How to Evaluate Plant Biostimulant Performance:

Field Trials, Yield Response, and ROI

Why Field Trials Matter

Controlled laboratory and greenhouse experiments are valuable.

They allow researchers to investigate mechanisms under carefully managed conditions.

However, commercial agriculture is much more complex.

Field crops experience continuously changing conditions involving:

• Temperature
• Rainfall
• Solar radiation
• Soil moisture
• Nutrient availability
• Pest and disease pressure
• Soil variability
• Competition
• Irrigation
• Management operations

A biostimulant that produces a strong response under controlled conditions may therefore produce a smaller, larger, or different response in the field.

Field trials help bridge the gap between biological potential and commercial performance.

Biostimulant Performance Is Context Dependent

One of the most important lessons from biostimulant research is that performance is not uniform across all production systems.

A large meta-analysis of open-field studies found that biostimulant yield responses varied according to factors including:

• Biostimulant category
• Crop type
• Application method
• Climate
• Soil characteristics

The analysis included more than 1,000 paired observations from 180 qualified studies and reported substantial variation across production conditions.

This is important for commercial decision-making.

An average response reported across many studies should not be interpreted as a guaranteed response on an individual farm.

The relevant question is:

How does this product perform under conditions similar to mine?

Start with a Clear Agronomic Question

A good field trial begins with a specific question.

For example:

Does this biostimulant increase marketable tomato yield under our current fertigation program?

Or:

Does this microbial biostimulant improve phosphorus acquisition in maize grown in this soil?

Or:

Can this seaweed-based biostimulant improve crop performance under moderate salinity stress?

These questions are much stronger than:

Does this biostimulant work?

The word "work" is too vague.

A field trial should define what success means before the trial begins.

Define the Claim Before Measuring the Result

Different biostimulants may be intended to support different functions.

Depending on the product and regulatory framework, these may relate to areas such as:

• Nutrient use efficiency
• Nutrient availability
• Tolerance to abiotic stress
• Crop quality

The trial should measure parameters directly connected to the intended benefit.

If the objective is improved nutrient use efficiency, simply measuring plant height may not provide sufficient evidence.

If the objective is improved crop quality, total biomass alone may not answer the commercial question.

The measurement strategy should follow the claim.

The Importance of a Control Treatment

One of the most important components of a field trial is the control.

Without an appropriate control, it can be difficult to determine whether the observed crop response was due to the biostimulant or to normal variation.

A simple comparison might include:

Treatment A — Control

Standard crop-management program without the tested biostimulant.

Treatment B — Biostimulant

The same standard program plus the biostimulant.

Everything else should remain as similar as practically possible.

If Treatment B receives additional fertilizer, different irrigation, and a biostimulant while Treatment A does not, the trial cannot clearly isolate the effect of the biostimulant.

Compare Like with Like

The control and biostimulant treatments should ideally receive the same:

• Variety
• Planting date
• Fertilizer program
• Irrigation
• Crop-protection program
• Plant population
• Soil-management practices
• Harvest method

The primary planned difference should be the treatment being tested.

This principle is fundamental.

If multiple management variables change simultaneously, identifying the cause of the final response becomes much more difficult.

Why a Single Treated Strip Can Be Misleading

A common demonstration method is to treat one large strip of a field and compare it with another untreated strip.

This may be visually impressive.

But fields are rarely uniform.

Differences can exist in:

• Soil texture
• Organic matter
• Drainage
• Fertility
• Compaction
• Elevation
• Previous crop
• Irrigation distribution

If the treated strip happens to occupy better soil, the treatment may appear more effective than it actually is.

If it occupies poorer soil, a useful treatment may appear ineffective.

This is why proper experimental design uses replication and, where appropriate, randomization.

What Is Replication?

Replication means repeating each treatment in several experimental units.

Instead of:

One control plot vs. one treated plot

a trial might contain several control and treated plots distributed across the experimental area.

For example:

• Control — Replicate 1
• Biostimulant — Replicate 1
• Control — Replicate 2
• Biostimulant — Replicate 2
• Control — Replicate 3
• Biostimulant — Replicate 3
• Control — Replicate 4
• Biostimulant — Replicate 4

The exact experimental design should be selected according to the crop, field, trial objective, and statistical requirements.

