United States 

Department of

Agriculture

National

Agricultural

Statistics

Service

Research and

Development Division

Washington DC 20250

RDD Research Report

RDD-09-09

May 2009

Exploring Quarterly 

Agricultural Survey 

Questionnaire Version

Reduction Scenarios 

Morgan Earp

Scott Cox

Jody McDaniel

Chadd Crouse

This paper was prepared for limited distribution to the research community outside the United 

States Department of Agriculture. The views expressed herein are not necessarily those of the 

National Agricultural Statistics Service or of the United States Department of Agriculture. 

i

EXECUTIVE SUMMARY

The United States Department of Agriculture (USDA), National Agricultural Statistics 

Service’s (NASS) Agricultural Survey Program (ASP) collects national agricultural data 

from farmers and ranches quarterly and annually to estimate the size of local and national 

crop production and stock inventories using the Quarterly Agricultural Survey 

Questionnaire (QAS). The QAS provides a basis for estimating seasonal and annual crop 

production, supplies, and grain storage. The farming industry uses QAS estimates for 

both short-term and long-term crop planning. 

Currently the QAS collects data on a variety of crops and stocks across all 50 states

anywhere from one to four times a year. Variations in the crop data collection from state

to state are believed to reduce respondent burden (e.g., Maine farmers are not surveyed 

for soybeans and Georgia farms are not surveyed for alfalfa); however, such 

customization in turn creates a number of QAS versions, which is both time-consuming

and more costly to administer. Therefore, this report explores multiple scenarios for 

reducing the number of QAS versions.

A table consisting of all 31 crops and stocks surveyed by each state was analyzed using 

hierarchical clustering to identify possibilities for regional versions. The number of

clusters was limited to 20 in order to reduce the number of potential QAS versions. The

resulting clusters’ (regions’) crop/stock survey frequencies were determined by 

comparing the crop/stock survey frequency across states within the given cluster. 

ii

RECOMMENDATIONS

1. There are no current plans to pursue regionalizing the QAS. However, should 

regionalization be pursued in the future, this report demonstrates a useful 

methodology for doing so. This approach should also be explored when 

proposing regionalization of questionnaires in other surveys.
Exploring Quarterly Agricultural Survey Questionnaire Version Reduction 

Scenarios 

Morgan Earp, Scott Cox, Jody McDaniel, & Chadd Crouse1 

Abstract

The United States Department of Agriculture’s National Agricultural 

Statistics Service (NASS) conducts the Agricultural Survey Program 

(ASP), which consists of crop/stocks and livestock surveys. The Quarterly 

Agricultural Survey (QAS) questionnaire serves as a primary data 

collection instrument for the Agency’s estimates of seasonal and annual

crop production, supplies, and grain storage, which are used by the 

farming industry for both short and long term planning. The QAS is

administered in all 50 states and collects data on 31 different crops and 

stocks, in varying combinations and frequencies throughout the year 

depending on the state. Such variation allegedly reduces respondent 

burden; however, it greatly increases the complexity of the survey 

administration process. Hierarchical clustering was done to investigate the 

potential of creating 20 regional QAS versions. Such an approach, if 

implemented operationally, would reduce the number of QAS versions by 

60 percent (50 to 20). The research explored further clustering the QAS

into only five regional versions, which, if implemented, would reduce the 

number of QAS versions by 90 percent (50 to 5). 

 

Key Words: Quarterly Agricultural Survey Questionnaire; Questionnaire Version

Reduction; Item Reduction

1. BACKGROUND

In September of 2007, the United States Department of Agriculture’s (USDA) National 

Agricultural Statistics Service (NASS) created a team to improve the efficiency of the 

Quarterly Agricultural Survey Program through a general review of survey content and

by reducing the number of questionnaire versions. The Quarterly Agricultural Survey 

(QAS) Questionnaire Reduction and Review Team was established to improve the 

efficiency of the quarterly agricultural survey program through a reduction in the number 

 

1

Scott Cox and Jody McDaniel initiated this research while serving as the Commodity Surveys Section 

Head and the Quarterly Crops/Stocks Survey Administrator with the USDA/NASS – Census and Survey 

Division (CSD). Chadd Crouse provided and defined the data for this research while a mathematical 

statistician with the USDA/NASS – CSD. Morgan Earp is a survey and mathematical statistician with the 

USDA/NASS in its Research and Development Division (RDD), located in Room 305, 3251 Old Lee 

Highway, Fairfax, VA 22030.

