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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.

IntroductionGrass-livestock interaction is of great value in maintaining ecological balance, regional economic development, and the sustainability of grassland husbandry, which has gained attention in recent years in both academia and practice, however, study on grass-livestock interaction possesses cognitive bias and gaps.MethodsThis study clarified the latest research progress and development trend in the grass-livestock interaction field by illuminating publication trend, cooperative network, keyword co-occurrence relationship, keyword clustering during 2000 to 2022 based on the Web of Science core database.ResultsThe results indicated that the annual number of publications in the grass-livestock interaction field showed a globally increasing with interdisciplinary and international collaboration, and the United States of America ranked firstly, followed by Brazil, England, Australia, China. Agricultural Research Service, the U.S. Department of Agricultures was the organization with the highest number of publications, and Rangeland Ecology & Management, Agriculture, Ecosystems & Environment, Grass and Forage Science were the top three journals. Furthermore, in terms of the temporal evolution, the research targeting the grass-livestock interaction mainly went through three stages: initial exploration period (2000–2011), to the period of rapid development (2012–2016) to transitional development period (2017–2022), and the notable existing keywords was Management, Livestock, Cattle, Vegetation, Growth, Soil. In terms of the structural composition, four broad divisions including mechanism of human activities, grassland resource monitoring and management, grassland livestock competition/adaptability, grassland land/soil research were identified. The results provide positive and objective insights into that grassland sustainability and grazing control through strengthening cross-disciplinary and cross-regional cooperation, and applying of standard methods based on evaluation system in the grass-livestock interaction field.DiscussionThe overall contribution of the work is provision of novel insight into the intellectual structure and temporal progression of grass-livestock interaction research at a macroscopic scale and the directions in which the field is headed currently.

Abstract Mapping grazing intensity (GI) using satellites is crucial for developing adaptive utilization strategies according to grassland conditions. Here we developed a monitoring framework based on a paired sampling strategy and the classification probability of random forest algorithm to produce annual grazing probability (GP) and GI maps at 10-m spatial resolution from 2015 to 2021 for the largest temperate meadow in China (Hulun Buir grasslands), by harmonized Landsat 7/8 and Sentinel-2 images. The GP maps used values of 0–1 to present detailed grazing gradient information. To match widely used grazing gradients, annual GI maps with ungrazed, moderately grazed, and heavily grazed levels were generated from the GP dataset with a decision tree. The GI maps for 2015–2021 had an overall accuracy of more than 0.97 having significant correlations with the statistical data at city (r = 0.51) and county (r = 0.75) scales. They also effectively captured the GI gradients at site scale (r = 0.94). Our study proposed a monitoring approach and presented annual 10-m grazing information maps for sustainable grassland management.

The paddy field is a major source of gaseous carbon and nitrogen emissions, and reducing these emissions is of great significance for mitigating greenhouse effects and non-point source pollution in farmland. Biochar, derived from agricultural waste, possesses a stable structure, large specific surface area, abundant pore structures, and surface functional groups. These characteristics could enhance soil physicochemical properties and microbial activity, thereby facilitating the dual goals of increasing crop yield and reducing emissions. Based on numerous studies, this review summarizes the effects of biochar on the emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ammonia volatilization (NH3), as well as on global warming potential (GWP) and greenhouse gas emission intensity (GHGI). It elucidates the mechanism of emission reduction by biochar amendment from the perspective of carbon and nitrogen conversion processes and soil physicochemical and biological properties. Numerous studies showed the application of 5~40 t ha−1 biochar reduced CO2, CH4, N2O, and NH3 emissions by 1.64~89.6%, 8.6~89.6%, 10~90%, and 12.27~53%, respectively. A small number of studies found that the application of 5~48 t ha−1 biochar increased CO2, CH4, N2O, and NH3 emissions by 12~37%, 19.85~176%, 13~84.23%, and 5.47~70.9%, respectively. Most scholars have found that biochar has varying degrees of emission reduction capabilities in different parts of the world. Therefore, future research directions have been suggested for utilizing biochar to reduce carbon and nitrogen emissions in paddy fields.

ABSTRACT This study aimed to investigate the effects of Lactiplantibacillus plantarum (L. plantarum) or Lentilactobacillus buchneri (L. buchneri) on the fermentation characteristics and microbial community of oat silages in response to additives. The oat was harvested at the milk stage of maturity and was chopped into 30 mm in size. Then, the oat was treated with distilled water (control, CON treatment), L. buchneri, and L. plantarum; the addition of L. buchneri and L. plantarum was in 1 × 106 colony-forming units/g of fresh matter and stored at room temperature (25°C). Results showed that the addition of L. plantarum inoculations could increase the lactic acid concentrations of oat silages compared with the control, and the addition of L. buchneri could increase the acetic acid concentrations, whereas the addition of L. plantarum could decrease the fiber contents and increase the crude protein content. The Shannon index of bacterial community was markedly (P < 0.05) lower in the L. buchneri- and L. plantarum-treated oat silages, and the Shannon index of fungal community was significantly (P < 0.05) higher in L. buchneri- and L. plantarum-treated oat silages compared with the CON treatment for 7 and 10 days of ensiling. From 7 to 90 days of ensiling, Lactobacillus was the dominant genus during the whole fermentation process in the three treatments. The homofermentative L. plantarum regulated the fermentation quality and microbial community by enhancing the Emden–Meyerhoff pathway, phosphoketolase pathway, and pentosephosphate pathway, and the heterofermentative L. buchneri modulated the ensiling performance and microbial community via improving the pentosephosphate pathway. These results suggested that the addition of lactic acid bacteria could improve the ensiling performance by regulating the microbial community in oat silage, and L. plantarum was more beneficial than L. buchneri for enhancing the fermentation quality. IMPORTANCE Ensiled whole-plant oats are an important feedstuff for ruminants in large parts of the world. Oat silage is rich in dietary fibers, minerals, vitamins, and phytochemicals beneficial to animal health. The fermentation of oat silage is a complex biochemical process that includes interactions between various microorganisms. The activity of many microbes in silage may cause an extensive breakdown of nutrition and lead to undesirable fermentation. Moreover, it is difficult to make high-quality oat silage because the number of epiphytic lactic acid bacterium microflora was lower than the requirement. Understanding the complex microbial community during the fermentation process and its relationship with community functions is therefore important in the context of developing improved fermentation biotechnology systems. These results suggested that the addition of Lactobacillus plantarum or Lactobacillus buchneri regulated the ensiling performance and microbial community in oat silage by shaping the metabolic pathways.

Remote sensing images (RSIs) are widely used in various fields due to their versatility, accuracy, and capacity for earth observation. Direct application of RSIs to harvest optimal results is generally difficult, especially for weak information features in the images. Thus, extracting the weak information in RSIs is reasonable to promote further applications. However, the current techniques for weak information extraction mainly focus on spectral features in hyperspectral images (HSIs), and a universal weak information extraction technology for RSI is lacking. Therefore, this study focused on mining the weak information from RSIs and proposed the deep multi-order spatial–spectral residual feature extractor (DMSRE). The DMSRE considers the global information and three-dimensional cube structures by combining low-rank representation, high-order residual quantization, and multi-granularity spectral segmentation theories. This extractor obtains spatial–spectral features from two derived sequences (deep spatial–spectral residual feature (DMSR) and deep spatial–spectral coding feature (DMSC)), and three RSI datasets (i.e., Chikusei, ZY1-02D, and Pasture datasets) were employed to validate the DMSRE method. Comparative results of the weak information extraction-based classifications (including DMSR and DMSC) and the raw image-based classifications showed the following: (i) the DMSRs can improve the classification accuracy of individual classes in fine classification applications (e.g., Asphalt class in the Chikusei dataset, from 89.12% to 95.99%); (ii) the DMSC improved the overall accuracy in rough classification applications (from 92.07% to 92.78%); and (iii) the DMSC improved the overall accuracy in RGB classification applications (from 63.25% to 63.6%), whereas DMSR improved the classification accuracy of individual classes on the RGB image (e.g., Plantain classes in the Pasture dataset, from 32.49% to 39.86%). This study demonstrates the practicality and capability of the DMSRE method to promote target recognition on RSIs and presents an alternative technique for weak information mining on RSIs, indicating the potential to extend weak information-based applications of RSIs.

