[ A growing number of farmers are leveraging artificial
intelligence (AI) and large language models (LLMs), like ChatGPT, to
tackle daily tasks. One farmer said “I’ve been blown away by the
information it can kick out in a host of different areas."]
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PORTSIDE CULTURE

FARMERS ARE TENTATIVELY EMBRACING AI  
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Hannah Macready
October 6, 2023
Ambrook Research
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_ A growing number of farmers are leveraging artificial intelligence
(AI) and large language models (LLMs), like ChatGPT, to tackle daily
tasks. One farmer said “I’ve been blown away by the information it
can kick out in a host of different areas." _

As ag producers adopt ChatGPT, concerns about accuracy, utility, and
environmental damage abound., Graphic by Ali Aas

 

Marc Arnusch, a third-generation farmer, has spent his career raising
sugar beets, onions, and barley grains in Colorado’s Prospect
Valley. Recently, when his seventy-year-old friend and neighbor
reached out because he was considering exiting the farming business,
Arnusch found himself consulting an unexpected ally for advice:
ChatGPT.

Arnusch wanted to investigate every outcome before offering guidance
on such a personal and life-altering decision. ChatGPT helped Arnusch
lay out a strategy, with options for changing markets, leaving the
industry completely, or selling the farm for a stake.

Eventually, his friend decided to rent his farm holdings to
Arnusch’s son and nephew — the fourth generation of Arnusch Farms.
Now, the two farms are in business together. “I feel ChatGPT brought
us to a point where we had a really good conversation and drove the
principal pieces of that discussion,” said Arnusch. “It’s almost
like a robotic business coach that I’m learning to confide in.”

Arnusch is part of a small but growing number of farmers leveraging
artificial intelligence (AI) and large language models (LLMs), like
ChatGPT, to tackle daily tasks. He’s used it for everything from
drafting employee evaluation questions to researching Colorado’s
wolf recovery initiatives. “I’ve been blown away by the
information it can kick out in a host of different areas,” he said.

Still, Arnusch says he’s wary of using the tool for questions
related to his farming products. “When I ask it about animal
agriculture, sustainability, or GMOs, it produces things I don’t
agree with as a farmer, or that are blatantly false,” he said.

Earlier this year, Farmers Business Network (FBN), an ag-tech data
research platform based out of San Carlos, California, released its
own version of ChatGPT called Norm. Norm is built on OpenAI’s
GPT-3.5 model, and uses public data like weather reports, soil data,
and product labels to answer ag-related questions. It also taps into
FBN’s exclusive agronomic data and assets from the USDA’s National
Agriculture Statistic Service, in an effort to avoid some of the
common misinformation its predecessor has become known for. Referred
to as hallucinations, instances of misinformation occur when a model
generates incorrect information but presents it as fact. OpenAI,
ChatGPT’s creator, has already faced legal challenges
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to these hallucinations.

Kit Barron, head of data science and analytics at FBN, said the
company recognized the dangers of getting farming information wrong
early on. “You do not want a chatbot hallucinating recommendations
for highly regulated products that have a significant impact on the
viability of your family farm,” Barron said.

While still in beta testing, Norm is trained to answer animal health,
crop protection, and product usage questions for farmers across North
America. Barron said the kinds of questions Norm receives range from
mundane to serious. “[At first], there were a lot of novelty
questions, probing, having fun with it,” Barron recalled. “One
farmer was even asking us to help him write his wedding vows.”

“Over time it’s become more of a useful tool, we’re seeing a lot
more seasonally relevant, directed questions. People are treating it
as an ag advisor, or another trusted consultant on their farm. Now,
they’re asking about post-harvest [tactics], fertilizer regimes, or
herbicide applications, and that’s been really great to see.”

Digital Green, a global development organization, created a similar
tool, Farmer.CHAT, which is currently in use in India and Africa. This
multilingual chatbot was also built on GPT-3.5. The app aims to close
information gaps for rural farmers, who often lack access to real-time
agricultural information.

