Mapping Ash CaCO3, pH, and Extractable Elements Using Principal Component Analysis

Agata Novara, Yones Khaledian, Eric C. Brevik, Jesus Rodrigo Comino, Igor Bogunovic, Paulo Pereira, Marcos Francos, Artemi Cerdà, Xavier Úbeda

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7 Citazioni (Scopus)


Ash cover in fire-affected areas is an important factor in the reduction of soil erosion and increased availability of soil nutrients. Thus it is important to understand the spatial distribution of ash and its capacity for soil protection and to provide nutrients to the underlying soil. In this work, we aimed to map ash CaCO3, pH, and select extractable elements using a principal component analysis (PCA). Four days after a medium to severe wildfire, we established a grid in a 9 Ã27m area on a west facing slope and took ash samples every 3m for a total of 40 sampling points. The PCA carried out retained five different factors. Factor 1 had high positive loadings for ash with electrical conductivity, calcium, and magnesium and negative with aluminum and iron. Factor 2 had high positive loadings in total phosphorous and silica and factor 3 in manganese and zinc. Factor 4 had high negative loadings in CaCO3and pH and finally, factor 5 had high positive loadings in sodium and potassium. The spatial pattern of the factors was different. The Gaussian model was the best fit for factor 1, the linear model the most accurate for factor 4, and the wave hole effect for the loadings of factors 2, 3, and 5. The map generated with the factor scores of factor 1 had a specific pattern, while the map of factor 4 scores had a low accumulation of the explained elements in one area and high in the other. The maps produced from the factor scores of factors 2, 3, and 5 showed a cycled pattern. Ordinary kriging provided the best estimate for factors 1, 2, and 4. Mapping ash in the period immediately after the fire is very important to identify the level of soil protection and the ash nutrient input in the underlying soil.
Lingua originaleEnglish
Titolo della pubblicazione ospiteSoil Mapping and Process Modeling for Sustainable Land Use Management
Numero di pagine16
Stato di pubblicazionePublished - 2017

All Science Journal Classification (ASJC) codes

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