Soil physical and chemical properties and crop yields vary spatially and temporally on different scales. The variability is largely influenced by pedogenesis processes (Gilliam and Dick, 2010; Moradi et al., 2016), topography (Jankowski et al., 2011), and agricultural practices including tillage operations, compaction, chemical application, and harvesting (Alaoui et al., 2011; Gajda et al., 2016; Ozpinar and Ozpinar, 2015; Schjønning et al., 2009). Knowledge about soil variability is essential in precise determination of the most appropriate and localized management practices and amendments to improve and align soil conditions and quality for an effective use of water and nutrients and crop growth (Bölenius et al., 2017; Kumhálová and Matějková, ۲۰۱۷; Usowicz and Lipiec, 2017). For example, variable-rate management practices such as fertilization and irrigation based on spatial data of chemical and soil water status (Sadler et al., 2005; Pedrera-Parrilla et al., 2016; Mubarak et al., 2016) help to limit the use of agricultural chemical and water and to reduce leaching and environmental pollution (Adamchuk and Viscarra Rossel, 2011; Bogunovic et al., 2014; Hedley and Yule, 2009). Further, analysis of the spatial dependency of soil water status along with weather conditions are key issues for modelling soil water dynamics and balance (Awe et al., 2015; Kędzior and Zawadzki, 2016; Schwen et al., 2014). The spatial distribution of different soil properties can be evaluated by classical and spatial statistics using direct semivariograms and cross-semivariograms (Goovaerts, 1999; Webster, 2008). Semivariograms describe the dependence of the values of a given variable on the distance between the sampling sites and thereby the spatial structure of the variation. Thus, they help in designing a sampling setup including the number of samples required for adequate description of the soil and yield in agricultural areas (Jabro et al., 2010; Moradi et al., 2016). When different variables are related, their joint spatial patterns can be evaluated by cross-semivariograms. Cross-semivariogram data and maps obtained with the co-kriging procedure allow prediction of time-consuming and/or costly variables from those measured more easily. Using cross-semivariograms, Jabro et al. (2010) found that soil penetration resistance was spatially correlated with water content, total porosity, and saturated hydraulic conductivity. The study by Walter et al. (2002) showed spatial interdependence between weed species density and soil properties such as clay, phosphorus contents and pH, and the extent of the spatial dependence varied among the study years. However, little information is available about the spatial and inter-annual variability of crop yields and soil properties, especially on a field scale, although it is the main methodological means for implementing precision agriculture technology of different intensity to compensate and improve soil conditions for strengthening crop growth (Diacono et al., 2013; Usowicz et al., 2009; Webster, 2008).