## 4 Discussion
### Comparison with Similar Studies  
The existing research aims at the visualization of uncertainty in (precision) agricultural data primarily beyond (geo)spatial visualizations (e.g., [70; 72]). In such cases, visualization of uncertainty was expressed in a graph rather than on a map. [PERSON] [69] used an approach similar to Figure 10 of this paper, that is, two map windows: one for the presented values and the second for the expression of uncertainty. From a cartographic perspective, uncertainty was visualized through saturation, and this was used in this paper as well. [PERSON] [69] also foresees a presentation of the uncertainty layer as a semi-transparent one together with the primary information layer. Recently, a study on a similar topic was conducted by [PERSON] et al. [71] with a focus on uncertainty expressions on a global seasonal climate prediction. [PERSON] et al. [71] distinguish between the following methods: bubble plot (that equals the combination of colour hue, size, and saturation methods presented in this paper), pie charts (where saturation is used for uncertainty), and reliability categories (colour hue in a form of a modified traffic light metaphor). The main differences between [PERSON] et al. [71] and the results presented in this paper are the following:  
* The extended/modified traffic light metaphor uses six colour hues for five classification classes (note that the'marginally useful' class is presented by two distinct colour hues). Moreover, only the hues on both ends of the spectrum (red and green) are compliant with traffic lights. The remaining four hues (yellow, light brown, cyan) lie outside the traditional traffic light scheme and are also perceptually demanding. Potential users must use the legend to define the order of hues and their corresponding meanings (perfect--dangerously useless). Strictly sticking to the traffic lights hues (green, orange, and red and their lightness) as used in our study helps users make more intuitive decisions. * [PERSON] et al. [71] employed size and transparency to express uncertainty in a regular grid that avoided overlaps. Such a method was not feasible within our study as a consequence of spatial closeness of the measured data. However, the use of transparency for uncertainty visualization is considered highly intuitive [57]. On the other hand, only three transparency levels have been empirically tested and using more categories could complicate map reading. Three-dimensional uncertainty visualizations have so far been used only rarely in the agriculture domain [76], with a primary goal to provide information about the terrain. [PERSON] et al. [60] presented a sophisticated tool for 3D visualization of terrain, data, and associated visualization in a single map window. They also stressed the importance of scenario-aware visualization highlighting either the data or uncertainty in more detail. Unlike [PERSON] et al. [60], we used two parallel plots--one for terrain and data and the other for the associated uncertainty. Both plots are connected and changes in detail (zoom in and out, pan) and directions (rotate, tilt) are automatically performed in both windows. Parallel interactivity supports the possibility of prioritisation in detail in both the data/terrain and uncertainty views. A combination of 2D and 3D visualization of agricultural data was presented by [PERSON] et al. [80], who also performed a subjective qualitative empirical testing of applications on a mobile platform. The users praised the 3D visualization for the portrayal of terrain and other 3D objects (buildings), but their personal preferences varied depending on age and technological skills. The possibility to choose a data presentation according to the users' preference and practice is a consideration we pay attention to in preparing our visualizations.