Ecosystem Management of Grasslands

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Ecosystem, Environment and Climate Change in Agriculture".

Deadline for manuscript submissions: 5 June 2025 | Viewed by 894

Special Issue Editor


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Guest Editor
School of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
Interests: rodent; sustainable development

Special Issue Information

Dear Colleagues,

Grassland ecosystems play a vital role in biodiversity maintenance, soil conservation, and water resource management. With the increasingly serious global environmental problems, the research and practice of grassland ecological management has become particularly urgent. This Special Issue aims to discuss the latest progress and cutting-edge technologies of grassland ecological management and provide a platform for academic exchanges in this field. The purpose of this Special Issue is to promote academic research and exchange in the field of grassland ecological management and to explore the sustainable management methods of grassland ecosystems in depth. We welcome research papers on grazing management, vegetation restoration, biodiversity conservation, climate change adaptation, and more. Other topics of interest include but are not limited to the following articles: grassland ecosystem stability research; new grassland ecological management technology; carbon cycle and greenhouse gas emissions in grassland ecosystem; and the response mechanism of grassland ecosystem to climate change. We invite submissions of the following types: original research papers; review article; technical reports; practice case sharing; reviews; and perspective articles.

Prof. Dr. Zhenggang Guo
Guest Editor

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Keywords

  • ecosystem management
  • grassland
  • plant
  • soil
  • microorganism

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Published Papers (1 paper)

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Research

18 pages, 3872 KiB  
Article
Comparing Stacking Ensemble Learning and 1D-CNN Models for Predicting Leaf Chlorophyll Content in Stellera chamaejasme from Hyperspectral Reflectance Measurements
by Xiaoyu Li, Yongmei Liu, Huaiyu Wang, Xingzhi Dong, Lei Wang and Yongqing Long
Agriculture 2025, 15(3), 288; https://doi.org/10.3390/agriculture15030288 - 28 Jan 2025
Viewed by 496
Abstract
Stellera chamaejasme, a toxic invasive species widespread in degraded alpine grasslands, Qinghai Province, causes a significant threat to the local ecological balance. Accurate monitoring of the leaf chlorophyll content is essential for preventing its expansion over large areas. This study presents an [...] Read more.
Stellera chamaejasme, a toxic invasive species widespread in degraded alpine grasslands, Qinghai Province, causes a significant threat to the local ecological balance. Accurate monitoring of the leaf chlorophyll content is essential for preventing its expansion over large areas. This study presents an optimal approach by integrating hierarchical dimensionality reduction, stacking ensemble learning, and 1D-CNN models to estimate leaf chlorophyll content in S. chamaejasme using hyperspectral reflectance data. Field spectrometry analysis demonstrates that the combination of Pearson correlation, first derivative, and SPA algorithms can efficiently select the most chlorophyll-sensitive wavelengths, red-edge parameters, and spectral indices related to S. chamaejasme leaves. The stacking ensemble model outperforms the 1D-CNN model in predicting leaf chlorophyll content of S. chamaejasme over the whole growth stage, while the 1D-CNN excels at prediction in each individual growth stage. Comparatively, the 1D-CNN model achieved higher accuracy (R2 > 0.5) in all five growth stages, with optimal performance during the flower bud stage (R2 = 0.787, RMSE = 2.476). This study underscores the potential of combining feature spectra selection with machine learning and deep learning models to monitor S. chamaejasme growth, offering valuable insights for invasive species control and ecological management. Full article
(This article belongs to the Special Issue Ecosystem Management of Grasslands)
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