United States 

Department of

Agriculture

National

Agricultural

Statistics

Service

Research and

Development Division

Washington DC 20250

RDD Research Report

Number RDD-09-01

September 2009

Using Previously

Reported Cropland 

Acreage in Data Collection

 

HoaiNam N. Tran

Michael W. Gerling

 

This report 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 National Agricultural Statistics Service (NASS) conducts surveys on the 

United States’ and Puerto Rico’s agriculture for the purpose of estimating crops, 

livestock, production practices, farm economics, and related items of interest. 

In an effort to reduce respondent burden, NASS’ Research and Development 

Division examined the relationship between the June and September Agricultural 

Survey data to determine whether certain questions (cropland, land owned, land 

rented to, land rented from, and storage capacity) could be dropped from the 

September survey if the respondent had already answered the same question in 

June. 

Five states, representing high, middle, and low agricultural production were 

selected for a detailed analysis of the potential results of carrying forward the 

previously reported data. Cropland was the first item analyzed since it is asked on 

most surveys that NASS conducts and is one of the questions that respondents 

complain about being asked repeatedly. The June and September Agricultural 

Survey questions on cropland acreage operated were examined, and the 

differences between June and September responses for the same operation were 

measured.

The initial results for cropland data varied by state, so to obtain a broader view 

across states, a less detailed analysis based solely on the change in aggregate 

results was done for all states. Both the five-state and all-state results for 

cropland are documented in this report. A subsequent report will encompass 

more of the items for which the use of previously reported data has potential. 

The total cropland analyses show that June Agricultural Survey data could be 

carried forward to the September Agricultural Survey for the following twentytwo states: Arkansas, Delaware, Georgia, Idaho, Illinois, Indiana, Iowa, 

Louisiana, Maine, Michigan, Minnesota, Mississippi, Montana, Nebraska, North 

Dakota, Ohio, Pennsylvania, South Dakota, Utah, Virginia, Washington, and 

Wisconsin. However, for this to occur, the quality of June data needs 

improvement. One of the ways this could be accomplished is by including 

additional critical edit checks on cropland data during the editing process.

ii

RECOMMENDATIONS

1. In the context of an overall review of the potential increased use of 

previously reported data with the agency’s surveys, additional items 

(beyond the cropland focused on in this report) should be studied to 

determine whether data quality could be adequately preserved by carrying 

forward June Agricultural Survey responses to the September Agricultural

Survey. The continued research should focus on land owned, land rented to, 

land rented from, and storage capacity. 

2. If additional items are so identified for which carrying forward data would 

not jeopardize data quality, consider reducing respondent burden for the 

September Agricultural Survey by carrying theses items forward and not reasking the questions in September. Multiple questionnaire items would 

need to be carried forward for the resulting respondent burden reduction to 

be sufficient to make this use of previously reported data practical and cost 

effective. The research documented in this report indicated that this would 

only by possible, even for cropland, in twenty-two states (Arkansas, 

Delaware, Georgia, Idaho, Illinois, Indiana, Iowa, Louisiana, Maine, 

Michigan, Minnesota, Mississippi, Montana, Nebraska, North Dakota, Ohio, 

Pennsylvania, South Dakota, Utah, Virginia, Washington, and Wisconsin). 

a. If future research identifies additional questionnaire items that can be 

carried forward from the June Agricultural Survey into the September 

Agricultural Survey, the following are also recommended:

i. Develop an analysis table in NASS’ Interactive Data Analysis 

System (IDAS). This analysis table would indicate all of the

operations with positive values for those items reported in June that 

were also sampled for September. The table would also display the 

operations' expansion weights for September, their reported values

for these items in June, and the resulting indications derived by 

multiplying these two items. Statisticians would then be able to see 

the impact of carrying the June data forward and identify operations 

that may be problematic.

ii. Do not carry forward cropland acreages from June Agricultural 

Survey into the September Agricultural Survey for the following 

twenty-one states: Alabama, Arizona, California, Colorado, Florida, 

Kansas, Kentucky, Maryland, Missouri, Nevada, New Jersey, New 

Mexico, New York, North Carolina, Oklahoma, Oregon, South 

Carolina, Tennessee, Texas, West Virginia, and Wyoming. 
iii

3. Add a critical edit check to the Survey Processing System (SPS) Edit to flag 

records reporting relatively large differences in cropland compared to 

historical data.

4. Office survey administrators should reemphasize the importance of

collecting accurate data at the field and office enumerator training 

workshops and to the office staff responsible for reviewing and editing the 

questionnaires.
1

Using Previously Reported Cropland Acreage in Data Collection

HoaiNam N. Tran

Michael W. Gerling/1

Abstract

The National Agricultural Statistics Service (NASS) conducts surveys on the 

United States’ and Puerto Rico’s agriculture for the purpose of estimating crops, 

livestock, production practices, and farm economics.

