United States

Department of

Agriculture

National

Agricultural

Statistics

Service

Research and

Development Division

Washington DC 20250

RDD Research Report 

Number RDD-11-03

September 2011

A Method for 

Improving List 

Building: Cluster 

Profiling

Will Cecere, Denise A. Abreu, Jaki 

McCarthy, and Thomas Jacob

This report has been 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 the United States Department of Agriculture.
i

EXECUTIVE SUMMARY

The 2007 June Area Survey (JAS) was used to identify farming operations that were not found 

on the Census Mail List (CML). These Not-on-the-Mail-List (NML) operations were used as a 

measure of undercoverage for the 2007 Census of Agriculture. The operations were mailed a 

census report form to collect information about them. The NML farms consisted of 4,810 JAS 

tracts, representing an expanded number of 361,687 operations.

Given that all NML farms are not alike, an examination of their differences was proposed. Using 

a selected number of variables obtained from the census questionnaire and the JAS, a variety of 

clustering techniques were performed on the data. The objective was to partition or group 

observations such that differences were minimized within each cluster while maximizing 

differences across clusters. After several cluster methods were performed, a solution was chosen 

that used 5 clusters to describe the data. Segment profiling was applied to characterize each 

cluster in terms of the variables that best defined them. Results showed that the clusters were 

able to distinguish operations that had such characteristics as: a low Total Value of Production 

(TVP) and a lot of point farms, a high TVP, part-time status and renting land, and idle cropland.

Variables of interest were examined across the clusters. This analysis pointed out such things as, 

the majority of the operations in the cluster with many point farms were of part-time status, as 

well as that almost two thirds of the operations in the cluster with rented land had an operator 

who became the primary operator after the year 2000. An additional approach was taken to 

compare the same cluster definitions when applied to the CML. This gave perspective as to how 

the NML compared to the CML. The results showed that the NML contained more operations in 

the land rented cluster and roughly double the number of operations in the point farm cluster as 

the CML. The clusters were also used to compare the CML to the NML across matching 

variables of interest. The results of this cluster analysis could be used to target operations for 

future building of the list frame.
ii

RECOMMENDATIONS

1. Use comparisons between the CML and NML by variable as a gauge for what is missing 

on the CML and needs to be targeted for list building.

2. Examine needed areas of CML list building using the results of the NML clusters across 

variables examined and utilize this information to match to outside list sources. 
iii
1

A Method for Improving List Building: Cluster Profiling

Will Cecere, Denise A. Abreu, Jaki McCarthy and Thomas Jacob1

Abstract

The National Agricultural Statistics Service (NASS) conducts the quinquennial Census of 

Agriculture in years ending in 2 and 7. Also, NASS conducts an annual area frame based survey, 

the June Area Survey (JAS). The census employs a dual frame: an independent list frame and the 

area frame component from the JAS. The JAS is used to identify farming operations missed on 

the list frame. In 2007, a full census questionnaire was sent to all JAS records that were not 

found on the census mail list. Multiple clustering techniques were used to characterize farming 

operations missed during the census mail list building. Hierarchical methods (average linkage, 

centroid, and Ward’s method) and non-hierarchical k-means clustering were used to identify 

groupings. Through cluster profiling, potential improvements to future list building efforts are 

discussed. 

Key Words: hierarchical clustering, k-means, cluster profiling, dual frame 

 

1 Will Cecere, Denise A. Abreu and Jaki McCarthy are statisticians with the National Agricultural Statistics Service 

(NASS) Research and Development Division located at Room 305, 3251 Old Lee Highway, Fairfax, VA 22030. 

Thomas Jacob is a NASS statistician with the Agency’s Information Services Section, located at 1400 Independence 

Avenue SW, Washington, DC 20250.
2

1. INTRODUCTION

The National Agricultural Statistics Service (NASS) conducts the quinquennial Census of 

Agriculture in years ending in 2 and 7. The Census of Agriculture is a complete enumeration of 

United States (U.S.) farms and ranches as well as the people who operate them. A farm is 

defined as a place from which $1,000 or more of agricultural products were produced and sold, 

or normally would have been sold during the census year, including agriculturally related 

government payments. The census collects data on land use, ownership, operator characteristics, 

production practices, income and expenditures, and many other characteristics. The outcome, 

when compared to earlier censuses, helps to measure trends and new developments in the 

agricultural sector of the national economy. The information is used only for statistical purposes 

and data are published only in tabulated totals. The census provides the only source of uniform, 

comprehensive agricultural data for every county in the nation. 

NASS maintains a list of farmers and ranchers from which the Census Mail List (CML) is 

compiled. Census forms are sent using the CML to all known and potential agricultural 

operations in the U.S. The goal is to build as complete a CML as possible of all agricultural 

places that meet the NASS farm definition. NASS builds and improves the list on an ongoing 

basis. To achieve this, NASS obtains information from outside sources as well as special 

commodity lists. 

Despite the agency’s best efforts in building as complete a list as possible, there will ultimately 

be some level of incompleteness in covering the farm population in the resulting CML. To 

measure this incompleteness, NASS uses its area frame based June Area Survey (JAS). For the 

2007 JAS, prior to the census, an additional supplemental area sample was selected which 

targeted farming demographics that typically have lower coverage rates on the list frame, the 

foundation for the CML. Any farming operations found on the 2007 JAS or the supplemental 

sample that did not match those on the CML were determined to be Not-on-the-Mail-List 

(NML). These operations were mailed a census report form to collect information about them. 

Data from the NML operations provided a measure of the undercoverage of the CML as well as 

information on their size, commodities produced, operator demographics and other descriptive 

information. 

1.1 The Census of Agriculture and Mail List Development

The goal with the CML is to build as complete a list as possible of agricultural places that meet 

the NASS farm definition. The CML compilation begins with the list used to define sampling 

populations for NASS surveys conducted for its annual agricultural estimates program. NASS 

builds and improves the list on an ongoing basis by obtaining information from outside sources. 

