Spatial prediction of USDA‐ great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types

By A. Jafari, Peter Finke, J. V. Wauw, Shamsollah Ayoubi, Hossein Khademi. 2012

96 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/fecb63c23fdd347c7d65399cab55b0a138537557

How researchers see this paper

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

In Ecology

1. THOMAS C. EDWARDS JR.

In Environmental Engineering

1. THOMAS C. EDWARDS JR.
2. J. Boettinger
3. Michael C. Duniway
4. C. Brungard
5. S. Wills
6. Budiman Minasny
7. Alex B. McBratney

In Management, Monitoring, Policy and Law

1. C. Brungard

Citations in the corpus

• Machine learning for predicting soil classes in three semi-arid landscapes (2015, methodology): “91 Of the studies that used soil pedon observations to construct soil class-landscape relationships (e.g., 92 Barthold et al., 2013; Jafari et al., 2012; Kempen et al., 2012) few compared more than two machine 93 learning models, and none compared multiple machine learning models at more than one…”