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resources. Advances in information and communication technology combined with
big data analytics offer the potential to reduce the amount of resources needed.
Deadweight loss is a term that describes system inefficiencies that can be reduced
by enhanced coordination within and between firms. Even in advanced agricultural
settings, reduction of deadweight loss is perceived to be an attractive potential use
of big data innovations.
In this context, deadweight loss refers to the processes by which inputs and outputs are delivered (when and where). A more intriguing issue for many is whether
application of big data can fundamentally alter decision making as to “what” should
be done. Can we further optimize the biology of agricultural production, especially
in the context of the larger food and agricultural system? Earlier it was noted that
new sensing technologies offer the potential to monitor and document what actually
occurs as agricultural production takes place. The resulting data potentially would
be available at never before levels of detail, in terms of time and space, and at low
cost. Furthermore, analytic capabilities could combine diverse sources of data to
discern previously unknown patterns and provide insights not available previously.
A result of application of these innovations would be optimization of agricultural
production systems, simultaneously reducing its environmental impact and improving profitability. There are two interrelated factors that need to be addressed in considering the possible evolution of this optimization:
• Production agriculture involves biologic processes subject to considerable uncertainty. Therefore, even if one knows exactly what occurred in one production
season and what actions would have optimized performance under those circumstances, that information may not be a good predictor of what actions should
occur in the next season.
– Some assert that capturing massive amounts of agricultural data, spread
across large geographic regions, will provide sufficient information so that the
effects of weather and location can be estimated. Doing that would enable big
data analytics to answer the question, “why does production variability occur?”
– Agricultural science has been devoted to discerning the why of agricultural
production. Rather than solely relying on big data analytics, others assert that
agricultural science techniques and knowledge will need to be integrated
within big data techniques to truly optimize system performance.
• In most systems of agricultural production today, even the knowledge of what
occurred does not necessarily reside within one organization. Furthermore, as
was noted for precision agriculture, individual entities at the production level
typically do not have the scale to produce sufficient data nor to have the capabilities needed to analyze that data.
Because of these two factors, collaboration across organizational boundaries will
be required to fully exploit the potential benefits of big data’s application to agriculture. A host of factors, beyond technological effectiveness, will influence the speed
and extent of this exploitation. These relate to intellectual property and competitive
8 Digital Technologies, Big Data, and Agricultural Innovation
resources. Advances in information and communication technology combined with
big data analytics offer the potential to reduce the amount of resources needed.
Deadweight loss is a term that describes system inefficiencies that can be reduced
by enhanced coordination within and between firms. Even in advanced agricultural
settings, reduction of deadweight loss is perceived to be an attractive potential use
of big data innovations.
In this context, deadweight loss refers to the processes by which inputs and outputs are delivered (when and where). A more intriguing issue for many is whether
application of big data can fundamentally alter decision making as to “what” should
be done. Can we further optimize the biology of agricultural production, especially
in the context of the larger food and agricultural system? Earlier it was noted that
new sensing technologies offer the potential to monitor and document what actually
occurs as agricultural production takes place. The resulting data potentially would
be available at never before levels of detail, in terms of time and space, and at low
cost. Furthermore, analytic capabilities could combine diverse sources of data to
discern previously unknown patterns and provide insights not available previously.
A result of application of these innovations would be optimization of agricultural
production systems, simultaneously reducing its environmental impact and improving profitability. There are two interrelated factors that need to be addressed in considering the possible evolution of this optimization:
• Production agriculture involves biologic processes subject to considerable uncertainty. Therefore, even if one knows exactly what occurred in one production
season and what actions would have optimized performance under those circumstances, that information may not be a good predictor of what actions should
occur in the next season.
– Some assert that capturing massive amounts of agricultural data, spread
across large geographic regions, will provide sufficient information so that the
effects of weather and location can be estimated. Doing that would enable big
data analytics to answer the question, “why does production variability occur?”
– Agricultural science has been devoted to discerning the why of agricultural
production. Rather than solely relying on big data analytics, others assert that
agricultural science techniques and knowledge will need to be integrated
within big data techniques to truly optimize system performance.
• In most systems of agricultural production today, even the knowledge of what
occurred does not necessarily reside within one organization. Furthermore, as
was noted for precision agriculture, individual entities at the production level
typically do not have the scale to produce sufficient data nor to have the capabilities needed to analyze that data.
Because of these two factors, collaboration across organizational boundaries will
be required to fully exploit the potential benefits of big data’s application to agriculture. A host of factors, beyond technological effectiveness, will influence the speed
and extent of this exploitation. These relate to intellectual property and competitive
8 Digital Technologies, Big Data, and Agricultural Innovation