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Background Grazing is a major modulator of biodiversity and productivity in grasslands. However, our understanding of grazing-induced changes in below-ground communities, processes, and soil productivity is limited. Here, using a long-term enclosed grazing meadow steppe, we investigated the impacts of grazing on the soil organic carbon (SOC) turnover, the microbial community composition, resistance and activity under seasonal changes, and the microbial contributions to soil productivity. Results The results demonstrated that grazing had significant impacts on soil microbial communities and ecosystem functions in meadow steppe. The highest microbial α-diversity was observed under light grazing intensity, while the highest β-diversity was observed under moderate grazing intensity. Grazing shifted the microbial composition from fungi dominated to bacteria dominated and from slow growing to fast growing, thereby resulting in a shift from fungi-dominated food webs primarily utilizing recalcitrant SOC to bacteria-dominated food webs mainly utilizing labile SOC. Moreover, the higher fungal recalcitrant-SOC-decomposing activities and bacterial labile-SOC-decomposing activities were observed in fungi- and bacteria-dominated communities, respectively. Notably, the robustness of bacterial community and the stability of bacterial activity were associated with α-diversity, while this was not the case for the robustness of fungal community and its associated activities. Finally, we observed that microbial α-diversity rather than SOC turnover rate can predict soil productivity. Conclusions Our findings indicate the strong influence of grazing on soil microbial community, SOC turnover, and soil productivity and the important positive role of soil microbial α-diversity in steering the functions of meadow steppe ecosystems.

In the face of a series of challenges, such as climate change, population growth, and agricultural intensification, as well as the issue of how to promote sustainable development and guarantee food security, biodiversity, with its unique genetic, ecological, and traditional socio-cultural values, has become an important way to solve this dilemma. Urban biodiversity has continued to decline in recent decades due to rapid urbanization. The agroecosystem health of the Beijing-Tianjin-Hebei region, a typical urban agglomeration economic area, is facing a critical situation. Therefore, assessing the potential of ecosystem diversity in the Beijing-Tianjin-Hebei region and exploring the assessment mechanisms and methods of ecosystem health can provide theoretical support for biodiversity conservation and utilization. In this thesis, the overall ecosystem health of the Beijing-Tianjin-Hebei region was assessed based on the land cover data from 1992 to 2022 and the projected land cover data up to 2032, as well as using the habitat quality indicated by the Fragstats and InVEST models and the landscape pattern index, habitat quality, and mean species abundance (MSA) indicators of the GLOBIO module. The main results are as follows: Habitat quality and mean species abundance (MSA) in the Beijing-Tianjin-Hebei region were observed to show a continuous downward trend over 40 years from a landscape level perspective, and landscape fragmentation due to urbanization was the main reason. Habitat loss and habitat degradation caused by landscape fragmentation led to a decline in biodiversity. The spatial distribution of habitat quality in the Beijing-Tianjin-Hebei region is closely correlated with topography and landscape, being higher in the northwest and lower in the southeast, forming a clear spatial pattern that declined from 0.599 to 0.564 between 1992 and 2032. The mean species richness (MSA) value of the Beijing-Tianjin-Hebei region is significantly affected by infrastructure, especially road construction. With the continuous expansion of the road network, the MSA values in the region generally show a decreasing trend from 0.270 to 0.183 between 1992 and 2032. Based on the above results, it is recommended to carry out several aspects of agrobiodiversity conservation and ecosystem restoration.

