The Qinghai-Tibet Plateau is the third-largest glaciated area of the world. It is one of the most sensitive regions due to climate change, such as fast-melting permafrost, dust blow, and overgrazing in recent decades. It covers an area of more than 2.4 million km2 with an average altitude exceeding 4,000 m above sea level. In the past 50 years, the warming rate is higher than the global average warming rate, which is 0.40 ± 0.05 °C per decade, and it has become much warmer since 1998. The climate warming is most distinct in the northeastern plateau, implying warming of the air, surface temperatures as well as duration and depth of thawing. Also, the alpine grassland ecosystem of the Qinghai-Tibet Plateau is fragile and sensitive to climate change and related alterations of precipitation and temperature regarding species composition and biomass production. Human factors like land use, including overgrazing, the foundation of the settlement, road construction, and other activities, interfere with the fragile ecosystem more obvious. Many species showed their distributions by climate-driven shifts towards higher elevation, in Tibetan Plateau; however, the elevational variations of the alpine grassland are rare. It might have urgent needs to seek the responses of soil from the environment and the vegetation (Biodiversity, Biomass production) in high ecosystem stability and potential risks. The overall objective of this research project is to investigate the feedbacks between soils in alpine grassland ecosystems on the Tibetan Plateau and climate. More specifically, to:

1. examine the relationship between the quality of soils, their spatial distribution, and biomass production to detect the potential impact of climate change on the grassland ecosystem, especially to better understand the position and dynamics of the green line in high mountain ecosystems,
2. provide new data and ideas about the relationship between soil nutrient status and plant nutrition for quantitative simulation of vegetation change in the context of climate warming,
3. establish Fourier transformation near and mid-infrared spectroscopy (FT-NMIRS) to measure soil phosphorous fractions rapid and for large numbers of soil samples,
4. analyze environmental factors, including temperature, precipitation, soil development, soil fertility, plant growth, meadow plant populations and the ability of plants to adapt to the environmental impact of climate using FT-NMIRS,
5. predict the spatial distribution of soil and grassland properties for large areas using machine learning.

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