The Theory of Innovation Adoption in Agriculture: An Application

Three months ago, I published Predictably Irrational: The Dilemma of AgTech Adoption, which was intended to bring to light that we often oversimplify agtech adoption and underappreciate important aspects of innovation adoption within agriculture.

This led me to begin talking more with founders and farmers, reading 3rd party research, and being retrospective on my experience to find more concrete considerations.

There has been a lot of work done surrounding innovation adoption within agriculture.

In fact, what is not often acknowledged is the fact that the “Innovation Adoption Curve”, made popular by Geoffrey Moore in the book Crossing the Chasm, was based on corn hybrid adoption research in Iowa done in the 1940s to learn about the diffusion of innovation.

Source

The adoption research has been known to have shortcomings, such as the fact that the context of each individual is always different.

Digging into contextual considerations led me to research from Geoff Kaine out of New Zealand.

He applies many consumer-driven insights to technology adoption within ag, something I have been adamant about within farming. We think of farming as a purely B2B situation— dollars and cents driven. But farmers have much in common with the decisions of a consumer, such as a desire for simplicity or the realities of being socially accepted and signaling specific attributes to friends or neighbors. This is distinct from generally accepted B2B business considerations.

Consumer Driven Dynamics

Broadly speaking, there are three sources of consumer-driven adoption - interest, sign, and hedonic.

Interest concerns the performance of the product or activity in utilitarian, economic and functional terms. Sign concerns the contribution of the product or activity to how they view themselves and impression management (signaling). Hedonic is the extent to which the product or activity satisfies pleasure or experiential goals.

This can similarly be applied to farmers.

In The Adoption of Agricultural Innovations, Kaine introduces five important considerations from previous research that can be used to identify a farmers willingness to adopt:

1. The relative advantage of the innovation over the current standard or other options.

2. Compatibility of the technology within the farmer's operational context.

3. The level of complexity of the innovation.

4. Trialability of the innovation.

5. Observability of the result.

I would add a 6th, based on the work of Ron Adner— adoption chain risk, or value chain incentives that considers how an innovation makes it to the farm.

At a high level, these all make sense, and they are worth breaking down to consider how agribusinesses and agribusiness professionals can leverage these insights as a framework for improving the uptake of their innovations.

Relative Advantage

Relative advantage refers to how much better a new innovation is compared to the existing practices and technologies it aims to replace. It's a farmer-specific assessment of the benefits of adopting a new method or tool, which can vary regarding economic gains, social benefits, or time efficiencies.

The specific advantages that matter most can depend on the innovation itself and also on the characteristics of the farmer/workers who might use it. For instance, a farmer might consider a new type of variable rate service or crop protection product as having a relative advantage if it promises improved yield and quality, lower risk, cost savings, perform better on target issue, or is easier to use compared to what they are currently using.

The relative advantage is about recognizing two key things:

1. That the new option is technically better in some way than what it's replacing (like being more efficient or effective and delivering an ROI and immediacy of ROI)

2. This technical improvement aligns well with the farmer's practical, social, and personal goals. The quicker and more evident these advantages are, the more likely it is that the innovation will be adopted by farmers and agribusiness professionals.

We often emphasize economics, and this should be priority number one— however, those who have worked in the industry will recognize that there are many other contributors that go into a farmer adopting a technology.

Compatibility

'Compatibility' refers to how well a new innovation aligns with the established beliefs, experiences, appetite for risk, farm size (ability to spread fixed costs) and skill requirements of farmers. This, along with portions of relative advantage, gets at the core of considering farmer psychology and the sociology of a local market.

When evaluating new technologies or practices, farmers consider how these changes fit with their current operations, goals, and values. This compatibility impacts the likelihood of adopting these innovations.

For example, a new biological product is more likely to be adopted if the farmer believes that soil health benefits their farming operation. The more compatible an innovation is with a farmer's existing system and values, the greater the likelihood it will be successfully implemented. This insight also shares the opportunity for the “long game” approach of applying tactics such as those from “Pre-suasion” by Robert Cialdini where there are foundations laid to increase the prospect of adoption.

The appetite for risk doesn’t mean just economic risk in this instance, but perception risk. If some new technology doesn’t perform, how will their landlord view renewing the lease if things go awry? What will the neighbors think about a divergence from generally accepted norms? The emphasis a farmer puts on this type of risk will influence their appetite for adoption.