Jaki McCarthy provided assistance with this research while the Chief Cognitive Research Methodologist 

with the USDA/NASS/RDD. 
of questionnaire versions and a general review of survey content (National Agricultural 

Statistics Service, 2008). The desired result of this team was to facilitate standardization 

and regionalization of NASS data collection efforts and produce the following benefits: 

allow maximization of resources within Data Collection Centers, promote consistency of 

data collection within regions, ensure consistent standards between the Agricultural 

Survey and Census Program, guarantee the quality of estimates, potentially reduce survey 

cost and respondent burden, reduce processing time for headquarters units and gain 

efficiency in survey training. 

The team hoped to produce the following deliverable after the work was completed:

1. Draft regional Quarterly Agricultural Survey instruments that meet NASS 

questionnaire design standards and improve the overall efficiency of the data 

collection process. (Goal was dropped).

2. Complete review of Quarterly Agricultural Survey instruments to determine if all 

items are needed for the NASS estimation program and to determine if 

efficiencies can be gained by either changing the format or number survey items 

collected. (Goal was achieved).

3. Review all modes of collection (paper, Blaise, EDR) for the Quarterly 

Agricultural Survey program to ensure that questions are standardized across each 

mode of data collection. (Goal was achieved).

4. Ensure that all Quarterly Agricultural Survey paper instruments are 12 pages or 

less to ensure that NASS standard survey mailing procedures can be utilized. 

(Goal was achieved).

Each above deliverable focuses on improving the overall efficiency of the QAS. The 

relative success of each deliverable affected the efficiency of the QAS program. The 

collective success of all four ultimately was expected to provide the greatest opportunity 

for improving efficiency of the QAS program. Deliverables two through four were 

completed and are contributing towards the efficiency of the QAS program.

The last three deliverables are being used to make the QAS program more efficient; 

however, deliverable one was not completed. The goal of drafting regional QAS survey 

instruments was dropped after much discussion with CSD management. This was due to 

the realization that the continued use of the Questionnaire Repository System (QRS) 

provides many of the efficiencies that would be gained by regionalizing QAS survey 

instruments. Specifically, it was determined that the benefits achieved by using the QRS 

for questionnaire development outweighed potential gains of using regional 

questionnaires, when it came to reducing respondent burden, in the QAS program. The 

original intent of having regional questionnaire versions was to improve the overall 

efficiency of the QAS program. This includes but is not limited to the following items: 

reducing respondent burden, improving the quality of estimates, potentially reducing 

survey cost, improving survey training, and better utilizing DCCs. However, the change 
in objectives for the team did not allow completion of all steps (such as testing, etc.) that 

would have been completed if the regional questionnaires had been adapted. 

Furthermore, this report should be a starting point for any future discussions regarding 

the regionalization of QAS questionnaires (or any other survey instruments). The main 

principals discussed could probably be applied to other survey programs without any 

major changes in the methodology.

2. INTRODUCTION

This report documents options for reducing the number of QAS versions administered by 

the Agricultural Survey Program (ASP). The QAS is administered in all 50 states and 

collects data on 31 different crops and stocks throughout the year, in varying 

combinations and frequencies depending on the state. 

The QAS provides clear indications of the potential production and supply of major 

commodities in the United States. NASS surveys producers on their total acres operated, 

acres planted and harvested of specific commodities, and quantities of grains and oilseeds 

produced and stored on-farm, in order to set national and state estimates. NASS 

publishes the results of the QAS in a series of reports, including the annual acreage and 

quarterly grain stocks reports. The entire agricultural community including producers, 

buyers, providers, processors, state and federal agencies, and policymakers depends on 

the estimates set using the QAS. Users of these estimates include commodity markets, 

educational institutions, state and federal agencies, and the farming and ranching

operations themselves. 

In an effort to reduce respondent burden and maintain high response rates, state-specific 

versions of the QAS are used in the ASP data collection process. The content of these 

QAS versions varies by state and the time of year the questionnaire is administered. 

1.1 Problem

Currently, each state utilizes its own version of the QAS. Although this process is 

thought to reduce respondent burden, it requires considerable resources to develop the 

survey instruments, administer the survey, and summarize the data.

1.2 Purpose

This report describes the potential for regionalizing the QAS. It is merely an exploratory 

summarization of current QAS survey frequencies across states, and does not account for 

state preferences. The purpose of the report is to improve the efficiency of the quarterly 

agricultural survey program by reducing the number of QAS versions. It provides insight 

into possibilities for reducing the number of QAS versions and, thus, the associated 

survey administration burden.

