Journal of Integrative Agriculture 2017, 16(2): 312–323

RESEARCH ARTICLE

Available online at www.sciencedirect.com

ScienceDirect

Developing crop specific area frame stratifications based on 

geospatial crop frequency and cultivation data layers

Claire G. Boryan1, Zhengwei Yang1, Patrick Willis1, Liping Di2

1 Research and Development Division, USDA National Agricultural Statistics Service, VA 22030, USA

2 Center for Spatial Information Science and Systems, George Mason University, VA 22030, USA

Abstract

Area Sampling Frames (ASFs) are the basis of many statistical programs around the world. To improve the accuracy, 

objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting 

frequency and cultivation data is proposed. This paper investigates using 2008–2013 geospatial corn, soybean and wheat 

planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota 

(SD) U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF 

stratifications are developed based on crop frequency statistics derived at the primary sampling unit (PSU) level based 

on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop 

stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF 

stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service 

(NASS) ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual 

corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications 

consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency (FSA) 

Common Land Unit (CLU) and 578 administrative data. This demonstrates that the new stratifications based on crop planting 

frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that 

the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates. 

Keywords: cropland data layer, crop planting frequency data layers, automated stratification, crop specific stratification, 

multi-crop stratification

1. Introduction 

It is well known that the purpose of stratification is to increase 

the efficiency of estimators by grouping sampling units into 

relatively homogeneous strata. Area Sampling Frames 

(ASFs) have been used by the National Agricultural Statistics 

Service (NASS) since 1954 as a primary tool for conducting 

surveys to gather information on crop acreage and other agricultural information. They are considered “the backbone to 

the agricultural statistics program of the National Agricultural 

Received 22 October, 2015 Accepted 9 May, 2016

Correspondence Claire G. Boryan, Tel: +1-202-690-0449, 

E-mail: Claire.boryan@nass.usda.gov 

© 2017, CAAS. Published by Elsevier Ltd. This is an open 

access article under the CC BY-NC-ND license (http://

creativecommons.org/licenses/by-nc-nd/4.0/)

doi: 10.1016/S2095-3119(16)61396-5
Claire G. Boryan et al. Journal of Integrative Agriculture 2017, 16(2): 312–323 313

Statistics Service” (Ford et al. 1986; Vogel 1995; Nusser 

and House 2009; Arroway et al. 2010; Cotter et al. 2010). 

NASS’s primary area frame based survey is the June Area 

Survey (JAS) in which approximately 11000 one square mile 

sample segments are visited by enumerators each year at 

the beginning of the growing season to collect crop type 

and acreage information. Estimates of crop acreage and 

livestock inventories are based on the data collected during 

the JAS. The NASS stratification is based on a categorization of the land into agricultural intensity groups based on 

percent cultivation. This traditional stratification of land cover 

has been conducted using visual interpretation of aerial or 

satellite data, or topographic maps since the 1950s. This 

traditional state area frame stratification method has been 

widely used around the world (Cotter and Tomczac 1994; 

Hanuschak and Morrisey 1978; Villalabos and Wallace 1998; 

Cotter et al. 2010). 

There are different varieties of the area frame stratification method and its applications. The European Union 

(EU) Monitoring Agricultural Resources (MARS) remote 

sensing project used the NASS traditional ASF construction 

method to conduct the Land Use/Land Cover Area Frame 

Survey (LUCAS) in 1988. They proposed using a regular 

grid of square segments instead of physical boundaries 

(roads, railroads and rivers) for segment delineation to 

reduce cost (Gallego et al. 1994). The LUCAS sample 

units are points defined as 3 m diameter circles (Gallego 

and Delincè 2010). The original objective of the LUCAS 

was to provide annual European crop estimates. These 

objectives were expanded to include the collection of land 

use and environmental monitoring data (Gallego and Delincè 

2010). The Italian Agricultural Survey (AGRIT) program 

uses an area frame created from a regular grid of points 

with a resolution of 500 m. Point sampling is used for data 

collection for the multi-purpose survey to derive combined 

estimates of crop acreage, yields and land use (Carfagna 

and Gallego 2005; Benedetti et al. 2015). The Utilization du 

territoire (TER-UTI) is an annual survey conducted by the 

French Ministry of Agriculture, in which a grid area frame and 

sample points are used to collect land use and land cover 

information as well as to derive an estimate of land cover 

change. In 2005, the survey was redesigned to provide 

consistency with the TER-UTI. The survey is now known as 

the TER-UTI LUCAS (Benedetti et al. 2015). A combination 

of area sampling frames and list sampling frames are used 

for agricultural surveys in Brazil, Canada, Honduras, the 

U.S. and Albania, while an ASF alone is used in Argentina, 

Morocco, Nicaragua, Pakistan, Columbia, Costa Rica, Dominican Republic, El Salvador and Guatemala, France, Italy 

and Spain (Villalabos and Wallace 1998). In addition, ASFs 

were used in a variety of agricultural research studies such 

as: 1) evaluation of the prevalence of brown stem rot in the 

north central U.S. (Workneh et al. 1999), 2) improvement of 

agricultural ground survey estimates as part of the MARS 

project launched by the European Union in 1989 (Tsiligirides 

1998), 3) comparing the utility of frequently used estimators 

based on an ASF utilized in agricultural surveys (Faulkenberry and Garoui 1991) and 4) development of crop area 

estimation at a regional level in the Islamic Republic of Iran 

(Pradhan 2001). 

