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500m gridded surfaces for changes in climate suitability for coffee production in Risaralda, Colombia

We used 500m gridded historical and future climate surfaces for Risaralda, Colombia and coffee presences and absences to train species distribution models (suitability). Five methods were used: Generalized Boosting Model (GBM) (Friedman, 2001), Random Forest (RF) (Breiman, 2001), Maxent (Phillips et...

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Main Authors: Vallejo Arango, Eliana, Navarro Racines, Carlos Eduardo, Ramírez Villegas, Julián Armando, Aguilar-Ariza, Andrés, Delerce, Sylvain Jean
Format: Conjunto de datos
Language:Inglés
Published: 2017
Subjects:
suitability
coffee
machine learning
climate change
agriculture
Online Access:https://hdl.handle.net/10568/89764
Summary:We used 500m gridded historical and future climate surfaces for Risaralda, Colombia and coffee presences and absences to train species distribution models (suitability). Five methods were used: Generalized Boosting Model (GBM) (Friedman, 2001), Random Forest (RF) (Breiman, 2001), Maxent (Phillips et al., 2006), Generalized Linear Model (GLM) and Generalized Additive Model (GAM) (Guisan et al., 2002).

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