Comparing Geographic 

Information System (GIS) 

Calculated Acreage to Farmer 

Reported Acreage Utilizing a 

Mobile Mapping Instrument 

Claire G. Boryan

Linda A. Lawson

Michael Hyman

Denise A. Abreu

Michael W. Gerling

Rick Hardin

United States 

Department of

Agriculture

National

Agricultural

Statistics

Service

Research and

Development Division

Washington DC 20250

RDD Research Report

Number RDD-17-02

July 27, 2017

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 United States Department of Agriculture’s National Agricultural Statistics Service (NASS) 

conducts the annual June Area Survey (JAS) to obtain information on U.S. crops; livestock; grain 

storage capacity; and number, type and size of farms. During the JAS, field enumerators conduct 

personal interviews of farmers to obtain information on all agricultural activity occurring within a 

one-square-mile (approximately) sample unit or segment. Field enumerators are provided paper 

questionnaires and aerial photography that display the JAS segment boundaries. Field enumerators 

draw field boundaries on the aerial photo and use the paper questionnaire to record a farmer’s

agricultural activity within the segment and additional information about their entire operation. 

June Area Survey segment boundaries are based on physical features (roads, railroads, rivers) and 

field enumerators use these physical features to identify segment locations. As a cost saving 

initiative, NASS is evaluating the use of 1) a permanent grid area sampling frame, with units of 

roughly equal size and area (one-square-mile) and 2) a prototype mobile mapping instrument for 

JAS data collection. This permanent grid sampling frame could be developed based on the Public 

Land Survey System’s (PLSS’s) one-square-mile sections in the 30 states in which the PLSS is 

the primary surveying method. The sample units of the permanent grid sampling frame are referred 

to as grid cells in this paper. In the 30 PLSS states, roads are often closely aligned with PLSS 

section lines. There are, however, exceptions to this rule and gaps in PLSS coverage exist. In 

states where land surveying is based on alternate systems (non-PLSS states), a grid sampling 

frame, with approximately one-square-mile grid cells, which are not based on physical features, 

could be generated using ESRI’s ArcGIS software.

Due to the characteristics described above and the fact that both PLSS section lines and grid cells 

commonly cut across fields, a prototype mobile mapping instrument was developed to use 

Geographic Information Systems (GIS) technology for JAS data collection. In 2012, a team of 

researchers from NASS and Iowa State University’s Center for Survey Statistics and Methodology 

developed a prototype mobile mapping instrument called Geographic Information Running Area 

Frame Forms Electronically (GIRAFFE). The instrument was designed to operate on an iPad and 

could be utilized to collect data for either grid cells or JAS segments. 

The primary objective of this research was to determine whether the GIS calculation of acreage, 

based on fields digitized in a mobile mapping instrument, would result in field acreages that were

comparable to acreages reported by JAS farmers. A permanent frame, based on grid cells for the 

JAS, would require the use of a mobile mapping instrument for data collection. If no significant 

difference was identified between the GIS calculation of acreage and JAS farmer reported 

acreages, research into the use of a permanent frame based on grid cells could proceed. 

Additionally, research into the use of a mobile mapping instrument for JAS data collection could

advance with increased reliance on the GIS calculation of acreage

To compare the GIS calculation of acreage with JAS farmer reported acreage, a sample of 90 

segments was selected in Indiana, Pennsylvania and Washington (30 segments per state). Indiana 
ii

and Washington are PLSS states and Pennsylvania is a non-PLSS state. Field enumerators were 

provided the completed aerial photos from the 2013 JAS and tasked with replicating field 

boundaries within the prototype mobile mapping instrument. The calculated GIS acreage was then 

compared with the acres reported by JAS farmers. Acreage comparisons were based on segment 

totals and at the field level for all crop fields, interior crop fields (< 25% of a field’s border was 

also the segment border), and exterior or boundary crop fields (> 25% of a field’s border was also 

the segment border).

To determine whether there was bias in the acreage of fields calculated using the GIS software, 

the median difference in acreage was examined and found to be zero. Wilcoxon signed-rank tests 

were performed for segment totals and at the crop field level for the three crop field categories. 

Although the results indicate that differences in the GIS calculation of acreage and JAS farmer 

reported acreage exist, p-values from the Wilcoxon signed-rank tests showed no evidence that 

median acreage differences were not equal to zero at the 95% confidence level, with the exception 

of exterior fields in Indiana. Because the distribution of acreage differences are centered at zero, 

show little bias, and have acceptable standard deviations, the authors believe that these differences 

between the GIS calculation of acreage and JAS farmer reported acreage will have a negligible

impact on the estimates for the JAS. 
iii

RECOMMENDATIONS

1. Continue development of a prototype mobile mapping instrument, in an effort to modernize 

JAS data collection and reduce costs, with increased reliance on the GIS calculation of acreage

2. Study the feasibility of grid cell data collection using the prototype mobile mapping 

instrument, by conducting interviews with farmers in 2014
iv

ACKNOWLEDGEMENTS

The authors would like to thank:

National Agricultural Statistics Service

Research and Development

Wendy Barboza

Jeff Bailey

Matt Deaton

Irwin Anolik

Field Operations

Debbie Dunham

Greg Matli

Kevin Pautler

Eric Stebbins

Jillayne Weaber

Indiana, Pennsylvania and Washington Field enumerators

Iowa State University

Center for Survey Statistics and Methodology

Alan Dotts

Eric Henely

Andrew Vardeman
v

TABLE OF CONTENTS

1. INTRODUCTION ........................................................................................ 2

2. NASS AREA FRAME AND THE JUNE AREA SURVEY........................................ 4

3. PERMANENT GRID FRAME .......................................................................... 5

4. STUDY AREAS ............................................................................................ 6

5. METHOD ................................................................................................... 7

5.1. Replication of JAS tract and field boundaries in the mobile mapping Instrument ......... 7

5.2 GIS Calculation of Acreage .......................................................................... 8

5.3 Statistical Tests ....................................................................................... 8

6. RESULTS.................................................................................................. 10

6.1 Segment Total Analysis ............................................................................. 10

6.2 Crop Field Analysis.................................................................................. 12

6.2.1 Interior Crop Fields ........................................................................... 14

6.2.2 Exterior or Boundary Crop Fields .......................................................... 16

7. DISCUSSION ............................................................................................ 18

8. CONCLUSION ........................................................................................... 19

9. RECOMMENDATIONS ............................................................................... 20

10. REFERENCES ............................................................................................ 20

11. APPENDIX A: FIELD ENUMERATOR’S DATA COLLECTION FORM .................... A1

12. APPENDIX B: ERRORS FOUND IN THE COMPARISON OF JAS FARMER

REPORTED AND GIS CALCULATED ACREAGE .............................................. B1
1

Comparing Geographic Information System (GIS) Calculated Acreage to Farmer Reported 