3. METHOD 
3.1 Data

Crop and stock survey frequencies were assessed and compared across all states using 

hierarchical clustering. Appendix A shows all crops and stocks included in one or more 

QAS questionnaires across the column headings. The table cells are coded by sampling 

frequency, not publication frequency. The QAS surveying frequencies of crops and 

stocks are coded using the Field Crops Section classifications: annual (A), full season (F), 

“included” (I), all other states (AOS), silage (S) or not surveyed (NS) (Table A-1). The 

notation used to classify states does not necessarily reflect the crop/stock specifically 

sampled for the QAS. For the purposes of this analysis, “annual” crops/stocks are 

considered to be surveyed annually, “full season” crops/stocks are considered to be

surveyed quarterly, “included” crops/stocks are special crops considered to be surveyed 

at least annually2, “all other states” crops/stocks are grouped and published together with 

other crops by state (survey frequency varies), “silage” corn is considered to be surveyed 

annually to estimate silage (not grain), and “not surveyed” crops/stocks are not surveyed. 

All six survey frequencies were generated for purposes of analysis; however, they were 

consolidated as follows for purposes of summary: crops and stocks identified as annual, 

included, or silage were considered annual; crops and stocks identified as full season, all 

other states, or not surveyed were left as is. The analytical data set consists of 50 states 

and 31 variables: Each variable represents one of the 31 crops/stocks surveyed. 

3.2 Procedure

States sharing similar crops and stocks surveyed were dynamically grouped together 

using a dendogram to form clusters that were ultimately referred to as regions. A 

dendogram is a tree with individual elements at one end, building (agglomeratively) into 

a single cluster containing every element at the other end (JMP, 2008). Dendograms may 

be cut at any point to provide a specific number of clusters. For the purposes of this 

report, two target numbers of clusters were specified as follows: 1) the QAS 20 regions 

(versions) were identified using 20 clusters; and 2) the QAS 5 regions (versions) were

identified by consolidating all 20 original clusters into 5 clusters. 

4. RESULTS 

Clustering was used to reduce the number of QAS versions by 60 percent (50 to 20) and 

then ultimately by 90 percent (50 to 5). Survey administration frequency data, comprised 

of indicator variables as to whether specific items are surveyed as well as the frequency 

at which they are surveyed across states, were combined with state sampling data to 

create regional state groupings known as clusters using JMP’s hierarchical clustering

algorithm. Using hierarchical clustering, these data were compared across states to create 

clusters of like states in terms of survey administration frequency. Clusters were first 

limited to 20 and then to 5 to identify regional survey version scenarios. The initial

hierarchical clustering, using the 20 cluster cutoff, revealed 20 regions (Figure 1).

 

2

Special crops included in the full program are not necessarily sampled quarterly; therefore, they are 

classified as annual for purposes of this study given that they are surveyed at least annually.
The frequency (annually, seasonally, all other states or not surveyed) at which 

crops/stocks were surveyed within each cluster was examined and used to determine the 

frequency at which they would be surveyed for a given region. When the survey 

frequency was tied, annual, full season, or all other survey trumped not surveyed, and 

seasonal trumped annual. When survey frequency was not tied between annual, full 

season, all other survey, or not surveyed, the frequency at which the majority of states 

within the cluster surveyed specific crops/stocks was used (even if it meant reclassifying

crops/stocks from surveyed to not surveyed)
Cluster State Dendogram

One

Two

Three

Four

Five

Six

Seven

Eight

Nine

Ten

Eleven

Twelve

Thirteen

Fourteen

Fifteen

Sixteen

Seventeen

Eighteen

Nineteen

Twenty

Figure 1. State Cluster Dendogram
4.1 Twenty Region Survey Design

4.1.1 Region 1: Alabama, Mississippi, Georgia, South Carolina, North Carolina, & 

Virginia

The first cluster identified Region 1, which includes six states: Alabama, Mississippi, 

Georgia, South Carolina, North Carolina, and Virginia (Figure 2). Analysis of individual 

crops/stocks survey frequency within Cluster 1 indicates that Region 1 would survey 

oats, potatoes, sorghum, sweet potatoes, tobacco, and watermelons annually; and corn, 

cotton, other hay, peanuts, soybeans, and winter wheat seasonally (Table 1). 

