Changing Our Perspective
I have often marveled at the evolution of perspectives in art over the many centuries. Art ranged from simple stick figures on walls of caves in prehistoric times to multi-dimensional cubism in the twentieth century. Art, it has been said, mirrors culture. It reflects not only the culture of today but possible directions in the future. As realism gave way to abstraction and linearity gave way to non-linear perspectives, art suggested new ways of looking at our surroundings and stimulated our imagination of what could be possible.
Just like art, perspectives are important to precision agriculture. And like art, new perspectives are stimulating innovation in product development. These new perspectives are being forged by different kinds of data, analysis tools, and presentations. It is also being forged by the fusion of existing technologies into new forms to support precision agriculture.
To appreciate how these new perspectives are impacting precision agriculture, it may be helpful to discuss them in the context of a decision-making paradigm.
One popular decision-making paradigm is “data –> analysis –> interpretation –> decision.” That is, to make an informed decision, one must have a source of data, tools to analyze the data, an interpretation of the analyzed data, and lastly, a decision based on the interpreted and analyzed information. The implementation of this paradigm in an agricultural production setting may involve an individual or an organization. For instance, one person or entity may be responsible for collecting and analyzing field data, while a second interprets the analysis and a third makes the decision.
Therefore, individuals or organizations can have different “roles” depending on which point they participate in this paradigm. For example, the person or entity responsible for data could have the role of an “observer,” while those responsible for analysis could have the role of “analysts.” As we will see, the many kinds of perspectives are closely linked to roles in the decision-making paradigm.
A Richer Picture
Early agricultural production was like early art in that the decision making was fairly simple and linear. For each crop in a particular geography, there was a set of established practices that began after harvest in one season and ended with a harvest in the next season. These practices were based on past experience and a bit of trial and error. They may not have been the best set but the practices were practical and proven. In these early times, there were minimum records and virtually no data being collected in the field other than a measure of final yields at a point of sale. The spatial perspective for these early production practices was a field and the temporal perspective was a season.
Precision agriculture, like non-linear, multi-dimensional art, brought a new perspective. It afforded a three-dimensional view of decision making. There was now a two-dimensional perspective of points across a field, as well as, the dimension of time for the collection of data during a season. The production picture immediately got richer with subfield detail of applications and yields coupled with the understanding of how timing of practices impacted end-of-season outcomes. Global positioning systems (GPS) and geographic information systems (GIS) allowed growers to both track and view production details in the form of images over a growing season. Through precision agriculture, data were literally turning into art in the form of landscape maps. By viewing a time series of maps, which displayed the movement of pests or the development and growth of a crop, a decision maker could interpret data and make informed decisions for a field and across a farm.
The introduction of guidance and controls on equipment brought another perspective to precision agriculture. The detailed, landscape maps being viewed by individuals could now be interpreted by machinery, which could make decisions as to where to plant a seed or apply a fertilizer. Production like art was becoming more abstract as knowledge and not experience became shared season-to-season.
Landscape maps, themselves, became more sophisticated as background imagery could now have a street-level perspective as well as a bird’s eye view. They have also evolved from a two-dimensional, single layer to three-dimensional, multi-layer renditions of data complete with observation points. In addition, many analysis tools include the ability to “onion skin” overlays of different reference information, such as roads, cities, or important areas of interest.
The proliferation of data and the expansion of the analytic toolbox have opened up many possibilities for interpretation and many avenues for decision making. Avenues, originally singular based on legacy, are now multiple based on logic. With the many new sets of data and tools for analysis, old roles are being redefined and new roles are emerging in production agriculture. Consultants and organizations advising growers must now second as information technologists. Equipment operators must be able to manipulate embedded computer programs and data input in addition to carrying traditional tasks such as driving. Farm managers, responsible for operational activities, can now have the “big picture” of production and can make confident decisions that not only support a current season’s crop, but also the future sustainability of their enterprise.
The success stories in this current edition of Precision Ag Special Reports reflect not only the continued improvements in technology but also new perspectives on how that technology is impacting data analysis, interpretation, and production decision making.