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Spatial variation of soil water content in topsoil and subsoil of a Typic Ustifluvent
agricultural water management 83 (2006) 79–86
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Spatial variation of soil water content in topsoil and subsoil of a Typic Ustifluvent Sabit Ersahin *, A. Resit Brohi Department of Soil Science, Agricultural Faculty, Gaziosmanpasa University, 60250 Tokat, Turkey
article info
abstract
Article history:
Water content is a fundamental property affecting plant growth, transport and transforma-
Accepted 18 September 2005
tion of soil nutrients, and water and energy budgets in the soil–plant system. Its spatial
Published on line 13 December 2005
variation has important implications for all these processes. In this study, spatial variation in soil water contents was evaluated at pressures of 0.033 (u
Keywords:
1.50 MPa (u
0.033 MPa),
0.10 (u
0.10 MPa), and
1.50 MPa) at the soil depth from surface to the lower and of a plow layer in a Typic
Soil water content
Ustifluvent in an 8.5 ha level area (1–2% slope). Topsoil (0.0–0.30 m) and subsoil (0.31–0.60 m)
Soil water potential
were sampled based on a regular grid spacing of 25 m-by-25 m. Variables u
Spatial variability
u
Geostatistics Topsoil
0.10 MPa,
and u
1.50 MPa
0.033 MPa,
were measured with a pressure plate apparatus. Results were
evaluated and compared with conventional statistics and geostatistics. Coefficient of variation (CV) for soil water content increased as soil water pressure decreased in both
Subsoil Ustifluvents
topsoil and subsoil. Semivariance analysis and ordinary block kriging-produced surface maps for u
0.033 MPa,
u
0.10 MPa,
and u
1.50 MPa
showed that topsoil and subsoil were similar in
spatial variation of water content at all three corresponding soil water pressures. The geostatistical range values were between 298 m (u
0.10 MPa
in topsoil) and 486 m (u
0.10 MPa
in subsoil), both indicating a strong spatial dependence of soil water content. The semivariograms for u
0.033 MPa,
u
0.10 MPa,
and u
1.50 MPa
were similar in shape in both topsoil and
subsoil. Cross-dependence between sand content and soil water contents showed that soil texture controlled the spatial variation of water contents at the site, at all depths and pressures evaluated. # 2005 Elsevier B.V. All rights reserved.
1.
Introduction
Soil water controls plant growth and influences a variety of soil processes including erosion, chemical exchange, microbial activity, transport of solutes and water, energy balance of the soil–plant system, and pedogenesis (Western et al., 2003). The relationship between water content and water potential determines, in part, the nature of these effects. Soil water represents a small portion of the water in the hydrologic cycle. However, due to its vital role in the ecosystem, the temporal and spatial variability of soil water
has a controlling influence on ecosystem processes at a variety of scales (Western et al., 2003). Soil properties vary spatially. This variability may be random or systematic. Contrary to the random variation, a systematic variation has a consistent spatial pattern which allows one to estimate a particular property at subjected locations. Studies (Vauclin et al., 1983; Greminger et al., 1985; Yates and Warrick, 1987; van Vesenbeeck and Kachanoski, 1988; Mulla, 1988; Rockstro¨m et al., 1999; and many others) showed that soil water content exhibited organized features under majority of the conditions. However, degree of the organization varies based on the soil and climatic conditions.
* Corresponding author. Tel.: +90 356 252 1480x2177; fax: +90 356 252 1488. E-mail address: [email protected] (S. Ersahin). 0378-3774/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2005.09.002
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agricultural water management 83 (2006) 79–86
Spatial variability in soil water potential may be more important than water content because this determines plant water availability and percolation (drainage) rates. Spatial variability in soil water properties have been studied widely since 1975. Most of these studies focused on nature of spatial variability and its relation to soil properties (Cassel and Bauer, 1975; Gajem et al., 1981; Vauclin et al., 1983; Mulla, 1988; Burden and Selim, 1989; Mallants et al., 1996) and others on terrain properties (Western et al., 2003; Timlin et al., 2003), on soil management (Arya et al., 1975; van Vesenbeeck and Kachanoski, 1988), and on spatial interpolation of soil water content (Yates and Warrick, 1987). The knowledge of spatial variability of soil water content within the range of plant available water content is important for managing soil water in spatially variable soils. The characterization of soil water profiles along the root zone may yield important consequences that should be taken into account in modeling water budget and travel time variations in nutrient leaching out of the root zone. The objectives of this study were to analyze and compare spatial variation in soil water content at 0.033, 0.10, and 1.50 MPa soil water pressures; and to assess their spatial correlations with soil textural separates, soil bulk density, and soil organic matter content in topsoil (0.0–0.30 m) and subsoil (0.31–0.60 m) of a Typic Ustifluvent formed under semi-arid conditions.
2.
Materials and methods
2.1.
Study site
November and harvested in mid July, and the sugarbeets is planted in mid March and harvested in late October. In general, about 140 kg N ha 1 is applied in wheat and 250 kg N ha 1 is applied in sugarbeets. The average yield for sugarbeets is 50.0 Mg ha 1, and for wheat is 4.5 Mg ha 1. Wheat is irrigated rarely. However, sugarbeets is irrigated more often.
2.2.
Methods
2.2.1.
Soil sampling and analysis
The field was intensively sampled on a regular grid spacing of 25 m in March, 1998 (Fig. 1). At each sampling site, 140 soil samples were taken from topsoil (0.0–0.30 m) and subsoil (0.31–0.60 m). All 280 samples were analyzed for clay, silt, and sand contents by the hydrometer method (Gee and Bauder, 1986), for organic matter content by Walkley-Black Procedure (Nelson and Sommers, 1982), and for CaCO3 content with a Scheibler Calcimeter (McLean, 1982), and water contents at 0.033 (u 0.033 MPa), 0.10 (u 0.10 MPa), and 1.50 MPa (u 1.50 MPa) soil water pressures were determined with a pressure plate apparatus (Klute, 1986). The bulk density of repacked soils in the rings was determined and then used to calculate the volumetric soil water content from the gravimetric values. In addition, at each test site, undisturbed soil samples were taken from the topsoil and the subsoil using 100 cm3 steel cores to determine soil bulk density at time of sampling (Blake and Hartge, 1986). All the soil analyses were conducted as duplicated.
2.2.2.
This study was conducted as a component of a project to investigate the spatial variability of nitrate leaching in an 8.5ha (850 m 100 m) level (1–2% slope) and well drained alluvial area (loamy mesic Typic Ustifluvents) located in 25 km northeast of Tokat City in Central Anatolia of Turkey. The study area is an old river terrace, 680 m above the see level. Soils investigated are very deep, formed in alluvium over clayey lacustrine deposits. The study area, where an approximately 3 m deep shallow water table is present, was initially a swamp. The area was drained by government in 1970s for agricultural development. The topsoil (0–0.30 m) is characterized by a dark grayish brown color (10YR 4/2; dry); a moderate fine to moderate medium granular structure; common fine and very fine roots; common fine pores; and a clear wavy boundary. A plow layer is present between the 0.31 and 0.60 m depths, which formed as a result of tillage. The plow layer is characterized by a very dark grayish brown color (10YR 2/2; moist); a moderate fine and weak fine platy structure; few root channels filled with dark material from the upper horizon; few, fine tubular pores; common fine iron manganese concretions; and many prominent dark brown (10YR 3/3; moist) clay films on ped faces. The annual average precipitation is 420 mm most of which falls between October and May, and the annual average air temperature is 12 8C. The area has been under a rotation of winter wheat (Triticum aestivum L.) and sugarbeet (Beta vulgaris L.) for at least 10 years at the discretion of the farmer. Both crops are grown in a single year. The wheat is planted in early
Descriptive statistics
Descriptive statistics including minimum, maximum, mean, standard error of mean, coefficient of variation, skewness, and kurtosis were calculated for each variable. The Shapiro–Wilks normality test was conducted to test the hypothesis that assumes each property has a normal distribution.
