Precompression stress, compression index, and swelling index are used for characterizing the compressive behavior of soils, and are essential soil properties for establishing decision support tools to reduce the risk of soil compaction. Because measurements are time-consuming, soil compressive properties are often derived through pedotransfer functions. This study aimed to develop a comprehensive database of soil compressive properties with additional information on basic soil properties, site characteristics, and methodological aspects sourced from peer-reviewed literature, and to develop random forest models for predicting precompression stress using various subsets of the database. Our analysis illustrates that soil compressive properties data primarily originate from a limited number of countries. There is a predominance of precompression stress data, while little data on compression index or recompression index are available. Most precompression stress data were derived from the topsoils of conventionally tilled arable fields, which is not compatible with knowledge that subsoil compaction is a serious problem. The data compilation unveiled considerable variations in soil compression test procedures and methods for calculating precompression stress across different studies, and a concentration of data at soil moisture conditions at or above field capacity. The random forest models exhibited unsatisfactory predictive performance although they performed better than previously developed models. Models showed slight improvement in predictive power when the underlying data were restricted to a specific precompression stress calculation method. Although our database offers broader coverage of precompression stress data than previous studies, the lack of standardization in methodological procedures complicates the development of predictive models based on combined datasets. Methodological standardization and/or functions to translate results between methodologies are needed to ensure consistency and enable data comparison, to develop robust models for precompression stress predictions. Moreover, data across a wider range of soil moisture conditions are needed to characterize soil mechanical properties as a function of soil moisture, similar to soil hydraulic functions, and to develop models to predict the parameters of such soil mechanical functions.

Soil corrosivity is a term used to describe the corroding susceptibility (risk) of metal infrastructure in different soil environments. Soil corrosivity mapping is a crucial step in identifying potentially problematic, high-maintenance fence lines and can help improve fence longevity by identifying soil environments where the use of more expensive, corrosion-resistant materials would be more cost-effective in the long term. Soil corrosion damage sustained on exclusion fences can be a serious management issue for conservation programs and initiatives, as it weakens the fence netting and provides opportunities for invasive animal migration and occupation (e.g. feral cats and foxes) into areas of high conservation value. The increasing accessibility of geospatial analysis software and the availability of open-source soil data provide land managers with the opportunity to implement digital soil databases and pedotransfer functions to produce fence corrosion risk maps using commonly measured soil attributes. This paper uses open-source government agency soil data (shapefiles) to map fence corrosion risk in the southern part of the Yorke Peninsula in South Australia, with the intention to assist with the installation of a new barrier (exclusion) fence as part of the Marna Banggara rewilding project. The risk classifications (low, moderate and high risk) made by this map were compared with rates of zinc corrosion (mu m/year zinc loss) observed at field sites and correctly predicted the amount of fence damage sustained at five of the eight sites. The mapping approach outlined in this study can be implemented by environmental managers in other areas to inform strategies for enhancing fence longevity.