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• In agriculture, therefore, knowledge from science will need to be effectively integrated within efforts to accomplish the goals of predictive and prescriptive analytics. Even with this additional complication, the potential of tools based upon
emerging data science capabilities offers significant promise to more effectively
optimize operations and create value within the agricultural sector.
8.5 Digital Agriculture and the Food System
To this point, this chapter has focused on individual technologies and concepts that
can affect the manner in which big data and digital technologies affect agricultural
innovation. This section will attempt to depict a more unified picture of the future
setting that we might call digital agriculture. First, the emphasis will continue at the
level of production agriculture. The focus will emphasize managerial capabilities,
which in most cases will rely upon multiple technical factors. Illustrative examples
will be provided. Second, production agriculture is just one component of the
broader food system. A system which increasingly is employing digital technologies and big data to improve efficiency and effectiveness. Linkages between those
efforts and implementation within production agriculture will be explored in the
section’s second segment. The role of societal expectations for that system will be
discussed as well.
8.5.1 Components of a Potential Digital Agriculture
This section will attempt to paint a picture of the multiple components that could
inform farmers in tomorrow’s digital agriculture. This is not intended to be a prediction, as these components currently are being employed to some extent. Rather, the
discussion will hopefully provide insights as to the potentials that exist as integration across technical capabilities occurs. Of course, one needs to keep in mind the
reality that just because something is technically possible, there may not be sufficient justification for managers to adopt that innovation.
Figure 8.3 graphically identifies several components that could form digital agriculture. Examples of each will be provided to illustrate their potential application.
Precision agriculture has been discussed at length previously. While routinely
employed by many farmers, advances in technical capabilities are continually offering new opportunities. For example, sensors embedded in the maize planter now can
sense soil moisture, depth, and other factors in each furrow to optimize the placement of seed as planting is being done.
Similarly prior discussion addressed big data applications. Farmers now can subscribe to services that provide extremely localized weather information and/or
receive agronomic guidance based upon insights gained from analysis of production
on thousands of acres, in addition to their own experience. As more data is captured
S. T. Sonka
• In agriculture, therefore, knowledge from science will need to be effectively integrated within efforts to accomplish the goals of predictive and prescriptive analytics. Even with this additional complication, the potential of tools based upon
emerging data science capabilities offers significant promise to more effectively
optimize operations and create value within the agricultural sector.
8.5 Digital Agriculture and the Food System
To this point, this chapter has focused on individual technologies and concepts that
can affect the manner in which big data and digital technologies affect agricultural
innovation. This section will attempt to depict a more unified picture of the future
setting that we might call digital agriculture. First, the emphasis will continue at the
level of production agriculture. The focus will emphasize managerial capabilities,
which in most cases will rely upon multiple technical factors. Illustrative examples
will be provided. Second, production agriculture is just one component of the
broader food system. A system which increasingly is employing digital technologies and big data to improve efficiency and effectiveness. Linkages between those
efforts and implementation within production agriculture will be explored in the
section’s second segment. The role of societal expectations for that system will be
discussed as well.
8.5.1 Components of a Potential Digital Agriculture
This section will attempt to paint a picture of the multiple components that could
inform farmers in tomorrow’s digital agriculture. This is not intended to be a prediction, as these components currently are being employed to some extent. Rather, the
discussion will hopefully provide insights as to the potentials that exist as integration across technical capabilities occurs. Of course, one needs to keep in mind the
reality that just because something is technically possible, there may not be sufficient justification for managers to adopt that innovation.
Figure 8.3 graphically identifies several components that could form digital agriculture. Examples of each will be provided to illustrate their potential application.
Precision agriculture has been discussed at length previously. While routinely
employed by many farmers, advances in technical capabilities are continually offering new opportunities. For example, sensors embedded in the maize planter now can
sense soil moisture, depth, and other factors in each furrow to optimize the placement of seed as planting is being done.
Similarly prior discussion addressed big data applications. Farmers now can subscribe to services that provide extremely localized weather information and/or
receive agronomic guidance based upon insights gained from analysis of production
on thousands of acres, in addition to their own experience. As more data is captured
S. T. Sonka