Figure 2. Region 1 States

Table 1: Region 1 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Oats

Potatoes

Sorghum

Sweet Potatoes

Tobacco

Watermelons

Seasonal Corn

Cotton

Other Hay

Peanuts

Soybeans

Winter Wheat
4.1.2 Region 2: Arkansas & Louisiana 

The second cluster identified Region 2, which includes two states: Arkansas and 

Louisiana (Figure 3). Analysis of individual crops/stocks survey frequency within 

Cluster 2 indicates that Region 2 would survey alfalfa, potatoes, sweet potatoes, and 

watermelons annually and corn, cotton, other hay, rice, sugar cane, sorghum, soybeans, 

and winter wheat seasonally (Table 2). 

Figure 3. Region 2 States

Table 2: Region 2 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual

Alfalfa

Potatoes

Sweet Potatoes

Watermelons

Seasonal

Corn

Cotton

Other Hay

Rice

Sugar Cane

Sorghum

Soybeans

Winter Wheat
4.1.3 Region 3: Missouri & Oklahoma

The third cluster identified Region 3, which includes two states: Missouri and Oklahoma 

(Figure 4). Analysis of individual crops/stocks survey frequency within Cluster 3 

indicates that Region 3 would survey forage, oats, potatoes, rye, tobacco, and 

watermelons annually; alfalfa, corn, cotton, other hay, peanuts, rice, sorghum, soybeans, 

and winter wheat seasonally; and canola and sunflower in unison with other crops/stocks 

as the region sees fit (Table 3).

Figure 4. Region 3 States

Table 3: Region 3 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Forage

Oats

Potatoes 

Rye

Tobacco

Watermelons

Seasonal

Alfalfa

Corn

Cotton

Other Hay

Peanuts

Rice

Sorghum

Soybeans

Winter Wheat

All Other States Canola 

Sunflower
4.1.4 Region 4: California

The fourth cluster identified Region 4, which includes one state: California (Figure 5). 

Analysis of individual crops/stocks survey frequency within Cluster 4 indicates that 

Region 4 would survey dry edible beans, forage, garbanzo beans, potatoes, safflower, 

sorghum, sweet potatoes, and watermelons annually; alfalfa, corn, cotton, durum wheat, 

oats, other hay, rice, and winter wheat; and sunflower in unison with other crops/stocks 

as California sees fit (Table 4).

Figure 5. Region 4 States

Table 4: Region 4 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Dry Edible Beans

Forage

Garbanzo Beans 

Potatoes

Safflower

Sorghum

Sweet Potatoes

Watermelons

Seasonal Alfalfa

Corn

Cotton

Durum Wheat

Oats

Other Hay

Rice

Winter Wheat

All Other States Sunflower
4.1.5 Region 5: Texas

The fifth cluster identified Region 5, which includes one state: Texas (Figure 6). 

Analysis of individual crops/stocks survey frequency within Cluster 5 indicates that 

Region 5 would survey dry edible beans, forage, potatoes, sweet potatoes, and 

watermelons annually; alfalfa, corn, cotton, oats, other hay, peanuts, rice, sugar cane, 

sorghum, soybeans, sunflower, and winter wheat seasonally; and rye in unison with other 

crops/stocks as Texas sees fit (Table 5).

Figure 6. Region 5 States

Table 5: Region 5 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual

Dry Edible Beans

Forage

Potatoes

Sweet Potatoes

Watermelons

Seasonal

Alfalfa

Corn

Cotton

Oats

Other Hay

Peanuts

Rice

Sugar Cane

Sorghum

Soybeans

Sunflower

Winter Wheat

All Other States Rye
4.1.6 Region 6: Alaska & Hawaii

The sixth cluster identified Region 6, which includes two states: Alaska and Hawaii 

(Figure 7). Analysis of individual crops/stocks survey frequency within Cluster 6 

indicates that Region 6 would survey oats annually and sugar cane seasonally (Table 6).

Figure 7. Region 6 States

Table 6: Region 6 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Oats

Seasonal Sugar Cane
4.1.7 Region 7: Connecticut, Maine, Massachusetts, Nevada, New Hampshire, Rhode 

Island, & Vermont

The seventh cluster identified Region 7, which includes seven states: Connecticut, Maine, 

Massachusetts, Nevada, New Hampshire, Rhode Island, and Vermont (Figure 8). 

Analysis of individual crops/stocks survey frequency within Cluster 7 indicates that 

Region 7 would survey alfalfa, corn (silage), other hay, and potatoes annually (Table 7).

