[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3217":3,"related-3217":102},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":37,"classification":38,"search_phrases":97,"slug":100,"view_count":21,"doi":8,"paper":8,"created_at":101},3217,"十二年追踪万余农户！三份重磅报告今日在浙江发布——《浙江省农业新质生产力研究报告2026》等三份聚焦\"三农\"的报告9-19在杭州集中发布","https:\u002F\u002Fm.10jqka.com.cn\u002F20260919\u002Fc680083964.shtml","9-19 三份聚焦\"三农\"发展的重量级报告在浙江杭州集中发布。《中国农业农村微观数据观测报告2026》依托观测系统，覆盖全国20个省份、新疆生产建设兵团及计划单列市，对578个行政村、12480户农户开展2012-2023连续12年跟踪调研。数据显示：12年间样本区长期种粮农户水稻单产提升17.8%，玉米单产提升14.3%；社会化服务普及，水稻种植亩均用工下降40.5%，小麦亩均用工下降61.9%。户均经营耕地由15.24亩扩大至22.11亩，地块由碎片化的12.72块缩减到8.07块。乡风文明持续向好，彩礼均值从4.67万元回落至3.86万元；样本村自来水普及率达到93.4%，互联网入户占比81.0%，快递进村比例72.3%。",null,"![Image 1: 同花顺 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arrowRight](https:\u002F\u002Fs.thsi.cn\u002Fcd\u002Fnews-p-fe-app-news-flow-home\u002FnewsDetail\u002F_next\u002Fstatic\u002Fmedia\u002FarrowRight.e37d2cb7.svg)[财经](https:\u002F\u002Fnews.10jqka.com.cn\u002F)![Image 5: arrowRight](https:\u002F\u002Fs.thsi.cn\u002Fcd\u002Fnews-p-fe-app-news-flow-home\u002FnewsDetail\u002F_next\u002Fstatic\u002Fmedia\u002FarrowRight.e37d2cb7.svg)[地区经济资讯](https:\u002F\u002Fnews.10jqka.com.cn\u002Fregion_list\u002F)![Image 6: arrowRight](https:\u002F\u002Fs.thsi.cn\u002Fcd\u002Fnews-p-fe-app-news-flow-home\u002FnewsDetail\u002F_next\u002Fstatic\u002Fmedia\u002FarrowRight.e37d2cb7.svg)正文\n\n# 十二年追踪万余农户！三份重磅报告今日在浙江发布 利好\n\n2026-09-19 09:08:49\n\n来源：[潮新闻](https:\u002F\u002Ftidenews.com.cn\u002Fnews.html?id=3563481)\n\n分享\n\n![Image 7: wechat](https:\u002F\u002Fs.thsi.cn\u002Fcd\u002Fnews-p-fe-app-news-flow-home\u002FnewsDetail\u002F_next\u002Fstatic\u002Fmedia\u002Fwx1.52541d4c.svg)\n\n![Image 8: weibo](https:\u002F\u002Fs.thsi.cn\u002Fcd\u002Fnews-p-fe-app-news-flow-home\u002FnewsDetail\u002F_next\u002Fstatic\u002Fmedia\u002Fweibo1.e6767779.svg)![Image 9: qq](https:\u002F\u002Fs.thsi.cn\u002Fcd\u002Fnews-p-fe-app-news-flow-home\u002FnewsDetail\u002F_next\u002Fstatic\u002Fmedia\u002Fqq1.e6746dda.svg)\n\n文章提及标的\n\n乡村振兴--\n\n玉米--\n\n土地流转--\n\n农机--\n\n智慧农业--\n\n人工智能--\n\n[查看股票机会 下载客户端](https:\u002F\u002Fdownload.10jqka.com.cn\u002F)\n\n9月19日，三份聚焦“三农”发展的重量级报告在浙江杭州集中发布。\n\n中国农业科学院农业经济与发展研究所、浙江省农业科学院新质生产力智库等机构，相继发布《中国农业农村微观数据观测报告2026》《“千万工程”引领浙江乡村蝶变——中国农村微观数据观测点(浙江基地)研究报告》《浙江省农业新质生产力研究报告2026》。\n\n报告依托追踪调研，以全国万余农户微观样本结合浙江实地案例，用实打实的数据，记录农业农村现代化的鲜活变迁，提炼[乡村振兴（885705）](https:\u002F\u002Fq.10jqka.com.cn\u002Fthshy\u002Fdetail\u002Fcode\u002F885705)的浙江样本。\n\n![Image 10](https:\u002F\u002Fe.thsi.cn\u002Fimg\u002Fb6cdf09a90fbe9d)\n\n## **受访者供图**\n\n## **十二年追踪万余农户**\n\n## **微观记录“三农”历史性变迁**\n\n《中国农业农村微观数据观测报告2026》，依托观测系统，覆盖全国20个省份、新疆生产建设兵团及计划单列市，对578个行政村、12480户农户开展2012-2023连续12年跟踪调研。\n\n数据显示，“大国小农”背景下农业生产稳步提质。12年间样本区长期种粮农户水稻单产提升17.8%，[玉米（885811）](https:\u002F\u002Fq.10jqka.com.cn\u002Fthshy\u002Fdetail\u002Fcode\u002F885811)单产提升14.3%；社会化服务普及，水稻种植亩均用工下降40.5%，小麦亩均用工下降61.9%，小农户种地劳动负担显著减轻。户均经营耕地由15.24亩扩大至22.11亩，地块由碎片化的12.72块缩减到8.07块，土地集约度持续提升。\n\n农民生活品质显著升级，样本农户每周肉类购买量从1.24公斤增至3.34公斤；百户农家小汽车拥有量从16.6辆增至46.5辆。乡风文明持续向好，彩礼均值从4.67万元回落至3.86万元，人情支出占比由17.1%下降至11.5%。乡村硬件提档，样本村自来水普及率达到93.4%，互联网入户占比81.0%，快递进村比例72.3%。山地地区[土地流转（885439）](https:\u002F\u002Fq.10jqka.com.cn\u002Fthshy\u002Fdetail\u002Fcode\u002F885439)率达到22%，山地与平原[玉米（885811）](https:\u002F\u002Fq.10jqka.com.cn\u002Fthshy\u002Fdetail\u002Fcode\u002F885811)亩均净收益差距从103元收窄至18元；脱贫村集体资产总额增长7.7倍，危房实现清零，村庄绿化覆盖率增至42%，外部帮扶转化为村庄内生发展动力。\n\n## **解剖麻雀看千万工程**\n\n## **三地样本诠释从一村美到一片富**\n\n《“千万工程”引领浙江乡村蝶变——中国农村微观数据观测点(浙江基地)研究报告》，依托上虞、黄岩、淳安三个固定观测县区，分类剖析平原数智先行、山区产业富民、库区生态共富三种模式，拆解“千万工程”带来的全方位蝶变。\n\n比如，上虞全域环境整治投入50亿元，打造“四季仙果”产业，产值突破15亿元，带动果农增收3.5亿元；黄岩推进村庄“红黑榜”考核，农污治理率达98.99%，蜜橘全产业链产值20亿元，带动3.2万人就业；作为特别生态功能区，淳安主要入湖河流水质优秀率100%，茶叶全产业链产值达35.1亿元，千岛湖有机鱼养殖户年均增收5万至6万元。\n\n人才归巢为乡村注入活力，上虞集聚1500余名农创客、5400余名新农人；淳安依靠“新农人12条”吸引35岁以下310名青年返乡创业。片区化组团、飞地抱团成为共富抓手，黄岩82个村通过飞地抱团实现每村分红5000元；淳安大下姜联合体25个核心村2025年经营性收入达2318.5万元。报告总结出八大核心经验，为[乡村振兴（885705）](https:\u002F\u002Fq.10jqka.com.cn\u002Fthshy\u002Fdetail\u002Fcode\u002F885705)提供可复制的实践范式。\n\n## **新质生产力赋能现代农业**\n\n## **浙江走出人地相适发展路径**\n\n《浙江省农业新质生产力研究报告2026》构建了包含15个子维度的测算体系，首次系统评估全省农业新质生产力发展现状。在省级层面分析之外，《报告》还对11个设区市逐一专题分析，提出针对性建议。《报告》显示，全省各地农业新质生产力发展水平呈近似正态分布，体现了各地协调共进的整体性发展态势。这一格局的形成，得益于浙江持续深化“千万工程”、缩小“三大差距”、深入实施科技强[农机（885706）](https:\u002F\u002Fq.10jqka.com.cn\u002Fthshy\u002Fdetail\u002Fcode\u002F885706)械强农“双强”行动。\n\n![Image 11](https:\u002F\u002Fe.thsi.cn\u002Fimg\u002F76e4679035e2c74a)\n\n## **受访者供图**\n\n截至2025年底，浙江农业科技进步贡献率超70%，高出全国平均约6个百分点。“十四五”时期，浙江选育获得国家超级稻认定品种14个，占全国总数27.5%，居全国首位。从田间良种到数字农场，新场景不断涌现：自主良种刷新亩产纪录，象山RAS循环水养殖基地实现三文鱼工厂化繁育；杭州集聚近2300家[智慧农业（000816）](https:\u002F\u002Fstockpage.10jqka.com.cn\u002F000816)企业，浙江省农业科学院自研“浙里良田”等农业智能体落地田野。《报告》提出，浙江农业新质生产力是人地相适、人业相融的内涵式发展。全省形成多极共兴的格局，11个设区市立足资源禀赋差异化发展。\n\n面向未来，《报告》提出实施“八大工程”：关键核心技术强基、农业科研体系培优、[农机（885706）](https:\u002F\u002Fq.10jqka.com.cn\u002Fthshy\u002Fdetail\u002Fcode\u002F885706)装备升级、“[人工智能（885728）](https:\u002F\u002Fq.10jqka.com.cn\u002Fthshy\u002Fdetail\u002Fcode\u002F885728)+农业”、新兴产业科技赋能、“土特产富”优质优价、农业全面绿色转型、“新农人”培育工程，覆盖技术、装备、数字、产业、人才全链条，为浙江率先探索农业农村现代化科学路径绘制施工图。\n\n免责声明：风险提示：本文内容仅供参考，不代表同花顺观点。同花顺各类信息服务基于人工智能算法，如有出入请以证监会指定上市公司信息披露平台为准。