Explicit Representation of Grazing Activity in a Diagnostic Terrestrial Model: A Data‐Process Combined Scheme

Abstract

Grazing activity is a fundamental behavior in pasture ecosystems and, globally, is a major disturbance that leads to destruction of terrestrial biomass. However, its impact on ecosystem C sequestration at large scales is not well understood due to its obvious anthropogenic property. In this study, we proposed a Data‐Process combined Grazing Scheme (DPGS) to quantify the regional grazing impact on ecosystem C sequestration in the typical pasture ecosystem, Temperate Eurasian Steppe. First, a pixel‐based livestock distribution map was generated based on fine‐scale (province/prefecture) inventory data using a resource‐oriented livestock distribution approach. Then the C consumption due to grazing (Closs,graze) was simulated by combining a late version of a remote‐sensing‐based terrestrial model, the Boreal Ecosystem Productivity Simulator and the Shiyomi grazing model. The modeled regional livestock density was evaluated against the Gridded Livestock of the World data set. The DPGS was able to reproduce the spatial distribution of livestock. Because extralarge herbivores (camel and horse) were involved in the calculation, the DPGS predicts higher livestock density than the Gridded Livestock of the World data set over 70% of the region. The modeled Closs,graze and its seasonal variability were validated against multiple site‐based data sets. The results showed good agreements with the field observations of Closs,graze. With further tests and data incorporations, this scheme has the potential to produce high‐resolution data sets of livestock distribution and Closs,graze and become a useful diagnostic instrument for model evaluation, parameterization, and intercomparison.

Analyzing NPP Response of Different Rangeland Types to Climatic Parameters over Mongolia

“…Recently it has been applied and validated to monitor the dynamic changes in the productivity of various vegetation types, such as shrubland, farmland, and grassland ecosystems [52]. Accordingly, the model was used in mapping NPP spatial and temporal variations as a response to multiple distributors (e.g., forest fires, extreme climate events, drought, and climate change) at regional, continental, and global scales [50,[53][54][55][56][57] to quantify the water-use efficiency in dryland ecosystems [58], and to assess the grazing impact on ecosystem carbon sequestration [59].…”

Annual high-resolution grazing intensity maps on the Qinghai-Tibet Plateau from 1990 to 2020

“…Nearly half of the grasslands on the QTP have been reported to be degraded over the past four decades (Wang et al 2018;Dong et al 2020), with some reports even indicating that the degraded grassland has reached 90% (Wang et al 2021). It is widely recognized that overgrazing is the predominant and most pervasive unsustainable human activity continuing to drive grassland degradation on the QTP (Wang et al 2018;Chen et al 2019). However, identifying overgrazed areas remains an important challenge that can be effectively addressed by grazing intensity maps.…”

“…Among them, machine learning technology is providing new opportunities towards more accurate predictions of livestock intensity (Garcí a et al, 2020). Random forest regression, for instance, is currently widely used to construct global, national as well as regional livestock spatialization dataset, and has been proved to have much better accuracy than traditional mapping techniques (Rokach, 2016;Nicolas et al, 2016;Gilbert et al, 2018;Chen et al, 2019;Dara et al, 2020;. Nevertheless, other more advanced machine learning methods with superior feature learning and more robust generalization capabilities, remains largely untapped for modelling geographic data (Ahmad et al, 2018;Heddam et al, 2020;Long et al, 2022).…”

“…Livestock grazing activities are often affected by abiotic and biotic resources, including climatic and environmental factors (Waha et al, 2018), herd foraging and grazing behaviours (Garrett et al, 2018;Miao et al, 2020), and conservation-oriented policies . For instance, regions exceeding elevations of 5600 m or slope greater than 40% are customarily unsuitable for grazing (Mack et al, 2013;Robinson et al, 2014;Chen et al, 2019). The livestock generally prefer areas abundant in water and pasture resources for foraging .…”

Movement drives population dynamics of one of the most mobile ungulates on Earth: Insights from a mechanistic model

“…In addition, the mechanistic, ecophysiological nature of our herbivore–vegetation model allows one to alter process representations according to different ecological hypotheses, making it ideal for examining the implications of competing hypotheses or for gaining mechanistic understanding of the processes at work. While herbivores have been added to DGVMs (Chang et al, 2013; Chen et al, 2019; Luo et al, 2012; Pachzelt et al, 2013; Zhu et al, 2018), to our knowledge, moving herbivores have not. We therefore incorporated moving large herbivores into the DGVM Lund‐Potsdam‐Jena General Ecosystem Simulator (LPJ‐GUESS) to test two hypotheses: long‐distance movements (1) lead to an increase in the abundance of herbivores and (2) stabilize population dynamics.…”