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.
The infiltration of water into the soil is a necessary parameter for irrigation systems design. Characterizing its spatial behavior allows a site-specific management of water according to soil conditions and crop requirements. The aim of this study is to establish the spatial distribution of infiltration in an Andisol by means of two geostatistical approaches: on the one hand by means of functional kriging, taking as input infiltration curves (obtained after a smoothing stage), and on the other hand by using classical ordinary kriging on the parameters of the Kostiakov and Phillip models. The comparison between these methodologies is carried out taking as a criterion the sum of squared errors of a leave-one-out cross-validation analysis. The results show a high correlation between observations and predictions (R 2 values around 99%), which indicates that the use of functional geostatistics in this context could be a good alternative. Moreover, from a descriptive point of view, we can point out that the contour maps of basic infiltration (BI), cumulative infiltration (Ci), saturated hydraulic conductivity (Ks), and sorptivity (S) obtained with the observed data, as well as the predictions by functional geostatistics, show a very similar behavior, which empirically validates the use of this methodology.
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