Simulation of the nutritional requirements and energy balance of adult cows in a northern temperate grassland.

The forage-livestock balance is an important component of natural grassland management, and realizing a balance between the nutrient energy demand of domestic animals and the energy supply of grasslands is the core challenge in forage-livestock management. This study was performed at the Xieertala Ranch in Hulunbuir City, Inner Mongolia. Using the GRAZPLAN and GrazFeed models, we examined the forage-livestock energy balance during different grazing periods and physiological stages of livestock growth under natural grazing conditions. Data on pasture conditions, climatic factors, supplemental feeding, and livestock characteristics, were used to analyze the metabolizable energy (ME), metabolizable energy for maintenance (MEm), and total metabolizable energy intake (MEItotal) of grazing livestock. The results showed that the energy balance between forage and animals differed for adult cows at different physiological stages. In the early lactation period, although the MEItotal was greater than MEm, it did not meet the requirement for ME. MEItotal was greater than ME during mid-lactation, but there was still an energy imbalance in the early and late lactation periods. In the late lactation period, MEItotal could meet ME requirements from April-September. Adult gestational lactating cows with or without calves were unable to meet their ME requirement, especially in the dry period, even though MEItotal was greater than MEm. Adult cows at different physiological stages exhibited differences in daily forage intake and rumen microbial crude protein (MCP) metabolism, and the forage intake by nonpregnant cows decreased as follows: early lactation > mid-lactation > late lactation, pregnant cows' lactation > dry period. For the degradation, digestion and synthesis of rumen MCP, early-lactation cows were similar to those in the mid-lactation group, but both were higher than those in the late-lactation group, while pregnant cows had greater degradation, digestion, and synthesis of MCP in the lactation period relative to the dry period. For lactating cows, especially those with calves, grazing energy requirements, methane emission metabolism and heat production were highest in August, with increased energy expenditure in winter. Overall, grazing energy, methane emissions and heat production by dry cows were low. In the context of global climate change and grassland degradation, managers must adopt different strategies according to the physiological stages of livestock to ensure a forage-livestock balance and the sustainable utilization and development of grasslands.

Anthropogenic and climate impacts on carbon stocks of grassland ecosystems in Inner Mongolia and adjacent region.

Up to date, most studies reported that degradation is worsened in the grassland ecosystems of Inner Mongolia and adjacent regions as a result of intensified grazing. This seems to be scientific when considering the total forage or total above-ground biomass as a degradation indicator, but it does not hold true in terms of soil organic carbon density (SOCD). In this study, we quantified the changes of grassland ecosystem carbon stock in Inner Mongolia and adjacent regions from the 1980s to 2000s and identified the major drivers influencing these variations, using the National Grassland Resource Inventory and Soil Survey Dataset in 1980s and the Inventory data during 2002 to 2009 covering 624 sampling plots concerned vegetal traits and edaphic properties across the study region. The result indicated that the above-, below-ground and total vegetation biomass declined from the 1980s to 2000s by ∼ 10 %. However, total forage production increased by 6.72 % when considering livestock intake. SOCD remained stable despite a 67 % increase in grazing intensity. A generalized linear model (GLIM) analysis suggested that an increase in grazing intensity from the 1980s to 2000s could only explain 1.04 % of the total biomass change, while changes in precipitation and temperature explained 17.7 % (p < 0.05) of total vegetation biomass (TVB) change. Meanwhile, SOCD change during 1980s - 2000s could be explained 10.08 % by the soil texture (p < 0.05) and <1.6 % by changes in climate and livestock. This implies that the impacts of climate change on grassland biomass are more significant than those of grazing utilization, and SOCD was resistant to both climate change and intensified grazing. Overall, intensified grazing did not result in significant negative impacts on the grassland carbon stocks in the study region during the 1980s and 2000s. The grassland ecosystems possess a mechanism to adjust their root-shoot ratio, enabling them to maintain resilience against grazing utilization.