NASS’ Research and Development Division examined the agency’s June and 

September Agricultural Surveys to determine whether identical questions asked 

on both surveys could be eliminated on the September survey for June 

respondents, thereby reducing respondent burden. The questionnaires for these 

two surveys for five states (Delaware, Iowa, Nebraska, Texas, and Washington)

were reviewed and identical questions (cropland, land owned, land rented to, land 

rented from, and storage capacity) were identified. This report specifically 

focuses on cropland. A second report focusing on additional items is scheduled 

for late 2009.

Initially, between-quarter differences in cropland for the same operation were

measured. If the indicated variation in the answers was not statistically 

significant, it was concluded that the questions could be omitted in September for 

those operators responding in June. This initial test showed that four of the five 

test states could have the June cropland data carried forward to the September 

Agricultural Survey.

The analyses were then expanded to include all states participating in the June and 

September Agricultural Surveys. The results indicated that if the quality of the 

cropland data collected in June is improved then using the June reported data in

September is feasible in the following twenty-two states: Arkansas, Delaware, 

Georgia, Idaho, Illinois, Indiana, Iowa, Louisiana, Maine, Michigan, Minnesota, 

Mississippi, Montana, Nebraska, North Dakota, Ohio, Pennsylvania, South 

Dakota, Utah, Virginia, Washington, and Wisconsin.

KEY WORDS: Previously Reported Data, Agricultural Survey, Data Collection

 

/1 HoaiNam N. Tran and Michael W. Gerling are Mathematical Statisticians with the National 

 Agricultural Statistics Service - Research & Development Division, located at 3251 Old Lee 

 Highway, Fairfax, VA 22030. 
2

1. INTRODUCTION

The National Agricultural Statistics Service’s (NASS) primary mission is to provide timely, 

accurate, and useful statistics on the United States’ and Puerto Rico’s agriculture. NASS 

conducts hundreds of agricultural surveys annually.

The Quarterly Agricultural Survey Program is conducted four times each year, in March, June, 

September, and December, with June being the base quarter. Each quarter, survey participants 

are asked to report acres of cropland operated as of a stated reference date. In the past, 

respondents have questioned NASS’ rational in asking some of the same questions each quarter. 

Hence, a research project was constructed to examine the practicality of omitting repetitive 

questions and thereby reducing respondent burden.

The goals of this research project were to:

1. Determine if the cropland question could be omitted in September for operations 

who were sampled for both quarters and responded to the question in June. If so, 

the June responses could be carried forward to the September data set for 

processing and analyses. This procedure would reduce respondent burden to some 

extent since the length of the September instrument would be reduced for those 

operations responding in June.

 

2. Examine the impact of carrying June cropland data forward to the September data 

set as described in goal number 1.

3. Document ways to improve the data collection and analysis processes as discovered 

by this research. 

2. JUNE AND SEPTEMBER AGRICULTURAL SURVEYS

The Quarterly Agricultural Survey Program obtains agricultural data from farmers and ranchers 

across the nation to estimate crop production, grain storage supply, and livestock and poultry

production. All agricultural operations across the United States are eligible to be selected in the 

sample. The annual sample sizes for the United States for years 2003 through 2005 ranged from 

50,000 to 90,000 operations. See Appendix A for sample sizes by state and year. The survey 

questions typically refer to either the first day of the survey month or to a specific crop year as 

reference points. 

Data collection is a multi-mode combination of mail, telephone interview, Computer Assisted 

Telephone Interview (CATI), personal enumeration, and Web data collection. 
3

2.1 CROPLAND

For this report, cropland acreage was the item examined. On both the June and September 

Agricultural Survey questionnaires, the precise wording of the question on cropland is “Of the 

total acres operated, how many acres are considered cropland, including land in hay, summer 

fallow, cropland idle, cropland used for pasture and cropland in government programs?” The 

reference date is June 1st or September 1st, depending on the survey period. 

This question is one of a series of questions concerning the operator’s land. Questions pertaining 

to land owned, rented to others and rented from others are asked to assist the operator in deriving 

the total acres in the operation. 

Total cropland is used in imputing missing individual crop acreage values for inaccessible and 

refusal records. It is also utilized in the calibration process of the Quarterly Agricultural Survey 

data. NASS, however, does not publish a cropland estimate based on the June or September data.

In general, an agricultural operation’s total cropland should not significantly change between 

June and September. In June, most crops have been planted or are being planted and any land 

rental arrangements have been finalized. By September, crops are either growing or being 

harvested. While total cropland can change between June and September if an operation 

purchases or sells some acreage during this time period, such valid mid-season acreage changes 

are rare.

