It might have been instinct that led hunter-gatherers to collect a handful of seeds and sow them into prepared soil or perhaps it was accidental opportunity. Either way, human’s history of cultivating plants to develop beneficial characteristics evolved slowly into the domestic crops we know today.

Figs are the oldest known domesticated plant dating back 11,400 years in the lower Jordan Valley, West Bank. A thousand years later, founder plants native to southwest Asia, such as emmer wheat, einkhorn wheat and barley, were domesticated. Pulses such as lentil, pea and chickpea were domesticated for their oil. Maize was domesticated 9,000 years ago in southern Mexico. Today, the category of founder plants includes nearly 20 other species of cereals and legumes.

Although the first farmers probably didn’t know they were breeding plants, the ones that thrived survived.

Fast-forward 10,000 years and we’re still at it. Plant breeding is now a tad more sophisticated and with a warming planet, the challenge becomes breeding plants that are climate-resilient.

Modern challenges in warming world

Researchers at the University of Hawaii (UH) have pioneered a new way to breed climate-resilient crops faster by combining plant gene-bank data with DNA and climate analysis. Their research focuses on sorghum and the method they’ve developed could speed up ways to secure future food supplies worldwide.

“Plant breeding is defined as the selection of plants for human benefit,” says Michael Kantar, associate professor in the UH Mānoa College of Tropical Agriculture and Human Resilience.

“Every plant breeding program relies on diversity to find new things that people want. Historically, this has been food, fibre, feed and medicine. Germplasm collections have been around for thousands of years informally. While some traits have been well characterized in (gene-bank) collections, climate resilience has not been. This is largely because measuring drought, heat or waterlogging stress is very hard and expensive.”

The research, published in Nature Climate Change, identified the best potential plant parents and geographical areas that support the most promising genotypes for crop improvements.

Plant gene-banks are repositories of millions of diverse seeds, plants and other genetic material collected from around the world. They serve as insurance against natural or human-induced catastrophes that could decimate global food, and they are vital resources for plant breeders developing new crop varieties with a range of traits from drought resistance to disease tolerance.

“These collections are absolutely amazing,” says Kantar. “They are living libraries but often they are more like museums. We really wanted to make use of the library part. We wanted to try and find some way to see if we could develop a smaller set of individuals to use for crossing to make experiments easier.”

The innovation: environmental genomic selection

The research team used 1,937 unique samples, or accessions (unique samples of seeds representing a breeding line or a distinct population) of sorghum valued for its heat and drought tolerance to test a new method called environmental genomic selection. The method combined DNA data with climate information to predict which plants would be best suited to future warmer conditions.

“What environmental genomic selection does is, instead of predicting the value of an individual plant for a specific trait like yield, we predict its response to environmental conditions,” says Kantar. “Genomic selection takes thousands of small differences in DNA across the population, and we calculate the effects of each change with respect to an environmental variable like heat. We get a ranked list of what plant will survive under hot temperatures.”

As an example, Kantar says that a change from an A (adenine) to a T (thymine) on chromosome 3 may result in the ability for the plant to tolerate temperatures that are 0.0001 degrees higher than the average temperature the crop lives in.

“We do this same calculation thousands of times and eventually end up with a score that says Plant A will be able to survive in temperatures 5.6 degrees higher, so this now becomes a candidate to be used as a parent in a breeding program.”

The system can be applied to any crop with the right data, not only sorghum but others such as barley, cannabis and pepper to name a few. Instead of testing thousands of plants in a field, scientists use a smaller, diverse mini-core group to forecast crop performance in different climate environments to help breeders more quickly select the best parents for new, climate-resilient varieties.

“Germplasm collections are really big, often tens of thousands of accessions,” Kantar says. “This has led to many folks creating different ways to subset the accessions to try to find traits useful to humans. Most of these methods focused on different types of sampling genetic, geographic, use or phenotypes. Each way of sampling has pros and cons. Starting in the late 1980s, it became popular to create core collections, subsets of collections based on maximizing genetic diversity.”

Kantar says that a threshold of diversity was set, usually based on a metric like allelic richness. Alleles are variant forms of a particular gene and over time the combined inherited differences are the basis for genetic diversity. A researcher would find the minimum number of individual plants that had 80 per cent of total diversity. But those core collections were still very large, often thousands of individual plants.

“In the 21st century, a lot of effort was placed on creating smaller (easy to control) collections — mini-cores — in which individual researchers could grow usually less than 500 accessions,” Kantar says. “These mini-cores represent a huge amount of genetic diversity of important crops but in a small enough package that an individual researcher could grow and evaluate all of them at once.”

Predicting performance for the year 2100

Clarifying critical climate information means projecting what the temperature will be in the region where farmers want to grow a specific plant. For example, Kantar says that if a plant is currently thriving in a place where the average temperature during the growing season is 24 C but by 2100 is expected to be 27 C, then it is important to source a plant accession that will be able to grow under that 3-degree change. The genomic selection method predicts which plants will grow well under these kinds of temperature changes.

“For the environmental genomic selection specifically, and genome environment association in general, you need certain types of data,” says Kantar. “You need to have the latitude and longitude of where plants were collected from. You need to have genome-wide markers, which can be from a wide range of technologies, the most common now being sequencing. And the plants need to be either wild relatives of crops or landraces.”

