Xiaomin Zhao

Returning rice straw to fields is key for sustainable agriculture, yet its slow decomposition conflicts with tight schedules in double-cropping rice systems. Whether laboratory-effective microbial inoculants can consistently enhance straw decomposition and nutrient turnover in the field remains unclear. We conducted a two-season field experiment using nylon-mesh bags that prevented direct mixing of straw from soil-applied inoculants (laboratory-developed decomposing inoculant, LABIN; conventional commercial decomposing inoculant, COMIN, both Bacillus subtilis-based), thereby evaluating their primarily indirect effects via stimulation of the indigenous soil microbiome. Neither inoculant significantly increased net dry matter loss in either season ( p >0.05). However, LABIN exerted profound effects on nutrient release dynamics, it markedly elevated phosphorus release in the late-season period and persistently drove greater lignin degradation as well as unique structural alterations within straw residues, which was verified via Fourier transform infrared spectroscopy (FTIR). LEfSe analysis identified enrichment of putative decomposer taxa such as Vicinamibacter and Candida tropicalis , without affecting alpha diversity. Collectively, these results indicate that LABIN did not accelerate overall mass loss under conditions of limited direct inoculant–straw contact. Nevertheless, it altered straw residue chemistry and nutrient release, likely through selective shifts in the soil microbial community. This finding provides proof-of-concept evidence for a primarily indirect, microbiome-mediated mode of action of LABIN in paddy systems.

Understanding the long-term spatiotemporal dynamics of soil nutrients in land consolidation areas is essential for sustainable land management. In this study, the changes in soil organic matter (SOM), available nitrogen (AN), available phosphorus (AP), available potassium (AK), and soil pH from 1980 to 2023 in the land consolidation area of Anyi County, China, were investigated. A random forest (RF) model was developed using multisource environmental variables, including climate, topography, remote sensing, vegetation, parent material, distance to rivers, and land use. The results revealed three key findings. First, while land consolidation significantly increased overall soil nutrient levels, trends for individual nutrients were highly variable: AP and AK continuously increased over the 40-year period. SOM and AN increased after consolidation but decreased in later years. This process was accompanied by systematic soil acidification. Second, land consolidation substantially restructured the spatial patterns of soil nutrients. SOM and AN became more homogeneously distributed, whereas AP and AK showed increased variability. High-value nutrient areas shifted from contiguous expansion in the early stages to local aggregation and patchy fragmentation over the long term. Third, the RF model demonstrated high predictive accuracy (R² = 0.70 for pH, 0.68 for SOM, 0.63 for AP, and 0.54 for AK), with variable importance analysis identifying distinct environmental drivers for each nutrient. These findings provide a scientific basis for precision fertilization and evidence-based land consolidation planning.

Timely and accurate estimation of soil organic matter (SOM) is vital for monitoring soil fertility and enabling precision management. This study investigates how combining soil texture data with visible and near‐infrared (Vis–NIR) spectroscopy can improve SOM prediction under both laboratory (dry) and in situ (moist) conditions. We also identify which soil properties cause the most significant differences between dry and moist spectral readings. Four modelling strategies were compared: (1) Pure spectra (using Vis–NIR data alone), (2) texture class and (3) texture merged (integrating quantitative clay, silt and sand content) and (4) texture dummy (integrating categorical texture classes determined by hand feel). We used the Cubist algorithm to assess the contribution of different soil properties, while partial least squares regression (PLSR) and random forest (RF) models were used for SOM prediction. Results show that soil texture was the most significant factor driving the differences between the moist and dry spectra. Texture was also the primary variable explaining the prediction errors (residuals) when using spectra alone. Critically, incorporating simple, field‐assessed texture data via the Texture Dummy strategy significantly improved SOM prediction accuracy compared to models relying on pure spectra, with the highest R ² increasing from 0.65 to 0.78 and from 0.79 to 0.86 and the root mean square error decreasing from 5.73 to 4.56 g kg ⁻¹ and from 4.47 to 3.62 g kg ⁻¹ for wet and dry samples, respectively. The Texture merged strategy provided a modest improvement ( R ² increasing from 0.63 to 0.66 using in situ spectra), while the Texture class strategy showed the lowest performance, with slightly reduced R ² values for both dry and in situ spectra. The accuracy achieved using the Texture dummy strategy with in situ spectra ( R ² = 0.76, LCCC = 0.86) was comparable to that of the general model developed from laboratory spectra ( R ² = 0.79, LCCC = 0.88). Our findings demonstrate that soil texture is the primary factor driving the spectral differences between in situ and laboratory soil samples, except within the known water‐absorption wavelengths; incorporating the texture class determined by hand‐feel can effectively guide spectral techniques, enhancing the potential of vis–NIR spectroscopy in rapid estimation of SOM. However, further verification is needed using soils from a wider range of areas and larger spectral libraries, as well as other spectroscopic techniques across different wavelength ranges.

