[PERSON]  
2 Counel for Agricultural Research and Analysis of Agricultural Economics-Agriculture and Environment Research, Center (CREA-AA), 70125 Bari, Italy; [EMAIL_ADDRESS] 3  
Concotti for Agricultural Research and Analysis of Agricultural Economics-Research Centre for Animal Production and Aquaculture, Bella Muro, PZ, S.S. 7, 85051 Potenza, Italy; [EMAIL_ADDRESS] 3  
Concotti for Agricultural Research and Analysis of Agricultural Economics-Agriculture and Production and Aquaculture, Bella Muro, PZ, S.S. 7, 85051 Potenza, Italy; [EMAIL_ADDRESS] 3  
Concotti for Agricultural Research and Analysis of Agriculturalsoil resources must be protected from degradation, and increased the need for effective and reliable information on soil quality modifications/alterations as a consequence of its management [4]. Intensive agricultural activity and not sustainable management practices may give rise to soil degradation processes with a consequent decline of crop yield and soil quality [4, 7], and an associated reduction in ecosystem functions and services [8, 9]. Among land-use and soil management practices, tillage strongly affects soil physical quality since it directly modifies soil aggregation and structural stability [10], soil porosity [11, 12] and hydrological properties [13] and thus the soil ability to store and transmit water and air [14, 15]. In addition, tillage management plays a key role in soil organic matter turnover, microbial abundance and diversity, carbon storage and thus CO\({}_{2}\) emissions [16, 17]. Several researches described the beneficial effects of no-tillage management on soil properties [17, 18, 19, 20]. However, significant effects on soil carbon changes and overall soil quality are often difficult to detect in the short- and medium-term period, due to the two-stage responses to change exhibited by many fundamental soil properties [17, 21]. In this context, long-term field experiments may be considered ideal research tools where relatively stable conditions are likely to be observed [12, 15, 21, 22, 23]. Soil quality can be inferred by identifying and measuring the soil quality indicators, which are specific soil properties and processes sensitive to land use and management [5, 24, 25, 26, 27]. Most Authors have used single indicators to assess soil quality and its relationship with land uses [5]; however, univariate approaches do not always allow a comprehensive judgement on soil status. In addition, increasing the number of indicators may increase collinearity or provide conflicting results, making difficult the soil quality evaluation [15, 28]. A selection and combination of indicators of different nature (physical, chemical and biological), through the computation of soil quality indices (SQIs), is essential to gain a \"holistic image of soil quality\" [28]. Generally, the assessment of a SQI comprises three main steps: (1) measurement and selection of the most relevant soil attributes; (2) normalization and scoring of the selected soil properties using appropriate scoring functions; (3) integration of the scored attributes to construct the final index [6, 29, 30]. The selection of the most relevant soil properties is a critical step as it is the basis of the soil quality index. Selected indicators represent the minimum dataset variables [28]. Many studies have focused on selecting soil variables, exploring a broad range of approaches from qualitative -expert opinion and literature review results- to semi-quantitative and quantitative statistically based methodologies [30, 31]. There is, however, a general consensus on the need of relying on rigorous approaches based on feature selection methods to avoid biased results. Feature selection methods result in a subset of the original features or variables [32, 33], selecting \"relevant\" variables which capture most of the information on processes and functioning of the system investigated, while excluding \"irrelevant\" and/or \"redundant\" variables.