engleski

Mapping soil organic matter in the Baranja region (Croatia): Geological and anthropic forcing parameters

Spatial mapping of soil organic matter (SOM) and evaluation of the related natural and anthropic influencing factors are crucial to monitor the extent of degraded land and the evolution of soil functions. The objective of this work is to study the spatial distribution of SOM in a highly exploited agricultural area in the Baranja Region (Croatia). The spatially dense dataset available (4825 top-soil samples from 0 to 30 cm) allowed to produce reliable SOM maps using geostatistical interpolation kriging algorithms and to study the relationships with possible influencing factors. The interpolation has been conducted by means of two approaches. In one approach, the overall data set is considered for computing a global variogram and performing a direct interpolation of SOM values. In the second approach, the data are stratified according to two different geological and morphogenetic domains, Holocene Domain (HD) and Pleistocene Domain (PD), and a distinct geostatistical analysis is performed in each domain. The results showed that average SOM in the studied region was 2.29%, indicating a future need for adopting sustainable soil management practices in this region. SOM was significantly higher in HD (2.64%) than PD (1.97%) domain. SOM in PD generally had a much lower global variability. Global dataset analysis reveals that regional intrinsic factors prevail over local intrinsic and extrinsic factors in determining SOM spatial patterns. In contrast, the stratified approach can filter the effect of regional variability related to the main geological and geomorphological setting. The structural spatial correlation in PD is weaker than in HD, as manifested by spatial patches of low and high SOM content with smaller extension in PD with respect to HD. The strong relationships between SOM spatial patterns and geological/geomorphological factors suggest the possibility of adopting finer subdivision criteria in future research.

agroecosystem ; auxiliary data ; geology ; geostatistics ; kriging ; soil quality

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