Figure 8. Region 7 States

Table 7: Region 7 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Alfalfa

Corn (Silage)

Other Hay

Potatoes 
4.1.8 Region 8: Arizona & Florida

The eighth cluster identified Region 8, which includes two states: Arizona and Florida 

(Figure 9). Analysis of individual crops/stocks survey frequency within Cluster 8 

indicates that Region 8 would survey corn, other hay, potatoes, sorghum, soybeans, 

tobacco, watermelons, and winter wheat annually; alfalfa, cotton, durum wheat, peanuts, 

and sugarcane seasonally; and safflower in unison with other crops/stocks as the region 

sees fit (Table 8).

Figure 9. Region 8 States

Table 8: Region 8 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Corn

Other Hay

Potatoes

Sorghum

Soybeans

Tobacco

Watermelons

Winter Wheat

Seasonal Alfalfa

Cotton

Durum Wheat

Peanuts

Scane

All Other States Safflower
4.1.9 Region 9: New Mexico

The ninth cluster identified Region 9, which includes one state: New Mexico (Figure 10). 

Analysis of individual crops/stocks survey frequency within Cluster 9 indicates that 

Region 9 would survey dry edible beans, forage, other hay, potatoes, and winter wheat 

annually; and alfalfa, corn, cotton, peanuts, and sorghum seasonally (Table 9).

Figure 10. Region 9 States

Table 9: Region 9 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Dry Edible Beans

Forage

Other Hay

Potatoes

Winter Wheat

Seasonal Alfalfa

Corn

Cotton

Peanuts

Sorghum
4.1.10 Region 10: Delaware, Maryland, & New Jersey

The tenth cluster identified Region 10, which includes three states: Delaware, Maryland, 

and New Jersey (Figure 11). Analysis of individual crops/stocks survey frequency 

within Cluster 10 indicates that Region 10 would survey alfalfa, other hay, potatoes, and 

watermelons annually; and corn, soybeans, and winter wheat seasonally (Table 10).

Figure 11. Region 10 States

Table 10: Region 10 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual

Alfalfa

Other Hay

Potatoes

Watermelons

Seasonal Corn

Soybeans

Winter Wheat
4.1.11 Region 11: Indiana & Iowa

The eleventh cluster identified Region 11, which includes two states: Indiana and Iowa 

(Figure 12). Analysis of individual crops/stocks survey frequency within Cluster 11 

indicates that Region 11 would survey forage and watermelons annually; and alfalfa, 

corn, other hay, soybeans, and winter wheat seasonally (Table 11).

Figure 12. Region 11 States

Table 11: Region 11 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Forage

Watermelons

Seasonal Alfalfa 

Corn

Other Hay

Soybeans

Winter Wheat
4.1.12 Region 12: Kentucky, Tennessee, & West Virginia 

The twelfth cluster identified Region 12, which includes three states: Kentucky, 

Tennessee, and West Virginia (Figure 13). Analysis of individual crops/stocks survey 

frequency within Cluster 12 indicates that Region 12 would survey alfalfa, sorghum, and 

tobacco annually; and corn, other hay, soybeans, and winter wheat seasonally (Table 12).

Figure 13. Region 12 States

Table 12: Region 12 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual

Alfalfa

Sorghum

Tobacco

Seasonal Corn

Other Hay

Soybeans

Winter Wheat
4.1.13 Region 13: Colorado

The thirteenth cluster identified Region 13, which includes one state: Colorado (Figure 

14). Analysis of individual crops/stocks survey frequency within Cluster 13 indicates

that Region 13 would survey dry edible beans, oats, potatoes, proso millet, and spring 

wheat annually; alfalfa, corn, other hay, sorghum, sunflower, and winter wheat 

seasonally; and canola and sunflower in unison with other crops/stocks /stocks as 

Colorado sees fit (Table 13).

Figure 14. Region 13 States

Table 13: Region 13 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Dry Edible Beans

Oats

Potatoes

Proso Millet

Spring Wheat

Seasonal Alfalfa

Corn

Other Hay

Sorghum

Sunflower

Winter Wheat

All Other States Canola

Safflower
4.1.14 Region 14: Utah & Wyoming

The fourteenth cluster identified Region 14, which includes two states: Utah and 

Wyoming (Figure 15). Analysis of individual crops/stocks survey frequency within 

Cluster 14 indicates that Region 14 would survey corn, dry edible beans, oats, spring 

wheat, and winter wheat annually; alfalfa and other hay seasonally; and safflower and 

sunflower in unison with other crops/stocks as the region sees fit (Table 14).

