## 3 Results
### Comparisons with Other PTF Models  
It is useful to compare the results from the present PTF described here with other models available from the literature, to see how well the model performs and check if another PTF option with better performance is viable using S-map information. Almost all PTFs tend to be built for use with locally available explanatory variables, so the nature of any comparison is inherently flawed, if not unfair. The vast majority of PTFs in the literature were ruled out for comparison because the required explanatory variables were not readily available from conventional soil mapping from New Zealand, or required laboratory estimates that would be difficult to obtain in the field (e.g., bulk density, carbon concentration). A further group of potentially useful PTFs were ruled out since code for them was not straightforwardly available.  
One set of PTFs that has been extensively described are the EUPTF models [26], which can use various sets of explanatory variables. After consideration of the available models, only one of the EUPTF models was employed, using only sand, silt, and clay content. The clear flaw in this approach is that the soil types used to fit EUPTF differ from those available in New Zealand, so systematic differences in predictions might be expected.  
Figure 8 shows the measured versus predicted plot for TAW using the EUPTF2 model, by soil order. TAW in this case was calculated using the difference between the EUPTF2 estimates of \(-10\) and \(-1500\) kPa, and comparing this with the measured difference between \(-10\) and \(-1500\) kPa from New Zealand sample data. Over the practical range of TAW (approximately \(10\%\) to \(30\%\)), the tendency was for EUPTF-derived estimates to be biased low.  
Figure 8: Measured against predicted total available water content (TAW) response for the EUPTF2 model. The predicted EUPTF2 TAW value was calculated from the difference between the EUPTF2 predicted responses at \(-10\) and \(-1500\) kPa. The long-dashed line is the linear fit between measured and modeled values, while the short-dashed line is the 1:1 line.  
It is difficult to draw definitive conclusions from the results in Figure 8. Although the EUPTF results appear to be biased with respect to the PTF developed here, this could be due to the different constituent soil types used in each PTF, or a combination of other relevant explanatory variables. The result suggests that it is important to be aware of the potential for bias when attempting to generate predictions for areas of New Zealand where mapping from S-map is not available.  
Figure 9 shows plots of goodness-of-fit (GOF) measures for the previous New Zealand model [1], the New Zealand model developed in this paper, and the EUPTF2 model. The best of the models was the New Zealand model developed in this paper, while the EUPTF2 model gave the poorest results. In particular, the EUPTF2 model provided estimates that were generally biased below the true value (as observed in Figure 8). It should be noted that the available explanatory variables for use in the EUPTF2 model are very restricted, and much better results would be obtained if, for example, bulk density or total porosity were available from S-map. Unfortunately, using a richer range of explanatory variables was not a viable option in the New Zealand case, since the objective was to estimate the responses using variables readily available from S-map.