Grazing intensity and driving factors affect soil nitrous oxide fluxes during the growing seasons in the Hulunber meadow steppe of China

Yan, Ruirui; Tang, Huajun; Xin, Xiaoping; Chen, Baorui; Murray, Philip J.; Yan, Yunchun; Wang, Xu; Yang, Guoxiang

doi:10.1088/1748-9326/11/5/054004

Cited by 34 publications

(24 citation statements)

References 37 publications

(40 reference statements)

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“…However, during grazing seasons with normal precipitation or dry conditions this affect was not evident, indicating that the grazing intensity and precipitation (soil moisture) interact to effect N 2 O emissions from this grazed rough fescue grassland soil. Our results are both in agreement and in contrast to previous studies, with some showing grazing may increase (Luo et al, ; Frank et al, ; Saggar et al, ) or decrease N 2 O emissions (Wang et al, ; Xu et al, ; Liu et al, ; Peng et al, ; Yan et al, ). We attribute this to the three‐year study period providing a range of annual precipitation.…”

Section: Discussionsupporting

confidence: 92%

“…The soil N 2 O flux from the rough fescue grassland was highly variable over the grazing season. Grazing by cattle may reduce soil N 2 O flux (Wang et al, 2005;Xu et al, 2008;Liu et al, 2010;Peng et al, 2011;Yan et al, 2016), or increase soil N 2 O flux (Luo et al, 1999;Frank et al, 2000;Saggar et al, 2004). The contradictory findings suggest that soil N 2 O emissions under grazing conditions are complex and affected by factors other than cattle stocking rate, probably interactions between soil moisture (O 2 availability) and other soil properties (Chapus-Lardy et al, 2007).…”

Section: Trace Gas Fluxes Over the Grazing Seasonmentioning

confidence: 99%

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Long‐Term Grazing Alters Soil Trace Gas Fluxes from Grasslands in the Foothills of the Rocky Mountains, Canada

et al. 2017

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Long-term cattle grazing may degrade grassland soils, but how soil CO 2 , CH 4 and N 2 O fluxes respond to long-term cattle grazing is poorly understood. Therefore, we quantified soil CO 2 , CH 4 and N 2 O fluxes in response to four levels (none, light, heavy, very heavy) of long-term (>65 years) cattle grazing on a rough fescue grassland in the foothills of the Rocky Mountains, Canada over three grazing seasons. The grazed grassland soils emitted 37 to 51% more CO 2 than non-grazed soils. Grazed grassland soils were small CH 4 sinks and small N 2 O sources each season, and their cumulative fluxes were significantly affected by a cattle stocking rate × year interaction, indicating the grazing effect was influenced by environmental conditions. Soil CH 4 uptake was negatively correlated with soil moisture (r = À0·59). The 2013 grazing season had about 41% greater precipitation than average and grazing significantly decreased CH 4 uptake 31 to 38% compared with non-grazed soils. The N 2 O emissions were 122 to 179% greater with heavy and very heavy grazing than none in the wet season, unaffected by grazing in the normal precipitation season and 72% lower with light grazing than none in the dry season. Predicting trace gas fluxes from grazed grassland soils across space and time is difficult because of interactions among weather conditions, edaphic properties and grazing intensity. However, long-term cattle grazing increased soil CO 2 fluxes, while the grazing effect on CH 4 uptake depended on precipitation and the soil N 2 O flux responded as a function of grazing intensity and precipitation.

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Section: Discussionsupporting

confidence: 92%

Section: Trace Gas Fluxes Over the Grazing Seasonmentioning

confidence: 99%

Long‐Term Grazing Alters Soil Trace Gas Fluxes from Grasslands in the Foothills of the Rocky Mountains, Canada

et al. 2017

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“…The Hulunber meadow steppe is one of the largest natural grasslands in the world and accounts for approximately 60% of the total temperate meadow steppe area in China [25,26]. The Hulunber grassland provides much high-quality forage for livestock and is a fundamental source of livelihood for local residents, in addition to providing various ecological services at regional and national scales.…”

Section: Introductionmentioning

confidence: 99%

“…The Hulunber grassland provides much high-quality forage for livestock and is a fundamental source of livelihood for local residents, in addition to providing various ecological services at regional and national scales. However, long-term, high-intensity grazing has resulted in serious degradation of the Hulunber grasslands [25]. In recent years, much effort has been directed to the restoration and sustainable management of the Hulunber grasslands, including fencing to exclude grazers, retiring of livestock, and returning farmlands to grasslands.…”

