Data-intensive technologies such as precision agriculture require new approaches for acquisition of soil data on landscapes. We compared the ability of near infrared (NIR; 400–2500 nm) and mid infrared (MIR; 2,500– 25,000 nm) spectroscopy for field-scale acquisition of soil fertility parameters. Samples were obtained in a grid pattern (25 m spacing) from the surface (0–10 cm) and sub-surface (1...

Aiming to evaluate the soil textural classes distribution and the spatial correlation between the soil textural classes and both infiltration rate and salinity in an alluvial area, in Pernambuco State “agreste” region, indicator geostatistics has been applied, adopting a binary distribution based on the presence/absence of loam soils. Considering sampling points and test locations along the mai...

Soils of riparian wetlands are highly effective at phosphorus (P) sorption. However, these soils exhibit extreme spatial variability across riparian zones. We used a spatially explicit sampling design in two riparian wetlands in North Carolina to better understand the relationships among P sorption, soil properties, and spatial variability. Our objectives were to quantify patterns of spatial va...

The aim of this paper is to propose a methodology combing multi-temporal X-band SAR images (TerraSAR-X) with continuous ground thetaprobe measurements, for the retrieval of surface soil moisture and texture at a high spatial resolution. Our analysis is based on seven radar images acquired at a 36° incidence angle in the HH polarization, over a semi-arid site in Tunisia (North Africa). The soil ...

Spatially resolved soil parameters are some of the most important pieces of information for precision agriculture. These parameters, especially the particle size distribution (texture), are costly to measure by conventional laboratory methods, and thus, in situ assessment has become the focus of a new discipline called proximal soil sensing. Terahertz (THz) radiation is a promising method for n...

Bacterial diversity in transgenic and nontransgenic corn rhizospheres was determined. In greenhouse and field studies, metabolic profiling and molecular analysis of 16S rRNAs differentiated bacterial communities among soil textures but not between corn varieties. We conclude that bacteria in corn rhizospheres are affected more by soil texture than by cultivation of transgenic varieties.

An artificial neural network (ANN) model was employed to predict the soil cohesion and soil internal friction angle. The soil samples were collected from different cultivated sites in seven regions in Saudi Arabia. Direct shear box method was used to determine soil cohesion and soil internal friction angle. The input factors to ANN model were soil dry density, soil moisture content and soil tex...