Bootstrapped local regression (LOESS) for soil depth profile comparison
Keith, A.M., Henrys, P.A., Rowe, R.L., McNamara, N.P. (2015). Bootstrapped local regression (LOESS) for soil depth profile comparison. NERC Environmental Information Data Centre. https://doi.org/10.5285/d4f92cd8-43e8-49e4-8f9e-efcc0e3b2478
This is an application providing code for the non-parametric comparison of soil depth profiles, and testing for significant differences between soil depth profiles, using bootstrapped Loess (local) regressions (BLR). The BLR approach was developed to be able to compare and test for significant differences in potentially non-linear depth profiles of soil properties across land use transitions, which does not need to meet any parametric distribution assumptions, and is intended to be generally applicable regardless of specific contexts of land use and soil type. A small dataset is provided with the code to demonstrate the BLR approach and its outputs. The code was written using the R statistical programming language and provides two examples of the BLR approach. This application was created by the Centre for Ecology & Hydrology at Lancaster in 2015 during the ELUM (Ecosystem Land Use Modelling & Soil Carbon GHG Flux Trial) project, which was commissioned and funded by the Energy Technologies Institute (ETI).
The code was written using the R statistical programming language at CEH Lancaster, and provides two examples of the BLR approach. This application was created by the Centre for Ecology & Hydrology at Lancaster in 2015 during the ELUM (Ecosystem Land Use Modelling & Soil Carbon GHG Flux Trial) project, which was commissioned and funded by the Energy Technologies Institute (ETI).
copyright UK Centre for Ecology & Hydrology
NERC Environmental Information Data Centre
Reinsch, S., Lebron, I., Brentegani, M. et al. Co-located ecological data for exploring top- and subsoil carbon dynamics across grassland-woodland contrasts. Sci Data 11, 478 (2024).