Comparison of soil quality indexes calculated by network and principal component analysis for carbonated soils under different uses

There is an urgent need to conserve and improve the quality of agricultural soils in coming decades. Decision tools capable providing reliable information about soil are needed, index (SQI) one most used. Principal component analysis (PCA) common methodology calculate it, however some cases fails differentiate properly. Therefore, aim this work assess a SQI through different as network (NTA) compare it with PCA, assuming that uses affect qualities differently. From (rainfed, olive grove forest) principal have been used select minimum dataset (MDS) generate from 36 physical, chemical biological variables. Using NTA, geometric mean enzyme activities (GMEAN), bulk density (BD) phosphatase activity (phos) where selected indicators, while PCA total organic carbon (TOC), free Fe oxides (FeF), crystalline Mn (MnX), pH, electrical conductivity (EC) percentage coarse sand (CS). Four were calculated each MDS linear non-linear scoring equations by additive integration weights. The generated NTA more useful than those addition having fewer indicators they able better study. This greater resolution capacity would be consequence selection using method PCA.