E.U. Onweremadu and F.O.R. Akamigbo
Research Journal of Forestry, 2007, 1(2), 55-65.
The study quantified spatial variability in exchangeable basic cations in soils of forested hilly terrain in 2006. A transect technique was used in aligning profile pits along three physiographic units of interfluve, colluvial footslope and channel bed. Collected soil samples from the profile pits were analysed using routine and special laboratory techniques. Soil data were subjected to conventional and geostatistical analyses. Exchangeable cations varied spatially and vertically irrespective of physiographic position. Strong degree of spatial dependence was exhibited by these cations in epipedal horizons while moderate degree of spatial dependence dominated in their subsurface distribution using nugget values. Values of coefficient of variation confirm pronounced anisotropy among these attributes in their vertical distribution. Studies in exchange chemistry and other related edaphic properties using other multivariate techniques will enhance knowledge gained in this study.
Nugget values were used to estimate spatial dependence and were ranked according to the procedure of Cambardella et al. (1994). RESULTS AND DISCUSSION Soil Morphology
Soil macromorphological features are indicated in Table 1, showing differences in elevation and slope attributes. However at horizon levels A-horizon thickness was least in soils of the interfluve. Soil structure was weakly developed in soil of the interfluve and colluvial footslope and this is consistent with the findings of Weisenborn and Schaetzl (2005) in soils of a Summit in Michigan, United States of America.
Nugget values were used to estimate spatial dependence and were ranked according to the procedure of Cambardella et al. (1994).
RESULTS AND DISCUSSION
|Table 1:||Morphology of studies pedons|
|Pedon 1 = Soils of interfluve, Pedon 2 = Soils of colluvial slope, Pedons 3 = Soils of channel bed, NE = Northeast, SF = Secondary Forest|
|Table 2:||Properties of studied soils|
|OC = Organic Carbon, BD = Bulk Density, Ks = Saturated hydraulic conductivity|
Soils of the interfluve were grayish, typical of results of Schaetzl (2002) in a Spodosol-Entisol transition in Northern, Michigan, while soil colours downslope were redder. These results could be in response to drainage differences in the site. Soils of the interfluve were proximal to the consolidated bedrock and this is impermeable causing some capillarity irresespective of seepage characteristic of the site. The is in contrast to other soil groups which are deeper, thereby allowing a wider range for soil water re-distribution. Soils of the site are very geomorphically unstable, as erosive activities may have resulted in differential A-horizon thickness. This is in contrast to the findings of Williams (1998).
Soils are sandy, of high bulk density and low organic carbon. These properties are in response to parent material climate and land use (Table 2). Values of saturated hydraulic conductivity (Ks) (Table 2) are similar to reports of Ezeaku and Anikwe (2006) on soils of Ukpabi-Nsukka in the northernmost part of southeastern Nigeria. Values of Ks were higher in epipedal samples and this could be attributed to greater aggregation caused by higher organic matter values at surface soils (Igwe and Stahr, 2004) or increased biological channels (Bouma, 1991). It is hypothesized that bioturbation includes the filling in of very coarse voids left by tree roots and burrowing animals (Ezeaku and Anikwe, 2006) and as a consequence, Ks values were higher in surface soils, irrespective of physiographic position. The implication of this is that more exchangeable cations will be leached out of the surface horizons.
Cation Exchange Characteristics
Cations distribution in soils are given in Table 3 and 4 indicating results of surface and subsurface soil, respectively. Surface and subsurface soil horizons differed significantly (t-tests; p<0.001) in all chemical parameters measured (Table 5) except in Na Mean basic cation concentrations increased downslope while Exchangeable Acidity (EA) decreased in the direction of channel bed soils. Means of exchangeable basic cations in both horizons occurred in the order of abundance as follows:
Ca > Mg > K > Na
Also, these basic cations had higher values in the subsurface horizons irrespective of geomorphic setting. In contrast, EA values were lower in subsurface horizons, suggesting pronounced pedogenic processes of leaching and eluviation in surface horizons and consequent illuviation of translocated basic cations in deep subsurface soils. While leaching and eluviation leave a preponderance of acidic cations (aluminium and hydrogen) hence higher EA values in surface soils, the basic cations (Ca, Mg, K and Na) are readily transported and deposited in the illuvial layers. Exchangeable Ca was the highest in soils irrespective of physiography, possibly due to low adsorption capacity of exchangeable Mg to the exchange site (Foth, 1984) while exchangeable K has low values in sandy soils (Brady and Weil, 1999).
