A regional comparison of two soil health assessment frameworks

Soil health assessment frameworks translate soil measurements into scores that inform management decisions, yet different frameworks can yield different conclusions about the same soils. Few studies evaluate how framework design shapes these interpretations. Using a management-explicit dataset (n = 262) from Ontario’s Greater Golden Horseshoe, we compared distribution-based scoring with reference-based benchmarking. We sampled Luvisols, Gleysols, and Cambisols planted in grains and oilseeds with contrasting tillage and cover cropping, alongside perennial references, and evaluated indicator redundancy, management sensitivity, and the influence of sampling design and statistical inference. Carbon and nitrogen indicators were strongly correlated. Aggregate stability by image analysis was less correlated with carbon indicators and robust to organic amendment effects. All indicators except aggregate stability by wet sieving showed management sensitivity. Both frameworks yielded similar score rank ordering, though distribution-based scores were lower because row-cropped and perennial soils were pooled. Interpretations were sensitive to database composition under the distribution approach and to reference management types under the reference approach. Assessment outcomes therefore depend on framework design, experimental design, and assumptions, not measurements alone. For soil health frameworks to be effective decision-support tools, they must be transparent about assumptions, responsive to and inclusive of management differences, and based on balanced representation of the soils and management systems they aim to inform.