Estimation of Grassland Carrying Capacity by Applying High Spatiotemporal Remote Sensing Techniques in Zhenglan Banner, Inner Mongolia, China

Abstract

Overgrazing directly leads to grassland degradation, which is a serious constraint to the sustainable development of animal husbandry. In drylands, grassland biomass is highly heterogeneous in space and time. It is difficult to achieve sustainable utilization of grassland resources by focusing only on the average annual carrying capacity assessment obtained from grass yield. Here, we proposed a novel approach for assessing grassland carrying capacity, taking Zhenglan Banner (County) in Inner Mongolia as the study area. First, monthly grass yield at 30 m spatial resolution was estimated, derived from Carnegie–Ames–Stanford Approach (CASA) model and spatial and temporal adaptive reflectance fusion model (STARFM). Then, based on the degree of sand mobility and degradation condition of typical steppe, the utilization patterns for sandy land and typical steppe in different grazing seasons were developed separately to obtain available grass yield. Finally, the carrying capacity at the Gacha (Village)-scale was estimated and the current livestock carrying status was evaluated to facilitate the grassland refined management. In Zhenglan Banner, the carrying capacity was 237.46 thousand cattle-units in summer. The grassland resources are being overgrazed, with an overload rate of 19.32%. At Gacha-scale, the maximum reasonable stock density was ranged from 0.06 cattle-unit/ha to 0.42 cattle-unit/ha. Fifty-one Gachas exhibited livestock overload. This study is expected to provide technical support and scientific reference data for ecological conservation and grassland management in the study area, as well as in dryland pastoral areas of northern China.

Global trends in grassland carrying capacity and relative stocking density of livestock

“…RSD falls within the “overstocked” class (i.e., exceeding grassland CC) in large parts of southern Asia, eastern Asia, northwestern Europe, and the Sahel region (Figure 4a). Overgrazing has indeed been reported to occur in many of these regions, such as in the Three‐River Headwaters region in China (Zhang et al, 2014), in Inner Mongolia (Qin et al, 2021), and in the Patagonian rangelands in Argentina (Gaitán et al, 2018). Most parts of southern Africa, Australia, Canada, and Russia fall, in turn, within the “low pressure” RSD category (Figure 4a).…”

“…CC assessments typically use a proper use factor (PUF) or similar coefficients to define this fraction (see e.g. De Leeuw et al, 2019; Petz et al, 2014; Qin et al, 2021). However, we considered it inappropriate to apply a single PUF for all grassland worldwide, because PUFs vary significantly between ecosystems (Fetzel, Havlik, Herrero, & Erb, 2017).…”

“…(3) TA B L E 2 Overview of uncertainty estimates used in the analysis. See the justifications for the limits in Appendix S2 Petz et al, 2014;Qin et al, 2021). However, we considered it inappropriate to apply a single PUF for all grassland worldwide, because PUFs vary significantly between ecosystems .…”

Assessment of remotely sensed inventories for land cover classification of public grasslands in Manitoba, Canada

“…Grasslands are one of the most important ecosystems on the Earth because of their several ecosystem services, such as forage production and carbon sequestration. They account for roughly 26% of the terrestrial surface and about 70% of total global agricultural land (Conant, 2010; Qin et al, 2021; Reinermann et al, 2020). The North American Great Plains contains one of the largest grassland areas in the world with Canada accounting for 16% of the total land area (Gauthier & Wiken, 2003).…”

“…et al, 2014) Zhang et al (2014). andQin et al (2021) used a land cover classification differentiating montane grassland types, such as alpine steppe and desert grasslands, to implement forage utilisation rates for carrying capacity estimations in China. Likewise, a provincial forage benchmarking project carried out in Manitoba also adjusted forage utilisation rates across lowland, upland and transitional grasslands, shrublands and forests, based on soil ecosites (Agriculture…”