Background and aims Understanding the effects of livestock grazing on the ecosystem biomass and soil nitrogen processes of grassland ecosystems is critical to improving knowledge on the mechanisms underlying grassland degradation and accurately assessing the influence of grazing management on grassland functions. Methods We examined the interannual patterns of ecosystem biomass and soil nitrogen mineralization in response to cattle grazing in a Chinese meadow steppe. The soil core incubation method was employed for soil N transfer estimation, whilst the fumigation extraction method, a modified Baermann funnel method and harvest method were used for various measurements of ecosystem biomass parameters. Results We found that cattle grazing caused consistent significant increases in soil temperature, irrespective of the stocking rate and year, whereas significant effects on soil moisture and edaphic properties were observed only in individual years and/or at specific stocking rates. Consistent positive effects at moderate stocking rates were observed for aboveground net primary production and soil nematode biomass in all study years. The across stocking rate pattern of N mineralization in response to cattle stocking appeared to be year-specific, although negative effects were found in most cases. In contrast, the interannual pattern of N mineralization was determined principally by the interannual patterns in precipitation and soil moisture and was much less affected by cattle grazing. Conclusions Soil N mineralization in this meadow steppe was affected by cattle grazing via two major mechanisms, i.e., its effects on the aboveground net primary production (ANPP) and thus the quantity of plant litter input into the soil and its effects on soil temperature and moisture. Overall, our study spanned the longest consecutive years with the broadest range of stocking rates thus far of its kind, which revealed for the first time that the soil nitrogen mineralization pattern with respect to stocking rate was year-specific. Our findings have important implications for adaptive management and sustainable utilization of Chinese grasslands.

Fundamental sheep behaviours, for instance, walking, standing, and lying, can be closely associated with their physiological health. However, monitoring sheep in grazing land is complex as limited range, varied weather, and diverse outdoor lighting conditions, with the need to accurately recognise sheep behaviour in free range situations, are critical problems that must be addressed. This study proposes an enhanced sheep behaviour recognition algorithm based on the You Only Look Once Version 5 (YOLOV5) model. The algorithm investigates the effect of different shooting methodologies on sheep behaviour recognition and the model’s generalisation ability under different environmental conditions and, at the same time, provides an overview of the design for the real-time recognition system. The initial stage of the research involves the construction of sheep behaviour datasets using two shooting methods. Subsequently, the YOLOV5 model was executed, resulting in better performance on the corresponding datasets, with an average accuracy of over 90% for the three classifications. Next, cross-validation was employed to verify the model’s generalisation ability, and the results indicated the handheld camera-trained model had better generalisation ability. Furthermore, the enhanced YOLOV5 model with the addition of an attention mechanism module before feature extraction results displayed a mAP@0.5 of 91.8% which represented an increase of 1.7%. Lastly, a cloud-based structure was proposed with the Real-Time Messaging Protocol (RTMP) to push the video stream for real-time behaviour recognition to apply the model in a practical situation. Conclusively, this study proposes an improved YOLOV5 algorithm for sheep behaviour recognition in pasture scenarios. The model can effectively detect sheep’s daily behaviour for precision livestock management, promoting modern husbandry development.

Aims Grazing is a key driver of plant communities and soil functions in grassland ecosystems. Soil nematodes play a vital role in soil ecological functions. The aim of this study was to explore how grazing shapes soil nematode community in different soil layers. Methods We investigated the composition, abundance, diversity, metabolic footprint, and food web metrics of soil nematodes over a gradient of grazing in the 0–10 cm and 10–20 cm soil layers in a meadow steppe. The relationships between nematode community structure and biotic and abiotic factors were analyzed by principal component analysis and structural equation model analysis. Results Light grazing increased the abundance of total soil nematodes by 18.5%. Intensive grazing decreased the carbon used in production and metabolic footprints of plant parasites, fungivores, and total soil nematodes in 0–10 cm soils. There was no difference in the carbon used in production and metabolic footprints of soil nematodes among different grazing intensities in the 10–20 cm soil layer. Soil moisture, aboveground biomass, belowground biomass and Shannon diversity of grass contributed more to changes in soil nematode composition in both soil layers. In the 0–10 cm soil layer, grazing directly and indirectly affected soil nematode diversity via soil moisture and aboveground biomass, while grazing directly affected soil nematode diversity in 10–20 cm soil layer. Conclusions Our results indicate that increasing soil depth can weaken the effect of grazing intensities on soil nematode fauna. Grazing affected the soil nematode community structure via different paths in different soil layers.

