Regenerative grazing, which generally involves some form of rotational grazing with strategic rest, is increasingly seen as a profitable management approach that will accelerate landscape recovery. However, there is limited quantitative evidence supporting the benefits of this approach in northern Australia. This space-for-time study collected vegetation and soil data from a range of properties in the Burdekin catchment in Queensland that have implemented regenerative grazing strategies for between 5 and 20 years. Data were also collected at adjacent control sites that did not undergo regenerative grazing, but where more traditional continuous set-stocking grazing approaches were applied. Coincident data were also collected from several sites where grazing had been excluded for ~30 years. Data suggested that improvements in vegetation, soil and land condition can be obtained from implementing regenerative grazing principles, although it is likely to take at least 3–5 years, and up to 15–20 years for statistically significant improvements to be measurable at a site, particularly for areas that are moving from a degraded baseline condition. Vegetation attributes such as plant biomass and basal area and litter incorporation all appeared to be better surrogates than percentage ground cover for representing improved landscape condition and soil health. Sites that maintained remotely sensed percentage ground cover at or above the minimally disturbed reference benchmark levels for >10 years, as well as having statistically higher biomass, basal area and litter, had significant increases in total nitrogen (TN) and soil organic carbon (SOC) relative to the local control site. Although there are indications that regenerative grazing can lead to improvements in land condition, this study does not enable us to conclude whether regenerative grazing will accelerate improvements compared with other best-practice grazing land management (GLM) approaches, and further research on the social and economic dimensions of regenerative grazing is needed.

Core Ideas: Soil‐specific pedotransfer functions can be incorporated into the HYDRUS model. Electrolyte concentration reduces the adverse effect of pH and Na on soil structural stability. Clay content is important in governing soil hydraulic reduction dynamics due to pH. Hydraulic conductivity is a key soil property governing agricultural production and is thus an important parameter in hydrologic modeling. The pH scaling factor for saturated hydraulic conductivity ( K _s ) reduction in the HYDRUS model was reviewed and evaluated for its ability to simulate K _s reduction. A limitation of the model is the generalization of K _s reduction at various levels of electrolyte concentration for different soil types, i.e., it is not soil specific. In this study, a new generalized linear regression model was developed to estimate K _s reduction for a larger set of Australian soils compared with three American soils. A nonlinear pedotransfer function was also produced, using the Levenberg–Marquardt optimization algorithm, by considering the pH and electrolyte concentration of the applied solution as well as the soil clay content. This approach improved the estimation of the pH scaling factor relating to K _s reduction for individual soils. The functions were based on K _s reduction in nine contrasting Australian soils using two sets of treatment solutions with Na adsorption ratios of 20 and 40; total electrolyte concentrations of 8, 15, 25, 50, 100, 250, and 500 mmol _c L ⁻¹ ; and pH values of 6, 7, 8, and 9. A comparison of the experimental data and model outputs indicates that the models performed objectively well and successfully described the K _s reduction due to the pH. Further, a nonlinear function provided greater accuracy than the generalized function for the individual soils of Australia and California. This indicates that the nonlinear model provides an improved estimation of the pH scaling factor for K _s reduction in specific soils in the HYDRUS model and should therefore be considered in future HYDRUS developments and applications.

Dispersive spoil/soil management is a major environmental and economic challenge for active coal mines as well as sustainable mine closure across the globe. To explore and design a framework for managing dispersive spoil, considering the complexities as well as data availability, this paper has developed a Bayesian Belief Network (BBN)-a probabilistic predictive framework to support practical and cost-effective decisions for the management of dispersive spoil. This approach enabled incorporation of expert knowledge where data were insufficient for modelling purposes. The performance of the model was validated using field data from actively managed mine sites and found to be consistent in the prediction of soil erosion and ground cover. Agreement between predicted soil erosion probability and field observations was greater than 74%, and greater than 70% for ground cover protection. The model performance was further noticeably improved by calibration of Conditional Probability Tables (CPTs). This demonstrates the value of the BBN modelling approach, whereby the use of currently best-available data can provide a practical result, with the capacity for significant model improvement over time as more (targeted) data come to hand.