A Geostatistical Analysis of Soil Properties in the Davis Pond Mississippi Freshwater Diversion

2012 ◽  
Vol 76 (3) ◽  
pp. 1107-1118 ◽  
Author(s):  
Filip Kral ◽  
Ron Corstanje ◽  
John R. White ◽  
Fabio Veronesi
Keyword(s):  
Soil Properties ◽  
Geostatistical Analysis ◽  
Freshwater Diversion

scholarly journals Geostatistical Analysis of Yield, Soil Properties and Crop Management Practices in Paddy Rice Fields

2004 ◽  
Vol 7 (2) ◽  
pp. 230-239 ◽  
Author(s):  
Tatsuya Inamura ◽  
Kei Goto ◽  
Michihisa Iida ◽  
Kazuyoshi Nonami ◽  
Hiromo Inoue ◽  
...  
Keyword(s):  
Soil Properties ◽  
Management Practices ◽  
Crop Management ◽  
Paddy Rice ◽  
Geostatistical Analysis ◽  
Rice Fields

scholarly journals Spatial Variability and Geostatistical Analysis of Soil Properties in the Diversified Cropping Regions of Bangladesh Using Geographic Information System Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Md. Zulfikar Khan ◽  
Md. Rafikul Islam ◽  
Ahmed Bin Abdus Salam ◽  
Tama Ray
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Nutrient Management ◽  
Sampling Site ◽  
Geostatistical Analysis ◽  
Kriging Interpolation ◽  
Total N ◽  
Study Region ◽  
Site Specific ◽  
Classical Statistics

Mapping of soil properties is an important operation as it plays an important role in the knowledge about soil properties and how it can be used sustainably. The study was carried out in a local government area in Bangladesh in order to map out some soil properties and assess their variability within the area. From the study area, a total of 92 soil samples (0–20 cm) were collected from different cropping patterns at an interval of 2.2 × 2.2 km2 on a regular grid design. A portable global positioning system (GPS) was used to collect coordinates of each sampling site. Then, soil properties, that is, pH, electrical conductivity (EC), soil organic carbon (SOC), total nitrogen (Total N), and soil available nutrients (P, K, and S) were measured in the laboratory. After the normalization of data, classical statistics were used to describe the soil properties, and geostatistical analysis was used to illustrate the spatial variability of the soil properties by using kriging interpolation techniques in a GIS environment. Results show that the spatial distribution and spatial dependency level of soil properties can be different even within the small or large scale. According to cross-validation results, for most soil properties, the kriging interpolation method provided the least interpolation error. The generated maps of soil properties that indicate soil nutrient status over the study region could be helpful for farmers and decision-makers to enhance site-specific nutrient management. Also, these prototype maps would be helpful for future nutrient and fertilizer applications management, including a site-specific condition to not only reduce the cost of input management but also prevent any environmental hazard. It also demonstrates that the effectiveness of geostatistics and GIS techniques provided a powerful tool for this study in terms of regionalized nutrient management.

GEOSTATISTICAL ANALYSIS OF SOIL PROPERTIES OF MID-WEST TAIWAN SOILS

Soil Science ◽  
1997 ◽  
Vol 162 (4) ◽  
pp. 291-298 ◽  
Author(s):  
Yi-Ju Chien ◽  
Dar-Yuan Lee ◽  
Horng-Yuh Guo ◽  
Kun-Huang Houng
Keyword(s):  
Soil Properties ◽  
Geostatistical Analysis

An analysis of variability in the activity of nitrifiers in a soil under pasture .II. Some problems in the geostatistical analysis of biological soil properties

Soil Research ◽  
1991 ◽  
Vol 29 (1) ◽  
pp. 109 ◽  
Author(s):  
RGV Bramley ◽  
RE White
Keyword(s):  
Soil Properties ◽  
Spatial Variation ◽  
Sampling Design ◽  
High Variability ◽  
Geostatistical Analysis ◽  
Sample Variance ◽  
Geostatistical Methods ◽  
Sample Data ◽  
Principal Directions ◽  
The Relationship

An investigation of spatially dependent variability in the activity of soil nitrifiers indicated a number of shortcomings in the application of geostatistical methods to the analysis of spatial variation in biological soil properties which inherently have high variability. The relationship between the sill variance (Co+C) and sample variance (s2)), problems in the identification of anisotropic variation, and the effect of the scale of sampling on s2 are considered in relation to the sampling design used for collection of sample data. It is concluded that for minimum complication, sampling designs should be symmetrical in at least four principal directions, and that some sampling sites should be positioned at points between the nodes of the grid to ensure as wide a spread as possible in the distances between pairs of observations.

scholarly journals ESTIMATION OF PHYSICAL AND CHEMICAL SOIL PROPERTIES BY ARTIFICIAL NEURAL NETWORKS

2018 ◽  
Vol 31 (3) ◽  
pp. 704-712 ◽  
Author(s):  
ROBERTO DIB BITTAR ◽  
SUELI MARTINS DE FREITAS ALVES ◽  
FRANCISCO RAMOS DE MELO
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Properties ◽  
Ordinary Kriging ◽  
Interpolation Method ◽  
Soil Samples ◽  
Geostatistical Analysis ◽  
Computational Technique ◽  
Artificial Neural ◽  
Physical And Chemical

ABSTRACT Soil physical and chemical analyses are relatively high-cost and time-consuming procedures. In the search for alternatives to predict these properties from a reduced number of soil samples, the use of Artificial Neural Networks (ANN) has been pointed out as a great computational technique to solve this problem by means of experience. This tool also has the ability to acquire knowledge and then apply it. This study aimed at using ANNs to estimate the physical and chemical properties of soil. The data came from the physical and chemical analysis of 120 sampling points, which were submitted to descriptive analysis, geostatistical analysis, and ANNs training and analysis. In the geostatistical analysis, the semivariogram model that best fitted the experimental variogram was verified for each soil property, and the ordinary kriging was used as an interpolation method. The ANNs were trained and selected based on their assertiveness in the mapping of considered standards, and then used to estimate all soil properties. The mean errors of ordinary kriging estimates were compared to those of ANNs and then compared to the original values using Student's t-Test. The results showed that the ANN had an assertiveness compatible with ordinary kriging. Therefore, such technique is a promising tool to estimate soil properties using a reduced number of soil samples.

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