A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression
Pedotransfer functions (PTFs) have been developed for many regions to estimate values missing from soil profile databases. However, globally there are many areas without existing PTFs, and it is not advisable to use PTFs outside their domain of development due to poor performance. Further, developed...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Geoderma |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0016706123003518 |
| _version_ | 1797634016290013184 |
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| author | Adrienne Arbor Margaret Schmidt Daniel Saurette Jin Zhang Chuck Bulmer Deepa Filatow Babak Kasraei Sean Smukler Brandon Heung |
| author_facet | Adrienne Arbor Margaret Schmidt Daniel Saurette Jin Zhang Chuck Bulmer Deepa Filatow Babak Kasraei Sean Smukler Brandon Heung |
| author_sort | Adrienne Arbor |
| collection | DOAJ |
| description | Pedotransfer functions (PTFs) have been developed for many regions to estimate values missing from soil profile databases. However, globally there are many areas without existing PTFs, and it is not advisable to use PTFs outside their domain of development due to poor performance. Further, developed PTFs often lack accompanying uncertainty estimations. To address these issues, a framework is proposed where existing equation-based PTFs are recalibrated using a nonlinear least squares (NLS) approach and validated on two regions of Canada; this process is coupled with the use of quantile regression (QR) to generate uncertainty estimates. Many PTFs have been developed to predict soil bulk density, so this variable is used as a case study to evaluate the outcome of recalibration. New coefficients are generated for existing soil bulk density PTFs, and the performance of these PTFs is validated using three case study datasets, one from the Ottawa region of Ontario and two from the province of British Columbia, Canada. The improvement of the performance of the recalibrated PTFs is evaluated using root mean square error (RMSE) and the concordance correlation coefficient (CCC). Uncertainty estimates produced using QR are communicated through the mean prediction interval (MPI) and prediction interval coverage probability (PICP) graphs. This framework produces dataset-specific PTFs with improved accuracy and minimized uncertainty, and the method can be applied to other regional datasets to improve the estimations of existing PTF model forms. The methods are most successful with large datasets and PTFs with fewer variables and minimal transformations; further, PTFs with organic carbon (OC) as one of or the sole input variable resulted in the highest accuracy. |
| first_indexed | 2024-03-11T12:01:53Z |
| format | Article |
| id | doaj.art-9e3d7ff0f7e848ff8596113891b21b4b |
| institution | Directory Open Access Journal |
| issn | 1872-6259 |
| language | English |
| last_indexed | 2024-03-11T12:01:53Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geoderma |
| spelling | doaj.art-9e3d7ff0f7e848ff8596113891b21b4b2023-11-08T04:08:44ZengElsevierGeoderma1872-62592023-11-01439116674A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regressionAdrienne Arbor0Margaret Schmidt1Daniel Saurette2Jin Zhang3Chuck Bulmer4Deepa Filatow5Babak Kasraei6Sean Smukler7Brandon Heung8Soil Science Lab, Department of Geography, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, CanadaSoil Science Lab, Department of Geography, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, CanadaOntario Ministry of Agriculture and Rural Affairs, 1 Stone Rd. W, Guelph, ON N1G 4Y2, CanadaSoil Science Lab, Department of Geography, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada; Department of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, PO Box 550, 21 Cox Rd., Truro, NS B2N 5E3, CanadaBritish Columbia Ministry of Forests, Lands, Natural Resource Operations & Rural Development, Vernon, BC V1B 2C7, CanadaBritish Columbia Ministry of Water, Land and Resource Stewardship, PO Box 9358 Stn Prov Govt, Victoria, BC V8W 9M2, CanadaSoil Science Lab, Department of Geography, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, CanadaFaculty of Land and Food Systems, The University of British Columbia, 123-2357 Main Mall, Vancouver, BC V6T 1Z4 CanadaDepartment of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, PO Box 550, 21 Cox Rd., Truro, NS B2N 5E3, Canada; Corresponding author.Pedotransfer functions (PTFs) have been developed for many regions to estimate values missing from soil profile databases. However, globally there are many areas without existing PTFs, and it is not advisable to use PTFs outside their domain of development due to poor performance. Further, developed PTFs often lack accompanying uncertainty estimations. To address these issues, a framework is proposed where existing equation-based PTFs are recalibrated using a nonlinear least squares (NLS) approach and validated on two regions of Canada; this process is coupled with the use of quantile regression (QR) to generate uncertainty estimates. Many PTFs have been developed to predict soil bulk density, so this variable is used as a case study to evaluate the outcome of recalibration. New coefficients are generated for existing soil bulk density PTFs, and the performance of these PTFs is validated using three case study datasets, one from the Ottawa region of Ontario and two from the province of British Columbia, Canada. The improvement of the performance of the recalibrated PTFs is evaluated using root mean square error (RMSE) and the concordance correlation coefficient (CCC). Uncertainty estimates produced using QR are communicated through the mean prediction interval (MPI) and prediction interval coverage probability (PICP) graphs. This framework produces dataset-specific PTFs with improved accuracy and minimized uncertainty, and the method can be applied to other regional datasets to improve the estimations of existing PTF model forms. The methods are most successful with large datasets and PTFs with fewer variables and minimal transformations; further, PTFs with organic carbon (OC) as one of or the sole input variable resulted in the highest accuracy.http://www.sciencedirect.com/science/article/pii/S0016706123003518Soil bulk densityNonlinear least squaresUncertainty analysisQuantile regressionPedotransfer functionsModel recalibration |
| spellingShingle | Adrienne Arbor Margaret Schmidt Daniel Saurette Jin Zhang Chuck Bulmer Deepa Filatow Babak Kasraei Sean Smukler Brandon Heung A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression Geoderma Soil bulk density Nonlinear least squares Uncertainty analysis Quantile regression Pedotransfer functions Model recalibration |
| title | A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression |
| title_full | A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression |
| title_fullStr | A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression |
| title_full_unstemmed | A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression |
| title_short | A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression |
| title_sort | framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression |
| topic | Soil bulk density Nonlinear least squares Uncertainty analysis Quantile regression Pedotransfer functions Model recalibration |
| url | http://www.sciencedirect.com/science/article/pii/S0016706123003518 |
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