Building an Agronomic Strategy for Scaling Regenerative Ag: Aligning Agronomic Fit with Product Capabilities (Part 2)

Aug 1

12 min read

This is the fourth post in our series on building effective agronomic strategies for regenerative agriculture product adoption. In our first post, we introduced the framework, in our second post, we explored what agronomic strategy means for scaling regenerative ag, and in our third post, we dove deep into defining the problem from the farmgate out. Now we're tackling Step 2: Aligning Agronomic Fit with Product Capabilities — the critical bridge between understanding farmer needs and building solutions they'll actually adopt.

TL;DR

Having solid science isn't enough if your product doesn't fit into farmers' real-world systems and decision-making processes. This article provides:

A framework for asking the right research questions that drive purchasing decisions
The Modified BRICE Prioritization system to evaluate which farmer questions deserve your R&D investment
Real-world strategies for designing trials that reflect actual adoption barriers
How to honestly assess and communicate your product's system compatibility

Whether you're in Product Development, Research, or Strategy, this approach helps you build regenerative ag solutions that farmers can actually implement… NOT just products that work in ideal conditions.

The Reality Check

A lot of companies in regenerative ag are building incredible biological and soil health products.

But even good products can stall in the field if they're developed in isolation from the real-world context of how farmers make decisions.

Here's the tough question I often ask product, agronomy, and sales leaders: Are the questions you're asking in research the same questions your growers (and their trusted advisors) are asking when deciding whether to adopt?

Too often, the answer is no.

And here's why that disconnect is costing you adoption, time, and resources: You might be generating mountains of data that impress scientists but leave farmers wondering "How does this actually help me?"

Farmers Don't Just Ask Simple Questions

Farmers don't just ask:

"Will it boost yield?"

They also ask:

"What's the return on investment compared to my current program?"
"Can I reduce input spend elsewhere while maintaining or improving profitability?"
"Will this save me time or create operational efficiencies?"
"Is this going to create complications with my equipment, timing, or logistics?"
"Do I trust that it'll work consistently in variable conditions?"
"How does this stack up against competitive options in the market?"
"What happens if weather doesn't cooperate or conditions aren't ideal?"
"What's the learning curve and support system for implementation?"

Yield still matters, but it's one component of the profitability equation, not the whole story. Research confirms that economic returns (either anticipated or demonstrated) from technology investment serve as the primary factor influencing adoption.

The challenge for regenerative ag/ soil health companies is that farmers' questions are often practical and/or systems-focused, while your R&D might be focused on biological mechanisms and efficacy under specific conditions. This creates a gap between what you're proving and what farmers need to know to make purchasing decisions.

The Modified BRICE Framework: Prioritizing What Really Matters

To bridge this gap, I've developed a Modified BRICE Prioritization Framework that helps companies evaluate whether answering a particular farmer question is a good investment of resources.

This framework is based on years of qualitative data from farmer interviews and field conversations. It's designed specifically for small to medium companies who are commercializing a new product or have launched a product within the past 5-7 years. This can also apply to large, established companies who may be struggling to achieve product-market fit with new offerings.

How the Framework Works

The Modified BRICE framework adapts the established RICE prioritization methodology developed by Intercom for the specific context of agricultural product development. BRICE stands for:

B - Business Importance (0.5-3): How does generating this data impact your company's goals and strategic direction?

R - Reach (1-100%): The percentage of target growers who will benefit from or be influenced by this insight

I - Impact (0.25-3): How critical is this information for driving farmer action or purchasing decisions?

C - Confidence (1-100%): How confident are you in your assumptions regarding Reach, Impact, and Effort?

E - Effort (1-3): The estimated effort to generate this insight (in full-time equivalent work)

BRICE Score = (B × R × I × C) ÷ E

Score Range: 0.1 (lowest priority) to 9.0 (highest priority). Additional context is below:

High Priority (>4.0): Questions that significantly influence purchasing decisions and should be answered first
Medium Priority (2.0-4.0): Important questions that support adoption but aren't deal-breakers
Lower Priority (<2.0): Nice-to-have information that may not justify immediate resource investment

System Compatibility = Clarity of Fit

One of the most overlooked pieces of agronomic strategy is system fit AND this is where many promising products fail in the field.

If a product only works in-furrow, you're likely looking at 25–30% of row crop growers. And that's NOT a problem IF you're clear about it.

The Cost of Poor System Fit

I once worked with a grower who was interested in testing a new soil priming and residue decomposition product. It was recommended that the product be applied in the fall over corn residue with a carrier of water. However, the recommended rate was no less than 50 gallons per acre.

For those not familiar with application rates, this is a lot. Most herbicides, foliar fertilizers, and other biologicals applied with a sprayer are applied at a full mix rate of 8-20 gallons per acre maximum and lower if applied in furrow.

We really wanted this product to work, and fortunately, the applicator is one of my best friends. We only applied the product to 30 acres, and if we had done a full 80, my best friend might not have spoken to me for a few weeks 😄. His screens plugged multiple times, and it took him 6 times longer than it would normally take to apply the most commonly used residue digester in the area.

