Production and characterization of coconut shell charcoal-based bio-briquettes as an alternative energy source for rural communities.

The increasing demand for sustainable energy solutions has driven interest in the utilization of agricultural residues, such as coconut shells, for bio-briquette production. This study investigates the impact of binder types (cassava and corn) and concentrations (5 wt%, 10 wt%, 15 wt%) on the properties of bio-briquettes made from dried coconut shells with two particle sizes (40 mesh and 60 mesh). The experimental evaluation focuses on several performance indicators, including density, shatter index, percentage moisture content (PMC), percentage volatile matter (PVM), percentage ash content (PAC), percentage fixed carbon (PFC), higher heating value (HHV), ignition time, burning time, and boiling time. The results indicate that briquettes with 10 % fine charcoal cassava binder achieved the highest density of 0.764 g/cm³ due to improved compaction. Briquettes with 15 % coarse charcoal corn binder demonstrated the highest shatter resistance at 96.99 %, reflecting their superior structural integrity. The highest PMC and PVM values were observed in briquettes with 15 % coarse charcoal cassava binder, at 8.13 % and 31.25 %, respectively. Conversely, the highest PAC was 16.34 % for 5 % coarse charcoal cassava binder. Briquettes with 10 % fine charcoal corn binder exhibited the highest PFC of 70.79 % and HHV of 31.51 MJ/kg. Boiling times ranged from 15 min 53 s to 36 min 35 s, with the shortest boiling time for briquettes with 10 % fine charcoal corn binder. The findings highlight the superior mechanical properties and energy performance of bio-briquettes with specific binder concentrations and particle sizes. This study demonstrates the potential of coconut shell bio-briquettes as a viable and sustainable energy source, offering economic and environmental benefits through the effective utilization of agricultural waste and reduction of carbon emissions.

Temporal changes in net ecosystem CO2 exchange and influential factors in an apple orchard in Northeast China.

The apple orchards in Liaoning, one of the four major apple-producing areas in Bohai Bay, Northeast China, play a crucial role in regulating the carbon sink effect. However, there is limited information on the variation in carbon flux and its influential factors in apple orchards in this region. To address this, CO2 flux data were monitored throughout the entire apple growth seasons from April to November in 2017 and 2018 in the apple (Malus pumila Mill. cv Hanfu) orchard in Shenyang, China. The energy closure of the apple orchard was calculated, and variations in net ecosystem exchange (NEE) at different time scales and its response to environmental factors were analyzed. Our results showed that the energy balance ratio of the apple was 0.74 in 2017 and 1.38 in 2018. NEE was generally positive in April and November and negative from May to October, indicating a strong carbon sink throughout the growth season. The daily average NEE ranged from - 0.103 to 0.094 mg m-2 s-1 in 2017 and from - 0.134 to 0.059 mg m-2 s-1 in 2018, with the lowest values observed in June and July. NEE was negatively correlated with net radiation, atmospheric temperature, saturated vapor pressure deficit, and soil temperature. These findings provide valuable insights for predicting carbon flux in orchard ecosystems within the context of global climate change.

Moisture Impact on Static and Dynamic Modulus of Elasticity in Structural Normal-Weight Concretes.

In the case of concrete built into a structure, the static secant modulus of elasticity (Ec,s) is often estimated based on its dynamic value (Ed) measured by the ultrasonic pulse velocity method instead of direct tests carried out on drilled cores. Meanwhile, the prevailing equations applied to estimate Ec,s often overlook the impact of concrete moisture. This study aimed to elucidate the moisture impact across two normal-weight structural concretes differing in compressive strength (51.6 and 71.4 MPa). The impact of moisture content was notably more evident only for the weaker concrete, according to dynamic modulus measurements. In other cases, contrary to the literature reports and expectations, this effect turned out to be insignificant. These observations may be explained by two factors: the relatively dense and homogeneous structure of tested concretes and reduced sensitivity of Ec,s measurements to concrete moisture condition compared to Ed measurements obtained using the ultrasonic method. Additionally, established formulas to estimate Ec,s were verified. The obtained modulus results tested under different moisture conditions of normal-weight concretes were also compared with those of lightweight aggregate concretes of identical volume compositions previously obtained in a separate study.

Regional wood chip quality parameters decomposition and price linkage with impact on Polish energy sustainability: Time frequency analysis between 2013 and 2019.

