## 4 Discussion
### Soil Volatilomics in Decoding Complex Land Use-Specific Patterns
[34], who reported that agricultural management practices tend to standardize soil microbial communities and their metabolic outputs. Forest soils presented moderate internal variability, reflecting the natural heterogeneity of woodland ecosystems. This finding aligns with studies by [PERSON] et al. [35] and [PERSON] [36], who demonstratedthat the structural complexity of forest ecosystems promotes diverse microbial niches and, consequently, varied volatile signatures. The patterns observed in the grassland soils, which presented the greatest internal variability, align with the concept of the intermediate disturbance hypothesis, where moderate levels of disturbance promote greater biological diversity and metabolic variation ([PERSON] and [PERSON], 2017). The implementation of PLS-DA revealed robust discrimination between land use types, with particularly strong differentiation between agricultural and forest soils (AUC = 0.868). This marked separation suggests fundamental differences in biochemical processes and microbial communities between these contrasting ecosystems. The high explained variance (77.7-81.5%) achieved through the first two latent variables demonstrates the robust discriminatory power of soil volatilomes as indicators of land use change, supporting similar findings of [PERSON] et al. (2014) and [PERSON] et al. (2018) in their volatilomic and metabolomic studies of different land use and management practices. The intermediate discrimination observed between the grassland and other land uses (AUC = 0.810 for both F vs. G and G vs. A) suggests that grassland ecosystems may represent a transitional state between forest and agricultural systems in terms of their volatile profiles. This observation aligns with ecological succession theories and supports the findings of [PERSON] et al. (2019) and [PERSON] and [PERSON] (2019) regarding the intermediate nature of the metabolic signatures of grassland ecosystems. The identification of different volatile organic compounds could provide insight into the biochemical adaptations associated with different land uses. The greater number of differential compounds between forest and agricultural soils (63 compounds, Figure 5) than between the other pairs reflects the contrasting natures of these ecosystems. This finding supports the works of [PERSON] et al. (2014), [PERSON] et al. (2014), and [PERSON] et al. (2016), who demonstrated that land use intensification significantly alters soil metabolic networks. The prevalence of specific volatile compounds, such as hexanal, benzaldehyde, and nonanal, as key differentiators suggests their potential role as biomarkers of land use change. These compounds, which have high VIP scores in the PLS-DA models, may serve as indicators of specific soil processes or microbial activities. For example, the elevated levels of benzothiazole in agricultural soils align with previous studies by [PERSON] et al. (2018), who linked this compound to agricultural management practices and altered microbial communities. The observation that grassland soils harbor the greatest number of unique volatile organic compounds (64 compounds, Table S3) suggests that these ecosystems maintain distinct metabolic processes that may be crucial for ecosystem resilience. This finding aligns with recent work by [PERSON] et al. (2013) on the role of soil volatile diversity in maintaining ecosystem stability. The multi-analytical approach employed in this study reveals that soil volatilomes serve as sensitive indicators of the effects of land use on soil biochemical processes. The clear differentiation between land use types, particularly between agricultural and forest systems, suggests that human management significantly impacts soil metabolic networks. These findings have important implications for land use management and soil health monitoring, as volatile profiles could serve as early warning indicators of ecosystem changes.