Collecting Data from a 

Permanent Grid Sampling 

Frame via a Mobile Mapping 

Instrument

Denise A. Abreu

Linda A. Lawson

Michael Hyman

Rick Hardin

Michael Gerling

United States 

Department of

Agriculture

National

Agricultural

Statistics

Service

Research and

Development Division

Washington DC 20250

RDD Research Report

Number RDD-17-01

April 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

In recent years, the National Agricultural Statistics Service (NASS) has made strides to improve 

its data collection processes and to evaluate a number of cost-saving initiatives. The June Area

Survey (JAS) is one of the largest annual surveys NASS conducts and is based on an area frame. 

This is also NASS’s only survey collected via in-person interviews using pencil and paper. Frame 

construction and face-to-face data collection efforts are expensive and time-consuming processes. 

It has been proposed to replace the area frame with a permanent grid sampling frame. 

The sampling units for the JAS are segments of land. In the segment creation process, segment 

borders follow physical features on the ground (i.e., an edge of a field, a road, a river, etc.). 

Fitting segment boundaries to physical features is a manual and laborious process, with overall 

annual costs of approximately 2.6 million dollars. One way to lower the labor costs for this 

survey is through the use of a permanent frame requiring limited manual adjustments. The 

proposed grid frame has roughly equal-sized units and shaped areas called grid cells. This frame 

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

which the PLSS is the primary surveying method. 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. 

Because grid cells do not necessarily follow the infrastructure on the ground and often cut across 

fields, a mobile mapping instrument was developed to automatically calculate acreages of 

surveyed fields. A team of researchers from NASS and Iowa State University’s Center for Survey 

Statistics and Methodology developed the prototype mobile mapping instrument in 2012. The 

instrument was designed to operate on an iPad and can be used to collect data for either grid cells 

or JAS segments. 

This paper describes the results of research initiated to test collecting data using grid cells utilizing

the mobile mapping prototype instrument. Enumerators (field interviewers) in North Carolina, 

Pennsylvania, and South Dakota visited with farm operators during the summer of 2014. A 

random sample of 20 grid cells was selected in each state. South Dakota was selected because it 

is based on the PLSS system. Because North Carolina and Pennsylvania are not based on the 

PLSS system, a fishnet was created using ArcGIS software and grid cells were selected. 

Enumerators identified a total of 917 tracts, which are unique land operating arrangements (457 

agricultural and 460 non-agricultural). Enumerators delineated all the tracts and attempted to 

conduct interviews with farm operators for all tracts with agriculture. Within each agricultural 

tract, they delineated the field boundaries for any kind of agriculture in the specified field. In 

addition, they were asked to record any challenges while enumerating the grid cells, time spent 

with and without the farm operator, and any issues related to the use of a mobile mapping 

instrument such as connectivity and glare associated with the iPad. 
ii

Results indicate that the lack of physically identifiable boundaries for grid cells presented a 

substantial problem to enumerate and increased respondent burden as additional operators had to 

be contacted to complete a survey form based upon a very small portion of a field. Another lesson 

learned is that the average time it took enumerators to draw off the fields with the mobile mapping 

instrument was not reasonable for the approach to work operationally.

The best practices to enhance usability of the mobile mapping instrument is to simplify the design. 

Many enumerators in rural areas do not have high speed Wi-Fi in their homes; therefore, it is 

important to be mindful of connectivity limitations. Sophisticated GIS features may not be 

practical due to connectivity requirements. Further, enumerators need to have a higher level of 

technical expertise than with the current operational procedures. Future efforts should continue to 

obtain feedback from enumerators on instrument enhancements and to test the instrument in all 

possible environments.

In conclusion, the use of grid cells as an enumeration unit for the JAS is not feasible for NASS. 

However, the mobile mapping instrument is still a promising tool for modernizing the agency data 

collection activities. Because the instrument can be used with both grid cells and JAS segments, 

research should continue with the application being on JAS segments. One way to reduce the time 

taken to enumerate segments is to provide segments with pre-delineated field boundaries. 

Information from the Farm Service Agency Common Land Units, imagery from the National 

Agricultural Imagery Program (NAIP), and topology maps could be used to create the segment 

pre-delineated field boundaries. 
iii

RECOMMENDATIONS

1. Do not use grid cells as an enumeration unit to collect data for the JAS.

2. Provide enumerators with JAS segments with pre-delineated field boundaries using 

sources such as Farm Service Agency’s Common Land Units, imagery from the National 

Agricultural Imagery Program (NAIP), and topology maps among other sources. 

3. Implement the following instrument and training modifications:

a. Store all required imagery on the same server as the instrument.

b. Test the instrument in all possible environments.

c. Make the following enhancements to the instrument:

i. Increase the width of the segment border to distinguish it from roads.

ii. Retain the vertices of the incomplete polygon when a split fails.

iii. Add a pan tool to allow enumerators to navigate around the imagery 

without selecting. 

iv. Clarify warning messages to indicate the number of fields involved in a 

merge and the reason a merge failed.

v. Ignore slight finger movements when a button is pushed.

vi. Display the calculated GIS acres for each field at the bottom of the Section 

D form.

d. Remove the following features from the instrument:

i. Option to double tap to complete a split.

ii. The geo-location feature. 

iii. Menu requiring the selection of the type of other crop.

e. Incorporate more role-play practice exercises that mimics live interviews during 

field enumerators training.

f. Reinforce the use of the iPad’s airplane mode and the invert colors options.

