安徽农学通报 >
2026 , Vol. 32 >Issue 10: 138 - 140
DOI: https://doi.org/10.16377/j.cnki.issn1007-7731.2026.10.037
农产品质量安全预警防控体系发展现状及对策
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谢梦雅(1994—),女,安徽宁国人,助理农艺师,从事农产品质量安全监管、检测和质量认证等工作。 |
收稿日期: 2025-03-11
网络出版日期: 2026-05-28
Current situation and countermeasures of the early warning and prevention system for agricultural product quality and safety
Received date: 2025-03-11
Online published: 2026-05-28
本文分析了农产品质量安全预警防控体系发展现状和有待提升的环节,并提出针对性策略,旨在为基层农产品质量安全预警防控体系建设提供参考。研究区生态禀赋优越、特色产业优势突出,农业产业基础扎实,农产品生产规模持续壮大;农产品质量安全监管体系与监测网络逐步健全,智慧监管建设初见成效。然而,该体系在基层风险防控力量建设、生产技术标准落地应用、风险预警评估精准能力等方面存在提升空间。对此,可通过实地教学、线上平台等多元形式,开展农产品质量安全法律法规培训;加大监测频次、拓宽监测覆盖范围,逐步扩项认证;强化宣传引导,提高生产技术标准应用实效;同时修订完善农产品质量安全突发事件应急预案,运用大数据技术采集前端生产信息,分析监测相关数据,提高风险预警评估精准能力。
谢梦雅 . 农产品质量安全预警防控体系发展现状及对策[J]. 安徽农学通报, 2026 , 32(10) : 138 -140 . DOI: 10.16377/j.cnki.issn1007-7731.2026.10.037
This article analyzed the current development status of the agricultural product quality safety early warning and prevention system, as well as the aspects that need improvement, and proposed targeted strategies, aiming to provide reference for the construction of the grassroots agricultural product quality safety early warning and prevention system. The study area has superior ecological endowment, prominent characteristic industries, a solid agricultural industry foundation, and continuously expanding agricultural production scale; the agricultural product quality safety supervision system and monitoring network have been gradually improved, and the intelligent supervision construction has achieved initial results. However, this system still has room for improvement in aspects such as the construction of grassroots risk prevention and control forces, the implementation of production technical standards, and the accuracy of risk warning and assessment. To address this, various forms such as on-site teaching and online platforms can be utilized to conduct training on agricultural product quality safety laws and regulations knowledge; increase the frequency of monitoring, expand the coverage of monitoring, gradually expand the scope of certification, and achieve full coverage of routine risk parameters; at the same time, the emergency response plan for agricultural product quality safety incidents will be revised and improved. Big data technology will be used to collect front-end production information, analyze relevant data for supervision and monitoring, and enhance the accuracy of risk warning and assessment.
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