Urine contains most of the nutrients excreted by humans. They are mainly released into the environment and contribute to the strong disruption of planetary biogeochemical cycles. These nutrients could be separated at source and used in agriculture. Once the urine is separated different treatments can be applied to stabilize nitrogen, reduce the volume, treat contaminants, or extract nutrients. These treatments and the resulting fertilizers have different characteristics and environmental impacts that must be assessed. We reviewed the characteristics of six treatments and the resulting fertilizers. The studied treatments were restricted to those that retain the most nutrients: storage, acidification, alkalinization, nitrification, and mixture with organic substrate. Phosphorus precipitation was also included, as it is one of the most developed treatments. Volume reduction posttreatments were also considered. We compared these treatments and the resulting fertilizers in terms of nutrient forms and concentrations, fertilizer efficiency, contaminant removal, energy, and chemical consumption. Although some parameters, such as fertilizer efficiency or pharmaceutical removal were in the same range, the nutrient concentrations and the energy and chemical consumption requested by fertilizer production varied widely. Holistic studies and pilot facilities implemented at a larger scale are needed in order to assess the whole value chain. Graphical abstract

Changes in soil organic carbon (SOC) stocks are a major source of uncertainty for the evolution of atmospheric CO₂ concentration during the 21st century. They are usually simulated by models dividing SOC into conceptual pools with contrasted turnover times. The lack of reliable methods to initialize these models, by correctly distributing soil carbon amongst their kinetic pools, strongly limits the accuracy of their simulations. Here, we demonstrate that PARTY_SOC, a machine-learning model based on Rock-Eval^® thermal analysis, optimally partitions the active- and stable-SOC pools of AMG, a simple and well-validated SOC dynamics model, accounting for effects of soil management history. Furthermore, we found that initializing the SOC pool sizes of AMG using machine learning strongly improves its accuracy when reproducing the observed SOC dynamics in nine independent French long-term agricultural experiments. Our results indicate that multi-compartmental models of SOC dynamics combined with a robust initialization can simulate observed SOC stock changes with excellent precision. We recommend exploring their potential before a new generation of models of greater complexity becomes operational. The approach proposed here can be easily implemented on soil monitoring networks, paving the way towards precise predictions of SOC stock changes over the next decades.