Baseflow estimation based on a self-adaptive non-linear reservoir algorithm in a rainy watershed of eastern China.

Accurate baseflow estimation is critical for water resources evaluation and management, and non-point source pollution quantification. Nonlinear reservoir algorithm (NRA) has been increasingly applied to baseflow separation because of its good approximation to the real groundwater discharge (commonly dominated by the unconfined aquifer) in most watersheds. However, in the rainy regions, large uncertainties may remain in the traditional NRA-separated baseflow sequences due to its empirical transition function for the rising limb of discharge process, and the evident variations of baseflow recession in the initial period of the falling limb caused by the disturbance from surface flow or rainfall events. To improve the reliability of baseflow separation, a self-adaptive non-linear reservoir algorithm (SA-NRA) was developed in this study based on the NRA, a self-adaptive groundwater discharge modified parameter, and the Particle Swarm Optimization algorithm (PSO). The validation of SA-NRA in a rainy watershed of eastern China showed that SA-NRA could be the approach to provide a goodness-of-fit for baseflow recession behaviors in the rainy regions. The traditional NRA and Eckhardt's two-parameter recursive digital filter (ERDF), calibrated (or validated) only with the pure baseflow recession data, can hardly provide reliable baseflow predictions for the non-pure baseflow recession periods (including the rising limb and the falling limb with surface flow or rainfall disturbance) due to the apparent variations of baseflow recession behavior. Therefore, more attentions should be paid to the uncertainties of baseflow separation for the non-pure baseflow recession periods in the rainy regions.

Vegetation patterns governing the competitive relationship between runoff and evapotranspiration using a novel water balance model at a semi-arid watershed.

Vegetation patterns play an important role in precipitation partitioning into hydrological components, especially evapotranspiration and runoff. However, few studies focus on their competitive relationship and the influence of the vegetation on them. In this study, a vegetation threshold was postulated to prevent further decrease of runoff by determining a new hydrological component continuing evapotranspiration (partitioned from total and initial evapotranspiration) through a novel model coupled with the Budyko model (dimensional form) and two-stage partitioning model (nondimensional form) in the semi-arid watershed. The results showed significant correlations between model parameters ε (underlying surface index), λ (ratio of initial evapotranspiration) and vegetation coverage (M) (R2 = 0.95 and 0.97, p < 0.01) b Based on the modified Budyko model and λ. The nondimensional model showed high-precise estimations of KH (Horton index Fraction), KB (Baseflow Fraction), KV (evapotranspiration Fraction), KR (runoff Fraction), and KC (continuing evapotranspiration Fraction) (R2 > 0.98, p < 0.01) as a function of a new aridity index φ. KH, KB, KV, KR, showed symmetrical patterns correlated with φ both at between-subwatershed and between-year scale based on the dimensional model and λ. However, KC showed asymmetrical different correlation with M3 and φ (KC/M3 ∼ φ: in between-subwatershed: R2 = 0.92, p < 0.01; and between-year scale: R2 = 0.74, p < 0.01). Based on the solution of continuing evapotranspiration, the vegetation threshold has been solved with M = 0.73 (+0.09/-0.02) to prevent further decreasing runoff. The framework presented can be applied in other semi-arid watersheds worldwide to better protect the sustainability of the hydro-ecosystems.

Pathogen Identification and Factors Influencing Infection Frequency and Severity of Fungal Rust in Four Native Grasses in Hulunber Grassland, China.

A serious rust infection present in 2014 and 2015 on the dominant grass species (Leymus chinensis) in the Hulunber grassland of Inner Mongolia, China, and also present on three other grass species (Agropyron cristatum [wheat grass], Bromus inermis, and Festuca ovina) was investigated. Field surveys, laboratory determination of morphological characteristics, pathogenicity tests, and molecular identification methods were integrated to identify two rust-causing pathogens on L. chinensis. It was found that Puccinia elymi was the major pathogen of L. chinensis, and also infected A. cristatum and F. ovina. This is the first report of P. elymi on A. cristatum in China. P. striiformis caused stripe rust on L. chinensis and B. inermis. The incidence and severity of rust infection increased through the growing season, presumably from asexual spread by urediniospores, and was higher on grass species phylogenetically more closely related to common crop hosts of the pathogens. High host grass density and presence of a potential alternate host for P. elymi, Thalictrum squarrosum, were two further factors promoting rust incidence. These results provide insight into ecological factors linked to the rust epidemic and provide a theoretical basis for the formulation of control strategies.