The value of the team’s work lies in the speed and efficiency of developing a new genetic order for crops to meet climate challenges. Kantar has been talking with crop breeders about options for not only meeting climate challenges but also resistance to diseases that may develop because of a warming world.

“Breeding is a complex decision problem,” says Kantar. “People want a lot of things. There has been a lot of work on trying to deal with these sometimes-contradictory goals. In this case we use this method to try and find both quantitative and qualitative variations. This allows breeders to do both population improvement and backcrossing. A major goal is to get breeders a small set of lines that they are confident will have the traits they need so they will put them into their breeding program.”

The researchers discovered that nations with high sorghum use will likely need genetic resources from other countries to effectively adapt to climate change.

A global effort for future food security

“There are national and international (germplasm) collections,” says Kantar. “Some countries have all the variation they need within these national programs or they can collect it from local farmers. They may have easier access due to not having phytosanitary (plant health) regulations to move plant material. Other countries will need to get germplasm from international collections. In some cases, this means a delay in being able to breed.”

The environmental genomic selection approach saves time. And, by using the diverse mini-core approach to forecast how crops will perform in different environments, breeders will be able to quickly select the best parents for new, climate-resilient varieties.

The method could be expanded to a variety of crops and used to support future breeding.

The post How scientists are using DNA and climate data to breed crops of the future appeared first on Country Guide.

This might sound dramatic, but it’s becoming a real possibility for Canadian farmers. As agriculture embraces automation, cloud platforms and remote monitoring, the risks of cyberattacks are growing — and most farms aren’t ready.

In fact, a recent study by MNP and RealAgristudies found that only seven per cent of farmers say they feel very knowledgeable about cybersecurity and nearly 80 per cent don’t have an incident response plan. And while tech adoption is accelerating, basic cyber hygiene often lags.

So, what should you do if your farm goes offline? And more importantly, how can you prevent it from happening in the first place?

The new front line: Cybersecurity in agriculture

Farms today are more connected than ever. Robotic milking systems, GPS-enabled ear tags, automated feeding and cleaning systems, and cloud-based management platforms are all part of the modern operation. These tools bring efficiency, but they also create new vulnerabilities.

Take automated milking systems, for example. Many people use proprietary communication protocols that aren’t encrypted. That means a hacker could intercept or alter commands, disrupting the milking process and potentially harming cow health.

Or consider the sensors monitoring barn temperature and humidity. If those devices still use default passwords (which many do), they’re easy targets. A cybercriminal could manipulate the data, leading to poor decisions or even equipment failure.

Even GPS tracking isn’t immune. Spoofing or jamming signals from cow ear tags can throw off herd management, delaying health interventions or causing animals to go missing.

And then there’s the cloud. Many farms rely on cloud-based platforms to manage everything from herd health to finances. But weak authentication or misconfigured access controls can leave critical data exposed or even enable attackers to shut down operations remotely.

Real risks, real consequences

The impact of a cyberattack on a farm isn’t just technical; for the modern farm, it’s deeply operational. If your feed system is compromised, cows might be underfed or overfed. If your cleaning schedule is disrupted, hygiene suffers. If your milk cooling system is tampered with, spoilage can occur before you even realize there’s a problem.

These aren’t hypothetical scenarios. They’re increasingly common in sectors such as dairy, where automation is widespread, but cybersecurity isn’t always top of mind.

And the consequences go beyond the farm gate. A breach in your supply chain tracking system could delay deliveries, tamper with safety data or damage your reputation with buyers and consumers.

Building a cyber-resilient farm

Now that we’ve got the bad news out of the way, the good news is that you don’t need to be a tech expert to protect your farm. Cyber hygiene starts with a few simple, practical steps:

• Change default passwords on all devices. It sounds basic, but it’s one of the most common entry points for attackers.

• Make sure your systems, especially those controlling feeding, cleaning and environmental conditions, are running the latest firmware. Updates often include security patches that close known vulnerabilities.

• Segment your network so that your IoT devices (such as sensors and trackers) aren’t on the same system as your business operations. That way, if one part is compromised, the rest remains protected.

• Don’t forget about data encryption. Whether it’s temperature readings or herd health records, secure transmission helps prevent tampering.

• It’s worth investing in monitoring tools that can flag unusual activity, such as a sudden spike in barn temperature or a feed schedule that doesn’t match your usual routine.

• Limit remote access. If vendors need to log in to your systems, make sure it’s done securely and that you know exactly who has access and when.

What happens if you’re hit?

Even with safeguards in place, no system is bulletproof. That’s why having an incident response plan is critical.

Know who to call, whether it’s your IT provider, your insurance broker or a cybersecurity expert. Have a checklist of systems to shut down, data to back up and steps to take to get back online.

In terms of insurance, make sure your policy covers cyber incidents. Many farms are underinsured in this area. Coverage should include data recovery, business interruption, legal costs and notification requirements. Increasingly, farmers are choosing specific cyber policies to protect their modern farm from modern risks. These policies typically also include coverage for payments that are made due to fraudulent payment requests.

