Vol 5 No 8 (2019): International Journal For Research In Agricultural And Food Science (ISSN: 2208-2719)
Articles

APPLICATION OF REGRESSION TREE IN MODELLING AND MAPPING OF CATION EXCHANGE CAPACITY OF SOILS IN AKWA IBOM STATE, NIGERIA

Udeme Akpan
University of Uyo
Bio
Uwah I.D.
Akwa Ibom State College of Technology
Bio
Nkannga, N.A.
University of Uyo
Bio
Published September 3, 2019
Keywords
  • modelling,
  • regression tree,
  • CEC,
  • soil of Akwa Ibom State
How to Cite
Akpan, U., Uwah I.D., & Nkannga, N.A. (2019). APPLICATION OF REGRESSION TREE IN MODELLING AND MAPPING OF CATION EXCHANGE CAPACITY OF SOILS IN AKWA IBOM STATE, NIGERIA. International Journal For Research In Agricultural And Food Science (ISSN: 2208-2719), 5(8), 71-92. Retrieved from https://gnpublication.org/index.php/afs/article/view/960

Abstract

 Regression tree   was used in modelling and mapping cation exchange capacity of soils in Akwa Ibom State, Nigeria.The aim was to provide an alternative techinque of estimating ECEC from more readily available soil data and map the distribution for site-specific soil management. The study area (Akwa Ibom State) was grouped into four major mapping units based on parent materials, namely:  coastal plain sand, sandstone, shale and beach ridge sand. Each parent material (major mapping unit) was subdivided into four commonly practice landuse types/soil management systems namely homestead or compound farmland, oil palm plantation, secondary forest of 3 years and above and cultivated farmland.  In each landuse type/soil management system, mini- soil profile pit was dug to a depth of 100cm at representative location. Soil samples were collected from designated depths of 0-20, 20-60 and 60-100 cm. A total of 144 samples were generated for laboratry analysis. The study revealed that ECEC can be predicted using soil organic carbon, clay, silt and soil pH in the study area. The results of independent variable importance to the model showed that organic carbon was the most significant predictor of ECEC with 22.1 % contribution, followed by clay with 17.3 %, followed by silt with 8.5% while soil pH was the least predictor of ECEC with 0.8% contribution in the study area. Based on the model, organic carbon content predicted ECEC of 32.4 cmol/kg in sandstone soil while organic carbon in combination with clay predicted ECEC of 40.24 cmol/kg in soils developed from shale parent material. In coastal plain sand soils, organic carbon in combination with clay and silt predicted ECEC of 24.3 cmol/kg. In beach ridge sand soils, organic carbon in combination with clay and silt predicted ECEC of 17.8 cmol/kg. The model showed that organic carbon content was the only significant predictor of ECEC in sandstone soils while organic carbon in combination with clay made significant prediction of ECEC in shale parent material. In coastal plain sand and beach ridge sand soils, Organic carbon in combination with clay and silt made significant prediction of ECEC. In the application of the model, independent variables included in the final model and measured in the same unit should be used.

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