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• Soil compaction increases bulk density and reduces saturated hydraulic conductivity. • A soil rheology and animal movement models estimate changes in these properties. • The model predicts changes in macroporosity and saturated hydraulic conductivity. • Compaction-induced changes in these soil properties depend on the soil water content. • Predicted temporal changes in soil properties are validated with literature data. Increased soil compaction resulting from livestock treading and use of heavy machinery is a major environmental hazard often linked to degradation of the soil ecosystem and economic services. However, there is a weak quantitative understanding of the spatial and temporal extent of soil compaction and how it modifies soil properties and associated functions. To address this challenge, we developed a framework for systematic modelling soil compaction caused by grazing animals. We considered random movement of livestock in a confined field to describe the spatial variation in the soil that is discretized in square cells with given properties. We then used a rheology model based on Bingham's law to infer compaction-induced changes in soil bulk density and porosity. An associated reduction of saturated hydraulic conductivity is obtained from soil porosity predictions by empirically accounting for macroporosity reduction using a dual-porosity permeability model. This model is coupled with an empirical model of soil structure recovery to account for biological activity (i.e., earthworms and roots). The modelling framework effectively captures primary effects of soil compaction on key soil properties despite lack of explicit consideration of complex effects of compaction such as redistribution of pore sizes and changes in pore connectivity. We tested the model using bulk density, macroporosity and saturated hydraulic conductivity data from a grazing study at the Tussock Creek experimental platform in New Zealand. Data were successfully reproduced by the model. Compaction and recovery trends can be interpreted in terms of model properties associated with management, soil texture and environmental conditions. If data are available for calibration of such properties, the model could be used in agro-ecosystem modelling applications to assess the environmental impacts (such as surface runoff and green-house gas emissions) of livestock-grazing systems and inform management strategies for ameliorating these. [ABSTRACT FROM AUTHOR]

Efficient simulation of water-flow processes in the vadose zone is crucial to increase agricultural productivity within environmental limits. This requires deriving detailed soil hydraulic parameters of the soil profile that is highly challenging, particularly for heterogeneous soils. We therefore developed an alternative indirect methodology to calibrate the hydraulic parameters from soil water content time series measured at multiple depths by using the new physically based hydrological model HyPix. We propose a novel, efficient, multistep optimization algorithm for layered soils that derives an optimal set of hydraulic parameters for a desired number of soil layers. For each selected soil layer, HyPix derives five physical, bimodal, Kosugi hydraulic parameters that describe the soil water retention and hydraulic conductivity by using a novel algorithm that reduces the degree of sensitivity and freedom of the parameters. The optimization algorithm upscales the soil hydraulic parameters by gradually incorporating the soil heterogeneity. This method overcomes the problems associated with optimization of the hydraulic parameters of each layer individually, which leads to poor results because it does not represent the cohesive soil water dynamics across the unsaturated zone. We tested the method using soil water content measurements at different depths at five heterogeneous experimental sites in New Zealand. We show how the accuracy of the simulated water balance components increases with the number of soil layers. The multistep optimization upscales a detailed, layered profile of soil hydraulic parameters into a model with fewer layers. The methodology developed provides an estimate of the uncertainty of using a reduced number of soil layers. We also show that a pedological description can provide an indication of the minimum soil layers of vertical discretization required to accurately compute the soil water balance components. • A hydrological model that solves the Richardson–Richards' equation allowing vertical scaling of hydraulic properties. • Vertical multistep optimization of bimodal Kosugi hydrological parameters for layered soils. • Accuracy of simulated water balance components and soil water content with different number of soil layers. • Soil description informs the vertical discretization required to compute water balance components. [Display omitted] [ABSTRACT FROM AUTHOR]

• Hydraulic conductivity at field capacity (FC) is important for bimodal pore system. • Hydraulic conductivity at FC should be adjusted for soil drainage classes/texture. • Saturated hydraulic conductivity is less important for the temporal soil saturation. • Hydraulic conductivity at FC affects pasture yield. Accurate simulation of soil water dynamics is a key factor when using agricultural models for guiding management decisions. However, the determination of soil hydraulic properties, especially unsaturated hydraulic conductivity, is challenging and measured data are scarce. We investigated the use of APSIM (Agricultural Production Simulation Model) with SWIM3 as the water module, based on Richards equation and a bimodal pore system, to determine likely ranges of the hydraulic conductivity at field capacity (K -10 ; assumed at a matric potential of −10 kPa) for soils representing different drainage characteristics. Hydraulic conductivity measurements of soils with contrasting soil drainage characteristics and values for K -10 were extracted from New Zealand's national soil database. The K -10 values were then varied in a sensitivity analysis from 0.02 to 5 mm d−1 for well-drained soils, from 0.02 to 1 mm d−1 for moderately well-drained soils, and from 0.008 to 0.25 mm d−1 for poorly drained soils. The value of K -10 had a large effect on the time it took for the soil to drain from saturation to field capacity. In contrast, the saturated hydraulic conductivity value had little effect. Simulations were then run over 20 years using two climatic conditions, either a general climate station for all seven different soils, or site-specific climate stations. Two values for K -10 were used, either the APSIM default value, or the soil-specific measured K -10. The monthly average soil saturation level simulated with the latter has a better correspondence with the morphology of the seven soils. Finally, the effect of K -10 on drainage and pasture yield was investigated. Total annual drainage was only slightly affected by the choice of K -10 , but pasture yield varied substantially. [ABSTRACT FROM AUTHOR]