Acreage Utilizing a Mobile Mapping Instrument

Claire G. Boryan, Linda A. Lawson, Michael Hyman, Denise A. Abreu, Michael Gerling, and 

Rick Hardin 

Abstract

The National Agricultural Statistics Service (NASS) conducts the annual June Area Survey (JAS), 

which is based on an area sampling frame. Segments of land comprise the JAS sampling units. 

Field enumerators use hard copy aerial photos that contain outlines of the sample unit or segment 

boundaries to locate and to interview all farmers within each sampled unit. Paper questionnaires 

are used to record the farmer’s agricultural activity occurring within each segment and additional 

information about their entire operation.

 

June Area Survey sampled segment boundaries follow physical features (roads, railroads, rivers) 

on the ground. As a cost saving initiative, NASS is evaluating the use of a permanent grid area 

frame with sampling units of roughly equal size and area with data collection conducted using a 

mobile mapping instrument. The permanent grid frame would be based on the Public Land Survey 

System’s (PLSS’s) one-square-mile sections in the 30 states in which the PLSS is the primary 

surveying method. The sample units of the permanent grid frame are referred to as grid cells in this 

paper. In these 30 states, roads are often aligned with the PLSS section lines. However, exceptions 

to this rule and gaps in PLSS coverage exist. In states where land surveying is based on alternate 

systems (non-PLSS states), a grid frame, with one-square-mile sections would be generated using 

ESRI’s ArcGIS software. These grid cells would not be aligned with physical features on the 

ground. Due to these characteristics and the fact that grids cells commonly cut across agricultural 

fields, a mobile mapping instrument was developed to use Geographic Information Systems (GIS) 

technology to calculate the acreage of the fields located in the grid cells. 

The primary objective of this study is to determine whether a GIS calculation of acreage is 

comparable to the acreage reported by JAS farmers. If so, research into the use of a permanent grid 

sampling frame can move forward. Additionally, research into the use of a mobile mapping 

instrument for JAS data collection, based on grid cells or JAS segments, can advance with increased 

reliance on the GIS calculation of acreage. For this study, field enumerators used the aerial photos 

from previously collected 2013 JAS data to delineate field boundaries in the prototype mobile 

mapping instrument. The calculated GIS acreage, at the segment and crop field level, was then 

compared to the acreage reported by JAS 2013 farmers. Ninety segments in Indiana, Pennsylvania 

and Washington were included to assess segment and crop field acreage differences. Summary 

statistics and p-values from Wilcoxon signed-rank tests for total segment and crop field acreage

differences between GIS calculated values and JAS farmer reported values are reported. 

To determine whether there was any bias in segment-level and crop-field-level acreage, estimated 

using the GIS software, the median difference in acreage was examined and was found to be zero. 

Wilcoxon signed-rank tests were performed for segment totals and at field level for three crop field 

categories. Results indicated that, for segment totals and for all crop field types in all states, with 
2

the exception of Indiana’s boundary fields (p-value = 0.04), the GIS calculation of acreage and the 

JAS farmer reported acreages were not statistically different. 

Key Words: Mobile Mapping, GIS Calculated Acreage, Data Collection, Area Frame Survey

1.0 INTRODUCTION 

The National Agricultural Statistics Service (NASS) conducts the June Area Survey (JAS), which 

obtains information on U.S. crops; livestock; grain storage capacity; and the number, type and size 

of farms. During the annual JAS, field enumerators conduct personal interviews with farmers to 

obtain information on all agricultural activity occurring within a sample unit or segment that is 

approximately one-square-mile. Field enumerators are provided a hard copy aerial photo that

displays the sampled segment boundaries, and a paper questionnaire. The field enumerators draw 

the field boundaries on the aerial photo and use the paper questionnaire to record information on 

the farmer’s agricultural activity within the segment and additional information about the farmer’s

entire operation (Cotter et al., 2010; Ford, et al., 1986; Vogel, F., 1995; Nusser and House, 2009; 

Arroway et al., 2010).

June Area Survey segment boundaries are based on physical features (roads, railroads, rivers) and 

the field enumerators use these physical features to identify segment locations. As a cost saving 

initiative, NASS is evaluating the use of a permanent grid area frame, with sampling units of 

roughly equal size and area and a mobile mapping instrument for JAS data collection. The 

permanent frame would be based on the Public Land Survey System’s (PLSS’s) one-square-mile 

sections in the 30 states in which the PLSS is the primary surveying method (USGS, 2016). The 

sample units of the permanent grid frame are referred to as grid cells (Figure 4). In these 30 states, 

roads are often aligned with the PLSS section lines. In states, where land surveying is based on 

alternate systems (non-PLSS states), a grid sampling frame, with one square mile sections would 

be generated using ESRI’s ArcGIS software. Figure 1 illustrates the grid sampling frame concept 

(outlined in yellow) compared with a JAS segment based on physical features (outlined in red). 

Figure 1: A grid sampling frame with grid cells (outlined in yellow) compared 

with a JAS segment boundary based on physical features (outlined in red)
3

Due to the general characteristics of the PLSS and the fact that PLSS section lines and grid cells 

commonly cut across fields, a mobile mapping instrument was developed to use Geographic 

Information Systems (GIS) technology for JAS data collection and specifically to calculate the 

acreage of the fields surveyed. In 2012, a team of researchers from NASS and Iowa State 

University’s Center for Survey Statistics and Methodology developed a prototype mobile mapping 

instrument called Geographic Information Running Area Frame Forms Electronically (GIRAFFE) 

(Gerling et al., 2015). The instrument was designed to operate on an iPad and could be utilized to 

collect data for either grid cells or JAS segments. 

