Spatial Variability of Some Soil Properties in Paddy Fields (Case Study: Siyahkal, Guilan Province)

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1 AGRICULTURAL COMMUNICATIONS, 2017, 5(1): Spatial Variability of Some Soil Properties in Paddy Fields (Case Study: Siyahkal, Guilan Province) NAFISEH YAGHMAEIAN MAHABADI *1 AND ZAHRA AMIRI 2 1 Department of Soil Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran. 2 Department of Agricultural Economics, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran. *Corresponding Author: yaghmaeian_na@guilan.ac.ir (Accepted: 20 Dec. 2016) ABSTRACT Characterization of spatial variability of soil properties in the landscape is a key step in precision farming or site specific management. This study was conducted to investigate the spatial variability of some soil properties of paddy fields in Siayhkal region, Guilan province, Iran. To know the spatial variability of soil properties, 40 topsoil samples (0 30 cm) were taken from a regular grid of m in a m plot in paddy field with a uniform farm management. Ordinary kriged maps were achieved for studied soil attributes including percent of clay, silt, sand, organic matter, available P, ph and EC. The results showed a moderate to high variability across the paddy fields for all selected soil properties by values of the coefficient of variation (CV), excluding soil ph, which had low variability with CV value of 1.25%. Variography showed a good spatial structure for the studied variables. The ranges of spatial dependency showed a variation from 65.4 m for clay up to m for silt. The range of all variables was less than the original sampling distance. Therefore, soil sampling in this area can be carried out by decreasing the sampling interval to monitor spatial pattern of soil variation. Kriged maps demonstrated that soil properties did not have a random pattern but had a spatial distribution. The results of this studies provides necessary information for strategy management and precision farming. Keywords: Kriged maps, Kriging, paddy fields, precision farming, spatial dependency, variogram. Abbreviations: CV: Coefficient of Variation; EC: Electrical Conductivity; ME: Mean Error; P: Available Phosphorous; RMSE: Root Mean Square Error; RNE: Relative Nugget Effect; SOM: Soil Organic Matter. INTRODUCTION Several studies have documented that soil properties vary across farm fields; causing spatial variability in crop yields (Gaston et al., 2001; Kaspar et al., 2003; Shukla et al., 2004). Precision farming or site specific management is aimed at managing soil spatial variability by applying inputs in accordance with the sitespecific requirements of a specific soil and crop (Fraisse et al., 1999). Such management practices require quantification of soil spatial variability across the field. Soil properties vary spatially from a field to a larger regional scale affected by both intrinsic (soil forming factors) and extrinsic factors (soil management practices, fertilization, and crop rotation) (Viera et al., 2010), that most often does not run independently. Moreover, soil forming factors and processes can operate at various scales, so that the spatial variability of soil properties may exhibit nested effects. Consequently, either soil properties that are constant over time or those highly dynamic can display a rich spatial variability (Dafonte et al., 2010; Montanari et al., 2011) or spatial and temporal (Vidal Vazquez et al., 2012; Morales et al., 2014). There have been growing interests in the study of spatial variability of soil characteristics using geostatistics since 1970s, as geostatistics were well developed and successful in characterizing the spatial variations of soil characteristics (Liu et al., 2008). In recent years, some researchers focused on using geostatistics and different kriging methods to better understand soil properties spatial variability pattern over small to large spatial scale (Yasrebi et al., 2008; Zhu and Lin, 2010; Sun et al., 2012). Aishah et al. (2010) studied the spatial variability of selected soil chemical properties of paddy soils in the Barat Laut Paddy Project area by geostatistical techniques. Their results showed that the need for a site specific approach in managing