4. Investigate new devices and screen protectors as they come on the market to further 

address the issue of screen visibility on the iPad.
iv

ACKNOWLEDGEMENTS

The authors would like to thank:

National Agricultural Statistics Service

Research and Development

Wendy Barboza

Claire G. Boryan

Matt Deaton

Mark Gorsak

Dao Light

David Twiner

Field Operations

Karen Bryan

Kathy Neas

Steve Susemihl

Jillayne Weaber

North Carolina, Pennsylvania and South Dakota Field enumerators

Iowa State University

Center for Survey Statistics and Methodology

Alan Dotts

Eric Henely 

Andrew Vardeman
v

TABLE OF CONTENTS

1. Introduction and Background .......................................................................1

2. NASS Area Frame Construction and the June Area Survey ....................................3

3. Theoretical Grid Sampling Frame ..................................................................4

4. Prototype Mobile Mapping Instrument ............................................................6

4.1 Aerial Imagery Part of the Instrument .........................................................7

4.2 Instrument’s Electronic Section D Form ......................................................8

5. Phase III Study – Field Data Collection & Results ................................................9

5.1 Reported Issues: Aerial Imagery Part of the Instrument ...................................15

5.2 Reported Issues: Electronic Section D Form ................................................16

5.3 Reported Issues: Connectivity .................................................................18

5.4 Reported Issues: Visibility .....................................................................19

6. Conclusions ...........................................................................................20

7. Recommendations ....................................................................................21

8. References .............................................................................................22

 APPENDIX A: Section D Form .......................................................................A1

 APPENDIX B: Enumerator’s Data Collection Form ................................................B1

 
1

Collecting Data Using a Permanent Grid Sampling Frame via a Mobile Mapping 

Instrument

Denise A. Abreu, Linda A. Lawson, Michael Hyman, Rick Hardin and Michael Gerling

Abstract

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

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

and constructing an area frame is expensive and time consuming. The agency is evaluating the 

use of a permanent grid sampling frame as a cost-saving initiative. The proposed grid frame would 

include sample units having roughly equal-sized and shaped areas called grid cells. The grid cells 

would be stratified by agricultural intensity and content and then a stratified random sample drawn.

A challenge associated with this proposed approach is that only a portion of an agricultural field 

may lie within the selected grid cells whereas currently sampled segments have boundaries that 

follow roads or other physical features. Because of the presence of partial fields, mobile mapping 

technology may be critical for proper identification of the areas to be included in a sampled grid 

cell. Using mobile mapping technology would also allow for replacing the aerial photo and paper 

questionnaires, thus modernizing NASS’s data collection efforts and improving data quality. To 

test the concept of permanent grid cells in conjunction with the mobile mapping instrument, 

enumerators (field interviewers) in North Carolina, Pennsylvania and South Dakota visited with 

farm operators during the summer of 2014. For each sampled grid cell, the enumerators completed 

an evaluation form to obtain information on a variety of issues including 1) those associated with 

the grid concept, 2) use of a mobile mapping instrument, 3) connectivity and 4) visualization 

problems associated with the iPad (e.g. sun glare). This report documents the challenges faced, as 

well as the lessons learned, from collecting data using grid cells with the mobile mapping prototype 

and discusses future instrument enhancements. 

Key Words: Mobile Mapping, Area Frame Survey, GIS, Data Collection, Interface Design

1. Introduction and Background

In recent years, the National Agricultural Statistics Service (NASS) is making strides to improve 

its data collection processes and to evaluate a number of cost-saving initiatives. The June Area

Survey (JAS) is one of the largest annual surveys NASS conducts and is based on an area frame. 

This is also NASS’s only survey solely collected via in-person interviews using pencil and paper. 

Frame construction and face-to-face data collection efforts are expensive and time-consuming 

processes. It has been proposed to replace the area frame with a permanent grid sampling frame. 

The sampling units for the JAS are segments of land. In the segment creation process, segment 

borders are adjusted to follow physical features on the ground (i.e., an edge of a field, a road, a 

river, etc.) (Cotter et. al, 2010). Determination and preparation of segments are labor-intensive 

and expensive with overall costs around 2.6 million dollars per annum. The proposed grid frame 

has roughly equal-sized units and shaped areas called grid cells which require very limited manual 

intervention. The grid frame is based on the Public Land Survey System’s (PLSS’s) one-squaremile grid cells in the 30 states in which the PLSS exists. In these 30 states, roads are often aligned 
2

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 grid cells can be generated using ESRI’s ArcGIS software. Figure 1 illustrates 

the grid frame concept (outlined in yellow) compared with a JAS segment (outlined in red). 

Figure 1: Grid cell (outlined in yellow) vs. JAS segment (outlined in red)

Because grid cells do not necessarily follow the infrastructure on the ground and often cut across 

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

technology to calculate the acreage of 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 is designed to operate on an iPad 

and can be used to collect data for either grid cells or JAS segments. A series of studies were 

needed to evaluate whether or not NASS could implement the permanent grid frame operationally. 

Phase I was the actual development of the instrument in 2012. The objective of the 2013 study 

(or Phase II) was to determine whether the mobile mapping instrument’s calculated GIS acreages 

were comparable to the acreages reported by farmers during the JAS before moving forward with 

testing grid cells. Enumerators (field interviewers) were provided the completed aerial photos 

from previously collected data and tasked with replicating the field boundaries within the mobile 

mapping instrument. Results indicated that there was strong agreement between JAS farmer 

reported and GIS acreage (Boryan et. al, 2017). Based on this result, this study was initiated to 

test collecting data using grid cells with the mobile mapping prototype instrument (Abreu et. al, 

2015; Lawson et. al, 2015). This paper documents the challenges faced as well as the lessons 

learned from collecting data using these grid cells and describes future instrument enhancements. 