An important question to answer before NASS could adopt the use of a mobile mapping instrument, 

based on GIS technology, was to determine if there was a statistically significant difference in the 

GIS calculation of acreage and JAS farmer reported acreage. Although GIS technology has 

advanced significantly in recent years, the JAS farmer reported acreages remain the basis for the 

NASS JAS crop estimates. Consequently, understanding if there is a statistically significant

difference in the GIS calculation of acreage and JAS farmer reported acreage is foundational to the 

modernization of both the NASS area sampling frame and JAS data collection procedures.

GIS technology is currently used to calculate acreage within the USDA NASS and the Farm Service 

Agency (FSA). For example, NASS currently utilizes the GIS calculation of acreage within JAS 

segment boundaries as the basis for a 10% tolerance rule in which the JAS farmer reported acreages 

must sum to within 10% of the acreage calculated using the GIS software (USDA NASS, 2016). 

The USDA FSA relies upon the GIS calculation of acreage for many aspects of their acreage and 

compliance procedures (USDA FSA, 2016-a). GIS calculations of acreage are the foundation of 

the FSA Common Land Unit (CLU) program used to obtain crop acreage for FSA fields (USDA 

FSA, 2016-b). Historically, FSA calculated acreage on hard copy aerial photos using planimeters. 

However, FSA has transitioned to a comprehensive geospatial basis for the FSA farm records,

which utilizes GIS software and aerial photography. Acreage for all CLU polygons is determined 

by digitizing around managed areas and field boundaries that are displayed over National 

Agricultural Imagery Program (NAIP) aerial photography. The FSA relies on farmer reports of 

crop acreage for CLU subfields but the CLU acreage calculation is made using GIS software. In 

letters sent to farmers notifying them of acreage changes and included as a reference in the FSA 

Handbook on Acreage and Compliance Procedures the “ FSA reports that their tests have shown 

that the acreage calculation using computer software are equal to or slightly more accurate than the 

methodology used in the past” (USDA FSA, 2016-a). Other GIS technology, including Geographic 

Positioning System (GPS), is used to support staking and referencing procedures. GPS points are 

loaded into ArcGIS software and acreage calculations computed. The FSA recommends that 

calculated or measured acreage can be entered or maintained to “tenths or hundredths for higher 

precision and mitigation of rounding problems that may occur between acreage on FSA-578 and 

acreage in Farm Records”. This high level of precision in the acreage calculation infers complete 

reliance upon the GIS calculation of acreage by the FSA (USDA FSA, 2016-a). 

Based on the common usage of the GIS calculation of acreage both within NASS, and the FSA for 

acreage and compliance procedures, a statistical comparison of the GIS calculation of acreage and 
4

the JAS farmer reported acreage is warranted. The primary objective of this study is to determine 

whether a GIS calculation of acreage, using the mobile mapping instrument, is comparable to the 

acreage reported by JAS farmers. For this study, a sample of 90 segments was selected from the 

2013 JAS conducted in Indiana, Pennsylvania and Washington (30 segments per state). 

This paper is organized into nine sections. Section 2.0 includes background on the NASS Area 

Sampling Frame and the JAS. Section 3.0 describes the proposed permanent grid area sampling 

frame research. Section 4.0 describes the study areas. Section 5.0 describes the method used to 

conduct the assessments including 1) replication of JAS segments and field boundaries in the 

mobile mapping instrument; 2) the GIS calculation of acreage; and 3) the statistical tests. Section 

6.0 includes the research results for segment totals and at the crop field level for each state 

individually and for all three states combined. Crop field level results are reported for 1) all crop 

fields, 2) interior fields, and 3) exterior or boundary fields. Section 7.0 includes a discussion of the 

research results and section 8.0 includes the conclusion. Section 9.0 describes recommendations for 

future research. 

2.0 THE NASS AREA SAMPLING FRAME AND JUNE AREA SURVEY (JAS)

The NASS JAS is conducted annually utilizing an area sampling frame, which ensures complete 

coverage of all land in the U.S. Land within the area frame is divided into homogeneous strata 

based on percent cultivated land and further into substrata based on similarity of agricultural 

content. Within each stratum, the land is divided into primary sampling units (PSUs). A sample 

of PSUs is selected within substrata and smaller, similar-sized segments of land (approximately 

one- square-mile) are delineated within the selected PSUs. One segment is randomly selected from 

each selected PSU to be fully enumerated during the JAS. Selected segments usually have 

boundaries that follow physical features (roads, railroads and rivers) (Cotter, et al., 2010). 

June Area Survey segments (outlined in red in Figure 2) are pre-screened during the month of May 

prior to the June data collection period. During pre-screening, field enumerators divide each 

segment into separate tracts of land that represent each unique farm operating arrangement. Each 

tract is assigned a letter and drawn in blue on the aerial photo (Figure 2). Tracts are then screened 

for agricultural activity of which about 42,000 of them are classified as agricultural tracts. June 

Area Survey data collection is conducted during the first two weeks of June. At this time, field

enumerators return to only the agricultural tracts and conduct interviews using the JAS 

questionnaire, which collects detailed agricultural information about the farmer’s land, both inside

and outside the segment. Field enumerators complete a separate paper questionnaire for each 

agricultural tract operation within the segment. Farmers identify all field boundaries (outlined in 

red in Figure 3) on the aerial photo and report acreage and the crop planted or other land use of 

each individual field (pasture, woods, wasteland, etc.) (Cotter et al., 2010).
5

Figure 5: PLSS section lines (red) 

displayed over NAIP aerial photography

 

3.0 PERMANENT GRID SAMPING FRAME

 

NASS is evaluating the potential use of a permanent grid area sampling frame to replace the area 

sampling frame and segments based on physical features, as a cost saving initiative. The permanent 

grid frame sample units are roughly equal size and shape (approximately one-square-mile) and are

referred to as grid cells in this paper. The permanent grid sampling frame could be based on the

PLSS as described in Section 1.0 Introduction (USGS, 2016). In Figure 4, the 30 states included 

in the PLSS are identified in blue. Figure 5 illustrates PLSS section lines over a NAIP aerial photo 

(USDA FSA, 2016c). In Figure 5, the PLSS section lines are closely aligned with physical features 

on the ground. However, even in PLSS states, section lines do not perfectly coincide with roads, 

railroads or rivers, causing fields to be split. Further, in non-PLSS states, the grid cell boundaries

are not related to physical features on the ground. 