|Table 3:||Epipedal (Surface) distribution of selected soil properties (N = 36)|
|EA = Exchangeable Acidity, CEC = Cation Exchange Capacity, BS = Base Saturation, Ca = Calcium, Mg = Magnesium, K = Potassium, Na = Sodium, LV = Little Variation, MV = Moderate Variation, HV = High Variation, Al sat: Aluminum saturation|
Higher values of aluminium saturation in surface horizons were recorded when compared with their subsurface counterpart, implying possibility of A1-toxicity for shallow-rooted crops. In addition to this, soil of the study site are strongly to moderately acidic (Table 3 and 4) in both horizons and this accentuates a greater possibility of Al-toxicity in soils. Earlier, Yamaguchi et al. (2004) reported that soluble aluminium is phytotoxic but phytotoxicity of aluminium depends on its chemical form (Rayan et al., 1995; Ma et al., 1997; Ginting et al., 1998). Aluminium preferentially occupies exchange sites with lowered pH (Gillman and Sumpter, 1986) and such exponential increases in aluminium saturation are of remarkable agronomic importance in terms of acid soil infertility. But studies (Alva et al., 1986) found that aluminium toxicity is not correlated with total aluminium and this is due to complexation of soluble Al-forms by organic molecules (Gillman et al., 1989). The presence of humic fractions of organic matter in soils causes the disappearance of phytotoxic forms of aluminium (Yamaguchi et al., 2004). While organic matter reduced the toxicity of Al and other metals in the rhizosphere, organic acids in these forested soils can induce acidification and acid leaching (Akamigbo, 1999), resulting in unavailability of exchangeable cations particularly Ca and Mg in the root zone (Osemwota et al., 2003). Similar findings in were reported in European forest soils (Thimonier et al., 2001) and North American forest soils (Raben et al., 2000).
In these soils, a good number of chemical properties showed high variations (CV ≥50%) in the subsurface horizons while in surface soils little (CV <20%) to moderate (CV = 21-49%) variations were exhibited by soil exchangeable cations (Table 5). These classifications and rankings were consistent with the report of Aweto (1982) in slope soils of southwest Nigeria.
|Table 4:||Subsurface distribution of selected soil properties (N = 36)|
|Ca = Calcium, Mg = Magnesium, K = Potassium, Na = Sodium, EA = Exchangeable Acidity, CEC = Cation Exchange Capacity, BS = Base Saturation, Alsat = Aluminium saturation, LV = Little Variation, MV = Moderate Variation, HV = High Variation|
|Table 5:||Two sample t-test of horizons differences among the soil groups (N = 18)|
|Ca = Calcium, Mg = Magnesium, K = Potassium, Na = Sodium, EA = Exchangeable acidity, CEC = Cation Exchange Capacity, BS = Base Saturation, Alsat = Aluminium saturation, ***: Significant at the rate of p = 0.001, **: Significant at the rate of p = 0.01, *: Significant at the rate of p = 0.05, NS = Not Significant|
However, greater variability in subsurface horizons could be ascribed to lateral drainage. Nonetheless, differences in surface chemical properties may arise due to variation in nature of organic materials and vegetation types. Onweremadu et al. (2006) reported differences in soil properties in response to variability in vegetal types. Moderate to high variations in exchangeable cations were observed in a similar work by Goderya (1998) which he attributed to differences in management practices. However, variability in CEC was more than values reported by Butt and Park (1999) and this is attributable to climatic and edaphic differences in addition to varying land use history.