The study aims to analyze and decompose the qualitative parameters of wood chips in Poland. The European Green Deal brings the new framework to support sustainability and elimination of emissions. The Wavelet coherence and Wavelet Discrete Decomposition are used for determination of relations among significant qualitative parameters. Thus, the possible uses are discussed. For the obvious relationship between moisture and calorific value there is evidence of strong correlation. The behaviour of these interrelations are different at frequencies in the long and short time. The wood chip price is inter-transmitter from moisture parameter to calorific value in a positive (in-phase) relationship. At both low and high frequencies there is evidence that the variables of moisture and calorific value are highly correlated. The transient effect of linkage is presented at values between 0.1 and 0.3 in coherence map. The empirical findings provide implication for local producers and policymakers.

Experimental study on the effect of freeze-thaw cycles to the cohesion and moisture content of geogrid reinforced silty clay.

The freezing and thawing cycle is one of the primary causes of damage and instability to buildings in seasonal frost regions. During this process, the mechanical properties of soil are affected, leading to settlement, cracking, or deformation of infrastructure. Mitigating or reducing the occurrence of building frost damage in seasonal frost regions has become an important subject of study. Freeze-thaw (F-T) action will influence the distribution of moisture inside the reinforced soil and change the strength of thawing soil, which is closely related to the main influencing factors, such as initial moisture content, compaction degree, reinforced spacing, number of freeze-thaw cycles (FTC), freezing temperature, and effective vertical stress. Cohesion is an important index to determine the shear strength of clay, which is important to analyze the change in cohesion after F-T. Meanwhile, cohesion is closely related to soil moisture content. This study conducted orthogonal experiments on these primary influencing factors (6 factors at 5 levels) through FTC tests, triaxial tests, and moisture content tests to determine the undrained cohesion and moisture content of the clay after FTC, thereby establishing the influence of reinforcement on soil strength under freeze-thaw conditions. Based on the experimental results, SPSS software was used to fit the regression equations of undrained cohesion and moisture content expressed by the main influencing factors at different heights of the clay. Optimization options for the main influencing factors were obtained with Matlab software when the highest undrained cohesion values 6.8, 10.6, 8.9 kPa and lowest moisture content values 24.0%, 24.3%, 26.2% appeared in upper, middle and lower parts of the testing clay structure respectively, in conditions of - 15 °C freezing temperature and 5 times FTC. And determined the optimal combinations of moisture content, reinforcement spacing, compaction density, and vertical load at different heights. Decreasing reinforced spacing in silty clay was beneficial for liquid underwater seepage after F-T. The redistribution of internal moisture in the soil sample strengthened its undrained cohesion, thereby increasing the soil's shear strength. Comparing reinforcement conditions at different locations, it was found that when there were 3 layers of reinforcement with a spacing of 150 mm between them, this spacing was optimal. It played a significant role in improving the soil's shear strength and enhancing its bearing capacity. For reinforced clay itself, the order of the main factors influencing the undrained cohesion of soil after F-T, from high to low, was initial moisture content, reinforced spacing, and compaction degree.

Effects of moisture content and packaging on color stability of red pepper (Capsicum annuum L.) powder.

The color stability and quality changes of red pepper powder of various initial moisture content (7%, 10%, and 13%) and packaged with different materials, low-density polyethylene (LDPE) and nylon layered polyethylene (Ny/PE) film pouches or plastic and glass bottles, were compared during storage at 20 °C for 126 days. In the 7% moisture content sample packaged in LDPE film and plastic bottles, the L* and b*-values increased from 48.2 ± 0.7 and 15.4 ± 1.1 to 65.2-65.9 and 29.5-30.3, respectively, while the a*-value decreased from 26.1 ± 0.7 to 14.2-15.9, indicating a noticeable color change from red to orange. However, the decrease in the capsaicinoid and free sugar content of red pepper powder over time was found to have no significant correlation with moisture content and packaging type. As a result, the color change during the long-term storage of red pepper powder was greatly influenced by the initial moisture content and gas permeability of the packaging material.

Prediction and analysis of dominant factors influencing moisture content during vacuum screening based on machine learning.