Again, we really wanted this product to work, but unfortunately, I won't be able to find an applicator to try it again, nor do I want to test it unless improvements are made.

This is the reality for farmers: You usually have one, maybe two chances. Time is critical, and having to fight through clogged screens, impractical application requirements, and similar issues will scare away any customer quickly.

Being Honest About Product Fit

Being honest about product fit actually strengthens your market position by:

Defining your ideal customer profile (ICP) more precisely
Making your sales team more efficient by focusing on qualified prospects
Building credibility with farmers who value straightforward answers
Setting realistic expectations that lead to successful implementations

Over time, if adoption grows, you can always invest in R&D to expand compatibility. But trying to be everything to everyone from the start often results in being nothing meaningful to anyone.

Questions to Ask About System Fit

Application Compatibility:

What equipment do farmers need to apply this product?
How does our application timing align with their current operations?
Are there any equipment modifications required?

Operational Integration:

Does this fit into their existing crop rotation?
How does this interact with their current input program?
What's the learning curve for implementation?

Economic Integration:

Can they reduce spending elsewhere if they adopt this product or solution?
How does the ROI timeline align with their cash flow needs?
What's the risk if conditions aren't ideal?

Design Trials That Reflect Real-World Risk

If your trials don't address farmer or advisor concerns, they won't move the needle. It's not enough to show that your product works in ideal conditions. You need trials that reflect variability, risk, and implementation tradeoffs.

Think about it: A new product or practice might promise $10–20/acre in ROI, but if it costs a farmer time, slows down planting, or introduces uncertainty? They'll walk away.

Here's an analogy most decision-makers can relate to: Imagine you're considering switching to a new software platform for your business. The vendor promises it will save you $2,000 per month (+10 bu/ac) and has better features (improved plant health and increased nitrogen cycling). But the implementation will disrupt your operations for 2-3 months (refill the planter every 30 acres), requires retraining your entire team (major planter modifications), and has limited customer support (no product agronomist). Even with clear financial benefits (Long term ROI), would you make the switch? That's exactly the same calculation farmers make when evaluating new products, the total cost of adoption often extends far beyond the product price.

Moving Beyond Perfect Conditions

Most companies design trials to prove their product works. But farmers need to know it works in their specific context with their specific constraints (especially the early and late majority).

Instead of asking: "Does our product increase yield?"

Ask: "Does our product increase profitability AND what is the yield impact when applied with typical farm equipment, under variable weather conditions, and integrated with existing input programs?"

Instead of asking: "What's the optimal rate?"

Ask: "What rate provides the best ROI given typical application variability and economic constraints and at what crop stage?"

Trial Design Principles for Decision-Driving Data

I understand the importance of greenhouse trials and small plots in the commercialization process. They are critical for the development and early stage proof of concept. However, when the product shows promise and confidence, test under farm conditions as early as possible. Below are some considerations that will fundamentally change how you approach research.

1. Test Under Farmer Conditions that MATCH your ideal customer

Use typical application equipment that matches the profile of your ideal customer. Use typical application equipment that matches the profile of your ideal customer. Smart researchers might ask about confirmation bias. Here's why this approach actually reduces bias: when you understand your ideal customer's constraints intimately, you design trials that reflect their reality rather than idealized conditions that don't exist in practice. This provides you with the opportunity to learn what challenges are likely to come up, and you can speak to what your customers may be facing firsthand..
Include variable weather scenarios in multi-year trials in regions you plan to establish a presence.
Test across different soil types and management systems to better understand your product’s response and define where it fits best.

2. Address Implementation Friction

Compare application timing options that fit into real schedules. You'll be able to speak from firsthand experience to growers who may miss the mark by one or two growth stages.
Test compatibility with commonly used tank mixes. If it's a microbe, how much does performance degrade when mixed with fungicides? If it's a biostimulant, what happens when tank-mixed with herbicides?

3. Measure What Farmers Care About

Track total profitability, not just yield. This seems obvious, yet most trials focus solely on yield response.
Measure operational efficiency (time savings, labor reduction). I rarely see input companies doing this. Operational efficiency is a key metric for software, hardware, and equipment companies but rarely for biological inputs.
Document any operational challenges encountered and be upfront about them. Transparency here often leads to productive brainstorming sessions with customers who can shed light on how to make your product work better in their systems.

4. Include Risk Scenarios

Document conditions of failure and frequency to identify trends that help farmers better understand the context of failure, then share it openly. Often, a small operational adjustment can turn a loss into a win.

Aligning Research Investment with Adoption Reality

Most biological product companies invest heavily in mechanism studies and optimal-condition trials while the questions that actually drive farmer purchasing decisions get minimal attention. This disconnect explains why products with strong lab data often struggle with field adoption.

The BRICE framework forces a different approach: prioritize research investment based on the questions farmers are actually asking. Use Table 1 to evaluate which questions deserve investigation across all departments, not just trials.

