McCain: Using Digital Tools to Scale Sustainable Agriculture

Climate volatility and rising input costs are pushing agricultural systems toward collapse. Farmers face pressure to transition from crisis management to operational resilience.

Regenerative agriculture could provide a pathway to long-term sustainability and profitability. McCain Foods has committed to implementing regenerative practices across 100% of its global potato acreage by 2030.

The food company is deploying data-backed solutions designed for commercial farming environments rather than laboratory conditions. McCain operates 4,400 grower relationships across multiple continents, creating a complex network of diverse farming systems that must be integrated into a cohesive digital framework.

The transition represents a fundamental shift in how industrial agriculture approaches environmental stewardship. Traditional farming models prioritised short-term yield maximisation, often at the expense of soil health and ecosystem resilience. Regenerative practices flip this equation, focusing on building soil organic matter, enhancing biodiversity and creating closed-loop nutrient cycles that reduce dependency on synthetic inputs.

For McCain, this is not merely an environmental initiative but a business imperative. Climate unpredictability threatens the consistency of potato supplies, whilst regulatory pressures and consumer expectations are reshaping market dynamics. The company recognises that operational resilience and environmental sustainability are increasingly inseparable objectives.

Managing inconsistent farm data

Precision agriculture tools have demonstrated capability to reduce nitrogen fertiliser use by up to 53% without sacrificing crop yields, according to McCain. The challenge lies in deploying these systems across geographically diverse farming operations where infrastructure, climate conditions and technological readiness vary dramatically.

Integration bottlenecks emerge between cloud-based analytical models and legacy tractor hardware. Dr Michelle Lynn D'Souza, Leader in Research and Innovation of Global Agriculture at McCain, says the company's digital twin is designed to integrate with existing GIS-enabled equipment.

"Our digital twin is designed to integrate with existing GIS-enabled equipment to simulate real farm conditions, model regenerative practices, test outcomes and guide decision making," she says.

Commercial agriculture involves managing massive data fragmentation across disparate systems. Michelle says the technical hurdle lies in the infrastructure itself, where standardisation remains elusive.

"The challenge is that farm data is often inconsistent, fragmented and unreliable," she says. "Data varies in quality across hardware, often manually ingested due to legacy systems and lacking standardisation, with added risks around commercial sensitivity."

"These complexities are compounded globally, where varying conditions, from climate and heat to power and connectivity, affect performance."

McCain's engineering strategy relies on establishing flexible protocols rather than demanding pristine infrastructure. The company is establishing a minimum viable data standard that defines thresholds for quality and frequency, allowing analytical tools to function effectively even when data inputs are imperfect.

"To address this, we are establishing a 'minimum viable data standard' that defines thresholds for quality and frequency so tools can operate reliably with imperfect, real-world data across diverse global farming systems," Michelle adds.

This pragmatic approach acknowledges the economic realities facing commercial growers. Demanding comprehensive sensor networks and cutting-edge hardware would create insurmountable barriers to adoption. Instead, McCain is building systems that work with what farmers already have, creating upgrade pathways that align with their operational budgets and technical capabilities.

Scaling regenerative farming systems

McCain's Farms of the Future initiative operates as live innovation testbeds across Canada, South Africa and the UK. These sites, alongside 30 global innovation farms, allow the company to trial controlled-traffic systems, new crop species and advanced data architectures under genuine commercial conditions.

The University of New Brunswick backs the project alongside McCain through digital agriculture investments. The primary challenge is synthesising inputs from different sources into a unified platform that commercial growers can afford to replicate.

McCain's philosophy avoids forcing growers to buy hyper-expensive, high-density hardware networks. "We are building a modular, replicable data stack anchored in widely available inputs: satellite imagery, weather models, yield monitors and GPS-guided equipment, with more advanced sensors as optional layers," Michelle says.

"Rather than requiring high-density soil sensor networks, which are not always feasible, we focus on interoperability and accessibility."

This modular architecture allows the technology to meet farmers where they currently are. McCain allows growers to calculate their own digital maturity curves by deploying this framework. Michelle says this approach translates directly to the field.

