STRATIFICATION OF AN AGRICULTURAL AREA 

SAMPLING FRAME USING GEOSPATIAL 

CULTIVATION AND CROP PLANTING FREQUENCY 

DATA LAYERS

Claire G. Boryan, Zhengwei Yang and Patrick Willis

USDA National Agricultural Statistics Service (NASS) 

Claire.Boryan@nass.usda.gov

Federal Committee on Statistical Methodology Research Conference 

December 1 - 3, 2015 Washington, D.C. 
OUTLINE

• RATIONALE

• BACKGROUND

• OBJECTIVES

• STUDY AREA 

• DATA

• METHODOLOGY 

• RESULTS/DISCUSSION

• CONCLUSIONS

• FUTURE RESEARCH

• REFERENCES

Federal Committee on Statistical Methodology Research Conference

December 1 - 3, 2015 Washington, D.C. 
RATIONALE

• The National Agricultural Statistics Service (NASS) 

– provides timely, accurate and useful statistics in service to U.S. agriculture

– collects and disseminates data on all facets of agriculture

• NASS conducts June Agricultural Survey (JAS) and other agricultural 

statistics programs based on NASS Area Sampling Frame (ASF) [1]

• The ASF stratification is based on percent cultivated cropland at the 

primary sampling unit (PSU) level

• However, percent cultivation information does not provide crop 

specific predictive planting information, which is critical for 

sampling and crop estimation

• To iマpro┗e the A“F’s prediItaHility, ┘e iミ┗estigated usiミg Crop 

Planting Frequency Data Layers for ASF development

Federal Committee on Statistical Methodology Research Conference 

December 1 - 3, 2015 Washington, D.C. 
OBJECTIVES

• To develop a new ASF stratification with predicative crop 

planting information at the ASF Primary Sampling Unit level:

– Develop three crop specific ASF stratifications using the 

NASS Geospatial Crop Planting Frequency Data Layers and

the NASS Cultivated Layer derived from multi-year NASS 

Cropland Data Layers (CDLs) and 

– Evaluate them using 2014 Farm Service Agency (FSA) 

Common Land Unit (CLU) Data 

Federal Committee on Statistical Methodology Research Conference 

December 1 - 3, 2015 Washington, D.C. 
BACKGROUND 

NASS AREA SAMPLING FRAME (ASF)
ASF PSUs, with percent 

cultivation, overlaying the NASS 

cultivated layer 

BACKGROUND 

ASF BASED ON NASS CULTIVATED LAYER [2-3]

Each PSU labeled with a stratum 

category based on the state 

specific stratum definitions
STUDY AREA 

SOUTH DAKOTA (SD) - U.S.A

• Large quantities of corn, soybeans, and wheat 

• Greater than 95% coverage with Farm Service Agency Common Land Unit 

data for in situ validation and assessment of ASF performance

Federal Committee on Statistical Methodology Research Conference 

December 1 - 3, 2015 Washington, D.C. 
DATA 

NASS SOUTH DAKOTA AREA SAMPLING FRAME

Roberts Co., SD Detail - Primary Sampling Units (PSUs)
DATA

THE NASS CROPLAND DATA LAYER (CDL) [4]

The CDL product is an annually produced, 30 m, geo-referenced, crop 

specific, land cover data set 

Available on NA““’s 

CropScape

http://nassgeodata.g

mu.edu/CropScape/
DATA

NASS CULTIVATED LAYER and 

CROP FREQUENCY DATA LAYERS [5] [6]
DATA 

FARM SERVICE AGENCY (FSA) COMMON LAND UNIT (CLU) 

& 578 ADMINI“TRATIVE さCROPざ DATA 
METHODOLOGY

CALCULATE MEAN CORN FREQUENCY (YEARS)

AT THE PSU LEVEL 

(Using the 2008-2013 Corn Frequency Layer) 

PSUs with corn frequency means over the 

2008-2013 SD Corn Planting Frequency 

Data Layer 

Each PSU labeled with a stratum category 

based on corn planting frequency
METHODOLOGY

CALCULATE PERCENT CULTIVATION AT PSU LEVEL 

(Using the 2013 Cultivated Layer) 

