## 5 Discussion  
This investigation was carried out on repacked samples of two relatively coarse-textured soils that, in the absence of any treatment, exhibited a very low macroporosity, a rather low or a nearly acceptable plant available water capacity, an insufficient ability to store water relative to the soil's total pore volume, and an excessive aeration. Therefore, the SPQ was poor for both soils, and it was worse at CAS than at GIN, since the _BD_ and the _AC_ were higher and the _PAWC_ and the _RFC_ were lower in the former case than the latter one. Indeed, the _PMAC_ was higher at CAS than at GIN, but the _PMAC_ values were very low in both cases. The experiment demonstrated that adding any of the tested amendments in these soils determined an increase of the relevant soil water content, \(\theta_{h}\), values for the SPQ assessment [3], since \(\theta_{s}\), \(\theta_{10}\), \(\theta_{100}\), and \(\theta_{15,300}\) increased with the applied amendment rate. Of course, detecting increasing relationships with the _ar_ for the considered soil water content values is not enough to draw any conclusion about the effects of the treatment on the  
Figure 6: Relative field capacity, _RFC_, against the amendment rate for the three tested amendments (COM = compost; VER = vermicompost; BIO = biochar) at (**a**) the Castellaneta and (**b**) the Ginosa field sites. For a given site, the continuous lines are the significant linear regression lines (black for COM; red for VER; blue for BIO). The dashed grey lines define the range of values inside which the soil physical quality is considered good. SPQ. An improvement of the SPQ requires that the slopes of the \(\theta_{h}\) vs. the \(ar\) relationships combine with each other in such a way as to effectively determine an improvement of the soil air and water storage parameters. According to this investigation, in soils similar to the tested ones, the objective of the treatment should be inducing a decrease of the \(AC\) and an increase of both the \(PAWC\) and the \(RFC\). This result can be obtained if the \(\theta_{100}\) increases with the \(ar\) more than both the \(\theta_{s}\) and the \(\theta_{15,300}\). To also obtain an increase of the \(P_{MMC}\), the \(\theta_{s}\) should increase more than the \(\theta_{10}\). Determining the relationships between the applied amendment dose and both the \(\theta_{h}\) and the SPQ parameters [16,34] appears therefore advisable in SPQ investigations, since this approach allows us to predict the variation of any SPQ parameter by varying the intensity of the treatment. Considering linear relationships in a range of amendment rates appears practically useful, since the intercept of the fitted relationship represents the value of the considered parameter in the absence of any treatment, whereas the slope is expressive of the rate at which the parameter varies with the addition of the amendment. This approach is frequently used in the literature. For example, [PERSON] et al. [39] used linear regression analysis procedures to establish the impact of VER addition on water stable aggregates. The same approach was applied by [PERSON] et al. [40] in an evaluation of different soil quality indices, and by [PERSON] [41] and [PERSON] et al. [42] in investigations focused on soil amendment with BIO. A limit of this choice, that should perhaps be taken into account in future research, is that the actual shape of the relationship between an SPQ parameter and an \(ar\) could contain an information, such as a stabilization of a parameter above a threshold \(ar\) value, that is not predicted by the linear approach. Both the \(AC\) and \(RFC\) are calculated as a function of the \(\theta_{s}\) and \(\theta_{100}\) and, hence, the starting information is the same for these two indicators. [PERSON] et al. [43] and [PERSON] et al. [44] recognized a strong correlation between the \(AC\) and \(RFC\), and a similar result was obtained in this investigation by considering all the \(RFC\) vs. \(AC\) data points (Figure 7).