such as large 

fluctuations in discount rates in different quality ranges, which appears unreasonable. 

Another common problem encountered in this step is that many of the discount schedules cover different 

levels of deficiency of the quality factor. For example, one discount schedule would cover 5% through 

 

15 The measure of similarity is the Euclidean distance between two discount schedules, which is the square root of the sum of 
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15% damage, one would cover 10%-20% and then indicate that prices for damage above 20% were 

subject to negotiation, and yet another would specify a discount rate per percent above 15% without 

specifying an ending point or maximum damage amount. For the last case, we simply could not determine 

if this discount rate would apply all the way up to 20%, 30%, or higher damage, and when we inquired of 

a few country elevators about this issue, the responses were frequently vague (such as “farmers rarely or 

never bring in anything below a certain level”). We thus picked the ending points of the discount ranges 

simply by looking at where most other schedules stop. If beyond a certain point the discount rate is based 

on negotiation, then data entry would stop at that point. Indeed, even if the discount schedule specifies an 

end point, based on conversations with country elevators, it is possible that the country elevator would 

still accept crops beyond that point of quality problem with a discount, depending on market conditions, 

relationship with the producer, and the like. It is thus difficult to determine the ending point for a discount 

schedule. 

The results of all of the analyses are voluminous. To minimize the volume in the main text of this report, 

we present only selected results from the comparisons of discount levels, the nonparametric tests of the 

equality of discount levels across RMA regions, and the recommended discount schedules based on the 

average discount levels computed. Summary statistics for the correlation coefficients among the discount 

schedules of elevators are provided in the Appendix. For the convenience of reporting, the regions 

belonging to each of the RMA regional offices are assigned a number as shown in the following table. 

Table 1. Definition of RMA Regions (in random order) 

Region No. States 

1 IA, MN, WI 

2 MT, ND, SD, WY 

3 CO, KS, MO, NE 

4 IL, IN, MI, OH 

5 AK, ID, OR, WA 

6 NM, OK, TX 

7 AZ, CA, HI, NV, UT 

8 AR, KY, LA, MS, TN 

9 AL, FL, GA, SC 

10 CT, DE, ME, MA, MD, NH, NJ, NY, NC, PA, RI, VT, VA, WV 

Data Transformation. For each particular quality factor, the discount schedules of various elevators are 

frequently in a mixture of percentage and dollar discount units. In such a case, the data must be 

transformed to a common unit so that comparisons and groupings are feasible. For that purpose, we either 

multiplied percentage discounts by the average December 2001 FSA relevant posted county price to 

 

square distance between the discount rates across all common quality ranges. 
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obtain the dollar discount or divided dollar discounts by the same posted county price to obtain the 

percentage discount. The average posted county price appears to be the relevant price because, based on 

conversations with elevators, the discount schedules do not vary with daily price variations, and the 

survey was conducted during December 2001. 

For certain terminal elevators or large corporations purchasing crops through multiple elevators from 

different locations, their discount schedules may apply to large regions or the whole country. In that 

event, we used the simple average of the PCP prices of all counties included in the relevant region as the 

PCP price. 

In addition, the discount schedules of different elevators often have different increments. For example, 

company A’s discount schedule might state an incremental discount of 2 cents for every 2% kernel 

damage from 14% up to 20%. Company B’s discount schedule might state 1 percent discount for each 

additional 0.5% from 15% up to 19.5% kernel damage. To make them consistent, we would convert the 

discount schedule of company A to the scale of every half percent from 14% up to 20% using linear 

interpolation (drawing a straight line to join the two adjacent points). 

Corn Analysis 

The corn crop insurance contract applies quality adjustment for low test weight, kernel damage, musty, 

sour, or commercially objectionable odor (COFO) leading to a quality level of U.S. Standard Grade 5 or 

worse; or substance or conditions injurious to human or animal health. 

Discount Level Comparisons. Since not all discount schedules cover the same range of deficiencies, it 

is not meaningful to compare the averages of each discount schedule for each quality factor. Instead, we 

compare the average discount rates for the ranges of deficiency shared by all discount schedules. The 

distribution of the averages is shown in the following tables. It indicates two general patterns: 

• large discount variations, and 

• highly skewed discount distribution. 

The first suggests that there is potential for regional discount rates to improve the accuracy of quality 

discount rates. The second suggests that parametric analysis of variance, which usually assumes normal 
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distribution in the underlying variables, is not the proper approach for testing the similarity of discounts 

across different regions. 

Table 2. Average Discounts for Test Weights 52-53 (cents) 

 ID Discount ID Discount ID Discount

1 NE166 -7 19 NE184 -2 37 NE205 -1.5 

2 SD056 -7 20 NE186 -2 38 IL206 -1.5 

3 KS037 -6 21 IL009 -1.5 39 IL207 -1.5 

4 KS121 -6 22 CO012 -1.5 40 IL212 -1.5 

5 NE071 -5.5 23 WI029 -1.5 41 KS214 -1.5 

6 MN026 -5 24 MN031 -1.5 42 MO215 -1.5 

7 IA088 -3.5 25 NE051 -1.5 43 NE216 -1.5 

8 SD002 -3 26 CO102 -1.5 44 IO218 -1.5 

9 SD049 -3 27 IA114 -1.5 45 IL219 -1.5 

10 SD067 -3 28 IL117 -1.5 46 IL221 -1.5 

11 TX068 -3 29 IA122 -1.5 47 US222 -1.5 

12 NE069 -3 30 WI156 -1.5 48 IL223 -1.5 

13 IA074 -3 31 IA163 -1.5 49 IL224 -1.5 

14 IL119 -3 32 KS172 -1.5 50 MN225 -1.5 

15 SD127 -3 33 NE181 -1.5 51 IL226 -1.5 

16 KS013 -2.5 34 AX182 -1.5 52 IA227 -1.5 

17 WI148 -2 35 IA198 -1.5 53 WI171 -1 

18 IA157 -2 36 IL200 -1.5 
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Table 3. Average Discounts for 5%-7% Damage (cents) 

 ID Discount ID Discount ID Discount

1 SD046 -5 15 IA122 -2 29 NE181 -1 

2 SD056 -4 16 IA157 -2 30 IL200 -1 

3 KS037 -3 17 NE184 -2 31 IL206 -1 

4 KS172 -3 18 NE186 -2 32 IL207 -1 

5 MN031 -2.5 19 SD002 -1 33 IL212 -1 

6 IA114 -2.3 20 IL009 -1 34 IO218 -1 

7 IA198 -2.3 21 SD049 -1 35 IL221 -1 

8 NE051 -2 22 GA065 -1 36 US222 -1 

9 SD067 -2 23 IL117 -1 37 IL223 -1 

10 TX068 -2 24 SD127 -1 38 IL224 -1 

11 NE069 -2 25 WI148 -1 39 MN225 -1 

12 NE071 -2 26 WI156 -1 40 IL226 -1 

13 IA074 -2 27 IA163 -1 41 IA227 -1 

14 IL119 -2 28 WI171 -1 

Table 4. Discount for COFO (cents) 

 ID Discount ID Discount ID Discount

1 IL119 -20 7 IL207 -10 13 IA114 -5 

2 MN026 -20 8 NE069 -10 14 IA198 -5 

3 IL200 -10 9 NE184 -10 15 IL206 -5 

4 IL219 -10 10 NE186 -10 16 IL212 -5 

5 IL226 -10 11 WI148 -10 17 NE216 -5 

6 IL117 -10 12 IA074 -5 

Table 5. Discount for Musty (cents) 

 ID Discount ID Discount ID Discount

1 AX182 -10 15 IA074 -5 29 MO215 -5 

2 IA088 -10 16 IA114 -5 30 NE051 -5 

3 IL119 -10 17 IA198 -5 31 NE216 -5 

4 KS172 -10 18 IA157 -5 32 NE069 -5 

5 KS013 -10 19 IL223 -5 33 NE181 -5 

6 MN026 -10 20 IL206 -5 34 NE205 -5 

7 NE184 -10 21 IL224 -5 35 SD002 -5 

8 NE186 -10 22 IL200 -5 36 SD127 -5 

9 NE071 -10 23 IL212 -5 37 US222 -5 

10 WI171 -10 24 IL219 -5 38 WI156 -5 

11 IA122 -7 25 IL226 -5 39 WI029 -5 

12 TX068 -7 26 KS037 -5 40 IL117 -3 

13 GA065 -5 27 KS214 -5 41 IL207 -3 

14 IA163 -5 28 MN031 -5 42 IO218 -3 
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Table 6. Discounts for Sour (cents) 

 ID Discount ID Discount ID Discount

1 IL119 -15 14 NE184 -10 27 IA198 -5 

2 AX182 -10 15 NE186 -10 28 IL206 -5 

3 GA065 -10 16 NE205 -10 29 IL224 -5 

4 IA163 -10 17 NE071 -10 30 IL200 -5 

5 IA088 -10 18 SD127 -10 31 IL212 -5 

6 IA157 -10 19 TX068 -10 32 IL219 -5 

7 IL223 -10 20 US222 -10 33 IL117 -5 

8 IL226 -10 21 WI156 -10 34 IL207 -5 

9 KS037 -10 22 WI171 -10 35 NE051 -5 

10 KS172 -10 23 WI148 -10 36 NE216 -5 

11 KS214 -10 24 IA122 -7 37 SD002 -5 

12 MO215 -10 25 IA074 -5 38 WI029 -5 

13 NE069 -10 26 IA114 -5 

Statistical Tests. We performed the Kruskal-Wallis test for the equality of the average discount rates 

across RMA regions. The KW test is a nonparametric test that does not assume that the discount rates 

have a particular distribution. The results are shown in the table below. Except for Sour, all of the pvalues are significant at the 5% level. This indicates that the average discounts differ significantly across 

regions.16

Table 7. Results of Testing the Equality of Discounts Across RMA Regions 

Analysis 

Variable 

Kruskal-Wallis 

Statistic 

Degree of 

Freedom p-value 

Damage 16.1 4 0.003 

TW 15.3 4 0.004 

Musty 9.3 4 0.055 

Sour 4.8 4 0.307 

Discount Schedules Computation. The statistical tests suggest that discount schedules based on RMA 

regions could be more accurate than a national schedule. We thus computed the actual average discount 

schedule for each region. 

 

16 The p-value is an indicator of the significance of a test. The lower the p-value, the less likely that the observed difference across 

regions is a coincidence instead of a pattern. A p-value of 10% is a common convention used by academic studies as an evidence 

of statistical significance. 
-45-

MILLIMAN USA 

For U.S. Standard Grade discounts, the average discount for Grade 5 due to test weight, kernel damage or 

total broken and foreign material was computed for each elevator. Some elevators have discounts for only 

one or two of the three quality factors, in which case the missing item was ignored for this computation. 

Based on the average of each elevator, we computed the average discount for Grade 5 for each region. 

This process was repeated to obtain the regional discount rates for sample grade. Similarly, the averages 

for musty, sour, COFO, and the incremental discounts for test weight, damage, aflatoxin, and TB & FM 

were computed separately. For these computations, the discount schedules for each region were based on 

the elevators that purchase crops exclusively from that region. Although some terminal elevators may 

have provided a discount schedule applying to the whole country, we could not identify the parts of the 

country from which they purchase crops. Thus, these discount schedules were included in the 

computation of the national average. 

The average discount rates are displayed in the table below. Discounts for RMA regions 1, 2, 3, 4, and 6 

are based on survey discount schedules from these regions. The national discount schedule is the average 

of all discount schedules. The current discount schedule is also displayed to allow for a comparison of the 

current and the computed discount schedules. Discounts for aflatoxin and infested are also included, in 

case that they are covered perils. We obtained only one discount schedule for aflatoxin, hence the 

recommendation is based on one discount schedule only. 

Regional discount schedules for corn are broadly similar, and generally comparable to the current 

schedule. (An exception to this is that the proposed incremental discounts for test weight are greater than 

the current discounts.) Although the current schedule shows discounts for test weight below 54 lbs., the 

requirement for indemnity under the corn policy is a quality deficiency that renders the corn grade 5 or 

below. We provide a grade 5 discount, and note that the lowest test weight for grade 5 is 46 lbs., hence 

our proposed schedule shows an incremental discount only below that weight. This is consistent with the 

basic principle that quality discounts should be used when a crop falls below the threshold specified in the 

policy, and that incremental discounts should be applied beyond that level. 
-46-

MILLIMAN USA 

Table 8. Current Discount Schedule and Average Discount Schedules 

Based on Survey 

Corn 

 Region 

 Current 1 2 3 4 6 National 

Grade 5 15 19 21 10 16 15 

Sample Grade 15 20 20 21 14 18 20 

TW (lbs) 

below 54 1/lb N/A N/A N/A N/A N/A N/A 

TW (lbs) 

below 50 2/lb N/A N/A N/A N/A N/A N/A 

TW (lbs) 

below 46 2/lb 4/lb 5/lb 4/lb 3/lb 3/lb 3/lb 

Kernel Damage 

(%) above 5 2/% N/A N/A N/A N/A N/A N/A 

Kernel Damage 

(%) above 15 3/% 3/% 2/% 4/% 4/% 2/% 2/% 

Musty 10 7 5 7 5 7 6 

Sour 10 8 8 9 7 10 8 

COFO 10 9 9 9 10 9 9 

Aflatoxin N/A 25/20-120 ppb; 40/121-200 ppb; 60/201-250 ppb; 75/251-300 ppb 

Infested N/A 7 10 6 8 10 7 

Observations 16 5 17 12 1 53 

Notes: 1. All discounts are in cents/bushel. 

2. N/A implies no data available. 

3. "Observations" is the number of discount schedules used to compute the regional averages. It is possible that 

not all entries in the same region used the total number of observations.
-47-

MILLIMAN USA 

Soybean Analysis 

The current soybean crop insurance policy applies quality adjustment to US sample grade soybeans 

caused by weight or kernel damage, musty, sour, commercially objectionable odor, or garlicky; or 

substances or conditions injurious to human or animal health. 

Comparisons of Average Discount Rates. For kernel damage and other damages, the discount rates 

vary by up to a few hundred percents from elevator to elevator and are highly skewed. The test weight 

discounts are still skewed, but less so than damage discounts. More than half of the average discounts are 

about 1 cent/bushel for test weight 52-54 pounds/bushel. 

Table 9. Average Discounts for 2% - 3% Damage 

 ID Discount ID Discount ID Discount 

1 SD049 -0.78% 9 IA074 -0.35% 17 IL009 -0.22% 

2 KS003 -0.62% 10 MS204 -0.35% 18 IL224 -0.22% 

3 IA122 -0.54% 11 WI006 -0.25% 19 IL200 -0.15% 

4 SD067 -0.52% 12 MN225 -0.25% 20 SD127 -0.14% 

5 IL119 -0.44% 13 IA227 -0.23% 21 MN050 -0.12% 

6 NE184 -0.37% 14 KS081 -0.23% 22 WI148 -0.12% 

7 NE069 -0.36% 15 IL226 -0.23% 23 IL207 -0.11% 

8 NE051 -0.36% 16 IL206 -0.22% 24 IL117 -0.11% 

Table 10. Average Discounts for Test Weights 52 – 54 lbs. 

