Asking ten companies in the biologicals market what “quality” means for their product will most likely yield ten different answers, and most of them will be incomplete. CFU count. Potency measurements. Purity profile. Shelf life under refrigerated storage. These are all cited as quality metrics, but none of them, alone or combined, can guarantee that the product will actually perform in the field.
The problem runs deeper than quality control, and it originates from how quality is defined in the first place.
The biological ag industry has largely inherited quality frameworks from adjacent industries, like pharmaceutical microbiology, food safety, and chemical inputs, and applied them to biological actives without questioning whether those frameworks actually transfer. “Quality” has been defined around what is convenient to measure during production, rather than what matters for agronomic outcomes. This situation led to a market where products can pass quality control and still fail commercially.[1],[2] This disconnect evolved from a minor inefficiency to a structural bottleneck, one that prevents biologicals from achieving consistent, scalable commercial impact.
The Default Currency of Quality – CFU Count
Colony-forming units (CFU) per gram (for solid formulations) or milliliter (for liquid formulations) has become the prime metric producers put on their product labels as an assurance of efficacy and quality. It defines the minimal number of viable cells in one gram or one milliliter of a product. The appeal of the CFU count is obvious: it is easy to measure, compare, and communicate across the supply chain.
However, the number of viable cells is not a reliable metric of actual performance under real-world conditions, which is why the product can leave the production facility with a high CFU count and still fail to deliver desired effects. Why? Because that same product may:
- Lose viability during storage or transport
- Exhibit low functional activity at the moment of application
- Deliver inconsistent performance from batch to batch
While increasing CFU count can reduce the risks that lead to these failure modes, it can only do so partially, and without tackling the true cause of poor performance. This doesn’t imply that CFU is a bad metric or that it shouldn’t be used, but rather that it shouldn’t be taken as a holistic indicator of quality.
Three Dimensions That Actually Define Quality for Biological Actives
Getting a biological product from the lab to the field is a long process with multiple steps – development, production, formulation, downstream processing, packaging, transport, storage and application. Each of these steps carries different inherent risks of quality loss, but extensive quality testing can only be performed before the product leaves the production facility. As a result, the solution requires a more complete framework, one that predicts how biological actives will behave across their entire lifecycle.
1. Stability Across the Delivery Chain
Quality at the point of production is something that end users of biological ag inputs do not think about a lot, as they seek quality at application. For them, a quality product is one that performs in the field.
Considering that biological actives are often living organisms that can be very sensitive to various environmental stressors, they need to be adequately stabilized to persist through realistic storage, transport, and handling conditions. Stable performance is achieved by yielding the most potent cells and helping them keep their biological activity through adequate formulation approaches.
2. Functional Activity, Not Just Viability
A living cell is not a guarantee of performance. For many biological actives, especially those relying on metabolite production, root colonization, or enzymatic processes, efficacy depends on physiological state, not just survival. Cells may be alive but inactive, stressed, or unable to perform their intended function at the time of application.
Quality, therefore, must include measures of functional activity:
- Are the organisms metabolically active?
- Can they express the traits required for their mode of action?
- Are they in the right state to interact with the plant or environment as intended?
Viability metrics that cannot answer these questions risk overstating real-world performance.
3. Batch-to-Batch Consistency
A single high-performing batch demonstrates what is possible. It does not demonstrate that the same outcome can be reproduced at the next run, at a different scale, or at a partner facility. In commercial agriculture, where growers are used to stability and consistency of chemical solutions, delivering reliable performance is crucial for achieving and maintaining trust in product efficacy. Achieving reproducibility in biological actives is fundamentally harder than in chemical or pharmaceutical products, because consistency is not a property of the organism. It is a property of the manufacturing process, and it has to be engineered deliberately.
Quality Must Be Designed, Not Inspected
The prevailing model in biological actives treats quality as a checkpoint: produce a batch, test it, release or reject it. This retrospective approach is fundamentally limited, as it identifies failures after costs have already been incurred, instead of preventing them. Running additional tests or measuring another convenient metric doesn’t address the root of the issue. What does is designing quality into the production process and making it a premise of every step of the biological products’ lifecycle.
The companies that consistently deliver high-quality biological actives are not simply better at testing. They are better at understanding the relationship between process and outcome, and controlling that relationship deliberately, treating quality as a design principle.
This is a more demanding capability to build. It requires deeper process insight, tighter integration between R&D and manufacturing, more sophisticated analytics, and the willingness to treat quality as a design principle rather than a compliance function. But it is also the only approach that produces quality reliably, rather than probabilistically.
At Evologic, this is the foundation of how we develop and supply biological actives. We define critical quality attributes first, based on what the product needs to do in the field, and derive manufacturing requirements from those definitions. The result is a supply that is reproducible by design, not by chance.
The Strategic Consequence: Quality Definition as Competitive Advantage
Biological crop inputs have the scientific validation, regulatory support, and market demand to become a mainstream component of modern agriculture. Yet their commercial performance has been inconsistent and limited to only a handful of products. Most of that inconsistency is a quality problem in disguise. Products defined, produced, and evaluated against metrics that do not predict field performance will continue to disappoint, regardless of how promising their underlying biology may be.
The path forward requires quality frameworks built around outcome-relevant specifications: stability through the delivery chain, functional activity at the point of application, and reproducibility across production runs. Companies that have already made this transition hold a meaningful advantage. CFU counts can be matched. A manufacturing process built around a causal understanding of biology cannot.
If your product development is constrained by quality that is hard to stabilise, supply that varies across batches, or performance that does not transfer from trials to commercial volumes, that is a conversation worth having.
[1] Matthias J. Salomon, Stephanie J. Watts-Williams, Michael J. McLaughlin, Heike Bücking, Brajesh K. Singh, Imke Hutter, Carolin Schneider, Francis M. Martin, Miroslav Vosatka, Liangdong Guo, Tatsuhiro Ezawa, Masanori Saito, Stéphane Declerck, Yong-Guan Zhu, Timothy Bowles, Lynette K. Abbott, F. Andrew Smith, Timothy R. Cavagnaro, Marcel G.A. van der Heijden. Establishing a quality management framework for commercial inoculants containing arbuscular mycorrhizal fungi. iScience, Volume 25, Issue 7, 2022, 104636, ISSN 2589-0042.
[2] Sadanov, A.K.; Baimakhanova, G.; Baimakhanova, B.B.; Orazymbet, S.; Ratnikova, I.A.; Smirnova, I.; Aitkaliyeva, G.S.; Belkozhayev, A.M.; Kossalbayev, B.D. Microorganism-Based Biological Products for Agriculture: From Strain Selection to Production Organization. Microorganisms 2026, 14, 775. https://doi.org/10.3390/microorganisms14040775
