J. Soil Sci. Soc. Sri Lanka, Vol. 23, 2011 ORIGINAL PAPER ACCEPTED FOR PUBLICATION IN JOURNAL OF SOIL SCIENCE SOCIETY OF SRI LANKA SPATIAL VARIABILITY OF SOIL TEXTURE, ORGANIC CARBON AND CATION EXCHANGE CAPACITY OF A REDDISH BROWN LATASOLIC SOIL IN A SLOPY LANDSCAPE V.G.D.Nayanaka, U.W.A. Vitharana * and R.B. Mapa Department of Soil Science, Faculty of Agriculture, University of Peradeniya * Corresponding Author: uvithara@pdn.ac.lk ABSTRACT Detailed and quantitative information on spatial variability of soil properties is a pre-requisite for decision making in sitespecific soil management and land use planning. Classical maps are published at a coarse scale thus they are not sufficient to provide detailed soil spatial information requirement. Therefore, there is a need of exploring short-scale variability of soil properties and produce detailed maps in different soilscapes. This study was conducted at the Dodangolla experimental station of Faculty of Agriculture, University of Peradeniya to investigate the spatial variability of key soil properties in a selected slopy landscape and to produce digital maps of soil variability. A seven hectare pasture land that consisting of Reddish Brown Latasolic Great Soil Group (Ultisols) was selected for the study. Clay, sand, organic carbon and cation exchange capacity (CEC) of top (0-30 cm) and subsoil (30-60 cm) were determined at 55 sampling locations identified using a combined grid and random sampling scheme. The variogram analysis indicated high to moderate spatially structured variability of top and subsoil properties. It was also evident that a large proportion of total variation (52% to 100%) was accounted by the spatially structured component. Range of spatial dependencies of topsoil varied from 75 m to 95 m whereas that of subsoil properties ranged from 68 m to 81 m. These variogram parameters could be used to develop optimum sampling schemes for detailed characterization of these key soil properties in a similar soilscape. Digital map layers of each soil property further explained the short-scale variability of the area. This short-scale variability can be used to implement sustainable land management practices while minimizing the detrimental effects on environment. This study also revealed that short-scale variability of soil properties is governed by the landscape processes. Keywords: site-specific soil management, sampling scheme, Variograms, Geo-statistics INTRODUCTION Spatial variability of soil properties has been recognized for many years (Burrough, 1993) and this could be divided into two main categories, namely systematic and random variability. Systematic variation is a gradual change of soil properties as a function of soil forming factors and/or soil management. The random variation entails either spatial difference of soil properties which cannot be explained in terms of known soil-forming factors or resulted due to the measurement errors at the scale of the study (Jenny, 1980; Quine and Zahng, 2002). Systematic variation leads to develop short-scale variability of soil properties which can be quantified using spatial analysis and mapping. Traditional soil management practices do not account for the short scale variability of soil properties and only considers that the soil properties are more homogenous within and among agricultural fields (Vitharana, 2008). Moreover, many researchers have revealed that the information on short scale variability of soil properties can be used as a basis for sustainable land management strategies such as site specific soil management, process based land use planning and environmental modeling. Cahn et al. (1994) emphasized that the spatial patterns of soil properties and nutrient concentrations need to be characterized at detailed level to develop site-specific soil management practices. Further, accuracy of soil spatial information as inputs for many logical, empirical and physical models of soil-landscape processes can greatly influence the reliability of results (Burrough, 1983; Foussereau et al., 1993; Wilding et al., 1994). Traditionally, the information on spatial variation of soil properties are inventoried as area-class maps which are published at coarser scales (1:250000 and 1:500000). Therefore, these maps are not detailed enough to provide soil information for sustainable land management strategies. Moreover, the soil spatial variability is not quantitatively expressed by these maps (Heuvelink and Webster, 2001). Besides, producing detailed soil maps by conventional soil surveying methods are expensive and laborious as it requires large number of field observations. Therefore, there is a need to construct quantitative (continuous) maps of key soil properties by means of accurate and cost-effective methods. Classical statistical techniques are not sufficient to characterize the spatial variability of soil (Goovaerts, 1997). In this context, geo-statistics is considered as an alternative approach in which spatial correlation (autocorrelation) of variables those can be quantified through variogram analysis (Webster & Oliver, 2001) and subsequent accurate mapping of key soil properties. The objectives of this study were to quantify the spatial variability of key soil properties which influence crop production through variogram analysis, use this information to produce accurate maps by means of ordinary kriging and to identify the relationships between variability of soil properties and topography.