These sources include lists from state and federal government agencies, producer associations, 

seed growers, pesticide applicators, veterinarians, marketing associations, and a variety of other 

agriculture related areas. NASS also obtains special commodity lists to address specific list 

deficiencies. These outside source lists are matched to the NASS list using record linkage 

programs. Most names on newly acquired lists are already on the NASS list. Records not on the 

NASS list are treated as potential farms until NASS can confirm their existence as a qualifying 

farm. 
3

List building activities for developing the 2007 CML started in 2004. Between 2004 and 2007, 

NASS conducted a series of Agricultural Identification Surveys (AIS) to screen approximately 

1.7 million records for agriculture activity, which included nonrespondents from the 2002 

Census of Agriculture and newly added records from outside list sources. The AIS report form 

collected information that was used to determine farm/non-farm status. Reports identified as 

farms were added to the NASS list and subsequently to the CML. The official CML was 

finalized on September 1, 2007 and contained 3,194,373 records. Within this, there were 

2,198,410 records that were thought to meet the NASS farm definition and 995,963 potential 

farm records. 

To account for farming operations not on the CML, NASS used its area frame. The NASS area 

frame covers all land in the U.S. and thus includes all farms. The land in the U.S. is stratified by 

characteristics of the land. Segments of approximately equal size are delineated within each 

stratum and designated on aerial photographs (See red outlined boundary in Figure 1). A 

probability sample of segments is drawn within each stratum for the NASS annual area framebased JAS. 

 Figure 1. JAS segment with tract boundaries

The JAS sample of segments is allocated to strata to provide accurate measures of acres planted 

to widely grown crops and not-on-the-list cattle inventory. Sampled segments in the JAS are 

personally enumerated. Each operation identified within a segment boundary is known as a tract

(See blue outlined areas labeled A through H in Figure 1). The 2007 JAS consisted of 10,912 

regular sampled segments and a supplemental sample of 3,692 Agricultural Coverage Evaluation 

Survey (ACES) segments. ACES segments were selected to provide measures of small and 

minority-owned farms. These additional ACES segments targeted farming demographics that 

typically had lower coverage rates on the list. The information from each tract (operation) within 
4

a segment is matched against operations on the CML to determine the NML operations to which 

a census report form was mailed. 

Data from the NML operations provided a measure of the undercoverage of the CML operations. 

In general, NML farms tended to be small in acreage, production, and sales of agricultural 

products (Eldridge, 2007). However, it is important to keep in mind that NML operations are not 

all alike. Farm operations were missed for various reasons, including the possibility that the 

operation started after the mail list was developed, the operation was so small that it did not 

appear in any agriculture related source lists, or the operation was erroneously classified as a 

non-farm prior to mailout.

The objective of this research was to find ways to improve our list through a better understanding 

of our NML population. It was thought that knowing more about distinct subgroups within the 

NML would help NASS find farm operations more easily from outside sources. In order to 

achieve this, a way to partition or group similar operations was needed to identify areas where 

list building efforts could be targeted.

2. METHODS

In order to achieve the goal of characterizing the NML operations, we must look at techniques 

that allow for the partitioning of the operations based on a set of variables. One insightful way of 

looking at this problem is through the use of a multivariate technique called cluster analysis. 

Cluster analysis seeks to find optimal groupings or clusters which minimize differences within a 

cluster while maximizing differences across clusters. 

The intended use of cluster analysis in the context of this research is similar to that of businesses 

using an application of cluster analysis called customer segmentation. Here, clustering is 

performed to segment a customer base in order to get useful results; in this context, useful

typically means that the results will aid in a marketing process. The usual goals in this process 

are to build customer segments in order to understand how to best market a product or set of 

products to each customer group. These techniques gained popularity due to the fact that

businesses could avoid mass marketing and thus save on costs by having their marketing plan 

customized to specific marketing groups (Collica 2007). This concept is related to the objectives 

of this research in that the NML population represents a portion of the NASS user or customer

base. It is important to better understand the NML operations with the use of clustering in order 

to better target common groupings of operations to optimize list building efforts.

One important aspect of cluster analysis is the use of similarity or proximity measures. To 

accurately depict the degree of closeness from one observation to another, a quantitative measure 

of distance must be selected for all variables used in the analysis. Common measures of 

similarity for categorical data often involve calculating a similarity coefficient for whether two 

observations have the same values. For continuous data, there are more options for measures of 

distance, ranging from a simple Euclidean distance to correlation measures such as Pearson’s. A 

common situation is to have mixed-mode data, continuous and categorical, in which case a 

similarity matrix is often used as a measure of proximity.
5

Variables used in cluster analysis in most cases are not measured using the same units. For 

example, continuous variables Total Value of Production (TVP) and Cropland Harvested are

recorded using a different scale. Therefore, it would not make sense to treat measures of 

distance the same for variables using different units. A common solution to this problem is to 

standardize each continuous variable using the standard deviations calculated from the dataset.

2.1 Clustering techniques

There are numerous techniques available for cluster analysis due to its wide range of 

applications. A popular approach to clustering is to employ hierarchical methods, all of which 

use a series of partitions to arrive at the final number of clusters. There are two categories of 

hierarchical clustering: agglomerative and divisive. In an agglomerative method, we start out 

with n clusters and end with a single cluster containing all observations. In a divisive method, a 

single cluster with all observations is broken up until there are n clusters. Criteria are examined 

in either case to determine which set of clusters most appropriately distinguishes the data.

For purposes of this research, three agglomerative hierarchical methods were evaluated: average

linkage, centroid, and Ward’s method. In the average linkage method, the distance between two 

clusters A and B is the average of the distances between all observations in A and all 

observations in B. The centroid method examines the Euclidean distance between the mean 

vectors of two clusters to determine distance. Ward’s method seeks to minimize the total withincluster error sum of squares. Consequently, Ward’s method selects the minimum between-cluster 

distances before merging them.

Another approach to clustering is to use optimization techniques. These techniques involve 

maximizing or minimizing a set of numerical criteria in order to produce a pre-selected number 

of clusters. One such popular method examined is called the k-means method. Once the number 

of clusters k is pre-selected, various algorithms depending on the software package are 

performed so that the sum of squares within each cluster is minimized.

