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The spatial variability of erodibility and its relation to soil types: a study from northern Ghana
Geoderma 106 Ž2002. 101–120 www.elsevier.comrlocatergeoderma
The spatial variability of erodibility and its relation to soil types: a study from northern Ghana Anita Veihe ) Department of Geography and International DeÕelopment Studies, Roskilde UniÕersity, Hus 19.2, P.O. Box 260, 4000 Roskilde, Denmark Received 14 March 2000; received in revised form 11 December 2000 and 27 June 2001; accepted 9 July 2001
Abstract In many parts of Africa, where regional mapping of soil erosion risk or related land-use planning exercises take place, existing soil maps are the only sources of information available when estimating erodibility. The current study examines the spatial variability of erodibility and the extent to which it is related to soil types based on a case study from northern Ghana. Soil samples from 136 test sites were analysed to determine erodibility parameters and surface characteristics were described in the field. Through ANOVA analysis, the intraclass correlations and within-class variations related to soil types were examined which showed an overall poor relationship. The fraction of sand was found to be the best indicator of erodibility due to its low spatial heterogeneity and determined the friability of water-stable aggregates. Further studies investigating the uncertainty associated with the use of soil maps in erosion studies should be carried out with the aim of producing soil maps with statistical information relevant for land resource studies. q 2002 Elsevier Science B.V. All rights reserved. Keywords: Erodibility; Spatial variability; USLE; Savanna ecosystem; Ghana
1. Introduction Soil erodibility expresses the soil’s susceptibility to erosional processes and can be defined as ‘the ease with which soil is detached by splash during rainfall andror by the shear of surface flow’ Ž Young et al., 1990. . The concept of erodibility and how to assess it is complicated since the susceptibility of the soil to erosion is influenced by a large number of properties such as physical, )
Fax: q45-46-74-30-32. E-mail address: [email protected] ŽA. Veihe..
0016-7061r02r$ - see front matter q 2002 Elsevier Science B.V. All rights reserved. PII: S 0 0 1 6 - 7 0 6 1 Ž 0 1 . 0 0 1 2 0 - 3
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mechanical, hydrologic, chemical, rheological, mineralogical and biological, not to mention the soil profile characteristics such as the depth of the soil and its influence on vegetative growth. Two major time-dependent subprocesses related to sheet erosion have been identified. Initially, just after tillage, agricultural soils are considered noncoherent, the reason why entrainment forces will be resisted by particle size, shape and density. A measure suitable for an assessment of erodibility at this stage is aggregate stability or any related index. Aggregate breakdown is also the main process responsible for crusting, a phenomenon considered to be widespread in the savanna ecosystem. During storms, however, the surface progressively evolves into a coherently sealed or crusted structure. This is the reason why erodibility in this situation is determined by the cohesion of the soil, a parameter that can be measured with a torvane. Shear strength is, nevertheless, linked to some of the same bonding mechanisms involved in aggregation Ž Bryan, 2000; Bryan et al., 1989.. Several attempts have been made to devise a simple index of erodibility based on the properties of the soil determined either in the laboratory or in the field. Finding a suitable erodibility index for soils in the savanna ecosystem specifically, poses a number of problems because the majority of the existing indices were developed for soils from temperate regions. For an erosion risk assessment, the use of the erodibility factor K in the Universal Soil Loss Equation ŽUSLE. is desirable because it makes the erodibility assessment comparable with other assessments in the region. The K-value represents the soil loss per unit of EI 30 , as measured in the field on a standard plot with a length of 22 m and 9% slope ŽWischmeier and Smith, 1978.. Whereas the USLE is applicable to soils in West Africa, where kaolinite is the dominant clay mineral ŽRoose, 1977., it may not be applied to vertisols characterised by expanding clay minerals and soils with a high-coarse sand fraction ŽRoose, 1977; Vanelslande et al., 1987. . These soils appear to be far more erodible than what the USLE K factor indicate, while loamy and silt loamy soils with a high silt content appeared to have lower K factor values ŽVanelslande et al., 1987. . In fact, several studies have been unable to find a correlation between predicted and observed K factors and generally found that the USLE K factor was being underestimated Ž Vanelslande et al., 1987; Jaiyeoba and Ologe, 1990; Igwe et al., 1995; Torri et al., 1997. . Estimation of K factors from soil types in general can be problematic because soil classifications are often not based on those parameters reflecting erodibility ŽRoose and Sarrailh, 1990.. Many soil maps in Africa do not contain detailed information on soil texture and during soil surveys, emphasis is placed on map legends and soil classification schemes, whereas the interpretation of soils in terms of land evaluation is scarce. Nevertheless, it is useful to know the spatial distribution of different soil characteristics and through geostatistical procedures, it is possible to check existing map legends Ž Bouma, 1985. . In many
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parts of West Africa, the only means of carrying out regional erosion risk assessments and related land-use planning exercises is to use existing soil maps as erodibility maps and knowledge about the uncertainty associated with the use of these maps becomes essential. The aim of this study was to evaluate the extent to which a FAO soil map in Ghana can be used as an erodibility map for erosion risk assessments at a regional scale. This was done by investigating the spatial variability of potential soil erodibility and its relation to soil type and to assess the uncertainty of erodibility assessments based on point soil sampling without taking into account the issue of scale. Future erodibility assessment and mapping in the savannah ecosystem of northern Ghana is discussed. 2. Materials and methods 2.1. Soil location and sampling The current study was carried out in the Bolgatanga district of Upper East Region, Ghana. The soils are characterised as coarse textured with extreme moisture relationships, having a low accumulation of organic matter Ž mostly less than 2%., a low cation-exchange capacity and low fertility Ž Quansah, 1990; Asiamah, 1992; Berhe and Tetteh, 1993. . Adu Ž 1972. made the first soil map of the region at a scale of 1:250,000 where 55 soil series were given names after local towns and rivers. This soil map has been redrawn at a scale of 1:125,000 classified based on the morphological characteristics into the Revised Legend of the FAO-UNESCO Legend of the soil map of the World Ž Asiamah, 1992; personal communication.. The collection of soil samples was carried out using systematic sampling as described by Webster and Oliver Ž1990. and aided by Global Positioning System ŽGPS. . The 10 = 10-km grid covered four major soil types being Luvisolsr Lixisols, Leptosols, Plinthosols and Vertisols Ž Asiamah, 1992. and consisted of 100 measurement points ŽFig. 1.. Within each of the soil types, smaller grids were laid out consisting of nine sampling points each with a spacing of 60 m to enable sampling within single fields. The smaller grids also made it possible to investigate the variation of the erodibility parameters in relation to scale and it ensured sufficient samples for a statistical analysis for the four soil types. However, sampling points from the small grids were not used for kriging due to their specific location within the larger grid. At each sampling site, a general description was made according to the FAO guidelines for soil description ŽFAO, 1990. and soil texture was systematically determined in the field as described in detail by Møberg et al. Ž1990.. This consisted of identifying sand grains and rolling out soil samples in the hand with varying soil moisture content in order to estimate the span between the liquid limit and the plastic limit. Surface soil samples were collected and this was followed by the
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Fig. 1. Location of the study area in Upper East Region and the grid used for soil sampling.
