Assessing the spatial distribution of weeds within a field is key step to success site-specific weed management strategies. Centaurea diluta (knapweed) an emerging that causing major agronomic problem in southern and central Spain because its large size, difficulty controlling it, high competitive ability. The main objectives this study were examine variability C. density two wheat fields by mu...

1 Abstract Automatically generating maps of a measured variable of interest can be problematic. In this work we focus on the monitoring network context where observations are collected and reported by a network of sensors, and are then transformed into interpolated maps for use in decision making. Using traditional geostatistical methods, estimating the covariance structure of data collected in...

The zeolitic threshold method used in previous chapters is replaced with more rigorous geostatistical modeling. With this more sophisticated modeling approach, we examine whether using a threshold cutoff for zeolitic abundance is an appropriate upscaling method for characterizing small scale heterogeneity. Geostatistical techniques are used to develop variograms, kriging maps, and conditional s...

This paper applies a novel Kriging model to the interpolation of stochastic simulation with high computational expense. The novel Kriging model is developed by using Taylor expansion to construct a drift function for Kriging, thus named Taylor Kriging. The interpolation capability of Taylor Kriging for stochastic simulation is empirically compared with those of Simple Kriging and Ordinary Krigi...

In geostatistics, spatial data will be analysed that often come from irregularly distributed sampling locations. Interest is in modelling the data, i.e. estimating distributional parameters, and then to predict the phenomenon under study at unobserved sites within the corresponding sampling domain. The method of universal kriging for spatial prediction was introduced to cover the problem of spa...

In geostatistics, spatial data will be analysed that often come from irregularly distributed sampling locations. Interest is in modelling the data, i.e. estimating distributional parameters, and then to predict the phenomenon under study at unobserved sites within the corresponding sampling domain. The method of universal kriging for spatial prediction was introduced to cover the problem of spa...

BACKGROUND Smoothing methods have been developed to improve the reliability of risk cancer estimates from sparsely populated geographical entities. Filtering local details of the spatial variation of the risk leads however to the detection of larger clusters of low or high cancer risk while most spatial outliers are filtered out. Static maps of risk estimates and the associated prediction varia...

The usage of spatial tools might be helpful in the optimization decision-making regarding soil management, with technologies that assist interpretation information related to fertility. Therefore, present study evaluated variability chemical attributes under an agroforestry system compared a native forest municipality Tomé-açu, Eastern Amazon, Brazil. Soil samples were performed at 36 points ar...

Mapping of groundwater level observations often makes very little use of auxiliary data and is often undertaken simply by manual interpolation or ordinary kriging of the heads. Recently, a number of geostatistical methods have emerged that significantly improve estimates by incorporating the land surface elevation and groundwater flow or drawdown equations. However, at the regional scale heads ...

abstract the quality of soil maps depends upon their ability to show the soils variability. thus, the accuracy of the maps used for crop recommendations is due to the accuracy of soil maps. this study was performed to investigate the amount of soil properties and crop yield spatial variability in s 2 and s3 units of a semi-detailed quantitative suitability map (1:50000 scale) for irrigated whea...