Geostatistical-based index for spatial variability in soil properties

Abstract

The assessment of spatial variability of environmental variables such as soil properties is important for site-specific management. A geostatistical index that allows quantifying and characterizing the structure of spatial variability is fundamental in this context. Thus, this study aimed to develop a new spatial dependency index, called the Spatial Dependence Measure (SDM) for the spherical, exponential, Gaussian, cubic, pentaspherical, and wave semivariogram models; and comparing it with some of the indexes available in the literature. The SDM is also dimensionless, in the same way as the Spatial Dependence Index (SDI), also considering more parameters of the semivariogram, when compared to the Spatial Dependence Degree (SPD) and Relative Nugget Effect (NE) indexes. In a simulation data study, it is observed that the SDI and SDM indexes showed an advantage over the SPD (or NE). To exemplify the application of the SDM in the proposal for the classification of soil properties, we used estimates of geostatistical parameters presented in the two studies. The results indicate that the SDM can be a measure that, analyzed together with the SDI, can help to improve the description of the spatial variability structure. Thus, this study expands the number of geostatisitcal-based measures and increases the power of decision on the description of the degree of spatial variability of agricultural and soil attributes.

Spatial and seasonal dynamics of rainfall in subtropical Brazil

“…On the other hand, weak structures of spatial variability are observed in January, March, August, November, and December, which can generate lower quality estimates on the maps. Appel Neto et al (2020) suggest that the SDI and SDM indices should be used together to improve the process of description and classification of the spatial variability structure. Assessing the mean and median annual rainfall in RS, Ananias et al (2021) observe a strong degree of spatial dependence.…”

Directional Differences in Thematic Maps of Soil Chemical Attributes with Geometric Anisotropy

“…Directional trends represent a linear association between the respective values of the soil chemical properties with the coordinates of the X or Y axis, and were assessed by Pearson's linear correlation coefficient (r(x), r(y)), in which values above 0.30 in a module indicate a directional trend [17]. Spatial dependence was assessed by the spatial dependence index (SDI), being classified as weak when SDI ≤ 9%, moderate when 9% < SDI ≤ 20% and strong when SDI ≥ 20% [4,18]. Still, the directional comparison analyses described above were also performed.…”

“…The estimated values of the practical range and the spatial dependence index (SDI) indicated the existence of spatial dependence for all soil chemical properties, with a radius of spatial dependence from 149.57 to 371.89 m, and spatial dependence classified as weak for the soil carbon and calcium contents (SDI ≤ 9%) and moderate for the soil potassium content (9% < SDI ≤ 20%) [4,18].…”

Prediction, mapping, and implication for better soil organic carbon management in Ethiopia

“…Moreover, with fewer data, the OK and IDW models failed to capture the SOC fluctuation. Soil properties change with time and space due to inherent and external influences (Abreu et al., 2003; Apple Neto et al., 2020; Marins et al., 2018; Reichert et al., 2020). Understanding soil quality is critical for optimizing fertilizer use, increasing agricultural output, and reducing environmental risks (Guan et al., 2017; Panday et al., 2018; Tesfahunegn et al., 2011).…”