Model-based Estimation of the Number of U.S.

Farms and Land in Farms from Survey and

Administrative Data

Habtamu Benecha1, Luca Sartore2,1, Arthur Rosales1,

Grace Yoon1, Tara Murphy1, Denise Abreu1

1United States Department of Agriculture

National Agricultural Statistics Service (USDA NASS)

2National Institute of Statistical Science (NISS)

FCSM 2022 – Survey Research Methods Section

October 27, 2022

“. . . providing timely, accurate, and useful statistics in service to U.S. agriculture.” 1
Disclaimer

The findings and conclusions in this presentation are those

of the authors and should not be construed to represent any

official USDA, or US Government determination or policy

I This research was supported in part by the intramural research

program of the U.S. Department of Agriculture, National

Agricultural Statistics Service (NASS)

FCSM 2022 – Survey Research Methods Section – Habtamu Benecha 2
PART I

MOTIVATION

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The June Area Survey (JAS)

I The June Area Survey is one of the largest surveys conducted

by NASS

I Based on an area frame, conducted every year during the first

two weeks of June

I All land in all states except Alaska stratified by land use

I Land divided into homogeneous strata (e.g., cultivated land,

urban area, etc.)

I Each of the strata is divided into several substratum containing

agriculturally similar areas

I Each substratum is then divided into primary sampling units

(PSU)

I PSUs are sampled, divided into smaller areas called segments

FCSM 2022 – Survey Research Methods Section – Habtamu Benecha 4
The June Area Survey (JAS)

I One segment is randomly selected from each PSU

I Each segment is

divided into several

smaller areas called

tracts

I Each tract is

screened: agricultural

or non-agricultural

I Agricultural tracts

within selected

segments are

enumerated

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Estimation from the JAS

I Annual estimates of the number of U.S. farms and land in

farms are produced by using JAS data

I A farm is any place from which $1,000 or more of agricultural

products were produced and sold or normally would have been

sold during the year.

I Studies have shown that the number of U.S. farms derived

from the JAS are underestimated, mainly due to

misclassification during screening

I A capture-recapture approach is employed to make estimation

I NASS’s list frame used as the second sample

I JAS and list frame records are linked by using probabilistic

record linkage

I Different adjustment weights are estimated by using the

matched dataset

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Estimation of the number of farms and land in farms

For a farm tract i, the capture-recapture weight is estimated by

wi =

ti

πi

Pr(Fi|SiAiRi Ji)

Pr(Ji|SiAiRiFi)Pr(Ri|SiAiFi)Pr(Ai|SiFi)

,

where,

i = Tract on the JAS,

ti = Proportion of a farm represented by tract

πi = Sample inclusion probability

Si = Tract is in the sample

Ai = Tract passed Agricultural screening process

Ri = Tract responds

Ji = Tract is recorded as a farm on the JAS

Fi = Tract is truly a farm

The Number of farms estimates are obtained by summing the

capture-recapture weights over farm tracts

FCSM 2022 – Survey Research Methods Section – Habtamu Benecha 7
Estimation of the number of farms and land in farms

I A total of five logistic regression models

I Matched JAS & list-frame records are used

I Both agricultural & non-agricultural tracts used in the analysis

I Model covariates

I JAS data available for agricultural tracts

I JAS data not collected for non-agricultural tracts, challenging

to get model covariates

Goal: Using adminstrative & remote sensing data to create

model covariates

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PART II

PROPOSED

METHODOLOGY

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Farm Service Agency (FSA) data

• FSA Common Land Units (CLUs) 

• The smallest unit of land that has a permanent 

contiguous boundary, a common land cover and 

land management, a common owner, and a 

common producer

• FSA-578 Form

• Available for all land associated with a USDA 

program in a calendar year

• Provides crop information

https://www.agridatainc.com/Home/Products/Mapping

%20Features/Land%20Resource%20Intelligence/FSA%20

Field%20Boundaries%20(CLU)

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Cropland Data Layer (CDL)