The majority of area sampling frames are developed 

using visual interpretation of aerial photography or satellite 

data for stratification. Geospatial data are not used in a fully 

automated manner in any of these applications. Boryan 

et al. (2015) recently developed a new automated stratification method that utilized available NASS geospatial data 

Cropland Data Layers (CDLs) (Boryan et al. 2011), rather 

than using visual interpretation of satellite imagery and 

aerial photography (traditional method) to define percent 

cultivation of land areas. The new method resulted in ASFs 

with significantly improved accuracy, in strata with greater 

than 15% cultivation, as well as, improved objectivity and 

efficiency at reduced cost (Boryan et al. 2014a). This automated stratification method was integrated into NASS Area 

Frame construction operations and evolved into a hybrid 

process which combines automated geospatial stratification 

with manual editing and review (Boryan et al. 2014a; Boryan 

and Yang 2014).

Information on the geospatial distribution of future crop 

specific planting is critical for improving agricultural survey 

estimates (increasing the efficiency), and other agricultural 

business related decision making, such as agricultural 

production planning and agricultural product commodity 

inventory control. The NASS’s traditional area sampling 

frame stratification and the CDL based automated stratification method (Boryan et al. 2014a), are based on spatial 

percent cultivation data, which have very limited predictability of future crop planting. To improve geospatial predicative 

information regarding future crop planting, Boryan et al.

(2015) developed US national 30 m resolution crop specific 

planting frequency data layers using multi-year CDL data 

(Boryan et al. 2014b). These data sets provide crop specific 

planting frequency information for major crops such as corn, 

soybean, wheat and cotton. The Crop Planting Frequency 

Data Layers and Cultivation Layers are highly accurate 

(USDA, NASS 2016). Using Crop Planting Frequency Data 

Layers for crop specific area frame stratification is therefore 

proposed in this paper to improve the predictive capability 

of the developed area frame so that ultimately the obtained 

estimates will be more accurate and with smaller coefficients 

of variation. 

In this paper, three single crop (corn, soybean and wheat) 

specific stratifications are developed individually using the 

specific Crop Frequency Data Layers of interest. The corn, 
314 Claire G. Boryan et al. Journal of Integrative Agriculture 2017, 16(2): 312–323

soybean or wheat specific planting frequency strata are substratified based on percent cultivation resulting in 32 unique 

strata (per frame). The substratification was conducted to 

determine if it was possible to identify where “other” crops 

(other than targeted crops), would be planted. The single 

crop specific stratifications are not intended for direct use to 

stratify the NASS ASFs for use in the JAS, in which multiple 

crops are estimated. However, by conducting substratification of the crop planting frequency based stratifications, with 

the cultivation data, the single crop specific stratifications are 

potentially applicable to multi-crop surveys and estimation. 

In addition, a multi-crop specific stratification is also developed to illustrate how these single Crop Frequency Layers 

can be used together to create one unified multi-crop specific 

stratification. This paper presents the following results: 1) 

an evaluation of the percent cultivation accuracy of the three 

single crop ASF stratifications in comparison to the current 

NASS South Dakota ASF, 2) a determination whether the 

primary sampling unit (PSU) distributions in the three single 

crop stratifications are consistent with the current NASS ASF 

based on percent cultivation alone and, most importantly, 

3) a determination whether the crop specific stratifications 

based on 2008–2013 data can predict patterns of future 

crop specific planting at the PSU level as verified by 2014 

Farm Service Agency (FSA) Common Land Unit (CLU) Data 

(Heard 2002; FSA 2014).

2. Materials and methods

2.1. Study site and experimental data

South Dakota (SD) U.S. is selected as the study area for 

this investigation because 1) it is a major corn, soybean 

and wheat producing state and serves as a good example 

of the range of agriculture grown in the U.S. and 2) the 

USDA FSA has digitized the SD field boundaries and attributes them annually with crop and non-crop information, 

known as FSA CLU data, for approximately 98% of SD’s 

land cover. The FSA CLU data are utilized for validation of 

the crop specific stratifications. The geospatial data used 

in this research include: the NASS SD 2008–2013 CDLs; 

the NASS SD 2013 Cultivation Layer, the SD Corn, Soybean and Wheat-Crop Planting Frequency Data Layers; 

the NASS SD Area Sampling Frame; and 2014 SD Farm 

Service Agency Common Land Unit data. The Cropland 

Data Layers are annually updated, georeferenced crop 

specific land cover classifications with a 30–56 m resolution. 

The CDLs are 85–95% accurate for major crops in large 

agricultural states (Boryan et al. 2011). The NASS 2013 

Cultivation Layer is created using 2009–2013 CDL data 

and identifies land as cultivated or non-cultivated at a 30-m 

resolution (Boryan et al. 2012). The NASS 2013 national 

scale Cultivated Layer has a producer accuracy of 97.3% 

(non-cultivation) and 98.5% (cultivation) and a user accuracy 

of 97.8% (non-cultivation) and 97.5% (cultivation) (Boryan 

et al. 2012). The 2008–2013 SD Crop Planting Frequency 

Data Layers identify the number of years that a 30-m pixel 

is classified as planted to a specific crop over a defined 

time period in the CDL input data. The accuracies of the 

national scale Crop Planting Frequency Data Layers are 

91.00, 90.13, 87.67 and 85.96% for corn, cotton, soybeans 

and wheat, respectively (Boryan et al. 2014b). Fig. 1-A 

illustrates the U.S. NASS 2013 Cultivated Layer. The black 

box identifies the state of SD. The dark pixels identify cultivation and the light pixels identify non-cultivation. Fig. 1-B 

illustrates the SD 2008–2013 Corn Planting Frequency 

Data Layer with a zoom that indicates the levels of corn 

planting frequency from 2008–2013. In the Crop Planting 

Frequency Data Layers, the darker shades identify pixels 

that are planted more years to the specific crop. The lighter 

shades indicate lower planting frequency from 2008–2013. 