 ID Discount ID Discount ID Discount 

1 SD067 -2 15 IA122 -1 29 NE069 -0.5 

2 IL119 -2 16 WI148 -1 30 KS081 -0.5 

3 SD127 -2 17 IA163 -1 31 IL200 -0.5 

4 NE184 -1.3 18 IL206 -1 32 MN202 -0.5 

5 SD002 -1 19 IL207 -1 33 MS204 -0.5 

6 WI006 -1 20 IL217 -1 34 NE205 -0.5 

7 IL009 -1 21 US222 -1 35 OK210 -0.5 

8 WI029 -1 22 IL223 -1 36 IA211 -0.5 

9 MN050 -1 23 IL224 -1 37 NE213 -0.5 

10 NE051 -1 24 IL226 -1 38 IA220 -0.5 

11 NE071 -1 25 SD049 -0.6 39 MN225 -0.5 

12 IA074 -1 26 KS003 -0.5 40 IA227 -0.5 

13 IL117 -1 27 NE004 -0.5 

14 KS121 -1 28 KS013 -0.5 
-48-

MILLIMAN USA 

Table 11. Average Discounts for COFO 

 ID Discount ID Discount ID Discount

1 WI148 -25 6 IL117 -10 11 NE184 -10 

2 IL119 -20 7 IL200 -10 12 NE205 -10 

3 IL207 -15 8 IL206 -10 13 NE213 -10 

4 IA211 -10 9 IL217 -10 14 OK210 -10 

5 IA220 -10 10 NE004 -10 15 IA074 -5 

Table 12. Average Discounts for Musty 

 ID Discount ID Discount ID Discount

1 IA088 -10 13 NE213 -10 25 IL206 -5 

2 IA163 -10 14 OK210 -10 26 IL207 -5 

3 IA211 -10 15 SD002 -10 27 IL223 -5 

4 IL119 -10 16 SD127 -10 28 IL224 -5 

5 IL217 -10 17 WI006 -10 29 IL226 -5 

6 KS013 -10 18 WI148 -10 30 KS003 -5 

7 KS081 -10 19 IA122 -7 31 MS204 -5 

8 MN202 -10 20 IA074 -5 32 NE051 -5 

9 NE004 -10 21 IA157 -5 33 US222 -5 

10 NE069 -10 22 IA220 -5 34 WI029 -5 

11 NE184 -10 23 IL117 -5 

12 NE205 -10 24 IL200 -5 

Table 13. Average Discounts for Sour 

 ID Discount ID Discount ID Discount

1 IL119 -15 13 MN202 -10 25 MN050 -10 

2 IA088 -10 14 NE004 -10 26 IA122 -7 

3 IA163 -10 15 NE069 -10 27 IA074 -5 

4 IA211 -10 16 NE184 -10 28 IA157 -5 

5 IA220 -10 17 NE205 -10 29 IL200 -5 

6 IL117 -10 18 NE213 -10 30 IL224 -5 

7 IL206 -10 19 OK210 -10 31 KS003 -5 

8 IL207 -10 20 SD002 -10 32 MS204 -5 

9 IL217 -10 21 SD127 -10 33 NE051 -5 

10 IL223 -10 22 US222 -10 34 WI029 -5 

11 IL226 -10 23 WI006 -10 

12 KS081 -10 24 WI148 -10 

Statistical Tests. We then performed a nonparametric Kruskal-Wallis test on the average discount rates 

of elevators among the RMA regions. The results of the KW tests are displayed in the table below. The 

results are somewhat mixed; there is strong statistical evidence suggesting that the discount rates for 

musty are different, and less strong evidence (about 10% statistical significance) that discounts for kernel 

damage are different among the regions. For test weight discounts, there is an almost 10% level statistical 

significance for the variation of test weight discounts among RMA regions. The evidence in general 

suggests that discounts for sour and COFO are not significantly different across RMA regions. 
-49-

MILLIMAN USA 

Table 14. Results of Testing the Equality of Discounts Across RMA Regions 

Analysis 

Variable 

Kruskal-Wallis 

Statistic 

Degree of 

Freedom p-value 

Damage 7.1 3 0.070 

TW 7.1 4 0.132 

COFO 2.1 3 0.555 

Musty 13.6 4 0.009 

Sour 5.7 4 0.225 

Discount Schedules Computation. The computation procedure for the regional discount schedules is 

similar to that for corn. The regional discount schedules are displayed in the following table. The current 

crop insurance policy also specify quality adjustment for substances or conditions injurious to human or 

animal health. We have included discounts for green, frost and infested damage, in case that they are 

covered perils. The recommendation for frost damage is based on the only one discount schedule for frost 

we obtained. 

There is considerable variation in the sample grade discount rates across regions. Other than that, the 

proposed discount levels seem relatively similar to the current discounts. Also, the current schedule has 

discounts for test weight and kernel damage beginning at levels before those required to reach sample 

grade. Since quality deficiency due to a covered peril must at least equal the amount of the deficiency 

required to reach sample grade, our recommended incremental discounts begin at those levels. 
-50-

MILLIMAN USA 

Table 15. Current Discount Schedule and Average Discount Schedule 

Based on Survey 

Soybeans 

 Region 

 Current 1 2 3 4 6 National 

Sample Grade 15 8 18 13 11 N/A 12 

TW (lbs) 

below 54 .5/lb N/A N/A N/A N/A N/A N/A 

TW (lbs) 

below 49 1/lb N/A N/A N/A N/A N/A N/A 

TW (lbs) 

below 48 N/A 1/lb 2/lb 1/lb 1/lb 1/lb 1/lb 

Damage (%) 

above 2 1/half % N/A N/A N/A N/A N/A N/A 

Damage (%) 

above 8 2/half % N/A N/A N/A N/A N/A N/A 

Damage (%) 

above 9 N/A 1.5/half % 2/half % 2.5/half % 2/half % 2/half % 2/half % 

Musty 5 8 10 9 6 10 8 

Sour 10 8 10 9 9 10 9 

COFO 20 13 11 10 13 10 11 

Garlicky N/A N/A N/A N/A N/A N/A N/A 

Green N/A 2/% above 

9 up to 15% N/A 3/% above 

9 up to 15%

1% for 

10% green N/A 2/% above 

9 up to 15%

Frost N/A 2/half % above 9 

Infested N/A 9 10 N/A 8 N/A 9 

Observations N/A 13 4 11 10 1 40 

 Notes: 1. All discounts are in cents/bushel. 

2. N/A implies no data available. 

3. "Observations" is the number of discount schedules used to compute the regional 

averages. It is possible that not all entries in the same region used the total number 

of observations 
-51-

MILLIMAN USA 

Grain Sorghum Analysis 

The current quality adjustment factors for grain sorghum crop insurance are sample grade caused by test 

weight damage, kernel damage, musty, sour and COFO; or substances or conditions injurious to human or 

animal health. 

Comparison of Average Discount Rates. The following tables display the variations of discount rates 

across elevators. In general, discount rates vary a great deal among elevators, suggesting that there might 

be improvements in accuracy in using regional discount schedules. 

Table 16. Average Discounts for 5% - 10% Damage (cents) 

 ID Discount ID Discount ID Discount 

1 US222 -6 4 NE184 -5 7 KS037 -3 

2 IL224 -6 5 NE186 -5 8 NE188 -3 

3 KS081 -5 6 KS209 -5 9 KS003 -1 

Table 17. Average Discounts for Test Weights 51 - 54 lbs. 

 ID Discount ID Discount ID Discount

1 IL223 -2.50% 9 MO215 -1.81% 17 KS081 -1.33% 

2 KS121 -2.38% 10 NE205 -1.76% 18 KS037 -1.22% 

3 TX062 -2.33% 11 US222 -1.75% 19 KS013 -1.07% 

4 KS021 -2.13% 12 TX147 -1.59% 20 NE184 -0.87% 

5 OK210 -2.06% 13 KS214 -1.55% 21 NE186 -0.87% 

6 KS209 -1.85% 14 NE181 -1.38% 22 KS010 -0.66% 

7 TX060 -1.84% 15 NE216 -1.37% 23 NE188 -0.65% 

8 IL224 -1.82% 16 NE071 -1.36% 24 KS003 -0.61% 

Table 18. Average Discounts for Musty 

 ID Discount ID Discount ID Discount

1 NE216 -20 7 NE071 -10 13 IL223 -8 

2 KS081 -15 8 NE184 -10 14 IL224 -5 

3 KS013 -10 9 NE186 -10 15 KS003 -5 

4 KS209 -10 10 NE205 -10 16 KS037 -5 

5 KS214 -10 11 OK210 -10 17 US222 -5 

6 MO215 -10 12 TX147 -10 18 NE188 -3 
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MILLIMAN USA 

Table 19. Average Discounts for Sour 

 ID Discount ID Discount ID Discount

1 NE216 -20 7 KS214 -10 13 OK210 -10 

2 KS081 -15 8 MO215 -10 14 TX147 -10 

3 KS209 -12 9 NE071 -10 15 US222 -10 

4 IL223 -10 10 NE184 -10 16 IL224 -5 

5 KS003 -10 11 NE186 -10 17 NE188 -3 

6 KS037 -10 12 NE205 -10 

Table 20. Average Discounts for COFO 

 ID Discount ID Discount ID Discount

1 NE216 -20 2 NE184 -10 3 NE186 -10 

Statistical Tests. The test results displayed below suggest that there is statistical evidence of significant 

variation in test weight discounts among RMA regions, but not for other quality factors. Considering that 

test weight is an important quality factor which is frequently due to insurable cause, we believe that this 

provides a reason to suggest regional discount schedules. 

. 

Table 21. Results of Testing the Equality of Discounts Across RMA Regions 

Analysis 

Variable 

Kruskal-Wallis 

Statistic 

Degree of 

Freedom 

p-value 

Damage 1.2 1 0.27 

TW 6.8 2 0.03 

Musty 2.6 2 0.28 

Sour 3.2 2 0.20 

Discount Schedules Computation. Based on the statistical test results, we compute the regional average 

discount rates. They are shown in the table below. We included Tan discounts in case that it is a covered 

peril. All Tan discount schedules we received are identical. The recommended Tan discount schedule is 

identical in all regions. 

The proposed sample grade discount for grain sorghum is substantially lower than the current discounts 

for sample grade, musty sour and COFO. The proposed discounts, however, has higher discounts for 

kernel damage. Also, the current schedule has discounts for test weight and kernel damage beginning at 

levels before those required to reach sample grade. Since quality deficiency due to a covered peril must at 

least equal the amount of the deficiency required to reach sample grade, our recommended incremental 

discounts begin at those levels. 
-53-

MILLIMAN USA 

Table 22. Current Discount Schedule and Average Discount Schedule 

Based on Survey 

Grain Sorghum 

 Region 

 Current 3 4 6 National 

Sample Grade 30 12 13 19 14 

TW (lbs) below 55 2/lb N/A N/A N/A N/A 

TW (lbs) below 51 4/lb N/A N/A N/A N/A 

TW (lbs) below 50 4/lb 3/lb 4/% 5/% 4/% 

TW (lbs) below 44 4/lb 4 /lb 4/% 5/% 4/% 

Kernel Damage (%) 

above 5 2/% N/A N/A N/A N/A 

Kernel Damage (%) 

above 6 3/% N/A N/A N/A N/A 

Kernel Damage (%) 

above 16 3/% 2/% 3/% 2/% 2/% 

Kernel Damage (%) 

above 20 3/% 3/% 3/% 3/% 3/% 

Kernel Damage (%) 

above 25 2/% 3/% 3/% 3/% 3/% 

Musty 10 10 7 10 9 

Sour 20 11 8 10 10 

COFO 20 13 13 13 13 

Special Grade Reqd. for 

Smutty N/A N/A N/A N/A N/A 

Tan N/A 4/% above 1% Tan 

Observations 17 2 4 24 

 Notes: 1. All discounts are in cents/bushel. 

2. N/A implies no data available. 

3. "Observations" is the number of discount schedules used to compute the regional averages. It is possible that not all 

entries in the same region used the total number of observations. 

Wheat Analysis 

The current wheat policy applies quality adjustment to Grade 5 or Sample grade wheat caused by test 

weight damage, kernel damage, shrunken or broken kernels defects, musty, sour, COFO, grading 

garlicky, light smutty, smutty, or ergoty, and substances or conditions injurious to human or animal 

health. 

Discount Rates Comparison. The following tables compare the average discount rates among RMA 

regions for each peril covered by the current policy. Only six elevators have separate grade discounts in 

the quality discount schedules. 
-54-

MILLIMAN USA 

Table 23. Average Test Weight Discount Rates in 58-56 lbs.(cents/bushel) 

 ID Discount ID Discount ID Discount

1 ND034 -30 17 KS121 -6 32 IL117 -4 

2 ND145 -16 18 TX022 -6 33 IL201 -4 

3 ID112 -7 19 US222 -5 34 NE184 -4 

4 KS214 -7 20 SH113 -5 35 NE186 -4 

5 MO215 -7 21 KS021 -5 36 TX083 -4 

6 NE205 -7 22 WI029 -5 37 WI148 -4 

7 NE228 -7 23 US187 -4 38 CO072 -4 

8 SD002 -7 24 NE166 -4 39 OK066 -3 

9 US175 -7 25 NE180 -4 40 WA208 -3 

10 IL119 -7 26 SD127 -4 41 KS209 -3 

11 IL223 -7 27 OK160 -4 42 CO012 -3 

12 IL226 -7 28 OK210 -4 43 CO102 -3 

13 IL123 -6 29 MT058 -4 44 NE181 -2 

14 IL224 -6 30 OK176 -4 45 TX060 -1 

15 KS013 -6 31 CO141 -4 

Table 24. Average Discount Rates for Grade 5 

ID Discount ID Discount ID Discount

1 MT058 -8 3 WA208 -4 5 TX022 -2 

2 MN050 -8 4 US187 -3 6 SH113 -2 

Table 25. Average Discount Rates for 4-5% Damage (cents/bushel) 

 ID Discount ID Discount ID Discount

1 ND034 -45 10 NE184 -5 19 IL119 -2 

2 ND145 -23 11 NE186 -5 20 IL117 -1 

3 SD049 -15 12 KS214 -4 21 IL123 -1 

4 MN225 -14 13 MO215 -4 22 IL223 -1 

5 MN050 -12 14 NE205 -4 23 IL224 -1 

6 SD002 -11 15 NE228 -4 24 IL226 -1 

7 US175 -11 16 KS209 -3 25 US222 -1 

8 SD127 -10 17 NE180 -3 

9 NE166 -5 18 OK210 -3 

Table 26. Average Test Weight Discount Rates for White Club Wheat and 

Hard Red Spring Wheat in 58-54 lbs. (cents/bushel)