Area Frame: PSUs with percent cultivation 

based on the SD 2013 Cultivated Layer
METHODOLOGY

STRATIFICATION

STRATA

NUMBER 

OF 

PSUs

CORN PLANTING

FREQUENCY RANGE

(Number of years out of 6)

1 4634 0.0000 - 0.2558

2 2443 0.2560 - 0.6508

3 2497 0.6510 - 1.0550

4 2500 1.0551 - 1.4593

5 2268 1.4596 - 1.8645

6 1909 1.8651 - 2.2901

7 1544 2.2902 - 2.8491

8 356 2.8506 - 4.8648

K-means clustering was used on the mean corn planting frequency 

variable to create eight strata (same number as the current SD ASF)

Federal Committee on Statistical Methodology Research Conference 

December 1 - 3, 2015 Washington, D.C. 
METHODOLOGY 

SUB-STRATIFICATION

SD corn planting frequency strata are sub-stratified by percent cultivation

Federal Committee on Statistical Methodology Research Conference 

- December 1 - 3, 2015 Washington, D.C. 

Strata NUMBER OF

PSUs

PERCENT 

CULTIVATION

2a 214 >75%

2b 376 >50 % - 75%

2c 1768 >15 % - 50%

2d 85 ≥ ヰ.ヰ - 15%

Total 2443 N/A

STRATA NUMBER OF

PSUs

PERCENT 

CULTIVATION

1a 293 >75%

1b 379 >50 % - 75%

1c 1291 >15 % - 50%

1d 2671 ≥ ヰ.ヰ - 15%

Total 4634 N/A

STRATA

CORN PLANTING

FREQUENCY RANGE

(Number of years out of 6)

NUMBER 

OF

PSUs

1 0.00-0.2558 4634

2 0.256-0.6508 2443

.

.

.

8 2.8506 - 4.8648 356
RESULTS

CORN STRATIFICATION
SOYBEAN WHEAT 

RESULTS 

SOYBEAN AND WHEAT STRATIFICATIONS

Federal Committee on Statistical Methodology Research Conference 

December 1 - 3, 2015 Washington, D.C. 
STRATA NUMBER of 

PSUs

CORN PLANTING

FREQUENCY RANGE

(Number of years out of 6)

MEAN CORN 

ACRES

MEAN CORN 

PERCENT

1 4634 0.0000 - 0.2558 58.402 2.575

2 2443 0.2560 - 0.6508 203.782 12.041

3 2497 0.6510 - 1.0550 288.300 19.077

4 2500 1.0551 - 1.4593 400.515 26.975

5 2268 1.4596 - 1.8645 490.888 32.978

6 1909 1.8651 - 2.2901 638.074 39.241

7 1544 2.2902 - 2.8491 812.784 44.667

8 356 2.8506 - 4.8648 922.534 51.578

1-8 18151 0.0000 - 4.8648 N/A N/A

Federal Committee on Statistical Methodology Research Conference 

December 1 - 3, 2015 Washington, D.C. 