When working with larger data files, often it is easier to use a two-stage clustering approach. 

Under this method, a pre-cluster stage is performed in order to reduce a large data file into 

cluster seeds. From the cluster seeds, typically a hierarchical method is used to determine a final 

number of clusters. One major advantage of the two-stage clustering approach is that it offers a 

Euclidean distance measure for continuous variables as well as a likelihood function for 

categorical variables, making it convenient for mixed-mode data. One critical assumption for 

using a two-stage clustering approach is that all continuous variables follow the normal 

distribution.

An important aspect of cluster analysis is that there is no “correct” solution. Results may vary 

greatly depending on what method is employed and how the data are used. The goal of the 

researcher in using cluster techniques should be to produce practical results. If the clusters that 

result from using any method cannot be linked to some form of useful interpretation with respect 

to the subject matter, then the results are of no use. A quote by Dr. George Box accurately 

describes our approach. He stated about statistical models in general “All models are wrong, 
6

some are useful”. Therefore, we must be discriminating with results so that we may get some use 

out of them.

2.2 Data and software preparation

Of the 14,604 tracts in the 2007 JAS, there were 4,810 tracts utilized for this project. These tracts 

represented all of the NML operations qualifying as farms and they expanded to a total of 

361,687 farming operations. The data analyzed came from 2007 Census of Agriculture 

questionnaires that were sent to these operations.

Starting with a data file with over 400 variables, criteria were established in order to trim the 

number of variables to a more appropriate list from which useful interpretation could be drawn. 

If a variable had a large number of missing observations or valid zeros, we removed it from the

analysis. For several specialty commodity variables which were sparse with data but for which 

we thought could be deterministic (e.g. fruits, nuts, and livestock), indicator variables were 

created to account for them. If a variable displayed an unusually high correlation with another 

variable, it was also removed. Highly correlated variables have a tendency to skew cluster 

formations in their direction, which in turn conceals other variables that may be more significant 

in the cluster formation. Additional subject matter knowledge and expertise were used to remove 

further variables not eliminated previously.

A final list of 70 variables was arrived at for our analysis. A broad representation of the kind of 

variables used is shown in Table 1. For a complete list of variables along with their descriptions, 

see Appendix A.

Table 1. Types of variables used in cluster analysis

Operator expenditures Commodities raised

Farm Type Value of sales

Operator Demographics Cropland

The SAS software package JMP was initially used to examine one-stage methods. The 

hierarchical methods as well as k-means clustering were tested using JMP’s procedures. It was 

very difficult to arrive at any form of interpretable results from the one-stage clustering methods. 

The software struggled with the mixed-mode data as well as the quantity of variables used as 

inputs. 

The SAS Enterprise Miner data mining software package was used to examine two-stage cluster 

methods. For the Enterprise Miner two-stage cluster procedure, the first stage utilized an 

optimization method and the final stage used a hierarchical method. The k-means method was 

used for all analysis to make the cluster seeds and then the three hierarchical methods discussed

(average linkage, centroid, and Ward’s method) were performed separately in the second stage. 

Since the variables in the study are not all measured in the same units (i.e. acres, dollars, etc), 

they were standardized by dividing by their respective standard deviations. This assured that no 
7

additional weight was given to variables with a larger scale. Log transforms were used in order 

for the positively skewed continuous variables to meet the normality assumptions. 

As previously stated, the cluster procedure in Enterprise Miner used a k-means algorithm to 

select the cluster seeds in the first stage. In the second stage, the smallest number of clusters was

selected such that two constraints were met. The first was that at least two clusters and no more 

than the maximum number of clusters requested were produced. The second was that the cubic 

clustering criteria (which tested the hypothesis that all data are from the same uniform 

distribution) had to be greater than the pre-set cutoff. After the clusters were formed, they could

be further analyzed by using segment profiling in order to gain a greater understanding of the 

variable values in each cluster.

3. RESULTS

The clustering was performed using the three hierarchical methods in the second stages. Both the 

centroid and the average linkage yielded a five cluster solution while Ward’s method gave a 

three cluster result. A closer look at the solution given by Ward’s method showed that it was 

difficult to distinguish the defining variable values. For each cluster, the values for the variables 

most important to that cluster were not distinctly separate from those of the other clusters. This 

made characterizing the clusters difficult, so the solution from Ward’s method was not chosen.

The two separate five cluster solutions from the centroid and average linkage methods were

practically identical so either one could have been used for interpretation. This report will show 

results from the centroid method. The sizes of the clusters in terms of the number of tracts and 

expanded farms in each cluster are displayed in Table 2. Further clustering results can be found 

in Appendix B.

Table 2. Cluster sizes for Centroid Method

Cluster 1 2 3 4 5 Total

Tracts 1,800 1,783 588 323 316 4810

Expanded 

number of farms

158,687 141,053 19,458 18,566 23,922 361,687

Cluster 1 is the largest group and represents an expanded number of almost 160,000 farm 

operations. It is characterized by a high quantity of point farms. A point farm is defined as an 

operation that reports less than $1,000 of sales, but has enough agriculture inventory to qualify as 

a farming operation. When compared to the overall NML population, this point farm cluster has 

a much higher proportion of cattle, equine, and other livestock. 

One aspect that the segment profiling examined in SAS Enterprise Miner is the logworth 

statistic, which measures how well a variable partitions observations into a cluster. For each 

cluster, the defining variables of the cluster are listed in order of their logworth value. Some 

defining variables with a high logworth value for Cluster 1 included Total Value of Production 

(TVP) and Farm Type. Figure 2 shows the overall distribution of TVP as compared to the 
8

operations in the point farms cluster. The inner circle displays the overall NML population 

distribution, while the outer circle shows the cluster distribution. 

Figure 2. Segment profile of TVP for Cluster 1

Here the yellow indicates a Total Value of Production ranging from $0 to $900. The blue 

indicates values from $900 - $8,500 and the red represents values above $8,500. It is clear from 

this chart that Cluster 1 in the outer ring or the point farm cluster has observations with a low 

TVP relative to the overall NML population.

