Spatial prediction of soil penetration resistance using functional geostatistics

Abstract

Knowledge of agricultural soils is a relevant factor for the sustainable development of farming activities. Studies on agricultural soils usually begin with the analysis of data obtained from sampling a finite number of sites in a particular region of interest. The variables measured at each site can be scalar (chemical properties) or functional (infiltration water or penetration resistance). The use of functional geostatistics (FG) allows to perform spatial curve interpolation to generate prediction curves (instead of single variables) at sites that lack information. This study analyzed soil penetration resistance (PR) data measured between 0 and 35 cm depth at 75 sites within a 37 ha plot dedicated to livestock. The data from each site were converted to curves using non-parametric smoothing techniques. In this study, a B-splines basis of 18 functions was used to estimate PR curves for each of the 75 sites. The applicability of FG as a spatial prediction tool for PR curves was then evaluated using cross-validation, and the results were compared with classical spatial prediction methods (univariate geostatistics) that are generally used for studying this type of information. We concluded that FG is a reliable tool for analyzing PR because a high correlation was obtained between the observed and predicted curves (R 2 = 94 %). In addition, the results from descriptive analyses calculated from field data and FG models were similar for the observed and predicted values.

Establishment of homogeneous zones in a soil of alluvial origin

“…According to Cambardella et al (1994), the variables with strong spatial dependence are more influenced by soil formation factors ( Table 2). The evaluation of UK interpolation was intermediate, with a range of cross-validation coefficients between 0.53 and 0.73, i.e., lower than those found by Varón-Ramírez, Camacho-Tamayo and González, (2018), and similar to those found by Cortés, Camacho-Tamayo and Giraldo (2016). The pH interpolation range fluctuated between 3.5 and 7.4, with a mean of 5.49 and a standard deviation of 0.98.…”

“…To establish the goodness of the predictions made by different methods, cross-validations were made, and the best model was selected by the highest cross-validation coefficient (CVC), the smallest root of the mean square error (RMSE), the reduced error (RE) value closer to zero, the value of the standard deviation of the reduced errors (SDRE) closer to one (Faraco et al, 2008;Johann et al, 2010;Cortés;Giraldo, 2016) and the best degree of spatial dependence (DSD) of each of the chemical soil properties, according to the classification proposed by Cambardella et al (1994). These authors consider the degree of spatial dependence as strong when DSD ≤ 25%, moderate when 25 < DSD ≤ 75%, and weak when DSD > 75%.…”