The bottom line

Cybersecurity might not be the first thing you think about when you walk into the barn, but as technology becomes central to farm operations, it needs to be part of your daily management.

You don’t have to overhaul your entire system overnight. Start with the basics: strong passwords, regular updates and a plan for what to do if things go wrong.

Because when your farm goes offline, it’s not just about lost data. It’s also about lost time, lost product and, potentially, lost trust.

Meghan Mackenzie Bell is a seasoned farm insurance specialist with deep expertise in equine and agricultural coverage. As associate vice-president of sales for NFP’s equine and farm division, she leads strategic initiatives to enhance sales operations and streamline proposal creation, helping Canadian equine and farm clients protect what matters most. You can contact Meghan at meghan.mackenziebell@nfp.com

Is your farm hackable?

As newer digital technologies such as AI, robotics, drones, digital twinning and wearables are increasingly used in agriculture, cybersecurity is becoming a big issue for today’s farms and it’s one that isn’t being talked about nearly enough.

“I think farmers need to be aware that cybersecurity is an issue and it’s a growing issue,” says Dr. Emily Duncan of the department of sociology and social studies at the University of Regina, who recently published results of a survey of farmers and industry experts called I grow food, IT people do cybersecurity: Addressing cybersecurity risks in Canada’s agri-food sector.

“The basics of cyber hygiene — regularly updating passwords, using antivirus software, backing up files often — are things that more farmers need to be learning and implementing into their business practices,” she says.

Duncan has put together a checklist of some cyber hygiene best practices for farmers and retailers (see Cyber Hygiene Best Practices Checklist).

The agri-tech industry lags behind many sectors. Duncan says it needs to step up and make sure that when they are developing digital platforms for farmers that they have high cyber security standards in place, such as firewalls and multi-factor authentication.

“There is a lot of room to grow and when we are dealing with living systems, whether it’s animals or crops, they are fragile and in fragile environments,” she says.

“When we think about controlled environment agriculture, such as vertical farming or temperature-controlled barns with 40,000 chickens inside it, if someone hacks that, things can go wrong pretty quickly. Putting those protections in place is super important to protect farming businesses and food security.

— Angela Lovell, CG contributor

The post When hackers hit the barn appeared first on Country Guide.

According to the Canadian Agri-Food Policy Institute (CAPI), “Digital agricultural tools available to producers today have been proven to boost productivity and competitiveness and reduce environmental impacts with the potential to unlock a further $750 million to $1.5 billion in annual net revenue over the next decade.”

The rate of adoption can depend on any number of factors, for instance, the age of the farm owner, available labour, type of technology, type of operation and infrastructure. It could also depend on the cost to implement, potential return on investment, compatibility with existing technology on the farm and how available are the knowledge and skills needed to utilize the technology and the data it produces.

What’s driving digital technology adoption on farms?

Invariably, the reasons farmers adopt any technology is to improve productivity and efficiency, increase profits and help solve major pain points such as labour shortages. Staying competitive and reducing costs are major drivers.

“Farmers today are competing not just inside their province, or Canada, but internationally,” says Maryna Ivus, manager, economics research at the Information and Communications Technology Council (ICTC). “So, competitiveness is an important driver of technology adoption.”

Farmers across the board are struggling to find labour and existing technologies that have helped to address this issue for many producers. The digital era, with advancements in areas such as autonomous farming, remote sensing and sophisticated AI-driven systems to integrate and manage multiple functions and operations, could solve it once and for all.

“Whether it’s true automation technology or just more updated equipment that makes things run a little bit smoother, farmers are investing in what’s needed to allow the farm to operate,” says Phyllis MacCallum, senior program manager, research and knowledge mobilization, Canadian Agricultural Human Resource Council (CAHRC). “It can allow a production facility to continue on if they are short-staffed. And herd health is important whether you are in milk or beef production, so these technologies can allow farmers to monitor and ensure that the herd is staying healthy while they manage everything else.”

Setting the stage for autonomous agriculture

AI, machine learning and autonomous technology are at the forefront of technological trends in agriculture and many other industries. In agri-food, AI is being used for diverse applications from managing greenhouse operations to evaluating embryo viability in cattle.

“Automation and AI allow for 24/7 operations, which means constant quality control as well as safety control when it comes to worker performance, as well as reducing costs through things like precise use of fertilizers, water and other inputs,” says Ivus.

“AI-driven planning tools for co-ordinating field equipment are starting to gain traction, especially in Western Canada where large grain and oilseed farms rely on multiple machines working together,” says Godard, CEO and co-founder of Verge Ag, a company that creates AI-driven software designed to help farmers plan, simulate and streamline how their equipment operates in the field. The aim is to make existing equipment more efficient, ease the workload on operators by optimizing routes, and reduce costs by minimizing overlap and reducing fuel consumption.

“Since most farm equipment already comes equipped with GPS and autosteer, these optimized routes leverage existing capabilities, bringing us closer to a future of fully autonomous farming,” says Godard.

The tech can work for any type or size of farm, although larger farms tend to see the most immediate impact because even small efficiency improvements can lead to significant cost savings. However, benefits such as reduced overlap, lower fuel consumption and improved timing apply across the board.

