ACS agricultural science & technology最新文献

Xanthomonas oryzae pv. oryzae (Xoo) is a widespread bacterial pathogen in rice with worldwide implications. This pathogen causes bacterial blight in rice and is a concern for global food security, causing up to 50% yield loss. This review provides a comprehensive analysis of Xoo, including its global distribution, disease cycle, and current management strategies, while critically evaluating the limitations of existing diagnostic methods. By focusing on Xoo, the paper addresses a gap in research that mostly focuses on the wider Xanthomonas genus. Emphasizing the role of Xoo in maintaining rice health, the review underscores the importance of detecting Xoo for successful disease management. Conventional approaches such as visual inspection, biochemical assays, and PCR-based techniques often lack the sensitivity, specificity, and scalability required for early and accurate detection, especially in resource-limited settings. To address these challenges, the review explores both current and emerging diagnostic technologies, including molecular, serological, and innovative field-deployable methods. Particular attention is given to advanced tools like biosensors, artificial intelligence, and IoT-enabled systems, which promise to enhance precision and efficiency in pathogen detection. By identifying research gaps and proposing actionable pathways, this work underscores the need for integrating traditional and modern diagnostic methods to achieve accessible, scalable, and effective solutions. These advancements hold the potential to revolutionize Xoo management, ensuring sustainable rice production and global food security.

From decorative houseplants to the crops that feed the world, plants are subjected to various environmental stresses over their lifetimes. Factors like changes in climate, pollution, and disease threaten plant health, requiring time-sensitive interventions to prevent widespread crop losses. We present a bioinspired colorimetric sensing strategy for measuring proline, a biomarker of stress in plants, by leveraging the condensation reaction between sinapaldehyde and proline to form a natural red pigment called nesocodin. We prepared paper-based sensors embedded with sinapaldehyde that supported nesocodin synthesis when we introduced the proline analyte. Signals range from pale yellow, indicative of unreacted sinapaldehyde, to deep red, indicative of proline-dependent formation of nesocodin. These sensors can quantitatively differentiate between 0 and 15 mM proline, which sufficiently measured relative increases in proline concentrations of plants exposed to controlled stresses. This approach highlights the opportunity to design field-deployable, user-friendly tools for agricultural monitoring, improved farming efficiency, and strengthened food security.

Carbon dioxide (CO₂) is the material substance of plant photosynthesis, yet its concentration remains insufficient to meet plant photosynthesis demands. Therefore, the formation of CO₂-enriched regions around leaf stomata is expected to improve the efficiency of plant photosynthesis. Herein, a photoresponsive metal–organic framework (Zr-ABTC) was constructed from azobenzene bonds, while T(n)/Zr-ABTC was prepared by the incorporation of tetraethyl pentamine (TEPA) with an adsorption ability for CO₂. The photoresponsive material could capture CO₂ in darkness and release it under ultraviolet irradiation, thus establishing a CO₂ “enrichment-release” cycle under dark/light cycles. Upon application of Zr-ABTC onto Chinese little green leaves, scanning electron microscopy (SEM) revealed that the material is distributed around plant stomata, resulting in an 87.5% increase in crop yield compared with the blank control group not treated by Zr-ABTC (dry weight). The photothermal responsive materials created in this article may be used to improve the photosynthetic efficiency and enhance agricultural productivity.

This study evaluated rice samples (Oryza sativa L.)─rice husk, husk and grain, polished grain, and unpolished grain─exposed to imazapyr, imazapic, and clomazone using high-performance liquid chromatography coupled to high-resolution mass spectrometry (HPLC-HRMS) and chemometric analysis. Partial least squares discriminant analysis (PLS-DA) was applied to HPLC-HRMS data, successfully distinguishing between herbicide-treated and control samples. Additionally, variable importance in projection (VIP) scores were then computed to identify key metabolites contributing to class differentiation, with higher scores indicating the most influential m/z values. These findings revealed metabolites affected by herbicide exposure and variations in the rice matrix. Furthermore, the most relevant m/z values were putatively annotated using spectral libraries, enabling the assessment of herbicide-induced metabolomic changes in rice. Herbicide treatment resulted in reduced free sugar levels across all rice matrices and led to a decrease in flavonoid content in the husk, indicating a potential suppressive effect on flavonoid accumulation. In addition, the herbicide treatment markedly disrupted the phenylpropanoid biosynthesis pathway. Overall, the combination of HPLC-HRMS analysis with multivariate approaches proved effective in detecting significant variations in the rice metabolome cultivated under herbicide application, paving the way for understanding the effects of herbicides in crop cultivation.