Long-term rice-crayfish farming alters soil dissolved organic carbon quality and biodegradability by regulating microbial metabolism and iron oxidation.

The biodegradability of dissolved organic carbon (DOC) is a crucial process in the migration and transformation of soil organic carbon (SOC), and play a vital role in the global soil carbon (C) cycle. Although the significance of DOC in SOC transportation and microbial utilization is widely acknowledged, the impact of long-term rice-crayfish (RC) farming on the content, quality, and biodegradability of DOC in paddy soils, as well as regulatory mechanisms involved, remains unclear. To address this gap, a space-for-time method was employed to investigate the effects of different RC farming durations (1-, 5-, 10-, 15-, and 20- years) on the quality and biodegradability of DOC, as well as their relationship with soil microbial metabolism and minerals in this study. The results revealed that continuous RC farming increased the soil DOC content, but reduced DOC biodegradability. Specifically, after 20 years of continuous RC farming, the DOC content increased by 52.7% compared to the initial year, whereas the DOC biodegradability decreased by 63.4%. Analysis using three-dimensional fluorescence and ultraviolet spectroscopy demonstrated that continuous RC farming resulted in a decrease in the relative abundance of humus-like fractions, humification, and aromaticity indexes in DOC, but increased the relative abundance of protein-like fractions, biological, and fluorescence index, indicating that long-term RC farming promoted the simple depolymerization of the molecular structure of DOC. Continuous RC farming increased the activity of hydrolase involved in soil nitrogen (N) and phosphorus (P) cycles and oxidase, but decreasing the hydrolase C/N and C/P acquisition ratios; moreover, it also stimulated an increase in soil iron oxides and exchangeable calcium content. Structural equation modeling suggests that soil hydrolases and iron oxides are the primary drivers of DOC quality change, with DOC biodegradability being driven solely by soil iron oxides and not regulated by DOC quality. In conclusion, long-term RC farming promotes the catalytic decomposition of DOC aromatic substances and the production of DOC protein-like components by increasing soil oxidase activity and decreasing the hydrolase C/N acquisition ratio; these processes collectively contribute to the simple depolymerization of DOC molecular structure. Additionally, long-term RC farming induced legacy effects of soil iron oxides and enhanced chemical protection role leading to reduced DOC biodegradability. These findings suggested that long-term RC farming may reduce the rapid turnover and loss of DOC, providing a negative feedback on climate warming.

Rice-crayfish farming system promote subsoil microbial residual carbon accumulation and stabilization by mediating microbial metabolism process.

Rice-crayfish farming systems (RCs) can help mitigate climate change by enhancing soil organic carbon (SOC) sequestration. However, the mechanisms that govern the responses of microbial residues carbon (MRC), a key component of SOC, in RCs are not fully understood. We conducted a 6-year field experiment comparing RCs and rice monoculture systems (RMs). Specifically, we explored how MRC formation and stabilization differ between the two systems and how those differences are linked to changes in the metabolic processes of microbes. Results showed that MRC levels in RCs were 5.2 % and 40.0 % higher in the topsoil and subsoil, respectively, compared to RMs, indicating depth-dependent effects. Notably, MRC accumulation and stabilization in RCs were promoted through a cascade of processes of dissolved organic carbon (DOC) accessibility-microbial metabolism-mineral protection. In addition, the mechanism of MRC accumulation in subsoil differed between the two systems. Specifically, RMs improved accessibility of DOC by reducing humification and aromaticity of subsoil DOC, which helped microbes access to resources at lower cost. This decreased the respiration rate of microbes, thereby increasing microbial carbon pump (MCP) efficiency and thus promoting MRC accumulation. By contrast, the crayfish in RCs facilitated carbon exchange between topsoil and subsoil through their burrowing behaviors. This increased carbon allocation for microbial metabolism in the subsoil, supporting a larger microbial population and thus enhancing the MCP capacity, while reducing MRC re-decomposition via enhanced mineral protection, further increasing subsoil MRC accumulation. That is, MRC accumulation in the subsoil of RCs was predominantly driven by microbial population numbers (MCP capacity) whereas that of RMs was mostly driven by microbial anabolic efficacy (MCP efficiency). Our findings reveal a key mechanism by which RCs promoted soil MRC accumulation and stabilization, highlighting the potential role of DOC accessibility-microbial metabolism-mineral protection pathway in regulating MRC accumulation and stabilization.

Intelligent Fault Diagnosis of Rolling Bearings Based on a Complete Frequency Range Feature Extraction and Combined Feature Selection Methodology.

The utilization of multiscale entropy methods to characterize vibration signals has proven to be promising in intelligent diagnosis of mechanical equipment. However, in the current multiscale entropy methods, only the information in the low-frequency range is utilized and the information in the high-frequency range is discarded. In order to take full advantage of the information, in this paper, a fault feature extraction method utilizing the bidirectional composite coarse-graining process with fuzzy dispersion entropy is proposed. To avoid the redundancy of the full frequency range feature information, the Random Forest algorithm combined with the Maximum Relevance Minimum Redundancy algorithm is applied to feature selection. Together with the K-nearest neighbor classifier, a rolling bearing intelligent diagnosis framework is constructed. The effectiveness of the proposed framework is evaluated by a numerical simulation and two experimental examples. The validation results demonstrate that the extracted features by the proposed method are highly sensitive to the bearing health conditions compared with hierarchical fuzzy dispersion entropy, composite multiscale fuzzy dispersion entropy, multiscale fuzzy dispersion entropy, multiscale dispersion entropy, multiscale permutation entropy, and multiscale sample entropy. In addition, the proposed method is able to identify the fault categories and health states of rolling bearings simultaneously. The proposed damage detection methodology provides a new and better framework for intelligent fault diagnosis of rolling bearings in rotating machinery.

Genome-Wide Association Study of Xian Rice Grain Shape and Weight in Different Environments.

Drought is one of the key environmental factors affecting the growth and yield potential of rice. Grain shape, on the other hand, is an important factor determining the appearance, quality, and yield of rice grains. Here, we re-sequenced 275 Xian accessions and then conducted a genome-wide association study (GWAS) on six agronomic traits with the 404,411 single nucleotide polymorphisms (SNPs) derived by the best linear unbiased prediction (BLUP) for each trait. Under two years of drought stress (DS) and normal water (NW) treatments, a total of 16 QTLs associated with rice grain shape and grain weight were detected on chromosomes 1, 2, 3, 4, 5, 7, 8, 11, and 12. In addition, these QTLs were analyzed by haplotype analysis and functional annotation, and one clone (GSN1) and five new candidate genes were identified in the candidate interval. The findings provide important genetic information for the molecular improvement of grain shape and weight in rice.