One of the best things food safety research projects can give isn’t certainty. It’s narrower uncertainty. 

Think about our agricultural systems. Food safety in agriculture operates in an inherently variable, uncertain and dynamic environment. Weather changes. Water moves. Animals move. Microbial populations fluctuate. Agricultural activities change throughout the season. Conditions upstream and adjacent to a farm may change without the grower controlling, or even immediately knowing about them.  

Growers manage this complexity every day, adjusting production decisions to deliver safe, nutritious fresh produce. However, it’s also very unlikely that we can learn enough to control the complexity and dynamic nature of this system. 

Research alone will never provide certainty across every fresh produce environment.  

First, variability vs uncertainty. 

I often see variability and uncertainty used interchangeably, yet they are very different things.  

• Variability refers to the inherent diversity of data. For example, with population height there will be shorter and taller people. Variability cannot be reduced (yet it can be better characterized). 

• Uncertainty refers to the lack of data or understanding of the context around a decision. Uncertainty can be reduced with better data and information. 

Research increasingly points us toward the need for site-specific risk management systems that use local conditions, data and knowledge to continuously optimize food safety outcomes. 

Dynamic risk management is the real-world intersection of what we know from research about our fresh produce systems, and the design of functional tools to help manage inherent variability, risks and uncertainty. 

In these agricultural environments, we cannot expect to be able to eliminate inherent variability or expect to remove all uncertainty. What we can do in these systems is get much better at separating, characterizing and visualizing them. 

Imagine that we begin with a very large list labeled “We don’t know.” Research, surveillance and operational data should be designed to progressively make that list smaller and smaller. 

From that effort, data and research can help us understand when and under what environmental conditions a hazard is more likely to become a meaningful risk. More plainly, we can identify pathways that matter more during certain parts of the season because of rainfall, hydrology, temperature, animal activity or agricultural practices. 

Most of these factors may be unremarkable on their own. But when certain factors occur together, they can create conditions that meaningfully increase risk. That is an important distinction: the goal should not be to manage individual factors in isolation, but to recognize, monitor and manage the combinations and relationships that create meaningful risk pathways. 

With systems capable of monitoring these relationships, we gain something we didn’t have before: the opportunity to change what we do when, and only when, the risk changes. 

That is the value of on-going operational risk management. 

Instead of treating every acre, every day, every water source and every hazard equally, a risk-management system continuously asks: What is happening right now, and does it meaningfully change risk? 

When conditions change, monitoring can change. Sampling can increase or move. A water source can receive additional scrutiny or treatment, but only when it needs it. Harvest decisions can incorporate new information and be delayed or modified to reduce risk. And when conditions return toward baseline, resources can be redirected elsewhere.  

That is fundamentally different from a static checklist. It gives food safety systems the ability to adapt as conditions change.   

Checklists ask whether something was done. Risk-management systems ask whether what we are doing still makes sense given what we currently know. 

This also changes how we should think about data, the type we collect, and how we do it.  

Sometimes it may feel like everyone just keeps asking for more data and more information. Ultimately, that can often just feel like more work. But the goal is not simply to collect more of it. More samples, sensors and tests do not automatically create a safer food system. We need the right type of data, not any type.  

The right type of data helps characterize normal variation in our fresh produce environments and reduce uncertainty about where observed change may indicate a meaningful pathway(s) of risk. The systems we need to build must be capable of translating that information into decisions – if not, it’s just another exercise in data collection.  

Agriculture has already begun to embrace this concept in other areas. Precision agriculture uses increasingly sophisticated information about soil, weather, water, plant health and other variables to optimize decisions rather than treating an entire production system as uniform. The opportunity is that much of this same information may also help us understand food safety risks. We do not necessarily need to build an entirely separate system. We just need to expand how we use the information agricultural systems are already beginning to collect. 

Precision agriculture should not only help us optimize yield, water use and crop health. It can also help us understand when conditions are changing in ways that matter for food safety. 

We will probably never have perfect information about every hazard, pathway or field. We also will never have one research study that answers all our questions. But the good news is we do not need to wait for them either. 

The future of food safety isn’t knowing everything. It’s getting much better at knowing when, where and why risk changes—and knowing what to do when it does.