Understanding appropriate UX requirements for on-boarding farmers to your agtech solution

The world of technology has only recently begun to design services for farmers. But the numbers show that this line of business is already growing rapidly. Investment records are broken every semester.

Underlying this development is the boon of the huge amount of data that a farm now generates. Like every other human being, farmers do book keeping, are active online, and use smartphones. They use such tools to solve problems on the farm, and by doing so, they’re generating data. There is also farm machinery, loaded with sensors generating lots of data. By combining such farm operations data with other data repositories, like open satellite data on water tables, precipitation, and nutrition data from the feed manufacturer, etc, data can be combined into useful applications that can help with farm management.

Despite the opportunity for the developer to build valuable solutions, there are also barriers that significantly obstruct adoption of digital solutions for farm management. An important barrier is created by technology solutions imposing certain implicit requirements regarding a farmer’s level of data saviness, to collect, organize, and distribute data. Even farmers who do understand the value of data management, generally don’t use computer or web-based tools.

The transfer of farm management practice to an online environment, is hardly seamless, and this transfer problem is a problem for many companies and startups that are developing services in this space. More often than not, I see solutions being launched that are at quite a distance from the kind of tech usability that a farmer can muster.

A Thinking Model
A rule of thumb for nailing product design is that you can best meet your farmers where they are. To help position where the farmers you’re targeting are in terms of tech saviness, I’ve developed a model form making farm data personas. This model is presented below.

A model for making farm data persona's
A model for making farm data persona’s

The basic idea is that any farmer, regardless of the technology, has to follow a flow of datamanagement in order to utilize and implement insights drawn from data in the farming practice. This datamanagement flow consists of 4 steps: Data Input, Data Storage/Retrieval, Data Sharing, and Data Analysis.

For each step there are a variety of options available to the farmer to handle their data. These options range from low-tech solutions, like paper-based record keeping, to high-tech solutions, like cloud computing storage, and SaaS for analyzing farm performance. (These can of course be adapted to whatever fits the sliding technology scale that matches your own circumstances.)

Next to profiling farmers according to their tech saviness, this model can also be used to indicate what level your solution currently requires your farmers to be in. By comparing these parameters, you can start to see, and understand the gaps.

Making technology appropriate.
Lets take an example of a solution of herd management for dairy farmers. Say you have an idea for a SaaS analysis solution, to offer online software for dairy herd management, like Farmeron. You will need to figure out a way to connect all the necessary data flows to feed your analytical solution. You want farmers to somehow get their cows to the cloud for analyzing herd performance, and you’re going to need a way to get them to digitize their input.

In the case that your farmers are still working with paper-based recording keeping, which is likely, you’re probably going to have to physically on-board their data. (As stated by Matija Kopic himself, founder of Farmeron)

Once you’ve identified this gap, the question then becomes what intermediate steps you can build into your solution to take farmers up the tech ladder. Can you redesign your product to include a learning curve, which will take your farmers on a journey to comprehend your solution, take control, and eventually make it fully usable?

Sticking with the example of Farmeron, they’re streamlining their person-to-person onboarding service with other support tools to educate farmers, and keep the learning going. This is something they had to configure on the fly whilst already being deeply rooted into their developed solution.

The upshot is that agtech initiatives will likely need to design for the farmer’s learning curve. Adoption for agtech solutions will be slower, and acquisition costs relatively high compared to other tech industries. This is something startups in this space will spend of lot of time on, and their investors will need to provide for the adequate runway.

Thanks to some valuable discussions I’ve had about this model with my colleagues Lan Ge, and Marc-Jeroen Boogaardt at Wageningen University in our Farm Digital project, and also with my dear wife Anne Bruinsma, who happens to be a leading figure in agtech with Hackwerk Advies.

The overlooked ecosystem problem for technology innovation in agriculture

Agriculture is lifting off in the world of start-ups. Google’s Eric Schmidt recently announced an accelerator dedicated to backing startups in the domain. This is an encouraging development that could bring agriculture to the forefront of the digital tech ecosystem, and might even give it a top position in the field of tech innovation.

But despite the opportunity of commercial venture capital directing itself to agriculture, there is a major overlooked problem that tech development in ag faces, and that is the condition of its ecosystem around entrepreneurship and startups.

The agricultural sector suffers from a backlog in terms of informed entrepreneurship skills. Generally speaking, agricultural engineers, who either design solutions for farmers, or are farmers themselves, know very little about entrepreneurship. They build things animals and plants like, but not necessarily their human users. Agriculture knows tons about engineering, but little about the innovation that is required to successfully support new technology adoption in the farmers’ market.

Leadership is the second ecosystem problem in agriculture. In a dynamic landscape, like that of agriculture at the moment, it’s important that there are leaders out there that can define an end to which the game will likely play out. From defining such an end, you can then work backwards, and make the hard decision about what activities and initiatives you should be undertaking now, to move in a trajectory towards that end, even if this goes against the grain of conventional wisdom. (I strongly encourage you to read John Hagel’s recent two posts on the future shape of strategy)

However, many of agriculture’s leaders don’t give much for landscape thinking and futurism. They prefer to work with linear progression, departing from the handful of business models that have ruled agriculture for the past 50 years, for their predictions of the future. This is not such a fruitful perspective for a market environment in which technology tends to blur, rather than affirm classic industry boundaries.

The ecosystem challenges are not only of the broad landscape definition kind. They also lie in the specificities of tech design for farmers. Particularly the basic technology user experience for farmers has not been understood thus far. For instance, a startup called Farmobile is betting on its own hardware module to function as a data bridge between reading out data from tractor and machine sensors, and mass storage on the cloud. Farmobile explicitly states that they don’t work with mobile phones and tablets, because they break, get lost, have batteries that drain too quickly, and are cumbersome to use in pairing for farmers and their farms hands.

Convention would dictate that the mobile platform be used. Such a choice for explicit distantiation as Farmobile has taken from mobile would generally be considered as Silicon sacrilege. But I am convinced Farmobile knows their users better than convention dictates, and is making the right bet on UX. A bold decision which nobody, or no precursor could support them to make as of yet.

The bottom line is that there is no ecosystem yet of founding teams with experience and mentors and investors alike, who understand what lies ahead for agriculture and the practical challenges to overcome. That ecosystem is yet to be built. I predict, nay warn, that if “ecosystem” remains optimistically overlooked in the new investment strategies that are popping up for agriculture, that the lack of entrepreneurship, leadership, and design is going to be one of the big, hard walls that tech development in agriculture will hit.