Jan Vanderborght

Overview of inert tracer experiments in key belgian soil types: Relation between transport and soil morphological and hydraulic properties

To investigate relations between solute transport, soil properties, and experimental conditions, we summarize results from leaching experiments that we carried out in a range of soils, at different scales (column (0.3-1.0 m ID, 1.0 m length) and field plot scale), and using, different leaching rates (0.5-30 cm d(-1)). The lateral mixing regime and longitudinal dispersion were derived from time series of tracer concentrations at several depths in the soil. Field- and column-scale transport were similar in loam and silt loam soils. The mixing regime was related to soil morphological features, such as vertical tongues, stratification, macropores, and a water-repellent layer. The dispersion increased in all soils more than linearly with increasing leaching rate, implying that the dispersivity is not an intrinsic soil characteristic. The change of dispersivity with leaching rate was linked to the unsaturated hydraulic conductivity using a multidomain conceptualization of the pore space.

Parameter uncertainty in the mobile-immobile solute transport model

Abstract The four-parameter mobile-immobile model (MIM) is often used to describe solute transport processes in aggregated porous media by assuming no advective solute transport in the aggregates or immobile zone and a first-order kinetic diffusion transport process between the mobile and immobile zones. When the concentration in the immobile zone reaches an equilibrium with the concentration in the mobile zone (local equilibrium assumption, LEA), the MIM ‘degenerates’ to a two-parameter convective-dispersive model (CDE) and the MIM parameters cannot be accurately obtained using inverse optimization techniques. Two criteria to test the LEA applicability—the difference in skewness and a criterion derived from the SKIT (separation of kinetically influenced terms, Bahr and Rubin, 1987, Water Resourc. Res., 23: 438–452) method—are evaluated using statistical inference about model building applied to the MIM and CDE model. The results show that the criterion derived from the SKIT method evaluates the LEA applicability the best. To assess MIM parameter uncertainty as a function of the data uncertainty and the input boundary condition, asymptotic confidence intervals are obtained from sensitivity matrices. The parameter uncertainty as a function of the data uncertainty is well predicted by the criterion proposed by Bahr and Rubin, 23: 438–452) and depends on the type of input boundary condition and the type of optimization used, i.e. ordinary or weighted least squares. Using relationships between the MIM parameters and the size and shape of the aggregates, it is shown that for soils consisting of spherical aggregates, the MIM parameters are not identifiable from miscible displacement experiments using water fluxes prevailing under climatic boundary conditions. For soils with cylindrical macropores, the MIM parameters can be identified even for relatively low values of the pore velocity, v.

Combining δ13C measurements and ERT imaging: improving our understanding of competition at the crop-soil-hedge interface

Background and aimsHedgerow cropping decreases erosion in hillside agriculture but also competes for water and nutrients with crops. This study combined two methods for an improved understanding of water and nutrient competition at the crop-soil-hedge interface.Methodsδ13C isotopic discrimination in plants and soil electrical resistivity tomography (ERT) imaging were used in a field trial with maize monocropping (MM) vs. leucaena hedgerow intercropping with and without fertilizer (MHF+ and MHF−) in Thailand.ResultsHedges significantly reduced maize grain yield and aboveground biomass in rows close to hedgerows. ERT revealed water depletion was stronger in MM than in MHF+ and MHF- confirming time domain reflectometry and leaf area data. In MHF+, water depletion was higher in maize rows close to the hedge compared to rows distant to hedges and maize grain δ13C was significantly less negative in rows close to hedges (-10.33‰) compared to distant ones (-10.64‰). Lack of N increased grain δ13C in MHF- (-9.32‰, pu2009≤u20090.001). Both methods were correlated with each other (ru2009=u20090.66, pu2009≤u20090.001). Combining ERT with grain δ13C and %N allowed identifying that maize growth close to hedges was limited by N and not by water supply.ConclusionCombining ERT imaging and 13C isotopic discrimination approaches improved the understanding of spatial-temporal patterns of competition at the hedge-soil-crop interface and allowed distinguishing between water and N competition in maize based hedgerow systems.