Cluster 2 is slightly smaller than Cluster 1 and can be described as a group of operations that 

represent the overall NML population closely. All variables examined for Cluster 2 showed that 

they were reflective of the overall NML population. Defining variables for this cluster include 

Total Sales and Cropland Harvested. This cluster is of relatively little use since it does not 

distinguish any unique features of the NML.

Cluster 3 can be described as the high value of sales cluster. The majority of the operations in 

this cluster have a high sales value and the defining variables are primarily sales variables such 

as TVP and total sales. This group is much smaller than the previous two clusters with 588 tracts 

representing over 19,000 operations. It contains mainly full-time operators (primarily males)

who have been involved in the operation for more than 20 years. 
9

Figure 3. Census final farm value of sales for Cluster 3

The discrepancy in value of sales between Cluster 3 and the overall NML population is shown in 

Figure 3. In the inner circle representing the NML population, the highest sales class displayed 

ranges from $50,000 to $100,000 and is shown by the light blue. The majority of the outer circle, 

representing the distribution of value of sales for Cluster 3, shows that most of the operations 

have a sales class of greater than $50,000 with several over $1,000,000.

Cluster 4 is characterized by operations that rented land. These are mostly part-time operations 

that have not been in operation for a long time. Its defining variables include Land Rented from 

Others and low Dollar Value of Owned Land. 

Lastly, Cluster 5, the fifth and smallest cluster contains mostly operations that have idle 

cropland. Many operations in this cluster have hay or idle cropland. 

A common practice once the clusters are formed is to examine all variables of interest across the 

clusters. These variables of interest are not limited to ones used in the clustering procedures. 

This can provide insight as to additional characteristics that each cluster may possess and 

ultimately will aid in targeting a specific subgroup. A total of 8 variables of interest were 

examined across the clusters ranging from operator characteristics to geographic variables. 

$50,000 – 100,000 

Count = 110

$100,000-250,000 

Count = 144

$250,000–500,000

Count = 102 

$500,000-1,000,000

Count = 60

>$1,000,000

Count = 27
10

Table 3. Part-Time operator status across clusters

Table 3 shows the expanded number of farms for a binary variable called Part-Time that tells 

whether an operator is full-time or part-time. Included with the cluster number is a description of 

the cluster in terms of what best defines it from the NML segment profiling. Although the 

description does not give the entire picture of the cluster, it is a way to attach a name to the 

cluster that characterizes it. It can be seen that the majority of the operators across the NML 

tracts are part-time, 250,198 out of 361,687. However, the number of full-time operators within 

Cluster 3 (the high sales cluster) is almost double that of part-time operators. This variable 

illustrates a good example of how differences can be identified by examining variables across the 

clusters. Results for all variables examined across clusters may be found in Appendix C.

3.1 CML vs. NML comparisons

After presenting results of this research to the NASS List Frame Section, it was recommended 

that analysis be done to compare the clusters formed from the NML records above, to clusters on 

the CML, formed using the same definitions. Research conducted by Eldridge (2007), compared 

CML vs. NML for the 2002 Census for a number of characteristics. His research identified the 

characteristics of records on the NML and whether or not they were properly covered on the 

CML. This section of the report intends to supplement the 2002 results with information from 

the clustering to fine tune the CML categories into other areas not previously explored.

Due to the amount of information needed to score cluster definitions to the CML, only 

respondents from the 2007 Census were used. There were 1,517,338 of these records used from 

the CML in this analysis. As opposed to using the nonresponse weighted total of CML records, 

the unweighted records were utilized to avoid any potential effects of nonresponse adjustment 

bias.

Cluster 

Number

1 2 3 4 5 

Description Point 

Farms

Typical 

NML

High 

Sales

Rented 

Land

Idle 

Cropland Total

Full-time 41,780 43,049 12,595 7,714 6,347 111,488

Part-time 116,907 98,003 6,862 10,851 17,574 250,198

Total 158,687 141,053 19,458 18,566 23,921 361,687
11

Table 4. NML vs. CML by cluster

Table 4 displays the cluster definitions fitted to the 2007 CML along with the original NML 

number of farms used to make the clusters. This analysis highlighted which areas of the CML 

we are missing most in proportion to the NML. A simple examination of the clusters when 

applied to the CML showed that while the high sales cluster in the NML looks concerning, over 

40 times the number of operations are assigned to this cluster for the CML. This indicates that

high sales operations are well represented, making up over 53 percent of the CML. Highlighted 

in green in Table 4, the point farms cluster shows that there are roughly double the number of 

operations in the NML than in the CML within this cluster. This cluster makes up over 43 

percent of the total NML while it accounts for only about 5 percent of the CML. Also 

highlighted is the rented land cluster. While the number of farms in the NML is not much 

greater for this cluster, it accounts for over 5 percent of the NML compared to over 1 percent of 

the CML.

Just as specific variables were compared across clusters for the NML population, 8 variables 

were compared in the same way across the CML. The results are shown in Appendix C.

Figure 4 shows a comparison of the CML vs. the NML across Cluster 3 for a variable called 

Start Year. The years on the bottom indicate the decade that the operation started, i.e., 30 means 

that an operation started in the 1930s and 0 means an operation began in the 2000s. From the 

data, it is clear that a much larger percentage of the NML population in Cluster 3 began 

operating in the 2000s. This makes sense given that newer operations would be more difficult to 

capture on the CML. However, information such as this also provides a valuable comparison of 

the NML cluster to the CML. Histograms comparing the CML and NML results for the 

remaining variables are shown in Appendix D.
12

Figure 4. NML vs. CML comparison of Start Year in Cluster 3

4. CONCLUSIONS

Analyzing variables across clusters gives an ability to target multiple characteristics that are 

specific to subgroups. For instance, in the example of the Part-time variable, adding more 

knowledge of the high sales cluster can potentially make it easier for operators with those 

characteristics to be found on an outside source list and thus, added to the CML. 