determination of shear strength on saturated soil using a torvane Ž type SOILTEST CL-600A. and soil strength using a pocket penetrometer Ž type SOILTEST CL-700A.. Ten replicates were made within 1 m2. 2.2. Laboratory analysis Aggregate stability was determined by wet-sieving using a method proposed by Hartge and Horn Ž 1989. but modified according to Schjønning Ž personal communication.. The modification meant that sieves with diameters of 8, 5, 3 and 2 mm were used rather than 8, 6, 4 and 2 mm and because of the weak aggregate structure, aggregates were not broken prior to sieving. Furthermore, stones identified visually were removed from the sieves prior to weighing the material to ensure measurements were done on aggregates only. This proved of particular importance due to the high stone content of the soils. Organic matter content was estimated by determining the carbon content through dry combustion using a LECO apparatus. Iron and aluminium associated with organic
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matter was extracted with sodium pyrophosphate as described by Sheldrick Ž1984., since this had proven to be related to aggregate stability in previous studies in the region Ž Folly, 1995.. 2.3. Statistics, geostatistics and erodibility modelling The data were analysed using descriptive statistics, including the calculation of coefficient of variations of the means of the studied soil parameters in order to use it as indicators of spatial heterogeneity. Since data were spatially clustered due to preferential sampling, an analysis was done on both clustered and declustered data. The cell-declustering technique described by Goovaerts Ž1997. was used whereby data in densely sampled areas Ž smaller grids. receive less weight than those in sparsely sampled areas Ž larger grid. . The equal-weighted mean Ž m ˆ . is, therefore, calculated as: 1 B m Ž1. ˆ s Ý mˆ b B bs1 where B is the number of cells that contain at least one datum; m ˆ is the equal-weighted mean of z-values within cell b. The declustered variance Ž sˆ 2 . is then computed as ŽGoovaerts, 1997.: n
sˆ 2 s Ý la z Ž ua . y m ˆ
2
Ž2.
as1
where la are the declustering weights; z Ž ua . is the value at a datum location. An analysis of variance Ž ANOVA. was carried out as described by Webster and Beckett Ž1968. and Webster and Oliver Ž 1990. in which the total, the within-class and the among-class variances were calculated and compared based on the assumption that data sets are normally distributed. Histograms were, hence, produced for the various erodibility parameters. The within-class variation Ž s w2 ., which in this case refers to the variation of parameters within a particular soil type, was calculated by pooling the individual ones: k
nk
Ý Ý Ž xi j yxi . s w2 s
is1 js1 k
2
Ž3.
Ý Ž n i y 1. is1
where k is the number of classes, the ith class containing n i observations; x is the mean for the whole sample; x i is the mean for the ith class. To obtain information about the correlation among individuals within the same class Ž soil type., the intraclass correlation Ž r i . , was calculated: ri s
sT2 s w2 q sT2
Ž4.
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where: sT2 s Ž T y s w2 . rn 0 Ž5. and where T is an estimate of the variance between classes, whereas n 0 is an adjustment necessary because of n i being different from n: k
n0 s
Ts
1 ky1 1
Ý n2i Ny
is1
N
k
Ý ni Ž x i y x . ky1
0 2
Ž6. Ž7.
is1
where N is the total size of the sample.
Fig. 2. Overall methodology for assessing the use of the FAO soil map as an erodibility map Žnames in italics refer to software packages..
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Kriging was then used to estimate the values at unsampled places of properties that vary in space. The difference as compared to a linear estimation is that data carry different weights according to their positions both in relation to the unknown point and to one another ŽOliver and Webster, 1991. . In order to describe mathematically the way the variance of a property changes as the distance and direction separating any two points vary, a semivariogram is used derived with the Variowin software Ž Pannatier, 1994. . The semivariance is estimated as:
g Ž h. s
1
N Ž h.
Ý z Ž x i . y z Ž x i q h. 2 N Ž h . iy1
2
Ž8.
with w z Ž x i ., z Ž x i q h.x s a pair of observations of the variable at any two points; h s lag Ž a vector having both distance and direction. ; N Ž h. s the number of pairs separated by h. Point data were used to draw contour maps using the Surfer software Ž Surfer, 1995. using either kriging or interpolation. The maps were exported to ArcrInfo Ž1996. to be overlain on the FAO soil map and this provided the basis of the validation of the soil maps for their use as erodibility maps. The overall methodology is outlined in Fig. 2.
3. Results and discussion 3.1. Characteristics of erodibility soil parameters The declustering of data showed that in general the preferentially sampled values had little influence on the histograms except for the iron and aluminium content for the vertisols in particular. Hence, soil properties were characterised based on the declustering results ŽTable 1., whereas the ANOVA analysis has been carried out using the clustered data. No correlation was found between the various erodibility parameters though a correlation had been found between iron and aluminium associated with organic matter and aggregate stability in a previous study within the region Ž Folly, 1995.. Whereas the lack of correlation between organic matter content and shear strength corresponds with findings from Australia by Spain et al. Ž 1990. , they are in disagreement with Davies Ž1985. studies on English soils. This supports the idea that findings from one region cannot necessarily be extrapolated to other ecological zones. The average weight diameter Ž DVGD. and the mean weight diameter ŽMWD. are virtually the same for all soil types ŽTable 1. . Aggregate stability was the same magnitude when compared with previous studies in the region Ž Folly, 1995., but was much lower than in Denmark Ž Schjønning and Rasmussen, 1989.. The impact of aggregate stability on erodibility mostly points to its
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Table 1 Characteristic soil properties The equal-weighted mean Ž m ˆ . and the coefficient of variation ŽCV. for the four soil types Žluvisolsrlixisols, leptosols, plinthosols and vertisols. Luvisolsrlixisols Leptosols DVGD Žmm. STAB Ž%. STAB532 Ž%. STAB53 Ž%. STAB5 Ž%. MWD Žmm. OMC Ž%. Fe Ž‰. Al Ž‰. Stones Ž%. Clay Ž%. Silt Ž%. Sand Ž%. Shear strength ŽkPa. Soil strength ŽkPa.
Plinthosols
Vertisols
m ˆ
CV
m ˆ
CV
m ˆ
CV
m ˆ
CV
2.3 52.8 44.7 39.8 31.5 3.3 1.8 0.29 0.26 14.9 12.8 25.9 61.3 40.9 41.2
0.34 0.33 0.40 0.49 0.58 0.18 0.47 0.66 0.86 0.93 0.53 0.37 0.23 0.21 0.19
2.0 61.5 52.6 47.2 40.0 3.3 3.0 0.33 0.28 38.9 15.8 36.9 47.3 35.2 34.8
0.44 0.27 0.32 0.41 0.51 0.21 0.42 0.55 0.66 0.87 0.38 0.40 0.36 0.26 0.27
2.1 52.9 45.1 39.2 33.0 3.2 2.3 0.45 0.26 12.4 13.3 30.0 56.7 40.1 49.5
0.35 0.33 0.40 0.52 0.82 0.13 0.34 0.34 0.83 1.55 0.35 0.24 0.21 0.28 0.30
2.2 50.4 45.3 40.6 35.9 2.4 3.2 0.38 0.37 12.4 12.3 33.9 53.8 38.0 39.7
0.40 0.35 0.38 0.46 0.53 0.44 0.27 0.69 0.53 1.06 0.30 0.44 0.32 0.24 0.37
DVGD: change in average weight diameter Žmm., STAB: aggregates which have not been shattered to a diameter -1 mm, STAB532: stable aggregates 2–3 mm, STAB53: stable aggregates 3–5 mm, STAB5: stable aggregates 5–8 mm, MWD: mean weight diameter, OMC: organic matter content.
