Precision agriculture is a journey. Lars Petter Blikom shares his journey straddling the twin worlds of agriculture and technology.

I am a farmer. And for the past 7 years, I have gotten to know a lot of fellow farmers, both in my home country Norway and across the world. I have developed the deepest respect for what farmers do.

Day in and day out, they hone the skills of cultivating the land and growing massive quantities of high-quality food. After thousands of years, there is quite a bit of precision to what farmers do. It’s not easy to produce food.

Day in and day out, they hone the skills of cultivating the land and growing massive quantities of high-quality food. After thousands of years, there is quite a bit of precision to what farmers do. It’s not easy to produce food.

Who speaks for the farmer?

My background is in other industries, my education is in technology, and my 17 years of professional experience have taken me through the hypes of internet-of-things, machine learning, and all the other buzzwords of the past few decades. And as the oldest kid on a farm, I was born with the questionable rights to a rewarding life as a farmer, and 7 years ago, my time had come.

I am writing this as a farmer primarily, perhaps with an unusual background in technology, but a farmer nonetheless.

Agriculture is a massively large industry. Some of the world’s largest companies are dependent on it. And the whole food and grocery industry is dependent on it too.

As with any large industry, all the usual suspects are present; the researchers, the experts, the financiers, the law-makers, the big corp executives, the investors – they meet at conferences and seminars, and they exchange papers, articles, and business ideas. They talk and discuss important things, such as “what is precision agriculture?”

But who’s not there? Who´s voice is not present? The farmers’.

Farmers don’t have time to hang around conferences and chat.

There is an entire ecosystem of players in this industry, fully dependent on the farmer as the core value creator – yet they are surprisingly disinterested in what the farmer needs, wants, and thinks.

I could go on for a long time about the imbalance of power in the agriculture industry, how even the largest of farmers are no bigger than a bug than their suppliers and customers, and how the value created ends up with those with the best negotiation position – never the farmer. But this is not today’s topic.

Defining precision agriculture

Naturally, “precision agriculture” is defined by above mentioned important people. You can read on Wikipedia what they say:

• Vegetation indexes (NDVI) from satellites will tell us that our weak plants are… you guessed it; weak.
• Cameras with image recognition will tell us what type of weeds we have in the fields… as if we didn’t already know.
• IoT-sensors will tell us when to harvest for optimal ripeness… forgetting details like logistics, labour force management, weather, and a few other things that also impact the harvest time.

Don´t conclude I am against these concepts; I am a technologist, believe in the powers of image recognition, know that multispectral cameras in the sky can see things our human eyes can’t see, and I know that sensors will give us a better decision basis. The problem is that the development is technology driven – someone observes a shiny new thing and concludes, “let´s deploy this in agriculture.” The better way to guide development is to let it be driven by user needs.

But they forget to ask the farmers what they need.

This disconnect between farmers’ needs and the new technologies makes them too hard to adopt. NDVI imagery is a good example, it is readily available, and I can get it for my farm cheaply.

I get a yellow/green/red heat map indicating areas with low to high biomass density. It’s interesting; I can go out in the field and compare the heat map with real life – see if it fits. But then what? I don’t trust it enough to create variable zones based on it.

I don’t have the equipment to make variable-rate applications for my orchards. And even if I did, how do I adjust the rates? What’s the right spray application rate for a yellow area on the heat map? There is just not enough available knowledge in my ecosystem to jump into this yet.

It’s like the early days of the internet – remember all that work with dial-up modems and manually fiddling with protocol settings until the damn thing works? It’s like that, plus in farming, you don’t know until 3 months later if it worked or not. And if it didn’t, you can try something else next season – it’s the slowest learning cycle of all. You just can’t afford a lot of experimentation when there’s a year between each iteration.

Precision agriculture is a journey.

It’s a journey the whole agriculture industry has been on for thousands of years and which will continue for as long as people eat food. “Precision” is a word that describes diligence, accuracy, and knowledge in the farmers’ work and decision-making. I will let no external expert claim that word and make it synonymous with drones, satellites, and machine learning. Precision is realized through a farmer’s best judgment in the field. And the farmers will always use the best tools available to inform their judgment.

In the late 1800s, we got the first tractor. In the early 1900s, we got granulated fertilizers. In the early 2000s, maybe we are getting multispectral cameras. No big deal. Mostly, the production of food goes on. Business as usual.

In the next article of this series, we´ll discuss what problems this so-called “precision agriculture” needs to solve. What does the farmer need it to do? And I think we have to debunk a couple of myths too.

This is part one in a seven-part series on a farmer’s journey to precision agriculture.

Part 2: What problems will precision agriculture solve

Part 3: Reinvent how you gather, organize and use your data

Part 4: Why measuring return on investment per field is still a challenge

Part 5: What’s the biggest cost in agriculture? Labour.

Part 6: How to get your orchard future ready? Start automating documentation

Part 7: How variable zoning can lead to more precision in agriculture