Construction of an Early Warning System Based on a Fuzzy Matter-Element Model for Diagnosing the Health of Alpine Grassland: A Case Study of Henan County, Qinghai, China

Abstract

To maintain alpine grassland in a healthy and sustainable state, a sound warning system was developed to diagnose its potential degradation risk. Data related to grassland quality (six indicators), habitat (six indicators), and eco-carrying capacity (three indicators) at eight sampling plots were collected from Henan Mongol Autonomous County of West China in 2014 and 2017, representing five types of grassland and three grazing styles. Compared to the warning level in 2014, alpine grassland had a higher warning level in 2017, demonstrating the degradation of grassland ecosystems. Kobresia tibetica exhibited the lowest level of warning, while Kobresia humilis had the highest, indicating its corresponding safety and unsafety under the environmental change. Grassland quality is the most important index for grassland health, and soil total carbon and available phosphorus are the most important indices of habitat quality, which finally greatly influence the warning level of alpine grassland. Further analysis results suggested that winter grazing is beneficial for the health of grassland, and moderate grazing can accelerate the self-recovery of the alpine grassland due to the increase in organic matter. This study is crucial for understanding the health level of alpine grassland and its further change trends, and providing an important scientific basis for rational grazing.

Keywords

• alpine grassland
• early warning system
• grassland type
• grazing style
• fuzzy matter-element model
• analytic hierarchy process