Aims Livestock grazing is one of the most common utilization methods and exerts a significant effect on the carbon allocations between the above- and belowground components of a grassland ecosystem. The major aim of this study were to evaluate the proportions of 13 C allocation to various C pools of the plant-soil system of a meadow steppe ecosystem in response to changes of stocking rate. Methods In situ stable  13 C isotope pulse labeling was conducted in a long-term grazing experiment with 4 stocking rate. Plant materials and soil samples were taken at eight occasions (0, 3, 10, 18, 31, 56 and 100 days after labeling) to analyze the decline in 13 C over time, and their composition signature of 13 C were analyzed by the isotope ratio mass spectrometer technique. Results We found a significantly greater decline in assimilated 13 C of shoot and living root for the heavily grazed swards compared to other stocking rates, with the highest relocation rate of 13 C into soil C pool compared to other fractions. In addition, light grazing significantly allocated 13 C assimilates in the belowground pool compared to other stocking rates, especially in the live root and topsoil C-pools. Conclusions In this study, the effects of grazing on the carbon transfers and stocks within the plant-soil system of the meadow steppe were highly grazing pressure dependent. Plant-soil system in light stocking rate presented the highest C utilization efficiency, however, plants allocated more C to soil C pools with heavily stocking rate.

Accurately estimating grassland carbon stocks is important in assessing grassland productivity and the global carbon balance. This study used the regression kriging (RK) method to estimate grassland carbon stocks in Northeast China based on Landsat8 operational land imager (OLI) images and five remote sensing variables. The normalized difference vegetation index (NDVI), the wide dynamic range vegetation index (WDRVI), the chlorophyll index (CI), Band6 and Band7 were used to build the RK models separately and to explore their capabilities for modeling spatial distributions of grassland carbon stocks. To explore the different model performances for typical grassland and meadow grassland, the models were validated separately using the typical steppe, meadow steppe or all-steppe ground measurements based on leave-one-out crossvalidation (LOOCV). When the results were validated against typical steppe samples, the Band6 model showed the best performance (coefficient of determination (R2) = 0.46, mean average error (MAE) = 8.47%, and root mean square error (RMSE) = 10.34 gC/m2) via the linear regression (LR) method, while for the RK method, the NDVI model showed the best performance (R2 = 0.63, MAE = 7.04 gC/m2, and RMSE = 8.51 gC/m2), which were much higher than the values of the best LR model. When the results were validated against the meadow steppe samples, the CI model achieved the best estimation accuracy, and the accuracy of the RK method (R2 = 0.72, MAE = 8.09 gC/m2, and RMSE = 9.89 gC/m2) was higher than that of the LR method (R2 = 0.70, MAE = 8.99 gC/m2, and RMSE = 10.69 gC/m2). Upon combining the results of the most accurate models of the typical steppe and meadow steppe, the RK method reaches the highest model accuracy of R2 = 0.69, MAE = 7.40 gC/m2, and RMSE = 9.01 gC/m2, while the LR method reaches the highest model accuracy of R2 = 0.53, MAE = 9.20 gC/m2, and RMSE = 11.10 gC/m2. The results showed an improved performance of the RK method compared to the LR method, and the improvement in the accuracy of the model is mainly attributed to the enhancement of the estimation accuracy of the typical steppe. In the study region, the carbon stocks showed an increasing trend from west to east, the total amount of grassland carbon stock was 79.77 × 104 Mg C, and the mean carbon stock density was 47.44 gC/m2. The density decreased in the order of temperate meadow steppe, lowland meadow steppe, temperate typical steppe, and sandy steppe. The methodology proposed in this study is particularly beneficial for carbon stock estimates at the regional scale, especially for countries such as China with many grassland types.

