Effects of grassland degradation and precipitation on carbon storage distributions in a semi-arid temperate grassland of Inner Mongolia, China

Li, Xiaobing; Bai, Yunxiao; Wen, Wanyu; Hong, Wei; Li, Ruihua; Li, Guoqing; Wang, Han

doi:10.1016/j.actao.2017.09.008

Cited by 18 publications

(7 citation statements)

References 44 publications

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“…However, most studies have assessed C and N storage values for specific grassland types, especially temperate grasslands (Conant and Paustian, 2002;Piao et al, 2007;Yang et al, 2010) and alpine grasslands (Li et al, 2014;Yan et al, 2015;Chen et al, 2017). Additionally, numerous studies have studied soil C and N in the grasslands of Inner Mongolia (Bai et al, 2004;He et al, 2014;Li et al, 2017) and the Qinghai-Tibetan Plateau (QTP) (Yang et al, 2008;Chang et al, 2014) in China. However, few studies have investigated soil organic carbon (SOC) and soil total nitrogen (STN) storages variations among different grassland types in the southern foot of the Altai Mountains due to limited soil surveys and the high spatial variability of soils in the arid region.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of soil organic carbon and total nitrogen under various grassland types along a transect in a mountain-basin system in Xinjiang, China

Nan

et al. 2018

J. Arid Land

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Soil organic carbon (SOC) and soil total nitrogen (STN) in arid regions are important components of global C and the N cycles, and their response to climate change will have important implications for both ecosystem processes and global climate feedbacks. Grassland ecosystems of Funyun County in the southern foot of the Altay Mountains are characterized by complex topography, suggesting large variability in the spatial distribution of SOC and STN. However, there has been little investigation of SOC and STN on grasslands in arid regions with a mountain-basin structure. Therefore, we investigated the characteristics of SOC and STN in different grassland types in a mountain-basin system at the southern foot of the Altai Mountains, north of the Junggar Basin in China, and explored their potential influencing factors and relationships with meteorological factors and soil properties. We found that the concentrations and storages of SOC and STN varied significantly with grassland type, and showed a decreasing trend along a decreasing elevation gradient in alpine meadow, mountain meadow, temperate typical steppe, temperate steppe desert, and temperate steppe desert. In addition, the SOC and STN concentrations decreased with depth, except in the temperate desert steppe. According to Pearson's correlation values and redundancy analysis, the mean annual precipitation, soil moisture content and soil available N concentration were significantly positively correlated with the SOC and STN concentrations. In contrast, the mean annual temperature, pH, and soil bulk density were significantly and negatively correlated with the SOC and STN concentrations. The mean annual precipitation and mean annual temperature were the primary factors related to the SOC and STN concentrations. The distributions of the SOC and STN concentrations were highly regulated by the elevation-induced differences in meteorological factors. Mean annual precipitation and mean annual temperature together explained 97.85% and 98.38% of the overall variations in the SOC and STN concentrations, respectively, at soil depth of 0-40 cm, with precipitation making the greatest contribution. Our results provide a basis for estimating and predicting SOC and STN concentrations in grasslands in arid regions with a mountain-basin structure.

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Section: Introductionmentioning

confidence: 99%

Characteristics of soil organic carbon and total nitrogen under various grassland types along a transect in a mountain-basin system in Xinjiang, China

Nan

et al. 2018

J. Arid Land

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“…The selection of suitable UD as a reference benchmark formed the scientific basis for the assessment of grassland degradation, but in practice, the selection criterion was inconsistent. In studies at the sample plot level, scholars have used enclosed grassland as the reference benchmark for UD and have assigned degrees of grassland degradation based on grazing intensity [57]. For the study of grassland degradation monitoring at the regional level, it has been difficult to find an undegraded natural sample plot, and the selection of a reference benchmark became challenging [58,59].…”

Section: Academic Value Of Studymentioning

confidence: 99%

Comprehensive Grassland Degradation Monitoring by Remote Sensing in Xilinhot, Inner Mongolia, China