如有投资者据此操作，风险自担，同花顺对此不承担任何责任。\n\n相关阅读\n\n[![Image 12: 智慧农业：9月21日获融资买入529.56万元](https:\u002F\u002Fs.thsi.cn\u002Fcd\u002Fnews-p-fe-app-news-flow-home\u002FnewsDetail\u002F_next\u002Fstatic\u002Fmedia\u002Frelated-read-fallback-1.8a2b64a7.png) 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arrowRight](https:\u002F\u002Fs.thsi.cn\u002Fcd\u002Fnews-p-fe-app-news-flow-home\u002FnewsDetail\u002F_next\u002Fstatic\u002Fmedia\u002FarrowRight.e37d2cb7.svg)\n\n![Image 17: 资金流向](https:\u002F\u002Fs.thsi.cn\u002Fcd\u002Fnews-p-fe-app-news-flow-home\u002FnewsDetail\u002F_next\u002Fstatic\u002Fmedia\u002FziJinLiuXiangIcon.53a9ce1e.svg)\n\n资金流向\n\n![Image 18: 盘面分析](https:\u002F\u002Fs.thsi.cn\u002Fcd\u002Fnews-p-fe-app-news-flow-home\u002FnewsDetail\u002F_next\u002Fstatic\u002Fmedia\u002FpanMianFenXiIcon.eb53de78.svg)\n\n盘面分析\n\n![Image 19: 涨停聚焦](https:\u002F\u002Fs.thsi.cn\u002Fcd\u002Fnews-p-fe-app-news-flow-home\u002FnewsDetail\u002F_next\u002Fstatic\u002Fmedia\u002FzhangTingJuJiaoIcon.547e7b98.svg)\n\n涨停聚焦\n\n![Image 20: 公告速递](https:\u002F\u002Fs.thsi.cn\u002Fcd\u002Fnews-p-fe-app-news-flow-home\u002FnewsDetail\u002F_next\u002Fstatic\u002Fmedia\u002FgongGaoSuDiIcon.a622ea50.svg)\n\n公告速递\n\n头条\n\n更多头条![Image 21: 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阿根廷抗议英方支持在马岛争议区域延长油气开采许可\n\n阿根廷外交部22日向英国驻阿大使馆发出照会，抗议马尔维纳斯群岛（简称马岛，英国称福克兰群岛）当局在英方支持下延长相关企业在马岛及争议海域的油气开采许可证有效期。阿根廷外交部在一份声明中说，在阿根廷大陆架上单方面勘探与开采自然资源是违反国际法和侵犯阿根廷国家主权的行为。阿根廷政府将动用外交、经济和法律等手段捍卫马岛主权，并制裁那些试图非法开采自然资源的企业和个人。（央视新闻）\n\n07:43\n\n### 高端产品缺货严重 华强北MLCC商家爆单\n\n“好忙，我们爆单了”，“现在主要是拿不到货”，“原厂需求太旺盛了，直接多数签了长协，没什么货出来”，“AI类、有的高端MLCC（多层陶瓷电容器）确实价格波动比较大，缺货严重”，“今年MLCC产品整体是上涨的，因为原材料都涨了，但具体涨幅和是否缺货，还是要看具体型号”。MLCC被业内称为“电子工业大米”，具有稳压、储能、滤波等作用，广泛应用于手机、电脑、汽车、AI服务器等领域。“中国电子第一街”深圳华强电子世界一层是国内MLCC现货集散地之一，在这里，聚集着村田、三星电机、太阳诱电、京瓷、国巨、信昌、华新科、风华高科等全球主要MLCC品牌柜台，普通料、高容、AI服务器规格MLCC都能找到。9月21日下午，记者来到现场，忙碌、热闹、行业景气度高的场景，与MLCC概念在资本市场的火热情况相互印证。对于后续MLCC产品会否涨价，业内普遍认为，AI类产品在未来较长一段时间内会持续缺货，价格看涨，但消费类产品要看原厂扩产情况，以及需求端消费电子市场情况。（人民财讯）\n\n风华高科--\n\n07:32\n\n### OpenAI拟在AI模型开发早期阶段引入第三方机构评估安全风险\n\n据报道，OpenAI计划让第三方机构在AI模型开发周期的更早阶段评估安全风险，此举旨在继续努力应对围绕AI潜在危害的担忧。OpenAI还列出了其认为此类评估要有效开展所需优先关注的事项，包括“强有力的独立机制、科学严谨性、稳健的安全实践和明确的责任划分”。\n\n07:31\n\n### 创新药行业开启管线出清 转向“质量竞赛”\n\n国内创新药行业研发管线数量快速增长，随之而来出现了同质化立项、热门靶点扎堆的问题。大量企业同步推进结构与疗效高度相近的药物研发，大量资金、临床资源投入低水平重复项目，形成“靶点内卷”。进入2026年，行业迎来一系列转变：主管部门通过完善一系列制度，引导研发回归临床价值；部分企业主动评估管线价值，终止缺乏差异化优势的在研项目，自发开启管线出清。行业正从管线“数量竞争”转向“质量竞赛”，探索从跟随式创新走向源头创新的破局路径。（人民财讯）\n\n07:25\n\n### 供应端增量超过需求增量，猪价节前承压下行\n\n中秋国庆双节临近，生猪市场价格走势却未因需求提振有所好转。8月下旬以来，国内生猪期货价格不断探底。9月22日盘中，生猪期货主力合约2611最低报10865元\u002F吨，较月初下跌超9%，年内最大跌幅超过22%。虽然近期生猪市场需求有所回升，但供应端增量超过需求增量，猪价承压下行。不过相关数据显示，三季度生猪产能去化加速。进入下半月，双节集中备货全面开启，终端需求快速回暖，供需格局短期改善，猪价有望止跌反弹。（人民财讯）\n\n07:22\n\n### 产品布局持续拓宽 被动指数债基规模超过两万亿元\n\n今年以来，债券投资指数化持续推进。数据显示，截至9月21日，53只债券ETF合计规模9871.29亿元，距离万亿元关口仅差128.71亿元。结合场外被动指数型债券基金半年报披露的规模数据，全市场384只被动指数型债券基金合计规模达20663.38亿元。天风证券固收首席分析师谭逸鸣分析称，我国债券ETF市场未来前景广阔：一方面是在产品种类和覆盖度上可持续创新，满足机构差异化配置需求；另一方面是在交易机制上可进一步完善，加快健全做市商机制、跨市场交易体制，提升折溢价套利机制的顺畅度，进而增强市场交易活跃度与定价效率。（上海证券报）\n\n07:20\n\n### 华安证券：四季度有望出现“吃饭行情”\n\n展望后市，华安证券策略团队发布观点称，在海外扰动逐步缓释的条件下，A股有望逐步从“震荡区间”摆脱，年末“情绪修复”机会仍可期待，四季度有望出现幅度可观的“吃饭行情”，AI算力硬件及配套方向仍是首选，具备景气或催化的方向也值得重视。本轮AI产业景气周期行情正演绎至第二阶段——业绩驱动上涨过程的中段，参考历史规律，中段震荡期结束后将迎来一波加速上涨行情。（上海证券报）\n\n07:13\n\n### 境外机构持续增持中国国债 总规模近2.03万亿元\n\n中央结算公司发布的数据显示，8月份境外机构连续第四个月增持中国国债约16亿元，持有总规模已升至近2.03万亿元。专家指出，中国经济基本面的坚实与稳健是境外机构青睐人民币债券的关键原因。在全球市场波动加剧的背景下，中国国债的避险属性和收益稳定性日益凸显，成为国际资金配置的重要选择。（中国证券报）\n\n07:05\n\n### 高通发布智能手机芯片\n\n高通发布智能手机芯片Snapdragon 8 Elite Gen 6和Snapdragon 8 Elite Extreme Gen 6。\n\n高通--\n\n07:04\n\n### 火变形虫改写真核生物耐热上限\n\n一项关于真核生物的研究，将复杂生命的耐热上限推高。22日《细胞》杂志正式发表美国雪城大学等联合团队成果：从加利福尼亚拉森火山国家公园温泉中分离出的“喀斯喀特火变形虫”，可在63℃完成有丝分裂，并在70℃形成保护外层、降温后恢复，打破此前真核生物约60℃的生长上限。\n\n07:03\n\n### AI “条形码”可识别衰老组织中“僵尸细胞”\n\n据《自然·衰老》杂志21日报道，美国麻省理工学院团队开发出一种借助人工智能（AI）识别衰老细胞的新方法。他们将拉曼显微镜获取的细胞生化信息与单细胞基因表达数据结合，再利用机器学习寻找衰老细胞的特征，生成一种独特的AI“条形码”，可用于识别衰老组织中的“僵尸细胞”。这种方法无需破坏细胞，有望为癌症、组织退化等衰老相关疾病的诊断提供新思路。\n\n06:43\n\n异动\n\n### 全球最大的白银ETF iShares Silver Trust持仓量较前日减少53.38吨\n\n截至2026年09月22日，全球最大的白银ETF iShares Silver Trust持仓量为15375.88吨，较前一个交易日减少53.38吨。\n\n06:41\n\n### 美联储10月加息的概率为54.2%\n\n据CME“美联储观察”：美联储10月会议维持利率在3.75%-4.00%不变的概率为45.8%，加息25个基点的概率为54.2%。美联储到12月维持利率在3.75%-4.00%不变的概率为10.8%，累计加息25个基点的概率为47.8%，累计加息50个基点的概率为41.4%。\n\n06:21\n\n### 台风“杜鹃”已致日本7人死亡\n\n受今年第25号台风“杜鹃”带来的强降雨影响，日本关东地区和伊豆群岛遭遇创纪录规模暴雨，多地接连发生山体滑坡、河流泛滥引发的洪水灾害。截至目前，千叶县和神奈川县共有7人死亡、5人失踪。此外，住宅受灾情况也不断增加，目前已确认千叶县、神奈川县和茨城县共有500多栋房屋受损。（央视新闻）\n\n06:02\n\n异动\n\n### NYMEX天然气主力合约涨超1%\n\nNYMEX天然气主力合约涨超1%，现报3.16美元\u002F百万英热。\n\n05:24\n\n### 比利时宣布和卢旺达恢复外交关系\n\n当地时间22日，比利时副首相兼外交大臣普雷沃宣布，比利时与卢旺达已同意恢复外交关系。当天，普雷沃与卢旺达外交部长奥利维耶·恩杜洪吉雷海就此签署了联合声明。声明指出，两国将重新互开大使馆。此前，因在刚果（金）东部冲突相关问题上存在分歧，卢旺达方面曾于2025年3月17日与比利时断绝外交关系。作为回应，比利时驱逐了卢旺达外交官，并终止了双边政府合作协议。（央视新闻）\n\n04:58\n\n### 伊朗外长与美特使在纽约会晤\n\n当地时间22日，伊朗外长阿拉格齐与美国总统特使威特科夫在第81届联合国大会会议期间于纽约举行会晤。据伊朗方面消息称，此举是“应美方要求”，伊朗同意威特科夫的会晤请求，是出于“传达伊朗关于重新开放霍尔木兹海峡条件”的需要。据悉，伊朗重新开放霍尔木兹海峡的条件包括立即解除海上封锁，立即解冻所有伊朗资产以及结束在地区各战线的战争。特朗普：美伊代表会谈“非常有建设性”美国总统特朗普表示，美国官员当天与伊朗代表举行了一场“非常好的”会谈。特朗普称，这场会谈持续了3个小时，并称会谈“非常有建设性”。他还表示，美伊双方已安排在近期再次举行会谈。