Xu Wang

Impacts of Differing Grazing Rates on Canopy Structure and Species Composition in Hulunber Meadow Steppe

ABSTRACT In this study, the impacts of cattle grazing with differing grazing rates on species composition, canopy structural traits, standing crop of canopy biomass, and plant species diversity were examined in a meadow steppe of the Hulunber grasslands, Northeastern China. Six stocking-rate treatments (0, 0.23, 0.34, 0.46, 0.69, and 0.92 AU.ha-1) with three replicates were established, and observations were conducted from 2009 to 2011. Our findings demonstrate that short-term grazing substantially altered the species composition and relative dominance, standing crop of aboveground biomass, and canopy structural traits, whereas no significant changes in species diversity and evenness occurred in response to different-rated grazing in this meadow steppe, which has a long-term evolutionary grazing history and high-resources availabilities. We found that perennial graminoid significantly decreased, while forbs and annuals increased at the same time, with increasing grazing intensity and duration; canopy height and coverage decreased substantially with increasing stocking rates, whereas significant changes in plant density occurred only at heavy grazing in the second and third years; and significant negative linear relations were found between the standing crop of biomass and grazing intensity in each individual year or for 3 years on average. Significantly highest species richness and canopy dominance occurred only at the intermediate grazing rate in the third year, and intermediate grazing intensity also maintained a highly constant standing crop of canopy biomass in the 3 years, all being in accordance with the intermediate disturbance hypothesis. Our findings imply that monitoring changes in species composition, canopy traits, and standing crop of biomass in grassland communities can provide important references for assessing current grazing management scenarios and conducting timely adaptive practices to maintain the long-term ability of grassland systems to perform their ecological functions.

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

In this study, the effects of cattle grazing intensity on soil nitrous oxide (N2O) fluxes were examined in the Hulunber meadow steppe of north-eastern China. Six stocking-rate treatments (0, 0.23, 0.34, 0.46, 0.69, and 0.92 AU ha−1) with three replicates were established, and observations were conducted from 2010 to 2014. Our results showed that substantial temporal fluctuations in N2O flux occurred amongst the different grazing intensities, with peak N2O fluxes after natural rainfall. Grazing had a long-term effect on the soil N2O flux in the grasslands. After 4–5 years of grazing, the N2O fluxes under increased levels of grazing intensity began to decrease significantly by 31.4%–60.2% in 2013 and 32.5%–50.5% in 2014 compared to the non-grazing treatment. We observed a significant negative linear relationship between the soil N2O fluxes and grazing intensity for the five-year mean. The soil N2O flux was significantly affected each year in all of the treatments. Over the five years, the temporal coefficient of variation (CVs) of the soil N2O flux generally declined significantly with increasing grazing intensity. The soil N2O emission rate was significantly positively correlated with soil moisture (SM), soil available phosphorus (SAP), soil soil above-ground biomass (AGB), plant ground cover and height and was negatively correlated with total soil nitrogen (TN). Stepwise regressions showed that the N2O flux was primarily explained by SM, plant height, TN, soil pH, and soil Using structural equation modelling, we show that grazing significantly directly influenced the plant community and the soil environment, which then influenced the soil N2O fluxes. Our findings provide an important reference for better understanding of the mechanisms and identifying the pathways of grazing effects on soil N2O emission rates, and the key drivers plant community and soil environment within the nitrogen cycle that are mostly likely to affect N2O emissions in the Inner Mongolian meadow steppes.

The implications of serial correlation and time-lag effects for the impact study of climate change on vegetation dynamics – a case study with Hulunber meadow steppe, Inner Mongolia

Ecological time series data are widely used in ecological research thanks to the development of remote-sensing technologies and fixed ecological research stations. However, the serial correlation issue with time series, which violates the fundamental assumption of independence for traditional statistical models or analysis, is rarely considered by ecologists in vegetation–climate relationship research. In addition, the issue of time lags between climate change and vegetation response is also often ignored. Inadequate consideration of these issues produces misleading results in some cases. In this article, we propose an approach based on the Autoregressive Integrated Moving Average (ARIMA) model and the nonparametric test to address serial correlation issue and distribution requirements for the valid statistical analysis of time series data. With Hulunber meadow steppe as a case, we applied this approach to analyse the role of climate factors in vegetation dynamics based on leaf area index (LAI) data and climatic data. The results showed that the LAI dynamics of Hulunber meadow steppe were mainly related to temperature with the time lag of zero, whereas the impact of precipitation on LAI dynamics was not statistically obvious. The comparison of regression models that deal with serial correlation and residual normality to different extents showed that ignoring the serial correlation issue with time series data likely produces misleading results, highlighting the importance of serial correlation removal. The combination of nonparametric correlation tests with ARIMA-based cross-correlation analysis also proved quite useful in reducing the chance of spurious correlation and time lags resulting from outlier values in ARIMA-based cross-correlation.

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

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 m2/m2 and 0.23 m2/m2 for RK and RFRK, respectively), followed by the RF model (RMSE = 0.27 m2/m2), 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 m2/m2 and 0.35 m2/m2 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 m2/m2 to 0.28 m2/m2 for PLSR and from 0.33 m2/m2 to 0.28 m2/m2 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 m2/m2 to 0.32 m2/m2).

Current Situation and Prospect of Grassland Management Decision Support Systems in China

China, with wide grassland areas of the second rank throughout the world, is faced with a severe challenge on how to manage its vast and degenerating/degenerated grassland. Computer and network technologies are more and more widely applied in grassland production, research and education, which is just a greatly encouraging field. Tremendous achievements have been made in grassland management decision support system (GMDSS) research in developed countries at present, but there is still a long way to obtain a great development for developing countries, such as China. This paper reviewed the research progress and current situation in the GMDSS research and application in China. Concept models and empirical models are still hugely focused on the corresponding research fields in China, but the integrated GMDSS has not been well developed. Therefore, Chinese scientists must develop the integrated models from the existing models, and accordingly sinicize the GMDSS of models used in the developed countries for availably application. In the other hand, there is a same direction of research and development about the GMDSS not only for developed countries but also for China, which is going to be combined with internet, 3S (GIS, RS and GPS) and virtual technology.