2 AGRICULTURAL COMMUNICATIONS paddy soils particularly with regard to nutrient management. Tola et al. (2016) characterized the spatial variability of soil physicochemical properties and its impact on grass productivity. The results showed high spatial variability across the experimental field based on soil compaction, clay and silt; indicated by values of the coefficient of variation (CV) of 22.08%, 21.89% and 21.02%, respectively. However, low to very low variability was observed for soil EC, sand and ph; with CV values of 13.94%, 7.20% and 0.53%, respectively. Cilic et al. (2012) evaluated the effects of duration of intensive cultivation practices on some soil chemical and physical properties and to characterize spatial variability of soil properties. Their results showed that variation of the soil variables was fairly homogenized in the cultivated fields compared to the native grassland. Relatively few studies have thoroughly investigated the spatial variability of soil properties in paddy field on a large scale (Yanai et al., 2000; Liu et al., 2008) and little information is available on the soil related crop yield potential for monsoon Asia (Yanai et al., 2001). The objectives for this study were (1) to characterize the spatial variability of the measured properties; and (2) to map the spatial distribution of each property. These aspects have not previously been studied at this level in the study area and the study could provide insight for agronomic management and environmental evaluations. MATERIALS AND METHODS Study Area: The area under investigation is located between and N, and and E in Siayhkal region, Guilan province, northern Iran (Fig. 1). The area has a mean annual rainfall of 1448 mm, mean annual temperature of 17.7 C and mean relative humidity of 71.4 %. Based on U.S. Soil Taxonomy (Soil Survey Staff, 2014), the soil moisture and temperature regimes of the area are udic and thermic, respectively. Alluvial plain is the main geomorphology unit of the study area. The land use of the study area is mainly rice (Oryza sativa) paddy fields. Iran Guilan province N E N E Fig. 1. Location of the study area along with sampling points. 8

3 YAGHMAEIAN MAHABADI AND AMIRI Soil Sampling and Analysis: A regular grid sampling method, which is the most usual sampling approach for geostatistical purposes (Webster and Oliver, 2001) was designed; consequently, 40 surface soil samples (0 30 cm) were taken from a regular grid of m in a m plot in paddy field with a uniform farm management (Fig. 1). To determine the location of sampling points, topographic map (1: scale) of the area was digitized in ArcGIS (ESRI, Inc., 2010) software. The geographical position of all sample points was determined by GPS in the field. The soil samples were taken to the laboratory, where they were airdried overnight and then passed through a 2 mm sieve. Particle size analyses were subsequently conducted using hydrometer method (Gee and Bauder, 1986). Extractable phosphate (P) was determined using a colorimetric method (Kuo, 1996). Soil ph was measured in a 1:2.5 soil/kcl mixture potentiometrically (Thomas, 1996) and electrical conductivity (EC) was measured in the saturated extract using a conductimeter (Rhoades, 1982). Soil organic matter (SOM) was determined using a wet combustion method (Nelson and Sommers, 1982). Geostatistical Studies: Geostatistics is based on the theory of a regionalized variables which is distributed in space (with spatial coordinates) and shows spatial autocorrelation. Geostatistical techniques play an important role in the quantitative evaluation of spatial variability within a field (Yang et al., 2011). Geostatistical analyses consist of variography and kriging steps. Kriging is characterized as a method of optimal prediction or estimation in geographical space and is often referred to as being the best linear unbiased predictor (Oliver, 2010). Hence, the collected soil physicochemical data were assigned to the respective geo coordinates and exported to a GIS domain (ArcGIS Software) as a shape file for geostatistical analysis. In kriging, interpolation algorithm was developed. Kriging estimation was made and compared with the measured values. In the variography stage, the spatial structure of each soil properties was characterized by experimental semivariogram γ(h) using the following equation (Goovaerts, 1997): (1) where N(h) is the number of sample value pairs within the distance interval h; z(xi) and z(xi+h) are sample values at two points separated by the distance interval h. Thorough analysis of the semivariogram, a suitable authorized model (spherical or exponential) and its parameters (range, nugget effect and sill) were obtained. The ratio between the nugget effect (C0) and the sill (C0 + C) characterizes the importance of the random component in the whole field spatial variability of the data and provides quantitative measures of spatial dependence for each soil variable. In order to see the relative contribution of nugget to the total variance, the relative nugget effect (RNE) was calculated according to: 100% (2) In the kriging stage, the semivariogram parameters extracted from each fitted model were used to interpolate the values at un sampled locations using ordinary block kriging (Webster and Oliver, 2001). To check the validity of fitted models and to compare values estimated from the semivariogram models with actual values, some error measurements can be used. The most commonly used indices were mean error (ME) and root mean square error (RMSE) (Li and Heap, 2008). RMSE and ME were calculated as follows: (3) (4) where N is the number of observations, Z and are the observed and estimated values at xi point, respectively. Geostatistical analysis, cross validation and kriging were conducted on measured and calculated variables using the GS+ (Gamma Design, 2001) software and kriged maps were generated using this software. RESULTS AND DISCUSSION Statistical Analysis: Descriptive statistics (minimum, maximum, arithmetic mean, median, standard deviation, coefficient of variation (CV), Kurtosis and Skewness) results for each soil characteristic for 40 samples are presented in Table 1. Although normality may not be strictly required in the geostatistical analysis, normal distribution may lead to more reliable results (Webster and Oliver, 2001). Therefore, prior to the spatial analysis, normality of each soil property was checked using the Kolmogorov Smirnov test statistics at 95% confidence level of significance. This revealed that all the soil attributes showed a normal distribution. The skewness values confirmed normality results that in all variables were located between +1 to 1. Also, this pretension was supported with the proximity of mean and median values (Table 1). According to Wilding (1985), all of the soil properties had moderate to high variability, with CVs varying from 15% to 35% and more than 35%, respectively, excluding soil ph which had low variability, with CV less than 15%. This low 9