3. ANALYSIS PROCESS EXPLAINED

Initially, five states (Delaware, Iowa, Nebraska, Texas, and Washington) were selected to be 

analyzed. Delaware was chosen due to its relatively small number of operations, which make the 

data sets manageable for testing purposes. The other states were selected based on their use of 

standardized questionnaires and their collective representativeness of U.S. agriculture.

Questions occurring on both the June and September Agricultural Survey questionnaires were 

identified. 

All list operations sampled in June and September were analyzed with the following restrictions: 

1. Complete, usable reports were obtained for both quarters.

2. Operations which were inaccessible, refused to complete the survey, or provided 

estimated data were removed from the study.

3. Operations which responded as “out-of-business” were also eliminated.

Data plots (Figure 1, page 7) were created from the total cropland information for the remaining 

operations to obtain a visual picture of the distribution of the data to identify and examine any 
4

outliers. A review of these plots revealed extreme outliers that required additional study. Those

outlier data points determined to be in error were removed from the study. 

F-tests, linear regression analyses, 95% confidence intervals, and percent differences of 

expanded data were used to determine whether or not the reported June and September cropland 

acreages were statistically different.

3.1 TESTING THE DIFFERENCES BETWEEN JUNE AND SEPTEMBER

RESPONSES USING LINEAR REGRESSION

The regression model under consideration was

y ax b

, where 

x

and y are the June and 

September values, respectively. For this study, June and September data always have a nontrivial linear relationship, meaning that the slope, a, is never zero (and, in fact, should be close to 

1) at the designated confidence level. A 95 percent confidence level was used in this study. The 

hypotheses for the slope a and intercept b are as follows:

 

: 1 0

: 1 0

H a

H a

a

o

 and,

: 0

: 0

H b

H b

a

o

If a sampled operation’s responses are not significantly different between June and September,

the estimated intercept has to be small (near zero) and the estimated slope near 1. This causes 

the formula (y = ax + b) to become approximately

y 1x 0

(or

y x

).

3.2 TESTING THE DIFFERENCES BETWEEN JUNE AND SEPTEMBER

RESPONSES USING AN F-TEST

For testing the differences between the quarterly responses, let

ijk J , Sijk

, and 

Wijk

be the June 

data, September data, and September weight respectively for operation

i

, state j, and year k, 

where 

i

is indexed over the number of matching records. Further, letting

ijk ijk ijk y S J

, the Ftest p-value of the regression model 

ijk ijk y J

would also tell the relationship between the June 

and September data. If the resulting p-value is smaller than , then the matching data items for

June and September are significantly different. P-values closer to 1 indicate less dissimilarity

between the June and September values. 

3.3 EXAMINING THE AGGREGATE EFFECT OF CARRYING JUNE CROPLAND 

DATA FORWARD

To measure the effect of carrying June data forward to September, historical data sets were used 
5

and the process was simulated for 2003, 2004, and 2005. The corresponding September weights

for each year were used on this simulated data set to obtain new expanded totals. The expanded 

total from the simulated September records was then compared to the original expanded 

September total. This procedure was first applied for the five test states then applied to all the 

surveyed states (43 states) for 2003, 2004, and 2005. Alaska and Hawaii were excluded since 

they do not participate in either the June or September Agricultural Survey. Connecticut, 

Massachusetts, New Hampshire, Rhode Island, and Vermont were also excluded since they do 

not participate in the September Agricultural Survey.

Let

ijk J ,ijk S

, and 

Wijk

be defined as above. The two expanded totals were compared by 

considering the percent change in state j and year k with the following statistic:

ijk

ijk ijk

ijk

ijk

ijk

ijk

ijk ijk

jk S W

S W J W

p 100 * . 

The comparison measured the potential impact of re-using the data from the good June reports

for September, rather than re-asking the questions.

3.3.1 ABSOLUTE UPPER CONFIDENCE INTERVAL LIMIT

Definition: The absolute upper confidence interval limit of the expanded percent 

differences,( ,..., ,..., ) p1k pjk pnkfor the year k, is defined as follows:

,

( 1)

(| | )

1

2

/ 2

n n

p p

p t

n

j

jk k

k

where

n

p

p

n

j

jk

k

1

| |

.

Taking the absolute values of the expanded differences allows an assessment of the overall 

magnitude of the aggregate June to September differences, without considering the direction of 

any individual changes that might have occurred. Hence, this procedure reveals any changes 

that otherwise would be masked by similar but opposite expanded differences elsewhere.