ID Discount ID Discount ID Discount

1 NE166 -14 5 MN225 -5 9 ND034 -4 

2 WI029 -11 6 ND145 -5 10 MT058 -3 

3 CO141 -7 7 SD049 -5 11 WA208 -3 

4 SD127 -5 8 MN014 -5 12 US187 -2 
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MILLIMAN USA 

Table 27. Average Discount Rate for 5-6.5% Shrunken and Broken 

 ID Discount ID Discount ID Discount

1 US175 -7 8 NE184 -3 15 IL117 -2 

2 CO012 -6 9 NE186 -3 16 IL123 -2 

3 SD127 -6 10 KS214 -2.8438 17 IL226 -2 

4 MN225 -4 11 MO215 -2.8438 18 IL223 -0.75 

5 TX022 -4 12 NE205 -2.8438 19 IL224 -0.75 

6 KS209 -3 13 OK160 -2.4063 20 US222 -0.75 

7 NE180 -3 14 OK210 -2.4063 

Table 28. Discounts for COFO 

 ID Discount ID Discount ID Discount

1 US175 -25 7 KS081 -20 13 SH113 -10 

2 US175 -25 8 US187 -20 14 OK160 -10 

3 ID112 -25 9 IL119 -20 15 NE184 -10 

4 US187 -20 10 US187 -20 16 NE186 -10 

5 SD002 -20 11 TX022 -15 

6 SD002 -20 12 NE180 -15 

Table 29. Discounts for Garlicky 

 ID Discount ID Discount ID Discount

1 US187 -20 5 ID112 -15 9 OK160 -10 

2 WA208 -15 6 US187 -12 10 NE180 -10 

3 WA208 -15 7 US187 -12 11 NE184 -5 

4 WA208 -15 8 SH113 -10 12 NE186 -5 

Table 30. Discounts for Light Smutty 

 ID Discount ID Discount ID Discount

1 ID112 -20 9 OK160 -10 17 US187 -5 

2 MT058 -15 10 NE205 -10 18 WA208 -5 

3 MT058 -15 11 WA208 -10 19 NE180 -5 

4 MT058 -15 12 OK210 -10 20 US222 -5 

5 IL119 -15 13 KS214 -10 21 IL226 -5 

6 MT058 -15 14 MO215 -10 22 US187 -5 

7 WA208 -10 15 NE228 -10 23 WA208 -5 

8 SH113 -10 16 IL123 -10 
-56-

MILLIMAN USA 

Table 31. Discounts for Musty 

 ID Discount ID Discount ID Discount 

1 US187 -20 17 WA208 -10 33 IL123 -10 

2 US187 -20 18 SD002 -10 34 IL201 -10 

3 ID112 -20 19 KS013 -10 35 IL224 -10 

4 MT058 -15 20 TX022 -10 36 KS209 -7 

5 WA208 -15 21 CO072 -10 37 KS081 -5 

6 MT058 -15 22 KS121 -10 38 SH113 -5 

7 MT058 -15 23 US175 -10 39 SD127 -5 

8 MN225 -15 24 NE184 -10 40 OK160 -5 

9 IL119 -15 25 NE186 -10 41 NE180 -5 

10 MN225 -15 26 NE205 -10 42 MN014 -5 

11 MT058 -15 27 WA208 -10 43 WI029 -5 

12 WA208 -15 28 OK210 -10 44 SD127 -5 

13 US187 -12 29 KS214 -10 45 WI029 -5 

14 US187 -12 30 MO215 -10 46 US222 -5 

15 SD002 -10 31 NE228 -10 47 IL223 -5 

16 US175 -10 32 IL117 -10 48 IL226 -5 

Table 32. Discounts for Smutty 

 ID Discount ID Discount ID Discount 

1 ID112 -50 11 NE205 -20 21 SH113 -15 

2 MT058 -25 12 OK210 -20 22 OK160 -15 

3 MT058 -25 13 KS214 -20 23 WA208 -15 

4 MT058 -25 14 MO215 -20 24 US187 -10 

5 MT058 -25 15 NE228 -20 25 WA208 -10 

6 US187 -20 16 IL117 -20 26 NE180 -10 

7 TX022 -20 17 IL119 -20 27 IL201 -10 

8 NE184 -20 18 IL123 -20 28 US222 -10 

9 NE186 -20 19 WA208 -15 29 US187 -10 

10 US187 -20 20 CO072 -15 30 WA208 -10 
-57-

MILLIMAN USA 

Table 33. Discounts for Sour 

 ID Discount ID Discount ID Discount 

1 SD002 -20 17 US187 -12 33 NE228 -10 

2 US175 -20 18 US187 -12 34 MN014 -10 

3 US187 -20 19 WA208 -10 35 IL117 -10 

4 SD002 -20 20 KS013 -10 36 IL123 -10 

5 US175 -20 21 TX022 -10 37 IL201 -10 

6 US187 -20 22 CO072 -10 38 US222 -10 

7 ID112 -20 23 KS081 -10 39 IL223 -10 

8 MT058 -15 24 KS121 -10 40 IL224 -10 

9 MT058 -15 25 NE180 -10 41 IL226 -10 

10 WA208 -15 26 NE184 -10 42 SH113 -5 

11 MT058 -15 27 NE186 -10 43 SD127 -5 

12 WA208 -15 28 NE205 -10 44 OK160 -5 

13 MN225 -15 29 KS209 -10 45 WI029 -5 

14 IL119 -15 30 OK210 -10 46 SD127 -5 

15 MN225 -15 31 KS214 -10 47 WI029 -5 

16 MT058 -15 32 MO215 -10 

Statistical Tests. We then performed nonparametric tests on the average discount rates of elevators 

among the RMA regions. The results, as displayed in the table below, indicate strong statistical evidence 

suggesting that the discount rates are different across regions for damage, test weight, shrunken and 

broken (SB), sour, smutty, and garlicky. 

Table 34. Results of Testing the Equality of Discounts Across RMA Regions 

Analysis 

Variable 

KruskalWallis 

Statistic 

Degree of 

Freedom p-value 

Damage 19.96 4 0.001 

TW 6.58 3 0.086 

SB 12.49 4 0.014 

COFO 6.17 4 0.187 

Musty 7.09 5 0.214 

Sour 10.21 5 0.069 

Light smutty 6.81 4 0.146 

Smutty 9.53 4 0.049 

Ergoty 0.47 1 0.495 

Garlicky 6.48 2 0.039 
-58-

MILLIMAN USA 

Quality Discounts Computation. The computation procedure for the regional discount schedules is 

similar to that for corn, except that Grade 5 and sample grade discounts are based on the average of grade 

discounts, when the elevator has one, or else average Grade 5 or sample grade discounts due to test 

weight, kernel damage, or SB from each elevator. The average discount rates are displayed in the table 

below. Discounts for RMA regions 1, 2, 3, 4, 5 and 6 are based on survey discount schedules specifically 

for these regions. We also included mold, sprout and infested discounts, in case that these perils may be 

covered by the current crop insurance policy. 
-59-

MILLIMAN USA 

Table 35. Current Discount Schedule and Average Discount Schedule 

Based on Survey 

Wheat 

 Region 

 Current 1 2 3 4 5 6 National 

Grade 5 65 37 19 24 45 9 N/A* 24 

Sample Grade 70 51 26 34 63 12 N/A* 37 

TW (lbs) 

below 51 6/lb 10/lb 3/lb 6/lb 12/lb 3/lb 7/lb 12/lb 

TW (WCL & 

HRS) (lbs) 

below 50 

2/lb 10/lb 5/lb 20/lb 7/lb 2/lb 7/lb 9/lb 

TW (WCL & 

HRS) (lbs) 

below 47 

2/lb 10/lb 8/lb 20/lb 7/lb 2/lb 7/lb 9/lb 

TW (WCL & 

HRS) (lbs) 

below 45 

3/lb 10/lb 8/lb 20/lb 7/lb 2/lb 7/lb 9/lb 

TW (WCL & 

HRS) (lbs) 

below 40 

6/lb 10/lb 8/lb 20/lb 7/lb 2/lb 7/lb 9/lb 

Damage (%) 

above 15 3/% 4/% 8/% 2/% 4/% 2/% 3/% 2/% 

Damage (%) 

above 20 3/% 4/% 8/% 4/% 4/% 2/% 3/% 4/% 

S.B. (%) 

above 20 N/A 2/% 2/% 2/% 2/% 2/% 1/% 2/% 

COFO 50 18 20 14 20 25 13 18 

Ergoty 18 18 18 18 14 18 18 

Light Smut 10 10 15 9 10 10 10 10 

Smut 20 19 25 18 18 20 18 19 

Garlic 1/bulblet 12 12 7 12 15 10 12 

Musty 10 9 11 9 9 14 8 10 

Sour 25 10 14 10 11 15 8 12 

Mold 

above 1% N/A N/A 5/% N/A 4/% N/A N/A 4/% 

Sprout 

above 1% N/A N/A 9/% 1/% 5/% 7/% N/A 7/% 

Sprout 

above 8 N/A N/A N/A 6/% N/A N/A N/A N/A 

Sprout 

above 5 N/A N/A N/A N/A N/A 8/% N/A N/A 

Sprout 

above 4 N/A N/A N/A 4/% N/A N/A N/A N/A 

Infested N/A 15 15 8 11 11 8 11 

Observations N/A 2 8 19 7 4 7 54 

Notes: 1. All discounts are in cents/bushel. 

2. N/A implies no data available. 

3. N/A* means not enough observations to derive reasonable results. 

4. "Observations" is the number of discount schedules used to compute the regional averages. It is possible that not all 

entries in the same region used the total number of observations. 
-60-

MILLIMAN USA 

The proposed discounts from the survey are considerably lower than the current grade 5, sample grade, 

sour and COFO discounts; however, they are substantially higher for test weight. Also, with the exception 

of ergoty, there is considerable variation in discounts from region to region, consistent with the results of 

the statistical tests. 

Barley Analysis 

The barley policy specifies quality adjustment for test weight, percentage sound barley, kernel damage, 

thin, black, musty, sour, COFO, grading blighted, garlicky or ergoty; or substances or conditions 

injurious to human or animal health. We received only 8 discount schedules for barley, but since they 

covered different quality factors, the highest number of discount schedules for any individual factor is 

only 6. There are few (and in some cases no) discount schedules for some of the minor quality factors 

such as black discount. 

Comparison of Average Discount Rates. The following tables display the distributions of the average 

discount rates for the quality factors with the largest number of observations. Similar to all other crops, 

the discounts vary a great deal among elevators. We thus performed statistical tests to find out whether 

they vary by RMA regions. 

Table 36. Average Discount for Test Weights 40 – 45 

 ID Discount ID Discount ID Discount

1 MT234 -9 3 MT234 -9 5 MN026 -7 

2 ND174 -8 4 ND174 -8 6 ND193 -4 

Table 37. Average Discounts for 5% - 8% Thin 

 ID Discount ID Discount 

1 MT233 -28 3 US175 -8 

2 MT234 -26 4 ND191 -3 

Table 38. Average Discount for Musty 

 ID Discount ID Discount 

1 MT233 -25 3 MN026 -10 

2 MT234 -25 4 WI029 -5 

Statistical Tests. Considering that no more than 4 observations are available for other quality factors, we 

only performed statistical tests of the equality of test weight discounts across regions. The p-values of the 

Kruskal-Wallis test are 1, indicating no evidence that the regional differences are statistically significant. 
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This could easily be due to the fact that there are only a small number of observations. On the other hand, 

one reason that after contacting 360 elevators, we have only received a small number of discount 

schedules for barley is that barley is a relatively minor crop in comparison to corn, or soybeans. The 

volume of insurance premium is also relatively small. It is thus the case that the benefit of regional 

discount schedules is less likely to outweigh the additional administrative costs. Considering all these, we 

recommend a national discount schedule for barley. 

Table 39. Results of Testing the Equality of Discounts Across RMA Regions 

Analysis 

Variable 

Kruskal-Wallis 

Statistic 

Degree of 

Freedom p-value 

Test Weight 0 1 1 

Discount Schedules Computation. The following table is the computed average national discount rates. 

Discounts for Don, Plump and Stones are also included, in case that they are covered perils. The 

computed schedule for barley is reasonably comparable to the current schedule; it shows that the grade 

discounts are somewhat lower than the current FSA schedule, but that the individual deficiency discounts 

are somewhat greater than current. Thus, when the proposed schedule is applied, the fact that the 

incremental discounts are greater than current will tend to offset the lower grade discounts. Only a 

national schedule is shown because there was no statistical evidence that the regional discount schedules 

differed significantly. A few covered deficiencies are shown as N/A because we were unable to obtain 

any FSA discounts for these perils. 
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Table 40. Current Discount Schedule and Average Discount Schedule 

Based on Survey 

Barley 

 Current National 

Grade 5 30 22 

Sample Grade 30 25 

TW (lbs) 3/lb below 36 4/lb below 36 

Thin (%) 5/5% above 75% 10/5% above 75% 

Black 5/5% above 75% N/A 

Damage (%) 2/% above 10% 

5/% above 15% 5/% above 10% 

Don N/A

17/1 ppm, 

28/1.1-2 ppm, 

35/2.1-3 ppm, 

52/3.1-4ppm. 

Plump N/A 2/% below 70 

7/% below 65 

Stones N/A 

10 up to 8 stones 

1.5/stone in excess of 8 

Garlicky 5 N/A 

Musty 10 15 

Sour 10 20 

COFO 10 20 

Blighted Smutty N/A N/A 

Ergoty N/A N/A 

Mycotoxins N/A N/A 

Observations 6 

Notes: 1. All discounts are in cents/bushel. 

2. N/A implies no data available. 

3. "Observations" is the number of discount schedules 

used to compute the column averages. It is possible 

that not all entries used the total number of observations. 

Oats Analysis 

The oat crop insurance policy specifies quality adjustment for sample grade oats due to test weight, 

percentage of damage, musty, sour, COFO, or smutty; or substances or conditions injurious to human or 

animal health. According to agronomists, these perils are all difficult to control by producers. 

Comparison of Average Discount Rates. We received 11 discount schedules for oats. Of the quality 

factors covered by the current program, only test weight and musty discounts have more than 4 schedules. 
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The distribution of the discounts for these two factors is shown below. There are substantial variations 

across schedules and across regions. 

Table 41. Average Discounts for Test Weight 33 

 ID Discount ID Discount ID Discount

1 IA122 -16 5 ND034 -10 9 IA074 -2 

2 SD049 -14 6 KS081 -6 10 IA157 -2 

3 IL223 -12 7 KS121 -6 11 WI029 0 

4 MN026 -10 8 IA163 -5 

Table 42. Average Discounts for Musty 

 ID Discount ID Discount 

1 IA163 -10 3 KS081 -5 

2 MN026 -10 4 WI029 -5 

Statistical Tests. Similar to barley, only test weight has enough observations for a meaningful statistical 

test of the equality of discounts among regions. The p-value of Kruskal-Wallis test indicates no evidence 

that the regional differences are statistically significant. Further considering that the farm value of oat is 

not as large as some other crops, we recommend a national discount schedule for oats. 

Table 43. Results of Testing the Equality of Discounts Across RMA Regions 

Analysis Variable 

Kruskal-Wallis 

Statistic 

Degree of 

Freedom p-value 

MTW 3.5 3 0.321 

Discount Schedules Computation. The average national discount rates are computed and displayed in 

the following table. Discounts for infested are also included, in case that they are covered perils. 
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Table 44. Current Discount Schedule and Average Discount Schedule 

Based on Survey 

Oats 

 Current National 

Sample Grade 15 25 

TW (lbs) 

5 from 32.5-32 

3/lb below 32 

4/lb below 26 

6/lb below 25 

Sound-Cultivated (%) 2/% below 94 

3/% below 80 1/% below 75 

Garlicky 5 N/A 

Smutty N/A N/A 

Ergoty N/A N/A 

Infested N/A 7 

Observations 11 

Notes: 1. All discounts are in cents/bushel. 

2. N/A implies no data available. 

3. "Observations" is the number of discount schedules used to compute the regional 

averages. It is possible that not all entries in the same region used the total number of 

observations. 

The proposed schedule is considerably different than the current FSA discounts: the sample grade and test 

weight discounts are higher, but the sound cultivated discount is lower. Also, the FSA schedule shows 

discounts for test weight below 32.5 lbs., whereas the test weight requirement for sample grade oats is 26 

lbs. or less. Also, none of the schedules we received had discounts for smutty, garlicky or ergoty oats. 

Rye Analysis 

We obtained only one discount schedule for rye, most likely due to the fact that the farm value of rye is 

now relatively modest. Based on conversations with breeders, extension agronomists and elevators, rye is 

mainly used as a cover crop or as forage. The current total crop insurance premium is only $98,638. 
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Table 45. Current Discount Schedule and Average Discount Schedule 

Based on Survey 

Rye 

 Current National 

Grade 4 N/A 28 

Sample Grade 48.3 44 

Test Weight N/A 4/half lb below 49.0 

Thin N/A N/A 

Smutty N/A N/A 

Garlicky N/A N/A 

Ergoty 

$0.98/3.51-3.6% 

4/0.1% below 3.6% 

5/0.1% below 4% down to 5%. 

N/A 

Notes: 1. All discounts are in cents/bushel. 

2. N/A implies no data available. 

3. The survey discount is based on one purchaser. 

4. The current discount levels are the latest FSA rye discount rates. 

The proposed discount for sample grade is quite similar to the current schedule; otherwise, the current 

schedule (which we understand became effective in 1995 and has not been updated since) contains no 

other quality discounts. 

Flax Analysis 

The current crop insurance policy allows quality adjustment for sample grade flax due to kernel damage; 

or substances or conditions injurious to human or animal health. With assistance from Jan Topp of the 

North Dakota Oilseed Council, we obtained one discount schedule for flax. According to Ms. Topp, this 

discount schedule is fairly standard in North and South Dakota and Minnesota, and has been in use for a 

long period of time. We thus relied on this discount schedule to make recommendations for the quality 

adjustment factors shown in the following table. We also included discounts for stones and test weights, 

in case that they are covered perils. 
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Table 46. Current Discount Schedule and Average Discount Schedule 

Based on Survey 

Flaxseed 

 Current National 

Sample Grade 7% 100% 

Damaged Kernels (%) 24%/15.1-16% 

7%/% below 16% 100% 

Stones N/A 100% 

Test Weight N/A 100% below 40 lbs. 

Notes: 1. The survey discounts were based on a single purchaser’s 

discount schedule, which specifies rejection for all sample 

grade flaxseeds. 

2. Cents/cwt discounts were converted to % discounts based on 

average FSA flaxseed PCP for December 2001. 

The schedule above is based on the discount schedule of a single purchaser. In this case, the schedule 

indicated that any flax at sample grade would be subject to rejection. If a crop is rejected, the producer 

would suffer a 100% loss, unless the crop could be sold for an alternative purpose (presumably at 

substantial discount). We do not necessarily recommend a 100% discount in the presence of sample grade 

for flax; however, we have no basis for developing an alternative schedule. One possible solution would 

be to base the flax discount on the observed discounts for similar crops. 

Canola Analysis 

The current canola crop insurance program applies quality adjustment on U.S. sample grade canola 

caused by kernel damage, musty, sour, COFO and sources or conditions injurious to human or animal 

health. We also included discounts for stones and green damage, in case that they are covered perils. We 

obtained discount schedules from two of the largest canola purchasers. Of all of the quality factors 

covered by the current insurance policy, only one of the two companies specifies the damage discount. 