4 AGRICULTURAL COMMUNICATIONS variability can be caused by intrinsic factors (such as parent material in study area) effect on the behavior of this variables. Soil ph has made low variance and coefficient of variation compared with other soil properties in most studies (Vieira and Paz Gonzalez, 2003; Mahdavi Firoozabadi et al., 2016). The highest coefficient of variation for available P may be attributed to the effect of management factors in studied paddy field. Ayoubi and Jalalian (2006) reported that long term land use and monotonic management can be lead to soil uniformity and reduce the coefficient of variation. The results (Table 1) revealed that sand was the dominant soil texture component in the study area (52.55%), followed by silt (32.02%) and clay (15.42%). As indicated by the values of the coefficient of variation (CV), it was observed that the spatial variability of the sand component across the experimental field was the lowest (CV of 18.31%) compared to silt (CV of 21.02%) and clay (CV of 23.35%). In general, the results revealed that, in terms of soil texture components, the experimental field was relatively homogeneous in sand with a low spatial variability in clay and silt components. Soil Property Sand (%) Clay (%) Silt (%) Organic matter (%) ph EC (ds m Available P (mgkg 1 ) Mean Table 1. Statistics of the studied soil properties. Coefficient of Median Minimum Maximum Variation (%) Standard Deviation Skewness Kurtosis This variation of soil properties might be due to errors in measurements, soil properties, paddy variety, tillage practices, soil mineralogy, clay content, pesticide applications and moisture availability. Results of the descriptive statistics indicated that except for EC, all selected soil properties revealed almost symmetric data, while the distribution of EC observations skewed to the right. Kurtosis results indicated that all soil properties revealed a lower and broader central peak with shorter and thinner tails (Table 1). In comparison to the optimum values of chemical characteristics for paddy requirement, as recommended by Aishah et al. (2010), it is shown that the mean ph for the area was higher than the optimum range ( ), while the mean concentration of organic carbon have already declined the optimum level (2 3%).The mean concentration of available P was lower than the optimum level (> 40 mg kg 1). Geostatistical Analysis: Although descriptive statistical analyses provide useful information about the soil properties distribution, they do not describe the spatial continuity of the data, i.e., the relationship between the value for a property in one location and its values at other locations through the landscape. Hence, the geostatistical techniques were applied to better understand of spatial distribution pattern of the studied variables. Since the variogram surfaces did not show any spatial anisotropy, omni directional variograms for studied properties were calculated and the suitable model was fitted to them (Fig. 2). However, authorized models, interpolation parameters and cross validation statistics of the soil properties are given in Table 2. Table 2. Authorized models, interpolation parameters and cross validation statistics of soil properties. Nugget Spatial Soil Property Model Range Sill RNE (%) RMSE Effect Dependency Level Sand (%) Spherical Moderate Clay (%) Spherical Strong Silt (%) Exponential Moderate Organic matter (%) Spherical Moderate ph Exponential Strong EC (ds m Spherical Moderate Available P (mg kg 1) Spherical Strong ME 10