Response of Plant Fungal Diseases to Beef Cattle Grazing Intensity in Hulunber Grassland.

The effects of grazing by large herbivores on biodiversity and ecosystem functioning have been extensively studied, whereas how grazing influence plant diseases, especially in natural grasslands, remains poorly understood. Therefore, we undertook a field study regarding a grazing trial in a temperate meadow steppe grassland to investigate mechanisms underlying grazing-host-pathogen interactions. The effects of cattle grazing at different grazing intensities of 0, 0.23, 0.34, 0.46, 0.69, and 0.92 AU/ha (where 1 AU= 500 kg of adult cattle) on the microenvironment, vegetation characteristics, and occurrence of diseases were evaluated. At the population level, the effects of grazing on grassland vegetation characteristics and disease varied with grassland plant species. Compared with nongrazing, grazing directly decreased the average density, coverage, and disease incidence of palatable and edible forages by 51.4, 62.4, and 82.4% in the 0.92 AU/ha treatment but increased the occurrence and prevalence of disease in remaining small herbs by 752.1%. At the community level, with the increase of grazing intensity, the pathogen load of the whole community in grassland was positively related to host coverage. In addition, there was a trend toward increased microtemperature and decreased microhumidity with increased grazing. Although occurrence of plant diseases in natural grasslands is influenced by a range of factors, comprehensive analysis highlighted the major role that cattle grazing intensity plays in the occurrence of plant diseases in natural grasslands. In addition to its direct effect, grazing also indirectly affects disease occurrence by shifting plant community structure and the microenvironment. However, direct effects of grazing intensity affected disease occurrence more than indirect effects.

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.

Managed grassland alters soil N dynamics and N2O emissions in temperate steppe.

Reclamation of degraded grasslands as managed grasslands has been increasingly accelerated in recent years in China. Land use change affects soil nitrogen (N) dynamics and nitrous oxide (N2O) emissions. However, it remains unclear how large-scale grassland reclamation will impact the grassland ecosystem as a whole. Here, we investigated the effects of the conversion from native to managed grasslands on soil N dynamics and N2O emissions by field experiments in Hulunber in northern China. Soil (0-10cm), nitrate (NO3-), ammonium (NH4+), and microbial N were measured in plots in a temperate steppe (Leymus chinensis grassland) and two managed grasslands (Medicago sativa and Bromus inermis grasslands) in 2011 and 2012. The results showed conversion of L. chinensis grassland to M. sativa or B. inermis grasslands decreased concentrations of NO3--N, but did not change NH4+-N. Soil microbial N was slightly decreased by the conversion of L. chinensis grassland to M. sativa, but increased by the conversion to B. inermis. The conversion of L. chinensis grassland to M. sativa (i.e., a legume grass) increased N2O emissions by 26.2%, while the conversion to the B. inermis (i.e., a non-legume grass) reduced N2O emissions by 33.1%. The conversion from native to managed grasslands caused large created variations in soil NO3--N and NH4+-N concentrations. Net N mineralization rates did not change significantly in growing season or vegetation type, but to net nitrification rate. These results provide evidence on how reclamation may impact the grassland ecosystem in terms of N dynamics and N2O emissions.

Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar.

