The development of pedotransfer functions offers a potential means of alleviating cost and labour burdens associated with bulk‐density determinations. As a means of incorporating a priori knowledge into the model‐building process, we propose a conceptual model for predicting soil bulk density from other more regularly measured properties. The model considers soil bulk density to be a function of soil mineral packing structures (ρm) and soil structure (Δρ). Bulk‐density maxima were found for soils with approximately 80% sand. Bulk densities were also observed to increase with depth, suggesting the influence of over‐burden pressure. Residuals from the ρm model, hereby known as Δρ, correlated with organic carbon. All models were trained using Australian soil data, with limits set at bulk densities between 0.7 and 1.8 g cm−3 and containing organic carbon levels below 12%. Performance of the conceptual model (r2 = 0.49) was found to be comparable with a multiple linear regression model (r2 = 0.49) and outperformed models developed using an artificial neural network (r2 = 0.47) and a regression tree (r2 = 0.43). Further development of the conceptual model should allow the inclusion of soil morphological data to improve bulk‐density predictions.

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Mid-infrared diffuse reflectance spectroscopy can provide rapid, cheap and relatively accurate predictions for a number of soil properties. Most studies have found that it is possible to estimate chemical properties that are related to surface and solid material composition. This paper focuses on prediction of physical and mechanical properties, with emphasis on the elucidation of possible mechanisms of prediction. Soil physical properties that are based on pore-space relationships such as bulk density, water retention and hydraulic conductivity cannot be predicted well using MIR spectroscopy. Hydraulic conductivity was measured using a tension-disc permeameter, excluding the macropore effect, but MIR spectroscopy did not give a good prediction. Properties based on the soil solid composition and surfaces such as clay content and shrink-swell potential can be predicted reasonably well. Macro-aggregate stability in water can be predicted reasonably as it has a strong correlation with carbon content in the soil. We found that most of the physical and mechanical properties can be related back to the fundamental soil properties such as clay content, carbon content, cation exchange capacity and bulk density. These connections have been explored previously in pedotransfer functions studies. The concept of a spectral soil inference system is reiterated: linking the spectra to basic soil properties and connecting basic soil properties to other functional soil properties via pedotransfer functions.

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Mid‐infrared spectroscopy has been proposed as a cheap yet accurate alternative to a number of laboratory methods for measuring soil properties. While accurate predictions of a number of basic soil constituents have been reported, properties associated with soil structure have received far less attention. In this study, we looked at the efficacy of mid‐infrared reflectance spectroscopy in predicting moisture retention and whether better predictions can be achieved using pedotransfer functions using spectroscopic predictions of basic soil constituents as inputs. Three methods were used to predict volumetric moisture retention: (i) mid‐infrared (MIR) spectra and partial least squares regression, (ii) a neural network pedotransfer function (PTF) using laboratory particle‐size distribution and bulk density data, and (iii) a pedotransfer function with MIR‐predicted particle‐size distribution and bulk density as inputs. We used Lin's concordance correlation coefficient as a goodness‐of‐fit measure. Predictions of volumetric moisture retention on intact structured soils were generally poor, particularly at the wet end. Improved predictions were observed at dry‐end matric potentials, where moisture retention was more correlated with particle‐size distribution than soil structure. The neural network PTF was found to have better goodness of fit for all matric potentials; however, predictions at larger matric potentials (wet end) were still poor due to poor bulk density predictions. In light of this work, we propose that while MIR spectroscopy may be a valuable predictor of fundamental soil constituents such as particle‐size fractions and organic C, predictions of soil properties dependant on soil structure, such as volumetric moisture retention, may prove difficult. Mid‐infrared spectroscopy in combination with PTFs should provide improvements to moisture retention predictions through improved representation of the influential processes, namely soil structure and adsorptive forces.