title: Comparison of soil quality index using three methods.
abstract: Assessment of management-induced changes in soil quality is important to sustaining high crop yield. A large diversity of cultivated soils necessitate identification development of an appropriate soil quality index (SQI) based on relative soil properties and crop yield. Whereas numerous attempts have been made to estimate SQI for major soils across the World, there is no standard method established and thus, a strong need exists for developing a user-friendly and credible SQI through comparison of various available methods. Therefore, the objective of this article is to compare three widely used methods to estimate SQI using the data collected from 72 soil samples from three on-farm study sites in Ohio. Additionally, challenge lies in establishing a correlation between crop yield versus SQI calculated either depth wise or in combination of soil layers as standard methodology is not yet available and was not given much attention to date. Predominant soils of the study included one organic (Mc), and two mineral (CrB, Ko) soils. Three methods used to estimate SQI were: (i) simple additive SQI (SQI-1), (ii) weighted additive SQI (SQI-2), and (iii) statistically modeled SQI (SQI-3) based on principal component analysis (PCA). The SQI varied between treatments and soil types and ranged between 0-0.9 (1 being the maximum SQI). In general, SQIs did not significantly differ at depths under any method suggesting that soil quality did not significantly differ for different depths at the studied sites. Additionally, data indicate that SQI-3 was most strongly correlated with crop yield, the correlation coefficient ranged between 0.74-0.78. All three SQIs were significantly correlated (r = 0.92-0.97) to each other and with crop yield (r = 0.65-0.79). Separate analyses by crop variety revealed that correlation was low indicating that some key aspects of soil quality related to crop response are important requirements for estimating SQI.
authors.affiliation: Carbon Management and Sequestration Center, School of Environment and Natural Resources, The Ohio State University, Columbus, Ohio, United States of America.
authors.forename: Atanu
authors.initials: A
authors.lastname: Mukherjee
authors.affiliation: Carbon Management and Sequestration Center, School of Environment and Natural Resources, The Ohio State University, Columbus, Ohio, United States of America.
authors.forename: Rattan
authors.initials: R
authors.lastname: Lal
mesh_terms: D018556:Crops, Agricultural / Q000254:growth & development*; D015233:Models, Statistical; D008962:Models, Theoretical; D009820:Ohio; D025341:Principal Component Analysis; D012987:Soil / Q000737:chemistry; D013025:Glycine max / Q000254:growth & development; D003313:Zea mays / Q000254:growth & development
chemical_list: D012987:Soil