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Big data and Ag-Analytics

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  • Joshua Woodard

Abstract

Purpose - – The purpose of this paper is to provide a brief and necessarily partial overview of the design, motivation, and use of the Ag-Analytics platform (ag-analytics.org), focussing on integration and warehousing of publicly available research data for broad communities of researchers, including those in the area of agricultural finance. Design/methodology/approach - – The paper walks the reader through an overview of the layout and utilization of the Ag-Analytics platform, including a few example applications of some of the tools and web API’s. Findings - – Much of the data researchers routinely use in agricultural and environmental finance and related fields are often – strictly speaking – publicly available; however the form in which they are distributed leads to great inefficiencies in data sourcing and processing which can be greatly improved. The goal of the Ag-Analytics open data/open source platform is to help researchers centralize and share in such efforts. Development of systems for disseminating, documenting, and automating the processing of such data can lead to more transparency in research, better routes for validation, and a more robust research community. Practical implications - – Some of the tools and methods are discussed, as well as practical issues in data sourcing and automation for research. A few high level introductory examples and applications are illustrated. Originality/value - – Development and adoption of such systems and data resources remains seriously lacking in social science research, particularly in the economics, natural resource, environmental, and agricultural finance spheres. This brief provides an overview of one such system which should be of value to researchers in this field and many others.

Suggested Citation

  • Joshua Woodard, 2016. "Big data and Ag-Analytics," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 76(1), pages 15-26, May.
  • Handle: RePEc:eme:afrpps:v:76:y:2016:i:1:p:15-26
    DOI: 10.1108/AFR-03-2016-0018
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    Citations

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    Cited by:

    1. Xu, Chang & Katchova, Ani L., 2019. "Predicting Soybean Yield with NDVI Using a Flexible Fourier Transform Model," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 51(3), pages 402-416, August.
    2. Aglasan, Serkan & Goodwin, Barry K. & Rejesus, Roderick, 2020. "Genetically Modified Rootworm-Resistant Corn, Risk, and Weather: Evidence from High Dimensional Methods," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 305181, Agricultural and Applied Economics Association.
    3. 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.
    4. 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.
    5. 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.
    6. Tao Luo, 2018. "Research on Decision-Making of Complex Venture Capital Based on Financial Big Data Platform," Complexity, Hindawi, vol. 2018, pages 1-12, December.
    7. Coppess, Jonathan & Navarro, Christopher & Satheesan, Sandeep Puthanveetil & Naraharisetty, Vara Veera Gowtham & Bhattarai, Rabin & Armstrong, Shalamar & Gupta, Rishabh, . "Introducing the Cover Crop Decision Support Tool," farmdoc daily, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics, vol. 10(176).

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