Spatial Variability of Soil Properties of a Reddish Brown Latasolic Soil. Nayanaka et.al. MATERIALS AND METHODS Study area Study area (Fig. 1) was a seven hectare pasture land in the University experimental station, Dodangolla at Kundasale (central coordinates: 7º17' 51" N, 80º 42' 12" E) which has been previously used for annual crop cultivation. Reddish Brown Latasolic soils (Ultisols: taxonomic equivalent) is the soil great group found in this area (Pushpananda, 1985). median, mean, standard deviation (SD), coefficient of skewness and coefficient of variation (CV). Spatial analysis Spatial analysis involved the calculation of omnidirectional experimental variograms and subsequent fitting of theoretical models. Variowin spatial analysis software (Pannatier, 1997) was used for spatial analysis. The strength of spatial variability of each soil property was explained using the variogram model parameters. Further, Relative nugget effect (RNE) which is the ratio between nugget variance to sill variance was calculated to understand the degree of spatially structured variability. Ordinary point kriging interpolation technique was used to create continuous digital maps of each soil property using GSLIB software (Deutsch and Journel, 1998). RESULTS AND DISCUSSION Descriptive statistics of the soil properties are given in the Table 1. The Kolmogrov-Smirnov normality test indicated that top and subsoil organic matter content, cation exchange capacity, sand content were normally distributed whereas top and subsoil clay were not normally distributed at 5% probability level. This was further explained by the corresponding coefficient of skewness values (Table 1). Fig. 1. Satellite image showing the Dodangolla University Farm (top) and the study area (bottom) (yellow line shows the boundary) Soil sampling and laboratory analysis A coupled random and grid sampling scheme was used to obtain total of 55 soil samples (Fig. 2) from both top soil (0-30cm) and sub soil (30-60cm). First, half of the sampling points were located on each grid node of a 50 m regular grid and the remaining sampling points were assigned as random pairs within each grid cell. All sampling locations were georeferenced using a Garmin GPS (Garmin Inc. USA) receiver. Three sub samples were taken from both top and subsoil within a one meter radius at each sampling location. Sub samples were bulked to obtain a composite sample for top and subsoil. Soil samples were air dried and sieved through 2 mm sieve prior to laboratory analysis. Soil samples were analyzed for soil texture (Gee and Or, 2002), CEC (Summer and Miller, 1996) and organic carbon (Nelson and Sommer, 1996). Exploratory data analysis Exploratory data analysis was performed for all variables. This included calculation of statistical parameters such as Topsoil clay content ranged from 3.5% to 30.9% whereas that of the subsoil varied from 3.5% to 26.3% indicating a similar variability of clay fractions in the two soil layers. This is also reflected with almost similar CV values for the top and the subsoil clay fractions (Table 1). Both top and subsoil sand fractions had almost similar variability as reflected by CV values. Three textural classes namely sandy loam, loamy sand and sandy clay loam were identified for the topsoil according to the USDA soil textural triangle whereas four textural classes ranging from sandy loam to sand were observed for the subsoil. Topsoil organic matter content ranged from 0.99% to 2.41% whereas that of subsoil ranged from 0.20% to 2.16% indicating a larger variability of subsoil in compared with the topsoil. According to the classification by Hillel (1980) all the properties showed a medium variability (CV=10-100%). Spatial variability of soil properties Experimental variograms of top and subsoil clay, sand, organic matter and CEC and theoretical models fitted are given in the Fig. 3. Spherical model was found to be the best fit for all experimental variograms of all properties. The corresponding model parameters are listed in Table 2. The lag distance at which the variogram reaches the sill variance represents the range of spatial correlation. It is considered that the observations within the range are spatially correlated (auto-correlated) whereas those spaced greater than the range are considered as spatially independent (Goovaerts, 1997).