Therefore, we are able to make recommendations only for damage discounts based on this discount 

schedule. 
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Table 47. Current Discount Schedule and Average Discount Schedule 

Based on Survey 

Canola 

 Current National (cents/bushel) 

Sample Grade 50 68 

Kernel Damage 50/% above 20% 45/30.1-40% 

45/40.1-50% 

Stones N/A 20 

Green Damage N/A 40/increment of up to 5% 

below 20% 

Musty 40 N/A 

Sour 40 N/A 

COFO 70 N/A 

Notes: 1. All discounts are in cents/cwt. 

2. N/A implies no data available. 

3. The survey discount is based on one purchaser. 

Although the proposed sample grade discount is higher than current, the incremental discounts above 

20% kernel damage are smaller than current, tending to offset some of the sample grade difference. 

Oil Sunflowers Analysis 

The current crop insurance policy applies quality adjustment for weight damage, kernel damage, musty, 

sour and COFO; or substances or conditions injurious to human or animal health. We also included 

discounts for oil and stones, in case that they are covered perils. We obtained four discount schedules for 

oil type sunflowers which are almost all identical to each other. There are, however, no discounts for 

COFO in any of the four discount schedules. Based on this information from the four discount schedules 

we obtained, we recommend a uniform national discount schedule. The recommended discount schedule 

is shown in the following table. 

While the sample grade discounts for the current and proposed schedules are similar, the test weight and 

kernel damage discounts differ. Also, we were informed that significant quality deficiencies cause 

sunflowers to be useless except for sale as birdseed, and in such cases the producer would expect to sell 

the crop at a discount of approximately 50%. Thus the discounts should not exceed 50%, the approximate 

salvage value of the crop. 
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Table 48. Current Discount Schedule and Average Discount Schedule 

Based on Survey 

Oil Sunflower 

 Current National 

Sample Grade 6% 5% 

TW (lb) 2.5%/24.9lbs-24 lbs 

2.5%/lb below 24 lbs. 1%/half lb below 25 

Kernel Damage (%) 

22/5.1%-6% 

22/% above 6% 

44/% above 10% 

2%/percentage point above 10%, 

but no more than 50% total discount

Oil N/A 3%/% below 38%, 

3.5%/% below 32% 

Stone ($/cwt) N/A 0.01/each stone in excess of 10 

Musty 6% 2% 

Sour 6% 2% 

COFO 9% N/A 

Notes: 1. The survey discounts were based on 4 purchasers. Since all 4 

purchasers used % discount in their discount schedules, we 

converted all discounts to % discounts for averaging and comparison. 

2. Cents/cwt discounts were converted to % discounts based on 

average FSA oil sunflower PCP for December 2001. 

3. N/A implies no data available. 

Confection Sunflower Analysis 

The current crop insurance policy applies quality adjustment for test weight below 22 lbs, damage in 

excess of 5%, musty, sour, and COFO; or substances or conditions injurious to human or animal health. 

Based on our conversations with major purchasers, kernel damage includes black damage and dark roast 

damage. 

We received three quality discount schedules for confection sunflower, from three companies within 80 

miles from each other. Contrary to oil type sunflowers, the discount schedules for the three purchasers 

differ in both range and magnitude. Also, transactions appear to frequently be based on negotiations. 

Furthermore, conversations with the major purchasers indicated that they purchase the product for 

different markets which may have very different quality requirements. Thus, some buyers may be less 

tolerant of low quality sunflower than others. 

We also included discounts for burrs, insects and sclerotinia, however we note that discounts or these 

factors should apply only if they are covered perils (i.e., if they are injurious to human or animal health). 

Since there is no current value, we specify the discounts for the levels of damage when the magnitude of 

discount reaches 50% or beyond; 50% is the lowest discount rate for all other perils. 
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Table 49. Current Discount Schedule and Recommended (FSA) Discount Schedule 

Confection Sunflower 

 Current National 

TW (lb) 5%/22.9-22.1 lbs. 50% below 22 lbs. 

Black Kernel Damage (%) N/A 50%/above 5%. 

Burrs N/A 50%/above 0.5% 

100%/2.5% or above 

Insects N/A 50%/above 4.5%. 

Sclerotinia N/A 50%/above 1%. 

Musty 5% N/A 

Sour 5% N/A 

COFO 8% N/A 

Notes: 1. The survey discounts were based on 3 purchasers. Since 2 out of 3 

purchasers used % discount in their discount schedules, we converted 

all discounts to % discounts for averaging and comparison. 

2. Cents/cwt discounts were converted to % discounts based on the average 

of FSA confection sunflower posted county prices for December 2001. 

3. N/A implies no data available. 

The three schedules we received were from the largest purchasers of sunflowers. In discussions with these 

purchasers, we were informed that any quality deficiencies in excess of the levels shown in the table 

would render the crop useless except for sale as birdseed. As a consequence, the proposed national 

discounts are considerably larger than the current FSA discounts, but are still limited to a maximum of 

50%. 

Safflower Analysis 

The safflower policy does not require reaching any specific grade before quality adjustment; however, it 

specifies that kernel damage will be adjusted for quality if such damage exceeds 25%. Also quality 

adjustment is allowed if test weight is below 50 lbs./bushel. We received two discount schedules, only 

one of which has a discount for heat damage. Thus, the proposed discounts reflect the discount that would 

be applied at the 25% level for heat damage, along with incremental discounts thereafter. 

Safflower production is usually contracted. The best price and discount information are available from 

contracts of a small number of major purchasers. We suggest that, if possible, RMA survey the major 

purchasers before the beginning of each season and update the discount schedules accordingly. 
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Table 50. Current Discount Schedule and Average Discount Schedule 

Based on Survey 

Safflower 

 Current National 

Test Weight N/A N/A 

Damage 42%/ 25.1-26% 

3%/% above 26%. 

62.5% for 25% damage 

2.5%/percentage point above 25.5% 

Notes: 1. The survey discounts were based on a single purchaser’s discount 

 schedule. 

2. Cents/cwt discounts were converted to % discounts based on average 

 FSA safflower PCP for December 2001. 

3. N/A implies no data available. 

Rice Analysis 

We contacted staff members of the U.S. Rice Federation, major rice farmer cooperatives in Arkansas and 

California, and rice mills in Arkansas and Louisiana. The following summarizes the information we 

gathered from these sources. 

There are two distinct regions of rice production in the U.S.: California and the rest of the country, 

including Arkansas, Louisiana, Mississippi, Texas and Missouri. The two markets have very different 

pricing behavior. 

For the areas outside of California, we were told consistently that the FSA discount schedule reflects the 

typical discount rates used by the industry for quality deficiencies down to grade 6. For grade 6 and 

sample grade, the market is so sparse that it has the tendency to rely on the results of bargaining on a 

case-by-case basis. Nonetheless, the FSA discounts are still frequently used as a reference point in these 

bargaining processes. For these grades we were unable to obtain any information on the recent or historic 

transaction prices. We were also informed that all mills strictly follow the FSA discount schedule for 

purchasing rice lower than grade two. We were, however, unable to verify this information. This 

credibility problem is important in light of the fact that many of the people we spoke to opined that 

quality adjustment, and to a lesser extent crop insurance, is not as important for rice farmers as for some 

other crops. 

As a whole, it seems to be commonly agreed upon that the market for deficient rice, which is anything 

lower than grade 2 outside of California, is so thin that the FSA discount schedule is what buyers and 

sellers would use as a commonly agreed upon price in their bargaining process. 
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For areas outside of California, anything lower than grade 1 is considered off-grade and is subject to deep 

discounts. Thus, the FSA discount schedule is largely inapplicable for the California market. The offgrade market is so limited that there is no established marketing channel to sell off-grade rice other than 

as animal feed. Nonetheless, rice harvested from the field is almost always grade 1, and when it is less 

than grade 1, the vast majority of the time it is due to damage during the storage process, such as 

warehouse roof leakage. It is thus considered unimportant for rice farmers to purchase crop insurance. It 

was suggested to us that grade 2 rice would receive a 50% discount in California, although the figure 

could fluctuate greatly from case to case. Anything grade 4 or less is considered almost useless and would 

be dumped. 

Based on all this information, it is our opinion that the current FSA discount schedule is appropriate for 

quality adjustment for areas outside of California. For California, assuming that the average quality is 

extremely high, it appears that the economic value of quality adjustment is probably limited such that the 

cost of administrating a quality adjustment program would exceed the benefit of the program. 

As evident from the table below, we recommend using the current FSA discount schedule for rice. As 

noted, we make this recommendation based on discussions with the Rice Federation of America, rice 

cooperatives and rice mills in different locations across the U.S., which suggested that most purchasers 

outside of California rely on the FSA schedules only. 

Table 51. Current Discount Schedule and Average Discount Schedule 

Based on Survey 

Rice 

 Current National 

Grade 4 $0.60 $0.60 

Grade 5 $1.00 $1.00 

Grade 6 $2.00 $2.00 

Sample Grade $5.50 $5.50 

Note: 1. Discount data are from FSA of USDA. 

Dark Roast Confection Sunflower Analysis 

Of the three schedules we received for confection sunflower, one indicates purchase for birdseed for dark 

kernel damage more than 1.5%, one does not specify a discount for more than 2% dark kernel damage, 

and one specifies rejection or negotiation for dark kernel damage above 1%. Based on our conversations 

with one of the purchasers, confection sunflowers with significantly dark kernels can be used only for 

birdseed, where the percentage of dark kernels is of little importance. Therefore, we suggest a 50% 
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discount for more than 5% dark roast for confection sunflower. (We start the discount at 5% because the 

current policy indicates that quality adjustment applies only if dark roast exceeds that amount.) 

Buckwheat Analysis 

Buckwheat is a very thinly traded crop with major production in only two areas of the U.S. There appears 

to be only two major buyers, and one failed to provide a discount schedule despite repeated requests. Of 

the discount schedule we received, we were told that this schedule had been in use for three years and was 

last changed when a new buckwheat variety was adopted. Currently, there is no crop insurance for 

buckwheat. Of all of the quality factors covered by the discount schedule we received, we suggest quality 

adjustment for test weight, soundness, musty, garlicky, and aflatoxin for two reasons. First, agronomists 

suggested to us that these perils are not well within the control of producers. Second, they are the kind of 

perils usually covered by the insurance policies of other major grains. It would thus be fair to producers 

producing other grains. 

The proposed national schedule shows 100% discount for four of the five covered perils because the 

single schedule stated that the presence of the indicated conditions made the crop subject to rejection. 

Assuming the crop is rejected, the producer would suffer a 100% loss, unless the crop could be sold for an 

alternative purpose (presumably at substantial discount). We do not necessarily recommend a 100% 

discount in the presence of these quality deficiencies, however, we have no basis for developing a 

discount specifically for buckwheat in the presence of these conditions. One possible solution would be to 

base the buckwheat discount for these deficiencies on the observed discounts for similar crops. 

Table 52. Current and Average Discount Schedules 

Based on Survey 

Buckwheat 

 Current National 

Test Weight (lb) N/A 1%/each half lb below 45. 

Soundness N/A 100% 

Musty N/A 100% 

Garlicky N/A 100% 

Aflatoxin N/A 100%/5 p.p.b. aflatoxin or above 

 Note: 1. The discount schedule is based on one purchaser only. 
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RISK ANALYSIS 

We performed risk analysis to address two issues: 

• For all crops, we identified the relative importance of different perils, as well as the relative costs 

of insuring those perils. 

• For cotton, we estimated the additional costs of insuring by bale instead of insuring by unit. 

Crops in General 

To identify the relative importance of different perils, one approach would be to estimate the frequency 

and severity of loss for each peril. There is, however, no data available for this approach as quality data is 

not available in the RMA claim database and we are not aware of any other database that contains 

sufficiently complete yield and quality data for this type of analysis. As a consequence, we used a 

different approach. We interviewed elevator operators and agronomists and obtained estimates of the 

relative frequency and severity of the quality factors for each crop. For most crops, we obtained what 

appeared to be credible opinions on the relative importance of each quality factor for each crop. In the 

section on the economic importance of quality adjustment in crop insurance, we discuss the estimates 

provided to us by the warehousemen, elevator operators and agronomists we interviewed. 

In addition, we recommend that, if possible, resources be invested in the future to collect complete quality 

information in the RMA insurance database. This information will be useful for both projecting future 

quality of output for producers and estimating the expected loss frequency and severity of the program. 

Cotton 

Objective. For cotton, risk analysis was performed to estimate the difference in loss frequency and 

severity when coverage is by bale instead of by unit of insurance. 

Data Source. The data used here are the daily price data for upland cotton in each of the seven cotton 

regions, and the weekly quality of classified cotton, both obtained from AMS for the crop year 2000. 

Analysis. The cotton quality data from AMS reports, for each bale, the classing office number and a 

description of the quality of the bale. For each bale of classified cotton, we matched the quality 
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description to the price data of the relevant market in the last weekday of the week. Based on suggestions 

from the AMS staff, we used the following to match the classing offices with the cotton markets: 

Table 53. The Relevant Spot Market for the Cotton Classing Offices 

Classing Office Cotton Market 

Abilene, Corpus Christi East Texas-Oklahoma 

Florence, Macon, Birmingham Southeastern 

Rayville, Dumas South Delta 

Lubbock, Lamesa West Texas 

Memphis North Delta 

Phoenix Desert Southwest 

Visalia San Joaquin Valley 

We then computed the ratio between the price of each bale of cotton to 85% of the price of the base grade 

cotton of the respective spot market. If this ratio is less than one (that is, if the actual price is less than 

85% of the base grade), then the quality adjustment factor is defined as the ratio; otherwise the quality 

adjustment factor is set to equal one. The average of the quality adjustment factors for each bale is then 

used as the expected quality adjustment factor. Then, under the assumption that quality variation is 

independent of yield variation, and given the historic distribution of yield variation as a percentage of 

expected yield17, we computed the relative indemnity between quality adjustment coverage by bale versus 

by unit. However, since the unit of indemnity is a percentage of expected yield, we then divided the 

indemnity by the level of coverage to obtain an estimate of indemnity as a percentage of the level of 

coverage. This measure provides a better approximation of the percentage impact of quality adjustment 

coverage by bale on the premium levels. 

The following tables show the difference in expected indemnities between quality adjustment coverage by 

bale versus by unit. It appears that on average, coverage by bale leads to around 10% higher indemnity 

level, although the percentage difference is the highest (at around 14%) at the lowest coverage level of 

50% and then decreases to around 5% as the coverage level increases to 85%. 

 

17 The distribution of yield variation is based on the underwriting experience of the RMA crop programs. 
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Table 54. Indemnity Comparison: 

QA Coverage by 

Bale vs. by Unit 

Expected Indemnity 

Level of Insurance by Bale by Unit 

Difference 

from by Unit

% Difference 

from by Unit 

50% 12.50% 10.95% 1.55% 14.14% 

65% 13.83% 12.67% 1.15% 9.10% 

85% 16.88% 16.08% 0.79% 4.92% 

Table 55. QA Portion of Indemnity Comparison: 

Insurance by 

Bale vs. by Unit 

Expected Indemnity 

Level of Insurance by Bale by Unit 

Difference 

from by Unit 

50% 1.59% 0.04% 1.55% 

65% 1.22% 0.07% 1.15% 

85% 0.93% 0.14% 0.79% 

We caution that this is an estimate based on the 2000 crop year data alone. Several factors could cause 

this estimate to be biased. First, the absolute levels of discounts do not appear to fluctuate over time, as do 

the base prices. It is thus possible that as the base prices declined since the year 2000, both the frequency 

and severity of quality adjustment could be higher now. In contrast, if the base prices increase in the 

future (above the year 2000 levels), then our estimates would be biased upward. Second, advances in 

technology may improve the quality of cotton in the future, which would reduce quality adjustment 

frequency and severity, unless base quality is also adjusted upward in the future accordingly. Third, a 

major determinant of quality adjustment is weather, and crop year 2000 may not represent the typical 

weather over time. Our estimates would likely be underestimates for future years when the weather is less 

favorable than the 2000 crop year, or overestimates for future years when the weather is more favorable 

for the cotton crop than 2000. 

We strongly recommend RMA implement any coverage changes on a pilot basis, monitor results, and 

make adjustments accordingly. 
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CLOSING 

We appreciate the opportunity to provide this actuarial review to the Risk Management Agency. We 

would like to acknowledge the assistance and efforts of the numerous parties mentioned throughout the 

report who provided all of the expert advice, suggestions, program materials and other information 

necessary for our analysis. 