Replication helps estimate natural variability and makes the comparison more informative.

Why Randomization Matters

Suppose every control plot is placed on one side of a field, and every biostimulant plot on the other.

Any underlying field gradient may become confused with the treatment effect.

Randomization helps reduce this problem by preventing treatment placement from systematically following field position.

In many agricultural experiments, treatments are randomized within blocks designed to account for known field variability.

This can improve the reliability of the comparison.

Blocking Can Help Manage Field Variability

Fields often contain gradients.

For example:

• One side may be wetter.
• One section may contain more organic matter.
• Elevation may change across the field.
• Irrigation pressure may vary.
• Soil texture may gradually change.

A randomized complete block design is one common approach for managing this type of variability.

Each block contains the treatments being compared, and treatments are randomized within the block.

The purpose is to compare treatments under more similar local conditions.

Establish the Trial Before Seeing the Outcome

Trial design should be determined before results are available.

Important decisions include:

• Treatments
• Replication
• Plot size
• Application rate
• Application timing
• Measurements
• Sampling method
• Harvest procedure
• Statistical analysis

Changing the evaluation method after seeing the results increases the risk of interpreting random variation as a meaningful response.

A written trial protocol is therefore valuable.

Record the Product Precisely

The trial record should identify the exact product tested.

Useful information includes:

• Commercial product name
• Manufacturer
• Product category
• Batch or lot number
• Application rate
• Dilution
• Application method
• Application dates
• Number of applications

For microbial products, additional information may include:

• Microorganism
• Strain identification
• Viable concentration
• Storage conditions
• Product age at application

Without accurate product records, reproducing the trial becomes difficult.

Record the Starting Conditions

The crop environment should also be documented.

Useful information may include:

• Crop
• Variety
• Planting date
• Plant population
• Previous crop
• Soil type
• Soil pH
• Organic matter
• Electrical conductivity
• Nutrient status
• Irrigation system
• Fertilizer program
• Weather conditions

The objective is not to collect data simply for the sake of collecting data.

The objective is to understand the conditions under which the response occurred.

Soil Testing Before the Trial

Soil analysis can be particularly valuable when the product claim involves plant nutrition.

Depending on the crop and objective, soil testing may include:

• pH
• Organic matter
• Electrical conductivity
• Available phosphorus
• Exchangeable potassium
• Mineral nitrogen
• Micronutrients
• Other relevant soil parameters

Without knowing the baseline nutrient status, interpreting a nutrient-related response can be difficult.

For example, a product may perform differently in a severely phosphorus-deficient soil than in one with sufficient available phosphorus.

Tissue Analysis Can Add Valuable Information

Plant tissue analysis may help evaluate whether a treatment influences nutrient status.

Depending on the trial objective, tissue testing can measure nutrients such as:

• Nitrogen
• Phosphorus
• Potassium
• Calcium
• Magnesium
• Sulfur
• Iron
• Zinc
• Manganese
• Boron

However, tissue nutrient concentration should not automatically be interpreted as proof of improved nutrient use efficiency.

Concentration can be influenced by biomass production and dilution effects.

Nutrient data should therefore be interpreted together with crop growth, nutrient supply, and yield.

Measuring Root Development

Some biostimulants are promoted for their effects on root development.

Possible measurements include:

• Root biomass
• Root length
• Root volume
• Root density
• Root architecture
• Root-to-shoot ratio

These measurements can provide useful physiological information.

But a larger root system does not automatically mean the treatment has generated economic value.

If root development is the claimed mechanism, root measurements may help explain the response.

Commercial evaluation should still consider outcomes relevant to the grower.

Yield Is One of the Most Important Commercial Measurements

For many crops, yield remains one of the clearest commercial endpoints.

Depending on the production system, growers may measure:

• Total yield
• Marketable yield
• Yield per hectare
• Yield per plant
• Harvested biomass
• Grain yield
• Fruit yield
• Tuber yield

Marketable yield can be particularly important.

