Estimation of the Carrying Capacity and Relative Stocking Density of Mongolian grasslands under various adaptation scenarios

Abstract

Mongolia's vast grasslands, crucial for both environmental and economic stability, are currently facing challenges due to overgrazing, climate change, and land-use changes. Understanding and effectively managing their Carrying Capacity (CC) and Relative Stocking Density (RSD) is essential for maintaining ecological balance. This study rigorously evaluates the CC and RSD of Mongolia's grasslands through an innovative approach that integrates ecological models with socio-economic data, aimed at improving grazing management practices. Data from the National Agency for Meteorology and Environmental Monitoring validates the model, providing precise CC and RSD estimates at the Soum level from 2000 to 2019. The study reveals significant regional variations in CC: northern grasslands exhibit a high CC of 2.8 Sheep Units (SU) per hectare, contrasting with the fragile CC in some southern regions, like the Gobi Desert, where it is as low as 0.3 SU per hectare. Approximately 38.8 % of Mongolia's territory maintains a CC exceeding 1.0 SU per hectare, indicative of sustainable grasslands. In contrast, 41.7 % of the land, primarily in southern regions, shows CCs below 0.5 SU per hectare, highlighting ecosystem vulnerability. The RSD, reflecting livestock numbers relative to CC, averages 1.07, suggesting a high livestock concentration near Ulaanbaatar but a more sustainable density across 43.2 % of the country. The research also explores adaptation scenarios against desertification and degradation, as well as improving pasture accessibility, providing insights for future grassland management strategies. In conclusion, this study emphasizes the need for sustainable land management practices to balance carrying capacity and stocking rates, offering a vital tool for policymakers and stakeholders in grassland conservation.

Keywords: Carrying Capacity; Mongolian grasslands; Relative Stocking Density; Scenario modeling; Smart-grazing systems; Sustainable management.