In agricultural fields, observations near in space share more similarities than observations far apart. This phenomenon of closely related points, the so-called spatial autocorrelation or in that case, the within-field spatial variability, is well-recognized and needs to be characterized to consider site-specific management. Quantifying spatial dependency is fundamental for understanding the underlying factors affecting field productivity. An examination of multiple scientific papers was carried out to assess why and how practitioners were evaluating the spatial variability across their fields. An analysis of the existing descriptors of within-field variability was performed to identify the most relevant indicators to use based on (i) the case studies that practitioners employed, (ii) the different nature of data to which users were confronted, and (iii) the complexity and ease of access to information in support of these available approaches. Finally, this paper provides users with a comprehensive decision tree that should help them select an appropriate index of spatial variability for their work. Results also highlight the needs for further investigation, especially regarding the implementation of more general and standardized approaches that will enable cross-study comparison.