Section: Introductionmentioning

confidence: 99%

Grazing Affects the Ecological Stoichiometry of the Plant–Soil–Microbe System on the Hulunber Steppe, China

et al. 2019

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Grazing affects nutrient cycling processes in grasslands, but little is known by researchers about effects on the nutrient stoichiometry of plant–soil–microbe systems. In this study, the influence of grazing intensity (0, 0.23, 0.34, 0.46, 0.69, and 0.92 AU ha−1) on carbon (C), nitrogen (N) and phosphorus (P) and their stoichiometric ratios in plants, soil, and microbes was investigated in a Hulunber meadow steppe, Northeastern China. The C:N and C:P ratios of shoots decreased with grazing increased. Leaf N:P ratios <10 suggested that the plant communities under grazing were N-limited. Heavy grazing intensities increased the C:N and C:P ratios of microbial biomass, but grazing intensity had no significant effects on the stoichiometry of soil nutrients. The coupling relationship of C:N ratio in plant–soil–microbial systems was tightly significant compared to C:P ratio and N:P ratio according to the correlation results. The finding suggested grazing exacerbated the competition between plants and microorganisms for N and P nutrition by the stoichiometric changes (%) in each grazing level relative to the no grazing treatment. Therefore, for the sustainability of grasslands in Inner Mongolia, N inputs need to be increased and high grazing intensities reduced in meadow steppe ecosystems, and the grazing load should be controlled within G0.46.

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“…In the mowed grassland, the predicted LAI values were greater than 1 m 2 /m 2 on all six experimental dates. The five models exhibited a similar performance throughout the season but greater uncertainty was observed in the late-middle and end stages of the growing season (28 July 2014 and 10 August 2015), possibly resulting from an increasing amount of litter in the grassland [68,69]. After this time, grass cutting activity reduced the proportion of canopy litter, and the prediction uncertainty decreased on 26 August 2015.…”

Section: Model Performance In Different Grassland Types and At Differmentioning

confidence: 99%

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Wang

Tang

et al. 2016

Remote Sensing

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Leaf area index (LAI) is a key parameter used to describe vegetation structures and is widely used in ecosystem biophysical process and vegetation productivity models. Many algorithms have been developed for the estimation of LAI based on remote sensing images. Our goal was to produce accurate and timely predictions of grassland LAI for the meadow steppes of northern China. Here, we compare the predictive power of regression approaches and hybrid geostatistical methods using Chinese Huanjing (HJ) satellite charge coupled device (CCD) data. The regression methods evaluated include partial least squares regression (PLSR), artificial neural networks (ANNs) and random forests (RFs). The two hybrid geostatistical methods were regression kriging (RK) and random forests residuals kriging (RFRK). The predictions were validated for different grassland types and different growing stages, and their performances were also examined by adding several groups of vegetation indices (VIs). The two hybrid geostatistical models (RK and RFRK) yielded the most accurate predictions (root mean squared error (RMSE) = 0.21 m 2 /m 2 and 0.23 m 2 /m 2 for RK and RFRK, respectively), followed by the RF model (RMSE = 0.27 m 2 /m 2 ), which was the most accurate among the regression models. These three models also exhibited the best temporal performance across the duration of the growing season. The PLSR and ANN models were less accurate (RMSE = 0.33 m 2 /m 2 and 0.35 m 2 /m 2 for ANN and PLSR, respectively), and the PLSR model performed the worst (exhibiting varied temporal performance and unreliable prediction accuracy that was susceptible to ground conditions). By adding VIs to the predictor variables, the predictions of the PLSR and ANN models were obviously improved (RMSE improved from 0.35 m 2 /m 2 to 0.28 m 2 /m 2 for PLSR and from 0.33 m 2 /m 2 to 0.28 m 2 /m 2 for ANN); the RF and RFRK models did not generate more accurate predictions and the performance of the RK model declined (RMSE decreased from 0.21 m 2 /m 2 to 0.32 m 2 /m 2 ).

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Grazing intensity and driving factors affect soil nitrous oxide fluxes during the growing seasons in the Hulunber meadow steppe of China

Cited by 34 publications

References 37 publications

Long‐Term Grazing Alters Soil Trace Gas Fluxes from Grasslands in the Foothills of the Rocky Mountains, Canada

Long‐Term Grazing Alters Soil Trace Gas Fluxes from Grasslands in the Foothills of the Rocky Mountains, Canada

Grazing Affects the Ecological Stoichiometry of the Plant–Soil–Microbe System on the Hulunber Steppe, China

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

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