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A comparative Digital Soil Mapping (DSM) study using a non-supervised clustering analysis and an expert knowledge based model - A case study from Ahuachapán, El Salvador

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Title A comparative Digital Soil Mapping (DSM) study using a non-supervised clustering analysis and an expert knowledge based model - A case study from Ahuachapán, El Salvador
 
Creator Martín López, Javier M.
Silva, Mayesse A. da
Valencia, Jefferson
Quintero, Marcela
Keough, Adam
Casares, Francisco
 
Subject soil
soil properties
models
 
Description DSM is the inference of spatial and temporal soil property variations using mathematical models based on quantitative relationships between environmental information and soil measurements. The quality of DSM information depends on the method and environmental covariates used for its estimations. We compared two DSM methods to predict soil properties such as Organic Matter “MO” (%), Sand (%), Clay (%), pH (H2O), Phosphorus (mg/kg), Effective Cationic Exchange Capacity “CICE” (cmol/L), Potassium (cmol/L) and Water Holding Capacity (mm/m) for the department of Ahuachapán in El Salvador to support the activities of the Agriculture Landscape Restoration Initiative (ALRI) in the country
 
Date 2019
2020-01-29T13:19:43Z
2020-01-29T13:19:43Z
 
Type Poster
 
Identifier Martín-López, Javier M.; da Silva Mayesse; Valencia, Jefferson; Quintero, Marcela; Keough, Adam & Casares, Francisco (2019). A comparative Digital Soil Mapping (DSM) study using a non-supervised clustering analysis and an expert knowledge based model - A case study from Ahuachapán, El Salvador.Presented at:Joint Workshop for Digital Soil Mapping and Global Soil Map March 12-16 2019. 1 p.
https://hdl.handle.net/10568/106786
 
Language en
 
Rights Other
Open Access
 
Format 1 p.
application/pdf
 
Publisher International Center for Tropical Agriculture