2.2.3.
Geostatistical analysis
Sample semivariogram functions for u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa were calculated (Isaaks and Srivastava, 1989; Goovaerts, 1997). Hypothetical semivariogram models were fitted to experimental semivariograms. The best models were determined by the leaving-one-out method of cross-validation (Yates and Warrick, 1987; Shouse et al., 1990). To crossvalidate, we remove one known value at a time from the data set and estimate this value from a neighborhood around it, but not itself. This procedure allows comparisons of the different models and search strategies on the results of interpolation (Goovaerts, 1997). To determine the anisotropy, lagged distances corresponding to the fixed sample semivariance values were determined in the directions 08, 458, 908, and 1358 and then the resulting distances were plotted on a rose
Fig. 1 – Layout of the experiment: x represents location of soil sampling.
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agricultural water management 83 (2006) 79–86
diagram. The severity and direction of geometric anisotropy was determined comparing minor and major axes of resultant ellipse (Isaaks and Srivastava, 1989). Since the shape of the field was long and narrow (100 m wide and 850 m long), the test of anisotropy was confined in the lagged distance 75 m.
2.2.4.
saturation (us) was the least and CaCO3 was the most variable in the topsoil, while organic matter content was the most and silt content was the least variable in the subsoil (Tables 1 and 2). Water content values at 0.033, 0.10, and 1.50 MPa soil water pressures (u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa, respectively) showed a left skewed distribution. The degree of skewness was greater in subsoil. Soil water contents had a relatively flat distribution in subsoil compared to topsoil. Plant available water content (PAW, %) exhibited a similar variation in both topsoil and subsoil (Tables 1 and 2). In both topsoil and subsoil, u0.033 MPa, u0.10 MPa, and u1.50 MPa exhibited a high variability. Similar results were reported by Greminger et al. (1985). Cassel and Bauer (1975) reported a 19% of CV for u 1.50 MPa of a silty loam, highly similar to values obtained in our study. Rogowski (1972) suggested that a soil may be considered uniform in the case of that CV in u1.50 MPa does not exceed 15%. Our values for CV% for u1.50 MPa in both topsoil and subsoil exceeded the above specified value by Rogowski. Decreasing water potential resulted in CV of soil water content to slightly increase in both topsoil and subsoil (Tables 1 and 2). Burden and Selim (1989) reported CV% values of 9.6, 17.7, and 20.5 for u 0.03 MPa, u 0.10 MPa, and u 1.50 MPa, respectively, from their study conducted on a bare field of Oliver silt loam (fine-silty, mixed, thermic, Aquic Fragiudalfs), sampled 0–10 cm depth by 30 cm intervals. Mulla and McBratney (2001) reported CV% ranges from 14 to 45% for u 1.50 MPa and from 4 to 20% for u 0.010 MPa, indicating lower values of CV% at greater soil water potentials. Western et al. (2003) studied soil water measured over different (and sometimes multiple) depths at 13 areas around the world, with climate changing from semi-arid to humid, soils ranging from sand to clay, and topography from gently undulating to steep. They observed that the variance increased with average moisture in dry catchments and decreased when average water content increased in wet catchments. However, where the mean moisture had a sufficiently large range over time, the variance peaked at intermediate values. Mallants et al. (1996) reported results on the hydraulic properties of a well-drained sandy loam along a 30 m transect.
Spatial estimations
Ordinary point kriging procedure was applied to estimate u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa at unsampled locations of the field, using the version 5.0 of GS+ (Robertson, 2000). The water content values were estimated within a 12.5 m-by12.5 m grid. Each time, the estimates in kriging were controlled by the procedure cross-validation to determine a suitable number of neighboring data points to be used in the estimation (Vieira et al., 1981). Linear correlation coefficient calculated between measured and calculated values, and mean absolute error (MAE) calculated using residuals were considered, and the normality of residuals from crossvalidation was tested to check the validity of kriging estimations. A maximum of 15 and a minimum of 10 neighboring data points were used along with semivariograms in kriging estimation. The surface maps of estimates from point kriging and surface maps of error values were examined to asses the quality of the estimations.
3.
Results and discussion
3.1.
Characterization of soil properties
The average textural classification of topsoil (0.0–0.30 m) was loam and of subsoil (0.31–0.60 m) was clay loam. However, in some areas of the field, loamy clay, and silty clay textures were present in the topsoil, and loamy clay in the subsoil. The average organic matter content was greater in the topsoil, and the average bulk density was greater in the subsoil probably due to the plow layer. Calcium carbonate bounds and groups the soil particles together to form stable lumps or aggregates, improving the water holding capacity of the soils, being important especially in the local areas with high sand and low organic matter content. Volumetric water content at
Table 1 – Summary statistics for some properties of topsoil Soil property Sand (%) Silt (%) Clay (%) Bulk density (Mg m 3) us (%)a u 0.033 MPa (%)a u 0.10 MPa (%) u 1.50 MPa (%) PAWC (%)a CEC (cmol kg 1) CaCO3 (%) Organic matter (%) a
Mean
Minimum
Maximum
33.2 41.9 24.9 1.25 52.89 36.8 30.4 24.3 12.5 0.41 14.4 3.8
21.9 30.8 17.8 1.03 46.0 25.6 19.7 7.5 4.2 0.20 7.4 1.9
46.9 52.2 31.1 1.43 61.1 49.2 37.7 34.6 21.4 1.02 20.9 5.6
CV (%)a 18.2 12.3 10.4 8.1 7.3 14.4 13.4 20.1 25.4 29.3 40.4 21.6
Sea 0.51 0.44 0.22 8.5 10 0.32 0.45 0.35 0.41 1.06 9.8 10 0.21 0.32
3
3
Ska
Kra
0.13 0.06 0.01 0.19 0.18 0.16 0.65 0.58 0.37 1.56 0.11 0.14
2.25 2.14 3.65 1.82 1.82 2.32 2.62 3.22 3.82 8.32 2.75 2.25
CV: coefficient of variation; Se: standard error of mean; Sk: skewness; Kr: kurtosis; PAWC: plant available water content (volumetric); u 0.033 MPa: volumetric water content at 0.033 MPa soil water pressure; us: saturated water content (volumetric); CEC: cation exchange capacity.
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Table 2 – Summary statistics for some properties of subsoil Soil property
Mean
Sand (%) Silt (%) Clay (%) Bulk density (Mg m 3) us (%)a u 0.033 MPa (%)a u 0.10 MPa (%) u 1.50 MPa (%) PAW (%)a CEC (cmol kg 1)a CaCO (%) Organic matter (%) a
20.9 43.9 35.2 1.33 49.80 37.2 31.3 25.9 11.4 0.37 15.5 1.4
Minimum 7.9 35.2 22.1 1.14 35.85 15.7 11.2 8.7 3.5 0.01 7.3 0.01
Maximum
CV (%)a
Sea
Ska
Kra
37.8 56.3 49.6 1.70 56.98 46.9 40.4 35.1 25.4 0.68 22.6 4.0
31.9 9.1 14.6 9.7 9.7 17.6 18.3 19.4 28.3 39.9 19.6 62.9
0.56 0.34 0.44 0.01 0.41 0.55 0.48 0.42 0.27 0.01 0.26 0.08
0.34 0.05 0.37 0.87 0.87 0.95 1.04 1.05 0.71 0.57 0.39 0.46
2.54 2.29 2.98 3.04 3.04 3.58 3.97 4.32 5.29 3.18 2.99 2.76
CV: coefficient of variation; Se: standard error of mean; Sk: skewness; Kr: kurtosis; PAWC: plant available water content (volumetric); u 0.033 MPa: volumetric water content at 0.033 MPa soil water pressure; us: saturated water content (volumetric); CEC: cation exchange capacity.
