The Complementary Conference Room sent a clear message throughout the day. Industry standards are strengthened when information is translated into operational criteria. In a context where decision-making coexists with variability and pressure for consistency, both workshops agreed on one essential point: the value lies not in accumulating data, but in interpreting it correctly and using it to prioritize actions.

Following this approach, Claudia Moggia explored the fundamental principles that allow a trial to be useful and comparable in R&D&I. Juan Hirzel, meanwhile, addressed how to interpret data analyses using agronomic criteria before turning a result into a decision. Together, the sessions established a common technical language for evaluating evidence more rigorously and reducing hasty interpretations.

Comparable trials in R&D&I to make confident decisions

The advanced workshop on the design and analysis of R&D trials focused on the elements that make a trial transferable to management decisions. The emphasis was on formulating clear questions, defining measurable variables, establishing comparable methodologies, and ensuring consistent interpretation of results, especially in a crop with high variability associated with the interaction between environment, management, and production system performance.

Moggia emphasized that statistical analysis is the foundation for drawing reliable and comparable conclusions. “Statistical analysis allows us to obtain reliable results and work with a probability of error that does not exceed 5%,” she noted, stressing that the quality of the result depends on the design, the sampling method, and the evaluation. From this perspective, a trial becomes useful when it starts with a well-defined research question and a design capable of capturing the desired effect without compromising comparability between treatments.

The session also reinforced that the usefulness of the trial is diminished when it attempts to measure too many factors simultaneously. This approach increases uncertainty and makes it difficult to draw conclusions, hindering the transfer of results to decision-making. In contrast, working with design criteria, repetitions, and error control allows for distinguishing striking results from applicable ones, providing evidence to support management adjustments or validate alternatives under comparable conditions.

Claudia Moggia Advanced Workshop on Design and Analysis of Trials for R&D&I © Blueberries Consulting

Interpretation of data with agronomic criteria and relevance

In the data analysis interpretation workshop, Juan Hirzel delved into how to read information to make more accurate decisions. The content covered understanding variability, the consistency of results, and the agronomic relevance of the analysis, before translating conclusions into concrete actions. The message connected to a cross-cutting scenario for the industry. As varietal diversity increases, it becomes more relevant to work with specific references and standards, rather than applying general criteria.

Hirzel stated it directly, pointing out that “it’s not correct to use the same standard for all varieties; we need to work with references for each variety.” This idea was linked to the need to make decisions based on contextualized evidence, considering that factors don’t always carry the same weight in production. From this perspective, interpreting data involves first examining its consistency and variability, and then evaluating whether the result has a magnitude and context that justify a change.

The workshop reinforced the idea that sound interpretation relies on minimum reading criteria, validation of the data context, and conclusions proportionate to the actual scope of the analysis. In practice, this means avoiding reactive decisions based on incomplete readings and strengthening the consistency of programs through well-interpreted evidence.

Juan Hirzel Data Analysis Interpretation Workshop © Blueberries Consulting

A common framework for consistent decision-making

Taken together, both workshops delivered a key benefit for the technical teams. A common framework was established to align communication across departments, reduce interpretation gaps, and improve consistency in decision-making. The overarching idea was to move toward methodical decision-making, formulating better questions, measuring comparably, and reaching technically sound conclusions.

The Complementary Conference Room thus established a practical and demanding approach. Data quality, its interpretation, and its transfer to decision-making became the guiding thread of a morning focused on strengthening criteria, especially in a crop where variability is part of the system and where consistency depends, to a large extent, on how what happens in the field is measured and interpreted.