Accurate canopy structure datasets, including canopy height and fractional cover, are required to monitor aboveground biomass as well as to provide validation data for satellite remote sensing products. In this study, the ability of an unmanned aerial vehicle (UAV) discrete light detection and ranging (lidar) was investigated for modeling both the canopy height and fractional cover in Hulunber grassland ecosystem. The extracted mean canopy height, maximum canopy height, and fractional cover were used to estimate the aboveground biomass. The influences of flight height on lidar estimates were also analyzed. The main findings are: (1) the lidar-derived mean canopy height is the most reasonable predictor of aboveground biomass (R² = 0.340, root-mean-square error (RMSE) = 81.89 g·m-2, and relative error of 14.1%). The improvement of multiple regressions to the R² and RMSE values is unobvious when adding fractional cover in the regression since the correlation between mean canopy height and fractional cover is high; (2) Flight height has a pronounced effect on the derived fractional cover and details of the lidar data, but the effect is insignificant on the derived canopy height when the flight height is within the range (<100 m). These findings are helpful for modeling stable regressions to estimate grassland biomass using lidar returns.

The effects of organic modifier on physicochemical and chromatographic characteristics of self-assembled micelle from poly (stearyl methacrylate-co-methacrylic acid) in electrokinetic chromatography.

In our previous work, organic solvents, especially 1-butanol, play a key role to separate highly hydrophobic analytes in EKC using the polymeric micelle self-assembled from amphiphilic random copolymer poly (stearyl methacrylate-co-methyl acrylic acid) (P(SMA-co-MAA)) as a novel pseudostationary phase. Herein, the influences of organic solvents on polymeric micelle physicochemical properties including environmental micropolarity and dimension, as well as chromatographic characteristics containing elution window, hydrophobic selectivity and polar group selectivity were investigated in detail. P(SMA-co-MAA) has extremely low CMC of 1.26 × 10(-6) g/mL, and the self-assembled micelles with selective solvent method had regular spherical structure with diameter about 50 nm. The experimental results showed that methanol, isopropanol or acetonitrile molecules could not penetrate into the interior of the polymeric micelles, and mainly affected the properties of surrounding running buffer. Too much these organic solvents led to elution window narrowed down, methylene selectivity and group selectivity weaken, even the micelles collasped. Whereas, addition of 2% 1-butanol did not influence the elution window, instead, improved the hydrophobic selectivity. Furthermore, both better group selectivity and faster migration for relatively hydrophobic analytes could be achieved simultaneously. It indicated that 1-butanol could insert into the polymeric micelle and 2% 1-butanol was enough to modify the structure of the micelles.

Optimization of Maximum Light Use Efficiency in Inner Mongolian Steppe.

For the case that the value of the maximum light use efficiency (MLUE) is not optimized for different steppes, we simulated the MLUE for meadow steppe, typical steppe and desert steppe in Inner Mongolia based on the field observed NPP and CASA ecosystem model, and analyzed the spatial and temporal pattern of the LUE and net primary productivity (NPP) in Inner Mongolia. The result indicate that the MLUE is optimized to be 0.654，0.553 and 0.511 gC·MJ-1 for meadow steppe, typical steppe and desert steppe in Inner Mongolia, respectively, with an average of 0.573 gC·MJ-1. Compared to the result that used same value of 0.541 gC·MJ-1 for MLUE, the correlation coefficient and relative mean square error was improved 0.024 and 2.62 gC·(m2·month-1)-1, respectively after optimization. Affected by the hydrothermal condition and distribution of grassland types, the LUE and NPP in Inner Mongolia decreased from northeast to southwest, and showed one crest shape. However, the maximum value of LUE and NPP was appeared in August and July. This difference could be attributed to the difference in the maximum value between absorbed photosynthetically active radiation and LUE. The LUE and NPP decreased by meadow steppe, typical steppe and desert steppe.

Evaluation and intercomparison of MODIS and GEOV1 global leaf area index products over four sites in North China.

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.

Comparative effectiveness of carbon nanoparticles and biochar in alleviating copper stress in corn (Zea mays L.).