Elucidating the connection between the total supply and demand of ecosystem services (ESTSD) can provide comprehensive decision support for regional eco-management. Previous studies have focused on the spatial and temporal evolution of ESTSDs in the horizontal dimension but studies in the vertical dimension are lacking; too much attention has been given to single dimensions for ecological management zoning while omitting multilevel and multiobjective considerations; and there are limitations in knowledge about the threshold scale effect of ecosystem service (ES) influencing factors. Therefore, this study aimed to quantify the ESTSD in Jiangxi Province; to reveal the spatial and temporal evolution of the matching of total supply and total demand, the coupling and coordination, and the vertical spatial gradient effect of the ESTSD; to propose a multiobjective zoning framework that integrates ‘supply–demand–coordination–function’ and ‘strategic guidance–zoning control–functional guidance’; and to delineate three levels of integrated ecological management zones. Constraint lines were used to explore and complement the threshold scale effect. The results show that the ESTSD values are low overall. The high supply‒low demand region is large, and the degree of coupling coordination shows an overall imbalance. The matching and coupling coordination of the ESTSD shows significant vertical spatial gradient effects. Four strategic guidance zones at the primary level, 5 secondary regulation zones, and 5 advantageous function guidelines are delineated. As the scale increases, the optimal thresholds for the digital elevation model, slope, and precipitation all decrease, whereas those for the temperature increase. It is proposed that this study will further refine the management of regional ecosystems, regulate the contradiction within the ESTSD, realize a multiobjective ecological zoning control, and promote regional coordination and sustainable development. This research provides favorable support for ecological management and can serve as a reference for similar regions.

Aboveground biomass (AGB) serves as a crucial indicator for assessing vegetation carbon sequestration capacity. While AGB levels vary significantly across different vegetation types and regions, the spatial distribution of AGB for specific wetland communities remains poorly characterized. To address this, we integrated field-collected data with Sentinel-2 spectral bands and remote sensing indices, employing random forest (RF) regression and Backpropagation Neural Network (BPNN) for AGB modeling. Through comparative evaluation of their inversion performance, the optimal model was selected to estimate vegetation AGB in the Nanji Wetland. By incorporating wetland classification data, we further generated spatial distribution maps of AGB for four dominant vegetation types during the dry season. The main findings are as follows. Important variables for the RF model included spectral bands B12, B11, B3, B2, B9, B1, B8, B6, and B4 and the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI), Kernel Normalized Difference Vegetation Index (KNDVI), and Simple Ratio Index (SR). RF demonstrated significantly higher predictive accuracy (R² = 0.945, RMSE = 109.205 g·m⁻²) compared to the BPNN (R² = 0.821, RMSE = 176.025 g·m⁻²). The total estimated AGB reached 4.03 × 10⁹ g; Carex spp. dominated AGB accumulation (1.49 × 10⁹ g), followed by P. australis spp. (6.69 × 10⁸ g), M. lutarioriparius spp. (4.60 × 10⁸ g), and Polygonum spp. (3.61 × 10⁸ g). The AGB exhibited a clear spatial gradient, decreasing from higher-elevation lakeshore areas towards the central lake. The results provide detailed spatial quantification of AGB stocks across dominant vegetation types, revealing distinct spatial characteristics and interspecies variations in AGB. This study offers a valuable baseline and methodological framework for monitoring wetland carbon dynamics.