（央视新闻）\n\n04:46\n\n### 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金豆","手机同花顺财经","2026-09-19T00:00:00Z","报道",10,false,83,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},24,23,18,12,6,1,"国家级科研机构联合发布的三份三农报告，含12年万余农户追踪数据与浙江新质生产力测算体系，信息增量与专业深度突出，值得进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","智慧农业","农业新质生产力","千万工程","微观数据观测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":8},"A","field-crops","大田作物","\u002Ftopic\u002Ffield-crops",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"partial","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47,"reason":96},[44,52,58,64,70,76,81,86,91],{"code":45,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},"A1",3,true,[],[50],"水稻",[],{"code":53,"score":46,"supported":47,"title":54,"abstract":55,"tags":57},"A2",[],[56],"小麦",[],{"code":59,"score":46,"supported":47,"title":60,"abstract":61,"tags":63},"A3",[],[62],"玉米",[],{"code":65,"score":46,"supported":47,"title":66,"abstract":67,"tags":69},"A8",[],[68],"单产提升",[],{"code":71,"score":46,"supported":47,"title":72,"abstract":73,"tags":75},"G2",[],[74],"社会化服务",[],{"code":77,"score":22,"supported":14,"title":78,"abstract":79,"tags":80},"G4",[],[],[30],{"code":82,"score":22,"supported":14,"title":83,"abstract":84,"tags":85},"I1",[],[],[28],{"code":87,"score":22,"supported":14,"title":88,"abstract":89,"tags":90},"I6",[],[],[27],{"code":92,"score":22,"supported":14,"title":93,"abstract":94,"tags":95},"J2",[],[],[29],"一级领域明确，二级方向需要细分",[98,99],"农业新质生产力 微观数据观测 千万工程 数字乡村","农业新质生产力 微观数据观测","农业新质生产力微观数据观测千万工程数字乡村-3217","2026-09-23T00:04:29.368579Z",{"total":21,"page":22,"page_size":21,"items":103},[104,170,306,382,447,527],{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":8,"source_name":110,"source_url":107,"published_at":111,"category":112,"cover_url":8,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":118,"tags":120,"primary_topic":125,"secondary_topics":126,"classification":127,"search_phrases":149,"slug":152,"view_count":153,"doi":154,"paper":155,"created_at":169},5404,"Validation of a Day-of-Year Standardized Canopy Indicator for Regional Crop Monitoring: Suitability and Decoupling Boundaries","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202610.1707.v1","Translating satellite-derived crop canopy anomalies into regional production signals is critical for agricultural monitoring. However, standard anomaly indicators assume a stable, direct coupling between greenness and production, ignoring phenological shifts, sub-seasonal cropping dynamics, and harvested acreage fluctuations. I evaluate the validation boundaries and expected conditions of a pixel-level, day-of-year (DOY)-first Leaf Area Index (LAI) Z-score anomaly indicator with a leave- year-out baseline (2003–2022) across five contrasting global breadbaskets: Australia, Indo-Gangetic Plains (IGP), North China Plain (NCP), US Corn Belt, and Ukraine. The analysis shows that DOY- first standardization reduces temporal composite timing mismatches, though coupling with regional statistics varies systematically. Retrospective validation reveals that direct canopy-yield coupling remains robust exclusively in rainfed Australia (Pearson’s r = 0.90), where yield variability dominates production variance (explaining 90% of variance) and moisture stress limits canopy development. In contrast, three interpretation boundaries are identified: (1) greening trend-sensitivity in the IGP, where detrending reduces the association to r = 0.53; (2) temporal representativeness bias in the double-cropped NCP, requiring crop-calendar windowing to recover the wheat signal (r = 0.51); and (3) area-shock-sensitivity in Ukraine (where harvested area contractions explain 78% of production variance), decoupling canopy vigor from production (r = 0.40). Furthermore, the LAI Z-score provides a more interpretable canopy-response measure than the Vegetation Condition Index (VCI) in dense canopies where NDVI saturates. These validation levels are synthesized into a prototype diagnostic decision framework to support indicator configuration in operational monitoring systems.","将卫星反演的作物冠层异常转化为区域产量信号对农业监测至关重要。然而，标准异常指标假定绿度与产量之间存在稳定、直接的耦合关系，忽略了物候偏移、次季节作物动态以及收获面积波动。我评估了一种像元级、以年积日（DOY）为首要标准化维度的叶面积指数（LAI）Z分数异常指标在五个对比鲜明的全球粮仓——澳大利亚、印度河-恒河平原（IGP）、华北平原（NCP）、美国玉米带和乌克兰——的验证边界与预期适用条件，该指标采用留一年基线（2003—2022年）。分析表明，以DOY为首要标准化维度可减少时间合成时相不匹配，但其与区域统计数据的耦合关系存在系统性差异。回顾性验证显示，冠层与产量的直接耦合仅在雨养条件下的澳大利亚保持稳健（Pearson's r = 0.90），该地区产量变异性主导产量方差（解释90%的方差），且水分胁迫限制冠层发育。相比之下，识别出三个解释边界：（1）IGP的绿度趋势敏感性，去趋势后关联性降至r = 0.53；（2）双季种植的NCP的时间代表性偏差，需按作物日历开窗以恢复小麦信号（r = 0.51）；（3）乌克兰的面积冲击敏感性（收获面积缩减解释78%的产量方差），导致冠层活力与产量解耦（r = 0.40）。此外，在NDVI饱和的密集冠层中，LAI Z分数提供了比植被条件指数（VCI）更具可解释性的冠层响应度量。