The increasing global demand for sustainable agriculture requires accurate and efficient soil analysis methods. Conventional laboratory techniques are often time-consuming, costly and environmentally damaging. To address this challenge, we developed and validated locally calibrated mid-infrared (MIR) spectroscopy models for predicting key soil properties pH, phosphorus (P) and exchangeable cations in soil samples from South Africa's Western Highveld region, using a dataset of 979 soil samples and machine learning algorithms Cubist, partial least squares regression (PLSR) and random forest (RF). A subset of spectra was also submitted to the newly developed Open Soil Spectral Library's (OSSL) prediction models to determine whether global prediction models could be used for local soil property prediction. Accurate predictions for pH, calcium (Ca) and magnesium (Mg), with coefficient of determination (R-2) values exceeding 0.76 were obtained with the local calibration algorithms. The predictions for P, potassium (K) and sodium (Na) did not meet the requirements for reliability. Soil spectroscopic prediction models calibrated with local soils outperformed the corresponding global prediction models considered. The OSSL prediction results were inaccurate, with a RPIQ <1, and consistently underpredicted all soil properties. Furthermore, the OSSL collection of prediction models does not include a pH (KCl) model, the routinely used pH measurement method in South Africa. These findings highlight the importance of local calibration for accurate soil property prediction and underscore the need for regional representation in global spectral libraries. This research serves as the first local calibration of MIR spectroscopy models for the Western Highveld region of South Africa and provides a foundation for future local soil property inference model development. It also serves as a potential starting point for a comprehensive South African soil spectral library that can be contributed to global spectral libraries.

Soil microbial communities in the Arctic play a critical role in regulating the global carbon (C) cycle. Vast amounts of C are stored in northern high latitude soils, and rising temperatures in the Arctic threaten to thaw permafrost, making relatively inaccessible C sources more available for mineralization by soil microbes. Few studies have characterized how microbial community structure responds to thawing permafrost in the context of varying soil chemistries associated with contrasting tundra landscapes. We subjected active layer and permafrost soils from upland and lowland tundra sites on the North Slope of Alaska to a soil-warming incubation experiment and compared soil bacterial community profiles (obtained by 16S rRNA amplicon sequencing) before and after incubation. The influence of soil composition (characterized by mid-infrared [MIR] spectroscopy) on bacterial community structure and class abundance was analyzed using redundancy and correlation analyses. We found increased abundances of Alphaproteobacteria, Gammaproteobacteria, and Bacteroidetes [Sphingobacteriia] post incubation, particularly in permafrost soils. The categorical descriptors site and soil layer had the most explanatory power in our predictive models of bacterial community structure, highlighting the close relationship between soil bacteria and the soil environment. Specific soil chemical attributes characterizing the soil environments that were found to be the best predictors included MIR spectral bands associated with inorganic C, silicates, amide II (C=N stretch), and carboxylics (C-O stretch), and MIR peak ratios representing C substrate quality. Overall, these results further characterize soil bacterial community shifts that may occur as frozen environments with limited access to C sources, as is found in undisturbed permafrost, transition to warmer and more C-available environments, as is predicted in thawing permafrost due to climate change.