A Framework for Visualization and Analysis of Agronomic Field Trials from On‐Farm Research Networks

An on‐farm research network is an organization of farmers that conducts agronomic experiments under local conditions. It is common that an elementary statistical analysis be conducted for individual studies. However, there is unexplored potential in detecting yield response variability patterns for better decision making. We developed a data‐analytics framework and web‐application program that allows users to analyze multiple studies that use a common protocol and can identify the conditions where an imposed treatment may or may not be effective. The development of this data‐analytics framework is needed to improve predictions at the farm level that can lead to more cost‐effective, sustainable and environmentally sound agricultural production. Data visualization is an important part of data‐analytics. In this paper, we have developed and tested a Bayesian hierarchical model that can be used to assess the general agronomic performance of different management practices. Decision making related to new management practices should be based on the complete evidence, local conditions and economic considerations. The web‐application includes dynamic data visualization features to enhance communication and sharing of information with the goal to reach a broader audience. Core Ideas We develop a data‐analytics framework and web‐application for on‐farm research trials. A Bayesian hierarchical model quantifies the uncertainty in yield response. The model helps assess alternative practices, products, and technologies among trial locations. The framework provides a reactive break‐even economic analysis of alternative management practices.