David Appel Richard B. Lord 

Ph.D. Fellow, Casualty Actuarial Society 

 Member, American Academy of Actuaries 

Milliman USA, Inc. 

June 4, 2002 
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REFERENCE 

Hoy, M. “Causing Risks in the Insurance Industry.” The Quarterly Journal of Economics, 97, May 1982: 

321-36. 

Hueth, Darrell L. and William H. Furtan, Economics of Crop Insurance: Theory and Evidence. Kluwer 

Academic Publishers. 1994. 

Makki, Shiva and Agapi Somwaru, “Asymmetric Information in the Market for Yield and Revenue 

Insurance Products.” USDA Economic Research Service Technical Bulletin Number 1892, April 2001. 

National Grain and Feed Association, Barley Production Marketing and Use. 

National Grain and Feed Association, Corn Production Marketing and Use. 

National Grain and Feed Association, Flaxseed Production Marketing and Use. 

National Grain and Feed Association, Oats Production Marketing and Use. 

National Grain and Feed Association, Rye Production Marketing and Use. 

National Grain and Feed Association, Soybeans Production Marketing and Use. 

National Grain and Feed Association, Sunflower Production Marketing and Use. 

National Grain and Feed Association, Wheat Production Marketing and Use. 

Smith, Donald L. and Chantal Hamel (eds.), Crop Yield Physiology and Processes, Springer-Verlag. 

1999. 

Smith, C. Wayne and J. Tom Cothren edited, Cotton: Origin, History, Technology, and Production, 

Wiley Series in Crop Science. 1999. 

Zepp, Glenn; Joy Harwood; Charles Hammond; and Agapi Sowaru, “Buckwheat: An Economic 

Assessment of the Feasibility of Providing Multiple-Peril Crop Insurance.” USDA Risk Management 

Agency, November 1996. 
MILLIMAN USA 

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APPENDIX A 

The following displays the minimum and mean of the autocorrelation coefficient tables for each discount 

schedule for various quality deficiencies as well or the autocorrelation coefficients themselves when the 

number of discount schedules is only two. For example, the first four columns in row 1 of the following 

table indicate that elevator IL009 has an average correlation coefficient with all other elevators to be 

0.997. Its minimum correlation coefficient with all other elevators is 0.894. In general, the correlation 

coefficients between each discount schedule and all other discount schedules are very high, indicating that 

the discount schedules usually vary in proportions. 

Corn 

Table A1. Maximum and Minimum Correlation Coefficients Among Damage Discount Schedules 

 ID Mean Min ID Mean Min 

1 IL009 0.997 0.894 22 IL206 0.996 0.982 

2 MN031 0.987 0.894 23 IL207 0.996 0.982 

3 SD049 0.996 0.968 24 IL221 0.996 0.982 

4 WI156 0.986 0.968 25 NE181 0.996 0.982 

5 KS172 0.996 0.968 26 NE184 0.996 0.982 

6 IA163 0.996 0.968 27 NE186 0.996 0.982 

7 IA227 0.996 0.968 28 IO218 0.990 0.983 

8 IL117 0.996 0.968 29 IL200 0.999 0.985 

9 NE051 0.996 0.968 30 IL223 0.999 0.985 

10 GA065 0.996 0.968 31 NE071 0.999 0.985 

11 IA114 0.996 0.971 32 SD056 0.999 0.985 

12 IA198 0.996 0.971 33 TX068 0.999 0.985 

13 WI148 0.995 0.973 34 US222 0.999 0.985 

14 IL224 0.996 0.976 35 IA122 0.995 0.986 

15 SD067 0.997 0.982 36 MN225 0.995 0.986 

16 IA157 0.997 0.982 37 SD127 0.993 0.986 

17 NE069 0.997 0.982 38 SD002 0.995 0.986 

18 IL212 0.991 0.982 39 WI171 0.992 0.990 

19 IL226 0.992 0.982 40 IA074 0.997 0.990 

20 SD046 0.996 0.982 41 KS037 0.996 0.991 

21 IL119 0.996 0.982 
MILLIMAN USA 

-79-

Table A2. Maximum and Minimum Correlation Coefficients Among 

Total Damage Discount Schedules 

 ID Mean Min ID Mean Min 

1 CO012 0.986 0.973 6 MO215 0.998 0.984 

2 IA165 0.995 0.973 7 NE205 0.998 0.984 

3 NE216 0.996 0.978 8 AX182 0.994 0.986 

4 IA088 0.997 0.984 9 IL219 0.997 0.986 

5 KS214 0.998 0.984 

Table A3. Maximum and Minimum Correlation Coefficients Among 

Foreign Material Discount Schedules 

 ID Mean Min ID Mean Min 

1 IA114 0.993 0.980 22 IL226 0.998 0.989 

2 IA198 0.993 0.980 23 IL206 0.997 0.990 

3 IL223 0.996 0.980 24 SD049 0.997 0.990 

4 US222 0.996 0.980 25 WI148 0.997 0.990 

5 IL117 0.992 0.983 26 MN031 0.999 0.992 

6 IA088 0.997 0.984 27 KS172 0.996 0.992 

7 IA122 0.997 0.984 28 NE069 0.997 0.992 

8 SD056 0.997 0.984 29 TX068 0.997 0.992 

9 IA074 0.997 0.984 30 WI029 0.998 0.992 

10 IL119 0.997 0.984 31 WI171 0.998 0.992 

11 NE051 0.997 0.984 32 KS214 0.998 0.992 

12 GA065 0.997 0.986 33 MO215 0.998 0.992 

13 NE184 0.997 0.986 34 NE071 0.998 0.992 

14 NE186 0.997 0.986 35 NE205 0.998 0.992 

15 IA165 0.997 0.986 36 SD002 0.999 0.993 

16 IA227 0.997 0.986 37 KS037 0.998 0.993 

17 IL009 0.999 0.986 38 NE216 0.997 0.994 

18 WI156 0.997 0.986 39 IL224 0.998 0.995 

19 IA163 0.998 0.987 40 IA157 0.998 0.996 

20 IL221 0.998 0.987 41 MN225 0.998 0.996 

21 SD127 0.997 0.988 
MILLIMAN USA 

-80-

Table A4. Maximum and Minimum Correlation Coefficients Among 

Total Broken & Foreign Material Discount Schedules18

 ID Mean Min ID Mean Min 

1 IL207 0.986 0.951 6 IL212 0.988 0.965 

2 SD067 0.974 0.951 7 IL219 0.987 0.965 

3 IO218 0.987 0.965 8 IL200 0.987 0.968 

4 CO012 0.978 0.965 9 AX182 0.997 0.992 

5 CO102 0.978 0.965 

Table A5. Maximum and Minimum Correlation Coefficients Among 

Heat Damage Discount Schedules 

 ID Mean Min ID Mean Min 

1 NE216 0.849 0.655 14 IL219 0.979 0.845 

2 IA088 0.982 0.655 15 AX182 0.989 0.845 

3 IA227 0.982 0.763 16 IL212 0.985 0.845 

4 KS037 0.982 0.792 17 KS172 0.988 0.845 

5 IL221 0.982 0.792 18 MN225 0.989 0.845 

6 IA163 0.986 0.792 19 WI148 0.978 0.845 

7 IL117 0.986 0.792 20 GA065 0.991 0.866 

8 IL119 0.986 0.792 21 IO218 0.985 0.866 

9 WI171 0.986 0.792 22 IL224 0.977 0.868 

10 IL207 0.985 0.792 23 NE069 0.971 0.868 

11 IL226 0.972 0.823 24 TX068 0.971 0.868 

12 SD127 0.952 0.845 25 IL200 0.961 0.878 

13 IL206 0.979 0.845 

 

18 Total Broken & Foreign Material is a discount some elevators Used to include all discounts for broken, shrunken and foreign 

