Assessment of Soil Quality under Different Soil Management Strategies: Combined Use of Statistical Approaches to Select the Most Informative Soil Physico-Chemical Indicators

Assessment of soil quality under different management practices is crucial for sustainable agricultural production and natural resource use. In this study, statistical methods (principal component analysis, PCA; stepwise discriminant SDA; partial least squares regression with VIP statistics, PLSR) were applied to identify the variables that most discriminated status minimum tillage no-tillage. Data collected in 2015 from a long-term field experiment on durum wheat (Triticum Desf.) used twenty indicators (chemical, physical biological) quantified upper layer (0–0.20 m). The iteration strategies affected quality, showing greater bulk density, relative capacity (RFC), organic extractable carbon contents (TOC TEC) exchangeable potassium PCA SDA confirmed these results underlined also role available phosphorous fractions as treatments investigated. PLSR, including information plant response (grain yield protein content), selected, important variables, nutrients, indicators, pH cations. research showed effectiveness combining variable selection summarize deriving multivariate datasets improving understanding system approaches compared provided terms selected ranking variables. combined use three allowed smaller number (TOC, TEC, Olsen P, water nitrogen, RFC, macroporosity, air capacity), which able provide clear discrimination between compared, shown by carried out reduced dataset. presence PLSR considerably drove feature process.