## 1 Introduction
### Data Quality and Uncertainty  
The issue of data quality and uncertainty as a general phenomenon in the natural world has been at the centre of the geospatial community since the beginning of geographic data quality research. [PERSON] [30] introduced a broader framework for evaluating geographic data quality and proposed several measures (accuracy, consistency, completeness, and lineage) as essential components to be used as possible dimensions describing geographic data. Other quality dimensions representing particular characteristics of quality have been proposed by scholars [31; 32; 33] and standardisation organisations for both data and services. ISO, OGC (Open Geospatial Consortium), and W3C (World Wide Web Consortium) have agreed on ISO 19157 as a common standardization framework for geographic data quality [25; 34; 35]. In summary, the dimensions of quality depend on the application domain. [PERSON] et al. [36] discusses these dimensions in the context of agriculture and the soil in general.  
The international standard ISO 19157:2013 [25] establishes the principles for describing the quality of geographic data. Among others, it defines components for describing data quality (also known as data quality elements and sub-elements) and a set of data quality measures for use in evaluating and reporting data quality. A variety of measures are based on counting erroneous items, such as if 'number of missing objects in the dataset' equals '5'. However, there are also several measures dealing with the uncertainty of numerical values. The uncertainty-related data quality basic measures according to ISO 19157 are based on the concept of modelling the uncertainty of measurements with statistical methods. None of the uncertainty concepts/expressions presented in ISO 19157 are applicable to the measured point Big Data with respect to the scope presented in this study. The elementary premise of all ISO 19157 uncertainty measures, that is, reference values ('ground truth') to quantify the derivations with a certain probability, is not met.  
The quality of data also plays a crucial role in agriculture. Food and Agriculture Organisation (FAO) stressed the importance of data quality measures (completeness, precision, consistency) for collecting and combining heterogeneous land use data [37]. The FAO has further developed procedures for using statistical techniques and integrating data sources from different regions of the world [38]. The CountrySTAT framework [39], serving as an integration platform for agricultural statistics, has defined the following six dimensions of data quality: relevance and completeness, accessibility and clarity, timeliness, comparability, subjectiveness, and coherence. [PERSON] et al. [40] downsized the quality measures from global to national perspective and proposed an alternative set of quality dimensions encompassing both quantifiable and subjective ones. [PERSON] and [PERSON] [41] developed a meta-study surveying the use of quality measures/dimensions in agricultural applications as well as the context of use. Their observations revealed that the most often mentioned (and probably used) dimensions are accuracy, timeliness, and completeness, followed by consistency and relevancy. The same authors also pointed out that particular measures vary according to the spatial scale (local to global), measured factors (natural and human--soil management practices), and types of devices. Nevertheless, none of the analysed studies dealt with uncertainty of spatially-located data in (precision) agriculture in general and farm machinery in particular.