Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics
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 propert...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Applied and Environmental Soil Science |
| Online Access: | http://dx.doi.org/10.1155/2018/9263296 |
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| author | Jacob Kaingo Siza D. Tumbo Nganga I. Kihupi Boniface P. Mbilinyi |
| author_facet | Jacob Kaingo Siza D. Tumbo Nganga I. Kihupi Boniface P. Mbilinyi |
| author_sort | Jacob Kaingo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-144b566e75dd4e31892ed5d80a9a9b33 |
| institution | Kabale University |
| issn | 1687-7667 1687-7675 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied and Environmental Soil Science |
| spelling | doaj-art-144b566e75dd4e31892ed5d80a9a9b332025-02-03T00:59:29ZengWileyApplied and Environmental Soil Science1687-76671687-76752018-01-01201810.1155/2018/92632969263296Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid TropicsJacob Kaingo0Siza D. Tumbo1Nganga I. Kihupi2Boniface P. Mbilinyi3DEST, Sokoine University of Agriculture, P.O. Box 3003, Morogoro, TanzaniaDEST, Sokoine University of Agriculture, P.O. Box 3003, Morogoro, TanzaniaDEST, Sokoine University of Agriculture, P.O. Box 3003, Morogoro, TanzaniaDEST, Sokoine University of Agriculture, P.O. Box 3003, Morogoro, TanzaniaSoil 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.http://dx.doi.org/10.1155/2018/9263296 |
| spellingShingle | Jacob Kaingo Siza D. Tumbo Nganga I. Kihupi Boniface P. Mbilinyi Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics Applied and Environmental Soil Science |
| title | Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics |
| title_full | Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics |
| title_fullStr | Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics |
| title_full_unstemmed | Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics |
| title_short | Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics |
| title_sort | prediction of soil moisture holding capacity with support vector machines in dry subhumid tropics |
| url | http://dx.doi.org/10.1155/2018/9263296 |
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