## 2 Materials and Methods  
### Available Predictors  
Both the existing and the proposed newly derived PTF in this paper have a limited range of explanatory variables that could be used, dictated by the information available from the S-map soil mapping system [2]. Specifically, this refers to the soil order and group (group is a subdivision of the soil order), along with the soil drainage class and rock class of fines (the dominant rock type that forms the fine soil material with a particle size < 2 mm) as defined by the New Zealand Soil Classification (NZSC) [9]. A correlation between NZSC soil order and the corresponding WRB Reference Soil Groups is given in Appendix A in Table 11. Also available as explanatory variables were the numeric estimates of sand, silt, and clay particle size fractions (soil texture), and the soil horizon depths. Categorical information concerning soil morphology was available from the S-map functional horizon description. This description is a character string that, when appropriately parsed, determines whether the soil is a topsoil or not and provides a texture class (see Table 1), information on the size of structure, soil strength or consistency, and identifies whether the soil is formed from basic or acidic volcanic material [10; 11]. The collection of parameters available for PTF training are shown in Table 1, some of which were expected to have very little influence on soil water content. Additionally, the S-map functional horizon string also provided coding describing the stoniness of the soil (e.g., not stony, extremely stony), but there are no soil water content data for stony soils. Stony soils are handled by a procedure applied to profiles based on 2021 work by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [8], as described in a later section. Organic horizons, which are indicated by the organic categorical variable defined as True in Table 1, required special treatment. For these soils, the only explanatory variables used were the sample depth, carbon content (available for organic horizons), and the organic matter composition (fibrous peat or humic peat). Fibrous peat is weakly decomposed and is comprised of dominantly well-preserved fibrous plant remains. Humic peat is composed of moderately to strongly decomposed humic peat. The range of explanatory variables was limited because there are too few samples of organic horizons in the training dataset to include other explanatory variables or categories. As a result, a separate PTF model was developed for organic horizons. It is important to note that some other potentially relevant explanatory variables were not available from the S-map soil mapping system. Examples include fine-earth bulk density, particle density, total porosity, and wilting point, all of which are used with success in various PTFs described in the literature. For example, the PTF described in [13] requires the \(-1500\) kPa response, bulk density, carbon concentration, and sample depth, producing estimates of \(0\) and \(-1500\) kPa, as well as estimates of saturated soil hydraulic conductivity. In that approach, the model is physically based and defined in terms of the \(-1500\) kPa response, so that the soil hydraulic conductivity and \(-1500\) kPa response are consistent with one another. The result is a model that produces near-exact estimates of soil water content at specific tensions such as \(0\) or \(-1500\) kPa, along with physical model estimates of soil hydraulic conductivity. By contrast, the model described here is a soil classification and soil morphology model, designed to estimate the soil water content at a specific set of tensions, for which explanatory variable information is available for extensive areas of New Zealand for thousands of sites in soil surveys.