Yield maps play a fundamental role in on-farm experimentation, providing valuable insights into performance variability within a field. This technology, which records georeferenced harvest data, helps farmers visualize how different areas of the field respond to management practices over time. By integrating this information into the planning and analysis of experiments, it is possible to make more informed, specific, and accurate decisions.
One of the main advantages of yield maps is the ability to identify heterogeneous zones within a field. Not all areas of a field produce the same way due to differences in soil, drainage, topography, fertility, and even microclimates. With detailed maps, farmers can recognize spatial patterns and separate areas of high, medium, and low productivity. This division is essential to select representative locations where on-farm experiments will be conducted, ensuring that the results reflect the real conditions of the field.
Furthermore, yield maps allow evaluating the impact of previous management practices. They provide a valuable history that can be used to determine if experimental practices, such as different doses of inputs or seed varieties, are improving productivity compared to previous years. This comparison helps validate the effectiveness of interventions and adjust future strategies with greater confidence.
In experimental planning, yield maps help define areas where variability is more significant or where conditions are more uniform, depending on the objective of the study. During the experiment, yield maps also help monitor results. By cross-referencing harvest data with experimental plots, it is possible to quickly identify how each treatment influenced production in different parts of the field. This ensures a more detailed and assertive analysis, fundamental to validate the conclusions of the experiment.
Another important application is the integration of yield maps with other digital agriculture tools. With the Eiwa platform, for example, it is possible to combine yield maps with other layers of field information to create a complete view of the field. This integration enhances the planning, execution, and analysis of experiments, increasing the accuracy of recommendations.
Finally, yield maps are not just a technical tool, but also a strategic one. They allow farmers to prioritize areas of greater potential, optimize the use of inputs, and adjust management practices based on concrete evidence. In on-farm experimentation, their importance goes beyond data collection: they become the basis for smarter and more targeted decisions, maximizing the benefits of experimentation and contributing to a more efficient and sustainable agriculture.
This article was written in collaboration with Dr. Guilherme Sanches (Ph.D.), a specialist in Precision Agriculture, Digital Agriculture, and Artificial Intelligence applied to agribusiness, with extensive academic and international experience.