material combined. 
MILLIMAN USA 

-81-

Table A6. Maximum and Minimum Correlation Coefficients Among 

Test Weight Discount Schedules 

 ID Mean Min ID Mean Min 

1 GA065 0.981 0.391 28 MN031 0.990 0.849 

2 US222 0.909 0.391 29 KS172 0.988 0.852 

3 IA157 0.864 0.599 30 IL206 0.992 0.870 

4 IL009 0.986 0.599 31 IO218 0.992 0.870 

5 KS214 0.986 0.599 32 NE166 0.993 0.872 

6 MO215 0.986 0.599 33 SD056 0.987 0.873 

7 NE205 0.986 0.599 34 MN026 0.987 0.873 

8 WI156 0.982 0.630 35 NE069 0.992 0.874 

9 NE184 0.987 0.632 36 IA122 0.992 0.890 

10 NE186 0.987 0.632 37 WI148 0.985 0.894 

11 IL223 0.986 0.642 38 IL200 0.994 0.894 

12 NE071 0.988 0.657 39 NE181 0.994 0.894 

13 IL224 0.982 0.681 40 IA088 0.984 0.898 

14 AX182 0.987 0.700 41 MN225 0.993 0.906 

15 NE216 0.987 0.700 42 WI029 0.993 0.906 

16 SD049 0.989 0.724 43 IA114 0.989 0.906 

17 IL221 0.989 0.739 44 IA163 0.989 0.906 

18 IL212 0.990 0.767 45 IA198 0.989 0.906 

19 SD002 0.992 0.769 46 WI171 0.989 0.908 

20 IL207 0.987 0.785 47 IL117 0.993 0.914 

21 SD067 0.987 0.806 48 NE051 0.993 0.914 

22 IA227 0.985 0.820 49 IA074 0.993 0.916 

23 KS121 0.987 0.825 50 IL226 0.991 0.922 

24 KS013 0.991 0.828 51 KS037 0.991 0.923 

25 IL219 0.988 0.830 52 TX068 0.993 0.935 

26 CO102 0.992 0.841 53 SD127 0.992 0.940 

27 CO012 0.992 0.841 54 IL119 0.987 0.945 

Soybean 

Table A7. Maximum and Minimum Correlation Coefficients Among 

Corn Admixture Discount Schedules 

 ID Mean Min 

1 NE004 1 1 

2 NE205 1 1 

3 OK210 1 1 

4 SD002 1 1 
MILLIMAN USA 

-82-

Table A8. Maximum and Minimum Correlation Coefficients Among 

Damage Discount Schedules 

 ID Mean Min ID Mean Min 

1 IA122 0.974 0.853 13 IL117 0.992 0.970 

2 MN050 0.977 0.853 14 IA227 0.993 0.970 

3 MN225 0.993 0.892 15 KS003 0.994 0.971 

4 MS204 0.987 0.892 16 SD127 0.995 0.977 

5 WI006 0.994 0.930 17 NE069 0.994 0.980 

6 WI148 0.983 0.941 18 IL226 0.995 0.981 

7 IL207 0.991 0.959 19 NE184 0.995 0.981 

8 IL009 0.990 0.970 20 KS081 0.995 0.981 

9 IL206 0.988 0.970 21 IL119 0.995 0.981 

10 IL224 0.989 0.970 22 NE051 0.995 0.981 

11 IA074 0.991 0.970 23 IL200 0.995 0.981 

12 SD067 0.991 0.970 24 SD049 0.994 0.986 

Table A9. Maximum and Minimum Correlation Coefficients Among 

Foreign Material Discount Schedules 

 ID Mean Min ID Mean Min 

1 IA157 0.997 0.920 21 IL117 1.000 1.000 

2 IL200 0.987 0.920 22 IL207 1.000 1.000 

3 IA165 0.997 0.938 23 IL217 1.000 1.000 

4 NE184 0.997 0.938 24 IL223 1.000 1.000 

5 SD046 0.997 0.938 25 IL224 1.000 1.000 

6 SD067 0.997 0.938 26 KS013 1.000 1.000 

7 WI029 0.997 0.938 27 KS081 1.000 1.000 

8 KS003 0.998 0.978 28 KS121 1.000 1.000 

9 NE004 0.998 0.978 29 MN101 1.000 1.000 

10 OK210 0.998 0.978 30 MN225 1.000 1.000 

11 NE051 0.998 0.979 31 MS204 1.000 1.000 

12 MN202 0.998 0.979 32 NE069 1.000 1.000 

13 IA088 0.998 0.982 33 NE071 1.000 1.000 

14 IL226 1.000 1.000 34 NE205 1.000 1.000 

15 IA074 1.000 1.000 35 NE213 1.000 1.000 

16 IA163 1.000 1.000 36 SD002 1.000 1.000 

17 IA211 1.000 1.000 37 US222 1.000 1.000 

18 IA220 1.000 1.000 38 WI006 1.000 1.000 

19 IA227 1.000 1.000 39 WI148 1.000 1.000 

20 IL009 1.000 1.000 

 
MILLIMAN USA 

-83-

Table A10. Maximum and Minimum Correlation Coefficients Among 

Heat Discount Schedules 

 ID Mean Min ID Mean Min 

1 MN050 0.915 0.517 16 IL206 0.981 0.845 

2 MN225 0.956 0.517 17 IL217 0.981 0.845 

3 MN202 0.925 0.686 18 IA163 0.982 0.888 

4 IA211 0.979 0.717 19 WI006 0.982 0.888 

5 NE213 0.979 0.717 20 IL117 0.993 0.966 

6 IA220 0.973 0.717 21 US222 0.993 0.966 

7 SD002 0.956 0.783 22 IA122 0.993 0.966 

8 SD127 0.940 0.797 23 IL223 0.993 0.966 

9 IL226 0.987 0.839 24 IL207 0.993 0.966 

10 IA227 0.990 0.839 25 NE205 0.995 0.967 

11 IL224 0.988 0.839 26 KS003 0.995 0.967 

12 IL119 0.990 0.839 27 OK210 0.995 0.967 

13 NE004 0.981 0.845 28 WI148 0.995 0.967 

14 MS204 0.981 0.845 29 IL200 0.993 0.967 

15 NE069 0.969 0.845 30 IA088 0.999 0.972 

Table A11. Maximum and Minimum Correlation Coefficients Among 

Split Discount Schedules 

 ID Mean Min ID Mean Min 

1 IA088 0.977 0.911 16 OK210 0.990 0.966 

2 MS204 0.975 0.911 17 IA227 0.990 0.966 

3 IL217 0.975 0.911 18 SD067 0.991 0.966 

4 IL223 0.986 0.921 19 SD049 0.978 0.968 

5 US222 0.986 0.921 20 MN050 0.997 0.969 

6 NE069 0.987 0.925 21 IL224 0.997 0.969 

7 IL200 0.985 0.927 22 MN225 0.997 0.969 

8 NE213 0.985 0.927 23 IA211 0.997 0.969 

9 KS003 0.991 0.943 24 WI006 0.997 0.969 

10 KS013 0.991 0.943 25 NE004 0.997 0.969 

11 IL226 0.991 0.943 26 IA220 0.997 0.969 

12 SD002 0.992 0.946 27 WI148 0.997 0.969 

13 IL207 0.996 0.948 28 IL117 0.997 0.969 

14 MN202 0.970 0.948 29 SD127 0.997 0.969 

15 NE205 0.988 0.948 30 NE051 0.995 0.977 
MILLIMAN USA 

-84-

Table A12. Maximum and Minimum Correlation Coefficients Among 

Total Damage Discount Schedules 

 ID Mean Min ID Mean Min 

1 SD002 0.991 0.979 9 IA163 0.993 0.985 

2 IA220 0.990 0.979 10 IA165 0.993 0.985 

3 IL217 0.991 0.980 11 NE213 0.995 0.987 

4 OK210 0.991 0.980 12 MN202 0.994 0.989 

5 IL223 0.992 0.982 13 IA211 0.995 0.989 

6 NE004 0.992 0.985 14 NE069 0.996 0.990 

7 IA088 0.993 0.985 15 NE205 0.995 0.990 

8 US222 0.994 0.985 

Table A13. Maximum and Minimum Correlation Coefficients Among 

Test Weight Discount Schedules 

 ID Mean Min ID Mean Min 

1 MN202 0.978 0.958 21 IA227 0.996 0.981 

2 IA122 0.997 0.958 22 MN225 0.996 0.981 

3 IA163 0.997 0.958 23 IL224 0.996 0.981 

4 IL009 0.997 0.958 24 IL226 0.996 0.981 

5 IL117 0.997 0.958 25 KS013 0.997 0.981 

6 NE051 0.997 0.958 26 NE069 0.997 0.981 

7 NE071 0.997 0.958 27 KS003 0.997 0.981 

8 IA074 0.997 0.972 28 SD067 0.997 0.981 

9 IA211 0.997 0.972 29 IL119 0.997 0.981 

10 IA220 0.997 0.972 30 IL217 0.996 0.981 

11 IL223 0.997 0.972 31 WI029 0.991 0.985 

12 KS081 0.997 0.972 32 NE184 0.996 0.986 

13 KS121 0.997 0.972 33 MN050 0.999 0.986 

14 NE004 0.997 0.972 34 WI148 0.992 0.986 

15 NE205 0.997 0.972 35 IL206 0.992 0.986 

16 NE213 0.997 0.972 36 IL207 0.992 0.986 

17 OK210 0.997 0.972 37 IL200 0.989 0.986 

18 US222 0.997 0.972 38 SD002 0.989 0.986 

19 SD049 0.995 0.974 39 SD127 0.989 0.986 

20 MS204 0.995 0.981 40 WI006 0.989 0.986 
MILLIMAN USA 

-85-

Table A14. Maximum and Minimum Correlation Coefficients Among 

Green Discount Schedules 

 ID Mean Min ID Mean Min 

1 KS121 0.995 0.986 5 IL217 0.997 0.991 

2 NE213 0.995 0.986 6 KS013 0.996 0.991 

3 MN202 0.995 0.986 7 IL200 0.998 0.991 

4 IA211 0.995 0.986 

Table A15. Maximum and Minimum Correlation Coefficients Among 

Other Color Discount Schedules 

 ID Mean Min ID Mean Min 

1 KS003 1 1 8 IL207 1 1 

2 MS204 1 1 9 IL217 1 1 

3 NE004 1 1 10 MN225 1 1 

4 NE069 1 1 11 NE205 1 1 

5 NE051 1 1 12 NE213 1 1 

6 IA211 1 1 13 IL200 1 1 

7 IA227 1 1 

Wheat 

Club Wheat 

Table A16. Maximum and Minimum Correlation Coefficients Among 

Contrasting Color Discount Schedules 

 ID Mean Min ID Mean Min 

1 US187 1 1 3 WA208 1 1 

2 WA118 1 1 4 MT058 1 1 

Table A17. Maximum and Minimum Correlation Coefficients Among 

Dockage Discount Schedules 

 ID Mean Min ID Mean Min 

1 MT058 0.973 0.904 3 US187 0.996 0.991 

2 WA208 0.974 0.904 4 WA118 0.999 0.995 
MILLIMAN USA 

-86-

Table A18. Maximum and Minimum Correlation Coefficients Among 

Frost Discount Schedules 

 ID Mean Min 

1 MT058 1 1 

2 WA208 1 1 

3 US187 1 1 

Table A19. Maximum and Minimum Correlation Coefficients Among 

Germ Discount Schedules 

 ID Mean Min 

1 MT058 1 1 

2 US187 1 1 

3 WA208 1 1 

Table A20. Correlation Coefficients Among 

Grade Discount Schedules 

 US187 WA208

US187 1 0.997 

WA208 0.997 1 

Table A21. Maximum and Minimum Correlation Coefficients Among 

Heat Discount Schedules 

 ID Mean Min 

1 MT058 1 1 

2 US187 1 1 

3 WA208 1 1 

Table A22. Maximum and Minimum Correlation Coefficients for 

Insect Damaged Kernels Discounts 

 ID Mean Min 

1 US187 1 1 

2 WA208 1 1 

3 MT058 1 1 

Table A23. Maximum and Minimum Correlation Coefficients Among 

Mold Discount Schedules 

 ID Mean Min 

1 MT058 1 1 

2 US187 1 1 

3 WA208 1 1 

Table A24. Maximum and Minimum Correlation Coefficients Among 

Stone Discount Schedules 

 ID Mean Min ID Mean Min 

1 WA208 3 WA118 0.974 0.949 

2 US187 0.974 0.949 4 MT058 1 1 
MILLIMAN USA 

-87-

Table A25. Maximum and Minimum Correlation Coefficients Among 

Test Weight Discount Schedules 

 ID Mean Min 

1 MT058 0.998 0.996 

2 WA208 0.997 0.996 

3 US187 0.998 0.997 

Table A26. Maximum and Minimum Correlation Coefficients Among 

Wheat of Other Classes Discount Schedules

 ID Mean Min 

1 MT058 0.998 0.995 

2 WA208 0.996 0.995 

3 US187 0.998 0.995 

Durum Wheat 

Table A27. Correlation Coefficients Among 

Contrasting Color Discount Schedules 

 ND034 ND145

ND034 1 1 

ND145 1 1 

Table A28. Correlation Coefficients Among 

Damage Discount Schedules 

 ND034 ND145

ND034 1 1 

ND145 1 1 

Table A29. Correlation Coefficients for 

Falling Numbers Discounts 

 ND039 ND034

ND039 1 

ND034 1 
MILLIMAN USA 

-88-

Table A30. Maximum and Minimum Correlation Coefficients for 

Hard Vitreous Amber Kernels Discounts 

 ID Mean Min 

1 ND039 1 1 

2 ND145 1 1 

3 ND034 1 1 

Table A31. Maximum and Minimum Correlation Coefficients Among 

Test Weight Discount Schedules

 ND034 ND145

ND034 1 0.993 

ND145 0.993 1 

Dark Northern Spring 

Table A32. Maximum and Minimum Correlation Coefficients Among 

Black Tip Fungus Discount Schedules

 ID Mean Min 

1 MT058 0.968 0.946 

2 US187 0.977 0.946 

3 WA208 0.981 0.958 

Table A33. Maximum and Minimum Correlation Coefficients Among 

Contrasting Color Discount Schedules 

 ID Mean Min ID Mean Min 

1 WA208 1 1 4 US175 1 1 

2 MT058 1 1 5 US187 1 1 

3 WA118 1 1 
MILLIMAN USA 

-89-

Table A34. Maximum and Minimum Correlation Coefficients Among 

Dark and Hard Vitreous Kernels Discount Schedules

 ID Mean Min 

1 MT058 1 1 

2 US187 1 1 

3 WA118 1 1 

Table A35. Maximum and Minimum Correlation Coefficients Among 

Damage Discount Schedules 

 ID Mean Min 

1 MN050 1 1 

2 SD002 1 1 

3 US175 1 1 

Table A36. Maximum and Minimum Correlation Coefficients Among 

Dockage Discount Schedules 

 ID Mean Min ID Mean Min 

1 MT058 0.964 0.935 4 WA118 0.989 0.956 

2 MN050 0.980 0.935 5 US187 0.991 0.970 

3 US175 0.973 0.945 6 WA208 0.990 0.975 

Table A37. Maximum and Minimum Correlation Coefficients Among 

Foreign Material Discount Schedules 

 ID Mean Min ID Mean Min 

1 MN050 0.811 0.693 4 MT058 0.887 0.825 

2 SD002 0.901 0.693 5 US187 0.965 0.859 

3 US175 0.903 0.693 

Table A38. Maximum and Minimum Correlation Coefficients Among 

Frost Discount Schedules 

 ID Mean Min 

1 US187 1 1 

2 WA208 1 1 

3 MT058 1 1 
MILLIMAN USA 

-90-

Table A39. Maximum and Minimum Correlation Coefficients Among 

Germ Discount Schedules 

 ID Mean Min 

1 US187 0.998 0.994 

2 WA208 0.997 0.994 

3 MT058 0.999 0.997 

Table A40. Maximum and Minimum Correlation Coefficients Among 

Grade Discount Schedules 

 ID Mean Min ID Mean Min 

1 MN050 0.997 0.986 3 MT058 1.000 1.000 

2 WA208 0.997 0.986 4 US187 1.000 1.000 

Table A41. Maximum and Minimum Correlation Coefficients Among 

Heat Discount Schedules 

 ID Mean Min 

1 MT058 0.989 0.968

2 WA208 0.989 0.968

3 US187 1.000 1.000

Table A42. Maximum and Minimum Correlation Coefficients Among 

Insect Damaged Kernels Discount Schedules 

 ID Mean Min ID Mean Min 

1 US175 1 1 3 US187 1 1 

2 MT058 1 1 4 WA208 1 1 

Table A43. Maximum and Minimum Correlation Coefficients Among 

Mold Discount Schedules 

 ID Mean Min 

1 WA208 0.995 0.988

2 US187 0.996 0.988

3 MT058 0.999 0.996

Table A44. Maximum and Minimum Correlation Coefficients Among 

Scab Discount Schedules 

 MT058 WA208

MT058 1 1 

WA208 1 1 
MILLIMAN USA 

-91-

Table A45. Maximum and Minimum Correlation Coefficients Among 

Sprout Discount Schedules 

 ID Mean Min 

1 MT058 1 1 

2 US187 1 1 

3 WA208 1 1 

Table A46. Maximum and Minimum Correlation Coefficients Among 

Stone Discount Schedules 

 ID Mean Min ID Mean Min 

1 MT058 4 US175 0.934 0.868 

2 WA118 0.934 0.868 5 US187 0.934 0.868 

3 WA208 0.934 0.868 

Table A47. Maximum and Minimum Correlation Coefficients Among 

Test Weight Discount Schedules

 ID Mean Min ID Mean Min 

1 SD002 0.9987 0.9978 4 WA208 0.9991 0.9978 

2 US175 0.9987 0.9978 5 MT058 0.9991 0.9978 

3 US187 0.9991 0.9978 

Table A48. Maximum and Minimum Correlation Coefficients Among 

Wheat of Other Classes Discount Schedules

 ID Mean Min ID Mean Min 

1 MT058 0.987 0.979 3 US187 0.995 0.982 

2 WA208 0.993 0.979 4 US175 0.995 0.988 

Hard Red Winter 

Table A49. Maximum and Minimum Correlation Coefficients Among 

Black Tip Fungus Discount Schedules 

 ID Mean Min 

1 WA208 1 1 

2 MT058 1 1 

3 US187 1 1 

Table A50. Maximum and Minimum Correlation Coefficients Among 

Contrasting Color Discount Schedules

 ID Mean Min ID Mean Min 

1 MT058 1 1 4 WA208 1 1 

2 US187 1 1 5 US175 1 1 

3 WA118 1 1 
MILLIMAN USA 

-92-

Table A51. Maximum and Minimum Correlation Coefficients Among 

Damage Discount Schedules 

 ID Mean Min ID Mean Min 

1 KS214 0.987 0.894 8 KS209 0.994 0.989 

2 MO183 0.967 0.894 9 NE184 0.997 0.991 

3 MO215 0.987 0.894 10 NE186 0.997 0.991 

4 NE205 0.987 0.894 11 OK210 0.997 0.991 

5 NE228 0.987 0.894 12 NE166 0.996 0.992 

6 SD002 0.994 0.985 13 US175 0.996 0.993 

7 NE180 0.994 0.986 

Table A52. Maximum and Minimum Correlation Coefficients Among 

Dockage Discount Schedules 

 ID Mean Min ID Mean Min 

1 CO102 0.870 0.563 13 NE205 0.971 0.823 

2 KS013 0.952 0.563 14 NE228 0.971 0.823 

3 CO012 0.882 0.612 15 CO141 0.969 0.828 

4 KS121 0.960 0.614 16 MT058 0.947 0.828 

5 KS081 0.954 0.689 17 OK210 0.973 0.831 

6 MO183 0.942 0.689 18 KS209 0.974 0.836 

7 US175 0.957 0.733 19 OK066 0.925 0.837 

8 WA208 0.947 0.733 20 TX022 0.978 0.846 

9 SH113 0.970 0.777 21 US187 0.979 0.850 

10 OK176 0.966 0.800 22 OK160 0.978 0.872 

11 KS214 0.971 0.823 23 WA118 0.976 0.876 

12 MO215 0.971 0.823 24 TX083 0.978 0.881 
MILLIMAN USA 

-93-

Table A53. Maximum and Minimum Correlation Coefficients Among 

Foreign Material Discount Schedules

 ID Mean Min ID Mean Min 

1 OK210 0.968 0.862 11 CO072 0.985 0.924 

2 SD002 0.962 0.862 12 OK160 0.979 0.931 

3 US175 0.959 0.873 13 KS214 0.991 0.965 

4 TX022 0.964 0.877 14 MO215 0.991 0.965 

5 MO183 0.959 0.878 15 NE205 0.991 0.965 

6 SH113 0.986 0.878 16 KS121 0.991 0.969 