• Georeferenced, crop-specific land 

cover raster dataset

• Captures planted acres

• National scale, 30m, 9 billion+ 

pixels

• Annually produced since 2008

• 85%-95% accuracy for major crops

• Freely available and open to the 

public

FCSM 2022 – Survey Research Methods Section – Habtamu Benecha 11
Predictive Cropland Data Layer (PCDL)

Illinois (2021)

Entropy Layer

Illinois (2021)

PCDL and Segments

Entropy

High

Low

PCDL b

High-O

Entrop

on norm

from th

Accura

F1-scor

F1-scor

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PART III

APPLICATION

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Case study

I Data available for 2021 have been considered in this study

I The JAS & digitized tracts data merged by using state,

segment and tract identification numbers

I Tract-level PCDL data are used to create covariates for four

models (Pr(Ji|SiAiRiFi), Pr(Ri|SiAiFi), Pr(Ai|SiFi), and

farm status)

I Predicted CDL for corn, cotton and rice

I Estimates of the number of farms and land in farms produced

based on the capture-recapture model

I State & U.S. level estimates produced without accounting for

Alaska & Hawaii

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Results

Figure: Percent relative differences of unadjusted and adjusted number of

farms and land in farms against published (Year 2021)
PART IV

CONCLUSION

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Final remarks

I The model-based adjustment improved state and national

estimates

I Tract-level administrative and remote sensing data enabled to

obtain more accurate adjustment weights

I Digitized tracts data can be used to obtain information on

non-agricultural JAS tracts

I Challenges:

I Complete digitized tracts data available only for 2021

I Digitized tracts data not available for some of the JAS tracts

I Future research opportunities:

I Summarizing the entropy layer at the tract level to be used as

a covariate

I Testing generalized additive models or other non-parametric

models to better account for nonlinear effects

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Select References

Abreu, D. A., McCarthy, J. S., Colburn, L. A., et al. (2010). Impact of the screening procedures of the June Area

Survey on the number of farms estimates. Research and Development Division. RDD Research Report Number

RDD-10-03. Washington, DC: USDA, National Agricultural Statistics Service.

Alho, J. M. (1990). Logistic regression in capture-recapture models. Biometrics, pages 623–635.

Boryan, C., Yang, Z., Mueller, R., and Craig, M. (2011). Monitoring us agriculture: the us department of

agriculture, national agricultural statistics service, cropland data layer program. Geocarto International,

26(5):341–358.

Congalton, R. G. and Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices.

CRC press.

Hyman, M., Sartore, L., and Young, L. J. (2021). Capture–recapture estimation of characteristics of US Local

Food Farms using a web-scraped list frame. Journal of Survey Statistics and Methodology.

Johnson, D. M. and Mueller, R. (2021). Pre-and within-season crop type classification trained with archival land

cover information. Remote Sensing of Environment, 264:112576.

Mahammad, S. S. and Ramakrishnan, R. (2003). Geotiff-a standard image file format for gis applications. Map

India, pages 28–31.

Osman, J., Inglada, J., and Dejoux, J.-F. (2015). Assessment of a markov logic model of crop rotations for early

crop mapping. Computers and Electronics in Agriculture, 113:234–243.

S ̈arndal, C.-E., Swensson, B., and Wretman, J. (2003). Model assisted survey sampling. Springer Science &

Business Media.

Shannon, C. E. (1948). A mathematical theory of communication. The Bell system technical journal,

27(3):379–423.

USDA, FSA (2017). Farm Service Agency (FSA) Common Land Unit (CLU) information worksheet.

https://www.fsa.usda.gov/Assets/USDA-FSA-Public/usdafiles/APFO/support-documents/pdfs/clu_

infosheet_2017_Final.pdf.

Young, L. J., Lamas, A. C., and Abreu, D. A. (2017). The 2012 Census of Agriculture: a capture–recapture

analysis. Journal of Agricultural, Biological and Environmental Statistics, 22(4):523–539.

FCSM 2022 – Survey Research Methods Section – Habtamu Benecha 18
Thank you!

Questions?

Habtamu Benecha, PhD

habtamu.benecha@usda.gov

Luca Sartore, PhD

luca.sartore@usda.gov

FCSM 2022 – Survey Research Methods Section – Habtamu Benecha 19