A treatment may increase total biological production without increasing the portion of the crop that can actually be sold.

Quality Can Matter as Much as Yield

In many crops, revenue depends on both quality and quantity.

Relevant measurements may include:

• Fruit size
• Uniformity
• Soluble solids
• Protein concentration
• Oil content
• Color
• Firmness
• Shelf life
• Dry matter
• Nutrient composition
• Market grade

A treatment that does not substantially increase total yield could still create economic value if it increases the percentage of production that enters a higher-value commercial grade.

The opposite can also occur.

Higher yield does not necessarily guarantee higher profit if quality declines.

Evaluate the Claimed Function

Different products require different measurements.

For Nutrient Use Efficiency

Possible measurements may include:

• Yield
• Nutrient input
• Plant nutrient uptake
• Tissue nutrient status
• Nutrient recovery

For Abiotic Stress Tolerance

Measurements may include:

• Yield under stress
• Biomass
• Water status
• Photosynthetic parameters
• Crop survival
• Recovery after stress

For Crop Quality

Measurements should directly assess the relevant quality characteristic.

For Nutrient Availability

Soil, root, and plant nutrient measurements may be appropriate depending on the mechanism and claim.

Measurements should be selected before the trial begins.

Visual Appearance Is Not Enough

A darker green crop can look impressive.

A larger canopy can also appear convincing.

But visual observations alone can be misleading.

Darker leaves do not necessarily mean:

• Higher yield
• Better quality
• Higher profit
• Improved nutrient use efficiency

Visual observations can be included as supporting information, but they should not replace quantitative measurements.

Calculate Yield Response

A simple way to express treatment response is:

Yield Response (%) = [(Treated Yield − Control Yield) ÷ Control Yield] × 100

Suppose:

Control yield = 8.0 tonnes per hectare

Biostimulant yield = 8.6 tonnes per hectare

The difference is:

0.6 tonnes per hectare

The percentage response is:

(0.6 ÷ 8.0) × 100 = 7.5%

The biostimulant treatment therefore produced a 7.5% higher yield in this example.

But this number alone is not sufficient.

The next question is whether the difference is consistent and whether it exceeds normal experimental variability.

Statistical Significance and Agronomic Significance Are Different

A result can be statistically significant without being commercially important.

Likewise, an agronomically interesting trend may fail to reach statistical significance in a small or highly variable trial.

These are different questions.

Statistical significance asks:

How strong is the evidence that the observed difference is not simply explained by experimental variation under the assumptions of the analysis?

Agronomic significance asks:

Is the size of the response meaningful for crop production?

Economic significance asks:

Does the response create enough additional value to justify the treatment?

A professional evaluation should consider all three.

Variability Should Be Reported

Reporting only average yield can hide important information.

Imagine two treatments both average 10 tonnes per hectare.

One produces highly consistent results across replicates.

The other produces very different results across plots.

These treatments do not provide the same level of confidence.

Useful statistical information can include:

• Number of replicates
• Standard deviation
• Standard error
• Confidence intervals
• Appropriate statistical test results

For formal research or product-claim substantiation, experimental design and statistical analysis should be planned by qualified experts.

One Successful Trial Is Not Universal Proof

Suppose a biostimulant produces an excellent response in one field during one season.

That result is useful.

But it does not establish that the same response will occur:

• In every soil
• On every crop
• In every climate
• At every application rate
• During every season

Biostimulant performance is influenced by environmental and management conditions.

Confidence strengthens when positive responses are replicated across relevant conditions.

Multi-Location Trials

Testing across several locations can reveal whether a response is broadly consistent or highly site-specific.

Locations may differ in:

• Soil
• Climate
• Fertility
• Irrigation
• Stress conditions
• Management practices

If a product consistently performs under the conditions for which it is intended, confidence in the recommendation increases.

If it performs only under specific conditions, that information is also valuable.

It can help define where the product should—and should not—be recommended.

Multi-Season Trials

Agricultural seasons differ.

One year may be:

• Hotter
• Colder
• Wetter
• Drier
• More stressful
• Less stressful

A biostimulant intended to support abiotic stress tolerance may show a stronger response during a stressful season than during an ideal season.

