for optimum planning, utilization, and management of lands, recognition of spatial variability of soil properties is necessary. this research was conducted to evaluate spatial variability of soil properties in 5000 ha of arsenjan plain in fars province. for this purpose, soil salinity, ph, particle size percentage, sodium, potassium, and organic carbon (oc) were measured on a systematic squared...

The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is important in both global carbon-nitrogen cycle and climate change research. There has been little research on the spatial distribution of SOC and STN at the watershed scale based on geographic information systems (GIS) and geostatistics. Ninety-seven soil samples taken at depths of 0-20 cm were collected dur...

estimating the amount of variation due to spatial dependence at different scales provides a basis for designing effective experiments. accurate knowledge of spatial structures is needed to inform silvicultural guidelines and management decisions for long term sustainability of forests. furthermore, geostatistics is a useful tool to describe and draw map the spatial variability and estimation of...

An accurate estimation of soil organic matter (SOM) content for spatial non-point prediction is an important driving force for the agricultural carbon cycle and sustainable productivity. This study proposed a hybrid geostatistical method of extreme learning machine-ordinary kriging (ELMOK), to predict the spatial variability of the SOM content. To assess the feasibility of ELMOK, a case study w...

Prediction of soil organic carbon (SOC) at unsampled locations is central to statistical modeling of regional SOC stocks. This is often accomplished by applying geostatistical techniques to plot inventory data. However, in many cases inventory data is sparsely sampled (<0.1 plots/km) relative to the region of interest, and it is unknown if geostatistics provides any advantage. Our objective was...