SPATIAL VARIABILITY OF SOIL pH AND CEC USING PREDICTIVE MODELS IN THE MOUNTAINOUS REGION OF OBANLIKU, CROSS RIVER STATE, NIGERIA
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This study predicted the distribution pH and CEC in Obudu Cattle Ranch, Obanliku Local Government Area of Cross River State. Sixty (60) composite soil samples were collected (0-30 cm) between 200 to 500 m apart for the study. Normalized difference moisture index (NDMI), land surface temperature (LST), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), clay, soil organic carbon and pH were used as covariates. Multiple linear regression (MLR), random forest (RF), ordinary kriging (OK), cubist regression (CR) and regression kriging (RK) were used to predict soil pH and CEC and were evaluated using bias, coefficient of determination (R2 ), and root mean square error (RMSE), mean square error (MSE) and Lin’s concordance correlation coefficient (CCC). The soils had pH varying from strongly acidic (pH=5.2) to slightly acid (pH=6.6) with high OC (>2 %). CEC of the soil was high (25 to 40 cmol/kg). CEC model estimated using OK had strong degree of spatial dependence while pH model had a moderate degree of spatial dependence. CEC predicted models showed almost similar spatial pattern with high CEC found in western part of the study. The highest predicted pH values dominated western part while the least predominated eastern parts of the study area in all the models. Cubist model better predicted CEC (CCC=0.605; MSE=28.79; RMSE=5.366) followed by MLR (R2=0.552) than other models whereas pH was better modeled by OK (CCC=0.636; MSE=0.103; RMSE=0.322) followed by RF (RF=0.664). Cubist and OK should be used for assessment of spatial variation of CEC and pH in mountainous areas.
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