High-Priority Questions (BRICE score >4.0):

ROI demonstrations across multiple conditions
Head-to-head comparisons with competitive products
Cost-effectiveness studies

Medium-Priority Questions (BRICE score 2.0-4.0):

Soil health impact demonstrations
Rate reduction studies for other inputs
Crop-specific performance validation

Lower-Priority Questions (BRICE score <2.0):

Detailed mechanism of action studies
Optimal timing research under perfect conditions
Equipment modification requirements

This framework impacts trials conducted, but it also requires other departments to think differently about which data they need to collect and how they communicate value to farmers. When R&D, marketing, and sales are all optimizing for the same farmer questions, adoption accelerates.

Ready to apply this framework to your research strategy? Access the complete BRICE Prioritization Framework here

Building Strategic Alignment

When the right data meets the right fit, adoption doesn't feel like a risk, it feels like the next logical step.

Cross-Functional Alignment

A well-aligned agronomic strategy ensures that:

Product Development focuses on features that address high-BRICE questions and real system constraints
Research Teams design trials that generate decision-driving data, not just publishable results
Marketing creates messaging that speaks to farmer priorities and addresses their specific concerns
Sales Teams are equipped with answers to the questions that actually drive purchasing decisions
Field Teams understand both the "what" and the "how" of implementation

Implementation Framework

Phase 1: Assessment

Conduct farmer interviews using the framework from Part 1
Apply the BRICE prioritization to identify critical questions
Honestly evaluate your product's system compatibility

Phase 2: Strategic Trial Design

Design trials that address high-BRICE questions
Include real-world variability and constraints
Measure farmer-relevant outcomes

Phase 3: Alignment and Communication

Align internal teams around farmer-driven priorities
Develop messaging that addresses system fit honestly
Create implementation support that reduces adoption friction

Learning From the Field: A Personal Example

This experience taught me exactly what companies with innovative agricultural tools face. Sap analysis is like a blood test for plants, helping assess nutritional deficiencies many weeks ahead of visual symptoms. It's a novel tool with limited research behind it — market penetration is less than 10% by my estimate and may actually be less than 5% depending on region and crop.

The barriers to adoption aren't just about proving the science works, they're about practical implementation: high cost relative to established alternatives ($80-$100 vs $25-$40 for tissue or $15-$25 for soil tests), limited interpretation resources, and unclear guidance on when and how to use the data profitably.

Sound familiar? The few companies developing sap analysis technology are still focused primarily on proving the tool works rather than answering the questions farmers are actually asking: "How do I use this profitably compared to my current monitoring approach?" "What specific decisions can I make that justify the extra cost?" "How do I interpret results without extensive training?"

In my own consulting, I first became interested in this tool thanks to John Kempf's Regenerative Agriculture Podcast. To be honest, the first three to four years of pulling samples and interpreting the results were hard. It was a challenge making decisions with limited resources to guide us in the right direction. I kept asking, how can I make this relevant, actionable, and profitable for growers this year and next year? What challenges can we address and when can we address those?

This is exactly why the BRICE framework matters for novel products. When you're introducing something new to the market, every research dollar spent on questions farmers aren't asking delays adoption. The companies that will succeed with sap analysis will be those that prioritize research around farmer decision-making questions rather than just technical validation.

The Strategic Impact

This approach to aligning agronomic fit with product capabilities delivers measurable business outcomes:

Reduced Development Costs: By focusing R&D on questions that drive adoption rather than just scientific curiosity
Faster Time-to-Market: When trials address real farmer concerns, adoption happens more quickly
Higher Conversion Rates: Sales teams equipped with answers to farmer-priority questions close more deals
Improved Customer Success: Products designed for real-world systems have higher implementation success rates
Stronger Market Position: Honest communication about fit builds trust and credibility

The Bottom Line

The gap between laboratory success and field adoption often comes down to alignment, or the lack thereof.

When your product development, research priorities, and go-to-market strategy are all aligned around what farmers actually need to know to make purchasing decisions, adoption stops feeling forced.

Instead of pushing products that work in ideal conditions, you're offering solutions that fit into real farming systems and address real farmer priorities.

That's when growth becomes sustainable, and your agronomic strategy starts driving the business outcomes you're seeking.

What's Next in This Series

We've now covered defining the problem from the farmgate out and aligning your capabilities with real farmer needs. In our remaining articles, we'll explore:

Part 3: Identify WHERE Adoption is Most Likely — mapping your ideal markets and customer segments
Part 4: Equip the Right People to Tell the Story — building the communication strategy that drives adoption

Each article provides field-tested frameworks you can immediately apply to your regenerative ag products and strategies.

Ready to align your product with farmer reality? 🚀

If your regenerative ag product has the science but struggles with real-world adoption, it's time to examine whether you're asking the right questions and designing for actual farming systems.

At Living Roots, we help companies translate farmer insights into strategic product development and trial design that drives measurable adoption.

Let's build solutions that work in the real world, not just the laboratory.

Let's save tomorrow's soils today.