Digital twin technology

Digital twin technology, while well established in the gaming industry, is a relatively new concept for agriculture. On its website, IBM describes a digital twin as “a virtual representation of a physical object or system that uses real-time data to accurately reflect its real-world counterpart’s behaviour, performance and conditions.”

“If you are looking to invest, to innovate and create better efficiencies on the farm, this technology has the ability to do that,” MacCallum says.

Whether you are bringing innovation in, or you are remodelling, you can use digital twinning technology to simulate what it would look like once everything is put in place, and assess whether that would create efficiencies and, overall, more profitability. It allows you to physically see how things will look in a 3D form before you execute.

Phyllis MacCallum
Canadian Agricultural Human Resource Council (CAHRC)

“For most of the people taking this program, digital twinning is a new technology, and they are learning about its capabilities. There is so much capability for the technology, no matter the farm or the application. There is always a way to incorporate this. The technology can be as simplistic or as complex as someone wants it to be.”

But digital twins are not just for planning; they also form the basis of a fully functional system connected to sensors, robots, cameras and other technology that can allow for continued remote monitoring and managing of operations. It could also be used to create virtual reality models for things such as health and safety training.

CAHRC is working with various partners and Palette Skills to offer a free 12-week training program called Seeding Digital Skills that introduces farmers and employees in agriculture and the food and beverage processing industries to digital twin technology. The program, valued at $12,000, is currently being funded by Upskills Canada.

“There is so much opportunity to co-ordinate with other applications like sensors, so it is monitoring the field and growth,” MacCallum says. “Or for sustainability questions in terms of crop production, to determine where the most efficient use of the land is, (for example) if you have marginal land, whether that needs to be left because it’s not returning an ROI or be put into a different level of production.”

Using AI to manage risk

A real-world example of digital twinning is offered by Agi3. The platform brings crop planning and risk management tools into one AI workflow. Farmers can create a field-level digital twin that connects with land, climate, management and market data to generate a risk profile and individualized insurance options.

“Farmers gain fair, individualized coverage and pricing that reflect their actual fields and practices, not provincial averages, so strong management is recognized rather than averaged away,” says David Hodge, chief strategy officer for Agi3 Group.

“With risk and coverage properly aligned, farms can invest in productivity-enhancing practices with greater confidence, instead of ‘farming to the average’ and leaving margin on the table.

“Claims are faster and more defensible because losses are verified with time-stamped, geo-referenced evidence, which improves program integrity and reduces friction. Agi3’s platform also provides a clean digital twin of the operation, simplifying planning and interactions with lenders, insurers and other partners. Overall, these elements deliver better protection, stronger economics and clearer, data-driven decisions for farmers.”

What is holding some farmers back from adopting digital technology?

CAPI’s report The Future is Digital: Digital Agriculture and Canadian Agriculture Policy, notes that adoption rates of digital technologies among Canadian farmers remain low due to factors such as poor rural connectivity, costs and uncertainty about the return on investment (ROI) and concerns over stewardship of the data that these technologies generate.

“If farmers are financing a technology, that is a risk, and that technology often requires additional finance for things like energy or additional infrastructure, for example broadband access. Securing an autonomous tractor doesn’t mean that you can utilize it tomorrow. There are additional expenses that sometimes are not clear. So, financial concerns are definitely number one for most farmers.”

Maryna Ivus
Information and Communications Technology Council (ICTC)

It’s often difficult to calculate the ROI of digital technology because it can take several years before benefits, such as productivity or profitability, start to show. Furthermore, some of the benefits can be intangible, such as improved safety.

“It has to be able to pencil out, whether that is over a number of years or over per head of cattle,” says MacCallum. “For producers who may only have a few more years in the industry, or those just entering the industry, they have to ensure that the investment that they’re making is worth the time and the commitment, and that in trying to find the efficiency, it doesn’t slow them down in profitability and production by investing too big or too fast. There is a lot to consider.”

Another big hurdle to adoption for many farm owners or managers is having the skills and knowledge to actually use these technologies and understand the data that they generate.

“It’s not about just acquiring the technology, it’s having someone who can use it and understand and analyze the data properly to make sure that it’s not underutilized,” says Ivus. “Someone who has the background in technology and agriculture is hard to find.”

Compatibility with other technologies already being used on the farm and ensuring that they can both connect and communicate with each other, can be another challenge. It may require some specialized skills to overcome but may also require farmers to think differently about how they approach getting the work done on their operations.

“The main hurdles are more about behaviour than technology,” says Godard. “Most farms already have the necessary hardware, for example, GPS, autosteer and precision guidance, but aren’t yet using planning software to co-ordinate machine operations across their fields. This requires a shift in how farmers approach their work, with more emphasis on planning digitally before heading into the field.”

There’s gold in the data

Digital technologies continuously create a monumental amount of data. Every operation in the field or cycle of production generates information that has tremendous value to help improve operations, offer solutions to create more efficiencies, identify and address problems, and create more profitability on the farm, but it’s not much use unless it has been properly analyzed and made useful. AI is going to play a big part in unlocking the ‘gold’ in that data.

