Agronomic Intelligence: How Multi-Regional Field Trial Data Drives Personalized On-Farm Recommendations

Decision-making in the field is increasingly data-driven. However, having information is not enough — it is essential to identify patterns, interpret scenarios, and convert dispersed data into actionable intelligence. Across Brazil, Paraguay, and the United States, an extensive experimental network generates the data that DigiFarmz leverages to reinforce its information foundation, serving as the backbone for personalized recommendations.

The diversity of climate conditions, soil types, cultivars, and management strategies enriches our database and provides deeper qualification of the production environments our clients face in the field.

“Our focus is to capture and understand how each variable interacts and influences crop performance. This insight allows us to build robust models capable of anticipating scenarios and delivering recommendations adapted to diverse production contexts,” says Ricardo Balardin, CSO & Founder of DigiFarmz.

Connected Data Driving Predictability

Data collected from different locations feeds DigiFarmz’s artificial intelligence models, which, with each new dataset, expand the platform’s ability to recognize productive patterns — increasing algorithm accuracy.

When a customer — whether a grower, agronomist, or commercial consultant — accesses our products, such as DigiFarmz Cropper or DigiFarmz Linkage, the algorithms automatically cross-reference farm-specific data with local datasets and consolidated agronomic information. This process enables continuous learning, resulting in an ever-increasing level of customization within DigiFarmz.

Recommendation insights are no longer generic — they become personalized, built by comparing the user’s real conditions with observed field scenarios. “Even when one or more parameters are not obtained from the same region or field, DigiFarmz algorithms can reproduce learnings from other similar regions by combining more than 50 parameters related to genetics, geographic location, sowing dates, inputs, and more — achieving a high degree of precision. The experimental plot is no longer just a piece of land in a research station — it becomes every productive region expressing both the technology applied and the grower’s technical capability,” explains Ricardo Balardin.

Currently, DigiFarmz is strongly focused on developing scoring systems that will not only define the Technical Quality Index of the grower but also assess soil productivity potential, climate and phytosanitary risks, and the quality of pest and disease management programs. The production process is complex, and DigiFarmz’s goal is to consolidate data to understand the limitations and strategies that explain productivity outcomes in each scenario.

“Our models can identify patterns and transfer learnings in a contextualized way. This gives growers predictability and confidence when defining management strategies,” reinforces Balardin.

Efficiency, Risk Mitigation, and Better Use of Resources

This integrated, data-based approach has a direct impact on operational efficiency and crop performance. By understanding more precisely how each combination of factors influences the final result, DigiFarmz provides customers with a solid foundation for more assertive management decisions.

With contextualized recommendations, it becomes possible to optimize input use, directing resources more efficiently. In addition, the ability to anticipate crop behavior under different scenarios strengthens plant health management and allows for earlier risk identification, reducing both production and financial uncertainty.

“This applied intelligence is continuously validated and refined. Large volumes of data help strengthen our understanding of how production systems respond to different conditions — resulting in increasingly consistent recommendations that have a real impact on profitability for both the grower and the agricultural value chain as a whole,” concludes Balardin.

Expanding the Crop Database

As part of the ongoing evolution of its technical foundation, DigiFarmz is expanding its agronomic decision models. Currently in the final validation phase, the corn, barley, and oat models will soon offer the same level of intelligence, recommendation insights, and advanced management algorithms that already power DigiFarmz, broadening the range of crops available to customers.

This expansion will allow growers and consultants to better evaluate how crop rotation and succession explain yield variations — making decision-making easier and strengthening the strategic use of data in planning and management execution.

A New Standard for Decision-Making in Agriculture

By connecting science, technology, and real-world farming, DigiFarmz is establishing itself as a tool capable of delivering highly reliable, personalized recommendations for growers and consultants in any region. DigiFarmz introduces a new dimension to agronomic research — one in which algorithms learn from millions of acres of commercial production, offering technologies fine-tuned to diverse agricultural realities with minimal margin of error.