## 1 Introduction
### Uncertainty Visualization  
The revised and modified spatial data quality research agenda proposed by National Center for Geographic Information and Analysis (NCGIA) [28] also included a role of data quality visualization in decision-making and dispute resolution. This agenda established grounds for further research and strongly influenced activities in data quality visualization for the next decades, including conceptual models, computation of uncertainties, as well as the investigation and evaluation of visualization methods themselves [42]. [PERSON] et al. [43] stated that most research directed towards uncertainty visualization had focused on developing representation methods [44; 45], on software applications for the display of uncertainty [46], and on developing the uncertainty visualization theory [47; 48]. Less has been done on the empirical evaluation of the role of uncertainty in geographic decisions and, more specifically, on the role of maps and uncertainty representation in that decision making. [PERSON] and [PERSON] [49] have demonstrated that visual form can improve the communication about spatial data uncertainty within spatial analysis and spatial decision support. On the basis of their review and synthesis of the literature on uncertainty visualization, [PERSON] et al. [43] identified seven core challenges requiring an interdisciplinary effort to be accomplished. One of these challenges dealt directly with the understanding of how knowledge of information uncertainty influences information analysis,decision making, and decision outcomes. Despite reasonable amounts of work done in the field of uncertainty visualization methods [50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61] and the testing of the impact of visualization on decision making [62; 63; 64; 65; 66; 67; 68], there is still a wide gap between the uncertainty visualization theory and widely accepted use of uncertainty representation within the decision-making process. The same conclusions have been reached in the field of visualization of special data uncertainties related to precision agriculture [69; 70; 71; 72]. Specifically, uncertainty visualizations were applied in tasks such as the sustainability of tropical fodder cultivation [69], in support of weed management [70], seasonal climate estimates on a global scale [71], and estimating the amount of biomass in individual fields [72]. To overcome the gap between the theory of data quality visualization and real use, several authors have proposed visualization typologies for spatial data in general [48; 73] and for selected contexts of use [47; 74; 75]. [PERSON] et al. [64] evaluated the usability of visualization methods and proposed a methodology and a web-based tool (uncertainty visualization selector) to determine suitable methods. The selection is a decision tree process based on the following parameters: data type, data format, uncertainty type, and interaction. If the \"fitness of use\" concept is accepted (see above), we need to focus on the current use of uncertainty visualization in the area of interest. [PERSON] et al. [76] elaborated a meta-analysis focusing on the role of visualizations in agricultural decision support systems (DSSs). They included several types of supportive visualization techniques including maps and uncertainty/quality visualization, aiming at design guidelines for a better and more user-optimised concept of DSS. Generalising their findings, the display of uncertainty is rather exceptional in agricultural data and, if applied, it should be provided in a cognitively plausible way. In other words, it is crucial to display the quality data in a way the end users can find the added value in terms of their decision making. When visualizing uncertainty, we are no longer limited only by the classic 2D cartographic visualization. Several approaches and techniques have been proposed for 3D visualization of phenomena related to the precision agriculture domain (e.g., [9; 15; 77; 78; 79; 80; 81]).