The efforts of the cluster analysis have yielded a combination of results, some of which were 

known anecdotally, and some that provided new insights about NML operations. The use of this 

exploratory technique allowed for the ability to use a wide variety of variables in order to gain 

insight as to which operations on the NML are most similar and why. It was clear from our 

results that all NML operations are not alike. It is useful to know the characteristics of clusters 

within the NML and the relative size of the clusters. Through efforts of examining the details 

shown in this research, we hope to make improvements to the CML for the 2012 Census of 

Agriculture.

5. RECOMMENDATIONS

1. Use comparisons between the CML and NML by variable as a gauge for what is missing 

on the CML and needs to be targeted for list building.

2. Examine needed areas of CML list building using the results of the NML clusters across 

variables examined and utilize this information to match to outside list sources. 

0

5

10

15

20

25

30

35

40

45

30 40 50 60 70 80 90 0

Cluster percentage

Start Year

Start Year Cluster 3 (High Sales)

CML

NML
13

6. REFERENCES

Collica, R.S. (2007), CRM Segmentation and Clustering Using SAS Enterprise

Miner, SAS Press Series

Eldridge, H.H. (2007). NASS Census Not on Mail List (NML) Farms. Research and 

Development Division. RDD Research Report: RDD-07-02.

Everitt, B.S., Landau, S, Leese, M. (2001), Cluster Analysis (Fourth Edition), Arnold

Rencher, A.C. (2002), Methods of Multivariate Analysis (Second edition), Wiley Series in 

Probability and Statistics
14

Appendix A. Variables included in the cluster procedures

VARIABLE DESCRIPTION

EXP_K930 Principal Operator--year Began Operation 

FARMTYPE 

K1080 Possible Duplicate -- Y/N? 

K1086 Any Other Farm - Y/N? 

K1153 Any woodland crops, Y/N 

K1157 Any woodland crops, Y/N 

K1237 Any Other Livestock?--Y/N 

K1671 Type of Organization 

K55 Principal County 

K924 Principal Operator retired, Y/N 

K925 Principal Operator - age 

K926 Principal Operator - sex 

K927 Principal Operator – Spanish Origin 

K928 Principal Operator – Principal Occupation 

K9903 Reporting mode code 

LOG_CALCPTS Calculated census points 

LOG_CLANDNTR Non-ag tract acres

LOG_CTRACTAC Ag tract acres 

LOG_FARM_WT Tract to farm weight 

LOG_K1021 Acres from Which All Hay & Forage was Harvested 

LOG_K103 Alfalfa Hay Harvested, Acres 

LOG_K106 Small Grain Hay Harvested, Acres 

LOG_K1062 Cropland Idle or Used for Cover Crops, Acres 

LOG_K1229 Layers- table egg types Inventory 

LOG_K1347 Total Sales--NUPC (Not Under Production Contract) 

LOG_K1501 Operator's (+LL*) Expenditure for Commercial Fertilizer 

LOG_K1503 Operator's (+LL) Expenditure for Seeds, Bulbs, Etc 

LOG_K1506 Operator's (+LL) Expenditure for Feed 

LOG_K1507 Operator's (+LL) Expenditure Dollars for Fuels and Oils 

LOG_K1509 Operator's (+LL) Expenditure for Supplies, Repairs, and 

LOG_K1513 Operator's (+LL) Cash Rent Paid for Land & Buildings 

LOG_K1517 Operator's (+LL) Property Taxes Paid 

LOG_K1518 Operator's (+LL) All Other Production Expenses 

LOG_K1520 Operator's Depreciation Expenses 

LOG_K1540 Operators's (+LL) Total Production Expenses 

LOG_K43 Land Owned, Acres 

LOG_K44 Land Rented from Others, Acres

LOG_K45 Land Rented to Others, Acres 

LOG_K46 Total Acres of Land in This Place 
15

* LL = Landlord

LOG_K685 Government Payments Received from CRP and WRP 

LOG_K787 Cropland Harvested, Acres 

LOG_K788 Cropland Used for Pasture, Acres 

LOG_K790 Cropland on Which All Crops Failed, Acres 

LOG_K791 Cropland in Summer Fallow, Acres 

LOG_K794 Woodland Pastured, Acres 

LOG_K796 Permanent Pasture and Rangeland (Name Change Only 

 from Other Pasture), Acres 

LOG_K797 All Other Land, Acres 

LOG_K803 Total Cattle and Calf--inventory 

LOG_K805 Milk Cow--inventory 

LOG_K904 Duck Inventory 

LOG_K996 Dollar Value of Owned Land 

LOG_TVP Total value of production 

LOG_TVPG Total value of production minus government payments 

LOG_gfarmpnt Area farm points 

MOLNOLAC Overlap indicator 

STRATUM Area stratum 

gfarmdef Area farm value of sales 

gfarmedt Area edited farm value of sales = edited to include point 

farms 

gfarmtyp Area type of farm

gqstrsps Area response code (helps identify refusals and 

inaccessible) 

mdemhisp Census Hispanic status 

mdemoage Census age of operator 

mdemosex Census gender 

mdemrace Census race indicator 

mfarmedt Census final farm value of sales 

yqstrsps Census response code

Livestock A binary variable indicating the presence of any one of 

(K830, K892, K898, K1221, K916, K1225, K910, K908, 

K820, K852, or K825)

Fruits_nuts A binary variable indicating the presence of any one of 

(K1045, K121, K137, K299, K125, K368)
16

Appendix B. Centroid Cluster Results

Variable Importance

Name Label Importance

Log_K1347 Transformed Total Sales 1.0000

Log_TVPG Transformed TVPG 0.9061

Log_TVP Transformed TVP 0.8377

Log_1501 Transformed Fertilizer Expenditures 0.8336

Log_K787 Transformed Cropland Harvested 0.8333

Log_CALCPTS Transformed Calculated Points 0.832

Our five cluster solution is shown in the chart above with the number of tracts as the top number 

and the expanded number of farms in parenthesis. In the table above are the variables that 

defined the cluster breaks in order of their importance.