The illustration on Fig. 1-C is the SD 2008–2013 Soybean 

Planting Frequency Data Layer. Illustration (Fig. 1-D) 

is the SD 2008–2013 Wheat Planting Frequency Data 

Layer. In SD, corn and soybeans are commonly rotated in 

the same areas and wheat is planted more often in central 

and western SD.

In SD, a relatively small amount of acreage is planted to 

multiple crops in the same year, due to the short growing 

season. For example, approximately 11054 acres of double 

crop winter wheat and corn, 406.0 acres of double crop corn 

and soybeans, and 3658 acres of double crop winter wheat 

and soybeans were grown in 2014 out of a total of 43257079 

million acres of farmland in SD (Census of Agriculture 2012). 

As shown in Table 1, crops planted more than once during a 

growing season are defined in one single category such as 

CDL code 225 and 226. For this assessment, 30 m pixels 

(0.22 acre) that are identified as planted to two crops in the 

same year are included in both Crop Planting Frequency 

Data Layers. Table 1 illustrates the CDL categories that are 

recoded to create the Corn, Soybean and Wheat Planting 

Frequency Data Layers (Boryan et al. 2014b). 

The state level ASFs, as illustrated in Fig. 2-A, in shape 

file format, are constructed with PSUs defined by physical 

features on the ground (roads, railroads and rivers) and stratified based on percent cultivation (all crop types included). 

The new crop specific frequency data layer based area 

frames are constructed based on the existing PSUs. As 

illustrated in Fig. 2-B, the FSA CLU data are standardized 

geographic information system (GIS) data layers which are 

updated annually with farmer reports that provide crop type 

and acreage information of the nation’s farms and fields that 

were established to support farm commodity, conservation 

and disaster response (Heard 2002; FSA 2014). The NASS 
Claire G. Boryan et al. Journal of Integrative Agriculture 2017, 16(2): 312–323 315

CDLs, the Crop Planting Frequency and Cultivated Layer, 

with metadata, are publically available on the NASS web 

GIS application CropScape (Han et al. 2012). 

2.2. Methodology

In this study, 2008–2013 Crop Frequency Data Layers are 

used to derive single and multiple crop-specific stratifications 

based on ASF PSUs rather than using Cultivation Data Layers. Crop Frequency Data Layers are used to automatically 

calculate crop specific planting frequency means at the PSU 

level for stratification because the Crop Planting Frequency 

Data Layers provide improved predictive information of 

future crop specific planting. In this study, the K-means 

clustering method was selected to group the ASF primary 

sampling units into eight primary strata based on corn, 

soybean or wheat planting frequency means (depending 

on crop targeted) where PSUs are grouped into strata with 

the nearest crop specific planting frequency mean. The 

K-means clustering method was selected because it is a 

robust algorithm, which has been used for many decades 

and is well documented (Forgy 1965; MacQueen 1967; 

Hartigan and Wong 1979). Further, the K-means clustering 

algorithm is readily available in ESRI’s ArcGIS software 

which is used within NASS for Area Frame construction. 

Crop specific planting frequencies were clustered to define 

strata that are more homogeneous within strata and heterogeneous among strata (based on the crop specific planting 

frequency mean values calculated at the PSU level). The 

detailed procedures to construct the single and multi-crop 

stratification using the Crop Frequency Data Layers are 

provided in the following subsections. 

2.3. Single crop specific stratifications

The proposed crop specific stratifications are based on the 

individual Crop Frequency Data Layers. The Cultivation 

Layer is used for sub-stratification only. Three single crop 

Fig. 1 A, United States 2013 Cultivated Layer. Black box identifies South Dakota. B, South Dakota Corn Planting Frequency Data 

Layer (2008–2013). C, South Dakota Soybean Planting Frequency Data Layer (2008–2013). D, South Dakota Wheat Planting 

Frequency Data Layer. The small black boxes in B–D identify the location of the zoomed areas in the Corn, Soybean and Wheat 

Planting Frequency Data Layers. The darker shades in B–D identify pixels with higher planting frequency and lighter shades 

identify pixels with lower planting frequency. 
316 Claire G. Boryan et al. Journal of Integrative Agriculture 2017, 16(2): 312–323

specific stratifications are derived independently with the 

same procedure including 1) a corn frequency stratification, 2) a soybean frequency stratification and 3) a wheat 

frequency stratification. The detailed steps of the procedure 

to conduct single crop stratification based on crop specific 

planting frequency and cultivation, are given as follows:

1) Load a SD Crop Specific (corn, soybeans or wheat) 

Planting Frequency Layer.

2) Load a SD ASF PSU boundary layer and the 2013 

Cultivation Layer individually.

