Spatial Continuity of Electrical Conductivity, Soil Water Content and Texture on a Cultivated Area With Cane Sugar

Abstract

Spatial variability of soil attributes affects crop development. Thus, information on its variability assists in soil and plant integrated management systems. The objective of this study was to assess the spatial variability of the soil apparent electrical conductivity (ECa), electrical conductivity of the saturation extract (ECse), water content in the soil (θ) and soil texture (clay, silt and sand) of a sugarcane crop area in the State of Pernambuco, Brazil. The study area had about 6.5 ha and its soil was classified as orthic Humiluvic Spodosol. Ninety soil samples were randomly collected and evaluated. The attributes assessed were soil apparent electrical conductivity (ECa) measured by electromagnetic induction with vertical dipole (ECa-V) in the soil layer 0.0.4 and horizontal dipole (ECa-H) in the soil layer 0.0-1.5 m; and ECse, θ and texture in the soil layers 0.0-0.2 m and 0.2-0.4 m. Spatial variability of the ECa was affected by the area relief, and had no direct correlation with the electrical conductivity of the saturation extract (ECse). The results showed overestimated mean frequency distribution, with means distant from the mode and median. The area relief affected the spatial variability maps of ECa-V, ECa-H, ECse and θ, however, the correlation matrix did not show a well-defined cause-and-effect relationship. Spatial variability of texture attributes (clay, site and sand) was high, presenting pure nugget effect.

Sampling density to detect spatial dependence of potassium, calcium and magnesium in sandy soils

“…Defining regions or homogeneous units in the crop leads to improvement of agricultural practices (BARBOSA et al, 2019) through soil fertility management with site-specific techniques (SILVA et al, 2018). This significantly increases efficiency (CARNEIRO et al, 2016), resulting in increased yield (LIMA et al, 2015), input saving and environmental preservation (DALCHIAVON et al, 2017), as it greatly assists in decision making regarding variable rate application and site-specific fertilizer rates (SILVA et al, 2020).…”