The resulting absolute upper confidence interval limit was compared to the absolute value of the 

expanded percent difference for each state/year combination. The two possible conclusions are:

a. If the absolute percent difference is less than its respective absolute upper confidence 

interval limit, then there is not a significant difference between the June and 

September values for that state and year. 

b. If the absolute percent difference is greater than or equal to the respective upper 

confidence interval limit, then the difference may not be accounted for by 

randomness. Therefore, a closer review of the June and September cropland data 

would need to be conducted to see why the cropland acreages were different.
6

3.3.2 T-VALUES

The t-values used throughout the study are based on the sample sizes (See Appendix A.) from

the respective states, and are shown in Table 1 below. Delaware has different t-values since it 

has small and varying sample sizes. In comparison, Iowa, Nebraska, Texas, and Washington

sample sizes are larger than 1,500, so the t-value of 1.96 was sufficient. The t-values for these 

four states differ only at the thousandth decimal place, which was considered negligible for this 

study.

Table 1: T-Values Used For Each State

State(s) Year Degrees of

Freedom (df)

df , / 2

t

 Delaware

2005

2004

2003

 96

104

 63

1.99

1.98

2.00

 Iowa

 Nebraska

 Texas

 Washington

All Years >300 1.96

4. CROPLAND ANALYSES

Figure 1, located on the next page, displays data plots for the years of 2003, 2004, and 2005 for 

the five test states. The scales of the horizontal and vertical axes are the same for each plot. The 

red line represents the function y = x, where x and y are June and September total cropland, 

respectively. 

If the operation’s values were the same from June to September then all of the points, denoted in 

black, would fall on this line. If a point was far away from the line then its values were reviewed 

for legitimacy and points representing data errors were removed.

Visually, all of the data points trend around the line

y(x) x . However, the 2004 Nebraska data 

and the 2004 and 2005 Texas data had more points lying further away from the line than the 

other state/year combinations. 
7

Figure 1: June Cropland Acres versus September Cropland Acres by Operation /1

State & Year 2003 2004 2005

DE

IA

NE

TX

WA

/1 The horizontal (June data) and vertical (September data) axes are the same for each plot. 
8

Table 2, on the following page, displays the F-test, slope, intercept, confidence intervals for 

slope and intercept, expanded percent difference, and the absolute expanded percent difference 

upper confidence interval (C.I.) limit for the five test states in 2003, 2004, and 2005. 

Overall, the data sets that passed all the tests were Delaware’s 2003 and 2004 data, all three 

years of data for Iowa, and the 2003 Texas data. The p-values for these six data sets ranged from 

0.07 to 0.74 and were greater than the alpha level. The slope estimates were also very close to 1, 

ranging from 0.97 to 1.03. The intercept estimates ranged from -14.66 to 34.90. While 

statistically some of these estimates are significantly different from zero (the ideal intercept 

value for these regressions), they are all small relative to the scale of the data and are practically 

insignificant. Both the slope and intercept confidence intervals contain the values of 1 and 0, 

respectively.

The absolute expanded percent difference between the two quarters’ estimates for these passing 

state/year combinations ranged from 0.03 to 2.52, which were statistically insignificant

compared to their respective absolute upper confidence interval limits. Thus, this implies that 

the June Agricultural Survey cropland data for Delaware in 2003 and 2004; for Iowa in all three 

years; and for Texas in 2003 could have been brought forward to the corresponding September’s 

quarter without dramatically affecting data quality.

Before moving forward on the instances that passed these initial statistical tests, a closer 

examination of the occurrences that failed them was conducted. An examination of the data 

plots for these failed occurrences (Figure 1) shows that a majority of the records are located on 

or near the line 

y x. Also, records that are not located near the line of symmetry are generally 

located above the line, meaning that these respondents reported more cropland in September than 

in June. However, a review of the historical data showed some of these increases were due to 

errors in the data.
9

Table 2: F-Test, Slope, Intercept, Slope and Intercept Confidence (C.I.) Intervals,

 Expanded Percent Difference, and Absolute Expanded Percent Difference Upper 

 Confidence Interval Limit

State/

Year

F-test 

P-value Slope Intercept Slope 95% C.I. Intercept 95% C.I.

