Field capacity (FC) and permanent wilting point (PWP) are two critical input parameters required in various biophysical models. There are limited published data on FC and PWP of dryland cropping soils across north-western Victoria. Direct measurements of FC and PWP are time-consuming and expensive. Reliable prediction of FC and PWP from their functional relationships with routinely measured soil properties can help to circumvent these constraints. This study provided measured data on FC using undisturbed samples and PWP as functions of geomorphological unit, soil type, and soil texture class for dryland cropping soils of north-western Victoria. We used a balanced, nested sampling strategy and developed functional relationships of FC and PWP with routinely measured soil properties using residual maximum likelihood based mixed-effects regression modelling. Using the data, we also tested the adequacy of nine published pedotransfer functions (PTFs) in predicting FC and PWP. Significant differences were observed among the three soil types and nine texture classes for most soil properties. FC and PWP were higher for Grey Vertosols (FC 43.7% vol, PWP 29.1% vol) than Hypercalcic Calcarosols (38.4%, 23.5%) and Red Sodosols (20.2%, 9.2%). Of the several functional relationships developed for prediction of FC and PWP, a quadratic single-predictor model based on dg (geometric mean particle size diameter) performed better than other models for both FC and PWP. It was nearly bias-free, with a root mean square error (RMSE) of 3.18% vol and an R2 of 93% for FC, and RMSE 3.47% vol and R2 89% for PWP. Another useful model for FC was a slightly biased, two-predictor quadratic model based on clay and silt, with RMSE 3.14% vol and R2 94%. For PWP, two other possibly useful, though slightly biased, models included a single-predictor quadratic model based on clay (RMSE 3.45% vol, R2 89%) and a three-predictor model based on clay, silt, and σg (geometric standard deviation of particle size diameter) (RMSE 3.27% vol, R2 90%). We observed a strong quadratic relationship of FC with PWP (RMSE 1.61% vol, R2 98%). This suggests the possibility to further improve the prediction of FC indirectly through PWP. These predictive models for FC and PWP, though developed for the dryland cropping soils of north-western Victoria, may be applicable to other regions with similar soil and climatic conditions. Some validation is desirable before these models are confidently applied in a new situation. Of the nine published PTFs, the multiple linear regression and artificial neural network based NTh5 for FC and NTh3 and CAM for PWP performed better on our data for the prediction of FC and PWP. The root mean square deviation of these PTFs, for both FC and PWP, was higher than the RMSE of our models. Our models are therefore likely to perform better under the dryland cropping soils of north-western Victoria than these PTFs. As a safeguard against arriving at optimistic inferences, we suggest that the modelling of functional relationships needs to account for the hierarchical structure of the sampling design using appropriate mixed effects regression models.

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Pasture-based animal production systems, which occupy a significant proportion of the landscape in Victoria, Australia, have historically been nutrient-limited, with phosphorus (P) often the most limiting nutrient. The Permanent Top-Dressed (PTD) pasture experiment was established in 1914 at the Rutherglen Research Station, Victoria, to investigate the management of this deficiency. The main objective of the PTD experiment was to demonstrate the value of adding P fertiliser at two rates to increase pasture productivity for lamb and wool production. We report on the status of the PTD soils after 100 years, investigating the long-term implications of continuous grazing and fertiliser management (0, 125 and 250 kg/ha of superphosphate every second year) of non-disturbed pasture. We investigated the long-term effects of P fertiliser on the forms and distribution of P and other relevant soil parameters. In the fertilised treatments, P has accumulated in the surface soils (0–10 cm) as both orthophosphate and organic P, with an Olsen P of 16–21 mg P/kg, which is non-limiting for pasture production. In the treatment with 250 kg superphosphate, there has also been movement of P down through the soil profile, probably due to the high sand content of the surface soil and the transfer through the profile of small quantities of water-soluble P and P bound to organic ligands. Over time, the site has continued to acidify (surface 0–10 cm); the soil acidity combined with aluminium (Al) concentrations in the fertilised treatments approach a level that should impact on production and where broadcast lime would be recommended. After 100 years of non-disturbed pasture, the surface soils of these systems would be in a state of quasi-equilibrium, in which the fertilised systems have high levels of carbon (C), nitrogen, P and exchangeable Al. The continued stability of this system is likely dependent upon maintaining the high C status, which is important to nutrient cycling and the prevention of Al phytotoxicity. There are two risks to this system: (i) the declining pH; and (ii) soil disturbance, which may disrupt the equilibrium of these soils and the bio-chemical processes that maintain it.

In the past, uncertainty analysis in soil research was often reduced to consideration of statistical variation in numerical data relating to model parameters, model inputs or field measurements. The simplified conceptual approach used by modellers in calibration studies can be misleading, because it relates mainly to error minimisation in regression analysis and is reductionist in nature. In this study, a large number of added uncertainties are identified in a more comprehensive attention to the problem. Uncertainties in soil analysis include errors in geometry, position and polygon attributes. The impacts of multiple error sources are described, including covariate error, model error and laboratory analytical error. In particular, the distinction is made between statistical variability (aleatory uncertainty) and lack of information (epistemic uncertainty). Examples of experimental uncertainty analysis are provided and discussed, including reference to error disaggregation and geostatistics, and a systems-based analytic framework is proposed. It is concluded that a more comprehensive and global approach to uncertainty analysis is needed, especially in the context of developing a future soils modelling process for incorporation of all known sources of uncertainty.