Feng Li

Composting agricultural waste is a cornerstone of circular agriculture but carries the risk of disseminating antibiotic resistance genes (ARGs). While persistent free radicals (PFRs) are known to continuously suppress ARGs, their natural yield in compost is often insufficient. Here, we demonstrate that hydroxamate siderophores (HDS) can capture Fe(III) to form stable redox-active complexes that act as potent electron shuttles, driving the generation of semiquinone radicals (SQ^•–) to inhibit ARG proliferation while promoting the humification process. In a 40 day cocomposting experiment with rice straw and pig manure, HDS amendment accelerated humification by 24.7% and enriched the quinone moieties within the humic matrix by 22.3%. HDS–Fe(III) complexes were predominantly reduced via microbial activity to HDS–Fe(II), which then served as a potent electron donor due to its significantly lowered redox potential. This drove spontaneous electron transfer to quinone moieties, increasing SQ^•– production by 2.1–7.4 times. The sustained oxidative pressure imposed by SQ^•– significantly inhibited plasmid-mediated conjugative transfer frequency by 65.3–84.4% via inducing membrane damage of critical host bacteria (such as Streptomyces). Consequently, mid- to high-risk ARGs in HDS-treated compost products were reduced by 11.6–26.6%. Application of HDS-treated compost products to paddy soil confirmed the persistent mitigation of ARG propagation risks. Overall, this HDS-based approach achieves the dual goals of compost valorization and biosafety, advancing the sustainability of circular agriculture within the “One Health” framework.

Sequestration of cadmium (Cd) in rice phytolith can effectively restrict its migration to the grains, but how hydroxamate siderophore (HDS) affects phytolith formation within rice plants especially the fate of Cd and silicon (Si) remains poorly understood. Here, we found that the addition of HDS increased the content of dissolved Si and Cd in soil pore water as well as its absorption by the rice roots during the reproductive growth stage. HDS effectively trapped orthosilicic acid and Cd ions at the third stem nodes of rice plants via hydrogen bonds and chelation interactions, which then rapidly deposited on the xylem cell wall through hydrophobic interactions. Ultimately, Cd was immobilized as phytolith-like particulates in the form of CdSiO₃. Field experiments verified that Cd accumulation was significantly reduced by 46.4% in rice grains but increased by 41.2% in rice stems after HDS addition. Overall, this study advances our understanding of microbial metabolites enhancing the instinctive physiological barriers within rice plants.

Soil cadmium contamination poses a significant threat to global food security and human health, making the timely and accurate diagnosis of cadmium stress in rice crucial for effective pollution control and agricultural management. However, during the early growth stages of rice, particularly the tillering stage, the spectral response to cadmium stress is subtle, rendering traditional remote sensing methods inadequate. This study aims to develop an efficient early diagnosis index, the Cadmium Early Stress Index (CESI), for rapid and accurate detection of cadmium stress in rice at a regional scale. By integrating field surveys with Sentinel-2 satellite data, the study extracts multi-angle spectral features and employs an enhanced Generalized Additive Model Neural Network (E-GAMI-Net) for analysis. E-GAMI-Net analysis identified key indicators for early diagnosis, including log-transformed reflectance at 941 nm (R941_log), Optimized Soil-Adjusted Vegetation Index (OSAVI), and the interaction between Red Edge Amplitude and Chlorophyll content. Based on these findings, CESI was constructed, demonstrating superior diagnostic performance (R² = 0.77, RMSE = 0.09 mg/kg) compared to existing methods. CESI also exhibited high stability under noise interference, with only a 5.6% reduction in R² under 15% noise. In regional-scale remote sensing applications, CESI successfully generated cadmium stress distribution maps, identifying previously undetected moderate stress areas. CESI’s high accuracy (R² = 0.6073, RMSE = 0.3021) and stability make it a promising tool for large-scale cadmium stress monitoring and precision agriculture management.