significance for surface sealing and crusting Ž Casenave and Valentin, 1989; Bissonais, 1996. and with the low aggregate stability observed in the study area, it is remarkable that no crusting phenomena were identified during the field survey. According to Valentin Ž personal communication. , this may be due to faunal activity that seasonally can destroy crusts as observed in the northern part of the Ivory Coast. On the other hand, crusts may exist without being conspicuous because they are structural andror depositional as described by Valentin Ž1993.. The average organic matter content was in the range 1.8–3.2% which categorises the soil as erodible ŽEvans, 1980. . Not much difference was found between the soil types in terms of iron and aluminium content except for the plinthosols with a higher iron content as compared to the other soil types. The textural composition of the soil types was quite similar and with the clay content being much lower than expected for the vertisols. This may be attributed to the scale of the map where inclusions of other soil types within the vertisols is to be expected. The surface stone content varied considerably between the leptosols with a stone content of 38.9% and the remaining soil types with a stone content between 12.4% and 14.9%. The majority of the sites had stones in the size range
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0.2–0.6 cm though medium and course stones Ž 0.6–6 cm. were found in some places. However, the varying percentages of embedding in the topsoil found in the study area proved a problem in terms of assessing the effect of stone content on soil erosion. Shear strength values for the study area on average ranged from 35.2 to 40.9 kPa, with the leptosols having lower values as compared to the other soil types. Observed shear strength values were rather high as compared to recommended guide values for both compacted and uncompacted soils in the European Soil Erosion Model Ž EUROSEM. User Guide Ž Morgan et al., 1998. based on the study of English soils. Similar observations have been made by Rey Ž personal communication. and Zimbone et al. Ž 1996. on soils in Mexico and Italy, respectively. When it comes to soil strength, only slight differences were observed between the various soil types with figures ranging from 34.8 kPa for the leptosols to 49.5 kPa for the plinthosols. 3.2. Spatial heterogeneity of erodibility soil parameters The coefficient of variation overall varied between 0.13 and 1.55, with spatial heterogeneity being high particularly for the stone content, aggregate stability and the content of iron and aluminium. The spatial heterogeneity of the stone content may be controlled by hillslope gradient as indicated by Poesen et al. Ž1998., who found the spatial variability to vary according to slope position with the highest variability upslope as compared to downslope. Low heterogeneity of iron and aluminium was caused by a few high values in distinct areas within the study area, which could not be related to a particular parameter andror environment measured or observed in the field. The lowest coefficient of variation was found for the means of the sand fractions indicating this may be a suitable parameter to describe spatial erodibility. For both the shear and soil strength measurements, a large variation was observed between the measurements taken at each sampling point, with a coefficient of variation for soil strength exceeding one. The coefficient of variation for the SOILTEST device varied depending on the soil texture Ž Table 2. and corresponds with findings by Zimbone et al. Ž 1996. although the coefficient of variation observed in Ghana is much lower indicating a higher uniformity. On the other hand, the high coefficient of variation observed for the soil strength measurements raises the question whether it is possible to estimate soil strength confidently for a particular soil type. The spatial heterogeneity indexes were related to their mean counterparts. A relationship was found for the equal-weighted mean water stable aggregates with the associated coefficient of variation decreasing exponentially with increasing mean water stable aggregates ŽFig. 3.. For the aggregates this means that the more cohesive are the aggregates, less friable they are and the friability is controlled by the sand fraction as depicted on Fig. 4.
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Table 2 Mean values Ž x . and coefficient of variance ŽCV. of surface soil shear strength for different soil texture measured with the SOILTEST CL-600A device Ždata from Italy after Zimbone et al., 1996. Soil texture
Sicily, Italy
Upper East Region
x
CV
x
CV
Sandy loam Silty loam Clay All tested soils
22.3 12.9 15.1 16.3
0.46 0.77 0.10 0.57
34.7 39.8 – 36.6
0.31 0.17 – 0.28
Fig. 5 shows the histograms for the erodibility parameters. Whereas the histograms for parameters associated with aggregate stability, shear and soil strength are symmetric, the histogram for the stone content does not seem to fit any distribution at all, the reason why results from the ANOVA analysis regarding this parameter should be interpreted with caution. The histograms for iron and aluminium are positively skewed due to the few high values mentioned earlier. A large part of these high values came from soil sample points characterised as vertisols with a larger part of them having been sampled from the small vertisol grid. The mean associated with the histograms can be seen in Table 3 that also shows the results from the ANOVA analysis. The aggregate stability indices ŽSTAB, STAB532, STAB53 and STAB5. all have a high within-class variance Ž s w2 . which is also observed for the stone content, shear and soil strength, as well as silt and sand content.
Fig. 3. The relationship between the equal-weighted mean water stable aggregates Ž%. and their associated coefficient of variations ŽCV..
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Fig. 4. Equal-weighted mean water stable aggregates Ž%. and coefficient of variation of equalweighted mean water stable aggregates as a function of the sand fraction.
The intraclass correlations Ž r i ., expressing the variance attributed to differences between classes, are high for the silt and sand content and partly for the shear strength measurements. Very low values were found for the DVGD and the percentage of clay while the remaining erodibility parameters had a high level of significance for the differences between the soil types. This applied to
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Fig. 5. Histograms for the various soil erodibility parameters. For iron and aluminium are histograms representing all observations and observations from the large grid, respectively.
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Table 3 Degrees of freedom Ž df ., means Ž x ., within-class variances Ž s w2 ., intraclass correlations Ž r i ., the F-value Ž F sT r s w2 s between-class variationrwithin-class variation. and the level of significance
DVGD Žmm. STAB Ž%. STAB532 Ž%. STAB53 Ž%. STAB5 Ž%. MWD Žmm. OMC Ž%. Fe Ž‰. Al Ž‰. Stones Ž%. Clay Ž%. Silt Ž%. Sand Ž%. Shear strength ŽkPa. Soil strength ŽkPa.
df
x
S w2
ri
F
Level of significance
102 102 102 102 101 102 103 121 121 132 126 126 126 93 94
2.2 55.8 47.9 42.7 35.8 3.3 2.8 0.36 0.31 26.9 15.4 34.8 49.8 36.3 35.1
0.704 286.42 284.47 353.94 380.77 0.465 1.257 0.0424 0.0452 822.53 34.85 171.73 258.25 394.98 114.18
0.0091 0.1063 0.0766 0.0712 0.0717 0.0172 0.1723 0.2152 0.1474 0.1832 0.0092 0.7920 0.8876 0.4580 0.1000
1.19 3.44 2.70 2.57 2.58 1.35 2.92 7.76 5.26 6.78 1.23 97.74 201.09 16.96 2.14
NS P s 0.975 P s 0.95 NS NS NS P s 0.95 P s 0.99 P s 0.99 P s 0.99 NS P s 0.99 P s 0.99 P s 0.99 NS
the iron and aluminium content, the percentage of stones, the silt and sand content and the shear strength. However, the results for the first three parameters may not fully reflect reality because of the histograms not meeting the requirements for an ANOVA analysis. The high intraclass correlations for silt and sand content found through the ANOVA analysis corresponds with findings by Igwe et al. Ž 1995., who found that clay and sand content rather than textural composition determines the ranking of soils in terms of erodibility, an observation that supports the proportion of sand as the controlling factor of soil friability as shown in Figs. 3 and 4. A ranking based on the sand fraction as opposed to textural composition also questions the use of the USLE K factor for estimating erodibility in the savanna ecosystem. 3.3. Spatial Õariability of erodibility soil parameters When calculating the semivariograms for the soil erodibility parameters, only the parameters shear strength, stone, sand and clay content could be fitted to a bounded semivariogram using an exponential function Ž Fig. 6. . Since there was geometric anisotropy in the data, a linear transformation of the coordinates of the locations was carried out. For the shear strength and the stone content, a relatively high nugget variance was observed as compared to the sand and clay content. Data with a much shorter spacing would have been desirable in order to improve the estimation of the nugget variance but because of the position of the
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Fig. 6. Omni-directional semivariograms for the four erodibility parameters Žshear strength and stone, sand and clay content. fitted with exponential functions Ž x-axiss lag distance in km, y-axisssemivariogram..
smaller grids relative to the larger grid, it was not possible to use these data for the semivariogram calculations. This highlights the problem of choosing the right sampling interval when carrying out geostatistical analysis. While Van Meirvenne and Hofman Ž 1989. advocated for a sampling interval of 14 m for loamy material and 7.5 m for sandy loam to obtain a continuous characterisation of soil texture, Uehara et al. Ž1985. found a structure in the variance at distances greater than 1 km. Contour maps were generated using ordinary kriging for the parameters shear strength, stone, sand and clay content, whereas triangulation was used for the remaining erodibility parameters as the semivariogram for these parameters did not show a spatial dependence in the data. The resulting contour maps were
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Fig. 7. The spatial distribution of the erodibility parameters wsoil strength ŽkPa., shear strength ŽkPa., iron Ž‰., aluminium Ž‰., organic matter content Ž%. and mean weight diameter Žmm.x.
