Abstract. Soil aggregate stability is a useful indicator of soil physical health and can be used to monitor condition through time. A novel method of quantifying soil aggregate stability, based on the relative increase in the footprint area of aggregates as they disintegrate when immersed in water, has been developed and can be performed using a smartphone application – SLAKES. In this study the SLAKES application was used to obtain slaking index (SI) values of topsoil samples (0 to 10 cm) at 158 sites to assess aggregate stability in a mixed agricultural landscape. A large range in SI values of 0 to 7.3 was observed. Soil properties and land use were found to be correlated with observed SI values. Soils with clay content >25 % and cation exchange capacity (CEC) : clay ratio >0.5 had the highest observed SI values. Variation in SI for these soils was driven by organic carbon (OC) content which fit a segmented exponential decay function. An OC threshold of 1.1 % was observed, below which the most extreme SI values were observed. Soils under dryland and irrigated cropping had lower OC content and higher observed SI values compared to soils under perennial cover. These results suggest that farm managers can mitigate the effects of extreme slaking by implementing management practices to increase OC content, such as minimum tillage or cover cropping. A regression-kriging method utilising a Cubist model with a suite of spatial covariates was used to map SI across the study area. Accurate predictions were produced with leave-one-out cross-validation, giving a Lin's concordance correlation coefficient (LCCC) of 0.85 and a root-mean-square error (RMSE) of 1.1. Similar validation metrics were observed in an independent test set of samples consisting of 50 observations (LCCC = 0.82; RMSE = 1.1). The potential impact of implementing management practices that promote soil OC sequestration on SI values in the study area was explored by simulating how a 0.5 and 1.0 % increase in OC would impact SI values at observation points and then mapping this across the study area. Overall, the maps produced in this study have the potential to guide management decisions by identifying areas that currently experience extreme slaking and highlighting areas that are expected to have a significant reduction in slaking by increasing OC content.

Abstract. Soil aggregate stability is a useful indicator of soil physical health and can be used to monitor condition through time. A novel method to quantify soil aggregate stability, based on the relative increase in the footprint area of aggregates as they disintegrate when immersed in water, has been developed and can be performed using a smartphone application – SLAKES. In this study the SLAKES application was used to obtain slaking index (SI) values of topsoil samples (0 to 10 cm) at 158 sites to assess aggregate stability in a mixed agricultural landscape. A large range in SI values of 0 to 7.3 was observed. Soil properties and land use were found to be correlated with observed SI values. Soils with clay content > 25 % and CEC : clay ratio > 0.5 had the highest observed SI values. Variation in SI for these soils was driven by OC content which fit a segmented exponential decay function. An OC threshold of 1.1 % was observed below which the most extreme SI values were observed. Soils under dryland and irrigated cropping had lower OC content and higher observed SI values compared to soils under perennial cover. These results suggest that farm managers can mitigate the effects of extreme slaking by implementing management practices to increase OC content, such as minimum tillage or cover-cropping. A regression-kriging method utilising a Cubist model with a suite of spatial covariates was used to map SI across the study area. Accurate predictions were produced with leave-one-out cross-validation (LOOCV) giving an LCCC of 0.85 and an RMSE of 1.1. Similar validation metrics were observed in an independent test set of samples consisting of 50 observations (LCCC = 0.82; RMSE = 1.1). The potential impact of implementing management practices that promote soil OC sequestration on SI values in the study area was explored by simulating how a 1 % increase in OC would impact SI values at observation points, and then mapping this across the study area. Overall, the maps produced in this study have the potential to guide management decisions by identifying areas that currently experience extreme slaking, and those areas that are expected to have a significant reduction in slaking by increasing OC content.

<p>A new methodology for the assessment of soil slaking using a mobile app named SLAKES was developed. The app uses an image recognition algorithm that measures the increasing area of soil aggregates immersed in water at regular intervals over a 10 minutes period. This method measures the kinetics of the slaking process and returns a continuous stability index from 0 (very stable) to higher numbers (higher than 7 as very unstable with 14 as a commonly observed maxima).</p><p>The methodology was originally presented in  Fajardo et al. (2016) using a dataset covering a great part of the agro-ecological variability of New South Wales (NSW), Australia. By 2020 the app is already present in 36 countries from 6 continents in its Android version (released in 2017) and the iPhone version is gradually reaching an increasing audience (released in December 2019).</p><p>This work presents a study made in a medium sized farm in New South Wales, Australia. Top-soil (0-10 cm) samples were surveyed and analysed by undergraduate students using the app. Different maps of soil aggregate stability were created showing evident aggregate stability geographical patterns at medium scale. The use of SLAKES has shown reliability compared with traditional methods as shown in third party scientific publications. The simplicity of SLAKES makes this app a simple yet powerful way to assess aggregate stability and shows great potential to be included in both citizen and open science educational programs.</p><p>Fajardo, M., McBratney, A.B., Field, D.J., Minasny, B., 2016. Soil slaking assessment using image recognition. Soil and Tillage Research 163, 119-129.</p>

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