J. Soil Sci. Soc. Sri Lanka, Vol. 23,2011 Table 1. Descriptive statistics for the measured soil properties Mean Median Min Max SD CV Skewness Topsoil Clay (%) 12.6 11.2 3.5 30.9 6.7 53.1 0.92 Sand (%) 74.3 75.2 50.5 87.3 7.9 10.7-0.68 Organic matter (%) 1.54 1.56 0.99 2.41 0.31 19.94 0.22 CEC (cmol c /kg) 13.84 13.90 6.50 23.78 4.32 31.19 0.32 Subsoil Clay (%) 10.8 10.6 3.5 26.3 5.5 50.8 0.53 Sand (%) 74.2 74.1 57.3 90.3 8.2 11.1 0.05 Organic matter (%) 1.26 1.35 0.20 2.16 0.56 43.96-0.1 CEC(cmol c /kg) 14.13 13.40 7.0 24.40 4.23 29.92 0.34 Range of spatial dependencies for the topsoil properties varied from 75 m to 95 m whereas that of the subsoil varied from 68 m to 81 m. Webster and Oliver (2001) suggested that the range value of a variogram can be used to find out the maximum sampling interval. These results indicate that the samples should be taken within the sampling interval of less than 68 m for detailed characterization of these soil properties. Further, when spatial variability of individual soil properties is concerned, corresponding ranges can be considered when designing sampling schemes for each soil property. Cambardella et al. (1994) proposed the use of RNE% to quantify the degree of spatial dependency of a variable. Accordingly, top soil clay, subsoil clay and CEC showed strong spatial dependency (RNE <25%) whereas other properties showed moderate level (RNE = 25% -75%) of spatial dependencies. Top and subsoil clay showed a nugget variance of zero indicating a negligible level of random variation. According to Cambardella et al. (1994) strong spatially dependent properties are controlled by the intrinsic factors such as soil forming processes. Table 2. Model parameters of the fitted variogram models of top and subsoil properties Property Model Variogram parameters Nugget Sill Range(m) RNE*% Topsoil Clay Spherical 0.00 43.99 80.6 0.00 Sand Spherical 19.43 68.34 75.6 28.43 OM Spherical 0.03 0.09 95.4 33.33 CEC Spherical 2.09 18.24 94.5 11.45 Subsoil Clay Spherical 0.00 30.00 67.5 0.00 Sand Spherical 18.75 67.66 72.0 27.71 OM Spherical 0.15 6.31 81.4 48.38 CEC Spherical 5.94 17.28 78.0 34.37 * Relative nugget effect (a) (b) Fig. 3. Omnidirectional experimental variograms (dots) fitted models (curves) for top soil (a) clay, (b) sand, (c) organic matter, (d) CEC and subsoil (e) clay (f) sand (g) organic matter (h) CEC
Spatial Variability of Soil Properties of a Reddish Brown Latasolic Soil. Nayanaka et.al. (c) (d) (e) (f) (g) (h) Fig. 3. Continued Digital maps of topsoil properties draped over the Digital elevation model (DEM) of the study area are presented in the Fig. 4a to d to explain the spatial variability of soil properties and their relationships with the landscape. Topsoil clay map (Fig. 4a) showed a large variability across the hilly landscape. Large part of the area had moderate clay content (10% to 20%) whereas smallest clay contents (<5%) were observed in the north-western corner of the field which is the highest position of the field. All most opposite spatial distribution was observed for the topsoil sand content (Fig. 5b). Moor et al. (1993) and Gessler et al. (2000) have observed distributions of large sand content and smaller clay contents in the upper positions (crest and shoulder positions) of the landscape. Water erosion occurring in the hilly landscape is the reason for such spatial distributions of sand and clay fractions in the upper positions of the field. Finer particles such as clay and silt are more susceptible to erosion by water compared to the sand particles. Therefore, such finer particles can be removed from upper landscape positions and deposited in low-lying areas in the hilly landscape. This study clearly revealed this by showing a comparatively large clay contents (20% to 30%) in the center and the bottom part of the field (Fig. 4a) and lower clay contents at the upslope areas. High topsoil organic matter content (1.7% to 2.7%) was observed in a part of the southern corner of the field where enrichment of clay was also evidenced (Fig. 4c). Many researchers have documented that the low lying landscape positions are likely to accumulate organic materials due to slow decomposition rates caused by anaerobic conditions and deposition of organic material from upper landscape positions. Furthermore, organic matter content can increase with clay content as organic matter form organo-mineral complexes which are resistant to decomposition (Brady and Weil, 1998). However, the spatial distribution of topsoil organic matter did not significantly coincide with the clay contents of the other parts of the field. Poor correlation between topsoil organic matter and clay (r = 0.1) also confirmed the absence of a strong linear relationship. According to the crop history, this land has been used for annual crop cultivation and later it has been converted to a pasture land. Therefore, addition of organic manure may have masked the distribution of soil organic carbon under natural conditions. Spatial distribution of CEC was similar to that of the organic carbon (Fig. 4d). Larger CEC (15 cmol c /Kg to 24 cmol c /Kg of soil) was observed in the same low lying landscape positions where organic matter was also high (Fig. 4c). Comparatively low CEC values were distributed in northwestern corner (hill top) and the ridges in the center