Testing over multiple seasons can therefore provide valuable information about consistency.

However, the appropriate evidence package depends on the claim, product, crop, and regulatory context.

Record Weather During the Trial

Weather data can help explain treatment response.

Useful measurements may include:

• Rainfall
• Maximum temperature
• Minimum temperature
• Relative humidity
• Solar radiation
• Extreme heat events
• Frost events

For irrigated crops, irrigation amounts should also be documented where practical.

This information is particularly important when the product is intended to support tolerance to drought, heat, or other abiotic stresses.



Nutrient Use Efficiency Requires the Right Comparison

Biostimulants are increasingly discussed in relation to nutrient use efficiency.

But claims about fertilizer efficiency require careful trial design.

A useful experiment may include more than two treatments.

For example:

Treatment A: Standard fertilizer program

Treatment B: Standard fertilizer program + biostimulant

Treatment C: Reduced fertilizer program

Treatment D: Reduced fertilizer program + biostimulant

This structure can help distinguish between:

• The effect of the biostimulant
• The effect of fertilizer reduction
• The interaction between the two

Simply reducing fertilizer and adding a biostimulant does not prove improved nutrient use efficiency unless the appropriate comparison is included.

Do Not Reduce Fertilizer Without Testing

A common commercial claim is that a biostimulant can allow growers to reduce fertilizer inputs.

This should never be assumed automatically.

If nutrient supply becomes insufficient, yield and crop quality can decline.

Any fertilizer-reduction strategy should therefore be evaluated experimentally.

Measurements should include:

• Yield
• Crop quality
• Nutrient status
• Fertilizer input
• Treatment cost
• Economic return

The objective is not simply to use less fertilizer.

The objective is to optimize nutrient productivity without compromising the production target.

Calculating the Treatment Cost

A commercial evaluation should calculate the full treatment cost.

Suppose a biostimulant program includes:

Product cost: USD 25 per hectare

Application cost: USD 12 per hectare

Total treatment cost: USD 37 per hectare

If the product requires three separate applications:

Product and application cost per treatment: USD 37 per hectare

Three treatments: USD 111 per hectare

If the application is tank-mixed with an existing compatible operation, the incremental application cost may differ.

The real cost should be calculated for the actual production system.

Calculating Additional Revenue

Suppose:

Control marketable yield = 10.0 tonnes per hectare

Biostimulant marketable yield = 10.5 tonnes per hectare

Additional yield:

0.5 tonnes per hectare

Suppose the farm-gate value is:

USD 400 per tonne

Additional gross revenue:

0.5 × USD 400 = USD 200 per hectare

If the total biostimulant program costs:

USD 80 per hectare

Then the simplified additional margin before other possible cost changes is:

USD 200 − USD 80 = USD 120 per hectare

This provides much more useful information than simply reporting a 5% increase in yield.

A Simple ROI Calculation

A simplified return-on-investment calculation can be expressed as:

ROI (%) = [(Additional Revenue − Treatment Cost) ÷ Treatment Cost] × 100

Using the previous example:

Additional revenue = USD 200 per hectare

Treatment cost = USD 80 per hectare

Therefore:

ROI = [(200 − 80) ÷ 80] × 100

ROI = 150%

This simplified example shows how agronomic performance can be translated into a commercial decision.

Actual farm economics may require a more detailed calculation.

Break-Even Analysis

Another useful metric is the break-even response.

Suppose:

Treatment cost = USD 60 per hectare

Crop value = USD 300 per tonne

The additional yield required to recover the treatment cost is:

USD 60 ÷ USD 300 per tonne = 0.20 tonnes per hectare

The treatment therefore needs at least:

0.20 tonnes per hectare of additional marketable yield

just to recover its direct cost, assuming no other economic effects.

This calculation can help growers determine whether the expected biological response is commercially meaningful.

Quality Premiums Should Be Included

Yield is not always the only source of additional revenue.

Suppose a biostimulant does not substantially increase total fruit yield but does increase the percentage of fruit entering a premium market grade.

