Current World Environment

Introduction

The conventional method of soil fertility management consider entire fields as a single group of soil and also while calculating requirement of fertilizer as a single field. Recitation of soil spatial variability in the field has a huge difficulty, in the use of latest advanced tools and technologies Viz. Global Positioning Systems (GPS), Geographic Information Systems (GIS) and many others were commenced. Many scientists demonstrated in studies conducted at various locations, GIS is an effective set of tools for to collect, store, retrieve, transform and display spatial data.¹ It is also seen that the scientists working in natural resource management groups has extensively used GIS for the production of soil fertility map of an area that helps to understand the soil fertility status spatially and temporally, which will be useful to calculate the site-specific suggestion for application of the appropriate quantity of fertilizers. Technologies like GPS and GIS allow fields to be mapped precisely and to help in understanding the complex spatial relationships between soil fertility factors.² Noticeably, a prepared soil fertility status map of selected location can help in  guiding the various stakeholders such as the people from farming community, manufacturers  from industrial sectors and  planners in deciding the need of  various  soil available nutrients in a different cropping seasons in the year and making  predictions for  increased  demand based on intensity of crops and cropping pattern.^2,3 stated that the soil properties can vary spatially due to a range of factors viz. parent material, topography, climate, vegetation, and land management. It is also revealed that the spatial variability in soil is essential to pinpoint the nutrient limit zones in relation of production area to lessen the use of nutrients. Therefore, the precision agriculture is mainly depends on the management of spatial variability in soil fertility in agriculture and which is a major constraint for food production.⁴ Therefore, in the present study conventional statistical analysis and ArcGIS tool has been applied to get the information about distribution and spatial variability of soil available nutrients.

Materials and Methods

Study Area

The study carried out in Vandurga Village of Shahapur taluk as geographically lies between 16.64 N to 16.65 N latitude and 76.69 E to 76.70 E longitudes (Fig. 1). The total study area is 40 ha. This village come under the north-east dry zone (Zone-2) of Karnataka and partly irrigated from Krishna River by Shahapur Branch Canal Study area is characterized by undulating to rolling topography and geographically the rock system is granite-genesis complex belongs to Archean period. It’s also enjoys semiarid climates with average annual rainfall 656 mm and minimum temperature of 21⁰C, where maximum temperature is 35 ⁰C.

Figure 1: Location of study area and sampling points  Click here to View figure

Soil sampling and analysis

A total of 25 soil samples (0-20 cm depth) were collected from farmer's fields randomly 2 km radius. Concurrently, from all the sampling point's global positioning data was recorded by GPS (Trimble Juno-3D) device before sowing of Kharif (onset of south –west monsoon) crops to assess the spatial variability in the soil fertility status. All collected soil samples from different locations were taken for laboratory analysis. Before analysis of soil available nutrients [Electrical conductivity (EC), p^H, soil organic carbon (OC), Nitrogen (N), Phosphorous (P₂O₅) and Potassium (K₂O)] all the collected soil samples were air dried and sieved through a 2 mm size brass sieve of BSS(8) and ASTM(10) by following the standard laboratory procedures.^5,6,7,8,9 The laboratory analysis was done to know the fertility status of all soil samples for chemical characteristics.

Database preparation

The entire analysed data were further processed using SPSS and ArcGIS. Initially, conventional statistical analysis was performed using SPSS (version 19) and spatial analysis was carried out using ArcMap GIS (version 9.2). The various maps produced in ArcMap GIS are presented in Fig. 1 and 2. The specific distribution of soil available nutrients was evaluated for normality using Kolmogorov–Smirnov (K-S) test.

Results and Discussion

Descriptive statistics

The descriptive statistics values of all analyzed soil samples from study area for various soil available nutrients (EC, pH, OC, N, P₂O₅ and K₂O) are presented in the Table 1. Mean, maximum, minimum, standard deviation (SD) and coefficient of variation (CV) values for all the available soil nutrients have been calculated for 25 locations of the study area. The obtained results were found in close agreement with¹⁰ in Mukona, Uganda study area. It was also observed that because of regular application of various nutrients in the past the mean values of N, P, K and OC elevated.

Table 1: Descriptive statistics of soil available nutrients.

Test of Distributional Adequacy

The Kolmogorov-Smirnov (K-S) test applied to decide if the analyzed soil samples come from datasets with a specific distribution.¹¹ The applied test is based on the empirical distribution function (ECDF). K-S Test reports has been presented in the Table 2 (a and b). Some outlier’s values were removed as per definition of ^[12] for OC, N, P₂O₅, and K₂O to achieve the more precise outcome from study. K-S Test found consistent for pH, EC, and OC for normal distribution with 0.49, 0.88 and 0.38 probability, respectively and unlikely consistent for  N, P₂O₅ and K₂O. It is also observed that for log normal distribution K-S test is consistent for all available nutrients evaluated for study area.

Table 2a: Kolmogorov-Smirnov (K-S) test report.

Table 2b: Kolmogorov-Smirnov (K-S) test report.

Spatial variability maps of all soil available nutrients were prepared after interpolation of point values by Inverse Distance Weighted (IDW) method and which is presented in Fig. 2 (i to vi). To classify the spatial variability of soil available nutrients in specific locations in study area spatial variability maps prepared and it clearly shows where management of nutrients is required. Similar results also observed by various scientists viz.¹³ used interpolation technique of kriging to prepare the landslide susceptibility analysis map of Kota Kinabalu in Malaysia to locate areas prone to landslides.¹⁴ reported that the degree of accuracy of kriging technique in the prediction of soil properties and the descriptive  tools of semi variogram to characterize  the spatial patterns of continuous and categorical soil attributes.

Figure 2: Spatial distribution maps of soil available nutrients in study area.  Click here to View figure

Conclusions

In the present study, the spatial variability in soil fertility were analysed for Vandurga Village, Yadgir district of Karnataka. This study showed that huge spatial variability in available nutrients in most of the farmer’s field. Few farmers field found deficient in nutrients availability and some found with adequate. This appeared due to lack of balanced application of nutrients by the farmers. Thus suggesting that, the appropriate nutrients applications needed for based of soil test values. The present study reveals that usefulness of GIS to know the spatial variability of soil available nutrients in the study area as well for spatial interpolation and mapping.

Acknowledgement

Authors would like to thanks to Director of Research, University of Agricultural Sciences, Raichur for supporting this work. Special thanks to unknown reviewer of journal who helped us to improve manuscript.

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