J. Soil Sci. Soc. Sri Lanka, Vol. 23,2011 positions of the landscape. Larger sand content (>75%) was also observed in those areas in field (Fig. 4b). it is a well known fact that soil texture is one of the important factors contributing to the cation exchange capacity in soil. Sand fraction of the texture usually consists of primary minerals such as Quartz and Feldspars which have low CEC compared with the clay minerals (Brady and Weil, 1998). Therefore, both organic matter and sandy texture can be the possible reason for spatial distribution of CEC in the field. Correlation coefficients of 0.5 and -0.4 between CEC and organic matter and CEC and sand also supported this evidence. Subsoil clay and sand contents showed small difference in spatial patterns to that of topsoil clay (Fig. 5a and b). Further, compared to topsoil, areas with low clay content (<5%) appeared prominently in the subsoil indicating a minimum disturbance of previous management practices to subsoil clay. Fig. 5c shows that the low-lying area in the southern corner of the field and concave slope positions (north-east direction) had high subsoil organic matter content (1.5% to 2.1%) whereas a small area with low organic matter (<0.5%) was noticed in the highest position of the landscape at the north western corner. Fig. 4. Digital map layers of topsoil (a) clay%, (b) sand%, (c) organic matter%, (d) and CEC cmol (+) /Kg (g) draped on the digital elevation model Many researchers including Moor et al. (1993) and Gessler et al. (2000) documented high organic matter associated with footslope and concave areas in landscape. However, it is important that such soil-landscape relationships need to be quantified in order to use such relationships to map soil properties accurately. Low-lying area in the southern corner, concave area in north-eastern corner and the area along the western corner were identified with large CEC values (Fig. 5d). Comparatively high organic matter and clay content in low lying area of northern corner and concave area of north eastern corner (Fig. 5a and c) had attributed to high CEC values in those areas.
Spatial Variability of Soil Properties of a Reddish Brown Latasolic Soil. Nayanaka et.al. Fig. 5. Digital map layers of subsoil (a) clay%, (b) sand%, (c) organic matter%, (d) and CEC cmol (+) /Kg (g) draped on the digital elevation model CONCLUSIONS This study was an attempt to quantify the spatially structured variability of key soil properties for crop production by means of geo-statistical analysis and use of information to produce detailed and accurate maps. The variogram analysis showed the presence of structured spatial variability of soil properties. The spatial dependencies of topsoil ranged from 75 m to 95m whereas that in subsoil properties exhibited a range of 68 m to 81 m. Digital maps exhibited a clear spatial variability of soil properties further strengthening the information revealed by variograms. Further, spatial distribution of all soil properties except for soil organic matter largely resembled the general topography of the slopy landscape. This indicated the presence of satisfactory soil-landscape relationships which can be used for cost-effective characterization of the investigated soil properties. This study clearly showed that the within field variability of soil properties is significant and should be considered in site-specific soil management and land use planning. REFERENCES Brady, N. C. and R. R. Weil. 1998. The nature and properties of soils: R.R. Weil (ed.). A Simon and Schuster company, Prentice Hall, New Jersey. Burrough, P.A., 1993. Fractals and Geostatistical methods in landscape studies. p. 87-112. In: N. Lam and L. de Cola (ed.) Fractals in geography. Prentice Hall, Englewood Clifts, NJ. Burrough, P.A., 1983. Multi-scale sources of spatial variation in soil: The application of fractal concepts to nested levels of soil variation. Journal of Soil Science 34: 577 597. Cahn, M.D., J.W. Hummel and B.H. Brouer. 1994. Spatial Analysis of Soil Fertility for Site Specific Crop Management. Soil.Sci. Soc. Am.J. 58:1240-1248. Cambardella, C.A., T.B. Moorman, J.M. Novak, T.B. Parkin, D.L. Karlen, R.F. Turco, and A.E. Konopka. 1994. Field-scale variability of soil properties in central Iowa soils. Soil Sci. Soc. Am. J. 58:1501-1511. Deutsch, C.V. and A.G. Journel. 1998. GSLIB, Geostatistical software library and user guide, 2ed. Oxford University press. NY. Foussereau, X., A.G. Hornsby, and R.B. Brown. 1993. Accounting for variability within map units when linking a pesticide fate model to soil survey. Geoderma 60: 257 276. Gee, G.W., and J.W. Bauder. 2002. Particle size analysis, p. 383-411, In A. Clute, (ed.) Methods of soil analysis, part 1. Physical and mineralogical methods. Agronomy monograph 9. American Society of Agronomy, Medison, WI. Gessler, P. E., O. A. Chadwick, F. Chamran, L. Althouse and K. Holmes. 2000. Modeling Soil Landscape and
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