“As producers innovate at a pace that’s comfortable for them, how do we ensure that they can harness the data that they are collecting?” MacCallum asks. “How do we ensure that data is available to them and that they can use it to better their production system? A producer who is busy running his operation doesn’t have time to sit down and look through thousands of data points, so how do we set up AI to analyze and generate reports or summaries that show the trends through this data collection so producers can better use that information?”

Who owns the data?

Farmers are understandably concerned about who actually owns the data that is being generated by all the technologies on their farms, where that data ends up and what is being done with it.

“It comes down to an issue of trust,” says Dr. Emily Duncan, department of sociology and social studies, University of Regina. She carried out a large-scale survey of 1,000 farmers across Canada around the adoption of digital technology, which was summarized in the report I grow food, IT people do cybersecurity: Addressing cybersecurity risks in Canada’s agri-food sector.

“Farmers had a lot of concerns around where the data goes once it leaves their farm, because end user license agreements, or the terms and conditions of using these platforms are not very clear about what that company can do with the data,” she says. “That does hold farmers back because the decisions that you make on your farm, how you get to that level of productivity, or what you use, is information that you might not want shared widely with third parties.”

Although farmers acknowledge that there is value to data sharing, Duncan learned that they are more comfortable sharing certain types of data than others.

“Farmers were more comfortable sharing environmental information like weather data that they might collect from an on-farm weather station, or soil data that they might collect from soil testing or soil mapping,” Duncan says. “When it came to data about decisions around use of inputs and productivity levels, these were types of data that farmers were much less comfortable sharing.”

It also matters to farmers how their data is shared. Farmers were most uncomfortable sharing raw data that comes off the sensors or implements on their farms. Once that data is uploaded to their service provider’s platform and is combined and averaged (called summarized data), they were a little more comfortable sharing it. The data they were most comfortable sharing was fully aggregated data that is pulled from many different farms in the region and remains anonymous.

Finally, Duncan asked who farmers are most comfortable sharing data with.

“Farmers were generally more comfortable sharing with industry groups, other farmers and researchers, and rated tech service providers as the actors they were least comfortable sharing data with,” Duncan says. “These are the people who have access to the data and are the people that farmers have little trust in.”

Dr. Emily Duncan
Department of sociology and social studies University of Regina

That could be because farmers are also skeptical about who is benefiting most from the data that they are subscribing to and paying for.

“Farmers aren’t really seeing any of the returns from the data that they are generating that these companies are making profits off,” Duncan says.

Despite all the misgivings that farmers may have about digital technologies, Duncan believes the industry is heading in the right direction.

“Farmers are interested in technology and are keen to improve their productivity and sustainability through new tools,” she says. “The challenge with new tools is sometimes they create a whole new set of problems, like issues around cybersecurity or trust and data sharing governance, but I think the industry is moving in the right direction in terms of trying to develop sector-specific solutions that empower farmers, and that is the most important thing.”

The post Are farmers ready for the digital ag revolution? appeared first on Country Guide.

“We’ve always been practical,” says Melinda McArthur, store manager and part of the family behind Heatherlea Farm in Caledon, Ont.

“But for a long time, like many farms, our record-keeping was pen and paper, with the occasional Excel sheet.”

The farm was originally a cattle business, but as the family added a butcher shop, café and online store, the McArthurs realized they needed to modernize to sustain growth. The business supports multiple generations of the family, including the store and farm owners, Pat and Gord McArthur, their son and daughter-in-law, Melinda and Don McArthur, and their three children, Kayleigh, Chloe and James.

These days, beneath that homespun charm hums a quiet current of modern technology, blending tradition with innovation.

Heatherlea uses digital systems to track every detail of their herd from weight gain and feed efficiency to fat content and lineage. Their operation is as data-driven as it is down to earth.

Don, who runs 90 head of Angus cattle on the 125-acre farm, says that his methods were traditional until he attended a Beef Farmers of Ontario feedlot tour several years ago. His main takeaway from the event was, “If you can’t measure it, you can’t manage it.”

That advice sparked a quiet tech revolution at Heatherlea. The farm invested in a Gallagher scanner system and an updated handling setup with built-in scales. Today, they can track weight gain and adjust feed programs with precision, ensuring both butcher-ready cattle and breeding stock maintain healthy growth.

“With the increasing number of cattle in our operation, we needed to adjust our handling facility from a few wood gates in a barn and a simple squeeze (chute) to a proper cattle-handling system with the ability to ship cattle simply, easily and more safely,” Don said.

“Our main reason for adopting the technology was to handle larger amounts of animals in a safe and ethical way. It’s good business and good for animal welfare.”

During a recent visit to the farm, Don demonstrated the system and his chosen cow moved through the chute like it was part of her everyday routine. She was calm and relaxed. Eleven-year-old James scratched the cow’s ears as Don completed a full health scan, including a pregnancy check and data on her weight and body composition. Each animal’s tag integrates with the Gallagher electronic identification and weighing system. The information collected from each EID tag is automatically sent to the herd management software.

“This system not only ensures the highest standards of animal care and handler safety but also gives us accurate, real-time insights into growth and performance,” Don said.

“It helps us make informed decisions to maintain the quality and consistency our customers expect from Heatherlea beef.”

“It’s not outrageously high-tech,” Melinda said. “But the difference is massive. Even small improvements in information and efficiency ripple across everything else we do.”