A non-destructive, convenient, and low-cost yield estimation at the field scale is vital for precision farming. Significant progress has been made in using UAV-based canopy features to predict crop yield during the mid-growth stages. However, there has been limited effort to explore yield estimation specifically after crop maturity. Researching the effectiveness of artificial intelligence for estimating wheat yield utilizing phenotypic features extracted from UAV images, this study applied a deep learning algorithm (Mask R-CNN) to extract three wheat ear phenotypic features at ripening stage, including ear count, ear size, and ear anomaly index. Subsequently, machine learning algorithms (i.e., multiple linear regression, support vector regression, and random forest regression) driven by ear features were intercompared to obtain the optimal grain yield estimation. Based on the findings, (1) field observed ear count which was linearly associated with grain yield (R2 = 0.93), can be largely detected by UAV images (81 %); (2) Mask R-CNN demonstrated satisfactory performance in ear segmentation, achieving an F1 score of 0.87; (3) random forest regression resulted in the most accurate yield estimation (R2 = 0.86 and rRMSE = 17.53 %), when all three ear phenotypic features were combined. Overall, this study demonstrates that utilizing ear phenotypic features is an alternative approach for estimating wheat grain yield at ripening stage, showing potential as a viable substitute to tedious field sampling methods.

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.

Biochar has been extensively studied as a soil amendment to reduce nutrients losses. However, the comparative effectiveness of biochar adsorption capacity for ammonium (NH4-N), nitrate (NO3-N), and phosphate (PO4-P) remains unknown. In the present study, the effects of feedstock (banana stem and coconut shell) and temperature (300, 500, and 700°C) on biochar adsorption ability for NH 4-N, NO 3-N, and PO 4-P were investigated and fitted by three adsorption models, viz Freundlich, Langmuir, and linear. Freundlich (R 2 = 0.95–0.99) and Langmuir (R 2 = 0.91–0.95) models were found suitable for adsorption of NH 4-N. The maximum adsorption capacity (Q m) for coconut shell biochar increased with pyrolysis temperature (Q m = 12.8–15.5 mg g-1) and decreased for banana stem biochar (Q m = 12.9–9.7 mg g-1). In the case of NO 3-N adsorption, Freundlich (R 2 = 0.82–0.99) and linear model (R 2 = 1.00) were found suitable while Langmuir model showed much less contribution, similarly adsorption of PO 4-P, was not supported by these three models. The minimum concentrations required for adsorption of phosphate were recorded as 36, 8, and 3 mg L -1 using pyrolyzed biochar at the temperatures of 300, 500, and 700°C, respectively. These results indicate that the feedstock and pyrolysis temperature, as well as aquatic nutrient concentration, were important factors for the adsorption of inorganic nitrogen and phosphorus.

Estimating forage yield and nutrient composition using hyperspectral remote sensing is a major challenge. However, there is still a lack of comprehensive research on the optimal wavelength for the analysis of various nutrients in pasture. In this research, conducted in Hailar District, Hulunber City, Inner Mongolia Autonomous Region, China, 126 sets of hyperspectral data were collected, covering a spectral range of 350 to 1800 nanometers. The primary objective was to identify key spectral bands for estimating forage dry matter yield (DMY), nitrogen content (NC), neutral detergent fiber (NDF), and acid detergent fiber (ADF) using principal component analysis (PCA), random forests (RF), and SHapley Additive exPlanations (SHAP) analysis methods, and then the RF and Extra-Trees algorithm (ERT) model was used to predict aboveground biomass (AGB) and nutrient parameters using the optimized spectral bands and vegetation indices. Our approach effectively minimizes redundancy in hyperspectral data by selectively employing crucial spectral bands, thus improving the accuracy of forage nutrient estimation. PCA identified the most variable bands at 400 nm, 520–550 nm, 670–720 nm, and 930–950 nm, reflecting their general spectral significance rather than a link to specific forage nutrients. Further analysis using RF feature importance pinpointed influential bands, predominantly within 930–940 nm and 700–730 nm. SHAP analysis confirmed critical bands for DMY (965 nm, 712 nm, and 1652 nm), NC (1390 nm and 713 nm), ADF (1390 nm and 715–725 nm), and NDF (400 nm, 983 nm, 1350 nm, and 1800 nm). The fitting accuracy for ADF estimated using RF was lower (R2 = 0.58), while the fitting accuracy for other indicators was higher (R2 ≥ 0.59). The performance and prediction accuracy of ERT (R2 = 0.63) were noticeably superior to those of RF. In conclusion, our method effectively identifies influential bands, optimizing forage yield and quality estimation.

The interrelation between grassland vegetation greenness and hydrothermal conditions on the Tibetan Plateau demonstrates a significant correlation. However, understanding the spatial patterns and the degree of this correlation, especially in relation to minimum and maximum air temperatures across various vertical gradient zones of the Plateau, necessitates further examination. Utilizing the normalized difference phenology index (NDPI) and considering four distinct hydrothermal conditions (minimum, maximum, mean temperature, and precipitation) during the growing season, an analysis was conducted on the correlation of NDPI with hydrothermal conditions across plateau elevations from 2000 to 2021. Results indicate that the correlation between vegetation greenness and hydrothermal conditions on the Tibetan Plateau grasslands is spatially varied. There is a pronounced negative correlation of greenness to maximum temperature and precipitation in the northeastern plateau, while areas exhibit stronger positive correlations to mean temperature. Additionally, as elevation increases, the positive correlation and sensitivity of alpine grassland vegetation greenness to minimum temperature significantly intensify, contrary to the effects observed with maximum temperature. The correlations between greenness and mean temperature in relation to elevational changes primarily exhibit a unimodal pattern across the Tibetan Plateau. These findings emphasize that the correlation and sensitivity of grassland vegetation greenness to hydrothermal conditions are both elevation-dependent and spatially distinct.

Leaf area index (LAI) is a key indicator of vegetation structure and function, and its products have a wide range of applications in vegetation condition assessment and usually act as important input parameters for ecosystem modeling. Grassland plays an important role in regional climate change and the global carbon cycle and numerous studies have focused on the product-based analysis of grassland vegetation changes. However, the performance of various LAI products and their discrepancies across different grassland types in drylands remain unclear. Therefore, it is critical to assess these products prior to application. We evaluated the accuracy of four commonly used LAI products (GEOV2, GLASS, GLOBMAP, and MODIS) using LAI reference maps based on both bridging and cross-validation approaches. Under different grassland types, the GLASS LAI performed better in meadow steppe (R2 = 0.26, RMSE = 0.41 m2/m2) and typical steppe (R2 = 0.32, RMSE = 0.38 m2/m2); the GEOV2 LAI performed better in desert steppe (R2 = 0.39, RMSE = 0.30 m2/m2). When we assessed their spatial and temporal discrepancies during the period from 2010 to 2019, the four LAI products overall showed a high spatial and temporal consistency across the region. Compared with GLASS LAI, the most consistent to least consistent correlations can be ordered by GEOV2 LAI (R2 = 0.94), MODIS LAI (R2 = 0.92), and GLOBMAP LAI (R2 = 0.87). The largest differences in LAI throughout the year occurred in July for all grassland types. Limited by the location and number of sample plots, we mainly focused on spatial and temporal variations. The spatial heterogeneity of land surface is pervasive, especially in vast grassland areas with rich grassland types, and the results of this study can provide a basis for the application of the product in different grassland types. Furthermore, it is essential to develop highly accurate and reliable satellite-based LAI products focused on grassland from the regional to the global scale according to these popular approaches, which is the next step in our work plan.

IntroductionGrasslands are the most important land use in China and have experienced extensive degradation in the past few decades due to overgrazing. However, regionally viable solutions to grazing intensity alleviation remained elusive to date.MethodsHere, we evaluated the grazing intensity effects of sown alfalfa pastures in northern China using an experiment-modeling combined approach that involved six sites in field experiments and five provinces in DNDC modeling of sown alfalfa pasture’s forage production and carbon sequestration potentials in marginal lands.ResultsOur results showed that the sown alfalfa pasture’s dry-matter yield varied between 4.5 and 9.0 Mg ha-1 under rainfed and irrigated conditions, respectively, from 2025 to 2035. If half of the available marginal lands were mobilized for alfalfa forage production, these yield levels meant that livestock grazing intensity on natural grasslands may drop 8-13% under rainfed and 20-33% under irrigated conditions. Our results also showed that marginal land’s soil organic carbon contents were systematically higher under sown alfalfa pasture than under fallow management by a big margin of 8.5 and 9.9 g kg-1 (i.e., +79 and +95%), under rainfed and irrigated conditions, respectively, during 2025-2035.DiscussionOverall, these results demonstrated that sown alfalfa pasture on marginal lands represents an effective grassland conservation pathway over the short- to medium-term time horizon based on current technologies.

