The aim of this work was to elaborate a new methodology that can allow for the identification topsoil homogeneous area (tSHA) distribution along land parcels, supporting farmers in keeping low-cost, sustainable, and light logistic management precision agriculture (PA) practices. This paper shows assessment tSHA variability over two production units (PUs), considering radiometric response (optic...

A geostatistical analysis of soil salinity in an agricultural area in the San Joaquin Valley included measurements of electrical conductivity of soil paste extract (EC3 and water content of soil samples supplemented by surface measurements of apparent electrical conductivity (EM”). Prediction of soil salinity at unsampled points by cokriging log@C3 and EMa is worthwhile because EM” measurements...

Spatial variables in the geosciences often display different patterns of variability at different spatial scales. When these variables are cross-correlated, the magnitude of the cross-correlation may differ at the different scales of spatial variability. This scale-dependent spatial variability and cross-correlation can be investigated by geostatistical factor analysis, which consists of four s...

The precision agriculture hopes to manage the variation in soil nutrient status continuously, which requires reliable predictions at places between sampling sites. For the long time, ordinary kriging has been used as one prediction method when the data are spatially dependent and a suitable variogram model exists. However, even if data are spatially correlated, there are often few soil sampling...

The benefits of an integrated geographical information system (GIS) and a geostatistics approach to accurately model the spatial distribution pattern of precipitation are known. However, the determination of the most appropriate geostatistical algorithm for each case is usually neglected, i.e. it is important to select the best interpolation technique for each study area to obtain accurate resu...

The two sources of information commonly available for modeling the top of a structure, depth data from wells and geophysical measurements from seismic surveys, are often Miicult to integrate. W bile, the well data provide t he most accurate measurements of depths there are rarely enough wells to permit an accurate appraisal from well data alone. On the other hand, the seismic data are generally...

Cokriging is the common method of spatial interpolation (best linear unbiased prediction) in multivariate geostatistics. While best prediction has been well understood univariate statistics, literature for case elusive so far. The new challenges provided by modern datasets, being typically multivariate, call a deeper study cokriging. In particular, we deal with problem misspecified cokriging wi...