The economic benefit may come from:

• Higher selling price
• Lower rejection rate
• Improved pack-out
• Longer shelf life
• Reduced post-harvest loss

Quantify these benefits where possible.

Economic analysis should reflect how the crop is actually sold.

Cost Savings Can Also Create Value

Some biostimulant strategies may aim to improve resource efficiency rather than simply increase yield.

Potential economic value could come from validated reductions in:

• Fertilizer use
• Water use
• Crop losses
• Other production costs

But these savings should be demonstrated.

A claimed saving is not the same as a measured saving.

For example, if a program uses less fertilizer but loses enough yield to reduce total profit, the lower input cost does not necessarily represent an economic improvement.

Evaluate Profit, Not Just Yield

Consider two hypothetical products.

Product A

Yield increase: 8%

Treatment cost: USD 200 per hectare

Product B

Yield increase: 5%

Treatment cost: USD 40 per hectare

The larger yield response does not automatically identify the better commercial option.

Crop price, quality, application cost, consistency, and risk all matter.

Agronomic response and economic response should therefore be evaluated together.

Consistency Has Economic Value

Imagine a treatment produces:

• +20% in one trial
• +1% in another
• −5% in another
• +15% in another

Another product produces:

• +6%
• +7%
• +5%
• +6%

The average response alone does not tell the whole story.

Commercial agriculture also values predictability.

A smaller but more consistent response may have a different risk profile from a larger but highly variable response.

This is why replication, multiple environments, and proper documentation matter.

Negative and Neutral Results Are Valuable

Not every trial should produce a positive result.

A neutral result can show that:

• The crop did not require the treatment under those conditions
• The application timing may have been inappropriate
• The product may not suit that production system
• Environmental conditions may not have favored the response
• The claimed function may not have been relevant

A negative result can be equally informative.

Professional product evaluation should learn from unsuccessful trials rather than ignore them.

Avoid Cherry-Picking Results

Suppose a company performs ten trials and publishes only the two strongest positive results.

Customers receive an incomplete picture of product performance.

A better evaluation considers the complete evidence base.

Questions should include:

• How many trials were performed?
• How many produced positive responses?
• Under what conditions?
• How large was the variation?
• Were neutral or negative results observed?
• Were the same methods used across trials?

Transparency improves agronomic decision-making.

Greenhouse Results and Field Results Serve Different Purposes

Greenhouse trials are valuable for:

• Screening products
• Studying mechanisms
• Controlling environmental variables
• Comparing doses
• Investigating stress responses

Field trials are valuable for:

• Commercial validation
• Real production conditions
• Soil variability
• Weather interactions
• Operational practicality
• Economic evaluation

One does not replace the other.

Strong product development can use controlled experiments to understand biological activity and field trials to evaluate commercial relevance.

Comparing Several Biostimulants

If several products are being compared, the trial should remain fair.

Products may have different:

• Recommended rates
• Application methods
• Application timings
• Number of applications

Applying every product at the same arbitrary dose or on the same schedule may not reflect proper commercial use.

A useful comparison should test each product according to a defensible application program while maintaining an appropriate common control.

Treatment cost should also be recorded.

Product Performance Should Match the Target Market

A biostimulant intended for saline soils should be tested under relevant salinity conditions.

A product intended for high-value greenhouse vegetables should not rely only on evidence from open-field cereals.

A product promoted for phosphorus efficiency should provide evidence relevant to phosphorus nutrition.

Evidence becomes more commercially useful when the trial conditions resemble the intended market.

A Practical On-Farm Trial Framework

Growers can use a structured process:

Define → Design → Baseline → Apply → Measure → Analyze → Calculate → Repeat

Define
Specify the agronomic question and expected benefit.

Design

Establish control treatments, replication, and plot layout.

Baseline

Record soil, crop, nutrient, and environmental conditions.

Apply

Follow the intended commercial dose, method, and timing.

Measure

Collect quantitative data related to the claim and commercial outcome.

Analyze

Compare treatments while accounting for variability.

Calculate

Determine treatment cost, additional revenue, and ROI.

Repeat

Validate the response across relevant conditions when practical.