5 YAGHMAEIAN MAHABADI AND AMIRI Fig. 2. Omni directional variograms for the studied soil properties. In Table 2, exponential and spherical models were fitted for the spatial structure of all variables. Spherical and exponential models are the most common models in the study of soil properties (Cambardella et al., 1994; Vieira and Paz Gonzalez, 2003) and were used successfully to describe spatial variability patterns of soil properties by other researchers (Iqbal et al., 2005; Miao et al., 2006). The semivariograms for soil EC, available P, organic matter, sand and clay content were fitted to spherical model and exponential model was fitted for soil ph and silt content. The range of the model establishes the outer limit at which points in space still interact 11

6 AGRICULTURAL COMMUNICATIONS spatially (Webster and Oliver, 2001). This parameter is a function of scale, distance between sample points and position of landscape (Cambardella et al., 1994). Among various studied attributes, clay and silt contents exhibited the largest and shortest ranges, indicating significant spatial structure extending and 65.4 m, respectively. Such differences in the range were reported for soil properties by other researchers (Cambardella et al., 1994; Vieira and Paz Gonzalez, 2003). However, the range of all variables was less than 200 m, indicating the presence of spatial structure interior the original sampling distance. This finding can be a good indicator for decreasing the sampling distance to monitor these properties in the future. Nugget variance represents the experimental error and field variation within the minimum sampling spacing. The nugget effect can be defined as an indicator of the continuity at close distances. Soil ph and EC indicated approximate a zero nugget. Sun et al. (2003) reported that the nugget effect was decreased with land use alteration and soil management practices. The low nugget effect showed the homogeneous of soil properties. However, the higher nugget effect can be related to the presence of micro heterogeneity on the sampling grids. To better understand the spatial correlation of soil properties, the RNE was calculated. A variable is considered to have a strong, moderate and week spatial dependency if RNE is less than 25%, between 25 75% and greater than 75%, respectively. According to spatial class ratios presented by Cambardella et al. (1994), the spatial dependency level for soil properties had moderate and strong values (25% <RNE < 75% and 25% <RNE, respectively) (Table 2). These suggest that the extrinsic factors such as fertilization, plowing and other soil management practices weakened their spatial correlation after a long history of cultivation. Some researchers have found the moderate spatial class for the soil properties (Granados et al., 2002; Iqbal et al., 2005). In addition, spatial dependence is defined as weak if the best fit semivariogram model has an R 2 < 0.5 (Duffera et al., 2007). Cambardella et al. (1994) also reported that strong spatial dependency of soil characteristics can be attributed to intrinsic factors (soil formation factors, such as parent materials), and weak spatial dependency can be attributed to extrinsic factors (soil management practices, such as fertilization). The cross validation statistic (RMSE and ME) in Table 2 shows how well soil properties can be estimated by application of the ordinary kriging method. Except for clay content, all RMSE for the other studied variables were lower than 40%. Hengl et al. (2004) stated that a value of RMSE below 40% meant a fairly satisfactory accuracy prediction. Therefore, the kriging estimator performed best for almost all the soil properties in the studied area. The average estimation error (ME) close to zero, which signifies unbiased estimates of the kriging method. Spatial Distributions: The main application of geostatistics to soil science has been the estimation and mapping of soil attributes in unsampled areas. Fig. 3 shows the spatial patterns of each soil characteristic in paddy field in Siyahkal region generated from kriging analysis based on their semivariograms parameters (Table 2). The prediction maps were generated using ordinary Kriging methods with original values. Kriged maps demonstrated that soil properties did not have a random pattern but had a spatial distribution. The kriged maps of the studied soil could establish information about their spatial distribution over long distances clearly. The results of this study can also be used for soil survey and evaluation. It is also found that the sampling interval could be decreased in future studies depending on the soil characteristics. Based on the variability existed, it is strongly recommended that site specific nutrient management should be carried out in paddy field in Siyahkal region with emphasis on increasing available P and organic matter values to the optimum level. CONCLUSION The present study showed the good spatial structure of soil attributes in paddy rice fields of Siyahkal region. Over a long history of various land management, the spatial variability of soil properties was caused not only by the soil parent materials but also by anthropogenic activity. Strong spatial correlation may be caused by the inherent variability of soil properties and weaker spatial dependency may be controlled by extrinsic factors such as fertilization and tillage. Hence, it can be expressed that the difference in soil properties variability cab be affected by both soil forming factors and land management practices. The utilized geostatistical method revealed moderately and strongly spatial correlation for the soil attributes across the study area. The range of all variables was less than 200 m, indicating the presence of spatial structure interior the original sampling distance. This indicates that more samples should be taken at smaller sampling intervals in the area to determine the spatial dependency for heterogeneous data. 12