Before discussing the study and the results, it is necessary to understand NASS’s area frame and 

the JAS (Section 2); the origins of the permanent grid sampling frame (Section 3); and the features 

and functionality of the mobile mapping prototype instrument (Section 4).
3

2. NASS Area Frame Construction and the June Area Survey

NASS’s JAS is based on an area frame, which ensures complete coverage of all land in the U.S. 

First, all land in a state is stratified using GIS technology, such as satellite imagery, aerial 

photography, and geo-referenced crop-specific land cover data known as the Cropland Data Layer 

(CDL) (Boryan et al., 2014). This step is a manual process where Primary Sampling Units (PSUs) 

are digitized (electronically identified using GIS software) based on physical boundaries and other 

features on the ground (i.e. railroads, roads, etc.) and classified into the defined strata for a state. 

A sample of PSUs is selected and smaller, similar-sized segments (each about a square mile [640

acres]) of land are delineated within selected PSUs. Segment boundaries follow natural boundaries 

that can be easily identified outdoors, such as roads, ditches, edges of fields, rivers, tree lines, etc. 

This “tweaking” of boundaries is a labor-intensive process. In general, staff divide a PSU of four 

square miles into four segments, one-square mile each. Next, one segment is randomly chosen 

from within each sampled PSU (see Figure 2). This process avoids segment delineation for nonselected PSUs, thereby saving resources. In the current sampling scheme, the JAS replaces the 

oldest 20% of the segments with new segments rotated in each year. Eight staff working yearround are required to select the incoming rotation for the sample. A state receives a completely 

new area frame approximately every fifteen years. This on-going process takes twenty-five staff 

with salary and benefits totaling about 2.6 million dollars and another 100,000 dollars in 

equipment, software, printing, and mailing of materials. (See Cotter et. al 2010 for further details 

on the JAS design). 

Figure 2: NASS area sampling frame for North Carolina
4

JAS segments (outlined in red in Figure 3) are pre-screened during the month of May prior to the 

June data collection period. During pre-screening, enumerators divide each segment into separate 

tracts of land that represent each unique operating arrangement. Each tract is assigned a letter and 

drawn in blue on the aerial photo (Figure 3). Tracts are then screened for agricultural activity of 

which about 42,000 of them are classified as agricultural tracts. JAS data collection is conducted 

during the first two weeks of June. At this time, enumerators return to only the agricultural tracts 

and conduct interviews using the JAS questionnaire, which collects detailed agricultural 

information about the operator’s land, both inside and outside the segment. Enumerators complete 

a separate paper questionnaire for each agricultural tract operation within the segment. Farm 

operators identify all field boundaries (outlined in red in Figure 4) on the aerial photo and report 

acreage and the crop planted or other land use of each individual field (pasture, woods, wasteland, 

etc.). 

 

3. Theoretical Grid Sampling Frame

NASS is exploring the concept of a permanent grid area frame to replace the area frame segments 

as a cost-saving initiative. The permanent frame sample units are roughly equal size and shape 

(one-square mile) and are called grid cells. The permanent grid frame concept can be based on 

the PLSS when it is available (USGS, 2016). PLSS is a surveying method used over large parts 

of 30 states in the United States to spatially identify parcels of land, especially in rural and 

undeveloped areas. Land is divided into (mostly) rectangular areas ranging from a 24-mile by 24-

mile quadrangle down to a one-mile by one-mile square grid cells (See Figure 5). In Figure 6, 

states included in the PLSS are identified in blue. Figure 5 illustrates PLSS section lines over a 

National Agricultural Imagery Program (NAIP) aerial photo (USDA FSA, 2015). 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 match with roads, railroads or rivers causing fields to 

be split. In non-PLSS states, a grid frame can be generated using ESRI’s ArcGIS software. In this 

case, the grid cell lines do not have any correlation with physical features on the ground. In the 

end, the United States can be divided into roughly one-mile by one-mile square grid cells. This 

Figure 3: The area outlined in red is 

the segment. Tracts are outlined in 

blue and labeled with letters.

Figure 4: Tracts are outlined in blue and 

labeled. Individual fields are outlined in red 

within the tracts and labeled with numbers.
5

can reduce the resources required in the preparation for the JAS. The sampling process can then 

be automated to handle stratification and sample selection of these grid cells using NASS’s current 

methods. The enumerator can then be responsible for collecting all agricultural data within the 

defined grid cells. 

 

A challenge with grid cells is that fields may not be fully contained within a grid cell boundary. 

In these instances, information must be collected for just the portion of the field that lies within 

the grid cell. In Figure 7, the pink boundary identifies the middle of the road, while the red line 

shows the actual grid cell boundary. In this case, the enumerator must collect information on the 

portion of land to the south of the road (labeled as “Partial Field” in Figure 7). It may be difficult 

for an agricultural operator to report the acreage correctly viewing a printed aerial photo. Thus, 

having a mobile mapping instrument that uses GIS technology to automatically calculate the 

acreage can be used to eliminate the need for farm operators to report acreage for land within the 

grid cell. An overview of the mobile mapping instrument and all its features is described in the 

next section.

Figure 7: Partial field identified at the left bottom corner of the grid cell

Figure 6: States included in the Public Land 

Survey System (PLSS)

Figure 5: Public Land Survey System (PLSS)

section lines overlaying the NAIP imagery

 States Included in the PLSS
6

4. Prototype Mobile Mapping Instrument

Development of the prototype mobile mapping instrument began in 2012. The instrument is an

offline-capable web application designed to run within the Safari browser on an iPad. A substantial

amount of the JAS data collection takes place in rural areas that tend to have intermittent signal;

therefore, it is essential that the instrument be able to operate without an Internet connection. Prior

to data collection, enumerators run a cache routine to store the required imagery in the iPad’s

memory. The instrument has two main parts (Figure 8). The left side of the screen contains the

aerial imagery and the right side of the screen displays general field information that replaces the

Section D (Appendix A) part of the paper questionnaire. 