7 SD127 0.984 0.878 17 KS209 0.992 0.969 

8 NE166 0.979 0.884 18 NE180 0.990 0.972 

9 US187 0.981 0.884 19 CO141 0.994 0.984 

10 OK066 0.978 0.888 

Table A54. Maximum and Minimum Correlation Coefficients Among 

Frost Discount Schedules 

 ID Mean Min 

1 WA208 0.998 0.994 

2 US187 0.997 0.994 

3 MT058 0.999 0.996 

Table A55. Maximum and Minimum Correlation Coefficients for 

Germ Discount Schedules 

 ID Mean Min 

1 MT058 0.999 0.996 

2 US187 0.998 0.996 

3 WA208 1.000 1.000 

Table A56. Maximum and Minimum Correlation Coefficients Among 

Grade Discount Schedules 

 ID Mean Min ID Mean Min 

1 US187 0.995 0.988 4 SH113 0.997 0.992 

2 WA208 0.995 0.988 5 MT058 1.000 1.000 

3 TX022 0.996 0.989 
MILLIMAN USA 

-94-

Table A57. Maximum and Minimum Correlation Coefficients Among 

Heat Discount Schedules 

 ID Mean Min ID Mean Min 

1 MT058 0.954 0.866 8 US187 0.986 0.943 

2 CO072 0.940 0.866 9 WA208 0.986 0.943 

3 KS214 0.963 0.866 10 OK160 0.986 0.943 

4 MO215 0.963 0.866 11 OK210 0.986 0.943 

5 NE205 0.963 0.866 12 SH113 0.986 0.943 

6 NE180 0.966 0.939 13 KS209 0.991 0.971 

7 TX022 0.986 0.942 

Table A58. Maximum and Minimum Correlation Coefficients for 

Insect Damaged Kernels Discount Schedules

 ID Mean Min ID Mean Min 

1 SH113 0.982 0.932 13 NE205 0.997 0.988 

2 KS021 0.995 0.932 14 NE228 0.997 0.988 

3 NE166 0.992 0.932 15 CO072 0.997 0.988 

4 OK176 0.989 0.938 16 TX022 0.997 0.988 

5 NE184 0.996 0.949 17 OK210 0.995 0.988 

6 NE186 0.996 0.949 18 KS013 0.996 0.988 

7 KS081 0.996 0.954 19 OK160 0.997 0.993 

8 US175 0.993 0.971 20 SD127 1.000 0.999 

9 KS209 0.997 0.986 21 US187 1.000 1.000 

10 MT058 0.999 0.986 22 WA208 1.000 1.000 

11 KS214 0.997 0.988 23 MO183 1.000 1.000 

12 MO215 0.997 0.988 

Table A59. Correlation Coefficients Among 

Mold Discount Schedules 

 MT058 WA208

MT058 1 0.941 

WA208 0.941 1 

Table A60. Maximum and Minimum Correlation Coefficients Among 

Rye Discount Schedules 

 ID Mean Min ID Mean Min 

1 NE184 0.990 0.961 4 CO012 0.990 0.961 

2 NE186 0.990 0.961 5 CO102 1.000 1.000 

3 OK176 0.977 0.961 
MILLIMAN USA 

-95-

Table A61. Maximum and Minimum Correlation Coefficients for 

Shrunken and Broken Kernels Discount Schedules

 ID Mean Min ID Mean Min 

1 NE180 0.977 0.931 9 KS209 0.994 0.981 

2 TX022 0.981 0.931 10 NE184 0.994 0.981 

3 OK160 0.988 0.941 11 NE186 0.994 0.981 

4 OK210 0.988 0.941 12 SD127 0.994 0.981 

5 US175 0.983 0.941 13 CO012 0.994 0.983 

6 KS214 0.991 0.965 14 MO183 0.999 0.986 

7 MO215 0.991 0.965 15 SH113 1.000 1.000 

8 NE205 0.991 0.965 

Table A62. Maximum and Minimum Correlation Coefficients Among 

Sprout Discount Schedules 

 ID Mean Min ID Mean Min 

1 WA208 0.989 0.957 3 MT058 0.989 0.957 

2 KS081 0.979 0.957 4 US187 1.000 1.000 

Table A63. Maximum and Minimum Correlation Coefficients Among 

Stone Discount Schedules 

 ID Mean Min ID Mean Min 

1 MT058 0.910 0.820 4 WA118 0.954 0.868 

2 US175 0.911 0.820 5 WA208 0.934 0.868 

3 US187 0.954 0.868 

Table A64. Maximum and Minimum Correlation Coefficients Among 

Total Damage Discount Schedules

 ID Mean Min ID Mean Min 

1 KS214 0.991 0.943 6 US175 0.970 0.943 

2 MO215 0.991 0.943 7 OK160 0.996 0.986 

3 NE205 0.991 0.943 8 SH113 0.992 0.987 

4 NE228 0.991 0.943 9 SD127 0.992 0.987 

5 OK210 0.991 0.943 10 TX022 0.995 0.989 
MILLIMAN USA 

-96-

Table A65. Maximum and Minimum Correlation Coefficients Among 

Test Weight Discount Schedules

 ID Mean Min ID Mean Min 

1 US175 0.991 0.805 18 NE228 0.993 0.975 

2 MO183 0.980 0.805 19 CO102 0.992 0.976 

3 TX060 0.973 0.894 20 CO012 0.991 0.976 

4 SH113 0.990 0.894 21 TX083 0.992 0.978 

5 MT058 0.989 0.894 22 OK160 0.994 0.979 

6 US187 0.989 0.894 23 OK210 0.994 0.979 

7 NE180 0.992 0.952 24 SD127 0.992 0.979 

8 NE166 0.988 0.955 25 KS121 0.995 0.980 

9 WA208 0.993 0.955 26 CO072 0.994 0.982 

10 OK066 0.986 0.969 27 KS209 0.994 0.982 

11 SD002 0.991 0.970 28 KS021 0.995 0.983 

12 NE184 0.993 0.974 29 TX022 0.996 0.983 

13 NE186 0.993 0.974 30 OK176 0.995 0.986 

14 NE181 0.995 0.975 31 KS081 0.995 0.986 

15 KS214 0.993 0.975 32 KS013 0.995 0.986 

16 MO215 0.993 0.975 33 CO141 0.996 0.986 

17 NE205 0.993 0.975 

Table A66. Maximum and Minimum Correlation Coefficients Among 

Wheat of Other Classes Discount Schedules

 ID Mean Min ID Mean Min 

1 SH113 0.917 0.894 7 MO183 0.981 0.894 

2 MO215 0.979 0.894 8 WA208 0.979 0.896 

3 NE205 0.979 0.894 9 NE180 0.979 0.896 

4 NE228 0.979 0.894 10 US175 0.980 0.914 

5 KS214 0.979 0.894 11 US187 0.980 0.914 

6 OK160 0.917 0.894 12 MT058 0.982 0.919 

Spring Wheat 

Table A67. Maximum and Minimum Correlation Coefficients Among 

Contrasting Color Discount Schedules

 ID Mean Min 

1 ND034 1 1 

2 MN225 1 1 

3 ND145 1 1 
MILLIMAN USA 

-97-

Table A68. Maximum and Minimum Correlation Coefficients Among 

Damage Discount Schedules 

 ID Mean Min ID Mean Min 

1 SD049 0.986 0.972 4 ND034 0.996 0.986 

2 NE166 0.993 0.972 5 ND145 0.996 0.986 

3 SD127 0.996 0.984 6 MN225 0.997 0.992 

Table A69. Correlation Coefficients Among 

Dockage Discount Schedules 

 CO141 ND034

CO141 1 0.780 

ND034 0.780 1 

Table A70. Maximum and Minimum Correlation Coefficients Among 

Foreign Material Discount Schedules

 ID Mean Min ID Mean Min 

1 MN225 5 CO141 0.994 0.981 

2 ND145 0.993 0.972 6 SD127 0.996 0.981 

3 NE166 0.982 0.972 7 ND034 0.996 0.982 

4 WI029 0.995 0.976 

Table A71. Maximum and Minimum Correlation Coefficients Among 

Falling Numbers Discount Schedules

 ID Mean Min 

1 ND145 0.984 0.962 

2 MN014 0.983 0.962 

3 ND034 0.992 0.986 

Table A72. Correlation Coefficients Among 

Insect Damaged Kernels Discount Schedules 

 NE166 SD127 

NE166 1 1 

SD127 1 1 
MILLIMAN USA 

-98-

Table A73. Correlation Coefficients Among 

Protein Discount Schedules 

 ND034 SD049 

ND034 1 0.999 

SD049 0.999 1 

Table A74. Correlation Coefficients Among 

Shrunken and Broken Kernels Discount Schedules 

 MN225 SD127 

MN225 1 0.987 

SD127 0.987 1 

Table A75. Maximum and Minimum Correlation Coefficients Among 

Test Weight Discount Schedules 

 ID Mean Min ID Mean Min 

1 SD049 0.991 0.979 6 CO141 0.995 0.987 

2 SD127 0.994 0.979 7 NE166 0.996 0.987 

3 MN014 0.996 0.982 8 ND145 0.997 0.989 

4 ND034 0.993 0.984 9 WI029 0.997 0.991 

5 MN225 0.996 0.986 

Soft Red Winter 

Table A76. Maximum and Minimum Correlation Coefficients Among 

Damage Discount Schedules 

 ID Mean Min ID Mean Min 

1 IL117 0.992 0.985 5 IL226 0.998 0.986 

2 IL224 0.998 0.985 6 IL123 0.999 0.993 

3 IL119 0.998 0.986 7 MN225 1.000 1.000 

4 IL223 0.998 0.986 8 US222 1.000 1.000 

Table A77. Maximum and Minimum Correlation Coefficients Among 

 Dockage Discount Schedules 

 ID Mean Min ID Mean Min 

1 IL119 1 1 4 IL223 1 1 

2 IL226 1 1 5 IL224 1 1 

3 IL201 1 1 6 US222 1 1 
MILLIMAN USA 

-99-

Table A78. Maximum and Minimum Correlation Coefficients Among 

Foreign Material Discount Schedules

 ID Mean Min ID Mean Min 

1 IL117 0.952 0.746 6 IL119 0.985 0.952 

2 IL223 0.922 0.746 7 IL226 0.987 0.961 

3 IL123 0.960 0.795 8 IL224 0.987 0.961 

4 IL201 0.987 0.939 9 US222 0.984 0.962 

5 WI029 0.988 0.939 

Table A79. Maximum and Minimum Correlation Coefficients Among 

Garlic Discount Schedules 

 ID Mean Min 

1 IL201 0.985 0.922 5 IL224 0.985 0.922 

2 IL123 0.947 0.922 6 IL117 0.992 0.970 

3 US222 0.985 0.922 7 IL119 0.991 0.970 

4 IL226 0.985 0.922 

Table A80. Maximum and Minimum Correlation Coefficients Among 

Heat Discount Schedules 

 ID Mean Min ID Mean Min 

1 IL119 0.858 0.548 4 IL223 0.995 0.974 

2 US222 0.910 0.548 5 IL123 0.999 0.994 

3 IL226 0.955 0.775 

Table A81. Maximum and Minimum Correlation Coefficients Among 

Shrunken and Broken Kernels Discount Schedules

 ID Mean Min ID Mean Min 

1 MN225 1 1 5 IL223 1 1 

2 IL123 1 1 6 US222 1 1 

3 IL224 1 1 7 IL117 1 1 

4 IL226 1 1 

Table A82. Maximum and Minimum Correlation Coefficients Among 

Total Damage Discount Schedules

 ID Mean Min 

1 IL201 1 1 

2 IL223 1 1 

3 IL226 1 1 
MILLIMAN USA 

-100-

Table A83. Maximum and Minimum Correlation Coefficients Among 

Test Weight Discount Schedules

 ID Mean Min ID Mean Min 

1 MN225 7 IL123 0.994 0.959 

2 IL224 0.985 0.891 8 WI148 0.995 0.966 

3 IL226 0.974 0.891 9 US222 0.996 0.966 

4 IL119 0.983 0.896 10 IL117 0.995 0.966 

5 IL201 0.969 0.950 11 IL223 0.998 0.997 

6 WI029 0.994 0.953 

Soft White 

Table A84. Maximum and Minimum Correlation Coefficients Among 

Black Tip Fungus Discount Schedules

 ID Mean Min 

1 ID112 1 1 

2 MT058 1 1 

3 WA208 1 1 

Table A85. Maximum and Minimum Correlation Coefficients Among 

Contrasting Color Discount Schedules

 ID Mean Min ID Mean Min 

1 US187 1 1 4 MT058 1 1 

2 WA118 1 1 5 ID112 1 1 

3 WA208 1 1 6 CO141 1 1 

Table A86. Maximum and Minimum Correlation Coefficients Among 

Dockage Discount Schedules 

 ID Mean Min ID Mean Min 

1 MT058 0.966 0.900 4 US187 0.993 0.974 

2 CO141 0.978 0.900 5 WA118 0.999 0.995 

3 WA208 0.982 0.904 6 ID112 0.999 0.997 
MILLIMAN USA 

-101-

Table A87. Maximum and Minimum Correlation Coefficients Among 

Foreign Material Discount Schedules

 ID Mean Min 

1 CO141 0.998 0.997 

2 ID112 0.999 0.997 

3 US187 0.999 0.997 

Table A88. Maximum and Minimum Correlation Coefficients Among 

Frost Discount Schedules 

 ID Mean Min ID Mean Min 

1 MT058 0.999 0.997 3 US187 0.999 0.997 

2 WA208 0.999 0.997 4 ID112 1.000 1.000 

Table A89. Maximum and Minimum Correlation Coefficients Among 

Germ Discount Schedules 

 ID Mean Min ID Mean Min 

1 ID112 1 1 3 US187 1 1 

2 MT058 1 1 4 WA208 1 1 

Table A90. Maximum and Minimum Correlation Coefficients Among 

Grade Discount Schedules 

 ID Mean Min 

1 US187 0.999 0.997 

2 WA208 0.999 0.997 

3 MT058 1.000 1.000 

Table A91. Maximum and Minimum Correlation Coefficients Among 

Heat Discount Schedules 

 ID Mean Min ID Mean Min 

1 ID112 1 1 3 US187 1 1 

2 MT058 1 1 4 WA208 1 1 

Table A92. Maximum and Minimum Correlation Coefficients Among 

Insect Damaged Kernels Discount Schedules

 ID Mean Min ID Mean Min 

1 ID112 0.993 0.978 3 US187 1.000 1.000 

2 MT058 0.993 0.978 4 WA208 1.000 1.000 

Table A93. Maximum and Minimum Correlation Coefficients Among 

Mold Discount Schedules 

 ID Mean Min ID Mean Min 

1 ID112 1 1 3 US187 1 1 

2 MT058 1 1 4 WA208 1 1 
MILLIMAN USA 

-102-

Table A94. Maximum and Minimum Correlation Coefficients Among 

Sprout Discount Schedules 

 ID Mean Min 

1 MT058 1 1 

2 WA208 1 1 

3 ID112 1 1 

Table A95. Maximum and Minimum Correlation Coefficients Among 

Stone Discount Schedules 

 ID Mean Min ID Mean Min 

1 ID112 0.848 0.691 4 WA208 0.932 0.780 

2 MT058 0.845 0.691 5 WA118 0.972 0.949 

3 US187 0.932 0.780 

Table A96. Maximum and Minimum Correlation Coefficients Among 

Test Weight Discount Schedules

 ID Mean Min ID Mean Min 

1 CO141 0.992 0.983 4 ID112 0.995 0.991 

2 WA208 0.995 0.983 5 US187 0.997 0.992 

3 MT058 0.996 0.989 

Table A97. Maximum and Minimum Correlation Coefficients Among 

 Wheat of Other Classes Discount Schedules

 ID Mean Min ID Mean Min 

1 ID112 0.997 0.986 3 MT058 0.999 0.995 

2 WA208 0.994 0.986 4 US187 0.999 0.995 

Grain Sorghum 

Table A98. Maximum and Minimum Correlation Coefficients Among 

Damage Discount Schedules 

 ID Mean Min ID Mean Min 

1 IL224 0.996 0.985 6 NE186 0.996 0.986 

2 KS003 0.993 0.985 7 KS081 0.997 0.995 

3 US222 0.996 0.985 8 KS209 0.997 0.995 

4 KS037 0.993 0.985 9 NE188 1.000 1.000 

5 NE184 0.996 0.986 
MILLIMAN USA 

-103-

Table A99. Maximum and Minimum Correlation Coefficients Among 

Foreign Material Discount Schedules

 ID Mean Min ID Mean Min 

1 KS037 0.976 0.950 8 NE188 0.994 0.969 

2 KS081 0.990 0.950 9 NE205 0.994 0.969 

3 NE216 0.973 0.957 10 OK210 0.994 0.969 

4 KS003 0.993 0.966 11 IL224 1.000 1.000 

5 KS209 0.994 0.969 12 NE184 1.000 1.000 

6 KS214 0.994 0.969 13 NE186 1.000 1.000 

7 MO215 0.994 0.969 

Table A100. Maximum and Minimum Correlation Coefficients Among 

Heat Discount Schedules 

 ID Mean Min ID Mean Min 

1 MO215 0.833 0.655 5 KS209 0.850 0.655 

2 NE216 0.833 0.655 6 TX147 0.850 0.655 

3 OK210 0.833 0.655 7 IL224 0.937 0.868 

4 KS003 0.850 0.655 

Table A101. Maximum and Minimum Correlation Coefficients Among 

Tan Discount Schedules 

 ID Mean Min ID Mean Min 

1 KS214 1 1 3 NE205 1 1 

2 MO215 1 1 4 OK210 1 1 
MILLIMAN USA 

-104-

Table A102. Maximum and Minimum Correlation Coefficients Among 

Total Broken and Foreign Material Discount Schedules

 ID Mean Min ID Mean Min 

1 KS037 0.987 0.949 7 NE188 0.987 0.976 

2 NE216 0.987 0.949 8 OK210 0.991 0.976 

3 TX147 0.984 0.949 9 KS214 0.991 0.976 

4 KS003 0.987 0.964 10 NE205 0.991 0.976 

5 US222 0.987 0.966 11 IL223 0.989 0.978 

6 MO215 0.991 0.976 12 KS209 0.993 0.985 

Table A103. Maximum and Minimum Correlation Coefficients Among 

Total Damage Discount Schedules

 ID Mean Min ID Mean Min 

1 KS214 0.999 0.995 5 MO215 0.999 0.995 

2 NE205 0.999 0.995 6 NE216 0.998 0.996 

3 OK210 0.999 0.995 7 IL223 1.000 1.000 

4 TX147 0.997 0.995 

Table A104. Maximum and Minimum Correlation Coefficients Among 

Test Weight Discount Schedules

 ID Mean Min ID Mean Min 

1 TX147 0.981 0.949 13 KS021 0.996 0.984 

2 IL223 0.991 0.949 14 NE181 0.997 0.984 

3 US222 0.991 0.949 15 NE071 0.996 0.984 

4 KS081 0.994 0.967 16 OK210 0.996 0.986 

5 IL224 0.995 0.967 17 KS121 0.996 0.986 

6 NE216 0.995 0.967 18 KS214 0.995 0.986 

7 KS037 0.992 0.967 19 NE184 0.996 0.986 

8 NE188 0.993 0.967 20 NE186 0.996 0.986 

9 KS003 0.995 0.967 21 NE205 0.995 0.986 

10 KS209 0.995 0.967 22 MO215 0.996 0.986 

11 TX060 0.983 0.967 23 KS013 0.996 0.986 

12 KS010 0.994 0.980 24 TX062 0.996 0.986 
MILLIMAN USA 

-105-

Barley 

Table A105. Correlation Coefficients Among 

Germination Discount Schedules 

 US175 ND191

US175 1 1 

ND191 1 1 

Table A106. Maximum and Minimum Correlation Coefficients Among 

Plump Discount Schedules 

 ID Mean Min 

1 ND034 0.997 0.992 

2 US175 0.997 0.992 

3 ND191 1.000 1.000 

Table A107. Correlation Coefficients Among 

Protein Discount Schedules 

 ND191 US175 

ND191 1 0.99 

US175 0.99 1 

Table A108. Correlation Coefficients for 

Shrunken and Broken Discount Schedules 

 ND191 US175 

ND191 1 1 

US175 1 1 

Table A109. Correlation Coefficients Among 

Thin Discount Schedules 

 ND191 US175 

ND191 1 1 

US175 1 1 
