Report from the conference, ‘identifying obstacles to applying big data in agriculture’

被引:0
|
作者
Emma L. White
J. Alex Thomasson
Brent Auvermann
Newell R. Kitchen
Leland Sandy Pierson
Dana Porter
Craig Baillie
Hendrik Hamann
Gerrit Hoogenboom
Todd Janzen
Rajiv Khosla
James Lowenberg-DeBoer
Matt McIntosh
Seth Murray
Dave Osborn
Ashoo Shetty
Craig Stevenson
Joe Tevis
Fletcher Werner
机构
[1] Texas A&M University,Journalist and Communications Professional
[2] Texas A&M AgriLife Research and Extension Center at Amarillo,undefined
[3] USDA-ARS Cropping Systems and Water Quality Research,undefined
[4] University of Southern Queensland,undefined
[5] IBM T.J. Watson Research Center,undefined
[6] University of Florida,undefined
[7] Janzen Agricultural Law LLC,undefined
[8] Colorado State University,undefined
[9] Harper Adams University,undefined
[10] Land,undefined
[11] Farm and Agribusiness Management,undefined
[12] Mc Communications,undefined
[13] VTX1 Companies,undefined
[14] Amazon Web Services,undefined
[15] BASF Canada Agricultural Solutions,undefined
[16] Vis Consulting Inc,undefined
[17] The Climate Corporation,undefined
来源
Precision Agriculture | 2021年 / 22卷
关键词
Automation; Big data; Farm profitability; Food security;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:306 / 315
页数:9
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