Below are tables of the variables that define each cluster in order of their log worth. Following 

each table are graphic depictions of the distribution of each variable for that cluster.
17

Cluster 1 Variable Importance Profile

Variable Label Log Worth Rank

Log_TVPG Transformed TVPG 0.331 1

Log_TVP Transformed TVP 0.304 2

Log_K1347 Transformed Total Sales 0.300 3

Log_CALCPTS Transformed Calculated Points 0.287 4

FARMTYPE Farm Type 0.135 5

The red outlines show the total NML distribution across transformed TVPG whereas the blue represents 

the cluster. The large bar on the left represents missing values.
18

Total Sales - NUPC
19

Farmtype 11 is Cattle and Calves

Farmtype 13 is Horse, Ponies, Mules, Burros and Donkeys

Value = 13

Freq = 498

Value = 11

Freq = 590
20

Cluster 2 Variable Importance Profile

Variable Label Log Worth Rank

Log_1347 Transformed Total Sales 0.286 1

Log_TVP Transformed TVP 0.286 2

Log_TVPG Transformed TVPG 0.261 3

Log_CALCPTS Transformed Calculated Points 0.153 4

Log_787 Transformed Cropland Harvested 0.086 5
21
22

Cluster 3 Variable Importance Profile

Variable Label Log Worth Rank

Log_K1347 Transformed Total Sales 0.131 1

Log_TVP Transformed TVP 0.129 2

Log_TVPG Transformed TVPG 0.129 3

Log_K787 Transformed Cropland Harvested 0.125 4

Mfarmedt Census Value of Sales 0.124 5

Total Sales - NUPC
23

Value = 8500+

Freq = 561

Value = 900 -

8500+

Freq = 24

Value = 8925+

Freq = 561
24

“Cropland Harvested, acres”

“Census Final Farm Value of Sales”

More than half of the cluster is above the $50,000 category

Value = 7

Freq = 110

Value = 8

Freq = 144

Value = 9

Freq = 102

Value = 20+

Freq = 533
25

Cluster 4 Variable Importance Profile

Variable Label Log Worth Rank

Log_K996 Transformed Land Owned Value 0.0927 1

Log_K43 Transformed Land Owned 0.0909 2

Log_K1517 Transformed Property Taxes Paid 0.0656 3

Log_K44 Transformed Land Rented From 0.0347 4

Log_K1513 Transformed Cash Rent Paid 0.0146 5

“Dollar Value of Owned Land”

“Land Owned, Acres”
26

“Operator's (+LL) Property Taxes Paid” 

“Land Rented from Others, Acres”
27

“Operator's (+LL) Cash Rent Paid for Land & Buildings”

Cluster 5 Variable Importance Profile

Variable Label Log Worth Rank

Log_K1062 Transformed Cropland Idle 0.0505 1

Log_K685 Transformed Government 

Payments Received

0.0245 2

Gfarmtyp JAS farmtype 0.0156 3

FARMTYPE Farm Type 0.0156 4

Log_K43 Transformed Land Owned 0.0106 5

“Idle Cropland”
28

“Government Payments Received from CRP and WRP”

“June Area Farm Type”

Value = 8

Freq = 178

Value = 0

Freq = 52
29

“Census Farm Type”

Value = 16

Freq = 36

Value = 8

Freq = 227
30

Appendix C. Cluster frequency tables by variable (Expanded number of farms)

NML Farm Type

FarmTypes Cluster Number

1 2 3 4 5 Total

Grain(1) 1293 10927 7766 3423 698 24107

Tobacco(2) 0 796 146 0 0 942

Cotton(3) 0 159 229 212 0 600

Vegetable(4) 69 6859 269 919 0 8116

Fruit(5) 1020 13338 994 913 325 16590

Nursery(6) 194 2786 185 295 0 3461

Christmas 

Tree(7)

1195 309 8 63 96 1672

Other Crop(8) 21820 39552 1548 2897 17983 83800

Hog(9) 3470 2907 463 504 31 7375

Dairy(10) 0 2723 1216 45 0 3984

Cattle(11) 49774 31427 6208 4114 1409 92932

Sheep(12) 10097 3796 0 343 187 14423

Horse(13) 49454 10328 213 3222 59 63276

Poultry(14) 5605 13561 125 767 817 20875

Aquaculture(15) 0 33 8 0 34 75

Other 

Animal(16)

