P.V.R.M. REDDY*, K.V. NAGA MADHURI AND T.N.V.K.V. PRASAD
Institute of Frontier Technologies, RARS, ANGRAU, Tirupati-517 502, Chittoor Dt., A.P.
Soil nutrients denotes the soil fertility status and play a vital role in crop production for sustainability. Based on geo-statistics and GIS, the spatial variation of pH, EC, available nitrogen, phosphorus, potassium and DTPA extractable micronutri-ents in the soils of Ramakuppam mandal of Andhra Pradesh was studied. The spatial variation of available nitrogen, phosphorus, potassium and micro nutrients were greatly affected by soil structural factors. The spatial distribution of soil nutrients in Ramakuppam mandal was intuitively characterized by Kriging interpolation. It is very important to understand the spatial distri-bution of soil nutrients, which will provide guidance for adjusting agricultural management measures in general and fertilizer application in particular.
Spatial Analysis, Geo-Statistics, Soil Nutrients, Kriging, Soil Nutrient Maps
The introduction of high yielding varieties and chemical fertilizers during the post independence era in India made a great influence on Indian soils (Bouma and Finke, 1993; Cattle et al., 1994; Yao et al., 2005) and soil nutrients leading to a high spatial heterogeneity. The spatial heterogeneity is the main factor that influences the yield and quality of crops (Eghball and Schepers, 2003), which also forms an important basis of agricultural management and the foundation of soil resource management. Therefore, it is of greater significance to strengthen the study of spatial heterogeneity of soil nutrients to realize the spatial layout of agricultural production and also to provide the basic information and suggestions for food production and land use planning. In recent years with the advancement of ICTs, the GIS and remote sensing, technologies have been extensively used in the research of soil science, especially in the study of the spatial variability of soil properties (Wang et al., 2003; Si et al., 2009; Liu et al., 2010; Qiu et al., 2010; Zhang et al., 2010), which has provided effective guidance for agricultural production. In this paper, Ramakuppam mandal of Chittoor District of Andhra Pradesh was selected for studying the spatial heterogeneity of soil nutrients quantifiably using Kriging interpolation technique, which would be of great significance for the effective use and management of soil nutrients, and also to provide a reference point for the application of fertilizers.
One thousands and fifty two surface soil samples were collected at the rate of one sample per every 10 hectares of arable land in 39 villages with the help of Global Positioning System (GPS).Samples were then kept in labeled plastic bags and brought to the laboratory for analyses. The soil samples were air-dried and sieved and passed through 2 mm mesh sieve.
The available nitrogen was determined by alkaline permanganate method outlined by Subbaih and Asija (1956) and the results are expressed in kg ha-1.The available phosphorus content was determined by extracting the soil with 0.5M NaHCO3 (Olsen et al., 1954) and estimated by developing blue colour using ascorbic acid as reductant on colorimeter (Olsen and Watanabe, 1965). Available potassium in the soils was extracted by neutral normal ammonium acetate and determined by the flame photometer (Jackson, 1973).The available micronutrients viz., Zn, Cu, Fe and Mn were determined in the DTPA extract of soil (pH 7.3) using Atomic Absorption Spectrophotometer as outlined by Lindsay and Norwell (1978).
Soil variation is spatial variable and this has been recognized for many years (Burrough, 1993). Quantification of spatial variability of soil fertility parameters is essential
for formulating land management and to increase fertilizer utilization efficiency. Hence, in this study the spatial distribution of soil properties namely pH, EC, available macro and micro nutrients were assessed. Spatial variability maps were prepared through Kriging interpolation technique. Kriging interpolation theory is a method of interpolation which predicts unknown values from data observed at known locations. This method uses variogram to express the spatial variation, and it minimizes the error of predicted values which are estimated by measuring the spatial distribution of the predicted values. In this paper, semivariance analysis of soil nutrients was studied using the ArcGIS10.3 software to do the Kriging interpolation.