Figure 15. Region 14 States

Table 14: Region 14 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Corn

Dry Edible Beans

Oats

Spring Wheat

Winter Wheat

Seasonal Alfalfa

Other Hay

All Other States Safflower 

Sunflower
4.1.15 Region 15: Idaho, Montana, & Oregon

The fifteenth cluster identified Region 15, which includes three states: Idaho, Montana, 

and Oregon (Figure 16). Analysis of individual crops/stocks survey frequency within 

Cluster 15 indicates that Region 15 would survey Austrian winter peas, corn, dry edible 

beans, dry edible peas, garbanzo beans, lentils, and potatoes annually; alfalfa, durum 

wheat, oats, other hay, spring wheat, and winter wheat seasonally; and canola, rapeseed, 

and mustard seed in unison with other crops/stocks as the region sees fit (Table 15).

Figure 16. Region 15 States

Table 15: Region 15 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Austrian Winter Peas

Corn

Dry Edible Beans

Dry Edible Peas

Garbanzo Beans

Lentils 

Potatoes

Seasonal Alfalfa

Durum Wheat

Oats

Other Hay

Spring Wheat

Winter Wheat

All Other States Canola

Rapeseed

Mustard Seed
4.1.16 Region 16: Washington

The sixteenth cluster identified Region 16, which includes one state: Washington (Figure 

17). Analysis of individual crops/stocks survey frequency within Cluster 16 indicates

that Region 16 would survey dry edible beans, dry edible peas, forage, garbanzo beans, 

lentils, oats, and potatoes annually; alfalfa, corn, other hay, spring wheat, and winter 

wheat seasonally; and canola and mustard seed in unison with other crops/stocks as 

Washington sees fit (Table 16).

Figure 17. Region 16 States

Table 16: Region 16 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Dry Edible Beans 

Dry Edible Peas

Forage

Garbanzo Beans

Lentils

Oats

Potatoes

Seasonal Alfalfa

Corn

Other Hay

Spring Wheat

Winter Wheat

All Other States Canola 

Mustard Seed
4.1.17 Region 17: Minnesota & North Dakota

The seventeenth cluster identified Region 17, which includes two states: Minnesota and 

North Dakota (Figure 18). Analysis of individual crops/stocks survey frequency within 

Cluster 17 indicates that Region 17 would survey dry edible beans, dry edible peas, 

flaxseed, forage, garbanzo beans, lentils, potatoes, and winter wheat annually; alfalfa, 

canola, corn, durum wheat, oats, other hay, soybeans, spring wheat, and sunflower 

seasonally; and rapeseed, mustard seed, rye, safflower in unison with other crops/stocks 

as the region sees fit (Table 17).

Figure 18. Region 17 States

Table 17: Region 17 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Dry Edible Beans

Dry Edible Peas

Flaxseed

Forage

Garbanzo Beans

Lentils

Potatoes

Winter Wheat

Seasonal

Alfalfa

Canola

Corn

Durum Wheat

Oats

Other Hay

Soybeans

Spring Wheat

Sunflower

All Other States Rapeseed

Mustard Seed 

Rye

Safflower
4.1.18 Region 18: Illinois, Michigan, New York, Ohio, Pennsylvania, & Wisconsin

The eighteenth cluster identified Region 18, which includes six states: Illinois, Michigan, 

New York, Ohio, Pennsylvania, and Wisconsin (Figure 19). Analysis of individual 

crops/stocks survey frequency within Cluster 18 indicates that Region 18 would survey 

forage and potatoes annually; alfalfa, corn, oats, other hay, rye, soybeans, sunflower, and 

winter wheat seasonally; and rye and sunflower in unison with other crops/stocks as the 

region sees fit (Table 18).

Figure 19. Region 18 States

Table 18: Region 18 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Forage

Potatoes

Seasonal Alfalfa

Corn

Oats

Other Hay

Soybeans

Winter Wheat

All Other States Rye

Sunflower
4.1.19 Region 19: Kansas & Nebraska

The nineteenth cluster identified Region 19, which includes two states: Kansas and 

Nebraska (Figure 20). Analysis of individual crops/stocks survey frequency within 

Cluster 19 indicates that Region 19 would survey dry edible beans, forage, garbanzo 

beans, lentils, potatoes, and proso millet annually; alfalfa, corn, cotton, oats, other hay, 

sorghum, soybeans, sunflower, and winter wheat seasonally; and canola and rye in unison 

with other crops/stocks as the region sees fit (Table 19).