In their soil horizons, CV of water contents at 0.33 and 1.50 MPa were, respectively; 13 and 10% in the Ap horizon, 8 and 13% in the C1 horizon, and 6 and 15% in the C2 horizon. The increase in CV with decreasing soil water potential generally agrees to our findings (Tables 1 and 2).
3.2.
Geostatistical parameters
Table 3 lists the geostatistical parameters for isotropic semivariograms of u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa in topsoil and subsoil. The anisotropy was checked in the directions 08, 458, 908, and 1358 at the lagged distance 75 m, as described under Section 2. Nugget variance (C0) represents variance due to measurement error or micro-variability of the property which cannot be detected with current scale of sampling. The semivarince increases as separation distance between sample locations increases, rising to an approximately constant value called sill (C0 + Cs) (Trangmar et al., 1985; Goovaerts, 1997). The difference between sill and nugget variance is called the structural component (Cs), and ratio of C0 to C0 + Cs is called the nugget effect (Mallants et al., 1996). Ju´nior et al. (2005) reported from their study that spatial structure of gravimetric moisture content of surface soil decreased due to the damage caused by tillage on the soil structure, introducing an important source of randomness. They further stated that the effect of tillage decreased with the increase of the sampling depth, where the
effect of tillage on the soil structure is weaker. In our study, greater values for nugget effect (C0/C0 + Cs) occurred in topsoil, indicating a greater short-range variation of water contents at the corresponding water potentials. We believe that relatively greater nugget variance in topsoil was caused by conventional tillage on soil properties that resulted in more heterogeneous conditions. However, in general, little nugget effect occurred for water content at the corresponding soil water potentials in subsoil (Table 3). The semivariogram range depends on the scale of observation, as well as factors that influence the soil’s spatial variation (Trangmar et al., 1985; Isaaks and Srivastava, 1989; Goovaerts, 1997). Gajem et al. (1981) reported that geostatistical ranges for water contents at 0.10 and 1.50 MPa increased as sampling interval increased. They obtained a geostatistical range of 0.6 m for u 1.50 MPa with 0.20 m sampling-spacing and of 15 m with 20 m samplingspacing. They further observed a similar increase in geostatistitcal range value for u 0.10 MPa. Vauclin et al. (1983) reported a range value of 26.0 m from their study conducted in a 70 m-by-40 m field, measuring soil water content retained at 0.033 MPa in the bulk samples that they collected from 20–40 cm depth on a square grid at every 10 m. Our findings of geostatistical range values indicated that u 0.03 MPa, u 0.10 MPa, and u 1.50 MPa were spatially dependent over a distance of approximately 350 m in topsoil and 480 m in subsoil (Table 3 and Fig. 2a–f).
Table 3 – Coefficients of the theoretical semivariograms of soil water content at S0.033, S0.10, and S1.50 MPa soil water pressures in topsoil and subsoil Model
C0a
Csa
C0/(C0 + Cs)
Range (m)
RSSa
R2
Topsoil u 0.03 MPa (%)a u 0.10 MPa (%) u 1.50 MPa (%)
Spherical Spherical Spherical
9.25 5.28 6.68
35.65 20.47 30.96
0.26 0.26 0.22
375 280 369
95.1 48.9 146.0
0.95 0.96 0.94
Subsoil u 0.033 MPa (%)a u 0.10 MPa (%) u 1.50 MPa (%)
Spherical Spherical Spherical
3.50 4.10 5.97
65.08 50.18 36.61
0.05 0.08 0.16
455 486 481
492 246 148
0.91 0.92 0.89
Variable
a
Volumetric water content at
0.033 MPa soil water pressure; C0: nugget variance; Cs: structural component; RSS: residual sum of squares.
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Fig. 2 – Semivariograms for volumetric water content (%): (a) at S0.033 MPa, (b) at S0.10 MPa, and (c) at S1.50 MPa soil water pressure in topsoil; and (d) at S0.033 MPa, (e) at S0.10 MPa, and (f) at S1.50 MPa soil water pressure in subsoil. Solid lines represent the spherical model fitted to experimental values.
The shape of the experimental semivariogram may differ depending on data, sampling scheme, and sampling interval used (Trangmar et al., 1985). Semivariogram also indicates spatial processes of the soil at the sampling scale (Trangmar et al., 1985). Fig. 2a–f show relatively similar shape of semivariograms for water content values at corresponding water potentials, which indicates presence of a strong crossdependence between soil water content and some soil
properties that influence the spatial variability in topsoil and subsoil. Cross-semivariogram analyses were conducted between water contents (at 3 potentials) and soil properties (organic matter content, bulk density, and textural separates) to identify those soil properties with the strongest influence on spatial patterns of water content. Of these cross-semivariograms, those between water content and textural separates, particularly silt and sand, showed the strongest cross-depen-
Table 4 – Coefficients of the theoretical cross-semivariograms between sand content and soil water content at S0.033, S0.10, and S1.50 MPa soil water pressures in topsoil and subsoil Model
C0a
Csa
Topsoil u 0.033 MPa sanda u 0.10 MPa sand u 150 MPa sand
Gaussian Gaussian Gaussian
0.01 0.02 0.01
26.20 18.64 24.73
3.8 10 1.1 10 4.0 l0
Subsoil u 0.033 MPa sand u 0.10 MPa sand u 150 MPa sand
Gaussian Gaussian Gaussian
0.02 0.01 0.01
31.01 30.92 21.01
6.4 10 3.2 10 4.8 10
Variable
a
Volumetric water content (%) at
C0/(C0 + Cs) 4 5 4
4 4 4
Range (m)
RSSa
R2
649 545 621
53.0 40.2 74.5
0.96 0.95 0.96
737 862 746
118.0 22.9 18.7
0.96 0.99 0.99
0.033 MPa soil water pressure; C0: nugget variance; Cs: structural component; RSS: residual sum of squares.
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Fig. 3 – Cross-semivariograms for between sand content and volumetric water content (%): (a) at S0.033 MPa, (b) at S0.10 MPa, and (c) at S1.50 MPa soil water pressure in topsoil; and (d) at S0.033 MPa, (e) at S0.10 MPa, and (f) at S1.50 MPa soil water pressure in subsoil. Solid lines represent the gaussian model fitted to experimental values.
dence and were similar in shape (Fig. 3 and Table 4 show the cross-semivariograms for sand). This suggested that soil texture was responsible for the dominant spatial patterns of water contents in topsoil and topsoil at all three water potentials. All cross-semivariograms in Fig. 3a–f and Table 4 are Gaussian. The parabolic behavior of the curve near the origin, typical for Gaussian type of semivariograms as stated by Isaaks and Srivastava (1989), indicated that the sand content was not effective to control the spatial variations in soil water content within short distances but longer distances.
3.3.
Estimation
Ordinary point kriging procedure was used along with isotropic semivarograms to estimate water content at corresponding soil water pressures. Based on the results from the cross-validation procedure, a search ellipse with a radius of 200 m was used, and a minimum of 10 and a maximum of 15 closest data values were included in the kriging estimations. Table 5 compares measured values with kriged and crossvalidated values against some of the estimation criteria.
