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Cited by 325

The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.

Big Data in Agriculture: A Challenge for the Future

Coble, Keith H, Mishra, Ashok K, Ferrell, Shannon, Griffin, Terry·2018·Applied Economic Perspectives and Policy

Abstract

This article examines the challenge and opportunities of Big Data, and concludes that these technologies will lead to relevant analysis at every stage of the agricultural value chain. Big Data is defined by several characteristics beyond size, particularly, the volume, velocity, variety, and veracity of the data. We discuss a set of analytical techniques that are increasingly relevant to our profession as one addresses these issues. Ultimately, we resolve that agricultural and applied economists are uniquely positioned to contribute to the research and outreach agenda on Big Data. We believe there are relevant policy, farm management, supply chain, consumer demand, and sustainability issues where our profession can make major contributions. The authors are thankful to the anonymous reviewers and editor Craig Gundersen for helpful comments. Support was provided by the Mississippi Agricultural and Forestry Experiment Station Special Research Initiative.

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Crossref · 10.1093/aepp/ppx056

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Metadata

Authors
Coble, K. H., Mishra, A. K., Ferrell, S., & Griffin, T.
Venue
Applied Economic Perspectives and Policy
Publisher
Wiley
Published
2018-02-16
Volume
40
Issue
1
Pages
79-96
ISSN
  • 2040-5790 (ISSN-L)
  • 2040-5804
OpenAlex
W2790729546
Cited by
325
References
38
FWCI
20.97
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Big Data in Agriculture: A Challenge for the Future. https://app.cochat.ai/papers/10.1093/aepp/ppx056
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Coble, K. H., Mishra, A. K., Ferrell, S., & Griffin, T. (2018). Big Data in Agriculture: A Challenge for the Future. Applied Economic Perspectives and Policy. https://doi.org/10.1093/aepp/ppx056

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.