The study of the dominant factors influencing moisture content is essential for investigating vacuum filtration mechanisms. In view of the present situation where there is insufficient experimental data and the dominant factors influencing the moisture content of a filter cake have not been identified, in this study a vacuum filtration apparatus was designed and constructed. Quartz sand particles were used as the filtration material. 300 datasets of moisture contents of a filter cake were obtained under different experimental conditions. Multiple Linear Regression, artificial neural network, decision tree, random forest, and extreme gradient boosting were used to establish a prediction model for moisture content during vacuum screening. By comprehensively analyzing the feature importance rankings and the effects of positive and negative correlations, the dominant factors influencing the moisture content of the filter cake during vacuum screening were the particle ratio, screen mesh, and airflow rate. This finding not only provides a scientific basis for the optimization of vacuum screening technology but also points the way for improving screening efficiency in practical applications. It is of significant importance for deepening the understanding of the vacuum screening mechanism and promoting its extensive application.

Investigating the crystallinity of hard candies prepared and stored at different temperatures with low field-NMR relaxometry.

BACKGROUND: In this study, hard candies were produced by using sucrose, glucose syrup and water. They were cooked at different temperatures, changing from 135 to 145 °C with an interval of 2.5 °C. They were stored at different storage temperatures, which were 25, 4, -18 and -80 °C. Hard candies placed at room temperature were stored for 2 months. In order to understand the crystallization characteristics of the hard candies, time domain (TD) proton nuclear magnetic resonance (1H-NMR) parameters of longitudinal relaxation time (T1) and second moment (M2) measurements were conducted. Moisture contents of the hard candies were determined by Karl-Fischer titration. X-ray diffraction experiments were also conducted as the complementary analysis. RESULTS: Increasing cooking temperature increased the crystallinity and decreased the moisture content of the hard candies significantly (P ≤0.05). Furthermore, storage temperature and storage time had significant effects on the crystallinity of the hard candies (P ≤0.05). The results of T1 and M2 correlated with each other (r > 0.8, P ≤ 0.5) and both produced the highest value at the cooking temperature of 145 °C and storage temperature of 4 °C (P ≤ 0.05). The values of T1 and M2 were obtained as 245.9 ms and 13.0 × 10-8 Hz2, respectively, for the cooking temperature of 145 °C and storage temperature of 4 °C. CONCLUSION: This study demonstrated that the crystallinity of hard candies can be observed and examined by TD-NMR relaxometry, as an alternative to commonly used methods. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Erratum: Utilisation of Lactiplantibacillus plantarum and propionic acid to improve silage quality of amaranth before and after wilting: fermentation quality, microbial communities, and their metabolic pathway.

[This corrects the article DOI: 10.3389/fmicb.2024.1415290.].

Compressibility characteristics of municipal solid waste considering multiple factors.

Borehole samples were collected from a municipal solid waste (MSW) landfill in Xi'an, China, and subjected to a series of basic geotechnical and compression tests. This study aims to investigate the influence of composition, dry unit weight, moisture content, organic content, and landfill age on the compressibility of MSW. The results show that with increasing landfill age, the compressible components and organic content exhibit a decreasing trend while the dry unit weight increases. The moisture content does not vary significantly. There is also a linear trend between the logarithm of the primary compression strain and vertical stress. In addition, with an increase in compressible components content, moisture content, and organic content, the modified primary compression index (Cc') shows an increasing trend, whereas with an increase in dry unit weight and landfill age, Cc' shows a decreasing trend. Furthermore, regarding the 34 sets of data, authors only selected five data points for a detailed comparative analysis, this decision was made on the basis that these data points are representative. A modified primary compression index prediction model that considers the dry unit weight, moisture content, and landfill age of the MSW as influencing factors results in a fitting coefficient of 0.797. The Cc' values in this study are within the range of 0.12 to 0.36. These findings provide a reference for the vertical expansion design of existing landfills.

Tree species explain only half of explained spatial variability in plant water sensitivity.