Moderate grazing increased carbon, nitrogen and phosphorus storage in plants and soil in the Eurasian meadow steppe ecosystem.

The effects of grazing on the cycling of carbon (C), nitrogen (N) and phosphorus (P) in grassland ecosystems are complex. Uncertainty still exists as regards the allocation of C, N and P storage amounts in grazed ecosystems in Inner Mongolia, situated at the eastern end of the Eurasian dryland. Based on the long-term cattle grazing experimental platform in the Hulun Buir meadow steppe of Inner Mongolia, a 3-year (2019-2021) field control experiment was conducted to assess how the grazing intensity influenced the quantities of C, N and P stored in canopy biomass, root, litter and soil compartments. We examined the relationships between the different pools and their regulatory pathways at the ecosystem level across six grazing intensities. In general, grazing increased the aboveground N and P contents but decreased the aboveground biomass C content and nutrient storage amounts in aboveground biomass, roots and litter. The grazing intensity of 0.34 AU ha-1 increased soil organic carbon, total nitrogen and total phosphorus storage amounts, with the soil accounting for 98 % of total reserves on average. Grazing affected soil pH， nutrient contents, above- and belowground biomass and soil environmental factors such as soil bulk density, which in turn affected C, N and P storage in the ecosystem according to the results of the structural equation model; therefore, grazing intensity can be an important factor regulating the input and output of nutrients in the ecosystem. In the future, for adaptive management of grasslands, moderate grazing could effectively increase C, N and P storage in meadow steppe ecosystems and ensure the nutrient balance and long-term sustainable development.

Effects of different grassland utilization methods on the germinable soil seed bank of the Hulunbuir meadow steppe.

Seed banks are crucial regenerative resources for aboveground vegetation. The pattern of their changes holds immense significance in understanding alterations in the belowground seed bank. This understanding is pivotal for uncovering both short-term and long-term shifts in plant communities. Additionally, it contributes to the restoration of grassland ecosystems. To better protect grassland biodiversity and provide a theoretical basis for the restoration of degraded grasslands, in this study, the germination characteristics of soil seed banks in free-grazed, enclosed and mown areas were compared, and the results were combined with those of previous studies for a comprehensive analysis. The density of soil seed bank and perennial forage soil seed bank were significantly affected by different grassland utilization and soil depths. Grazing and enclosure grassland utilization methods increased the content of the soil seed bank, and mowing reduced the content of the seed bank. The soil seed bank density of perennial grasses accounted for the highest proportion under grazing, followed by mowing, and its lowest proportion was observed in the enclosures. Grazing not only facilitated the germination of the perennial grass seed bank but also substantially augmented its content. Mowing inhibited the germination of the upper growth grasses seed bank, which was particularly significant in the 0-2 cm soil layer under grazing. The content of the upper growth grasses seed bank affected the total seed bank to a certain extent, mainly in the 5-10 cm layer. The general correlations among the perennial grasses, upper growth grasses and soil germination seed bank resulted in 84.58% information extraction, and this information has practical significance for grassland ecological restoration.

Grazing Sheep Behaviour Recognition Based on Improved YOLOV5.

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.

Research on Six-Axis Sensor-Based Step-Counting Algorithm for Grazing Sheep.

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.

Grazing-induced cattle behaviour modulates the secondary production in a Eurasian steppe ecosystem.

