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Data Science and Management for Large Scale Empirical Applications in Agricultural and Applied Economics Research

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  • Joshua D. Woodard

Abstract

The increased availability of high resolution data and computing power has spurred enormous interest in "Big Data". While analysts typically source data from a wide variety of agencies, even within the USDA no comprehensive data warehouse exists with which researchers can interact. This leads to massive duplication in efforts, inefficient data sourcing, and great potential for error. The purpose of this article is to provide a brief overview of this state of affairs within the community. An overview of a prototype warehouse is also provided, as are thoughts on future directions.

Suggested Citation

  • Joshua D. Woodard, 2016. "Data Science and Management for Large Scale Empirical Applications in Agricultural and Applied Economics Research," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 38(3), pages 373-388.
    • Handle: RePEc:oup:apecpp:v:38:y:2016:i:3:p:373-388.
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    File URL: http://hdl.handle.net/10.1093/aepp/ppw009
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    References listed on IDEAS

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    1. Sonka, Steve, 2014. "Big Data and the Ag Sector: More than Lots of Numbers," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 17(1), pages 1-20, February.
    2. Joshua Woodard, 2016. "Big data and Ag-Analytics," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 76(1), pages 15-26, May.
    3. Joshua Elliot & Michael Glotter & Neil Best & Ken Boote & Jim Jones & Jerry Hatfield & Cynthia Rozenweig & Leonard A. Smith & Ian Foster, 2013. "Predicting agricultural impacts of large-scale drought: 2012 and the case for better modeling," GRI Working Papers 111, Grantham Research Institute on Climate Change and the Environment.
    4. Doering, Otto C. & Lawrence, Douglas J. & Helms, J. Douglas, 2013. "Agricultural Conservation & Environmental Programs: The Challenge of Data-Driven Conservation," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 28(2), pages 1-5.
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    Cited by:

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    2. Yongtong Shao & Tao Xiong & Minghao Li & Dermot Hayes & Wendong Zhang & Wei Xie, 2021. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1082-1098, May.
    3. Bucheli, Janic & Dalhaus, Tobias & Finger, Robert, 2022. "Temperature effects on crop yields in heat index insurance," Food Policy, Elsevier, vol. 107(C).
    4. Li, Hong & Porth, Lysa & Tan, Ken Seng & Zhu, Wenjun, 2021. "Improved index insurance design and yield estimation using a dynamic factor forecasting approach," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 208-221.
    5. Woodard, Joshua & Wang, Diane & McClung, Anna & Ziska, Lewis & Dutta, Tridib & McCouch, Susan, 2016. "Integrating Variety Data into Large-Scale Crop Yield Models," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236170, Agricultural and Applied Economics Association.
    6. Joshua D. Woodard & Leslie J. Verteramo‐Chiu, 2017. "Efficiency Impacts of Utilizing Soil Data in the Pricing of the Federal Crop Insurance Program," American Journal of Agricultural Economics, John Wiley & Sons, vol. 99(3), pages 757-772, April.
    7. Ariel Ortiz‐Bobea, 2020. "The Role of Nonfarm Influences in Ricardian Estimates of Climate Change Impacts on US Agriculture," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(3), pages 934-959, May.
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