Land use and land cover (LULC) change has greatly altered ecosystem carbon storage and exerted an enormous impact on terrestrial carbon cycling. Characterizing its impact on ecosystem carbon storage is critical to balance regional carbon budgets and make land use decisions. However, due to the availability of LULC data and the strong variability in LULC change, uncertainty remains high in quantifying the effect of LULC change on the historical and future carbon stock. Based on four historical LULC maps and one future LULC projection, this study combined the Land Use and Carbon Scenario Simulator (LUCAS) with a process-based CENTURY model to evaluate the historical and future LULC change and its impact on grassland carbon storage from 1991 to 2050 in northern China. Results showed that grassland experienced a drastic decrease of 16.10 × 103 km2 before 2005, while agriculture and barren land increased by 16.91 × 103 km2 and 3.73 × 103 km2, respectively. After that, grassland was projected to increase, agriculture kept steady, and barren land decreased. LULC change has resulted in enormous total ecosystem carbon loss, mainly in agro-pasture areas; the maximum 8.54% of carbon loss happened in 2000, which was primarily attributed to agriculture to grassland, forest to grassland, grassland to agriculture, and grassland to barren. Before 2000, the grassland net biome productivity was projected to be −15.54 Tg C/yr and −2.69 Tg C/yr with and without LULC change. After 2001, the LULC change showed a positive impact on the grassland carbon balance, and the region was projected to be a carbon sink. Ecological projects have made a significant contribution to grassland carbon storage. The paper provides a framework to account for the effects of LULC change on ecosystem carbon and highlights the importance of improving grassland management in balancing the grassland carbon budget, which is helpful to understand the regional carbon budget and better inform local land use strategies.

(1) Estimation of grazing livestock intake is the basis for studying animal–plant relationships and the nutritional status of grazing livestock and has important implications for grassland composition and productivity. (2) We used the saturated alkanes method to determine the feed intake and vegetation nutrient digestibility of livestock at different grazing intensities and in different months. (3) We found that C31 had the highest concentration in both pasture and fecal output, and the average recovery of C31 was 77.99%. The different grazing intensities significantly affected livestock intake. As the grazing intensity increased, there was a decreasing trend of livestock intake and the highest livestock feed intake was 6.11 kg DM/day in light grazing. With the increase in grazing season months, the highest livestock intake was 6.67 kg DM/day in the cold period in September. The month also had a significant effect on the digestibility of livestock for all nutrient variables when compared to the grazing intensity. Livestock weight and medium palatability species are more important for livestock intake. (4) Our study provides a more accurate measurement of grazing livestock intake, which can be used as a reference for the scientific management of grazing livestock and the rational use of grazing pastures.

Long-term grazing leads to soil degradation in Inner Mongolia grassland. Based on the Hulunbeier meadow steppe, the variation characteristics of soil nitrogen content and storage in soil layers between 0–40 cm, under six different grazing intensities, and the response of vegetation and other physical and chemical properties of soil to grazing were studied. The main results were as follows: (1) Moderate grazing increased soil total nitrogen (TN), soluble total nitrogen (STN) and microbial biomass nitrogen (MBN) contents, while heavy grazing decreased MBN content. In the year with more rain, heavy grazing increased nitrate nitrogen (NO3−-N) content and storage, while less rain increased ammonium nitrogen (NH4+-N) content. (2) The proportion of 0–40 cm nitrogen components showed an upward trend in the year with more rain, and the opposite in the years with less rainfall with the increase of grazing intensity. Soil soluble organic nitrogen (SON) and NO3−-N storages decreased and MBN storage increased in rainy years. (3) Soil nitrogen component contents and storages were correlated with plant growth status, soil moisture (SM) and soil bulk density (SBD), and were significantly negatively correlated with soil temperature (ST) and pH (p < 0.05). The content and storage of soil nitrogen were affected by grazing, soil, vegetation, meteorological and other environmental factors. Moderate grazing was more conducive to the improvement of soil nitrogen storage capacity and the healthy development of grassland.

Temperate meadow steppe is a representative ecosystem in northeast China. A process-oriented biogeochemistry model, denitrification–decomposition (DNDC) biogeochemistry model was employed and adapted to interpret and integrate field observations to determine whether temperate meadow steppe ecosystem was a carbon dioxide (CO2) sink during the growing seasons from 2010 to 2011. Then, we applied the model to predict the long-term impacts of climate change on the carbon dynamics in the ecosystem. Daily weather data for 2010–2011 in conjunction with the soil properties data and management practices data for this site were utilized as inputs to simulate the grass growth and soil carbon dynamics. The modeled carbon fluxes were compared with eddy tower data. The observed and modeled CO2 fluxes data were consistent, with both showing that the temperate meadow steppe is a stable carbon sink. Prediction tests were conducted with a baseline and eight alternative climate scenarios for the period 2017–2050. Simulations for the years 2017–2050 found that (1) the temperate meadow steppe will be a carbon sink under the baseline climate conditions; (2) the decelerated warming and drier climate scenario produced the lowest grass production and lowest carbon sequestration; and (3) the accelerated warming and wetter climate scenario produced the highest biomass and highest carbon sequestration. By using DNDC model we can understand carbon dynamics in temperate meadow steppe. The ecosystem production is limited by precipitation, a wetter future climate would substantially elevate the carbon sequestration capacity of the ecosystem. However, the carbon sequestration potential could significantly decrease if the climate is more arid from 2017 to 2050.

Abstract Background In mammals, it is known that the estradiol-17β (E2) is mainly synthetized in ovarian granulosa cells (GCs), and the excessive apoptosis of GCs induces the follicular atresia. Many studies have implicated the essential role of KISS1, with the pro-synthetic effect of E2 and the anti-apoptotic effect on GCs, in the mammalian folliculogenesis, and several STAT4 potential binding sites were previously predicted on the promoter of KISS1 in pigs. However, the biological effects of STAT4 on GCs and the molecular regulation between STAT4 and KISS1 remained largely unknown. Methods Using the porcine GCs as the cellular model, the overexpression plasmid, small interfering RNA, 5′-deletion and luciferase assay were applied to investigate the molecular mechanisms for STAT4 regulating the expression of KISS1. Results In this study, the STAT4 negatively regulated the mRNA and protein levels of KISS1 in porcine GCs, and the mRNA level of STAT4 was observed to significantly decrease from immature to mature follicles, which was inversed with that of KISS1. The relative luciferase activity of KISS1 promoter was significantly increased with deletion of the fourth potential binding site (− 305/− 295), and ChIP further confirmed that the STAT4 bound at − 305/− 295 region of KISS1. Besides, the STAT4 significantly regulated the mRNA levels of PDK1, FOXO3 and TSC2 of PI3K signaling pathway to promote the cell apoptosis and the percentage of cells at G0/G1 phase of cell cycle in GCs. Alternatively, the STAT4 significantly decreased the mRNA levels of CYP17, 3B-HSD, 17B-33 HSD, ESR1, and ESR2, as well as the concentration of E2 in GCs. Furthermore, interfering with the expression of STAT4 was observed to significantly stimulate the pro-synthetic effect of E2 and anti-apoptotic effect of KISS1 in GCs. Conclusions Collectively, the STAT4 might directly target at − 305/− 295 region of KISS1 to negatively regulate the transcription of KISS1, promote the cell apoptosis via PI3K signaling pathway, suppress the synthesis of E2 through the estrogen signaling pathway in porcine GCs. These proposed works could provide useful insight in further investigations on the molecular functionalities of STAT4 and KISS1 in the folliculogenesis of mammals.

Climate change and human activities are critical to fractional vegetation cover (FVC). However, until now, their combined effects on FVC have not been well quantified. In this paper we analyze the temporal and spatial variations in the fractional vegetation cover in the Hulun Lake region from 1986 to 2017 and its response to changes in climate parameters and human activities; additionally, the impact of changes in climate parameters and human activities on FVC are discussed. The results showed that according to the Mann-Kendall (M−K) trend test analysis, 65.01% of the FVC in the Hulun Lake region decreased to varying degrees, of which 24.55% showed a significant decrease and only 8.61% increased significantly. Based on the M−K mutation test analysis of FVC and analysis of driving factors from 1986 to 2017, 1999 was the time point at which abrupt changes in FVC, annual precipitation and moisture index occurred in the Hulun Lake region, and water was an important factor affecting FVC. The regression analysis of FVC and the driving factors from 1986 to 2017 showed that the moisture index and the number of livestock could not be eliminated from the regression equation. Additionally, there was a significant correlation between the FVC and the climate livestock index (P

Understanding the grassland ecosystem is crucial for improving grassland ecosystem functions and services such as climate regulation, water and soil conservation, carbon sequestration, and biodiversity and gene pool maintenance. However, a systematic and comprehensive review of the relevant literature is still unclear and lacking. The VOSviewer software and cluster analysis were used to visually analyze and perform dimension reduction classification on the 27,778 studies related to grassland ecosystem research based on the Web of Science database. The number of publications targeting grassland ecosystem increased rapidly from 2006 to 2021. Ecology, agronomy, and environmental science were the most popular research categories, and the top journal sources were Remote Sensing, Journal of Ecology, and Ecology and Evolution. The leading publishing countries were the United States, China, and Germany. The top three institutions were the Chinese Academy of Sciences, the University of Chinese Academy of Sciences, and Colorado State University. Cooperation between different countries and institutions had increased. Keyword co-occurrence network analysis showed that Biodiversity, Vegetation and Conservation were the most popular study areas, grassland management, climate change, land use pattern, and ecosystem impact were the hot research topics. All studies could be divided into three categories by cluster analysis: grassland ecological characteristics including basic physicochemical properties, vegetation community characteristics, aboveground and belowground biomass, and soil structural quality of grassland; driving mechanisms that demonstrated effects of human activities and climate change on grassland ecosystem function; and grassland ecosystem services that focused the influences of different grassland management strategies on ecological services, animal welfare and human well-being. The three topic categories of reviewed studies were interrelated and consistent with each other, and the performances were progressive. This paper reviewed the trend evolution through keyword hotspots and analyzed the future research directions to provide an important reference for scientists to better respond to the balance of herbage and sustainable utilization of grassland and maintenance of ecological security.

