An Improved Method for Quantifying Soil Aggregate Stability

Abstract

Core Ideas We propose a metric that integrates aggregate stability measured using laser diffraction across size classes. The indicator accounts for underlying particle‐size differences and can differentiate micro‐ and macroscale aggregates. This integrated indicator shows high correlation with traditional wet sieving methods (R2 ≥ 0.5). By quantifying the percentage of aggregated particles, the metric can be used to compare different soils. Soil aggregate stability influences many biophysical and agronomic processes while acting as a key soil health indicator, yet current quantification methods suffer shortcomings including lack of repeatability, inadequate control over input energy, and inaccuracies in coarse‐textured soils or those with multi‐modal size distributions. In response, we propose a new method deemed integrated aggregate stability (IAS) to interpret aggregate stability using a laser diffraction (LD) machine. This method corrects for underlying particle‐size distributions and provides a comprehensive metric of aggregate stability. As verification, we presented repeatability tests that demonstrate the precision of the IAS method, and then compared IAS measurements to wet sieving results for three different soils. Overall, IAS showed higher correlation with the wet sieving method (R2 = 0.49 to 0.59) than the median aggregate size (d50), which represents the most common current method for quantifying aggregate stability (R2 = 0.09 to 0.27). Further, IAS can estimate the proportions of macro (>0.25 mm) and micro (<0.25 mm) aggregates, and thereby quantify shifts between those fractions under different applied energy levels. As an example, we compared IAS estimates of macro‐ and micro‐aggregates from three different soils that because of differences in texture and previous land use showed varying levels of aggregation. While d50 identified some of the between‐site differences in macro‐aggregation, only IAS was able to consistently detect and quantify micro‐aggregate fractions. Altogether, these results reveal that IAS can convey more consistent and relevant information about aggregate stability compared with traditionally used metrics.