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draped over the FAO soil map Ž Figs. 7 and 8. except for the contour maps showing the distribution of aggregate stability indices which overall are similar to the distribution for the mean weight diameter. Based on a visual interpretation of the erodibility maps ŽFigs. 7 and 8., none of the erodibility parameters seemed to be related to the soil types according to FAO. On the other hand, the intraclass correlation Ž r i . for some of the erodibility parameters was high Ž Table 3., particularly the sand and silt content. This discrepancy between the maps and the table values may be attributed to the factthat the ANOVA analysis included data from the small grids while the contour maps did not. Secondly, Goovaerts
Fig. 8. The spatial distribution of the erodibility parameters Žclay, silt, sand and stone content; in percentage..
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Ž1999. showed how kriging tends to smooth out local detail of the spatial variation of the soil attribute. A spatial gradient associated with the topography in the study area is observed for the stones perpendicular to the spatial gradients for sand and silt. This supports the observation of a nonstationary spatial distribution of the studied soil parameters. A much higher stone content is observed for the leptosols as compared to the other soil types as expected. The luvisolsrlixisols appeared to have a low clay, silt and organic matter content, whereas the sand content was relatively high. An important question is how best to assess the uncertainty associated with erodibility when using the soil map in the future? This may be done through multi-Gaussian and indicator-based algorithms as described by Goovaerts Ž 1997. or simply by overlaying interpolated point measurements onto the soil map as proposed by Rogowski Ž1996. . The spatial information about the sand fraction needs to be incorporated as a key parameter rather than single-point estimates which may be associated with considerable uncertainty.
4. Conclusions The current study illustrates the complexity of erodibility. Not only is erodibility determined by a wide range of interlinked parameters, but it was also shown that only some of these erodibility parameters can be related to the soil types on the FAO soil map. One very important erodibility parameter that could not be related to soil type according to the ANOVA analysis is aggregate stability. With an overall low aggregate stability in the study area, conditions for crusting and excessive runoff generation is likely. Low aggregate stability also indicates that agricultural soils are expected to have high erodibility just after tillage. However, the content of sand and silt, shear strength, iron, aluminium and stone content appeared to be associated with soil type. The sand content was found to be the best indicator of spatial erodibility supporting other studies in the region and this parameter controlled the friability of the soil expressed through the aggregate stability of the soil. The cohesion of the soil was high as compared with other studies and indicates that during storms when the surface has evolved into a sealed or crusted structure, erodibility is expected to be low. This study illustrated some of the problems associated with the use of FAO soil maps at a scale of 1:125,000 for erodibility mapping because the spatial variation of erodibility parameters is not properly reflected in the soil map and with many inclusions of other soil types. The erodibility assessment consequently is generalised and can only be used for regional planning purposes. Future work should, therefore, focus on exploring how best to assess the uncertainty associated with the soil map through multi-Gaussian and indicator-
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based algorithms and the associated use of GIS rather than using single-point measurements which may not be representative.
Acknowledgements This research project was sponsored by the Council for Development Research, Denmark Ž project no. j.nr.104.Dan.8r652. , whose generous grant is gratefully acknowledged. The author would like to thank the Department of Geography and Resource Development, University of Ghana, Ghana, for providing logistical support and laboratory facilities. Mr. Saaka now at the UNDP office, Accra, Ghana, is also thanked, as well as Dr. Marc van Meirvenne, Dr. Bartoli and two anonymous referees whose comments significantly improved the manuscript. A big thank you to Ingrid Jensen for making the drawings.
References Adu, S.V., 1972. Eroded savannah soils of the Navrongo–Bawku area, Upper region of Ghana. Ghana Journal of Agricultural Science 5, 3–12. ArcrInfo, 1996. ArcrInfo Version 7.04. Environmental Systems Research Institute, 360 New York St., Redlands, CA 92973, USA. Asiamah, R.D., 1992. Soils and Soil Degradation of the Upper East Region of Ghana. Soil Research Institute, Kwadaso-Kumasi, Ghana. Berhe, T., Tetteh, F.M., 1993. Soils of Ghana: A Special Report on Result of Analysis of Samples Collected from the 10 Regions. Sasakawa-Global 2000 and Soil Research Institute, Accra, Ghana, July. Bissonais, Y. Le., 1996. Soil characteristic and aggregate stability. In: Agassi, M. ŽEd.., Soil Erosion, Conservation, and Rehabilitation. Marcel Dekker, USA, pp. 41–60. Bouma, J., 1985. Soil variability and soil survey. In: Nielsen, D.R., Bouma, J. ŽEds.., Soil Spatial Variability. Proceedings of a Workshop of the ISSS and the SSSA, Las Vegas, USA, 30 November–1 December 1984, Pudoc, Wageningen, The Netherlands, pp. 130–145. Bryan, R.B., 2000. Soil erodibility and processes of water erosion on hillslope. Geomorphology 32, 385–415. Bryan, R.B., Govers, G., Poesen, J., 1989. The concept of erodibility and some problems of assessment and application. Catena 16, 393–412. Casenave, A., Valentin, C., 1989. Les etats de surface de la zone Sahelienne. Influence sur ´ ´ l’infiltration. Editions de l’ORSTOM, Paris, France. Davies, P., 1985. Influence of organic matter content, moisture status and time after reworking on soil shear strength. Journal of Soil Science 36, 299–306. Evans, R., 1980. Mechanics of water erosion and their spatial and temporal controls: an empirical viewpoint. In: Kirkby, M.J., Morgan, R.P.C. ŽEds.., Soil Erosion. Wiley, England, pp. 109–128. FAO, 1990. Guidelines for soil description. Soil Resources, Management and Conservation Service and Land and Water Development Division, 3rd edn. ŽRevised.. FAO and ISRIC, Rome, Italy.