That mindset travels from the field to the store, where managing inventory, sales and staff is done with modern point of sale (POS) systems, cloud-based documents and an e-commerce presence.

“We look for practical tools that let us work smarter, not harder,” Melinda said. “You can’t cling to the horse and cart when everyone else is driving a tractor.”

Melinda wears multiple hats at Heatherlea: farmer, store operator, HR, IT support and more. She says artificial intelligence (AI) is a practical solution for handling repetitive or time-consuming tasks.

“We use it for everything from coming up with product names and writing marketing copy to troubleshooting printer errors and creating staff training materials,” she said. “It’s especially useful for writing policies or summarizing meetings — things that take hours but don’t require a human touch for decision-making.”

But she doesn’t put blind trust in AI. “It doesn’t replace people. You still need to guide it. If you just let it write, it shows — either by saying a lot without really saying anything or overdoing the punctuation. You have to steer it to get good results.”

Looking ahead, Heatherlea sees tech being increasingly integrated into every facet of farm life. AI will quietly work in the background of tools such as Gmail, Google Analytics and note-taking apps, pulling insights and simplifying decisions. But the family is also keenly aware that authentic, human storytelling will remain vital.

“As AI content multiplies, real experiences and voices will matter even more,” Melinda said.

“As a small farm, we are grateful for technology and AI to help humankind, but it’s the humans that still bring the value,” Pat said.

Heatherlea Farm demonstrates that embracing technology doesn’t mean abandoning tradition. Tech and AI allow the family to focus on what truly matters: their animals, their customers and the people who make the farm run.

The family believes that small farms like Heatherlea may not need autonomous tractors or AI-driven drones to thrive, but thoughtful adoption of tech can transform how they manage, market and grow.

As for what’s next on the technological horizon at Heatherlea, Don says, “I’ve got my eyes on Ranch Bot. It’s a remote water management system that gives you all kinds of stats and control. It sends alerts if you’re low or out of water, because even being low can affect the cattle and takes them time to recover. It’s all about working smarter, measuring what matters and letting the tools handle the rest so we can get back to the heart of the farm. And for us, that’s raising the best Angus in the area.”

Gord adds, “I might not be the most tech-savvy, but I’m enjoying watching the evolution of agriculture.”

The post Where convention and innovation meet appeared first on Country Guide.

But is AI everywhere except on your farm? Are you missing out on the new revolution?

Here’s the good news: You’re already using AI and other people are using it in ways that benefit your farm. So, taking the first step is not as scary as you might think.

But first, what exactly is AI?

Defining AI

There’s no single definition for artificial intelligence. AI is used in all kinds of ways to describe many different things. Generally, AI is a process where computers do something normally done by humans. This could be something complicated, like a computer model that forecasts climate change, or something simple like a heater that turns on when the temperature drops.

Some people default to thinking of “chatbots,” such as ChatGPT, when they think of AI. Chatbots are software programs that can answer questions based on information they’ve “read,” and sometimes generate new information.

There’s more to AI than chatbots, but chatbots are a key piece of the puzzle.

For this article, ChatGPT was asked to define AI for a farm audience, using an account personalized to use a “critical and sarcastic” tone. This is ChatGPT’s response:

Imagine a smart hired hand that never sleeps, doesn’t complain about the weather, and works from data instead of gut feeling. It can read soil maps, predict yields, plan spray timing, monitor livestock health, or even drive machinery — though it still can’t fix a seized bearing or find that one missing wrench in the shop. In short: AI uses information to make smarter, faster choices. The farmer’s brain is still the boss; AI just crunches the numbers faster than anyone with a coffee thermos ever could.

You’re already using AI

Dr. Felippe Karp, instructor and researcher at the Werklund School of Agriculture Technology at Olds College of Agriculture & Technology in Alberta, opens his presentations about AI by telling farmers that it’s not the future, “It’s the present.”

Do you use an app to check the weather? “Guess what?” Karp said. “There’s AI behind that.”

Machinery automation is also AI. “Auto-steering is one of the most adopted precision agriculture systems,” Karp said.

Auto-steer equipment is driven by an algorithm that uses data from satellites and field locations. The algorithm directs the machinery so it can steer without human intervention.

Automation in newer combines also uses AI. It’s changed combines to the point that, Karp said:

“Someone with no experience running the equipment can jump in there and run it.”

Given the shortage of ag employees Karp added, “This is quite amazing.”

Knowing we already use AI makes it easier to take the next step. “We need to demystify AI,” Karp said. “It runs in a hidden way where we don’t directly interact with it, but it makes our lives easier.”

Smartphone apps that identify weeds, insects and disease are also AI at work. “This can empower people that might not have the expertise to go in the field and do some scouting for you,” he said.

However, powerful AI doesn’t replace real farmers and experts. For example, Karp suggests, imagine your phone app misidentifies a bug. You still need a farmer or agronomist with field experience to detect errors.

“This critical thinking portion is key,” he said.

Karp and other instructors at Olds College are training students to think critically about results from AI and help students see where they can add value in a world of rapidly changing technology.