3) Overlay ASF PSU boundary on the Crop Planting 

Frequency Layer and on the 2013 Cultivation layer.

4) Compute the crop (corn, soybeans or wheat) planting 

frequency mean and percent cultivation of each ASF PSU 

within the PSU boundary. 

5) Add values of the computed PSU level crop specific 

frequency mean and the percent cultivation as new attributes 

to the SD ASF PSU boundary layer for all PSUs.

6) Conduct a K-means clustering based on the crop 

specific frequency mean variable (no spatial constraint) to 

create eight primary strata. 

7) Sub-stratify the eight primary crop specific strata, 

based on crop planting mean frequency ranges, by percent 

cultivation into four substrata including: 1) >75% cultivation, 

2) >50–≤75%), 3) >15–≤50%, 4) ≥0.0–≤15%.

For this assessment, all SD ASF PSUs are grouped into 

eight strata because this is the same number of strata in the 

current NASS SD Area Frame. Eight strata were determined 

to provide a sufficient number of homogeneous groups to 

improve efficiency while maintaining a sufficiently large sample size within strata. Utilizing the same number of strata, 

as the current SD ASF, will facilitate the comparison of the 

new crop frequency stratification method, with traditional 

stratification, based on percent cultivation, in the future. 

Finally, the corn, soybean or wheat planting frequency strata 

are substratified, based on percent cultivation, resulting in 

32 unique strata (per frame). The substratification based 

on cultivation was conducted to identify where crops, other 

than those targeted with the specific Crop Frequency Data 

Layer, were located. The single crop specific stratifications 

are potentially applicable to multi-crop surveys and estimation due to the substratification based on percent cultivation.

2.4. Multi-crop specific stratification

In reality, single crop surveys are not common. Most agricultural surveys target multiple crops in a single survey. 

The single Crop Frequency Layers are naturally for single 

crop stratification. However, they can be easily used for 

multi-crop specific stratification. The procedure to create 

the multiple crop specific stratification based on the crop 

planting frequency and cultivation are similar to that of single 

crop stratification. There are three changes in the multi-crop 

stratification procedure: 1) in the first step, all targeted Crops’ 

(in this study, SD corn, soybean and wheat) Frequency 

Layers are loaded (no Cultivation Layer); 2) in step 6, the 

K-means clustering is based on multivariable corn, soybean

and wheat planting frequency means which are calculated 

at the PSU level (no spatial constraint) to create eight primary strata; and 3) there is no sub-stratification step. Every 

created stratum has three variables: corn, soybean and 

wheat frequencies. The proportions of crops are different 

within stratum and among strata. This information will help 

multivariate sample allocation (Bethel 1989). 

2.5. Crop specific stratification assessment

For this research, three single crop specific stratifications 

and one multi-crop specific stratification were created. The 

2014 FSA CLU data were used to assess their predictive 

capabilities. The 2014 FSA CLU/ 578 data are a comprehensive source of in situ data reported in 2014. The three 

single crop specific stratifications are evaluated based on: 

1) mean corn, soybean or wheat acreage increasing from 

strata 1–8 in 2014, 2) how accurately the new frame’s PSU 

Table 1 Cropland Data Layer (CDL) categories recoded to 

build crop planting frequency data layers 

CDL code Crop description

Corn

1 Corn

225 Double crop winter wheat/Corn

226 Double crop oats/Corn

237 Double crop barley/Corn

241 Double crop corn/Soybeans 

Cotton 

2 Cotton

232 Double crop lettuce/Cotton

238 Double crop winter wheat/Cotton

239 Double crop soybeans/Cotton

Soybean

5 Soybeans

26 Double crop winter wheat/Soybeans

239 Double crop soybeans/Cotton

240 Double crop soybeans/Oats

241 Double crop corn/Soybeans

254 Double crop barley/Soybeans

Wheat

22 Durum wheat

23 Spring wheat

24 Winter wheat

26 Double crop winter wheat/Soybeans

225 Double crop winter wheat/Corn

230 Double crop lettuce/Durum wheat

234 Double crop durum wheat/Sorghum

236 Double crop winter wheat/Sorghum

238 Double crop winter wheat/Cotton
Claire G. Boryan et al. Journal of Integrative Agriculture 2017, 16(2): 312–323 317

percent cultivation matches the percent cultivation reported 

in the 2014 FSA CLU data, 3) if the PSU distribution of 

the three stratifications are similar to the PSU distribution 

of the current NASS ASFs and 4) if the corn, soybean and 

wheat frequency and cultivation stratifications accurately 

predict crop specific planting patterns in 2014. The multicrop (corn, soybean and wheat) frequency stratification is 

evaluated primarily based on how reliably it predicts crop 

specific planting patterns as well. 

3. Results and discussion 

3.1. Single crop specific stratifications

In the three single crop specific stratifications, strata are 

defined by crop specific planting frequency ranges as 

illustrated in Tables 2–4. For example, in the corn specific 

stratification as shown in Table 2, stratum 8, the highest 

frequency range, includes PSUs with a mean corn frequency between 2.85–4.86 years out of 6 (2008–2013) years. 

