Developing Integer Calibration Weights for Census of Agriculture

Sartore, Luca; Toppin, Kelly; Young, Linda J.; Spiegelman, Clifford H.

doi:10.1007/s13253-018-00340-4

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…The probability of correct classification of a farm by the Census, π CFCC , is estimated from a separate logistic regression model (see [5] for details). The published Census numbers are obtained after the model-based adjusted estimates are calibrated to ensure consistency of estimates [25]. Standard errors of the adjusted Census estimates are calculated using a combination of jackknife and bootstrap methodologies.…”

Section: Estimation From the Census Of Agriculturementioning

confidence: 99%

Data reconciliation and estimation in an agricultural survey

Benecha¹,

Abreu²,

Jennings³

et al. 2023

SJI

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The Puerto Rico Census of Agriculture (Census) is the leading source of statistics about the island’s agricultural production. The Census is conducted every 5 years, in years ending in 2 and 7. However, the most recent Census was administered in 2018 due to delays caused by Hurricane Maria. Because the Census list frame is incomplete, a separate survey is conducted to inform measures of undercoverage, nonresponse, and misclassification, leading to adjusted weights. At the time of Hurricane Maria, the 2017 survey had already been conducted in preparation for the planned Census that year. In 2018, the survey was repeated using the same sample. Linking and analyzing the 2017 and 2018 survey data provide insights into the impacts of Hurricane Maria on the island’s agriculture. Furthermore, the 2018 survey gives an opportunity to evaluate the 2018 Census results. Although the same sample was used in 2017 and 2018, automated record linkage methods are not suitable to link records from the two surveys. This paper discusses record linkage and estimation approaches used to determine the number of farms and land in farms in Puerto Rico before and after Hurricane Maria based on the two surveys and the Census of Agriculture.

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Section: Estimation From the Census Of Agriculturementioning

confidence: 99%

Data reconciliation and estimation in an agricultural survey

Benecha¹,

Abreu²,

Jennings³

et al. 2023

SJI

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“…Although some outliers were identified when generating the direct survey estimates, other outliers were found during modeling. The schedule had to include the time to investigate each of the outliers to ensure that they properly represented the reported data (had no errors) and to then use the integer calibration algorithm [34] to distribute the outliers' weights within the state. For the reviews within the state field offices and by the Agricultural Statistics Board, tools are available to facilitate the review process, but were not designed for the inclusion of modeled estimates or their measures of uncertainty.…”

Section: Moving the Models Into Productionmentioning

confidence: 99%

Using Small Area Estimation to Produce Official Statistics

Young

Chen

2022

Stats

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The USDA National Agricultural Statistics Service (NASS) and other federal statistical agencies have used probability-based surveys as the foundation for official statistics for over half a century. Non-survey data that can be used to improve the accuracy and precision of estimates such as administrative, remotely sensed, and retail data have become increasingly available. Both frequentist and Bayesian models are used to combine survey and non-survey data in a principled manner. NASS has recently adopted Bayesian subarea models for three of its national programs: farm labor, crop county estimates, and cash rent county estimates. Each program provides valuable estimates at multiple scales of geography. For each program, technical challenges had to be met and a strenuous review completed before models could be adopted as the foundation for official statistics. Moving models out of the research phase into production required major changes in the production process and a cultural shift. With the implemented models, NASS now has measures of uncertainty, transparency, and reproducibility of its official statistics.

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“…Mail survey response rates have declined by roughly 0.76% per year over nearly five decades (Stedman et al, 2019), despite efforts to reverse such trends (Avemegah et al, 2021; Glas et al, 2019). This extends to surveys from the US Department of Agriculture (USDA) and its agency, the National Agricultural Statistics Service (NASS), which is responsible for the Census of Agriculture, the basis of numerous social and farm programs, funding formulas, as well as planning and operational activities (Sartore et al, 2019). Response rates for NASS crop acreage and production surveys have declined from 80% to 85% in the early 1990s to less than 60% in some cases and declining at an accelerating rate in the last few years (Johansson et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Payment versus charitable donations to attract agricultural and natural resource survey participation

Penn

2023

J of Agr & App Econ Assoc

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This analysis provides two case studies comparing direct payment versus charitable donations on respondents' behalf as survey participation incentives. The first examines agricultural producers' willingness to participate in several hypothetical surveys. Donation to charity significantly increases producers' willingness to participate relative to direct payment. The second case study examines forest landowners' willingness to participate in an actual, one‐time survey. Neither payment nor donation incentives improve participation among mail respondents and donations are less effective versus payment among online respondents. If they do not decrease participation, then donations may be preferred, requiring less effort to process a smaller number of transactions.

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Developing Integer Calibration Weights for Census of Agriculture

Cited by 5 publications

References 15 publications

Data reconciliation and estimation in an agricultural survey

Data reconciliation and estimation in an agricultural survey

Using Small Area Estimation to Produce Official Statistics

Payment versus charitable donations to attract agricultural and natural resource survey participation

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