Researchers are using AI for you

Dr. Rozita Dara is the director of Artificial Intelligence for Food (AI4Food), a technology hub at the University of Guelph that promotes responsible development and use of AI in Canada’s ag and food sector. Dara agreed that Canadian farmers already use AI in many ways, most of which are rather simple to explain.

For example, many AI tools have been developed for greenhouses. “Because it’s a relatively controlled environment, it’s easier to develop AI solutions,” Dara said. AI-enabled equipment helps greenhouse managers decide when to spray and how much to irrigate.

In poultry barns, cameras connected to AI systems can monitor animals for stress. Human experts “teach” the systems what kinds of movements and behaviours to “watch” for before the system is operable.

“When we build AI solutions it’s always with humans in the loop,” Dara said.

More complicated cases

“Generative algorithms” don’t just analyze data; they also build on it to create new information. These algorithms are complicated, so the results can be less predictable than, say, a steering wheel that makes a turn if a rock knocks the tractor off its straight course.

Because they’re more complex, generative AI systems have been known to go off the rails and provide very incorrect results. This has been referred to as “AI hallucination.”

Because this can happen, we’ll always need people to verify complicated results. Dara encourages farmers to “trust the technology but also validate.”

Dara’s current project is an example of a complicated algorithm guided by humans. Dara and her team are combining several data sources to detect avian flu outbreaks in its early stages, with the goal of getting information to decision makers.

Their data includes everything from satellite images to social media posts. AI looks for patterns in reams of data, identifying correlations that humans might miss.

“Our goal is to extract risk factors from these data sets.”

Dr. Rozita Dara, director
Artificial Intelligence for Food (AI4Food)

Dara’s team can detect potential avian influenza outbreaks two or sometimes three weeks before it’s been officially confirmed. The extra time helps decision makers monitor and act before an outbreak becomes severe.

Meanwhile, scores of other ag researchers are using AI in their labs to analyze genetics for faster plant breeding, correlate satellite data with in-field information for optimal cropping information and speed up every area of ag research you can imagine.

It comes down to good data

Much of the work is less than glamorous.

“AI is 90 per cent data,” Dara said. “People don’t understand the effort we put into data cleaning. It’s 90 per cent of the work.”

Karp has also spent more than his share of time processing data for AI. “We have to guarantee that good data is coming in,” he said, referencing the “garbage in, garbage out” maxim of computer models.

For example, crop yield maps show variations throughout the field. “We know that this is based on sensors,” he said. “Sensors can get things wrong.” Yield maps are only useful after any sensor errors are found and corrected.

There is a lot of data to clean. But AI becomes more powerful when it has access to multiple data sets, what Karp calls “data fusion.”

For example, adding yield data to rainfall data, along with soil test and EC (electrical conductivity) data makes a database that would drown an Excel spreadsheet.

“Imagine how powerful this information could be if we were to build models based on this data.”

Dr. Felippe Karp
Werklund School of Agriculture Technology, Olds College of Agriculture & Technology

He believes data fusion will let AI empower ag decision makers at a new level, bringing on a “digital revolution in agriculture.”

It’s all coming together, but a lot of the work is still at the “data collection” point.

Eventually, data-empowered AI will identify patterns and forecast results, giving farmers better insights into questions such as: Do your strawberries need pest control? Is it a good time to price your feed barley?

“But who makes the final decision?” Karp said. “The farmers.”

Here’s how to start now

You can start your AI journey at home, right now, with Farm Credit Canada’s (FCC) chatbot designed just for Canadian farmers.

Nicole Hayes, director of FCC’s Innovation Hub, said FCC found “producers were feeling the gap in availability of advisory services across Canada.” A chatbot that could answer agronomy and farm management questions could fill that gap and also be a safe space for farmers to learn to integrate AI into their businesses. The first version of FCC’s chatbot called Root was released in July 2024.

“Root is a great starting point,” Hayes said.

“It’s free, it’s available to everybody. It’s a great tool for people to start becoming more comfortable asking questions and getting responses, engaging in that dialogue.”

Nicole Hayes, director
Farm Credit Canada Innovation Hub

Unlike other chatbots, with Root, Hayes said, “We can ensure that the dialogue that we’re engaging in is focused on agriculture.” FCC only allows Root to base its chats on reliable information. “We’ve done some of that fact checking and validation behind the scenes,” Hayes added.

Root can “chat” with farmers based on Agriculture and Agri-Food Canada information and databases, FCC information and other sources FCC deems trustworthy. “We’ve got quite a substantial database that exists underneath the surface,” she said.

For now, users might find Root’s answers to be a bit limited, but that will change. “We’re at the early stages of development. It’s something we’re continuing to build and expand on. We are continuing to expand the knowledge sources that we’re using and who we’re partnering with in the industry.”

Even more options

In October 2025, Wharton Business School listed the top chatbots used by business leaders to analyze data, summarize reports, make presentations and generate ideas. ChatGPT and Microsoft Copilot were the most common off-the-shelf chatbots. Some businesses build their own custom AI models, with access to confidential internal data.

Create your own free account at ChatGPT.com and ask it about your farm (or anything, really). It finds information faster than a Google Search, does math quicker than your calculator, and writes business letters more formally than your seventh-grade English teacher.