Tracts and fields are delineated on digital imagery in place of the paper aerial photo. Detailed field

information is captured in a streamlined electronic version of the Section D questionnaire. Field

boundaries are captured as polygons with the Section D data linked as attributes.

If a wireless broadband connection is available, the instrument transmits a copy of the data to the

web server as it is entered or modified by an enumerator. Otherwise, the data remains stored

locally on the iPad. All data are automatically transmitted to the web server whenever a wireless

broadband connection is available. Up-to-date traffic light symbols are displayed to indicate if the

data has been stored locally on the iPad, saved to the server, or both.

The instrument contains a wide range of GIS tools and features. Enhancements are made each

year to improve usability. A sophisticated geolocation feature was added to the prototype

instrument in 2014 to help the enumerators orient themselves in relation to the segment or grid cell

(Figure 9).

Figure 8: Mobile mapping instrument Figure 9: Geolocation feature
7

4.1 Aerial Imagery Part of the Instrument

The aerial imagery part of the instrument displays a red segment or grid cell boundary overlaid on

digital imagery on the left side of the screen which can also be run in full screen mode (Figure 10). 

The digital imagery is obtained from the National Agricultural Imagery Program (NAIP), which

acquires aerial imagery during the agricultural growing seasons in the continental U.S. Typically,

this digital ortho-rectified aerial photography is available to governmental agencies and the public

within two to four months after acquisition.

 

The instrument is capable of presenting additional resource material using Web Map Service 

overlays. This allows the ability to replace the NAIP imagery with another layer, such as roads 

or the Cropland Data Layer (CDL) (Figure 11). The CDL is an annual crop specific land cover 

product, depicting more than one hundred unique crop categories across the nation. NASS 

derived this cropland area monitoring program via remote sensing (satellite data) using a 

supervised land cover classification approach. The national CDL product (Boryan et. al, 2011) is 

available on NASS’s CropScape web portal at http://nassgeodata.gmu.edu/CropScape. 

The majority of the functions are performed within the aerial imagery part of the instrument using 

the various tools created within OpenLayers. OpenLayers is an open-source JavaScript mapping 

library and provides basic web and GIS functionality. 

In the JAS enumeration process, enumerators use a blue grease pencil to outline tracts and a red 

grease pencil to outline fields on the paper aerial photo. The process within the mobile mapping 

instrument requires “splitting” a segment or grid cell into tracts and fields instead of outlining 

them. The polygons representing each of the fields are created by using the split feature tool. 

Splitting ensures that all land parcels are included within the segment or grid cell boundary.

The split tool is integrated into a toolbar on an OpenLayers map in the instrument. The map allows

a loaded segment or grid displayed over NAIP aerial imagery to be repeatedly split into component 

Figure 10: Instrument shown in full screen mode Figure 11: Displays the CDL in place of NAIP imagery
8

tracts and fields. A merge tool is used for updating/correcting errors made when splitting. Several 

additional tools are part of the OpenLayers map, including zoom tools, selection tools, and 

undo/redo buttons to make it more user friendly. The mobile mapping instrument has touch screen 

pinch zooming capabilities, but also includes buttons to quickly zoom to preset levels.

4.2 Instrument’s Electronic Section D Form

The right side of the mobile mapping instrument’s main screen (Figure 12) displays the calculated

GIS acreage along with general information about all of the polygons or fields that have been

delineated on the aerial imagery. A button to the right of each field is used to open the electronic

Section D form (Figure 13).

The mobile mapping instrument provides a highly-optimized version of the paper Section D form.

The specific detailsfor each field are captured in a survey-like format containing drop-down menus

and basic edit checks. Skip rules and validation logic are specified per-question dynamically. This

effectively reduces the complex paper table as shown in Appendix A to a handful of questions that

relate to the specific crop or land use (Figure 14).

 Figure 12: The right side of main screen displays the Figure 13: A view of the opened Section D

 calculated GIS acreage and general field information form for the first field in the table
9

5. Phase III Study - Field Data Collection & Results

This study was conducted to evaluate whether grid cells could replace JAS segments as the data 

collection unit for the survey and to identify any issues of collecting data electronically using a 

mobile mapping instrument instead of pencil and paper procedures. During the summer of 2014, 

enumerators in North Carolina (NC), Pennsylvania (PA) and South Dakota (SD) visited with farm 

operators to test the concept of grid cells in conjunction with the mobile mapping instrument. A 

random sample of 20 grid cells was selected in each state. South Dakota was selected because it 

is based on the PLSS. Because North Carolina and Pennsylvania are not based on the PLSS, a 

fishnet was created using ArcGIS software, and grid cells were selected. Figure 15 shows the 

location of the grid cells in each of the participating states. 

 

Figure 15: Sampled grid cell locations in participating states

North Carolina Pennsylvania South Dakota

Figure 14: Skip rules facilitate streamlined questions specific to each crop or land use. Indicators 

pinpoint missing information and any data inconsistencies found during basic edit checks.
10

Enumerators identified a total of 917 tracts of land representing unique operating arrangements. 

Each tract was screened for agricultural activity based on questions on the screening questionnaire. 

Of these, there were 457 classified as agricultural and 460 as non-agricultural. Enumerators 

delineated all of the tracts and attempted to conduct interviews with the operators for all tracts with 

agricultural activity. Enumerators were asked to complete a questionnaire for each grid cell as 

shown in Appendix B. The form collected information on a variety of issues including 1) those 

associated with the grid concept, 2) use of a mobile mapping instrument, 3) connectivity and 4) 

visualization problems associated with the iPad (e.g. sun glare). In addition to providing a yes/no 

response, enumerators were asked to supply additional information for positive responses. This 

enabled proper determination of the exact cause of the issue encountered. Table 1 shows the total 

counts of agricultural and non-agricultural tracts by state. Table 2 displays the interview results 

for the agricultural tracts by whether an interview was completed, refusal, or estimated (i.e., no 

contact).