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Folly, A., 1995. Estimation of erodibility in the savanna ecosystem, northern Ghana. Communications in Soil Science and Plant Analysis 26 Ž5–6., 799–812. Goovaerts, P., 1997. Geostatistics for natural resources evaluation. Applied Geostatistics Series. Oxford Univ. Press, NY, USA. Goovaerts, P., 1999. Geostatistics in soil science: state-of-the-art and perspectives. Geoderma 89, 1–45. Hartge, K.H., Horn, R., 1989. Die physikalische Untersuchung von Boden. Ferdinand Enke ¨ Verlag, Germany. Igwe, C.A., Akamigbo, F.O.R., Mbagwu, J.S.C., 1995. The use of some soil aggregate indices to assess potential soil loss in soils of south-eastern Nigeria. International Agrophysics 9 Ž2., 95–100. Jaiyeoba, I.A., Ologe, K.O., 1990. Soil erodibility measurements in Nigeria. Zeitschrift fur ¨ Geomorphologie 34 Ž3., 313–321. Møberg, J.P., Petersen, L., Borggaard, O.K., 1990. Geologi og Jordbundslære–Pedologiske Analyser, 3rd edn. Chemical Institute, The Royal Veterinary and Agricultural University, Copenhagen, Denmark. Morgan, R.P.C., Quinton, J.N., Smith, R.E., Govers, G., Poesen, J.W.A., Auerswald, K., Chisci, G., Torri, D., Styczen, M.E., Folly, A.J.V., 1998. The European Soil Erosion Model ŽEUROSEM.: Documentation and User Guide. Cranfield University at Silsoe, Bedford, England. Oliver, M.A., Webster, R., 1991. How geostatistics can help you. Soil Use Management 7 Ž4., 206–217. Pannatier, Y., 1994. Variowin 2.1. Institute of Mineralogy, BFSH2, University of Lausanne, Switzerland. Poesen, J., van Wesemael, B., Bunte, K., Sole´ Benet, A., 1998. Variation of rock fragment cover and size along semiarid hillslopes: a case study from southeast Spain. Geomorphology 23, 323–335. Quansah, C., 1990. Soil erosion and conservation in the northern and upper regions of Ghana. Topics in Applied Resource Management 2, 135–157. Rogowski, A.S., 1996. Quantifying soil variability in GIS applications: II. Spatial distribution of soil properties. International Journal of Geographical Information Systems 10 Ž4., 455–475. Roose, E.J., 1977. Application of the Universal Soil Loss Equation of Wischmeier and Smith in West Africa. In: Greenland, D.J. ŽEd.., Soil Conservation and Management in the Humid tropics, pp. 177–189. Roose, E., Sarrailh, J., et al., 1990. Erodibilite´ de quelques sols tropicaux. Ving Annees ´ de mesure en parcelles d’erosion sous pluies naturelles. Cahiers de ORSTOM, Serie ´ ´ Pedologie XXV Ž1–2., 7–30. Schjønning, P., Rasmussen, K.J., 1989. Long-term reduced cultivation: I. Soil strength and stability. Soil and Tillage Research 15, 79–90. Sheldrick, B.H., 1984. Analytical Methods Manual 1984. Land Resource Research Institute, Ottawa, Canada, LRRI Contribution no. 84-30. Spain, A.V., Prove, B.G., Hodgen, M.J., Lee, K.E., 1990. Seasonal variation in penetration resistance and shear strength of three rainforest soils from northeastern Queensland. Geoderm 47, 79–92. Surfer, 1995. Surfer version 6.01. Surface Mapping System. Golden Software, 809 14th Street, Golden, CO 80401-1866, USA. Torri, D., Poesen, J., Borselli, L., 1997. Predictability and uncertainty of the soil erodibility factor using a global dataset. Catena 31, 1–22. Uehara, G., Trangmar, B.B., Yost, R.S., 1985. Spatial variability of soil properties. In: Nielsen, D.R., Bouma, J. ŽEds.., Soil Spatial Variability. Proceedings of a Workshop of the ISSS and the SSSA, 30 November–1 December 1984, Las Vegas, USA, pp. 61–92.
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Valentin, C., 1993. Soil crusting and sealing in West Africa and possible approaches to improved management. Soil Tillage in Africa: Needs and Challenges. FAO Soils Bulletin, FAO, Rome, Italy, pp. 95–128. Vanelslande, A., Lal, R., Gabriels, D., 1987. The erodibility of some Nigerian soils: a comparison of rainfall simulator results with estimates obtained from the Wischmeier nomogram. Hydrological Processes 1, 255–265. Van Meirvenne, M., Hofman, G., 1989. Spatial variability of soil texture in a Polder area: I. Kriging. Pedologie 39 Ž1., 69–87. Webster, R., Beckett, H.T., 1968. Quality and usefulness of soil maps. Nature 219, 680–682. Webster, R., Oliver, M.A., 1990. Statistical Methods in Soil and Land Resource Survey. Spatial Information Systems, Oxford Univ. Press, England. Wischmeier, W.H., Smith, D.D., 1978. Predicting rainfall erosion losses—a guide to conservation planning. Agriculture Handbook, vol. 537. U.S. Department of Agriculture, Washington, D.C., USA. Young, R.A., Romkens, M.J.M., McCool, D.K., 1990. Temporal variations in soil erodibility. In: ¨ Bryan, R.B. ŽEd.., Soil Erosion—Experiments and Models. Catena, supplement 17, Verlag, Cremlingen-Destedt, pp. 101–111. Zimbone, S.M., Vickers, A., Morgan, R.P.C., Vella, P., 1996. Field investigations of different techniques for measuring surface soil shear strength. Soil Technology 9, 101–111.
The spatial variability of erodibility and its relation to soil types: a study from northern Ghana Anita Veihe ) Department of Geography and International DeÕelopment Studies, Roskilde UniÕersity, Hus 19.2, P.O. Box 260, 4000 Roskilde, Denmark Received 14 March 2000; received in revised form 11 December 2000 and 27 June 2001; accepted 9 July 2001
Abstract In many parts of Africa, where regional mapping of soil erosion risk or related land-use planning exercises take place, existing soil maps are the only sources of information available when estimating erodibility. The current study examines the spatial variability of erodibility and the extent to which it is related to soil types based on a case study from northern Ghana. Soil samples from 136 test sites were analysed to determine erodibility parameters and surface characteristics were described in the field. Through ANOVA analysis, the intraclass correlations and within-class variations related to soil types were examined which showed an overall poor relationship. The fraction of sand was found to be the best indicator of erodibility due to its low spatial heterogeneity and determined the friability of water-stable aggregates. Further studies investigating the uncertainty associated with the use of soil maps in erosion studies should be carried out with the aim of producing soil maps with statistical information relevant for land resource studies. q 2002 Elsevier Science B.V. All rights reserved. Keywords: Erodibility; Spatial variability; USLE; Savanna ecosystem; Ghana
1. Introduction Soil erodibility expresses the soil’s susceptibility to erosional processes and can be defined as ‘the ease with which soil is detached by splash during rainfall andror by the shear of surface flow’ Ž Young et al., 1990. . The concept of erodibility and how to assess it is complicated since the susceptibility of the soil to erosion is influenced by a large number of properties such as physical, )
Fax: q45-46-74-30-32. E-mail address: [email protected] ŽA. Veihe..
