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ASSESSMENT OF HETEROGENITY IN SOIL PROPERTIES OF NIMBIA FOREST RESERVEIN SOUTHERN GUINEA SAVANNAH OF NIGERIAFOR SITE SPECIFIC MANAGEMENT
ABSTRACT
Spatial analysis of soil heterogeneity is a vital prerequisite for site specific forest management. This study was aimed at describing the field-scale horizontal spatial variability and developing models for predicting soil aggregate stability from other soil propertiesin Nimbia Forest Reserve of the Southern Guinea Savanna zone of Nigeria. Georeferenced topsoil samples (0-0.15 m) and field infiltration were obtained systematically from block NF80 along 300 m transect at 3 m interval (n=100). These samples were analyzed for both physical and chemical properties. Soil data were analyzed using classical and spatial statistical tools. Results revealed large differences between minimum and maximum values of the investigated soil properties. The results showed high degree of variabilityin the majorityof the soil properties with coefficients of variability (CV) ranging from 0.74% to 90.83%. Using the students’ t-test statistics at 5 % level of probability, autocorrelation (ACF) and crosscorrelation functions (CCF) were calculated. Calculated ACF revealed strong spatial dependence between adjacent observations (19 lags) of large macroaggregate-associated organic carbon (OCa) and no autocorrelation in total iron. Based on the result of the CCF of wet sieved mean weight diameter (MWDw) versus other soil properties; correlated soil properties such as clay, total phosphorus (TP), dithionite iron (Fed), pyrophosphate-extractable iron (Fep), moisture content and aggregate fraction associated organic carbon (OCa, OCb, OCc, OCd, OCe) were selected and used in various combinations for multiple linear regression (MLR) and state-space analyses. The results showed that state-space models described the variation in MWDw better than the equivalent MLR models. The best model performance explained 99.85 % of the MWDw variation when clay, Total P, Fed, OCa, OCc, OCd and OCe were used in prediction. The relationship of clay, Fed and OCa to all the selected best performed models of each scenarios suggested that these soil properties have effective contribution to the spatial variation of MWDw. Semivariograms were used to quantify the spatial structure of soil properties. All properties, except total iron, exhibited a definable spatial structure, which were described by spherical, exponential, gaussian or linear models. Majority of the soil properties showed strong spatial dependence. Alternative analytical tools such as the statespace and semivariogram under forest conditions; considered the underlying processes of soil properties along transect, identify the local relations between MWDw and selected properties and; quantify these relationships taking measurement and model errors into account. Analysis of variation in soil properties is complex. Nevertheless, spatial statistical tools provided concise information about spatial variation and relationships between soil properties than the classical statistics tools and; this information is crucial for precision in site-specific soil management planning in such forest ecosystems.
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