"This enables a practical 'gap analysis' allowing growers to benchmark their current data and hardware, identify missing components and calculate the investment required to close those gaps to scale incrementally," she says.

"The result is a realistic blueprint that reflects the constraints of commercial farming whilst still enabling robust, data-driven decision making."

The incremental adoption model reduces financial risk for farmers experimenting with regenerative practices. Rather than requiring complete system overhauls, growers can add capabilities progressively as they validate the return on investment and build internal expertise.

Testing hardware in field conditions

McCain has collaborated with Dalhousie University's Faculty of Agriculture since 2018 to support field-based innovations on their Canadian Farm of the Future. One pilot involved upgrading standard farm sprayers with RTK-GPS and high-resolution cameras.

The system scans fields for insect pests at an individual plant level during routine field operations. The physical environment of an active farm pushed the hardware to its limits, revealing assumptions that held true in controlled settings but failed under real-world stress.

"One lesson from this prototype was mounting cameras on a sprayer boom," Michelle says. "It worked in controlled conditions, but in the field, constant movement caused mechanical damage and data loss."

"Other issues, like overheating hardware and limited space for GPUs, reinforced how unpredictable farm environments are."

This reality reshaped how McCain approaches its core digital twin hardware assumptions. The systems are now engineered to survive chaotic conditions rather than assuming stable operating environments.

"The lesson is clear: design for failure," Michelle says. "Systems must assume data dropouts, physical stress, with modular hardware and validation layers that distinguish between true data gaps and hardware disruption."

This represents a conceptual shift in how the tech industry views digital twins. In a manufacturing plant, a digital twin models highly controlled variables with extreme precision.

When mapping an open-air farm, the system deals with highly complex, living biological systems where predictability is limited. "In a farm environment, unpredictability is the standard, not the exception," Michelle says.

"Unlike factories, where variables are controlled and models can be highly precise, regenerative ecosystems involve biological processes that introduce constant variability."

McCain's digital twin does not chase perfect mathematical forecasts. The focus shifts toward agility and actionable intelligence rather than theoretical precision.

"Our digital twin focuses on actionable insight rather than perfect prediction, combining precise modelling where possible with validated heuristics where it is not," Michelle says.

"The value lies in giving farmers real-time visibility of emerging changes and enabling earlier intervention in a dynamic, living system."

Sustainability leaders won’t want to miss Sustainability LIVE: The Leadership Summit at London Climate Action Week, taking place at Code Node on 25 June 2026.

Register now for this exclusive invite-only event.

Connecting farms to consumers

The goal of accumulating this dataset extends beyond helping farmers optimise their inputs. McCain is connecting the entire supply chain to meet consumer demand for ecological transparency and verifiable sustainability claims.

McCain's Taste Good Feel Good campaign connects sustainable farming directly to consumer choices. This requires transforming raw agronomic data into verifiable, auditable pipelines that can back up environmental claims with empirical evidence.

"We're integrating on-farm data into a global, standardised dataset with auditable dataset, linking real farming practices to consumer-facing transparency," Michelle says.

"This underpins campaigns that help consumers feel confident about their planet-friendly choices."

Asking farmers to change soil management practices involves financial risk. Financial technology could become a tool for scaling these practices by creating new funding mechanisms that reward environmental outcomes.

Data architecture must evolve to satisfy financial underwriters and risk modellers. This could create an ecosystem where sustainable practices are directly rewarded with capital, reducing the upfront investment barriers that currently slow adoption.

"Fintech could play an important role in scaling adoption by quantifying ROI, modelling risk and enabling outcome-based financing," Michelle says.

"By combining data, transparency and financial tools, we can reduce upfront investment barriers and support farmers in transitioning to more regenerative systems."

The convergence of agricultural data, consumer transparency and financial innovation establishes a comprehensive framework for industrial food production. McCain's approach demonstrates that sustainability at scale requires not just agronomic innovation but systemic integration across technology, finance and supply chain management.