Figure 2: The segment is outlined in red. 

Tracts are outlined in blue and labeled 

with letters

Figure 3: Tracts are outlined in blue and 

labeled. Individual fields are outlined in red 

within the tracts and labeled with numbers

Figure 4: The thirty states included in 

the PLSS are highlighted in blue
6

Figure 6: Prototype Mobile Mapping Instrument

with a grid cell (red) displayed over NAIP aerial 

photography. The GIS tools are displayed are

on the right side of the instrument.

Because grid cells commonly do not coincide with physical features and cut across crop fields, a 

mobile mapping instrument was developed to calculate field acreages using GIS technology. In 

2012, NASS and Iowa State University’s Center for Survey Statistics and Methodology researchers

developed GIRAFFE, a prototype mobile mapping instrument. Gerling et al. provide detailed 

information about the instrument’s functionality. The prototype mobile mapping instrument was 

designed to operate on an iPad and can be utilized to collect data for either grid cells or JAS 

segments (Gerling et al., 2015). Figure 6 illustrates a grid cell digitized in the prototype mobile 

mapping instrument. 

4.0 STUDY AREAS

Prior to determining whether the permanent grid sampling frame based on the PLSS and/or a mobile 

mapping instrument can be used within NASS, it was necessary to determine whether acreage 

information collected using a mobile mapping instrument and calculated using GIS software was

comparable to acreage reported by farmers within JAS segments. Three states were selected as the 

study areas for this assessment. Indiana (IN) and Washington (WA) were selected as states with 

land surveyed using the PLSS system (Figure 4) and Pennsylvania (PA) was selected as an example

of a state that was not surveyed using the PLSS system. The GIS calculation of acreage and the JAS 

farmer reported acreages were compared for 90 segments in IN, PA and WA (30 segments per 

state). The 90 segments were randomly selected from the 2013 JAS. Figure 7 illustrates the 

locations of the states and segments included in this assessment.

Figure 7: Study areas include: Indiana (IN), 

Pennsylvania (PA), and Washington (WA). 

Segment locations are identified 

by the black dots.

IN PA

Test States WA
7

5.0 METHOD

This section describes the method used to conduct the assessments including 1) replication of JAS 

segment and field boundaries in the mobile mapping instrument; 2) the GIS calculation of acreage; 

and 3) the statistical analysis in which acreage comparisons were conducted using Wilcoxon 

signed-rank tests.

5.1 Replication of JAS tract and field boundaries in the mobile mapping instrument

The 90 segments selected for this research study were identified prior to the field enumeration stage 

of the 2013 JAS. Field enumerators were told that these 90 segments would be used for both the 

2013 JAS and for the research described in this paper. The field enumerators were provided the 

instructions outlined in Appendix A. After the 2013 JAS was completed, the field enumerators, 

who participated in this study, were instructed to digitize the tract and field boundaries into the 

mobile mapping instrument (Figure 9) exactly as they were drawn on the 2013 JAS paper aerial 

photo (Figure 8). If a field enumerator conducted the original 2013 JAS interview for a segment,

they were not assigned that segment.

Figure 8: A paper aerial photo identifying 

segment tract and field information 

collected during the 2013 JAS 

Figure 9: 2013 JAS segment in Figure 8 

digitized in the mobile mapping 

instrument
8

5.2 GIS Calculation of Acreage

Once the field boundaries were digitized in the mobile mapping instrument, the corresponding field 

acreages were calculated directly from the polygon data (field boundaries) using ESRI’s ArcGIS 

software. The polygons digitized in the mobile mapping instrument were imported into ArcGIS and 

the acreage calculated based on an appropriate projection and coordinate system. The GIS 

calculation of acreage was conducted using a customized Albers Equal Area Projection for the 

specific areas of interest. This is the same projection used in the construction of the NASS area 

frame and the selection of JAS segments.

A review of the GIS shapefiles, which included all field delineations, was conducted in order to 

identify potential problems or data anomalies. The only additional editing that occurred was the 

removal of four minor slivers of land. The slivers were not fields and were the result of an error 

that occurred when digitizing the field boundaries using the mobile mapping instrument. The slivers 

were merged into the appropriate adjacent fields. The sliver fields/polygons were so small (less 

than a tenth of an acre) that they had no effect on any of the acreage calculations (Figure 10).

5.3 Statistical Tests

An analysis was conducted to examine segment and crop field level differences between the GIS

calculation of acreage and JAS farmer reported acreages. Acreage differences were determined by 

subtracting the JAS reported acreage for a segment or crop field from the acreage calculated using 

the GIS software. The acreage comparisons were conducted using Wilcoxon signed-rank tests to 

determine whether JAS farmer reported acreages for segments and crop fields were statistically 

significantly different from the GIS calculated acreages. The Wilcoxon signed-rank test is a 

nonparametric statistical test used to assess the null hypothesis that the distribution of a random 

Figure 10: Sliver from a JAS segment identified in the 

mobile mapping instrument
9

variable is symmetric and centered at zero. The distributions that were tested included differences 

between GIS calculated acreage and JAS reported acreage for segments and three crop field 

categories. This statistical test was chosen due to non-normality of the acreage differences and small 

sample sizes for specific types of crop fields for which the sampling distribution was not assumed

to be normal. Assumptions for the Wilcoxon signed-rank test are that observations are randomly 

selected and independent. In other words, the acreage differences between GIS and JAS acreages 

for a field or segment are independent from the acreage differences of other fields or segments. 

This assumption is valid for segment acreages because they do not share any boundaries. 

The purpose of these comparisons was to assess whether the median acreage calculated using GIS 

software was statistically different from zero at the 95% confidence level when compared to the 

JAS farmer reported acreage values. Due to non-normality of the acreage differences, performing 

a statistical test to directly evaluate bias was difficult. The Wilcoxon signed-rank test evaluates 

whether the distribution of data is symmetrical and that the median of the distribution is equal to 

zero. The null hypothesis for these tests was that the median difference between GIS calculated 

acreage and JAS reported acreage for a segment is zero, versus the alternative hypothesis that the 

median acreage difference is not equal to zero. The tests were performed for segment acreage totals 

and for acreages at the crop field level, for each state individually and for the three states combined. 

