# From nitrogen addition to productivity: above–belowground mechanisms and nonlinear thresholds in Grasslands

## TL;DR

Adding nitrogen to grasslands can boost plant growth at first, but too much causes harmful effects like biodiversity loss and soil acidification.

## Contribution

The paper introduces a framework to detect when nitrogen addition shifts from beneficial to harmful, based on measurable indicators like soil pH and microbial diversity.

## Key findings

- Moderate nitrogen addition increases both above- and belowground plant productivity in nitrogen-limited systems.
- Exceeding nitrogen thresholds leads to biodiversity loss and soil acidification.
- A management-ready framework is proposed to detect transitions from moderate to excessive nitrogen addition.

## Abstract

Grasslands harbor high biodiversity and regulate continental carbon and nitrogen cycling, yet rising anthropogenic nitrogen (N) inputs are reshaping their structure, function, and stability. Synthesizing recent evidence, we show that in N-limited systems moderate N addition tends to raise both ANPP and BNPP by elevating leaf N, optimizing canopy structure, and rebalancing carbon allocation. However, once ecosystem-specific thresholds are exceeded, gains plateau or reverse, coinciding with biodiversity loss, functional-trait homogenization, declines in root-associated mutualists, and soil acidification. N effects are context dependent: thresholds shift lower in dry–hot or semi-arid grasslands and under intense grazing, while soil pH, available phosphorus, and microbial assemblages act as proximal controls that determine whether short-term productivity gains convert into long-term carbon sequestration. We propose a management-ready indicator framework organized along three axes—N dose × water–energy balance × P availability—and paired with field diagnostics (pH, available P, leaf N:P, microbial diversity and key enzyme activities, N2O fluxes) to detect early transitions from “moderate” to “excessive” N addition. Priorities include long-term, multifactor experiments and observation–remote sensing–model integration that jointly track plant traits, microbial dynamics, and coupled C–N processes to improve cross-scale prediction and provide actionable guidance for N application and grazing management.