14699 1552 80 849 2281 19460

Total 158687 141053 19458 18566 23921 361687

NML Gender

Gender Cluster Number

1 2 3 4 5 Total

Male 123174 114810 17563 13873 16233 285653

Female 35514 26243 1895 4693 7689 76034

Total 158687 141053 19458 18566 23921 361687

NML Part-time Operator Status

Status Cluster Number

1 2 3 4 5 Total

Full-time 41780 43049 12595 7714 6347 111488

Part-time 116907 98003 6862 10851 17574 250198

Total 158687 141053 19458 18566 23921 361687
31

NML Retired Status

Status Cluster Number

1 2 3 4 5 Total

Retired 40677 38466 2954 3241 9670 95008

Not Retired 118011 102587 16504 15325 14252 266678

Total 158687 141053 19458 18566 23921 361687

NML Spanish Origin

Status Cluster Number

1 2 3 4 5 Total

Hispanic 12595 7278 355 1202 261 21691

NonHispanic146092 133775 19104 17364 23661 339995

Total 158687 141053 19458 18566 23921 361687

NML Start Year

Decade Cluster Number

1 2 3 4 5 Total

1930s 0 136 0 0 0 136

1940s 922 707 286 0 639 2555

1950s 2325 3007 1091 258 1219 7899

1960s 5494 5931 1934 475 2389 16223

1970s 13569 13810 2039 1442 2995 33855

1980s 22090 19311 2531 1778 2327 48037

1990s 48268 35173 4082 3555 6950 98028

2000+ 66019 62989 7495 11058 7403 154954

Total 158687 141053 19458 18566 23921 361687

NML State

State Cluster Number

1 2 3 4 5 Total

AL(1) 3564 3332 178 113 598 7785

AZ(4) 2352 1426 30 206 0 4015

AR(5) 2417 2463 384 157 69 5490

CA(6) 7403 7264 746 834 427 16674

CO(8) 4516 1574 403 177 504 7175
32

CT(9) 295 45300 141 889

DE(10) 116 144 15 266 34 574

FL(12) 6224 3042 289 293 150 9997

GA(13) 4293 3292 579 799 347 9309

HI(15) 251 15952 6094 2461

ID(16) 2512 1602 123 259 405 4901

IL(17) 3541 3998 983 681 1139 10343

IN(18) 4040 3684 816 680 569 9790

IA(19) 1328 3128 1602 421 757 7236

KS(20) 4212 3296 1040 346 524 9419

KY(21) 4516 5752 364 665 1578 12875

LA(22) 3268 2334 262 112 644 6620

ME(23) 777 130160 42 2125

MD(24) 773 1482 44 69 124 2491

MA(25) 809 908 11 1180 1847

MI(26) 3073 4200 448 454 666 8841

MN(27) 2765 3699 395 256 776 7890

MS(28) 2463 1897 310 316 556 5542

MO(29) 4679 5252 579 385 1485 12381

MT(30) 1238 878 129 2420 2487

NE(31) 781 825 657 288 731 3283

NV(32) 292 326000 617

NH(33) 449 807 43 42 118 1460

NJ(34) 826 485 36 80 54

1480

NM(35) 1253 2121 48 13680 4790

NY(36) 2316 2766 7050 487 6274

NC(37) 5202 4102 167 751 248 10470

ND(38) 590 888 332 424 1504 3737

OH(39) 3283 3966 610 465 130 8455

OK(40) 7823 5904 1367 868 1537 17499

OR(41) 3008 2784 501 147 214 6654

PA(42) 6578 5492 741 742 1801 15353

RI(44)0 403000 403

SC(45) 1977 1317 477 106 585 4462

SD(46) 360 205 790 79 722 2156

TN(47) 5216 2788 207 251 313 8775

TX(48) 24009 14024 1292 1400 1511 42235

UT(49) 628 1708 33 239 127 2734

VT(50) 731 786 100 57 144 1815

VA(51) 5897 3597 588 537 412 11032

WA(53) 6083 6217 174 795 98 13367

WV(54) 2987 2650 70 231 451 6389

WI(55) 5218 7976 707 786 1171 15857

WY(56) 1758 918 78 454 23 3232

Total 158687 141053 19458 18566 23921 361687
33

NML Operator Status

K1671 Cluster Number

1 2 3 4 5 Total

Family or 

Individual 1

138354 130147 15548 16404 17527 317980

Partnerships 

2

9642 7114 1932 695 3020 22403

Incorporated 

3

8251 2528 1592 896 885 14152

Other 4 2440 1265 386 571 2489 7152

Total 158687 141053 19458 18566 23921 361687

CML Farm Type

FarmType Cluster Number

1 2 3 4 5 Total

Grain(1) 121 17086 224472 1568 8715 251962

Tobacco(2) 0 1464 5152 101 129 6846

Cotton(3) 1 187 7507 30 130 7855

Vegetable(4) 166 14457 11437 704 1249 28013

Fruit(5) 1219 33714 28331 405 3505 67174

Nursery(6) 38 14622 14682 571 535 30448

Christmas 

Tree(7)

305 5252 2363 86 1956 9962

Other Crop(8) 6047 83750 88871 2039 169276 349983

Hog(9) 969 5857 12970 263 504 20563

Dairy(10) 26 2095 43000 43 66 45230

Cattle(11) 27272 128052 301610 7579 20398 484911

Sheep(12) 8602 21119 6514 893 2328 39456

Horse(13) 28736 31235 20291 1893 3520 85685

Poultry(14) 1183 16590 22842 260 1715 42590

Aquaculture(15) 3 1357 2283 15 128 3786

Other 

Animal(16)