Table 5 – Results of cross-validation and ordinary point kriging estimations of water content at corresponding soil water pressures in topsoil and subsoil Variable
Mean a
Cross-validation
Experimental
Ordinary point kriging
Mean
r
MAEc
Topsoil u0.033 MPad u0.10 MPa u1.50 MPa
36.78 30.40 24.27
36.74 30.35 24.24
36.50 29.92 24.15
0.71 0.72 0.75
2.84 2.15 2.37
Subsoil u0.033 MPa u0.10 MPa u1.50 MPa
37.34 31.28 25.86
37.28 30.83 25.55
37.17 31.25 25.81
0.83 0.80 0.79
2.45 2.25 1.33
a b c d
b
Based on 140 measured values. Based on 280 ordinary point-kriged values. MAE: mean absolute error calculated from cross-validation results. Volumetric water content at 0.033 MPa soil water pressure.
agricultural water management 83 (2006) 79–86
Fig. 4 – Spatial patterns in 280 ordinary point kriging estimated values: (a) at S0.033 MPa, (b) at S0.10 MPa, and (c) at S1.50 MPa soil water pressure in topsoil; and (d) at S0.033 MPa, (e) at S0.10 MPa, and (f) at S1.50 MPa soil water pressure in subsoil.
Correlation coefficient (r) calculated between estimated and measured values can be a good indicator for spread of measured and estimated values on the 458 line. The higher values for r are indicative of closeness of the points on the line (Isaaks and Srivastava, 1989). The Table 5 shows r values greater than 0.7 and low values for MAE, suggesting that point kriging successfully estimated u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa in both topsoil and subsoil. The Table 5 further indicates that the point kriging estimates were more accurate in subsoil compared to topsoil. Pattern in 280 ordinary point-kriged values of u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa in topsoil and subsoil are similar (Fig. 4a–f), suggesting that difference between surface and subsurface do not depend on the location. Regions with low water holding capacity pose a potential thread for water and nutrient (especially nitrate) losses by deep percolation. Limited irrigation scheduling and split application of nitrogen fertilizers may be implemented to reduce nitrate leaching in these localities. This may be achieved by applying limited amount of water at critical grown stages to avoid possible yield reductions caused by water stress. Growing crops with greater water use efficiency may help reduce the quantity of water loss from the root zone by deep percolation. In addition, organic matter content can be improved by application of plant residues and manure to increase water holding capacity in these regions. In contrast, water may be lost via evaporation in the areas with high water holding capacity. Deep rooted crops with high leaf area index may be preferred in these areas to decrease water loss by evaporation and to evaluate water stored in the subsoil. Summary statistics for the estimates and error values cannot provide information on the distribution of the error over the study area. Fig. 5a–f compare the distribution of error values for u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa in topsoil and
85
Fig. 5 – Spatial patterns in the residuals (measured– estimated): (a) at S0.033 MPa, (b) at S0.10 MPa, and (c) at S1.50 MPa soil water pressure in topsoil; and (d) at S0.033 MPa, (e) at S0.10 MPa, and (f) at S1.50 MPa soil water pressure in subsoil.
subsoil, showing that despite the model over estimated in some local areas in topsoil in the west side of the study area, in general, the error values were distributed relatively randomly over the study area both in topsoil and subsoil, which indicated that the ordinary point kriging adequately estimated the water content over the study area. These patterns of over and underestimations, as indicated by Isaaks and Srivastava (1989) is inherent in kriging as it tends to underestimate the high values and overestimate the low values.
4.
Conclusions
Spatial variation in soil water content at 0.033 (u 0.033 MPa), 0.10 (u 0.10 MPa), and 1.50 MPa (u 1.50 MPa) soil water pressures in topsoil (0.00–0.30 m) and subsoil (0.31–0.60 m) of a Typic Ustifluvent was examined using geositatistical methods. Analysis of semivariograms suggested that spatial variations of u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa were similar in both topsoil and subsoil, particularly at the ranges <300 m. The cross-dependence between soil texture and each of u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa indicated that the sand content and silt content, sand content being more effective, were the determinants of the spatial variation in soil water content in both topsoil and subsoil. Ordinary point kriging adequately estimated the u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa values in unsampled locations in both topsoil and subsoil. Spatial variability of soil water is ubiquitous affecting many moisture-dependent processes. The unique relationship between soil water content and soil water potential controls the effectiveness of the soil water to modulate these processes. Strong cross-
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correlation existing between soil water content and sand content in both topsoil and subsoil at all three corresponding soil water potentials studied can be useful in water budget modeling. The relatively uniform distribution of water content by depth, which is controlled by spatial variability of sand content, accommodates determining the management zones by spatial pattern of sand content in a variable water application program to be implemented in this field. The results of this study are important, showing the spatial relationship between soil texture and soil water content at different water potentials that can be of an important value for farmers, decision makers, and researchers.
Acknowledgments The authors thank the Turkish Scientific and Technical Research Institute (TUBITAK) for the financial support provided to this study (Grant: TOAG-TARP 1871).
references
Arya, L.M., Blake, G.R., Farrell, D.A., 1975. A field study of soil water depletion patterns in presence of growing soybean roots. II. Effect of plant growth on soil water pressure and water loss patterns. Soil Sci. Soc. Am. Proc. 39, 430–436. Blake, G.R., Hartge, K.H., 1986. Bulk density. In: Klute, A. (Ed.), Methods of Soil Analysis. Part 1. second ed. Agron. Mongr. 9. ASA, Madison, WI, pp. 363–375. Burden, D.S., Selim, H.M., 1989. Correlation of a spatially variable soil water retention for a surface soil. Soil Sci. 148, 436–447. Cassel, D.K., Bauer, A., 1975. Spatial variability in soil below depth of tillage: bulk density and fifteen atmosphere percentage. Soil Sci. Soc. Am. Proc. 39, 247–250. Gajem, Y.M., Warrick, A.W., Myers, D.E., 1981. Spatial variability of a Typic Torrifluvent soil. Soil Sci. Soc. Am. J. 45, 709–715. Gee, G.W., Bauder, J.W., 1986. Particle size analysis. In: Klute, A. (Ed.), Methods of Soil Analysis. Part 1. second ed. Agron. Mongr. 9. ASA, Madison, WI, pp. 383–411. Greminger, P.J., Sud, Y.K., Nielsen, D.R., 1985. Spatial variability of field-measured soil–water characteristics. Soil Sci. Soc. Am. J. 49, 1075–1082. Goovaerts, P., 1997. Geostatistics for Natural Resource Evaluation. Oxford University Press, New York. Isaaks, H.E., Srivastava, R.M., 1989. An Introduction to Applied Geostatistics. Oxford University Press, New York. Ju´nior, V.V., Carvalho, M.P., Dafonte, J, Freddi, O.S., Va´zques, E.V., Ingaramo, O.E., 2005. Spatial variability of soil water
