A standardized soil quality index for diverse field conditions

Understanding the nexus between soil quality and productivity is constrained by data artifacts, compounded by limitations of the existing models. Here, we explore the potential of 4 regression methods (i.e., Reduced Regression (RR), SIMPLS, Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR)), to synthesize 10 soil physical and chemical properties acquired from 3 major management practices and different soil layers, into an unbiased soil quality index (SQI) capable of evaluating soil functions (e.g., biomass production). The data was acquired from privately owned fields within the state of Ohio, USA, at the following land use and management sites: natural vegetation (NV) or woodlands, conventional till (CT), and no-till (NT). The soils were sampled at similar landscape positions (i.e., summit) at depth intervals of 0-10, 10-20, 20-40 and 40-60 cm, and analyzed for bulk density (ρb), carbon/nitrogen (C/N) ratio, soil organic C (SOC), total N (TN), available water capacity (AWC), pH and electrical conductivity (EC). Preliminary analyses revealed the PLSR method as the most robust. The PLSR Variable Importance of Projection (VIP) was calculated, transformed into the SQI score and compared with yield data. SOC, ρb, C/N and EC were identified as the major variables influencing soil quality status. The data shows that the quality of Pewamo silty clay loam (Pw) soil was higher than Crosby Celina loams (CtA), Kibbie fine sandy loam (kbA), Glynwood silt loam (GWA) and Crosby silt loam (CrA), respectively. In 2012, the mean SQI was 42.9%, with corn and soybean yields of 7 and 2Mg/ha. The R(2) of SQI versus yield was 0.74 for corn (Zea mays L.), and 0.89 for soybean (Glycine max (L.) Merr.). Future studies will investigate techniques for mapping this SQI.