Table 1: Agricultural and Non-Agricultural Tracts by State

North Carolina Pennsylvania South Dakota All 3 States

Number Percent Number Percent Number Percent Number Percent

Agricultural Tract 136 36.3 239 54.4 82 79.6 457 49.8

Non-Agricultural Tract 239 63.7 200 45.6 21 20.4 460 50.2

Total 375 100.0 439 100.0 103 100.0 917 100.0

Table 2: Completed, Partial and Estimated Agricultural Tract Interviews by State

North Carolina Pennsylvania South Dakota All 3 States

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Complete Live 

Interview 86 63.3 171 71.5 66 80.5 323 70.7

Refusal Partial 

Interview 18 13.2 11 4.6 3 3.7 32 7.0

Estimated

(No Contact) 32 23.5 57 23.9 13 15.8 102 22.3

Total 136 100.0 239 100.0 82 100.0 457 100.0

Enumerators were asked to record their experience and any challenges encountered while 

enumerating the grid cells by indicating ‘yes’, ‘no’ or ‘sometimes.’ In addition, they explained any 

issues they encountered. Table 3 displays the results of the question pertaining to challenges the 

enumerators faced while enumerating the grid cells. 

The results shown in Table 3 indicate that enumerators encountered issues more than 20% of the 

time in all three states. Non-PLSS states were expected to report numerous issues due to the lack 
11

Figure 17: Grid cell in Pennsylvania with 

difficult boundaries. Ovals show grid 

boundary cutting across multiple fields.

Figure 16: South Dakota grid cell with partial 

fields highlighted in purple

of physical boundaries, but enumerators in South Dakota (a PLSS state) answered ‘yes’ 21.8% of 

the time versus 12.2% in North Carolina and 4.7% in Pennsylvania, which are non-PLSS states. 

The primary problem in South Dakota was that the grid cells were very close to the infrastructure 

on the ground but slightly shifted causing numerous partial fields. As an example, in Figure 16, 

the South Dakota grid boundary does not align exactly with the road. As a consequence, the edges 

of fields are excluded from the left side of the grid cell and small portions of fields are included 

on the right side of the grid cell. In these cases, enumerators were required to contact additional 

operators and to complete a survey form based upon a very small portion of a field. 

Table 3: Did You Encounter Any Issues While Enumerating the Grid Cells?

North Carolina

(non-PLSS)

Pennsylvania

(non-PLSS)

South Dakota

(PLSS) All 3 States

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Yes 14 12.2 9 4.7 17 21.8 40 10.4

Sometimes 20 17.4 29 15.2 4 5.1 53 13.8

No 81 70.4 153 80.1 57 73.1 291 75.8

Total1/ 115 100.0 191 100.0 78 100.0 384 100.0

1/ There were 73 no-response to this question that are not included in the total. Counts by state: NC-21, PA-48 and SD-4.

The goal of the Phase II research (Boryan et. al, 2017) comparing reported acres to GIS calculation 

of acres was to determine whether asking farm operators the acres for split fields could be 

eliminated. While enumerating the grid cells (Phase III), the enumerators reported that farm 

operators had difficulty estimating acreage for just the part of the field inside the grid cell 

boundary. Pennsylvania and North Carolina enumerators indicated that their main issue was 

identifying clear boundaries for the grid cells (See Figure 17). In addition, they also reported that 

grid cell boundaries cut across numerous crop fields. 
12

Once enumerators contacted farm operators, they identified and delineated the fields and collected 

the crop-specific data on the Section D portion of the instrument. Table 4 displays the number of 

fields per tract by state and all states combined. Pennsylvania has more tracts than both South 

Dakota and North Carolina. It is important to note that this was a result of the counties that were 

available for sampling in Pennsylvania. This is not representative of all the counties in the state. 

Figure 18 shows an example of the number of fields delineated for each state. This helps depict

how many more fields needed to be delineated in Pennsylvania as compared to the other two states. 

Table 4: Number of Fields per Tract by State

North Carolina Pennsylvania South Dakota All 3 States

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

1 Field 41 30.1 44 18.4 24 29.3 109 23.9

2 Fields 19 14.0 51 21.3 16 19.5 86 18.8

3-4 Fields 28 20.6 47 19.7 14 17.1 89 19.5

5-7 Fields 23 16.9 28 11.7 13 15.8 64 14.0

8-10 Fields 8 5.9 32 13.4 8 9.8 48 10.5

Over 10 Fields 17 12.5 37 15.5 7 8.5 61 13.3

Total 136 100.0 239 100.0 82 100.0 457 100.0

Because limiting burden on farm operators is essential to NASS, the study focused on gaging how 

long it took enumerators to collect data using grid cells utilizing the mobile mapping instrument. 

The time spent with each farm operator was examined for each state. Figure 19 describes the 

enumeration times spent with the farm operator for each of the three states. The black vertical line 

represents the mean enumeration time with the farm operator for that particular state. 