When asked “What’s the best fertilizer rate for canola?” ChatGPT led with: “There’s no magic number that fits every field — if there were, agronomists would be out of work and the rest of us could just read the bag.”

It also provided some general NPKS guidelines and a recommendation of 80 to 150 pounds of nitrogen per acre.

A free account on Microsoft’s Copilot (which comes bundled with most Microsoft Office software) said the best rate “depends on your yield goals, soil nutrient levels and environmental conditions.” Copilot suggested 125 to 150 pounds of N per acre and also listed reference links, with one to the Canola Council of Canada.

Root wouldn’t go out on a limb with a suggested rate, which was sensible given that it didn’t know how much N is already in the soil. However, Root listed factors to consider, mentioned 4R fertilizer practices and referenced an Agriculture and Agri-Food Canada article titled “Managing nitrogen use efficiently.”

Using Root feels a bit like asking your sensible uncle. You’re not likely to get the wrong answer, and you might need to use its suggestions (links) to find the answer on your own. Other chatbots respond more like your wild cousin.

Did ChatGPT or Copilot give better responses? That depends on your field and your own expertise.

Ask harder questions

Don’t be afraid to ask more difficult questions. Chatbots are great at brainstorming and they may come up with answers you weren’t expecting, but do take a critical look at the answers.

When asked for suggestions for new crops in southeast Saskatchewan, Root provided great links and resources. When pushed, it suggested quinoa.

ChatGPT suggested a long list of crops that included soybeans and sunflowers but not quinoa. When asked, “What about quinoa?” ChatGPT “answered”:

“Ah, quinoa — the hipster of grains. Everybody loved it until they realized it was actually hard to grow here. Then they went back to complaining about canola.”

ChatGPT was willing to forecast revenues and expenses for a new crop — making the kind of assumptions Root would never make. It wasn’t always 100 per cent correct, but it wasn’t ludicrously wrong. Responses like this are either extremely helpful, or very dangerous. As Dr. Dara would say, “verify.”

You know more about your farm than a chatbot, but interaction can still help you come up with new ideas.

What’s next?

“In the coming months or years,” Dara said, “there will be many more AI assistant tools that are more reliable. They will definitely become much smarter.”

Whether or not you use it, Dara said “this technology is here. It’s not going to go away.” AI will be embedded in many of the tools we use, on or off the farm.

“Just as we can’t say ‘no’ to mathematics, we can’t say ‘no’ to AI,” Dara said.

“Eventually AI will be everywhere, and agriculture and food cannot fall behind.”

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Why it matters: The failure of some agriculture startup companies is tied to their inability to understand problems that farmers need to be solved.

Three companies with close connections to the agriculture sector answered questions about how they made sure their products met the needs of farmers during the AgTech Breakfast at Canada’s Outdoor Farm Show 2025.

Jesse Wiebe of Startup TNT, an organization that brings together investors in agriculture technology, hosted the panel discussion.

Colin Yates, who co-founded VETSon, a virtual veterinary app for farm animals, with his father, veterinarian Glen Yates, had a built-in connection to his major clients. Glen had years of experience in veterinary practice, so he understood the needs of animal healthcare providers.

“We didn’t have to go find a partner. We didn’t have to go find a business willing to do it,” said Colin Yates.

“It’s about having those relationships. And luckily, my relationship was with my father, who’s already had those 50 years of experience that we could develop and build upon that.”

Finding farmers to test and use VETSon’s service was easy, as a shortage of veterinarians created an unmet need for virtual veterinarians.

In fact, Yates found that farmers were pushing veterinarians to adopt the virtual veterinary technology.

“The farmers wanted this technology, the farmers wanted access and were willing to adopt it.”

Cattlytics founder Shari van de Pol invested time figuring out what help dairy farmers needed as she developed the numerous dairy data and programming services her company provides.

The computer engineer and large animal veterinarian developed trust with farmers, who then allowed her to analyze their dairy farm data. A revelation about a drop in milk production when cows were fed unfermented corn silage harvested in October was the first step, but the really valuable data showed the months it took for the cows to recover.

“That was the moment where I’m like, we can do so much here, we can do so much in such an interesting, complicated system,” she said.

Van de Pol developed trust with farmers with her technical expertise as a veterinarian, but she’s also a fiddler and played during dairy farmer gatherings, creating another level of connection to her future customers.

Matt Stevens of Finite Farms found another way to connect directly with farm-level demand and information for the apple thinning and harvesting machine — he bought an orchard.

Stevens called the orchard a “fixer-upper” based near Simcoe, Ont., but it enabled him to become a commercial apple grower. The investment in the game helped create relationships with other apple growers, including one of the leading apple growers in Ontario. That apple grower insisted that Stevens use their farms to test his machine, which he’s been developing through numerous iterations quickly.

The robot evolved from a first-generation idea to a functioning unit by the end of this past summer.

Wiebe said that the ability of company founders to have problems of their own to solve, such as Stevens needing more automation to run his orchard, is a test for those companies.

“If you’re a farmer here, and you’re looking for those technology companies that you may want to use on a farm, ask them, ‘are they from the industry?’”

For more coverage of Canada’s Outdoor Farm Show, visit Farmtario’s landing page.

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