0016-7061r02r$ - see front matter q 2002 Elsevier Science B.V. All rights reserved. PII: S 0 0 1 6 - 7 0 6 1 Ž 0 1 . 0 0 1 2 0 - 3
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mechanical, hydrologic, chemical, rheological, mineralogical and biological, not to mention the soil profile characteristics such as the depth of the soil and its influence on vegetative growth. Two major time-dependent subprocesses related to sheet erosion have been identified. Initially, just after tillage, agricultural soils are considered noncoherent, the reason why entrainment forces will be resisted by particle size, shape and density. A measure suitable for an assessment of erodibility at this stage is aggregate stability or any related index. Aggregate breakdown is also the main process responsible for crusting, a phenomenon considered to be widespread in the savanna ecosystem. During storms, however, the surface progressively evolves into a coherently sealed or crusted structure. This is the reason why erodibility in this situation is determined by the cohesion of the soil, a parameter that can be measured with a torvane. Shear strength is, nevertheless, linked to some of the same bonding mechanisms involved in aggregation Ž Bryan, 2000; Bryan et al., 1989.. Several attempts have been made to devise a simple index of erodibility based on the properties of the soil determined either in the laboratory or in the field. Finding a suitable erodibility index for soils in the savanna ecosystem specifically, poses a number of problems because the majority of the existing indices were developed for soils from temperate regions. For an erosion risk assessment, the use of the erodibility factor K in the Universal Soil Loss Equation ŽUSLE. is desirable because it makes the erodibility assessment comparable with other assessments in the region. The K-value represents the soil loss per unit of EI 30 , as measured in the field on a standard plot with a length of 22 m and 9% slope ŽWischmeier and Smith, 1978.. Whereas the USLE is applicable to soils in West Africa, where kaolinite is the dominant clay mineral ŽRoose, 1977., it may not be applied to vertisols characterised by expanding clay minerals and soils with a high-coarse sand fraction ŽRoose, 1977; Vanelslande et al., 1987. . These soils appear to be far more erodible than what the USLE K factor indicate, while loamy and silt loamy soils with a high silt content appeared to have lower K factor values ŽVanelslande et al., 1987. . In fact, several studies have been unable to find a correlation between predicted and observed K factors and generally found that the USLE K factor was being underestimated Ž Vanelslande et al., 1987; Jaiyeoba and Ologe, 1990; Igwe et al., 1995; Torri et al., 1997. . Estimation of K factors from soil types in general can be problematic because soil classifications are often not based on those parameters reflecting erodibility ŽRoose and Sarrailh, 1990.. Many soil maps in Africa do not contain detailed information on soil texture and during soil surveys, emphasis is placed on map legends and soil classification schemes, whereas the interpretation of soils in terms of land evaluation is scarce. Nevertheless, it is useful to know the spatial distribution of different soil characteristics and through geostatistical procedures, it is possible to check existing map legends Ž Bouma, 1985. . In many
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parts of West Africa, the only means of carrying out regional erosion risk assessments and related land-use planning exercises is to use existing soil maps as erodibility maps and knowledge about the uncertainty associated with the use of these maps becomes essential. The aim of this study was to evaluate the extent to which a FAO soil map in Ghana can be used as an erodibility map for erosion risk assessments at a regional scale. This was done by investigating the spatial variability of potential soil erodibility and its relation to soil type and to assess the uncertainty of erodibility assessments based on point soil sampling without taking into account the issue of scale. Future erodibility assessment and mapping in the savannah ecosystem of northern Ghana is discussed. 2. Materials and methods 2.1. Soil location and sampling The current study was carried out in the Bolgatanga district of Upper East Region, Ghana. The soils are characterised as coarse textured with extreme moisture relationships, having a low accumulation of organic matter Ž mostly less than 2%., a low cation-exchange capacity and low fertility Ž Quansah, 1990; Asiamah, 1992; Berhe and Tetteh, 1993. . Adu Ž 1972. made the first soil map of the region at a scale of 1:250,000 where 55 soil series were given names after local towns and rivers. This soil map has been redrawn at a scale of 1:125,000 classified based on the morphological characteristics into the Revised Legend of the FAO-UNESCO Legend of the soil map of the World Ž Asiamah, 1992; personal communication.. The collection of soil samples was carried out using systematic sampling as described by Webster and Oliver Ž1990. and aided by Global Positioning System ŽGPS. . The 10 = 10-km grid covered four major soil types being Luvisolsr Lixisols, Leptosols, Plinthosols and Vertisols Ž Asiamah, 1992. and consisted of 100 measurement points ŽFig. 1.. Within each of the soil types, smaller grids were laid out consisting of nine sampling points each with a spacing of 60 m to enable sampling within single fields. The smaller grids also made it possible to investigate the variation of the erodibility parameters in relation to scale and it ensured sufficient samples for a statistical analysis for the four soil types. However, sampling points from the small grids were not used for kriging due to their specific location within the larger grid. At each sampling site, a general description was made according to the FAO guidelines for soil description ŽFAO, 1990. and soil texture was systematically determined in the field as described in detail by Møberg et al. Ž1990.. This consisted of identifying sand grains and rolling out soil samples in the hand with varying soil moisture content in order to estimate the span between the liquid limit and the plastic limit. Surface soil samples were collected and this was followed by the
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Fig. 1. Location of the study area in Upper East Region and the grid used for soil sampling.
determination of shear strength on saturated soil using a torvane Ž type SOILTEST CL-600A. and soil strength using a pocket penetrometer Ž type SOILTEST CL-700A.. Ten replicates were made within 1 m2. 2.2. Laboratory analysis Aggregate stability was determined by wet-sieving using a method proposed by Hartge and Horn Ž 1989. but modified according to Schjønning Ž personal communication.. The modification meant that sieves with diameters of 8, 5, 3 and 2 mm were used rather than 8, 6, 4 and 2 mm and because of the weak aggregate structure, aggregates were not broken prior to sieving. Furthermore, stones identified visually were removed from the sieves prior to weighing the material to ensure measurements were done on aggregates only. This proved of particular importance due to the high stone content of the soils. Organic matter content was estimated by determining the carbon content through dry combustion using a LECO apparatus. Iron and aluminium associated with organic
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matter was extracted with sodium pyrophosphate as described by Sheldrick Ž1984., since this had proven to be related to aggregate stability in previous studies in the region Ž Folly, 1995.. 2.3. Statistics, geostatistics and erodibility modelling The data were analysed using descriptive statistics, including the calculation of coefficient of variations of the means of the studied soil parameters in order to use it as indicators of spatial heterogeneity. Since data were spatially clustered due to preferential sampling, an analysis was done on both clustered and declustered data. The cell-declustering technique described by Goovaerts Ž1997. was used whereby data in densely sampled areas Ž smaller grids. receive less weight than those in sparsely sampled areas Ž larger grid. . The equal-weighted mean Ž m ˆ . is, therefore, calculated as: 1 B m Ž1. ˆ s Ý mˆ b B bs1 where B is the number of cells that contain at least one datum; m ˆ is the equal-weighted mean of z-values within cell b. The declustered variance Ž sˆ 2 . is then computed as ŽGoovaerts, 1997.: n
sˆ 2 s Ý la z Ž ua . y m ˆ
2
Ž2.
as1
where la are the declustering weights; z Ž ua . is the value at a datum location. An analysis of variance Ž ANOVA. was carried out as described by Webster and Beckett Ž1968. and Webster and Oliver Ž 1990. in which the total, the within-class and the among-class variances were calculated and compared based on the assumption that data sets are normally distributed. Histograms were, hence, produced for the various erodibility parameters. The within-class variation Ž s w2 ., which in this case refers to the variation of parameters within a particular soil type, was calculated by pooling the individual ones: k
nk
Ý Ý Ž xi j yxi . s w2 s
is1 js1 k
2
Ž3.
Ý Ž n i y 1. is1
where k is the number of classes, the ith class containing n i observations; x is the mean for the whole sample; x i is the mean for the ith class. To obtain information about the correlation among individuals within the same class Ž soil type., the intraclass correlation Ž r i . , was calculated: ri s
sT2 s w2 q sT2
Ž4.
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where: sT2 s Ž T y s w2 . rn 0 Ž5. and where T is an estimate of the variance between classes, whereas n 0 is an adjustment necessary because of n i being different from n: k
n0 s
Ts
1 ky1 1
Ý n2i Ny
is1
N
k
Ý ni Ž x i y x . ky1
0 2
Ž6. Ž7.
is1
where N is the total size of the sample.
Fig. 2. Overall methodology for assessing the use of the FAO soil map as an erodibility map Žnames in italics refer to software packages..
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Kriging was then used to estimate the values at unsampled places of properties that vary in space. The difference as compared to a linear estimation is that data carry different weights according to their positions both in relation to the unknown point and to one another ŽOliver and Webster, 1991. . In order to describe mathematically the way the variance of a property changes as the distance and direction separating any two points vary, a semivariogram is used derived with the Variowin software Ž Pannatier, 1994. . The semivariance is estimated as:
g Ž h. s
1
N Ž h.
Ý z Ž x i . y z Ž x i q h. 2 N Ž h . iy1
2
Ž8.
with w z Ž x i ., z Ž x i q h.x s a pair of observations of the variable at any two points; h s lag Ž a vector having both distance and direction. ; N Ž h. s the number of pairs separated by h. Point data were used to draw contour maps using the Surfer software Ž Surfer, 1995. using either kriging or interpolation. The maps were exported to ArcrInfo Ž1996. to be overlain on the FAO soil map and this provided the basis of the validation of the soil maps for their use as erodibility maps. The overall methodology is outlined in Fig. 2.