Figure 18: Examples of grid cells in NC, PA and SD. Letters represent each tract or unique operating 

arrangement. Tract boundaries are shown in blue and fields within each agricultural tract are shown in yellow.
13

Based on a one-way analysis of variance, the mean time enumerators spent with the farm operator 

(see Table 5) was not the same for all states (p < 0.0001). Fisher’s Least Significant Difference 

was then used to conduct pairwise comparisons between states (see Table 6). Only those times for 

which an interview was completed with a farm operator were included in the analysis. The mean 

enumeration times with farm operators differed significantly between North Carolina (non-PLSS)

and South Dakota (PLSS) and between Pennsylvania (non-PLSS) and South Dakota (PLSS). The 

mean interview times did not differ significantly between North Carolina and Pennsylvania. The 

average time taken to conduct an interview in South Dakota is significantly less than that for the 

other two states. The variability of this time is also significantly smaller in South Dakota than for 

Figure 19: Interview time in each state taken to input data into the mobile mapping instrument.

 0 20 40 60 80 100 120

 Time (Minutes)

 0 20 40 60 80 100 120

 Time (Minutes)

Number of Interviews

0 20 40 60 80 100 

Number of Interviews

0 20 40 60 80 100 

Number of Interviews

0 20 40 60 80 100 

 Histogram of Enumeration Time (NC) Histogram of Enumeration Time (PA)

 Histogram of Enumeration Time (SD) Box Plot of Enumeration Time by State

 0 20 40 60 80 100 120

 Time (Minutes)

 0 20 40 60 80 100 120

 Time (Minutes)

NC PA SD 
14

the other two states. This indicates that it was faster and easier to conduct interviews in the PLSS 

states as compared to the non-PLSS states.

Table 5: Summary Statistics for Enumeration Time Spent with the Farm Operator for Each State

State Number of

Interviews

Mean Time

(minutes)

Standard Deviation 

(minutes)

North Carolina 84 24.5 20.1

Pennsylvania 155 20.8 16.8

South Dakota 65 9.7 8.8

All 3 States 304 19.5 17.3

Table 6: P-values for Two Sample T-test Comparing Enumeration Times Spent with the Farm

Operator for Each Combination of Two States

P-values

North Carolina Pennsylvania South Dakota

North Carolina NA 0.156 < 0.0001

Pennsylvania 0.156 NA < 0.0001

South Dakota < 0.0001 < 0.0001 NA

Linear regression was used to examine the potential association between the number of fields in a 

tract and enumeration time. Although the correlation was not strong (R

2 < 0.35 for all states), the 

enumeration time did tend to increase as the number of fields within a tract increased. The 

estimated slopes from the linear regression coefficients (coefficient associated with fields) from 

each state are shown in Table 7. The estimated increase in the number of minutes, on average, for 

each additional field within a tract was 1.7, 1.5, and 0.6 minutes for North Carolina, Pennsylvania, 

and South Dakota, respectively; all of the slopes were significantly different from 0. A plot of the 

enumeration time versus the number of fields in the tract is shown in Figure 20 along with 

regression lines for each state.

Table 7: Coefficients and R2 Values for Regression of Enumeration Time vs Number of Fields

North Carolina Pennsylvania South Dakota

Beta (Fields) 1.7 1.5 0.6

p-value <0.0001 <0.0001 0.0012

R

2 0.34 0.25 0.13
15

Figure 20: Enumeration time vs the number of fields per tract

5.1 Reported Issues: Aerial Imagery Part of Instrument

Enumerators were asked whether they had any problems using the aerial imagery part of the 

instrument. Their responses were tabulated for each of the 457 agricultural tracts completed. Table 

8 displays the number of times that enumerators experienced difficulty navigating within the aerial 

imagery portion of the mobile mapping instrument. It was concerning that issues occurred during 

almost 40% of all agricultural tract interviews. The Pennsylvania enumerators may have 

responded “yes” more often due to the complexity of their grid cells, which contained numerous 

small fields. Enumeration Time (Minutes)

Number of Fields

North Carolina

Pennsylvania

South Dakota

0 20 40 60 80 100 120

 0 5 10 15 20 25 30 35
16

Table 8: Did You Encounter Any Issues While Using the Aerial Imagery Part of Instrument?

North Carolina Pennsylvania South Dakota All 3 States

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Yes 11 9.6 42 22.0 10 12.7 63 16.4

Sometimes 36 31.3 41 21.5 11 13.9 88 22.8

No 68 59.1 108 56.5 58 73.4 234 60.8

Total 1/ 115 100.0 191 100.0 79 100.0 385 100.0

1/ There were 72 no—response to this question that are not accounted for in the total. Counts by state: NC-21, PA-48 and SD-3.

All positive responses were investigated, and the vast majority of the issues reported were based 

upon difficulty splitting fields, a failed merge, or unresponsiveness of the touch screen. Various 

factors can contribute to an enumerator struggling with splitting fields. The main difficulty 

reported was accidentally double-tapping on the screen before the polygon was complete. This 

erased all of the vertices and forced them to start over. It is recommended that the option to double

tap to complete a split be removed from the instrument. It is also recommended that the vertices 

be retained if a split fails.

A merge will fail if all of the selected areas are not adjacent to one another. The enumerators were 

inadvertently selecting additional fields while using the select tool to move around. A suggestion 

to address this issue would be to add a pan tool that would allow enumerators the ability to navigate 

without selecting. In addition, pop-up warning messages should be clarified to indicate the number 

of fields involved and the reason a merge failed. One important suggestion is to have enumerator

training incorporate more role-play practice that mimics live interviews and focus on more specific 

skills of the various instrument tools and features. 

Several enumerators commented that the iPad touch screen was unresponsive at times. They noted 

that this seemed to mainly occur when they were trying to delineate the field boundaries in the 

middle of an interview. It was discovered that the touch screen became unresponsive when the 

user’s finger wiggled as it pressed a button. This instability was previously undetected because 

testing and training were primarily done with the iPad on a flat surface whereas these tests were 

conducted holding the iPad in one hand while standing outside. For future iterations of the 

instrument, programmers should specify that slight finger movements be ignored. 

