The Future of Agronomic Recommendations: Can We Trust AI with Automated Recs?
Many agronomic recommendations today are still generated through standardizations of lab and extension services. Soil and tissue samples are processed, values are compared to fixed sufficiency ranges, and generalized fertilizer suggestions are produced—typically one nutrient at a time. While this process has served agriculture for decades, it's not always responsive to additional sources of insight—such as tissue tests, biological activity, or microbial indicators—that can clarify nutrient availability, timing, and uptake limitations. As a result, recommendations can sometimes remain unchanged despite evolving conditions.
As more data becomes available and expectations rise for precision and efficiency, there's growing interest in more dynamic decision support systems. While the term "AI" is often used, most of what exists today are rule-based engines or logic-driven platforms that help interpret soil and tissue data in more structured ways. These tools are beginning to bridge the gap between lab numbers and actionable field insights—but they come with their own limitation
What These Systems Actually Do Most current agronomic platforms use conditional logic or fixed-rule engines to interpret test results. They process inputs like nutrient levels, crop type, and CEC, then apply pre-programmed agronomic rules to generate a recommendation. For example, if potassium is low and soil texture is sandy, they may recommend a split potassium application. These are not adaptive learning systems—they apply consistent rules derived from past agronomic knowledge.
Why Agronomy Is Hard to Automate Agronomy isn't plug-and-play. Nutrient behavior depends on dozens of interacting variables: soil test values, crop stage, prior applications, microbial activity, root zone moisture, test timing, and more. These systems often treat variables independently or apply generic thresholds. But agronomic decisions demand context.
Take a different example: a mid-season tissue test in soybeans shows low calcium. At first glance, it might make sense to apply foliar calcium nitrate to correct the deficiency. But when other variables are factored in—elevated potassium, low boron, and a Haney test showing low microbial activity and carbon availability—the picture shifts.
Step 1: Evaluate interactions – High potassium can inhibit calcium uptake, and low boron limits internal calcium mobility.
Step 2: Assess compound effects – Applying calcium nitrate could worsen the nitrogen-potassium balance while still failing to move calcium effectively within the plant.
Step 3: Choose an integrated fix – With this multi-variable context, a calcium-boron acetate blend is selected to support internal movement of Ca. It's paired with a light molasses drench to stimulate microbial activity and improve near-term availability.
Decision: What seemed like a straightforward calcium deficiency turns out to be a mobility and interaction problem. The final in-season fix is still nutritional—but more precise, better-timed, and less disruptive to other nutrient balances.
Where It Adds Value
Scale: These systems can scan hundreds of tests and surface potential issues across multiple management zones instantly.
Consistency: Standardizes logic across agronomists and avoids oversights.
Pattern Recognition: Helps uncover trends that only emerge when multiple data layers—like soil nutrients, tissue results, and biological indicators—are viewed together over time.
Where It Fails
Even well-structured decision tools can miss the mark when they don’t account for biological context, interactions between nutrients, or variation across zones.
Lack of context: A system recommends increasing nitrogen based on low tissue N. But sulfur is also low—and in adjacent zones with better sulfur status, tissue N is sufficient despite the same nitrogen program. Without sulfur to support protein synthesis, added N would be inefficient. The right response is balancing N and S, not simply increasing N.
Wrong root cause: A grower applies foliar manganese throughout the season. Later, tissue tests flag low iron, and the system recommends foliar Fe. But the deficiency isn’t due to low Fe supply—it’s Mn-induced suppression of Fe uptake. The solution is to pause or space Mn applications, not stack on more iron.
Over Reliance on static rules: A foliar boron application is triggered by a tissue test showing low B. But the sample was pulled shortly after irrigation, when dilution can depress B levels temporarily. No deficiency symptoms are present, and soil B is adequate. The actual fix is better sampling timing—not more boron.
Misinterpreting nutrient trends: A system recommends more phosphorus based on low tissue P. But the soil already has high P, and biological indicators—like low fungal biomass and weak mycorrhizal activity—suggest the issue is poor biological access, not undersupply. Instead of applying more P, a biological amendment is used to support nutrient availability and uptake.
Building Toward Smarter Decision Support
The future of nutrient analytics isn’t automation—it’s augmentation. Agronomic software should help experts work faster and with greater clarity, not replace their judgment. The next generation of tools will surface issues across soil, tissue, and biological datasets; show the reasoning behind each flag; and allow agronomists to apply their own expertise to finalize the response. The role of software is to structure the complexity—not simplify it away.