7 YAGHMAEIAN MAHABADI AND AMIRI Sand Silt Clay EC ph Available P 13

8 AGRICULTURAL COMMUNICATIONS SOM Fig. 3. Kriged maps of the studied soil properties. These findings indicate the importance of gathering information in each agricultural zone for precision farming. Thus, the kriging estimator can be used as an efficient tool to detect area for precision farming and improve the quality of soil studies. The methodology used in this study can characterize and acquire quantitative information for detecting and monitoring variability of soil properties. These results would provide further knowledge for management strategies and precision farming. Aishah, W.A., S. Zauyah, A.R. Anuar and C.I. Fauziah Spatial variability of selected chemical characteristics of paddy soils in Sawah Sempadan, Selangor, Malaysia. Malaysian Journal of Soil Science. 14: Ayoubi, SH. and A. Jalalian Land evaluation (agriculture and natural resources). Isfahan University of Technology Press. Isfahan, Iran. 396p. Cambardella, C.A., T.B. Moorman, J.M. Novak, T.B. Parkin, D.L. Karlen, R.F. Turco and A.E. Konopka Fieldscale variability of soil properties in Central Iowa. Soil Science Society American Journal. 58: Childs, C Interpolating Surfaces in ArcGIS Spatial Analyst. ArcUser, ESRI Education Services. ESRI ARC GIS. pp: Cilic, K., S. Kilic and R. Kocyigit Assessment of spatial variability of soil properties in areas under different land use. Bulgarian Journal of Agricultural Science. 18(5): Dafonte, J., M. Guitian, J. Pazz Ferrreiro, G. Machado Siqueira and E. VidalVazquez Mapping of soil micronutrients in an European Atlantic agricultural landscape using ordinary kriging and indicator approach. Bragantia. 69: Duffera, M., J.G. White and R. Weisz Spatial variability of Southeastern U.S. Coastal Plain soil physical properties: Implication for site specific management. Geoderma. 137: REFERENCES Fraisse, C.W., K.A. Sudduth, N.R. Kitchen and J.J. Fridgen Use of unsupervised clustering algorithms for delineating within field management zones. ASAE Paper, No International Meeting, Toronto, Ontario, Canada. Pp: Gaston, L.A., M.A. Locke, R.M. Zablotowicz and K.N. Reddy Spatial variability of soil properties and weed populations in the Mississippi delta. Soil Science Society American Journal. 65: Gee, G.W. and J.W. Bauder Particle size analysis. In: Klute, A. (Ed.). Methods of soil analysis. Madison, WI. USA. pp: Goovaerts, P Geostatistics for Natural Resources Evaluation. Oxford University Press, New York, USA. 512 p. Granados, F.L., M.J. Exposito, S. Atenciano, A.G. Ferrer, M.S. Orden and L.G. Torres Spatial variability of agricultural soil parameters in southern Spain. Plant and Soil. 246: Hengl, T., G.B.M. Heuvelink and A. Stein A generic framework for spatial prediction of soil variables based on regression kriging. Geoderma. 120: Iqbal, J., J.A. Thomasson, J.N. Jenkins, P.R. Owens and F.D. Whisler Spatial variability analysis of soil physical properties of alluvial soils. Soil Science Society American Journal. 69: Kaspar, T.C., T.S. Colvin, D.B. Jaynes, D.L. Karlen, D.E. James and D.W. Meek Relationships between 14

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