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• Using digital tools and techniques to summarize, analyze, synthesize, and
communicate digital and other information to improve decision-making
2. Within that broad perspective, it also is useful to distinguish between three types
of digital technology application:
• Precision agriculture: Although having 20+ years of history, precision agriculture technologies continue to markedly improve. Powered by GPS-enabled
equipment and machine-based sensors, precision agriculture focuses on measurement and differential input application at sub-field levels. Managerial
analysis focuses on the use of data captured from individual farm fields to
improve productivity. Over the last two decades, farmers have been exposed
to and, in many cases, have had experience with precision agriculture.
However, today’s advances in sensor capabilities continue to enhance the
effectiveness of precision agriculture practices meaning that farmers have an
on-going opportunity to choose whether to employ new practices or not.
• Big data analytics: The ability to cheaply capture extraordinarily large sets of
data has fueled numerous big data applications throughout society. However,
the existence of massive datasets is only part of the story. Big data analytics
requires extensive computational power as well as application of fundamentally different means of analysis to provide probabilistically based insights to
improve decision making.
– A potential for the application of big data analytics in farming is the pooling
of production-related data from many farming operations across potentially
millions of acres to discern previously unknown managerial insights.
– Terms such as “big data” and “artificial intelligence” are relatively new to
society, let alone agriculture. Tracking of the mention of those terms in media
publications (for all uses) indicates that such mentions barely existed in 2007.
However, over 150,000 mentions were identified by the year 2014, only
7 years later (Gandomi and Haider 2015). The media hype associated with
such rapid growth, however, often contributes to confusion and uncertainty
regarding the managerial application of such innovations (Sonka 2015).
– Some applications of big data analytics in agriculture (weather forecasting,
autonomous steering of machines in the field) do not require that detailed
farm production data from one field/farm be compared with data from other
farm operations.
• Communication and social media: This category includes two somewhat different applications:
– The use of social media to communicate with personal and business contacts.
– The use of telecommunication-based wireless, WIFI, and the Internet
• To transfer production-related data captured from sensors to devices where
that data can be stored/analyzed
• To transfer findings to the farmer and/or instructions directly to machines
as activities that should be conducted
S. T. Sonka
• Using digital tools and techniques to summarize, analyze, synthesize, and
communicate digital and other information to improve decision-making
2. Within that broad perspective, it also is useful to distinguish between three types
of digital technology application:
• Precision agriculture: Although having 20+ years of history, precision agriculture technologies continue to markedly improve. Powered by GPS-enabled
equipment and machine-based sensors, precision agriculture focuses on measurement and differential input application at sub-field levels. Managerial
analysis focuses on the use of data captured from individual farm fields to
improve productivity. Over the last two decades, farmers have been exposed
to and, in many cases, have had experience with precision agriculture.
However, today’s advances in sensor capabilities continue to enhance the
effectiveness of precision agriculture practices meaning that farmers have an
on-going opportunity to choose whether to employ new practices or not.
• Big data analytics: The ability to cheaply capture extraordinarily large sets of
data has fueled numerous big data applications throughout society. However,
the existence of massive datasets is only part of the story. Big data analytics
requires extensive computational power as well as application of fundamentally different means of analysis to provide probabilistically based insights to
improve decision making.
– A potential for the application of big data analytics in farming is the pooling
of production-related data from many farming operations across potentially
millions of acres to discern previously unknown managerial insights.
– Terms such as “big data” and “artificial intelligence” are relatively new to
society, let alone agriculture. Tracking of the mention of those terms in media
publications (for all uses) indicates that such mentions barely existed in 2007.
However, over 150,000 mentions were identified by the year 2014, only
7 years later (Gandomi and Haider 2015). The media hype associated with
such rapid growth, however, often contributes to confusion and uncertainty
regarding the managerial application of such innovations (Sonka 2015).
– Some applications of big data analytics in agriculture (weather forecasting,
autonomous steering of machines in the field) do not require that detailed
farm production data from one field/farm be compared with data from other
farm operations.
• Communication and social media: This category includes two somewhat different applications:
– The use of social media to communicate with personal and business contacts.
– The use of telecommunication-based wireless, WIFI, and the Internet
• To transfer production-related data captured from sensors to devices where
that data can be stored/analyzed
• To transfer findings to the farmer and/or instructions directly to machines
as activities that should be conducted
S. T. Sonka