content and mechanical resistance of Brazilian ferralsol. Soil Tillage Res. Available online at www.sciecendirect.com. Klute, A., 1986. Water retention: laboratory methods. In: Klute, A. (Ed.), Methods of Soil Analysis. Part 1. second ed. Agron. Monogr. 9. ASA, Madison, WI, pp. 635–662. Mallants, D., Binayak, P.M., Diederik, J., Feyen, J., 1996. Spatial variability of hydraulic properties in a multi-layered soil profile. Soil Sci. 161, 167–181. McLean, E.O., 1982. Soil pH and lime requirement. In: Page, A.L. (Ed.), Methods of Soil Analysis. Part 2. second ed. Agron. Monogr. 9. ASA, Madison, WI, pp. 199–224. Mulla, D.J., 1988. Estimating spatial patterns in water content, matric suction, and hydraulic conductivity. Soil Sci. Soc. Am. J. 52, 1547–1553. Mulla, D.J., McBratney, A.B., 2001. Soil spatial variability. In: Sumner, M.E. (Ed.), Handbook of Soil Science. CRC Press, Boca Raton, FL, pp. A321–A352. Nelson, D.W., Sommers, L.E., 1982. Total carbon, organic carbon, and organic matter. In: Page, A.L. (Ed.), Methods of Soil Analysis. Part 2. second ed. Agron. Monogr. 9. ASA, Madison, WI, pp. 539–579. Robertson, G.P., 2000. GS+: Geostatistics for the Environmental Sciences. Gamma Design Software, Plainwell, Michigan, USA. Rockstrom, J., Barron, J., Brouwer, J., Galle, S., de Rouw, A., 1999. On-farm spatial and temporal variability of soil and water in pearl millet cultivation. Soil Sci. Soc. Am. J. 63, 1308–1319. Rogowski, A.S., 1972. Watershed physics: soil variability criteria. Water Resour. Res. 8 (4), 1015–1023. Shouse, P.J., Gerik, T.J., Russell, W.B., Cassel, D.K., 1990. Spatial distribution of soil particle size and aggregate stability index in a clay soil. Soil Sci. 149, 351–360. Timlin, D.J., Pachepsky, Y.A., Walthall, C.L., 2003. A mix of scales: topography, point samples and yields. In: Pachepsky, Y., Rdcliffe, D.E., Selim, H.M. (Eds.), Scaling Methods in Soil Physics. CRC Press, Boca Raton, USA, pp. 227–241. Trangmar, B.B., Yost, R.S., Uehara, G., 1985. Application of geostatistics to spatial studies of soil properties. Adv. Agr. 38, 45–94. van Vesenbeeck, I.J., Kachanoski, R.G., 1988. Spatial and temporal distribution of soil water in the tilled layer under a corn crop. Soil Sci. Soc. Am. J. 52, 363–368. Vauclin, M., Vieira, S.R., Vachaud, G., Nielsen, D.R., 1983. The use of cokriging with limited field observations. Soil Sci. Soc. Am. J. 47, 175–184. Vieira, S.R., Nielsen, D.R., Biggar, J.W., 1981. Spatial variability of field-measured infiltration rate. Soil Sci. Soc. Am. J. 45, 1040– 1048. Western, A.W., Grayson, R.B., Blo¨schl, G., Wilson, D.J., 2003. Spatial variability of soil moisture and its implication for scaling. In: Pachepsky, Y., Radcliffe, D.E., Selim, H.M. (Eds.), Scaling Methods in Soil Physics. CRC Press, Boca Raton, USA, pp. 119–142. Yates, S.R., Warrick, A.W., 1987. Estimating soil water content using cokriging. Soil Sci. Soc. Am. J. 51, 23–30.
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"
Spatial variation of soil water content in topsoil and subsoil of a Typic Ustifluvent Sabit Ersahin *, A. Resit Brohi Department of Soil Science, Agricultural Faculty, Gaziosmanpasa University, 60250 Tokat, Turkey
article info
abstract
Article history:
Water content is a fundamental property affecting plant growth, transport and transforma-
Accepted 18 September 2005
tion of soil nutrients, and water and energy budgets in the soil–plant system. Its spatial
Published on line 13 December 2005
variation has important implications for all these processes. In this study, spatial variation in soil water contents was evaluated at pressures of 0.033 (u
Keywords:
1.50 MPa (u
0.033 MPa),
0.10 (u
0.10 MPa), and
1.50 MPa) at the soil depth from surface to the lower and of a plow layer in a Typic
Soil water content
Ustifluvent in an 8.5 ha level area (1–2% slope). Topsoil (0.0–0.30 m) and subsoil (0.31–0.60 m)
Soil water potential
were sampled based on a regular grid spacing of 25 m-by-25 m. Variables u
Spatial variability
u
Geostatistics Topsoil
0.10 MPa,
and u
1.50 MPa
0.033 MPa,
were measured with a pressure plate apparatus. Results were
evaluated and compared with conventional statistics and geostatistics. Coefficient of variation (CV) for soil water content increased as soil water pressure decreased in both
Subsoil Ustifluvents
topsoil and subsoil. Semivariance analysis and ordinary block kriging-produced surface maps for u
0.033 MPa,
u
0.10 MPa,
and u
1.50 MPa
showed that topsoil and subsoil were similar in
spatial variation of water content at all three corresponding soil water pressures. The geostatistical range values were between 298 m (u
0.10 MPa
in topsoil) and 486 m (u
0.10 MPa
in subsoil), both indicating a strong spatial dependence of soil water content. The semivariograms for u
0.033 MPa,
u
0.10 MPa,
and u
1.50 MPa
were similar in shape in both topsoil and
subsoil. Cross-dependence between sand content and soil water contents showed that soil texture controlled the spatial variation of water contents at the site, at all depths and pressures evaluated. # 2005 Elsevier B.V. All rights reserved.
1.
Introduction
Soil water controls plant growth and influences a variety of soil processes including erosion, chemical exchange, microbial activity, transport of solutes and water, energy balance of the soil–plant system, and pedogenesis (Western et al., 2003). The relationship between water content and water potential determines, in part, the nature of these effects. Soil water represents a small portion of the water in the hydrologic cycle. However, due to its vital role in the ecosystem, the temporal and spatial variability of soil water
has a controlling influence on ecosystem processes at a variety of scales (Western et al., 2003). Soil properties vary spatially. This variability may be random or systematic. Contrary to the random variation, a systematic variation has a consistent spatial pattern which allows one to estimate a particular property at subjected locations. Studies (Vauclin et al., 1983; Greminger et al., 1985; Yates and Warrick, 1987; van Vesenbeeck and Kachanoski, 1988; Mulla, 1988; Rockstro¨m et al., 1999; and many others) showed that soil water content exhibited organized features under majority of the conditions. However, degree of the organization varies based on the soil and climatic conditions.
* Corresponding author. Tel.: +90 356 252 1480x2177; fax: +90 356 252 1488. E-mail address: [email protected] (S. Ersahin). 0378-3774/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2005.09.002
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agricultural water management 83 (2006) 79–86
Spatial variability in soil water potential may be more important than water content because this determines plant water availability and percolation (drainage) rates. Spatial variability in soil water properties have been studied widely since 1975. Most of these studies focused on nature of spatial variability and its relation to soil properties (Cassel and Bauer, 1975; Gajem et al., 1981; Vauclin et al., 1983; Mulla, 1988; Burden and Selim, 1989; Mallants et al., 1996) and others on terrain properties (Western et al., 2003; Timlin et al., 2003), on soil management (Arya et al., 1975; van Vesenbeeck and Kachanoski, 1988), and on spatial interpolation of soil water content (Yates and Warrick, 1987). The knowledge of spatial variability of soil water content within the range of plant available water content is important for managing soil water in spatially variable soils. The characterization of soil water profiles along the root zone may yield important consequences that should be taken into account in modeling water budget and travel time variations in nutrient leaching out of the root zone. The objectives of this study were to analyze and compare spatial variation in soil water content at 0.033, 0.10, and 1.50 MPa soil water pressures; and to assess their spatial correlations with soil textural separates, soil bulk density, and soil organic matter content in topsoil (0.0–0.30 m) and subsoil (0.31–0.60 m) of a Typic Ustifluvent formed under semi-arid conditions.
2.
Materials and methods
2.1.
Study site
November and harvested in mid July, and the sugarbeets is planted in mid March and harvested in late October. In general, about 140 kg N ha 1 is applied in wheat and 250 kg N ha 1 is applied in sugarbeets. The average yield for sugarbeets is 50.0 Mg ha 1, and for wheat is 4.5 Mg ha 1. Wheat is irrigated rarely. However, sugarbeets is irrigated more often.