With a simple text or voice query on WhatsApp or Telegram, Farmer.CHAT
answers questions such as, “What do I do if there are white flies on
my chilies?” or, “How do I know when my onions are ready to
harvest?” Its knowledge base is trained on proprietary Digital Green
data, much like FBN’s Norm, to ensure only the most accurate
information is passed on. Digital Green stated that, to date, the app
has reached 5.2 million farmers.

Jona Repishti, head of global gender programs at Digital Green, feels
that bridging agricultural information gaps is one of the best
applications of the GPT technology she’s seen. She envisions a
future where AI and LLMs could be used in predicting yield
measurements, market timing, and pricing. “AI technology just has so
much potential. It’s so transformative,” she said, “but one of
the things that we see is that the landscape is lumpy, in terms of
adoption, in terms of availability and equity of access to the latest
technologies.” She continued, “Some sectors are further ahead than
others.”

Repishti also emphasized that quick access to agricultural information
is even more crucial in the face of climate change. Around the world,
farmers face mounting climate threats, including wildfire, drought,
and flood, as well as negative mental health effects
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living in an increasingly unpredictable environment.

Joseph Walton, a research fellow in arts, climate, and technology at
the Sussex Digital Humanities Lab, isn’t convinced of generative
AI’s efficacy in the agricultural sphere just yet. Generative AI
models have negative environmental impacts, which trickle down to
agriculture, said Walton.

“While it can be tempting to think of AI as this ghostly, magical
thing, it has a physical basis,” said Walton. “It runs on servers
built out of copper, steel, gold, silver, palladium, and cobalt. It
took energy to extract those materials and twist and twirl them into
servers.“

He continued, “Training and deploying AI models is computationally
intensive, and that has implications for embodied carbon in data
center hardware, electricity usage, water for cooling, e-waste, and so
on.”

“Tech impacts climate and climate impacts agriculture,” he said.

One study [[link removed]],
conducted by researchers from AI firms like Hugging Face, aimed to
quantify the carbon impact of a machine learning model called Bloom.
The study found Bloom’s training emitted enough carbon to power an
average American home for 41 years
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The same study reported that models of a similar parameter size, such
as OpenAI’s GPT-3 model, emitted nearly 20 times as much as Bloom.

Polina Levontin, an environmental policy researcher at Imperial
College London, has a more optimistic perspective. She sees AI as a
cost-effective way to offset agriculture’s existing carbon
footprint.

“There is enormous potential to use agriculture to regenerate soils,
capture carbon, use less water and nitrogen, and only apply chemicals
when really needed,” she said. “AI will make all this much

easier and cheaper ... and, combined with robotics, will limit
exposure for farm workers from heat waves and [the like].”

Her concern lies with who will benefit from the adoption of these AI
tools. “AI will benefit all farmers, small and large, but it is
likely to benefit larger farmers a lot more, exaggerating existing
inequalities,” she said.

Still, farmers like Arnusch are already getting creative. He hopes AI
can help take the guesswork out of decision-making, and make it easier
for smaller farms like his to run a profitable business. “I
appreciate the value it has in understanding alternatives that maybe
weren’t as obvious to me as I would have thought,” he said.

Walton notes that LLMs aren’t the only AI tools being used in
agriculture. Deep learning systems, which use large datasets to
recognize patterns and make decisions, are also increasingly popular.
These models can be used for crop disease detection, weed control, and
yield prediction. Researchers have also found ways to use AI to assess
animal emotions, a practice some think will have positive impacts on
global animal welfare.

However, he warned against thinking of AI as a silver bullet solution.
“We’ve really seen this gung-ho attitude this year … where
[suddenly] everyone is rushing to embed generative models in
everything, without talking about what the long game is.”

There are also still big gaps in tech companies‘ commitments to
climate change and the energy-intensive worlds they are creating, said
Walton. “Technology is going to be an important part of how we
address climate change. Techno-solutionism is not.”

Hannah Macready

Hannah Macready is a freelance writer focused on technology and
innovation. Her work has appeared in _The Globe & Mail_ and
the _Financial Post_, among others. In her free time, Hannah loves
exploring the dense forests of Canada’s West Coast with her two
dogs, Soup and Salad. Find more of Hannah’s work here
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* agriculture
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* artificial intelligence
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* AI
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