## 3 Results and Discussion
### Establishment of the SQI  
At the conclusion of the PCA and after a redundancy elimination process, the SMR and qCO\({}_{2}\) indicators were identified as the most related to the quality of the analysed soils. During this process, the \"more is better\" function was applied for the SMR indicator and the \"less is better\" function for the qCO\({}_{2}\) indicator. Then, the _SQI_ was established and Equation (4) was obtained. \[SQI=\left(0.626\times S_{i_{SMR}}\right)+\left(0.626\times S_{i_{qCO_{2}}}\right) \tag{4}\]  
The _SQI_ values obtained for the analysed soils revealed significant differences \((p~{}\leq~{}0.05)\). The results indicated that some soils presented low quality (Sa6), while the majority presented moderate quality (Ac1, Ac2, Ac3, Ac4, Ir1, Ir2, Ir3 and Sa2) and high quality (Sa1) (Table 6). Consequently, the _SQI_ made it possible to identify differences in soil quality as a function of conventional farming practices in corn cultivation. Considering the above-mentioned soil quality conditions, the Sa6 soil presents unfavourable conditions for the development of microbial communities due to its low TN concentration, acidic pH and high Na concentration (Table 3). These factors influence microbial activities and may affect nutrient cycling and availability to crops. This is confirmed by the low MBN index in the Sa6 soil. In addition, the high qCO\({}_{2}\) value suggests stress on the microbial community, possibly due to acidic pH, nutritional limitations due to a low N concentration, and osmotic stress. This stress could affect the ability of the microbial communities to develop structures and carry out metabolic processes. The Sa1 soil (high quality) offers more favourable conditions for microorganisms compared with the Sa6 soil. It presents better TN and Na concentrations and a more suitable pH (Table 3). These more favourable conditions allow the microorganisms to carry out their metabolic functions more efficiently, reducing stress and allowing them to maintain their cellular structures with less energy expenditure. The developed _SQI_, based on biological indicators (SMR and qCO\({}_{2}\)), offers greater sensitivity to detect small disturbances in the soil due to agricultural activity. These disturbances could be identified in a short period of time, as changes in environmental conditions rapidly affect the metabolisms of microbial communities, even before some physicochemical indicators show alterations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Soil** & _SQI_ & **Quality** & \(p\) \\ \hline Ac1 & 0.43 ab & Moderate & \textless{}0.001 \\ Ac2 & 0.59 ab & Moderate & \textless{}0.001 \\ Ac3 & 0.48 ab & Moderate & \textless{}0.001 \\ Ac4 & 0.53 ab & Moderate & \textless{}0.001 \\ Ir1 & 0.45 ab & Moderate & \textless{}0.001 \\ Ir2 & 0.57 ab & Moderate & \textless{}0.001 \\ Ir3 & 0.52 ab & Moderate & \textless{}0.001 \\ Sa1 & **0.63a** & High & \textless{}0.001 \\ Sa2 & 0.54 ab & Moderate & \textless{}0.001 \\ Sa6 & **0.39b** & Low & \textless{}0.001 \\ \hline \hline \end{tabular}  
\end{table} 
Table 6: One-way ANOVA with subsequent [PERSON]’s mean test on sampled soils from the Bajio region of Guanajuato. Other researchers have created several _SQI_s for corn cultivation in different regions of the world by considering a variety of environmental conditions and soil types. [PERSON] et al. [57] developed a PCA-based _SQI_ by using a minimum data set of reclaimed marsh soils using coal mining residues and evaluated its performance under corn-growing conditions. This _SQI_ was based on texture, pH, EC and TOC indicators. The _SQI_ values ranged from 0.18 (low quality) to 0.66 (high quality) for the analysed soils. However, the application of linear functions to score the indicators could generate incorrect evaluations due to the inclusion of indicators with non-linear behavior in the soil, such as texture and pH, also reducing the sensitivity of the _SQI_ by focusing only on physicochemical indicators.