Assessment of the grassland carrying capacity for winter-spring period in Mongolia. (February 2023)

• Record Type: Journal Article
• Authors: Yan, Nana; Zhu, Weiwei; Wu, Bingfang; Tuvdendorj, Battsetseg; Chang, Sheng; Mishigdorj, Odbayar; Zhang, Xiwang
• Is Part Of: Ecological indicators. Volume 146(2023)
• Publication Date: 2023-02
• Subjects: Aboveground biomass -- Grassland carrying capacity -- Remote sensing -- Google Earth Engine; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2023.109868
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: A framework was proposed to estimate the above-ground biomass and the carrying capacity of grassland based on the GEE environment. The modeled above-ground biomass data were verified in Mongolia. The grassland carrying capacity for winter-spring period were overexploited in half of provinces in Mongolia in recent years. The dramatic increase of livestock populations was the main driving factor on the change of grassland carrying status. Abstract: The grassland ecosystems of Mongolia are among the most sensitive to global climate change because of the arid and semiarid climate. As a key source of primary productivity for livestock, the quantification of the carrying capacity in grassland for the winter and early spring period is crucial for sustainable livestock management and livelihoods of herders in Mongolia. In this study, we used remote sensing data and ancillary data to propose a framework to estimate the aboveground biomass(AGB) and the carrying capacity of grassland (GCC) using the Google Earth Engine (GEE) environment. We analysed the spatial and temporal changes in the GCC for the winter-spring period in Mongolia during 2000–2020, and the grassland carrying status index for winter-spring period (GCSIW) was proposed to reflect grassland utilization and livestock carrying status over the past 21 years. Our study demonstrated the effectiveness of AGB and GCC estimation using the Carnegie-Ames-Stanford Approach (CASA) model with the root-to-crown ratio method within the GEE environment. The AGB model validation showed good performance with an R 2 of 0.67–0.71 and RMSE of 22.91–28.94 g/m 2 . Significant increases in AGB and GCC over the 21 years were found in Mongolian grasslands and most provinces. The average GCSIW increased significantly during 2000–2020 in the whole country and all provinces, indicating the increasing stocking density and the overexploited status of grassland in recent years. The multiregression analysis further showed that the dramatic increase in livestock populations contributed 87.5% and 55%-99% to the variations in the GSCIW for the grassland and seventeen provinces, respectively. These results will be useful and helpful in supporting sustainable grassland management and the sustainable livelihoods of herders in Mongolia.