Estimation of soil organic matter by geostatistical methods: Use of auxiliary information in agricultural and environmental assessment |
| |
| Institution: | 1. Faculty of Agriculture and Environment, The University of Sydney, 1 Central Avenue, Australia Technology Park, Eveleigh, NSW 2015, Australia;2. EcoSciences Precinct, Department of Science, Information Technology, Innovation and the Arts, GPO Box 5078, Brisbane, QLD 4001, Australia;1. Universidade Federal do Rio Grande do Sul, UFRGS, Faculdade de Agronomia, Av. Bento Gonçalves, 7712, Porto Alegre, RS 91540-000, Brazil;2. University of Wisconsin — Madison, Department of Soil Science, FD Hole Soils Lab, 1525 Observatory Drive, Madison, WI 53706, USA;3. CAPES Foundation, Ministry of Education of Brazil, Brasília, DF 70040-020, Brazil;1. Rubber Research Institute, Chinese Academy of Tropical Agriculture Sciences, Danzhou, Hainan 571737, China;2. College of Resources and Environment, Southwest University, Chongqing 400716, China;3. Hainan Agricultural Reclamation Academy of Sciences, Haikou 570206, China;1. Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan, Iran;2. Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran;3. Department of Geography, Brigham Young University, Provo, UT, USA |
| |
| Abstract: | Mapping soil properties such as soil organic matter (SOM), or soil organic carbon (SOC) content represent a problem often arising in agronomic and environmental surveys, since point data must be spatialized with the best interpolation. Deterministic methods (Inverse Distance Weighting, Splines, ecc.) do not account for the error, thus probabilistic methods as geostatistics (e.g. Ordinary Kriging, OK) have been successfully applied for many years. Maps derived from this kind of stochastic interpolation, based only on a recognized autocorrelation among measured points, could not be suitable in representing the reality, since they usually show a smoothed pattern. Hybrid interpolation methods, such as Regression Kriging (RK), combine an interpolation based only on point data and an interpolation based on a regression of the target variable with other continuous variables, spatially related, well known on the whole area.In the Teramo province, central Italy, a set of 250 soil samples, collected from the surface horizon of agricultural soils is available. From these samples the estimation of soil texture, SOC, SOM related to texture and C/N both by OK and RK was performed. The following predictors were used for RK: (i) indexes derived from Landsat TM imagery, (ii) morphometric parameters derived from DEM, (iii) soil subsystems map 1:250,000. The maps obtained by both OK and RK in this survey show substantial agreement, without significant improvement in map accuracy using auxiliary information. |
| |
| Keywords: | Soil organic matter Geostatistics Ordinary kriging Regression kriging C/N ratio |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