Table 2 summarizes the SD eight corn planting frequency 

strata, PSU distribution, and corn planting frequency ranges 

that define each stratum, as well as, mean corn acres and 

percent corn cover reported in the 2014 FSA CLU data by 

stratum. Results indicate that mean corn planting frequency 

calculated at the PSU level is a reliable predictor of corn 

planting with increasing quantities of mean corn acreage 

reported from strata 1 to 8 in 2014. Further, the number 

of PSUs, per stratum, decreases from strata 1 to 8 as the 

corn planting frequency stratum increases. This means that 

higher corn planting frequency is a strong indication of higher 

corn acreage. Moreover, the percent corn planting coverage 

increases from strata 1 to 8 as corn planting frequency increases. These results show the strong predictive power of 

the newly developed corn specific area frame stratification. 

Fig. 3 illustrates the spatial distribution of the strata 

derived from planting frequency and percent cultivation for 

three crops in SD U.S. Fig. 3-A illustrates the corn strata 

distribution. The dark green colors identify PSUs, grouped 

into strata with high corn planting frequency (strata 7 and 8) 

located predominantly in south eastern SD. The light green, 

yellow and orange illustrate the mid corn planting frequency 

Fig. 2 National Agricultural Statistics Service’s (NASS) South Dakota Area Frame Primary Sampling Units (PSUs) stratified by 

percent cultivation are illustrated in A and South Dakota Farm Service Agency (FSA) Common Land Unit ( CLU) ground truth data 

are illustrated in B with field boundaries and associated crop and non-crop attributes. The white box identifies a zoom area which 

includes approximately three PSUs to illustrate the FSA Common Land Unit crop fields included in the zoom area. 
318 Claire G. Boryan et al. Journal of Integrative Agriculture 2017, 16(2): 312–323

(strata 3–5) located in central SD and the brown colors identify low levels (strata 1–2) of corn planting frequency. Color 

saturation is used for the three crop specific stratifications 

to illustrate stratum percent cultivation. The dark shades 

identify PSUs with the highest percent cultivation and the 

light shades represent low levels of cultivation. Specifically, 

the darkest shade identifies PSUs which are greater than 

75% cultivation, while the lightest shade identifies PSUs 

with 15% or less cultivation. 

Table 3 summarizes the soybean planting frequency strata, PSU distribution, the soybean planting frequency ranges 

by stratum, 2014 FSA CLU reported mean soybean acres 

and mean percent soybean coverage by stratum. As shown 

in Table 3, the soybean planting frequency statistics are also 

a reliable predictor of soybean planting with increasing quantities of mean soybean acreage reported from strata 1 to 8. 

Mean percent soybean coverage by stratum also increases 

from strata 1 to 8. Fig. 3-B illustrates the distribution of SD 

soybean planting and percent cultivation. Dark blue colors 

identify strata with high soybean planting frequency (strata 

7 and 8). The spatial distribution of soybean planting in SD 

Table 2 South Dakota corn planting frequency strata based 

on corn frequency ranges, frame primary sampling unit (PSU) 

distribution, and 2014 Farm Service Agency reported mean 

corn acres/percent corn coverage by stratum

Strata No. of

PSUs

Corn frequency 

range 

Mean corn 

acres 

Mean corn 

(%) 

1 4 634 0.000–0.2558 58.402 2.575

2 2 443 0.256–0.6508 203.782 12.041

3 2 497 0.651–1.0550 288.300 19.077

4 2 500 1.0551–1.4593 400.515 26.975

5 2 268 1.4596–1.8645 490.888 32.978

6 1 909 1.8651–2.2901 638.074 39.241

7 1 544 2.2902–2.8491 812.784 44.667

8 356 2.8506–4.8648 922.534 51.578

Fig. 3 South Dakota Crop Specific Area Frame Stratifications. A, corn frequency and percent cultivation. B, soybean frequency 

and percent cultivation. C, wheat frequency and percent cultivation. D, multi-crop corn, soybean and wheat frequency stratification. 
Claire G. Boryan et al. Journal of Integrative Agriculture 2017, 16(2): 312–323 319

closely mirrors corn’s spatial distribution but soybeans are 

not planted as intensely in Central SD. The light blue and 

green color ranges indicate mid and low levels of soybean 

planting frequency. In Western SD, very little soybeans are 

planted as evidenced by Fig. 3-B. 

Table 4 summarizes similar results for the eight wheat 

frequency strata. Similar to the Corn and Soybean Planting 

Frequency Data Layers, the Wheat Planting Frequency Data 

Layer is a reliable predictor of wheat planting in 2014 with 

reported acreage increasing from strata 1 to 8. The percent of land planted to wheat in 2014 increases predictably 

from strata 1 to 8, as the wheat planting frequency ranges 

increase. Fig. 3-C illustrates the spatial distribution of the 

SD wheat crop and percent cultivation. Wheat is planted 

with high frequency in Central SD (strata 7 and 8) and in 

Western SD and at a low frequency in the far West and East 

of SD (strata 1 and 2). As shown in Fig. 3, the derived single 

crop specific stratifications provide more detailed geospatial 

distribution information of crop specific planting frequency 

and cultivation for sample selection when compared with 

an ASF stratification based on percent cultivation alone.

More detailed analysis of the soybean frequency and 

cultivation stratification results are summarized in Table 5 

as an example. The detailed results of the corn frequency/

cultivation (Boryan et al. 2015) and wheat frequency/cultivation stratifications are very similar to the soybean results. 

