Mapping soil texture classes using field texturing, particle size distribution and local knowledge by both conventional and geostatistical methods
We investigated the utility of three interpolation techniques that ignored descriptive `soft' information and one that used it for mapping topsoil texture classes: re‐coding of soil map units within Geographical Information Systems (GIS), Thiessen polygons, and classifcation of probability vectors e...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Wiley
1999
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| Online Access: | https://hdl.handle.net/10568/165783 |
| _version_ | 1855518301703110656 |
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| author | Oberthür, T. Goovaerts, P. Dobermann, A. |
| author_browse | Dobermann, A. Goovaerts, P. Oberthür, T. |
| author_facet | Oberthür, T. Goovaerts, P. Dobermann, A. |
| author_sort | Oberthür, T. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | We investigated the utility of three interpolation techniques that ignored descriptive `soft' information and one that used it for mapping topsoil texture classes: re‐coding of soil map units within Geographical Information Systems (GIS), Thiessen polygons, and classifcation of probability vectors estimated by ordinary indicator kriging and simple indicator kriging with local prior means. The results were compared with texture maps based on a classifcation of kriged maps of particle size distribution. The methods were applied to two distinct regions, which represent large areas in rain‐fed rice ecosystems and irrigated rice ecosystems. The `hard' databases for both areas contained soil information needed for mapping at regional scales (1:100 000‐1:150 000). These data were complemented with 'soft' information derived from farmers and soil experts (Northeast Thailand) and soil maps (Nueva Ecija, Philippines). Interpolated maps agreed with the texture map based on interpolation of particle size distribution, and ®eld estimates of soil texture proved to be valuable surrogates for laboratory measurements of soil texture classes. The interpolation of categorical data such as soil texture classes allows for upgrading and increasing the resolution of maps in sparsely sampled regions by using simple field measurements. Validation using independent test sets showed that indicator kriging with local prior means performed best in the rain‐fed lands, whereas soft information did not improve the predictions in Nueva Ecija. Local knowledge in a formalized form was valuable in Northeast Thailand and the interpolated soil texture map for this area had an accuracy and resolution to support agronomic decisions at the village scale. The poor quality of the soil map and the fact that the gradually changing variability in young alluvial soils can be modelled with fewer data explained the lower accuracy of simple indicator kriging with local prior means in Nueva Ecija. Thiessen polygons performed well in the undulating rain‐fed lands but were not as reliable as indicator kriging in the gradually changing irrigated lands. |
| format | Journal Article |
| id | CGSpace165783 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 1999 |
| publishDateRange | 1999 |
| publishDateSort | 1999 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1657832025-05-14T10:23:56Z Mapping soil texture classes using field texturing, particle size distribution and local knowledge by both conventional and geostatistical methods Oberthür, T. Goovaerts, P. Dobermann, A. mapping soil texture particle size distribution geographical information systems rainfed rice irrigated rice We investigated the utility of three interpolation techniques that ignored descriptive `soft' information and one that used it for mapping topsoil texture classes: re‐coding of soil map units within Geographical Information Systems (GIS), Thiessen polygons, and classifcation of probability vectors estimated by ordinary indicator kriging and simple indicator kriging with local prior means. The results were compared with texture maps based on a classifcation of kriged maps of particle size distribution. The methods were applied to two distinct regions, which represent large areas in rain‐fed rice ecosystems and irrigated rice ecosystems. The `hard' databases for both areas contained soil information needed for mapping at regional scales (1:100 000‐1:150 000). These data were complemented with 'soft' information derived from farmers and soil experts (Northeast Thailand) and soil maps (Nueva Ecija, Philippines). Interpolated maps agreed with the texture map based on interpolation of particle size distribution, and ®eld estimates of soil texture proved to be valuable surrogates for laboratory measurements of soil texture classes. The interpolation of categorical data such as soil texture classes allows for upgrading and increasing the resolution of maps in sparsely sampled regions by using simple field measurements. Validation using independent test sets showed that indicator kriging with local prior means performed best in the rain‐fed lands, whereas soft information did not improve the predictions in Nueva Ecija. Local knowledge in a formalized form was valuable in Northeast Thailand and the interpolated soil texture map for this area had an accuracy and resolution to support agronomic decisions at the village scale. The poor quality of the soil map and the fact that the gradually changing variability in young alluvial soils can be modelled with fewer data explained the lower accuracy of simple indicator kriging with local prior means in Nueva Ecija. Thiessen polygons performed well in the undulating rain‐fed lands but were not as reliable as indicator kriging in the gradually changing irrigated lands. 1999-09 2024-12-19T12:55:29Z 2024-12-19T12:55:29Z Journal Article https://hdl.handle.net/10568/165783 en Wiley Oberthür, T.; Goovaerts, P and Dobermann, A . 1999. Mapping soil texture classes using field texturing, particle size distribution and local knowledge by both conventional and geostatistical methods. European J Soil Science, Volume 50 no. 3 p. 457-479 |
| spellingShingle | mapping soil texture particle size distribution geographical information systems rainfed rice irrigated rice Oberthür, T. Goovaerts, P. Dobermann, A. Mapping soil texture classes using field texturing, particle size distribution and local knowledge by both conventional and geostatistical methods |
| title | Mapping soil texture classes using field texturing, particle size distribution and local knowledge by both conventional and geostatistical methods |
| title_full | Mapping soil texture classes using field texturing, particle size distribution and local knowledge by both conventional and geostatistical methods |
| title_fullStr | Mapping soil texture classes using field texturing, particle size distribution and local knowledge by both conventional and geostatistical methods |
| title_full_unstemmed | Mapping soil texture classes using field texturing, particle size distribution and local knowledge by both conventional and geostatistical methods |
| title_short | Mapping soil texture classes using field texturing, particle size distribution and local knowledge by both conventional and geostatistical methods |
| title_sort | mapping soil texture classes using field texturing particle size distribution and local knowledge by both conventional and geostatistical methods |
| topic | mapping soil texture particle size distribution geographical information systems rainfed rice irrigated rice |
| url | https://hdl.handle.net/10568/165783 |
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