Expanded

Percent 

Difference

Absolute

Expanded %

Difference 

Upper

C.I. Limit

 2003 0.06 1.03 -14.46 (-62.02, 33.10) (0.99, 1.05) 0.71 3.05

DE 2004 0.45 0.99 -11.15 (-54.30, 32.01) (0.97, 1.02) 2.52 4.38

 2005 0.04 1.02 29.62 (-16.80, 76.04) (0.99, 1.05) -10.50 6.51

 2003 0.07 1.03 -14.66 (-50.02, 20.70) (0.99, 1.05) 2.24 3.05

IA 2004 0.25 1.01 -0.11 (-21.13, 20.92) (0.99, 1.02) -1.15 4.38

 2005 0.74 0.99 18.84 (-30.71, 68.39) (0.96, 1.02) 0.03 6.51

2003 <0.0001 0.93 65.10 (23.01, 107.10) (0.91, 0.96) -4.03 3.05

NE 2004 0.0002 0.95 34.25 (-2.17, 70.65) (0.93, 0.97) 1.06 4.38

2005 <0.0001 0.96 40.20 (12.09, 68.32) (0.94, 0.97) 1.20 6.51

2003 0.14 0.97 34.90 (-26.06, 95.87) (0.94, 1.01) 1.35 3.05

TX 2004 <0.0001 0.83 168.78 (123.27, 214.30) (0.80, 0.85) 2.68 4.38

2005 <0.0001 0.89 114.03 (79.16, 148.90) (0.88, 0.91) -0.02 6.51

2003 0.01 0.96 99.95 (20.10, 179.79) (0.93, 0.98) -0.91 3.05

WA 2004 <0.0001 0.87 195.57 (103.60, 287.54) (0.83, 0.90) 5.88 4.38

2005 <0.0001 0.90 141.32 (66.10, 216.53) (0.88, 0.93) 0.95 6.51
10

Table 3 displays some of the outliers with erroneous data. The reasons for the data being in error 

are also provided in the table.

Table 3: Outliers with Erroneous Data

State Year Record June 

Cropland

September 

Cropland Reason in Error

DE 2005 1 800 3,800

Based on historical data, another 

operation’s acreage may have been 

incorrectly included in Sept.

NE 2003 2 0 4,855

Based on historical data, June cropland 

acreage should have been around 4,800 

acres.

NE 2003 3 125 2,850

Based on historical data, operation’s 

cropland in Wyoming is sometimes 

included.

TX 2004 4 24,500 11,000 Based on historical data, 2004’s June’s 

cropland data are incorrect.

TX 2004 5

90,000 5,700

One operator overseeing two 

operations. Data for first operation 

were mistakenly collected and posted to 

second operation and vice versa.

TX 2004 6 2,500 20,000 Based on historical data, June cropland 

acreage should be around 20,500 acres. 

WA 2004 7 0 15,000

Based on historical data, there are 

multiple operations. June cropland 

acreage should be around 15,000 acres.

WA 2004 8 2,000 20,200

Based on historical data, September 

cropland acreage should be around 

2,020 acres.

WA 2005 9 1,715 12,000

Based on historical data, there are 

multiple operations sometimes being 

included in the total cropland.

WA 2005 10 1,600 5,600

Based on historical data, September 

cropland acreage should be around 

1,600 acres.
11

Table 4 summarizes the number of operations with outlier cropland data found to be in error. 

The erroneous data were removed from the test data set and the analyses were re-run. 

Table 4: Number of Operations Whose Cropland Data Were Identified as Outliers, Found 

to be Incorrect, and Excluded from the Analyses

State

Number of Outliers Excluded

2003 2004 2005

DE 0 0 1

IA 0 0 0

NE 5 6 6

TX 0 10 13

WA 3 5 3

Table 5 displays the revised results of the five test states. The footnote (1/) indicates those 

particular states’ data sets where no erroneous data were detected. In these cases the results are 

identical to those presented earlier in Table 2.

Table 5: F-Test, Slope, Intercept, Slope and Intercept Confidence Intervals, Expanded 

Percent Difference, and Absolute Expanded Percent Difference Upper C.I. Limit 

Without the Erroneous Outlier Data

State/Year F-test’s 

P-value Slope Intercept Slope 95% C.I. Intercept 95% C.I.