The application of carbon nanoparticles (CNPs) and biochar in agriculture for improving plant health and soil quality and alleviating metal stress offers alternative approaches to meet the ever-increasing demand for food. However, poor understanding of their roles in improving crop production under Cu stress represents a significant obstacle to their wide application in agriculture. To clarify how CNPs and biochar affect corn (Zea mays L.) seed germination, seedling growth, plant health, and nutrient uptake under different Cu stress levels, soil-less Petri-dish and greenhouse soil-based bioassays were conducted. The results revealed that CNPs and biochar stimulated corn seed germination and seedling growth. Besides, they were effective in immobilizing Cu2+ sorption in sandy soil and alleviating Cu stress for plant growth, as shown by the increased plant height and dry biomass. The plant nutrient uptake efficiency (NUE) was significantly increased by CNPs, with a maximum increase of 63.1% for N and 63.3% for K at the highest Cu2+ stress level (400 mg Cu2+ L-1). In contrast, non-significant effects on NUE were observed with biochar treatments regardless of Cu stress levels. Interestingly, CNPs significantly increased plant uptake of Cu in the Petri dish test, while biochar inhibited plant uptake of Cu under both experimental conditions. Principle component analysis (PCA) and Pearson correlation analysis indicated that CNPs mitigated Cu stress mainly by elevating antioxidant enzyme activities, enhancing plant photochemical efficiency, and increasing plant uptake of N and K, while biochar was more likely to reduce bioavailability and uptake of Cu in the plant. These findings have great implications for the application of CNPs and biochar as plant growth stimulators and de-toxicity agents in agriculture.

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.

The greenhouse gas emissions from meat sheep production contribute double of household consumption in a Eurasian meadow steppe.

With the rapid development of the economy, household activities have emerged as an important source of greenhouse gas (GHG) emissions, making them a crucial focal point for research in the pursuit of sustainable development and carbon emission reduction. Hulunber, as a typical steppe region in eastern Eurasia, is representative of studying the GHG emissions from household ranches, which are the basic production units in this region. In this paper, based on survey data of 2018 and 2019, we quantified and assessed GHG emissions from household ranches by combining life cycle assessment (LCA) and structural equation modeling (SEM) approaches, with LCA to define household ranches system boundary and SEM to determine the key driving factors of emissions. The results showed that GHG emissions of meat sheep live weight was 23.54 kg CO2-eq/kg. The major contributor to household GHG emissions was enteric methane (55.23 %), followed by coal use (20.80 %) and manure management systems (9.16 %), and other contributing factors (14.81 %). The SEM results indicated that the GHG emissions from household ranches were derived primarily by economic level, while the economic level was significantly affected by income. This study also found a significant positive and linear correlation between household GHG emissions and the number of meat sheep (R2 = 0.89, P < 0.001). The GHG emissions from meat sheep production (67.52 %) were double times greater than household livelihood consumption (32.48 %). These findings emphasized the importance of reducing emissions from meat sheep production and adjusting the energy mix of household livelihood, contributing to the establishment of a low-carbon household livelihood operation.

A 10-m annual grazing intensity dataset in 2015-2021 for the largest temperate meadow steppe in China.

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.

Soil environmental anomalies dominate the responses of net ecosystem productivity to heatwaves in three Mongolian grasslands.

Climate change is causing more frequent and intense heatwaves. Therefore, it is important to understand how heatwaves affect the terrestrial carbon cycle, especially in grasslands, which are especially susceptible to climate extremes. This study assessed the impact of naturally occurring, simultaneous short-term heatwaves on CO2 fluxes in three ecosystems on the Mongolia Plateau: meadow steppe (MDW), typical steppe (TPL), and shrub-grassland (SHB). During three heatwaves, net ecosystem productivity (NEP) was reduced by 86 %, 178 %, and 172 % at MDW, TPL, and SHB, respectively. The changes in ecosystem respiration, gross primary production, evapotranspiration, and water use efficiency were divergent, indicating the mechanisms underlying the observed NEP decreases among the sites. The impact of the heatwave in MDW was mitigated by the high soil water content, which enhanced evapotranspiration and subsequent cooling effects. However, at TPL, insufficient soil water led to combined thermal and drought stress and low resilience. At SHB, the ecosystem's low tolerance to an August heatwave was heavily influenced by species phenology, as it coincided with the key phenological growing phase of plants. The potential key mechanism of divergent NEP response to heatwaves lies in the divergent stability and varying importance of environmental factors, combined with the specific sensitivity of NEP to each factor in ecosystems. Furthermore, our findings suggest that anomalies in soil environment, rather than atmospheric anomalies, are the primary determinants of NEP anomalies during heatwaves. This challenges the conventional understanding of heatwaves as a discrete and ephemeral periods of high air temperatures. Instead, heatwaves should be viewed as chronologically variable, compound, and time-sensitive environmental stressors. The ultimate impact of heatwaves on ecosystems is co-determined by a complex interplay of environmental, biological, and heatwave features.

