Abstract Agriculture provides food, raw materials, and employment opportunities for a significant percentage of the world's population. Climate, economic, political, social, and other conditions affect decision making in agricultural processes. In many cases, these conditions imply the loss of suitability of many areas for some traditional crops. In contrast, these areas can produce new crops by taking advantage of changing conditions. In this sense, having reliable tools and information for decision making is essential in adapting to new agricultural productivity scenarios. The above implies having sufficient and relevant data sources to reduce the uncertainty in the decision‐making processes. However, data by nature tend to be diverse in structure, storage formats, and access protocols. Data fusion tasks have been immersed in a multitude of applications and have been approached from different points of view when implementing a suitable solution. We propose a multi‐domain data fusion strategy to support data analysis tasks in agricultural contexts. We also describe all the data sources collected, which are the main input to the proposed strategy. The combined data sources were also evaluated through a preliminary exploratory analysis in a multi‐label learning approach. Finally, the data fusion strategy is explained through an example in agricultural crop production.