Objective The red soil is an important soil resource in our country with good hydrothermal conditions and large area，its quality is related to national food security and ecological security.Strengthening the monitoring of red soil quality is an important measure to ensure national food security.In-situ visible - near-infrared reflectance spectroscopy （vis-NIR） is a popular technical method for red soil quality monitoring.However，during the monitoring process，the influencing mechanism of factors on the in-situ reflectance spectrum remains unclear，which affects the monitoring accuracy.Clarifying the main influencing factors of the spectrum under in-situ conditions and understanding the action laws of these factors on the in-situ spectrum are crucial steps in the rapid monitoring of red soil quality. Method The soil in the red soil area of Jiangxi Province，which has the largest national area in the red soil region，was taken as the research object.The paddy soil and red soil decomposed from moorstone in Meilin Mountain，fluvial sediment in Yangzi Island，quaternary red clay and red sand from the Yujiang county were used in this study.The visible and near infrared reflectance spectra （VIS-NIR） of in-situ soil，in-situ air-dried soil，and air-dried and ground soil from the plow layer，the illuvial horizon，and the plow pan of different soil types were obtained.In addition，after the air-dried ground soil is remoistened to reach moisture contents of 15% and 25% respectively，its spectra were measured.Then，this study performed continuum removal on all spectra to characterize the positional relationships of characteristic absorption peaks in soils derived from different parent materials across various layers，and analyze the correlations between these peaks and soil moisture content，soil organic matter （SOM），and soil texture. Result The result showed that there were obvious differences among the four soil types soured the four parent materials.The soil from the ploughing layer，bedding layer and plow bottom layer of moisture soil with in situ condition and from the ploughing layer with milled soil were more clustered.Compared with mother material，soil texture and water content，the layer of tillage have the best impact on the spectra.The soil texture have impact on the water on the characteristic absorbance.And there is nonlinear relationship between the 1 900 nm and water content.In the soil with lower soil organic content，the water content in in-situ spectra can improve the correlation of SOM with characteristic absorbance and it should be considered that the soil texture when estimated the SOM using the in-situ spectra. Conclusion In addition to moisture content，soil texture is a critical factor influencing in-situ spectroscopy.The findings of this study provide theoretical support for understanding the role of moisture in rapid and accurate monitoring of cultivated land quality.Furthermore，they advance research on soil spectroscopy concerning the coupled effects of moisture and soil properties in in-situ spectral monitoring within red soil regions.

The construction and maintenance of an ecological security pattern (ESP) are important for promoting the regional development of ecological civilizations, realizing sustainable and healthy development, and creating a harmonious and beautiful space for human beings and nature to thrive. Traditional construction methods have the limitations of a single dimension, a single method, and excessive human subjective intervention for source and corridor identification, without considering the multidimensional quality of the sources and the structural connectivity and resilience optimization of the corridors. Therefore, an ecological civilization demonstration area (Jiangxi Province) was used as the study area, a new research method for ESP was proposed, and an empirical study was conducted. To evaluate ecosystem service (ES) importance–disturbance–risk and extract sustainability sources through the deep embedded clustering–self-organizing map (DEC–SOM) deep unsupervised learning clustering algorithm, ecological networks (ENs) were constructed by applying the minimum cumulative resistance (MCR) gravity model and circuit theory. The ENs were then optimized to improve performance by combining the comparative advantages of the two approaches in terms of structural connectivity and resilience. A comparative analysis of EN performance was constructed among different functional control zones, and the ESP was constructed to include 42 ecological sources, 134 corridors, 210 restoration nodes, and 280 protection nodes. An ESP of ‘1 nucleus, 3 belts, 6 zones, and multiple corridors’ was constructed, and the key restoration components and protection functions were clarified. This study offers a valuable reference for ecological management, protection, and restoration and provides insights into the promotion of harmonious symbiosis between human beings and nature and sustainable regional development.

Microbial necromass carbon (MNC) is the dominant contributor to soil organic carbon (SOC). However, the contribution of MNC in different soil compartments to SOC sequestration has not been comprehensively studied, especially under the organic fertilizers input. To address this gap, we conducted a rice root box experiment by adding organic fertilizer (straw and straw biochar) and chemical fertilizer alone to red loamy paddy soil, respectively. We found that although SOC accumulation was stimulated by both biochar and straw in the rhizosphere, more substantial SOC was sequestered in the rhizosphere due to biochar addition (increased by 25.82% compared to straw addition). Additionally, the input of organic fertilizers resulted in varying degrees of MNC retention in the different soil compartments. Compared with that in bulk soil, fungal necromass carbon (FNC) content was reduced by 1.37% and 7.06%, and bacterial necromass carbon (BNC) content was reduced by 5.53% and 9.49% in the rhizosphere and hyphosphere, respectively, following straw addition. Conversely, the addition of biochar leads to a significant increase of FNC (increased by 2.92%) and BNC (increased by 2.00%) in the rhizosphere compared with bulk soil. However, straw addition also significantly enhanced SOC thermal stability within the rhizosphere and hyphosphere soils. Based on partial least squares path modeling, we found that SOC thermal stability was significantly and positively influenced by FNC, which was strongly associated with carbon degradation gene abundance. These results emphasize the critical role of soil compartments in SOC sequestration under organic fertilizer application and underscore the importance of FNC in enhancing SOC stability in the rhizosphere.