Conclusions were drawn based on a 95% confidence or significance level of 0.05. The null 

hypothesis was rejected if the p-value was less than 0.05. Rejection of the null hypothesis was

evidence that the distribution of acreage differences is not centered at zero. As sample size for the 

test increases, the mean of a symmetric distribution converges to the median. Thus, for large enough 

sample sizes, conclusions drawn from the Wilcoxon tests provide some indication of bias in field 

acreages calculated using a GIS. In addition to statistical tests, the mean, median, and standard 

deviation are reported as indications of whether acreages calculated using a GIS are biased and how 

much variation exists between the GIS calculated acreage and the JAS farmer reported acreage 

measurements. 

The GIS calculation of acreage and the JAS farmer reported acreages were compared for 

agricultural tracts within 90 segments in IN, PA and WA (30 segments per state). Eight segments 

were removed from the analysis; three were considered outliers because they were composed of 

acreages much larger than other segments and were composed of only one, non-agricultural field. 

Five additional segments were removed due to errors in the JAS data collection (See Appendix B). 

Fields within each segment were digitized using the mobile mapping instrument and the field and 

segment acreages were compared to the acreages reported by farmers during the 2013 JAS. Tests 

regarding the distributions of differences in segment acreage totals consisted of the aggregated 

acreage differences over all fields within the segments. The final dataset consisted of 82 individual 

segments, which included 2,234 fields. These fields were further categorized based on the presence 

of cropland and the amount of border they shared with other fields and with the segment boundary.
10

6.0 RESULTS

This section includes the research results for segment totals and at the crop field level for each state 

individually and for all three states combined. Crop field level analysis was also conducted for 1)

all crop fields, 2) interior fields and 3) exterior or boundary fields.

6.1 Segment Total Analysis

A total of 82 segments were included to assess segment total acreage differences including 27 

segments in Indiana, 27 segments in Pennsylvania, and 28 segments in Washington (Table 1). To 

obtain segment total acreages for both GIS calculated and JAS farmer report values, field acreages 

were summed over all fields in a segment. Table 1 identifies the summary statistics and p-values 

from Wilcoxon signed-rank tests reporting total segment differences between the GIS calculation 

of acreage and JAS farmer reported acreage. These results are reported individually for each state 

and for all combined segments in the three states. A p-value of 0.05 or less indicates statistically

significant differences at the 95% confidence level. Figure 11 displays histograms of the segment

total acreage differences for each individual state and combined for all three states. 

Table 1: Summary Statistics and p-Values for Wilcoxon Signed-Rank Tests for 

Segment Totals. All units of summary statistics are in acres.

State Segments Mean

Difference

Standard

Deviation

Median

Difference p-value 

IN 27 -1.60 14.21 0.21 0.269

PA 27 4.64 26.02 1.30 0.835

WA 28 -0.89 35.14 -1.38 0.934

All Data 82 0.70 26.49 0.11 0.465

A p-value of 0.05 or less indicates significance at the 95% confidence level.

Mean differences between the GIS calculated segment acreage and JAS farmer reported segment 

acreage were less than five acres and the median acreage difference was less than one and a half 

acres for all three states. The mean and median acreage differences were 0.70 acres and 0.11 acres,

respectively. Standard deviations of the segment level acreage differences ranged from 14.21 acres 

in Indiana to 35.14 acres in Washington. These results indicated that there were acreage 

discrepancies between the GIS calculation and JAS farmer reported acreage at the segment level. 

These individual segment level acreage discrepancies were largest in Washington. Individual 

acreage differences ranged from -74.2 to 107.1 acres for segments. The null hypothesis of the 

Wilcoxon signed-rank test was that the median difference between GIS calculated acreage and JAS 

reported acreage for a segment is zero, versus the alternative hypothesis that the median acreage 

difference was not equal to zero. The test results, with p-values of 0.269, 0.835 and 0.934 for 

segments in IN, PA and WA respectively, indicated that the median acreage difference (GIS minus 

JAS) was not statistically significantly different from zero for any of the three states. 
11

Figure 11: Acreage difference for all segments and at the segment level for each state individually. 

The red line indicates zero acreage difference.

Acreage Differences – All Pennsylvania

Acreage Difference (GIS-JAS) Acreage Difference (GIS-JAS)

Number of SegmentsNumber of Segments

Number of SegmentsNumber of Segments

Acreage Difference (GIS-JAS) Acreage Difference (GIS-JAS)

Indiana Washington
12

6.2 Crop Field Analysis

A total of 2,234 fields composed the 82 segments in this analysis and 750 of these fields reported 

the presence of cropland on the 2013 JAS questionnaire: 351 in Indiana, 318 in Pennsylvania and 

81 in Washington (Table 2). Approximately 90% of all crop fields were 100% cultivated, and no 

crop field in this analysis reported less than 50% cultivation during the 2013 JAS. Table 2 illustrates 

the summary statistics and p-values from Wilcoxon signed-rank tests of differences between the 

GIS calculation of acreage and JAS farmer reported acreages for all crop fields. Figure 12 displays 

histograms of the acreage differences for all crop fields and for each individual state. 

Summary statistics showed that the mean and median differences between the GIS calculation of 

crop field acreage and the corresponding JAS farmer reported acreage for the same crop fields was

less than 0.4 acres for all three individual states. Standard deviations of crop field acreage 

differences ranged from 3.72 acres in Pennsylvania to 24.18 acres in Washington. This indicated

discrepancies between the GIS calculation and JAS farmer reported field acreage for individual 

crop fields and that these differences were larger in Washington. Individual acreage differences 

ranged from -99.43 to 66.99 acres for crop fields. However, the mean acreage difference for all 

crop fields was only 0.08 acres. The null hypothesis tested using a Wilcoxon signed-rank test was 

that the median difference between the GIS calculation and JAS farmer reported acreage for a crop 

field was equal to zero, versus the alternative hypothesis that the median acreage difference was

not equal zero. Test results indicated that the median acreage difference (GIS minus JAS) was not

statistically different from zero for any of the three states at the 5% significance level.