Therefore, the detailed analysis results of the corn and 

wheat stratification will not be included in this paper. Table 5 

illustrates the eight primary soybean frequency strata with 

substrata based on percent cultivation (a–d) with accuracies 

determined using the 2014 FSA CLU data. Table 5 further 

identifies by strata the soybean planting frequency range, 

number of PSUs, mean percent cultivation, percent cultivation accuracy (based on whether PSU percent cultivation 

matches the 2014 FSA CLU data summarized at the PSU 

level), mean values for corn acres, percent corn, soybean 

acres, percent soybean, wheat acres and percent wheat 

reported in the 2014 FSA CLU data. 

The percent cultivation substrata of the new SD ASF design has an overall accuracy of 72.365% (validated using the 

2014 FSA CLU data), which is consistent with the accuracy of 

the current NASS ASF of 74% based on percent cultivation, 

which is validated using the JAS data (Cotter et al. 2010). It 

should be noted that accuracy of cultivation is usually higher 

than the accuracy of an individual crop classification due to 

the error cancellation of the cultivation calculation. 

The new crop specific stratification’s PSU distribution is 

consistent with the PSU distribution of the current NASS 

ASF. As shown in Table 5, in stratum 1, which has the 

lowest soybean frequency range, the majority of PSUs are 

0.0–15% cultivation. In strata 2 and 3, which also have low 

soybean frequency, the majority of the PSUs are 15–50% 

cultivation. In strata 4 and 5 which are the mid soybean 

intensity strata, the majority of PSUs are 50–75% cultivation. 

Further, virtually all PSUs in the highest soybean intensity 

strata are greater than 75% cultivation. 

The soybean frequency/percent cultivation stratification indicates that soybean and corn are rotated closely, but they are 

not always grown together. As soybean planting increases 

by strata, corn planting also increases in parallel. In stratum 

1, soybean planting is low and corn planting is higher but in 

strata 3–8 the acreage and percentages are comparable. 

Further, wheat planting is almost a compliment of soybean 

planting in SD with the highest levels of wheat planting in 

strata 1 and 2 of the soybean frequency/percent cultivation 

stratification and the lowest levels of wheat in stratum 8. 

The substratification of eight primary soybean frequency 

strata into 32 total strata provides more PSU level crop 

specific information than the current NASS ASF based 

on percent cultivation alone. The novel ASF stratification 

creates strata where high concentrations of soybeans are 

likely planted (strata 7 and 8) and particularly in the PSUs in 

strata with greater than 75% cultivation (strata 7a and 8a). 

The crop specific stratification also identifies where wheat is 

Table 3 South Dakota soybean planting frequency strata based 

on soybean frequency ranges, frame primary sampling unit 

(PSU) distribution, and 2014 Farm Service Agency Common 

Land Unit reported mean soybean acres/percent soybean 

coverage by stratum

Strata No. of

PSUs

Soybean 

frequency 

range

Mean 

soybean 

acres

Mean 

soybean 

(%)

1 6 364 0.0000–0.1989 26.7632 1.4916

2 1 853 0.1995–0.5479 215.7756 13.4733

3 1 835 0.5481–0.9111 287.5780 19.7053

4 1 803 0.9119–1.2745 393.9005 26.9931

5 1 873 1.2749–1.6228 471.4280 31.8156

6 1 803 1.6233–1.9715 599.8244 37.4098

7 1 676 1.9721–2.3448 732.8084 42.0424

8 944 2.3456–3.5209 796.5825 47.0770

Table 4 South Dakota wheat planting frequency strata based 

on wheat frequency ranges, frame primary sampling unit 

distribution, and 2014 Farm Service Agency Common Land 

Unit reported mean wheat acres/percent wheat coverage by 

stratum

Strata No. of

PSUs

Wheat 

frequency 

range 

Mean 

wheat

 acres 

Mean 

wheat 

(%) 

1 8 112 0.0000–0.1626 23.255 0.968

2 3 640 0.1629–0.4262 86.111 4.892

3 2 467 0.4268–0.7499 174.050 9.910

4 1 521 0.7503–1.1463 251.572 15.682

5 1 006 1.1469–1.6249 346.729 23.217

6 749 1.6254–2.2204 501.761 31.395

7 550 2.2256–3.1929 752.500 40.113

8 106 3.2129–5.6657 1 002.771 56.010
320 Claire G. Boryan et al. Journal of Integrative Agriculture 2017, 16(2): 312–323

grown in higher concentrations (strata 1a and 2a). Soybeans 

and corn are planted in equivalent quantities, in strata 4a, 5a, 

6a. Furthermore, additional corn and wheat planting land 

cover statistics calculated at the PSU level and summed at 

the stratum level, as shown in Table 5, provide additional 

information regarding the corn and soybean rotation pattern 

and the distribution of wheat planting in these strata.

The single crop planting frequency stratifications are 

developed for individual crop surveys that are of great interest to commodity trading groups or researchers evaluating 

individual crops. However, the substratification of the crop 

planting frequency strata, by percent cultivation, can provide 

spatial distribution information of other crops. Therefore, 

the crop specific stratification results can also be used for 

multivariate sampling for other crops with appropriate sample allocation. A set of weights can be assigned for different 

strata. Of course, the appropriate sample allocation schema 

has to be developed.