2.2.
Methods
2.2.1.
Soil sampling and analysis
The field was intensively sampled on a regular grid spacing of 25 m in March, 1998 (Fig. 1). At each sampling site, 140 soil samples were taken from topsoil (0.0–0.30 m) and subsoil (0.31–0.60 m). All 280 samples were analyzed for clay, silt, and sand contents by the hydrometer method (Gee and Bauder, 1986), for organic matter content by Walkley-Black Procedure (Nelson and Sommers, 1982), and for CaCO3 content with a Scheibler Calcimeter (McLean, 1982), and water contents at 0.033 (u 0.033 MPa), 0.10 (u 0.10 MPa), and 1.50 MPa (u 1.50 MPa) soil water pressures were determined with a pressure plate apparatus (Klute, 1986). The bulk density of repacked soils in the rings was determined and then used to calculate the volumetric soil water content from the gravimetric values. In addition, at each test site, undisturbed soil samples were taken from the topsoil and the subsoil using 100 cm3 steel cores to determine soil bulk density at time of sampling (Blake and Hartge, 1986). All the soil analyses were conducted as duplicated.
2.2.2.
This study was conducted as a component of a project to investigate the spatial variability of nitrate leaching in an 8.5ha (850 m 100 m) level (1–2% slope) and well drained alluvial area (loamy mesic Typic Ustifluvents) located in 25 km northeast of Tokat City in Central Anatolia of Turkey. The study area is an old river terrace, 680 m above the see level. Soils investigated are very deep, formed in alluvium over clayey lacustrine deposits. The study area, where an approximately 3 m deep shallow water table is present, was initially a swamp. The area was drained by government in 1970s for agricultural development. The topsoil (0–0.30 m) is characterized by a dark grayish brown color (10YR 4/2; dry); a moderate fine to moderate medium granular structure; common fine and very fine roots; common fine pores; and a clear wavy boundary. A plow layer is present between the 0.31 and 0.60 m depths, which formed as a result of tillage. The plow layer is characterized by a very dark grayish brown color (10YR 2/2; moist); a moderate fine and weak fine platy structure; few root channels filled with dark material from the upper horizon; few, fine tubular pores; common fine iron manganese concretions; and many prominent dark brown (10YR 3/3; moist) clay films on ped faces. The annual average precipitation is 420 mm most of which falls between October and May, and the annual average air temperature is 12 8C. The area has been under a rotation of winter wheat (Triticum aestivum L.) and sugarbeet (Beta vulgaris L.) for at least 10 years at the discretion of the farmer. Both crops are grown in a single year. The wheat is planted in early
Descriptive statistics
Descriptive statistics including minimum, maximum, mean, standard error of mean, coefficient of variation, skewness, and kurtosis were calculated for each variable. The Shapiro–Wilks normality test was conducted to test the hypothesis that assumes each property has a normal distribution.
2.2.3.
Geostatistical analysis
Sample semivariogram functions for u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa were calculated (Isaaks and Srivastava, 1989; Goovaerts, 1997). Hypothetical semivariogram models were fitted to experimental semivariograms. The best models were determined by the leaving-one-out method of cross-validation (Yates and Warrick, 1987; Shouse et al., 1990). To crossvalidate, we remove one known value at a time from the data set and estimate this value from a neighborhood around it, but not itself. This procedure allows comparisons of the different models and search strategies on the results of interpolation (Goovaerts, 1997). To determine the anisotropy, lagged distances corresponding to the fixed sample semivariance values were determined in the directions 08, 458, 908, and 1358 and then the resulting distances were plotted on a rose
Fig. 1 – Layout of the experiment: x represents location of soil sampling.
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agricultural water management 83 (2006) 79–86
diagram. The severity and direction of geometric anisotropy was determined comparing minor and major axes of resultant ellipse (Isaaks and Srivastava, 1989). Since the shape of the field was long and narrow (100 m wide and 850 m long), the test of anisotropy was confined in the lagged distance 75 m.
2.2.4.
saturation (us) was the least and CaCO3 was the most variable in the topsoil, while organic matter content was the most and silt content was the least variable in the subsoil (Tables 1 and 2). Water content values at 0.033, 0.10, and 1.50 MPa soil water pressures (u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa, respectively) showed a left skewed distribution. The degree of skewness was greater in subsoil. Soil water contents had a relatively flat distribution in subsoil compared to topsoil. Plant available water content (PAW, %) exhibited a similar variation in both topsoil and subsoil (Tables 1 and 2). In both topsoil and subsoil, u0.033 MPa, u0.10 MPa, and u1.50 MPa exhibited a high variability. Similar results were reported by Greminger et al. (1985). Cassel and Bauer (1975) reported a 19% of CV for u 1.50 MPa of a silty loam, highly similar to values obtained in our study. Rogowski (1972) suggested that a soil may be considered uniform in the case of that CV in u1.50 MPa does not exceed 15%. Our values for CV% for u1.50 MPa in both topsoil and subsoil exceeded the above specified value by Rogowski. Decreasing water potential resulted in CV of soil water content to slightly increase in both topsoil and subsoil (Tables 1 and 2). Burden and Selim (1989) reported CV% values of 9.6, 17.7, and 20.5 for u 0.03 MPa, u 0.10 MPa, and u 1.50 MPa, respectively, from their study conducted on a bare field of Oliver silt loam (fine-silty, mixed, thermic, Aquic Fragiudalfs), sampled 0–10 cm depth by 30 cm intervals. Mulla and McBratney (2001) reported CV% ranges from 14 to 45% for u 1.50 MPa and from 4 to 20% for u 0.010 MPa, indicating lower values of CV% at greater soil water potentials. Western et al. (2003) studied soil water measured over different (and sometimes multiple) depths at 13 areas around the world, with climate changing from semi-arid to humid, soils ranging from sand to clay, and topography from gently undulating to steep. They observed that the variance increased with average moisture in dry catchments and decreased when average water content increased in wet catchments. However, where the mean moisture had a sufficiently large range over time, the variance peaked at intermediate values. Mallants et al. (1996) reported results on the hydraulic properties of a well-drained sandy loam along a 30 m transect.
Spatial estimations
Ordinary point kriging procedure was applied to estimate u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa at unsampled locations of the field, using the version 5.0 of GS+ (Robertson, 2000). The water content values were estimated within a 12.5 m-by12.5 m grid. Each time, the estimates in kriging were controlled by the procedure cross-validation to determine a suitable number of neighboring data points to be used in the estimation (Vieira et al., 1981). Linear correlation coefficient calculated between measured and calculated values, and mean absolute error (MAE) calculated using residuals were considered, and the normality of residuals from crossvalidation was tested to check the validity of kriging estimations. A maximum of 15 and a minimum of 10 neighboring data points were used along with semivariograms in kriging estimation. The surface maps of estimates from point kriging and surface maps of error values were examined to asses the quality of the estimations.
3.
Results and discussion
3.1.
