DOI: https://doi.org/10.5513/JCEA01/22.3.3193
Original scientific paper
Predicting soil organic matter content using soil color at three locations with different land use in Zagreb (Croatia)
2021, 22 (3) p. 646-656
Vedran Rubinić, Alan Pavlović, Ivan Magdić
Abstract
Soil organic matter (SOM) plays a key role in ecosystems. Reduction of its content due to land-use changes has a negative impact on the soil, but also on the wider environment. Accordingly, SOM content is routinely analyzed in the laboratory. As these are expensive and/or time-consuming, indirect ones are also tested. The aim of this study was to examine the possibility of predicting SOM content by linear regression using soil color as the predictor, at three locations in Zagreb (Croatia), with different soil types (eutric cambisol anthropogenic, humofluvisol, pseudogley) and different land uses (plough land, meadow, forest, respectively). At each location, 5 samples of the surface soil layer were taken. Soil color was determined using the Munsell system, and the hue was 2.5Y and 10YR in dry and moist soil, respectively. Laboratory analyzes showed that the soils are very acid to neutral silt loams. In line with the land-use, they differed significantly in SOM content and were poorly humic (plough land), moderately to highly humic (meadow), and highly humic (forest). Correlation between soil color dimensions and SOM content was significant only for the dry samples, between chroma and SOM and between value/chroma ratio and SOM. Regression analysis showed high coefficients of determination for these two relationships (R2 = 0.88 for chroma-SOM, R2 = 0.76 for value/chroma-SOM). The results suggest that visual soil color determination can be used to estimate SOM content, but only in dry soil. The model calibrated in this paper needs to be validated using samples of other (different) soils.
Keywords
humus, Munsell color system, linear regression, pedotransfer function
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