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.

Physicochemical characteristics and microbial community succession during oat silage prepared without or with Lactiplantibacillus plantarum or Lentilactobacillus buchneri.

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.

Carbon nanomaterials are a superior soil amendment for sandy soils than biochar based on impacts on lettuce growth, physiology and soil biochemical quality.

A significant bottleneck of current agricultural systems remains the very low agronomic efficiency of conventional agrochemicals, particularly in sandy soils. Carbon nanomaterials (CNMs) have been proposed to address this inefficiency in sandy soils, which could potentially improve soil fertility and enhance crop growth and physiological processes. However, the effects of different rates of CNMs on crop physiological and soil biochemical quality in sandy soils must be compared to other carbon sources (e.g., biochar) before CNMs can be broadly used. To address this, a 70-day pot experiment was set up, growing lettuce under ten treatments: a negative control with no CNMs, biochar or fertilizer; a fertilizer-only control; three CNMs-only unfertilized treatments (CNMs at 200, 400 and 800 mg kg-1 soil); two biochar treatments with fertilizer (biochar at 0.5% and 1% by soil mass + fertilizer); and three CNMs treatments with fertilizer (CNMs at 200, 400 and 800 mg kg-1 soil + fertilizer). A novel amorphous, water-dispersible, and carboxyl-functionalized CNMs with pH of 5.5, zeta potential of -40.6 mV and primary particle diameter of 30-60 nm was used for this experiment. Compared to the fertilizer-only control, CNMs applied at low to medium levels (200-400 mg kg-1) significantly increased lettuce shoot biomass (20-21%), total chlorophyll (23-27%), and fluorescence and photosynthetic activities (4-10%), which was associated with greater soil nutrient availability (N: 24-58%, K: 68-111%) and higher leaf tissue accumulation (N: 25-27%; K: 66%). Low to medium levels of CNMs also significantly increased soil biochemical properties, such as higher soil microbial biomass carbon (27-29%) and urease enzyme activity (34-44%) relative to fertilizer-only applications. In contrast, biochar (0.5%) increased lettuce biomass relative to fertilizer-only but had no significant effect on soil fertility and biological properties. These results suggest that CNMs at low to medium application rates are a superior carbon-based amendment relative to biochar in sandy soils.

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.

Zinc glycerolate (Glyzinc): A novel foliar fertilizer for zinc biofortification and cadmium reduction in wheat (Triticum aestivum L.).

Sustainable strategies are essential for zinc (Zn) biofortification and cadmium (Cd) reduction in staple food crops. Herein, we evaluated the phytotoxicity of Glyzinc under foliar and root application (FA&RA) in a lab-scale experiment, and then investigated its Zn efficiency and Cd reduction through foliar application on wheat (Triticum aestivum L.) under field conditions. Compared to RA, FA of Glyzinc exhibited no adverse effect on wheat growth and oxidative stresses at all doses. In field conditions, FA of Glyzinc remarkably increased Zn (28.7 %), S (10.4 %), Cu (17.3 %) and crude protein (9.1 %) content in wheat grain at 100 mg/L without damaging wheat yield. Furthermore, FA of Glyzinc significantly reduced the grain phytic acid (PA) (23.7 %) and Cd level (19.5 %), as well as PA to Zn molar ratio (32.3 %). Overall, our results indicate that Glyzinc has great potential as a high-efficiency foliar fertilizer for Zn biofortification and safe crop production in nano-enabled agriculture.