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8.2 “What Is, or Can Be, Agricultural Data?”
To many, maybe most, of us, the word data tends to generate little excitement.
Frankly data was just boring, as it suggested rows and columns of numbers on a
spreadsheet offering scant guidance or insight. Yet data in either its implicit (that
which we sense or feel) or explicit (that which is written down) forms is essential to
how we make decisions. Farmers, throughout time, have been constrained to making decisions based upon what they could observe, sense, and feel. This constraint
was imposed by technology and economics. Digital technologies and big data are
changing the parameters associated with those constraints. However, those forces
will continue to determine the extent and effectiveness of technology adoption.
This section addresses the links of technology, data, and decision making. Its
first segment employs a very simple example to demonstrate the interactions of
technology, economics, and farmer decision making. The second segment illustrates
how emerging technologies are fundamentally changing what is available as explicit
data for agricultural decision making. The section’s final segment provides a more
complete description of some of terms associated with digital technologies.
8.2.1 Measurement
“You can’t manage what you don’t measure!” is a phrase attributed to both Peter
Drucker and W. Edwards Deming. This phrase is as applicable to farmers as it is to
managers at Toyota or Amazon (Brynjolfsson and McAfee 2012). The relationship
between measurement and the ability to make improved decisions is critically
important in understanding the potential for digital technologies to affect agricultural management.
The author of this paper had the benefit of growing up on a small farm in the
Midwest region of the United States and, throughout his career, has learned extensively from farmers in the United States and globally. With apologies for a small
digression, let me use personal experience to focus on the linkage between measurement and management. Growing up on a farm, the linkage between what could be
measured and our ability to improve performance was straightforward. In those
days, we had to carry the, hopefully, full milking machine from the cow to the milk
tank. The weight of the bucket gave direct evidence as to which cows were producing more. And because there were less than 20 cows in the herd, it also was possible
to remember which were the higher-producing cows and give them an extra portion
of grain. Laggard producers received less grain.
On this same farm, about 120 egg producing chickens were housed in a building,
with ample room to roam outdoors as well. Eggs were collected twice a day.
Performance of the entire group was observable. Information that could lead to
improved performance of individual birds, however, was not observable. Technically,
it would have been possible to establish a production system where measurement of
8 Digital Technologies, Big Data, and Agricultural Innovation
8.2 “What Is, or Can Be, Agricultural Data?”
To many, maybe most, of us, the word data tends to generate little excitement.
Frankly data was just boring, as it suggested rows and columns of numbers on a
spreadsheet offering scant guidance or insight. Yet data in either its implicit (that
which we sense or feel) or explicit (that which is written down) forms is essential to
how we make decisions. Farmers, throughout time, have been constrained to making decisions based upon what they could observe, sense, and feel. This constraint
was imposed by technology and economics. Digital technologies and big data are
changing the parameters associated with those constraints. However, those forces
will continue to determine the extent and effectiveness of technology adoption.
This section addresses the links of technology, data, and decision making. Its
first segment employs a very simple example to demonstrate the interactions of
technology, economics, and farmer decision making. The second segment illustrates
how emerging technologies are fundamentally changing what is available as explicit
data for agricultural decision making. The section’s final segment provides a more
complete description of some of terms associated with digital technologies.
8.2.1 Measurement
“You can’t manage what you don’t measure!” is a phrase attributed to both Peter
Drucker and W. Edwards Deming. This phrase is as applicable to farmers as it is to
managers at Toyota or Amazon (Brynjolfsson and McAfee 2012). The relationship
between measurement and the ability to make improved decisions is critically
important in understanding the potential for digital technologies to affect agricultural management.
The author of this paper had the benefit of growing up on a small farm in the
Midwest region of the United States and, throughout his career, has learned extensively from farmers in the United States and globally. With apologies for a small
digression, let me use personal experience to focus on the linkage between measurement and management. Growing up on a farm, the linkage between what could be
measured and our ability to improve performance was straightforward. In those
days, we had to carry the, hopefully, full milking machine from the cow to the milk
tank. The weight of the bucket gave direct evidence as to which cows were producing more. And because there were less than 20 cows in the herd, it also was possible
to remember which were the higher-producing cows and give them an extra portion
of grain. Laggard producers received less grain.
On this same farm, about 120 egg producing chickens were housed in a building,
with ample room to roam outdoors as well. Eggs were collected twice a day.
Performance of the entire group was observable. Information that could lead to
improved performance of individual birds, however, was not observable. Technically,
it would have been possible to establish a production system where measurement of
8 Digital Technologies, Big Data, and Agricultural Innovation