这些验证层级被综合为一个原型诊断决策框架，以支持业务化监测系统中的指标配置。","Preprints.org","2026-10-19T00:00:00Z","论文",65,{"impact":20,"substance":115,"depth":19,"authority":21,"freshness":116,"relevant":22,"comment":117},21,8,"预印本提出按年积日标准化的LAI异常指标，并在五大农业区验证其适用边界，方法有新意但尚未经同行评审，属细分领域进展。",[119],{"name":110,"url":107},[28,121,122,123,124],"遥感","作物监测","产量估测","叶面积指数",{"code":33,"slug":34,"name":35,"path":36,"parent_code":8},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":128},{"candidates":129,"agricultural_evidence":47,"reason":96},[130,135,140,145],{"code":53,"score":21,"supported":47,"title":131,"abstract":132,"tags":134},[],[56,133],"wheat",[],{"code":59,"score":21,"supported":47,"title":136,"abstract":137,"tags":139},[],[62,138],"corn",[],{"code":141,"score":46,"supported":14,"title":142,"abstract":143,"tags":144},"I3",[],[],[123,122,124,121],{"code":82,"score":22,"supported":14,"title":146,"abstract":147,"tags":148},[],[],[28],[150,151],"LAI Z-score 作物监测","遥感 作物产量 估测","LAIZ-score作物监测-5404",0,"10.20944\u002Fpreprints202610.1707.v1",{"doi":154,"openalex_id":156,"authors":157,"venue":110,"cited_by_count":153,"oa_url":107,"card":161,"direction":167,"ingested_from":168},"W7222967882",[158],{"name":159,"orcid":160},"Xiaolong Song","https:\u002F\u002Forcid.org\u002F0000-0002-2106-6629",{"tldr":162,"method":163,"finding":164,"direction":165,"opportunity":166},"验证了按年积日标准化的LAI Z-score冠层指标在五大农业区的适用边界。","用2003–2022年留一年基线，在五个全球粮仓区做LAI DOY标准化与产量相","仅雨养澳大利亚冠层-产量强耦合(r=0.90)，其余区域受趋势、物候和面积冲击影响而解耦。","农业遥感与作物表型","可发展融合物候窗口、去趋势与收获面积因子的分区自适应冠层异常指标诊断框架。","农业人工智能与决策模型","openalex","2026-10-20T23:30:40.675672Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":8,"source_name":176,"source_url":173,"published_at":177,"category":112,"cover_url":8,"hotness":13,"is_selected":14,"score":178,"score_detail":179,"sources":184,"tags":186,"primary_topic":191,"secondary_topics":192,"classification":193,"search_phrases":244,"slug":247,"view_count":153,"doi":248,"paper":249,"created_at":305},5314,"A data‐driven image extraction and analysis pipeline for plant phenotyping in controlled environments","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppj2.70116","Abstract Advances in automation, imaging, and artificial intelligence have enabled large‐scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Automated Precision Phenotyping Greenhouse and expanded plant growth and phenotyping (PGP v2) data across maize ( Zea mays L.), cotton ( Gossypium hirsutum ), rice ( Oryza sativa ), and sorghum ( Sorghum bicolor L.). The pipeline integrates pseudo‐red‐green‐blue composite image generation, plant detection and segmentation, image stitching, vegetation‐index analysis, texture analysis, morphological trait extraction, and temporal comparison of image‐derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, segment anything model (SAM) v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. Segment anything model for long sequences (SAM2Long)maintained plant‐instance associations across vertically stacked frames, while scale‐invariant feature transform–based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863‐dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment‐level temporal analysis of mutagenized sorghum lines and cold‐stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatment‐ or stress‐related responses. The combined dataset and workflow provide structured, automated, and well‐documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlled‐environment plant science and crop improvement.","摘要　自动化、成像和人工智能的进步使得大规模植物表型分析成为可能，但图像分析仍然是作物改良和生物学发现的关键瓶颈。我们利用德克萨斯农工大学农业生命科学自动化精准表型温室（Texas A&M AgriLife Automated Precision Phenotyping Greenhouse）的图像，开发了一个集成多光谱表型分析框架，并扩展了玉米（Zea mays L.）、棉花（Gossypium hirsutum）、水稻（Oryza sativa）和高粱（Sorghum bicolor L.）的植物生长与表型分析（plant growth and phenotyping，PGP v2）数据。该流程整合了伪红绿蓝合成图像生成、植物检测与分割、图像拼接、植被指数分析、纹理分析、形态性状提取以及图像衍生特征的时间比较，以量化植物结构、光谱反射率和纹理随时间的变化。在评估的分割方法中，segment anything model（SAM）v3在不同作物结构下提供了最高且最一致的准确性，尽管其所需计算时间多于经典方法。面向长序列的segment anything model（SAM2Long）在垂直堆叠帧之间维持了植物实例关联，而基于尺度不变特征变换（scale-invariant feature transform）的拼接在单株植物超出单一视野时重建了植物拼接图。对于每株植物和每个成像日期，该流程生成了一个863维特征向量，涵盖植被指数、光谱统计量、纹理描述符和形态性状。该框架通过两个案例研究进行了评估：诱变高粱品系的处理水平时间分析，以及使用独立成像系统对玉米进行的冷胁迫表型分析。在这两项研究中，提取的特征支持了表型变异的统计分析和多变量分析，并能够根据处理或胁迫相关响应区分植物。合并的数据集和工作流程为受控环境植物科学和作物改良提供了跨多种作物、实验设置和成像系统的结构化、自动化且记录完善的表型分析。","The Plant Phenome Journal","2026-10-18T00:00:00Z",81,{"impact":19,"substance":180,"depth":19,"authority":181,"freshness":182,"relevant":22,"comment":183},22,14,9,"该研究提出一套面向多作物的自动化多光谱表型分析流程，方法新颖、数据规模可观，对智慧育种与受控环境植物科学研究具有较高参考价值。",[185],{"name":176,"url":173},[28,187,188,189,190],"农业人工智能","图像分割","表型组学","多光谱成像",{"code":33,"slug":34,"name":35,"path":36,"parent_code":8},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":194},{"candidates":195,"agricultural_evidence":47,"reason":96},[196,201,206,213,220,227,234,240],{"code":45,"score":21,"supported":47,"title":197,"abstract":198,"tags":200},[],[50,199],"rice",[],{"code":59,"score":21,"supported":47,"title":202,"abstract":203,"tags":205},[],[62,204],"maize",[],{"code":207,"score":21,"supported":47,"title":208,"abstract":209,"tags":212},"A5",[],[210,211],"高粱","sorghum",[],{"code":214,"score":21,"supported":47,"title":215,"abstract":216,"tags":219},"A6",[],[217,218],"棉花","cotton",[],{"code":221,"score":21,"supported":47,"title":222,"abstract":223,"tags":226},"B7",[],[224,225],"温室","greenhouse",[],{"code":228,"score":229,"supported":47,"title":230,"abstract":231,"tags":233},"I2",5,[],[232],"artificial intelligence",[187,188],{"code":141,"score":235,"supported":47,"title":236,"abstract":237,"tags":239},4,[],[238],"multispectral",[190],{"code":82,"score":22,"supported":14,"title":241,"abstract":242,"tags":243},[],[],[28],[245,246],"Texas A&M 自动化表型温室","玉米 高粱 