Expanded

Percent 

Difference

Absolute 

Expanded % 

Difference Upper 

C.I. Limit

 2003

/1 0.06 1.03 -14.46 (-62.02, 33.10) (0.99, 1.05) 0.71 3.05

DE 2004/1 0.45 0.99 -11.15 (-54.30, 32.01) (0.97, 1.02) 2.52 4.38

 2005 0.14 1.01 19.00 (-23.75, 61.75) (0.99, 1.05) -1.50 1.52

 2003/1 0.07 1.03 -14.66 (-50.02, 20.70) (0.99, 1.05) 2.24 3.05

IA 2004/1 0.25 1.01 -0.11 (-21.13, 20.92) (0.99, 1.02) -1.15 4.38

 2005/1 0.74 0.99 18.84 (-30.71, 68.39) (0.96, 1.02) 0.03 6.51

 2003 0.002 0.95 41.23 (8.90, 73.57) (0.93, 0.97) 1.40 1.85

NE 2004 0.19 0.98 17.34 (-15.32, 49.99) (0.96, 1.01) 0.44 1.84

 2005 0.12 0.99 20.61 (-1.53, 42.74) (0.98, 1.00) -0.15 1.52

2003/1 0.14 0.97 34.90 (-26.06, 95.87) (0.94, 1.01) 1.35 3.05

TX 2004 <0.0001 0.95 56.14 (18.88, 93.40) (0.93, 0.97) 0.50 1.84

 2005 <0.0001 0.96 53.41 (24.22, 82.61) (0.95, 0.98) -1.88 1.52

 2003 0.13 0.98 44.64 (-10.78, 100.07) (0.96, 1.01) -0.46 1.85

WA 2004 0.14 0.98 63.37 (-7.65, 134.39) (0.95, 1.01) -0.88 1.84

 2005 0.18 0.99 28.66 (-13.20, 70.53) (0.97, 1.01) -0.23 1.52

/1 Denotes the results were unchanged from the Table 2. No erroneous outlier data were removed from the data set.
12

By removing the erroneous outlier data, Delaware, Iowa, and Washington data passed the tests 

for all three years. Their F-test p-values were all larger than 0.05. Also, their slope and intercept 

confidence intervals contained both 1 and 0. The maximum absolute estimated percent difference 

between the June and September acreage data was 2.52 percent, corresponding to the percentage 

change of 2004 Delaware data. These differences were small, compared to their respective 

absolute upper confidence interval limits. These factors imply that the June cropland data could 

have been carried forward into September for those three states.

For Nebraska, the 2004 test results were improved substantially. The F-test p-value increased

from 0.0002 to 0.19. Both 1 and 0 were contained in the slope and intercept confidence 

intervals, respectively. Also, the percent difference between the two quarters dropped to 0.44

percent. 

Excluding the erroneous outlier data did not significantly improve Nebraska’s 2003 results. 

There were too many operations whose cropland acreage differed by +/-3,000 acres between 

June and September, causing the data to fail the F-test. However, the resulting estimated slope

(0.95), intercept (41.23), and the absolute percent difference (1.40) were marginally close 

enough to cautiously justify using the June cropland data in September.

For Texas, there were several records with significant fluctuations in cropland acres between 

June and September. Without going back to the operations, it was impossible to conclude in 

several cases, whether or not the data were erroneous. The significant differences in the 2003 

and 2004 data caused the F-test to fail. Also, the 2004 absolute percent difference between the 

two quarters (1.88) was larger than its respective absolute upper C.I. limit (1.50). Further 

research is needed to determine whether Texas’ June cropland data can be carried forward into 

September.

Overall, the tests for the five selected states only implied that the data could be carried forward

for certain states.

4.2 CROPLAND ANALYSIS EXPANDED

Next, the expanded percent differences in cropland between June and September Agricultural 

Surveys for all 43 states that participated in both surveys were considered. Reviewing Tables 6

and 7 (pages 14 and 15) along with the results of the initial five test states, there were twenty-two 

states (Arkansas, Delaware, Georgia, Idaho, Illinois, Indiana, Iowa, Louisiana, Maine, Michigan, 

Minnesota, Mississippi, Montana, Nebraska, North Dakota, Ohio, Pennsylvania, South Dakota, 

Utah, Virginia, Washington, and Wisconsin) whose expanded percent differences between June 

and September data were less than the respective absolute upper C.I. limit. This indicates that 

the June data could be carried forward into the September Agricultural Survey for these states.

On the other hand, there were twenty-one states (Alabama, Arizona, California, Colorado, 

Florida, Kansas, Kentucky, Maryland, Missouri, Nevada, New Jersey, New Mexico, New York, 

North Carolina, Oklahoma, Oregon, South Carolina, Tennessee, Texas, West Virginia, and 
13

Wyoming) whose expanded percent differences between June and September data were greater 

than or equal to the absolute upper C.I. limit in at least one year. This indicates that bringing 

June data forward would have an adverse effect on the data. Therefore, carrying June cropland 

data forward into September is not recommended for these states. 
14

Table 6: Expanded Cropland Percent Difference Between June and September Data by State and Year 