Minimum Information to Record

A useful trial record should include:

• Farm and field identification
• Crop and variety
• Planting date
• Soil characteristics
• Fertilizer program
• Irrigation program
• Product name
• Product batch
• Application rate
• Application method
• Application dates
• Weather conditions
• Number of replicates
• Plot size
• Measurements
• Harvest data
• Crop quality
• Product cost
• Application cost
• Selling price used for economic analysis

Good documentation turns a demonstration into useful agronomic evidence.

Common Field-Trial Mistakes

No Control

Without a suitable control, treatment effects are difficult to isolate.

No Replication

A single comparison cannot adequately characterize field variability.

Changing Several Variables

If fertilizer, irrigation, and biostimulant treatments all change simultaneously, attribution becomes difficult.

Measuring Only Appearance

Visual crop differences do not automatically translate into commercial value.

Measuring Too Many Parameters Without a Clear Objective

More data do not automatically create better evidence.

Measurements should address the original agronomic question.

Ignoring Economics

A treatment can increase yield and still fail economically.

Generalizing from One Trial

One positive result should not automatically become a universal recommendation.

Ignoring Neutral Results

Neutral and negative results can help identify where a product does not create value.

From Field Trial to Commercial Recommendation

A strong commercial recommendation should answer several questions.

What product was tested?

On which crop and variety?

Under what soil and climate conditions?

At what rate?

Using which application method?

At what growth stage?

Against what control?

What was measured?

How consistent was the response?

What was the economic result?

This is far more useful than simply stating:

“The product increased yield.”

The Future of Biostimulant Evaluation

Biostimulant trials are likely to become increasingly data-driven.

Future evaluation may combine:

• Soil sensors
• Weather stations
• Satellite imagery
• Drone imaging
• Chlorophyll measurements
• Plant tissue analysis
• Root imaging
• Yield mapping
• Automated irrigation records
• Machine learning
• Farm-management platforms

These technologies can help researchers understand not only whether a product worked, but also where, when, and under which conditions it worked best.

Large datasets across farms and seasons could eventually help identify environments where particular biostimulant strategies are most likely to create value.

This could move the industry away from generalized claims and toward more precise recommendations.

Conclusion

Evaluating a plant biostimulant requires more than observing whether treated plants look better.

A professional field trial should begin with a clearly defined agronomic question.

It should use an appropriate control, adequate replication, reliable measurements, and consistent management.

The evaluation should then examine:

Agronomic response.
Variability.
Crop quality.
Nutrient response where relevant.
Treatment cost.
Additional revenue.
Return on investment.
Consistency across relevant conditions.

Yield increases can be valuable, but yield alone does not determine commercial success.

A smaller, repeatable response with a strong economic return may be more useful than a large but inconsistent response.

Likewise, a biostimulant that performs well in one environment should not automatically be assumed to perform equally well everywhere.

The most useful question is therefore not:

“Does this biostimulant work?”

It is:

“Under which conditions does this biostimulant create a measurable, repeatable, and economically valuable response?”

That question transforms biostimulant evaluation from marketing into agronomy.

References

• Li, J., Van Gerrewey, T. & Geelen, D. (2022). A Meta-Analysis of Biostimulant Yield Effectiveness in Field Trials. Frontiers in Plant Science, 13, 836702.
• Ricci, M., Tilbury, L., Daridon, B. & Sukalac, K. (2019). General Principles to Justify Plant Biostimulant Claims. Frontiers in Plant Science, 10, 494.
• European Parliament and Council (2019). Regulation (EU) 2019/1009 — EU Fertilizing Products Regulation.
• du Jardin, P. (2015). Plant Biostimulants: Definition, Concept, Main Categories and Regulation. Scientia Horticulturae, 196, 3–14.
• Rouphael, Y. & Colla, G. (2020). Biostimulants in Agriculture. Frontiers in Plant Science, 11, 40.
• Thonar, C. et al. (2024). Effectiveness of Bio-Effectors on Maize, Wheat and Tomato Performance and Phosphorus Acquisition from Greenhouse to Field Scales in Europe and Israel: A Meta-Analysis. Frontiers in Plant Science, 15, 1333249.