Figure 20. Region 19 States

Table 19: Region 19 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Dry Edible Beans

Forage

Garbanzo Beans

Lentils

Potatoes

Proso Millet

Seasonal

Alfalfa

Corn

Cotton

Oats

Other Hay

Sorghum

Soybeans

Sunflower 

Winter Wheat

All Other States Canola 

Rye
4.1.20 Region 20: South Dakota

The twentieth cluster identified Region 20, which includes one state: South Dakota 

(Figure 21). Analysis of individual crops/stocks survey frequency within Cluster 20 

indicates that Region 20 would survey dry edible beans, durum wheat, flaxseed, forage, 

garbanzo beans, and proso millet annually; alfalfa, corn, oats, other hay, sorghum, 

soybeans, spring wheat, sunflower, and winter wheat seasonally; and rye and safflower in 

unison with other crops/stocks as South Dakota sees fit (Table 20).

Figure 21. Region 20 States

Table 20: Region 20 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual

Dry Edible Beans

Durum Wheat

Flaxseed

Forage

Garbanzo Beans

Proso Millet

Seasonal

Alfalfa

Corn

Oats

Other Hay

Sorghum

Soybeans

Spring Wheat

Sunflower 

Winter Wheat

All Other States Rye

Safflower
4.2 Five Region Survey Design

Hierarchical clustering was also done using five clusters, which created five QAS

regions, ultimately reducing the number of versions by 90 percent (from 50 to 5). 

3.2.1 Region One: Alabama, Arkansas, California, Georgia, Louisiana, Mississippi, 

Missouri, North Carolina, Oklahoma, South Carolina, Texas, & Virginia

Clusters 1 through 5 were merged to identify the first consolidated region, Region 1, 

which includes twelve states: Alabama, Arkansas, California, Georgia, Louisiana, 

Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Texas, and Virginia 

(Figure 22 & 23). Analysis of individual crops/stocks survey frequency across clusters 1 

through 5 indicates that Region 1 would survey oats, sorghum, potatoes, sweet potatoes, 

and watermelons annually; and alfalfa, corn, cotton, other hay, peanuts, rice, soybeans, 

and winter wheat seasonally (Table 21).

Figure 22. Region 1 States

Table 21: Region 1 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Oats

Sorghum

Potatoes

Sweet Potatoes

Watermelons

Seasonal Alfalfa

Corn

Cotton

Other Hay

Peanuts

Rice

Soybeans

Winter Wheat
Cluster State Dendogram

One 

Two

Three

Four

Five 

Figure 23. Five Region Dendogram: Region 1 States

4.2.2 Region Two: Alaska, Connecticut, Hawaii, Maine, Massachusetts, Nevada, New 

Hampshire, Rhode Island, & Vermont

Clusters 6 and 7 were merged to identify the second consolidated region, Region 2, which 

includes nine states: Alaska, Connecticut, Hawaii, Maine, Massachusetts, Nevada, New 

Hampshire, Rhode Island, and Vermont (Figure 24 & 25). Analysis of individual 

crops/stocks survey frequency across clusters 1 through 5 indicates that Region 2 would 

survey alfalfa, corn (silage), and other hay annually (Table 23).

Figure 24. Region 2 States

Table 22: Region 2 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Alfalfa

Other Hay

Corn (Silage)
Cluster State Dendogram

Six

Seven

 

Figure 25. Five Region Dendogram: Region 2 States

4.2.3 Region Three: Arizona, Florida, New Mexico, Delaware, Maryland, New Jersey, 

Indiana, Iowa, Kentucky, Tennessee, West Virginia, Colorado, Utah, & Wyoming

Clusters 8 through 14 were merged to identify the third consolidated region, Region 3, 

which includes fourteen states: Arizona, Florida, New Mexico, Delaware, Maryland, 

New Jersey, Indiana, Iowa, Kentucky, Tennessee, West Virginia, Colorado, Utah, and 

Wyoming (Figure 26 & 27). Analysis of individual crops/stocks survey frequency across 

Clusters 8 through 14 indicates that Region 3 would survey potatoes and winter wheat 

annually; and alfalfa, corn, other hay, and soybeans seasonally (Table 23).