Soil microorganisms promote material transformation and energy flow in the entire ecological environment and play a key role in the stability and development of grassland ecosystems. Studies on the impacts of grazing on the soil microbial community and the establishment of a reasonable grazing intensity are crucial to improve our knowledge of the mechanisms underlying grassland degradation and to accurately assess the influence of grazing management on grassland functions and the nutrient cycle. Based on the grassland grazing control experimental platform, we compared the structure and diversity characteristics of soil microbial communities under six grazing intensities (0.00, 0.23, 0.34, 0.46, 0.69, and 0.92 AU ha−1) (1 AU = 500 kg of adult cattle) on the Hulunbuir Leymus chinensis meadow steppe. The results showed that soil microbial biomass carbon (MBC) and nitrogen (MBN) decreased with increasing soil depth. The 0–10 cm soil layer of G0.34 had the highest MBC and MBN, and the G0.92 treatment had the lowest MBC and MBN. Heavy grazing significantly decreased the MBC and MBN contents in the soil surface layer. The soil bacterial diversity under light grazing treatment (0.23 AU ha−1) was higher than that under heavy grazing, and the fungal diversity under the no-grazing treatment was higher than that under the grazing treatment. Overgrazing reduced the bacterial species in the soil. The plant belowground biomass significantly (p = 0.039) influenced the bacterial community structure, and the soil pH (p = 0.032), total nitrogen (p = 0.011), and litter (p = 0.007) significantly influenced the fungal community. The effects of grazing on microbial communities were primarily driven by vegetation productivity, litter mass, and soil geophysical and chemical characteristics. This study deepened our understanding of the impacts of grazing practices on soil microbial communities on the meadow steppe, suggesting that moderate-disturbance grazing can promote the sustainable development of grassland vegetation-soil microorganisms.

Studies on the impacts of fertilization on plant production and species diversity are crucial for better maintaining the stability of grassland ecosystems and restoring degraded grasslands. Using a controlled fertilization experimental platform in a temperate Leymus chinensis meadow steppe ecosystem, the effects of different levels of nitrogen (N) and phosphorus (P) addition on plant community structure, biomass allocation, diversity, and the correlation relationship were explored. The major results were as follows: (1) The structural composition of the plant community changed after different levels of N and P addition; the dominance ratio and biomass of Poaceae plants increased gradually with increasing N and P addition levels. (2) The addition of N and P increased the height, density and coverage of the plant community, the biomass of the dominant L. chinensis and plant community and the total productivity of grassland, and reduced the root–shoot ratio of grassland biomass. For example, plant community biomass, gramineous plant biomass and grassland total productivity increased by 84.46–204.08%, 162.64–424.20%, and 38.12–46.44%, respectively, after N and P addition. (3) The community richness, diversity, and evenness indices decreased overall and showed binomial regression after N and P addition; the functional group of Poaceae plants was highly significantly negatively correlated with species diversity indices and was highly significantly positively correlated with the aboveground biomass of L. chinensis and community; Leguminosae plants and Ranunculaceae plants were highly significantly positively correlated with Margalef and Patrick richness indices; Ranunculaceae plants were highly significantly and negatively correlated with L. chinensis biomass, community biomass, and Poaceae plants. Moderate fertilization not only improved the plant community structure and productivity but was also beneficial for maintaining the grassland species diversity and stability.

PurposeHepatocellular carcinoma (HCC), a worldwide leading cause of morbidity and mortality, is the most frequent primary liver tumor. Most HCC patients are diagnosed with advanced liver cancer, resulting in a very low 5-year survival rate. Thus, there is an urgent need for the development of targeted therapies. In this study, we aimed to investigate the effect and mechanism of the miR-20a/EZH1 axis on the proliferation and metastasis of HCC and the inhibitory effect of the EZH1/EZH2 inhibitor UNC1999 on HCC.Materials and MethodsThe expression of miR-20a in human HCC tissues and cell lines was detected using quantitative real-time PCR (qRT-PCR). The expressions of proteins were analyzed with immunohistochemistry and Western blotting. Luciferase assay was used to verify whether miR-20a targets EZH1 or EZH2. The effect of miR-20a on HCC progression was studied in vivo and in vitro. The tumor inhibitory effect of UNC1999 was confirmed in vivo. CCK8 assay, wound healing assay, cell migration and invasion assay were used to evaluate the synergistic effect of UNC1999 with sorafenib. RNA sequencing (RNA-seq) was performed to screen the differentially expressed genes in the Huh7 and SMMC7721 cell lines after UNC1999, sorafenib, and combination treatments.ResultsIn this study, miR-20a showed a lower expression in both HCC tissues and cell lines. MiR-20a inhibited the proliferation and migration of SMMC7721 and Huh7 cells. The results of the luciferase assay and Western blot analysis revealed that miR-20a directly targeted EZH1, a histone methyltransferase. We demonstrated that miR-20a negatively regulated the expression of EZH1 and inhibited the proliferation and metastasis of HCC by reducing H3K27 methylation. We found UNC1999 inhibited tumor cells proliferation and enhanced the inhibitory effect of sorafenib.ConclusionWe demonstrated that miR-20a suppresses the tumor proliferation and metastasis in HCC by directly targeting EZH1. UNC1999 can inhibit tumor proliferation in vivo and increase the sensitivity of hepatoma cell lines to sorafenib.

Land reclamation and grassland management are important land utilization pattern for effective use and restoration of alkali-saline land in agriculture-pasture transition regions. These large-scale land cover/land use changes (LCLUC) could impose profound and divergent impacts on terrestrial carbon (C) and H2O cycling, hence require precise assessment of ecosystem CO2 uptake and water use differences due to specific land use of the same climate/soil landscape. We initiated clustered eddy covariance flux-tower observations and C/H2O fluxes analysis of 4 adjacent representative land cover and grassland managements types on alkali-saline Songnen Plain, including fenced meadow (as reference land use), clipped meadow, degraded alkali-saline land and paddy field in 2018–2019. Results showed that differences in land use influenced the magnitude and temporal variation of net ecosystem productivity (NEP) and evapotranspiration (ET). Compared to fenced meadow, paddy field increased growing season C uptake by 63.3% and ET by 35.4%, while degraded alkali-saline land and clipped meadow decreased C uptake by 43.0% and 30.6%, and ET by 32.1% and 3.2%. Degraded alkali-saline land barely sequestrate C in dry year, whereas paddy field is least sensitive to inter-annual precipitation variation. Growing season C uptake was advanced in clipped meadow due to increased surface temperature, while monthly ET peaked early at May in paddy field. Agricultural use and specific grassland management also caused large differences in ET partitioning. Fenced meadow had greater ET partitioned into transpiration (69%) than clipped meadow (58%), degraded alkali-saline land and paddy field (41% and 34%, respectively). Water use efficiency appeared greater in clipped meadow and degraded alkali-saline land than in fenced meadow, and responded differently to abiotic factors. Overall, large-scale LCLUC has exerted significant influence on terrestrial C/H2O balances and physical responses in alkali-saline Songnen Plain.

Grasslands contribute considerably to the global carbon cycle and livestock production. However, many of the world grasslands suffer from degradation caused mainly by overgrazing. Remote sensing methods are effective tools for monitoring and estimating grassland vegetation parameters. In this study, we compared the performance of vegetation indices (VIs) obtained from two different sensors to estimate grassland vegetation parameters under different vegetation and soil conditions based on typical grassland biomass gradients formed by long-term controlled grazing experiments. Sentinel-2 and Landsat 8 were selected as data sources to estimate two vegetation parameters, fresh aboveground biomass (AGB) and leaf area index (LAI). Field-measured fresh AGB and LAI data were collected from experimental grasslands with different grazing intensities (GI) in Hulunber, Inner Mongolia, China in 2019. Univariate linear mixed models were established between VIs and field measurements, and grazing intensities were considered as random factors. The results confirmed that vegetation parameters (AGB and LAI) and VIs decreased with increasing GI; however, the decreasing trend was insignificant when the GI exceeded 0.69 Au/ha. VIs derived from Sentinel-2 and Landsat 8 estimated fresh AGB and LAI at 80% accuracy. Sentinel-2 derived VIs yielded higher predictive accuracy than Landsat 8 for both fresh AGB and LAI. Comparing with the other VI inversion models, the normalised difference phenology index derived from Sentinel-2 images estimated the vegetation parameters the most effectively and accurately, with a coefficient of determination (R2) of 0.625 and relative root mean square error (RMSE%) of 18.105% for fresh AGB estimation and R2 of 0.559 and RMSE% of 14.953% for LAI estimation.