|Table 6:||Comparative analysis of selected soil parameters among soil groups given as means (± SD) (N = 18)|
|**: Significant at the rate of p = 0.01, NS = Not Significant, SD = Standard Deviation in parentheses|
|Table 7:||Relationships among some soil properties (N =36)|
|OM = Organic Matter, BD = Bulk Density, Ks = Saturated hydraulic conductivity, CEC = Cation Exchange Capacity, EA = Exchangeable Acidity, Ca = Calcium, Mg = Magnesium, K = Potassium, Na = Sodium, **: Significant at p = 0.01, *: Significant at p = 0.05, NS = Not Significant|
Hydraulic properties of studied soils are shown in Table 6, indicating significant differences (p>0.01) in organic matter and hydraulic conductivity. Bulk density, transmission pores and residual pores exhibited non-significant differences. These results suggest that organic matter content and hydraulic conductivity determine movement of exchangeable cations in the soils. Relationships between these attributes and other soil properties are shown in Table 7. In these soils as OM increased, soil water is conducted through the soil system. This is possibly due to increased aggregation and increased macro-porosity of soils. Organic matter creates negative exchange sites that attract exchangeable cations, thereby reducing their availability for transport. However, this ability depends on pH and concentration of ligand (Giesler et al., 2005). As Ks increases, exchangeable cations become unavailable in the soil system, especially when soils are sandy since Ks correlated significantly (p = 0.01) (Table 7). Although sandy soils are porous, their ability to conduct solutes depend on their pore space geometry (Ezeaku and Anikwe, 2006). Spatial dependence; Table 8 indicates geostatistical data on exchangeable cations on the hilly landscape at surface and subsurface horizons. Nugget values expressed in percentages showed marked differences in the degrees of spatial dependence for variable in all physiographic land units and at both surface and subsurface horizons. Earlier, Cambardella et al. (1994) classified nugget values into three categories, namely <25%, 25 to 75% and >75% defining strong, moderate and weak spatial dependence, respectively. In surface horizons, variables in soils of the interfluve indicated strong spatial dependence while soils of colluvial footslope and channel bed exhibited strong to moderate spatial dependence. On the other hand, soil variables in these soil groups indicted strong moderate spatial dependence. However, variables exhibited differences in their spatial dependence. In addition to the above, fits of spherical and exponential models on generated data were good for surface horizons irrespective of physiographic land units while spherical and linear models dominated in the subsurface horizons, suggesting the adoption of spherical models in predicting spatial dependence of soils of the study area. While exchangeable Ca, exchangeable Mg exchangeable acidity and CEC showed strong dependence in surface soils of interfluve and colluvial footslope, exchangeable acidity exhibited moderate spatial dependence on soils of the channel bed, suggesting that physiographic position has very great influence on its spatial behaviour.
|Table 8:||Variability within surface and subsurface horizons using geostatistical analysis|
|S = Spherical, E = Exponential, L = Linear, T = Transformed, U = Untransfomed|
These results were not the same at subsurface horizons. Exchangeable K and Na indicated moderate spatial dependence in both surface and subsurface horizons, especially at the interfluve.
With the exception of exchangeable Ca and Mg K with strong spatial dependence, other measured exchange attributes showed moderate spatial dependence in the subsurface horizons, irrespective of the geomorphic setting. Exchangeable Ca was the most variable in spatial behaviour while Na was the least variable, suggesting that Ca is most sensitive to pedogenic changes in the studied soils.
Soils indicated pronounced vertical anisotropy in these chemical attributes in the two horizons (Table 8) and this is attributable to the prevalence of biological activities especially cycling of nutrients coupled with the effects of climate and land use practices in the area. Shorter ranges observed in spatial dependence could e a reflection of scales of preferential flow pathways (Ezeaku and Anikwe, 2006). It could be inferred that local-scale variability in vegetation which was not investigated in this study may have contributed greatly to spatial dependence and pedogenetic processes in the sloping terrain.
This study revealed variability in the distribution of exchangeable cations with space, with exchangeable cations increasing downslope in both surface and subsurface horizons. Higher values of CV were obtained in subsurface soils for all the chemical properties, particularly at colluvial slope and channel bed physiographic positions. Conventional and geostatistical techniques indicated clear differences in the distribution of exchangeable basic cations. It was also found that spatial dependence of these chemical attributes varied from strong, moderate and weak categories in the study site. We also found that variations were best described using spherical and linear models in the subsurface attributes. In future studies, considerations should be made in areas of scaling, more intensive sampling and involvement of more geostatistical and multi-variate techniques. Investigations of ion exchange chemistry and mounting of similar studies in lowland landscapes will certainly enhance the generation of more reliable data, helpful in precision agriculture.
We are grateful to the Technical Staff, Department of Soil Science, University of Nigeria Nsukka, Nigeria for technical assistance during the field studies." class="btn btn-success" target="_blank">View Fulltext
Computational Geosciences, 2012, (), . DOI: 10.1007/s10596-012-9290-6