Abstract
Soil moisture-holding capacity data are required in modelling agrohydrological functions of dry subhumid environments for sustainable crop yields. However, they are hardly sufficient and costly to measure. Mathematical models called pedotransfer functions (PTFs) that use soil physicochemical properties as inputs to estimate soil moisture-holding capacity are an attractive alternative but limited by specificity to pedoenvironments and regression methods. This study explored the support vector machines method in the development of PTFs (SVR-PTFs) for dry subhumid tropics. Comparison with the multiple linear regression method (MLR-PTFs) was done using a soil dataset containing 296 samples of measured moisture content and soil physicochemical properties. Developed SVR-PTFs have a tendency to underestimate moisture content with the root-mean-square error between 0.037 and 0.042 cm3·cm−3 and coefficients of determination (R2) between 56.2% and 67.9%. The SVR-PTFs were marginally better than MLR-PTFs and had better accuracy than published SVR-PTFs. It is held that the adoption of the linear kernel in the calibration process of SVR-PTFs influenced their performance.
Highlights
Sustainability of crop yields in dry subhumid zones of marginal agricultural productivity requires integrated modelling approaches to provide the necessary feedback for adapting agrohydrological functions to changing seasonal soil moisture regimes [1]
Descriptive Statistics of Soil Datasets. e training and testing datasets were distributed within seven United States Department of Agriculture (USDA) soil texture classes (Figure 2)
Organic carbon ranged from 0.06% to 3.37%, and clay, sand, and silt contents ranged from 0.1% to 63.6%, 20% to 96.6%, and 1.4% to 35.4%, respectively
Summary
Sustainability of crop yields in dry subhumid zones of marginal agricultural productivity requires integrated modelling approaches to provide the necessary feedback for adapting agrohydrological functions to changing seasonal soil moisture regimes [1]. Soil moisture-holding capacity is an important parameter for modelling moisture availability. It is a measure of the difference between moisture at field capacity and wilting point [2]. Moisture-holding capacity facilitates the description of soil hydrological processes such as drainage, infiltration, and percolation and is vital input data in models such as Soil Water Assessment Tool (SWAT) [3], and AQUACROP [4]. Is is suggested to impart contrasting soil hydraulic characteristics [5, 11,12,13], limiting transferability of PTFs for modelling processes across their statistical and pedoclimatic calibration bounds [9, 14] Tropical soils have a bimodal particle-size distribution in contrast to the unimodal soils of the temperates [5, 10], with maximal weight percentage for clay- and sand-size fractions and low silt content [5]. is is suggested to impart contrasting soil hydraulic characteristics [5, 11,12,13], limiting transferability of PTFs for modelling processes across their statistical and pedoclimatic calibration bounds [9, 14]
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