State/Year Expanded Percent Difference Between June and September Data

2003 2004 2005

Alabama 7.91/1 1.79 -2.55

Arizona 17.95/1 1.45 1.42

Arkansas -1.48 1.87 -0.19

California -0.48 7.87/1

 4.06/1

Colorado 11.53/1

 -0.17 -1.10

Delaware 0.71 2.52 -10.50/1

Florida 1.08 1.55 8.99/1

Georgia -1.86 -1.80 0.01

Idaho 0.21 0.61 -1.90

Illinois 3.25 -0.43 0.98

Indiana -0.27 0.18 1.31

Iowa 2.24 -1.15 0.03

Kansas 4.82/1

 0.14 -1.55

Kentucky 4.43 4.07/1

 -1.53

Louisiana 1.83 0.75 0.90

Maine 0.36 -0.03 0.57

Maryland -2.53 3.09/1 1.44

Michigan -0.87 1.88 2.12

Minnesota -0.74 1.01 -1.09

Mississippi 0.93 0.75 1.41

Missouri 4.94/1

 5.43/1 -1.20

Montana -0.89 -1.51 0.14

Nebraska -4.04 1.06 1.20

Nevada -3.74 -3.70/1

 -1.49

New Jersey -2.48 -4.17/1 1.01

New Mexico 3.27 -1.54 -6.25/1

New York 0.38 -0.77 3.71/1

North Carolina 0.64 4.40/1 0.13

North Dakota 1.03 0.83 0.86

Ohio 1.76 2.49 0.83

Oklahoma 5.27/1 1.83 4.69/1

Oregon 5.88/1 1.04 2.08

Pennsylvania 3.98 0.56 -1.54

South Carolina 3.88 -0.03 -6.78/1

South Dakota 2.01 0.21 3.06

Tennessee 2.90 1.98 3.40/1

Texas 1.35 2.68/1

 -0.02

Utah 2.57 -0.37 -0.14

Virginia 0.97 -1.21 1.69

Washington -0.91 5.88/1 0.95

West Virginia 6.01/1 0.13 -3.98/1

Wisconsin 0.38 1.76 1.33

Wyoming -12.48/1

 3.15/1 -2.01

/1 Absolute percentage change is greater than or equal to the absolute upper C.I. limit
15

Table 7: The Averages, Standard Errors, and Absolute Upper C.I. Limits of Cropland 

Expanded Percent Difference between June and September Data, by Year

Type of Statistics Year

2003 2004 2005

Average 1.67 1.07 0.11

Absolute Average 4.73 2.54 3.14

Weighted Average 1.52 1.20 0.50

Absolute Standard Error 4.53 2.32 3.16

Absolute Upper Confidence 

Interval Limit 4.61 2.62 3.08

5. IMPROVING DATA QUALITY

The data collection and data review processes need to be improved prior to carrying the June 

cropland data forward to matching records in the September Agricultural Survey. Some sources 

for potential error in the cropland data include misunderstanding by the respondent as to what is 

included or excluded as cropland, data entry mistakes, illegible handwriting on the 

questionnaires, and confusion or misunderstanding between the respondent and the field 

enumerator. All of these need to be addressed in order to minimize error in the cropland data. 

Furthermore, a critical edit check that would flag those records in the June Survey whose 

cropland data differed substantially from historical data should be added to the June Agricultural 

SPS Edit.

Also, a table in NASS’ Interactive Data Analysis System (IDAS) showing all operations with

positive cropland values in June that was also sampled for September needs to be developed. 

The table would also display the operations' expansion weights for September, their reported 

cropland for June, and their contributions to the overall indications by multiplying these two 

items. NASS statisticians would then be able to see those operations having the most impact on 

September's cropland if the June cropland values are carried forward.

Finally, field office survey administrators need to reemphasize the importance of collecting 

accurate data at the field and office enumerator training workshops and to the office staff 

responsible for reviewing and editing the questionnaires. 

6. CONCLUSION

Once the quality of the cropland data is improved, it would be feasible to carry forward June 

cropland data to the September Agricultural Survey for Arkansas, Delaware, Georgia, Idaho, 

Illinois, Indiana, Iowa, Louisiana, Maine, Michigan, Minnesota, Mississippi, Montana, 

Nebraska, North Dakota, Ohio, Pennsylvania, South Dakota, Utah, Virginia, Washington, and 

Wisconsin. Multiple questionnaire items would need to be carried forward for the resulting 
16

respondent burden reduction to be sufficient to make this use of previously reported data 

practical and cost effective. If ultimately implemented, periodic data quality assessment should

be conducted periodically to measure the quality of any June data brought forward and to 

validate this use of previously reported data.

For the other states participating in the June and September Agricultural Surveys, future data 

quality checks need to be conducted to see if June cropland data become stable enough to be 

used in September. 

This study shows that using previously reported data for items that “seem” to be fairly stable can 

adversely affect the quality of the estimates. Only through careful study and research can the 

impact of using previously reported data be measured. 

The Research and Development Division will continue to examine using previously reported 

data as a way to reduce respondent burden while improving the quality of data.

7. REFERENCES

Myers, Raymond and Walpole, Ronald 1972, “Probability and Statistics for Engineers and 

Scientists”, The Macmillan Company.