Characterization of soil properties
The average textural classification of topsoil (0.0–0.30 m) was loam and of subsoil (0.31–0.60 m) was clay loam. However, in some areas of the field, loamy clay, and silty clay textures were present in the topsoil, and loamy clay in the subsoil. The average organic matter content was greater in the topsoil, and the average bulk density was greater in the subsoil probably due to the plow layer. Calcium carbonate bounds and groups the soil particles together to form stable lumps or aggregates, improving the water holding capacity of the soils, being important especially in the local areas with high sand and low organic matter content. Volumetric water content at
Table 1 – Summary statistics for some properties of topsoil Soil property Sand (%) Silt (%) Clay (%) Bulk density (Mg m 3) us (%)a u 0.033 MPa (%)a u 0.10 MPa (%) u 1.50 MPa (%) PAWC (%)a CEC (cmol kg 1) CaCO3 (%) Organic matter (%) a
Mean
Minimum
Maximum
33.2 41.9 24.9 1.25 52.89 36.8 30.4 24.3 12.5 0.41 14.4 3.8
21.9 30.8 17.8 1.03 46.0 25.6 19.7 7.5 4.2 0.20 7.4 1.9
46.9 52.2 31.1 1.43 61.1 49.2 37.7 34.6 21.4 1.02 20.9 5.6
CV (%)a 18.2 12.3 10.4 8.1 7.3 14.4 13.4 20.1 25.4 29.3 40.4 21.6
Sea 0.51 0.44 0.22 8.5 10 0.32 0.45 0.35 0.41 1.06 9.8 10 0.21 0.32
3
3
Ska
Kra
0.13 0.06 0.01 0.19 0.18 0.16 0.65 0.58 0.37 1.56 0.11 0.14
2.25 2.14 3.65 1.82 1.82 2.32 2.62 3.22 3.82 8.32 2.75 2.25
CV: coefficient of variation; Se: standard error of mean; Sk: skewness; Kr: kurtosis; PAWC: plant available water content (volumetric); u 0.033 MPa: volumetric water content at 0.033 MPa soil water pressure; us: saturated water content (volumetric); CEC: cation exchange capacity.
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agricultural water management 83 (2006) 79–86
Table 2 – Summary statistics for some properties of subsoil Soil property
Mean
Sand (%) Silt (%) Clay (%) Bulk density (Mg m 3) us (%)a u 0.033 MPa (%)a u 0.10 MPa (%) u 1.50 MPa (%) PAW (%)a CEC (cmol kg 1)a CaCO (%) Organic matter (%) a
20.9 43.9 35.2 1.33 49.80 37.2 31.3 25.9 11.4 0.37 15.5 1.4
Minimum 7.9 35.2 22.1 1.14 35.85 15.7 11.2 8.7 3.5 0.01 7.3 0.01
Maximum
CV (%)a
Sea
Ska
Kra
37.8 56.3 49.6 1.70 56.98 46.9 40.4 35.1 25.4 0.68 22.6 4.0
31.9 9.1 14.6 9.7 9.7 17.6 18.3 19.4 28.3 39.9 19.6 62.9
0.56 0.34 0.44 0.01 0.41 0.55 0.48 0.42 0.27 0.01 0.26 0.08
0.34 0.05 0.37 0.87 0.87 0.95 1.04 1.05 0.71 0.57 0.39 0.46
2.54 2.29 2.98 3.04 3.04 3.58 3.97 4.32 5.29 3.18 2.99 2.76
CV: coefficient of variation; Se: standard error of mean; Sk: skewness; Kr: kurtosis; PAWC: plant available water content (volumetric); u 0.033 MPa: volumetric water content at 0.033 MPa soil water pressure; us: saturated water content (volumetric); CEC: cation exchange capacity.
In their soil horizons, CV of water contents at 0.33 and 1.50 MPa were, respectively; 13 and 10% in the Ap horizon, 8 and 13% in the C1 horizon, and 6 and 15% in the C2 horizon. The increase in CV with decreasing soil water potential generally agrees to our findings (Tables 1 and 2).
3.2.
Geostatistical parameters
Table 3 lists the geostatistical parameters for isotropic semivariograms of u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa in topsoil and subsoil. The anisotropy was checked in the directions 08, 458, 908, and 1358 at the lagged distance 75 m, as described under Section 2. Nugget variance (C0) represents variance due to measurement error or micro-variability of the property which cannot be detected with current scale of sampling. The semivarince increases as separation distance between sample locations increases, rising to an approximately constant value called sill (C0 + Cs) (Trangmar et al., 1985; Goovaerts, 1997). The difference between sill and nugget variance is called the structural component (Cs), and ratio of C0 to C0 + Cs is called the nugget effect (Mallants et al., 1996). Ju´nior et al. (2005) reported from their study that spatial structure of gravimetric moisture content of surface soil decreased due to the damage caused by tillage on the soil structure, introducing an important source of randomness. They further stated that the effect of tillage decreased with the increase of the sampling depth, where the
effect of tillage on the soil structure is weaker. In our study, greater values for nugget effect (C0/C0 + Cs) occurred in topsoil, indicating a greater short-range variation of water contents at the corresponding water potentials. We believe that relatively greater nugget variance in topsoil was caused by conventional tillage on soil properties that resulted in more heterogeneous conditions. However, in general, little nugget effect occurred for water content at the corresponding soil water potentials in subsoil (Table 3). The semivariogram range depends on the scale of observation, as well as factors that influence the soil’s spatial variation (Trangmar et al., 1985; Isaaks and Srivastava, 1989; Goovaerts, 1997). Gajem et al. (1981) reported that geostatistical ranges for water contents at 0.10 and 1.50 MPa increased as sampling interval increased. They obtained a geostatistical range of 0.6 m for u 1.50 MPa with 0.20 m sampling-spacing and of 15 m with 20 m samplingspacing. They further observed a similar increase in geostatistitcal range value for u 0.10 MPa. Vauclin et al. (1983) reported a range value of 26.0 m from their study conducted in a 70 m-by-40 m field, measuring soil water content retained at 0.033 MPa in the bulk samples that they collected from 20–40 cm depth on a square grid at every 10 m. Our findings of geostatistical range values indicated that u 0.03 MPa, u 0.10 MPa, and u 1.50 MPa were spatially dependent over a distance of approximately 350 m in topsoil and 480 m in subsoil (Table 3 and Fig. 2a–f).
Table 3 – Coefficients of the theoretical semivariograms of soil water content at S0.033, S0.10, and S1.50 MPa soil water pressures in topsoil and subsoil Model
C0a
Csa
C0/(C0 + Cs)
Range (m)
RSSa
R2
Topsoil u 0.03 MPa (%)a u 0.10 MPa (%) u 1.50 MPa (%)
Spherical Spherical Spherical
9.25 5.28 6.68
35.65 20.47 30.96
0.26 0.26 0.22
375 280 369
95.1 48.9 146.0
0.95 0.96 0.94
Subsoil u 0.033 MPa (%)a u 0.10 MPa (%) u 1.50 MPa (%)
Spherical Spherical Spherical
3.50 4.10 5.97
65.08 50.18 36.61
0.05 0.08 0.16
455 486 481
492 246 148
0.91 0.92 0.89
Variable
a
Volumetric water content at
0.033 MPa soil water pressure; C0: nugget variance; Cs: structural component; RSS: residual sum of squares.
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agricultural water management 83 (2006) 79–86
Fig. 2 – Semivariograms for volumetric water content (%): (a) at S0.033 MPa, (b) at S0.10 MPa, and (c) at S1.50 MPa soil water pressure in topsoil; and (d) at S0.033 MPa, (e) at S0.10 MPa, and (f) at S1.50 MPa soil water pressure in subsoil. Solid lines represent the spherical model fitted to experimental values.