Spatiotemporal patterns of plant water uptake, loss, and storage exert a first-order control on photosynthesis and evapotranspiration. Many studies of plant responses to water stress have focused on differences between species because of their different stomatal closure, xylem conductance, and root traits. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density. Here, we seek to understand the relative importance of the dominant species for regional-scale variations in woody plant responses to water stress. We map plant water sensitivity (PWS) based on the response of remotely sensed live fuel moisture content to variations in hydrometeorology using an auto-regressive model. Live fuel moisture content dynamics are informative of PWS because they directly reflect vegetation water content and therefore patterns of plant water uptake and evapotranspiration. The PWS is studied using 21,455 wooded locations containing U.S. Forest Service Forest Inventory and Analysis plots across the western United States, where species cover is known and where a single species is locally dominant. Using a species-specific mean PWS value explains 23% of observed PWS variability. By contrast, a random forest driven by mean vegetation density, mean climate, soil properties, and topographic descriptors explains 43% of observed PWS variability. Thus, the dominant species explains only 53% (23% compared to 43%) of explainable variations in PWS. Mean climate and mean NDVI also exert significant influence on PWS. Our results suggest that studies of differences between species should explicitly consider the environments (climate, soil, topography) in which observations for each species are made, and whether those environments are representative of the entire species range.

Pinpointing Moisture: The Capacitive Detection for Standing Tree Health.

BACKGROUND: the feasibility of the capacitance method for detecting the water content in standing tree trunks was investigated using capacitance-based equipment that was designed for measuring the water content of standing tree trunks. METHODS: In laboratory experiments, the best insertion depth of the probe for standing wood was determined by measurement experiments conducted at various depths. The bark was to be peeled when specimens and standing wood were being measured. The actual water content of the test object was obtained by specimens being weighed and the standing wood being weighed after the wood core was extracted. RESULTS: A forecast of the moisture content of standing wood within a range of 0 to 180% was achieved by the measuring instrument. The feasibility of the device for basswood and fir trees is preliminarily studied. When compared to the drying method, the average error of the test results was found to be less than 8%, with basswood at 7.75%, and fir at 7.35%. CONCLUSIONS: It was concluded that the measuring instrument has a wide measuring range and is suitable for measuring wood with low moisture content, as well as standing timber with high moisture content. The measuring instrument, being small in size, easy to carry, and capable of switching modes, is considered to have a good application prospect in the field of forest precision monitoring and quality improvement.

Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy.

Detection of the moisture content (MC) and freshness for loquats is crucial for achieving optimal taste and economic efficiency. Traditional methods for evaluating the MC and freshness of loquats have disadvantages such as destructive sampling and time-consuming. To investigate the feasibility of rapid and non-destructive detection of the MC and freshness for loquats, optical fiber spectroscopy in the range of 200-1000 nm was used in this study. The full spectra were pre-processed using standard normal variate method, and then, the effective wavelengths were selected using competitive adaptive weighting sampling (CARS) and random frog algorithms. Based on the selected effective wavelengths, prediction models for MC were developed using partial least squares regression (PLSR), multiple linear regression, extreme learning machine, and back-propagation neural network. Furthermore, freshness level discrimination models were established using simplified k nearest neighbor, support vector machine (SVM), and partial least squares discriminant analysis. Regarding the prediction models, the CARS-PLSR model performed relatively better than the other models for predicting the MC, with R 2 P and RPD values of 0.84 and 2.51, respectively. Additionally, the CARS-SVM model obtained superior discrimination performance, with 100% accuracy for both calibration and prediction sets. The results demonstrated that optical fiber spectroscopy technology is an effective tool to fast detect the MC and freshness for loquats.

Macroscopic and microscopic analysis of the effects of moisture content and dry density on the strength of loess.

To clarify the impact of moisture content and dry density on the strength of loess, the remolded loess samples with different moisture content and dry density were prepared, and the influence of moisture content and dry density on loess strength was explored from the macro level by direct shear test without suction control. On this basis, the mechanism of the influence of moisture content and dry density on loess strength was explored from the micro level by nuclear magnetic resonance method. The research results indicate that: In the case of low water content, there are peak points in the stress-strain curve of remolded loess, exhibiting strain softening characteristics. In the case of high water content, there is no obvious peak in the stress-strain curve, exhibiting strain hardening characteristics. Moisture has a significant impact on the shear strength of remolded loess. As the moisture content of the soil sample increases, the cohesion decreases significantly, and the change in internal friction angle is not obvious. As the moisture content continues to increase, the free water content continues to increase. Free water will continuously soften the soil particle structure, reduce the bonding force between soil particles, and cause the cohesion to decrease with the increase of moisture content. The change in dry density also has a significant impact on the shear strength parameters of remolded loess. As the dry density of the soil sample increases, the cohesion increases. The smaller the dry density, the larger the pore ratio, and the looser the contact between soil particles, weakening the bonding effect. The larger the pore ratio, the more bound water is converted to free water, and the strong bonding force between the water film and soil particles disappears. Both of these microscopic factors can lead to a decrease in cohesion with a decrease in dry density.