This study aimed to investigate the effects of lactic acid bacteria (LAB) inoculants on the fermentation quality, microbial compositions, and predicted functional profiles of forage oat. The forage oat was inoculated with distilled water, Lentilactobacillus buchneri (LB), and Lactiplantibacillus plantarum (LP) as the control (CON), LB and LP treatments, respectively, and the addition of Lentilactobacillus buchneri (LB) or Lactiplantibacillus plantarum (LP) resulted in 1 × 106 colony-forming units/g of fresh weight. After 30 days of fermentation, the lowest pH (4.23) and the lowest content of ammoniacal nitrogen (NH3-N) in dry matter (DM, 4.39%) were observed in the LP treatment. Interestingly, there was a significant (p < 0.05) difference in lactic acid (LA) concentration among the three treatments. The LP treatment had the highest lactate concentration (7.49% DM). At the same time, a markedly (p < 0.05) elevated acetic acid (AA) concentration (2.48% DM) was detected in the LB treatment. The Shannon and Chao1 indexes of bacterial and fungal communities in all the silage samples decreased compared to those in the fresh materials (FM). Proteobacteria was the dominant phylum in the FM group and shifted from Proteobacteria to Firmicutes after ensiling. Lactobacillus (64.87%) and Weissella (18.93%) were the predominant genera in the CON, whereas Lactobacillus dominated the fermentation process in the LB (94.65%) and LP (99.60%) treatments. For the fungal community structure, the major genus was Apiotrichum (21.65% and 60.66%) in the FM and CON groups after 30 days of fermentation. Apiotrichum was the most predominant in the LB and LP treatments, accounting for 52.54% and 34.47%, respectively. The genera Lactococcus, Pediococcus, and Weissella were negatively associated with the LA content. The genus Ustilago and Bulleromyces were positively associated with the LA content. These results suggest that the addition of LAB regulated the microbial community in oat silage, which influenced the ensiling products, and LP was more beneficial for decreasing the pH and NH3-N and increasing the LA concentration than LB in forage oat silage.

Leymus chinensis is a primary plant in the meadow steppe and typical steppe of China. With global warming and increasing grazing intensity, grassland degradation is being exacerbated. To better protect the L. chinensis grassland in this area and provide a theoretical basis for restoring it, this paper compared the germinable seed bank in the soil and the germination characteristics of L. chinensis (including initial germination time, duration of germination, germination termination time, germination dynamics, and germination index) in free-grazing and enclosed areas. At the same time, combining information about the density of L. chinensis on the ground and previous research results, a comprehensive analysis was conducted. The major results were: (1) there was no significant difference in the number of germinable seeds in the soil between the free-grazing area and the enclosed area, and these seeds were mainly concentrated in the 0–2 cm soil layer. (2) The free-grazing area resulted in a significant increase in the number of germinable L. chinensis seeds and advanced the initial germination time. (3) The number of soil germinated seeds and the number of L. chinensis germinated seeds decreased with the increase in soil depth. (4) Livestock grazing behaviours increased seed burial, thus improving the L. chinensis germination rate. At the same time, the L. chinensis seed bearing percentage and seed quality and the number of germinable L. chinensis seeds were significantly higher in the free-grazing area than in the enclosed area. However, this result still needs to be further explored.

Understanding long-term seasonal and interannual patterns of soil respiration with their controls is essential for accurately quantifying carbon fluxes at a regional scale. During the period from 2009 to 2014, an automatic measurement system (LI-8150, Licor Ldt., Lincoln, NE, USA) was employed for the measurement of soil respiration in a meadow steppe of eastern Inner Mongolia. We found that the seasonal pattern of soil respiration was controlled mainly by the soil temperature, which explained about 82.19% of the variance. Annual soil respiration varied between 391.4 g cm−2 and 597.7 g cm−2, and significantly correlated with soil moisture, suggesting that soil moisture was the most predominant factor controlling the annual variations of soil respiration in this meadow steppe. A double factorial exponential model including both soil temperature (TS) and soil water content (SWC) (y = 6.084 × exp(0.098 TS × SWC) − 5.636) explains 72.2% of the overall variance in soil respiration. We also detected a temporal inconsistency of 2–3 months in the effects of precipitation on soil respiration versus canopy biomass production, which was presumably a main mechanism explaining the weak relationships between soil respiration and phytomass components in this ecosystem. Our findings have important implications for better understanding and accurately assessing the carbon cycling characteristics of terrestrial ecosystems in response to climate change in a temporal perspective.

Studies on the impacts of grazing on carbon, nitrogen, and phosphorus stoichiometry and storage are crucial for better understanding the nutrient cycles of grasslands ecosystems. Using a controlled grazing experimental platform in a meadow steppe ecosystem, the effects of different stocking rates (0.00, 0.23, 0.34, 0.46, 0.69, and 0.92 AU ha−1) on the carbon, nitrogen, and phosphorus contents of plant functional groups were explored. The major results were: (1) The carbon content of Gramineae Barnhart was significantly reduced by grazing intensity (p < 0.05), and the organic carbon content of Cyperaceae Rotundus was significantly higher than that of the other groups; the total nitrogen content of Cyperaceae and other groups and total phosphorus contents of Gramineae, Leguminosae Sp., Cyperaceae, and other groups all increased significantly with increasing grazing intensity (p < 0.05). (2) The carbon, nitrogen, and phosphorus storage amounts of Gramineae, Leguminosae, and Ranunculaceae L. decreased significantly with increasing grazing intensity. Heavy grazing reduced the carbon, nitrogen, and phosphorus storage amounts of Cyperaceae and other groups, while the carbon, nitrogen, and phosphorus storage amounts of Compositae were the largest under moderate grazing. (3) The nitrogen content of each functional group was highly significantly negatively correlated with the C/N ratio, and the phosphorus content was highly significantly negatively correlated with the C/P ratio. Grazing and foraging affected the growth of the different functional groups, which in turn affected their carbon, nitrogen, and phosphorus content, stoichiometry, and storage. Moderate grazing improved the nutrient utilization efficiency of grassland and is beneficial for promoting sustainable grassland development.

Understanding the effects of livestock grazing on ecosystem respiration (Re) of grassland ecosystems is critical for accurately assessing the feedback of grazing management to climate change. We examined ecosystem respiration in response to varying cattle grazing intensities during growing seasons from 2009 to 2018 in a meadow steppe ecosystem of eastern Inner Mongolia. We found that ungrazed swards had the highest mean annual Re rate, with seasonal CVs in Re ranging from 37.53% to 46.04% for all treatments. When all treatments were analysed as a whole, we identified a significant positive relationship between the annual Re rate and annual peak value of standing plant aboveground biomass. Our findings showed that controlling factors on the mean annual Re differed substantially with grazing intensity. In ungrazed and lightly-grazed plots (G0.00 and G0.23), the mean annual Re rate was controlled mainly by canopy height and/or rainfall, while it was controlled more predominantly by contents of NH4+-N and available phosphorus in moderately and heavily grazed plots. We detected significant positive relationships of the annual Re rate with rainfall, soil moisture, ammonium nitrogen, and soil available phosphorus during the entire study period, whereas significant negative relationships were detected between the annual ecosystem respiration rate and the mean growing season temperature, irrespective of grazing intensity. Our findings revealed that grazing could substantially simplify the relationship between the mean annual Re rate and biotic and abiotic parameters. It may be concluded that the relationship between the annual Re rate and the standing crop aboveground biomass was a principal mechanism underlying the effects of gradient grazing on the Re of Chinese meadow steppe ecosystems.