The shape of the experimental semivariogram may differ depending on data, sampling scheme, and sampling interval used (Trangmar et al., 1985). Semivariogram also indicates spatial processes of the soil at the sampling scale (Trangmar et al., 1985). Fig. 2a–f show relatively similar shape of semivariograms for water content values at corresponding water potentials, which indicates presence of a strong crossdependence between soil water content and some soil
properties that influence the spatial variability in topsoil and subsoil. Cross-semivariogram analyses were conducted between water contents (at 3 potentials) and soil properties (organic matter content, bulk density, and textural separates) to identify those soil properties with the strongest influence on spatial patterns of water content. Of these cross-semivariograms, those between water content and textural separates, particularly silt and sand, showed the strongest cross-depen-
Table 4 – Coefficients of the theoretical cross-semivariograms between sand content and soil water content at S0.033, S0.10, and S1.50 MPa soil water pressures in topsoil and subsoil Model
C0a
Csa
Topsoil u 0.033 MPa sanda u 0.10 MPa sand u 150 MPa sand
Gaussian Gaussian Gaussian
0.01 0.02 0.01
26.20 18.64 24.73
3.8 10 1.1 10 4.0 l0
Subsoil u 0.033 MPa sand u 0.10 MPa sand u 150 MPa sand
Gaussian Gaussian Gaussian
0.02 0.01 0.01
31.01 30.92 21.01
6.4 10 3.2 10 4.8 10
Variable
a
Volumetric water content (%) at
C0/(C0 + Cs) 4 5 4
4 4 4
Range (m)
RSSa
R2
649 545 621
53.0 40.2 74.5
0.96 0.95 0.96
737 862 746
118.0 22.9 18.7
0.96 0.99 0.99
0.033 MPa soil water pressure; C0: nugget variance; Cs: structural component; RSS: residual sum of squares.
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agricultural water management 83 (2006) 79–86
Fig. 3 – Cross-semivariograms for between sand content and volumetric water content (%): (a) at S0.033 MPa, (b) at S0.10 MPa, and (c) at S1.50 MPa soil water pressure in topsoil; and (d) at S0.033 MPa, (e) at S0.10 MPa, and (f) at S1.50 MPa soil water pressure in subsoil. Solid lines represent the gaussian model fitted to experimental values.
dence and were similar in shape (Fig. 3 and Table 4 show the cross-semivariograms for sand). This suggested that soil texture was responsible for the dominant spatial patterns of water contents in topsoil and topsoil at all three water potentials. All cross-semivariograms in Fig. 3a–f and Table 4 are Gaussian. The parabolic behavior of the curve near the origin, typical for Gaussian type of semivariograms as stated by Isaaks and Srivastava (1989), indicated that the sand content was not effective to control the spatial variations in soil water content within short distances but longer distances.
3.3.
Estimation
Ordinary point kriging procedure was used along with isotropic semivarograms to estimate water content at corresponding soil water pressures. Based on the results from the cross-validation procedure, a search ellipse with a radius of 200 m was used, and a minimum of 10 and a maximum of 15 closest data values were included in the kriging estimations. Table 5 compares measured values with kriged and crossvalidated values against some of the estimation criteria.
Table 5 – Results of cross-validation and ordinary point kriging estimations of water content at corresponding soil water pressures in topsoil and subsoil Variable
Mean a
Cross-validation
Experimental
Ordinary point kriging
Mean
r
MAEc
Topsoil u0.033 MPad u0.10 MPa u1.50 MPa
36.78 30.40 24.27
36.74 30.35 24.24
36.50 29.92 24.15
0.71 0.72 0.75
2.84 2.15 2.37
Subsoil u0.033 MPa u0.10 MPa u1.50 MPa
37.34 31.28 25.86
37.28 30.83 25.55
37.17 31.25 25.81
0.83 0.80 0.79
2.45 2.25 1.33
a b c d
b
Based on 140 measured values. Based on 280 ordinary point-kriged values. MAE: mean absolute error calculated from cross-validation results. Volumetric water content at 0.033 MPa soil water pressure.
agricultural water management 83 (2006) 79–86
Fig. 4 – Spatial patterns in 280 ordinary point kriging estimated values: (a) at S0.033 MPa, (b) at S0.10 MPa, and (c) at S1.50 MPa soil water pressure in topsoil; and (d) at S0.033 MPa, (e) at S0.10 MPa, and (f) at S1.50 MPa soil water pressure in subsoil.
Correlation coefficient (r) calculated between estimated and measured values can be a good indicator for spread of measured and estimated values on the 458 line. The higher values for r are indicative of closeness of the points on the line (Isaaks and Srivastava, 1989). The Table 5 shows r values greater than 0.7 and low values for MAE, suggesting that point kriging successfully estimated u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa in both topsoil and subsoil. The Table 5 further indicates that the point kriging estimates were more accurate in subsoil compared to topsoil. Pattern in 280 ordinary point-kriged values of u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa in topsoil and subsoil are similar (Fig. 4a–f), suggesting that difference between surface and subsurface do not depend on the location. Regions with low water holding capacity pose a potential thread for water and nutrient (especially nitrate) losses by deep percolation. Limited irrigation scheduling and split application of nitrogen fertilizers may be implemented to reduce nitrate leaching in these localities. This may be achieved by applying limited amount of water at critical grown stages to avoid possible yield reductions caused by water stress. Growing crops with greater water use efficiency may help reduce the quantity of water loss from the root zone by deep percolation. In addition, organic matter content can be improved by application of plant residues and manure to increase water holding capacity in these regions. In contrast, water may be lost via evaporation in the areas with high water holding capacity. Deep rooted crops with high leaf area index may be preferred in these areas to decrease water loss by evaporation and to evaluate water stored in the subsoil. Summary statistics for the estimates and error values cannot provide information on the distribution of the error over the study area. Fig. 5a–f compare the distribution of error values for u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa in topsoil and
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Fig. 5 – Spatial patterns in the residuals (measured– estimated): (a) at S0.033 MPa, (b) at S0.10 MPa, and (c) at S1.50 MPa soil water pressure in topsoil; and (d) at S0.033 MPa, (e) at S0.10 MPa, and (f) at S1.50 MPa soil water pressure in subsoil.
subsoil, showing that despite the model over estimated in some local areas in topsoil in the west side of the study area, in general, the error values were distributed relatively randomly over the study area both in topsoil and subsoil, which indicated that the ordinary point kriging adequately estimated the water content over the study area. These patterns of over and underestimations, as indicated by Isaaks and Srivastava (1989) is inherent in kriging as it tends to underestimate the high values and overestimate the low values.
4.
Conclusions
Spatial variation in soil water content at 0.033 (u 0.033 MPa), 0.10 (u 0.10 MPa), and 1.50 MPa (u 1.50 MPa) soil water pressures in topsoil (0.00–0.30 m) and subsoil (0.31–0.60 m) of a Typic Ustifluvent was examined using geositatistical methods. Analysis of semivariograms suggested that spatial variations of u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa were similar in both topsoil and subsoil, particularly at the ranges <300 m. The cross-dependence between soil texture and each of u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa indicated that the sand content and silt content, sand content being more effective, were the determinants of the spatial variation in soil water content in both topsoil and subsoil. Ordinary point kriging adequately estimated the u 0.033 MPa, u 0.10 MPa, and u 1.50 MPa values in unsampled locations in both topsoil and subsoil. Spatial variability of soil water is ubiquitous affecting many moisture-dependent processes. The unique relationship between soil water content and soil water potential controls the effectiveness of the soil water to modulate these processes. Strong cross-
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correlation existing between soil water content and sand content in both topsoil and subsoil at all three corresponding soil water potentials studied can be useful in water budget modeling. The relatively uniform distribution of water content by depth, which is controlled by spatial variability of sand content, accommodates determining the management zones by spatial pattern of sand content in a variable water application program to be implemented in this field. The results of this study are important, showing the spatial relationship between soil texture and soil water content at different water potentials that can be of an important value for farmers, decision makers, and researchers.
Acknowledgments The authors thank the Turkish Scientific and Technical Research Institute (TUBITAK) for the financial support provided to this study (Grant: TOAG-TARP 1871).
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