Livestock-grassland interactions are among the most important relationships in grazed grassland ecosystems, where herbivores play a crucial role in plant community and ecosystem functions. However, previous studies primarily have focused on the responses of grasslands to grazing, with few focussing on the effects of livestock behaviour that in turn would influence livestock intake and primary and secondary productivity. Through a 2-year grazing intensity experiment with cattle in Eurasian steppe ecosystem, global positioning system (GPS) collars were used to monitor animal movements, where animal locations were recorded at 10-min intervals during the growing season. We used a random forest model and the K-means method to classify animal behaviour and quantified the spatiotemporal movements of the animals. Grazing intensity appeared to be the predominant driver for cattle behaviour. Foraging time, distance travelled, and utilization area ratio (UAR) all increased with grazing intensity. The distance travelled was positively correlated with foraging time, yielding a decreased daily liveweight gain (LWG) except at light grazing. Cattle UAR showed a seasonal pattern and reached the maximum value in August. In addition, the canopy height, above-ground biomass, carbon content, crude protein, and energy content of plants all affected cattle behaviour. Grazing intensity and the resulting change in above-ground biomass and forage quality jointly determined the spatiotemporal characteristics of livestock behaviour. Increased grazing intensity limited forage resources and promoted intraspecific competition of livestock, which induced longer travelling distance and foraging time, and more even spatial distribution when seeking habitat, which ultimately led to a reduction in LWG. In contrast, under light grazing where there were sufficient forage resources, livestock exhibited higher LWG with less foraging time, shorter travelling distance, and more specialized habitat occupation. These findings support the Optimal Foraging Theory and the Ideal Free Distribution model, which may have important implications for grassland ecosystem management and sustainability.

Spatial patterns and driving factors of aboveground and belowground biomass over the eastern Eurasian steppe.

The Eurasian steppe is the largest steppe region in the world and is an important part of the global grassland ecosystem. The eastern Eurasian steppe has favorable hydrothermal conditions and has the highest productivity and the richest biodiversity. Located in the arid and semi-arid region, the eastern Eurasian steppe has experienced large-scale grassland degradation due to dramatic climate change and intensive human activities during the past 20 years. Hence, accurate estimation of aboveground biomass (AGB, gC m-2) and belowground biomass (BGB, gC m-2) is necessary. In this study, plenty of AGB and BGB in-situ measurements were collected among dominated grassland types during summer in 2013 and 2016-2018 in the eastern Eurasian steppe. Vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS), Digital Elevation Model (DEM) and climate variables were chosen as independent variables to establish predictive models for AGB and BGB with random forest (RF). Both AGB (R2 = 0.47, MAE = 21.06 gC m-2, and RMSE = 27.52 gC m-2) and BGB (R2 = 0.44, MAE = 173.02 gC m-2, and RMSE = 244.20 gC m-2) models showed acceptable accuracy. Then the RF models were applied to generate spatially explicit AGB and BGB estimates for the study area over the last two decades (2000-2018). Both AGB and BGB showed higher values in the Greater Khingan Mountains and decreased gradually to the east and west sides. The mean values for AGB and BGB were 62.16 gC m-2 and 531.35 gC m-2, respectively. The climatic factors were much more important in controlling biomass than anthropogenic drivers, and shortage of water and raising temperature were the main limiting factor of AGB and BGB, respectively, in the peak growth season. These findings provide scientific data for the scientific management of animal husbandry and can contribute to the sustainable development of grassland ecology in the eastern Eurasian steppe.

Estimating Grassland Carbon Stocks in Hulunber China, Using Landsat8 Oli Imagery and Regression Kriging.

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 ⅹ 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.

Grazing affects snow accumulation and subsequent spring soil water by removing vegetation in a temperate grassland.

By altering plant and soil properties and microclimate environments, grazing has a profound influence on the structure and function of grassland ecosystems. However, few studies have addressed the potential grazing effects on snow accumulation and subsequent spring soil water after snow melting and soil thawing. In this study, vegetation properties, snow accumulation and soil water were measured in experimental plots subjected to 8 years of cattle grazing comprising six different grazing intensity treatments in a typical temperate grassland in eastern Eurasia. The results indicated that heavy grazing reduced the snow depth by 51% and the snow mass by 40%. Snow accumulation first rapidly increased but then remained relatively stable with increases in both the aboveground biomass and canopy height. An obvious inflection point occurred at approximately 200 g m-2 aboveground biomass and at a 12.5 cm canopy height. The obvious difference in soil water content between the heavily grazed and ungrazed treatments occurred mainly in the spring after snow melting and soil thawing. The spring soil water content (0-30 cm) reached 31.5% in the ungrazed treatment (G0), which was 1.7 times that in the heavily grazed treatment (G0.92). The soil water content increased exponentially with increasing vegetation properties (aboveground biomass, canopy height and canopy cover), and a similar trend occurred with increasing snow mass and snow depth. Our structural equation modeling showed that both vegetation properties and snow accumulation had significant positive effects on spring soil water. By removing vegetation, grazing at increased intensities had significant, indirect suppressive effects on snow accumulation and spring soil water. Therefore, to obtain increased amounts of snow accumulation and spring soil water, land managers should consider reducing the grazing intensity or leaving some plots ungrazed.