Lyu

Gong

et al. 2020

Sustainability

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Grassland degradation is a complex process and cannot be thoroughly measured by a single indicator, such as fractional vegetation cover (FVC), aboveground biomass (AGB), or net primary production (NPP), or by a simple combination of these indicators. In this research, we combined measured data with vegetation and soil characteristics to establish a set of standards applicable to the monitoring of regional grassland degradation by remote sensing. We selected indicators and set their thresholds with full consideration given to vegetation structure and function. We optimized the indicator simulation, based on which grassland degradation in the study area during 2014–2018 was comprehensively evaluated. We used the feeding intensity of herbivores to represent the grazing intensity. We analyzed the effects of climate and grazing activities on grassland degradation using the constraint line method. The results showed degradation in approximately 69% of the grassland in the study area and an overall continued recovery of the degraded grassland from 2014 to 2018. We did not identify any significant correlation between temperature and grassland degradation. The increase in precipitation promoted the recovery of degraded grassland, whereas increased grazing may have aggravated degradation. Our findings can not only improve the scientific quality and accuracy of grassland degradation monitoring by remote sensing but also provide clear spatial information and decision-making help in sustainable management of grassland regions.

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“…The transformation and degradation of grasslands can be attributed to a range of drivers that include climatic variability, invasive plant species, injudicious land use and management, and ecosystem fragility (Li et al, 2022 ; Tiscornia et al, 2019 ). The degradation of grasslands leads to a reduction in forage quality and quantity at varying spatial extents (Hooper et al, 2012 ; Li et al, 2017 ), thereby adversely affecting livestock production and food security, especially within rangelands. Also, the literature shows that foliar nutrient status/quality in rangelands is regulated by environmental conditions such as changes in temperature, precipitation, and soil nutrients, causing spatial variation in rangelands (Getabalew & Alemneh, 2019 ; Ghorbani et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

The role of remote sensing in tropical grassland nutrient estimation: a review

Arogoundade

Mutanga

Odindi

et al. 2023

Environ Monit Assess

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The carbon (C) and nitrogen (N) ratio is a key indicator of nutrient utilization and limitations in rangelands. To understand the distribution of herbivores and grazing patterns, information on grass quality and quantity is important. In heterogeneous environments, remote sensing offers a timely, economical, and effective method for assessing foliar biochemical ratios at varying spatial and temporal scales. Hence, this study provides a synopsis of the advancement in remote sensing technology, limitations, and emerging opportunities in mapping the C:N ratio in rangelands. Specifically, the paper focuses on multispectral and hyperspectral sensors and investigates their properties, absorption features, empirical and physical methods, and algorithms in predicting the C:N ratio in grasslands. Literature shows that the determination of the C:N ratio in grasslands is not in line with developments in remote sensing technologies. Thus, the use of advanced and freely available sensors with improved spectral and spatial properties such as Sentinel 2 and Landsat 8/9 with sophisticated algorithms may provide new opportunities to estimate C:N ratio in grasslands at regional scales, especially in developing countries. Spectral bands in the near-infrared, shortwave infrared, red, and red edge were identified to predict the C:N ratio in plants. New indices developed from recent multispectral satellite imagery, for example, Sentinel 2 aided by cutting-edge algorithms, can improve the estimation of foliar biochemical ratios. Therefore, this study recommends that future research should adopt new satellite technologies with recent development in machine learning algorithms for improved mapping of the C:N ratio in grasslands.

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Effects of grassland degradation and precipitation on carbon storage distributions in a semi-arid temperate grassland of Inner Mongolia, China

Cited by 18 publications

References 44 publications

Characteristics of soil organic carbon and total nitrogen under various grassland types along a transect in a mountain-basin system in Xinjiang, China

Characteristics of soil organic carbon and total nitrogen under various grassland types along a transect in a mountain-basin system in Xinjiang, China

Comprehensive Grassland Degradation Monitoring by Remote Sensing in Xilinhot, Inner Mongolia, China

The role of remote sensing in tropical grassland nutrient estimation: a review

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