Background and aims: Grazing and mowing are widely adopted management practices for the semiarid steppe in China that profoundly affect the greenhouse gas (GHG) flux in grassland ecosystems. However, the general mechanisms for CH4 and N2O fluxes in response to grazing and mowing remain poorly understood. Thus, we conducted year-round flux measurements of CH4 and N2O fluxes to investigate the effect of grazing and mowing on CH4 and N2O fluxes. Methods: Using manual static chamber and gas chromatography, we measured the fluxes of CH4 and N2O from grazing-exclusion, grazing and mowing sites from June 2019 to June 2020 in the Hulunbuir Grassland, Inner Mongolia. Results: Grazing and mowing increased soil temperature and reduced soil water content and soil inorganic N content. Compared with grazing and mowing, lower mean CH4 uptake (−67.58 ± 8.9 μg m−2 h−1) and higher mean N2O emissions (29.17 ± 6.8 μg m−2 h−1) were found at the grazing-exclusion site. No pulse emissions of N2O were found for all sites during the spring thaw period (STP), and only two small N2O emission peaks due to soil thawing were captured on April 2nd and 5th. The contribution of the spring thaw to the total annual N2O budget was small and accounted for only 10%, 13% and 12% of the annual fluxes at the grazing-exclusion, grazing and mowing sites, respectively. Conclusion: Our results indicate that grazing and mowing enhance CH4 uptake and inhibit N2O emissions, primarily due to the increased soil temperature and reduced soil water content and soil inorganic N content. No apparent pulse N2O emissions were observed at any of the three sites during the STP in the Hulunbuir grassland.

The services provided by grassland ecosystems are important and irreplaceable in maintaining the balance and stability of ecosystems. The spatiotemporal variations of grassland ecosystem service value (ESV) and its influencing factors in Inner Mongolia from 2000 to 2019 were studied in this paper. Based on the socio-economic data, remote sensing data, geographic data, and meteorological data, a dynamic ESV assessment method based on the equivalent factors was used to calculate the grassland ESV for each year. The spatiotemporal dynamic variation and future trend of grassland ESV were studied by coefficient of variation index (CV), Theil–Sen median trend analysis, Mann–Kendall test, and Hurst index, and the Geodetector was used to determine the main factors affecting the distribution of ESV. The results indicated that (1) the annual average grassland ESV of Inner Mongolia was higher in the northeast than in southwest, the average ESV was 2.0794 million CNY/km2, and the pixels were concentrated from 1 to 3 million CNY/km2, accounting for 75.46% of the study area; (2) during the study period, the average grassland ESV increased slowly with time at an annual growth rate of 0.2, and the total ESV decreased first and then increased with the change in grassland area; (3) the average volatility was 0.16, and pixels with CV values between 0.1 and 0.2 accounted for 69.2% of the study area, indicating the fluctuation of ESV was relatively stable during the study period; (4) 37.16% of the grassland ESV in Inner Mongolia decreased slightly and 41.77% increased slightly during these years, and the two parts showed opposite trends in the future; and (5) the single factor influencing the spatial distribution of grassland ESV was mainly normalized vegetation index (NDVI) and precipitation, and the multi-factor interactions were NDVI∩slope and NDVI∩precipitation. All influencing factors exhibited a stronger impact through the two-factor interaction. This study can provide reference values for the policymaking of natural resource conservation or restoration.

Chinese traditional herbs are used widely as feed supplements to improve the immune response and antioxidant capacity of livestock. Twenty early-weaned 4-month-old yak calves (72.3 ± 3.65 kg) were divided randomly into four groups (n = 5 per group); three groups were provided with supplementary 80 mL/kg DMI of the root water extracts of either Angelica sinensis, Codonopsis pilosula or Glycyrrhiza uralensis, and one group (control) was not provided with a supplement. Compared to control calves, calves consuming the three herbal extracts increased serum concentrations of albumin (ALB) and glutathione peroxidase (GSH-Px), but decreased serum concentrations of free fatty acids (FFAs) and malondialdehyde (MDA) (p < 0.05). Calves consuming A. sinensis decreased (p < 0.05) serum concentration of total cholesterol (TC), and increased (p < 0.05) serum concentration of total proteins (TP). Serum FFA concentrations increased (p = 0.004) linearly with time in the control group, but not in the groups consuming herbs. Serum metabolomic data demonstrated that A. sinensis and C. pilosula regulate mainly amino acid metabolism, while G. uralensis regulates mainly carbon and amino acid metabolism. It was concluded that the three herbal root extracts, as dietary supplements, improved energy and nitrogen metabolism, and enhanced the antioxidant capacity of yak calves.

Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network regression (ANNR) and support vector regression (SVR) based on satellite data have been widely used for the estimation of LAI. However, the selection of input variables has a great impact on the estimation performance of MLAs. In this study, we aimed to improve the LAI inversion model of Inner Mongolia grassland based on MLAs incorporating empirical knowledge. Firstly, we used the ANNR, SVR and RFR approaches, respectively, to rank the input variables including vegetation indices, climate factors, soil factors and topography factors and found that Normalized Difference Phenology Index (NDPI) contributed the most to LAI estimation. Secondly, we selected four sets of input variables, namely, all variables—A, model selected variables—B, overlapping variables—C and self-defined variables—D, respectively. Subsequently, we built twelve LAI estimation models (RFR-A, RFR-B, RFR-C, etc.) based on three MLAs and four sets of input variables. The evaluation of them showed the RFR produced higher prediction accuracy, followed by ANNR and SVR. Furthermore, the RFR-D presented the highest accuracy in predicting LAI (R2 = 0.55, RMSE = 0.37 m2/m2, MAE = 0.29 m2/m2). Finally, we compared our results with MODIS LAI and GEOV2 LAI products and found that all of them showed a similar spatial distribution of grassland LAI in the four sub-regions covering all grassland types, but our model exhibited larger LAI values in the desert steppe and smaller LAI values in the others. These findings demonstrated that MLAs incorporating empirical knowledge could improve the accuracy of modelling LAI and further study is necessary to reduce the uncertainty in LAI mapping in grassland.

Grazing is the main utilization of native grassland, and forage fungal disease is one of the limiting factors of grassland productivity. The present research in the Hulunber meadow steppe grassland was conducted to investigate the responses of the dominant plant Leymus chinensis (Trin.) to beef cattle grazing, rust, and their interaction influence. Six grazing intensity treatments with three replicates were established. The response of L. chinensis to grazing and rust was systematically studied for two consecutive years. The main findings were that grazing and rust had significant effects (p < 0.05) on the growth and nutrient elements content of L. chinensis. Compared with the 0 cattle ha−1 treatment, the dry matter of L. chinensis in the 0.42, 0.63, and 1.67 cattle ha−1 treatments decreased by 42.2%, 90.5%, and 339.5%, respectively. Compared with non-infected plants, dry matter of rust-infected L. chinensis plants decreased by 45.6%. The N:C and P:C ratios of rust-infected plants were lower than in non-infected plants, and positively correlated with their relative growth rates. Therefore, we concluded that the growth rate hypothesis still applied in L. chinensis under the interactive effects of grazing and disease. Additionally, grazing can alleviate the loss of dry matter caused by disease.

This paper synthesizes the contemporary challenges for the sustainability of the social-environmental system (SES) across a geographically, environmentally, and geopolitically diverse region—the Asian Drylands Belt (ADB). This region includes 18 political entities, covering 10.3% of global land area and 30% of total global drylands. At the present time, the ADB is confronted with a unique set of environmental and socioeconomic changes including water shortage-related environmental challenges and dramatic institutional changes since the collapse of the Union of Soviet Socialist Republics. The SES of the ADB is assessed using a conceptual framework rooted in the three pillars of sustainability science: social, economic, and ecological systems. The complex dynamics are explored with biophysical, socioeconomic, institutional, and local context-dependent mechanisms with a focus on institutions and land use and land cover change (LULCC) as important drivers of SES dynamics. This paper also discusses the following five pressing, practical challenges for the sustainability of the ADB SES: (a) reduced water quantity and quality under warming, drying, and escalating extreme events, (b) continued, if not intensifying, geopolitical conflicts, (c) volatile, uncertain, and shifting socioeconomic structures, (d) globalization and cross-country influences, and (e) intensification and shifts in LULCC. To meet the varied challenges across the region, place-based, context-dependent transdisciplinary approaches are needed to focus on the human-environment interactions within and between regional landscapes with explicit consideration of specific forcings and regulatory mechanisms. Future work focused on this region should also assess the role of the following mechanisms that may moderate SES dynamics: socioeconomic regulating mechanisms, biophysical regulating mechanisms, regional and national institutional regulating mechanisms, and localized institutional regulating mechanisms.

Abstract 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.

The use of carbon nanoparticles (CNPs) as a fertilizer synergist to enhance crop growth has attracted increasing interest. However, current understanding about plant growth and soil response to CNPs is limited. In the present study, we investigated the effects of CNPs at different application rates on soil properties, the plant growth and nutrient use efficiency (NUE) of corn (Zea mays L.) in two agricultural soils (Spodosol and Alfisol). The results showed that CNPs affected corn growth in a dose-dependent manner, augmenting and retarding growth at low and at high concentrations, respectively. The amendment at the optimal rate of 200 mg CNPs kg−1 significantly enhanced corn growth as indicated by improved plant height, biomass yield, nutrient uptake and nutrient use efficiency, which could be explained by the higher availability of phosphorus and nitrogen in the amended soils. The application of CNPs largely stimulated soil urease activity irrespectively of soil types. However, the responses of dehydrogenase and phosphatase to CNPs were dose dependent; their activity significantly increased with the increasing application rates of CNPs up to 200 mg kg−1 but declined at higher rates (>400 mg kg−1). These findings have important implications in the field application of CNPs for enhancing nutrient use efficiency and crop production in tropical/subtropical regions.