5.2 Reported Issues: Electronic Section D Form

Enumerators were asked whether they had any problems completing the electronic Section D form. 

Their responses were tabulated for each of the 457 agricultural tracts completed. Table 9 displays 

the number of times that enumerators reported issues while answering the questions within Section 

D of the mobile mapping instrument. 
17

Table 9: Did You Encounter Any Issues While Completing the Electronic Section D Form?

North Carolina Pennsylvania South Dakota All 3 States

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Yes 6 5.3 21 11.3 4 5.1 31 8.2

Sometimes 5 4.4 44 23.7 3 3.8 52 13.7

No 103 90.3 121 65.0 72 91.1 296 78.1

Total 1/ 114 100.0 186 100.0 79 100.0 379 100.0

 1/ There were 78 no-response to this question that are not accounted for in the total. Counts by state: NC-22, PA-53 and SD-3.

North Carolina and South Dakota enumerators reported no issues completing over 90% of the 

agricultural tracts. The primary complaint of the Pennsylvania enumerators was having to scroll 

through a list of 32 “other” crops that were not alphabetized. The survey form designed for 

Pennsylvania was much more complicated than in the other states due to an extensive listing of 

other crops. In 2015, the other crop question was eliminated from the instrument due to a policy 

change eliminating the need to specify the type of other crop.

Enumerators reported that the placement of the calculated GIS acreage was not adequate for the 

interviews to flow smoothly. If the respondent did not know the number of acres in a field, the 

enumerator had to close the Section D form to view the calculated GIS acres in the general table 

and then reopen the form in order to continue. The GIS calculated acres should be displayed at 

the bottom of the Section D form as shown in Figure 21. 

Figure 21: Shows the additional placement of calculated GIS acreage
18

5.3 Reported Issues: Connectivity

Enumerators were asked to report any issues related to connectivity. Their responses were 

tabulated for each of the 457 agricultural tracts completed. Table 10 displays the number of times 

that enumerators reported connectivity problems. Even though the mobile mapping instrument 

was designed to function without an internet connection, connectivity problems were reported 

9.1% of the time in all 3 states.

Table 10: Did You Have Any Connectivity Related Issues?

North Carolina Pennsylvania South Dakota All 3 States

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Yes 24 20.9 7 3.7 4 5.1 35 9.1

Sometimes 32 27.8 41 21.6 0 0.0 73 19.0

No 59 51.3 142 74.7 75 94.9 276 71.9

Total 1/ 115 100.0 190 100.0 79 100.0 384 100.0

 1/ There were 73 no-response to this question that are not accounted for in the total. Counts by state: NC-21, PA-49 and SD-3.

In areas of low signal strength, the geo-location feature actually timed out and caused the 

instrument to malfunction. North Carolina may have experienced more issues due to the 

mountains and poor cellular coverage. The geo-location feature should be removed from future 

iterations of the instrument.

Pennsylvania enumerators misunderstood the cache routine. They thought that it automatically 

cached all zoom levels when in reality it was not designed to capture the higher zoom levels they 

needed to view the smaller fields. Enumerator training should be modified to help them identify 

when specific areas are not cached properly. In addition, enumerators should be instructed to 

switch the iPad to airplane mode after running the cache routine. This will prevent any signal and 

display a pink tile in place of any imagery that has not been cached. They should then turn airplane 

mode off and cache any additional imagery needed before heading out to the field.

Some of the connectivity issues were instances when the NAIP imagery was not available. The 

instrument is dependent on public servers for hosting aerial photography (www.nationalmap.gov). 

To avoid the NAIP unreliability, it is recommended that all required imagery be stored on the same 

server as the mobile mapping instrument.
19

5.4 Reported Issues: Visibility

Up to this point, summary tables refer to all 457 agricultural tracts. To study visibility issues and, 

in particular, whether farm operators had difficulty viewing the iPad screen, it was necessary to 

remove any instances of refusals or in-accessible interviews. This allowed the focus to be on the 

323 completed interviews. Table 11 displays the results to the screen visibility question.

Table 11: Did You Encounter Any Issues with Screen Visibility?

North Carolina Pennsylvania South Dakota All 3 States

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Number of 

Agricultural 

Tracts

Percent

Yes 21 26.2 87 57.6 6 9.2 114 38.5

Sometimes 15 18.8 17 11.3 22 33.9 54 18.2

No 44 55.0 47 31.1 37 56.9 128 43.3

Total 1/ 80 100.0 151 100.0 65 100.0 296 100.0

 1/ There were 27 no-response to this question that are not accounted for in the total. Counts by state: NC-6, PA-20 and SD-1.

Glare, especially in bright sunlight, hinders the ability to collect data and needs to be significantly 

reduced to avoid visibility difficulties, which were observed in over half of all interviews. 

Pennsylvania reported the most issues with screen visibility. This is likely due to difficulty 

viewing smaller fields since Pennsylvania had almost twice the number of fields per grid cell than 

in the other two states.

One suggestion to help improve visibility is to adjust the color scheme on the iPad. Currently, the 

iPad has the invert colors option that could be turned on and off to allow a different view of the 

sampled segment (Figures 22 and 23). Another suggestion for better segment view is to increase 

the width of the segment border to distinguish it from roads. 