Lohr, Sharon 1999, “Sampling: Design and Analysis”, Brooks/Cole Publishing Company.
17

Appendix A

June Agricultural Survey and September Agricultural Survey

Sample Sizes and Matching Records

For Years

2003, 2004 and 2005

By State
18

June Agricultural Survey and September Agricultural Survey Sample Sizes and Matching Records

2003 2004 2005

No. % No. % No. %

A L 998 1,288 1,268 662 971 950 384 58% 596 61% 572 60%

A Z 419 462 426 389 476 456 249 64% 315 66% 295 65%

A R 2,132 2,092 2,022 1,477 1,535 1,439 970 66% 1,001 65% 942 65%

CA 1,939 2,133 2,103 1,247 1,642 1,540 868 70% 1,153 70% 1,095 71%

CO 1,600 1,905 1,973 943 1,188 1,175 686 73% 866 73% 864 74%

DE 308 372 341 212 281 273 159 75% 206 73% 203 74%

FL 462 567 552 433 599 591 241 56% 356 59% 360 61%

GA 1,305 1,568 1,562 1,050 1,270 1,243 599 57% 779 61% 770 62%

ID 1,645 1,848 1,771 1,144 1,455 1,346 840 73% 1,102 76% 1,013 75%

IL 2,289 2,455 2,389 2,169 2,290 2,171 1,442 66% 1,522 66% 1,434 66%

IN 1,861 2,191 2,195 1,884 2,094 2,063 1,203 64% 1,363 65% 1,376 67%

IA 1,908 2,258 2,388 1,867 2,297 2,471 1,060 57% 1,367 60% 1,508 61%

KS 4,006 4,257 4,226 2,383 2,754 2,719 1,578 66% 1,842 67% 1,810 67%

KY 1,549 1,784 1,783 1,318 1,295 1,310 816 62% 795 61% 814 62%

LA 1,276 1,488 1,389 824 1,021 985 527 64% 673 66% 660 67%

ME 281 302 311 256 291 303 222 87% 256 88% 261 86%

MD 695 1,001 932 451 733 711 337 75% 535 73% 504 71%

MI 1,607 1,908 1,885 1,155 1,497 1,431 757 66% 982 65% 951 66%

MN 2,172 2,400 2,359 1,835 2,125 2,046 1,181 64% 1,404 66% 1,370 67%

MS 1,221 1,536 1,499 932 1,350 1,283 612 66% 940 70% 903 70%

MO 2,303 2,718 2,707 2,083 2,486 2,465 1,346 65% 1,627 65% 1,626 66%

MT 1,500 1,777 1,828 1,510 1,959 1,926 1,024 68% 1,344 69% 1,404 73%

N E 2,580 2,944 3,015 1,851 2,362 2,333 1,261 68% 1,630 69% 1,665 71%

N V 222 258 273 185 207 218 119 64% 135 65% 142 65%

N J 305 428 453 213 409 416 145 68% 275 67% 283 68%

N M 569 778 762 368 660 601 242 66% 464 70% 455 76%

N Y 1,083 1,257 1,221 689 887 850 445 65% 566 64% 542 64%

N C 1,553 1,609 1,590 1,149 1,425 1,286 801 70% 961 67% 918 71%

N D 2,249 2,371 2,366 1,609 1,788 1,833 1,197 74% 1,327 74% 1,381 75%

OH 1,493 1,683 1,668 1,232 1,442 1,443 811 66% 962 67% 958 66%

OK 2,031 2,378 2,365 1,577 2,072 2,120 1,042 66% 1,385 67% 1,420 67%

OR 764 1,006 1,006 496 765 745 357 72% 542 71% 557 75%

PA 1,297 1,467 1,436 983 1,092 1,085 664 68% 767 70% 752 69%

SC 806 1,214 1,179 669 1,143 1,098 435 65% 756 66% 726 66%

SD 2,002 2,527 2,490 1,770 2,219 2,130 1,202 68% 1,557 70% 1,516 71%

TN 1,624 1,724 1,790 1,068 1,146 1,140 684 64% 750 65% 729 64%

TX 3,871 4,511 4,257 2,913 3,770 3,419 1,910 66% 2,570 68% 2,376 69%

UT 495 779 747 449 903 812 320 71% 610 68% 565 70%

V A 1,131 1,505 1,440 703 1,116 1,052 438 62% 745 67% 706 67%

WA 1,510 1,764 1,798 1,218 1,514 1,571 874 72% 1,070 71% 1,157 74%

W V 512 559 524 398 534 506 231 58% 315 59% 292 58%

W I 1,886 2,116 2,105 1,790 1,986 2,010 1,101 62% 1,262 64% 1,286 64%

W Y 764 1,013 988 468 729 650 298 64% 479 66% 429 66%

S t

June Ag. Sample September Ag. Sample Matching

2003 2004 2005 2003 2004 2005