3. Results and discussion 3.1. Characteristics of erodibility soil parameters The declustering of data showed that in general the preferentially sampled values had little influence on the histograms except for the iron and aluminium content for the vertisols in particular. Hence, soil properties were characterised based on the declustering results ŽTable 1., whereas the ANOVA analysis has been carried out using the clustered data. No correlation was found between the various erodibility parameters though a correlation had been found between iron and aluminium associated with organic matter and aggregate stability in a previous study within the region Ž Folly, 1995.. Whereas the lack of correlation between organic matter content and shear strength corresponds with findings from Australia by Spain et al. Ž 1990. , they are in disagreement with Davies Ž1985. studies on English soils. This supports the idea that findings from one region cannot necessarily be extrapolated to other ecological zones. The average weight diameter Ž DVGD. and the mean weight diameter ŽMWD. are virtually the same for all soil types ŽTable 1. . Aggregate stability was the same magnitude when compared with previous studies in the region Ž Folly, 1995., but was much lower than in Denmark Ž Schjønning and Rasmussen, 1989.. The impact of aggregate stability on erodibility mostly points to its
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Table 1 Characteristic soil properties The equal-weighted mean Ž m ˆ . and the coefficient of variation ŽCV. for the four soil types Žluvisolsrlixisols, leptosols, plinthosols and vertisols. Luvisolsrlixisols Leptosols DVGD Žmm. STAB Ž%. STAB532 Ž%. STAB53 Ž%. STAB5 Ž%. MWD Žmm. OMC Ž%. Fe Ž‰. Al Ž‰. Stones Ž%. Clay Ž%. Silt Ž%. Sand Ž%. Shear strength ŽkPa. Soil strength ŽkPa.
Plinthosols
Vertisols
m ˆ
CV
m ˆ
CV
m ˆ
CV
m ˆ
CV
2.3 52.8 44.7 39.8 31.5 3.3 1.8 0.29 0.26 14.9 12.8 25.9 61.3 40.9 41.2
0.34 0.33 0.40 0.49 0.58 0.18 0.47 0.66 0.86 0.93 0.53 0.37 0.23 0.21 0.19
2.0 61.5 52.6 47.2 40.0 3.3 3.0 0.33 0.28 38.9 15.8 36.9 47.3 35.2 34.8
0.44 0.27 0.32 0.41 0.51 0.21 0.42 0.55 0.66 0.87 0.38 0.40 0.36 0.26 0.27
2.1 52.9 45.1 39.2 33.0 3.2 2.3 0.45 0.26 12.4 13.3 30.0 56.7 40.1 49.5
0.35 0.33 0.40 0.52 0.82 0.13 0.34 0.34 0.83 1.55 0.35 0.24 0.21 0.28 0.30
2.2 50.4 45.3 40.6 35.9 2.4 3.2 0.38 0.37 12.4 12.3 33.9 53.8 38.0 39.7
0.40 0.35 0.38 0.46 0.53 0.44 0.27 0.69 0.53 1.06 0.30 0.44 0.32 0.24 0.37
DVGD: change in average weight diameter Žmm., STAB: aggregates which have not been shattered to a diameter -1 mm, STAB532: stable aggregates 2–3 mm, STAB53: stable aggregates 3–5 mm, STAB5: stable aggregates 5–8 mm, MWD: mean weight diameter, OMC: organic matter content.
significance for surface sealing and crusting Ž Casenave and Valentin, 1989; Bissonais, 1996. and with the low aggregate stability observed in the study area, it is remarkable that no crusting phenomena were identified during the field survey. According to Valentin Ž personal communication. , this may be due to faunal activity that seasonally can destroy crusts as observed in the northern part of the Ivory Coast. On the other hand, crusts may exist without being conspicuous because they are structural andror depositional as described by Valentin Ž1993.. The average organic matter content was in the range 1.8–3.2% which categorises the soil as erodible ŽEvans, 1980. . Not much difference was found between the soil types in terms of iron and aluminium content except for the plinthosols with a higher iron content as compared to the other soil types. The textural composition of the soil types was quite similar and with the clay content being much lower than expected for the vertisols. This may be attributed to the scale of the map where inclusions of other soil types within the vertisols is to be expected. The surface stone content varied considerably between the leptosols with a stone content of 38.9% and the remaining soil types with a stone content between 12.4% and 14.9%. The majority of the sites had stones in the size range
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0.2–0.6 cm though medium and course stones Ž 0.6–6 cm. were found in some places. However, the varying percentages of embedding in the topsoil found in the study area proved a problem in terms of assessing the effect of stone content on soil erosion. Shear strength values for the study area on average ranged from 35.2 to 40.9 kPa, with the leptosols having lower values as compared to the other soil types. Observed shear strength values were rather high as compared to recommended guide values for both compacted and uncompacted soils in the European Soil Erosion Model Ž EUROSEM. User Guide Ž Morgan et al., 1998. based on the study of English soils. Similar observations have been made by Rey Ž personal communication. and Zimbone et al. Ž 1996. on soils in Mexico and Italy, respectively. When it comes to soil strength, only slight differences were observed between the various soil types with figures ranging from 34.8 kPa for the leptosols to 49.5 kPa for the plinthosols. 3.2. Spatial heterogeneity of erodibility soil parameters The coefficient of variation overall varied between 0.13 and 1.55, with spatial heterogeneity being high particularly for the stone content, aggregate stability and the content of iron and aluminium. The spatial heterogeneity of the stone content may be controlled by hillslope gradient as indicated by Poesen et al. Ž1998., who found the spatial variability to vary according to slope position with the highest variability upslope as compared to downslope. Low heterogeneity of iron and aluminium was caused by a few high values in distinct areas within the study area, which could not be related to a particular parameter andror environment measured or observed in the field. The lowest coefficient of variation was found for the means of the sand fractions indicating this may be a suitable parameter to describe spatial erodibility. For both the shear and soil strength measurements, a large variation was observed between the measurements taken at each sampling point, with a coefficient of variation for soil strength exceeding one. The coefficient of variation for the SOILTEST device varied depending on the soil texture Ž Table 2. and corresponds with findings by Zimbone et al. Ž 1996. although the coefficient of variation observed in Ghana is much lower indicating a higher uniformity. On the other hand, the high coefficient of variation observed for the soil strength measurements raises the question whether it is possible to estimate soil strength confidently for a particular soil type. The spatial heterogeneity indexes were related to their mean counterparts. A relationship was found for the equal-weighted mean water stable aggregates with the associated coefficient of variation decreasing exponentially with increasing mean water stable aggregates ŽFig. 3.. For the aggregates this means that the more cohesive are the aggregates, less friable they are and the friability is controlled by the sand fraction as depicted on Fig. 4.
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Table 2 Mean values Ž x . and coefficient of variance ŽCV. of surface soil shear strength for different soil texture measured with the SOILTEST CL-600A device Ždata from Italy after Zimbone et al., 1996. Soil texture
Sicily, Italy
Upper East Region
x
CV
x
CV
Sandy loam Silty loam Clay All tested soils
22.3 12.9 15.1 16.3
0.46 0.77 0.10 0.57
34.7 39.8 – 36.6
0.31 0.17 – 0.28
Fig. 5 shows the histograms for the erodibility parameters. Whereas the histograms for parameters associated with aggregate stability, shear and soil strength are symmetric, the histogram for the stone content does not seem to fit any distribution at all, the reason why results from the ANOVA analysis regarding this parameter should be interpreted with caution. The histograms for iron and aluminium are positively skewed due to the few high values mentioned earlier. A large part of these high values came from soil sample points characterised as vertisols with a larger part of them having been sampled from the small vertisol grid. The mean associated with the histograms can be seen in Table 3 that also shows the results from the ANOVA analysis. The aggregate stability indices ŽSTAB, STAB532, STAB53 and STAB5. all have a high within-class variance Ž s w2 . which is also observed for the stone content, shear and soil strength, as well as silt and sand content.
Fig. 3. The relationship between the equal-weighted mean water stable aggregates Ž%. and their associated coefficient of variations ŽCV..