Table 2: Summary Statistics and p-Values for Wilcoxon Signed-Rank Test for All 

Crop Fields. All units of summary statistics are in acres.

State Crop Fields Mean

Difference

Standard

Deviation

Median

Difference p-value 

IN 351 0.05 4.37 0.15 0.223

PA 318 0.22 3.72 0.07 0.668

WA 81 -0.32 24.18 0.40 0.706

All Data 750 0.08 8.79 0.15 0.184

A p-value of 0.05 or less indicates significance at the 95% confidence level.
13

Figure 12: Acreage difference for all crop fields and crop fields for each state individually. 

The red line indicates zero acreage difference.

Acreage Differences – All Pennsylvania

Acreage Difference (GIS-JAS) Acreage Difference (GIS-JAS)

Indiana Washington

Acreage Difference (GIS-JAS) Acreage Difference (GIS-JAS)

Number of FieldsNumber of Fields

Number of FieldsNumber of Fields 
14

6.2.1 Interior Crop Fields

For this assessment, interior crop fields were defined as crop fields that had at least 75% of their 

perimeter bordering neighboring fields (i.e., less than 25% of the field’s border was also the 

segment border). Thus, interior fields shared a border with at least one adjacent field. Particular 

interest lies in GIS calculations of acreage for interior fields because often, only a single border is 

digitized in the mobile mapping device separating the two adjacent fields. Both field’s GIS acreages 

are determined by this border so there is a potential for correlation between neighboring fields’

acreages. Interior fields share a larger percentage of their border with adjacent fields and have a 

higher potential for larger acreage differences. Thus, the distribution of the difference between the 

GIS calculation of acreage and JAS farmer reported acreage was examined. The segments used in 

this analysis contained 579 interior crop fields, including 259 in Indiana, 271 in Pennsylvania and 

51 in Washington. Table 3 shows the summary statistics and p-values from Wilcoxon signed-rank 

tests for interior crop fields’ differences between the GIS calculation and the JAS farmer reported 

acreages. These results are reported individually for each state and for all interior crop fields in the 

three state combined. Figure 13 displays histograms of the acreage differences for all interior crop 

fields for each individual state and combined for all three states. 

For interior crop fields, mean differences between the GIS calculation and JAS farmer reported 

acreage ranged from -0.19 acres in Indiana to 0.7 acres in Washington. The overall mean acreage 

difference was 0.08 acres for all three states. The median acreage difference was closer to zero 

than the mean acreage difference for all three states, and was 0.06 acres for all interior crop fields. 

Indiana and Pennsylvania had little variation in field acreage differences, with standard deviations 

of 4.08 and 3.93 acres respectively. However, Washington had crop fields with large discrepancies 

between the GIS calculated acreage and JAS reported acreage, and had a standard deviation of 

24.63 acres. The acreage difference of individual fields ranged from -99.43 acres to 66.99 acres in 

Washington. A Wilcoxon signed-rank test was used to test the null hypothesis that the median 

difference between the GIS calculation of acreage and JAS farmer reported acreage for interior crop 

fields was equal to zero, versus the alternative hypothesis that the median acreage difference was

not equal to zero. The tests indicated that the median acreage difference (GIS minus JAS) was not 

statistically different from zero for any of the three states.

Table 3: Summary Statistics and p-Values for Wilcoxon Signed-Rank Test for 

Interior Agricultural Fields. All units of summary statistics are in acres. 

State Interior Crop

Fields

Mean

Difference

Standard

Deviation

Median

Difference p-value 

IN 259 -0.19 4.08 0.07 0.848

PA 271 0.22 3.93 0.01 0.971

WA 51 0.70 24.63 0.40 0.407

All Data 579 0.08 8.19 0.06 0.629

A p-value of 0.05 or less indicates significance at the 95% confidence level.
15

Figure 13: Acreage difference for all interior crop fields and for interior crop fields for each state individually. 

The red line indicates zero acreage difference.

Acreage Differences – All Pennsylvania

Acreage Difference (GIS-JAS) Acreage Difference (GIS-JAS)

Indiana Washington

Acreage Difference (GIS-JAS) Acreage Difference (GIS-JAS)

Number of FieldsNumber of Fields

Number of FieldsNumber of Fields 
16

6.2.2. Exterior or Boundary Crop Fields

The differences between the GIS calculation and JAS farmer reported acreage were also examined 

for exterior or boundary crop fields in the three test states. For this assessment, exterior or boundary 

crop fields were fields that had less than 75% of their perimeter bordering neighboring fields (i.e., 

at least 25% of the field’s border was also the segment border) and also reported containing 

cropland during the 2013 JAS. Table 4 identifies the summary statistics and p-values from the 

Wilcoxon signed-rank tests comparing the GIS calculation and JAS farmer reported acreages for 

the exterior or boundary crop fields. The test results are reported individually for each state and for 

all exterior fields in the three states combined. Figure 14 displays histograms of the field acreage 

differences. The 82 segments used in this analysis contained a total of 71 exterior crop fields

including 94 in Indiana, 47 in Pennsylvania and 30 in Washington (Table 4).

Table 4: Summary Statistics and p-Values for Wilcoxon Signed-Rank Test for 

Exterior Crop Fields.

State Exterior Crop

Fields

Mean

Difference

Standard

Deviation

Median

Difference p-value 

IN 94 0.70 5.05 0.43 0.045*

PA 47 0.26 2.10 0.27 0.241

WA 30 -2.06 23.70 0.21 0.670

All Data 171 0.09 10.58 0.36 0.072

A p-value of 0.05 or less indicates significance at the 95% confidence level.

*Significant at the 0.05 level.