3.2. Multi-crop specific stratifications

A multi crop specific (corn, soybeans, and wheat) stratification is also created by running the multivariate K-means 

clustering algorithm based on the corn, soybean and wheat 

Table 5 The primary eight soybean frequency strata with substrata based on percent cultivation (a–d)

Strata Soybean frequency 

range

No. of 

PSUs

Cultivation 

(%)1)

Cultivation 

accuracy (%)

Mean 

soybean

acres

Mean

soybean 

(%)1)

Mean corn

acres

Mean corn

(%)1)

Mean 

wheat acres

Mean 

wheat

(%)1)

1 0.0000–0.1989 6364 78.6 26.76 1.49 112.28 6.01 214.63 11.61

1a 829 >75 33.27 2.04 225.20 13.59 704.83 40.07

1b 876 >50–75 23.54 1.78 167.11 11.80 327.52 23.06

1c 1971 >15–50 34.62 2.03 136.91 6.93 199.42 9.47

1d 2688 ≥0.0–15 20.04 0.83 41.52 1.11 37.81 0.66

2 0.1995–0.5479 1853 78.7 215.78 13.47 267.07 16.48 178.25 10.27

2a 205 >75 240.12 15.10 458.09 26.33 688.80 35.32

2b 331 >50–75 263.27 19.93 364.62 26.53 251.03 17.37

2c 1249 >15–50 205.44 11.87 220.65 12.86 83.72 4.78

2d 68 ≥0.0–15 100.99 6.68 69.01 4.36 21.19 1.11

3 0.5481–0.9111 1835 77.9 287.58 19.71 303.62 20.49 118.34 7.50

3a 227 >75 387.23 25.99 459.30 29.95 385.89 21.94

3b 559 >50–75 331.94 23.59 354.69 24.84 132.20 9.17

3c 1048 >15–50 242.56 16.23 242.94 16.11 53.10 3.48

3d 1 ≥0.0–15 0.00 0.00 20.92 28.28 0.00 0.00

4 0.9119–1.2745 1803 65.1 393.90 26.99 389.71 26.27 102.08 6.59

4a 318 >75 528.24 33.25 556.85 34.76 205.74 11.73

4b 1121 >50–75 400.44 27.15 390.63 25.96 88.69 6.00

4c 364 >15–50 256.41 21.04 240.88 19.83 52.76 3.85

4d 0 ≥0.0–15 0.00 0.00 0.00 0.00 0.00 0.00

5 1.2749–1.6228 1873 64.2 471.43 31.82 476.03 31.56 83.09 5.64

5a 585 >75 603.70 37.52 615.29 37.23 134.07 8.42

5b 1245 >50–75 419.46 29.56 419.20 29.13 60.99 4.41

5c 43 >15–50 176.70 19.58 226.87 24.83 29.30 3.44

5d 0 ≥0.0–15 0.00 0.00 0.00 0.00 0.00 0.00

6 1.6233–1.9715 1803 62.2 599.82 37.41 591.75 36.62 78.45 5.20

6a 1130 >75 690.28 40.31 678.82 38.78 94.20 5.93

6b 668 >50–75 449.84 32.60 448.11 33.14 51.36 3.88

6c 5 >15–50 195.35 24.40 101.26 12.15 137.26 16.59

6d 0 ≥ 0.0–15 0.00 0.00 0.00 0.00 0.00 0.00

7 1.9721–2.3448 1676 60.8 732.81 42.04 726.33 41.85 63.82 3.94

7a 1565 >75 756.07 42.55 749.36 42.28 64.49 3.90

7b 111 >50–75 404.83 34.92 401.51 35.78 54.38 4.63

7c 0 >15–50 0.00 0.00 0.00 0.00 0.00 0.00

7d 0 ≥0.0–15 0.00 0.00 0.00 0.00 0.00 0.00

8 2.3456–3.5209 944 78.4 796.53 47.08 804.43 47.07 60.92 3.99

8a 933 >75 801.64 47.18 810.21 47.22 61.06 3.97

8b 11 >50–75 367.34 38.79 314.56 34.22 49.28 5.09

8c 0 >15–50 0.00 0.00 0.00 0.00 0.00 0.00

8d 0 ≥0.0–15 0.00 0.00 0.00 0.00 0.00 0.00

1) Percentages were calculated based on total PSU and cover. 
Claire G. Boryan et al. Journal of Integrative Agriculture 2017, 16(2): 312–323 321

Table 6 The eight multi-crop specific strata definitions, mean crop frequency values, and mean soybean, corn and wheat acres 

reported in 2014 

Variables Mean Mean 2014 soybean acres Mean 2014 corn acres Mean 2014 wheat acres

Stratum 1

(PSU no. 4254)

Soybean frequency 0.0649 2.3255

Corn frequency 0.1244 78.3554

Wheat frequency 0.0954 57.8367

Stratum 2 

(PSU no. 1675)

Soybean frequency 0.1239 78.8352

Corn frequency 0.3902 174.9277

Wheat frequency 0.8391 286.2222

Stratum 3

(PSU no. 500)

Soybean frequency 0.0882 60.7055

Corn frequency 0.4988 235.8626

Wheat frequency 2.9082 831.4245

Stratum 4

(PSU no. 1224)

Soybean frequency 0.1766 100.7665

Corn frequency 0.7117 241.3915

Wheat frequency 1.8089 498.2871

Stratum 5

(PSU no. 2807 )