Abstract Background Companioned by economic development, a dietary shift toward higher meat consumption is seen in developing countries and transitional economies, where the demand for livestock production has been increasing in response to such a dietary shift. In the Asian Dryland Belt, approaches to meet this demand have focused on grazing intensification, cropland conversion for animal feed, and supplemental feeding. With the scarcity of water, energy, and food in the region, a key question is whether or not the current approaches are sustainable. If not, what are the pathways to increase livestock production while protecting the region’s environment for a sustainable future? We provide our reviews and discuss current approaches in response to these dietary shifts and assess their environmental resilience with a focus on the grassland ecosystems in the Asian Dryland Belt. Results While current approaches alleviate the urgent need for short-term livestock production, they lead to long-term vulnerability in food security. Trade-offs between short gains and long-term losses, between food for humans and for animals, and between agricultural intensification and environmental degradation need to be holistically examined for the sustainable development of the region. A grassland water, energy, and food nexus framework is proposed with specific recommendations to increase livestock production while considering other ecosystem services of the dryland grassland ecosystems in the Asian Dryland Belt. Conclusions Current practices to increase livestock production are likely to lead to long-term, large-scale ecological degradation of the grassland ecosystems in the Asian Dryland Belt and are thus unsustainable. By considering the trade-offs in the nexus of water, land, food, and livelihoods, sustainable pathways were articulated and recommended. Future pilot studies are needed for validation and adoption.

The accurate estimation of grassland vegetation parameters at a high spatial resolution is important for the sustainable management of grassland areas. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) sensors with a single laser beam emission capability can rapidly detect grassland vegetation parameters, such as canopy height, fractional vegetation coverage (FVC) and aboveground biomass (AGB). However, there have been few reports on the ability to detect grassland vegetation parameters based on RIEGL VUX-1 UAV LiDAR (Riegl VUX-1) systems. In this paper, we investigated the ability of Riegl VUX-1 to model the AGB at a 0.1 m pixel resolution in the Hulun Buir grazing platform under different grazing intensities. The LiDAR-derived minimum, mean, and maximum canopy heights and FVC were used to estimate the AGB across the entire grazing platform. The flight height of the LiDAR-derived vegetation parameters was also analyzed. The following results were determined: (1) The Riegl VUX-1-derived AGB was predicted to range from 29 g/m2 to 563 g/m2 under different grazing conditions. (2) The LiDAR-derived maximum canopy height and FVC were the best predictors of grassland AGB (R2 = 0.54, root-mean-square error (RMSE) = 64.76 g/m2). (3) For different UAV flight altitudes from 40 m to 110 m, different flight heights showed no major effect on the derived canopy height. The LiDAR-derived canopy height decreased from 9.19 cm to 8.17 cm, and the standard deviation of the LiDAR-derived canopy height decreased from 3.31 cm to 2.35 cm with increasing UAV flight altitudes. These conclusions could be useful for estimating grasslands in smaller areas and serving as references for other remote sensing datasets for estimating grasslands in larger areas.

Grazing is one of the predominant human activities taking place today inside protected areas, with both direct and indirect effects on the vegetation community. We analyzed the effects of grazing intensity on grass composition during four grazing seasons containing 78 plant species belonging to eight plant functional groups, which include perennial tall grass (6 species), perennial short grass (6 species), shrubs (3 species), legumes (9 species), Liliaceae herb (8 species), annual/biennial plants (11 species), perennial short forbs (16 species) and perennial tall forbs (18 species). We estimated grazing intensity at four levels, control, light, moderate and heavy grazing intensity corresponding to 0.00, 0.23, 0.46 and 0.92 animal units ha−1, respectively. We found that each plant functional group showed a different response to grazing intensity. Perennial tall grasses that were dominated by high palatable mesophyte and mesoxerophyte grass showed a significant decrease with grazing intensity, while the medium palatable xerophyte and widespread grasses that were the predominant short perennial increases with grazing intensity. The perennial tall forbs that were dominated by the mesophyte grass also decreased, but the decrease was statistically insignificant. The influence of grazing density on species is also related to soil factors (soil nutrient, soil moisture and soil temperature and soil bulk density). Some functional groups such as tall fescue and Liliaceae herbs, remained stable—which may be related to the changes in the soil environment caused by grazing activities. The findings of this study could provide a standpoint for assessing the current grazing management scenarios and conducting timely adaptive practices to maintain the long-term ability of grassland systems to perform their ecological functions.

The management practices required for grazing management will continue to increase, as necessitated by the reported rate of reduction in productivity, coupled with the degradation of Inner Mongolian steppe ecosystems. The current study was conducted to (i) examine the responses of aboveground net primary production (ANPP) to different grazing intensities and its relationship with soil factors and (ii) study the effects of grazing intensity on herbage growth and dry matter intake in Hulunber grasslands, Northeastern China. Six grazing rate treatments (G0.00, G0.23, G0.34, G0.46, G0.69, and G0.92 animal unit (AU ha−1) for zero, two, three, four, six, and eight young cows with ranging weight of 250–300 kg/plot), with three replications, were established during two consecutive growing seasons in 2017 and 2018. Our study concentrated on the grazing-induced degradation processes by different intensities of grazing. The highest decrease in aboveground biomass (AGB) was 64.1% and 59.3%, in 2017 and 2018, respectively, by the G0.92 treatment as compared with the G0.00 treatment. There was a positive relationship between yearly precipitation and ANPP. The grazing tolerance and growth rate of forage were higher in the wet year than in the dry year. Understanding the ecological consequences of grazing intensity provides useful information for assessing current grazing management scenarios and taking timely adaptation measures to maintain grassland capacity in a short and long-term system.

Species composition and biomass are two important indicators in assessing the effects of restoration measures of degraded grasslands. In this paper, we present a field study on the temporal changes in plant community characteristics, species diversity and biomass production in a degraded temperate meadow steppe in response to an enclosure measure in Hulunbuir in Northern China. Our results showed that the plant community responded positively to the fence enclosure in terms of vegetation coverage, height, above- and belowground biomass. A year-to-year increase in aboveground biomass was observed, and this increase plateaued at the ninth year of the enclosure. Our results also showed that the existing dominant and foundation species gained predominance against other species. The sum of the biomass of these two species was more than doubled after the ninth year of the enclosure. However, belowground biomass only briefly increased until the fifth year of the enclosure and then decreased until the end of the experimental period. Plant diversity, evenness, and richness indices showed similar trends to that of belowground biomass. Overall, we found that the degraded temperate meadow steppe responded significantly positively to the enclosure treatment, but an optimal condition was only reached after approximately 5–7 years of continuous protection, providing a solid use case for grassland conservation and management at regional scales.

Understanding the joint impact of anthropologic and climatic changes on ecosystem function and dynamics is among the frontiers in global environmental change studies. Carbon and water balances are especially crucial to the sustainable ecosystem and functional returns in sensitive regions such as the Mongolian Plateau. In this study, the significance of non-climatic component (NCC) on carbon and water use efficiency (CUE and WUE) is quantified among the ecosystem types on the Mongolian Plateau. We mapped the spatial gradients of carbon/water balance and delineated the hotspots of NCC-driven CUE and WUE for 2000–2013 using gross and net primary production (GPP and NPP) and evapotranspiration (ET) products derived from the MODIS databases. Significantly higher CUE and WUE values were found in Mongolia (MG) than in Inner Mongolia (IM) due to both climatic forcing (CF) and NCC. NCC was found to dominate the changes in CUE and WUE in the steppes on the plateau by over 16% and 22%, respectively, but with spatially uneven distributions. NCC-driven WUE values were much higher than those driven by CF. The hotspots for NCC-driven CUE did not overlap with those of WUE, with CUE hotspots concentrated in the east of MG and northeast of IM; WUE hotspots were found in the central and Khangai regions of MG and eastern regions of IM. The NCC-driven CUE area in MG was from population growth and the industrial shares in gross domestic product, while the NCC-driven WUE area was due to livestock growth in MG but driven by the growth of cultivated lands in IM. In sum, we conclude that NCC provoked substantial spatiotemporal changes on carbon and water use. CF and NCC effects on carbon and water balance varied in space, by ecosystem type, and between the two political entities.