Study on the Influence of Temperature and Water Content on the Static Mechanical Properties of Sandstone.

The area of permafrost worldwide accounts for approximately 20% to 25% of land area. In cold-climate regions of China, which are garnering international attention, the study of low-temperature and moisture effects on rock mass mechanical properties is of significant importance. China has a wide area of cold regions. This research can provide a foundation for China's exploration activities in such extreme environments. This paper examines the mechanical behavior of rock specimens subjected to various low temperatures and water contents through uniaxial compression tests. The analysis encompasses failure modes, stress-strain relationships, uniaxial compressive strength (UCS), and elastic modulus (EM) of these specimens. Findings reveal that at lower temperatures, the rock specimens' fracture patterns transition from compressive shear failure to cleavage failure, reflecting a shift from a plastic-elastic-plastic to a plastic-elastic response. Specifically, saturated rocks exhibit a 40.8% decrease in UCS and an 11.4% reduction in EM compared to their dry counterparts. Additionally, in cold conditions, an increased water content in rocks primarily leads to vertical cracking. Under such conditions, saturated rocks show a 52.3% decline in UCS and a 15.2% reduction in EM, relative to their dry state.

Soybean (Glycine max L.) Leaf Moisture Estimation Based on Multisource Unmanned Aerial Vehicle Image Feature Fusion.

Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021-2022), soybean (Glycine max L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients (p < 0.05) were selected as input variables for the model (combination 1: vegetation indices; combination 2: texture features; combination 3: randomly extracted texture indices in combination; combination 4: combination of vegetation indices, texture features, and randomly extracted texture indices). Subsequently, extreme learning machine (ELM), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN) were utilized to model the leaf moisture content. The results indicated that most vegetation indices exhibited higher correlation coefficients with soybean leaf moisture compared with texture features, while randomly extracted texture indices could enhance the correlation with soybean leaf moisture to some extent. RDTI, the random combination texture index, showed the highest correlation coefficient with leaf moisture at 0.683, with the texture combination being Variance1 and Correlation5. When combination 4 (combination of vegetation indices, texture features, and randomly extracted texture indices) was utilized as the input and the XGBoost model was employed for soybean leaf moisture monitoring, the highest level was achieved in this study. The coefficient of determination (R2) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth.

Prediction Distribution Model of Moisture Content in Laminated Wood Components.

Shrinkage cracks are some of the most common defects in timber structures obtained from woods with an uneven distribution of moisture content and are subject to external dynamic environmental changes. To accurately predict the changes in the moisture content of wood components at any time and position, this study first applied the principles of food drying and established a moisture field model for laminated wood based on the analogy between heat and humidity transfer. A model for predicting the moisture content of wood that considers time and spatial distribution was then proposed. Second, by collecting relevant experimental data and establishing a finite element analysis model, three moisture absorption conditions (0-9.95%, 0-13.65%, and 0-17.91%) and four desorption conditions (34-5.5%, 28-8.3%, 31-11.8%, and 25.5-15.9%) were analyzed. In the moisture absorption comparison, the time needed to reach 95% equilibrium moisture content was 2.43 days, 4.07 days, and 6.32 days. The rate at which the internal components reached equilibrium moisture content exceeded 10 days. The temporal and spatial distribution of wood moisture content revealed the correctness of the proposed wood moisture field model. Finally, the moisture content prediction model was applied in the order of characteristic equation solutions, moisture content gradient difference, and laminated wood size. The results revealed that the established humidity field model can predict the wood moisture content and how it changes over time and in space. Notably, 1-2 orders for the solution of the characteristic equation are recommended when applying the prediction model. The greater the difference in moisture content, the faster the equilibrium moisture content is reached. The moisture content varies greatly based on the component size and position. Notably, the influence of moisture gradient and wood size on the average wood moisture content cannot be ignored.

Hyperspectral imaging for in situ visual assessment of Industrial-Scale ginseng.