MILLIMAN USA 

-106-

Table A110. Maximum and Minimum Correlation Coefficients Among 

DON Discount Schedules 

 ID Mean Min ID Mean Min 

1 ND034 0.90 0.73 4 US175 0.94 0.88 

2 ND191 0.87 0.73 5 ND145 0.94 0.89 

3 ND174 0.90 0.73 

Table A111. Maximum and Minimum Correlation Coefficients Among 

Test Weight Discount Schedules

 ID Mean Min ID Mean Min 

1 WI029 0.998 0.993 3 ND174 0.997 0.995 

2 ND193 0.997 0.993 4 MN026 0.999 0.996 

Oats 

Table A112. Maximum and Minimum Correlation Coefficients Among 

Foreign Material Discount Schedules

ID Mean Min

1 IL223 0.999 0.998

2 MN02 0.999 0.998

3 WI029 1.000 1.000

Table A113. Maximum and Minimum Correlation Coefficients Among 

Test Weight Discount Schedules

 ID Mean Min ID Mean Min 

1 SD049 0.994 0.985 7 IA157 0.996 0.986 

2 WI029 0.994 0.985 8 KS081 0.993 0.986 

3 IA163 0.994 0.985 9 KS121 0.994 0.986 

4 ND034 0.995 0.985 10 IA122 0.996 0.986 

5 MN02 0.996 0.985 11 IL223 0.999 0.993 

6 IA074 0.996 0.986 

Canola 

Table A114. Correlation Coefficients Among 

Green Discount Schedules 

US170 US196

US170 1 0.98 

US196 0.98 1 
MILLIMAN USA 

-107-

Table A115. Correlation Coefficients Among 

Heat Discount Schedules 

US170 US196

US170 1 0.82 

US196 0.82 1 

Table A116. Correlation Coefficients Among 

In Admix Discount Schedules 

US170 US196

US170 1 0.99

US196 0.99 1 

Oil Sunflower 

Table A117. Maximum and Minimum Correlation Coefficients Among 

Damage Discount Schedules

ID Mean Min

1 KS013 1 1

2 KS121 1 1 

3 KS197 1 1 

Table A118. Maximum and Minimum Correlation Coefficients Among 

Foreign Material Discount Schedules

ID Mean Min

1 KS197 0.999 0.997

2 KS013 0.998 0.997

3 KS121 0.999 0.998

Table A119. Correlation Coefficients Among 

Heat Discount Schedules 

KS121 KS197

KS121 1 1 

KS197 1 1 

Table A120. Maximum and Minimum Correlation Coefficients Among 

Oil Discount Schedules 

ID Mean Min

1 KS01 1 1

2 KS12 1 1 

3 KS19 1 1 
MILLIMAN USA 

-108-

Table A121. Maximum and Minimum Correlation Coefficients Among 

Test Weight Discount Schedules

ID Mean Min

1 KS01 1 1

2 KS12 1 1 

3 KS19 1 1 
MILLIMAN USA 

-109-

APPENDIX B 

Covered Quality Deficiencies 

Table B1: Summarized Provisions by Crop

Crop Provision to be met for Quality Adjustment Exception Notes 

Barley 

U.S. Grade 5 or worse 

Weight or kernel damage 

Percentage of sound barley 

Thin or black barley 

Musty, sour, or commercially objectionable odor 

Grading smutty, garlicky or ergoty 

Substances (including mycotoxins) or conditions 

injurious to human or animal health 

Smut or garlic odor 

As identified by FDA or 

other U.S. health 

organizations 

Buckwheat N/A N/A N/A 

Canola 

U.S. Sample Grade 

Kernel damage 

Musty, sour, or commercially objectionable odor 

Substances (including mycotoxins) or conditions 

injurious to human or animal health 

Heat damage As identified by FDA or 

other U.S. health 

organizations 

Corn 

U.S. Grade 5 or worse 

Weight or kernel damage 

Musty, sour, or commercially objectionable odor 

Substances (including mycotoxins) or conditions 

injurious to human or animal health 

Heat damage 

As identified by FDA or 

other U.S. health 

organizations 

Cotton 

Price quotation “A” is less than 75% of price 

quotation “B” 

Ginning must have been completed at a gin 

Using roller equipment 

Colored cotton lint 

Flax 

U.S. sample grade 

Damaged kernels 

Substances (including mycotoxins) or conditions 

injurious to human or animal health 

 As identified by FDA or 

other U.S. health 

organizations 

Grain 

Sorghum 

U.S. sample grade 

Weight or kernel damage 

Musty, sour, or commercially objectionable odor 

Smutty grain sorghum 

Substances or conditions injurious to human or 

animal health 

Heat damage 

Smut odor 

As identified by FDA or 

other U.S. health 

organizations 

Oats 

U.S. sample grade 

Weight damage 

Percentage of sound oats 

Grading smutty, garlicky or ergoty 

Substances (including mycotoxins) or conditions 

injurious to human or animal health 

 

As identified by FDA or 

other U.S. health 

organizations 

Source: “Crop Provisions” documents, www. RMA.USDA.GOV 
MILLIMAN USA 

-110-

Crop Provision to be met for Quality Adjustment Exception Notes 

Rice 

U.S. Grade 4 or worse 

Total milling yield of less than 68 pounds per 

hundredweight 

Whole kernel weight is less than 55 pounds per 

hundredweight of milled rice 

Whole kernel weight is less than 48 pounds per 

hundredweight of milled rice 

Substances conditions injurious to human or 

animal health 

 Because of red rice, 

chalky kernels, or 

damaged kernels 

For medium and short 

grain varieties 

For long grain varieties

As identified by FDA or 

other U.S. health 

organizations 

Rye 

U.S. Grade 4 or worse 

Weight or kernel damage 

Thin Rye 

Grading smutty, garlicky or ergoty 

Substances (including mycotoxins) or conditions 

injurious to human or animal health 

 

As identified by FDA or 

other U.S. health 

organizations 

Safflower 

Test weight below 35 pounds per bushel 

Seed damage in excess of 25% 

Substances injurious to human or animal health 

 As identified by FDA or 

other U.S. health 

organizations 

Soybeans 

Grade U.S. sample grade 

Weight or kernel damage 

Musty, sour, or commercially objectionable odor 

Garlicky soybeans 

Substances injurious to human or animal health 

Heat damage 

Garlic odor 

As identified by FDA or 

other U.S. health 

organizations 

Sunflower 

(Oil Type) 

U.S. sample grade 

Weight or kernel damage 

Musty, sour, or commercially objectionable odor 

Substances injurious to human or animal health 

Heat damage 

As identified by FDA or 

other U.S. health 

organizations 

Sunflower 

(Non-Oil 

Type) 

Test weight below 22 pounds per bushel 

Kernel damage in excess of 5% 

Musty, sour, or commercially objectionable odor 

Substances injurious to human or animal health 

Heat damage 

As identified by FDA or 

other U.S. health 

organizations 

Wheat 

U.S. Grade 5 or worse 

Weight or kernel damage 

Shrunken or broken kernels 

Defects 

Musty, sour, or commercially objectionable odor 

Grading garlicky, light smutty, smutty or ergoty 

Substances (including mycotoxins) or conditions 

injurious to human or animal health 

Heat damage 

Foreign material and 

heat damage will not 

be considered defects 

As identified by FDA or 

other U.S. health 

organizations 

Source: “Crop Provisions” documents, www. RMA.USDA.GOV 
MILLIMAN USA 

-111-

FSA Quality Discounts 

Table B2: Summarized Provisions by Crop

Crop Unit Provision to be met for Quality Adjustment Discount Notes 

Barley Bushel U.S. Sample Grade $0.30 For 2001 crop 

 U.S. Grade 5 $0.30 

 Weight Damage $0.02 $0.02 for test weight of 35.9 to 35.0 

pounds and $0.02 for each whole or 

fraction of a pound under 35.0 

pounds per bushel 

 Heat-Damaged Kernels $0.02 $0.02 for 3.1 percent plus an 

additional $0.02 for each 10th of a 

percent over 3.1 percent 

 Damaged Kernels (Total) Percent $0.02 $0.02 for 10.1 percent plus an 

additional $0.02 for each whole 

percent or fraction of a percent over 

11.0 percent through 15.0 percent 

plus an additional $0.05 for each 

whole percent or fraction of a percent 

over 15.0 percent 

 Thin or Black Barley $0.05 $0.05 for thin percentages from 75.1 

to 80.0 plus $0.05 for each additional 

thin percentages in 5% increments 

 Special Grade Discount of Garlicky $0.10 Plus $0.01 for each whole percent or 

fraction of a half percent over 4.5 

percent 

 Musty, Sour $0.10 

 Foreign Material $0.01 $0.01 for 5.1 to 6.0 percent plus an 

additional $0.01 for each whole 

percent or fraction of a percent over 

6.0 percent through 10.0 percent plus 

an additional $0.02 for each whole 

percent or fraction of a percent over 

10.0 percent 

Buckwheat N/A N/A N/A N/A 

Canola Hundredweight Damaged Kernels (Total) Percent $0.25 $0.06 for 2.1 to 3.0 percent, with an 

additional $0.06 for each percent or 

fraction of a percent up to 9.0 

percent, an additional $0.10 for each 

percent or fraction of a percent up to 

10.0 percent, an additional $0.25 for 

each percent or fraction of a percent 

up to 15.0 and, above that, it is an 

additional $0.50 for each percent or 

fraction of a percent 

 U.S. Sample Grade $0.50 For 2001 crop 

 Musty, Sour $0.40 

Source: “Premiums & Discounts” schedules, www.FSA.USDA.GOV 
MILLIMAN USA 

-112-

Crop Unit Provision to be met for Quality Adjustment Discount Notes 

Corn Bushel U.S. Sample Grade $0.15 For 2001 crop 

 Weight Damage $0.01 $0.01 for test weight of 53.9 to 53.0 

pounds, with an additional $0.01 for 

each pound or fraction of a pound 

down to 50.0 pounds, and, below that, 

it is an additional $0.02 for each 

pound or fraction of a pound 

 Damaged Kernels (Total) Percent $0.02 $0.02 for 5.1 to 6.0 percent, with an 

additional $0.02 for each percent or 

fraction of a percent up to 15.0 

percent, and, above that, an 

additional $0.03 for each percent or 

fraction of a percent 

 Broken Corn & Foreign Material $0.02 $0.02 for 3.1 to 4.0 percent, with an 

additional $0.02 for each percent or 

fraction of a percent up to 8.0 

percent, an additional $0.03 for each 

percent or fraction of a percent up to 

10.0 percent, and, above that, it is an 

additional $0.04 for each percent or 

fraction of a percent 

 Sour $0.10 

 Musty $0.05 

Cotton N/A N/A N/A N/A 

Flax Hundredweight U.S. Sample Grade $0.50 For 2001 crop 

 Heat-Damaged Kernels $0.12 $0.06 for 0.3 to 0.5 percent, with an 

additional $0.06 for each half percent 

or fraction of a percent up to 1.0 

percent, an additional $0.12 for each 

half percent or fraction of a percent 

up to 1.5 percent, an additional $0.24 

for each half percent or fraction of a 

percent up to 2.0 percent, an 

additional $0.52 for each 1.0 percent 

or fraction of a percent up to 3.0 

percent, and, above that, an 

additional $1.00 for each 1.0 percent 

or fraction of a percent 

 Damaged Kernels (Total) Percent $0.50 $0.22 for 10.1 to 11.0, with an 

additional $0.22 for each 1.0 percent 

or fraction of a percent up to 14.0 

percent, and, above that, it is an 

additional $.50 for each 1.0 percent or 

fraction of a percent 

Source: “Premiums & Discounts” schedules, www.FSA.USDA.GOV 
MILLIMAN USA 

-113-

Crop Unit Provision to be met for Quality Adjustment Discount Notes 

Grain 

Sorghum 

Hundredweight U.S. Sample Grade $0.30 For 2001 crop 

 Weight Damage $0.02 $0.02 for 54.9 to 54.0 pounds, with an 

additional $0.02 for each pound or 

fraction of a pound down to 51.0 

pounds, and an additional $0.04 for 

each pound or fraction of a pound 

below that 

 Damaged Kernels (Total) Percent $0.02 $0.02 for 5.1 to 6.0 percent, with an 

additional $0.03 for each percent or 

fraction of a percent up to 15.0 

percent, an additional $0.03 for each 

percent or fraction of a percent up to 

25.0 percent, and an additional $0.02 

for each percent or fraction of a 

percent above that 

 Broken Kernels, Foreign Material $0.01 $0.01 for 7.1 to 8.0 percent, with an 

additional $0.02 for each percent or 

fraction of a percent up to 15.0 

percent, and an additional $0.03 for 

each percent or fraction of a percent 

above that 

 Musty $0.10 

 Sour $0.20 

Oats Bushel U.S. Sample Grade $0.15 For 2001 crop 

 Weight Damage $0.05 $0.05 for 32.5 to 32.1 pounds, with an 

additional $0.03 for each half pound 

or fraction of a pound below that 

 Sound-Cultivated Oats $0.02 $0.02 for 93.9 to 93.0 percent, with an 

additional $0.02 for each percent or 

fraction of a percent down to 80.0 

percent, and an additional $0.03 for 

each percent or fraction of a percent 

below that 

 Garlicky $0.05 

 Foreign Material $0.01 $0.01 for 3.1 to 3.5 percent, with an 

additional $0.01 for each half percent 

or fraction of a half percent up to 8.0 

percent, an additional $0.02 for each 

half percent or fraction of a half 

percent up to 15.0 percent, and an 

additional $0.01 for each half percent 

or fraction of a half percent above that 

Rice Hundredweight U.S. Sample Grade $5.50 

 U.S. Grade 4 $0.60 

 U.S. Grade 5 $1.00 

 U.S. Grade 6 $2.00 

Rye N/A N/A N/A N/A 

Safflower Hundredweight Total Damage $0.05 $0.05 for 3.1 to 4.0 percent, with an 

additional $0.05 for each percent or 

fraction of a percent up to 10.0 

percent, an additional $0.10 for each 

percent or fraction of a percent up to 

15.0 percent, and, above that, an 

additional $0.25 for each percent or 

fraction of a percent 

Source: “Premiums & Discounts” schedules, www.FSA.USDA.GOV 
MILLIMAN USA 

-114-

Crop Unit Provision to be met for Quality Adjustment Discount Notes 

Soybeans U.S. Sample Grade $0.15 For 2001 crop 

 

 Weight Damage $0.005 $0.005 for 53.9 to 53.0 pounds, with 

an additional $0.005 for each pound 

or fraction of a pound down to 49.0 

pounds, and, below that, an additional 

$0.01 for each pound or fraction of a 

pound 

 Total Damage $0.02 $0.02 for 2.1 to 3.0 percent, with an 

additional $0.02 for each percent or 

fraction of a percent up to 8.0 

percent, and, above that, an 

additional $0.02 for each half percent 

or fraction of a half percent 

 Musty $0.05 

 Sour $0.10 

 COFO $0.20 

Sunflower 

(Oil Type) 

Hundredweight U.S. Sample Grade $0.50 For 2001 crop 

 Weight Damage $0.20 $0.20 for 24.9 to 24.0 pounds, below 

that, with an additional $0.20 for each 

pound or fraction of a pound 

 Total Damage $0.44 $0.22 for 5.1 to 6.0 percent, with an 

additional $0.22 for each percent or 

fraction of a percent up to 10.0 

percent, and, above that, with an 

additional $0.44 for each percent or 

fraction of a percent 

 Musty, Sour $0.50 

Sunflower 

(Non-Oil Type) 

Hundredweight Musty, Sour $0.50 

Wheat Bushel U.S. Sample Grade $0.70 For 2001 crop 

 U.S. Grade 5 $0.65 

 Weight Damage $0.06 $0.06 for test weight of 50.9 to 50.0 

pounds and $0.06 for each whole 

pound or fraction of a pound under 

40.0 pounds per bushel 

 Total Damage $0.03 $0.03 for 15.1 percent plus an 

additional $0.03 for each whole 

percent or fraction of a percent over 

16.0 percent 

 Smut $0.20 $0.10 for Light Smutty 

 Garlic $0.01 

 Musty $0.10 

 Sour $0.25 

Source: “Premiums & Discounts” schedules, www.FSA.USDA.GOV