Using a Web-scraped List-frame for an Agricultural Survey

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  Habtamu Benecha, Bruce A. Craig, Grace Yoon, Zachary Terner*

  Denise A. Abreu, Linda J. Young

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

FCSM 2021 Research & Policy Conference

November 3,  2021

*National Institute of Statistical Sciences (NISS)

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                             DisclaimerThe 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.

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Background

The June Area Survey (JAS)

Conducted annually

Based on an area-frame that covers all land in the continental U.S.

In-person enumeration

NASS uses the JAS to produce comprehensive estimates of land use and agricultural activities 

Number of farms and land in farms

     - $1000 in sales or potential sales

 Undercoverage adjustment for the Census of Agriculture and several   NASS surveys

The JAS is an expensive survey; in-person enumeration, maintenance                          of the area-frame, rotation of segments

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Background

NASS is exploring ways to lower costs and improve data collection by leveraging new statistical methods and technologies

The June Area Research Project (JARP): 2019 Pilot study

    -  Assess the viability of using a web-scraped list-frame to replace the area-frame

    - Evaluate the effectiveness of collecting data by mail, telephone and the web

Parallel study design: the 2018 JAS and the 2017 Census are used to evaluate the estimates and the quality of information from the pilot study 

Goals: Discuss the estimation approaches and explore alternatives

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2019 JARP: Frames and Surveys

Four states: KS, NE, NY, PA

A web-scraped list-frame developed for each state by scraping for potential farms 

State specific and national open-data sources used

    - National open-data sources for two of the states

    - National and state-specific sources for the other two states

Record linkage to NASS’ main list-frame 

Data collection in two phases

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 2019 JARP: Phase 1 Survey  

Sample from records on the web-scraped frame not linked to NASS’ list-frame  

Use the data to estimate coverage weights for NASS list frame records

Screening questionnaire

- Determine farm status  

- Collect information for farm records:  mail, web, and telephone

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2019 JARP: Phase 2 Survey  

Sample from records on NASS’ list-frame 

Some of the Phase 2 sample records linked to both frames

Obtain information consistent with JAS by using mail, web, and telephone

Produce JAS estimates using capture-recapture approach

Farms and non-farms

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Estimation

Number of farms, land in farms, and coverage adjustments for six surveys

Capture-recapture approach (Young et.al, 2017† ; Hyman et.al, 2021)

Coverage and response probabilities

Number of farms and land in farms: Capture-recapture weight

                 (1)     

Coverage probabilities () and response probabilities () – Logistic regression models

Categorical covariates

					† Young et al. (2017) The 2012 Census of Agriculture: A capture–recapture analysis. 					  Journal of Agricultural, Biological and Environmental Statistics. 22, 523-539.

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Results

Percent relative differences of the number of farms estimates from 2019 JARP and 2018 JAS compared to the 2017 Census estimates

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Reference to 2017 Census

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Results

Percent relative differences of the land in farms estimates from JARP and the JAS compared to the 2017 Census estimates 

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Reference to 2017 Census

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An Alternative Capture-recapture Model: Sampling from the Entire Web-scraped Frame

Neither frame covers the population of farms

Extension to Alho (1990)* 

   - Each frame represents a large sample from the population of potential farms

   - The two surveys ( & ) provide sub-samples from the population

Probability of inclusion in a survey is defined as a products of

The probability of capture by the corresponding frame 

The conditional probability of inclusion in the sample given the record was captured by the frame

			    * Alho (1990) Logistic Regression in Capture-Recapture Models. Biometrics, 46(3): 623-635

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An Alternative Capture-recapture Model

Capture-status: Multinomial distribution

Four sets of logistic regression models

The probability of inclusion in at least one survey, , estimated

Total number of potential farms  

=    ( the set of all captured potential farms)                (2)

The number of farms  

                                                                             (: the set of all captured farms)                                  (3)

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Simulation Study

Two sets of different covariates to generate records for the frames and the surveys; four Bernoulli distributions

Farm status: a function of one variable

500 replicates

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| True Number of Potential Farms () | Model EstimatedMean of |
|-----------------------------------|------------------------|
| 25,000                            | 1.064                  |
| 40,000                            | 1.015                  |
| 60,000                            | 1.010                  |
| 100,000                           | 1.009                  |
| 250,000                           | 1.006                  |
| True Number of Farms () | Model EstimatedMean of |
|-------------------------|------------------------|
| 21,031                  | 1.071                  |
| 33,618                  | 1.015                  |
| 50,576                  | 1.010                  |
| 84,296                  | 1.009                  |
| 210,543                 | 1.006                  |

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Summary and future work

Promising results

   - Many of the estimates are close to results from the JAS,

     the Census and multiple-frame analysis

 Main challenge: under-estimation of some commodity values

 Low response-rate

 Quality and quantity of covariates

 Scraping at state and national levels may improve estimates

Sampling from the entire web-scraped frame (several options for estimation)

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                                              Thank You!                                               Questions?                           habtamu.benecha@usda.gov

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