*4.2. Effect of Cropping System on Net N Mineralization*

Many studies of data from long-term experiments have demonstrated the influence of crop rotation and management practices on soil N availability [20,26,61–65]. The wide variety of cropping systems studied in the network confirmed this influence (Figure 5b) via the I\_Sys indicator, which integrated the influence of the crop rotation and organic waste application only over the medium term, since the experiment was designed so that no organic waste had been applied or grassland had been plowed within the previous three years. I\_Sys values varied widely, ranging from 14–185 kg N ha−1 and integrated the effects of crop rotation well, especially the presence of grassland in the rotation and the frequency of organic waste application [40]. I\_Sys values were low for forage maize monocultures and even lower in fields in which no organic waste had been applied. In contrast, I\_Sys values were highest for rotations with grazed grassland and increased as the age of grassland increased; it is well known that grassland age influences mineralization, since soil N mineralization increases as the age of temporary grassland increases and decreases for the annual crops planted for one–three years after a grassland [66].

Regular application of organic waste increases stocks of C and N over the medium term, which influence POM [26], SMB, and mineralization activity over the long term [67]. However, this influence depends greatly on the type of waste, the amount applied, and the frequency of application; frequent application of solid waste (e.g., manure, compost) has more influence on mineralization than that of liquid waste [29,68], which justifies the choice of a model that integrates these driving factors into the I\_Sys indicator [40].

#### *4.3. Correlations between Vn, Soil Properties, EON and I\_Sys*

The study confirms the significant correlation between Vnmean and SON, which has long been identified as an important variable that influences mineralization [9,12,28,69–71]. However, SON explained much less variance in Vnmean in our dataset (R2 = 0.10) than in the study of Clivot et al. [12] (R2 = 0.26) or the meta-analysis of Ros et al. [31] (R2 = 0.40). This difference may be explained by the non-linear relation between SON and Vnmean, in which mineralization plateaus at high SON (Figure 5a), due to a larger proportion of stable organic matter in soils in the network. In fact, Vnmean was much more strongly correlated with SMB than with SON, thus confirming that SMB is a valuable indicator of soil N availability [23,72] and is more important for nutrient turnover and availability to plants than SON [73].

Vnmean was also more strongly correlated with POM-N than with SON, thus confirming the utility of measuring POM-N to estimate mineralization, since POM-N can be considered an unprotected potential source of N. Some studies identify POM-N as a strong predictor of mineralization [14] or one of its main drivers [21], while other studies indicate only that the N available from it depends greatly on its chemical composition and C:N ratio [17].

Among important soil properties, soil texture has often been observed to influence the availability of substrate for mineralization, since high clay content decreases SON decomposition by better protecting SON chemically and physically [9,10,74]. We observed a weakly significant negative correlation between Vn and clay content; the correlation itself was also weak, perhaps due to a relatively small range of clay contents in the soils studied. In contrast, we observed a stronger positive correlation between Vnmean and the coarse sand content, since the soils with higher SON also had higher coarse sand contents.

Finally, our results confirm the utility of the EON method we used, since its correlation with Vnmean was much stronger than that between SON and Vnmean. However, the variance explained by EON (R2 = 0.23) was much lower than that observed by Gianello and Bremner [48] (R2 = 0.81), Schomberg et al. [75] (R2 = 0.58), and Ros et al. [31] (R2 = 0.50). This can be explained in part by the fact that these strong correlations were observed from laboratory measurements of mineralization, while mineralization and these indicators usually have weaker correlations when mineralization is measured under field conditions [31].