Soybean frequency 0.7160 271.4267

Corn frequency 0.8967 285.8428

Wheat frequency 0.2474 69.1390

Stratum 6

(PSU no. 1458)

Soybean frequency 1.4895 512.0829

Corn frequency 1.3613 447.7145

Wheat frequency 1.0274 224.2992

Stratum 7

(PSU no. 3008 )

Soybean frequency 1.4444 450.6398

Corn frequency 1.6290 460.4094

Wheat frequency 0.2083 50.7613

Stratum 8

(PSU no. 3225)

Soybean frequency 2.1224 749.7595

Corn frequency 2.3779 782.8406

Wheat frequency 0.1549 41.5645

Strata 1–8

(PSU no. 18151)

Soybean frequency 0.8878 316.9351

Corn frequency 1.0674 352.8414

Wheat frequency 0.4847 140.9791

frequency mean values together calculated at the PSU level. 

Table 6 illustrates the resulting stratum definitions (including 

no. of PSUs per stratum) for the eight strata in the multi-crop 

stratification, which include a combination of characteristics 

including soybean planting frequency means, corn planting 

frequency means and wheat planting frequency means calculated at the PSU level. Further, soybean, corn and wheat 

acreage, reported in the SD FSA CLU data, are calculated at 

the stratum level to evaluate the predictability of the multiple 

crop specific stratification.

Fig. 3-D illustrates the spatial distribution of the multi-crop 

stratification. Table 6 includes the eight multi-crop strata 

definitions, which include the stratification variables and 

mean stratum values for each variable, as well as mean 

corn, soybeans and wheat acres reported in the FSA CLU 

data in 2014 for each stratum. General observations of 

the multi-crop stratification are that stratum 1 (light tan) in 

Fig. 3-D would be expected, based on crop planting frequency mean values, listed in Table 6, to include the least 

amount of crop acreage. Stratum 2 illustrated in the tan 

color in Fig. 3 would be expected to include moderately low 

levels of crop acreage for all three crops (Table 6). Stratum 3 

(dark red/brown) would have the largest quantities of wheat 

planting based on the high wheat frequency mean values. 

Stratum 4 (lighter red/brown) would be anticipated to include 

large amounts of wheat planting but not as much as stratum 3. 

From stratum 5 to 8 (blues to dark green) progressively 

larger amounts of corn and soybeans are anticipated to be 

reported in these strata based on the corresponding higher 

corn and soybean planting frequency ranges in Table 6. 

Stratum 8 (darkest green) would be expected to have the 

largest amounts of corn and soybean acreage based on the 

high soybean and corn planting frequency mean values. 

The FSA CLU data, reported in 2014 and listed in Table 6, 

confirm these general predications. 

Fig. 4-A illustrates the current NASS SD traditional ASF 

based on percent cultivation. Fig. 4-B illustrates the new 

multi-crop stratification based on the Crop Planting Frequency Layers. The spatial distribution of crop planting is 

consistent between these two stratifications. However, the 

multi-crop specific stratification provides more targeted crop 

specific distribution information with the homogeneous strata 
322 Claire G. Boryan et al. Journal of Integrative Agriculture 2017, 16(2): 312–323

based on crop specific planting frequency statistics while the 

traditional ASF has no crop specific planting information. As 

shown in Fig. 4-B, the corn, soybeans and wheat planting 

areas are clearly identified within strata. 

 Crop specific ASF stratifications, such as those presented in this paper, can be useful in targeting individual crops 

for sampling and estimation. Future research will include 

continued assessments of the multi-crop stratification that 

can be used to select samples and conduct estimation 

using the 2014 FSA CLU and 578 administrative data as 

proxy survey data which can be compared with the 2014 

JAS estimates.

4. Conclusion

This paper proposed using a novel method for land cover 

ASF stratification based on crop specific planting frequency and percent cultivation to build four new crop specific 

ASF stratification designs. In this paper, new SD corn, 

soybean and wheat stratifications were derived based on 

crop planting frequency and cultivation. Three single crop 

specific stratifications and a multi-crop specific stratification 

produced strata where reliably predicted quantities of target 

crops were reported grown in 2014. The four novel ASF 

stratifications provided additional crop specific information 

when compared to traditional stratification based on percent 

cultivation alone. The additional mean corn, soybean and 

wheat frequency data provided strong indications about 

where these crops are planted in large quantities, which 

was confirmed using the 2014 FSA CLU and administrative 

data 578 as in situ validation. In SD, soybeans and corn are 

grown in similar areas and rotated. Consequently, strata 3–8 

in the soybean frequency/percent cultivation stratification 

identifies the areas where equivalent quantities of soybeans 

and corn are grown. It is observed that all four ASF stratifications predict corn, soybean and wheat planting patterns 

well as verified by the 2014 FSA and 578 administrative data. 

This demonstrates that the new stratifications based on geospatial crop planting frequency (2008–2013) and cultivation 

are crop type independent and applicable to all major crops 

in addition to the specified crops. These results indicate 

that new SD crop specific stratifications based on the Corn, 

Soybean and Wheat Planting Frequency (2008–2013) and 

Cultivation Data Layers have great potential to improve ASF 

accuracy, efficiency and crop estimates.

Acknowledgements

The authors would like to thank Ms. Lee Ebinger from the 

United States Department of Agriculture for creating the 

figures for this paper and Dr. Shirley Sampson from the 

United States Department of Agriculture for editorial review. 

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