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Updated: Oct 31, 2025

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Modelling of land nutrient cycles: recent progress and future development.

Ying-Ping Wang1, Daniel S Goll2

  • 1CSIRO Oceans and Atmosphere, PMB 1, Aspendale Victoria 3195, Australia.

Faculty Reviews
|July 1, 2021
PubMed
Summary

Global land models now better represent nutrient cycles, like nitrogen and phosphorus, by incorporating plant-microbial interactions and geological processes. Future advancements require machine learning and optimality principles to address data gaps and improve accuracy.

Keywords:
Nutrient cycleglobal modellingnitrogenphosphorus

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Area of Science:

  • Earth System Science
  • Biogeochemistry
  • Computational Modeling

Background:

  • Nutrient cycles, particularly nitrogen and phosphorus, are crucial for land biosphere productivity, a fact recognized for decades.
  • Global land models have recently begun incorporating nutrient cycle representations, with significant progress in the last three years.
  • Key processes like plant-microbial interactions, soil phosphorus transformation, and nitrogen release from rocks are now better understood.

Purpose of the Study:

  • To highlight recent advancements in modeling land nutrient cycles.
  • To identify current challenges and uncertainties in global-scale nutrient cycle modeling.
  • To propose novel approaches for improving the accuracy and scope of these models.

Main Methods:

  • Reviewing recent progress in understanding key nutrient cycle processes.
  • Analyzing the representation of these processes in global land models.
  • Recommending machine learning and optimality principles for future model development.

Main Results:

  • Improved understanding of the interplay between geology, biology, and climate in controlling nutrient cycles.
  • Identification of significant uncertainties in current models due to data limitations at relevant scales.
  • Recognition of the need for advanced techniques to integrate diverse data sources.

Conclusions:

  • Progress in global nutrient cycle modeling is accelerating due to new process understanding.
  • Significant uncertainties persist, particularly in evaluating coupled carbon and nutrient cycles.
  • Machine learning and optimality principles offer promising avenues to enhance global land nutrient cycle models.