多光谱表型","TexasAM自动化表型温室-5314","10.1002\u002Fppj2.70116",{"doi":248,"openalex_id":250,"authors":251,"venue":176,"cited_by_count":153,"oa_url":173,"card":300,"direction":165,"ingested_from":168},"W7222893982",[252,254,257,260,263,265,267,270,273,276,279,282,285,288,291,294,296,298],{"name":253,"orcid":8},"Fahimeh Orvati Nia",{"name":255,"orcid":256},"Joshua Peeples","https:\u002F\u002Forcid.org\u002F0000-0001-7861-7789",{"name":258,"orcid":259},"Seth C. Murray","https:\u002F\u002Forcid.org\u002F0000-0002-2960-8226",{"name":261,"orcid":262},"Andrew J. McFarland","https:\u002F\u002Forcid.org\u002F0009-0000-3516-8274",{"name":264,"orcid":8},"Troy Vann",{"name":266,"orcid":8},"Shima Salehi",{"name":268,"orcid":269},"Robert G. Hardin","https:\u002F\u002Forcid.org\u002F0000-0002-0714-8281",{"name":271,"orcid":272},"David D. Baltensperger","https:\u002F\u002Forcid.org\u002F0000-0003-4511-5680",{"name":274,"orcid":275},"Amir M. H. Ibrahim","https:\u002F\u002Forcid.org\u002F0000-0002-0584-753X",{"name":277,"orcid":278},"J. Alex Thomasson","https:\u002F\u002Forcid.org\u002F0000-0002-5557-105X",{"name":280,"orcid":281},"Henry Y. Fadamiro","https:\u002F\u002Forcid.org\u002F0000-0002-4530-9680",{"name":283,"orcid":284},"Nithya K. Subramanian","https:\u002F\u002Forcid.org\u002F0000-0002-1659-7396",{"name":286,"orcid":287},"Suresh D. Pillai","https:\u002F\u002Forcid.org\u002F0000-0002-9766-8765",{"name":289,"orcid":290},"Rebecca Roston","https:\u002F\u002Forcid.org\u002F0000-0002-3063-5002",{"name":292,"orcid":293},"Joslin Ishimwe","https:\u002F\u002Forcid.org\u002F0009-0003-6999-7718",{"name":295,"orcid":8},"Diptadeep Basak",{"name":297,"orcid":8},"Nazar Oladepo",{"name":299,"orcid":8},"Uday Santhosh Raju Vysyaraju",{"tldr":301,"method":302,"finding":303,"direction":165,"opportunity":304},"构建多光谱图像提取分析流程，实现多作物表型自动化量化。","用温室多光谱图像，集成SAM v3分割、SIFT拼接与863维特征提取。","SAM v3分割最准，特征可区分高粱诱变与玉米冷胁迫响应。","可探索轻量化分割模型与跨成像系统迁移，降低计算成本并拓展田间表型。","2026-10-19T23:30:17.276726Z",{"id":307,"title":308,"url":309,"summary":310,"summary_zh":311,"content":8,"source_name":312,"source_url":309,"published_at":313,"category":112,"cover_url":8,"hotness":13,"is_selected":14,"score":314,"score_detail":315,"sources":319,"tags":321,"primary_topic":324,"secondary_topics":325,"classification":326,"search_phrases":357,"slug":360,"view_count":153,"doi":361,"paper":362,"created_at":381},4901,"Feature-level fusion of Sentinel-1 SAR and Sentinel-2 optical imagery for crop-type mapping in humid tropical mountainous terrain","https:\u002F\u002Fdoi.org\u002F10.64960\u002Feasr.2026.263460","Accurate crop-type mapping in humid tropical mountains is challenged by persistent cloud cover, rugged topography, and fragmented smallholder parcels. This study evaluates a feature-level fusion workflow that integrates Sentinel-2 optical indices and Sentinel-1 SAR backscatter for crop-type mapping in Candipuro District, Lumajang, East Java, Indonesia. Sentinel-2 surface reflectance and Sentinel-1 IW data acquired in June–July 2023 were harmonised in Google Earth Engine to a common 10 m pixel grid, so that all comparison maps were evaluated on the same spatial support. The optical component comprised NDVI, NDBI, and NDWI, whereas the radar component used the VH\u002FVV polarisation ratio. A DEM was used only to characterise the mountainous setting and to assist terrain interpretation, and was not included as a predictor in the classifier. The stacked feature image was classified using a Random Forest model trained with 101 field-survey ground control points representing eight land-cover\u002Fcrop-related classes. Accuracy was assessed using 393 stratified random validation points. The fused-image classification achieved an overall accuracy of 93% and a kappa coefficient of 0.90. The fusion workflow yielded richer thematic differentiation than the optical-only comparison scenarios, particularly for water bodies, sugarcane, and shrubland. However, class-level results reveal substantial uncertainty for corn fields and moderate confusion in built-up and water classes. Therefore, the present workflow should be interpreted as a site-specific, single-date proof of concept rather than a fully transferable operational solution. The results indicate that Sentinel-1\u002FSentinel-2 feature fusion is promising for crop-type mapping in cloud-prone mountainous landscapes, while future work should incorporate multi-temporal phenology, richer vegetation indices, SAR texture features, uncertainty mapping, and broader field sampling.","在湿润的热带山区，准确的作物类型制图面临持续云覆盖、崎岖地形和破碎的小农户地块等挑战。本研究评估了一种特征级融合工作流，该工作流整合了Sentinel-2光学指数与Sentinel-1 SAR后向散射数据，用于印度尼西亚东爪哇省卢马姜县坎迪普罗区的作物类型制图。2023年6月至7月获取的Sentinel-2地表反射率和Sentinel-1 IW数据在Google Earth Engine中统一至共同的10 m像元网格，以确保所有对比制图均在相同的空间支撑上进行评估。光学组分包括NDVI、NDBI和NDWI，雷达组分则使用VH\u002FVV极化比值。DEM仅用于表征山区环境并辅助地形解译，未作为预测变量纳入分类器。堆叠特征影像采用随机森林模型进行分类，该模型以101个野外调查地面控制点进行训练，代表八个土地覆盖\u002F作物相关类别。精度评估使用393个分层随机验证点。融合影像分类的总体精度达到93%，Kappa系数为0.90。与仅光学对比方案相比，融合工作流产生了更丰富的专题区分能力，尤其在水体、甘蔗和灌木地方面。然而，类别层面的结果揭示了玉米田存在较大不确定性，建成区和水体类别存在中等程度的混淆。因此，本工作流应被解读为特定站点的单日期概念验证，而非完全可迁移的业务化方案。