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The determination of the coefficient of linear extensibility (COLE) of soils is crucial for enhancing structural stability in civil engineering applications. Traditional methods for measuring COLE have some practical limitations. In routine soil analyses, clay and soil organic carbon (OC) are often measured, while soil hygroscopic water content (w h ) is easy to determine on several soil samples simultaneously. The aims of the study were to (1) utilise two data partitioning approaches to develop regression models that estimate the soil COLE from hygroscopic water content, clay and OC contents, and (2) compare the model performance of the developed regression models. We used two data partitioning approaches. First, the calibration models were developed on 53 soil samples from Slovakia and validated with 24 soil samples from the United States (country-wise). Second, the calibration models were built from 67% of the entire dataset and validated with 33% (mixed data). Regression models based on hygroscopic water content accurately estimated COLE regardless of sorption direction or data partitioning approach (RMSE: 0.014-0.023 cm cm À1 ). The inclusion of OC in multiple linear regression models of clay only marginally improved COLE estimation compared to clay alone. For all models, the mixed data partitioning method showed better model validation performance than the countrywise approach. The COLE classes derived from the estimated COLE values compared favourably (72%-94% accurate) to the measured data. Thus, there is a great potential to estimate the COLE from readily available (clay and OC) or easily measurable (hygroscopic water content) soil properties. Highlights• Hygroscopic water content (w h ) is intimately linked to soil properties that determine COLE.• Regression models based on wh or clay, and organic carbon content accurately estimated COLE.• Data partitioning approach for modelling significantly impacted model performance

Litter decomposition is a critical process in carbon cycling, which can be affected by land use. The relationship between litter decomposition and soil properties under different land uses remains unclear. Litter decomposition can be quantified by the Tea Bag Index (TBI), which includes a decomposition rate k and a stabilization factor S.Our objective was to investigate linkages between TBI and soil physicochemical and gas transport properties and land use. We buried three pairs of tea bags in 20 sites (covering cropland, grassland, heathland, and forest land uses) in a transect from the western to the eastern coast of the Jutland peninsula, Denmark. The tea bags were retrieved after 90 d and TBI was determined. Disturbed and undisturbed (100 cm 3 soil cores) samples were collected from each site. Thereafter, clay content, organic carbon (OC), bulk density (ρ b ), pH, electrical conductivity (EC), oxalate-extractable phosphorus (P ox ), aluminum (Al ox ), and iron (Fe ox ) content, soil water content, gas diffusivity (D p /D 0 ), and air permeability (k a ) at −10 kPa were measured. Results showed that grasslands had the highest k and S among four land uses, and agricultural soils (croplands and grasslands) exhibited higher TBI values than seminatural soils (forest and heathland). The prediction of S was better than that of k based on multiple linear regression analysis involving soil physicochemical properties. Clay content and OC were not strong predictors. Including D p /D 0 and k a improved the prediction of S, and finally, the inclusion of land use enhanced the prediction of both k and S. The different trends between two distinct land-use groups can be attributed to pH, P ox , and ρ b . INTRODUCTIONThe decomposition of soil organic matter (SOM), the fundamental process in C cycling, is controlled by three main fac-Abbreviations: ε, air-filled porosity; ϕ, total porosity; Al ox , oxalate-extractable aluminum content; D p /D 0 , gas diffusivity; EC, electrical conductivity; Fe ox , oxalate-extractable iron content; k, decomposition rate; k a , air permeability; MLR, multiple linear regression; OC, organic carbon; P ox , oxalate-extractable phosphorus content; S, stabilization factor; SOM, soil organic matter; TBI, Tea Bag Index; ρ b , bulk density; ρ s , particle density.

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<p>Soil microbiome is an important indicator of soil quality and it is related to various soil functions, including soil carbon cycling. Plant litter decomposition is a key process in carbon cycling, and the use of standardized plant litter for the comparison of decomposition rates between different conditions is a promising method. In this study, we aimed to investigate the difference in microbial community composition in long-term manure amended soils with different crop rotations, and its relationship with litter decomposition by using the Tea Bag Index (TBI) protocol. Green tea and rooibos tea bags were buried pairwise in three long-term experimental sites (LTEs) in Germany, Denmark and Sweden for three months. The TBI, i.e. decomposition rate and stabilization factor, was calculated from the weight loss of tea. The three LTEs have contrasting soil textures and had been manured between 20 and 127 years. The rotation elements in the LTEs include spring barley, winter wheat, winter oat, maize, and grass/clover. The microbial community composition was characterized by biomarkers (phospholipid fatty acids and neutral lipid fatty acids) and 16S and ITS sequencing. Enzyme activity was quantified by fluorescein diacetate hydrolysis analysis. The linkage between TBI and several microbial properties including microbial biomass, enzyme activity, the fungal:bacterial ratio, and the abundance and the diversity of the microbial community, will be discussed. The interactive effect of soil texture and management on the TBI and microbial properties will be addressed, which shall provide implications for soil quality and soil management.</p>