2014 FSA CLU Mean Corn Acres/Percent Corn Increases from stratum 1 - 8

RESULTS
RESULTS 

PERCENT CULTIVATION &

PSU DISTRIBUTION

• Percent cultivation substrata of the new SD ASF design has an 

overall accuracy of 72.365%

– validated using the 2014 FSA CLU & 578 data

• Accuracy is consistent with the accuracy of the current SD ASF 

of 74% based on percent cultivation

– validated using 2014 June Agricultural Survey data 

• The new A“F’s P“U distribution is consistent with the current 

NASS SD ASF

Federal Committee on Statistical Methodology Research Conference -

December 1 - 3, 2015 Washington, D.C. 
RESULTS 

PREDICTION OF CROP SPECIFIC PLANTING 

PATTERNS IN 2014

• The sub-stratification of the eight primary corn, soybeans and 

wheat frequency strata provides additional crop specific 

information at the PSU level than the current NASS ASF based 

on percent cultivation

• The novel ASF method creates strata where high 

concentrations of corn, soybean and wheat are likely planted 

which was confirmed using the 2014 FSA CLU data as in situ 

validation data

Federal committee on Statistical Methodology Research 

Conference - December 1 - 3, 2015 Washington, D.C. 
CONCLUSIONS

• It was observed that all three ASF designs consistently 

predicted corn, soybean and wheat planting patterns well

as verified by the 2014 FSA CLU and 578 administrative 

data

• The new stratification based on crop frequency and 

cultivation is crop type independent and applicable to all 

major crops

• These results indicate that the new stratification method 

and the three novel SD crop specific ASF designs have great 

potential to improve ASF accuracy, efficiency and crop

estimates

Federal Committee on Statistical Methodology Research Conference 

December 1 - 3, 2015 Washington, D.C. 
FUTURE RESEARCH

• Future research will include 1) conducting estimation based 

on the new ASF stratifications using the 2014 FSA CLU & 

578 data as proxy survey data which can be compared with 

the 2014 JAS estimates and 2) creating and assessing a 

multivariate stratification based on the NASS Crop 

Frequency Data Layers

Federal Committee on Statistical Methodology Research Conference -

December 1 - 3, 2015 Washington, D.C. 
REFERENCES

[1] Cotter, J., Davies, C., Nealon, J. aミd RoHerts, R. ヲヰヱヰ. さArea Fraマe Desigミ for AgriIultural 

“ur┗eys,ざ iミ Agricultural Survey Methods (eds R. Benedetti, M. Bee, G. Espa and F. Piersimoni), 

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

[2] Boryan, C. Yang, Z. Di, L. and Hunter, K. さA ミe┘ autoマatiI stratifiIatioミ マethod for U.“. 

agriIultural area saマpliミg fraマe IoミstruItioミ Hased oミ the Croplaミd Data Layer,ざ IEEE Jourミal of 

Selected Topics in Applied Earth Observations and Remote Sensing, VOL. 7, NO. 11, NOVEMBER , 

2014

[3] Boryan, C. aミd Yaミg Z. ヲヰヱヴa, さOperatioミal iマpleマeミtatioミ of a ミe┘ autoマatiI stratifiIatioミ 

マethod usiミg geospatial Iroplaミd data layers iミ the NA““ area fraマe seItioミざ, ProIeediミgs of 

IGARSS 2014, IGARSS 2014 & 35th Canadian Symposium on Remote Sensing, Quebec City, 

Canada, July 13-18, 2014.

[4] Boryan, C., Yaミg, Z., Mueller, R., aミd Craig, M. ヲヰヱヱ. さMoミitoriミg U“ AgriIulture: The U“ 

Departマeミt of AgriIulture, Natioミal AgriIultural “tatistiIs “er┗iIe Croplaミd Data Layer Prograマ,ざ 

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

Federal Committee on Statistical Methodology Research Conference -

December 1 - 3, 2015 Washington, D.C. 
REFERENCES

[ヵ] Boryaミ, C., Yaミg Z., aミd Di, L., ヲヰヱヲ. さDeri┗iミg ヲヰヱヱ Iulti┗ated laミd Io┗er data sets usiミg 

U“DA ミatioミal agriIultural statistiIs ser┗iIe historiI Iroplaミd data layers,ざ ProI. of IEEE 

International Geoscience and Remote Sensing Symposium, July 22-27, 2012, Munich, Germany.

[6] Boryan, C. G., Yang Z., and Willis P. 2014 -C. "US Geospatial Crop Frequency Data Layers," 

Proc. Of the Third International Conference on Agro-geoinformatics (Agro-geoinformatics 2014), 

August 11-14 2014, Beijing, China, DOI:10.1109/Agro-Geoinformatics.2014.6910657.

Federal Committee on Statistical Methodology Research Conference 

December 1 - 3, 2015 Washington, D.C. 
Thank you

Questions?

Claire G. Boryan, Zhengwei Yang and Patrick Willis

USDA/NASS/RDD

claire.boryan@nass.usda.gov