结果表明，Sentinel-1\u002FSentinel-2特征融合在多云山区景观的作物类型制图中具有前景，未来工作应纳入多时相物候、更丰富的植被指数、SAR纹理特征、不确定性制图以及更广泛的野外采样。","Engineering and Applied Science Research","2026-10-12T00:00:00Z",66,{"impact":116,"substance":316,"depth":317,"authority":20,"freshness":182,"relevant":22,"comment":318},20,17,"方法扎实、精度可观的遥感作物制图概念验证研究，但属单点单时相案例，公共影响有限，适合作为技术参考而非每日精选头条。",[320],{"name":312,"url":309},[28,187,121,322,323],"作物识别","哨兵卫星",{"code":33,"slug":34,"name":35,"path":36,"parent_code":8},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":327},{"candidates":328,"agricultural_evidence":47,"reason":96},[329,333,339,345,349,353],{"code":59,"score":21,"supported":47,"title":330,"abstract":331,"tags":332},[],[62,138],[],{"code":214,"score":21,"supported":47,"title":334,"abstract":335,"tags":338},[],[336,337],"甘蔗","sugarcane",[],{"code":71,"score":21,"supported":47,"title":340,"abstract":341,"tags":344},[],[342,343],"小农户","smallholder",[],{"code":82,"score":22,"supported":14,"title":346,"abstract":347,"tags":348},[],[],[28],{"code":228,"score":22,"supported":14,"title":350,"abstract":351,"tags":352},[],[],[187],{"code":141,"score":22,"supported":14,"title":354,"abstract":355,"tags":356},[],[],[121],[358,359],"Sentinel-1 Sentinel-2 作物分类","印尼 东爪哇 作物制图","Sentinel-1Sentinel-2作物分类-4901","10.64960\u002Feasr.2026.263460",{"doi":361,"openalex_id":363,"authors":364,"venue":312,"cited_by_count":153,"oa_url":375,"card":376,"direction":165,"ingested_from":168},"W7221544367",[365,368,371,373],{"name":366,"orcid":367},"Indarto Indarto","https:\u002F\u002Forcid.org\u002F0000-0001-6319-6731",{"name":369,"orcid":370},"Gunawan Gunawan","https:\u002F\u002Forcid.org\u002F0000-0001-7914-3669",{"name":372,"orcid":8},"Mahrus Irsyam",{"name":374,"orcid":8},"Fahmi Arif Kurnanto","https:\u002F\u002Fph01.tci-thaijo.org\u002Findex.php\u002Feasr\u002Farticle\u002Fdownload\u002F263460\u002F176575",{"tldr":377,"method":378,"finding":379,"direction":165,"opportunity":380},"融合Sentinel-1 SAR与Sentinel-2光学特征，在印尼热带山地实现93%精度的作物类","Google Earth Engine中10米格网特征级融合NDVI\u002FNDBI\u002F","融合影像总体精度93%、kappa 0.90，优于仅光学方案，但玉米和建成区仍存在明显混淆。","可引入多时相物候、SAR纹理与不确定性制图，提升热带多云山区小农户破碎地块的作物识别与可迁移性。","2026-10-14T23:30:28.943594Z",{"id":383,"title":384,"url":385,"summary":386,"summary_zh":8,"content":387,"source_name":388,"source_url":8,"published_at":313,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":389,"score_detail":390,"sources":394,"tags":396,"primary_topic":402,"secondary_topics":403,"classification":404,"search_phrases":442,"slug":445,"view_count":22,"doi":8,"paper":8,"created_at":446},4765,"在农业农村现代化关键点上持续发力","https:\u002F\u002Fwww.qstheory.cn\u002F20261012\u002Fc6238e31742540c887fb80611fcdb460\u002Fc.html","求是网刊发关于兵团农业农村现代化的理论文章,部署三项重点。一是积极培育农业低空经济新业态,布局AI加农业专项行动;二是推动稳粮、优棉、强果、兴畜、创特色,扩大优质食味稻、专用小麦、高产高油大豆供给;三是推进农产品精深加工、发展智慧冷链物流,贯通仓储、运输、配送全环节。","  持续深化团场改革，激发农业发展活力。一是持续深化团场综合配套改革，完善土地、职工、民兵“三位一体”机制，持续增强团场发展的内生动力和发展活力。针对团场连队基础设施与公共服务短板，推动城乡要素双向流动，促进人才、资金、技术等现代生产要素向团场连队有序集聚。健全人才引进与培育机制，鼓励科技人员、高校毕业生、退役军人等高素质人才到团场连队创业就业。持续推进公共服务均等化，助力实现区域全面融合、共同繁荣的发展目标。二是在规划建设方面，坚持精准施策，全面提升规划、建设、治理融合发展水平，统筹推进城镇高质量发展。各师市要立足自身资源禀赋与区位条件，科学编制区域发展规划，明确城市、团场、连队的功能定位与发展方向，加快构建城乡融合、功能互补、共同繁荣的城乡发展格局。三是充分发挥兵团农业发展优势，健全兵地共同推进乡村振兴的沟通协商机制，统筹推进兵地基础设施建设和人居环境改善，深化公共服务资源共享，推动兵团连队与地方邻近村开展结对共建，进一步推动兵地深度嵌入、融合发展。\n\n  强化农业科技创新，培育农业新质生产力。一是深入实施种业振兴行动，加快突破性品种选育推广，有序推进生物育种产业化应用。发挥兵团在棉花、玉米种子领域的基础优势，加大生物育种技术研发力度，推进分子标记辅助选择、基因编辑等现代育种技术应用，培育更多具有自主知识产权的优良品种。支持兵团围绕种业振兴、智慧农业、深度节水、土壤改良等领域开展农业关键核心技术攻关，高水平建设种业创新平台，加快培育突破性品种，破解兵团农业领域“卡脖子”技术难题。二是加快高端智能农机装备研发应用，持续巩固提升兵团农业机械化水平。积极培育农业低空经济新业态，拓展无人机在植保作业、农田监测、农资运输等领域应用场景。构建农业科技领军企业培育库，支持企业牵头组建创新联合体，加快培育龙头型、成长型农业科技企业。推动人工智能与农业生产经营深度融合，实施“人工智能+农业”专项行动，促进物联网、大数据、机器人等技术在精准施肥、智能灌溉、病虫害监测等生产环节规模化应用。三是进一步提升节水技术水平，在现有膜下滴灌、“干播湿出”等技术基础上，加快攻克深度节水、智能灌溉关键技术，推广应用智能水肥一体化技术，持续巩固兵团农业高效节水灌溉全国领先地位。探索建立农业用水总量控制与定额管理制度，深化农业水价综合改革，促进水资源可持续利用。\n\n  提升农业综合生产能力，夯实农产品稳产保供根基。一是稳定粮食生产。深入实施新一轮千亿斤粮食产能提升行动，持续推进蔬菜、林果、畜禽、水产等特色优势产业发展。牢固树立并深入践行大食物观，坚持农林牧渔协同发展，推动构建多元化食物供给体系，全方位、多途径开发食物资源，拓宽食物供给来源。按照“稳粮、优棉、强果、兴畜、创特色”的农业发展要求，将棉花种植面积稳定在千万亩级，持续巩固棉花产业优势地位，同时优化粮食品种结构，扩大优质食味稻、专用小麦、高产高油大豆等优质农产品供给。二是推进高标准农田建设。按照分区分类原则高质量推进高标准农田建设，完善立项、建设、验收与管护的全链条管理机制，推进大中型灌区现代化建设与改造，强化盐碱地治理，提升耕地质量。进一步提升高标准农田占耕地面积比重，从根本上改善农业生产的土地基础条件，为农业稳产高产提供坚实支撑。三是推动农产品品质提升。推动兵团农业向产量与品质并重转型，健全农业标准体系，完善农产品质量安全承诺达标合格证制度，构建以市场需求为导向的品质评价与质量分级制度。强化兵团农产品质量安全监管，确保农产品质量安全监测总体合格率稳定在较高水平，推动更多兵团优质农产品实现优质优价。\n\n  构建现代农业产业体系，加快推动产业转型升级。一是推动农产品精深加工。围绕“优、精、特”农业发展方向，坚持智能化升级、绿色化生产、融合化发展路径，促进种养环节增值，推动农产品供给由“原字号”初级产品向高附加值“制成品”转型。延伸拓展棉花、玉米、红枣、辣椒等特色农产品加工链条，发展精深加工与生物制造产业，挖掘农产品多元利用价值；加快智慧冷链物流网络建设，贯通仓储、运输、配送全环节，保障农产品跨区域高效安全流通；促进农业与文旅、康养等产业深度融合，培育发展田园综合体等新业态，拓展农业多元功能。充分开发利用荒漠、草场、林地与水域资源，把戈壁荒滩的潜在价值转化为从田间到车间的产业发展优势。二是壮大兴农富民产业。健全紧密型联农带农利益联结机制，发展壮大各具特色的团场连队经济。培育一批产业链条长、科技含量高、带动能力强的产业领军企业，做强农产品精深加工产业集群。持续做强农业优势产业，大力发展农产品加工流通业，促进农产品增值，让职工群众共享产业增值收益。做大做强棉花及纺织服装全产业链，积极承接东部地区产业转移，建设综合性纺织工业基地与特色服装产业集群。\n\n  建设宜居宜业和美连队，提升职工群众生活品质。一是学习运用“千万工程”经验，分区分类推进和美连队建设。接续实施乡村振兴“百连示范工程”扩面提质行动，进一步扩大示范覆盖范围、提升示范建设质量，推进高水平示范连队建设，发挥典型引领带动作用。持续健全连队治理长效机制，坚持和发展新时代“枫桥经验”，深入推进文明乡风建设工程。二是大力发展连队本土特色经济，保护传承军垦文化。立足各连队的区位条件、资源特色，因地制宜发展庭院经济、休闲农业、乡村旅游等新产业新业态，激活连队零散闲置的各类资源，以千家万户庭院“小美”赋能连队生态宜居、产业兴旺的“大美”建设，拓宽职工群众就地就近增收渠道。在连队建设全过程中，注重保护兵团特有的军垦文化风貌，守护并延续兵团精神的历史文脉，让红色军垦基因得到传承弘扬，使连队建设既充分体现新时代现代化发展水平，配套完善现代化基础设施与公共服务，又保留兵团屯垦戍边的独特标识，打造彰显军垦特色、生态宜居的田园式家园。\n\n  推进绿色低碳循环发展，擦亮农业生态底色。一是深入推进节水增效。