## 3 Results and Discussion
### Establishment of the SQI
In contrast, the present study considered non-linear scoring functions, incorporating non-linear behavior of the indicators, and improving the sensitivity of the _SQI_ by incorporating biological indicators. [PERSON] et al. [58] developed an _SQI_ based on a total data set composed of 24 physicochemical and biological indicators. They conducted their study on Ultisol soil in long-term plots (17 years) with different corn management systems in the Coastal Plateaus of Sergipe State, Northeast Brazil. The authors analysed the influence of the type of ploughing and cover crops on soil quality and concluded that conventional ploughing significantly reduced soil quality, while non-ploughing led to the best soil quality. Regarding the cover crop, peas and millet showed good and excellent quality conditions, respectively, for the minimum- and no-ploughing systems. Although the use of many indicators improves the accuracy of an _SQI_, its applicability in other areas with similar conditions may be limited due to its complexity and cost of its implementation. Compared with the present study, the use of PCA allows for a reduction in the number of indicators in an _SQI_, reducing its cost and complexity and thus facilitating its application in other areas with similar characteristics. In addition, the analysis of 10 soils with different characteristics considers environmental variability, which is the opposite of the study conducted by [PERSON] et al. [58]. [PERSON] et al. [59] evaluated soil quality in a long-term experiment (15 years) of crop rotation (cotton and corn) and bio-cover crops under a no-tillage regime using the Soil Management Assessment Framework (SMAF). Their model includes indicators such as pH, TOC, bulk density (BD), soil extractable phosphorus (P) and K, EC and the sodium adsorption coefficient (SAR). The SMAF model could differentiate soil quality with respect to depth, showing moderate conditions. However, it was not able to satisfactorily differentiate quality in relation to crop rotation and bio-cover crops, showing moderate values in both cases. Although the SMAF model has been used successfully in various agricultural areas around the world, it did not perform well in this study, possibly due to its preset nature and lack of consideration of the specific conditions of the study area. In comparison, the PCA model used in the present study, which considered local conditions, improved the accuracy of the results by considering all sources of variation in soil quality. The inclusion of biological indicators in the _SQI_ also improves its sensitivity and ability to differentiate soil quality. The abovementioned studies offer valuable insights into the assessment of soil quality in different agricultural contexts. While the SMAF model used by [PERSON] et al. [59] showed limitations in its sensitivity to differentiate soil quality in their long-term study, the PCA-based approach used by [PERSON] et al. [57] and the present study addresses this limitation by considering the specific conditions of the study area. Furthermore, the study by [PERSON] et al. [58] highlights the importance of using a combination of physicochemical and biological indicators to improve the sensitivity of soil quality assessment. Compared with these studies, the present study adopted an integrated approach by using PCA and considering biological indicators. This approach improved the ability to distinguish soil quality in different agricultural contexts and environmental conditions.