Abstract
While widespread imitation of the productivity of the land biosphere by nutrients, like nitrogen and phosphorus, was demonstrated many decades ago, representation of nutrient cycles in global land models has been relatively recent. Over the last three years, significant progress has been made in understanding some of the key processes and their representation in global land models. They include the significance of plant-microbial interaction in affecting nutrient cycles, inorganic soil phosphorus transformation, and nitrogen release from rocks. As a result, our understanding of the linkages among geology, biology, and climate controlling nutrient cycles is improving. However, progress in modelling nutrient cycles at a global scale is still confronted with large uncertainties in representing key processes owing to lack of data at the relevant scales for evaluating coupled carbon and nutrient cycles. Here we recommend two approaches to advance modelling of land nutrient cycles: the application of machine learning techniques to bridge the gap between global modelling and scattered site-level information and the use of optimality principles to identify key mechanisms driving spatial and temporal patterns of nutrients.