健全高标准节水灌溉技术体系，在现有节水农业发展基础上，大面积推广应用智能水肥一体化技术，持续提高农业水资源利用效率，减少水资源无效消耗。探索建立符合兵团农业发展实际的农业用水总量控制与定额管理制度，以刚性制度约束引导农业合理配水用水，进一步促进区域水资源可持续利用。二是强化农业面源污染综合治理。深入推进化肥农药减量增效行动，大力推广有机肥替代化肥、病虫草害绿色防控等成熟技术，从源头减少农业面源污染排放。持续推进畜禽养殖粪污资源化利用，推动形成种养结合、循环发展的农业模式，实现养殖废弃物就地就近消纳、循环利用。健全农业废弃物回收利用体系。统筹推进农作物秸秆综合利用和农田残膜回收，全链条降低农业废弃物对生态环境的影响。","求是网 2026-10-12",82,{"impact":17,"substance":19,"depth":391,"authority":392,"freshness":182,"relevant":22,"comment":393},16,15,"央媒刊发的兵团农业现代化系统部署，政策条款与产业路径清晰，但属方向性综合论述，新数据与独家信息有限。",[395],{"name":388,"url":385},[28,397,398,399,400,401],"低空经济","高标准农田","种业振兴","农业现代化","智慧冷链",{"code":33,"slug":34,"name":35,"path":36,"parent_code":8},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":405},{"candidates":406,"agricultural_evidence":47,"reason":96},[407,412,416,422,428,433,438],{"code":92,"score":20,"supported":47,"title":408,"abstract":410,"tags":411},[409],"农业农村现代化",[409],[400],{"code":53,"score":46,"supported":47,"title":413,"abstract":414,"tags":415},[],[56],[],{"code":417,"score":46,"supported":47,"title":418,"abstract":419,"tags":421},"A4",[],[420],"大豆",[],{"code":423,"score":46,"supported":47,"title":424,"abstract":425,"tags":427},"E3",[],[426],"冷链",[],{"code":429,"score":22,"supported":14,"title":430,"abstract":431,"tags":432},"F1",[],[],[399],{"code":434,"score":22,"supported":14,"title":435,"abstract":436,"tags":437},"H1",[],[],[398],{"code":82,"score":22,"supported":14,"title":439,"abstract":440,"tags":441},[],[],[28],[443,444],"兵团 种业振兴 生物育种","兵团 农业低空经济 无人机植保","兵团种业振兴生物育种-4765","2026-10-13T00:08:34.007796Z",{"id":448,"title":449,"url":450,"summary":451,"summary_zh":452,"content":8,"source_name":453,"source_url":450,"published_at":454,"category":112,"cover_url":8,"hotness":13,"is_selected":14,"score":455,"score_detail":456,"sources":458,"tags":460,"primary_topic":465,"secondary_topics":466,"classification":467,"search_phrases":496,"slug":499,"view_count":22,"doi":500,"paper":501,"created_at":526},4328,"Agricultural water-land optimization for multiple cropping under crop growth stages and farmer budget constraints","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104990","CONTEXT Optimizing water and land resources for multiple cropping face three major challenges: temporal mismatches, periodic land overload, and disconnects from farmers' budget constraints. OBJECTIVE This study proposes a tiered framework that synchronizes crop-stage water needs, monthly supply, land limits, and budget ceilings to generate feasible, differentiated allocation plans. METHOD The year was divided into 35 sub-periods based on crop growth-stage transitions, with two-way mapping among stages, sub-periods, and months to resolve mismatches. Binary planting variables capture crop distribution and generate time-series area constraints. A dual-path budget grading framework is embedded in the multi-objective model to account for data availability. Where survey data exist, per-area budget is the clustering indicator; Ward's clustering method and silhouette coefficient determine K. Otherwise, standardized budget scenarios are constructed using minimum feasible cost and land-use potential. The model is solved by NSGA-II, and incremental benefit-cost ratio analysis guides tiered recommendations. RESULTS AND CONCLUSIONS Applied to Gaozhou Irrigation District, the method achieved water satisfaction 91.75%–100% across growth stages. Net benefit increased 6.4% from 96,426.51 CNY (13,393 USD) to 102,587.59 CNY (14,248 USD), and water deficit reduced 29.7% to 753 m 3 . With budget ceilings 25,700, 44,300, 65,600 CNY, rice area expands without per-unit irrigation change; peanut area and per-unit irrigation rise only at low-medium transition; further increments drive area expansion but raise water deficit. The incremental benefit-cost ratio values are 1.30 and 1.53 CNY\u002FCNY for low-to-medium and medium-to-high transitions, respectively. SIGNIFICANCE By converting theoretical optima into budget-differentiated feasible strategies, the framework supports precision irrigation scheduling and provides a practical basis for designing targeted agricultural subsidies and water-saving policies.","背景 优化水土地资源以实现多熟种植面临三大挑战：时间错配、周期性土地超载，以及与农户预算约束脱节。目的 本研究提出一个分层框架，将作物阶段需水、月度供给、土地限制和预算上限同步考虑，以生成可行且差异化的配置方案。方法 根据作物生长阶段转换将全年划分为35个子时段，并通过阶段、子时段和月份之间的双向映射来解决错配问题。二元种植变量用于刻画作物分布并生成时间序列面积约束。在多目标模型中嵌入双路径预算分级框架，以考虑数据可得性。在有调查数据的情况下，以单位面积预算作为聚类指标；采用Ward聚类法和轮廓系数确定K。否则，利用最小可行成本和土地利用潜力构建标准化预算情景。模型采用NSGA-II求解，并通过增量效益成本比分析指导分层建议。结果与结论 将该方法应用于高州灌区，各生长阶段的水分满足度达到91.75%–100%。净效益从96,426.51元（13,393美元）增加到102,587.59元（14,248美元），增幅为6.4%，水分亏缺减少29.7%至753 m³。在预算上限为25,700元、44,300元、65,600元时，水稻面积扩大而单位面积灌溉量不变；花生面积和单位面积灌溉量仅在中低过渡阶段上升；进一步增加会推动面积扩张，但会提高水分亏缺。低到中和中到高过渡阶段的增量效益成本比分别为1.30和1.53元\u002F元。