To investigate how added nutrient nitrogen (N) and phosphorus (P) affect subarctic peatlands, we sampled peat soils from bog and fen type peatlands in the Northwest Territories, Canada, and measured CO₂ and CH₄ production rates by means of laboratory incubations. Our short-term experiments show that changes in nutrient concentrations in soil water can significantly affect microbial carbon cycling, suggesting the necessity of additional considerations of wildfire and permafrost thaw impacts on peatland carbon storage.

This work aims to predict soil water content at a fine spatiotemporal resolution (80 m grids, daily) to support agricultural management in Tasmania. It proves that transfer learning can improve the accuracy of deep learning models to predict multilevel soil moisture. We address the challenge of mapping soil moisture at field-scale resolution and integrate the model into a near-real-time monitoring system. 

To use fertilizers more effectively, non-invasive geophysical methods can be used to understand nutrient distributions in the soil. We utilize, in a long-term field study, geophysical techniques to study soil properties and conditions under different fertilizer treatments. We compared the geophysical response with soil samples and soil sensor data. In particular, electromagnetic induction and electrical resistivity tomography were effective in monitoring changes in nitrate levels over time.

Despite the massive use of soil storage, its impact on soil microbial parameters (SMPs) has been assessed in a very scattered way. I analysed 73 research articles dealing with the impact of storage practices on various SMPs. The results show significant effects of all storage practices on SMPs in a vast majority of cases. Storage practices should be carefully selected according to the conditions that prevail in the native soil environment and to the SMPs of interest.