Abstract
Data-centric technology has not undergone widespread adoption in production agriculture but could address global needs for food security and farm profitability. Participants in the U.S. Department of Agriculture (USDA) National Institute for Food and Agriculture (NIFA) funded conference, “Identifying Obstacles to Applying Big Data in Agriculture,” held in Houston, TX, in August 2018, defined detailed scenarios in which on-farm decisions could benefit from the application of Big Data. The participants came from multiple academic fields, agricultural industries and government organizations and, in addition to defining the scenarios, they identified obstacles to implementing Big Data in these scenarios as well as potential solutions. This communication is a report on the conference and its outcomes. Two scenarios are included to represent the overall key findings in commonly identified obstacles and solutions: “In-season yield prediction for real-time decision-making”, and “Sow lameness.” Common obstacles identified at the conference included error in the data, inaccessibility of the data, unusability of the data, incompatibility of data generation and processing systems, the inconvenience of handling the data, the lack of a clear return on investment (ROI) and unclear ownership. Less common but valuable solutions to common obstacles are also noted.
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References
Anderson, D. (1994). Dorland’s illustrated medical dictionary (28th ed.). Philadelphia, PA, USA: J.B. Saunders.
Brooks, R. (2017). Farmer survey highlights need for agronomic data management services. Ag Professional. Retrieved November 21, 2019 from https://www.agprofessional.com/article/farmer-survey-highlights-need-agronomic-data-management-services.
Hunter, M. C., Smith, R. G., Schipanski, M. E., Atwood, L. W., & Mortensen, D. A. (2017). Agriculture in 2050: Recalibrating targets for sustainable intensification. BioScience, 67(4), 386–391.
Information Technology and Innovation Foundation. (2017). Defining and mapping broadband coverage in America. Retrieved August 10, 2019 from https://www2.itif.org/2017-defining-mapping-broadband.pdf.
Lowenberg-DeBoer, J. (2015). The precision agriculture revolution. Foreign Affairs, 94(3), 105–112.
Lowenberg-DeBoer, J., & Erickson, B. (2019). Setting the record straight on precision agriculture adoption. Agronomy Journal, 111(4), 1552–1569.
Chang, W. L., & Grady, N. (2015). NIST Big data interoperability framework: Volume 1, definitions. NIST Special Publication 1500-1.
Roberts, O. (2017). Survey shows gap between precision data collection and use. Real Agriculture.
Rowles, C. (2001). Sow lameness. Journal of Swine Health and Production, 9(3), 130–131.
United Nations. (2019). World population prospects. United Nations Department of Economic and Social Affairs, Population Division. Retrieved November 21, 2019 from https://population.un.org/wpp/.
U.S. Department of Agriculture. (2019). A case for rural broadband. Retrieved August 15, 2019 from https://www.usda.gov/sites/default/files/documents/case-for-rural-broadband.pdf.
USDA Economic Research Service. (2020a). Commodity costs and returns. Retrieved May 16, 2020 from https://www.ers.usda.gov/data-products/commodity-costs-and-returns/.
USDA Economic Research Service. (2020b). Inflation-adjusted price indices for corn, wheat, and soybeans show long-term declines. Retrieved May 16, 2020 from https://www.ers.usda.gov/data-products/chart-gallery/gallery/chart-detail/?chartId=76964.
Walker, D., Kurth, T., Van Wyck, J., & Tilney, M. (2016). Lessons from the frontlines of the Agtech revolution. Boston Consulting Group (BCG) Perspectives. Retrieved November 21, 2019 from https://www.bcg.com/en-us/publications/2016/process-industries-building-materials-strategy-lessons-frontlines-agtech-revolution.aspx.
Wikipedia. (2018). Big data. Retrieved January 20, 2018 from https://en.wikipedia.org/wiki/Big_data.
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming: A review. Agricultural Systems, 153, 69–80.
Acknowledgements
This work was supported by a United States Department of Agriculture—National Institute of Food and Agriculture, Food and Agriculture Cyberinformatics and Tools (USDA NIFA FACT) initiative conference Grant Number 2018-67021-28692.
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Conceptualized and developed conference: ELW, JAT; Wrote manuscript: ELW, JAT; Contributed technical material: CB, HH, GH, TJ, RK, JL-DB, MM, SM, DO, AS, CS, JT, JAT, FW; Organized scenarios: BA, NRK, LSP, DP; Revised: BA, LSP, DP, HH, GH, JL-DB, SM, JT, JAT.
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White, E.L., Thomasson, J.A., Auvermann, B. et al. Report from the conference, ‘identifying obstacles to applying big data in agriculture’. Precision Agric 22, 306–315 (2021). https://doi.org/10.1007/s11119-020-09738-y
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DOI: https://doi.org/10.1007/s11119-020-09738-y