Modelling Spatial Variability Of Soil Moisture Holding Capacity In A Dry Sub-Humid Landscape

EXTENDED ABSTRACT

Moisture scarcity is a limiting factor for sustainable agricultural productivity of dry sub-humid agroecosystemsof sub-Saharan Africa (SSA). Designing sustainable agricultural system management strategies responsive to the fluctuating soil moisture regime is essential. Detailed and accurate information on soil moisture storage conditions is essential for modelling agricultural system productivity. Moisture storage capacity of the soils is quantified by moisture holding capacity (MHC) which is defined as the difference between moisture content at field capacity (FC)and wilting point (WP). Data availability is limited for SSA due to high costs associated with direct measurement of MHC. Pedo-transfer functions (PTFs) and the digital soil mapping (DSM)framework offer an opportunity for characterising spatial variability of MHC through indirect approaches that integrate mathematical and statistical methods. Though various methods exist for prediction and mapping MHC, machine learning methods offer an avenue for more accurate characterisation of MHC. The main objective of this study was to improve understanding on estimation of soil moisture holding capacity at large spatial domains using machine learning algorithms. This was achieved througha probabilistic sampling scheme, development of MHC PTFs, and 3-dimensional characterisation of spatial variability of MHC. One hundred (100) sampling locations were established over a geographic area of about 44 km 2 by k-means clustering using R-statistical software. Two sampling strategies were evaluated for optimisation of the sampling locations –a stratified random sampling (STRS) and spatial coverage sampling (SPCS). Bulk soil samples and soil cores were taken at three depth intervals of 0-30cm, 30-60 cm, and iii 60-100 cm at each sampling location. Geostatistical analysis and cross-validation were performed for assessment of the sampling schemes using root mean square error (RMSE), coefficient of determination (R2 ) and Mean Error (ME) as indices. West-East anisotropy was evident in the MHC probably associated with topographic and land cover effects. Spatial dependence ratio for the stratified random sampling scheme (73 %) was higher than that of the spatial coverage sampling scheme (19 %). This implied that SPCSdesign had better spatial correlation than the STRS design due to a regular configuration of sampling nodes for SPCS design.Validation resultswere better for STRS design than SPCS design. Pedo-transfer functions were developed for FC and WP from support vector regression and multiple linear regression with soil physico-chemical properties as predictors. Support vector regression-PTFs had slightly better accuracy (RMSEs = 0.037 cm-3 cm-3 ) than multiple linear regression PTFs (RMSEs = 0.038 cm-3 cm-3 ) and other published PTFs. R2 values for SVR-PTFs were 66.3 and 67.9 % while those for MLR-PTFs were 64.5 and 67.3% for FC and WP, respectively.Two machine learning algorithms (Random forests(RF) and cubist decision trees (CB)) combined with soil depth functions were evaluated for 3-dimensional mapping of MHC. Two DSM scenarios were also evaluated (Measured data only (DSM-A) and measured plusPTFestimated data (DSM-B)).Principal component analysis was performed on spatial covariates layers representing soil forming factors for dimension reduction. Ten principal components with a cumulative variance > 70 % were selected for mapping process. Equal-area quadratic spline soil depth functions were fitted to model continuous vertical distribution of MHC data. Prediction accuracy was good with RMSEs ranging between 0.011-0.015 cm-3 cm-3 and R2 between 36 - 81.4 %. Random iv forests had better accuracy than the Cubist decision trees. A RF-CB ensemble improves prediction accuracy.