International ‘EURASIA Natural Nutrition and Healthy Life Summit’ 2017 GIS Based Geostatistical Prediction of Site Specific Organic Matter Variability on the Apple Orchard First Author*, Second Author, Third Author and Others *Corresponding author: Name of Organization, Department, City, Country, Tel., Fax., E-mail Aim Considerable relations have recently been given to the environmental hazardous caused by agricultural chemicals such as excess fertilizers. Evaluating of GIS based prediction of site specific variability of chemical fertilizers based on soil organic matter (SOM) values will provide valuable data especially for large agricultural areas. In this study, spatial variability of soil organic matter based on fertilizer levels was examined on the agricultural apple area. Site specific variations of SOM on this area were predicted by performing GIS based geostatistical analyst module. Introduction In many studies, importance of spatial analysis of soil fertility properties has been emphasized (Mahinakbarzadeh et al., 1991; Yanai et al., 2006; Xingmei et al., 2007). Monitoring of spatial changes of soil properties on large agricultural areas will provide valuable data for precision agriculture and environmental aspect. Recently, variable-rate fertilizer application based on measured patterns of plant-available nutrients had become a widely employed practice aimed at increasing crop yield and nutrient-use efficiency (Wollenhaupt et al., 1994; Schepers et al., 2000; Aggelopoulo et al., 2011). However, the main problems for additional soil sampling and analyses are economical limitations and other non linear properties of agricultural fields. Thus, classical statistical techniques are not appropriate enough to characterize the spatial variability of soil (Goovaerts, 1997; Nayanaka et al., 2010). Geostatistics offers an alternative approach where spatial correlation of variables can be quantified through variogram analysis (Webster and Oliver, 2001; Neilson, 2008; Nayanaka et al., 2010) and subsequent accurate mapping. Hence, geostatistical methods supplies proper solves for non linear systems (Webster, 1985; Mallants et al., 1996; Guo et al., 2001; Chen et al., 2004; Vitharana, 2008). It has been emphasized that agricultural consultants have imported spatial and temporal field data into geographic information systems to produce spatial distribution maps. By making use of observed values, through kriging method and semivariogram model that best fit to the data values of non-sampled points can be estimated. As a matter of fact, these geostatistical methods can be tested for environmental aspect or spatial analysis can also be made for many varied conditions (Yost et al., 1982; Karaman et al., 2009; Zhang and Xuan, 2011). These approaches will also help to effective fertilization schemes for optimal yield and quality with reduced environmental pollution. Results Descriptive Statistics for Experimental Fields The coefficient of variance (C.V.), kurtosis and skewness values for site-specific SOM values of top and subsoils on the apple area were calculated, which is the ratio of the standard deviation to mean expressed as a percentage is a useful measure of overall variability (Table 1). The maximum C.V. value of 27 % was measured for organic matter levels in the topsoil. The C.V. value of 24 % was also determined for subsoil. The findings were agreement with the results of a similar study carried out in an apple orchard, in which the C.V. value of 28.6 % was found for SOM (Aggelopoulou, 2011). Wilding (1985) classified the C.V. values as 0-15 (low), 16-35 (medium) and 36 < (high) variable, respectively. Hillel (1980) also classified it as low variability (<10 %, medium variability (10 - 100 %) and high variability (>100 %). The range of C.V. for the area suggested different degrees of heterogeneity among the properties studied (Nayanaka et al., 2010). Testing Results of Geostatistical Parameters Geoistatistical parameters of nugget, sill, nugget/sill and range for site specific SOMtopsoil and SOMsubsoil levels were presented Table 2. In this study, simple kriging method and guassian semivariogram model were determined as the best optimal interpolation method for SOMtopsoil and SOMsubsoil levels. Prediction values were significantly (p<0.01) correlated with measured values for topsoil and subsoil, (r = 0.995) and (r = 0.960), respectivly. Sill values were 0.087 and 0.038 for for SOMtopsoil and SOMsubsoil, respectively. In geostatistical aspect, spatial dependence was defined as the percentage ratio of nugget semivariance to the sill semivariance. In this study, strongly (< 25 %) dependences were observed for both for SOMtopsoil and SOMsubsoil values. The maximum range was reached at 15 m for SOM level at the topsoil layer, whereas it was 13 m for subsoil levels. Nayanaka et al. (2010) have also found that relative nugget effect (Nugget/Sill) for soil organic C which indicates the proportion of spatially unstructured variation in relation to the total variation, was 9.28 %. According to Cambardella (1994), this is a strongly structured spatial dependence (<25%). On the other hand, the spatial distribution maps were constructed by using Simple Kriging Method (SKM) with guassian semivariogram model. Based on the selected kriging method and semivariogram model, SOM levels were spatially varied within the study area. Three dimensional and contour maps of site specific SOM levels for topsoil and subsoil of the apple area indicated the importance of spatial variability of SOM. The 2D and 3D distribution maps and cross-validation charts for the related elements have been given in Figure 1. Conclusion Soil organic matter (SOM) content is one of the main factors to be considered in the evaluation of soil health and fertility (Marchetti et al., 2012). In this study, the experimental data concerning with topsoil and subsoil organic matter levels were analyzed by using computer based geostatistical interpolation method. As a result of the descriptive statistics, a great spatial variability occurred in SOM levels on the apple area. Geostatistical analysis technique was used for predicting the spatial structure of SOM levels for the experimental area. The spatial distribution maps were constructed by using Simple Kriging Method (SKM) with guassian semivariogram model. Based on the selected kriging method and semivariogram model, SOM levels were spatially varied within the study area. The results have indicated that spatial distribution faces of SOM were adequately predicted based on the suitable interpolation method. Kriging maps, three dimention visualitation and related semivariogram model of guassian demonstrated the spatial pattern of site specific levels of SOM in the experimental area. OM% (a) Materials and Methods The study was conducted in an apple area in Konya, Turkey, located on a flat plain. The soil samples were systematically taken from the study area. The grid system (20 m x 10 m) was used for locating the sample position. Fourty five soil samplings were taken at two depths (0-25 cm and 25-50 cm), the distance on the Y direction is 10 m and in the X direction is 20 m. The soil samples were prepared for analysis, and some physical and chemical analyses were made in the samples by routine methods. The processes of definitive statistical information of the chemical properties of soil samples and sorting of the excess values were done by SPSS 11.5 statistics program. Geostatistical analyses of research data were studied by Geostatiscial analyst module of ArcMap 9.1 GIS software by ESRI (ESRI, 2005). SOM data measured for topsoil and subsoil were analyzed through kriging analysis using geostatistical interpolation method of guassian. In the soil samples, organic matter contents were determined by the method of (Walkley, 1947). Determinations were also made for saturation percent (Richards, 1954), CaCO3 (Allison and Moodie, 1958), pH (Jackson, 1958), electrical conductivity (E.C.) (Richards, 1954) and available phosphorus analysis (Olsen et al., 1954) for both topsoil and subsoil samples. In the experimental topsoils; saturation percent was 67.10 %. Average value of CaCO3 was 34.9%, pH was 6.81, available soil phosphorus was 18.85 kg P2O5 da-1 and EC was 407 µmhos cm-1. In the subsoils; saturation percent was 66.00 %. Average value of CaCO3 was 46.9%, pH was 7.25, available soil phosphorus was 12.81 kg P2O5 da-1 and EC was 427 µmhos cm-1. (a) Topsoil OM % (b) (b) Subsoil Fig. 1. Kriging maps showing distribution of organic matter levels and cross-validation charts for (a) topsoil and (b) subsoil