221
and as research and development continues, the capabilities of such services are
likely to increase.
The Internet of Things is a concept closely linked to precision agriculture,
although it is worthy of separate consideration. Sensors that monitor conditions in
the field to inform irrigation decisions are one example. Similarly sensors in grain
bins can continually monitor conditions in the bin. In both instances, the associated
systems can inform managers of the actual and past status, can initiate action when
warranted, and can record information that can be employed (possibly with big data
approaches) to improve the algorithms within the system.
A key difference between factory-based manufacturing and much of agriculture is that agriculture occurs in the open and across significant distances.
Therefore monitoring what is happening as it happens has been a historic challenge on the farm. As noted previously remote sensing is being employed to
overcome this constraint. The source of the data can be from satellites, fixed
wing aircraft, UASs, stationary devices, or some combination of them. For example in Australia, efforts are underway which link satellite monitoring of pasture
conditions with sensors that monitor animal weight. Algorithms are being estimated which use that data to recommend when to move animals from a paddock
that is in danger of being overgrazed to another more suitable one. Applications
in developing countries are particularly exciting as the prior methods of gathering information have been both expensive and insufficient. Possibly, remotely
sensed data, in combination with other information sources, can improve agricultural and food systems in those settings. A marked improvement might be possible in a fashion similar to the way cell phones markedly improved
communications in developing countries.
This chapter’s second section described a story from the 1950s to illustrate that
farmers have always wanted to use evidence from their operations to improve the
farm’s performance. The high cost or infeasibility of measurement historically
limited their capabilities to do so. Labelled here as on-farm research, there appears
to be is considerable innovation and experimentation focused on the means by
which farmers can apply the digital technologies to learn how to improve their
own operations (Grains Research Development Council 2016). This interest is
being expressed in terms of actions by individual farmers, efforts of groups of
farmers (including cooperatives), and in collaboration with input providers. These
efforts are exciting because of the possibility of gain, but also because of the
potential for enhanced managerial control which previously was never available
to farmers.
Finally, coordination is a critically important aspect of digital agriculture.
Such coordination may involve relatively simple technologies. For example, a
group of woman farmers in an African country who learn that the trader can pay
higher prices for their chickens if they use a cell phone to inform the trader when
there are enough chickens available to fill the trader’s truck. In contrast, coordination may involve combining localized weather forecasts with a logistics model
of the most efficient use of equipment for a large farming operation.
8 Digital Technologies, Big Data, and Agricultural Innovation
and as research and development continues, the capabilities of such services are
likely to increase.
The Internet of Things is a concept closely linked to precision agriculture,
although it is worthy of separate consideration. Sensors that monitor conditions in
the field to inform irrigation decisions are one example. Similarly sensors in grain
bins can continually monitor conditions in the bin. In both instances, the associated
systems can inform managers of the actual and past status, can initiate action when
warranted, and can record information that can be employed (possibly with big data
approaches) to improve the algorithms within the system.
A key difference between factory-based manufacturing and much of agriculture is that agriculture occurs in the open and across significant distances.
Therefore monitoring what is happening as it happens has been a historic challenge on the farm. As noted previously remote sensing is being employed to
overcome this constraint. The source of the data can be from satellites, fixed
wing aircraft, UASs, stationary devices, or some combination of them. For example in Australia, efforts are underway which link satellite monitoring of pasture
conditions with sensors that monitor animal weight. Algorithms are being estimated which use that data to recommend when to move animals from a paddock
that is in danger of being overgrazed to another more suitable one. Applications
in developing countries are particularly exciting as the prior methods of gathering information have been both expensive and insufficient. Possibly, remotely
sensed data, in combination with other information sources, can improve agricultural and food systems in those settings. A marked improvement might be possible in a fashion similar to the way cell phones markedly improved
communications in developing countries.
This chapter’s second section described a story from the 1950s to illustrate that
farmers have always wanted to use evidence from their operations to improve the
farm’s performance. The high cost or infeasibility of measurement historically
limited their capabilities to do so. Labelled here as on-farm research, there appears
to be is considerable innovation and experimentation focused on the means by
which farmers can apply the digital technologies to learn how to improve their
own operations (Grains Research Development Council 2016). This interest is
being expressed in terms of actions by individual farmers, efforts of groups of
farmers (including cooperatives), and in collaboration with input providers. These
efforts are exciting because of the possibility of gain, but also because of the
potential for enhanced managerial control which previously was never available
to farmers.
Finally, coordination is a critically important aspect of digital agriculture.
Such coordination may involve relatively simple technologies. For example, a
group of woman farmers in an African country who learn that the trader can pay
higher prices for their chickens if they use a cell phone to inform the trader when
there are enough chickens available to fill the trader’s truck. In contrast, coordination may involve combining localized weather forecasts with a logistics model
of the most efficient use of equipment for a large farming operation.
8 Digital Technologies, Big Data, and Agricultural Innovation