Additionally, this concept illustrates where the value of local advisors comes in— if going to market through a retailer, for example, an innovation company can better align its product with farmers in the right context. Or, as we see more software being used by farmers, whether it’s software from Bushel, or Ever.Ag or AgVend, or its farm management software, there is an ability to derive better context about the farmer, such as behavioral tendencies and an application of behavioral science to determine the farmer's fit for a specific kind of technology.

Complexity

Innovations that are complex often require a farmer to have a deeper understanding of the underlying system, more effort in implementation, and greater foresight in anticipating the outcomes of their adoption.

This complexity can place significant demands (eg: increased cognitive load) on the farm decision-makers, who then need to learn new skills and also effectively train their staff, and change their systems (eg: new equipment or processes).

Unsurprisingly, the adoption rate of a new technology among farmers is inversely related to its complexity.

I have highlighted this multiple times within Upstream, including in USDA Precision Technology Adoption Report Highlights and Analysis, but Dan Northrup more concisely articulated the point in Agriculture – On the cusp of a rapid evolution:

Said another way, fast-adopted technology has been effectively integrated into current products, processes, and workflow with little change to the workload or skills required to implement.

There are multiple aspects that are important to adjust accordingly here, including go-to-market, and partnerships, but something that is very important is support to implement when a systems change is needed. This might be at the farmer level, or the agribusiness professional level (eg: trusted advisor). A farmer having confidence in the support they will receive for implementation is crucial to gaining innovation adoption when products are system changes.

Observability and Trialability

Observability refers to how easily the benefits and outcomes of a new technology can be seen and assessed by farmers. When the results of a technology are visible and can be easily demonstrated, farmers are more likely to adopt it. This means creating a tight feedback loop that is obvious and actionable to the farmer or user.

Take a harvest combine loss sensor technology— an in-cab feature that illustrates the exact harvest losses on a screen in real-time (eg: how many bushels are being lost) that can be adjusted by the operator in real-time gives a tight feedback loop from observation to tangible implication. This can be reinforced by creating a referencable map that shows after harvest has been completed what the adjustment in setting saved the farmer in losses. I believe this is also key in the less tangible biological and micronutrient space where observability is drastically lower than in the crop protection space where dead insects and weeds are obvious, whereas abiotic stress reduction or an increase in nutrient level is not. In these instances, companies need to get creative to illustrate the need and the impact, such as tissue tests.

Finally, trialability. This is about the extent to which a technology can be experimented with on a limited basis before fully committing. Technologies that can be tried out in smaller, less risky settings allow farmers to assess the benefits and risks without a significant initial investment. Some innovations are challenging to do this, such as farm management software, which reinforces one reason why there has been slower adoption compared to other technologies that can be tested more seamlessly.

Adoption Chain Management

I am bringing this concept in from outside the paper as I believe it is important in farming.

Acknowledging and overcoming adoption chain risk is essential for the successful adoption of technology— companies that went direct to farmers in the 2010’s found this out day after day.

The Wide Lens highlights that innovations rely on a chain of intermediaries between them and their end customers. In the instance of ag technology this includes distributors, retailers and agronomists as a few examples.

The farmer is the ultimate beneficiary of most new technology. But first, technology needs to get buy-in at the channel, whether to bring into inventory, or to have familiarity when recommending or not begrudge the technology as the farmer’s trusted advisor.

The technology needs to create value at all the various touch points of the channel, or what the book calls an adoption chain.

If any point in the value chain doesn't derive a benefit, the likelihood of success deteriorates precipitously. We even see this in regards to farmer adoption from Stratus Ag Research work:

Part of this adoption chain is the input manufacturer, but we know one of the most prominent detractors, or enablers, of any innovation is the retailer. Product use and new technology run heavily through the retail influence, so incentives must be aligned there.

Suppose there is a crucial player at any point of the value chain that does not understand, derive a benefit, or has something to lose from an innovation coming to fruition that needs to be managed. We can see from the below example that there can be an immense benefit at all points in the value chain, except one, and the innovation fails. Whereas, if we see a minor benefit at all points in the value chain, we can see positive utilization of the innovation:

Considering the entire value chain when delivering technology to a farmer is crucial for adoption success.

Bringing it Together

The above may not be surprising to most of you— but it reinforces that there is a need to do more than show a farmer a picture of bigger roots or a 7% increase in yield or save them 10% of their costs. Adoption success goes much deeper and needs to be thought of systematically from all points of failure.

The six considerations get at the need for communications, marketing, sales, business development and product to work together to more effectively create the right partnerships, go-to-market, product experience, and target the right farmers while creating the environment that is most conducive to successful adoption.

All six of these considerations can be evaluated to help guide where potential risks are:

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