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Journal of Data Science


Use of Farm Equipment Machine-Logged Data to Inform Crop Production Statistics✩
Pub. online: 8 June 2026      Type: Data Science In Action      Open accessOpen Access

✩ 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.
Received
29 September 2025
Accepted
29 May 2026
Published
8 June 2026

Abstract

The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) conducted a pilot study in 2024 to obtain data collected onboard farm machinery and explore their uses for statistical purposes. NASS has recognized high value in these machine-logged data (MLD) systems as they can potentially augment, or even replace, traditional survey efforts while providing additional benefits of reducing respondent burden and improving crop-related estimates. This pilot study ultimately addressed four topics: 1) understanding the obstacles in obtaining MLD from farmers; 2) creating geographic workflows to manage inherent geospatial MLD; 3) developing the linkages to NASS’s tabular list frame information; and 4) assessing the use of MLD to replace survey data for time-sensitive estimates. To study each topic, field-level information was gathered from the MLD systems of dozens of producers over hundreds of fields across the central United States (US) for the 2023 growing season. Results showed that 90% of the fields could be linked to a producer on the NASS list frame. Of those producers, the consistency of MLD versus traditional survey reporting was highly variable for those who were selected for a survey in 2023. Comparisons showed median MLD values were larger than historical NASS survey values. Approximately 48% of survey comparisons showed a difference of 25% or less between MLD and historical NASS survey values. MLD shows promise for use in official statistics; however, further analyses with additional producers’ data and enhancements to MLD collection processes are needed before supplementing traditional survey methods.

Supplementary material

 Supplementary Material
The supplementary material contains an R program that is made available in a zip file attached to this article.

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Copyright
2026 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
data linkage geospatial imputation MyAgDataⓇ precision agriculture unstructured data

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