Fine-resolution mapping of soil organic carbon based on multivariate secondary data

By G. Simbahan, A. Dobermann, Pierre Goovaerts, J. Ping, M. Haddix. 2006

198 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/bb843696f4c3ce7bcfcea18822e3340b2ded8728

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Alex B. McBratney
2. Budiman Minasny
3. Rattan Lal
4. M. Meirvenne
5. Sabine Grunwald

In Ecology

1. Budiman Minasny
2. Alex B. McBratney
3. Desheng Liu
4. Rattan Lal
5. Tomislav Hengl
6. Sabine Grunwald
7. R. M. Lark
8. G. Brookes

In Environmental Engineering

1. Budiman Minasny
2. Alex B. McBratney
3. Desheng Liu
4. Rattan Lal
5. M. Meirvenne
6. Sabine Grunwald
7. Tomislav Hengl
8. R. M. Lark

Citations in the corpus

• Spatial prediction of soil properties using EBLUP with the Matérn covariance function (2007, methodology): “Stein (1999) showed that Eq.”
• Predicting Soil Organic Carbon Stock Using Profile Depth Distribution Functions and Ordinary Kriging (2009, background): “Systematic (grid) sampling has been used extensively in geostatistical mapping (Baxter and Oliver, 2005; Minasny et al., 2006; Simbahan et al., 2006).”