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
Funding Information:This project was supported by the Office of Chief Economist cooperative agreement #58-0111-14-018. This work is written by US Government employees and is in the public domain in the US. The findings and conclusions in this work have not been formally disseminated by the U. S. Department of Agriculture and should not be construed to represent any agency determination or policy.
Funding Information:
This project was supported by the Office of Chief Economist cooperative agreement #58‐0111‐14‐018. This work is written by US Government employees and is in the public domain in the US. The findings and conclusions in this work have not been formally disseminated by the U. S. Department of Agriculture and should not be construed to represent any agency determination or policy.
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