Figure 26. Region 3 States
Table 23: Region 3 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Potatoes

Winter Wheat

Seasonal Alfalfa

Corn

Other Hay

Soybeans

Cluster State Dendogram

Eight

Nine

Ten

Eleven

Twelve

Thirteen

Fourteen

 

Figure 27. Five Region Dendogram: Region 3 States

4.2.4 Region Four: Idaho, Oregon, Montana, Washington, Minnesota, & North Dakota

Clusters 15 through 17 were merged to identify the fourth consolidated region, Region 4, 

which includes six states: Idaho, Oregon, Montana, Washington, Minnesota, and North 

Dakota (Figure 28 & 29). Analysis of individual crops/stocks survey frequency across 

Clusters 15 through 17 indicates that Region 4 would survey Australian winter peas, dry 

edible beans, dry edible peas, flaxseed, forage, garbanzo beans, lentils, and potatoes

annually; alfalfa, canola, corn, oats, other hay, spring wheat, and winter wheat seasonally;

and canola, rapeseed, and mustard seed in unison with other crops/stocks as the region 

sees fit (Table 24).
Figure 28. Region 4 States

Table 24: Region 4 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Australian Winter Peas

Dry Edible Beans

Dry Edible Peas

Flaxseed

Forage

Garbanzo Beans

Lentils

Potatoes

Seasonal Alfalfa

Canola

Corn

Oats

Other Hay

Spring Wheat

Winter Wheat

All Other Surveys Canola

Rapeseed

Mustard Seed

Cluster State Dendogram

Fifteen

Sixteen

Seventeen

 

Figure 29. Five Region Dendogram: Region 4 States
4.2.5 Region Five: Illinois, Wisconsin, Michigan, New York, Ohio, Pennsylvania, 

Kansas, Nebraska, & South Dakota

Clusters 18 through 20 were merged to identify the fifth consolidated region, Region 5, 

which includes nine states: Illinois, Wisconsin, Michigan, New York, Ohio, 

Pennsylvania, Kansas, Nebraska, and South Dakota (Figure 30 & 31). Analysis of 

individual crops/stocks survey frequency across Clusters 18 through 20 indicates that 

Region 5 would survey dry edible beans, forage, and potatoes annually; alfalfa, corn, 

oats, other hay, soybeans, and winter wheat seasonally; and rye in unison with other 

crops/stocks as the region sees fit (Table 24).

Figure 30. Region 5 States

Table 25: Region 5 Crops/Stocks Survey Frequency

Survey Frequency Crops/Stocks

Annual Dry Edible Beans

Forage

Potatoes

Seasonal Alfalfa

Corn

Oats

Other Hay

Soybeans

Winter Wheat

All Other Surveys Rye
Cluster State Dendogram

Eighteen

Nineteen

Twenty 

Figure 31. Five Region Dendogram: Region 5 States

5. DISCUSSION 

The QAS is used to collect data on up to 31 crops and stocks anywhere from one to four 

times a year, depending on the state. Since no two states are identical in their 

questionnaire content, national data collection and summary is highly complex. It 

appears that the survey administration process could be greatly simplified by 

regionalizing the QAS. Creating 20 QAS regions reduces the amount of questionnaire 

versions by 60 percent, helping standardize questionnaire administration and summaries, 

and ultimately allowing states to share in printing costs and data editing tools. Creating 

five QAS regions further reduces the number of questionnaire versions by 90 percent, 

further standardizing questionnaire administration and summary code, and allowing

numerous states to share in printing costs and data editing tools. Regionalizing the QAS

also reduces survey administration burden and helps simplify the process for setting 

national estimates. 

6. LIMITATIONS

Obviously, by reducing the number of QAS versions, states lose some autonomy and 

respondent burden may be slightly increased. It is expected that the respondent burden 

will be minimal using the 20-region design and only slightly increased by using the 5-

region design. However, respondent burden should be assessed by each state 

individually. States need to compare the number of items as well as the frequency at 

which they are asked before and after regionalization.

7. RECOMMENDATIONS

There are no current plans to pursue regionalizing the QAS. However, should 

regionalization be pursued in the future, this report demonstrates a useful 

methodology for doing so. This approach should also be explored when 

proposing regionalization of questionnaires for other surveys.
8. REFRENCES

JMP. (2007). Clustering. Statistics and Graphics Guide . SAS Institute Inc.

National Agricultural Statistics Service. (2008, January 15). Charter for the Quarterly 

Agricultural Survey Questionnaire Reduction and Review Team. Retrieved from nassnet: 

http://nassnet/library/info/teams/qas/index_qas_response.html
7. APPENDIX A 

3

 

3 Note: A = Annual, F = Full Season, I = “Included”, AOS = All Other States, S = Silage, and NS = Not 

Table 

Surveyed (classifications are based on sampling frequency, not on publication frequency).

A-1: Crops/Stocks Questionnaire Reduction Team Data
Table A-1: Crops/Stocks Questionnaire Reduction Team Data (Continued)