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 (R2 = 94 %). In addition, the results from descriptive analyses calculated from field data and FG models were similar for the observed and predicted values.
Highlights
Agricultural soils behave like a complex system that accumulates and transmits air, water, nutrients and heat to microorganisms and plants (Orjuela-Matta et al, 2012)
The applicability of functional geostatistics (FG) as a spatial prediction tool for penetration resistance (PR) curves was evaluated using cross-validation, and the results were compared with classical spatial prediction methods that are generally used for studying this type of information
Descriptive analysis of soil penetration resistance The soil examined for this study presents optimal root development at depths greater than 10 cm (Figure 1)
Summary
Agricultural soils behave like a complex system that accumulates and transmits air, water, nutrients and heat to microorganisms and plants (Orjuela-Matta et al, 2012). Soil penetration resistance (PR) is a good indicator of soil physical quality once it is measured and interpreted, and correlated with other soil attributes (Guimarães et al, 2013). Data analyses are performed by calculating univariate descriptive measures (location and dispersion), obtaining distribution graphs (histograms and box plots), performing multivariate analyses (correlations, classification and principal components) and using univariate geostatistical analyses (variogram estimation, Kriging prediction, and mapping). These tools are used to describe the spatial behavior of soil attributes (Medina et al, 2012) and to determine the existence of different zones that require specific management to achieve the expected agricultural yield (Camacho-Tamayo et al, 2013)
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