Vegetation phenology has been used in studies as an indicator of an ecosystem’s responses to climate change. Satellite remote sensing techniques can capture changes in vegetation greenness, which can be used to estimate vegetation phenology. In this study, a long-term vegetation phenology study of the Greater Khingan Mountain area in Northeastern China was performed by using the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index version 3 (NDVI3g) dataset from the years 1982–2012. After reconstructing the NDVI time series, the start date of the growing season (SOS), the end date of the growing season (EOS) and the length of the growing season (LOS) were extracted using a dynamic threshold method. The response of the variation in phenology with climatic factors was also analyzed. The results showed that the phenology in the study area changed significantly in the three decades between 1982 and 2012, including a 12.1-day increase in the entire region’s average LOS, a 3.3-day advance in the SOS and an 8.8-day delay in the EOS. However, differences existed between the steppe, forest and agricultural regions, with the LOSs of the steppe region, forest region and agricultural region increasing by 4.40 days, 10.42 days and 1.71 days, respectively, and a later EOS seemed to more strongly affect the extension of the growing season. Additionally, temperature and precipitation were closely correlated with the phenology variations. This study provides a useful understanding of the recent change in phenology and its variability in this high-latitude study area, and this study also details the responses of several ecosystems to climate change.

Validation is an essential and important step before the application of remote sensing products. This paper introduces a prototype of the validation network for remote sensing products in China (VRPC). The VRPC aims to improve remote sensing products at a regional scale in China. These improvements will enhance the applicability of the key remote sensing products in understanding and interpretation of typical land surface processes in China. The framework of the VRPC is introduced first, including its four basic components. Then, the basic selection principles of the observation sites are described, and the principles for the validation of the remote sensing products are established. The VRPC will be realized in stages. In the first stage, four stations that have improved remote sensing observation facilities have been incorporated according to the selection principles. Certain core observation sites have been constructed at these stations. Next the Heihe Station is introduced in detail as an example. The three levels of observation (the research base, pixel-scale validation sites, and regional coverage) adopted by the Heihe Station are carefully explained. The pixel-scale validation sites with nested multi-scale observation systems in this station are the most unique feature, and these sites aim to solve some key scientific problems associated with remote sensing product validation (e.g., the scale effect and scale transformation). Multi-year of in situ measurements will ensure the high accuracy and inter-annual validity of the land products, which will provide dynamic regional monitoring and simulation capabilities in China. The observation sites of the VRPC are open, with the goal of increasing cooperation and exchange with global programs.

This study investigated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) and GEOLAND2 Version 1 (GEOV1) Leaf Area Index (LAI) products using ground measurements and LAI reference maps over four sites in North China for 2011–2013. The Terra + Aqua MODIS and Terra MODIS LAI retrieved by the main algorithm and GEOV1 LAI within the valid range were evaluated and intercompared using LAI reference maps to assess their uncertainty and seasonal variability The results showed that GEOV1 LAI is the most similar product with the LAI reference maps (R2 = 0.78 and RMSE = 0.59). The MODIS products performed well for biomes with low LAI values, but considerable uncertainty arose when the LAI was larger than 3. Terra + Aqua MODIS (R2 = 0.72 and RMSE = 0.68) was slightly more accurate than Terra MODIS (R2 = 0.57 and RMSE = 0.90) for producing slightly more successful observations. Both MODIS and GEOV1 products effectively followed the seasonal trajectory of the reference maps, and GEOV1 exhibited a smoother seasonal trajectory than MODIS. MODIS anomalies mainly occurred during summer and likely occurred because of surface reflectance uncertainty, shorter temporal resolutions and inconsistency between simulated and MODIS surface reflectances. This study suggests that further improvements of the MODIS LAI products should focus on finer algorithm inputs and improved seasonal variation modeling of MODIS observations. Future field work considering finer biome maps and better generation of LAI reference maps is still needed.

The leaf area index (LAI) is a crucial parameter of vegetation structure. It provides key information for earth surface process simulations and climate change research on the global and regional scales. Focusing on the meadow steppe in Hulunber, Inner Mongolia, China, the present study assessed the accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product in the study area. First, seven field campaigns collecting ground-based measurements were conducted during the growing season in 2013, and 252 pairs of LAIs and spectra were collected. Then, seven scenes of high-resolution LAI maps were obtained from the corresponding 30 m Chinese HJ-1A/1B charge-coupled diode (CCD) images by employing a regression approach. Finally, comparisons between the MODIS LAI product and the high resolution LAI maps were made to determine the accuracy of the MODIS LAI product. Moreover, the corresponding 500 m MODIS LAI maps were derived from the daily MODIS surface reflectance product to support the findings using the 1 km HJ LAI product and the ground-based comparison. The results showed that, compared to the ground data, the MODIS LAI product followed a reasonable seasonal trajectory during the growing season. However, an anomaly existed at the beginning of the growing season. Also, a slight overestimation was found for the MODIS LAI product compared to the HJ-retrieved LAI maps. The average overestimation for the LAI was approximately 0.4 m2/m2, and the relative absolute errors of the product ranged from 10%–50%. The overestimation at the beginning and end of the growing season was higher due to the interference of soil background and grass variation. The results of this study provide a comprehensive understanding of the accuracy of the regional MODIS LAI product for the Hulunber meadow steppe. This research is important for improving regional modeling and prediction of vegetation biogeochemical processes and earth system productivity.

Previous studies have strongly recommended that KISS-1 metastasis suppressor (KISS1) plays an essential gatekeeper of the initiation of reproductive maturation in mammals. However, KISS1 has been recently reported to highly express in ovarian granulosa cells (GCs). But the biological functionalities of KISS1 on cell apoptosis, cell cycle, and synthesis of estradiol-17β (E2) have not been explored in GCs. In this study, using porcine GCs as a cellular model, the overexpression plasmid of KISS1 was built to explore the biological effects of KISS1 on the PI3K signaling pathway, estrogen signaling pathway, cell apoptosis, cell cycle, and E2 secretion. We found that mRNA of KISS1 highly expressed in the ovary and significantly increased from immature to mature follicles in gilts. Overexpression of KISS1 could significantly increase the mRNA expression of PIK3CG, PIK3C1, and PDK1, and significantly decreased the mRNA levels of FOXO3, TSC2, and BAD of PI3K signaling pathway. Furthermore, results of the flow cytometry showed that overexpression of KISS1 significantly inhibited the apoptosis of GCs and decreased the percentage of GCs at G0/G1 phase of the cell cycle. Additionally, overexpression of KISS1 could increase the mRNA levels of Star, CYP17, 3B-HSD, 17B-HSD of estrogen synthesis signaling pathway, significantly increase the concentration of E2 in the supernatant of the cultured GCs, and up-regulate the mRNA expression levels of ESR1 and ESR2. These results suggested that KISS1 might suppress cell apoptosis through activating the PI3K signaling pathway and stimulate synthesis of E2 via boosting the estrogen synthesis signaling pathway. This study would be of great interests for exploring the biological functionalities of KISS1 in the folliculogenesis and sex steroid production of the ovaries in mammals.

It is important to accurately evaluate ecosystem respiration (RE) in the alpine grasslands of the Tibetan Plateau and the temperate grasslands of the Inner Mongolian Plateau, as it serves as a sensitivity indicator of regional and global carbon cycles. Here, we combined flux measurements taken between 2003 and 2013 from 16 grassland sites across northern China and the corresponding MODIS land surface temperature (LST), enhanced vegetation index (EVI), and land surface water index (LSWI) to build a satellite-based model to estimate RE at a regional scale. First, the dependencies of both spatial and temporal variations of RE on these biotic and climatic factors were examined explicitly. We found that plant productivity and moisture, but not temperature, can best explain the spatial pattern of RE in northern China’s grasslands; while temperature plays a major role in regulating the temporal variability of RE in the alpine grasslands, and moisture is equally as important as temperature in the temperate grasslands. However, the moisture effect on RE and the explicit representation of spatial variation process are often lacking in most of the existing satellite-based RE models. On this basis, we developed a model by comprehensively considering moisture, temperature, and productivity effects on both temporal and spatial processes of RE, and then, we evaluated the model performance. Our results showed that the model well explained the observed RE in both the alpine (R2 = 0.79, RMSE = 0.77 g C m−2 day−1) and temperate grasslands (R2 = 0.75, RMSE = 0.60 g C m−2 day−1). The inclusion of the LSWI as the water-limiting factor substantially improved the model performance in arid and semi-arid ecosystems, and the spatialized basal respiration rate as an indicator for spatial variation largely determined the regional pattern of RE. Finally, the model accurately reproduced the seasonal and inter-annual variations and spatial variability of RE, and it avoided overestimating RE in water-limited regions compared to the popular process-based model. These findings provide a better understanding of the biotic and climatic controls over spatiotemporal patterns of RE for two typical grasslands and a new alternative up-scaling method for large-scale RE evaluation in grassland ecosystems.