意义 通过将理论最优解转化为预算差异化的可行策略，该框架支持精准灌溉调度，并为设计有针对性的农业补贴和节水政策提供了实践依据。","Agricultural Systems","2026-10-06T00:00:00Z",79,{"impact":391,"substance":180,"depth":19,"authority":181,"freshness":182,"relevant":22,"comment":457},"核心期刊论文，提出分阶段水土资源优化框架并在高州灌区验证，方法新颖、数据扎实，对精准灌溉与节水政策有参考价值。",[459],{"name":453,"url":450},[28,461,462,463,464],"农业补贴","多目标优化","灌溉调度","水资源优化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":8},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":468},{"candidates":469,"agricultural_evidence":47,"reason":96},[470,474,480,487,492],{"code":45,"score":21,"supported":47,"title":471,"abstract":472,"tags":473},[],[50,199],[],{"code":417,"score":21,"supported":47,"title":475,"abstract":476,"tags":479},[],[477,478],"花生","peanut",[],{"code":481,"score":21,"supported":47,"title":482,"abstract":483,"tags":486},"H3",[],[484,485],"灌溉","irrigation",[],{"code":488,"score":235,"supported":47,"title":489,"abstract":490,"tags":491},"G3",[],[461],[461],{"code":82,"score":22,"supported":14,"title":493,"abstract":494,"tags":495},[],[],[28],[497,498],"高州灌区 水稻 花生 灌溉","多目标优化 水资源优化 农业补贴 智慧农业","高州灌区水稻花生灌溉-4328","10.1016\u002Fj.agsy.2026.104990",{"doi":500,"openalex_id":502,"authors":503,"venue":453,"cited_by_count":153,"oa_url":450,"card":521,"direction":167,"ingested_from":168},"W7220721550",[504,506,509,512,514,516,518],{"name":505,"orcid":8},"杜宇",{"name":507,"orcid":508},"Yuzhi Tang","https:\u002F\u002Forcid.org\u002F0000-0003-2225-9905",{"name":510,"orcid":511},"Kaiwen Li","https:\u002F\u002Forcid.org\u002F0009-0007-0133-6277",{"name":513,"orcid":8},"Chengbin Yang",{"name":515,"orcid":8},"Yihang Zheng",{"name":517,"orcid":8},"LuJia",{"name":519,"orcid":520},"Li He","https:\u002F\u002Forcid.org\u002F0000-0002-2935-0069",{"tldr":522,"method":523,"finding":524,"direction":167,"opportunity":525},"提出分层框架，在作物生长阶段和农户预算约束下优化多熟制水与土地资源配置。","按35个子时段映射生长阶段，嵌入双路径预算分级，用NSGA-II求解多目标模型。","高州灌区水满足度91.75%-100%，净收益增6.4%，缺水减29.7%，预算分级影响作物面积与灌","可将预算分级框架与实时遥感、物联网数据结合，发展动态预算约束下的自适应灌溉决策。","2026-10-07T23:30:04.193429Z",{"id":528,"title":529,"url":530,"summary":531,"summary_zh":532,"content":8,"source_name":533,"source_url":530,"published_at":534,"category":112,"cover_url":8,"hotness":13,"is_selected":14,"score":535,"score_detail":536,"sources":538,"tags":540,"primary_topic":545,"secondary_topics":546,"classification":547,"search_phrases":580,"slug":583,"view_count":153,"doi":584,"paper":585,"created_at":604},4234,"Opportunity Cost of Precision Conservation","https:\u002F\u002Fdoi.org\u002F10.3368\u002Fle.103.2.110525-0095r1","Abstract Conservation practices provide public environmental benefits, but the associated private costs are chiefly borne by farmers. Precision agriculture technologies can help reduce farmer private costs by identifying low-yielding areas, thereby minimizing foregone revenue. This study estimates the opportunity cost of such precision conservation on 29 commercial fields in Michigan, USA, using fine-scale yield maps collected between 2020 and 2024. We employ spatial regression models and Monte Carlo simulations to account for spatial heterogeneity and price variability. Results show that under a corn-soybean crop rotation, precision conservation improves profitability in 22 of 29 fields, indicating potential for adoption without government subsidies.","摘要 保护性耕作实践能带来公共环境效益，但相关的私人成本主要由农民承担。精准农业技术有助于通过识别低产区域来降低农民的私人成本，从而最大限度地减少放弃的收入。本研究利用2020年至2024年间收集的精细尺度产量图，估算了美国密歇根州29块商业农田上此类精准保护性耕作的机会成本。我们采用空间回归模型和蒙特卡洛模拟来处理空间异质性和价格波动。结果表明，在玉米-大豆轮作下，精准保护性耕作提高了29块农田中22块的盈利能力，表明无需政府补贴也具备采用潜力。","Land Economics","2026-10-02T00:00:00Z",80,{"impact":19,"substance":180,"depth":19,"authority":181,"freshness":116,"relevant":22,"comment":537},"基于2020-2024年29块商业田块精细产量图，量化精准保护性耕作的机会成本，结论显示多数田块无需补贴即可盈利，对智慧农业落地与农业绿色政策有参考价值。",[539],{"name":533,"url":530},[28,541,542,543,544],"农业遥感","精准农业","保护性耕作","产量地图",{"code":33,"slug":34,"name":35,"path":36,"parent_code":8},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":548},{"candidates":549,"agricultural_evidence":47,"reason":96},[550,555,559,564,571,576],{"code":82,"score":116,"supported":47,"title":551,"abstract":552,"tags":554},[],[542,553],"precision agriculture",[28,542],{"code":59,"score":21,"supported":47,"title":556,"abstract":557,"tags":558},[],[62,138],[],{"code":417,"score":21,"supported":47,"title":560,"abstract":561,"tags":563},[],[420,562],"soybean",[],{"code":565,"score":21,"supported":47,"title":566,"abstract":567,"tags":570},"A7",[],[568,569],"轮作","crop rotation",[],{"code":572,"score":235,"supported":47,"title":573,"abstract":574,"tags":575},"H8",[],[543],[543],{"code":141,"score":22,"supported":14,"title":577,"abstract":578,"tags":579},[],[],[541],[581,582],"密歇根 精准保护性耕作 机会成本","精准农业 产量地图 玉米大豆轮作","密歇根精准保护性耕作机会成本-4234","10.3368\u002Fle.103.2.110525-0095r1",{"doi":584,"openalex_id":586,"authors":587,"venue":533,"cited_by_count":153,"oa_url":597,"card":598,"direction":165,"ingested_from":168},"W7215722779",[588,591,594],{"name":589,"orcid":590},"Seo Woo Lee","https:\u002F\u002Forcid.org\u002F0009-0000-4165-9520",{"name":592,"orcid":593},"Scott M. Swinton","https:\u002F\u002Forcid.org\u002F0000-0002-3751-1811",{"name":595,"orcid":596},"Bruno Basso","https:\u002F\u002Forcid.org\u002F0000-0003-2090-4616","https:\u002F\u002Fle.uwpress.org\u002Fcontent\u002Fwple\u002Fearly\u002F2026\u002F09\u002F24\u002Fle.103.2.110525-0095R1.full.pdf",{"tldr":599,"method":600,"finding":601,"direction":602,"opportunity":603},"基于密歇根29块田的精细产量图，估算精准保护的机会成本并评估其经济可行性。","2020-2024年精细产量图，空间回归与蒙特卡洛模拟。","玉米-大豆轮作下，29块田中22块精准保护可提高利润，无需政府补贴。","农业绿色发展与碳","可拓展至其他作物与区域，探究精准保护对农户收益与环境效益的协同机制。","2026-10-05T23:30:26.394346Z"]