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Geostatistics, a branch of applied statistics helps to analyze, process and represent a spatially distributed variable. It consists of both spatial data (incorporates spatial coordinates) and temporal data for a given parameter. Therefore, geostatistics can be applied to assess both spatial and temporal variability of a soil parameter due to its dependency to a particular point (sampling location) with space and time. Spatial variability assessment of soil fertility parameter through geostatistical techniques is essential for enhancing the nutrient use efficiency. It helps to design site specific nutrient management strategy. So, this article has been written to describe the methodology and applications of geostatistics to analyze the spatial variability of soil properties especially soil fertility properties.

With the advancement of computational facility, computer graphics, software, algorithms, big data science, these tools are increasingly used to solve the modern world’s problems. During last decade, a large number of soil data have been collected to analyse the soil properties. But the analysis of big soil data is a serious problem due to its complex and variable nature. Geostatistics can solve this problem through assessing the variability of large number of soil properties. Soil properties show a very high degree of both spatial and temporal variability depending on the scale (farm, local, regional or continental scale) used for the study. Spatial variability of any soil property arises due to several factors including both intrinsic and extrinsic factors. Intrinsic factors include intrinsic factors of soil formation like variation in soil parent materials, soil topography, geology, soil texture, climate, vegetation etc. On the other hand, common agricultural practices like fertilizer applications, irrigation water management etc. can be regarded as the sources of external variations of the soil property (Vasu et al. 2017). Soil spatial variability also arises due to the complex interactions between these two factors (Liu et al. 2015).

Spatial variability assessment of soil fertility properties at any scale especially at farm scale is very much crucial for soil survey, soil fertility mapping and for precision farming (Stutter et al. 2004). Geostatistics can provide a set of geostatistical tools to assess the variability of a variable at a specified scale, to predict that variable at an unsampled locations and to assess the uncertainty associated with this prediction (Goovaerts, 1999). Therefore, the modern geostatistical tools and techniques like semivariogram, spatial autocorrelogram, various kriging approaches can be applied to determine the spatial variability of the soil property.

In modern agricultural practices, excessive use of chemical fertilizers without considering the spatial variability of soil fertility parameter, creates nutritional imbalance in crop-field and also creates environmental pollution. To solve this problem, appropriate and balanced use of chemical input is necessary. Appropriate use of any chemical input (especially fertilizer containing nutrients) or any nutrient is only possible through the accurate prediction and spatial variability assessment of that nutrient. Geostatistics have the potential to perform these two jobs efficiently.

Application of Geostatistics

Geostatistics can be applied in various disciplines of Science:

1.       Agricultural Science (Soil Science: for mapping of nutrient status, generation of soil fertility maps)

2.       Environmental Science (to study the contaminant level)

3.       Mining industry (for quantification and mapping of the availability of mineral resources)

4.       Meteorological applications (for prediction of climatic variables)

5.       Others.

Comparison between classical statistics and geostatistics

The classical statistical technique normally applied the descriptive statistical tools like mean, median, mode, coefficient of variation etc. to measure the variability of a soil property without considering its spatial dependence to the sampling point. It cannot explain the continuous spatial variability pattern properly. In contrast with this, geostatistics can be successfully employed to draw the spatial variability pattern of a key soil fertility parameter by considering its spatial dependence. It uses the structural property of a semivariogram to predict the value of a soil parameter at unsampled locations using kriging technique.

Steps in geostatistical analysis

Several steps involved in analysing the spatial variability of any parameter through geostatistics. These steps have been described by renowned researcher (Goovaerts, 1999) viz:

1.       Selection of proper geostatistical tools, techniques and algorithms

2.       Quantitative modelling of spatial autocorrelation of soil properties

3.       Numerical prediction and mapping of soil properties

 

4.       Assessment of uncertainty in prediction of a variable.

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Geostatistics.pdf