Choosing Precision Agriculture Technologies

With the end of winter in sight, many in the agricultural industry are starting to get serious on their choices of precision agriculture technologies for the upcoming growing season. Whether the choice is equipment, software, or just taking soil samples in a new pattern, a lot of thought — accompanied with a lot of anxiety — precedes the actual decision. This decision is commonly made without an appreciation of the value expectations on the part of the user (e.g., grower, consultant, rep) and the hidden costs on the part of the provider of the technologies. In this article, I wish to discuss how the precision agriculture “ante” for making a choice is much greater than the purchase price — with some important implications for an organization. I have added some visuals to help elucidate my arguments.

The precision agriculture ante can be best understood with the interplay of two curves: perceived or demonstrated value of new technology to improve production (technology value) vs. user cost, and the necessary user education of the new technology (user education) vs. seller cost. I have assumed in my arguments that the seller will initially bear the cost of educating a user on a new technology. As a technology becomes mature, third-party vendors may provide another educational option. I will first discuss each curve and then discuss the interplay between them. A graph of the technology value (in terms of improved production) versus user cost curve is in Figure 1 , while a graph of the user education versus seller cost is in Figure 2.

The technology value vs. user cost curve in Figure 1 is the classic “diminishing return” curve. There is a diminishing return in the production value for each successive investment in new technology (dotted lines in figure). Viewed another way, you get the biggest bang for your buck on your first purchase of a new technology with lesser returns on future purchases relative to the improvement in production (e.g., higher yields, more efficiency).

The user education versus seller curve in Figure 2 has the inverse relationship to the “diminishing return” curve in Figure 1. There is an increasing return in the education of the user of a new technology for each successive investment in training (dotted lines in figure). In other words, a user can more quickly grasp new technologies after getting an initial grounding with the first technology. However, the increase in user education comes at an increasing cost to the seller. Now, with an understanding of what each curve means alone, we combine them to get greater insight into the precision agriculture ante.

The Education Equation

The interplay of the curves reveals the age-old struggle between introducing a new technology to a user and at the same time providing adequate education to support that technology. In the best case for both the user and seller, the introduced technology is simple to use (little education at little cost to seller) and has been proven to have high value towards improving production (high value at little cost to user). Since the best case rarely happens, we can explore other more likely scenarios, the extreme of which are dreaded by both the user and seller alike.

The first more likely scenario is that the user needs more education and support to use the new technology (more training at more cost to seller), but there is a demonstrated value of improved production after adapting the technology (high value at little cost to user). The result is that the seller is frustrated (because of additional training costs) even though there is delivery on the technology.

The second more likely scenario is that the user sees little value in the new technology even after receiving adequate education from seller. The result of this second scenario is the user is frustrated (because of diminishing return in value of the new technology relative to earlier technologies) even though there is delivery on the technology. The most dreaded interplay of the curves is when the user fails to see the value of the new technology in production and the seller keeps spending on the education of the user to adapt the technology. In this worst case, both the user and seller are frustrated and there is no delivery on the technology (at least as perceived by the user).

The best and worst cases for the interplay of curves have far reaching implications for an organization. In the best case of high value to user with little education cost to seller, both the user and seller end up on a positive note. The user sees the value that precision agriculture brings to its organization and will be favorably disposed to try new technologies in the future. Furthermore, the same user will be more patient if there is the need for more education with a new technology since the value of the first technology has been established. The seller needs, however, to be aware that the user may see a diminishing return on the next technologies relative to the success of the first and therefore must adjust marketing goals accordingly.

In the worst case of low or no value to user with ongoing education costs to seller, both the user and seller immediately start off on a negative note. The user rejects the technology and with it all future innovations in precision agriculture, while the seller goes out of business. In this worst case, the organization of the user very likely will become less competitive with its neighbors as the new technologies bring benefits to them. The “gun shy” user organization and the failed seller company represent a setback to all of precision agriculture.

The take-home message of the “curves” is that both the user and seller must be aware of the value of a new technology relative to previously adapted technologies and set expectations accordingly. Both the user and seller must be aware of the true educational needs of the user when introducing a new technology. Without this awareness, the ante for technology adoption may be so high that neither the user nor the seller will want to gamble on precision agriculture.

Editor’s note: This article first appeared in the Spring 2007 issue of PrecisionAg Special Reports.

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