Step counting is an effective method to assess the activity level of grazing sheep. However, existing step-counting algorithms have limited adaptability to sheep walking patterns and fail to eliminate false step counts caused by abnormal behaviors. Therefore, this study proposed a step-counting algorithm based on behavior classification designed explicitly for grazing sheep. The algorithm utilized regional peak detection and peak-to-valley difference detection to identify running and leg-shaking behaviors in sheep. It distinguished leg shaking from brisk walking behaviors through variance feature analysis. Based on the recognition results, different step-counting strategies were employed. When running behavior was detected, the algorithm divided the sampling window by the baseline step frequency and multiplied it by a scaling factor to accurately calculate the number of steps for running. No step counting was performed for leg-shaking behavior. For other behaviors, such as slow and brisk walking, a window peak detection algorithm was used for step counting. Experimental results demonstrate a significant improvement in the accuracy of the proposed algorithm compared to the peak detection-based method. In addition, the experimental results demonstrated that the average calculation error of the proposed algorithm in this study was 6.244%, while the average error of the peak detection-based step-counting algorithm was 17.556%. This indicates a significant improvement in the accuracy of the proposed algorithm compared to the peak detection method.

Many studies reported the influence of wind erosion on soil degradation and the effect of vegetation coverage on preventing wind erosion. However, fewer studies have quantitatively measured the grassland soil particle size fractions and nutrients' loss caused by wind erosion under different vegetation coverage. Aims: We conducted a field experiments to (1) to explore the effect of vegetation coverage on soil wind erosion; (2) examine quantitatively the effects of wind erosion on soil texture, and determine the most erodible particles fraction of soil; (3) to examine quantitatively the soil carbon, nutrients such as nitrogen and phosphorus loss caused by wind erosion under different vegetation coverage. Methods: Six vegetation coverage treatments (0 %, 15 %, 35 %, 55 %, 75 % and 95 %) were constructed. To be able to monitor wind erosion status under more diverse weather conditions, three consecutive repeat experiments under different weather condition were conducted. Results: The results show that all the residue soil samples after wind erosion became coarser than that of original soil samples. The degree of change for the soil particle size distribution before and after wind erosion gradually increased with the less of vegetation coverage. The critical particle size for distinguishing the original soil sample and the residue soil after wind erosion occurred in the range of 125 µm and 210 µm depending on the vegetation cover. The fractions below or above the critical particle size are either easy to deplete or favoured by wind erosion, respectively. The most reduction occurs between 50 and 90 µm depending on the different weather condition and vegetation coverage. Due to the disproportionately greater amounts of nutrients in the fine soil particles, the preferential depletion of fine particles directly lead to a preferentially significant depletion of organic carbon and nutrients. The organic carbon and nutrient contents in the residue soil after erosion decreased significantly compared to that in the original soil. The soil nutrient loss ratio decrease significantly with the increase of vegetation coverage. Conclusions: Wind erosion is an important factor to affect the evolution of soil texture and soil nutrient. Vegetation coverage has a major impact on both preventing wind erosion and decreasing loss ratio of fine particles and nutrients. If we want to effectively protect the fine particles and nutrients, the vegetation cover should be maintained at least above 35 %.

Wind erosion poses a significant challenge to agricultural sustainability in Northern China’s arid regions. This study investigated the effectiveness of alfalfa grassland versus conventional cropland in controlling wind erosion across nine study sites in three agroecological regions. Using Sentinel-2 satellite imagery and the Revised Wind Erosion Equation (RWEQ) model, we analyzed vegetation cover duration and quantified soil wind erosion from 2018 to 2020. The results showed that alfalfa grassland extended vegetation cover by 80 days annually compared to cropland, with most extension occurring in spring. Alfalfa grassland demonstrated superior erosion control, reducing soil losses by 50% (24.02 versus 50.70 t/ha/yr) and increasing soil retention threefold (1.52 versus 0.59 t/ha/yr) compared to cropland. The northwest region experienced the highest erosion rates, while management practices significantly influenced alfalfa’s soil conservation effectiveness. Multiple regression analysis revealed vegetation cover and annual precipitation as primary factors affecting wind erosion. These findings suggest integrating alfalfa into crop rotations could effectively enhance soil conservation in Northern China’s wind erosion-prone regions.