Figure 22: iPad screen with invert colors option turned on Figure 23: Normal view of iPad screen with invert 

colors turned off
20

6. Conclusions

The primary objective of this research was to determine whether grid cells could potentially replace 

JAS segments. This was evaluated as a cost and resource-saving initiative. The lack of physically 

identifiable boundaries for the grid cells presented a substantial problem for enumerators to 

identify where the imaginary grid boundaries fell in order to properly determine the areas to be 

included in a sampled grid cell. Partial fields also caused an increase in respondent burden since 

additional operators had to be contacted to complete a survey form based upon relatively small 

portions of land. Another lesson learned is that the average time it took enumerators to draw off 

the fields with the mobile mapping instrument was not reasonable for the approach to work

operationally. This was especially difficult in Pennsylvania where enumerators had many tracts 

within the grid cells as well as many small fields within each tract.

One key suggestion to enhance usability of the mobile mapping instrument is to simplify the

design. Many enumerators in rural areas do not have high speed WiFi in their homes; therefore,

it is important to be mindful of connectivity limitations. Sophisticated GIS features may not be 

practical due to connectivity requirements. Further, enumerators need to have a higher level of 

technical expertise than with the current operational procedures. Efforts should continue to obtain 

feedback from enumerators on instrument enhancements and to test the instrument in all possible 

environments.

Enumerator training should incorporate more role-play practice that mimics live interviews and 

focus on more specific skills. In addition, special features such as the use of the iPad’s airplane 

mode and the invert colors option should be provided to better prepare enumerators for what they 

will encounter during live interviews. Screen visibility on the iPad continues to be a problem and 

needs to be significantly reduced. Efforts should continue to identify new devices and screen 

protectors as they come on the market.

The use of grid cells as an enumeration unit for the JAS is not feasible for NASS. However, the 

mobile mapping instrument is still a promising tool for modernizing the agency data collection 

activities. Because the instrument can be used with both grid cells and JAS segments, research 

should continue with the application being on JAS segments. One way to reduce the time taken to 

enumerate segments is to provide pre-delineated field boundaries. Currently, information from 

the Farm Service Agency’s Common Land Units, NAIP imagery and topology maps could be used 

to create the segment delineations. 
21

7. Recommendations

1. Do not use grid cells as an enumeration unit to collect data for the JAS.

2. Provide enumerators with JAS segments with pre-delineated field boundaries using 

sources such as Farm Service Agency’s Common Land Units, imagery from the National 

Agricultural Imagery Program (NAIP), and topology maps among other sources. 

3. Implement the following instrument and training modifications:

a. Store all required imagery on the same server as the instrument.

b. Test the instrument in all possible environments.

c. Make the following enhancements to the instrument:

i. Increase the width of the segment border to distinguish it from roads.

ii. Retain the vertices of the incomplete polygon when a split fails.

iii. Add a pan tool to allow enumerators to navigate around the imagery 

without selecting. 

iv. Clarify warning messages to indicate the number of fields involved in a 

merge and the reason a merge failed.

v. Ignore slight finger movements when a button is pushed.

vi. Display the calculated GIS acres for each field at the bottom of the Section 

D form.

d. Remove the following features from the instrument:

i. Option to double tap to complete a split.

ii. The geo-location feature. 

iii. Menu requiring the selection of the type of other crop.

e. Incorporate more role-play practice exercises that mimics live interviews during 

field enumerators training.

f. Reinforce the use of the iPad’s airplane mode and the invert colors options.

4. Investigate new devices and screen protectors as they come on the market to further 

address the issue of screen visibility on the iPad.
22

8. References

Abreu Denise, Lawson L., Boryan C., Gerling M., and Hardin R., (2015). “Using a Mobile 

Mapping Instrument to Evaluate a Permanent Grid Sampling Frame” Proc. In the 2015 Federal 

Committee on Statistical Methodology.

Boryan, Claire, Lawson, L., Abreu, D.A., Weaber, J., Gerling, M., Hyman, M., and Hardin, R. 

(2017). “Comparing Geographic Information System (GIS) Calculated Acres to Farmer Reported 

Acreage Utilizing a Mobile Mapping Instrument” Research and Development Division. RDD 

Research Report #RDD-17-XX. Washington, DC: USDA, National Agricultural Statistics Service. 

Pending internal review.

Boryan, Claire, Yang, Z., Di, L., and Hunt, K., (2014). “A New Automatic Stratification Method 

for U.S. Agricultural Area Sampling Frame Construction Based on the Cropland Data Layer”

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, pp. 1939-

1404, May 2014, DOI: 10.1109/JSTARS.2014.2322584. 

Boryan, Claire, Yang, Z., Mueller R., and Craig M., (2011). “Monitoring US Agriculture: the U.S. 

Department of Agriculture, National Agricultural Statistics Service Cropland Data Layer 

Program” Geocarto International, 26, (5): 341-358.

Cotter, Jim, 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.

Gerling, Michael, Lawson L., Weaber J., Dotts A., Vardeman A., and Wilson E. (2015). “Field 

Data Collection Using Geographic Information Systems Technologies and iPads on the USDA’s 

June Area Frame Survey” Research and Development Report RDD-15-03, United States 

Department of Agriculture, National Agricultural Statistics Service.

Lawson, Linda A., Abreu D.A., Boryan, C., Gerling, M., and Hardin, R., (2015). “A Summary of 

Issues Associated with the Modernization of Data Collection” Proceedings of the 2015 Federal 

Committee on Statistical Methodology.

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

<http://nationalmap.gov/small_scale/a_plss.html> (last accessed 02/24/16).

USDA FSA (2015). National Agricultural Imagery Program (NAIP) Imagery,

<http://www.fsa.usda.gov/programs-and-services/aerial-photography/imageryprograms/naip-imagery/> (last accessed 02/24/2016).
A-1

APPENDIX A
B-1

APPENDIX B

ENUMERATOR’S DATA COLLECTION FORM (Front)
B-2

APPENDIX B

ENUMERATOR’S DATA COLLECTION FORM (Back)