Acreage differences in exterior crop fields were larger on average than for interior crop fields. The 

mean acreage differences for the three individual states ranged from -2.06 acres in WA to 0.26 acres 

in PA. However, the mean acreage difference for all exterior crop fields combined was only 0.09 

acres. The median acreage differences were similar across individual states and was 0.36 acres for 

all combined exterior crop fields. Indiana and Pennsylvania had little variation in exterior crop field 

acreage differences, with standard deviations of 5.05 and 2.10 acres respectively. However, 

Washington had fields with larger discrepancies between the GIS calculated acreage and JAS 

reported acreage, and had a standard deviation of 23.70 acres. A Wilcoxon signed-rank test was 

used to test the null hypothesis that the median difference between the GIS calculation of acreage 

and JAS farmer reported acreage for exterior crop fields was equal to zero, versus the alternative 

hypothesis that the median acreage difference was not equal to zero. The tests indicated moderately 

strong evidence that the median acreage difference (GIS minus JAS) was statistically different from 

zero for Indiana and marginal evidence that the median was not equal to zero for all combined 

fields. The median acreage difference was not significantly different from zero for Pennsylvania

and Washington. 
17

Figure 14: Acreage differences for all exterior or boundary fields and for exterior or boundary fields for each 

state individually. The red line indicates zero acreage difference.

Acreage Differences – All Pennsylvania

Acreage Difference (GIS-JAS) Acreage Difference (GIS-JAS)

Indiana Washington

Acreage Difference (GIS-JAS) Acreage Difference (GIS-JAS)

Number of FieldsNumber of Fields

Number of FieldsNumber of Fields 
18

7.0 DISCUSSION

Based on the analysis of eighty-two 2013 JAS segments in Indiana, Pennsylvania and Washington, 

segment level summary statistics showed that the mean acreage difference between the GIS 

calculated acreage and JAS farmer reported acreage at the segment level was less than five acres 

and the median acreage difference was less than one and a half acres for all three states examined. 

These were small differences considering that the average segment size was 640 acres. The mean 

and median acreage differences were 0.70 acres and 0.11 acres, respectively, for segment total 

acreages. Although discrepancies between the GIS calculation of acreage and the JAS farmer 

reported acreage existed at the segment level, the test results indicated that the median difference 

between GIS calculated acreage and the JAS farmer reported acreage was not significantly different 

from zero in any of the three states. 

As was the case with the segment level acreage differences, the p-values from Wilcoxon signedrank tests, which compared the GIS calculation of acreage and the JAS farmer reported acreage,

indicated that the median difference between GIS calculated acreage and the JAS farmer reported 

acreage was not significantly different from zero for all crop fields. Interior crop fields and exterior 

or boundary crop field acreages were also compared. The interior crop field calculations of acreage 

based on the GIS and the JAS famer reported acreages had p-values of 0.848 in Indiana, 0.971 in 

Pennsylvania and 0.407 in Washington, indicating that the median difference in GIS calculations 

of acreage and JAS farmer reported acreages were not significantly different from zero.

The only crop field category with significant evidence that the median difference between the GIS 

calculated acreage and JAS farmer reported acreages was not equal to zero was for exterior or 

boundary fields in Indiana. The Wilcoxon signed-rank test, for exterior or boundary crop fields in 

Indiana had a p-value of 0.045. After reviewing the completed 2013 JAS aerial photos, the paper 

questionnaires, and the digitized field boundaries in the mobile mapping instrument; it was 

determined that the 2013 JAS field enumerators included trees, hedgerows, and drainage ditches 

along the edge of the segment boundary, and the JAS farmer operators reported only their tillable 

land. Consequently, when the field enumerators were instructed to digitize the field boundaries 

into the mobile mapping instrument exactly as they were drawn on the aerial photos, a larger total 

acreage (which included the trees, hedgerows and drainage ditches) was calculated using the GIS 

software. Figures 15 and 16 illustrate two fields in which the difference between the GIS calculation 

of acreage and JAS farmer reported acreage was greater than 4 acres. Figure 15 illustrates a crop 

field, digitized in the mobile mapping instrument, that includes a road that extends along the south 

and east side and a group of trees contained within the boundary on the west side. Figure 16

illustrates a crop field that includes a road extending along the south and east sides. Although the 

2013 JAS field enumerators included these features within the boundaries of the crop fields, farmers 

were not likely to report this acreage during the JAS. Farmers generally report the acreage of tillable 

land or land that is useable for its purpose (cropland, pasture, etc.). Field enumerators used the JAS 

aerial photo field boundaries as a guideline for defining each field and thus followed them closely 

when digitizing fields in the mobile mapping instrument. This leads to greater acreage estimates 

when the GIS acreage is used as compared to the acreage reported by the farmers. This information 

is useful as field enumerators will need to be trained to identify these boundary features (roads, tree 
19

lines, hedgerows, etc.) and to not include them within the crop field boundary when using a mobile 

mapping instrument.

8.0 CONCLUSION

The main objective of this research was to determine whether a GIS calculation of acreage was

comparable to the acreage reported by JAS farmers. For this study, enumerators used the aerial 

photos from previously collected 2013 JAS data to digitize field boundaries in a prototype mobile 

mapping instrument. The calculated GIS acreage at the segment and crop field level was compared

to the acres reported by JAS 2013 farmers. Eighty-two segments in Indiana, Pennsylvania and 

Washington were used to assess segment and crop field acreage differences. Summary statistics 

and p-values from Wilcoxon signed-rank tests for total segment and crop field acreage differences 

between GIS calculated values and JAS farmer reported values were reported. Test results indicated

that all median acreages calculated at the segment and crop field level with the exception of exterior 

or boundary fields in Indiana had p-values less than 0.05 indicating that the median difference in 

acreage between the GIS calculation of acreage and JAS farmer reported acreages was not 

significantly different from zero. 

Although differences in the GIS calculation of acreage and JAS farmer reported acreage exist, pvalues from the Wilcoxon signed-rank tests showed no evidence that median acreage differences 

were not equal to zero at the 95% confidence level, with the exception of exterior fields in Indiana. 

Because the distribution of acreage differences are centered at zero, show little bias, and have 

acceptable standard deviations, the authors believe that these differences between the GIS 

calculation of acreage and JAS farmer reported acreage will have a negligible impact on the 

estimates for the JAS. Based on the results of this research, the authors recommend that NASS 1) 

continue development of a prototype mobile mapping instrument in an effort to modernize JAS 

data collection and reduce costs with increased reliance on a GIS calculation of acreage and 2) 

study the feasibility of grid cell data collection using the prototype mobile mapping instrument, by 

conducting interviews with farmers in 2014.

