Publication | Open Access
Spatial variation of soil quality indicators as a function of land use and topography
25
Citations
63
References
2020
Year
Precision AgricultureEnvironmental MonitoringEngineeringGeomorphologyLand UseSoil QualitySoil Organic MatterSpatial VariationSoil Organic CarbonLand DegradationPlant Available WaterEarth ScienceSocial SciencesSoil PropertySoil Quality IndicatorsLand Use PlanningBiogeochemistryCarbon SequestrationSoil ClassificationSoil ScienceGeographySoil Physical QualityPrecision Soil MappingDeforestationSoil EcologySpatial Statistics
Soil quality (SQ) indicators such as plant available water (PAW), soil organic carbon (SOC), and microbial biomass carbon (MBC) can reveal agroecological functions; however, their spatial variabilities across contrasting land uses need to be better understood. This study examined the spatial variation of these key SQ indicators as a function of two land-use systems and using topography covariates. We sampled a total of 116 point locations in a native grassland (NG) site and an irrigated cultivated (IC) site located near Brooks, Alberta. Compared with NG, cultivation altered soil pore-size distribution by sharply reducing macroporosity by 25%. However, conditions in the IC soil supported greater accrual of microbial growth (MBC of 601 vs. 812 nmol phospholipid fatty acids g −1 soil) probably due to more availability of water and nutrients. Focusing on the effects of topography on SQ indicators, terrain elevation (by light detection and ranging) and estimated depth-to-water were found to be key controllers of SQ at the two land-use systems. Also, there were gradual increases in both SOC and MBC where estimated water table was deeper, and higher SOC also associated with lower elevation. A comparison of ordinary kriging and cokriging (coK) geostatistical mapping indicated that the coK method performed better as demonstrated by improvements in the accuracies of spatial estimations of PAW, SOC concentration, and MBC. Thus, implementing coK using the aforementioned topography covariates enhances the capability for predictive mapping of SQ, which is particularly useful when spatial data for key SQ indicators are sparse and challenging to measure.
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