Spatial distribution characteristic of soil nutrients
Spatial distribution of different soil parameters like pH, EC, available nitrogen, phosphorus, potassium and DTPA extractable micronutrients were determined by using Kriging interpolation technique in ArcGIS10.3 software. The results were discussed here under
It was observed from Fig. 1 that pH values mainly vary from 5.2 to 9.2 in 39 villages of the mandal showing variation from slightly acidic to highly alkaline soils. Out of the 1052 samples analysed 75 samples were slightly acidic, 648 samples were neutral, 319 were slightly alkaline and the remaining 10 samples were highly alkaline (Table 1). The electrical conductivity of the samples were presented in Table 1 and depicted in Fig. 2 that showed EC range from 0.02 to 1.43 indicating that the entire area was suitable for crop cultivation.
Spatial distribution of available Nitrogen was presented in Table 1 and also depicted in fig.3 that showed that entire area (1048 samples out of 1052) was under low nitrogen status with a range of 70 to 275 kg ha-1. Only 4 samples recorded medium status of available nitrogen (290-375 kg ha-1).
The available phosphorous in the study area was presented in Table 1 that showed that there was a wide variability, with a range from 2.5 to 912.5 kg ha-1. The spatial distribution for phosphorus showed that 267 samples recorded low, 241samples recorded medium and 544 samples recorded high for available phosphorus.
However, when these samples were grouped on village basis they fell in to medium and high range as depicted in fig. 4. The spatial distribution of available potassium was presented in Table 1 that showed that the range was from 1 to 1125 kg/ha. The sample distribution revealed that 518 samples recorded low, 424 samples recorded medium and only 110 samples recorded high for available potassium. On grouping all the villages they fell under low available potassium status and as depicted in fig. 5
The available zinc status in the study area revealed that a significant number of samples (353 out of 1052) showed low zinc content amounting to 33.6 per cent of the total samples and the remaining 66.4 per cent samples (699) recorded sufficient zinc content. The range of available zinc in the study area was from 0.03- 6.5 mg kg-1 soil (Table 2 and Fig. 6). Available iron status of the study area ranged from 0.1 to 28.6 mg kg-1 soil and is presented in Table 2. The spatial distribution of available iron depicted in Fig. 7 revealed that 654 (62.2 %) samples recorded more than the critical limit for available iron and the remaining 398 (37.8 %) samples were having the available iron at below the critical limit. The range of available copper was from 0.01 to 13.24 mg kg-1 soil in
the study area. For available copper, 976 (92.8 %) samples recorded above the critical limit and 76 (7.2%) samples recorded below the critical limit (Table 2). The micronutrient analysis report for available manganese revealed that 1039(98.8%) samples recorded above the critical limit where as only 13(1.2 %) samples recorded below the critical limit with a range from 0.8 to 19.8 mg kg-1soil. The soil samples were grouped according to the availability and depicted in Fig.8 for copper and Fig.9 for manganese.
This paper studied the spatial variability of soil nutrients in Ramakuppam mandal of Andhra Pradesh by using geo-statistic and geographical information systems. The analysis report of 1052 soil samples collected from Ramakuppam mandal comprising 39 villages revealed that the soil reaction (pH) was slightly acidic to neutral in most of the soils and the electrical conductivity (EC) was normal in the entire study area.
Out of 1052 samples,
1. 99.6 per cent samples were low, 0.4 per cent were medium and 0per cent were high in available Nitrogen
2. 25.4 per cent were low, 22.9 5 were medium and 51.7 per cent were high in available Phosphorus.
3. 49.2per cent samples were low, 40.3 per cent were medium and 10.5 per cent samples were high in available Potassium.
4. 66.4per cent sample were sufficient and 33.6 per cent samples were deficient in Zinc.
5. 62.2 per cent samples were sufficient and 37.8per cent were sufficient in Iron.
6. 92.8 per cent sample were sufficient and 7.2 per cent samples were deficient in Copper.
7. 98.8 per cent samples were sufficient and 1.2 per cent samples were deficient in Manganese.