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Fig. 4. Equal-weighted mean water stable aggregates Ž%. and coefficient of variation of equalweighted mean water stable aggregates as a function of the sand fraction.
The intraclass correlations Ž r i ., expressing the variance attributed to differences between classes, are high for the silt and sand content and partly for the shear strength measurements. Very low values were found for the DVGD and the percentage of clay while the remaining erodibility parameters had a high level of significance for the differences between the soil types. This applied to
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Fig. 5. Histograms for the various soil erodibility parameters. For iron and aluminium are histograms representing all observations and observations from the large grid, respectively.
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Table 3 Degrees of freedom Ž df ., means Ž x ., within-class variances Ž s w2 ., intraclass correlations Ž r i ., the F-value Ž F sT r s w2 s between-class variationrwithin-class variation. and the level of significance
DVGD Žmm. STAB Ž%. STAB532 Ž%. STAB53 Ž%. STAB5 Ž%. MWD Žmm. OMC Ž%. Fe Ž‰. Al Ž‰. Stones Ž%. Clay Ž%. Silt Ž%. Sand Ž%. Shear strength ŽkPa. Soil strength ŽkPa.
df
x
S w2
ri
F
Level of significance
102 102 102 102 101 102 103 121 121 132 126 126 126 93 94
2.2 55.8 47.9 42.7 35.8 3.3 2.8 0.36 0.31 26.9 15.4 34.8 49.8 36.3 35.1
0.704 286.42 284.47 353.94 380.77 0.465 1.257 0.0424 0.0452 822.53 34.85 171.73 258.25 394.98 114.18
0.0091 0.1063 0.0766 0.0712 0.0717 0.0172 0.1723 0.2152 0.1474 0.1832 0.0092 0.7920 0.8876 0.4580 0.1000
1.19 3.44 2.70 2.57 2.58 1.35 2.92 7.76 5.26 6.78 1.23 97.74 201.09 16.96 2.14
NS P s 0.975 P s 0.95 NS NS NS P s 0.95 P s 0.99 P s 0.99 P s 0.99 NS P s 0.99 P s 0.99 P s 0.99 NS
the iron and aluminium content, the percentage of stones, the silt and sand content and the shear strength. However, the results for the first three parameters may not fully reflect reality because of the histograms not meeting the requirements for an ANOVA analysis. The high intraclass correlations for silt and sand content found through the ANOVA analysis corresponds with findings by Igwe et al. Ž 1995., who found that clay and sand content rather than textural composition determines the ranking of soils in terms of erodibility, an observation that supports the proportion of sand as the controlling factor of soil friability as shown in Figs. 3 and 4. A ranking based on the sand fraction as opposed to textural composition also questions the use of the USLE K factor for estimating erodibility in the savanna ecosystem. 3.3. Spatial Õariability of erodibility soil parameters When calculating the semivariograms for the soil erodibility parameters, only the parameters shear strength, stone, sand and clay content could be fitted to a bounded semivariogram using an exponential function Ž Fig. 6. . Since there was geometric anisotropy in the data, a linear transformation of the coordinates of the locations was carried out. For the shear strength and the stone content, a relatively high nugget variance was observed as compared to the sand and clay content. Data with a much shorter spacing would have been desirable in order to improve the estimation of the nugget variance but because of the position of the
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Fig. 6. Omni-directional semivariograms for the four erodibility parameters Žshear strength and stone, sand and clay content. fitted with exponential functions Ž x-axiss lag distance in km, y-axisssemivariogram..
smaller grids relative to the larger grid, it was not possible to use these data for the semivariogram calculations. This highlights the problem of choosing the right sampling interval when carrying out geostatistical analysis. While Van Meirvenne and Hofman Ž 1989. advocated for a sampling interval of 14 m for loamy material and 7.5 m for sandy loam to obtain a continuous characterisation of soil texture, Uehara et al. Ž1985. found a structure in the variance at distances greater than 1 km. Contour maps were generated using ordinary kriging for the parameters shear strength, stone, sand and clay content, whereas triangulation was used for the remaining erodibility parameters as the semivariogram for these parameters did not show a spatial dependence in the data. The resulting contour maps were
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Fig. 7. The spatial distribution of the erodibility parameters wsoil strength ŽkPa., shear strength ŽkPa., iron Ž‰., aluminium Ž‰., organic matter content Ž%. and mean weight diameter Žmm.x.
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draped over the FAO soil map Ž Figs. 7 and 8. except for the contour maps showing the distribution of aggregate stability indices which overall are similar to the distribution for the mean weight diameter. Based on a visual interpretation of the erodibility maps ŽFigs. 7 and 8., none of the erodibility parameters seemed to be related to the soil types according to FAO. On the other hand, the intraclass correlation Ž r i . for some of the erodibility parameters was high Ž Table 3., particularly the sand and silt content. This discrepancy between the maps and the table values may be attributed to the factthat the ANOVA analysis included data from the small grids while the contour maps did not. Secondly, Goovaerts
Fig. 8. The spatial distribution of the erodibility parameters Žclay, silt, sand and stone content; in percentage..
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Ž1999. showed how kriging tends to smooth out local detail of the spatial variation of the soil attribute. A spatial gradient associated with the topography in the study area is observed for the stones perpendicular to the spatial gradients for sand and silt. This supports the observation of a nonstationary spatial distribution of the studied soil parameters. A much higher stone content is observed for the leptosols as compared to the other soil types as expected. The luvisolsrlixisols appeared to have a low clay, silt and organic matter content, whereas the sand content was relatively high. An important question is how best to assess the uncertainty associated with erodibility when using the soil map in the future? This may be done through multi-Gaussian and indicator-based algorithms as described by Goovaerts Ž 1997. or simply by overlaying interpolated point measurements onto the soil map as proposed by Rogowski Ž1996. . The spatial information about the sand fraction needs to be incorporated as a key parameter rather than single-point estimates which may be associated with considerable uncertainty.
4. Conclusions The current study illustrates the complexity of erodibility. Not only is erodibility determined by a wide range of interlinked parameters, but it was also shown that only some of these erodibility parameters can be related to the soil types on the FAO soil map. One very important erodibility parameter that could not be related to soil type according to the ANOVA analysis is aggregate stability. With an overall low aggregate stability in the study area, conditions for crusting and excessive runoff generation is likely. Low aggregate stability also indicates that agricultural soils are expected to have high erodibility just after tillage. However, the content of sand and silt, shear strength, iron, aluminium and stone content appeared to be associated with soil type. The sand content was found to be the best indicator of spatial erodibility supporting other studies in the region and this parameter controlled the friability of the soil expressed through the aggregate stability of the soil. The cohesion of the soil was high as compared with other studies and indicates that during storms when the surface has evolved into a sealed or crusted structure, erodibility is expected to be low. This study illustrated some of the problems associated with the use of FAO soil maps at a scale of 1:125,000 for erodibility mapping because the spatial variation of erodibility parameters is not properly reflected in the soil map and with many inclusions of other soil types. The erodibility assessment consequently is generalised and can only be used for regional planning purposes. Future work should, therefore, focus on exploring how best to assess the uncertainty associated with the soil map through multi-Gaussian and indicator-
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based algorithms and the associated use of GIS rather than using single-point measurements which may not be representative.
Acknowledgements This research project was sponsored by the Council for Development Research, Denmark Ž project no. j.nr.104.Dan.8r652. , whose generous grant is gratefully acknowledged. The author would like to thank the Department of Geography and Resource Development, University of Ghana, Ghana, for providing logistical support and laboratory facilities. Mr. Saaka now at the UNDP office, Accra, Ghana, is also thanked, as well as Dr. Marc van Meirvenne, Dr. Bartoli and two anonymous referees whose comments significantly improved the manuscript. A big thank you to Ingrid Jensen for making the drawings.
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