Machine learning in soil nutrient dynamics of alpine grasslands

As a terrestrial ecosystem, alpine grasslands feature diverse vegetation types and play key roles in regulating water resources and carbon storage, thus shaping global climate. The dynamics of soil nutrients in this ecosystem, responding to regional climate change, directly impact primary productivi...

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Veröffentlicht in:The Science of the total environment 2024-10, Vol.946, p.174295, Article 174295
Hauptverfasser: Jiang, Lili, Wen, Guoqi, Lu, Jia, Yang, Hengyuan, Jin, Yuexia, Nie, Xiaowei, Wang, Zongsong, Chen, Meirong, Du, Yangong, Wang, Yanfen
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container_title The Science of the total environment
container_volume 946
creator Jiang, Lili
Wen, Guoqi
Lu, Jia
Yang, Hengyuan
Jin, Yuexia
Nie, Xiaowei
Wang, Zongsong
Chen, Meirong
Du, Yangong
Wang, Yanfen
description As a terrestrial ecosystem, alpine grasslands feature diverse vegetation types and play key roles in regulating water resources and carbon storage, thus shaping global climate. The dynamics of soil nutrients in this ecosystem, responding to regional climate change, directly impact primary productivity. This review comprehensively explored the effects of climate change on soil nitrogen (N), phosphorus (P), and their balance in the alpine meadows, highlighting the significant roles these nutrients played in plant growth and species diversity. We also shed light on machine learning utilization in soil nutrient evaluation. As global warming continues, alongside shifting precipitation patterns, soil characteristics of grasslands, such as moisture and pH values vary significantly, further altering the availability and composition of soil nutrients. The rising air temperature in alpine regions substantially enhances the activity of soil organisms, accelerating nutrient mineralization and the decomposition of organic materials. Combined with varied nutrient input, such as increased N deposition, plant growth and species composition are changing. With the robust capacity to use and integrate diverse data sources, including satellite imagery, sensor-collected spectral data, camera-captured videos, and common knowledge-based text and audio, machine learning offers rapid and accurate assessments of the changes in soil nutrients and associated determinants, such as soil moisture. When combined with powerful large language models like ChatGPT, these tools provide invaluable insights and strategies for effective grassland management, aiming to foster a sustainable ecosystem that balances high productivity and advanced services with reduced environmental impacts. [Display omitted] •Alpine ecosystems are critical in shaping global climate by influencing hydrological cycles and carbon sequestration.•Soil nutrient availability, equilibrium, and composition in alpine grasslands are notably affected by climate warming.•Machine learning shows promise in estimating soil nutrient temporal and spatial dynamics using diverse data sources.•The integration of machine learning in soil nutrient management can advance the sustainable development of ecosystems.
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subjects air temperature
Alpine grassland
carbon sequestration
climate
Climate change
environment
grassland management
Machine learning
mineralization
nitrogen
phosphorus
plant growth
primary productivity
remote sensing
Soil nitrogen and phosphorus
soil nutrient dynamics
soil nutrients
soil water
species diversity
spectral analysis
terrestrial ecosystems
vegetation
title Machine learning in soil nutrient dynamics of alpine grasslands
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