Figure 15: Exterior or boundary field in Indiana. 

This field includes a road that extends along the 

south and east side and a group of trees contained 

within the boundary on the west side.

Figure 16: Exterior or boundary field in Indiana. 

This field contains a road extending along the 

south and east sides.
20

9.0 RECOMMENDATIONS

1. Continue development of a prototype mobile mapping instrument in an effort to modernize 

JAS data collection and reduce costs with increased reliance on a GIS calculation of acreage

2. Study the feasibility of grid cell data collection, using the prototype mobile mapping 

instrument, by conducting interviews with farmers in 2014

10.0 REFERENCES

Arroway, P., Abreu, D., Lamas, A., Lopiano, K., and Young, L. (2010). An alternate approach 

to assessing misclassification in JAS. In: Proceedings of the section on survey research methods 

JSM 2010, American Statistical Association, Alexandria VA

Cotter, J. Davies, C., Nealon, J., & Roberts R. (2010). Area Frame Design for Agricultural Surveys 

in Agricultural Survey Methods (eds R. Benedetti, M. Bee, G. Espa and F. Piersimoni), John 

Wiley & Sons, TD, Chichester, UK, doi:10.1002/9780470665480.ch11

FAO, “Multiple Frame Agricultural Surveys. Volume 2 (1998): Agricultural Survey Programmes 

based on Area Frame or Dual Frame (Area and List) Sample Designs.” FAO Statistical 

Development Series no. 10. Rome: FO. 274.

Ford, B. I., Nealon, J., Tortora, R. D (1986). Area frame estimators in agricultural surveys; sampling 

versus non sampling errors. Agricultural Economics research 38 (2), 1-10.

Gerling, M., Lawson, L., Weaber, J., Dotts, A., Vardeman, A., and Wilson E. (2015). NASS Report: 

Field Data Collection using Geographic Information Systems Technologies and iPads on the 

USDA’s June Area Frame Survey.

Nusser S and House C. (2009) Sampling, data collection, and estimation in agricultural surveys. In: 

Pfeffermann D, Rao C (eds) Handbook of statistics 29A, sample surveys: design, methods and 

applications. Elsevier. The Netherlands, pp 471-486.

USDA FSA (2016-a). Farm Service Agency Handbook: Acreage and Compliance Determinations 

<https://www.fsa.usda.gov/Internet/FSA_File/2-cp_r15_a97.pdf last accessed 04/10/2016> 

(last accessed 04/12/17).

USDA FSA (2016-b) Farm Service Agency: Common Land Unit 

<https://www.fsa.usda.gov/programs-and-services/aerial-photography/imageryproducts/common-land-unit-clu/index> (last accessed 04/12/17).
21

USDA FSA (2016c). National Agricultural Imagery Program (NAIP) Imagery, 

<http://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naipimagery/> (last accessed 04/12/2017).

USDA NASS (2016). Survey Administration Manual (Internal Report) Section 6.2.3, Washington, 

D.C.

USGS (2016). The U.S. Map: The Public Land Survey System. 

<http://nationalmap.gov/small_scale/a_plss.html> (last accessed 04/12/17).

Vogel, F. A. (1995). The evolution and development of agricultural statistics at the United States 

Department of Agriculture. Journal of Official Statistics, 11: 161-180.
A-1

APPENDIX A

FIELD ENUMERATOR’S DATA COLLECTION FORM
B-1

APPENDIX B

EVALUATION OF ERRORS FOUND IN THE COMPARISON OF GIS VS. JAS ACREAGE

Table B1 displays the distribution of errors and acreage discrepancies by state. 

Table B1: Errors found in comparison of JAS vs. GIS Acreage

Discrepancy IN PA WA Total

Non-matching fields error 10 20 102 132

Large acreage difference 44 67 56 167

Total 54 87 158 299

For this evaluation, scanned images of the 2013 JAS questionnaires and supplementary forms 

specific to the fields in question, were used to derive information on the respondent and how easily 

acreage information was provided. A visual comparison of segments in the mobile mapping 

instrument with the aerial photos determined whether boundaries were positioned correctly by the 

enumerator. For the aerial photo, an acreage grid was used to verify acreage provided by the JAS 

respondent (farmer). Finally, error categories were created and all identified fields were classified 

as one of the resulting categories.

Table B2 describes the non-matching fields in which acreage was not reported in the JAS or not 

estimated in the mobile mapping instrument. The vast majority of these types of errors occurred in 

WA and resulted from other types of errors associated with the JAS. 

Table B2: Analysis of Reasons for Non-matches between JAS and GIS Acreage

Reason for Non-matching Field IN PA WA Total

JAS labeling procedures 0 0 41 41

JAS enumerator mislabeled field 4 2 8 14

Other errors associated with JAS 4 15 50 69

Mobile mapping instrument 

enumerator mislabeled field 2 2 0 4

Other errors associated with using 

the mobile mapping instrument 0 1 3 4

Total 10 20 102 132
B-2

Table B3 describes fields with large acreage differences (> 10 acres difference) in which errors were 

identified prior to the analysis. The majority of the large acreage differences were a result of 

enumerator gridding error. This finding was consistent across all states. 

Table B3: Errors associated with JAS data collection in fields with large acreage 

(>10 acres) differences between JAS and GIS Acreage

Reason for Acreage Difference IN PA WA Total

Enumerator gridding error 19 40 31 90

Incorrect field boundary position 6 3 5 14

Incorrect reported acreage 6 12 0 18

Other errors associated with JAS 4 9 15 28

Total 35 64 51 150

Table B4 describes the errors associated with the fields with large acreage differences between JAS 

and mobile mapping instrument estimates that were not due to either enumerator or farmer error. 

These errors are associated with the mobile mapping instrument GIS acreage calculation process.

Table B4: Errors Associated with the Mobile Mapping Instrument Occurring in 

Fields with Large Acreage (>10 Acres) Differences between JAS vs GIS Acreage

Reason for Acreage Difference IN PA WA Total

Precise boundary constraint 6 0 4 10

Incorrect field boundary position 3 3 1 7

Total 9 3 5 17