Abstract
Studies that seek to identify a smaller number of soil attributes that represent others can generate less expenditure of time and financial resources for monitoring cultivated areas. Thus, this study aimed to analyze the spatial distribution and spatial autocorrelation of physical attributes of an Oxisol (Latossolo Vermelho Amarelo, Brazilian Soil Classification System). The evaluated attributes consisted of soil density (SD), total porosity (TP), free porosity (FP), field capacity (FC), permanent wilt point (PW), and total water availability (TW). Semivariogram adjustments and semivariance estimates were performed to characterize the structure and magnitude of the spatial dependence of soil attributes. The attributes were distributed on thematic maps and the spatial autocorrelation was estimated by the Moran index, which quantifies the degree of autocorrelation. TP showed a high positive correlation with PWP. Soil TW showed a high positive correlation with SD and a high negative correlation with FP. In turn, FP showed a high negative correlation with SD. The results showed spatial dependence for all attributes, standing out the apparent soil density and permanent wilt point, which were good evaluators of strong spatial dependence.
Highlights
Changes in soil structure, evidenced by alterations in its density, affect different soil physico-hydric attributes (Tavanti et al, 2020)
Spatial autocorrelation can be defined as the coincidence of similar values in close locations or even the absence of randomness of a variable due to its spatial distribution (Neves et al, 2015)
Two forms of spatial autocorrelation can occur: a positive autocorrelation, when high or low values for a random variable tend to cluster in space, and a negative autocorrelation, when a dissimilarity is found in the data between the high and low values spatially distributed (Anselin et al, 2006; Neves et al, 2015)
Summary
Changes in soil structure, evidenced by alterations in its density, affect different soil physico-hydric attributes (Tavanti et al, 2020). These attributes include total porosity, pore diameter distribution, aeration porosity, water storage and availability for plants, and water dynamics on the surface and in the soil profile. All these attributes are important but evaluating them all together is not a quick and cheap task. Two forms of spatial autocorrelation can occur: a positive autocorrelation, when high or low values for a random variable tend to cluster in space, and a negative autocorrelation, when a dissimilarity is found in the data between the high and low values spatially distributed (Anselin et al, 2006; Neves et al, 2015)
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