Abstract
The USDA produces yield and supply estimates for many crops that influence commodity markets and are used for implementing the Title I program, Agriculture Risk Coverage. Precision agriculture advances have increased the potential for the private sector to capture near-real time yield data, however, it is unclear whether they provide advantages in setting market positions since the samples are typically non-random. Here, we use yield histories from a large population of corn farms to quantify biases associated with different non-random sampling schemes for estimating aggregate yield, and demonstrate the effectiveness of benchmarking procedures for removing systematic prediction error.
Original language | English |
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Pages (from-to) | 668-683 |
Number of pages | 16 |
Journal | Applied Economic Perspectives and Policy |
Volume | 41 |
Issue number | 4 |
DOIs | |
State | Published - Dec 1 2019 |
Bibliographical note
Publisher Copyright:© 2019 Agricultural and Applied Economics Association
Keywords
- Big data
- USDA reports
- market information
- precision agriculture
ASJC Scopus subject areas
- Development
- Economics and Econometrics