6425 9806 13955 760 11938 42884

Total 81113 386643 806280 17210 226092 1517338
34

CML Gender

Gender Cluster Number

1 2 3 4 5 Total

Male 62051 323245 749127 14414 180009 1328846

Female 19062 63398 57153 2796 46083 188492

Total 81113 386643 806280 17210 226092 1517338

CML Part-time Status

Status Cluster Number

1 2 3 4 5 Total

Full-time 20526 113370 535303 4783 49566 723548

Part-time 60587 273273 270977 12427 176526 793790

Total 81113 386643 806280 17210 226092 1517338

CML Retired Status

Status Cluster Number

1 2 3 4 5 Total

Retired 19475 126891 174181 2102 102059 424708

Not Retired 61638 259752 632099 15108 124033 1092630

Total 81113 386643 806280 17210 226092 1517338

CML Spanish Origin

Status Cluster Number

1 2 3 4 5 Total

Hispanic 2889 9879 10779 475 2110 26132

NonHispanic78224 376764 795501 16735 223982 1491206

Total 81113 386643 806280 17210 226092 1517338
35

CML Start Year

Decade Cluster Number

1 2 3 4 5 Total

1930s 31 267 1123 3 821 2245

1940s 330 2779 14500 30 6735 24374

1950s 1035 8581 45972 126 14258 69972

1960s 2872 20767 91171 328 21241 136379

1970s 7904 48712 175476 1046 34741 267879

1980s 13561 70644 167527 2156 39405 293293

1990s 26778 121315 187422 5240 60183 400938

2000+ 28594 113556 123064 8280 48685 322179

Total 81113 386643 806280 17210 226092 1517338

CML State

State Cluster Number

1 2 3 4 5 Total

AL(1) 2204 9791 15539 622 5367 33523

AZ(4) 2654 5155 2514 134 305 10762

AR(5) 2308 9551 20894 423 3693 36869

CA(6) 2470 19706 27674 596 1554 52000

CO(8) 2121 6358 13231 337 3899 25946

CT(9) 121 1446 1150 47 81 2845

DE(10) 70 491 1025 22 149 1757

FL(12) 3151 13795 11633 219 1717 30515

GA(13) 1819 7948 14833 266 5275 30141

HI(15) 992 5068 8892 191 1983 17126

ID(16) 1049 7641 33190 502 13169 55551

IL(17) 1824 10396 24090 347 6901 43558

IN(18) 991 7236 46222 523 17126 72098

IA(19) 992 5577 29621 411 9521 46122

KS(20) 3338 17921 28161 682 8451 58553

KY(21) 1344 5522 8894 452 3018 19230

LA(22) 189 1857 1974 63 610 4693

ME(23) 368 2462 4676 140 1306 8952

MD(24) 288 2170 1782 85 120 4445

MA(25) 1674 9856 20126 251 7825 39732

MI(26) 1145 7126 34918 337 15413 58939

MN(27) 1487 5971 12872 404 7966 28700

MS(28) 3632 16481 43942 699 13151 77905

MO(29) 1526 3804 12909 269 3833 22341

MT(30) 785 3401 26684 436 5344 36650

NE(31) 190 616 1233 26 105 2170
36

NV(32) 152 1162 920 44 123 2401

NH(33) 333 4365 2522 108 237 7565

NJ(34) 1716 4310 5353 284 1440 13103

NM(35) 1111 5673 14821 199 2354 24158

NY(36) 1723 11714 17498 569 3743 35247

NC(37) 186 687 14659 206 7150 22888

ND(38) 2112 15209 29866 539 7623 55349

OH(39) 3154 12415 32909 886 5499 54863

OK(40) 1874 12179 10478 375 1592 26498

OR(41) 1701 10710 21494 382 6389 40676

PA(42) 42 437 311 17 16 823

RI(44) 1233 5618 6656 223 3748 17478

SC(45) 495 1812 16727 230 3980 23244

SD(46) 4091 22357 23945 547 7278 58218

TN(47) 15111 50301 82893 2657 17504 168466

TX(48) 1100 4296 5222 224 795 11637

UT(49) 154 1366 2234 53 234 4041

VT(50) 1683 9286 15812 334 2710 29825

VA(51) 1697 9189 10564 251 2490 24191

WA(53) 883 6287 6459 191 1190 15010

WV(54) 1365 8642 31022 318 11367 52714

WI(55) 465 1282 5236 89 748 7820

Total 81113 386643 806280 17210 226092 1517338

CML Operator Status

K1671 Cluster Number

1 2 3 4 5 Total

Family or 

Individual 1

75477 361587 651374 15492 190242 1294172

Partnerships 

2

3616 16474 86961 988 20109 128148

Incorporated 

3

1506 6049 59560 473 6970 74558

Other 4 514 2533 8385 257 8771 20460

Total 81113 386643 806280 17210 226092 1517338
37

Appendix D. CML-NML comparison graphs

0

5

10

15

20

25

30

35

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Cluster percentage

Farmtype

Farmtype Cluster 1 (Point Farms)

CML

NML

0

5

10

15

20

25

30

35

40

45

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Cluster percentage

Farmtype

Farmtype Cluster 3 (High Sales)

CML

NML
38

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Cluster percentage

Farmtype

Farmtype Cluster 4 (Land Rented)

CML

NML

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Cluster percentage

Farmtype

Farmtype Cluster 5 (Idle Cropland)

CML

NML
39

0

20

40

60

80

100

male female

Cluster Percentage

Gender Cluster 1 (Point Farms)

CML

NML

0

20

40

60

80

100

male female

Cluster Percentage

Gender Cluster 3 (High Sales)

CML

NML

0

20

40

60

80

100

male female

Cluster Percentage

Gender Cluster 4 (Land Rented)

CML

NML
40

0

10

20

30

40

50

60

70

80

90

male female

Cluster Percentage

Gender Cluster 5 (Idle Cropland)

CML

NML

0

20

40

60

80

full part

Cluster percentage

Part Time Cluster 1 (Point 

Farms)

CML

NML

0

10

20

30

40

50

60

70

full part

Cluster Percentage

Part Time Cluster 3 (High Sales)

CML

NML
41

0

20

40

60

80

full part

Cluster Percentage

Part Time Cluster 4 (Land 

Rented)

CML

NML

0

20

40

60

80

100

full part

Cluster Percentage

Part Time Cluster 5 (Idle 

Cropland)

CML

NML

0

10

20

30

40

50

60

70

80

retired not retired

Cluster percentage

Retire Cluster 1 (Point Farms)

CML

NML
42

0

20

40

60

80

100

retired not retired

Cluster percentage

Retire Cluster 3 (High Sales)

CML

NML

0

20

40

60

80

100

retired not retired

Cluster percentage

Retire Cluster 4 (Land Rented)

CML

NML

0

10

20

30

40

50

60

70

retired not retired

Retire Cluster 5 (Idle Cropland)

CML

NML
43

0

20

40

60

80

100

120

hispanic non-hispanic

Spanish Origin Cluster 1 (Point 

Farms)

NML

CML

0

20

40

60

80

100

120

hispanic non-hispanic

Spanish Origin Cluster 3 (High 

Sales)

NML

CML

0

20

40

60

80

100

120

hispanic non-hispanic

Spanish Origin Cluster 4 (Land 

Rented)

NML

CML
44

0

5

10

15

20

25

30

35

40

45

30 40 50 60 70 80 90 0

Cluster percentage

Start Year

Start Year Cluster 1 (Point Farms)

CML

NML

0

5

10

15

20

25

30

35

40

45

30 40 50 60 70 80 90 0

Cluster percentage

Start Year

Start Year Cluster 3 (High Sales)

CML

NML
45

0

10

20

30

40

50

60

70

30 40 50 60 70 80 90 0

Cluster percentage

Start Year

Start Year Cluster 4 (Rented Land)

CML

NML

0

5

10

15

20

25

30

35

30 40 50 60 70 80 90 0

Cluster percentage

Start Year

Start Year Cluster 5 (Idle Cropland)

CML

NML