Vegetation patches increase wind-blown litter accumulation in a semi-arid steppe of northern China

Litter decomposition is an important source of soil organic matter and nutrients; however, few studies have explored how vegetation patches affect wind-driven litter mobility and accumulation. In this study, we aimed to test the following hypotheses: (1) vegetation patches can reduce litter removal and facilitate litter accumulation, (2) litter mobility results in the heterogeneous redistribution of carbon and nutrients over the land surface, and (3) litter removal rates differ among different litter types (e.g., leaf and stem). Four vegetation patch types and six litter types were used to investigate the impacts of vegetation patches on litter mobility and accumulation. The results show that compared with almost bare ground patches, patches with vegetation cover had significantly higher litter accumulation, with the shrub patch type having the highest accumulation amount. The rate of litter removal due to wind was highest for the almost bare surface type (P4) and lowest for the shrub patch (P1) and Stipa grandis community (P2) types. There were significant differences in the removal rate among the different litter types. These findings indicate that wind-based litter redistribution among bare, S. grandis-dominated, and shrub-dominated patches is at least partially responsible for increasing the spatial heterogeneity of resources on a landscape scale.

Mapping daily leaf area index at 30 m resolution over a meadow steppe area by fusing Landsat, Sentinel-2A and MODIS data

ABSTRACT The leaf area index (LAI) is a key vegetation canopy structure parameter and is closely associated with vegetation photosynthesis, transpiration, and energy balance. Developing a landscape-scale LAI dataset with a high temporal resolution (daily) is essential for capturing rapidly changing vegetation structure at field scales and supporting regional biophysical modeling efforts. In this study, two daily 30 m LAI time series from 2014 to 2016 over a meadow steppe site in northern China were generated using a spatial and temporal adaptive reflectance fusion model (STARFM) combined with an LAI retrieval radiative transfer model (PROSAIL). Gap-filled Landsat 7, Landsat 8 and Sentinel-2A surface reflectance (SR) images were used to generate fine-resolution LAI maps with the PROSAIL look-up table method. Two daily 500 m moderate-resolution imaging spectroradiometer (MODIS) LAI product-the existing MCD15A3H LAI product and one was generated from the MCD43A4 SR product and the PROSAIL model, were used to provide temporally continuous LAI variations. The STARFM model was then used to fuse the fine-resolution LAI maps with the two 500 m LAI products separately to generate two daily 30 m LAI time series. Both results were assessed for three types of pasture (mowed pasture, grazing pasture, and fenced pasture) using ground measurements from 2014–2015. The results showed that the PROSAIL-generated LAI maps all exhibited a high accuracy, and the root mean squared errors (RMSEs) for the Landsat 7 LAI and Landsat 8 LAI compared to the ground-measured LAI were 0.33 and 0.28 respectively. The Landsat LAI maps also showed good agreement and similar spatial patterns with the Sentinel-2A LAI with mean differences between ± 0.5. The MCD43A4_PROSPECT LAI product exhibited similar seasonal variability to the ground measurements and to the Landsat and Sentinel-2A LAIs, and these data are also smoother and contain fewer noisy points than the gap-filled MCD15A3H LAI product. Compared to the ground measurements, the daily 30 m LAI time series fused from the fine-resolution LAI maps and PROSPECT generated MODIS LAI product demonstrated better performance with an RMSE of 0.44 and a mean absolute error (MAE) of 0.34, which is an improvement from the LAI time series fused from the fine-resolution LAI maps and the existing MCD15A3H LAI product (RMSE of 0.56 and MAE of 0.42). The latter dataset also exhibited abnormal temporal fluctuations, which may have been caused by the interpolation method. The results also demonstrated the very good performance of the STARFM model in grazing and mowed pasture with homogeneous surfaces compared to fenced pasture with smaller patch sizes. The Sentinel-2A data offers increased landscape vegetation observation frequency and provides temporal information about canopy changes that occur between Landsat overpass dates. The scheme developed in this study can be used as a reference for regional vegetation dynamic studies and can be applied to larger areas to improve grassland modeling efforts.

Response of ecosystem CO 2 fluxes to grazing intensities – a five-year experiment in the Hulunber meadow steppe of China

Grazing is the primary land use in the Hulunber meadow steppe. However, the quantitative effects of grazing on ecosystem carbon dioxide (CO2) fluxes in this zone remain unclear. A controlled experiment was conducted from 2010 to 2014 to study the effects of six stocking rates on CO2 flux, and the results showed that there were significant differences in CO2 fluxes by year, treatment, and month. The effects of light and intermediate grazing remained relatively constant with grazing year, whereas the effects of heavy grazing increased substantially with grazing duration. CO2 flux significantly decreased with increasing grazing intensity and duration, and it was significantly positively correlated with rainfall, soil moisture (SM), the carbon to nitrogen ratio (C/N ratio), soil available phosphorus (SAP), soil NH4+-N, soil NO3−N, aboveground biomass (AGB), coverage, height, and litter and negatively correlated with air temperature, total soil N (TN) and microbial biomass N (MBN). A correspondence analysis showed that the main factors influencing changes in CO2 emissions under grazing were AGB, height, coverage, SM, NH4+-N and NO3−N. Increased rainfall and reduced grazing resulted in greater CO2 emissions. Our study provides important information to improve our understanding of the role of livestock grazing in GHG emissions.