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
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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R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
Adaptive Mixtures of Local Experts
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991) · Neural Computation · doi:10.1162/neco.1991.3.1.79 · Cited by 5,384
Big data: a revolution that will transform how we live, work, and think
(2013) · Choice Reviews Online · doi:10.5860/choice.50-6804 · Cited by 3,630
Sure Independence Screening for Ultrahigh Dimensional Feature Space
Fan, J., & Lv, J. (2008) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2008.00674.x · Cited by 2,711
Knowledge acquisition via incremental conceptual clustering
Fisher, D. H. (1987) · Machine Learning · doi:10.1007/bf00114265 · Cited by 1,991
Big data: a revolution that will transform how we live, work, and think
Armstrong, K. (2014) · Information, Communication & Society · doi:10.1080/1369118x.2014.923482 · Cited by 1,629
Big Data: New Tricks for Econometrics
Varian, H. R. (2014) · Journal of Economic Perspectives · doi:10.1257/jep.28.2.3 · Cited by 1,557
Challenges of Big Data analysis
Fan, J., Han, F., & Liu, H. (2014) · National Science Review · doi:10.1093/nsr/nwt032 · Cited by 1,482
Bayesian Variable Selection in Linear Regression
Mitchell, T. J., & Beauchamp, J. J. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478694 · Cited by 1,424
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Klerkx, L., Jakku, E., & Labarthe, P. (2019) · NJAS: Wageningen Journal of Life Sciences · doi:10.1016/j.njas.2019.100315 · Cited by 1,307
Computational sustainability: computing for a better world and a sustainable future
Gomes, C., Dietterich, T., Barrett, C., Conrad, J., Dilkina, B., Ermon, S., Fang, F., Farnsworth, A., Fern, A., Fern, X., et al. (2019) · Communications of the ACM · doi:10.1145/3339399 · Cited by 126
1.5 Degrees of Separation: Computer Science Education in the Age of the Anthropocene
Pollock, I., Alshaigy, B., Bradley, A., Krogstie, B. R., Kumar, V., Ott, L., Peters, A.-K., Riedesel, C., & Wallace, C. (2019) · Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education · doi:10.1145/3344429.3372500 · Cited by 10
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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