Grazing-induced microbiome alterations drive soil organic carbon turnover and productivity in meadow steppe.

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 ß-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.

MicroRNA-106a functions as an oncogene in human gastric cancer and contributes to proliferation and metastasis in vitro and in vivo.

Mounting evidences has shown that miRNAs are involved in the development and progression of gastric cancer acts as tumor suppressor genes or oncogenes. In our previous studies, we have found that the up-regulation of miR-106a occurs frequently in human gastric cancer tissues compared with that of normal tissues. Here, we investigate the role of the ectopic expressed miR-106a in the progression and metastasis of gastric cancer in vitro and in vivo. FFPE samples have the priority to be included and qRT-PCR was used to detect the miR-106a expression. Human gastric cancer cells and immortalized gastric epithelial cell were selected and the miR-106a mimic and inhibitor were transfected. Cell growth was determined by MTT method. The flow cytometric analysis for cell apoptosis and transwell assays for evaluating the cell migration and invasion were conducted. Luciferase assay and western blot confirmed the direct binding site of miR-106a and its target. BALB/c nude mice were randomly divided to explore the implantation of gastric cancer cells transfected with miR-106a antagomir. Abnormal over-expression of miR-106a significantly promoted gastric cancer cell proliferation, metastasis, inhibited the cell apoptosis. Functional experiment ascertained that miR-106a interacted with FAS and mediated caspase3 pathway. Knockdown of miR-106a leaded to the attenuation of gastric cancer implantation capacity in vivo. Moreover, expression of TIMP2 was inversely associated with miR-106a in nodule tissues. Apoptotic body was also seen under electron microscope accompanied by silencing of miR-106a. Together, this data indicated that miR-106a may act as an oncogene and contribute to gastric cancer development.

[Effect of grazing on the temperature sensitivity of soil respiration in Hulunber meadow steppe].

Grazing is one of the major human activities which lead to disturbance on grassland ecosystem. Quantifying the effect of grazing on the temperature sensitivity of soil respiration ( Q10 ) is essential for accurate assessment of carbon budget in grassland ecosystem. This study was conducted on the grazing gradients experiment platform in Hulunber meadow steppe. Soil respiration was measured by a dynamic closed chamber method (equipped with Li 6400-09, Lincoln, NE, USA) during the growing season in 2011. The results showed that soil respiration had significant seasonal variation and the maximum occurred in July, which was mainly dominated by temperature. The order of average soil respiration during the period from May to September in different treatments was G1 > GO > G2 > G3 > G4 > G5. Comparing with non-grazing treatment, Q10 under heavy grazing conditions (0. 92 Au hm-2) was reduced by about 10% , and was increased a little under light grazing conditions (0. 23 Au hm-2). There was a significant negative correlation between Q15 and grazing intensities (r = 0. 944, P <0. 05) . Grazing could decrease the temperature sensitivity of soil respiration to different degrees. The Q10 under different grazing gradients had positive linear regression relationships with aboveground biomass, belowground biomass, soil organic carbon and soil moisture. They could explain 71.0%-85.2% variations of Q10. It was suggested that the variation of Q10 was mainly determined by the change of biotic and environmental factors due to grazing.