Scale-Dependent Field Partition Based on Water Retention Functional Data

Abstract

Functional data are being used increasingly in recent years and in many environmental sciences, such as hydrology applied to agriculture. This means that the output, instead of a scalar variable represented by a spatial map, is given by a function. Furthermore, in site-specific management, there is a need to delineate the field into management areas depending on the agricultural procedure and on the scale of application. In this paper, an approach based on multivariate geostatistics is illustrated that uses the parameters of Dexter’s water retention model and some soil properties to arrive at a multiscale delineation of an agricultural field in Iran. One hundred geo-referenced soil samples were taken and subjected to various measurements. The volumetric water contents at the different suctions were fitted to Dexter’s model. The estimated curve parameters plus the measurements of the soil variables were transformed into standardized Gaussian variables and the values transformed were subjected to geostatistical cokriging and factorial cokriging procedures. These results show that soil properties (organic carbon, bulk density, saturated hydraulic conductivity and tensile strength of soil aggregates) influence the parameters of Dexter’s model, although to different extents. The thematic maps of both soil properties and water retention curve parameters displayed a varying degree of spatial association that allowed the identification of homogeneous areas within the field. The first regionalized factors (F1) at the scales of 508 m and 3000 m made it possible to provide different delineations of the field into homogeneous areas as a function of scale, characterized by specific physical and hydraulic properties. F1 at a short and long distance could be interpreted as “porosity indicator” and “hydraulic indicator”, respectively. Such type of field delineation proves particularly useful in sustainable irrigation management. This paper emphasizes the importance of taking the spatial scale into account in precision agriculture.