Ensemble Modeling on Near-Infrared Spectra as Rapid Tool for Assessment of Soil Health Indicators for Sustainable Food Production Systems
A novel total ensemble (TE) algorithm was developed and compared with random forest optimization (RFO), gradient boosted machines (GBM), partial least squares (PLS), Cubist and Bayesian additive regression tree (BART) algorithms to predict numerous soil health indicators in soils with diverse climat...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2021
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/116303 |
| _version_ | 1855517627515928576 |
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| author | Recha, John W.M. Olale, Kennedy O. Sila, Andrew Ambaw, Gebermedihin Radeny, Maren A.O. Solomon, Dawit |
| author_browse | Ambaw, Gebermedihin Olale, Kennedy O. Radeny, Maren A.O. Recha, John W.M. Sila, Andrew Solomon, Dawit |
| author_facet | Recha, John W.M. Olale, Kennedy O. Sila, Andrew Ambaw, Gebermedihin Radeny, Maren A.O. Solomon, Dawit |
| author_sort | Recha, John W.M. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | A novel total ensemble (TE) algorithm was developed and compared with random forest optimization (RFO), gradient boosted machines (GBM), partial least squares (PLS), Cubist and Bayesian additive regression tree (BART) algorithms to predict numerous soil health indicators in soils with diverse climate-smart land uses at different soil depths. The study investigated how land-use practices affect several soil health indicators. Good predictions using the ensemble method were obtained for total carbon (R2 = 0.87; RMSE = 0.39; RPIQ = 1.36 and RPD = 1.51), total nitrogen (R2 = 0.82; RMSE = 0.03; RPIQ = 2.00 and RPD = 1.60), and exchangeable bases, m3. Cu, m3. Fe, m3. B, m3. Mn, exchangeable Na, Ca (R2 > 0.70). The performances of algorithms were in order of TE > Cubist > BART > PLS > GBM > RFO. Soil properties differed significantly among land uses and between soil depths. In Kenya, however, soil pH was not significant, except at depths of 45–100 cm, while the Fe levels in Tanzanian grassland were significantly high at all depths. Ugandan agroforestry had a substantially high concentration of ExCa at 0–15 cm. The total ensemble method showed better predictions as compared to other algorithms. Climate-smart land-use practices to preserve soil quality can be adopted for sustainable food production systems. |
| format | Journal Article |
| id | CGSpace116303 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1163032025-08-15T13:21:15Z Ensemble Modeling on Near-Infrared Spectra as Rapid Tool for Assessment of Soil Health Indicators for Sustainable Food Production Systems Recha, John W.M. Olale, Kennedy O. Sila, Andrew Ambaw, Gebermedihin Radeny, Maren A.O. Solomon, Dawit algorithms climate-smart soil quality land use A novel total ensemble (TE) algorithm was developed and compared with random forest optimization (RFO), gradient boosted machines (GBM), partial least squares (PLS), Cubist and Bayesian additive regression tree (BART) algorithms to predict numerous soil health indicators in soils with diverse climate-smart land uses at different soil depths. The study investigated how land-use practices affect several soil health indicators. Good predictions using the ensemble method were obtained for total carbon (R2 = 0.87; RMSE = 0.39; RPIQ = 1.36 and RPD = 1.51), total nitrogen (R2 = 0.82; RMSE = 0.03; RPIQ = 2.00 and RPD = 1.60), and exchangeable bases, m3. Cu, m3. Fe, m3. B, m3. Mn, exchangeable Na, Ca (R2 > 0.70). The performances of algorithms were in order of TE > Cubist > BART > PLS > GBM > RFO. Soil properties differed significantly among land uses and between soil depths. In Kenya, however, soil pH was not significant, except at depths of 45–100 cm, while the Fe levels in Tanzanian grassland were significantly high at all depths. Ugandan agroforestry had a substantially high concentration of ExCa at 0–15 cm. The total ensemble method showed better predictions as compared to other algorithms. Climate-smart land-use practices to preserve soil quality can be adopted for sustainable food production systems. 2021-11-12 2021-11-25T17:03:49Z 2021-11-25T17:03:49Z Journal Article https://hdl.handle.net/10568/116303 en Open Access MDPI Recha JW, Olale KO, Sila A, Ambaw G, Radeny M, Solomon D. 2021. Ensemble Modeling on Near-Infrared Spectra as Rapid Tool for Assessment of Soil Health Indicators for Sustainable Food Production Systems. Soil Systems 5(4):69. |
| spellingShingle | algorithms climate-smart soil quality land use Recha, John W.M. Olale, Kennedy O. Sila, Andrew Ambaw, Gebermedihin Radeny, Maren A.O. Solomon, Dawit Ensemble Modeling on Near-Infrared Spectra as Rapid Tool for Assessment of Soil Health Indicators for Sustainable Food Production Systems |
| title | Ensemble Modeling on Near-Infrared Spectra as Rapid Tool for Assessment of Soil Health Indicators for Sustainable Food Production Systems |
| title_full | Ensemble Modeling on Near-Infrared Spectra as Rapid Tool for Assessment of Soil Health Indicators for Sustainable Food Production Systems |
| title_fullStr | Ensemble Modeling on Near-Infrared Spectra as Rapid Tool for Assessment of Soil Health Indicators for Sustainable Food Production Systems |
| title_full_unstemmed | Ensemble Modeling on Near-Infrared Spectra as Rapid Tool for Assessment of Soil Health Indicators for Sustainable Food Production Systems |
| title_short | Ensemble Modeling on Near-Infrared Spectra as Rapid Tool for Assessment of Soil Health Indicators for Sustainable Food Production Systems |
| title_sort | ensemble modeling on near infrared spectra as rapid tool for assessment of soil health indicators for sustainable food production systems |
| topic | algorithms climate-smart soil quality land use |
| url | https://hdl.handle.net/10568/116303 |
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