Spatial Variability of Soil Organic Matter and Cation Exchange Capacity in an Oxisol under Different Land Uses - Paz Ferreiro, Jorge; Pereira De Almeida, Vicente [UNESP]; Cristina Alves, Marlene [UNESP]; Aparecida De Abreu, Cleide; Vieira, Sidney R.; Vidal Vázquez, Eva (Taylor & Francis, 2016)
Soil properties may exhibit large spatial variability. Frequently this variability is auto-correlated at a certain scale. In addition to soil-forming factors, soil management, land cover, and agricultural system may affect the spatial variability of agricultural soils. Soil organic matter (OM) is an important soil property contributing toward soil fertility and a key attribute in assessing soil quality. Increasing soil OM increases cation exchange capacity (CEC) and enhances soil fertility. We analyzed the impact of land use on the spatial variability of OM and CEC in a tropical soil, an Oxisol, within São Paulo state, Brazil. Land uses were prairie, maize, and mango. Soil samples were taken at 0–10 and 10–20 cm depths at 84 points within 1-ha plots, i.e., 100 m × 100 m. Statistical variability was higher for soil OM than for CEC. The mango plot contained the highest soil OM, whereas prairie the lowest. Also, soil OM and CEC were significantly related at all land use treatments and depths studied. All soil OM data sets and most of the CEC data sets (with two exceptions) exhibited spatial dependence. When spatial variability was present, the semivariograms showed a nugget effect plus a spherical or an exponential structure. Patterns of soil OM and CEC spatial variability (i.e., model type, ranges of spatial dependence, and nugget effects) were different between land uses and soil depths. In general, CEC exhibited a lower spatial autocorrelation and a weaker spatial structure than soil OM. Moreover, soil OM displayed a higher autocorrelation and was more strongly structured at the 0–10 cm depth than at the 10–20 cm depth. Interpolation by kriging or inverse distance weighting (IDW) allowed to illustrate how the spatial variability of soil OM and CEC differed due to land cover and sampling depth. Modeling and mapping the spatial distribution of soil OM and CEC provided a framework for spatially implicit comparisons of these soil properties, which may be useful for practical applications.
Keywords: Geostatistics; kriging; land use; soil monitoring; spatial autocorrelation; spatial structure; tropical soil