In industrial production, the timely assessment of ginseng-derived ingredients is crucial and requires nondestructive techniques for identifying and analyzing composition. Hyperspectral imaging (HSI) effectively visualizes the three-dimensional spatial distribution of phytochemicals in dried ginseng. This study explores the in-situ prediction and visualization of moisture content (MC) and ginsenoside content (GC) in thermally processed ginseng using dual-band HSI. We collected hyperspectral images from 216 raw ginseng samples, which underwent dimensionality reduction, noise reduction, and feature enhancement via Principal Component Analysis (PCA) and Minimum Noise Separation (MNF). Linear regression models were developed following these pretreatments and evaluated using a validation set. The PCA-based models demonstrated superior performance over those based on MNF, especially in predicting GC in the near-infrared (NIR) spectrum. Similarly, models predicting MC in the visible spectrum showed favorable results. HSI enables rapid generation of distribution maps, facilitating real-time imaging for commercial applications. Repeated drying cycles and increased duration primarily affect the textural characteristics and visible color of the ginseng surface, without significantly altering its intrinsic properties. The deployment of this predictive model alongside real-time content inversion using HSI technology holds promise for integrating visual and intelligent quality monitoring in the trade of valuable herbal commodities.

Detection of moisture content of polyester fabric based on hyperspectral imaging and BP neural network.

To validate the feasibility and improve the accuracy of water content detection in polyester fabrics using hyperspectral imaging, 150 sets of hyperspectral images of polyester fabrics with varying thicknesses and moisture contents were obtained, and the characteristics of the spectral curves and impact of moisture content were elucidated. In addition, the area and full width at half maximum of the characteristic peaks around 1363 and 1890 nm were determined as spectral characteristic variables. Furthermore, the models of polyester fabric moisture content detection were developed using backpropagation neural networks, and their accuracy was evaluated using correlation coefficient and mean squared error. It was observed that the change in the moisture content of polyester fabrics not only affected the reflectance of the overall spectral curve of polyester fabrics but also altered the position and overall shape of the characteristic peaks. As the moisture content increased, the proportion of pure water spectra in the mixed spectra of water-containing polyester fabrics also increased, leading to a change in the overall shape of the characteristic peaks of polyester fabrics. Because of the overlap between the near-infrared absorption bands of pure water and the polyester fabric around 1363 and 1890 nm, the area and full width at half maximum of the characteristic peaks were considered to be more representative than the reflection for modeling. The established backpropagation neural network-based moisture content quantitative detection model has shown extremely high detection accuracy, with the correlation coefficient for the test set being higher than 0.999 and the root mean square error being lower than 0.3 %, indicating that the detection error of moisture content was only about 0.3 wt%.

Utilisation of Lactiplantibacillus plantarum and propionic acid to improve silage quality of amaranth before and after wilting: fermentation quality, microbial communities, and their metabolic pathway.

Objective: The aim of this study was to investigate the effects of Lactiplantibacillus plantarum (L. plantarum) and propionic acid (PA) on fermentation characteristics and microbial community of amaranth (Amaranthus hypochondriaus) silage with different moisture contents. Methods: Amaranth was harvested at maturity stage and prepared for ensiling. There were two moisture content gradients (80%: AhG, 70%: AhS; fresh material: FM) and three treatments (control: CK, L. plantarum: LP, propionic acid: PA) set up, and silages were opened after 60 d of ensiling. Results: The results showed that the addition of L. plantarum and PA increased lactic acid (LA) content and decreased pH of amaranth after fermentation. In particular, the addition of PA significantly increased crude protein content (p < 0.05). LA content was higher in wilted silage than in high-moisture silage, and it was higher with the addition of L. plantarum and PA (p < 0.05). The dominant species of AhGLP, AhSCK, AhSLP and AhSPA were mainly L. plantarum, Lentilactobacillus buchneri and Levilactobacillus brevis. The dominant species in AhGCK include Enterobacter cloacae, and Xanthomonas oryzae was dominated in AhGPA, which affected fermentation quality. L. plantarum and PA acted synergistically after ensiling to accelerate the succession of dominant species from gram-negative to gram-positive bacteria, forming a symbiotic microbial network centred on lactic acid bacteria. Both wilting and additive silage preparation methods increased the degree of dominance of global and overview maps and carbohydrate metabolism, and decreased the degree of dominance of amino acid metabolism categories. Conclusion: In conclusion, the addition of L. plantarum to silage can effectively improve the fermentation characteristics of amaranth, increase the diversity of bacterial communities, and regulate the microbial community and its functional metabolic pathways to achieve the desired fermentation effect.